System

Abstract

An object of a system according to an embodiment is to generate a high-quality 3D model based on satellite 3D.SOLUTION: A system includes a satellite 3D data-acquiring part, an analysis part, a mapping part, and a generation part. The satellite 3D information acquirer acquires satellite 3D information. The analyzing section analyzes the satellite 3D information acquired by the satellite 3D information acquiring section. The mapping unit maps 2D textures based on the information analyzed by the analysis unit. The generation unit generates a high-quality 2D model on the basis of the 3D textures mapped by the mapping unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the texture of geographic 3D models created from satellite 3D data was rough, making it difficult to use as a digital twin.

[0005] The system according to the embodiment aims to generate a high-quality 3D model based on satellite 3D data. [Means for solving the problem]

[0006] The system according to the embodiment includes a satellite 3D data acquisition unit, an analysis unit, a mapping unit, and a generation unit. The satellite 3D data acquisition unit acquires satellite 3D data. The analysis unit analyzes the satellite 3D data acquired by the satellite 3D data acquisition unit. The mapping unit maps a 2D texture based on the data analyzed by the analysis unit. The generation unit generates a high-quality 3D model based on the 2D texture mapped by the mapping unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate a high-quality 3D model based on satellite 3D data. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A 3D model creation system according to an embodiment of the present invention is a system that uses generative AI to map the 2D texture of a satellite and create a realistic 3D model. As a result, the 3D model creation system can create a realistic 3D model by mapping the 2D texture of the satellite.

[0029] A 3D model creation system according to an embodiment includes a satellite 3D data acquisition unit, an analysis unit, a mapping unit, and a generation unit. The satellite 3D data acquisition unit acquires satellite 3D data. For example, the satellite 3D data acquisition unit acquires LiDAR data. The satellite 3D data acquisition unit can also acquire stereo camera data. The satellite 3D data acquisition unit can also acquire data directly from a satellite. The analysis unit analyzes the satellite 3D data acquired by the satellite 3D data acquisition unit. For example, the analysis unit filters the data. The analysis unit can also perform noise removal. The analysis unit can also perform feature extraction. The mapping unit maps a 2D texture based on the data analyzed by the analysis unit. For example, the mapping unit calculates texture coordinates. The mapping unit can also position the 2D texture using a mapping algorithm. The mapping unit can also perform appropriate positioning of the 2D texture. The generation unit generates a high-quality 3D model based on the 2D texture mapped by the mapping unit. For example, the generation unit adjusts the number of polygons. The generation unit can also increase texture resolution. The generation unit can also pursue realism. As a result, the 3D model creation system according to the embodiment can use generation AI to map the 2D texture of the satellite and create a realistic 3D model.

[0030] The analysis unit corrects for the effects of weather and time of day when acquiring satellite 3D data, enabling more accurate data to be extracted. For example, when acquiring satellite 3D data, the generation AI analyzes weather data in real time and corrects for the effects of cloudiness and rain. For example, it removes the effects of clouds and extracts detailed data of the earth's surface. When acquiring satellite 3D data, the generation AI also corrects for changes in light depending on the time of day, providing consistent data regardless of day or night. For example, it converts nighttime data into daytime light conditions. When acquiring satellite 3D data, the generation AI also takes seasonal variations into account and integrates seasonal data to provide consistent topographical information. For example, it removes the effects of snow in winter to reveal detailed earth's surface. This allows more accurate data to be obtained by correcting for the effects of weather and time of day.

[0031] The analysis unit can detect changes in terrain and buildings in real time and reflect the latest information. For example, the generation AI in the analysis unit analyzes satellite 3D data and detects changes in terrain due to natural disasters such as earthquakes and floods in real time. For example, it immediately reflects changes in terrain after an earthquake. The analysis unit also detects changes in buildings due to urban development and construction work in real time and provides the latest city models. For example, it immediately reflects information on new buildings and demolition. The analysis unit also detects changes in terrain in agriculture and forest management in real time and provides the latest status of farmland and forests. For example, it immediately reflects changes in farmland after harvest. This allows changes in terrain and buildings to be detected in real time and reflect the latest information.

[0032] The analysis unit can also be applied to ocean and outer space data, covering a wide range of environmental data. For example, the analysis unit uses generative AI to acquire and analyze ocean data and create detailed models of seafloor topography and ocean currents. For example, it analyzes changes in seafloor topography and ocean currents in real time. The analysis unit also uses generative AI to acquire and analyze outer space data and create detailed models of planets and moons. For example, it analyzes the topography of Mars and craters on the Moon in detail. The analysis unit also uses generative AI to acquire and analyze polar data and create detailed models of ice sheets and glaciers. For example, it analyzes changes in the Antarctic ice sheet and the flow of Arctic glaciers in real time. This allows it to be applied to ocean and outer space data, covering a wide range of environmental data.

[0033] The analysis unit can integrate different satellite data sources and extract optimal information from multiple data sets. For example, the analysis unit integrates different satellite data sources (e.g., weather satellites and Earth observation satellites), and the generation AI extracts optimal information. For example, it combines weather data and topographical data for analysis. The analysis unit also integrates satellite data of different resolutions, and the generation AI extracts high-resolution detailed information. For example, it combines low-resolution wide-area data and high-resolution local data for analysis. The analysis unit also integrates satellite data acquired at different times, and the generation AI analyzes temporal variations. For example, it combines seasonal data to analyze variations in topography. This makes it possible to integrate different satellite data sources and extract optimal information from multiple data sets.

[0034] The mapping unit automatically adjusts the color tone and brightness of 2D textures to achieve a more natural and realistic appearance. For example, the mapping unit's generation AI automatically adjusts the color tone of 2D textures to reproduce natural colors. For example, it makes the colors of building walls and roofs more realistic. The mapping unit's generation AI also automatically adjusts the brightness of 2D textures to reproduce realistic shadows. For example, it expresses shadows according to the position of the sun. The mapping unit's generation AI also automatically adjusts the contrast of 2D textures to display details clearly. For example, it reproduces the texture of grass on the ground and roads in detail. This allows the color tone and brightness of 2D textures to be automatically adjusted to achieve a more natural and realistic appearance.

[0035] The mapping unit automatically maps 2D textures for different seasons and time periods, recreating the sense of the seasons and the changes in time of day. For example, the generation AI automatically maps 2D textures for each season (spring, summer, autumn, and winter) to recreate the sense of the seasons. For example, it realistically depicts cherry blossoms in spring and autumn leaves in autumn. The mapping unit also automatically maps 2D textures for the time of day and night to recreate the changes in time of day. For example, it realistically depicts bright daytime scenery and street light at night. The mapping unit also automatically maps 2D textures for specific events (such as Christmas or Halloween) to recreate the atmosphere of the event. For example, it realistically depicts Christmas illuminations and Halloween decorations. This allows the automatic mapping of 2D textures for different seasons and time periods to recreate the sense of the seasons and the changes in time of day.

[0036] The mapping unit can also be applied to the internal structure and interior design of a building, creating a detailed internal model. For example, the generation AI in the mapping unit analyzes the internal structure of a building and maps 2D textures to create a detailed internal model. For example, it realistically reproduces the textures of the walls and floors of a room. The generation AI in the mapping unit also analyzes the interior design and maps 2D textures to create a detailed internal model. For example, it realistically reproduces the textures of furniture and decorative items. The generation AI in the mapping unit also analyzes the internal lighting of a building and maps 2D textures to create a detailed internal model. For example, it realistically reproduces the reflections and shadows of lighting. This allows the system to be applied to the internal structure and interior design of a building, creating a detailed internal model.

[0037] The mapping unit can combine historical data and future prediction data to recreate past and future landscapes. For example, the mapping unit uses a generation AI to analyze historical data and map 2D textures to recreate past landscapes. For example, it can realistically recreate ancient cities and pre-war cityscapes. The mapping unit also uses a generation AI to analyze future prediction data and map 2D textures to recreate future landscapes. For example, it can realistically recreate future urban planning and architectural designs. The mapping unit also uses a generation AI to integrate past and future data and map 2D textures to recreate landscape changes over time. For example, it can realistically recreate the transition of a city from the past to the future. This allows it to combine historical data and future prediction data to recreate past and future landscapes.

[0038] The generation unit can automatically emphasize the details of the 3D model to create a more realistic model. For example, the generation unit's AI automatically emphasizes the decoration of a building to create a 3D model that is realistic down to the smallest detail. For example, the decoration of a window frame or door is reproduced in detail. The generation unit's AI also automatically emphasizes the fine features of nature (for example, the texture of leaves and grass) to reproduce a more realistic natural landscape. The generation unit's AI also automatically emphasizes the details of a city (for example, streetlights and signs) to reproduce a realistic urban atmosphere. This automatically emphasizes the details of the 3D model to create a more realistic model.

[0039] The generation unit simulates the physical properties of the 3D model and can reproduce physical behavior that is close to reality. For example, the generation AI in the generation unit simulates the materials of a building and reproduces physical behavior that is close to reality. For example, it realistically expresses the reflection of glass and the luster of metal. The generation unit also simulates the physical properties of nature (for example, the flow of water and the effects of wind) and reproduces realistic natural landscapes. For example, it realistically expresses the flow of a river and the swaying of trees. The generation unit also simulates the physical properties of a city (for example, the movement of cars and the flow of people) and reproduces a realistic urban environment. For example, it realistically expresses the flow of traffic and the movement of people. In this way, the physical properties of the 3D model can be simulated and physical behavior that is close to reality can be reproduced.

[0040] The generation unit can apply the created 3D model to a virtual reality or augmented reality environment to provide an interactive experience. For example, the generation unit uses a generation AI to apply the 3D model to a virtual reality environment, allowing a user to have an interactive experience through a VR headset. For example, exploring a city's panoramic view in VR. The generation unit can also apply the 3D model to an augmented reality environment, allowing a user to have an interactive experience through a smartphone or AR glasses. For example, exploring the interior of a building in AR. The generation unit can also apply the 3D model to a virtual reality or augmented reality environment to provide an interactive experience in education or training settings. For example, learning about historical buildings in VR. This allows the creation of the 3D model to be applied to a virtual reality or augmented reality environment to provide an interactive experience.

[0041] The generation unit can apply the creation of 3D models to simulations in the education and medical fields to support specialized training and diagnosis. For example, the generation unit's generative AI applies 3D models to simulations in the education field, allowing students to learn in a realistic environment. For example, 3D models are used in geography and history classes. The generation unit can also apply 3D models to simulations in the medical field, allowing doctors and nurses to receive specialized training. For example, surgery simulations are performed using 3D models. The generation unit can also apply 3D models to medical diagnosis, realistically reproducing a patient's condition to support diagnosis. For example, a 3D model is created based on CT scan data and used by doctors for diagnosis. This allows the creation of 3D models to be applied to simulations in the education and medical fields to support specialized training and diagnosis.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The analysis unit can integrate different satellite data sources and extract the optimal information from multiple data sets. For example, different satellite data sources (such as weather satellites and Earth observation satellites) can be integrated, and the generation AI can extract the optimal information. For example, weather data and topographical data can be combined and analyzed. The analysis unit can also integrate satellite data of different resolutions, and the generation AI can extract high-resolution detailed information. For example, low-resolution wide-area data can be combined and analyzed with high-resolution local data. The analysis unit can also integrate satellite data acquired at different times, and the generation AI can analyze temporal variations. For example, seasonal data can be combined to analyze topographical variations. This makes it possible to integrate different satellite data sources and extract the optimal information from multiple data sets.

[0044] The analysis unit can also be applied to ocean and space data, covering a wide range of environmental data. For example, it uses generative AI to acquire and analyze ocean data to create detailed models of seafloor topography and ocean currents. For example, it analyzes changes in seafloor topography and ocean currents in real time. The analysis unit also uses generative AI to acquire and analyze space data to create detailed models of planets and moons. For example, it analyzes the topography of Mars and craters on the Moon in detail. The analysis unit also uses generative AI to acquire and analyze polar data to create detailed models of ice sheets and glaciers. For example, it analyzes changes in the Antarctic ice sheet and the flow of Arctic glaciers in real time. This allows it to be applied to ocean and space data, covering a wide range of environmental data.

[0045] The mapping unit automatically adjusts the color tone and brightness of 2D textures to achieve a more natural and realistic appearance. For example, the generation AI automatically adjusts the color tone of 2D textures to reproduce natural colors. For example, it makes the colors of building walls and roofs more realistic. The mapping unit also automatically adjusts the brightness of 2D textures to reproduce realistic shadows. For example, it expresses shadows according to the position of the sun. The mapping unit also automatically adjusts the contrast of 2D textures to display details clearly. For example, it reproduces the texture of grass on the ground and roads in detail. This allows the color tone and brightness of 2D textures to be automatically adjusted to achieve a more natural and realistic appearance.

[0046] The mapping unit automatically maps 2D textures for different seasons and time periods, recreating the sense of the seasons and the changes in time of day. For example, the generation AI automatically maps 2D textures for each season (spring, summer, autumn, and winter) to recreate the sense of the seasons. For example, it realistically depicts cherry blossoms in spring and autumn leaves in autumn. The mapping unit also automatically maps 2D textures according to the time of day and night to recreate the changes in time of day. For example, it realistically depicts bright daytime scenery and street light at night. The mapping unit also automatically maps 2D textures according to specific events (such as Christmas or Halloween) to recreate the atmosphere of the event. For example, it realistically depicts Christmas illuminations and Halloween decorations. This allows the automatic mapping of 2D textures for different seasons and time periods to recreate the sense of the seasons and the changes in time of day.

[0047] The mapping unit can also be applied to the internal structure and interior design of a building to create a detailed internal model. For example, the generative AI analyzes the internal structure of a building and maps 2D textures to create a detailed internal model. For example, it realistically reproduces the textures of the walls and floors of a room. The mapping unit also analyzes the interior design and maps 2D textures to create a detailed internal model. For example, it realistically reproduces the textures of furniture and decorative items. The mapping unit also analyzes the internal lighting of a building and maps 2D textures to create a detailed internal model. For example, it realistically reproduces the reflections and shadows of lighting. This allows the system to be applied to the internal structure and interior design of a building to create a detailed internal model.

[0048] The mapping unit can combine historical data and future prediction data to recreate past and future landscapes. For example, the generation AI analyzes historical data and maps 2D textures to recreate past landscapes. For example, it can realistically recreate ancient cities and pre-war cityscapes. The mapping unit also analyzes future prediction data and maps 2D textures to recreate future landscapes. For example, it can realistically recreate future urban planning and architectural designs. The mapping unit also integrates past and future data and maps 2D textures to recreate landscape changes over time. For example, it can realistically recreate the transition of a city from the past to the future. This allows it to combine historical data and future prediction data to recreate past and future landscapes.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The satellite 3D data acquisition unit acquires satellite 3D data. For example, it can acquire LiDAR data, stereo camera data, or data directly from the satellite. Step 2: The analysis unit analyzes the satellite 3D data acquired by the satellite 3D data acquisition unit. For example, it can perform data filtering, noise removal, and feature extraction. Step 3: The mapping unit maps the 2D texture based on the data analyzed by the analysis unit. For example, the mapping unit can calculate texture coordinates and position the 2D texture using a mapping algorithm. Step 4: The generator generates a high-quality 3D model based on the 2D texture mapped by the mapping unit. For example, it can adjust the number of polygons, improve texture resolution, or pursue realism.

[0051] (Example 2) A 3D model creation system according to an embodiment of the present invention is a system that uses generative AI to map the 2D texture of a satellite and create a realistic 3D model. As a result, the 3D model creation system can create a realistic 3D model by mapping the 2D texture of the satellite.

[0052] A 3D model creation system according to an embodiment includes a satellite 3D data acquisition unit, an analysis unit, a mapping unit, and a generation unit. The satellite 3D data acquisition unit acquires satellite 3D data. For example, the satellite 3D data acquisition unit acquires LiDAR data. The satellite 3D data acquisition unit can also acquire stereo camera data. The satellite 3D data acquisition unit can also acquire data directly from a satellite. The analysis unit analyzes the satellite 3D data acquired by the satellite 3D data acquisition unit. For example, the analysis unit filters the data. The analysis unit can also perform noise removal. The analysis unit can also perform feature extraction. The mapping unit maps a 2D texture based on the data analyzed by the analysis unit. For example, the mapping unit calculates texture coordinates. The mapping unit can also position the 2D texture using a mapping algorithm. The mapping unit can also perform appropriate positioning of the 2D texture. The generation unit generates a high-quality 3D model based on the 2D texture mapped by the mapping unit. For example, the generation unit adjusts the number of polygons. The generation unit can also increase texture resolution. The generation unit can also pursue realism. As a result, the 3D model creation system according to the embodiment can use generation AI to map the 2D texture of the satellite and create a realistic 3D model.

[0053] The analysis unit corrects for the effects of weather and time of day when acquiring satellite 3D data, enabling more accurate data to be extracted. For example, when acquiring satellite 3D data, the generation AI analyzes weather data in real time and corrects for the effects of cloudiness and rain. For example, it removes the effects of clouds and extracts detailed data of the earth's surface. When acquiring satellite 3D data, the generation AI also corrects for changes in light depending on the time of day, providing consistent data regardless of day or night. For example, it converts nighttime data into daytime light conditions. When acquiring satellite 3D data, the generation AI also takes seasonal variations into account and integrates seasonal data to provide consistent topographical information. For example, it removes the effects of snow in winter to reveal detailed earth's surface. This allows more accurate data to be obtained by correcting for the effects of weather and time of day.

[0054] The analysis unit can detect changes in terrain and buildings in real time and reflect the latest information. For example, the generation AI in the analysis unit analyzes satellite 3D data and detects changes in terrain due to natural disasters such as earthquakes and floods in real time. For example, it immediately reflects changes in terrain after an earthquake. The analysis unit also detects changes in buildings due to urban development and construction work in real time and provides the latest city models. For example, it immediately reflects information on new buildings and demolition. The analysis unit also detects changes in terrain in agriculture and forest management in real time and provides the latest status of farmland and forests. For example, it immediately reflects changes in farmland after harvest. This allows changes in terrain and buildings to be detected in real time and reflect the latest information.

[0055] The analysis unit can prioritize analysis of geographical features that interest the user. For example, the analysis unit uses an emotion estimation function to prioritize analysis of tourist spots and famous places that interest the user. For example, popular tourist spots are analyzed in detail based on the user's search history and feedback. The analysis unit also uses the emotion estimation function to prioritize analysis of natural landscapes (e.g., mountains and lakes) that interest the user. For example, specific natural landscapes are analyzed in detail based on the user's emotional response. The analysis unit also uses the emotion estimation function to prioritize analysis of specific areas of a city that interest the user (e.g., areas with many historical buildings). For example, specific areas are analyzed in detail based on the user's emotional data. This allows the analysis of geographical features that interest the user to be prioritized.

[0056] The analysis unit can also be applied to ocean and outer space data, covering a wide range of environmental data. For example, the analysis unit uses generative AI to acquire and analyze ocean data and create detailed models of seafloor topography and ocean currents. For example, it analyzes changes in seafloor topography and ocean currents in real time. The analysis unit also uses generative AI to acquire and analyze outer space data and create detailed models of planets and moons. For example, it analyzes the topography of Mars and craters on the Moon in detail. The analysis unit also uses generative AI to acquire and analyze polar data and create detailed models of ice sheets and glaciers. For example, it analyzes changes in the Antarctic ice sheet and the flow of Arctic glaciers in real time. This allows it to be applied to ocean and outer space data, covering a wide range of environmental data.

[0057] The analysis unit can integrate different satellite data sources and extract optimal information from multiple data sets. For example, the analysis unit integrates different satellite data sources (e.g., weather satellites and Earth observation satellites), and the generation AI extracts optimal information. For example, it combines weather data and topographical data for analysis. The analysis unit also integrates satellite data of different resolutions, and the generation AI extracts high-resolution detailed information. For example, it combines low-resolution wide-area data and high-resolution local data for analysis. The analysis unit also integrates satellite data acquired at different times, and the generation AI analyzes temporal variations. For example, it combines seasonal data to analyze variations in topography. This makes it possible to integrate different satellite data sources and extract optimal information from multiple data sets.

[0058] The analysis unit can perform data analysis focusing on specific regions or events in which the user is interested. The analysis unit, for example, uses an emotion estimation function to perform data analysis focusing on specific regions in which the user is interested (e.g., tourist spots or famous places). For example, a detailed analysis of the specific region is performed based on the user's emotion data. The analysis unit also uses the emotion estimation function to perform data analysis focusing on specific events in which the user is interested (e.g., festivals or sporting events). For example, a detailed analysis of the event venue is performed based on the user's emotional response. The analysis unit also uses the emotion estimation function to perform data analysis focusing on specific natural phenomena in which the user is interested (e.g., aurora or meteor showers). For example, a detailed analysis of the natural phenomenon is performed based on the user's emotion data. This makes it possible to perform data analysis focusing on specific regions or events in which the user is interested.

[0059] The mapping unit automatically adjusts the color tone and brightness of 2D textures to achieve a more natural and realistic appearance. For example, the mapping unit's generation AI automatically adjusts the color tone of 2D textures to reproduce natural colors. For example, it makes the colors of building walls and roofs more realistic. The mapping unit's generation AI also automatically adjusts the brightness of 2D textures to reproduce realistic shadows. For example, it expresses shadows according to the position of the sun. The mapping unit's generation AI also automatically adjusts the contrast of 2D textures to display details clearly. For example, it reproduces the texture of grass on the ground and roads in detail. This allows the color tone and brightness of 2D textures to be automatically adjusted to achieve a more natural and realistic appearance.

[0060] The mapping unit automatically maps 2D textures for different seasons and time periods, recreating the sense of the seasons and the changes in time of day. For example, the generation AI automatically maps 2D textures for each season (spring, summer, autumn, and winter) to recreate the sense of the seasons. For example, it realistically depicts cherry blossoms in spring and autumn leaves in autumn. The mapping unit also automatically maps 2D textures for the time of day and night to recreate the changes in time of day. For example, it realistically depicts bright daytime scenery and street light at night. The mapping unit also automatically maps 2D textures for specific events (such as Christmas or Halloween) to recreate the atmosphere of the event. For example, it realistically depicts Christmas illuminations and Halloween decorations. This allows the automatic mapping of 2D textures for different seasons and time periods to recreate the sense of the seasons and the changes in time of day.

[0061] The mapping unit can generate 2D textures that reflect the user's preferred colors and designs. The mapping unit, for example, uses an emotion estimation function to generate 2D textures that reflect the user's preferred colors. For example, the mapping unit reproduces the user's preferred colors based on the user's emotion data. The mapping unit also uses the emotion estimation function to generate 2D textures that reflect the user's preferred designs. For example, the mapping unit reproduces the user's preferred design patterns based on the user's emotion data. The mapping unit also uses the emotion estimation function to generate 2D textures that reflect the user's preferred scenery and building styles. For example, the mapping unit reproduces the user's preferred scenery and building styles based on the user's emotion data. This makes it possible to generate 2D textures that reflect the user's preferred colors and designs.

[0062] The mapping unit can also be applied to the internal structure and interior design of a building, creating a detailed internal model. For example, the generation AI in the mapping unit analyzes the internal structure of a building and maps 2D textures to create a detailed internal model. For example, it realistically reproduces the textures of the walls and floors of a room. The generation AI in the mapping unit also analyzes the interior design and maps 2D textures to create a detailed internal model. For example, it realistically reproduces the textures of furniture and decorative items. The generation AI in the mapping unit also analyzes the internal lighting of a building and maps 2D textures to create a detailed internal model. For example, it realistically reproduces the reflections and shadows of lighting. This allows the system to be applied to the internal structure and interior design of a building, creating a detailed internal model.

[0063] The mapping unit can combine historical data and future prediction data to recreate past and future landscapes. For example, the mapping unit uses a generation AI to analyze historical data and map 2D textures to recreate past landscapes. For example, it can realistically recreate ancient cities and pre-war cityscapes. The mapping unit also uses a generation AI to analyze future prediction data and map 2D textures to recreate future landscapes. For example, it can realistically recreate future urban planning and architectural designs. The mapping unit also uses a generation AI to integrate past and future data and map 2D textures to recreate landscape changes over time. For example, it can realistically recreate the transition of a city from the past to the future. This allows it to combine historical data and future prediction data to recreate past and future landscapes.

[0064] The mapping unit can prioritize mapping scenes that evoke a specific emotion in the user. For example, the mapping unit uses an emotion estimation function to prioritize mapping scenery that the user can relax in. For example, natural landscapes and quiet places are realistically reproduced based on the user's emotion data. The mapping unit also uses the emotion estimation function to prioritize mapping scenes that excite the user. For example, a sporting event or concert venue is realistically reproduced based on the user's emotion data. The mapping unit also uses the emotion estimation function to prioritize mapping scenes that move the user. For example, a beautiful sunset or night view is realistically reproduced based on the user's emotion data. This allows the mapping of scenes that evoke a specific emotion in the user to be prioritized.

[0065] The generation unit can automatically emphasize the details of the 3D model to create a more realistic model. For example, the generation unit's AI automatically emphasizes the decoration of a building to create a 3D model that is realistic down to the smallest detail. For example, the decoration of a window frame or door is reproduced in detail. The generation unit's AI also automatically emphasizes the fine features of nature (for example, the texture of leaves and grass) to reproduce a more realistic natural landscape. The generation unit's AI also automatically emphasizes the details of a city (for example, streetlights and signs) to reproduce a realistic urban atmosphere. This automatically emphasizes the details of the 3D model to create a more realistic model.

[0066] The generation unit simulates the physical properties of the 3D model and can reproduce physical behavior that is close to reality. For example, the generation AI in the generation unit simulates the materials of a building and reproduces physical behavior that is close to reality. For example, it realistically expresses the reflection of glass and the luster of metal. The generation unit also simulates the physical properties of nature (for example, the flow of water and the effects of wind) and reproduces realistic natural landscapes. For example, it realistically expresses the flow of a river and the swaying of trees. The generation unit also simulates the physical properties of a city (for example, the movement of cars and the flow of people) and reproduces a realistic urban environment. For example, it realistically expresses the flow of traffic and the movement of people. In this way, the physical properties of the 3D model can be simulated and physical behavior that is close to reality can be reproduced.

[0067] The generation unit can incorporate visual effects that impress the user into the 3D model. For example, the generation unit uses an emotion estimation function to incorporate a sunset scene that impresses the user into the 3D model. For example, a beautiful sunset is realistically reproduced based on the user's emotion data. The generation unit also uses the emotion estimation function to incorporate a night view scene that impresses the user into the 3D model. For example, a shining night view is realistically reproduced based on the user's emotion data. The generation unit also uses the emotion estimation function to incorporate natural phenomena that impress the user (for example, an aurora or a meteor shower) into the 3D model. For example, a spectacular natural phenomenon is realistically reproduced based on the user's emotion data. This makes it possible to incorporate visual effects that impress the user into the 3D model.

[0068] The generation unit can apply the created 3D model to a virtual reality or augmented reality environment to provide an interactive experience. For example, the generation unit uses a generation AI to apply the 3D model to a virtual reality environment, allowing a user to have an interactive experience through a VR headset. For example, exploring a city's panoramic view in VR. The generation unit can also apply the 3D model to an augmented reality environment, allowing a user to have an interactive experience through a smartphone or AR glasses. For example, exploring the interior of a building in AR. The generation unit can also apply the 3D model to a virtual reality or augmented reality environment to provide an interactive experience in education or training settings. For example, learning about historical buildings in VR. This allows the creation of the 3D model to be applied to a virtual reality or augmented reality environment to provide an interactive experience.

[0069] The generation unit can apply the creation of 3D models to simulations in the education and medical fields to support specialized training and diagnosis. For example, the generation unit's generative AI applies 3D models to simulations in the education field, allowing students to learn in a realistic environment. For example, 3D models are used in geography and history classes. The generation unit can also apply 3D models to simulations in the medical field, allowing doctors and nurses to receive specialized training. For example, surgery simulations are performed using 3D models. The generation unit can also apply 3D models to medical diagnosis, realistically reproducing a patient's condition to support diagnosis. For example, a 3D model is created based on CT scan data and used by doctors for diagnosis. This allows the creation of 3D models to be applied to simulations in the education and medical fields to support specialized training and diagnosis.

[0070] The generation unit can reflect a scenario in which the user feels a particular emotion in the 3D model. The generation unit, for example, uses an emotion estimation function to reflect a scenario in which the user feels adventurous in the 3D model. For example, adventurous landscapes and buildings are realistically reproduced based on the user's emotion data. The generation unit also uses the emotion estimation function to reflect a scenario in which the user feels relaxed in the 3D model. For example, relaxing natural landscapes are realistically reproduced based on the user's emotion data. The generation unit also uses the emotion estimation function to reflect a scenario in which the user feels moved in the 3D model. For example, moving events and landscapes are realistically reproduced based on the user's emotion data. In this way, a scenario in which the user feels a particular emotion can be reflected in the 3D model.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The analysis unit can integrate different satellite data sources and extract the optimal information from multiple data sets. For example, different satellite data sources (such as weather satellites and Earth observation satellites) can be integrated, and the generation AI can extract the optimal information. For example, weather data and topographical data can be combined and analyzed. The analysis unit can also integrate satellite data of different resolutions, and the generation AI can extract high-resolution detailed information. For example, low-resolution wide-area data can be combined and analyzed with high-resolution local data. The analysis unit can also integrate satellite data acquired at different times, and the generation AI can analyze temporal variations. For example, seasonal data can be combined to analyze topographical variations. This makes it possible to integrate different satellite data sources and extract the optimal information from multiple data sets.

[0073] The analysis unit can prioritize analysis of geographical features that interest the user. For example, the analysis unit uses the emotion estimation function to prioritize analysis of tourist spots and famous places that interest the user. For example, popular tourist spots are analyzed in detail based on the user's search history and feedback. The analysis unit also uses the emotion estimation function to prioritize analysis of natural landscapes (e.g., mountains and lakes) that interest the user. For example, specific natural landscapes are analyzed in detail based on the user's emotional response. The analysis unit also uses the emotion estimation function to prioritize analysis of specific areas of a city that the user is interested in (e.g., areas with many historical buildings). For example, specific areas are analyzed in detail based on the user's emotional data. This allows the analysis of geographical features that interest the user to be prioritized.

[0074] The analysis unit can also be applied to ocean and space data, covering a wide range of environmental data. For example, it uses generative AI to acquire and analyze ocean data to create detailed models of seafloor topography and ocean currents. For example, it analyzes changes in seafloor topography and ocean currents in real time. The analysis unit also uses generative AI to acquire and analyze space data to create detailed models of planets and moons. For example, it analyzes the topography of Mars and craters on the Moon in detail. The analysis unit also uses generative AI to acquire and analyze polar data to create detailed models of ice sheets and glaciers. For example, it analyzes changes in the Antarctic ice sheet and the flow of Arctic glaciers in real time. This allows it to be applied to ocean and space data, covering a wide range of environmental data.

[0075] The analysis unit can perform data analysis focusing on specific regions or events in which the user is interested. For example, the emotion estimation function is used to perform data analysis focusing on specific regions in which the user is interested (e.g., tourist spots or famous places). For example, a detailed analysis of the specific region is performed based on the user's emotion data. The analysis unit also uses the emotion estimation function to perform data analysis focusing on specific events in which the user is interested (e.g., festivals or sporting events). For example, a detailed analysis of the event venue is performed based on the user's emotional response. The analysis unit also uses the emotion estimation function to perform data analysis focusing on specific natural phenomena in which the user is interested (e.g., aurora or meteor showers). For example, a detailed analysis of the natural phenomenon is performed based on the user's emotion data. This makes it possible to perform data analysis focusing on specific regions or events in which the user is interested.

[0076] The mapping unit automatically adjusts the color tone and brightness of 2D textures to achieve a more natural and realistic appearance. For example, the generation AI automatically adjusts the color tone of 2D textures to reproduce natural colors. For example, it makes the colors of building walls and roofs more realistic. The mapping unit also automatically adjusts the brightness of 2D textures to reproduce realistic shadows. For example, it expresses shadows according to the position of the sun. The mapping unit also automatically adjusts the contrast of 2D textures to display details clearly. For example, it reproduces the texture of grass on the ground and roads in detail. This allows the color tone and brightness of 2D textures to be automatically adjusted to achieve a more natural and realistic appearance.

[0077] The mapping unit automatically maps 2D textures for different seasons and time periods, recreating the sense of the seasons and the changes in time of day. For example, the generation AI automatically maps 2D textures for each season (spring, summer, autumn, and winter) to recreate the sense of the seasons. For example, it realistically depicts cherry blossoms in spring and autumn leaves in autumn. The mapping unit also automatically maps 2D textures according to the time of day and night to recreate the changes in time of day. For example, it realistically depicts bright daytime scenery and street light at night. The mapping unit also automatically maps 2D textures according to specific events (such as Christmas or Halloween) to recreate the atmosphere of the event. For example, it realistically depicts Christmas illuminations and Halloween decorations. This allows the automatic mapping of 2D textures for different seasons and time periods to recreate the sense of the seasons and the changes in time of day.

[0078] The mapping unit can generate 2D textures that reflect the colors and designs that the user prefers. For example, the mapping unit uses an emotion estimation function to generate 2D textures that reflect the colors that the user prefers. For example, the mapping unit reproduces the user's preferred colors based on the user's emotion data. The mapping unit also uses the emotion estimation function to generate 2D textures that reflect the user's preferred designs. For example, the mapping unit reproduces the user's preferred design patterns based on the user's emotion data. The mapping unit also uses the emotion estimation function to generate 2D textures that reflect the user's preferred scenery and building styles. For example, the mapping unit reproduces the user's preferred scenery and building styles based on the user's emotion data. This makes it possible to generate 2D textures that reflect the user's preferred colors and designs.

[0079] The mapping unit can also be applied to the internal structure and interior design of a building to create a detailed internal model. For example, the generative AI analyzes the internal structure of a building and maps 2D textures to create a detailed internal model. For example, it realistically reproduces the textures of the walls and floors of a room. The mapping unit also analyzes the interior design and maps 2D textures to create a detailed internal model. For example, it realistically reproduces the textures of furniture and decorative items. The mapping unit also analyzes the internal lighting of a building and maps 2D textures to create a detailed internal model. For example, it realistically reproduces the reflections and shadows of lighting. This allows the system to be applied to the internal structure and interior design of a building to create a detailed internal model.

[0080] The mapping unit can combine historical data and future prediction data to recreate past and future landscapes. For example, the generation AI analyzes historical data and maps 2D textures to recreate past landscapes. For example, it can realistically recreate ancient cities and pre-war cityscapes. The mapping unit also analyzes future prediction data and maps 2D textures to recreate future landscapes. For example, it can realistically recreate future urban planning and architectural designs. The mapping unit also integrates past and future data and maps 2D textures to recreate landscape changes over time. For example, it can realistically recreate the transition of a city from the past to the future. This allows it to combine historical data and future prediction data to recreate past and future landscapes.

[0081] The generation unit can reflect a scenario in which the user feels a particular emotion in the 3D model. For example, the emotion estimation function is used to reflect a scenario in which the user feels adventurous in the 3D model. For example, adventurous landscapes and buildings are realistically reproduced based on the user's emotion data. The generation unit also uses the emotion estimation function to reflect a scenario in which the user feels relaxed in the 3D model. For example, relaxing natural landscapes are realistically reproduced based on the user's emotion data. The generation unit also uses the emotion estimation function to reflect a scenario in which the user feels moved in the 3D model. For example, moving events and landscapes are realistically reproduced based on the user's emotion data. In this way, a scenario in which the user feels a particular emotion can be reflected in the 3D model.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The satellite 3D data acquisition unit acquires satellite 3D data. For example, it can acquire LiDAR data, stereo camera data, or data directly from the satellite. Step 2: The analysis unit analyzes the satellite 3D data acquired by the satellite 3D data acquisition unit. For example, it can perform data filtering, noise removal, and feature extraction. Step 3: The mapping unit maps the 2D texture based on the data analyzed by the analysis unit. For example, the mapping unit can calculate texture coordinates and position the 2D texture using a mapping algorithm. Step 4: The generator generates a high-quality 3D model based on the 2D texture mapped by the mapping unit. For example, it can adjust the number of polygons, improve texture resolution, or pursue realism.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] 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.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a satellite 3D data acquisition unit that acquires satellite 3D data; an analysis unit that analyzes the satellite 3D data acquired by the satellite 3D data acquisition unit; a mapping unit that maps a 2D texture based on the data analyzed by the analysis unit; a generation unit that generates a high-quality 3D model based on the 2D texture mapped by the mapping unit. A system characterized by:

2. The analysis unit When acquiring the satellite 3D data, the effects of weather and time of day are corrected to extract more accurate data.

2. The system of claim 1.

3. The analysis unit Detects changes in terrain and buildings in real time and reflects the latest information 2. The system of claim 1.

4. The analysis unit Prioritizing analysis of the geographical features that are of interest to the user 2. The system of claim 1.

5. The analysis unit It also applies to ocean and space data, covering a wide range of environmental data.

2. The system of claim 1.

6. The analysis unit Integrating different satellite data sources and extracting the best information from multiple such data sets 2. The system of claim 1.

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