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 Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the identification of abandoned farmland and its reuse are not efficiently carried out, and the contribution to improving the food self-sufficiency rate of the region and environmental protection is not sufficient.

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

Means for Solving the Problems

[0006] The system according to this embodiment comprises an identification unit, a proposal unit, and a supply unit. The identification unit identifies abandoned farmland by analyzing satellite imagery 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 farmland leased as proposed by the proposal unit to a specific restaurant. [Effects of the Invention]

[0007] The system according to this embodiment can improve the local food self-sufficiency rate by identifying abandoned farmland and promoting its reuse. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that uses generative AI to identify abandoned farmland and reuse it, thereby improving the local food self-sufficiency rate. This system uses generative AI to analyze satellite imagery and geographic information to identify abandoned farmland. Next, it proposes leasing the identified farmland to local farmers and residents. Furthermore, by supplying agricultural products grown on the leased farmland to collaborative restaurants, it provides environmentally friendly and inexpensive, nutritious local food. It also provides a platform for carbon offsetting, increasing CO2 absorption through afforestation activities and soil improvement, and enhancing the potential of green businesses. For example, the generative AI analyzes satellite imagery and geographic information to identify abandoned farmland. The accuracy of identification can be improved by considering past farmland use history, seasonal changes, and local climate data. It is also possible to estimate the user's emotions and adjust the identification accuracy based on the estimated emotions. Next, it proposes leasing the identified farmland to local farmers and residents. By reusing abandoned farmland in cooperation with local farmers and residents, the local food self-sufficiency rate can be improved. Furthermore, agricultural products grown on the leased farmland are supplied to collaborative restaurants. By supplying fresh, locally grown produce to partner restaurants, the system can provide environmentally friendly, affordable, and nutritious local ingredients. It also offers a platform for carbon offsetting, increasing CO2 absorption through afforestation and soil improvement. This enhances the potential of green businesses, improving local food self-sufficiency and contributing to environmental protection.

[0029] The system according to this embodiment comprises an identification unit, a proposal unit, and a supply unit. The identification unit identifies abandoned farmland by analyzing satellite imagery or geographic information. The identification unit identifies the location of abandoned farmland by analyzing satellite imagery or geographic information, for example, using a generation AI. The generation AI takes satellite imagery or geographic information as input and outputs the location of abandoned farmland. The generation AI improves the accuracy of identification by considering, for example, past farmland use history, seasonal changes, and local climate data. The proposal unit proposes leasing the farmland identified by the identification unit. The proposal unit proposes the reuse of abandoned farmland to, for example, local farmers or residents. The proposal unit can generate proposal content using AI and present it to the target recipients. The supply unit proposes supplying agricultural products grown on the farmland leased as proposed by the proposal unit to a collaborative restaurant. The supply unit supplies, for example, fresh agricultural products grown locally to the collaborative restaurant. The supply unit can formulate a supply plan using AI and notify the target restaurant. This allows the system to identify abandoned farmland and improve regional food self-sufficiency by reusing it. Some or all of the above-described processes in the identification unit may be performed using or without a generating AI. For example, the identification unit can input satellite images and geographic information into a generating AI to have the generating AI identify the location of abandoned farmland. Some or all of the above-described processes in the proposal unit may be performed using or without an AI. For example, the proposal unit can input information about farmland identified by the identification unit into an AI to have the AI ​​generate a farmland lease proposal. Some or all of the above-described processes in the supply unit may be performed using or without an AI. For example, the supply unit can input information about farmland proposed by the proposal unit into an AI to have the AI ​​formulate a supply plan. This allows the system according to the embodiment to improve regional food self-sufficiency by identifying abandoned farmland and reusing it.

[0030] The identification unit identifies abandoned farmland by analyzing satellite imagery or geographic information. For example, the identification unit uses a generative AI to analyze satellite imagery and geographic information to pinpoint the location of abandoned farmland. The generative AI takes satellite imagery and geographic information as input and outputs the location of abandoned farmland. The generative AI improves identification accuracy by considering, for example, past farmland use history, seasonal changes, and local climate data. Specifically, the generative AI first analyzes satellite imagery at high resolution to detect changes in the shape and color of the farmland. This allows it to determine whether the farmland has been abandoned. Furthermore, it uses a Geographic Information System (GIS) to analyze changes in topography and land use to pinpoint the location of abandoned farmland. The generative AI integrates this data to pinpoint the location of abandoned farmland with high accuracy. For example, the generative AI compares satellite imagery from the past several years and tracks changes in farmland to identify abandoned farmland. The generative AI also considers local climate data to evaluate the usability of the farmland. This allows the identification unit to quickly and accurately pinpoint the location of abandoned farmland and provide basic information for reuse. Furthermore, the Identification Department can store information on identified abandoned farmland in a database and utilize it in conjunction with other departments and systems. For example, the Identification Department can provide location information of identified abandoned farmland to the Proposal Department and the Supply Department to support the development of reuse plans. This allows the Identification Department to efficiently support the entire process from the identification of abandoned farmland to its reuse.

[0031] The Proposal Department proposes the leasing of farmland identified by the Identification Department. For example, the Proposal Department proposes the reuse of abandoned farmland to local farmers and residents. The Proposal Department can use AI to generate proposals and present them to the target audience. Specifically, based on information on abandoned farmland provided by the Identification Department, the Proposal Department evaluates the possibility of reuse and proposes the optimal use method. The AI ​​considers past farmland use data, market demand, and local climate conditions to propose the optimal crops and uses. For example, the AI ​​analyzes soil and climate data of the identified abandoned farmland and selects crops suitable for that land. The AI ​​also considers the needs of local farmers and residents and proposes the optimal leasing conditions and uses. This allows the Proposal Department to promote the reuse of abandoned farmland and improve regional agricultural productivity. Furthermore, the Proposal Department provides communication tools to effectively convey the proposals to local farmers and residents. For example, the Proposal Department uses AI to visualize the proposals in an easy-to-understand way and create presentation materials and reports. The Proposal Department can also communicate directly with local farmers and residents through an online platform and share the proposals. This will enable the proposal department to effectively communicate information about the reuse of abandoned farmland and support the promotion of agriculture in the region.

[0032] The supply department proposes supplying agricultural products grown on farmland leased as proposed by the proposal department to partner restaurants. For example, the supply department supplies fresh, locally grown produce to partner restaurants. The supply department can use AI to develop supply plans and notify the target restaurants. Specifically, the supply department develops cultivation plans based on farmland information provided by the proposal department and predicts harvest times and supply quantities. The AI ​​creates an optimal supply plan considering past harvest data, market demand, and climate conditions. For example, the AI ​​analyzes soil and climate data for identified farmland to develop an optimal cultivation schedule. The AI ​​also considers the demand of local restaurants and optimizes supply quantities and timing. This allows the supply department to efficiently supply fresh, locally grown produce and improve the region's food self-sufficiency rate. Furthermore, the supply department provides a system for notifying restaurants of supply plans and sharing supply schedules. For example, the supply department can communicate directly with restaurants through an online platform to adjust supply schedules and quantities. Furthermore, the supply department can monitor supply conditions in real time and modify supply plans as needed. This allows the supply department to efficiently supply local agricultural products and improve the region's food self-sufficiency rate.

[0033] The identification unit can identify the location of abandoned farmland by analyzing geographic information system data or satellite imagery. For example, the identification unit can analyze geographic information system data to identify the location of abandoned farmland. Geographic information system data includes, for example, GIS data, topographic data, and land use data. The identification unit takes geographic information system data as input and outputs the location of abandoned farmland. For example, the identification unit can analyze satellite imagery to identify the location of abandoned farmland. Satellite imagery includes, for example, resolution, time of capture, and angle of capture. The identification unit takes satellite imagery as input and outputs the location of abandoned farmland. In this way, the identification unit can accurately identify the location of abandoned farmland by analyzing geographic information system data or satellite imagery. Some or all of the above-described processing in the identification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the identification unit can input geographic information system data or satellite imagery into a generation AI and have the generation AI identify the location of abandoned farmland.

[0034] The proposal department can work with local farmers or residents to reuse abandoned farmland. For example, the proposal department can work with local farmers to develop a plan for reusing abandoned farmland. The proposal department can propose the reuse of abandoned farmland to local farmers and obtain their cooperation. For example, the proposal department can work with local residents to hold workshops on reusing abandoned farmland. The proposal department can propose the reuse of abandoned farmland to local residents and obtain their cooperation. For example, the proposal department can launch a project in which local farmers and residents jointly reuse abandoned farmland. The proposal department can propose the reuse of abandoned farmland to local farmers and residents and obtain their cooperation. In this way, the proposal department can promote the reuse of abandoned farmland by working with local farmers or residents. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input information on local farmers and residents into AI and have the AI ​​generate proposals for the reuse of abandoned farmland.

[0035] The supply department can supply locally grown fresh produce to partner restaurants. The supply department can, for example, supply locally grown fresh produce to partner restaurants. The supply department can deliver produce harvested from local farmers to partner restaurants. The supply department can, for example, work with local farmers to plan the supply of produce. The supply department can supply produce harvested from local farmers to partner restaurants. The supply department can, for example, utilize local logistics networks to supply produce to partner restaurants. The supply department can partner with local logistics companies to supply produce to partner restaurants. In this way, the supply department can provide environmentally friendly, inexpensive, and nutritious local ingredients by supplying locally grown fresh produce. Some or all of the processes described above in the supply department may or may not be performed using AI. For example, the supply department can input information on local farmers and logistics companies into AI and have the AI ​​formulate a supply plan for produce.

[0036] The system includes a provisioning unit, which can carry out activities to increase CO2 absorption, such as afforestation or soil improvement. For example, the provisioning unit can increase CO2 absorption by carrying out afforestation. The provisioning unit can increase CO2 absorption by planting specific tree species. The provisioning unit can increase CO2 absorption by improving soil, such as improving soil. The provisioning unit can increase CO2 absorption by improving soil using specific fertilizers. The provisioning unit can increase CO2 absorption by combining afforestation and soil improvement, such as planting specific tree species and using specific fertilizers. In this way, the provisioning unit can increase CO2 absorption and contribute to environmental protection by carrying out afforestation and soil improvement. Some or all of the above processes in the provisioning unit may be carried out using AI or not. For example, the provisioning unit can have AI plan afforestation activities or soil improvement.

[0037] The identification unit can improve identification accuracy by considering seasonal changes when analyzing satellite images or geographic information. For example, the identification unit improves identification accuracy by considering seasonal changes when analyzing satellite images or geographic information. The identification unit analyzes seasonal changes using a generation AI. The generation AI takes satellite images or geographic information as input and outputs identification accuracy that takes seasonal changes into account. For example, the generation AI adjusts identification accuracy in spring by considering the growth of new shoots. The generation AI improves identification accuracy in summer by considering the growth status of crops. The generation AI adjusts identification accuracy in winter by considering the effects of snow and frost. In this way, the identification unit can improve identification accuracy by considering seasonal changes. Some or all of the above processing in the identification unit may be performed using a generation AI or without a generation AI. For example, the identification unit can input satellite images or geographic information into a generation AI and have the generation AI perform the improvement of identification accuracy that takes seasonal changes into account.

[0038] The identification unit can improve the reliability of its identification of abandoned farmland by referring to past farmland use history. For example, the identification unit improves the reliability of its identification by referring to past farmland use history when identifying abandoned farmland. The identification unit analyzes past farmland use history using a generation AI. The generation AI takes past farmland use data as input and outputs information to improve the reliability of its identification. For example, the generation AI analyzes farmland use history for the past 10 years to improve the accuracy of identifying abandoned farmland. The generation AI improves the reliability of its identification by referring to past harvest data of farmland. The generation AI improves the reliability of its identification by referring to information on past landowners. In this way, the identification unit can improve the reliability of its identification by referring to past farmland use history. Some or all of the above processing in the identification unit may be performed using a generation AI or not using a generation AI. For example, the identification unit can input past farmland use data into a generation AI and have the generation AI perform the improvement of the reliability of its identification.

[0039] The identification unit can improve identification accuracy by considering local climate data when analyzing satellite imagery or geographic information. For example, the identification unit improves identification accuracy by considering local climate data when analyzing satellite imagery or geographic information. The identification unit analyzes local climate data using a generation AI. The generation AI takes local climate data as input and outputs information to improve identification accuracy. For example, the generation AI analyzes local annual precipitation data to improve identification accuracy. The generation AI refers to local temperature data and adjusts identification accuracy. The generation AI considers local wind speed data to improve identification accuracy. In this way, the identification unit can improve identification accuracy by considering local climate data. Some or all of the above processing in the identification unit may be performed using the generation AI or not. For example, the identification unit can input local climate data into the generation AI and have the generation AI perform the improvement of identification accuracy.

[0040] The identification unit can evaluate specific environmental impacts by referring to local ecosystem data when identifying abandoned farmland. For example, the identification unit evaluates specific environmental impacts by referring to local ecosystem data when identifying abandoned farmland. The identification unit analyzes local ecosystem data using a generative AI. The generative AI takes local ecosystem data as input and outputs information for evaluating specific environmental impacts. For example, the generative AI evaluates specific environmental impacts by referring to local flora and fauna habitat data. The generative AI evaluates specific environmental impacts by analyzing local water quality data. The generative AI evaluates specific environmental impacts by referring to local soil data. In this way, the identification unit can evaluate specific environmental impacts by referring to local ecosystem data. Some or all of the above processing in the identification unit may be performed using a generative AI or not. For example, the identification unit can input local ecosystem data into a generative AI and have the generative AI perform the evaluation of specific environmental impacts.

[0041] The identification unit can improve identification accuracy by considering local land use plans when analyzing satellite imagery or geographic information. For example, the identification unit improves identification accuracy by considering local land use plans when analyzing satellite imagery or geographic information. The identification unit analyzes local land use plans using a generation AI. The generation AI takes local land use plan data as input and outputs information to improve identification accuracy. For example, the generation AI improves identification accuracy by referring to local land use plan data. The generation AI analyzes local urban planning data and adjusts identification accuracy. The generation AI improves identification accuracy by considering local agricultural land use plans. In this way, the identification unit can improve identification accuracy by considering local land use plans. Some or all of the above processing in the identification unit may be performed using a generation AI or not using a generation AI. For example, the identification unit can input local land use plan data into a generation AI and have the generation AI perform the improvement of identification accuracy.

[0042] The identification unit can evaluate specific cultural values ​​by referring to local historical data when identifying abandoned farmland. For example, the identification unit evaluates specific cultural values ​​by referring to local historical data when identifying abandoned farmland. The identification unit analyzes local historical data using a generative AI. The generative AI takes local historical data as input and outputs information for evaluating specific cultural values. For example, the generative AI evaluates specific cultural values ​​by referring to local historical farmland use data. The generative AI evaluates specific cultural values ​​by analyzing local historical building data. The generative AI evaluates specific cultural values ​​by referring to local historical event data. In this way, the identification unit can evaluate specific cultural values ​​by referring to local historical data. Some or all of the above processing in the identification unit may be performed using a generative AI or not. For example, the identification unit can input local historical data into a generative AI and have the generative AI perform the evaluation of specific cultural values.

[0043] The identification unit can improve identification accuracy by considering local water resource data when analyzing satellite imagery or geographic information. For example, the identification unit improves identification accuracy by considering local water resource data when analyzing satellite imagery or geographic information. The identification unit analyzes local water resource data using a generation AI. The generation AI takes local water resource data as input and outputs information to improve identification accuracy. For example, the generation AI improves identification accuracy by referring to local groundwater data. The generation AI analyzes local river data and adjusts identification accuracy. The generation AI improves identification accuracy by considering local precipitation data. In this way, the identification unit can improve identification accuracy by considering local water resource data. Some or all of the above processing in the identification unit may be performed using a generation AI or not using a generation AI. For example, the identification unit can input local water resource data into a generation AI and have the generation AI perform the improvement of identification accuracy.

[0044] The identification unit can evaluate specific biodiversity by referring to local flora and fauna habitat data when identifying abandoned farmland. For example, the identification unit evaluates specific biodiversity by referring to local flora and fauna habitat data when identifying abandoned farmland. The identification unit analyzes local flora and fauna habitat data using a generative AI. The generative AI takes local flora and fauna habitat data as input and outputs information for evaluating specific biodiversity. For example, the generative AI evaluates specific biodiversity by referring to local animal habitat data. The generative AI analyzes local plant habitat data and evaluates specific biodiversity. The generative AI evaluates specific biodiversity by referring to local ecosystem data. In this way, the identification unit can evaluate specific biodiversity by referring to local flora and fauna habitat data. Some or all of the above processing in the identification unit may be performed using a generative AI or not. For example, the identification unit can input local flora and fauna habitat data into a generative AI and have the generative AI perform the evaluation of specific biodiversity.

[0045] The proposal department can work with local farmers or residents to reuse abandoned farmland. For example, the proposal department can work with local farmers to develop a plan for reusing abandoned farmland. The proposal department can propose the reuse of abandoned farmland to local farmers and obtain their cooperation. For example, the proposal department can work with local residents to hold workshops on reusing abandoned farmland. The proposal department can propose the reuse of abandoned farmland to local residents and obtain their cooperation. For example, the proposal department can launch a project in which local farmers and residents jointly reuse abandoned farmland. The proposal department can propose the reuse of abandoned farmland to local farmers and residents and obtain their cooperation. In this way, the proposal department can promote the reuse of abandoned farmland by working with local farmers or residents. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input information on local farmers and residents into AI and have the AI ​​generate proposals for the reuse of abandoned farmland.

[0046] The proposal unit can select the optimal proposal method by referring to the past cooperation history of local farmers and residents when making a proposal. For example, the proposal unit selects the optimal proposal method by referring to the past cooperation history of local farmers and residents when making a proposal. The proposal unit uses AI to analyze past cooperation history. The AI ​​takes the past cooperation history data of local farmers and residents as input and outputs the optimal proposal method. For example, the AI ​​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. In this way, the proposal unit can select the optimal proposal method by referring to past cooperation history. Some or all of the above processing in the proposal unit may be performed using AI or not using AI. For example, the proposal unit can input the past cooperation history data of local farmers and residents into the AI ​​and have the AI ​​perform the selection of the optimal proposal method.

[0047] The proposal department can leverage local community events to promote the acceptance of proposals. For example, the proposal department can leverage local community events to promote the acceptance of proposals. The proposal department makes proposals for the reuse of abandoned farmland at local community events. The proposal department explains the benefits of farmland reuse at local community events. The proposal department holds workshops on farmland reuse at local community events. In this way, the proposal department can leverage local community events to promote the acceptance of proposals. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input information about local community events into AI and have the AI ​​develop a plan to promote the acceptance of proposals.

[0048] The proposal department can, at the time of proposal, provide an educational program on farmland reuse in collaboration with local educational institutions. For example, the proposal department can, at the time of proposal, provide an educational program on farmland reuse in collaboration with local educational institutions. The proposal department can, in collaboration with local schools, provide an educational program on farmland reuse. The proposal department can, in cooperation with local universities, launch a research project on farmland reuse. The proposal department can, in collaboration with local educational institutions, hold seminars on farmland reuse. In this way, the proposal department can provide an educational program on farmland reuse in collaboration with local educational institutions. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input information on local educational institutions into AI and have the AI ​​plan the educational program.

[0049] The supply unit can select the optimal supply method by referring to the quality data of agricultural products at the time of supply. For example, the supply unit selects the optimal supply method by referring to the quality data of agricultural products at the time of supply. The supply unit analyzes the quality data of agricultural products using AI. The AI ​​takes the quality data of agricultural products as input and outputs the optimal supply method. For example, the AI ​​analyzes the quality data of agricultural products and selects the optimal supply method. The AI ​​refers to the freshness data of agricultural products and selects the optimal supply method. The AI ​​analyzes the nutritional value data of agricultural products and selects the optimal supply method. In this way, the supply unit can select the optimal supply method by referring to the quality data of agricultural products. Some or all of the above processes in the supply unit may be performed using AI or not using AI. For example, the supply unit can input the quality data of agricultural products into the AI ​​and have the AI ​​perform the selection of the optimal supply method.

[0050] The supply department can achieve efficient supply by utilizing the local logistics network at the time of supply. For example, the supply department can achieve efficient supply by utilizing the local logistics network at the time of supply. The supply department can achieve efficient supply by partnering with local logistics companies. The supply department can achieve rapid supply by utilizing the local delivery network. The supply department can achieve efficient supply by utilizing local logistics hubs. In this way, the supply department can achieve efficient supply by utilizing the local logistics network. Some or all of the above processes in the supply department may be performed using AI or not. For example, the supply department can input information on the local logistics network into AI and have the AI ​​formulate an efficient supply plan.

[0051] The supply department can expand the sales channels for agricultural products by utilizing local market events at the time of supply. For example, the supply department expands the sales channels for agricultural products by utilizing local market events at the time of supply. The supply department expands sales channels by selling agricultural products at local market events. The supply department expands sales channels by selling agricultural products at local festivals. The supply department expands sales channels by selling agricultural products at local farmers' markets. In this way, the supply department can expand the sales channels for agricultural products by utilizing local market events. Some or all of the above processes in the supply department may be performed using AI or not. For example, the supply department can input information on local market events into AI and have the AI ​​formulate a plan for expanding sales channels.

[0052] The service provider can select the optimal service delivery method by referring to local environmental data at the time of delivery. For example, the service provider selects the optimal service delivery method by referring to local environmental data at the time of delivery. The service provider analyzes local environmental data using AI. The AI ​​takes local environmental data as input and outputs the optimal service delivery method. For example, the AI ​​selects the optimal service delivery method by referring to local climate data. The AI ​​selects the optimal service delivery method by referring to local soil data. The AI ​​selects the optimal service delivery method by referring to local water quality data. In this way, the service provider can select the optimal service delivery method by referring to local environmental data. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input local environmental data into the AI ​​and have the AI ​​perform the selection of the optimal service delivery method.

[0053] The service provider can maximize the effectiveness of its offerings by collaborating with local environmental organizations at the time of offering. For example, the service provider can maximize the effectiveness of its offerings by collaborating with local environmental organizations at the time of offering. The service provider can maximize the effectiveness of its offerings by cooperating with local environmental organizations. The service provider can maximize the effectiveness of its offerings by launching projects in collaboration with local environmental organizations. The service provider can hold events in collaboration with local environmental organizations to maximize the effectiveness of its offerings. In this way, the service provider can maximize the effectiveness of its offerings by collaborating with local environmental organizations. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input information on local environmental organizations into AI and have the AI ​​develop a plan to maximize the effectiveness of its offerings.

[0054] The service provider can, at the time of delivery, provide environmental education programs in collaboration with local schools. For example, the service provider can provide environmental education programs in collaboration with local schools. The service provider can provide environmental education programs in collaboration with local schools. The service provider can hold workshops on environmental protection in cooperation with local schools. The service provider can hold seminars on environmental protection in collaboration with local schools. In this way, the service provider can provide environmental education programs in collaboration with local schools. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input information about local schools into AI and have the AI ​​plan environmental education programs.

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

[0056] The identification unit can evaluate the value of abandoned farmland as a tourist resource by referring to local tourism data when identifying abandoned farmland. For example, the identification unit can analyze local tourist spots and tourist trend data to evaluate whether abandoned farmland can be used as a tourist resource. The identification unit can refer to tourist inflow data to evaluate the possibility of abandoned farmland being incorporated into tourist routes. The identification unit can analyze local tourism event data to evaluate whether 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 tourist resource.

[0057] The proposal department can collaborate with local cultural organizations to propose abandoned farmland as a venue for cultural activities. For example, the department could work with local art groups to propose abandoned farmland as a venue for art installations. The department could collaborate with local music groups to propose abandoned farmland as a venue for music festivals. The department could collaborate with local history groups to propose abandoned farmland as a venue for historical reenactment events. In this way, the proposal department can propose new uses for abandoned farmland by collaborating with local cultural organizations, thereby revitalizing local cultural activities.

[0058] The supply department can collaborate with local school feeding programs to supply school lunches with produce grown on abandoned farmland. For example, the supply department can work with local schools to incorporate fresh vegetables and fruits grown on abandoned farmland into school lunches. The supply department can also collaborate with local educational institutions to provide students with programs that allow them to experience the entire process from cultivation to harvesting. The supply department can work with local nutritionists to create nutritionally balanced school lunch menus. In this way, the supply department, by collaborating with local school feeding programs, can provide children with fresh and nutritious ingredients and contribute to food education.

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

[0060] The proposal department can collaborate with local businesses to propose the use of abandoned farmland as part of the companies' CSR activities. For example, the proposal department can work with local businesses to propose abandoned farmland as a site for the companies' afforestation activities. The proposal department can work with local businesses to propose abandoned farmland as a site for the companies' employee training. The proposal department can work with local businesses to propose abandoned farmland as part of the companies' community contribution activities. In this way, the proposal department can collaborate with local businesses to propose new uses for abandoned farmland and support the companies' CSR activities.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The identification unit analyzes satellite imagery or geographic information to identify abandoned farmland. The identification unit uses a generating AI to analyze satellite imagery and geographic information to pinpoint the location of abandoned farmland. The generating AI improves identification accuracy by considering 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 proposals using AI, and presents them to the target parties. Step 3: The supply department proposes supplying agricultural products grown on the farmland leased as proposed by the proposal department to a specific restaurant. The supply department supplies fresh, locally grown agricultural products to the collaborating restaurant, uses AI to create a supply plan, and notifies the target restaurant.

[0063] (Example of form 2) The system according to an embodiment of the present invention is a system that uses generative AI to identify abandoned farmland and reuse it, thereby improving the local food self-sufficiency rate. This system uses generative AI to analyze satellite imagery and geographic information to identify abandoned farmland. Next, it proposes leasing the identified farmland to local farmers and residents. Furthermore, by supplying agricultural products grown on the leased farmland to collaborative restaurants, it provides environmentally friendly and inexpensive, nutritious local food. It also provides a platform for carbon offsetting, increasing CO2 absorption through afforestation activities and soil improvement, and enhancing the potential of green businesses. For example, the generative AI analyzes satellite imagery and geographic information to identify abandoned farmland. The accuracy of identification can be improved by considering past farmland use history, seasonal changes, and local climate data. It is also possible to estimate the user's emotions and adjust the identification accuracy based on the estimated emotions. Next, it proposes leasing the identified farmland to local farmers and residents. By reusing abandoned farmland in cooperation with local farmers and residents, the local food self-sufficiency rate can be improved. Furthermore, agricultural products grown on the leased farmland are supplied to collaborative restaurants. By supplying fresh, locally grown produce to partner restaurants, the system can provide environmentally friendly, affordable, and nutritious local ingredients. It also offers a platform for carbon offsetting, increasing CO2 absorption through afforestation and soil improvement. This enhances the potential of green businesses, improving local food self-sufficiency and contributing to environmental protection.

[0064] The system according to this embodiment comprises an identification unit, a proposal unit, and a supply unit. The identification unit identifies abandoned farmland by analyzing satellite imagery or geographic information. The identification unit identifies the location of abandoned farmland by analyzing satellite imagery or geographic information, for example, using a generation AI. The generation AI takes satellite imagery or geographic information as input and outputs the location of abandoned farmland. The generation AI improves the accuracy of identification by considering, for example, past farmland use history, seasonal changes, and local climate data. The proposal unit proposes leasing the farmland identified by the identification unit. The proposal unit proposes the reuse of abandoned farmland to, for example, local farmers or residents. The proposal unit can generate proposal content using AI and present it to the target recipients. The supply unit proposes supplying agricultural products grown on the farmland leased as proposed by the proposal unit to a collaborative restaurant. The supply unit supplies, for example, fresh agricultural products grown locally to the collaborative restaurant. The supply unit can formulate a supply plan using AI and notify the target restaurant. This allows the system to identify abandoned farmland and improve regional food self-sufficiency by reusing it. Some or all of the above-described processes in the identification unit may be performed using or without a generating AI. For example, the identification unit can input satellite images and geographic information into a generating AI to have the generating AI identify the location of abandoned farmland. Some or all of the above-described processes in the proposal unit may be performed using or without an AI. For example, the proposal unit can input information about farmland identified by the identification unit into an AI to have the AI ​​generate a farmland lease proposal. Some or all of the above-described processes in the supply unit may be performed using or without an AI. For example, the supply unit can input information about farmland proposed by the proposal unit into an AI to have the AI ​​formulate a supply plan. This allows the system according to the embodiment to improve regional food self-sufficiency by identifying abandoned farmland and reusing it.

[0065] The identification unit identifies abandoned farmland by analyzing satellite imagery or geographic information. For example, the identification unit uses a generative AI to analyze satellite imagery and geographic information to pinpoint the location of abandoned farmland. The generative AI takes satellite imagery and geographic information as input and outputs the location of abandoned farmland. The generative AI improves identification accuracy by considering, for example, past farmland use history, seasonal changes, and local climate data. Specifically, the generative AI first analyzes satellite imagery at high resolution to detect changes in the shape and color of the farmland. This allows it to determine whether the farmland has been abandoned. Furthermore, it uses a Geographic Information System (GIS) to analyze changes in topography and land use to pinpoint the location of abandoned farmland. The generative AI integrates this data to pinpoint the location of abandoned farmland with high accuracy. For example, the generative AI compares satellite imagery from the past several years and tracks changes in farmland to identify abandoned farmland. The generative AI also considers local climate data to evaluate the usability of the farmland. This allows the identification unit to quickly and accurately pinpoint the location of abandoned farmland and provide basic information for reuse. Furthermore, the Identification Department can store information on identified abandoned farmland in a database and utilize it in conjunction with other departments and systems. For example, the Identification Department can provide location information of identified abandoned farmland to the Proposal Department and the Supply Department to support the development of reuse plans. This allows the Identification Department to efficiently support the entire process from the identification of abandoned farmland to its reuse.

[0066] The Proposal Department proposes the leasing of farmland identified by the Identification Department. For example, the Proposal Department proposes the reuse of abandoned farmland to local farmers and residents. The Proposal Department can use AI to generate proposals and present them to the target audience. Specifically, based on information on abandoned farmland provided by the Identification Department, the Proposal Department evaluates the possibility of reuse and proposes the optimal use method. The AI ​​considers past farmland use data, market demand, and local climate conditions to propose the optimal crops and uses. For example, the AI ​​analyzes soil and climate data of the identified abandoned farmland and selects crops suitable for that land. The AI ​​also considers the needs of local farmers and residents and proposes the optimal leasing conditions and uses. This allows the Proposal Department to promote the reuse of abandoned farmland and improve regional agricultural productivity. Furthermore, the Proposal Department provides communication tools to effectively convey the proposals to local farmers and residents. For example, the Proposal Department uses AI to visualize the proposals in an easy-to-understand way and create presentation materials and reports. The Proposal Department can also communicate directly with local farmers and residents through an online platform and share the proposals. This will enable the proposal department to effectively communicate information about the reuse of abandoned farmland and support the promotion of agriculture in the region.

[0067] The supply department proposes supplying agricultural products grown on farmland leased as proposed by the proposal department to partner restaurants. For example, the supply department supplies fresh, locally grown produce to partner restaurants. The supply department can use AI to develop supply plans and notify the target restaurants. Specifically, the supply department develops cultivation plans based on farmland information provided by the proposal department and predicts harvest times and supply quantities. The AI ​​creates an optimal supply plan considering past harvest data, market demand, and climate conditions. For example, the AI ​​analyzes soil and climate data for identified farmland to develop an optimal cultivation schedule. The AI ​​also considers the demand of local restaurants and optimizes supply quantities and timing. This allows the supply department to efficiently supply fresh, locally grown produce and improve the region's food self-sufficiency rate. Furthermore, the supply department provides a system for notifying restaurants of supply plans and sharing supply schedules. For example, the supply department can communicate directly with restaurants through an online platform to adjust supply schedules and quantities. Furthermore, the supply department can monitor supply conditions in real time and modify supply plans as needed. This allows the supply department to efficiently supply local agricultural products and improve the region's food self-sufficiency rate.

[0068] The identification unit can identify the location of abandoned farmland by analyzing geographic information system data or satellite imagery. For example, the identification unit can analyze geographic information system data to identify the location of abandoned farmland. Geographic information system data includes, for example, GIS data, topographic data, and land use data. The identification unit takes geographic information system data as input and outputs the location of abandoned farmland. For example, the identification unit can analyze satellite imagery to identify the location of abandoned farmland. Satellite imagery includes, for example, resolution, time of capture, and angle of capture. The identification unit takes satellite imagery as input and outputs the location of abandoned farmland. In this way, the identification unit can accurately identify the location of abandoned farmland by analyzing geographic information system data or satellite imagery. Some or all of the above-described processing in the identification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the identification unit can input geographic information system data or satellite imagery into a generation AI and have the generation AI identify the location of abandoned farmland.

[0069] The proposal department can work with local farmers or residents to reuse abandoned farmland. For example, the proposal department can work with local farmers to develop a plan for reusing abandoned farmland. The proposal department can propose the reuse of abandoned farmland to local farmers and obtain their cooperation. For example, the proposal department can work with local residents to hold workshops on reusing abandoned farmland. The proposal department can propose the reuse of abandoned farmland to local residents and obtain their cooperation. For example, the proposal department can launch a project in which local farmers and residents jointly reuse abandoned farmland. The proposal department can propose the reuse of abandoned farmland to local farmers and residents and obtain their cooperation. In this way, the proposal department can promote the reuse of abandoned farmland by working with local farmers or residents. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input information on local farmers and residents into AI and have the AI ​​generate proposals for the reuse of abandoned farmland.

[0070] The supply department can supply locally grown fresh produce to partner restaurants. The supply department can, for example, supply locally grown fresh produce to partner restaurants. The supply department can deliver produce harvested from local farmers to partner restaurants. The supply department can, for example, work with local farmers to plan the supply of produce. The supply department can supply produce harvested from local farmers to partner restaurants. The supply department can, for example, utilize local logistics networks to supply produce to partner restaurants. The supply department can partner with local logistics companies to supply produce to partner restaurants. In this way, the supply department can provide environmentally friendly, inexpensive, and nutritious local ingredients by supplying locally grown fresh produce. Some or all of the processes described above in the supply department may or may not be performed using AI. For example, the supply department can input information on local farmers and logistics companies into AI and have the AI ​​formulate a supply plan for produce.

[0071] The system includes a provisioning unit, which can carry out activities to increase CO2 absorption, such as afforestation or soil improvement. For example, the provisioning unit can increase CO2 absorption by carrying out afforestation. The provisioning unit can increase CO2 absorption by planting specific tree species. The provisioning unit can increase CO2 absorption by improving soil, such as improving soil. The provisioning unit can increase CO2 absorption by improving soil using specific fertilizers. The provisioning unit can increase CO2 absorption by combining afforestation and soil improvement, such as planting specific tree species and using specific fertilizers. In this way, the provisioning unit can increase CO2 absorption and contribute to environmental protection by carrying out afforestation and soil improvement. Some or all of the above processes in the provisioning unit may be carried out using AI or not. For example, the provisioning unit can have AI plan afforestation activities or soil improvement.

[0072] The identification unit can estimate the user's emotions and adjust the accuracy of identifying abandoned farmland based on the estimated user emotions. The identification unit estimates the user's emotions using a generative AI. The generative AI takes the user's facial expressions, voice, and text data as input and outputs an emotion score. For example, if the user is excited, the generative AI performs a detailed analysis to improve identification accuracy. If the user is relaxed, the generative AI sets the identification accuracy to a standard level. If the user is stressed, the generative AI sets the identification accuracy lower and provides results quickly. This allows the identification unit to identify abandoned farmland more appropriately by adjusting the identification accuracy based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the identification unit may be performed using a generative AI or not. For example, the identification unit can input user emotion data into the generating AI and have the generating AI perform adjustments to the identification accuracy based on emotion.

[0073] The identification unit can improve identification accuracy by considering seasonal changes when analyzing satellite images or geographic information. For example, the identification unit improves identification accuracy by considering seasonal changes when analyzing satellite images or geographic information. The identification unit analyzes seasonal changes using a generation AI. The generation AI takes satellite images or geographic information as input and outputs identification accuracy that takes seasonal changes into account. For example, the generation AI adjusts identification accuracy in spring by considering the growth of new shoots. The generation AI improves identification accuracy in summer by considering the growth status of crops. The generation AI adjusts identification accuracy in winter by considering the effects of snow and frost. In this way, the identification unit can improve identification accuracy by considering seasonal changes. Some or all of the above processing in the identification unit may be performed using a generation AI or without a generation AI. For example, the identification unit can input satellite images or geographic information into a generation AI and have the generation AI perform the improvement of identification accuracy that takes seasonal changes into account.

[0074] The identification unit can improve the reliability of its identification of abandoned farmland by referring to past farmland use history. For example, the identification unit improves the reliability of its identification by referring to past farmland use history when identifying abandoned farmland. The identification unit analyzes past farmland use history using a generation AI. The generation AI takes past farmland use data as input and outputs information to improve the reliability of its identification. For example, the generation AI analyzes farmland use history for the past 10 years to improve the accuracy of identifying abandoned farmland. The generation AI improves the reliability of its identification by referring to past harvest data of farmland. The generation AI improves the reliability of its identification by referring to information on past landowners. In this way, the identification unit can improve the reliability of its identification by referring to past farmland use history. Some or all of the above processing in the identification unit may be performed using a generation AI or not using a generation AI. For example, the identification unit can input past farmland use data into a generation AI and have the generation AI perform the improvement of the reliability of its identification.

[0075] The identification unit can improve identification accuracy by considering local climate data when analyzing satellite imagery or geographic information. For example, the identification unit improves identification accuracy by considering local climate data when analyzing satellite imagery or geographic information. The identification unit analyzes local climate data using a generation AI. The generation AI takes local climate data as input and outputs information to improve identification accuracy. For example, the generation AI analyzes local annual precipitation data to improve identification accuracy. The generation AI refers to local temperature data and adjusts identification accuracy. The generation AI considers local wind speed data to improve identification accuracy. In this way, the identification unit can improve identification accuracy by considering local climate data. Some or all of the above processing in the identification unit may be performed using the generation AI or not. For example, the identification unit can input local climate data into the generation AI and have the generation AI perform the improvement of identification accuracy.

[0076] The identification unit can evaluate specific environmental impacts by referring to local ecosystem data when identifying abandoned farmland. For example, the identification unit evaluates specific environmental impacts by referring to local ecosystem data when identifying abandoned farmland. The identification unit analyzes local ecosystem data using a generative AI. The generative AI takes local ecosystem data as input and outputs information for evaluating specific environmental impacts. For example, the generative AI evaluates specific environmental impacts by referring to local flora and fauna habitat data. The generative AI evaluates specific environmental impacts by analyzing local water quality data. The generative AI evaluates specific environmental impacts by referring to local soil data. In this way, the identification unit can evaluate specific environmental impacts by referring to local ecosystem data. Some or all of the above processing in the identification unit may be performed using a generative AI or not. For example, the identification unit can input local ecosystem data into a generative AI and have the generative AI perform the evaluation of specific environmental impacts.

[0077] The identification unit can estimate the user's emotions and determine the priority of identified farmland based on the estimated user emotions. For example, the identification unit estimates the user's emotions and determines the priority of identified farmland based on the estimated user emotions. The identification unit estimates the user's emotions using a generative AI. The generative AI takes the user's facial expressions, voice, and text data as input and outputs an emotion score. For example, if the user is excited, the generative AI increases the priority of identified farmland. If the user is relaxed, the generative AI sets the priority of identified farmland to standard. If the user is stressed, the generative AI sets the priority of identified farmland to a lower level. In this way, the identification unit enables more appropriate use of farmland by determining the priority of farmland based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the identification unit may be performed using a generative AI or not using a generative AI. For example, the specific unit can input user emotion data into a generating AI and have the generating AI perform emotion-based priority determination.

[0078] The identification unit can improve identification accuracy by considering local land use plans when analyzing satellite imagery or geographic information. For example, the identification unit improves identification accuracy by considering local land use plans when analyzing satellite imagery or geographic information. The identification unit analyzes local land use plans using a generation AI. The generation AI takes local land use plan data as input and outputs information to improve identification accuracy. For example, the generation AI improves identification accuracy by referring to local land use plan data. The generation AI analyzes local urban planning data and adjusts identification accuracy. The generation AI improves identification accuracy by considering local agricultural land use plans. In this way, the identification unit can improve identification accuracy by considering local land use plans. Some or all of the above processing in the identification unit may be performed using a generation AI or not using a generation AI. For example, the identification unit can input local land use plan data into a generation AI and have the generation AI perform the improvement of identification accuracy.

[0079] The identification unit can evaluate specific cultural values ​​by referring to local historical data when identifying abandoned farmland. For example, the identification unit evaluates specific cultural values ​​by referring to local historical data when identifying abandoned farmland. The identification unit analyzes local historical data using a generative AI. The generative AI takes local historical data as input and outputs information for evaluating specific cultural values. For example, the generative AI evaluates specific cultural values ​​by referring to local historical farmland use data. The generative AI evaluates specific cultural values ​​by analyzing local historical building data. The generative AI evaluates specific cultural values ​​by referring to local historical event data. In this way, the identification unit can evaluate specific cultural values ​​by referring to local historical data. Some or all of the above processing in the identification unit may be performed using a generative AI or not. For example, the identification unit can input local historical data into a generative AI and have the generative AI perform the evaluation of specific cultural values.

[0080] The identification unit can improve identification accuracy by considering local water resource data when analyzing satellite imagery or geographic information. For example, the identification unit improves identification accuracy by considering local water resource data when analyzing satellite imagery or geographic information. The identification unit analyzes local water resource data using a generation AI. The generation AI takes local water resource data as input and outputs information to improve identification accuracy. For example, the generation AI improves identification accuracy by referring to local groundwater data. The generation AI analyzes local river data and adjusts identification accuracy. The generation AI improves identification accuracy by considering local precipitation data. In this way, the identification unit can improve identification accuracy by considering local water resource data. Some or all of the above processing in the identification unit may be performed using a generation AI or not using a generation AI. For example, the identification unit can input local water resource data into a generation AI and have the generation AI perform the improvement of identification accuracy.

[0081] The identification unit can evaluate specific biodiversity by referring to local flora and fauna habitat data when identifying abandoned farmland. For example, the identification unit evaluates specific biodiversity by referring to local flora and fauna habitat data when identifying abandoned farmland. The identification unit analyzes local flora and fauna habitat data using a generative AI. The generative AI takes local flora and fauna habitat data as input and outputs information for evaluating specific biodiversity. For example, the generative AI evaluates specific biodiversity by referring to local animal habitat data. The generative AI analyzes local plant habitat data and evaluates specific biodiversity. The generative AI evaluates specific biodiversity by referring to local ecosystem data. In this way, the identification unit can evaluate specific biodiversity by referring to local flora and fauna habitat data. Some or all of the above processing in the identification unit may be performed using a generative AI or not. For example, the identification unit can input local flora and fauna habitat data into a generative AI and have the generative AI perform the evaluation of specific biodiversity.

[0082] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is expressed based on the estimated emotions. For example, the suggestion unit estimates the user's emotions and adjusts the way the suggestion is expressed based on the estimated emotions. The suggestion unit estimates the user's emotions using generative AI. The generative AI takes the user's facial expressions, voice, and text data as input and outputs an emotion score. For example, if the user is excited, the generative AI uses an emphatic way of expressing the suggestion. If the user is relaxed, the generative AI makes the suggestion in a calm way. If the user is stressed, the generative AI makes the suggestion concise. In this way, the suggestion unit can make more effective suggestions by adjusting the way the suggestion is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the suggestion unit may be performed using a generative AI or not. For example, the proposal department can input user emotion data into a generation AI and have the generation AI adjust the way proposals are expressed based on those emotions.

[0083] The proposal department can work with local farmers or residents to reuse abandoned farmland. For example, the proposal department can work with local farmers to develop a plan for reusing abandoned farmland. The proposal department can propose the reuse of abandoned farmland to local farmers and obtain their cooperation. For example, the proposal department can work with local residents to hold workshops on reusing abandoned farmland. The proposal department can propose the reuse of abandoned farmland to local residents and obtain their cooperation. For example, the proposal department can launch a project in which local farmers and residents jointly reuse abandoned farmland. The proposal department can propose the reuse of abandoned farmland to local farmers and residents and obtain their cooperation. In this way, the proposal department can promote the reuse of abandoned farmland by working with local farmers or residents. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input information on local farmers and residents into AI and have the AI ​​generate proposals for the reuse of abandoned farmland.

[0084] The proposal unit can select the optimal proposal method by referring to the past cooperation history of local farmers and residents when making a proposal. For example, the proposal unit selects the optimal proposal method by referring to the past cooperation history of local farmers and residents when making a proposal. The proposal unit uses AI to analyze past cooperation history. The AI ​​takes the past cooperation history data of local farmers and residents as input and outputs the optimal proposal method. For example, the AI ​​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. In this way, the proposal unit can select the optimal proposal method by referring to past cooperation history. Some or all of the above processing in the proposal unit may be performed using AI or not using AI. For example, the proposal unit can input the past cooperation history data of local farmers and residents into the AI ​​and have the AI ​​perform the selection of the optimal proposal method.

[0085] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, the suggestion unit estimates the user's emotions and determines the priority of suggestions based on the estimated emotions. The suggestion unit estimates the user's emotions using generative AI. The generative AI takes the user's facial expressions, voice, and text data as input and outputs an emotion score. For example, if the user is excited, the generative AI increases the priority of suggestions. If the user is relaxed, the generative AI sets the priority of suggestions to standard. If the user is stressed, the generative AI sets the priority of suggestions to low. In this way, the suggestion unit can make more appropriate suggestions by determining the priority of suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the suggestion unit may be performed using a generative AI or not using a generative AI. For example, the proposal department can input user emotion data into a generating AI and have the AI ​​determine the priority of proposals based on those emotions.

[0086] The proposal department can leverage local community events to promote the acceptance of proposals. For example, the proposal department can leverage local community events to promote the acceptance of proposals. The proposal department makes proposals for the reuse of abandoned farmland at local community events. The proposal department explains the benefits of farmland reuse at local community events. The proposal department holds workshops on farmland reuse at local community events. In this way, the proposal department can leverage local community events to promote the acceptance of proposals. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input information about local community events into AI and have the AI ​​develop a plan to promote the acceptance of proposals.

[0087] The proposal department can, at the time of proposal, provide an educational program on farmland reuse in collaboration with local educational institutions. For example, the proposal department can, at the time of proposal, provide an educational program on farmland reuse in collaboration with local educational institutions. The proposal department can, in collaboration with local schools, provide an educational program on farmland reuse. The proposal department can, in cooperation with local universities, launch a research project on farmland reuse. The proposal department can, in collaboration with local educational institutions, hold seminars on farmland reuse. In this way, the proposal department can provide an educational program on farmland reuse in collaboration with local educational institutions. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input information on local educational institutions into AI and have the AI ​​plan the educational program.

[0088] The supply unit can estimate the user's emotions and adjust the supply method based on the estimated emotions. The supply unit estimates the user's emotions using a generative AI. The generative AI takes the user's facial expressions, voice, and text data as input and outputs an emotion score. For example, if the user is excited, the generative AI selects a rapid supply method. If the user is relaxed, the generative AI selects a standard supply method. If the user is stressed, the generative AI selects a simple supply method. This allows the supply unit to provide more appropriate supplies by adjusting the supply method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the supply unit may be performed using a generative AI or not. For example, the supply unit can input user emotion data into a generating AI and have the AI ​​adjust the supply method based on those emotions.

[0089] The supply unit can select the optimal supply method by referring to the quality data of agricultural products at the time of supply. For example, the supply unit selects the optimal supply method by referring to the quality data of agricultural products at the time of supply. The supply unit analyzes the quality data of agricultural products using AI. The AI ​​takes the quality data of agricultural products as input and outputs the optimal supply method. For example, the AI ​​analyzes the quality data of agricultural products and selects the optimal supply method. The AI ​​refers to the freshness data of agricultural products and selects the optimal supply method. The AI ​​analyzes the nutritional value data of agricultural products and selects the optimal supply method. In this way, the supply unit can select the optimal supply method by referring to the quality data of agricultural products. Some or all of the above processes in the supply unit may be performed using AI or not using AI. For example, the supply unit can input the quality data of agricultural products into the AI ​​and have the AI ​​perform the selection of the optimal supply method.

[0090] The supply department can achieve efficient supply by utilizing the local logistics network at the time of supply. For example, the supply department can achieve efficient supply by utilizing the local logistics network at the time of supply. The supply department can achieve efficient supply by partnering with local logistics companies. The supply department can achieve rapid supply by utilizing the local delivery network. The supply department can achieve efficient supply by utilizing local logistics hubs. In this way, the supply department can achieve efficient supply by utilizing the local logistics network. Some or all of the above processes in the supply department may be performed using AI or not. For example, the supply department can input information on the local logistics network into AI and have the AI ​​formulate an efficient supply plan.

[0091] The supply department can expand the sales channels for agricultural products by utilizing local market events at the time of supply. For example, the supply department expands the sales channels for agricultural products by utilizing local market events at the time of supply. The supply department expands sales channels by selling agricultural products at local market events. The supply department expands sales channels by selling agricultural products at local festivals. The supply department expands sales channels by selling agricultural products at local farmers' markets. In this way, the supply department can expand the sales channels for agricultural products by utilizing local market events. Some or all of the above processes in the supply department may be performed using AI or not. For example, the supply department can input information on local market events into AI and have the AI ​​formulate a plan for expanding sales channels.

[0092] The service provider can estimate the user's emotions and adjust the service delivery method based on the estimated emotions. For example, the service provider estimates the user's emotions and adjusts the service delivery method based on the estimated emotions. The service provider estimates the user's emotions using a generative AI. The generative AI takes the user's facial expressions, voice, and text data as input and outputs an emotion score. For example, if the user is excited, the generative AI selects a rapid service delivery method. If the user is relaxed, the generative AI selects a standard service delivery method. If the user is stressed, the generative AI selects a simple service delivery method. This allows the service provider to provide more appropriate service by adjusting the service delivery method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI or not. For example, the service provider can input user emotion data into a generating AI and have the AI ​​adjust the service delivery method based on those emotions.

[0093] The service provider can select the optimal service delivery method by referring to local environmental data at the time of delivery. For example, the service provider selects the optimal service delivery method by referring to local environmental data at the time of delivery. The service provider analyzes local environmental data using AI. The AI ​​takes local environmental data as input and outputs the optimal service delivery method. For example, the AI ​​selects the optimal service delivery method by referring to local climate data. The AI ​​selects the optimal service delivery method by referring to local soil data. The AI ​​selects the optimal service delivery method by referring to local water quality data. In this way, the service provider can select the optimal service delivery method by referring to local environmental data. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input local environmental data into the AI ​​and have the AI ​​perform the selection of the optimal service delivery method.

[0094] The service provider can maximize the effectiveness of its offerings by collaborating with local environmental organizations at the time of offering. For example, the service provider can maximize the effectiveness of its offerings by collaborating with local environmental organizations at the time of offering. The service provider can maximize the effectiveness of its offerings by cooperating with local environmental organizations. The service provider can maximize the effectiveness of its offerings by launching projects in collaboration with local environmental organizations. The service provider can hold events in collaboration with local environmental organizations to maximize the effectiveness of its offerings. In this way, the service provider can maximize the effectiveness of its offerings by collaborating with local environmental organizations. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input information on local environmental organizations into AI and have the AI ​​develop a plan to maximize the effectiveness of its offerings.

[0095] The service provider can, at the time of delivery, provide environmental education programs in collaboration with local schools. For example, the service provider can provide environmental education programs in collaboration with local schools. The service provider can provide environmental education programs in collaboration with local schools. The service provider can hold workshops on environmental protection in cooperation with local schools. The service provider can hold seminars on environmental protection in collaboration with local schools. In this way, the service provider can provide environmental education programs in collaboration with local schools. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input information about local schools into AI and have the AI ​​plan environmental education programs.

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

[0097] The identification unit can evaluate the value of abandoned farmland as a tourist resource by referring to local tourism data when identifying abandoned farmland. For example, the identification unit can analyze local tourist spots and tourist trend data to evaluate whether abandoned farmland can be used as a tourist resource. The identification unit can refer to tourist inflow data to evaluate the possibility of abandoned farmland being incorporated into tourist routes. The identification unit can analyze local tourism event data to evaluate whether 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 tourist resource.

[0098] The proposal department can collaborate with local cultural organizations to propose abandoned farmland as a venue for cultural activities. For example, the department could work with local art groups to propose abandoned farmland as a venue for art installations. The department could collaborate with local music groups to propose abandoned farmland as a venue for music festivals. The department could collaborate with local history groups to propose abandoned farmland as a venue for historical reenactment events. In this way, the proposal department can propose new uses for abandoned farmland by collaborating with local cultural organizations, thereby revitalizing local cultural activities.

[0099] The supply department can collaborate with local school feeding programs to supply school lunches with produce grown on abandoned farmland. For example, the supply department can work with local schools to incorporate fresh vegetables and fruits grown on abandoned farmland into school lunches. The supply department can also collaborate with local educational institutions to provide students with programs that allow them to experience the entire process from cultivation to harvesting. The supply department can work with local nutritionists to create nutritionally balanced school lunch menus. In this way, the supply department, by collaborating with local school feeding programs, can provide children with fresh and nutritious ingredients and contribute to food education.

[0100] The supply department can collaborate with local energy companies to utilize abandoned farmland as a renewable energy production base. For example, the supply department can work with local energy companies to install solar panels on abandoned farmland and generate solar power. The supply department can collaborate with local biomass energy companies to cultivate biomass crops on abandoned farmland and use them for energy production. The supply department can collaborate with local wind power companies to install wind power generation equipment on abandoned farmland. In this way, by collaborating with local energy companies, the supply department can utilize abandoned farmland as a renewable energy production base 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 those 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 only the necessary information. If the user is stressed, the identification unit performs a rapid analysis and provides concise information. In this way, the identification unit can provide the most optimal information for the user by customizing the identification method based on the user's emotions.

[0102] The suggestion function can estimate the user's emotions and personalize its suggestions based on those emotions. For example, if the user is excited, the suggestion function will make proactive suggestions to capture the user's interest. If the user is relaxed, the suggestion function will make calm suggestions to give the user a sense of security. If the user is stressed, the suggestion function will make concise and easy-to-understand suggestions to reduce the user's burden. In this way, the suggestion function can make more effective suggestions by personalizing its content based on the user's emotions.

[0103] The supply unit can estimate the user's emotions and adjust the supply schedule based on those emotions. For example, if the user is excited, the supply unit will set a rapid supply schedule. If the user is relaxed, the supply unit will set a standard supply schedule. If the user is stressed, the supply unit will set a flexible supply schedule to reduce the user's burden. This allows the supply unit to provide more appropriate supplies by adjusting the supply schedule based on the user's emotions.

[0104] The service provider can estimate the user's emotions and customize the content based on those emotions. For example, if the user is excited, the service provider will provide detailed information to capture the user's interest. If the user is relaxed, the service provider will provide standard information to reassure the user. If the user is stressed, the service provider will provide concise information to reduce the user's burden. This allows the service provider to deliver information more effectively by customizing the content based on the user's emotions.

[0105] The identification unit can estimate the user's emotions and determine the priority of identified farmland based on those emotions. For example, if the user is excited, the identification unit will increase the priority of identified farmland. If the user is relaxed, the identification unit will set the priority of identified farmland to standard. If the user is stressed, the identification unit will set the priority of identified farmland to a lower level. In this way, by determining the priority of farmland based on the user's emotions, the identification unit enables more appropriate use of farmland.

[0106] The proposal department can collaborate with local businesses to propose the use of abandoned farmland as part of the companies' CSR activities. For example, the proposal department can work with local businesses to propose abandoned farmland as a site for the companies' afforestation activities. The proposal department can work with local businesses to propose abandoned farmland as a site for the companies' employee training. The proposal department can work with local businesses to propose abandoned farmland as part of the companies' community contribution activities. In this way, the proposal department can collaborate with local businesses to propose new uses for abandoned farmland and support the companies' CSR activities.

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

[0108] Step 1: The identification unit analyzes satellite imagery or geographic information to identify abandoned farmland. The identification unit uses a generating AI to analyze satellite imagery and geographic information to pinpoint the location of abandoned farmland. The generating AI improves identification accuracy by considering 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 proposals using AI, and presents them to the target parties. Step 3: The supply department proposes supplying agricultural products grown on the farmland leased as proposed by the proposal department to a specific restaurant. The supply department supplies fresh, locally grown agricultural products to the collaborating restaurant, uses AI to create a supply plan, and notifies the target restaurant.

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

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

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

[0112] For example, the specific unit is implemented by the camera 42 of the smart device 14 and the specific processing unit 290 of the data processing device 12. For example, the proposal unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing device 12. For example, the supply unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing device 12. For example, the providing unit is implemented by the control unit 46A of the smart device 14 and 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 examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

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

[0128] For example, the specific unit is implemented by the camera 42 of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. For example, the proposal unit is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. For example, the supply unit is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. For example, the providing unit is implemented by the control unit 46A of the smart glasses 214 and 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 examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

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

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

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] For example, the specific unit is implemented by the camera 42 of the headset terminal 314 and the specific processing unit 290 of the data processing device 12. For example, the proposal unit is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing device 12. For example, the supply unit is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing device 12. For example, the providing unit is implemented by the control unit 46A of the headset terminal 314 and 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 examples described above, and various modifications are possible.

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

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

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

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

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

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

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

[0161] For example, the specific unit is implemented by the camera 42 of the robot 414 and the specific processing unit 290 of the data processing device 12. For example, the proposal unit is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12. For example, the supply unit is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12. For example, the providing unit is implemented by the control unit 46A of the robot 414 and 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 examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

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

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

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

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

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

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

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

[0180] (Note 1) The system includes an identification unit (a unit for identifying abandoned farmland) that analyzes satellite imagery or geographical information to identify abandoned farmland, and The Proposal Department (the Department that Proposes Leasing Farmland) proposes leasing the farmland identified by the aforementioned Identification Department, The system comprises a supply unit (a unit that supplies agricultural products) that proposes to supply agricultural products grown on farmland leased as proposed by the aforementioned proposal unit to a specific restaurant. A system characterized by the following features. (Note 2) The specified part is, Analyze geographic information system data or satellite imagery to identify the location of abandoned farmland. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, In cooperation with local farmers or residents, repurpose abandoned farmland. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is They supply fresh, locally grown produce to partner restaurants. The system described in Appendix 1, characterized by the features described herein. (Note 5) Equipped with a supply section, The aforementioned supply unit is, Implement specific measures to increase CO2 absorption, such as afforestation activities (e.g., planting specific tree species) or soil improvement (e.g., using specific fertilizers). The system described in Appendix 1, characterized by the features described herein. (Note 6) The specified part is, The system estimates the user's emotions (e.g., joy or sadness) and adjusts the accuracy of identifying abandoned farmland based on the estimated user emotions (e.g., increasing accuracy when the emotion is positive). The system described in Appendix 1, characterized by the features described herein. (Note 7) The specified part is, When analyzing satellite imagery or geographic information, consider seasonal variations to improve identification accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 8) The specified part is, When identifying abandoned farmland, referencing past land use history improves the reliability of the identification. The system described in Appendix 1, characterized by the features described herein. (Note 9) The specified part is, When analyzing satellite imagery or geographic information, consider local climate data to improve identification accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 10) The specified part is, When identifying abandoned farmland, local ecosystem data is used to assess specific environmental impacts. The system described in Appendix 1, characterized by the features described herein. (Note 11) The specified part is, The system estimates the user's emotions (e.g., joy or sadness) and determines the priority of identified farmland based on the estimated user emotions (e.g., prioritizing farmland with positive emotions). The system described in Appendix 1, characterized by the features described herein. (Note 12) The specified part is, When analyzing satellite imagery or geographic information, consider local land-use plans to improve identification accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 13) The specified part is, When identifying abandoned farmland, local historical data is used to assess its specific cultural value. The system described in Appendix 1, characterized by the features described herein. (Note 14) The specified part is, When analyzing satellite imagery or geographic information, consider local water resource data to improve identification accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 15) The specified part is, When identifying abandoned farmland, we assess specific biodiversity by referring to local flora and fauna habitat data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions (e.g., joy or sadness) and adjusts the way suggestions are presented based on the estimated emotions (e.g., emphasizing the expression if the emotion is positive). The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, In cooperation with local farmers or residents, repurpose abandoned farmland. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a proposal, we will select the most suitable proposal method by referring to the past cooperation history of local farmers and residents. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions (e.g., joy or sadness) and determines the priority of suggestions based on the estimated user emotions (e.g., prioritizing suggestions with positive emotions). The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, utilize local community events to encourage acceptance of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, we will collaborate with local educational institutions to provide an educational program on land reuse. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is The system estimates the user's emotions (e.g., joy or sadness) and adjusts the supply method based on the estimated user emotions (e.g., increases the supply if the emotion is positive). The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is When supplying crops, the optimal supply method is selected by referring to quality data of the crops. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is During supply, we utilize local logistics networks to achieve efficient supply. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is During supply, we will expand sales channels for agricultural products by utilizing local market events. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, The system estimates the user's emotions (e.g., joy or sadness) and adjusts the delivery method based on the estimated emotions (e.g., increases the amount delivered if the emotion is positive). The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected by referring to local environmental data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, we will collaborate with local environmental protection groups to maximize its effectiveness. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, we will collaborate with local schools to offer environmental education programs. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. A special unit that identifies abandoned farmland by analyzing satellite imagery or geographical information, A proposal department that proposes the leasing of farmland identified by the aforementioned identification department to local farmers or residents, A system comprising: a supply unit which formulates a supply plan for supplying agricultural products grown on farmland leased as proposed by the aforementioned proposal unit to a collaborative restaurant, and a supply unit which notifies the collaborative restaurant of the formulated supply plan, The identification unit acquires at least one of the user's facial expressions, voice, or text data of the system, estimates the user's emotional state, adjusts the accuracy of identifying the abandoned farmland based on the estimated emotional state of the user, and performs a detailed analysis to improve the identification accuracy if the user's emotional state is estimated to be a predetermined excited state. A system characterized by the following features.

2. The specified part is, Analyze geographic information system data or satellite imagery to identify the location of abandoned farmland. The system according to feature 1.

3. The aforementioned proposal section is, In cooperation with local farmers or residents, develop and present a plan for the reuse of the aforementioned abandoned farmland. The system according to feature 1.

4. Equipped with a supply section, The aforementioned supply unit is, Develop and present a concrete plan to increase CO2 absorption through afforestation activities or soil improvement. The system according to feature 1.

5. The specified part is, If the user's emotional state is estimated to be a predetermined relaxed state, the identification accuracy is set to a standard level; if the user's emotional state is estimated to be a predetermined stressed state, the identification accuracy is lowered to provide results quickly. The system according to feature 1.

6. The specified part is, By using a generative AI to analyze satellite images or geographic information, seasonal changes including the growth of new shoots in spring, the growth status of crops in summer, and the effects of snow and frost in winter are analyzed. The generative AI takes the satellite images or geographic information as input and outputs the identification accuracy that takes into account the analysis results of the seasonal changes, thereby improving the identification accuracy. The system according to feature 1.

7. The specified part is, Using a generation AI, when identifying abandoned farmland, past farmland use history including past harvest data or landowner information is analyzed, and the generation AI takes past farmland use data as input and outputs information to improve the reliability of the identification, thereby improving the reliability of the identification. The system according to feature 1.

8. The specified part is, Using a generation AI, when analyzing satellite imagery or geographic information, local climate data including at least one of annual precipitation data, temperature data, or wind speed data is analyzed, and the generation AI takes the local climate data as input and outputs information to improve the identification accuracy, thereby improving the identification accuracy. The system according to feature 1.

9. The specified part is, Using a generative AI, when identifying the abandoned farmland, local ecosystem data including at least one of local flora and fauna habitat data, water quality data, or soil data is analyzed, and the generative AI takes the local ecosystem data as input and outputs information for evaluating specific environmental impacts, thereby evaluating the specific environmental impacts that the reuse of the abandoned farmland will have. The system according to feature 1.

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