system

Abstract

The system according to this embodiment aims to efficiently collect information from a specific space and reproduce it in VR. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a reproduction unit, and an operation unit. The acquisition unit collects information of a specific space. The analysis unit analyzes the information collected by the acquisition unit. The reproduction unit reproduces the space in VR based on the information analyzed by the analysis unit. The operation unit operates within the space reproduced by the reproduction unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to efficiently collect information on a specific space and reproduce it on VR.

[0005] The system according to the embodiment aims to efficiently collect information on a specific space and reproduce it on VR.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a reproduction unit, and an operation unit. The collection unit collects information on a specific space. The analysis unit analyzes the information collected by the collection unit. The reproduction unit reproduces a space on VR based on the information analyzed by the analysis unit. The operation unit operates within the space reproduced by the reproduction unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect information from a specific space and reproduce it in VR. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The VR reproduction system according to an embodiment of the present invention is a technology that reads a limited space with a 3D laser scanner and reproduces it in VR. This technology works in conjunction with AI and a database to collect detailed information about a target room, and allows users to zoom in and out of the space, measure length and temperature, etc., using VR goggles and gloves. This makes it useful for various applications, such as on-site surveys and troubleshooting. For example, the VR reproduction system first uses a 3D laser scanner to collect information about a specific space. In this process, the laser scanner acquires detailed 3D data of the space. Next, the acquired data is analyzed in conjunction with AI and a database to collect detailed information about the target room. For example, information such as the dimensions of the room, temperature, and the arrangement of objects is collected. The collected information is reproduced in VR using VR goggles and gloves. The user can wear the VR goggles and use the gloves to freely move around in the space and zoom in and out. For example, the user can zoom in on a part of the room to check the details or measure the temperature. This technology is useful for various applications, such as on-site surveys and troubleshooting. For example, it can be used for remote equipment inspections, operation in difficult environments, and remote maintenance and control activities. Furthermore, this technology enables safe work even in hazardous environments, ensuring the safety of personnel. Moreover, this technology is extremely useful for companies that manage large-scale facilities in fields such as engineering, energy, and disaster relief, as well as for companies that require remote troubleshooting. For example, engineering companies can efficiently conduct remote facility surveys and maintenance, and energy companies can safely perform work in hazardous environments. In disaster relief, it allows for rapid on-site surveys and troubleshooting, potentially mitigating damage. The market size for this technology is estimated at approximately 2 trillion yen, applicable to a wide range of sectors including the telecommunications, automotive, and construction industries. In particular, it is attracting attention as a strategy that can achieve cost reduction and profit increase amidst expenditures such as reduced mobile phone charges and investments in 5G base station construction. By utilizing this technology, we can change the conventional wisdom that nothing can be done without being on-site.For example, conducting pre-pandemic simulations to prepare for pandemics and major disasters can lead to improved countermeasures and serve as a first step towards larger projects. This is possible because the VR recreation system can read a limited space with a 3D laser scanner and recreate it in VR.

[0029] The VR reproduction system according to the embodiment comprises an acquisition unit, an analysis unit, a reproduction unit, and an operation unit. The acquisition unit collects information about a specific space. The acquisition unit collects detailed 3D data of the space, for example, using a 3D laser scanner. The acquisition unit can collect information such as the dimensions, temperature, and arrangement of objects in the space. The acquisition unit acquires detailed 3D data of the space, for example, using a 3D laser scanner. The analysis unit analyzes the information collected by the acquisition unit. The analysis unit analyzes the collected data in conjunction with AI and a database to collect detailed information about the target room. The analysis unit analyzes the collected data using AI, for example, to collect detailed information such as the dimensions, temperature, and arrangement of objects in the room. The reproduction unit reproduces the space in VR based on the information analyzed by the analysis unit. The reproduction unit reproduces the space using VR goggles and gloves, for example, based on the collected information. The reproduction unit can, for example, wear VR goggles and use gloves to move freely within the space and zoom in and out. The control unit operates within the space reproduced by the reproduction unit. For example, the control unit can use VR goggles and gloves to zoom in and out of the space, or measure length and temperature. For example, the control unit can wear VR goggles and use gloves to move freely within the space and zoom in and out. Thus, the VR reproduction system according to the embodiment can collect, analyze, reproduce, and manipulate information about a specific space in VR.

[0030] The data collection unit collects information about a specific space. For example, it can use a 3D laser scanner to collect detailed 3D data of the space. Specifically, the 3D laser scanner uses laser light to measure the distance to each point in the space and generates a 3D model of the space based on that data. This allows for the acquisition of detailed information such as the dimensions, shape, and placement of objects in the space with high accuracy. Furthermore, the data collection unit can also collect environmental data in the space simultaneously by using temperature and humidity sensors. For example, the temperature sensor measures the temperature at each point in the space, and the humidity sensor measures the humidity in the space. This allows for a detailed understanding of the environmental conditions of the space. The data collection unit can also collect image data of the space using a camera. The camera is used to record the color and texture of objects in the space in detail and to give the 3D model a realistic appearance. This allows the data collection unit to comprehensively collect not only the physical structure of the space but also visual information. The collected data is transmitted in real time to a central database, making it accessible to the analysis unit. This allows the data collection unit to efficiently and effectively collect spatial information and improve the overall system performance.

[0031] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected data in conjunction with AI and a database to gather detailed information about the target room. Specifically, the AI ​​uses machine learning algorithms to analyze the collected 3D data and environmental data, extracting detailed information such as spatial dimensions, object placement, and temperature distribution. For example, the AI ​​identifies the positions of walls, ceilings, and floors from the 3D data and calculates the dimensions of the room. It can also use object recognition technology to identify the location and type of furniture and equipment in the space. Furthermore, it analyzes temperature and humidity data to gain a detailed understanding of the environmental conditions within the space. The analysis unit stores this information in a database, making it accessible to the reproduction unit. The analysis unit can also utilize historical data and statistical information to analyze changes and trends in the space. For example, based on historical data, it can predict temperature and humidity fluctuations during specific time periods or seasons and evaluate future environmental conditions. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to quickly and accurately analyze the collected data and grasp detailed spatial information.

[0032] The reproduction unit recreates the space in VR based on the information analyzed by the analysis unit. For example, the reproduction unit recreates the space using VR goggles and gloves based on the collected information. Specifically, the VR goggles realistically display the 3D space to the user's vision, and the gloves track the user's hand movements to enable operation within the space. The reproduction unit performs a detailed recreation of the space based on 3D models and environmental data provided by the analysis unit. For example, a 3D model can be displayed on the VR goggles, allowing the user to freely move around the space and manipulate objects. It can also realistically recreate environmental conditions within the space based on environmental data such as temperature and humidity. The reproduction unit optimizes the interface so that the user can intuitively operate within the space. For example, the user can grasp and move objects in the space by moving their hands. It can also perform operations within the space in response to the user's voice commands using voice recognition technology. In this way, the reproduction unit can provide the user with a realistic spatial experience and achieve a detailed recreation of the space.

[0033] The control unit operates within the space reproduced by the reproduction unit. For example, using VR goggles and gloves, the control unit can zoom in and out, and measure length and temperature within the space. Specifically, by wearing VR goggles and using gloves to track hand movements, users can intuitively operate within the space. For example, by spreading their hands, the user can zoom in on the entire space to examine details. Conversely, by closing their hands, the space can be zoomed out to grasp the overall layout. Furthermore, the control unit provides a function to measure the length and temperature of specific points within the space. For example, by pointing to a specific point with their hand, the dimensions and temperature of that point can be displayed in real time. This allows users to intuitively grasp detailed information within the space and quickly perform necessary operations. The control unit can collect user feedback and continuously improve the accuracy and usability of the operation interface. For example, inconveniences and problems experienced by users during operation can be collected as feedback and reflected in the next system update. The control unit also provides a multi-user interface, allowing multiple users to operate within the space simultaneously. This allows the control unit to provide users with an intuitive and efficient operating environment, enabling detailed control of the space.

[0034] The data collection unit can collect detailed 3D data of a space using a 3D laser scanner. For example, the data collection unit can acquire detailed 3D data of a space using a 3D laser scanner. The data collection unit can collect information such as the dimensions, temperature, and arrangement of objects in the space. For example, the data collection unit can acquire detailed 3D data of a space using a 3D laser scanner. This allows for the collection of detailed 3D data of a space using a 3D laser scanner. Detailed 3D data includes, but is not limited to, resolution and data format. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired by the 3D laser scanner into a generating AI, which can then analyze the data to generate detailed 3D data.

[0035] The analysis unit analyzes the collected data in conjunction with AI and a database to collect detailed information about the target room. For example, the analysis unit analyzes the collected data in conjunction with AI and a database to collect detailed information such as the room's dimensions, temperature, and the arrangement of objects. For example, the analysis unit uses AI to analyze the collected data and collect detailed information such as the room's dimensions, temperature, and the arrangement of objects. This allows for the collection of detailed information about the room through collaboration with AI and a database. AI includes, but is not limited to, machine learning and deep learning. Databases include, but are not limited to, SQL databases and NoSQL databases. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the collected data into a generating AI, which can then analyze the data to collect detailed information about the room.

[0036] The reproduction unit can reproduce a space in VR based on the collected information. For example, the reproduction unit can reproduce a space using VR goggles and gloves based on the collected information. For example, the reproduction unit can wear VR goggles and use gloves to move freely within the space, zoom in and out, etc. This allows the space to be reproduced in VR based on the collected information. Reproducing a space in VR includes, but is not limited to, the VR device used and the accuracy of the reproduction. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the collected information into a generating AI, and the generating AI can analyze the information and reproduce the space in VR.

[0037] The control unit can zoom in and out of space, measure length and temperature, etc., using VR goggles and gloves. For example, the control unit can move freely within the space, zoom in and out, etc., by wearing VR goggles and using gloves. For example, the control unit can zoom in on a specific part of the space to examine details, measure temperature, etc. This allows for manipulation within the space and measurement of length and temperature using VR goggles and gloves. Zooming in and out includes, but is not limited to, the control interface and the range of zooming in and out. Measuring length and temperature includes, but is not limited to, the sensors used and the accuracy of the measurement. Some or all of the above processing in the control unit may be performed using, for example, AI, or not using AI. For example, the control unit can input data acquired using VR goggles and gloves into a generating AI, which can then analyze the data to measure length and temperature.

[0038] The data collection unit can collect data from different angles and heights to generate more detailed 3D models. For example, the data collection unit can automatically collect data from multiple angles and integrate information from all directions to generate a detailed 3D model. For example, the data collection unit can collect data at different heights to generate a 3D model that includes detailed information about the ceiling and floor. For example, the data collection unit can prioritize data collection from specific angles and heights to generate a 3D model that highlights detailed information about important parts. This allows for the generation of detailed 3D models by collecting data from different angles and heights. Different angles and heights include, but are not limited to, collection ranges and collection points. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from different angles and heights into a generating AI, which can then analyze the data to generate a detailed 3D model.

[0039] The data collection unit can automatically adjust its collection method in response to environmental changes. For example, if the lighting changes, the data collection unit can automatically adjust its sensitivity to perform optimal data collection. For example, if the temperature changes, the data collection unit can change the sensor settings to collect accurate data. For example, if ambient noise increases, the data collection unit can activate the noise cancellation function to collect clear data. This allows for optimal data collection by adjusting the collection method in response to environmental changes. Environmental changes include, but are not limited to, changes in weather or lighting. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the acquired data in response to environmental changes into a generating AI, which can then analyze the data and adjust the collection method.

[0040] The data collection unit can accept voice commands and collect data from specific areas based on user instructions. For example, if the user gives a voice command such as "Scan this area," the data collection unit will prioritize collecting data from that area. For example, if the user instructs the data collection unit to "Collect data from the ceiling," the data collection unit will collect detailed data from the ceiling. For example, if the user instructs the data collection unit to "Get more details on this part," the data collection unit will collect detailed data from that part. In this way, by accepting voice commands, the data collection unit can collect data from specific areas based on user instructions. Voice commands include, but are not limited to, examples such as speech recognition technology and command types. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input a voice command into a generating AI, which can analyze the voice and collect data from specific areas.

[0041] The data collection unit can collect detailed physical properties of a space by combining multiple sensors. For example, the data collection unit can collect detailed environmental data of a space by combining a temperature sensor and a humidity sensor. For example, the data collection unit can understand the physical properties of a space in detail by combining a barometric pressure sensor and a light sensor. For example, the data collection unit can integrate multiple sensors to collect the overall physical properties of a space with high accuracy. This makes it possible to collect detailed physical properties of a space by combining multiple sensors. Multiple sensors include, but are not limited to, temperature sensors and distance sensors. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input data acquired from multiple sensors into a generating AI, and the generating AI can analyze the data to collect detailed physical properties of the space.

[0042] The analysis unit can analyze the collected data in real time and provide immediate feedback. For example, the analysis unit can analyze the data in real time and provide immediate feedback to the user. For example, the analysis unit can instantly analyze the collected data and display the results to the user in real time. For example, the analysis unit can process the data in real time and provide the user with immediate analysis results. This enables immediate feedback through real-time analysis. Real-time analysis includes, but is not limited to, the algorithms used and the analysis speed. Some or all of the processing described above in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI, which can then analyze the data in real time and provide immediate feedback.

[0043] The analysis unit can apply different analysis algorithms to provide the optimal analysis result. For example, the analysis unit can apply multiple analysis algorithms and select and provide the optimal result. For example, the analysis unit can apply different algorithms according to the characteristics of the data to provide the optimal analysis result. For example, the analysis unit can execute multiple algorithms in parallel and provide the user with the most suitable result. In this way, the optimal analysis result can be provided by applying different analysis algorithms. Different analysis algorithms include, but are not limited to, clustering and regression analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into a generating AI, and the generating AI can apply different analysis algorithms to provide the optimal analysis result.

[0044] The analysis unit can improve analysis accuracy by referencing different data sources. For example, the analysis unit can improve analysis accuracy by referencing historical data and comparing it with current data. For example, the analysis unit can improve analysis accuracy by referencing an external database and obtaining additional information. For example, the analysis unit can integrate multiple data sources and provide comprehensive analysis results. In this way, analysis accuracy can be improved by referencing different data sources. Different data sources include, but are not limited to, external databases and sensor data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data obtained from different data sources into a generating AI, and the generating AI can analyze the data to improve analysis accuracy.

[0045] The analysis unit can accept voice commands and perform specific analyses based on user instructions. For example, if the user gives a voice command such as "Analyze this data," the analysis unit will prioritize analyzing that data. For example, if the user instructs the analysis unit to "Perform a detailed analysis," it will perform a detailed analysis. For example, if the user instructs the analysis unit to "Perform a simple analysis," it will perform a simple analysis. In this way, by accepting voice commands, the analysis unit can perform specific analyses based on user instructions. Voice commands include, but are not limited to, examples such as speech recognition technology and command types. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input a voice command into a generating AI, which can then analyze the voice and perform a specific analysis.

[0046] The display unit can provide views from different viewpoints and allow users to freely switch viewpoints. For example, the display unit can provide views from multiple viewpoints and allow users to freely switch viewpoints. For example, the display unit can highlight a particular viewpoint so that users can examine important parts in detail. For example, the display unit can change the viewpoint based on user instructions and provide an optimal display. This allows users to freely switch viewpoints by providing views from different viewpoints. Views from different viewpoints include, but are not limited to, methods for switching viewpoints and display accuracy. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can input data acquired from different viewpoints into a generating AI, and the generating AI can analyze the data and provide views from different viewpoints.

[0047] The simulation unit can simulate environmental changes and provide a more realistic VR space. For example, the simulation unit can simulate changes in time of day and reproduce the difference between day and night. For example, the simulation unit can simulate changes in weather and reproduce conditions such as rain and snow. For example, the simulation unit can simulate changes in seasons and reproduce the differences between spring, summer, autumn, and winter. In this way, by simulating environmental changes, a more realistic VR space can be provided. Environmental changes include, but are not limited to, changes in weather and changes in lighting. Some or all of the above processing in the simulation unit may be performed using, for example, AI, or not using AI. For example, the simulation unit can input data acquired in response to environmental changes into a generating AI, and the generating AI can analyze the data to simulate environmental changes.

[0048] The reproduction unit can receive voice commands and reproduce a specific area based on user instructions. For example, if the user gives the voice command "Reproduce this area," the reproduction unit will prioritize reproducing that area. For example, if the user instructs "Reproduce the ceiling," the reproduction unit will perform a detailed reproduction of the ceiling. For example, if the user instructs "Detail this part," the reproduction unit will perform a detailed reproduction of that part. In this way, by receiving voice commands, it can reproduce a specific area based on user instructions. Voice commands include, but are not limited to, examples such as speech recognition technology and command types. Some or all of the above processing in the reproduction unit may be performed using, for example, AI, or not using AI. For example, the reproduction unit can input a voice command into a generating AI, which can analyze the voice and reproduce a specific area.

[0049] The reproduction unit can enable multiple users to share a VR space simultaneously and facilitate collaborative work. For example, the reproduction unit can enable multiple users to share a VR space simultaneously and facilitate collaborative work. For example, the reproduction unit can support communication between users and enable real-time exchange of opinions. For example, the reproduction unit can share user location information to improve the efficiency of collaborative work. This enables collaborative work by allowing multiple users to share a VR space simultaneously. Examples of how multiple users can share a VR space simultaneously include, but are not limited to, network technologies and synchronization methods. Some or all of the processing described above in the reproduction unit may be performed using, for example, AI, or not using AI. For example, the reproduction unit can input data from multiple users into a generating AI, which can then analyze the data and share the VR space.

[0050] The control unit can provide haptic feedback, allowing the user to gain a physical sensation. For example, the control unit can provide haptic feedback, allowing the user to feel the sensation of touching an object. For example, the control unit can provide vibration feedback, allowing the user to physically feel the result of an operation. For example, the control unit can provide pressure feedback, allowing the user to adjust the strength of the operation. In this way, by providing haptic feedback, the user can gain a physical sensation. Haptic feedback includes, but is not limited to, vibration feedback and pressure feedback. Some or all of the above processing in the control unit may be performed, for example, using AI, or not using AI. For example, the control unit can input haptic feedback data into a generating AI, which can then analyze the data to provide haptic feedback.

[0051] The control unit can perform operations based on the user's hand movements using gesture recognition. For example, the control unit can operate menus based on the user's hand movements using gesture recognition. For example, the control unit can move objects based on the user's hand movements using gesture recognition. For example, the control unit can zoom in and zoom out based on the user's hand movements using gesture recognition. In this way, operations can be performed based on the user's hand movements by using gesture recognition. Gesture recognition includes, but is not limited to, the sensors and recognition algorithms used. Some or all of the above-described processes in the control unit may be performed using, for example, AI, or not using AI. For example, the control unit can input gesture recognition data into a generating AI, and the generating AI can analyze the data and perform operations.

[0052] The control unit can accept voice commands and perform specific operations based on user instructions. For example, if the user gives the voice command "Move this object," the control unit will move that object. For example, if the user gives the command "Zoom in," the control unit will zoom in. For example, if the user gives the command "Open the menu," the control unit will open the menu. In this way, by accepting voice commands, the control unit can perform specific operations based on user instructions. Voice commands include, but are not limited to, voice recognition technology and command types. Some or all of the above processing in the control unit may be performed using, for example, AI, or not using AI. For example, the control unit can input a voice command into a generating AI, which can analyze the voice and perform a specific operation.

[0053] The control unit can enable multiple users to operate simultaneously and facilitate collaborative work. For example, the control unit can enable multiple users to operate simultaneously and facilitate collaborative work. For example, the control unit can synchronize user operations to support real-time collaborative work. For example, the control unit can share user operation histories to improve the efficiency of collaborative work. This enables collaborative work through simultaneous operation by multiple users. Simultaneous operation by multiple users includes, but is not limited to, network technologies and synchronization methods. Some or all of the above-described processes in the control unit may be performed using, for example, AI, or not. For example, the control unit can input operation data from multiple users into a generating AI, which can then analyze the data to enable collaborative work.

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

[0055] The VR reproduction system can further integrate different data sources to generate more detailed 3D models. For example, it can integrate satellite data to generate a 3D model containing extensive topographic information. It can integrate aerial data from drones to generate a 3D model containing detailed information from advanced viewpoints. It can integrate ground sensor data to generate a 3D model containing detailed physical properties of the Earth's surface. In this way, by integrating different data sources, it is possible to generate more detailed and accurate 3D models. Different data sources include, but are not limited to, satellite data, drone data, and ground sensor data. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data acquired from different data sources into a generating AI, which can then analyze the data to generate a detailed 3D model.

[0056] The VR reproduction system can also collect data in real time and instantly update the 3D model. For example, it can collect real-time progress data at a construction site and reflect it in the 3D model. It can collect real-time information on the situation at a disaster site to support a rapid response. It can collect real-time data on the flow of people at an event venue to understand the congestion level. As a result, by collecting data in real time and instantly updating the 3D model, it is possible to provide a 3D model that reflects the latest information. Real-time data collection includes, but is not limited to, sensors and cameras. Some or all of the above processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input the data acquired in real time into a generating AI, which can analyze the data and instantly update the 3D model.

[0057] The VR reproduction system can further improve its analytical accuracy by combining different analytical algorithms. For example, clustering algorithms and regression analysis can be combined to analyze data characteristics in detail. Deep learning and machine learning can be combined to analyze complex data patterns. Statistical analysis and time series analysis can be combined to analyze data fluctuations in detail. This allows for improved analytical accuracy by combining different analytical algorithms. These different analytical algorithms include, but are not limited to, clustering, regression analysis, and deep learning. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input different analytical algorithms into a generating AI, which can then analyze the data to improve analytical accuracy.

[0058] The VR reproduction system can also accept voice commands and perform specific analyses based on user instructions. For example, if the user gives the voice command "Analyze this data," that data can be prioritized for analysis. If the user instructs "Perform a detailed analysis," a detailed analysis can be performed. If the user instructs "Perform a simple analysis," a simple analysis can be performed. In this way, by accepting voice commands, the system can perform specific analyses based on user instructions. Voice commands include, but are not limited to, examples such as speech recognition technology and command types. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input a voice command into a generating AI, which can analyze the voice and perform a specific analysis.

[0059] The VR reproduction system can further enable multiple users to share a VR space simultaneously, facilitating collaborative work. For example, multiple users can share a VR space simultaneously and collaborate on design work. Multiple users can share a VR space simultaneously and exchange opinions in real time. Multiple users can share a VR space simultaneously and collaboratively solve problems. This enables collaborative work by allowing multiple users to share a VR space simultaneously. Examples of how multiple users can share a VR space simultaneously include, but are not limited to, network technology and synchronization methods. Some or all of the above-described processes in the reproduction unit may be performed using, for example, AI, or not using AI. For example, the reproduction unit can input data from multiple users into a generating AI, which can then analyze the data and share the VR space.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The collection unit collects information about a specific space. For example, a 3D laser scanner is used to collect detailed 3D data of the space, obtaining information such as the dimensions of the space, temperature, and the arrangement of objects. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes the collected data in conjunction with AI and a database to gather detailed information such as room dimensions, temperature, and object placement. Step 3: The reproduction unit reproduces the space in VR based on the information analyzed by the analysis unit. For example, the space can be reproduced using VR goggles and gloves, allowing users to move freely within the space and zoom in and out. Step 4: The control unit manipulates the space recreated by the reproduction unit. For example, using VR goggles and gloves, the user can zoom in and out of the space, or measure length and temperature.

[0062] (Example of form 2) The VR reproduction system according to an embodiment of the present invention is a technology that reads a limited space with a 3D laser scanner and reproduces it in VR. This technology works in conjunction with AI and a database to collect detailed information about a target room, and allows users to zoom in and out of the space, measure length and temperature, etc., using VR goggles and gloves. This makes it useful for various applications, such as on-site surveys and troubleshooting. For example, the VR reproduction system first uses a 3D laser scanner to collect information about a specific space. In this process, the laser scanner acquires detailed 3D data of the space. Next, the acquired data is analyzed in conjunction with AI and a database to collect detailed information about the target room. For example, information such as the dimensions of the room, temperature, and the arrangement of objects is collected. The collected information is reproduced in VR using VR goggles and gloves. The user can wear the VR goggles and use the gloves to freely move around in the space and zoom in and out. For example, the user can zoom in on a part of the room to check the details or measure the temperature. This technology is useful for various applications, such as on-site surveys and troubleshooting. For example, it can be used for remote equipment inspections, operation in difficult environments, and remote maintenance and control activities. Furthermore, this technology enables safe work even in hazardous environments, ensuring the safety of personnel. Moreover, this technology is extremely useful for companies that manage large-scale facilities in fields such as engineering, energy, and disaster relief, as well as for companies that require remote troubleshooting. For example, engineering companies can efficiently conduct remote facility surveys and maintenance, and energy companies can safely perform work in hazardous environments. In disaster relief, it allows for rapid on-site surveys and troubleshooting, potentially mitigating damage. The market size for this technology is estimated at approximately 2 trillion yen, applicable to a wide range of sectors including the telecommunications, automotive, and construction industries. In particular, it is attracting attention as a strategy that can achieve cost reduction and profit increase amidst expenditures such as reduced mobile phone charges and investments in 5G base station construction. By utilizing this technology, we can change the conventional wisdom that nothing can be done without being on-site.For example, conducting pre-pandemic simulations to prepare for pandemics and major disasters can lead to improved countermeasures and serve as a first step towards larger projects. This is possible because the VR recreation system can read a limited space with a 3D laser scanner and recreate it in VR.

[0063] The VR reproduction system according to the embodiment comprises an acquisition unit, an analysis unit, a reproduction unit, and an operation unit. The acquisition unit collects information about a specific space. The acquisition unit collects detailed 3D data of the space, for example, using a 3D laser scanner. The acquisition unit can collect information such as the dimensions, temperature, and arrangement of objects in the space. The acquisition unit acquires detailed 3D data of the space, for example, using a 3D laser scanner. The analysis unit analyzes the information collected by the acquisition unit. The analysis unit analyzes the collected data in conjunction with AI and a database to collect detailed information about the target room. The analysis unit analyzes the collected data using AI, for example, to collect detailed information such as the dimensions, temperature, and arrangement of objects in the room. The reproduction unit reproduces the space in VR based on the information analyzed by the analysis unit. The reproduction unit reproduces the space using VR goggles and gloves, for example, based on the collected information. The reproduction unit can, for example, wear VR goggles and use gloves to move freely within the space and zoom in and out. The control unit operates within the space reproduced by the reproduction unit. For example, the control unit can use VR goggles and gloves to zoom in and out of the space, or measure length and temperature. For example, the control unit can wear VR goggles and use gloves to move freely within the space and zoom in and out. Thus, the VR reproduction system according to the embodiment can collect, analyze, reproduce, and manipulate information about a specific space in VR.

[0064] The data collection unit collects information about a specific space. For example, it can use a 3D laser scanner to collect detailed 3D data of the space. Specifically, the 3D laser scanner uses laser light to measure the distance to each point in the space and generates a 3D model of the space based on that data. This allows for the acquisition of detailed information such as the dimensions, shape, and placement of objects in the space with high accuracy. Furthermore, the data collection unit can also collect environmental data in the space simultaneously by using temperature and humidity sensors. For example, the temperature sensor measures the temperature at each point in the space, and the humidity sensor measures the humidity in the space. This allows for a detailed understanding of the environmental conditions of the space. The data collection unit can also collect image data of the space using a camera. The camera is used to record the color and texture of objects in the space in detail and to give the 3D model a realistic appearance. This allows the data collection unit to comprehensively collect not only the physical structure of the space but also visual information. The collected data is transmitted in real time to a central database, making it accessible to the analysis unit. This allows the data collection unit to efficiently and effectively collect spatial information and improve the overall system performance.

[0065] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected data in conjunction with AI and a database to gather detailed information about the target room. Specifically, the AI ​​uses machine learning algorithms to analyze the collected 3D data and environmental data, extracting detailed information such as spatial dimensions, object placement, and temperature distribution. For example, the AI ​​identifies the positions of walls, ceilings, and floors from the 3D data and calculates the dimensions of the room. It can also use object recognition technology to identify the location and type of furniture and equipment in the space. Furthermore, it analyzes temperature and humidity data to gain a detailed understanding of the environmental conditions within the space. The analysis unit stores this information in a database, making it accessible to the reproduction unit. The analysis unit can also utilize historical data and statistical information to analyze changes and trends in the space. For example, based on historical data, it can predict temperature and humidity fluctuations during specific time periods or seasons and evaluate future environmental conditions. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to quickly and accurately analyze the collected data and grasp detailed spatial information.

[0066] The reproduction unit recreates the space in VR based on the information analyzed by the analysis unit. For example, the reproduction unit recreates the space using VR goggles and gloves based on the collected information. Specifically, the VR goggles realistically display the 3D space to the user's vision, and the gloves track the user's hand movements to enable operation within the space. The reproduction unit performs a detailed recreation of the space based on 3D models and environmental data provided by the analysis unit. For example, a 3D model can be displayed on the VR goggles, allowing the user to freely move around the space and manipulate objects. It can also realistically recreate environmental conditions within the space based on environmental data such as temperature and humidity. The reproduction unit optimizes the interface so that the user can intuitively operate within the space. For example, the user can grasp and move objects in the space by moving their hands. It can also perform operations within the space in response to the user's voice commands using voice recognition technology. In this way, the reproduction unit can provide the user with a realistic spatial experience and achieve a detailed recreation of the space.

[0067] The control unit operates within the space reproduced by the reproduction unit. For example, using VR goggles and gloves, the control unit can zoom in and out, and measure length and temperature within the space. Specifically, by wearing VR goggles and using gloves to track hand movements, users can intuitively operate within the space. For example, by spreading their hands, the user can zoom in on the entire space to examine details. Conversely, by closing their hands, the space can be zoomed out to grasp the overall layout. Furthermore, the control unit provides a function to measure the length and temperature of specific points within the space. For example, by pointing to a specific point with their hand, the dimensions and temperature of that point can be displayed in real time. This allows users to intuitively grasp detailed information within the space and quickly perform necessary operations. The control unit can collect user feedback and continuously improve the accuracy and usability of the operation interface. For example, inconveniences and problems experienced by users during operation can be collected as feedback and reflected in the next system update. The control unit also provides a multi-user interface, allowing multiple users to operate within the space simultaneously. This allows the control unit to provide users with an intuitive and efficient operating environment, enabling detailed control of the space.

[0068] The data collection unit can collect detailed 3D data of a space using a 3D laser scanner. For example, the data collection unit can acquire detailed 3D data of a space using a 3D laser scanner. The data collection unit can collect information such as the dimensions, temperature, and arrangement of objects in the space. For example, the data collection unit can acquire detailed 3D data of a space using a 3D laser scanner. This allows for the collection of detailed 3D data of a space using a 3D laser scanner. Detailed 3D data includes, but is not limited to, resolution and data format. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired by the 3D laser scanner into a generating AI, which can then analyze the data to generate detailed 3D data.

[0069] The analysis unit analyzes the collected data in conjunction with AI and a database to collect detailed information about the target room. For example, the analysis unit analyzes the collected data in conjunction with AI and a database to collect detailed information such as the room's dimensions, temperature, and the arrangement of objects. For example, the analysis unit uses AI to analyze the collected data and collect detailed information such as the room's dimensions, temperature, and the arrangement of objects. This allows for the collection of detailed information about the room through collaboration with AI and a database. AI includes, but is not limited to, machine learning and deep learning. Databases include, but are not limited to, SQL databases and NoSQL databases. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the collected data into a generating AI, which can then analyze the data to collect detailed information about the room.

[0070] The reproduction unit can reproduce a space in VR based on the collected information. For example, the reproduction unit can reproduce a space using VR goggles and gloves based on the collected information. For example, the reproduction unit can wear VR goggles and use gloves to move freely within the space, zoom in and out, etc. This allows the space to be reproduced in VR based on the collected information. Reproducing a space in VR includes, but is not limited to, the VR device used and the accuracy of the reproduction. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the collected information into a generating AI, and the generating AI can analyze the information and reproduce the space in VR.

[0071] The control unit can zoom in and out of space, measure length and temperature, etc., using VR goggles and gloves. For example, the control unit can move freely within the space, zoom in and out, etc., by wearing VR goggles and using gloves. For example, the control unit can zoom in on a specific part of the space to examine details, measure temperature, etc. This allows for manipulation within the space and measurement of length and temperature using VR goggles and gloves. Zooming in and out includes, but is not limited to, the control interface and the range of zooming in and out. Measuring length and temperature includes, but is not limited to, the sensors used and the accuracy of the measurement. Some or all of the above processing in the control unit may be performed using, for example, AI, or not using AI. For example, the control unit can input data acquired using VR goggles and gloves into a generating AI, which can then analyze the data to measure length and temperature.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of 3D data collection based on the estimated emotions. For example, if the user is tense, the data collection unit can delay the collection timing and wait until the user is relaxed. For example, if the user is relaxed, the data collection unit can start collecting data immediately and proceed with the work efficiently. For example, if the user is in a hurry, the data collection unit can collect data quickly and obtain the necessary information in the shortest possible time. This enables efficient data collection by adjusting the timing of 3D data collection based on the user's emotions. The user's emotions are estimated by, for example, facial recognition or voice analysis, but are not limited to such examples. Adjusting the collection timing includes, for example, real-time adjustment or pre-setting, but are not limited to such examples. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, for example, text generation AI (e.g., LLM) or multimodal generation AI, but are not limited to such examples. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's facial expression data into a generating AI, which can then estimate the emotion and adjust the timing of data collection.

[0073] The data collection unit can collect data from different angles and heights to generate more detailed 3D models. For example, the data collection unit can automatically collect data from multiple angles and integrate information from all directions to generate a detailed 3D model. For example, the data collection unit can collect data at different heights to generate a 3D model that includes detailed information about the ceiling and floor. For example, the data collection unit can prioritize data collection from specific angles and heights to generate a 3D model that highlights detailed information about important parts. This allows for the generation of detailed 3D models by collecting data from different angles and heights. Different angles and heights include, but are not limited to, collection ranges and collection points. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from different angles and heights into a generating AI, which can then analyze the data to generate a detailed 3D model.

[0074] The data collection unit can automatically adjust its collection method in response to environmental changes. For example, if the lighting changes, the data collection unit can automatically adjust its sensitivity to perform optimal data collection. For example, if the temperature changes, the data collection unit can change the sensor settings to collect accurate data. For example, if ambient noise increases, the data collection unit can activate the noise cancellation function to collect clear data. This allows for optimal data collection by adjusting the collection method in response to environmental changes. Environmental changes include, but are not limited to, changes in weather or lighting. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the acquired data in response to environmental changes into a generating AI, which can then analyze the data and adjust the collection method.

[0075] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit will prioritize collecting important data and provide results quickly. For example, if the user is relaxed, the data collection unit will collect overall data in a balanced manner and generate a detailed 3D model. For example, if the user is stressed, the data collection unit will prioritize collecting simple data to reduce the user's burden. This enables efficient data collection by prioritizing data based on the user's emotions. Data prioritization includes, but is not limited to, importance evaluation and real-time adjustments. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user facial expression data into a generative AI, which can estimate emotions and determine data priority.

[0076] The data collection unit can accept voice commands and collect data from specific areas based on user instructions. For example, if the user gives a voice command such as "Scan this area," the data collection unit will prioritize collecting data from that area. For example, if the user instructs the data collection unit to "Collect data from the ceiling," the data collection unit will collect detailed data from the ceiling. For example, if the user instructs the data collection unit to "Get more details on this part," the data collection unit will collect detailed data from that part. In this way, by accepting voice commands, the data collection unit can collect data from specific areas based on user instructions. Voice commands include, but are not limited to, examples such as speech recognition technology and command types. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input a voice command into a generating AI, which can analyze the voice and collect data from specific areas.

[0077] The data collection unit can collect detailed physical properties of a space by combining multiple sensors. For example, the data collection unit can collect detailed environmental data of a space by combining a temperature sensor and a humidity sensor. For example, the data collection unit can understand the physical properties of a space in detail by combining a barometric pressure sensor and a light sensor. For example, the data collection unit can integrate multiple sensors to collect the overall physical properties of a space with high accuracy. This makes it possible to collect detailed physical properties of a space by combining multiple sensors. Multiple sensors include, but are not limited to, temperature sensors and distance sensors. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input data acquired from multiple sensors into a generating AI, and the generating AI can analyze the data to collect detailed physical properties of the space.

[0078] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, a highly visible display becomes possible. Display methods of the analysis results include, but are not limited to, graph displays and text displays. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is 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 processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI, which can estimate emotions and adjust the display method of the analysis results.

[0079] The analysis unit can analyze the collected data in real time and provide immediate feedback. For example, the analysis unit can analyze the data in real time and provide immediate feedback to the user. For example, the analysis unit can instantly analyze the collected data and display the results to the user in real time. For example, the analysis unit can process the data in real time and provide the user with immediate analysis results. This enables immediate feedback through real-time analysis. Real-time analysis includes, but is not limited to, the algorithms used and the analysis speed. Some or all of the processing described above in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI, which can then analyze the data in real time and provide immediate feedback.

[0080] The analysis unit can apply different analysis algorithms to provide the optimal analysis result. For example, the analysis unit can apply multiple analysis algorithms and select and provide the optimal result. For example, the analysis unit can apply different algorithms according to the characteristics of the data to provide the optimal analysis result. For example, the analysis unit can execute multiple algorithms in parallel and provide the user with the most suitable result. In this way, the optimal analysis result can be provided by applying different analysis algorithms. Different analysis algorithms include, but are not limited to, clustering and regression analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into a generating AI, and the generating AI can apply different analysis algorithms to provide the optimal analysis result.

[0081] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on the estimated emotions. For example, if the user is excited, the analysis unit will prioritize the analysis of important data and provide results quickly. For example, if the user is relaxed, the analysis unit will analyze the overall data in a balanced manner and provide detailed results. For example, if the user is stressed, the analysis unit will prioritize the analysis of simple data to reduce the user's burden. This enables efficient analysis by determining the priority of the analysis based on the user's emotions. Prioritization of analysis includes, but is not limited to, importance evaluation and real-time adjustments. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generative AI, which can estimate emotions and determine the priority of the analysis.

[0082] The analysis unit can improve analysis accuracy by referencing different data sources. For example, the analysis unit can improve analysis accuracy by referencing historical data and comparing it with current data. For example, the analysis unit can improve analysis accuracy by referencing an external database and obtaining additional information. For example, the analysis unit can integrate multiple data sources and provide comprehensive analysis results. In this way, analysis accuracy can be improved by referencing different data sources. Different data sources include, but are not limited to, external databases and sensor data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data obtained from different data sources into a generating AI, and the generating AI can analyze the data to improve analysis accuracy.

[0083] The analysis unit can accept voice commands and perform specific analyses based on user instructions. For example, if the user gives a voice command such as "Analyze this data," the analysis unit will prioritize analyzing that data. For example, if the user instructs the analysis unit to "Perform a detailed analysis," it will perform a detailed analysis. For example, if the user instructs the analysis unit to "Perform a simple analysis," it will perform a simple analysis. In this way, by accepting voice commands, the analysis unit can perform specific analyses based on user instructions. Voice commands include, but are not limited to, examples such as speech recognition technology and command types. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input a voice command into a generating AI, which can then analyze the voice and perform a specific analysis.

[0084] The display unit can estimate the user's emotions and adjust the display method of the VR space based on the estimated user emotions. For example, if the user is tense, the display unit provides a simple and highly visible display method. For example, if the user is relaxed, the display unit provides a display method that includes detailed information. For example, if the user is in a hurry, the display unit provides a display method that gets straight to the point. By adjusting the display method of the VR space based on the user's emotions, a highly visible display becomes possible. The display method of the VR space includes, but is not limited to, methods for switching viewpoints and display accuracy. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can input the user's facial expression data into the generative AI, which can estimate emotions and adjust the display method of the VR space.

[0085] The display unit can provide views from different viewpoints and allow users to freely switch viewpoints. For example, the display unit can provide views from multiple viewpoints and allow users to freely switch viewpoints. For example, the display unit can highlight a particular viewpoint so that users can examine important parts in detail. For example, the display unit can change the viewpoint based on user instructions and provide an optimal display. This allows users to freely switch viewpoints by providing views from different viewpoints. Views from different viewpoints include, but are not limited to, methods for switching viewpoints and display accuracy. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can input data acquired from different viewpoints into a generating AI, and the generating AI can analyze the data and provide views from different viewpoints.

[0086] The simulation unit can simulate environmental changes and provide a more realistic VR space. For example, the simulation unit can simulate changes in time of day and reproduce the difference between day and night. For example, the simulation unit can simulate changes in weather and reproduce conditions such as rain and snow. For example, the simulation unit can simulate changes in seasons and reproduce the differences between spring, summer, autumn, and winter. In this way, by simulating environmental changes, a more realistic VR space can be provided. Environmental changes include, but are not limited to, changes in weather and changes in lighting. Some or all of the above processing in the simulation unit may be performed using, for example, AI, or not using AI. For example, the simulation unit can input data acquired in response to environmental changes into a generating AI, and the generating AI can analyze the data to simulate environmental changes.

[0087] The simulation unit can estimate the user's emotions and determine the priority of the spaces to be simulated based on the estimated emotions. For example, if the user is excited, the simulation unit will prioritize the simulation of important spaces and provide results quickly. For example, if the user is relaxed, the simulation unit will simulate the overall space in a balanced way and provide detailed results. For example, if the user is stressed, the simulation unit will prioritize the simulation of simple spaces to reduce the user's burden. This enables efficient spatial simulation by determining the priority of the spaces to be simulated based on the user's emotions. The priority of spaces includes, but is not limited to, importance evaluation and real-time adjustment. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI or not using AI. For example, the simulation unit can input user facial expression data into the generative AI, which can then estimate emotions and determine the priority of the spaces to be simulated.

[0088] The reproduction unit can receive voice commands and reproduce a specific area based on user instructions. For example, if the user gives the voice command "Reproduce this area," the reproduction unit will prioritize reproducing that area. For example, if the user instructs "Reproduce the ceiling," the reproduction unit will perform a detailed reproduction of the ceiling. For example, if the user instructs "Detail this part," the reproduction unit will perform a detailed reproduction of that part. In this way, by receiving voice commands, it can reproduce a specific area based on user instructions. Voice commands include, but are not limited to, examples such as speech recognition technology and command types. Some or all of the above processing in the reproduction unit may be performed using, for example, AI, or not using AI. For example, the reproduction unit can input a voice command into a generating AI, which can analyze the voice and reproduce a specific area.

[0089] The reproduction unit can enable multiple users to share a VR space simultaneously and facilitate collaborative work. For example, the reproduction unit can enable multiple users to share a VR space simultaneously and facilitate collaborative work. For example, the reproduction unit can support communication between users and enable real-time exchange of opinions. For example, the reproduction unit can share user location information to improve the efficiency of collaborative work. This enables collaborative work by allowing multiple users to share a VR space simultaneously. Examples of how multiple users can share a VR space simultaneously include, but are not limited to, network technologies and synchronization methods. Some or all of the processing described above in the reproduction unit may be performed using, for example, AI, or not using AI. For example, the reproduction unit can input data from multiple users into a generating AI, which can then analyze the data and share the VR space.

[0090] The control unit can estimate the user's emotions and adjust the operation method based on the estimated emotions. For example, if the user is tense, the control unit provides a simple and intuitive operation method. For example, if the user is relaxed, the control unit provides detailed operation options and suggests a customizable operation method. For example, if the user is in a hurry, the control unit prioritizes voice input to enable quick operation. This allows for intuitive operation by adjusting the operation method based on the user's emotions. Adjustment of the operation method includes, but is not limited to, examples such as changing the interface or simplifying operations. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, examples such as text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI or not using AI. For example, the control unit can input user facial expression data into a generative AI, which can estimate emotions and adjust the operation method.

[0091] The control unit can provide haptic feedback, allowing the user to gain a physical sensation. For example, the control unit can provide haptic feedback, allowing the user to feel the sensation of touching an object. For example, the control unit can provide vibration feedback, allowing the user to physically feel the result of an operation. For example, the control unit can provide pressure feedback, allowing the user to adjust the strength of the operation. In this way, by providing haptic feedback, the user can gain a physical sensation. Haptic feedback includes, but is not limited to, vibration feedback and pressure feedback. Some or all of the above processing in the control unit may be performed, for example, using AI, or not using AI. For example, the control unit can input haptic feedback data into a generating AI, which can then analyze the data to provide haptic feedback.

[0092] The control unit can perform operations based on the user's hand movements using gesture recognition. For example, the control unit can operate menus based on the user's hand movements using gesture recognition. For example, the control unit can move objects based on the user's hand movements using gesture recognition. For example, the control unit can zoom in and zoom out based on the user's hand movements using gesture recognition. In this way, operations can be performed based on the user's hand movements by using gesture recognition. Gesture recognition includes, but is not limited to, the sensors and recognition algorithms used. Some or all of the above-described processes in the control unit may be performed using, for example, AI, or not using AI. For example, the control unit can input gesture recognition data into a generating AI, and the generating AI can analyze the data and perform operations.

[0093] The control unit can estimate the user's emotions and determine the priority of operations based on the estimated emotions. For example, if the user is excited, the control unit will prioritize important operations and provide results quickly. If the user is relaxed, the control unit will perform operations in a balanced manner and provide detailed results. If the user is stressed, the control unit will prioritize simple operations to reduce the user's burden. This enables efficient operation by determining the priority of operations based on the user's emotions. Prioritization of operations includes, but is not limited to, importance assessment and real-time adjustments. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI or not using AI. For example, the control unit can input user facial expression data into a generative AI, which can estimate emotions and determine the priority of operations.

[0094] The control unit can accept voice commands and perform specific operations based on user instructions. For example, if the user gives the voice command "Move this object," the control unit will move that object. For example, if the user gives the command "Zoom in," the control unit will zoom in. For example, if the user gives the command "Open the menu," the control unit will open the menu. In this way, by accepting voice commands, the control unit can perform specific operations based on user instructions. Voice commands include, but are not limited to, voice recognition technology and command types. Some or all of the above processing in the control unit may be performed using, for example, AI, or not using AI. For example, the control unit can input a voice command into a generating AI, which can analyze the voice and perform a specific operation.

[0095] The control unit can enable multiple users to operate simultaneously and facilitate collaborative work. For example, the control unit can enable multiple users to operate simultaneously and facilitate collaborative work. For example, the control unit can synchronize user operations to support real-time collaborative work. For example, the control unit can share user operation histories to improve the efficiency of collaborative work. This enables collaborative work through simultaneous operation by multiple users. Simultaneous operation by multiple users includes, but is not limited to, network technologies and synchronization methods. Some or all of the above-described processes in the control unit may be performed using, for example, AI, or not. For example, the control unit can input operation data from multiple users into a generating AI, which can then analyze the data to enable collaborative work.

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

[0097] The VR reproduction system can further estimate the user's emotions and adjust the ambient sounds in the VR space based on those estimated emotions. For example, if the user is relaxed, calming music or nature sounds can be played to maintain a relaxed atmosphere. If the user is concentrating, ambient sounds can be minimized to enhance concentration. If the user is stressed, relaxing music can be played to reduce stress. By adjusting ambient sounds based on the user's emotions, a more comfortable VR experience can be provided. Adjustments to ambient sounds include, but are not limited to, volume and type of sound. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reproduction unit may be performed using AI, or not using AI. For example, the reproduction unit can input the user's facial expression data into the generative AI, which can then estimate emotions and adjust the ambient sounds.

[0098] The VR reproduction system can further estimate the user's emotions and adjust the lighting in the VR space based on the estimated emotions. For example, if the user is relaxed, warm-colored soft lighting can be used to create a relaxed atmosphere. If the user is concentrating, white-colored bright lighting can be used to enhance concentration. If the user is stressed, calm lighting can be used to reduce stress. In this way, a more comfortable VR experience can be provided by adjusting the lighting based on the user's emotions. Lighting adjustments include, but are not limited to, color temperature and brightness. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the user's facial expression data into the generative AI, which can estimate emotions and adjust the lighting.

[0099] The VR reproduction system can further estimate the user's emotions and adjust the VR space interface based on those emotions. For example, if the user is relaxed, a simple and intuitive interface can be provided to reduce the burden of operation. If the user is focused, detailed options can be provided to support efficient operation. If the user is stressed, the operation can be simplified to reduce stress. In this way, a more comfortable VR experience can be provided by adjusting the interface based on the user's emotions. Interface adjustments include, but are not limited to, button placement and menu structure. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI, or not using AI. For example, the control unit can input the user's facial expression data into the generative AI, which can estimate emotions and adjust the interface.

[0100] The VR reproduction system can further estimate the user's emotions and adjust the navigation of the VR space based on the estimated emotions. For example, if the user is relaxed, it can provide slow-paced navigation to maintain a relaxed atmosphere. If the user is focused, it can provide quick and efficient navigation to increase work efficiency. If the user is stressed, it can provide simple and intuitive navigation to reduce stress. In this way, a more comfortable VR experience can be provided by adjusting the navigation based on the user's emotions. Navigation adjustments include, but are not limited to, movement speed and guidance methods. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the user's facial expression data into the generative AI, which can estimate emotions and adjust the navigation.

[0101] The VR reproduction system can further estimate the user's emotions and adjust the feedback in the VR space based on the estimated emotions. For example, if the user is relaxed, gentle feedback can be provided to maintain a relaxed atmosphere. If the user is focused, detailed and specific feedback can be provided to improve work efficiency. If the user is stressed, simple and positive feedback can be provided to reduce stress. In this way, a more comfortable VR experience can be provided by adjusting the feedback based on the user's emotions. Adjustment of feedback includes, but is not limited to, the content and timing of the feedback. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the control unit may be performed using, for example, AI, or not using AI. For example, the control unit can input the user's facial expression data into the generative AI, which can estimate emotions and adjust the feedback.

[0102] The VR reproduction system can further integrate different data sources to generate more detailed 3D models. For example, it can integrate satellite data to generate a 3D model containing extensive topographic information. It can integrate aerial data from drones to generate a 3D model containing detailed information from advanced viewpoints. It can integrate ground sensor data to generate a 3D model containing detailed physical properties of the Earth's surface. In this way, by integrating different data sources, it is possible to generate more detailed and accurate 3D models. Different data sources include, but are not limited to, satellite data, drone data, and ground sensor data. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data acquired from different data sources into a generating AI, which can then analyze the data to generate a detailed 3D model.

[0103] The VR reproduction system can also collect data in real time and instantly update the 3D model. For example, it can collect real-time progress data at a construction site and reflect it in the 3D model. It can collect real-time information on the situation at a disaster site to support a rapid response. It can collect real-time data on the flow of people at an event venue to understand the congestion level. As a result, by collecting data in real time and instantly updating the 3D model, it is possible to provide a 3D model that reflects the latest information. Real-time data collection includes, but is not limited to, sensors and cameras. Some or all of the above processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input the data acquired in real time into a generating AI, which can analyze the data and instantly update the 3D model.

[0104] The VR reproduction system can further improve its analytical accuracy by combining different analytical algorithms. For example, clustering algorithms and regression analysis can be combined to analyze data characteristics in detail. Deep learning and machine learning can be combined to analyze complex data patterns. Statistical analysis and time series analysis can be combined to analyze data fluctuations in detail. This allows for improved analytical accuracy by combining different analytical algorithms. These different analytical algorithms include, but are not limited to, clustering, regression analysis, and deep learning. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input different analytical algorithms into a generating AI, which can then analyze the data to improve analytical accuracy.

[0105] The VR reproduction system can also accept voice commands and perform specific analyses based on user instructions. For example, if the user gives the voice command "Analyze this data," that data can be prioritized for analysis. If the user instructs "Perform a detailed analysis," a detailed analysis can be performed. If the user instructs "Perform a simple analysis," a simple analysis can be performed. In this way, by accepting voice commands, the system can perform specific analyses based on user instructions. Voice commands include, but are not limited to, examples such as speech recognition technology and command types. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input a voice command into a generating AI, which can analyze the voice and perform a specific analysis.

[0106] The VR reproduction system can further enable multiple users to share a VR space simultaneously, facilitating collaborative work. For example, multiple users can share a VR space simultaneously and collaborate on design work. Multiple users can share a VR space simultaneously and exchange opinions in real time. Multiple users can share a VR space simultaneously and collaboratively solve problems. This enables collaborative work by allowing multiple users to share a VR space simultaneously. Examples of how multiple users can share a VR space simultaneously include, but are not limited to, network technology and synchronization methods. Some or all of the above-described processes in the reproduction unit may be performed using, for example, AI, or not using AI. For example, the reproduction unit can input data from multiple users into a generating AI, which can then analyze the data and share the VR space.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The collection unit collects information about a specific space. For example, a 3D laser scanner is used to collect detailed 3D data of the space, obtaining information such as the dimensions of the space, temperature, and the arrangement of objects. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes the collected data in conjunction with AI and a database to gather detailed information such as room dimensions, temperature, and object placement. Step 3: The reproduction unit reproduces the space in VR based on the information analyzed by the analysis unit. For example, the space can be reproduced using VR goggles and gloves, allowing users to move freely within the space and zoom in and out. Step 4: The control unit manipulates the space recreated by the reproduction unit. For example, using VR goggles and gloves, the user can zoom in and out of the space, or measure length and temperature.

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

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

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

[0112] Each of the multiple elements described above, including the data collection unit, analysis unit, reproduction unit, and operation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the data collection unit is implemented by the camera 42 of the smart device 14 or the identification processing unit 290 of the data processing device 12. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the collected data in cooperation with the AI ​​and database 24. The reproduction unit is implemented by the control unit 46A of the smart device 14 and reproduces the space using VR goggles and gloves. The operation unit is implemented by the control unit 46A of the smart device 14 and operates within the space using VR goggles and gloves. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the data collection unit, analysis unit, reproduction unit, and operation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the camera 42 of the smart glasses 214 or the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data in cooperation with the AI ​​and the database 24. The reproduction unit is implemented, for example, by the control unit 46A of the smart glasses 214, and reproduces the space using VR goggles and gloves. The operation unit is implemented, for example, by the control unit 46A of the smart glasses 214, and operates within the space using VR goggles and gloves. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the data collection unit, analysis unit, reproduction unit, and operation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the camera 42 of the headset terminal 314 and the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data in cooperation with the AI ​​and database 24. The reproduction unit is implemented by the control unit 46A of the headset terminal 314 and reproduces the space using VR goggles and gloves. The operation unit is implemented by the control unit 46A of the headset terminal 314 and operates within the space using VR goggles and gloves. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 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.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the data collection unit, analysis unit, reproduction unit, and operation unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the camera 42 of the robot 414 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data in cooperation with the AI ​​and the database 24. The reproduction unit is implemented by the control unit 46A of the robot 414 and reproduces the space using VR goggles and gloves. The operation unit is implemented by the control unit 46A of the robot 414 and operates within the space using VR goggles and gloves. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A collection unit that collects information about a specific space, An analysis unit analyzes the information collected by the aforementioned collection unit, A reproduction unit that reproduces the space in VR based on the information analyzed by the aforementioned analysis unit, The system includes an operating unit for operating within the space reproduced by the reproduction unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Use a 3D laser scanner to collect detailed 3D data of the space. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed in conjunction with AI and databases to gather detailed information about the target room. The system described in Appendix 1, characterized by the features described herein. (Note 4) The reproduction unit is, Recreate the space in VR based on the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned operating unit is Using VR goggles and gloves, you can zoom in and out of a virtual space, and measure length and temperature. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of 3D data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is By collecting data from different angles and heights, a more detailed 3D model is generated. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Automatically adjusts collection methods in response to environmental changes. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It accepts voice commands and collects data from specific areas based on user instructions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is By combining multiple sensors, detailed physical properties of the space are collected. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The collected data is analyzed in real time, and feedback is provided immediately. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Applying different analysis algorithms provides the optimal analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, Improve analysis accuracy by referencing different data sources. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It accepts voice commands and performs specific analyses based on user instructions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The reproduction unit is, It estimates the user's emotions and adjusts the display method of the VR space based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The reproduction unit is, Provide views from different perspectives and allow users to freely switch between viewpoints. The system described in Appendix 1, characterized by the features described herein. (Note 20) The reproduction unit is, It simulates environmental changes and provides a more realistic VR space. The system described in Appendix 1, characterized by the features described herein. (Note 21) The reproduction unit is, It estimates the user's emotions and determines the priority of the spaces to recreate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The reproduction unit is, It accepts voice commands and recreates specific areas based on user instructions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The reproduction unit is, This allows multiple users to share a VR space simultaneously, enabling collaborative work. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned operating unit is It estimates the user's emotions and adjusts the operation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned operating unit is Provide haptic feedback, allowing users to experience physical sensations. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned operating unit is Gesture recognition is used to perform operations based on the user's hand movements. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned operating unit is It estimates the user's emotions and determines the priority of actions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned operating unit is It accepts voice commands and performs specific actions based on user instructions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned operating unit is Allows multiple users to operate simultaneously, enabling collaborative work. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects information about a specific space, An analysis unit analyzes the information collected by the aforementioned collection unit, A reproduction unit that reproduces the space in VR based on the information analyzed by the aforementioned analysis unit, The system includes an operating unit for operating within the space reproduced by the reproduction unit. A system characterized by the following features.

2. The aforementioned collection unit is Use a 3D laser scanner to collect detailed 3D data of the space. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed in conjunction with AI and a database to gather detailed information about the target room. The system according to feature 1.

4. The reproduction unit is, Recreate the space in VR based on the collected information. The system according to feature 1.

5. The aforementioned operating unit is Using VR goggles and gloves, you can zoom in and out of a virtual space, and measure length and temperature. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of 3D data collection based on the estimated user emotions. The system according to feature 1.

7. The aforementioned collection unit is By collecting data from different angles and heights, a more detailed 3D model is generated. The system according to feature 1.

8. The aforementioned collection unit is Automatically adjusts collection methods in response to environmental changes. The system according to feature 1.

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