System

Abstract

An object of the system according to the embodiment is to precisely perform color correction and 3D model generation in digital archiving.SOLUTION: A system according to an embodiment includes a data-collection unit, a color-correction unit, a 3D model-generation unit, and a data-storage unit. The data collector collects data for digital archiving. The color correction unit performs color correction based on the data collected by the data collection unit. The 3D model generation unit generates a 3D model based on the image corrected by the color correction unit. The 3D model generator is configured to generate a ground model of the ground.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately correct color and generate 3D models for digital archiving, leaving room for improvement.

[0005] The system according to the embodiment aims to perform precise color correction and 3D model generation in digital archiving. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a color correction unit, a 3D model generation unit, and a data storage unit. The data collection unit collects data for digital archiving. The color correction unit performs color correction based on the data collected by the data collection unit. The 3D model generation unit generates a 3D model based on the data corrected by the color correction unit. The data storage unit stores the data generated by the 3D model generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can perform precise color correction and 3D model generation for digital archiving. [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) The digital archiving support tool according to an embodiment of the present invention is a system for preserving artworks, cultural assets, and the like as digital images or 3D data. In particular, an AI tool for precise color representation is provided. This enables the digital archiving support tool to precisely preserve digital images and 3D data of artworks and cultural assets and faithfully reproduce color representation.

[0029] A digital archiving assistance tool according to an embodiment includes a data collection unit, a color correction unit, a 3D model generation unit, and a data storage unit. The data collection unit collects data on artworks and cultural properties. For example, it uses a high-resolution camera to capture detailed images of paintings. It can also use a 3D scanner to obtain shape data on sculptures and buildings. Furthermore, the data collection unit can collect information on invisible layers using an infrared camera or an X-ray scanner. For example, an infrared camera can detect underlying paint layers and restoration marks on paintings, and an X-ray scanner can analyze the internal structure of a sculpture. The color correction unit performs color correction based on the collected data. For example, a generation AI analyzes the color data of a painting and corrects colors discolored by aging to their original colors. The generation AI can also perform more accurate color correction by taking into account past restoration history and the chemical composition of the pigments used. The 3D model generation unit generates a 3D model based on the data corrected by the color correction unit. For example, the generation AI analyzes the shape data of a sculpture and accurately reproduces its three-dimensional structure. The generative AI can also simultaneously analyze data for reproducing physical materials and surface textures to generate realistic 3D models. The data storage unit stores the generated data. For example, it stores it in a database and manages it by adding metadata such as the title of the work, the artist, and the year of production. The data storage unit can also eliminate data redundancy and optimize storage capacity using efficient data compression technology. This allows the digital archiving assistance tool according to the embodiment to precisely store digital images and 3D data of works of art and cultural properties and faithfully reproduce color expression. For example, the stored data can be used as teaching materials in educational institutions or for exhibitions in museums and art galleries. It can also be made publicly accessible by publishing it on an online platform.

[0030] The data collection department uses high-resolution cameras, 3D scanners, infrared cameras, and X-ray scanners to collect information on invisible layers and integrate it into digital data. For example, the data collection department uses high-resolution cameras to capture surface details and infrared cameras to detect underlying paint layers and repair marks. This generates digital data that integrates surface and interior information. The data collection department also uses 3D scanners to obtain shape data and X-ray scanners to analyze the internal structure. For example, reinforcements and repairs hidden inside the sculpture are integrated into the digital data. Furthermore, the data collection department combines infrared cameras and X-ray scanners to include the painting's sketches and restoration history in the digital data. This allows the overall image of the work to be preserved in more detail. This allows information on invisible layers to be collected and integrated into the digital data, enabling a more detailed digital archive.

[0031] The data collection unit uses drones to collect data on large buildings and cultural assets at high altitudes, and accurately digitizes the overall image. For example, the data collection unit equips drones with high-resolution cameras to photograph the exteriors of large buildings. This allows for the collection of detailed data on high altitudes that cannot be photographed from the ground. The data collection unit also uses drones to collect data on the interior structure and decoration of buildings. For example, it takes detailed photographs of ceiling paintings and sculptures at high altitudes and integrates them into digital data. Furthermore, the data collection unit uses drones to 3D scan the entire image of the building and generate a 3D model that integrates exterior and interior data. This allows for the accurate digitization of the entire image of the building. This makes it possible to accurately collect data on large buildings and cultural assets at high altitudes by using drones.

[0032] When collecting data, the data collection unit simultaneously collects sensory information such as sound and smell and stores it as multi-sensor data. The data collection unit, for example, combines a high-resolution camera and microphone to simultaneously collect visual and audio information about the artwork. For example, the audio environment of the exhibition venue is recorded and integrated into digital data. The data collection unit also uses an odor sensor to collect odor information about the artwork's storage environment. For example, the odor of old books and the air quality of the exhibition room are included in the digital data. Furthermore, the data collection unit uses multi-sensor data to generate digital data that integrates the visual, audio, and odor information about the artwork. This allows for a more detailed record of the artwork's storage environment. By simultaneously collecting sensory information such as sound and smell, more detailed digital archiving is possible.

[0033] The data collection unit compares data collected at different times and generates time-lapse data to track changes over time. For example, the data collection unit compares data collected periodically and displays the changes in a work of art over time in time-lapse format. For example, it visually tracks the fading of a painting or the deterioration of a sculpture. The data collection unit also integrates 3D data collected at different times to analyze changes in a building over time. For example, it saves cracks and repair areas on a building as time-lapse data. Furthermore, the data collection unit develops algorithms to track changes over time and automatically analyzes the collected data to generate time-lapse data. This allows the long-term monitoring of the conservation status of a work of art. By comparing data collected at different times and tracking changes over time, the conservation status of a work of art can be monitored over the long term.

[0034] When analyzing color data using the generation AI, the color correction unit takes into account the restoration history and the chemical composition of the pigments to perform more accurate color correction. For example, the generation AI analyzes past restoration history and performs color correction based on color data before restoration. For example, it references pre-restoration photographs and records to reproduce the original color. The color correction unit also analyzes the chemical composition of the pigments used, and the generation AI performs color correction based on those components. For example, it makes corrections taking into account the oxidation and discoloration of the pigments. Furthermore, the color correction unit integrates the restoration history and the chemical composition of the pigments to build a system that enables the generation AI to perform more accurate color correction. For example, it compares data before and after restoration to make corrections. This enables more accurate color correction by taking into account past restoration history and the chemical composition of the pigments.

[0035] During color correction, the color correction unit simulates how colors appear under different light sources and provides color data under multiple light source conditions. For example, the color correction unit uses a generation AI to simulate different light source conditions and correct the color data. For example, it reproduces colors under light sources such as natural light, fluorescent light, and LED lighting. The color correction unit also takes into account the color temperature and intensity of the light source when making corrections. For example, it reflects the difference between natural light in the daytime and artificial lighting at night. Furthermore, the color correction unit provides color data under multiple light source conditions, allowing users to select the optimal color expression. For example, it displays color data under different light sources for comparison. This allows for more diverse color expression by providing color data under different light source conditions.

[0036] The color correction unit optimizes the results of color correction for display on different devices. For example, the color correction unit optimizes the results of color correction for display on smartphones and tablets. For example, it adjusts color data to match the screen characteristics of the device. The color correction unit also reproduces the results of color correction in a 3D environment to optimize display on a VR headset. For example, it simulates color expression in VR space. Furthermore, to optimize display on different devices, the color correction unit uses a generation AI to learn the color characteristics of each device and perform optimal color correction. For example, it takes into account the device's color profile. This allows the results of color correction to be used on a variety of devices by optimizing for display on different devices.

[0037] The color correction department provides the color correction process as interactive educational materials, allowing students to learn color correction techniques. For example, the color correction department provides the color correction process as interactive educational materials, allowing students to actually experience color correction. For example, students can learn by comparing data before and after correction. The color correction department also develops online courses for learning color correction techniques, explaining the mechanisms of generative AI and the principles of color correction. For example, it supports learning using videos and quizzes. Furthermore, the color correction department provides simulation tools that allow students to practice color correction techniques and perform color correction using actual data. For example, it conducts color correction exercises using generative AI. In this way, the color correction process is provided as interactive educational materials, allowing students to learn color correction techniques.

[0038] When generating a 3D model using generative AI, the 3D model generation unit simultaneously analyzes data for reproducing physical materials and surface textures to generate a realistic 3D model. For example, the generative AI analyzes physical material data and reproduces the surface texture of the 3D model. For example, it faithfully reproduces the texture of stone or wood in a sculpture. The 3D model generation unit also reproduces the fine details of the 3D model based on the surface texture data. For example, it reproduces the brushstrokes in a painting or the fine carvings in a sculpture. Furthermore, the 3D model generation unit integrates the physical material data and surface texture data to create a system in which the generative AI generates a realistic 3D model. For example, it reproduces combinations of different materials. This allows for the reproduction of physical materials and surface textures, thereby generating a more realistic 3D model.

[0039] During the 3D model generation process, the 3D model generation unit integrates data from different perspectives to create a 3D model that includes visual information from all directions. For example, the 3D model generation unit uses a generation AI to integrate data from different perspectives to generate a 3D model that includes visual information from all directions. For example, it integrates data from the front, side, and back of a sculpture. The 3D model generation unit also uses the generation AI to reproduce the details of the 3D model based on data from different perspectives. For example, it reproduces the internal structure and decoration of a building from all directions. Furthermore, the 3D model generation unit builds a system in which the generation AI analyzes and integrates data from multiple perspectives to generate a 3D model that includes visual information from all directions. For example, it integrates data taken by a drone. This allows the generation AI to integrate data from different perspectives to create a 3D model that includes visual information from all directions.

[0040] The 3D model generation unit displays the generated 3D model in an AR or VR environment, allowing the user to experience it interactively. The 3D model generation unit, for example, displays the generated 3D model in an AR environment, allowing the user to place the model in a real space and experience it. For example, AR display is performed using a smartphone or tablet. The 3D model generation unit also displays the 3D model in a VR environment, allowing the user to interactively experience the model in a virtual space. For example, the 3D model is displayed using a VR headset. Furthermore, the 3D model generation unit builds a system for displaying the generated 3D model in an AR or VR environment, allowing the user to experience it interactively. For example, it enables the model to be rotated and scaled. This allows the user to experience it interactively by displaying the 3D model in an AR or VR environment.

[0041] The 3D model generation unit customizes 3D models for different purposes and utilizes them in a wide range of fields. For example, the 3D model generation unit customizes the generated 3D models for game development so that they can be used in the game. For example, they can be used as character models or background objects. The 3D model generation unit also customizes 3D models for film production and incorporates them into film scenes. For example, they can be used for special effects and set design. Furthermore, the 3D model generation unit customizes 3D models for architectural design and utilizes them in the design process. For example, they can be used for building design and simulation. In this way, by customizing 3D models for different purposes, they can be utilized in a wide range of fields.

[0042] The data storage unit eliminates data redundancy when storing data in a database and optimizes storage capacity using efficient data compression technology. The data storage unit, for example, automatically detects duplicate data when storing data in a database and develops an algorithm to eliminate redundancy. For example, it combines identical image data into one. The data storage unit also optimizes storage capacity using efficient data compression technology. For example, it compresses image data to reduce file size while maintaining quality. Furthermore, the data storage unit eliminates data redundancy and combines compression technologies to build a system that improves database storage efficiency. For example, it automatically applies a compression algorithm. This makes it possible to optimize storage capacity by eliminating data redundancy and using efficient data compression technology.

[0043] The data storage unit adds detailed information such as the restoration history and exhibition history of the artwork to the database metadata, thereby providing comprehensive data management. For example, the data storage unit adds the restoration history of the artwork to the database metadata, recording the condition before and after restoration. For example, it records details of the restoration work and the materials used. The data storage unit also adds exhibition history to the metadata, recording which exhibition the artwork was exhibited at. For example, it records the name, period, and location of the exhibition. Furthermore, the data storage unit integrates the restoration history and exhibition history to build a system for comprehensive data management. For example, it links and displays the restoration history and exhibition history. This enables comprehensive data management by adding detailed information such as the restoration history and exhibition history.

[0044] The data storage unit migrates the database to a cloud environment to enable global access. The data storage unit, for example, migrates the database to a cloud environment to enable access by users around the world. For example, the database is built using AWS or Google Cloud. The data storage unit also makes data backup and recovery easier by migrating to a cloud environment. For example, it automates regular backups. Furthermore, the data storage unit strengthens data security in the cloud environment to enable global access. For example, it introduces access control and encryption. As a result, migrating the database to a cloud environment enables global access.

[0045] The data storage unit automatically converts data in the database into different formats, making it usable for a wide range of applications. The data storage unit, for example, builds a system that automatically converts data in the database into different formats. For example, it converts image data into JPEG or PNG. The data storage unit also converts 3D data into different formats such as OBJ or STL, making it usable for a wide range of applications. For example, it generates data for 3D printing. Furthermore, the data storage unit stores the converted data in a database, allowing users to download the data in the required format. For example, it provides output in PDF or DOCX format. This automatic conversion of data into different formats makes it usable for a wide range of applications.

[0046] When optimizing digital data using the generating AI, the data utilization unit learns the user's usage history and preferences and provides individually optimized data. For example, the generating AI in the data utilization unit learns the user's usage history and provides individually optimized data. For example, it suggests optimal display settings based on past browsing history. The data utilization unit also learns the user's preferences and the generating AI provides data that matches those preferences. For example, it displays optimal data for a user who likes specific colors or designs. Furthermore, the data utilization unit builds a system in which the generating AI provides individually optimized data based on the usage history and preferences. For example, it generates data customized for each user. This makes it possible to provide individually optimized data by learning the user's usage history and preferences.

[0047] The data utilization unit automatically generates digital data in different resolutions and formats to provide optimal data for each application. For example, the generation AI automatically generates digital data in different resolutions to provide optimal data for each application. For example, it generates low-resolution data for the web and high-resolution data for printing. The data utilization unit also automatically generates digital data in different formats (e.g., JPEG, PNG, TIFF) to provide data according to user needs. For example, it generates image data for presentation slides and reports. Furthermore, the data utilization unit stores the automatically generated data in different resolutions and formats in a database, allowing users to download the data in the format they require. For example, it provides a download option on an online platform. This makes it possible to automatically generate data in different resolutions and formats to provide optimal data for each application.

[0048] The data utilization department shares the digital data on social media and online platforms to collect feedback from a wide range of users. For example, the data utilization department shares the digital data on social media and collects feedback from users. For example, the department posts images of the work on Facebook or Instagram and collects comments and likes. The data utilization department also publishes the digital data on online platforms and collects feedback from users. For example, the department exhibits the work on a dedicated website or app and receives user reviews. Furthermore, the data utilization department analyzes the feedback collected on social media and online platforms to help improve the digital data. For example, the data with the most positive feedback is preferentially displayed. This makes it possible to collect feedback from a wide range of users by sharing the digital data on social media and online platforms.

[0049] The Data Utilization Department incorporates digital data into interactive exhibits and educational programs, providing users with experiences they can tactile directly. For example, the Data Utilization Department incorporates digital data into interactive exhibits, providing users with experiences they can tactile directly. For example, they can experience the artwork using a touch screen or a VR headset. The Data Utilization Department also incorporates digital data into educational programs, providing students with experiences they can actually tactilely interact with the artwork. For example, they can study the artwork through digital teaching materials or online courses. Furthermore, the Data Utilization Department builds a system for incorporating digital data into interactive exhibits and educational programs, providing users with experiences they can tactile directly. For example, it supports the use of the system in exhibitions and workshops. In this way, by incorporating digital data into interactive exhibits and educational programs, users can provide experiences they can tactile directly.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The data collection unit can also use light of different wavelengths to obtain detailed information about the surface and interior of a work of art. For example, ultraviolet light can be used to detect minute scratches and stains on the surface, while visible light can be used to obtain standard color information. Near-infrared light can also be used to detect sketches or repairs hidden beneath the painting. Furthermore, the data collection unit can combine light of different wavelengths to perform a detailed analysis of the work's multi-layered structure and integrate it into digital data. This allows for the use of light of different wavelengths to obtain detailed information about the surface and interior of a work of art, enabling more precise digital archiving.

[0052] The data collection unit can also use a temperature sensor to record temperature changes in the artwork's preservation environment and integrate the data into digital data. For example, the temperature in an exhibition room can be measured periodically to analyze the impact of temperature changes on the artwork. The data collection unit can also use a humidity sensor to record humidity changes in the preservation environment and integrate the data into digital data. For example, the impact of humidity fluctuations on the canvas or paper of a painting can be analyzed. Furthermore, the data collection unit can combine temperature and humidity data to integrate a detailed record of the preservation environment into digital data. In this way, by collecting environmental data such as temperature and humidity, the preservation condition of the artwork can be recorded in more detail.

[0053] The data collection unit can also simultaneously collect sensory information such as sound and smell and store it as multi-sensor data. For example, a high-resolution camera and microphone can be combined to simultaneously collect visual and audio information about the artwork. For example, the audio environment of the exhibition venue can be recorded and integrated into digital data. The data collection unit can also use an odor sensor to collect odor information about the artwork's storage environment. For example, the odor of old books and the air quality of the exhibition room can be included in the digital data. Furthermore, the data collection unit can use multi-sensor data to generate digital data that integrates the visual, audio, and odor information about the artwork. This allows for a more detailed record of the artwork's storage environment. By simultaneously collecting sensory information such as sound and smell, more detailed digital archiving becomes possible.

[0054] The data collection unit can also compare data collected at different times to generate time-lapse data for tracking changes over time. For example, it can compare data collected periodically and display the changes in a work of art over time in time-lapse format. For example, it can visually track the fading of a painting or the deterioration of a sculpture. The data collection unit also integrates 3D data collected at different times to analyze changes in a building over time. For example, it can save cracks and repair areas on a building as time-lapse data. Furthermore, the data collection unit develops algorithms to track changes over time and automatically analyzes the collected data to generate time-lapse data. This allows for long-term monitoring of the conservation status of a work of art. By comparing data collected at different times and tracking changes over time, it is possible to monitor the conservation status of a work of art over the long term.

[0055] When analyzing color data using the generation AI, the color correction unit can also perform more accurate color correction by taking into account the restoration history and the chemical composition of the pigments. For example, the generation AI can analyze past restoration history and perform color correction based on color data from before restoration. For example, it can refer to pre-restoration photographs and records to reproduce the original color. The color correction unit also analyzes the chemical composition of the pigments used, and the generation AI performs color correction based on those components. For example, it makes corrections taking into account the oxidation and discoloration of the pigments. Furthermore, the color correction unit integrates the restoration history and the chemical composition of the pigments to build a system that enables the generation AI to perform more accurate color correction. For example, it makes corrections by comparing data before and after restoration. This allows for more accurate color correction by taking into account past restoration history and the chemical composition of the pigments.

[0056] During color correction, the color correction unit can also simulate how colors appear under different light sources and provide color data under multiple light source conditions. For example, the generation AI simulates different light source conditions and corrects the color data. For example, it can reproduce colors under light sources such as natural light, fluorescent light, and LED lighting. The color correction unit also takes into account the color temperature and intensity of the light source when making corrections. For example, it can reflect the difference between natural light in the daytime and artificial lighting at night. Furthermore, the color correction unit provides color data under multiple light source conditions, allowing users to select the optimal color expression. For example, it can display color data under different light sources for comparison. This allows for more diverse color expression by providing color data under different light source conditions.

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

[0058] Step 1: The data collection unit collects data on artworks and cultural properties. For example, a high-resolution camera can be used to capture detailed images of paintings. A 3D scanner can also be used to obtain geometric data on sculptures and buildings. The data collection unit can also collect information on invisible layers using infrared cameras and X-ray scanners. For example, an infrared camera can detect underlying paint layers and repair marks on a painting, while an X-ray scanner can analyze the internal structure of a sculpture. Step 2: The color correction unit performs color correction based on the collected data. For example, the generation AI analyzes the color data of a painting and corrects colors that have discolored over time to their original colors. The generation AI can also take into account past restoration history and the chemical composition of the pigments used to perform more accurate color correction. Step 3: The 3D model generation unit generates a 3D model based on the data corrected by the color correction unit. For example, the generation AI analyzes the shape data of a sculpture and accurately reproduces its three-dimensional structure. The generation AI also simultaneously analyzes data to reproduce the physical materials and surface textures, allowing it to generate a realistic 3D model. Step 4: The data storage unit stores the generated data. For example, it stores it in a database and manages it by adding metadata such as the title of the work, the artist, and the year of production. The data storage unit can also eliminate data redundancy and optimize storage capacity using efficient data compression techniques.

[0059] (Example 2) The digital archiving support tool according to an embodiment of the present invention is a system for preserving artworks, cultural assets, and the like as digital images or 3D data. In particular, an AI tool for precise color representation is provided. This enables the digital archiving support tool to precisely preserve digital images and 3D data of artworks and cultural assets and faithfully reproduce color representation.

[0060] A digital archiving assistance tool according to an embodiment includes a data collection unit, a color correction unit, a 3D model generation unit, and a data storage unit. The data collection unit collects data on artworks and cultural properties. For example, it uses a high-resolution camera to capture detailed images of paintings. It can also use a 3D scanner to obtain shape data on sculptures and buildings. Furthermore, the data collection unit can collect information on invisible layers using an infrared camera or an X-ray scanner. For example, an infrared camera can detect underlying paint layers and restoration marks on paintings, and an X-ray scanner can analyze the internal structure of a sculpture. The color correction unit performs color correction based on the collected data. For example, a generation AI analyzes the color data of a painting and corrects colors discolored by aging to their original colors. The generation AI can also perform more accurate color correction by taking into account past restoration history and the chemical composition of the pigments used. The 3D model generation unit generates a 3D model based on the data corrected by the color correction unit. For example, the generation AI analyzes the shape data of a sculpture and accurately reproduces its three-dimensional structure. The generative AI can also simultaneously analyze data for reproducing physical materials and surface textures to generate realistic 3D models. The data storage unit stores the generated data. For example, it stores it in a database and manages it by adding metadata such as the title of the work, the artist, and the year of production. The data storage unit can also eliminate data redundancy and optimize storage capacity using efficient data compression technology. This allows the digital archiving assistance tool according to the embodiment to precisely store digital images and 3D data of works of art and cultural properties and faithfully reproduce color expression. For example, the stored data can be used as teaching materials in educational institutions or for exhibitions in museums and art galleries. It can also be made publicly accessible by publishing it on an online platform.

[0061] The data collection department uses high-resolution cameras, 3D scanners, infrared cameras, and X-ray scanners to collect information on invisible layers and integrate it into digital data. For example, the data collection department uses high-resolution cameras to capture surface details and infrared cameras to detect underlying paint layers and repair marks. This generates digital data that integrates surface and interior information. The data collection department also uses 3D scanners to obtain shape data and X-ray scanners to analyze the internal structure. For example, reinforcements and repairs hidden inside the sculpture are integrated into the digital data. Furthermore, the data collection department combines infrared cameras and X-ray scanners to include the painting's sketches and restoration history in the digital data. This allows the overall image of the work to be preserved in more detail. This allows information on invisible layers to be collected and integrated into the digital data, enabling a more detailed digital archive.

[0062] The data collection unit uses drones to collect data on large buildings and cultural assets at high altitudes, and accurately digitizes the overall image. For example, the data collection unit equips drones with high-resolution cameras to photograph the exteriors of large buildings. This allows for the collection of detailed data on high altitudes that cannot be photographed from the ground. The data collection unit also uses drones to collect data on the interior structure and decoration of buildings. For example, it takes detailed photographs of ceiling paintings and sculptures at high altitudes and integrates them into digital data. Furthermore, the data collection unit uses drones to 3D scan the entire image of the building and generate a 3D model that integrates exterior and interior data. This allows for the accurate digitization of the entire image of the building. This makes it possible to accurately collect data on large buildings and cultural assets at high altitudes by using drones.

[0063] The data collection unit uses the emotion estimation function to record the environment and the emotions of the worker during collection, thereby improving the accuracy and efficiency of data collection. For example, the data collection unit analyzes the worker's emotions in real time during data collection and suggests taking a break if stress or fatigue increases. This maintains the accuracy of the work. The data collection unit also uses the emotion estimation function to suggest the optimal collection method in response to changes in the collection environment. For example, it suggests using supplemental lighting if lighting conditions are poor. Furthermore, the data collection unit provides feedback to improve the efficiency of data collection based on the worker's emotion data. For example, it suggests reviewing work procedures if concentration decreases. In this way, the emotion estimation function can improve the accuracy and efficiency of data collection.

[0064] When collecting data, the data collection unit simultaneously collects sensory information such as sound and smell and stores it as multi-sensor data. The data collection unit, for example, combines a high-resolution camera and microphone to simultaneously collect visual and audio information about the artwork. For example, the audio environment of the exhibition venue is recorded and integrated into digital data. The data collection unit also uses an odor sensor to collect odor information about the artwork's storage environment. For example, the odor of old books and the air quality of the exhibition room are included in the digital data. Furthermore, the data collection unit uses multi-sensor data to generate digital data that integrates the visual, audio, and odor information about the artwork. This allows for a more detailed record of the artwork's storage environment. By simultaneously collecting sensory information such as sound and smell, more detailed digital archiving is possible.

[0065] The data collection unit compares data collected at different times and generates time-lapse data to track changes over time. For example, the data collection unit compares data collected periodically and displays the changes in a work of art over time in time-lapse format. For example, it visually tracks the fading of a painting or the deterioration of a sculpture. The data collection unit also integrates 3D data collected at different times to analyze changes in a building over time. For example, it saves cracks and repair areas on a building as time-lapse data. Furthermore, the data collection unit develops algorithms to track changes over time and automatically analyzes the collected data to generate time-lapse data. This allows the long-term monitoring of the conservation status of a work of art. By comparing data collected at different times and tracking changes over time, the conservation status of a work of art can be monitored over the long term.

[0066] The data collection unit uses the emotion estimation function to analyze the user's emotional response to the collected data and evaluate the value of the data. The data collection unit, for example, analyzes the user's emotional response to the collected data in real time and evaluates the value of the data based on the emotion score. For example, data with a high number of positive responses is given a high rating. The data collection unit also uses the emotion estimation function to evaluate the importance of the data based on the user's emotional response. For example, data with a strong emotional or surprised response is preferentially saved. Furthermore, the data collection unit builds a system that quantifies the value of the collected data based on the user's emotional data. For example, the emotion score is quantified and the priority of saving the data is determined. In this way, the value of the collected data can be evaluated using the emotion estimation function.

[0067] When analyzing color data using the generation AI, the color correction unit takes into account the restoration history and the chemical composition of the pigments to perform more accurate color correction. For example, the generation AI analyzes past restoration history and performs color correction based on color data before restoration. For example, it references pre-restoration photographs and records to reproduce the original color. The color correction unit also analyzes the chemical composition of the pigments used, and the generation AI performs color correction based on those components. For example, it makes corrections taking into account the oxidation and discoloration of the pigments. Furthermore, the color correction unit integrates the restoration history and the chemical composition of the pigments to build a system that enables the generation AI to perform more accurate color correction. For example, it compares data before and after restoration to make corrections. This enables more accurate color correction by taking into account past restoration history and the chemical composition of the pigments.

[0068] During color correction, the color correction unit simulates how colors appear under different light sources and provides color data under multiple light source conditions. For example, the color correction unit uses a generation AI to simulate different light source conditions and correct the color data. For example, it reproduces colors under light sources such as natural light, fluorescent light, and LED lighting. The color correction unit also takes into account the color temperature and intensity of the light source when making corrections. For example, it reflects the difference between natural light in the daytime and artificial lighting at night. Furthermore, the color correction unit provides color data under multiple light source conditions, allowing users to select the optimal color expression. For example, it displays color data under different light sources for comparison. This allows for more diverse color expression by providing color data under different light source conditions.

[0069] The color correction unit uses the emotion estimation function to analyze the user's emotional response to the color-corrected data and selects the color expression that most emotionally resonates with the user. The color correction unit, for example, analyzes the user's emotional response to the color-corrected data in real time and selects the optimal color expression based on the emotion score. For example, it selects colors that evoke a high number of positive responses. The color correction unit also uses the emotion estimation function to improve the accuracy of color correction based on the user's emotional response. For example, it prioritizes the use of colors that evoke strong emotions such as emotion and surprise. Furthermore, the color correction unit builds a system that selects the color expression that most emotionally resonates with the user based on the user's emotion data. For example, it quantifies the emotion score and reflects it in the selection of the color expression. In this way, the emotion estimation function can be used to select the optimal color expression based on the user's emotion.

[0070] The color correction unit optimizes the results of color correction for display on different devices. For example, the color correction unit optimizes the results of color correction for display on smartphones and tablets. For example, it adjusts color data to match the screen characteristics of the device. The color correction unit also reproduces the results of color correction in a 3D environment to optimize display on a VR headset. For example, it simulates color expression in VR space. Furthermore, to optimize display on different devices, the color correction unit uses a generation AI to learn the color characteristics of each device and perform optimal color correction. For example, it takes into account the device's color profile. This allows the results of color correction to be used on a variety of devices by optimizing for display on different devices.

[0071] The color correction department provides the color correction process as interactive educational materials, allowing students to learn color correction techniques. For example, the color correction department provides the color correction process as interactive educational materials, allowing students to actually experience color correction. For example, students can learn by comparing data before and after correction. The color correction department also develops online courses for learning color correction techniques, explaining the mechanisms of generative AI and the principles of color correction. For example, it supports learning using videos and quizzes. Furthermore, the color correction department provides simulation tools that allow students to practice color correction techniques and perform color correction using actual data. For example, it conducts color correction exercises using generative AI. In this way, the color correction process is provided as interactive educational materials, allowing students to learn color correction techniques.

[0072] The color correction unit uses the emotion estimation function to monitor the user's emotions in real time during the color correction process and make correction suggestions based on the emotions. For example, the color correction unit monitors the user's emotions in real time during the color correction process and makes correction suggestions based on the emotion score. For example, it suggests colors that evoke strong positive emotions. The color correction unit also uses the emotion estimation function to improve the accuracy of color correction based on the user's emotional response. For example, it prioritizes the use of colors that evoke strong reactions of emotion or surprise. Furthermore, the color correction unit builds a system that selects the color expression that most emotionally resonates with the user based on the user's emotion data. For example, it quantifies the emotion score and reflects it in the selection of color expression. In this way, the emotion estimation function makes it possible to make correction suggestions based on the user's emotions.

[0073] When generating a 3D model using generative AI, the 3D model generation unit simultaneously analyzes data for reproducing physical materials and surface textures to generate a realistic 3D model. For example, the generative AI analyzes physical material data and reproduces the surface texture of the 3D model. For example, it faithfully reproduces the texture of stone or wood in a sculpture. The 3D model generation unit also reproduces the fine details of the 3D model based on the surface texture data. For example, it reproduces the brushstrokes in a painting or the fine carvings in a sculpture. Furthermore, the 3D model generation unit integrates the physical material data and surface texture data to create a system in which the generative AI generates a realistic 3D model. For example, it reproduces combinations of different materials. This allows for the reproduction of physical materials and surface textures, thereby generating a more realistic 3D model.

[0074] During the 3D model generation process, the 3D model generation unit integrates data from different perspectives to create a 3D model that includes visual information from all directions. For example, the 3D model generation unit uses a generation AI to integrate data from different perspectives to generate a 3D model that includes visual information from all directions. For example, it integrates data from the front, side, and back of a sculpture. The 3D model generation unit also uses the generation AI to reproduce the details of the 3D model based on data from different perspectives. For example, it reproduces the internal structure and decoration of a building from all directions. Furthermore, the 3D model generation unit builds a system in which the generation AI analyzes and integrates data from multiple perspectives to generate a 3D model that includes visual information from all directions. For example, it integrates data taken by a drone. This allows the generation AI to integrate data from different perspectives to create a 3D model that includes visual information from all directions.

[0075] The 3D model generation unit uses an emotion estimation function to analyze the user's emotional response to the generated 3D model and selects the model that most emotionally resonates with the user. The 3D model generation unit, for example, analyzes the user's emotional response to the generated 3D model in real time and selects the optimal model based on the emotion score. For example, it selects a model that receives a large number of positive responses. The 3D model generation unit also uses the emotion estimation function to improve the accuracy of the 3D model based on the user's emotional response. For example, it prioritizes the adoption of models that receive strong reactions of emotion or surprise. Furthermore, the 3D model generation unit builds a system that selects the 3D model that most emotionally resonates with the user based on the user's emotion data. For example, it quantifies the emotion score and reflects it in the model selection. In this way, the emotion estimation function can be used to select the optimal 3D model based on the user's emotions.

[0076] The 3D model generation unit displays the generated 3D model in an AR or VR environment, allowing the user to experience it interactively. The 3D model generation unit, for example, displays the generated 3D model in an AR environment, allowing the user to place the model in a real space and experience it. For example, AR display is performed using a smartphone or tablet. The 3D model generation unit also displays the 3D model in a VR environment, allowing the user to interactively experience the model in a virtual space. For example, the 3D model is displayed using a VR headset. Furthermore, the 3D model generation unit builds a system for displaying the generated 3D model in an AR or VR environment, allowing the user to experience it interactively. For example, it enables the model to be rotated and scaled. This allows the user to experience it interactively by displaying the 3D model in an AR or VR environment.

[0077] The 3D model generation unit customizes 3D models for different purposes and utilizes them in a wide range of fields. For example, the 3D model generation unit customizes the generated 3D models for game development so that they can be used in the game. For example, they can be used as character models or background objects. The 3D model generation unit also customizes 3D models for film production and incorporates them into film scenes. For example, they can be used for special effects and set design. Furthermore, the 3D model generation unit customizes 3D models for architectural design and utilizes them in the design process. For example, they can be used for building design and simulation. In this way, by customizing 3D models for different purposes, they can be utilized in a wide range of fields.

[0078] The 3D model generation unit uses the emotion estimation function to monitor the user's emotions in real time while the 3D model is being displayed, and adjusts the display based on the emotions. For example, the 3D model generation unit monitors the user's emotions in real time while the 3D model is being displayed, and adjusts the display based on the emotion score. For example, it suggests display settings that evoke strong positive emotions. The 3D model generation unit also uses the emotion estimation function to improve the display accuracy of the 3D model based on the user's emotional response. For example, it prioritizes display settings that evoke strong reactions of emotion or surprise. Furthermore, the 3D model generation unit builds a system that selects the display settings that most emotionally resonate with the user based on the user's emotion data. For example, it quantifies the emotion score and reflects it in the selection of display settings. This makes it possible to use the emotion estimation function to adjust the display based on the user's emotions.

[0079] The data storage unit eliminates data redundancy when storing data in a database and optimizes storage capacity using efficient data compression technology. The data storage unit, for example, automatically detects duplicate data when storing data in a database and develops an algorithm to eliminate redundancy. For example, it combines identical image data into one. The data storage unit also optimizes storage capacity using efficient data compression technology. For example, it compresses image data to reduce file size while maintaining quality. Furthermore, the data storage unit eliminates data redundancy and combines compression technologies to build a system that improves database storage efficiency. For example, it automatically applies a compression algorithm. This makes it possible to optimize storage capacity by eliminating data redundancy and using efficient data compression technology.

[0080] The data storage unit adds detailed information such as the restoration history and exhibition history of the artwork to the database metadata, thereby providing comprehensive data management. For example, the data storage unit adds the restoration history of the artwork to the database metadata, recording the condition before and after restoration. For example, it records details of the restoration work and the materials used. The data storage unit also adds exhibition history to the metadata, recording which exhibition the artwork was exhibited at. For example, it records the name, period, and location of the exhibition. Furthermore, the data storage unit integrates the restoration history and exhibition history to build a system for comprehensive data management. For example, it links and displays the restoration history and exhibition history. This enables comprehensive data management by adding detailed information such as the restoration history and exhibition history.

[0081] The data storage unit uses the emotion estimation function to analyze the user's emotional response to the data in the database and prioritizes management of data with high emotional value. The data storage unit, for example, analyzes the user's emotional response to the data in the database in real time and evaluates the value of the data based on the emotion score. For example, data with a high number of positive responses is given a high rating. The data storage unit also uses the emotion estimation function to evaluate the importance of the data based on the user's emotional response. For example, data with a strong emotional or surprised response is prioritized for management. Furthermore, the data storage unit builds a system that prioritizes management of data with high emotional value based on the user's emotion data. For example, the emotion score is quantified and the data management priority is determined. In this way, by using the emotion estimation function, data with high emotional value can be prioritized for management.

[0082] The data storage unit migrates the database to a cloud environment to enable global access. The data storage unit, for example, migrates the database to a cloud environment to enable access by users around the world. For example, the database is built using AWS or Google Cloud. The data storage unit also makes data backup and recovery easier by migrating to a cloud environment. For example, it automates regular backups. Furthermore, the data storage unit strengthens data security in the cloud environment to enable global access. For example, it introduces access control and encryption. As a result, migrating the database to a cloud environment enables global access.

[0083] The data storage unit automatically converts data in the database into different formats, making it usable for a wide range of applications. The data storage unit, for example, builds a system that automatically converts data in the database into different formats. For example, it converts image data into JPEG or PNG. The data storage unit also converts 3D data into different formats such as OBJ or STL, making it usable for a wide range of applications. For example, it generates data for 3D printing. Furthermore, the data storage unit stores the converted data in a database, allowing users to download the data in the required format. For example, it provides output in PDF or DOCX format. This automatic conversion of data into different formats makes it usable for a wide range of applications.

[0084] The data storage unit uses the emotion estimation function to analyze the user's emotional response to the database search results and optimizes the search results based on the emotion. The data storage unit, for example, analyzes the user's emotional response to the database search results in real time and optimizes the search results based on the emotion score. For example, results with a high number of positive responses are displayed at the top. The data storage unit also uses the emotion estimation function to improve the accuracy of the search results based on the user's emotional response. For example, results with strong reactions of emotion or surprise are displayed preferentially. Furthermore, the data storage unit builds a system that optimizes search results based on emotion based on the user's emotion data. For example, the emotion score is quantified and the display order of the search results is adjusted. In this way, by using the emotion estimation function, it is possible to optimize search results based on emotion.

[0085] When optimizing digital data using the generating AI, the data utilization unit learns the user's usage history and preferences and provides individually optimized data. For example, the generating AI in the data utilization unit learns the user's usage history and provides individually optimized data. For example, it suggests optimal display settings based on past browsing history. The data utilization unit also learns the user's preferences and the generating AI provides data that matches those preferences. For example, it displays optimal data for a user who likes specific colors or designs. Furthermore, the data utilization unit builds a system in which the generating AI provides individually optimized data based on the usage history and preferences. For example, it generates data customized for each user. This makes it possible to provide individually optimized data by learning the user's usage history and preferences.

[0086] The data utilization unit automatically generates digital data in different resolutions and formats to provide optimal data for each application. For example, the generation AI automatically generates digital data in different resolutions to provide optimal data for each application. For example, it generates low-resolution data for the web and high-resolution data for printing. The data utilization unit also automatically generates digital data in different formats (e.g., JPEG, PNG, TIFF) to provide data according to user needs. For example, it generates image data for presentation slides and reports. Furthermore, the data utilization unit stores the automatically generated data in different resolutions and formats in a database, allowing users to download the data in the format they require. For example, it provides a download option on an online platform. This makes it possible to automatically generate data in different resolutions and formats to provide optimal data for each application.

[0087] The data utilization unit uses the emotion estimation function to monitor the user's emotions in real time while digital data is being used, and optimizes the data based on the emotions. For example, the data utilization unit monitors the user's emotions in real time while digital data is being used, and optimizes the data based on the emotion score. For example, it suggests data display settings that indicate strong positive emotions. The data utilization unit also uses the emotion estimation function to improve the display accuracy of digital data based on the user's emotional response. For example, it prioritizes displaying data that indicates strong reactions of emotion or surprise. Furthermore, the data utilization unit builds a system that selects the data display settings that most emotionally resonate with the user based on the user's emotional data. For example, it quantifies the emotion score and reflects it in the selection of data display. In this way, using the emotion estimation function makes it possible to optimize data based on emotions.

[0088] The data utilization department shares the digital data on social media and online platforms to collect feedback from a wide range of users. For example, the data utilization department shares the digital data on social media and collects feedback from users. For example, the department posts images of the work on Facebook or Instagram and collects comments and likes. The data utilization department also publishes the digital data on online platforms and collects feedback from users. For example, the department exhibits the work on a dedicated website or app and receives user reviews. Furthermore, the data utilization department analyzes the feedback collected on social media and online platforms to help improve the digital data. For example, the data with the most positive feedback is preferentially displayed. This makes it possible to collect feedback from a wide range of users by sharing the digital data on social media and online platforms.

[0089] The Data Utilization Department incorporates digital data into interactive exhibits and educational programs, providing users with experiences they can tactile directly. For example, the Data Utilization Department incorporates digital data into interactive exhibits, providing users with experiences they can tactile directly. For example, they can experience the artwork using a touch screen or a VR headset. The Data Utilization Department also incorporates digital data into educational programs, providing students with experiences they can actually tactilely interact with the artwork. For example, they can study the artwork through digital teaching materials or online courses. Furthermore, the Data Utilization Department builds a system for incorporating digital data into interactive exhibits and educational programs, providing users with experiences they can tactile directly. For example, it supports the use of the system in exhibitions and workshops. In this way, by incorporating digital data into interactive exhibits and educational programs, users can provide experiences they can tactile directly.

[0090] The data utilization unit uses the emotion estimation function to monitor the user's emotions in real time while the digital data is being used, collect feedback based on the emotions, and use this feedback to improve the data. For example, the data utilization unit monitors the user's emotions in real time while the digital data is being used, and collects feedback based on the emotion score. For example, data with strong positive emotions is preferentially displayed. The data utilization unit also uses the emotion estimation function to improve the accuracy of the digital data display based on the user's emotional reactions. For example, data with strong reactions of excitement or surprise is preferentially displayed. Furthermore, the data utilization unit builds a system that selects the data display settings that most emotionally resonate with the user based on the user's emotional data. For example, the emotion score is quantified and reflected in the selection of data display. In this way, the emotion estimation function can be used to collect emotion-based feedback and use this to improve the data.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The data collection unit can also use light of different wavelengths to obtain detailed information about the surface and interior of a work of art. For example, ultraviolet light can be used to detect minute scratches and stains on the surface, while visible light can be used to obtain standard color information. Near-infrared light can also be used to detect sketches or repairs hidden beneath the painting. Furthermore, the data collection unit can combine light of different wavelengths to perform a detailed analysis of the work's multi-layered structure and integrate it into digital data. This allows for the use of light of different wavelengths to obtain detailed information about the surface and interior of a work of art, enabling more precise digital archiving.

[0093] The data collection unit can also use a temperature sensor to record temperature changes in the artwork's preservation environment and integrate the data into digital data. For example, the temperature in an exhibition room can be measured periodically to analyze the impact of temperature changes on the artwork. The data collection unit can also use a humidity sensor to record humidity changes in the preservation environment and integrate the data into digital data. For example, the impact of humidity fluctuations on the canvas or paper of a painting can be analyzed. Furthermore, the data collection unit can combine temperature and humidity data to integrate a detailed record of the preservation environment into digital data. In this way, by collecting environmental data such as temperature and humidity, the preservation condition of the artwork can be recorded in more detail.

[0094] The data collection unit can also use the emotion estimation function to analyze the user's emotional response to the collected data and evaluate the value of the data. For example, the data collection unit can analyze the user's emotional response to the collected data in real time and evaluate the value of the data based on the emotion score. For example, data with a high number of positive responses can be given a high rating. The data collection unit can also use the emotion estimation function to evaluate the importance of data based on the user's emotional response. For example, data with a strong emotional or surprised response can be preferentially saved. Furthermore, the data collection unit can build a system to quantify the value of collected data based on the user's emotional data. For example, the emotion score can be quantified and the priority of saving data can be determined. In this way, the emotion estimation function can be used to evaluate the value of collected data.

[0095] The data collection unit can also use the emotion estimation function to record the environment and the emotions of the worker during data collection, thereby improving the accuracy and efficiency of data collection. For example, it can analyze the worker's emotions in real time during data collection and suggest taking a break if stress or fatigue increases. This maintains the accuracy of work. The data collection unit also uses the emotion estimation function to suggest the optimal collection method in response to changes in the collection environment. For example, it can suggest using supplemental lighting if lighting conditions are poor. Furthermore, the data collection unit provides feedback to improve the efficiency of data collection based on the worker's emotion data. For example, it can suggest reviewing work procedures if concentration decreases. In this way, the emotion estimation function can improve the accuracy and efficiency of data collection.

[0096] The data collection unit can also simultaneously collect sensory information such as sound and smell and store it as multi-sensor data. For example, a high-resolution camera and microphone can be combined to simultaneously collect visual and audio information about the artwork. For example, the audio environment of the exhibition venue can be recorded and integrated into digital data. The data collection unit can also use an odor sensor to collect odor information about the artwork's storage environment. For example, the odor of old books and the air quality of the exhibition room can be included in the digital data. Furthermore, the data collection unit can use multi-sensor data to generate digital data that integrates the visual, audio, and odor information about the artwork. This allows for a more detailed record of the artwork's storage environment. By simultaneously collecting sensory information such as sound and smell, more detailed digital archiving becomes possible.

[0097] The data collection unit can also compare data collected at different times to generate time-lapse data for tracking changes over time. For example, it can compare data collected periodically and display the changes in a work of art over time in time-lapse format. For example, it can visually track the fading of a painting or the deterioration of a sculpture. The data collection unit also integrates 3D data collected at different times to analyze changes in a building over time. For example, it can save cracks and repair areas on a building as time-lapse data. Furthermore, the data collection unit develops algorithms to track changes over time and automatically analyzes the collected data to generate time-lapse data. This allows for long-term monitoring of the conservation status of a work of art. By comparing data collected at different times and tracking changes over time, it is possible to monitor the conservation status of a work of art over the long term.

[0098] When analyzing color data using the generation AI, the color correction unit can also perform more accurate color correction by taking into account the restoration history and the chemical composition of the pigments. For example, the generation AI can analyze past restoration history and perform color correction based on color data from before restoration. For example, it can refer to pre-restoration photographs and records to reproduce the original color. The color correction unit also analyzes the chemical composition of the pigments used, and the generation AI performs color correction based on those components. For example, it makes corrections taking into account the oxidation and discoloration of the pigments. Furthermore, the color correction unit integrates the restoration history and the chemical composition of the pigments to build a system that enables the generation AI to perform more accurate color correction. For example, it makes corrections by comparing data before and after restoration. This allows for more accurate color correction by taking into account past restoration history and the chemical composition of the pigments.

[0099] The color correction unit can also use the emotion estimation function to analyze the user's emotional response to the color-corrected data and select the color expression that most emotionally resonates with the user. For example, the color correction unit analyzes the user's emotional response to the color-corrected data in real time and selects the optimal color expression based on the emotion score. For example, it selects colors that evoke a high number of positive responses. The color correction unit also uses the emotion estimation function to improve the accuracy of color correction based on the user's emotional response. For example, it prioritizes the use of colors that evoke strong emotions such as emotion or surprise. Furthermore, the color correction unit builds a system that selects the color expression that most emotionally resonates with the user based on the user's emotion data. For example, it quantifies the emotion score and reflects it in the selection of the color expression. In this way, the emotion estimation function can be used to select the optimal color expression based on the user's emotion.

[0100] During color correction, the color correction unit can also simulate how colors appear under different light sources and provide color data under multiple light source conditions. For example, the generation AI simulates different light source conditions and corrects the color data. For example, it can reproduce colors under light sources such as natural light, fluorescent light, and LED lighting. The color correction unit also takes into account the color temperature and intensity of the light source when making corrections. For example, it can reflect the difference between natural light in the daytime and artificial lighting at night. Furthermore, the color correction unit provides color data under multiple light source conditions, allowing users to select the optimal color expression. For example, it can display color data under different light sources for comparison. This allows for more diverse color expression by providing color data under different light source conditions.

[0101] The color correction unit can also use the emotion estimation function to monitor the user's emotions in real time during the color correction process and make correction suggestions based on the emotions. For example, the color correction unit can monitor the user's emotions in real time during the color correction process and make correction suggestions based on the emotion score. For example, it can suggest colors that evoke strong positive emotions. The color correction unit can also use the emotion estimation function to improve the accuracy of color correction based on the user's emotional response. For example, it can prioritize colors that evoke strong reactions of emotion or surprise. Furthermore, the color correction unit can build a system that selects the color expression that most emotionally resonates with the user based on the user's emotion data. For example, it can quantify the emotion score and reflect it in the selection of color expression. In this way, the emotion estimation function can be used to make correction suggestions based on the user's emotions.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The data collection unit collects data on artworks and cultural properties. For example, a high-resolution camera can be used to capture detailed images of paintings. A 3D scanner can also be used to obtain geometric data on sculptures and buildings. The data collection unit can also collect information on invisible layers using infrared cameras and X-ray scanners. For example, an infrared camera can detect underlying paint layers and repair marks on a painting, while an X-ray scanner can analyze the internal structure of a sculpture. Step 2: The color correction unit performs color correction based on the collected data. For example, the generation AI analyzes the color data of a painting and corrects colors that have discolored over time to their original colors. The generation AI can also take into account past restoration history and the chemical composition of the pigments used to perform more accurate color correction. Step 3: The 3D model generation unit generates a 3D model based on the data corrected by the color correction unit. For example, the generation AI analyzes the shape data of a sculpture and accurately reproduces its three-dimensional structure. The generation AI also simultaneously analyzes data to reproduce the physical materials and surface textures, allowing it to generate a realistic 3D model. Step 4: The data storage unit stores the generated data. For example, it stores it in a database and manages it by adding metadata such as the title of the work, the artist, and the year of production. The data storage unit can also eliminate data redundancy and optimize storage capacity using efficient data compression techniques.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

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

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

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the 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 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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 data collection unit that collects data for digital archiving; a color correction unit that performs color correction based on the data collected by the data collection unit; a 3D model generation unit that generates a 3D model based on the data corrected by the color correction unit; a data storage unit that stores the data generated by the 3D model generation unit. A system characterized by:

2. The data collection unit High-resolution cameras, the 3D scanner, infrared cameras, and the X-ray scanner are used to collect information from invisible layers and integrate it into digital data.

2. The system of claim 1.

3. The data collection unit Using drones to collect data on large buildings and high-altitude cultural properties, accurately digitizing the entire image 2. The system of claim 1.

4. The data collection unit Recording the environment and worker emotions during data collection improves the accuracy and efficiency of data collection.

2. The system of claim 1.

5. The data collection unit When collecting data, sensory information such as sound and smell is also collected and saved as multi-sensor data.

2. The system of claim 1.

6. The data collection unit Comparing the data collected at different times to generate time-lapse data for tracking changes over time.

2. The system of claim 1.

7. The data collection unit Analyzing users' emotional responses to the collected data and assessing the value of the data 2. The system of claim 1.

8. The color correction unit When analyzing color data using the AI, restoration history and the chemical composition of pigments are taken into account to perform more accurate color correction.

2. The system of claim 1.

Images