system

Abstract

The system according to the embodiment aims to utilize digital twin technology and blockchain technology in the management of farmland and forests. [Solution] A system according to an embodiment includes an acquisition unit, a generation unit, a division unit, an analysis unit, and a monitoring unit. The acquisition unit acquires geospatial information. The generation unit generates natural images based on the geospatial information acquired by the acquisition unit. The division unit divides the virtual land space generated by the generation unit and registers it as a tradable digital asset on a blockchain. The analysis unit analyzes environmental data. The monitoring unit monitors the health of farmland or forests based on the data analyzed by the analysis unit.

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 have not fully utilized digital twin technology and blockchain technology in farmland and forest management, and there is room for improvement.

[0005] The system according to the embodiment aims to utilize digital twin technology and blockchain technology in the management of farmland and forests. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a generation unit, a division unit, an analysis unit, and a monitoring unit. The acquisition unit acquires geospatial information. The generation unit generates nature images based on the geospatial information acquired by the acquisition unit. The division unit divides the virtual land space generated by the generation unit and registers it as a tradable digital asset on a blockchain. The analysis unit analyzes environmental data. The monitoring unit monitors the health of farmland or forests based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can utilize digital twin technology and blockchain technology in managing farmland and forests. [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) An agricultural management system according to an embodiment of the present invention acquires geospatial information, generates nature images using a generation AI, divides a virtual land space, analyzes environmental data, and monitors the health of farmland and forests. The agricultural management system acquires geospatial information, generates nature images using a generation AI, divides the virtual land space, and registers the images as tradable digital assets on a blockchain. Furthermore, the agricultural management system analyzes environmental data and monitors the health of farmland and forests. For example, the agricultural management system acquires geospatial information from drones or satellites. For example, the agricultural management system uses a generation AI to generate nature images based on the geospatial information. For example, the agricultural management system divides a virtual land space into multiple sections and registers each section as a tradable digital asset on a blockchain. For example, the agricultural management system uses sensors to collect data such as soil humidity and temperature and crop growth status and inputs it into a machine learning model. For example, the agricultural management system monitors the health of farmland and forests based on the analyzed data. This allows the agricultural management system to combine sustainable agriculture and environmental conservation, thereby realizing innovative agricultural management. This allows agricultural management systems to integrate sustainable agriculture with environmental conservation, enabling innovative agricultural management. For example, the health of farmland can be monitored in real time, allowing necessary measures to be taken promptly. Forest monitoring can also strengthen environmental conservation efforts.

[0029] An agricultural management system according to an embodiment includes an acquisition unit, a generation unit, a division unit, an analysis unit, and a monitoring unit. The acquisition unit acquires geospatial information. The geospatial information includes, but is not limited to, topographical data, meteorological data, and land use data. The acquisition unit acquires the geospatial information from, for example, a drone or a satellite. The acquisition unit can also acquire the geospatial information using a ground sensor. For example, a drone is used to efficiently acquire wide-area geospatial information. The satellite may be an earth observation satellite or a communication satellite. The ground sensor is used to acquire detailed geospatial information at a specific location. The generation unit generates a natural image based on the geospatial information acquired by the acquisition unit using a generation AI. The generation AI may use technologies such as a generative adversarial network (GAN) or a variational autoencoder (VAE). For example, the generation AI generates a natural image based on the geospatial information. The generation AI can also generate a 3D model based on the geospatial information. The generation unit can also allow the generation AI to generate a simulation environment based on geospatial information. For example, the generation AI inputs geospatial information acquired from a drone or satellite and outputs natural images. The division unit divides the virtual land space generated by the generation unit and registers it as a tradable digital asset on a blockchain. Examples of virtual land spaces include, but are not limited to, digital maps, 3D models, and simulation environments. For example, the division unit divides the virtual land space into multiple sections and registers each section as a tradable digital asset on a blockchain. The division unit can also register the virtual land space as an NFT (non-fungible token). The division unit can also register the virtual land space as a digital certificate. For example, the division unit divides the virtual land space into multiple sections and registers each section as a tradable digital asset on a blockchain. The analysis unit analyzes environmental data. Examples of environmental data include, but are not limited to, temperature, humidity, and soil composition. The analysis unit uses sensors to collect data such as soil humidity and temperature, and crop growth status, and inputs this data into a machine learning model.The analysis unit can also collect weather data and input it into the machine learning model. The analysis unit can also collect water quality data and input it into the machine learning model. For example, the analysis unit collects data such as soil humidity and temperature, crop growth status, etc. using sensors and inputs the data into the machine learning model. The monitoring unit monitors the health of farmland and forests based on the data analyzed by the analysis unit. Examples of the health of farmland include, but are not limited to, the growth status of crops and the nutritional state of the soil. Examples of forest monitoring include, but are not limited to, the health of trees, biomass volume, and the risk of forest fires. For example, the monitoring unit monitors the health of farmland based on the data analyzed by the analysis unit. The monitoring unit can also monitor forests based on the data analyzed by the analysis unit. The monitoring unit can also strengthen environmental conservation efforts based on the data analyzed by the analysis unit. For example, the monitoring unit monitors the health of farmland based on the data analyzed by the analysis unit. As a result, the agricultural management system of the embodiment can consistently perform tasks from acquiring geospatial information to generating natural images, dividing virtual land space, analyzing environmental data, and monitoring the health of farmland and forests.

[0030] The acquisition unit can acquire geospatial information from a drone or a satellite. Examples of drones include, but are not limited to, fixed-wing drones and multi-rotor drones. Examples of satellites include, but are not limited to, earth observation satellites and communication satellites. The acquisition unit, for example, optimizes the flight pattern of the drone to acquire wide-area geospatial information in the shortest time. The acquisition unit can also select the timing for most efficient acquisition of geospatial information by taking into account the satellite's orbit. The acquisition unit can also efficiently acquire geospatial information by combining both drones and satellites. For example, the acquisition unit can optimize the flight pattern of the drone to acquire wide-area geospatial information in the shortest time. This allows wide-area geospatial information to be efficiently acquired by using drones or satellites. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit can input geospatial information acquired from a drone or satellite into a generation AI and cause the generation AI to analyze the geospatial information.

[0031] The generation unit can generate natural images based on geospatial information using a generation AI. Examples of the generation AI include, but are not limited to, a GAN (generative adversarial network) and a VAE (variational autoencoder). For example, the generation unit can generate natural images based on geospatial information using the generation AI. The generation unit can also generate a 3D model based on the geospatial information using the generation AI. The generation unit can also generate a simulation environment based on the geospatial information using the generation AI. For example, the generation AI receives geospatial information acquired from a drone or satellite as input and outputs natural images. This makes it possible to generate highly accurate natural images based on geospatial information using the generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input geospatial information to the generation AI and cause the generation AI to generate natural images.

[0032] The dividing unit can divide the virtual land space into multiple sections and register each section as a tradable digital asset on the blockchain. Examples of virtual land spaces include, but are not limited to, digital maps, 3D models, and simulation environments. Examples of digital assets include, but are not limited to, non-fungible tokens (NFTs) and digital certificates. For example, the dividing unit can divide the virtual land space into multiple sections and register each section as a tradable digital asset on the blockchain. The dividing unit can also register the virtual land space as an NFT. The dividing unit can also register the virtual land space as a digital certificate. For example, the dividing unit can divide the virtual land space into multiple sections and register each section as a tradable digital asset on the blockchain. This facilitates land transactions by dividing the virtual land space and registering it as a tradable digital asset on the blockchain. Some or all of the above-described processing by the dividing unit can be performed, for example, using AI or without AI. For example, the division unit can input the virtual land space to the generation AI and cause the generation AI to divide the virtual land space.

[0033] The analysis unit can collect data such as soil humidity or temperature and crop growth status using a sensor and input the data into the machine learning model. Soil humidity includes, but is not limited to, the type of sensor and the frequency of measurement. Soil temperature includes, but is not limited to, the type of sensor and the frequency of measurement. Crop growth status includes, but is not limited to, the growth stage and biomass amount. Machine learning models include, but are not limited to, regression models and classification models. The analysis unit can also collect weather data and input it into the machine learning model. The analysis unit can also collect water quality data and input it into the machine learning model. For example, the analysis unit can collect data such as soil humidity or temperature and crop growth status using a sensor and input it into the machine learning model. By inputting the data collected by the sensor into the machine learning model, the accuracy of environmental data analysis can be improved. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data collected by a sensor into the generation AI and have the generation AI analyze the data.

[0034] The monitoring unit can monitor the health of agricultural land or forests based on the data analyzed by the analysis unit. Examples of agricultural land health include, but are not limited to, crop growth status and soil nutritional status. Examples of forest monitoring include, but are not limited to, tree health, biomass volume, and forest fire risk. The monitoring unit, for example, monitors the health of agricultural land based on the data analyzed by the analysis unit. The monitoring unit can also monitor forests based on the data analyzed by the analysis unit. The monitoring unit can also strengthen environmental conservation efforts based on the data analyzed by the analysis unit. For example, the monitoring unit monitors the health of agricultural land based on the data analyzed by the analysis unit. This enables appropriate management by monitoring the health of agricultural land and forests based on the analyzed data. Some or all of the above-mentioned processing by the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input the data analyzed by the analysis unit into a generation AI and have the generation AI monitor the health of agricultural land and forests.

[0035] The acquisition unit can optimize the flight patterns of drones and satellites to efficiently acquire geospatial information. For example, the acquisition unit can optimize the flight pattern of a drone to acquire wide-area geospatial information in the shortest time. The acquisition unit can also select the timing for most efficient acquisition of geospatial information, taking into account the satellite orbit. The acquisition unit can also combine both drones and satellites to efficiently acquire geospatial information. For example, the acquisition unit can optimize the flight pattern of a drone to acquire wide-area geospatial information in the shortest time. In this way, geospatial information can be efficiently acquired by optimizing the flight patterns of drones and satellites. Some or all of the above-described processing in the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input the flight patterns of a drone or satellite into a generation AI and cause the generation AI to optimize the flight patterns.

[0036] The acquisition unit can improve the accuracy of the geospatial information by taking into account weather and seasonal fluctuations when acquiring the geospatial information. For example, the acquisition unit acquires weather data in real time and adjusts the timing of acquiring the geospatial information. The acquisition unit can also select an optimal method for acquiring the geospatial information by taking into account seasonal fluctuations. The acquisition unit can also predict weather and seasonal fluctuations and plan the acquisition of the geospatial information in advance. For example, the acquisition unit acquires weather data in real time and adjusts the timing of acquiring the geospatial information. This improves the accuracy of the geospatial information by taking into account weather and seasonal fluctuations. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input weather data to a generation AI and cause the generation AI to execute a geospatial information acquisition plan that takes into account weather and seasonal fluctuations.

[0037] When acquiring geospatial information, the acquisition unit can customize the acquisition method based on the characteristics of a specific crop or land. For example, the acquisition unit selects the optimal method for acquiring geospatial information, taking into account the growth status of a specific crop. The acquisition unit can also customize the method for acquiring geospatial information based on land characteristics (topography, soil type, etc.). The acquisition unit can also adjust drone and satellite settings according to the characteristics of the crop or land. For example, the acquisition unit selects the optimal method for acquiring geospatial information, taking into account the growth status of a specific crop. In this way, by customizing the acquisition method based on the characteristics of a specific crop or land, more appropriate geospatial information can be acquired. Some or all of the above-described processing in the acquisition unit may be performed, for example, using AI or without AI. For example, the acquisition unit can cause the generation AI to execute a method for acquiring geospatial information based on the characteristics of a specific crop or land.

[0038] When acquiring geospatial information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, the acquisition unit prioritizes acquiring geospatial information closest to the user's current location. The acquisition unit can also analyze the user's movement patterns to acquire highly relevant geospatial information. The acquisition unit can also acquire highly relevant geospatial information by referring to the user's past location information. For example, the acquisition unit prioritizes acquiring geospatial information closest to the user's current location. This makes it possible to prioritize acquiring highly relevant geospatial information by taking the user's geographical location information into account. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant geospatial information.

[0039] When acquiring geospatial information, the acquisition unit can analyze the user's social media activity and acquire related information. The acquisition unit, for example, acquires geospatial information of locations where the user has checked in on social media. The acquisition unit can also analyze the content of the user's posts on social media to acquire related geospatial information. The acquisition unit can also acquire related geospatial information by referring to the activities of the user's friends on social media. For example, the acquisition unit acquires geospatial information of locations where the user has checked in on social media. In this way, related geospatial information can be acquired by analyzing the user's social media activity. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media activity data to the generation AI and cause the generation AI to acquire related geospatial information.

[0040] When acquiring geospatial information, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. For example, the acquisition unit selects an optimal method for acquiring geospatial information based on feedback provided by the user in the past. The acquisition unit can also analyze the user's past feedback and improve the acquisition method. The acquisition unit can also adjust the method for acquiring geospatial information by reflecting the user's feedback in real time. For example, the acquisition unit selects an optimal method for acquiring geospatial information based on feedback provided by the user in the past. In this way, the acquisition method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0041] When generating a natural image, the generation unit can adjust the accuracy of generation based on the level of detail of the geospatial information. For example, the generation unit generates a precise natural image based on high-detail geospatial information. The generation unit can also generate a simplified natural image based on low-detail geospatial information. The generation unit can also adjust the resolution of the natural image to be generated according to the level of detail of the geospatial information. For example, the generation unit generates a precise natural image based on high-detail geospatial information. As a result, by adjusting the accuracy of generation based on the level of detail of the geospatial information, it is possible to generate a natural image with appropriate accuracy. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the level of detail of the geospatial information to the generation AI and cause the generation AI to adjust the accuracy of generation.

[0042] The generation unit can improve the accuracy of generation by integrating different geospatial information sources when generating natural images. The generation unit, for example, integrates geospatial information acquired from a drone and a satellite to generate highly accurate natural images. The generation unit can also integrate data acquired from multiple sensors to generate detailed natural images. The generation unit can also integrate geospatial information acquired at different times to generate the latest natural images. For example, the generation unit integrates geospatial information acquired from a drone and a satellite to generate highly accurate natural images. In this way, the integration of different geospatial information sources improves the accuracy of generation. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input different geospatial information sources into the generation AI and cause the generation AI to integrate the information.

[0043] When generating a natural image, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit generates a highly accurate natural image by referring to natural images generated by the user in the past. The generation unit can also analyze the user's past generation results and improve the generation method. The generation unit can also improve the generation accuracy based on user feedback. For example, the generation unit generates a highly accurate natural image by referring to natural images generated by the user in the past. In this way, the generation accuracy is improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past generation results into the generation AI and cause the generation AI to improve the generation accuracy.

[0044] When generating natural images, the generation unit can determine a generation priority based on the acquisition time of the geospatial information. The generation unit, for example, preferentially generates natural images based on the latest geospatial information. The generation unit can also generate complementary natural images based on past geospatial information. The generation unit can also adjust the order of natural images to be generated depending on the acquisition time of the geospatial information. For example, the generation unit preferentially generates natural images based on the latest geospatial information. In this way, by determining the generation priority based on the acquisition time of the geospatial information, it is possible to generate natural images that preferentially reflect the latest information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the acquisition time of the geospatial information to the generation AI and cause the generation AI to determine the generation priority.

[0045] When generating natural images, the generation unit can adjust the order of generation based on the relevance of the geospatial information. For example, the generation unit preferentially generates natural images based on the most relevant geospatial information. The generation unit can also generate complementary natural images based on less relevant geospatial information. The generation unit can also adjust the order of the natural images to be generated according to the relevance of the geospatial information. For example, the generation unit preferentially generates natural images based on the most relevant geospatial information. In this way, by adjusting the order of generation based on the relevance of the geospatial information, natural images that preferentially reflect important information can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the relevance of the geospatial information to the generation AI and cause the generation AI to adjust the order of generation.

[0046] When generating a natural image, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user is an expert, the generation unit generates a natural image that makes extensive use of technical terminology. Furthermore, if the user is a beginner, the generation unit can generate a natural image that explains things in simple terms. Furthermore, the generation unit can adjust the content of the natural image to be generated according to the user's level of expertise. For example, if the user is an expert, the generation unit generates a natural image that makes extensive use of technical terminology. In this way, by adjusting the use of technical terminology according to the user's level of expertise, it is possible to generate a natural image that is easy for the user to understand. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0047] The division unit can improve the accuracy of division based on the characteristics and intended use of the land when dividing the virtual land space. The division unit selects the optimal division method based on, for example, the characteristics of the land (topography, soil type, etc.). The division unit can also adjust the accuracy of division depending on the intended use (agriculture, residential area, etc.). The division unit can also customize the division method taking into account the characteristics and intended use of the land. For example, the division unit selects the optimal division method based on the characteristics of the land (topography, soil type, etc.). This improves the accuracy of division based on the characteristics and intended use of the land, enabling appropriate division. Some or all of the above-mentioned processing in the division unit may be performed using, for example, AI, or may be performed without using AI. For example, the division unit can input the characteristics and intended use of the land into the generation AI and cause the generation AI to improve the accuracy of division.

[0048] When dividing a virtual land space, the division unit can select the optimal division method by referring to past transaction data. The division unit, for example, analyzes past transaction data and selects the optimal division method. The division unit can also improve the division method based on the past transaction data. The division unit can also customize the division method by referring to the past transaction data. For example, the division unit analyzes past transaction data and selects the optimal division method. In this way, the optimal division method can be selected by referring to the past transaction data. Some or all of the above-mentioned processing in the division unit may be performed, for example, using AI, or may be performed without using AI. For example, the division unit can input past transaction data into the generation AI and have the generation AI select the division method.

[0049] The division unit can improve the division method by reflecting user feedback when dividing the virtual land space. The division unit, for example, improves the division method based on user feedback. The division unit can also adjust the division method by reflecting user feedback in real time. The division unit can also analyze past user feedback and select the optimal division method. For example, the division unit improves the division method based on user feedback. In this way, the division method can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the division unit may be performed using, for example, AI, or may be performed without using AI. For example, the division unit can input user feedback data into the generation AI and cause the generation AI to improve the division method.

[0050] The division unit can divide the virtual land space taking into account the geographical distribution. For example, the division unit selects an optimal division method taking into account the geographical distribution. The division unit can also adjust the accuracy of the division based on the geographical distribution. The division unit can also customize the division method taking into account the geographical distribution. For example, the division unit selects an optimal division method taking into account the geographical distribution. This enables appropriate division by taking the geographical distribution into account. Some or all of the above-mentioned processing in the division unit may be performed using, for example, AI, or may be performed without using AI. For example, the division unit can input geographical distribution data to the generation AI and have the generation AI select a division method.

[0051] When dividing the virtual land space, the division unit can improve the accuracy of the division by referring to related literature. For example, the division unit refers to related literature and selects an optimal division method. The division unit can also improve the division method based on the related literature. The division unit can also customize the division method by referring to the related literature. For example, the division unit refers to related literature and selects an optimal division method. As a result, the accuracy of the division is improved by referring to the related literature. Some or all of the above-mentioned processing in the division unit may be performed using, for example, AI, or may be performed without using AI. For example, the division unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the division method.

[0052] The division unit can divide the virtual land space taking market value into consideration. For example, the division unit selects the optimal division method taking market value into consideration. The division unit can also adjust the accuracy of the division based on market value. The division unit can also customize the division method taking market value into consideration. For example, the division unit selects the optimal division method taking market value into consideration. This enables appropriate division by taking market value into consideration. Some or all of the above-mentioned processing in the division unit may be performed using AI, for example, or may be performed without using AI. For example, the division unit can input market value data into the generation AI and have the generation AI select the division method.

[0053] When analyzing environmental data, the analysis unit can predict current data by referring to past data. The analysis unit, for example, predicts current data based on past environmental data. The analysis unit can also analyze past data and predict trends in the current data. The analysis unit can also predict fluctuations in the current data by referring to past data. For example, the analysis unit predicts current data based on past environmental data. In this way, current data can be predicted by referring to past data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data into a generation AI and have the generation AI predict current data.

[0054] When analyzing environmental data, the analysis unit can apply different analysis methods to each data category. For example, the analysis unit can apply a specific analysis method to soil data to improve accuracy. The analysis unit can also apply a different analysis method to weather data to improve accuracy. The analysis unit can also apply a different analysis method to crop growth data to improve accuracy. For example, the analysis unit can apply a specific analysis method to soil data to improve accuracy. In this way, by applying different analysis methods to each data category, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data categories to the generation AI and cause the generation AI to apply different analysis methods.

[0055] When analyzing environmental data, the analysis unit can perform the analysis taking into account attribute information of the data submitter. For example, if the data submitter is an expert, the analysis unit can perform a detailed analysis. Furthermore, if the data submitter is a novice, the analysis unit can also perform a simplified analysis. Furthermore, the analysis unit can select the optimal analysis method based on the attribute information of the data submitter. For example, if the data submitter is an expert, the analysis unit can perform a detailed analysis. This enables appropriate analysis by taking into account the attribute information of the data submitter. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input attribute information of the data submitter into the generation AI and have the generation AI select the analysis method.

[0056] When analyzing environmental data, the analysis unit can analyze changes in the analysis based on the time the data was acquired. For example, the analysis unit analyzes seasonal fluctuations based on the time the data was acquired. The analysis unit can also analyze long-term trends taking into account the time the data was acquired. The analysis unit can also adjust the analysis method depending on the time the data was acquired. For example, the analysis unit analyzes seasonal fluctuations based on the time the data was acquired. In this way, by analyzing changes in the analysis based on the time the data was acquired, seasonal fluctuations and long-term trends can be understood. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time the data was acquired into the generation AI and have the generation AI execute changes in the analysis.

[0057] When analyzing environmental data, the analysis unit can perform the analysis by referring to related market data. For example, the analysis unit can integrate environmental data with related market data and perform a comprehensive analysis. The analysis unit can also analyze the impact of environmental data based on market data. The analysis unit can also analyze the correlation between environmental data and market data. For example, the analysis unit can integrate environmental data with related market data and perform a comprehensive analysis. This makes it possible to perform a comprehensive analysis by referring to the related market data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input related market data into a generation AI and have the generation AI perform a comprehensive analysis.

[0058] The analysis unit can perform the analysis taking into account technological maturity when analyzing environmental data. The analysis unit, for example, selects an optimal analysis method taking into account technological maturity. The analysis unit can also adjust the accuracy of the analysis based on technological maturity. The analysis unit can also customize the analysis method taking into account technological maturity. For example, the analysis unit selects an optimal analysis method taking into account technological maturity. In this way, the optimal analysis method can be selected by taking technological maturity into account. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input technological maturity data to a generation AI and cause the generation AI to select an analysis method.

[0059] During monitoring, the monitoring unit can predict the current situation by referring to past monitoring data. The monitoring unit, for example, predicts the current situation based on past monitoring data. The monitoring unit can also analyze past data and predict trends in the current situation. The monitoring unit can also predict fluctuations in the current situation by referring to past data. For example, the monitoring unit predicts the current situation based on past monitoring data. In this way, the current situation can be predicted by referring to the past monitoring data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past monitoring data into a generation AI and cause the generation AI to predict the current situation.

[0060] During monitoring, the monitoring unit can apply different monitoring methods to different data categories. For example, the monitoring unit can apply a specific monitoring method to soil data to improve accuracy. The monitoring unit can also apply a different monitoring method to weather data to improve accuracy. The monitoring unit can also apply a different monitoring method to crop growth data to improve accuracy. For example, the monitoring unit can apply a specific monitoring method to soil data to improve accuracy. In this way, by applying different monitoring methods to different data categories, monitoring accuracy is improved. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data categories to the generation AI and cause the generation AI to apply different monitoring methods.

[0061] The monitoring unit can perform monitoring while taking into consideration attribute information of the data submitter. For example, if the data submitter is an expert, the monitoring unit can perform detailed monitoring. Furthermore, if the data submitter is a novice, the monitoring unit can also perform simplified monitoring. Furthermore, the monitoring unit can select the optimal monitoring method based on the attribute information of the data submitter. For example, if the data submitter is an expert, the monitoring unit performs detailed monitoring. This enables appropriate monitoring by taking into consideration the attribute information of the data submitter. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input attribute information of the data submitter into the generation AI and have the generation AI select the monitoring method.

[0062] During monitoring, the monitoring unit can analyze changes in the monitoring based on the time the data was acquired. For example, the monitoring unit monitors seasonal fluctuations based on the time the data was acquired. The monitoring unit can also monitor long-term trends taking into account the time the data was acquired. The monitoring unit can also adjust the monitoring method depending on the time the data was acquired. For example, the monitoring unit monitors seasonal fluctuations based on the time the data was acquired. In this way, by analyzing changes in the monitoring based on the time the data was acquired, seasonal fluctuations and long-term trends can be understood. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the time the data was acquired into a generation AI and have the generation AI execute the changes in the monitoring.

[0063] During monitoring, the monitoring unit can perform monitoring by referring to related market data. The monitoring unit, for example, integrates environmental data with related market data to perform comprehensive monitoring. The monitoring unit can also monitor the impact of environmental data based on market data. The monitoring unit can also monitor the correlation between environmental data and market data. For example, the monitoring unit integrates environmental data with related market data to perform comprehensive monitoring. This makes it possible to perform comprehensive monitoring by referring to related market data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input related market data into a generation AI and have the generation AI perform comprehensive monitoring.

[0064] The monitoring unit can perform monitoring while taking into account the technological maturity. For example, the monitoring unit selects an optimal monitoring method while taking into account the technological maturity. The monitoring unit can also adjust the accuracy of monitoring based on the technological maturity. The monitoring unit can also customize the monitoring method while taking into account the technological maturity. For example, the monitoring unit selects an optimal monitoring method while taking into account the technological maturity. In this way, the optimal monitoring method can be selected by taking into account the technological maturity. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input technical maturity data into the generation AI and cause the generation AI to select a monitoring method.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] When generating a natural image, the generation unit can improve the accuracy of generation by referring to the user's past behavioral history. For example, it analyzes what kind of natural images the user has preferred to generate in the past and generates a new natural image based on that tendency. It can also improve the generation method based on feedback from images the user has generated in the past. Furthermore, it can also customize the content of the natural image to be generated by referring to the user's behavioral history. In this way, by referring to the user's past behavioral history, the accuracy of generation can be improved and the most suitable natural image can be provided for the user.

[0067] When dividing a virtual land space, the division unit can select a division method by referring to historical data on the land. For example, it can select the optimal division method based on past land use data. It can also adjust the precision of the division by taking into account historical changes in the land. Furthermore, it can also customize the division method by referring to the historical data on the land. This makes it possible to divide the land appropriately and maximize its useful value.

[0068] When analyzing environmental data, the analysis unit can evaluate the reliability of the data and adjust the analysis method based on that reliability. For example, it can apply a detailed analysis method to highly reliable data and a simplified analysis method to less reliable data. It can also set criteria for evaluating data reliability and select an analysis method based on those criteria. Furthermore, it can create a feedback loop to improve data reliability and continuously improve the analysis method. By adjusting the analysis method based on the reliability of the data, analysis accuracy can be improved and more reliable results can be provided.

[0069] The acquisition unit performs real-time data filtering when acquiring geospatial information, enabling it to acquire only the necessary information. For example, data can be filtered based on specific regions or conditions to eliminate unnecessary information. Real-time data filtering can also improve the accuracy of the acquired information. Furthermore, filtering conditions can be customized according to user requests to acquire optimal information. This allows for efficient acquisition of necessary information by performing real-time data filtering.

[0070] The generation unit can simulate different weather conditions when generating a nature image and reflect them in the generated image. For example, different weather conditions such as sunny, rainy, and snowy days can be simulated and nature images can be generated based on them. It can also reflect changes in weather conditions in real time and generate images based on the latest information. Furthermore, it can select specific weather conditions in response to a user request and generate images based on those conditions. By simulating different weather conditions, more realistic and diverse nature images can be provided.

[0071] When dividing the virtual land space, the division unit can select the division method taking into account the ecosystem data of the land. For example, the division unit selects the optimal division method based on data on the ecosystem of the land. The division accuracy can also be adjusted taking into account the protection of the ecosystem. Furthermore, the division method can be customized by referring to the ecosystem data. In this way, by taking into account the ecosystem data of the land, appropriate division that takes into account environmental conservation is possible.

[0072] When analyzing environmental data, the analysis unit can take into account the spatial distribution of the data. For example, it can analyze the characteristics of each region based on the spatial distribution of the data. It can also compare data from different regions based on the spatial distribution and derive analysis results. It can also customize analysis methods by taking the spatial distribution into account. By taking the spatial distribution of the data into account, it is possible to accurately grasp the characteristics of each region and provide appropriate analysis results.

[0073] The processing flow of the first embodiment will be briefly explained below.

[0074] Step 1: The acquisition unit acquires geospatial information. Geospatial information includes topographical data, meteorological data, land use data, etc. The acquisition unit acquires geospatial information using drones, satellites, and ground sensors. For example, drones efficiently acquire wide-area geospatial information, satellites use earth observation satellites and communication satellites, and ground sensors acquire detailed geospatial information at specific locations. Step 2: The generation unit uses a generation AI to generate natural images based on the geospatial information acquired by the acquisition unit. The generation AI can use technologies such as GAN (generative adversarial network) and VAE (variational autoencoder). In the generation unit, the generation AI generates natural images, 3D models, and simulation environments based on the geospatial information. Step 3: The division unit divides the virtual land space generated by the generation unit and registers it as a tradable digital asset on the blockchain. The virtual land space includes digital maps, 3D models, simulation environments, etc. The division unit divides the virtual land space into multiple sections and registers each section as a tradable digital asset, NFT (non-fungible token), or digital certificate on the blockchain. Step 4: The analysis unit analyzes the environmental data, which includes temperature, humidity, soil composition, etc. The analysis unit uses sensors to collect soil humidity and temperature, crop growth status, weather data, water quality data, etc., and inputs this data into the machine learning model. Step 5: The Monitoring Department will monitor the health of farmland and forests based on the data analyzed by the Analysis Department. Farmland health includes crop growth status and soil nutrition status, while forest monitoring includes tree health, biomass volume, and forest fire risk. The Monitoring Department will monitor the health of farmland and forests based on the data analyzed by the Analysis Department, strengthening environmental conservation efforts.

[0075] (Example 2) An agricultural management system according to an embodiment of the present invention acquires geospatial information, generates nature images using a generation AI, divides a virtual land space, analyzes environmental data, and monitors the health of farmland and forests. The agricultural management system acquires geospatial information, generates nature images using a generation AI, divides the virtual land space, and registers the images as tradable digital assets on a blockchain. Furthermore, the agricultural management system analyzes environmental data and monitors the health of farmland and forests. For example, the agricultural management system acquires geospatial information from drones or satellites. For example, the agricultural management system uses a generation AI to generate nature images based on the geospatial information. For example, the agricultural management system divides a virtual land space into multiple sections and registers each section as a tradable digital asset on a blockchain. For example, the agricultural management system uses sensors to collect data such as soil humidity and temperature and crop growth status and inputs it into a machine learning model. For example, the agricultural management system monitors the health of farmland and forests based on the analyzed data. This allows the agricultural management system to combine sustainable agriculture and environmental conservation, thereby realizing innovative agricultural management. This allows agricultural management systems to integrate sustainable agriculture with environmental conservation, enabling innovative agricultural management. For example, the health of farmland can be monitored in real time, allowing necessary measures to be taken promptly. Forest monitoring can also strengthen environmental conservation efforts.

[0076] An agricultural management system according to an embodiment includes an acquisition unit, a generation unit, a division unit, an analysis unit, and a monitoring unit. The acquisition unit acquires geospatial information. The geospatial information includes, but is not limited to, topographical data, meteorological data, and land use data. The acquisition unit acquires the geospatial information from, for example, a drone or a satellite. The acquisition unit can also acquire the geospatial information using a ground sensor. For example, a drone is used to efficiently acquire wide-area geospatial information. The satellite may be an earth observation satellite or a communication satellite. The ground sensor is used to acquire detailed geospatial information at a specific location. The generation unit generates a natural image based on the geospatial information acquired by the acquisition unit using a generation AI. The generation AI may use technologies such as a generative adversarial network (GAN) or a variational autoencoder (VAE). For example, the generation AI generates a natural image based on the geospatial information. The generation AI can also generate a 3D model based on the geospatial information. The generation unit can also allow the generation AI to generate a simulation environment based on geospatial information. For example, the generation AI inputs geospatial information acquired from a drone or satellite and outputs natural images. The division unit divides the virtual land space generated by the generation unit and registers it as a tradable digital asset on a blockchain. Examples of virtual land spaces include, but are not limited to, digital maps, 3D models, and simulation environments. For example, the division unit divides the virtual land space into multiple sections and registers each section as a tradable digital asset on a blockchain. The division unit can also register the virtual land space as an NFT (non-fungible token). The division unit can also register the virtual land space as a digital certificate. For example, the division unit divides the virtual land space into multiple sections and registers each section as a tradable digital asset on a blockchain. The analysis unit analyzes environmental data. Examples of environmental data include, but are not limited to, temperature, humidity, and soil composition. The analysis unit uses sensors to collect data such as soil humidity and temperature, and crop growth status, and inputs this data into a machine learning model.The analysis unit can also collect weather data and input it into the machine learning model. The analysis unit can also collect water quality data and input it into the machine learning model. For example, the analysis unit collects data such as soil humidity and temperature, crop growth status, etc. using sensors and inputs the data into the machine learning model. The monitoring unit monitors the health of farmland and forests based on the data analyzed by the analysis unit. Examples of the health of farmland include, but are not limited to, the growth status of crops and the nutritional state of the soil. Examples of forest monitoring include, but are not limited to, the health of trees, biomass volume, and the risk of forest fires. For example, the monitoring unit monitors the health of farmland based on the data analyzed by the analysis unit. The monitoring unit can also monitor forests based on the data analyzed by the analysis unit. The monitoring unit can also strengthen environmental conservation efforts based on the data analyzed by the analysis unit. For example, the monitoring unit monitors the health of farmland based on the data analyzed by the analysis unit. As a result, the agricultural management system of the embodiment can consistently perform tasks from acquiring geospatial information to generating natural images, dividing virtual land space, analyzing environmental data, and monitoring the health of farmland and forests.

[0077] The acquisition unit can acquire geospatial information from a drone or a satellite. Examples of drones include, but are not limited to, fixed-wing drones and multi-rotor drones. Examples of satellites include, but are not limited to, earth observation satellites and communication satellites. The acquisition unit, for example, optimizes the flight pattern of the drone to acquire wide-area geospatial information in the shortest time. The acquisition unit can also select the timing for most efficient acquisition of geospatial information by taking into account the satellite's orbit. The acquisition unit can also efficiently acquire geospatial information by combining both drones and satellites. For example, the acquisition unit can optimize the flight pattern of the drone to acquire wide-area geospatial information in the shortest time. This allows wide-area geospatial information to be efficiently acquired by using drones or satellites. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit can input geospatial information acquired from a drone or satellite into a generation AI and cause the generation AI to analyze the geospatial information.

[0078] The generation unit can generate natural images based on geospatial information using a generation AI. Examples of the generation AI include, but are not limited to, a GAN (generative adversarial network) and a VAE (variational autoencoder). For example, the generation unit can generate natural images based on geospatial information using the generation AI. The generation unit can also generate a 3D model based on the geospatial information using the generation AI. The generation unit can also generate a simulation environment based on the geospatial information using the generation AI. For example, the generation AI receives geospatial information acquired from a drone or satellite as input and outputs natural images. This makes it possible to generate highly accurate natural images based on geospatial information using the generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input geospatial information to the generation AI and cause the generation AI to generate natural images.

[0079] The dividing unit can divide the virtual land space into multiple sections and register each section as a tradable digital asset on the blockchain. Examples of virtual land spaces include, but are not limited to, digital maps, 3D models, and simulation environments. Examples of digital assets include, but are not limited to, non-fungible tokens (NFTs) and digital certificates. For example, the dividing unit can divide the virtual land space into multiple sections and register each section as a tradable digital asset on the blockchain. The dividing unit can also register the virtual land space as an NFT. The dividing unit can also register the virtual land space as a digital certificate. For example, the dividing unit can divide the virtual land space into multiple sections and register each section as a tradable digital asset on the blockchain. This facilitates land transactions by dividing the virtual land space and registering it as a tradable digital asset on the blockchain. Some or all of the above-described processing by the dividing unit can be performed, for example, using AI or without AI. For example, the division unit can input the virtual land space to the generation AI and cause the generation AI to divide the virtual land space.

[0080] The analysis unit can collect data such as soil humidity or temperature and crop growth status using a sensor and input the data into the machine learning model. Soil humidity includes, but is not limited to, the type of sensor and the frequency of measurement. Soil temperature includes, but is not limited to, the type of sensor and the frequency of measurement. Crop growth status includes, but is not limited to, the growth stage and biomass amount. Machine learning models include, but are not limited to, regression models and classification models. The analysis unit can also collect weather data and input it into the machine learning model. The analysis unit can also collect water quality data and input it into the machine learning model. For example, the analysis unit can collect data such as soil humidity or temperature and crop growth status using a sensor and input it into the machine learning model. By inputting the data collected by the sensor into the machine learning model, the accuracy of environmental data analysis can be improved. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data collected by a sensor into the generation AI and have the generation AI analyze the data.

[0081] The monitoring unit can monitor the health of agricultural land or forests based on the data analyzed by the analysis unit. Examples of agricultural land health include, but are not limited to, crop growth status and soil nutritional status. Examples of forest monitoring include, but are not limited to, tree health, biomass volume, and forest fire risk. The monitoring unit, for example, monitors the health of agricultural land based on the data analyzed by the analysis unit. The monitoring unit can also monitor forests based on the data analyzed by the analysis unit. The monitoring unit can also strengthen environmental conservation efforts based on the data analyzed by the analysis unit. For example, the monitoring unit monitors the health of agricultural land based on the data analyzed by the analysis unit. This enables appropriate management by monitoring the health of agricultural land and forests based on the analyzed data. Some or all of the above-mentioned processing by the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input the data analyzed by the analysis unit into a generation AI and have the generation AI monitor the health of agricultural land and forests.

[0082] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring geospatial information based on the estimated user emotions. For example, when the user is feeling stressed, the acquisition unit automates the acquisition of geospatial information, minimizing user intervention. Furthermore, when the user is relaxed, the acquisition unit can also allow the user to manually set the timing of acquiring geospatial information. Furthermore, when the user is in a hurry, the acquisition unit can select a method that can acquire geospatial information most quickly. For example, when the user is feeling stressed, the acquisition unit automates the acquisition of geospatial information, minimizing user intervention. This reduces the burden on the user by adjusting the timing of acquiring geospatial information according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0083] The acquisition unit can optimize the flight patterns of drones and satellites to efficiently acquire geospatial information. For example, the acquisition unit can optimize the flight pattern of a drone to acquire wide-area geospatial information in the shortest time. The acquisition unit can also select the timing for most efficient acquisition of geospatial information, taking into account the satellite orbit. The acquisition unit can also combine both drones and satellites to efficiently acquire geospatial information. For example, the acquisition unit can optimize the flight pattern of a drone to acquire wide-area geospatial information in the shortest time. In this way, geospatial information can be efficiently acquired by optimizing the flight patterns of drones and satellites. Some or all of the above-described processing in the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input the flight patterns of a drone or satellite into a generation AI and cause the generation AI to optimize the flight patterns.

[0084] The acquisition unit can improve the accuracy of the geospatial information by taking into account weather and seasonal fluctuations when acquiring the geospatial information. For example, the acquisition unit acquires weather data in real time and adjusts the timing of acquiring the geospatial information. The acquisition unit can also select an optimal method for acquiring the geospatial information by taking into account seasonal fluctuations. The acquisition unit can also predict weather and seasonal fluctuations and plan the acquisition of the geospatial information in advance. For example, the acquisition unit acquires weather data in real time and adjusts the timing of acquiring the geospatial information. This improves the accuracy of the geospatial information by taking into account weather and seasonal fluctuations. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input weather data to a generation AI and cause the generation AI to execute a geospatial information acquisition plan that takes into account weather and seasonal fluctuations.

[0085] When acquiring geospatial information, the acquisition unit can customize the acquisition method based on the characteristics of a specific crop or land. For example, the acquisition unit selects the optimal method for acquiring geospatial information, taking into account the growth status of a specific crop. The acquisition unit can also customize the method for acquiring geospatial information based on land characteristics (topography, soil type, etc.). The acquisition unit can also adjust drone and satellite settings according to the characteristics of the crop or land. For example, the acquisition unit selects the optimal method for acquiring geospatial information, taking into account the growth status of a specific crop. In this way, by customizing the acquisition method based on the characteristics of a specific crop or land, more appropriate geospatial information can be acquired. Some or all of the above-described processing in the acquisition unit may be performed, for example, using AI or without AI. For example, the acquisition unit can cause the generation AI to execute a method for acquiring geospatial information based on the characteristics of a specific crop or land.

[0086] The acquisition unit can estimate the user's emotions and determine the priority of geospatial information to be acquired based on the estimated user's emotions. For example, when the user is feeling stressed, the acquisition unit prioritizes acquiring the most important geospatial information. Furthermore, when the user is relaxed, the acquisition unit can also acquire detailed geospatial information. Furthermore, when the user is in a hurry, the acquisition unit can prioritize geospatial information that can be acquired quickly. For example, when the user is feeling stressed, the acquisition unit prioritizes acquiring the most important geospatial information. Thus, by determining the priority of geospatial information according to the user's emotions, important information can be acquired preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI. For example, the acquisition unit may input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0087] When acquiring geospatial information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, the acquisition unit prioritizes acquiring geospatial information closest to the user's current location. The acquisition unit can also analyze the user's movement patterns to acquire highly relevant geospatial information. The acquisition unit can also acquire highly relevant geospatial information by referring to the user's past location information. For example, the acquisition unit prioritizes acquiring geospatial information closest to the user's current location. This makes it possible to prioritize acquiring highly relevant geospatial information by taking the user's geographical location information into account. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant geospatial information.

[0088] When acquiring geospatial information, the acquisition unit can analyze the user's social media activity and acquire related information. The acquisition unit, for example, acquires geospatial information of locations where the user has checked in on social media. The acquisition unit can also analyze the content of the user's posts on social media to acquire related geospatial information. The acquisition unit can also acquire related geospatial information by referring to the activities of the user's friends on social media. For example, the acquisition unit acquires geospatial information of locations where the user has checked in on social media. In this way, related geospatial information can be acquired by analyzing the user's social media activity. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media activity data to the generation AI and cause the generation AI to acquire related geospatial information.

[0089] When acquiring geospatial information, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. For example, the acquisition unit selects an optimal method for acquiring geospatial information based on feedback provided by the user in the past. The acquisition unit can also analyze the user's past feedback and improve the acquisition method. The acquisition unit can also adjust the method for acquiring geospatial information by reflecting the user's feedback in real time. For example, the acquisition unit selects an optimal method for acquiring geospatial information based on feedback provided by the user in the past. In this way, the acquisition method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0090] The generation unit can estimate the user's emotions and adjust the presentation method of the natural image based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates a natural image with calm colors and a leisurely pace. If the user is in a hurry, the generation unit can also generate a concise natural image that emphasizes important information. If the user is excited, the generation unit can also generate a natural image with a visually stimulating effect. For example, if the user is relaxed, the generation unit generates a natural image with calm colors and a leisurely pace. This allows the optimal image for the user to be provided by adjusting the presentation method of the natural image according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI. For example, the generation unit may input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0091] When generating a natural image, the generation unit can adjust the accuracy of generation based on the level of detail of the geospatial information. For example, the generation unit generates a precise natural image based on high-detail geospatial information. The generation unit can also generate a simplified natural image based on low-detail geospatial information. The generation unit can also adjust the resolution of the natural image to be generated according to the level of detail of the geospatial information. For example, the generation unit generates a precise natural image based on high-detail geospatial information. As a result, by adjusting the accuracy of generation based on the level of detail of the geospatial information, it is possible to generate a natural image with appropriate accuracy. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the level of detail of the geospatial information to the generation AI and cause the generation AI to adjust the accuracy of generation.

[0092] The generation unit can improve the accuracy of generation by integrating different geospatial information sources when generating natural images. The generation unit, for example, integrates geospatial information acquired from a drone and a satellite to generate highly accurate natural images. The generation unit can also integrate data acquired from multiple sensors to generate detailed natural images. The generation unit can also integrate geospatial information acquired at different times to generate the latest natural images. For example, the generation unit integrates geospatial information acquired from a drone and a satellite to generate highly accurate natural images. In this way, the integration of different geospatial information sources improves the accuracy of generation. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input different geospatial information sources into the generation AI and cause the generation AI to integrate the information.

[0093] When generating a natural image, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit generates a highly accurate natural image by referring to natural images generated by the user in the past. The generation unit can also analyze the user's past generation results and improve the generation method. The generation unit can also improve the generation accuracy based on user feedback. For example, the generation unit generates a highly accurate natural image by referring to natural images generated by the user in the past. In this way, the generation accuracy is improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past generation results into the generation AI and cause the generation AI to improve the generation accuracy.

[0094] The generation unit can estimate the user's emotion and adjust the length of the natural image based on the estimated user emotion. For example, if the user is in a hurry, the generation unit generates a short, to-the-point natural image. Furthermore, if the user is relaxed, the generation unit can generate a longer natural image including detailed explanations. Furthermore, if the user is excited, the generation unit can generate a natural image with a visually stimulating effect. For example, if the user is in a hurry, the generation unit generates a short, to-the-point natural image. This allows the length of the natural image to be adjusted according to the user's emotion, thereby providing an image of optimal length for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI. For example, the generation unit may input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0095] When generating natural images, the generation unit can determine a generation priority based on the acquisition time of the geospatial information. The generation unit, for example, preferentially generates natural images based on the latest geospatial information. The generation unit can also generate complementary natural images based on past geospatial information. The generation unit can also adjust the order of natural images to be generated depending on the acquisition time of the geospatial information. For example, the generation unit preferentially generates natural images based on the latest geospatial information. In this way, by determining the generation priority based on the acquisition time of the geospatial information, it is possible to generate natural images that preferentially reflect the latest information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the acquisition time of the geospatial information to the generation AI and cause the generation AI to determine the generation priority.

[0096] When generating natural images, the generation unit can adjust the order of generation based on the relevance of the geospatial information. For example, the generation unit preferentially generates natural images based on the most relevant geospatial information. The generation unit can also generate complementary natural images based on less relevant geospatial information. The generation unit can also adjust the order of the natural images to be generated according to the relevance of the geospatial information. For example, the generation unit preferentially generates natural images based on the most relevant geospatial information. In this way, by adjusting the order of generation based on the relevance of the geospatial information, natural images that preferentially reflect important information can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the relevance of the geospatial information to the generation AI and cause the generation AI to adjust the order of generation.

[0097] When generating a natural image, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user is an expert, the generation unit generates a natural image that makes extensive use of technical terminology. Furthermore, if the user is a beginner, the generation unit can generate a natural image that explains things in simple terms. Furthermore, the generation unit can adjust the content of the natural image to be generated according to the user's level of expertise. For example, if the user is an expert, the generation unit generates a natural image that makes extensive use of technical terminology. In this way, by adjusting the use of technical terminology according to the user's level of expertise, it is possible to generate a natural image that is easy for the user to understand. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0098] The segmentation unit can estimate the user's emotions and adjust the division method of the virtual land space based on the estimated user emotions. For example, the segmentation unit provides a detailed division method when the user is relaxed. The segmentation unit can also provide a simplified division method when the user is in a hurry. The segmentation unit can also provide a division method with a visually stimulating effect when the user is excited. For example, the segmentation unit provides a detailed division method when the user is relaxed. This allows the segmentation method of the virtual land space to be adjusted according to the user's emotions, thereby providing an optimal division method for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the segmentation unit may be performed using an AI, or may be performed without an AI. For example, the segmentation unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0099] The division unit can improve the accuracy of division based on the characteristics and intended use of the land when dividing the virtual land space. The division unit selects the optimal division method based on, for example, the characteristics of the land (topography, soil type, etc.). The division unit can also adjust the accuracy of division depending on the intended use (agriculture, residential area, etc.). The division unit can also customize the division method taking into account the characteristics and intended use of the land. For example, the division unit selects the optimal division method based on the characteristics of the land (topography, soil type, etc.). This improves the accuracy of division based on the characteristics and intended use of the land, enabling appropriate division. Some or all of the above-mentioned processing in the division unit may be performed using, for example, AI, or may be performed without using AI. For example, the division unit can input the characteristics and intended use of the land into the generation AI and cause the generation AI to improve the accuracy of division.

[0100] When dividing a virtual land space, the division unit can select the optimal division method by referring to past transaction data. The division unit, for example, analyzes past transaction data and selects the optimal division method. The division unit can also improve the division method based on the past transaction data. The division unit can also customize the division method by referring to the past transaction data. For example, the division unit analyzes past transaction data and selects the optimal division method. In this way, the optimal division method can be selected by referring to the past transaction data. Some or all of the above-mentioned processing in the division unit may be performed, for example, using AI, or may be performed without using AI. For example, the division unit can input past transaction data into the generation AI and have the generation AI select the division method.

[0101] The division unit can improve the division method by reflecting user feedback when dividing the virtual land space. The division unit, for example, improves the division method based on user feedback. The division unit can also adjust the division method by reflecting user feedback in real time. The division unit can also analyze past user feedback and select the optimal division method. For example, the division unit improves the division method based on user feedback. In this way, the division method can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the division unit may be performed using, for example, AI, or may be performed without using AI. For example, the division unit can input user feedback data into the generation AI and cause the generation AI to improve the division method.

[0102] The division unit can estimate the user's emotions and determine the priority of the virtual land to be divided based on the estimated user's emotions. For example, when the user is feeling stressed, the division unit can prioritize dividing the most important virtual land. Furthermore, when the user is relaxed, the division unit can prioritize dividing detailed virtual land. Furthermore, when the user is in a hurry, the division unit can prioritize dividing virtual land that can be divided quickly. For example, when the user is feeling stressed, the division unit prioritizes dividing the most important virtual land. Thus, by determining the priority of the virtual land according to the user's emotions, important land can be divided preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the division unit can be performed using, for example, an AI. For example, the division unit can input the user's emotion data into the generation AI and cause the generation AI to perform emotion estimation.

[0103] The division unit can divide the virtual land space taking into account the geographical distribution. For example, the division unit selects an optimal division method taking into account the geographical distribution. The division unit can also adjust the accuracy of the division based on the geographical distribution. The division unit can also customize the division method taking into account the geographical distribution. For example, the division unit selects an optimal division method taking into account the geographical distribution. This enables appropriate division by taking the geographical distribution into account. Some or all of the above-mentioned processing in the division unit may be performed using, for example, AI, or may be performed without using AI. For example, the division unit can input geographical distribution data to the generation AI and have the generation AI select a division method.

[0104] When dividing the virtual land space, the division unit can improve the accuracy of the division by referring to related literature. For example, the division unit refers to related literature and selects an optimal division method. The division unit can also improve the division method based on the related literature. The division unit can also customize the division method by referring to the related literature. For example, the division unit refers to related literature and selects an optimal division method. As a result, the accuracy of the division is improved by referring to the related literature. Some or all of the above-mentioned processing in the division unit may be performed using, for example, AI, or may be performed without using AI. For example, the division unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the division method.

[0105] The division unit can divide the virtual land space taking market value into consideration. For example, the division unit selects the optimal division method taking market value into consideration. The division unit can also adjust the accuracy of the division based on market value. The division unit can also customize the division method taking market value into consideration. For example, the division unit selects the optimal division method taking market value into consideration. This enables appropriate division by taking market value into consideration. Some or all of the above-mentioned processing in the division unit may be performed using AI, for example, or may be performed without using AI. For example, the division unit can input market value data into the generation AI and have the generation AI select the division method.

[0106] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. For example, the analysis unit can provide a detailed analysis method when the user is relaxed. The analysis unit can also provide a simplified analysis method when the user is in a hurry. The analysis unit can also provide an analysis method with a visually stimulating effect when the user is excited. For example, the analysis unit can provide a detailed analysis method when the user is relaxed. This allows the analysis method to be adjusted according to the user's emotions, thereby providing optimal analysis results for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0107] When analyzing environmental data, the analysis unit can predict current data by referring to past data. The analysis unit, for example, predicts current data based on past environmental data. The analysis unit can also analyze past data and predict trends in the current data. The analysis unit can also predict fluctuations in the current data by referring to past data. For example, the analysis unit predicts current data based on past environmental data. In this way, current data can be predicted by referring to past data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data into a generation AI and have the generation AI predict current data.

[0108] When analyzing environmental data, the analysis unit can apply different analysis methods to each data category. For example, the analysis unit can apply a specific analysis method to soil data to improve accuracy. The analysis unit can also apply a different analysis method to weather data to improve accuracy. The analysis unit can also apply a different analysis method to crop growth data to improve accuracy. For example, the analysis unit can apply a specific analysis method to soil data to improve accuracy. In this way, by applying different analysis methods to each data category, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data categories to the generation AI and cause the generation AI to apply different analysis methods.

[0109] When analyzing environmental data, the analysis unit can perform the analysis taking into account attribute information of the data submitter. For example, if the data submitter is an expert, the analysis unit can perform a detailed analysis. Furthermore, if the data submitter is a novice, the analysis unit can also perform a simplified analysis. Furthermore, the analysis unit can select the optimal analysis method based on the attribute information of the data submitter. For example, if the data submitter is an expert, the analysis unit can perform a detailed analysis. This enables appropriate analysis by taking into account the attribute information of the data submitter. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input attribute information of the data submitter into the generation AI and have the generation AI select the analysis method.

[0110] The analysis unit can estimate the user's emotions and adjust the importance of analysis based on the estimated user emotions. For example, when the user is feeling stressed, the analysis unit prioritizes analyzing the most important data. Furthermore, when the user is relaxed, the analysis unit can also analyze detailed data. Furthermore, when the user is in a hurry, the analysis unit can prioritize data that can be analyzed quickly. For example, when the user is feeling stressed, the analysis unit prioritizes analyzing the most important data. Thus, by adjusting the importance of analysis according to the user's emotions, important data can be prioritized for analysis. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0111] When analyzing environmental data, the analysis unit can analyze changes in the analysis based on the time the data was acquired. For example, the analysis unit analyzes seasonal fluctuations based on the time the data was acquired. The analysis unit can also analyze long-term trends taking into account the time the data was acquired. The analysis unit can also adjust the analysis method depending on the time the data was acquired. For example, the analysis unit analyzes seasonal fluctuations based on the time the data was acquired. In this way, by analyzing changes in the analysis based on the time the data was acquired, seasonal fluctuations and long-term trends can be understood. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time the data was acquired into the generation AI and have the generation AI execute changes in the analysis.

[0112] When analyzing environmental data, the analysis unit can perform the analysis by referring to related market data. For example, the analysis unit can integrate environmental data with related market data and perform a comprehensive analysis. The analysis unit can also analyze the impact of environmental data based on market data. The analysis unit can also analyze the correlation between environmental data and market data. For example, the analysis unit can integrate environmental data with related market data and perform a comprehensive analysis. This makes it possible to perform a comprehensive analysis by referring to the related market data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input related market data into a generation AI and have the generation AI perform a comprehensive analysis.

[0113] The analysis unit can perform the analysis taking into account technological maturity when analyzing environmental data. The analysis unit, for example, selects an optimal analysis method taking into account technological maturity. The analysis unit can also adjust the accuracy of the analysis based on technological maturity. The analysis unit can also customize the analysis method taking into account technological maturity. For example, the analysis unit selects an optimal analysis method taking into account technological maturity. In this way, the optimal analysis method can be selected by taking technological maturity into account. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input technological maturity data to a generation AI and cause the generation AI to select an analysis method.

[0114] The monitoring unit can estimate the user's emotions and adjust the monitoring display method based on the estimated user emotions. For example, when the user is nervous, the monitoring unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the monitoring unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the monitoring unit can provide a display method that focuses on the main points. For example, when the user is nervous, the monitoring unit provides a simple, highly visible display method. This allows the monitoring display method to be adjusted according to the user's emotions, thereby providing an optimal display method for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0115] During monitoring, the monitoring unit can predict the current situation by referring to past monitoring data. The monitoring unit, for example, predicts the current situation based on past monitoring data. The monitoring unit can also analyze past data and predict trends in the current situation. The monitoring unit can also predict fluctuations in the current situation by referring to past data. For example, the monitoring unit predicts the current situation based on past monitoring data. In this way, the current situation can be predicted by referring to the past monitoring data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past monitoring data into a generation AI and cause the generation AI to predict the current situation.

[0116] During monitoring, the monitoring unit can apply different monitoring methods to different data categories. For example, the monitoring unit can apply a specific monitoring method to soil data to improve accuracy. The monitoring unit can also apply a different monitoring method to weather data to improve accuracy. The monitoring unit can also apply a different monitoring method to crop growth data to improve accuracy. For example, the monitoring unit can apply a specific monitoring method to soil data to improve accuracy. In this way, by applying different monitoring methods to different data categories, monitoring accuracy is improved. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data categories to the generation AI and cause the generation AI to apply different monitoring methods.

[0117] The monitoring unit can perform monitoring while taking into consideration attribute information of the data submitter. For example, if the data submitter is an expert, the monitoring unit can perform detailed monitoring. Furthermore, if the data submitter is a novice, the monitoring unit can also perform simplified monitoring. Furthermore, the monitoring unit can select the optimal monitoring method based on the attribute information of the data submitter. For example, if the data submitter is an expert, the monitoring unit performs detailed monitoring. This enables appropriate monitoring by taking into consideration the attribute information of the data submitter. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input attribute information of the data submitter into the generation AI and have the generation AI select the monitoring method.

[0118] The monitoring unit can estimate the user's emotions and adjust the importance of monitoring based on the estimated user's emotions. For example, when the user is feeling stressed, the monitoring unit prioritizes monitoring the most important data. Furthermore, when the user is relaxed, the monitoring unit can also monitor detailed data. Furthermore, when the user is in a hurry, the monitoring unit can prioritize data that can be monitored quickly. For example, when the user is feeling stressed, the monitoring unit prioritizes monitoring the most important data. Thus, by adjusting the importance of monitoring according to the user's emotions, important data can be prioritized for monitoring. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit can be performed using, for example, an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0119] During monitoring, the monitoring unit can analyze changes in the monitoring based on the time the data was acquired. For example, the monitoring unit monitors seasonal fluctuations based on the time the data was acquired. The monitoring unit can also monitor long-term trends taking into account the time the data was acquired. The monitoring unit can also adjust the monitoring method depending on the time the data was acquired. For example, the monitoring unit monitors seasonal fluctuations based on the time the data was acquired. In this way, by analyzing changes in the monitoring based on the time the data was acquired, seasonal fluctuations and long-term trends can be understood. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the time the data was acquired into a generation AI and have the generation AI execute the changes in the monitoring.

[0120] During monitoring, the monitoring unit can perform monitoring by referring to related market data. The monitoring unit, for example, integrates environmental data with related market data to perform comprehensive monitoring. The monitoring unit can also monitor the impact of environmental data based on market data. The monitoring unit can also monitor the correlation between environmental data and market data. For example, the monitoring unit integrates environmental data with related market data to perform comprehensive monitoring. This makes it possible to perform comprehensive monitoring by referring to related market data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input related market data into a generation AI and have the generation AI perform comprehensive monitoring.

[0121] The monitoring unit can perform monitoring while taking into account the technological maturity. For example, the monitoring unit selects an optimal monitoring method while taking into account the technological maturity. The monitoring unit can also adjust the accuracy of monitoring based on the technological maturity. The monitoring unit can also customize the monitoring method while taking into account the technological maturity. For example, the monitoring unit selects an optimal monitoring method while taking into account the technological maturity. In this way, the optimal monitoring method can be selected by taking into account the technological maturity. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input technical maturity data into the generation AI and cause the generation AI to select a monitoring method. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, generation unit, division unit, analysis unit, and monitoring unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires geospatial information using the camera 42 of the smart device 14 or a drone, and the acquired information is processed by the specific processing unit 290 of the data processing device 12. For example, the generation unit generates natural images using a generative AI by the specific processing unit 290 of the data processing device 12. For example, the division unit divides a virtual land space by the specific processing unit 290 of the data processing device 12 and registers the divided land as a tradable digital asset on a blockchain. For example, the analysis unit analyzes environmental data by the sensor of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the monitoring unit monitors the health of farmland and forests by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, generation unit, division unit, analysis unit, and monitoring unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires geospatial information using the camera 42 of the smart glasses 214 or a drone, and the acquired information is processed by the specific processing unit 290 of the data processing device 12. For example, the generation unit generates natural images using a generative AI by the specific processing unit 290 of the data processing device 12. For example, the division unit divides virtual land space by the specific processing unit 290 of the data processing device 12 and registers the divided land as tradable digital assets on a blockchain. For example, the analysis unit analyzes environmental data by the sensor of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the monitoring unit monitors the health of farmland and forests by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the acquisition unit, generation unit, division unit, analysis unit, and monitoring unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the acquisition unit acquires geospatial information using the camera 42 of the headset-type terminal 314 or a drone, and the acquired information is processed by the specific processing unit 290 of the data processing device 12. For example, the generation unit generates natural images using a generative AI by the specific processing unit 290 of the data processing device 12. For example, the division unit divides a virtual land space by the specific processing unit 290 of the data processing device 12 and registers the divided land as a tradable digital asset on a blockchain. For example, the analysis unit analyzes environmental data by the sensor of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the monitoring unit monitors the health of farmland and forests by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, generation unit, division unit, analysis unit, and monitoring unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires geospatial information using the camera 42 of the robot 414 or a drone, and the acquired information is processed by the specific processing unit 290 of the data processing device 12. For example, the generation unit generates natural images using generative AI by the specific processing unit 290 of the data processing device 12. For example, the division unit divides virtual land space by the specific processing unit 290 of the data processing device 12 and registers the divided land as tradable digital assets on the blockchain. For example, the analysis unit analyzes environmental data by the sensor of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the monitoring unit monitors the health of farmland and forests by the specific processing unit 290 of the data processing device 12.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The acquisition unit can monitor the user's health condition when acquiring geospatial information and adjust the acquisition method based on the health condition. For example, if the user is tired, the acquisition unit can acquire geospatial information using an automated method, reducing the burden on the user. Alternatively, if the user is healthy, the acquisition unit can allow manual operation and allow the user to configure detailed settings. Furthermore, if the user has a specific health condition, the acquisition unit can select the acquisition method optimal for that condition. This reduces the burden on the user by adjusting the acquisition method of geospatial information according to the user's health condition, enabling efficient information acquisition.

[0124] When generating a natural image, the generation unit can improve the accuracy of generation by referring to the user's past behavioral history. For example, it analyzes what kind of natural images the user has preferred to generate in the past and generates a new natural image based on that tendency. It can also improve the generation method based on feedback from images the user has generated in the past. Furthermore, it can also customize the content of the natural image to be generated by referring to the user's behavioral history. In this way, by referring to the user's past behavioral history, the accuracy of generation can be improved and the most suitable natural image can be provided for the user.

[0125] When dividing a virtual land space, the division unit can select a division method by referring to historical data on the land. For example, it can select the optimal division method based on past land use data. It can also adjust the precision of the division by taking into account historical changes in the land. Furthermore, it can also customize the division method by referring to the historical data on the land. This makes it possible to divide the land appropriately and maximize its useful value.

[0126] When analyzing environmental data, the analysis unit can evaluate the reliability of the data and adjust the analysis method based on that reliability. For example, it can apply a detailed analysis method to highly reliable data and a simplified analysis method to less reliable data. It can also set criteria for evaluating data reliability and select an analysis method based on those criteria. Furthermore, it can create a feedback loop to improve data reliability and continuously improve the analysis method. By adjusting the analysis method based on the reliability of the data, analysis accuracy can be improved and more reliable results can be provided.

[0127] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring frequency can be reduced to reduce the burden on the user. Also, if the user is relaxed, the monitoring frequency can be increased to collect more detailed data. Furthermore, if the user is in a hurry, monitoring can be performed quickly to provide the necessary information. In this way, by adjusting the monitoring frequency according to the user's emotions, it is possible to provide the user with an optimal monitoring experience.

[0128] The acquisition unit performs real-time data filtering when acquiring geospatial information, enabling it to acquire only the necessary information. For example, data can be filtered based on specific regions or conditions to eliminate unnecessary information. Real-time data filtering can also improve the accuracy of the acquired information. Furthermore, filtering conditions can be customized according to user requests to acquire optimal information. This allows for efficient acquisition of necessary information by performing real-time data filtering.

[0129] The generation unit can simulate different weather conditions when generating a nature image and reflect them in the generated image. For example, different weather conditions such as sunny, rainy, and snowy days can be simulated and nature images can be generated based on them. It can also reflect changes in weather conditions in real time and generate images based on the latest information. Furthermore, it can select specific weather conditions in response to a user request and generate images based on those conditions. By simulating different weather conditions, more realistic and diverse nature images can be provided.

[0130] When dividing the virtual land space, the division unit can select the division method taking into account the ecosystem data of the land. For example, the division unit selects the optimal division method based on data on the ecosystem of the land. The division accuracy can also be adjusted taking into account the protection of the ecosystem. Furthermore, the division method can be customized by referring to the ecosystem data. In this way, by taking into account the ecosystem data of the land, appropriate division that takes into account environmental conservation is possible.

[0131] When analyzing environmental data, the analysis unit can take into account the spatial distribution of the data. For example, it can analyze the characteristics of each region based on the spatial distribution of the data. It can also compare data from different regions based on the spatial distribution and derive analysis results. It can also customize analysis methods by taking the spatial distribution into account. By taking the spatial distribution of the data into account, it is possible to accurately grasp the characteristics of each region and provide appropriate analysis results.

[0132] The monitoring unit can estimate the user's emotions and adjust the monitoring notification method based on the estimated user emotions. For example, if the user is feeling stressed, notifications can be reduced to reduce the burden on the user. Also, if the user is relaxed, detailed notifications can be provided to provide the user with the information they need. Furthermore, if the user is in a hurry, only important information can be quickly notified. In this way, by adjusting the monitoring notification method according to the user's emotions, it is possible to provide the user with the optimal notification experience.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The acquisition unit acquires geospatial information. Geospatial information includes topographical data, meteorological data, land use data, etc. The acquisition unit acquires geospatial information using drones, satellites, and ground sensors. For example, drones efficiently acquire wide-area geospatial information, satellites use earth observation satellites and communication satellites, and ground sensors acquire detailed geospatial information at specific locations. Step 2: The generation unit uses a generation AI to generate natural images based on the geospatial information acquired by the acquisition unit. The generation AI can use technologies such as GAN (generative adversarial network) and VAE (variational autoencoder). In the generation unit, the generation AI generates natural images, 3D models, and simulation environments based on the geospatial information. Step 3: The division unit divides the virtual land space generated by the generation unit and registers it as a tradable digital asset on the blockchain. The virtual land space includes digital maps, 3D models, simulation environments, etc. The division unit divides the virtual land space into multiple sections and registers each section as a tradable digital asset, NFT (non-fungible token), or digital certificate on the blockchain. Step 4: The analysis unit analyzes the environmental data, which includes temperature, humidity, soil composition, etc. The analysis unit uses sensors to collect soil humidity and temperature, crop growth status, weather data, water quality data, etc., and inputs this data into the machine learning model. Step 5: The Monitoring Department will monitor the health of farmland and forests based on the data analyzed by the Analysis Department. Farmland health includes crop growth status and soil nutrition status, while forest monitoring includes tree health, biomass volume, and forest fire risk. The Monitoring Department will monitor the health of farmland and forests based on the data analyzed by the Analysis Department, strengthening environmental conservation efforts.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0156] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0166] 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.

[0167] 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.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0183] 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.

[0184] 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.

[0185] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0186] 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.

[0187] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Explanation of symbols]

[0207] 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. an acquisition unit that acquires geospatial information; a generation unit that generates a natural image based on the geospatial information acquired by the acquisition unit; A division unit that divides the virtual land space generated by the generation unit and registers it as a tradable digital asset on a blockchain; An analysis department that analyzes environmental data; a monitoring unit that monitors the health of farmland or forests based on the data analyzed by the analysis unit. A system characterized by:

2. The acquisition unit Obtaining geospatial information from drones or satellites 2. The system of claim 1.

3. The generation unit Generative AI generates natural images based on geospatial information 2. The system of claim 1.

4. The dividing unit Divide the virtual land space into multiple sections and register each section as a tradable digital asset on the blockchain.

2. The system of claim 1.

5. The analysis unit Sensors collect data such as soil moisture or temperature and crop growth status and input it into machine learning models.

2. The system of claim 1.

6. The monitoring unit Monitoring the health of farmland or forests based on the data analyzed by the analysis unit 2. The system of claim 1.

7. The acquisition unit Estimates user emotions and adjusts the timing of geospatial information acquisition based on the estimated user emotions.

2. The system of claim 1.

8. The acquisition unit Optimize drone and satellite flight patterns to efficiently acquire geospatial information 2. The system of claim 1.

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