System

Abstract

To provide a system that specifies an abandoned farmland and urges reuse thereof to improve the food self-supply ratio of the area.SOLUTION: A system comprises a specification part, a proposition part, and a supply part. The specification part analyzes satellite images or geographic information to specify an abandoned farmland. The proposition part proposes that the farmland specified by the specification part is rented. The supply part proposes that farm crops cultivated in the farmland rented by the proposition by the proposition part are supplied to specific restaurants.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has not been efficient in identifying abandoned farmland and reusing it, which has led to issues such as insufficient contributions to improving local food self-sufficiency and environmental protection.

[0005] The system according to the embodiment aims to improve the food self-sufficiency rate of a region by identifying abandoned farmland and promoting its reuse. [Means for solving the problem]

[0006] The system according to the embodiment includes an identification unit, a proposal unit, and a supply unit. The identification unit identifies abandoned farmland by analyzing satellite images or geographic information. The proposal unit proposes leasing the farmland identified by the identification unit. The supply unit proposes supplying agricultural products grown on the rented farmland proposed by the proposal unit to a specific restaurant. Effect of the Invention

[0007] The system according to the embodiment can improve a region's food self-sufficiency rate by identifying abandoned farmland and promoting its reuse. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The system according to the embodiment of the present invention is a system that uses a generating AI to identify abandoned farmland and reuse it to improve the food self-sufficiency rate of the region. In this system, the generating AI analyzes satellite images and geographic information to identify abandoned farmland. Next, the system proposes the lending of the identified farmland to local farmers and residents. Furthermore, by supplying agricultural products grown on the rented farmland to collaborating restaurants, the system protects the environment and provides inexpensive, nutritious local ingredients. In addition, the system provides a platform for carbon offsetting, and increases the absorption of CO2 by carrying out reforestation activities and soil improvement, thereby enhancing the possibility of green business. For example, the generating AI analyzes satellite images and geographic information to identify abandoned farmland. The accuracy of identification can be improved by taking into account past farmland use history, seasonal changes, and local climate data. It is also possible to estimate the user's emotions and adjust the accuracy of identification based on the estimated emotions. Next, the system proposes the lending of the identified farmland to local farmers and residents. By working with local farmers and residents to reuse abandoned farmland, the food self-sufficiency rate of the region can be improved. Furthermore, the system supplies agricultural products grown on the rented farmland to collaborating restaurants. By supplying cooperating restaurants with fresh, locally grown produce, the system can protect the environment and provide inexpensive, nutritious local food. It also provides a platform for carbon offsetting, and increases CO2 absorption through reforestation and soil improvement activities. By reforestation and soil improvement activities, the system can increase CO2 absorption and increase the potential for green business. This will increase the region's food self-sufficiency rate and contribute to environmental protection. This will increase the region's food self-sufficiency rate and contribute to environmental protection.

[0029] The system according to the embodiment includes an identification unit, a proposal unit, and a supply unit. The identification unit analyzes satellite images or geographic information to identify abandoned farmland. The identification unit analyzes satellite images or geographic information using, for example, a generation AI to identify the location of abandoned farmland. The generation AI takes satellite images or geographic information as input and outputs the location of abandoned farmland. The generation AI improves the identification accuracy by considering, for example, past farmland use history, seasonal changes, and local climate data. The proposal unit proposes the rental of the farmland identified by the identification unit. The proposal unit proposes, for example, the reuse of abandoned farmland to local farmers and residents. The proposal unit can generate proposal content using AI and present it to the proposal target. The supply unit proposes to supply agricultural products cultivated on the rented farmland proposed by the proposal unit to the collaborative restaurant. The supply unit supplies, for example, fresh agricultural products cultivated locally to the collaborative restaurant. The supply unit can develop a supply plan using AI and notify the restaurant to which the supply is to be made. As a result, the system can improve the food self-sufficiency rate of the region by identifying and reusing abandoned farmland. A part or all of the above-mentioned processing in the identification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input satellite images and geographic information into the generation AI, and cause the generation AI to identify the location of the abandoned farmland. A part or all of the above-mentioned processing in the proposal unit may be performed using the AI, or may be performed without using the AI. For example, the proposal unit can input information on the farmland identified by the identification unit into the AI, and cause the AI ​​to generate a proposal for renting the farmland. A part or all of the above-mentioned processing in the supply unit may be performed using the AI, or may be performed without using the AI. For example, the supply unit can input information on the farmland proposed by the proposal unit into the AI, and cause the AI ​​to create a supply plan. As a result, the system according to the embodiment can improve the food self-sufficiency rate of the region by identifying and reusing abandoned farmland.

[0030] The identification unit analyzes satellite images or geographic information to identify abandoned farmland. The identification unit, for example, uses a generation AI to analyze satellite images or geographic information to identify the location of abandoned farmland. The generation AI takes satellite images and geographic information as input and outputs the location of abandoned farmland. The generation AI improves the identification accuracy by taking into account, for example, past farmland use history, seasonal changes, and local climate data. Specifically, the generation AI first analyzes satellite images at high resolution to detect changes in the shape and color of the farmland. This determines whether the farmland is abandoned. Furthermore, a geographic information system (GIS) is used to analyze the topography and changes in land use to identify the location of abandoned farmland. The generation AI can integrate these data and identify the location of abandoned farmland with high accuracy. For example, the generation AI identifies abandoned farmland by comparing satellite images from the past few years and tracking changes in the farmland. The generation AI also considers local climate data to evaluate the usability of the farmland. This allows the identification unit to quickly and accurately identify the location of abandoned farmland and provide basic information for reuse. Furthermore, the Identification Department can store information on the identified abandoned farmland in a database and use it in cooperation with other departments and systems. For example, the Identification Department can provide location information on the identified abandoned farmland to the Proposal Department and the Supply Department to support the creation of reuse plans. In this way, the Identification Department can efficiently support a series of processes from identifying abandoned farmland to its reuse.

[0031] The proposal unit proposes the lease of the farmland identified by the identification unit. The proposal unit proposes, for example, the reuse of abandoned farmland to local farmers and residents. The proposal unit can generate the proposal content using AI and present it to the proposal target. Specifically, the proposal unit evaluates the possibility of reuse based on the information on the abandoned farmland provided by the identification unit and proposes the optimal use method. The AI ​​proposes the optimal crop and use method by taking into account past farmland use data, market demand, local climatic conditions, etc. For example, the AI ​​analyzes soil data and climatic data of the identified abandoned farmland and selects crops suitable for the land. In addition, the AI ​​considers the needs of local farmers and residents and proposes the optimal lease conditions and use method. In this way, the proposal unit can promote the reuse of abandoned farmland and improve agricultural productivity in the region. Furthermore, the proposal unit provides a means of communication to effectively convey the proposal content to local farmers and residents. For example, the proposal unit uses AI to visualize the proposal content in an easy-to-understand manner and create presentation materials and reports. In addition, the proposal unit can directly communicate with local farmers and residents through an online platform and share the proposal content. This will enable the proposal department to effectively communicate information regarding the reuse of abandoned farmland and support the promotion of agriculture in the region.

[0032] The supply department proposes to supply the collaborative restaurant with agricultural products grown on the rented farmland as proposed by the proposal department. The supply department, for example, supplies the collaborative restaurant with fresh agricultural products grown locally. The supply department can use AI to create a supply plan and notify the restaurant to which the products are to be supplied. Specifically, the supply department creates a cultivation plan based on the farmland information provided by the proposal department and predicts the harvest time and supply amount. The AI ​​creates an optimal supply plan by taking into account past harvest data, market demand, climatic conditions, etc. For example, the AI ​​analyzes soil data and climatic data of the identified farmland and creates an optimal cultivation schedule. In addition, the AI ​​optimizes the supply amount and supply timing by taking into account the demand of local restaurants. This allows the supply department to efficiently supply fresh agricultural products grown locally and improve the food self-sufficiency rate of the region. Furthermore, the supply department provides a system for notifying the restaurant of the supply plan and sharing the supply schedule. For example, the supply department can directly communicate with the restaurant through an online platform and adjust the supply schedule and supply amount. The supply department can also monitor the supply situation in real time and revise the supply plan as necessary, which allows the supply department to efficiently supply local agricultural products and improve the region's food self-sufficiency rate.

[0033] The identification unit can analyze geographic information system data or satellite images to identify the location of the abandoned farmland. The identification unit can, for example, analyze geographic information system data to identify the location of the abandoned farmland. The geographic information system data includes, for example, GIS data, topographical data, and land use data. The identification unit receives geographic information system data as input and outputs the location of the abandoned farmland. The identification unit can, for example, analyze satellite images to identify the location of the abandoned farmland. The satellite images include, for example, resolution, shooting time, and shooting angle. The identification unit receives satellite images as input and outputs the location of the abandoned farmland. In this way, the identification unit can accurately identify the location of the abandoned farmland by analyzing the geographic information system data or satellite images. Some or all of the above-mentioned processing in the identification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input geographic information system data or satellite images to the generation AI and have the generation AI identify the location of the abandoned farmland.

[0034] The proposal unit can cooperate with local farmers or residents to reuse the abandoned farmland. For example, the proposal unit cooperates with local farmers to make a plan to reuse the abandoned farmland. The proposal unit proposes the reuse of the abandoned farmland to local farmers and obtains their cooperation. For example, the proposal unit cooperates with local residents to hold a workshop for reusing the abandoned farmland. The proposal unit proposes the reuse of the abandoned farmland to local residents and obtains their cooperation. For example, the proposal unit launches a project in which local farmers and residents jointly reuse the abandoned farmland. The proposal unit proposes the reuse of the abandoned farmland to local farmers and residents and obtains their cooperation. In this way, the proposal unit can promote the reuse of the abandoned farmland by cooperating with local farmers or residents. A part or all of the above-mentioned processing in the proposal unit may be performed using AI, or may be performed without using AI. For example, the proposal unit can input information about local farmers and residents into AI and cause AI to generate a proposal for reuse of the abandoned farmland.

[0035] The supply unit can supply fresh locally grown agricultural products to the cooperating restaurant. The supply unit, for example, supplies fresh locally grown agricultural products to the cooperating restaurant. The supply unit delivers agricultural products harvested from local farmers to the cooperating restaurant. The supply unit, for example, cooperates with local farmers to create a supply plan for agricultural products. The supply unit supplies agricultural products harvested from local farmers to the cooperating restaurant. The supply unit, for example, utilizes a local logistics network to supply agricultural products to the cooperating restaurant. The supply unit partners with a local logistics company to supply agricultural products to the cooperating restaurant. In this way, the supply unit can provide environmental protection and inexpensive, nutritious local food by supplying fresh locally grown agricultural products. Some or all of the above-mentioned processing in the supply unit may be performed using AI, or may be performed without using AI. For example, the supply unit can input information about local farmers and logistics companies into AI and have AI create a supply plan for agricultural products.

[0036] The system includes a providing unit, and the providing unit can take steps to increase the amount of CO2 absorbed by carrying out reforestation activities or soil improvement. The providing unit, for example, carries out reforestation activities to increase the amount of CO2 absorbed. The providing unit plants a specific tree species to increase the amount of CO2 absorbed. The providing unit, for example, performs soil improvement to increase the amount of CO2 absorbed. The providing unit uses a specific fertilizer to perform soil improvement to increase the amount of CO2 absorbed. The providing unit, for example, combines reforestation activities and soil improvement to increase the amount of CO2 absorbed. The providing unit combines planting a specific tree species with the use of a specific fertilizer to increase the amount of CO2 absorbed. As a result, the providing unit can increase the amount of CO2 absorbed and contribute to environmental protection by carrying out reforestation activities and soil improvement. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can have AI draw up plans for reforestation activities and soil improvement.

[0037] The identification unit can improve the identification accuracy by considering seasonal changes when analyzing satellite images or geographic information. For example, the identification unit improves the identification accuracy by considering seasonal changes when analyzing satellite images or geographic information. The identification unit analyzes seasonal changes using the generation AI. The generation AI receives satellite images and geographic information as input, and outputs the identification accuracy taking into account seasonal changes. For example, the generation AI adjusts the identification accuracy by considering the growth of new shoots in spring. The generation AI improves the identification accuracy by considering the growth status of crops in summer. The generation AI adjusts the identification accuracy by considering the effects of snow and frost in winter. This allows the identification unit to improve the identification accuracy by considering seasonal changes. Part or all of the above-mentioned processing in the identification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input satellite images and geographic information to the generation AI, and cause the generation AI to execute the improvement of the identification accuracy by considering seasonal changes.

[0038] The identification unit can improve the reliability of the identification by referring to the past agricultural land use history when identifying abandoned agricultural land. For example, when identifying abandoned agricultural land, the identification unit improves the reliability of the identification by referring to the past agricultural land use history. The identification unit analyzes the past agricultural land use history using the generation AI. The generation AI takes past agricultural land use data as input and outputs information for improving the reliability of the identification. The generation AI analyzes, for example, the agricultural land use history for the past 10 years and improves the accuracy of identifying abandoned agricultural land. The generation AI refers to the past harvest data of the agricultural land and improves the reliability of the identification. The generation AI refers to the information of the past landowner and improves the reliability of the identification. As a result, the identification unit can improve the reliability of the identification by referring to the past agricultural land use history. Part or all of the above-mentioned processing in the identification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input past agricultural land use data to the generation AI and cause the generation AI to execute an improvement in the reliability of the identification.

[0039] The identification unit can improve the identification accuracy by considering local climate data when analyzing satellite images or geographic information. For example, the identification unit improves the identification accuracy by considering local climate data when analyzing satellite images or geographic information. The identification unit analyzes the local climate data using the generation AI. The generation AI takes the local climate data as input and outputs information for improving the identification accuracy. The generation AI analyzes, for example, local annual precipitation data and improves the identification accuracy. The generation AI refers to local temperature data and adjusts the identification accuracy. The generation AI improves the identification accuracy by considering local wind speed data. Thereby, the identification unit can improve the identification accuracy by considering the local climate data. Part or all of the above-mentioned processing in the identification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input local climate data to the generation AI and cause the generation AI to execute the improvement of the identification accuracy.

[0040] The identification unit can evaluate the specific environmental impact by referring to local ecosystem data when identifying abandoned farmland. For example, when identifying abandoned farmland, the identification unit evaluates the specific environmental impact by referring to local ecosystem data. The identification unit analyzes the local ecosystem data using the generation AI. The generation AI takes the local ecosystem data as input and outputs information for evaluating the specific environmental impact. The generation AI evaluates the specific environmental impact by referring to, for example, local animal and plant habitat data. The generation AI analyzes local water quality data and evaluates the specific environmental impact. The generation AI evaluates the specific environmental impact by referring to local soil data. Thereby, the identification unit can evaluate the specific environmental impact by referring to the local ecosystem data. Part or all of the above-mentioned processing in the identification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input local ecosystem data to the generation AI and cause the generation AI to execute the evaluation of the specific environmental impact.

[0041] The identification unit can improve the identification accuracy by taking into account the local land use plan when analyzing satellite images or geographic information. For example, the identification unit improves the identification accuracy by taking into account the local land use plan when analyzing satellite images or geographic information. The identification unit analyzes the local land use plan using the generation AI. The generation AI takes the local land use plan data as an input and outputs information for improving the identification accuracy. The generation AI, for example, refers to the local land use plan data and improves the identification accuracy. The generation AI analyzes the local urban planning data and adjusts the identification accuracy. The generation AI improves the identification accuracy by taking into account the local agricultural land use plan. Thereby, the identification unit can improve the identification accuracy by taking into account the local land use plan. Part or all of the above-mentioned processing in the identification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input the local land use plan data to the generation AI and cause the generation AI to execute the improvement of the identification accuracy.

[0042] The identification unit can evaluate the specific cultural value by referring to local historical data when identifying abandoned farmland. For example, when identifying abandoned farmland, the identification unit evaluates the specific cultural value by referring to local historical data. The identification unit analyzes the local historical data using the generation AI. The generation AI takes the local historical data as input and outputs information for evaluating the specific cultural value. The generation AI evaluates the specific cultural value by referring to, for example, local historical farmland use data. The generation AI analyzes local historical building data and evaluates the specific cultural value. The generation AI evaluates the specific cultural value by referring to local historical event data. Thereby, the identification unit can evaluate the specific cultural value by referring to the local historical data. Part or all of the above-mentioned processing in the identification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input local historical data to the generation AI and cause the generation AI to execute the evaluation of the specific cultural value.

[0043] The identification unit can improve the identification accuracy by considering local water resource data when analyzing satellite images or geographic information. For example, the identification unit improves the identification accuracy by considering local water resource data when analyzing satellite images or geographic information. The identification unit analyzes the local water resource data using the generation AI. The generation AI takes the local water resource data as an input and outputs information for improving the identification accuracy. The generation AI improves the identification accuracy by referring to local groundwater data, for example. The generation AI analyzes local river data and adjusts the identification accuracy. The generation AI improves the identification accuracy by considering local precipitation data. Thereby, the identification unit can improve the identification accuracy by considering the local water resource data. Part or all of the above-mentioned processing in the identification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input local water resource data to the generation AI and cause the generation AI to improve the identification accuracy.

[0044] The identification unit can evaluate the specific biodiversity by referring to the habitat data of local plants and animals when identifying abandoned farmland. For example, the identification unit evaluates the specific biodiversity by referring to the habitat data of local plants and animals when identifying abandoned farmland. The identification unit analyzes the habitat data of local plants and animals using the generation AI. The generation AI takes the habitat data of local plants and animals as input and outputs information for evaluating the specific biodiversity. The generation AI, for example, evaluates the specific biodiversity by referring to the local animal habitat data. The generation AI analyzes the local plant habitat data and evaluates the specific biodiversity. The generation AI evaluates the specific biodiversity by referring to the local ecosystem data. Thereby, the identification unit can evaluate the specific biodiversity by referring to the habitat data of local plants and animals. Part or all of the above-mentioned processing in the identification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input the habitat data of local plants and animals to the generation AI and cause the generation AI to execute the evaluation of the specific biodiversity.

[0045] The proposal unit can cooperate with local farmers or residents to reuse the abandoned farmland. For example, the proposal unit cooperates with local farmers to make a plan to reuse the abandoned farmland. The proposal unit proposes the reuse of the abandoned farmland to local farmers and obtains their cooperation. For example, the proposal unit cooperates with local residents to hold a workshop for reusing the abandoned farmland. The proposal unit proposes the reuse of the abandoned farmland to local residents and obtains their cooperation. For example, the proposal unit launches a project in which local farmers and residents jointly reuse the abandoned farmland. The proposal unit proposes the reuse of the abandoned farmland to local farmers and residents and obtains their cooperation. In this way, the proposal unit can promote the reuse of the abandoned farmland by cooperating with local farmers or residents. A part or all of the above-mentioned processing in the proposal unit may be performed using AI, or may be performed without using AI. For example, the proposal unit can input information about local farmers and residents into AI and cause AI to generate a proposal for reuse of the abandoned farmland.

[0046] The proposal unit can select the optimal proposal method by referring to the past cooperation history of local farmers and residents at the time of proposal. For example, the proposal unit selects the optimal proposal method by referring to the past cooperation history of local farmers and residents at the time of proposal. The proposal unit analyzes the past cooperation history using AI. The AI ​​takes the past cooperation history data of local farmers and residents as input and outputs the optimal proposal method. The AI, for example, refers to the past cooperation history of local farmers and selects the optimal proposal method. The AI ​​refers to the past cooperation history of local residents and selects the optimal proposal method. The AI ​​analyzes the past cooperation history of local farmers and residents and selects the optimal proposal method. Thereby, the proposal unit can select the optimal proposal method by referring to the past cooperation history. Part or all of the above-mentioned processing in the proposal unit may be performed using AI, or may be performed without using AI. For example, the proposal unit can input the past cooperation history data of local farmers and residents to AI and cause the AI ​​to select the optimal proposal method.

[0047] The proposal unit can promote acceptance of the proposal by utilizing a local community event when making a proposal. For example, the proposal unit promotes acceptance of the proposal by utilizing a local community event when making a proposal. The proposal unit makes a proposal for reuse of abandoned farmland at a local community event. The proposal unit explains the benefits of reuse of farmland at the local community event. The proposal unit holds a workshop on reuse of farmland at the local community event. In this way, the proposal unit can promote acceptance of the proposal by utilizing the local community event. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, or may be performed without using AI. For example, the proposal unit can input information about local community events into the AI ​​and cause the AI ​​to create a plan for promoting acceptance of the proposal.

[0048] The proposal unit can provide an educational program for agricultural land reuse in cooperation with a local educational institution at the time of proposal. For example, the proposal unit provides an educational program for agricultural land reuse in cooperation with a local educational institution at the time of proposal. The proposal unit provides an educational program for agricultural land reuse in cooperation with a local school. The proposal unit launches a research project for agricultural land reuse in cooperation with a local university. The proposal unit holds a seminar on agricultural land reuse in collaboration with a local educational institution. In this way, the proposal unit can provide an educational program for agricultural land reuse by collaborating with a local educational institution. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, or may be performed without using AI. For example, the proposal unit can input information about a local educational institution into AI and have the AI ​​draw up a plan for the educational program.

[0049] The supply unit can select the optimal supply method by referring to the quality data of the agricultural products at the time of supply. For example, the supply unit selects the optimal supply method by referring to the quality data of the agricultural products at the time of supply. The supply unit analyzes the quality data of the agricultural products using AI. The AI ​​takes the quality data of the agricultural products as input and outputs the optimal supply method. For example, the AI ​​analyzes the quality data of the agricultural products and selects the optimal supply method. The AI ​​refers to freshness data of the agricultural products and selects the optimal supply method. The AI ​​analyzes nutritional value data of the agricultural products and selects the optimal supply method. Thereby, the supply unit can select the optimal supply method by referring to the quality data of the agricultural products. Some or all of the above-mentioned processing in the supply unit may be performed using AI, or may be performed without using AI. For example, the supply unit can input the quality data of the agricultural products to AI and cause the AI ​​to select the optimal supply method.

[0050] The supply department can realize efficient supply by utilizing a local logistics network at the time of supply. For example, the supply department realizes efficient supply by utilizing a local logistics network at the time of supply. The supply department partners with a local logistics company to realize efficient supply. The supply department realizes rapid supply by utilizing a local delivery network. The supply department realizes efficient supply by utilizing a local logistics hub. In this way, the supply department can realize efficient supply by utilizing the local logistics network. A part or all of the above-mentioned processing in the supply department may be performed using AI, or may be performed without using AI. For example, the supply department can input information on the local logistics network into AI and have the AI ​​create an efficient supply plan.

[0051] The supplying unit can expand the sales channels of the agricultural products by utilizing local market events at the time of supplying. For example, the supplying unit expands the sales channels of the agricultural products by utilizing local market events at the time of supplying. The supplying unit sells the agricultural products at local market events to expand the sales channels. The supplying unit sells the agricultural products at local festivals to expand the sales channels. The supplying unit sells the agricultural products at local farm shops to expand the sales channels. In this way, the supplying unit can expand the sales channels of the agricultural products by utilizing local market events. Some or all of the above-mentioned processing in the supplying unit may be performed using AI, or may be performed without using AI. For example, the supplying unit can input information about local market events into AI and have the AI ​​develop a plan for expanding the sales channels.

[0052] The providing unit can select the optimal providing method by referring to the local environmental data at the time of providing. For example, the providing unit selects the optimal providing method by referring to the local environmental data at the time of providing. The providing unit analyzes the local environmental data using AI. The AI ​​takes the local environmental data as input and outputs the optimal providing method. For example, the AI ​​refers to local climate data and selects the optimal providing method. The AI ​​refers to local soil data and selects the optimal providing method. The AI ​​refers to local water quality data and selects the optimal providing method. Thereby, the providing unit can select the optimal providing method by referring to the local environmental data. Part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the local environmental data to the AI ​​and cause the AI ​​to select the optimal providing method.

[0053] The provision unit can maximize the effect of the provision in cooperation with a local environmental protection organization at the time of provision. For example, the provision unit maximizes the effect of the provision in cooperation with a local environmental protection organization at the time of provision. The provision unit cooperates with a local environmental protection organization to maximize the effect of the provision. The provision unit launches a project in collaboration with a local environmental protection organization to maximize the effect of the provision. The provision unit cooperates with a local environmental protection organization to hold an event to maximize the effect of the provision. In this way, the provision unit can maximize the effect of the provision by cooperating with a local environmental protection organization. A part or all of the above-mentioned processing in the provision unit may be performed using AI, or may be performed without using AI. For example, the provision unit can input information of a local environmental protection organization into AI and have the AI ​​develop a plan to maximize the effect of the provision.

[0054] The provision unit can provide an environmental education program in cooperation with a local school at the time of provision. The provision unit, for example, provides an environmental education program in cooperation with a local school at the time of provision. The provision unit provides an environmental education program in cooperation with a local school. The provision unit holds a workshop on environmental protection in cooperation with the local school. The provision unit holds a seminar on environmental protection in cooperation with the local school. In this way, the provision unit can provide an environmental education program by collaborating with the local school. Some or all of the above-mentioned processing in the provision unit may be performed using AI, or may be performed without using AI. For example, the provision unit can input information about a local school into AI and have the AI ​​draw up a plan for the environmental education program.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] When identifying abandoned farmland, the identification unit can refer to local tourism data to evaluate its value as a tourism resource. For example, the identification unit analyzes local tourist spots and tourist trend data to evaluate whether the abandoned farmland can be used as a tourism resource. The identification unit refers to tourist inflow data to evaluate the possibility of the abandoned farmland being incorporated into a tourist route. The identification unit analyzes local tourism event data to evaluate whether the abandoned farmland can be used as an event venue. In this way, the identification unit can contribute to the development of the local tourism industry by evaluating the value of abandoned farmland as a tourism resource.

[0057] The Proposal Department can work with local cultural organizations to propose the abandoned farmland as a place for cultural activities. For example, the Proposal Department works with a local art organization to propose the abandoned farmland as a place for art installations. The Proposal Department works with a local music organization to propose the abandoned farmland as a venue for a music festival. The Proposal Department works with a local history organization to propose the abandoned farmland as a place for historical reenactment events. In this way, the Proposal Department can propose new ways to use the abandoned farmland and revitalize local cultural activities by working with local cultural organizations.

[0058] The food supply department can work with local school lunch programs to provide school lunches with agricultural products grown on abandoned farmland. For example, the food supply department can work with local schools to incorporate fresh vegetables and fruits grown on abandoned farmland into school lunches. The food supply department can work with local educational institutions to provide programs that allow students to experience the process of growing and harvesting agricultural products. The food supply department can work with local nutritionists to create nutritionally balanced school lunch menus. In this way, the food supply department can provide fresh and nutritious ingredients to children and contribute to food education by working with local school lunch programs.

[0059] The provision department can work with local energy companies to use the abandoned farmland as a production base for renewable energy. For example, the provision department works with local energy companies to install solar panels on the abandoned farmland to generate solar power. The provision department works with local biomass energy companies to cultivate biomass crops on the abandoned farmland to use for energy production. The provision department works with local wind power companies to install wind power generation equipment on the abandoned farmland. In this way, the provision department can work with local energy companies to utilize the abandoned farmland as a production base for renewable energy and improve the region's energy self-sufficiency rate.

[0060] The Proposal Department can work with local companies to propose abandoned farmland as part of the company's CSR activities. For example, the Proposal Department can work with local companies to propose abandoned farmland as a site for the company's reforestation activities. The Proposal Department can work with local companies to propose abandoned farmland as a site for the company's employee training. The Proposal Department can work with local companies to propose abandoned farmland as part of the company's community contribution activities. In this way, the Proposal Department can work with local companies to propose new ways to use abandoned farmland and support the company's CSR activities.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The identification unit analyzes satellite images or geographic information to identify abandoned farmland. The identification unit uses the generation AI to analyze satellite images and geographic information to identify the location of abandoned farmland. The generation AI improves the accuracy of identification by taking into account past farmland use history, seasonal changes, and local climate data. Step 2: The proposal department proposes leasing the farmland identified by the identification department. The proposal department proposes the reuse of abandoned farmland to local farmers and residents, generates proposal content using AI, and presents it to the proposal recipients. Step 3: The supply department proposes to supply agricultural products grown on the rented farmland to specific restaurants as proposed by the proposal department. The supply department supplies fresh, locally grown agricultural products to the cooperating restaurants, creates a supply plan using AI, and notifies the target restaurants.

[0063] (Example 2) The system according to the embodiment of the present invention is a system that uses a generating AI to identify abandoned farmland and reuse it to improve the food self-sufficiency rate of the region. In this system, the generating AI analyzes satellite images and geographic information to identify abandoned farmland. Next, the system proposes the lending of the identified farmland to local farmers and residents. Furthermore, by supplying agricultural products grown on the rented farmland to collaborating restaurants, the system protects the environment and provides inexpensive, nutritious local ingredients. In addition, the system provides a platform for carbon offsetting, and increases the absorption of CO2 by carrying out reforestation activities and soil improvement, thereby enhancing the possibility of green business. For example, the generating AI analyzes satellite images and geographic information to identify abandoned farmland. The accuracy of identification can be improved by taking into account past farmland use history, seasonal changes, and local climate data. It is also possible to estimate the user's emotions and adjust the accuracy of identification based on the estimated emotions. Next, the system proposes the lending of the identified farmland to local farmers and residents. By working with local farmers and residents to reuse abandoned farmland, the food self-sufficiency rate of the region can be improved. Furthermore, the system supplies agricultural products grown on the rented farmland to collaborating restaurants. By supplying cooperating restaurants with fresh, locally grown produce, the system can protect the environment and provide inexpensive, nutritious local food. It also provides a platform for carbon offsetting, and increases CO2 absorption through reforestation and soil improvement activities. By reforestation and soil improvement activities, the system can increase CO2 absorption and increase the potential for green business. This will increase the region's food self-sufficiency rate and contribute to environmental protection. This will increase the region's food self-sufficiency rate and contribute to environmental protection.

[0064] The system according to the embodiment includes an identification unit, a proposal unit, and a supply unit. The identification unit analyzes satellite images or geographic information to identify abandoned farmland. The identification unit analyzes satellite images or geographic information using, for example, a generation AI to identify the location of abandoned farmland. The generation AI takes satellite images or geographic information as input and outputs the location of abandoned farmland. The generation AI improves the identification accuracy by considering, for example, past farmland use history, seasonal changes, and local climate data. The proposal unit proposes the rental of the farmland identified by the identification unit. The proposal unit proposes, for example, the reuse of abandoned farmland to local farmers and residents. The proposal unit can generate proposal content using AI and present it to the proposal target. The supply unit proposes to supply agricultural products cultivated on the rented farmland proposed by the proposal unit to the collaborative restaurant. The supply unit supplies, for example, fresh agricultural products cultivated locally to the collaborative restaurant. The supply unit can develop a supply plan using AI and notify the restaurant to which the supply is to be made. As a result, the system can improve the food self-sufficiency rate of the region by identifying and reusing abandoned farmland. A part or all of the above-mentioned processing in the identification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input satellite images and geographic information into the generation AI, and cause the generation AI to identify the location of the abandoned farmland. A part or all of the above-mentioned processing in the proposal unit may be performed using the AI, or may be performed without using the AI. For example, the proposal unit can input information on the farmland identified by the identification unit into the AI, and cause the AI ​​to generate a proposal for renting the farmland. A part or all of the above-mentioned processing in the supply unit may be performed using the AI, or may be performed without using the AI. For example, the supply unit can input information on the farmland proposed by the proposal unit into the AI, and cause the AI ​​to create a supply plan. As a result, the system according to the embodiment can improve the food self-sufficiency rate of the region by identifying and reusing abandoned farmland.

[0065] The identification unit analyzes satellite images or geographic information to identify abandoned farmland. The identification unit, for example, uses a generation AI to analyze satellite images or geographic information to identify the location of abandoned farmland. The generation AI takes satellite images and geographic information as input and outputs the location of abandoned farmland. The generation AI improves the identification accuracy by taking into account, for example, past farmland use history, seasonal changes, and local climate data. Specifically, the generation AI first analyzes satellite images at high resolution to detect changes in the shape and color of the farmland. This determines whether the farmland is abandoned. Furthermore, a geographic information system (GIS) is used to analyze the topography and changes in land use to identify the location of abandoned farmland. The generation AI can integrate these data and identify the location of abandoned farmland with high accuracy. For example, the generation AI identifies abandoned farmland by comparing satellite images from the past few years and tracking changes in the farmland. The generation AI also considers local climate data to evaluate the usability of the farmland. This allows the identification unit to quickly and accurately identify the location of abandoned farmland and provide basic information for reuse. Furthermore, the Identification Department can store information on the identified abandoned farmland in a database and use it in cooperation with other departments and systems. For example, the Identification Department can provide location information on the identified abandoned farmland to the Proposal Department and the Supply Department to support the creation of reuse plans. In this way, the Identification Department can efficiently support a series of processes from identifying abandoned farmland to its reuse.

[0066] The proposal unit proposes the lease of the farmland identified by the identification unit. The proposal unit proposes, for example, the reuse of abandoned farmland to local farmers and residents. The proposal unit can generate the proposal content using AI and present it to the proposal target. Specifically, the proposal unit evaluates the possibility of reuse based on the information on the abandoned farmland provided by the identification unit and proposes the optimal use method. The AI ​​proposes the optimal crop and use method by taking into account past farmland use data, market demand, local climatic conditions, etc. For example, the AI ​​analyzes soil data and climatic data of the identified abandoned farmland and selects crops suitable for the land. In addition, the AI ​​considers the needs of local farmers and residents and proposes the optimal lease conditions and use method. In this way, the proposal unit can promote the reuse of abandoned farmland and improve agricultural productivity in the region. Furthermore, the proposal unit provides a means of communication to effectively convey the proposal content to local farmers and residents. For example, the proposal unit uses AI to visualize the proposal content in an easy-to-understand manner and create presentation materials and reports. In addition, the proposal unit can directly communicate with local farmers and residents through an online platform and share the proposal content. This will enable the proposal department to effectively communicate information regarding the reuse of abandoned farmland and support the promotion of agriculture in the region.

[0067] The supply department proposes to supply the collaborative restaurant with agricultural products grown on the rented farmland as proposed by the proposal department. The supply department, for example, supplies the collaborative restaurant with fresh agricultural products grown locally. The supply department can use AI to create a supply plan and notify the restaurant to which the products are to be supplied. Specifically, the supply department creates a cultivation plan based on the farmland information provided by the proposal department and predicts the harvest time and supply amount. The AI ​​creates an optimal supply plan by taking into account past harvest data, market demand, climatic conditions, etc. For example, the AI ​​analyzes soil data and climatic data of the identified farmland and creates an optimal cultivation schedule. In addition, the AI ​​optimizes the supply amount and supply timing by taking into account the demand of local restaurants. This allows the supply department to efficiently supply fresh agricultural products grown locally and improve the food self-sufficiency rate of the region. Furthermore, the supply department provides a system for notifying the restaurant of the supply plan and sharing the supply schedule. For example, the supply department can directly communicate with the restaurant through an online platform and adjust the supply schedule and supply amount. The supply department can also monitor the supply situation in real time and revise the supply plan as necessary, which allows the supply department to efficiently supply local agricultural products and improve the region's food self-sufficiency rate.

[0068] The identification unit can analyze geographic information system data or satellite images to identify the location of the abandoned farmland. The identification unit can, for example, analyze geographic information system data to identify the location of the abandoned farmland. The geographic information system data includes, for example, GIS data, topographical data, and land use data. The identification unit receives geographic information system data as input and outputs the location of the abandoned farmland. The identification unit can, for example, analyze satellite images to identify the location of the abandoned farmland. The satellite images include, for example, resolution, shooting time, and shooting angle. The identification unit receives satellite images as input and outputs the location of the abandoned farmland. In this way, the identification unit can accurately identify the location of the abandoned farmland by analyzing the geographic information system data or satellite images. Some or all of the above-mentioned processing in the identification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input geographic information system data or satellite images to the generation AI and have the generation AI identify the location of the abandoned farmland.

[0069] The proposal unit can cooperate with local farmers or residents to reuse the abandoned farmland. For example, the proposal unit cooperates with local farmers to make a plan to reuse the abandoned farmland. The proposal unit proposes the reuse of the abandoned farmland to local farmers and obtains their cooperation. For example, the proposal unit cooperates with local residents to hold a workshop for reusing the abandoned farmland. The proposal unit proposes the reuse of the abandoned farmland to local residents and obtains their cooperation. For example, the proposal unit launches a project in which local farmers and residents jointly reuse the abandoned farmland. The proposal unit proposes the reuse of the abandoned farmland to local farmers and residents and obtains their cooperation. In this way, the proposal unit can promote the reuse of the abandoned farmland by cooperating with local farmers or residents. A part or all of the above-mentioned processing in the proposal unit may be performed using AI, or may be performed without using AI. For example, the proposal unit can input information about local farmers and residents into AI and cause AI to generate a proposal for reuse of the abandoned farmland.

[0070] The supply unit can supply fresh locally grown agricultural products to the cooperating restaurant. The supply unit, for example, supplies fresh locally grown agricultural products to the cooperating restaurant. The supply unit delivers agricultural products harvested from local farmers to the cooperating restaurant. The supply unit, for example, cooperates with local farmers to create a supply plan for agricultural products. The supply unit supplies agricultural products harvested from local farmers to the cooperating restaurant. The supply unit, for example, utilizes a local logistics network to supply agricultural products to the cooperating restaurant. The supply unit partners with a local logistics company to supply agricultural products to the cooperating restaurant. In this way, the supply unit can provide environmental protection and inexpensive, nutritious local food by supplying fresh locally grown agricultural products. Some or all of the above-mentioned processing in the supply unit may be performed using AI, or may be performed without using AI. For example, the supply unit can input information about local farmers and logistics companies into AI and have AI create a supply plan for agricultural products.

[0071] The system includes a providing unit, and the providing unit can take steps to increase the amount of CO2 absorbed by carrying out reforestation activities or soil improvement. The providing unit, for example, carries out reforestation activities to increase the amount of CO2 absorbed. The providing unit plants a specific tree species to increase the amount of CO2 absorbed. The providing unit, for example, performs soil improvement to increase the amount of CO2 absorbed. The providing unit uses a specific fertilizer to perform soil improvement to increase the amount of CO2 absorbed. The providing unit, for example, combines reforestation activities and soil improvement to increase the amount of CO2 absorbed. The providing unit combines planting a specific tree species with the use of a specific fertilizer to increase the amount of CO2 absorbed. As a result, the providing unit can increase the amount of CO2 absorbed and contribute to environmental protection by carrying out reforestation activities and soil improvement. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can have AI draw up plans for reforestation activities and soil improvement.

[0072] The identification unit can estimate the user's emotion and adjust the identification accuracy of the abandoned farmland based on the estimated user's emotion. The identification unit, for example, estimates the user's emotion and adjusts the identification accuracy of the abandoned farmland based on the estimated user's emotion. The identification unit estimates the user's emotion using the generation AI. The generation AI receives the user's facial expression, voice, and text data and outputs an emotion score. For example, when the user is excited, the generation AI performs detailed analysis to improve the identification accuracy. When the user is relaxed, the generation AI sets the identification accuracy to a standard level. When the user is stressed, the generation AI sets the identification accuracy to a low level and quickly provides the result. This allows the identification unit to adjust the identification accuracy based on the user's emotion, thereby enabling more appropriate identification of abandoned farmland. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input a user's emotion data to the generation AI and cause the generation AI to adjust the identification accuracy based on the emotion.

[0073] The identification unit can improve the identification accuracy by considering seasonal changes when analyzing satellite images or geographic information. For example, the identification unit improves the identification accuracy by considering seasonal changes when analyzing satellite images or geographic information. The identification unit analyzes seasonal changes using the generation AI. The generation AI receives satellite images and geographic information as input, and outputs the identification accuracy taking into account seasonal changes. For example, the generation AI adjusts the identification accuracy by considering the growth of new shoots in spring. The generation AI improves the identification accuracy by considering the growth status of crops in summer. The generation AI adjusts the identification accuracy by considering the effects of snow and frost in winter. This allows the identification unit to improve the identification accuracy by considering seasonal changes. Part or all of the above-mentioned processing in the identification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input satellite images and geographic information to the generation AI, and cause the generation AI to execute the improvement of the identification accuracy by considering seasonal changes.

[0074] The identification unit can improve the reliability of the identification by referring to the past agricultural land use history when identifying abandoned agricultural land. For example, when identifying abandoned agricultural land, the identification unit improves the reliability of the identification by referring to the past agricultural land use history. The identification unit analyzes the past agricultural land use history using the generation AI. The generation AI takes past agricultural land use data as input and outputs information for improving the reliability of the identification. The generation AI analyzes, for example, the agricultural land use history for the past 10 years and improves the accuracy of identifying abandoned agricultural land. The generation AI refers to the past harvest data of the agricultural land and improves the reliability of the identification. The generation AI refers to the information of the past landowner and improves the reliability of the identification. As a result, the identification unit can improve the reliability of the identification by referring to the past agricultural land use history. Part or all of the above-mentioned processing in the identification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input past agricultural land use data to the generation AI and cause the generation AI to execute an improvement in the reliability of the identification.

[0075] The identification unit can improve the identification accuracy by considering local climate data when analyzing satellite images or geographic information. For example, the identification unit improves the identification accuracy by considering local climate data when analyzing satellite images or geographic information. The identification unit analyzes the local climate data using the generation AI. The generation AI takes the local climate data as input and outputs information for improving the identification accuracy. The generation AI analyzes, for example, local annual precipitation data and improves the identification accuracy. The generation AI refers to local temperature data and adjusts the identification accuracy. The generation AI improves the identification accuracy by considering local wind speed data. Thereby, the identification unit can improve the identification accuracy by considering the local climate data. Part or all of the above-mentioned processing in the identification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input local climate data to the generation AI and cause the generation AI to execute the improvement of the identification accuracy.

[0076] The identification unit can evaluate the specific environmental impact by referring to local ecosystem data when identifying abandoned farmland. For example, when identifying abandoned farmland, the identification unit evaluates the specific environmental impact by referring to local ecosystem data. The identification unit analyzes the local ecosystem data using the generation AI. The generation AI takes the local ecosystem data as input and outputs information for evaluating the specific environmental impact. The generation AI evaluates the specific environmental impact by referring to, for example, local animal and plant habitat data. The generation AI analyzes local water quality data and evaluates the specific environmental impact. The generation AI evaluates the specific environmental impact by referring to local soil data. Thereby, the identification unit can evaluate the specific environmental impact by referring to the local ecosystem data. Part or all of the above-mentioned processing in the identification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input local ecosystem data to the generation AI and cause the generation AI to execute the evaluation of the specific environmental impact.

[0077] The identification unit can estimate the user's emotion and determine the priority of the identified farmland based on the estimated user's emotion. The identification unit, for example, estimates the user's emotion and determines the priority of the identified farmland based on the estimated user's emotion. The identification unit estimates the user's emotion using the generation AI. The generation AI receives the user's facial expression, voice, and text data and outputs an emotion score. For example, when the user is excited, the generation AI increases the priority of the identified farmland. When the user is relaxed, the generation AI sets the priority of the identified farmland to the standard. When the user is stressed, the generation AI sets the priority of the identified farmland to a low priority. This enables the identification unit to determine the priority of the farmland based on the user's emotion, thereby enabling more appropriate use of the farmland. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input a user's emotion data to the generation AI and cause the generation AI to determine priorities based on emotions.

[0078] The identification unit can improve the identification accuracy by taking into account the local land use plan when analyzing satellite images or geographic information. For example, the identification unit improves the identification accuracy by taking into account the local land use plan when analyzing satellite images or geographic information. The identification unit analyzes the local land use plan using the generation AI. The generation AI takes the local land use plan data as an input and outputs information for improving the identification accuracy. The generation AI, for example, refers to the local land use plan data and improves the identification accuracy. The generation AI analyzes the local urban planning data and adjusts the identification accuracy. The generation AI improves the identification accuracy by taking into account the local agricultural land use plan. Thereby, the identification unit can improve the identification accuracy by taking into account the local land use plan. Part or all of the above-mentioned processing in the identification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input the local land use plan data to the generation AI and cause the generation AI to execute the improvement of the identification accuracy.

[0079] The identification unit can evaluate the specific cultural value by referring to local historical data when identifying abandoned farmland. For example, when identifying abandoned farmland, the identification unit evaluates the specific cultural value by referring to local historical data. The identification unit analyzes the local historical data using the generation AI. The generation AI takes the local historical data as input and outputs information for evaluating the specific cultural value. The generation AI evaluates the specific cultural value by referring to, for example, local historical farmland use data. The generation AI analyzes local historical building data and evaluates the specific cultural value. The generation AI evaluates the specific cultural value by referring to local historical event data. Thereby, the identification unit can evaluate the specific cultural value by referring to the local historical data. Part or all of the above-mentioned processing in the identification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input local historical data to the generation AI and cause the generation AI to execute the evaluation of the specific cultural value.

[0080] The identification unit can improve the identification accuracy by considering local water resource data when analyzing satellite images or geographic information. For example, the identification unit improves the identification accuracy by considering local water resource data when analyzing satellite images or geographic information. The identification unit analyzes the local water resource data using the generation AI. The generation AI takes the local water resource data as an input and outputs information for improving the identification accuracy. The generation AI improves the identification accuracy by referring to local groundwater data, for example. The generation AI analyzes local river data and adjusts the identification accuracy. The generation AI improves the identification accuracy by considering local precipitation data. Thereby, the identification unit can improve the identification accuracy by considering the local water resource data. Part or all of the above-mentioned processing in the identification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input local water resource data to the generation AI and cause the generation AI to improve the identification accuracy.

[0081] The identification unit can evaluate the specific biodiversity by referring to the habitat data of local plants and animals when identifying abandoned farmland. For example, the identification unit evaluates the specific biodiversity by referring to the habitat data of local plants and animals when identifying abandoned farmland. The identification unit analyzes the habitat data of local plants and animals using the generation AI. The generation AI takes the habitat data of local plants and animals as input and outputs information for evaluating the specific biodiversity. The generation AI, for example, evaluates the specific biodiversity by referring to the local animal habitat data. The generation AI analyzes the local plant habitat data and evaluates the specific biodiversity. The generation AI evaluates the specific biodiversity by referring to the local ecosystem data. Thereby, the identification unit can evaluate the specific biodiversity by referring to the habitat data of local plants and animals. Part or all of the above-mentioned processing in the identification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input the habitat data of local plants and animals to the generation AI and cause the generation AI to execute the evaluation of the specific biodiversity.

[0082] The suggestion unit can estimate the user's emotion and adjust the expression method of the proposal based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the expression method of the proposal based on the estimated user's emotion. The suggestion unit estimates the user's emotion using a generation AI. The generation AI receives the user's facial expression, voice, and text data, and outputs an emotion score. For example, when the user is excited, the generation AI uses an expression method that emphasizes the proposal. When the user is relaxed, the generation AI makes the proposal in a gentle expression method. When the user is stressed, the generation AI makes the proposal briefly. This enables the suggestion unit to adjust the expression method of the proposal based on the user's emotion, thereby making it possible to make a more effective proposal. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input a user's emotion data to the generation AI and cause the generation AI to adjust the way the suggestion is expressed based on the emotion.

[0083] The proposal unit can cooperate with local farmers or residents to reuse the abandoned farmland. For example, the proposal unit cooperates with local farmers to make a plan to reuse the abandoned farmland. The proposal unit proposes the reuse of the abandoned farmland to local farmers and obtains their cooperation. For example, the proposal unit cooperates with local residents to hold a workshop for reusing the abandoned farmland. The proposal unit proposes the reuse of the abandoned farmland to local residents and obtains their cooperation. For example, the proposal unit launches a project in which local farmers and residents jointly reuse the abandoned farmland. The proposal unit proposes the reuse of the abandoned farmland to local farmers and residents and obtains their cooperation. In this way, the proposal unit can promote the reuse of the abandoned farmland by cooperating with local farmers or residents. A part or all of the above-mentioned processing in the proposal unit may be performed using AI, or may be performed without using AI. For example, the proposal unit can input information about local farmers and residents into AI and cause AI to generate a proposal for reuse of the abandoned farmland.

[0084] The proposal unit can select the optimal proposal method by referring to the past cooperation history of local farmers and residents at the time of proposal. For example, the proposal unit selects the optimal proposal method by referring to the past cooperation history of local farmers and residents at the time of proposal. The proposal unit analyzes the past cooperation history using AI. The AI ​​takes the past cooperation history data of local farmers and residents as input and outputs the optimal proposal method. The AI, for example, refers to the past cooperation history of local farmers and selects the optimal proposal method. The AI ​​refers to the past cooperation history of local residents and selects the optimal proposal method. The AI ​​analyzes the past cooperation history of local farmers and residents and selects the optimal proposal method. Thereby, the proposal unit can select the optimal proposal method by referring to the past cooperation history. Part or all of the above-mentioned processing in the proposal unit may be performed using AI, or may be performed without using AI. For example, the proposal unit can input the past cooperation history data of local farmers and residents to AI and cause the AI ​​to select the optimal proposal method.

[0085] The suggestion unit can estimate the user's emotion and determine the priority of the proposal based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and determines the priority of the proposal based on the estimated user's emotion. The suggestion unit estimates the user's emotion using a generation AI. The generation AI receives the user's facial expression, voice, and text data and outputs an emotion score. For example, when the user is excited, the generation AI increases the priority of the proposal. When the user is relaxed, the generation AI sets the priority of the proposal to the standard. When the user is stressed, the generation AI sets the priority of the proposal to a low level. This enables the suggestion unit to determine the priority of the proposal based on the user's emotion, thereby enabling more appropriate suggestions. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input a user's emotion data to the generation AI and cause the generation AI to determine the priority of suggestions based on emotions.

[0086] The proposal unit can promote acceptance of the proposal by utilizing a local community event when making a proposal. For example, the proposal unit promotes acceptance of the proposal by utilizing a local community event when making a proposal. The proposal unit makes a proposal for reuse of abandoned farmland at a local community event. The proposal unit explains the benefits of reuse of farmland at the local community event. The proposal unit holds a workshop on reuse of farmland at the local community event. In this way, the proposal unit can promote acceptance of the proposal by utilizing the local community event. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, or may be performed without using AI. For example, the proposal unit can input information about local community events into the AI ​​and cause the AI ​​to create a plan for promoting acceptance of the proposal.

[0087] The proposal unit can provide an educational program for agricultural land reuse in cooperation with a local educational institution at the time of proposal. For example, the proposal unit provides an educational program for agricultural land reuse in cooperation with a local educational institution at the time of proposal. The proposal unit provides an educational program for agricultural land reuse in cooperation with a local school. The proposal unit launches a research project for agricultural land reuse in cooperation with a local university. The proposal unit holds a seminar on agricultural land reuse in collaboration with a local educational institution. In this way, the proposal unit can provide an educational program for agricultural land reuse by collaborating with a local educational institution. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, or may be performed without using AI. For example, the proposal unit can input information about a local educational institution into AI and have the AI ​​draw up a plan for the educational program.

[0088] The supply unit can estimate the user's emotion and adjust the supply method based on the estimated user's emotion. The supply unit, for example, estimates the user's emotion and adjusts the supply method based on the estimated user's emotion. The supply unit estimates the user's emotion using the generation AI. The generation AI receives the user's facial expression, voice, and text data and outputs an emotion score. For example, when the user is excited, the generation AI selects a quick supply method. When the user is relaxed, the generation AI selects a standard supply method. When the user is stressed, the generation AI selects a simple supply method. Thereby, the supply unit adjusts the supply method based on the user's emotion, thereby enabling more appropriate supply. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the supply unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the supply unit can input a user's emotion data to the generation AI and cause the generation AI to adjust the supply method based on the emotion.

[0089] The supply unit can select the optimal supply method by referring to the quality data of the agricultural products at the time of supply. For example, the supply unit selects the optimal supply method by referring to the quality data of the agricultural products at the time of supply. The supply unit analyzes the quality data of the agricultural products using AI. The AI ​​takes the quality data of the agricultural products as input and outputs the optimal supply method. For example, the AI ​​analyzes the quality data of the agricultural products and selects the optimal supply method. The AI ​​refers to freshness data of the agricultural products and selects the optimal supply method. The AI ​​analyzes nutritional value data of the agricultural products and selects the optimal supply method. Thereby, the supply unit can select the optimal supply method by referring to the quality data of the agricultural products. Some or all of the above-mentioned processing in the supply unit may be performed using AI, or may be performed without using AI. For example, the supply unit can input the quality data of the agricultural products to AI and cause the AI ​​to select the optimal supply method.

[0090] The supply department can realize efficient supply by utilizing a local logistics network at the time of supply. For example, the supply department realizes efficient supply by utilizing a local logistics network at the time of supply. The supply department partners with a local logistics company to realize efficient supply. The supply department realizes rapid supply by utilizing a local delivery network. The supply department realizes efficient supply by utilizing a local logistics hub. In this way, the supply department can realize efficient supply by utilizing the local logistics network. A part or all of the above-mentioned processing in the supply department may be performed using AI, or may be performed without using AI. For example, the supply department can input information on the local logistics network into AI and have the AI ​​create an efficient supply plan.

[0091] The supplying unit can expand the sales channels of the agricultural products by utilizing local market events at the time of supplying. For example, the supplying unit expands the sales channels of the agricultural products by utilizing local market events at the time of supplying. The supplying unit sells the agricultural products at local market events to expand the sales channels. The supplying unit sells the agricultural products at local festivals to expand the sales channels. The supplying unit sells the agricultural products at local farm shops to expand the sales channels. In this way, the supplying unit can expand the sales channels of the agricultural products by utilizing local market events. Some or all of the above-mentioned processing in the supplying unit may be performed using AI, or may be performed without using AI. For example, the supplying unit can input information about local market events into AI and have the AI ​​develop a plan for expanding the sales channels.

[0092] The providing unit can estimate the user's emotion and adjust the method of providing based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the method of providing based on the estimated user's emotion. The providing unit estimates the user's emotion using the generation AI. The generation AI receives the user's facial expression, voice, and text data, and outputs an emotion score. For example, when the user is excited, the generation AI selects a quick providing method. When the user is relaxed, the generation AI selects a standard providing method. When the user is stressed, the generation AI selects a simple providing method. Thereby, the providing unit can adjust the method of providing based on the user's emotion, thereby enabling more appropriate provision. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the providing method based on the emotion.

[0093] The providing unit can select the optimal providing method by referring to the local environmental data at the time of providing. For example, the providing unit selects the optimal providing method by referring to the local environmental data at the time of providing. The providing unit analyzes the local environmental data using AI. The AI ​​takes the local environmental data as input and outputs the optimal providing method. For example, the AI ​​refers to local climate data and selects the optimal providing method. The AI ​​refers to local soil data and selects the optimal providing method. The AI ​​refers to local water quality data and selects the optimal providing method. Thereby, the providing unit can select the optimal providing method by referring to the local environmental data. Part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the local environmental data to the AI ​​and cause the AI ​​to select the optimal providing method.

[0094] The provision unit can maximize the effect of the provision in cooperation with a local environmental protection organization at the time of provision. For example, the provision unit maximizes the effect of the provision in cooperation with a local environmental protection organization at the time of provision. The provision unit cooperates with a local environmental protection organization to maximize the effect of the provision. The provision unit launches a project in collaboration with a local environmental protection organization to maximize the effect of the provision. The provision unit cooperates with a local environmental protection organization to hold an event to maximize the effect of the provision. In this way, the provision unit can maximize the effect of the provision by cooperating with a local environmental protection organization. A part or all of the above-mentioned processing in the provision unit may be performed using AI, or may be performed without using AI. For example, the provision unit can input information of a local environmental protection organization into AI and have the AI ​​develop a plan to maximize the effect of the provision.

[0095] The provision unit can provide an environmental education program in cooperation with a local school at the time of provision. The provision unit, for example, provides an environmental education program in cooperation with a local school at the time of provision. The provision unit provides an environmental education program in cooperation with a local school. The provision unit holds a workshop on environmental protection in cooperation with the local school. The provision unit holds a seminar on environmental protection in cooperation with the local school. In this way, the provision unit can provide an environmental education program by collaborating with the local school. Some or all of the above-mentioned processing in the provision unit may be performed using AI, or may be performed without using AI. For example, the provision unit can input information about a local school into AI and have the AI ​​draw up a plan for the environmental education program.

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

[0097] When identifying abandoned farmland, the identification unit can refer to local tourism data to evaluate its value as a tourism resource. For example, the identification unit analyzes local tourist spots and tourist trend data to evaluate whether the abandoned farmland can be used as a tourism resource. The identification unit refers to tourist inflow data to evaluate the possibility of the abandoned farmland being incorporated into a tourist route. The identification unit analyzes local tourism event data to evaluate whether the abandoned farmland can be used as an event venue. In this way, the identification unit can contribute to the development of the local tourism industry by evaluating the value of abandoned farmland as a tourism resource.

[0098] The Proposal Department can work with local cultural organizations to propose the abandoned farmland as a place for cultural activities. For example, the Proposal Department works with a local art organization to propose the abandoned farmland as a place for art installations. The Proposal Department works with a local music organization to propose the abandoned farmland as a venue for a music festival. The Proposal Department works with a local history organization to propose the abandoned farmland as a place for historical reenactment events. In this way, the Proposal Department can propose new ways to use the abandoned farmland and revitalize local cultural activities by working with local cultural organizations.

[0099] The food supply department can work with local school lunch programs to provide school lunches with agricultural products grown on abandoned farmland. For example, the food supply department can work with local schools to incorporate fresh vegetables and fruits grown on abandoned farmland into school lunches. The food supply department can work with local educational institutions to provide programs that allow students to experience the process of growing and harvesting agricultural products. The food supply department can work with local nutritionists to create nutritionally balanced school lunch menus. In this way, the food supply department can provide fresh and nutritious ingredients to children and contribute to food education by working with local school lunch programs.

[0100] The provision department can work with local energy companies to use the abandoned farmland as a production base for renewable energy. For example, the provision department works with local energy companies to install solar panels on the abandoned farmland to generate solar power. The provision department works with local biomass energy companies to cultivate biomass crops on the abandoned farmland to use for energy production. The provision department works with local wind power companies to install wind power generation equipment on the abandoned farmland. In this way, the provision department can work with local energy companies to utilize the abandoned farmland as a production base for renewable energy and improve the region's energy self-sufficiency rate.

[0101] The identification unit can estimate the user's emotions and customize the method of identifying abandoned farmland based on the estimated user's emotions. For example, if the user is excited, the identification unit performs a detailed analysis and provides more information. If the user is relaxed, the identification unit performs a standard analysis and provides the minimum necessary information. If the user is stressed, the identification unit performs a quick analysis and provides concise information. In this way, the identification unit can provide optimal information to the user by customizing the identification method based on the user's emotions.

[0102] The suggestion unit can estimate the user's emotions and personalize the suggested content based on the estimated user's emotions. For example, when the user is excited, the suggestion unit makes active suggestions to attract the user's interest. When the user is relaxed, the suggestion unit makes gentle suggestions to give the user a sense of security. When the user is stressed, the suggestion unit makes simple and easy-to-understand suggestions to reduce the user's burden. This enables the suggestion unit to make more effective suggestions by personalizing the suggested content based on the user's emotions.

[0103] The supply unit can estimate the user's emotion and adjust the supply schedule based on the estimated user's emotion. For example, when the user is excited, the supply unit sets a rapid supply schedule. When the user is relaxed, the supply unit sets a standard supply schedule. When the user is stressed, the supply unit sets a flexible supply schedule to reduce the burden on the user. This allows the supply unit to adjust the supply schedule based on the user's emotion, thereby enabling more appropriate supply.

[0104] The providing unit can estimate the user's emotions and customize the provided content based on the estimated user's emotions. For example, when the user is excited, the providing unit provides detailed information to attract the user's interest. When the user is relaxed, the providing unit provides standard information to give the user a sense of security. When the user is stressed, the providing unit provides concise information to reduce the user's burden. This allows the providing unit to provide information more effectively by customizing the provided content based on the user's emotions.

[0105] The identification unit can estimate the user's emotions and determine the priority of the identified farmland based on the estimated user's emotions. For example, the identification unit increases the priority of the identified farmland when the user is excited. The identification unit sets the priority of the identified farmland to standard when the user is relaxed. The identification unit sets the priority of the identified farmland to a low level when the user is stressed. In this way, the identification unit determines the priority of the farmland based on the user's emotions, enabling more appropriate use of the farmland.

[0106] The Proposal Department can work with local companies to propose abandoned farmland as part of the company's CSR activities. For example, the Proposal Department can work with local companies to propose abandoned farmland as a site for the company's reforestation activities. The Proposal Department can work with local companies to propose abandoned farmland as a site for the company's employee training. The Proposal Department can work with local companies to propose abandoned farmland as part of the company's community contribution activities. In this way, the Proposal Department can work with local companies to propose new ways to use abandoned farmland and support the company's CSR activities.

[0107] The process flow of the second embodiment will be briefly described below.

[0108] Step 1: The identification unit analyzes satellite images or geographic information to identify abandoned farmland. The identification unit uses the generation AI to analyze satellite images and geographic information to identify the location of abandoned farmland. The generation AI improves the accuracy of identification by taking into account past farmland use history, seasonal changes, and local climate data. Step 2: The proposal department proposes leasing the farmland identified by the identification department. The proposal department proposes the reuse of abandoned farmland to local farmers and residents, generates proposal content using AI, and presents it to the proposal recipients. Step 3: The supply department proposes to supply agricultural products grown on the rented farmland to specific restaurants as proposed by the proposal department. The supply department supplies fresh, locally grown agricultural products to the cooperating restaurants, creates a supply plan using AI, and notifies the target restaurants.

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

[0110] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0111] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0112] For example, the identifying unit is realized by the camera 42 of the smart device 14 or the identifying processing unit 290 of the data processing device 12. For example, the suggesting unit is realized by the control unit 46A of the smart device 14 or the identifying processing unit 290 of the data processing device 12. For example, the supplying unit is realized by the control unit 46A of the smart device 14 or the identifying processing unit 290 of the data processing device 12. For example, the providing unit is realized by the control unit 46A of the smart device 14 or the identifying processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0116] 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.

[0117] 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 the voice according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0119] 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 performed in a secure state.

[0120] Fig. 4 shows an example of 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.

[0121] 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.

[0122] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0126] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0128] For example, the identification unit is realized by the camera 42 of the smart glasses 214 or the identification processing unit 290 of the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the smart glasses 214 or the identification processing unit 290 of the data processing device 12. For example, the supply unit is realized by the control unit 46A of the smart glasses 214 or the identification processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart glasses 214 or the identification processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0132] 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.

[0133] 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 the voice according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0135] 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 performed in a secure state.

[0136] 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.

[0137] 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.

[0138] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0139] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0140] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0141] 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 voice indicating a user input for 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.

[0142] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0144] For example, the specifying unit is realized by the camera 42 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the suggesting unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the supplying unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the providing unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0146] 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.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0148] 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.

[0149] 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 the voice according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0151] 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 performed in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0153] 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.

[0154] 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.

[0155] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0156] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0157] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for 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.

[0159] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0161] For example, the specification unit is realized by the camera 42 of the robot 414 or the specification processing unit 290 of the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the robot 414 or the specification processing unit 290 of the data processing device 12. For example, the supply unit is realized by the control unit 46A of the robot 414 or the specification processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the robot 414 or the specification processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0163] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0165] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0166] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0167] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0169] 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.

[0170] 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 Universal Serial Bus (USB) 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.

[0171] In addition, 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 upon request from the data processing device 12.

[0172] 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.

[0173] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0174] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0175] As an example of a configuration using one 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 configuration in which a processor is used that realizes the functions of the 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.

[0176] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0178] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0180] (Appendix 1) An identification department that analyzes satellite images or geographic information to identify abandoned farmland (a department that identifies abandoned farmland); A proposal unit (a unit proposing the leasing of farmland) that proposes the leasing of the farmland identified by the identification unit; A supply unit (a unit for supplying agricultural products) that proposes to supply agricultural products cultivated on the rented farmland to a specific restaurant based on the proposal by the proposal unit. A system characterized by: (Appendix 2) The identification unit is Analyze geographic information system data or satellite imagery to identify the location of abandoned farmland 2. The system of claim 1, (Appendix 3) The suggestion unit, Working with local farmers or residents to repurpose abandoned farmland 2. The system of claim 1, (Appendix 4) The supply unit includes: Supplying fresh, locally grown produce to partner restaurants 2. The system of claim 1, (Appendix 5) A providing section is provided. The providing unit is Take concrete steps to increase CO2 uptake through reforestation (e.g. planting certain tree species) or soil improvement (e.g. using certain fertilizers). 2. The system of claim 1, (Appendix 6) The identification unit is Estimate the user's emotion (e.g., happy or sad), and adjust the accuracy of identifying abandoned farmland based on the estimated user emotion (e.g., increase accuracy when the emotion is positive). 2. The system of claim 1, (Appendix 7) The identification unit is Improve identification accuracy by taking seasonal changes into account when analyzing satellite imagery or geographic information 2. The system of claim 1, (Appendix 8) The identification unit is When identifying abandoned farmland, referencing the past farmland use history will improve the reliability of the identification. 2. The system of claim 1, (Appendix 9) The identification unit is When analyzing satellite imagery or geographic information, consider local climate data to improve identification accuracy 2. The system of claim 1, (Appendix 10) The identification unit is When identifying abandoned farmland, local ecosystem data will be consulted to assess specific environmental impacts. 2. The system of claim 1, (Appendix 11) The identification unit is Estimate the user's emotion (e.g., happy or sad), and prioritize the identified farmlands based on the estimated user emotion (e.g., increase the priority if the emotion is positive). 2. The system of claim 1, (Appendix 12) The identification unit is When analyzing satellite imagery or geographic information, consider local land use plans to improve identification accuracy 2. The system of claim 1, (Appendix 13) The identification unit is When identifying abandoned farmland, refer to local historical data to assess its specific cultural value. 2. The system of claim 1, (Appendix 14) The identification unit is When analyzing satellite imagery or geographic information, consider local water resource data to improve identification accuracy 2. The system of claim 1, (Appendix 15) The identification unit is When identifying abandoned farmland, refer to local plant and animal habitat data to assess its specific biodiversity. 2. The system of claim 1, (Appendix 16) The suggestion unit, Infer the user's emotion (e.g., happy or sad) and tailor the presentation of the suggestions based on the inferred emotion (e.g., emphasize the expression if the emotion is positive) 2. The system of claim 1, (Appendix 17) The suggestion unit, Working with local farmers or residents to repurpose abandoned farmland 2. The system of claim 1, (Appendix 18) The suggestion unit, When making a proposal, the most appropriate proposal method is selected by referring to the past cooperation history of local farmers and residents. 2. The system of claim 1, (Appendix 19) The suggestion unit, Infer the user's emotion (e.g., happy or sad) and prioritize suggestions based on the estimated user emotion (e.g., give higher priority if the emotion is positive) 2. The system of claim 1, (Appendix 20) The suggestion unit, At the time of proposal, leverage local community events to drive acceptance of the proposal 2. The system of claim 1, (Appendix 21) The suggestion unit, Partner with local educational institutions to provide educational programming on agricultural land reclamation at the time of proposal. 2. The system of claim 1, (Appendix 22) The supply unit includes: Infer the user's emotion (e.g., happiness or sadness) and adjust the supply method based on the estimated user emotion (e.g., increase the supply amount if the emotion is positive) 2. The system of claim 1, (Appendix 23) The supply unit includes: At the time of supply, the optimal supply method is selected by referring to the quality data of the agricultural products. 2. The system of claim 1, (Appendix 24) The supply unit includes: At the time of supply, we will utilize local logistics networks to ensure efficient supply. 2. The system of claim 1, (Appendix 25) The supply unit includes: When supplying, utilize local market events to expand sales channels for agricultural products. 2. The system of claim 1, (Appendix 26) The providing unit is Infer the user's emotion (e.g., happiness or sadness) and adjust the delivery method based on the estimated user emotion (e.g., increase the amount of delivery if the emotion is positive) 2. The system of claim 1, (Appendix 27) The providing unit is At the time of delivery, local environmental data will be referenced to select the optimal delivery method. 2. The system of claim 1, (Appendix 28) The providing unit is When donating, partner with local environmental organizations to maximize the impact of donations. 2. The system of claim 1, (Appendix 29) The providing unit is During the project, we will provide environmental education programs in collaboration with local schools. 2. The system of claim 1, [Explanation of symbols]

[0181] 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 identification unit that analyzes satellite images or geographic information to identify abandoned farmland; a proposal unit that proposes leasing of the farmland identified by the identification unit; A supply unit that proposes to supply agricultural products cultivated on the rented farmland to a specific restaurant based on the proposal by the proposal unit. A system characterized by:

2. The identification unit is Analyze geographic information system data or satellite imagery to identify the location of abandoned farmland 2. The system of claim 1 .

3. The suggestion unit, Working with local farmers or residents to repurpose abandoned farmland 2. The system of claim 1 .

4. The supply unit includes: Supplying fresh, locally grown produce to partner restaurants 2. The system of claim 1 .

5. A providing section is provided. The providing unit is Take concrete steps to increase CO2 absorption through reforestation or soil improvement.

2. The system of claim 1 .

6. The identification unit is Estimating user sentiment and adjusting the accuracy of identifying abandoned farmland based on the estimated user sentiment 2. The system of claim 1 .

7. The identification unit is Improve identification accuracy by taking seasonal changes into account when analyzing satellite imagery or geographic information 2. The system of claim 1 .

8. The identification unit is When identifying abandoned farmland, referencing the past farmland use history will improve the reliability of the identification.

2. The system of claim 1 .

9. The identification unit is When analyzing satellite imagery or geographic information, consider local climate data to improve identification accuracy 2. The system of claim 1 .

10. The identification unit is When identifying abandoned farmland, local ecosystem data will be consulted to assess specific environmental impacts.

2. The system of claim 1 .

Images