system

Abstract

We provide the system. [Solution] A means of processing geographic information obtained from multiple sources to identify abandoned farmland, A means of proposing appropriate crops to cultivate for identified farmland based on the needs of the local community, A means of matching the needs of businesses and local communities, and formulating plans for agricultural activities, A means of generating environmental contribution data and linking it to a company's CO2 absorption target, Means for creating and providing to the government or relevant agencies a sales strategy for the surplus crops generated, A system that includes this.

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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 recent years, the increase in abandoned farmland has become a serious problem for the regional economy and the environment. However, in order to effectively utilize these farmlands, cooperation between the local community and enterprises is essential, while there are many barriers from appropriate crop selection to solving complex problems such as CO2 absorption. Conventional methods lack efficiency and smooth cooperation in identifying farmland and its effective utilization, so these problems need to be solved.

Means for Solving the Problems

[0005] This invention provides a system that utilizes a generative model to process geographic information and quickly and accurately identify neglected farmland. This system proposes appropriate crops for the identified farmland based on local community demand, and efficiently plans agricultural activities by matching the needs of businesses with those of local communities. Furthermore, by generating environmental contribution data, it contributes to businesses achieving their CO2 absorption targets, and can automatically create sales strategies for surplus crops and provide them to the government or relevant organizations.

[0006] "Geographic information" refers to data that includes geographical features and location information about a specific place.

[0007] "Abandoned farmland" refers to farmland that is not currently in use and is not being cultivated or managed.

[0008] A "generative model" refers to a machine learning algorithm that learns from existing data and generates new data.

[0009] "Local community" refers to a group of people or organizations that live in a specific area and are involved in common interests or activities.

[0010] "Cultivated crops" refer to plants grown in agricultural activities.

[0011] "Matching" refers to the process of connecting multiple elements that meet each other's needs and conditions.

[0012] "CO2 absorption" refers to the process by which plants or specific processes absorb and reduce carbon dioxide in the atmosphere.

[0013] "Surplus crops" refer to additional agricultural products that remain in the region after consumption or processing and are not used there.

[0014] A "sales strategy" refers to a plan or method for effectively selling goods or services to the market. [Brief explanation of the drawing]

[0015] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined

Embodiments for Carrying Out the Invention

[0016] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 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.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] The 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.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] To implement this invention, it is necessary to build a system in which a server, a terminal, and a user work together to realize the function. In this system, the server first accesses a geographic information database to identify and analyze abandoned farmland. Specifically, the server collects satellite images and weather data and uses a generative model to detect candidates for abandoned farmland.

[0037] Next, the system collects information on local community demand and available resources from businesses via terminals, and performs matching that suits the conditions of both parties. Based on this information, the server proposes appropriate crops to cultivate, plans agricultural activities, and facilitates efficient collaboration. In addition, the server simulates the amount of CO2 absorbed by crops planned for farmland to contribute to the environment, and generates data that can be linked to the company's environmental targets.

[0038] Based on the information obtained through this system, users proceed with their farmland implementation plans and carry out specific agricultural activities. Furthermore, the server makes harvest forecasts for surplus crops, uses terminals to formulate market sales strategies, and evaluates the feasibility of selling to the government or relevant organizations.

[0039] As a concrete example, if a crop is identified as being planned for cultivation in a particular area, the server analyzes the soil and climate conditions of the abandoned farmland and generates a list of optimal crops. The terminal then notifies local specialty product vendors of this information, and the user coordinates the entire process from cultivation to sales. This makes it possible to revitalize the local economy through the effective use of abandoned farmland and contribute to achieving the environmental goals of companies.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server collects satellite imagery, weather data, and land use data from geographic information databases. This provides the basic data needed to easily identify areas that could become abandoned farmland.

[0043] Step 2:

[0044] The server inputs the collected geographic information data into a generative model to identify abandoned farmland. The model extracts land that is likely to be unused and records its location.

[0045] Step 3:

[0046] The terminal displays map data of abandoned farmland obtained from the server to the user. The user then uses this information to conduct on-site surveys and determine which farmlands are usable.

[0047] Step 4:

[0048] The server analyzes local specialties and community demand information to generate suitable crop candidates. This makes it easier to plan agricultural activities tailored to the region.

[0049] Step 5:

[0050] The terminal compares crop candidates generated by the server with databases of local restaurants and businesses, matching the needs of both parties. Based on this information, the user explores specific collaborations with businesses.

[0051] Step 6:

[0052] The server predicts CO2 absorption by crops and generates data that aligns with the company's environmental goals. Companies can use this data to help plan sustainable activities.

[0053] Step 7:

[0054] The server calculates the amount of surplus crop based on harvest forecast data and notifies the user via a terminal. The user uses this information to formulate a sales strategy for the surplus crop. The goal is to maximize its use, including considering sales to the government and related organizations.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] In modern society, a significant problem is that much farmland is left unmanaged and neglected. While such neglected land should be utilized as a valuable local economic resource, efficient utilization methods have not yet been established. Furthermore, businesses need to effectively manage CO2 absorption through agricultural activities to achieve environmental targets. A system is needed to address these challenges and achieve both the effective use of neglected farmland and environmental contribution.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] This invention includes a server that collects geographic information from multiple sources and detects abandoned farmland, a device that performs climate and soil analysis on the identified farmland and presents suitable crops using a generating AI model, and a device that integrates demand information collected from local communities and businesses through terminals and performs matching. This makes it possible to support the efficient utilization of abandoned farmland and the achievement of environmental goals by businesses.

[0060] "Information source" refers to the function or system that provides the data, and includes infrastructure that provides geographic information and weather data.

[0061] "Geographic information" refers to data related to a specific region or location, including location information, topography, and land use information.

[0062] "Abandoned farmland" refers to land that is not properly managed and is not being used for agricultural production.

[0063] A "generative AI model" refers to a framework or algorithm used to analyze and generate data using artificial intelligence, and represents a technology used for analysis and prediction.

[0064] "Climate conditions" refer to environmental factors that affect agriculture in a specific region, such as temperature, precipitation, and humidity.

[0065] Soil analysis is a method for investigating the physical and chemical properties of land, and it is a process for obtaining information that is useful for optimizing agricultural production.

[0066] "Matching" refers to the process of effectively connecting elements with different needs and conditions, aiming to integrate supply and demand.

[0067] "CO2 absorption" is an indicator that shows how much carbon dioxide a particular activity removes from the atmosphere, and it serves as an evaluation criterion for reducing environmental impact.

[0068] To implement this invention, the entire system must have a structure in which servers, terminals, and users cooperate. The following describes each component and its role.

[0069] The server is the core of the system, collecting and analyzing information. Specifically, it accesses geographic information databases, obtains satellite image data from Google® Earth Engine, and collects weather data through the Japan Meteorological Agency API. Based on the collected information, the server uses a generative AI model to detect potential abandoned farmland. The generative AI model is implemented using, for example, a common AI framework, and prompts such as "Please perform an analysis to identify abandoned farmland in this area and suggest suitable crops" are used for analysis.

[0070] The terminals play a role in collecting information on demand and available resources from local communities and businesses. Smartphones and tablets are used as terminals, and information is entered via a dedicated mobile application. This information is sent to a server and used in the matching process.

[0071] Users perform specific agricultural activities based on information provided via their terminals. They prepare, cultivate, and manage farmland according to crop cultivation plans proposed by the server. They also develop market strategies based on predictive information from the server (e.g., tomato harvest forecasts).

[0072] As a concrete example, if the server analyzes the climate and soil of a specific area and determines that tomatoes are suitable, it will notify local residents via their terminals. An example of a prompt message in this case would be, "Generate a list of crops best suited for producing local specialty products and propose a tomato cultivation plan to local residents."

[0073] This system enables the efficient use of abandoned farmland, revitalizes the local economy, and supports companies in achieving their environmental goals.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The server connects to a geographic information database and retrieves satellite image data and weather data. The input includes a request for geographic information for a specified region. The server retrieves satellite imagery from Google Earth Engine and weather data from the Japan Meteorological Agency API, and formats these into datasets for analysis. As output, the server generates datasets of geographic and weather information necessary for analysis.

[0077] Step 2:

[0078] The server sends a prompt to the generating AI model to detect abandoned farmland. The specific inputs include the dataset generated in step 1 and the prompt, "Identify and analyze abandoned farmland." The server inputs these into the generating AI model, which identifies candidate areas of abandoned farmland. As output, the server generates information about the location and characteristics of the identified farmland.

[0079] Step 3:

[0080] The terminal collects demand information and available resource information from local communities and businesses. The input here is the demand and resource information entered by local residents and businesses into the application. Specifically, the user enters the information using a smartphone or tablet app and presses the send button, transmitting the data from the terminal to the server. As output, the server receives a dataset of integrated demand and resource information.

[0081] Step 4:

[0082] The server uses the information received from the terminal to suggest crops to cultivate using a generative AI model. The input includes the demand and supply information obtained in step 3, and the prompt message "Please suggest the optimal crops to cultivate." The server analyzes the information using the generative AI model and generates a list of suitable crops. As output, the server outputs a list of recommended crops and sends it to the terminal.

[0083] Step 5:

[0084] Based on suggestions from the server, the user begins preparing the farmland and starting cultivation activities. Specific inputs include a list of crops to cultivate and a regional farming schedule provided by the server. The user uses this information to arrange necessary resources and plan cultivation activities. The output is the prepared farmland and the planned planting schedule for the crops.

[0085] Step 6:

[0086] The server predicts the harvest season and develops a market strategy. The input consists of a cultivation schedule and a harvest prediction prompt message generated by an AI model: "Predict the harvest yield and formulate a sales strategy." As output, the server generates and sends to the terminal a plan for the optimal sales strategy in the market, along with the harvest yield prediction data.

[0087] (Application Example 1)

[0088] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0089] To effectively utilize neglected farmland and achieve sustainable agricultural production, it is necessary to accurately analyze geographical information and effectively match the needs of local communities and businesses. Furthermore, automation using autonomous machinery is required to simultaneously achieve increased efficiency in agricultural activities and reduced environmental impact.

[0090] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0091] This invention includes a server that processes geographic information obtained from multiple sources to identify neglected farmland, proposes appropriate crops for the identified farmland based on local community needs, and matches the requirements of businesses and local communities to plan agricultural activities. This makes it possible to monitor and coordinate the production process of farmland using autonomous machinery and support sustainable agricultural activities.

[0092] "Means for processing geographic information" refers to technologies that analyze geographic-related data obtained from multiple sources to understand the characteristics of a particular land or region.

[0093] "Abandoned farmland" refers to agricultural land that is not properly managed or utilized, and is land where the potential for agricultural production lies dormant.

[0094] "Means for proposing crops to cultivate" refers to a system for selecting the most suitable seeds and crops based on the needs of the local community and climatic conditions.

[0095] "Methods for matching the needs of businesses and local communities" refers to an approach that maximizes the benefits for both parties by comparing a company's resource provision capabilities with the local agricultural demand.

[0096] An "autonomous machine" refers to a robot or device that can complete a task automatically without requiring external intervention.

[0097] An "automated work profile tailored to the characteristics of farmland" is a plan that automatically generates guidelines for machines to perform optimal tasks based on the conditions of each individual plot of land.

[0098] "Report data" refers to information that summarizes the results of activities, visualizes progress and achievements, and is presented in an easy-to-understand document.

[0099] This invention is implemented in a system in which a server, terminal, and user work together.

[0100] The server plays a crucial role in processing geographic information obtained from multiple sources. Specifically, the server collects and analyzes satellite imagery and weather data, and uses generative AI models to identify neglected farmland. The server also handles the process of suggesting appropriate crops to cultivate, taking into account demand data gathered from local communities and the resource availability of businesses. For this purpose, software technologies such as Python and machine learning algorithms are utilized.

[0101] Users receive cultivation plans and sales strategy proposals from the system using a terminal. These terminals include tablets and smartphones. Furthermore, users participate in agricultural production activities using autonomous machinery, receiving and executing work profiles tailored to the characteristics of their land from the system.

[0102] For example, if abandoned farmland is found in a specific area of ​​a region, the server will recommend corn as the most suitable crop for that land. Based on this, the user can download the suggested profile to an autonomous tractor and automate tasks such as sowing and fertilizing, enabling efficient agricultural activities.

[0103] As an example of a prompt, if you set a scenario such as, "Based on the soil conditions and climate data of this farmland, identify crops that can be grown and propose an optimal cultivation plan," the generated AI can then generate specific cultivation suggestions and profiles based on that request.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The server collects geographic information data from multiple sources. Inputs include satellite imagery and weather data, which are used to access databases and convert them into an analyzable format. Output is analyzed data ready for subsequent processing. This conversion process utilizes Python scripts and data cleansing techniques.

[0107] Step 2:

[0108] The server uses a generative AI model to identify abandoned farmland from collected geographic information. The input is the analyzed data obtained in step 1, and the AI ​​model processes the data to identify the locations of unused farmland. The output is a list of the locations of the identified abandoned farmland.

[0109] Step 3:

[0110] The server suggests appropriate crops for identified abandoned farmland based on local demand information. Inputs include the location of the abandoned farmland and demand data from the local community. A generative AI model evaluates the conditions and selects appropriate crops. The output is a list of recommended crops.

[0111] Step 4:

[0112] The terminal receives crop recommendation information from the server and notifies the user. The input is a list of crops from the server, which is visually presented to the user through a notification application on the terminal. The output is a crop information message that the user can recognize.

[0113] Step 5:

[0114] The user uses a terminal to generate an automated work profile based on the recommended crops to be grown and sends it to the autonomous machine. The input is the crop information displayed on the terminal, and the work profile is created using a dedicated app. The output is the operation instruction data received by the autonomous machine.

[0115] Step 6:

[0116] The user operates the autonomous machine to monitor production activities on the farmland and make adjustments as needed. The input is the operation instruction data received by the machine, which is then reflected in the actual agricultural work. The output is a report of the farmland's condition after the work has been completed.

[0117] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0118] To implement this invention, an emotion engine is incorporated into the system to enable the provision of feedback and services based on the user's emotions. First, the server collects and analyzes geographic information data and performs a process to identify abandoned farmland. Next, it acquires the user's emotion data through the terminal in order to suggest crops to cultivate that meet the needs of the local community.

[0119] Based on this sentiment data, the server optimizes agricultural activity plans. The sentiment engine analyzes user feedback and reactions to detect how users feel about proposed crops and business plans. Based on this information, the server adjusts the matching algorithm and modifies the plans to better match the needs of businesses and local communities.

[0120] Furthermore, the analysis results from the emotion engine are provided to companies via the terminal. This allows companies to improve their communication strategies and strengthen their relationships with local communities. For example, if a proposed crop in a certain area elicits a positive response from residents, it can serve as justification for promoting a cultivation plan for that crop.

[0121] As a concrete example, when a local community receives a proposal to cultivate a certain crop on abandoned farmland, the system senses the user's reaction via a terminal and analyzes that data on a server. Based on these results, the server, taking into account the output of the emotion engine, selects the proposal that is most supported by community members and concretizes a collaboration plan with a company. This process increases the likelihood of success for agricultural projects and provides effective solutions to local communities.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] The server retrieves the necessary data from the geographic information database and uses a generative model to identify unused farmland. This initiates the analysis to understand the status of abandoned farmland.

[0125] Step 2:

[0126] The terminal displays information about abandoned farmland provided by the server to the user. The user uses this information to prepare for on-site surveys and provide feedback.

[0127] Step 3:

[0128] Users provide feedback on the proposed farmland use plan. The device detects the user's emotions from their facial expressions and voice, and collects emotional data.

[0129] Step 4:

[0130] The server analyzes collected sentiment data and adjusts farming plans according to the user's emotions. For example, it prioritizes crop proposals that receive many positive responses.

[0131] Step 5:

[0132] The server uses analysis results from its emotion engine to optimize crop cultivation to meet the needs of the local community. It also submits the adjusted plan as feedback to the company.

[0133] Step 6:

[0134] The device helps deepen communication between companies and local communities by providing this feedback to company representatives.

[0135] Step 7:

[0136] Based on information from servers and terminals, users will prepare to launch agricultural activities in cooperation with local communities and businesses. This will strengthen communication and collaboration, promoting sustainable agricultural use.

[0137] (Example 2)

[0138] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0139] This invention aims to promote the use of fallow land and revitalize local communities by efficiently matching the needs of local communities and businesses through the collection and analysis of geographic information and the utilization of user sentiment data, thereby creating optimal agricultural activity plans. Furthermore, it aims to strengthen collaboration between businesses and local communities through improved communication strategies that reflect the opinions of residents.

[0140] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0141] This invention includes a server that processes geographic information obtained from multiple sources to identify fallow land, an emotion engine that obtains emotional data from users via a terminal and analyzes that data to optimize agricultural activity plans based on the user's emotions, and a means for providing the analysis results to companies to improve communication strategies. This makes it possible to plan agricultural projects that accurately reflect the needs of local communities.

[0142] "Information sources" refer to the foundational elements that provide the data a system needs, such as geographical information and user data.

[0143] "Geographic information" is a general term for data that indicates geographical conditions, such as the location and condition of abandoned land.

[0144] "Fallow land" refers to farmland that is currently not in use or has been left unattended.

[0145] A "device" is an electronic device used by a user to input emotional data, and includes, for example, smartphones and tablets.

[0146] "Emotional data" refers to emotional information expressed through text and ratings that users provide to the system.

[0147] An "emotion engine" is a software system that has algorithms for analyzing emotional data and classifying user emotions.

[0148] "Matching" refers to the process of creating an optimal agricultural plan by combining the needs and requirements of businesses and local communities.

[0149] "Communication strategy" refers to the plans and methodologies that companies use to build relationships with local communities and effectively communicate information.

[0150] In a mode for carrying out the invention, this system provides a means for planning optimal agricultural activities through the collection of geographic information data and the analysis of user sentiment data. Details are provided below.

[0151] The server uses Geographic Information System (GIS) software to collect and analyze local geographic data. By utilizing remote sensing technology and satellite imagery analysis, it can automatically identify neglected farmland. Image analysis algorithms visualize the data on a map, making it easily accessible to administrators.

[0152] The device plays a role in acquiring emotional data from users. Specifically, a mobile application is used, and users input feedback on suggestions and questions related to cultivation. Emotional data is collected in various formats, including text comments, emojis, and rating scores.

[0153] The server uses an emotion engine to analyze the collected emotion data. It employs natural language processing (NLP) techniques to analyze user text data and classify emotions as positive, negative, or neutral. Specifically, by using a natural language processing engine, a type of generative AI model, the emotion data can be numerically evaluated.

[0154] Furthermore, the server dynamically adjusts the matching algorithm between businesses and local communities based on the results of sentiment analysis. By prioritizing agricultural products supported by local residents and optimizing cultivation plans, it can benefit both residents and businesses.

[0155] For example, if the server detects positive reactions from residents to a proposed lavender cultivation plan in a certain area, it can use this result to provide the company with suggested countermeasures to help them decide whether to proceed with the cultivation plan. An example of a prompt message a user might access is, "What kind of emotional reactions are residents having to the proposed crop cultivation plan?" In response to this prompt, the system can provide detailed analysis results.

[0156] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0157] Step 1:

[0158] The server uses Geographic Information System (GIS) software to collect satellite imagery and remote sensing data. Based on this input data, it applies image analysis algorithms to identify fallow land. Specifically, it analyzes the pixel data of the images to identify vegetation and land-use patterns. The output of this analysis is the geographic location information of the identified fallow land.

[0159] Step 2:

[0160] The device collects sentiment data from users through a mobile application. Users input their opinions and feedback on cultivation suggestions. This input includes text comments, rating scores, and emojis. The device sends this information to a server. Specifically, it processes user input in the form of a form or questionnaire. The output is obtained as collected sentiment data.

[0161] Step 3:

[0162] The server analyzes the received sentiment data using an emotion engine. This analysis uses a generative AI model to perform linguistic analysis of user feedback and process the data to quantify emotional tendencies. Specifically, it utilizes natural language processing techniques to classify the sentiment in the text data into one of three categories: "positive," "negative," or "neutral." The output of this step is the analyzed sentiment score.

[0163] Step 4:

[0164] The server adjusts the matching algorithm based on the results of sentiment data analysis. Specifically, it receives input that re-evaluates the priorities of agricultural products that companies propose to local communities and selects the crops with the highest sentiment scores. The algorithm uses this data to dynamically update the plan and optimize the proposals. The output is a list of selected crops and the optimized plan.

[0165] Step 5:

[0166] The server provides companies with optimized cultivation plans and sentiment score analysis results via terminals. Based on this information, companies develop and improve their communication strategies with local communities. Specifically, they create presentation materials for their proposals and use them for promotions to facilitate dialogue with the local community. The output is provided as feedback reports and improvement suggestions for the companies.

[0167] (Application Example 2)

[0168] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0169] In modern society, the continued neglect of farmland in local communities leads to the waste of agricultural resources and hinders the revitalization of local communities. Furthermore, aligning the needs of businesses and local communities is not easy, and building appropriate cooperative relationships is a challenge. In addition, there is a need to realize effective commercial strategies by utilizing user sentiment data in agricultural planning.

[0170] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0171] This invention includes a server that processes spatial data obtained from multiple sources to identify neglected farmland, proposes appropriate plants for cultivation based on the needs of the local community for the identified farmland, and analyzes people's emotional information to optimize commercial activity plans based on their reactions to the proposed plants and plans. This enables the optimization of farmland utilization, the establishment of smooth cooperative relationships between companies and local communities, and the formulation of effective commercial strategies based on user emotions.

[0172] "Spatial data" refers to datasets that include geographical information, providing details about the location of farmland and its surrounding environment.

[0173] "Agricultural land" refers to land used for agricultural purposes, specifically areas where cultivation and harvesting take place.

[0174] "Cultivated plants" refer to plants grown on farmland, specifically varieties selected according to local demand.

[0175] "Emotional information" refers to information that indicates a user's emotional state and reactions, and is the data that is subject to analysis.

[0176] A "commercial activity plan" is a plan for developing sales and promotional strategies for agricultural products.

[0177] A "virtual exhibition space" is a space virtually created through a digital system, serving as a platform for promoting agricultural products and other goods.

[0178] To implement this invention, a server, a user's terminal, and a cloud-based service must work together. First, the server utilizes a geographic information system (GIS) to analyze spatial data. This makes it possible to identify neglected farmland and suggest plants to cultivate based on the needs of the local community.

[0179] Next, the user's device collects emotional information from the user through an application installed on their smartphone or tablet. Using the device's camera and microphone, the system analyzes the user's emotions from their facial expressions and tone of voice. Machine learning libraries such as TENSORFLOW® are used to analyze the user's emotions in real time. The collected emotional information is sent to a server and incorporated into a process to optimize the planning of commercial activities.

[0180] The server processes data using cloud services such as Amazon Web Services (AWS®) and suggests suitable plants for cultivation. It then provides users with promotions and suggestions using generative AI models through a virtual exhibition space. Visually appealing content can be created using 3D modeling software such as Unity.

[0181] As a concrete example, when a user shows interest in a crop in a virtual exhibition space, the server provides relevant content based on analyzed sentiment information. For instance, if a user shows a positive reaction to a particular vegetable, the server will then present recipes and nutritional information using that vegetable.

[0182] An example of a prompt might be, "Please tell me how to develop an optimal farmland use plan and present relevant content based on user sentiment data."

[0183] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0184] Step 1:

[0185] The server uses GIS to process spatial data and identify abandoned farmland. Geographic information and map data are provided as input, and the output is the location information and characteristic data of the farmland. In this identification process, software equipped with algorithms within the geographic information system is executed to generate coordinate data for the abandoned farmland.

[0186] Step 2:

[0187] The server suggests suitable plants for specific farmland based on local community needs. Input includes local demand data and farmland characteristics data, and output is a list of recommended plants. This includes querying market research databases and selecting the most suitable plants considering local consumption trends.

[0188] Step 3:

[0189] The user's device collects emotional data. The input consists of video and audio data from the device's camera and microphone, and the output is the analyzed emotional information sent to the server. This process utilizes facial recognition AI models and voice analysis algorithms (e.g., TensorFlow) to perform real-time emotional analysis.

[0190] Step 4:

[0191] The server optimizes commercial activity plans based on the received emotional information. Inputs include emotional data and local community demand data, and the output generates the most effective market strategy for the target user. This optimization uses an AI-powered data analysis platform to recognize patterns and adjust promotional strategies.

[0192] Step 5:

[0193] The server presents users with content generated using AI models through a virtual exhibition space. Optimized market strategy and visual content data are used as input, and visually appealing information is displayed to the user as output. 3D content creation software such as Unity is used here, enabling attractive exhibitions.

[0194] 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.

[0195] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0196] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0197] [Second Embodiment]

[0198] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0199] 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.

[0200] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0201] 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.

[0202] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0203] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0204] 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.

[0205] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0206] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0207] The 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.

[0208] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0209] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0210] To implement this invention, it is necessary to build a system in which a server, a terminal, and a user work together to realize the function. In this system, the server first accesses a geographic information database to identify and analyze abandoned farmland. Specifically, the server collects satellite images and weather data and uses a generative model to detect candidates for abandoned farmland.

[0211] Next, the system collects information on local community demand and available resources from businesses via terminals, and performs matching that suits the conditions of both parties. Based on this information, the server proposes appropriate crops to cultivate, plans agricultural activities, and facilitates efficient collaboration. In addition, the server simulates the amount of CO2 absorbed by crops planned for farmland to contribute to the environment, and generates data that can be linked to the company's environmental targets.

[0212] Based on the information obtained through this system, users proceed with their farmland implementation plans and carry out specific agricultural activities. Furthermore, the server makes harvest forecasts for surplus crops, uses terminals to formulate market sales strategies, and evaluates the feasibility of selling to the government or relevant organizations.

[0213] As a concrete example, if a crop is identified as being planned for cultivation in a particular area, the server analyzes the soil and climate conditions of the abandoned farmland and generates a list of optimal crops. The terminal then notifies local specialty product vendors of this information, and the user coordinates the entire process from cultivation to sales. This makes it possible to revitalize the local economy through the effective use of abandoned farmland and contribute to achieving the environmental goals of companies.

[0214] The following describes the processing flow.

[0215] Step 1:

[0216] The server collects satellite imagery, weather data, and land use data from geographic information databases. This provides the basic data needed to easily identify areas that could become abandoned farmland.

[0217] Step 2:

[0218] The server inputs the collected geographic information data into a generative model to identify abandoned farmland. The model extracts land that is likely to be unused and records its location.

[0219] Step 3:

[0220] The terminal displays map data of abandoned farmland obtained from the server to the user. The user then uses this information to conduct on-site surveys and determine which farmlands are usable.

[0221] Step 4:

[0222] The server analyzes local specialties and community demand information to generate suitable crop candidates. This makes it easier to plan agricultural activities tailored to the region.

[0223] Step 5:

[0224] The terminal compares crop candidates generated by the server with databases of local restaurants and businesses, matching the needs of both parties. Based on this information, the user explores specific collaborations with businesses.

[0225] Step 6:

[0226] The server predicts CO2 absorption by crops and generates data that aligns with the company's environmental goals. Companies can use this data to help plan sustainable activities.

[0227] Step 7:

[0228] The server calculates the amount of surplus crop based on harvest forecast data and notifies the user via a terminal. The user uses this information to formulate a sales strategy for the surplus crop. The goal is to maximize its use, including considering sales to the government and related organizations.

[0229] (Example 1)

[0230] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0231] In modern society, a significant problem is that much farmland is left unmanaged and neglected. While such neglected land should be utilized as a valuable local economic resource, efficient utilization methods have not yet been established. Furthermore, businesses need to effectively manage CO2 absorption through agricultural activities to achieve environmental targets. A system is needed to address these challenges and achieve both the effective use of neglected farmland and environmental contribution.

[0232] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0233] This invention includes a server that collects geographic information from multiple sources and detects abandoned farmland, a device that performs climate and soil analysis on the identified farmland and presents suitable crops using a generating AI model, and a device that integrates demand information collected from local communities and businesses through terminals and performs matching. This makes it possible to support the efficient utilization of abandoned farmland and the achievement of environmental goals by businesses.

[0234] "Information source" refers to the function or system that provides the data, and includes infrastructure that provides geographic information and weather data.

[0235] "Geographic information" refers to data related to a specific region or location, including location information, topography, and land use information.

[0236] "Abandoned farmland" refers to land that is not properly managed and is not being used for agricultural production.

[0237] A "generative AI model" refers to a framework or algorithm used to analyze and generate data using artificial intelligence, and represents a technology used for analysis and prediction.

[0238] "Climate conditions" refer to environmental factors that affect agriculture in a specific region, such as temperature, precipitation, and humidity.

[0239] Soil analysis is a method for investigating the physical and chemical properties of land, and it is a process for obtaining information that is useful for optimizing agricultural production.

[0240] "Matching" refers to the process of effectively connecting elements with different needs and conditions, aiming to integrate supply and demand.

[0241] "CO2 absorption" is an indicator that shows how much carbon dioxide a particular activity removes from the atmosphere, and it serves as an evaluation criterion for reducing environmental impact.

[0242] To implement this invention, the entire system must have a structure in which servers, terminals, and users cooperate. The following describes each component and its role.

[0243] The server is the core of the system, collecting and analyzing information. Specifically, it accesses geographic information databases, obtains satellite image data from Google Earth Engine, and collects weather data through the Japan Meteorological Agency API. Based on the collected information, the server uses a generative AI model to detect potential abandoned farmland. The generative AI model is implemented using, for example, a common AI framework, and prompts such as "Please perform an analysis to identify abandoned farmland in this area and suggest suitable crops" are used for analysis.

[0244] The terminals play a role in collecting information on demand and available resources from local communities and businesses. Smartphones and tablets are used as terminals, and information is entered via a dedicated mobile application. This information is sent to a server and used in the matching process.

[0245] Users perform specific agricultural activities based on information provided via their devices. They prepare, cultivate, and manage farmland according to crop cultivation plans proposed by the server. They also develop market strategies based on predictive information from the server (e.g., tomato harvest forecasts).

[0246] As a concrete example, if the server analyzes the climate and soil of a specific area and determines that tomatoes are suitable, it will notify local residents via their terminals. An example of a prompt message in this case would be, "Generate a list of crops best suited for producing local specialty products and propose a tomato cultivation plan to local residents."

[0247] This system enables the efficient use of abandoned farmland, revitalizes the local economy, and supports companies in achieving their environmental goals.

[0248] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0249] Step 1:

[0250] The server connects to a geographic information database and retrieves satellite image data and weather data. The input includes a request for geographic information for a specified region. The server retrieves satellite imagery from Google Earth Engine and weather data from the Japan Meteorological Agency API, and formats these into datasets for analysis. As output, the server generates datasets of geographic and weather information necessary for analysis.

[0251] Step 2:

[0252] The server sends a prompt to the generating AI model to detect abandoned farmland. The specific inputs include the dataset generated in step 1 and the prompt, "Identify and analyze abandoned farmland." The server inputs these into the generating AI model, which identifies candidate areas of abandoned farmland. As output, the server generates information about the location and characteristics of the identified farmland.

[0253] Step 3:

[0254] The terminal collects demand information and available resource information from local communities and businesses. The input here is the demand and resource information entered by local residents and businesses into the application. Specifically, the user enters the information using a smartphone or tablet app and presses the send button, transmitting the data from the terminal to the server. As output, the server receives a dataset of integrated demand and resource information.

[0255] Step 4:

[0256] The server uses the information received from the terminal to suggest crops to cultivate using a generative AI model. The input includes the demand and supply information obtained in step 3, and the prompt message "Please suggest the optimal crops to cultivate." The server analyzes the information using the generative AI model and generates a list of suitable crops. As output, the server outputs a list of recommended crops and sends it to the terminal.

[0257] Step 5:

[0258] Based on suggestions from the server, the user begins preparing the farmland and starting cultivation activities. Specific inputs include a list of crops to cultivate and a regional farming schedule provided by the server. The user uses this information to arrange necessary resources and plan cultivation activities. The output is the prepared farmland and the planned planting schedule for the crops.

[0259] Step 6:

[0260] The server predicts the harvest season and develops a market strategy. The input consists of a cultivation schedule and a harvest prediction prompt message generated by an AI model: "Predict the harvest yield and formulate a sales strategy." As output, the server generates and sends to the terminal a plan for the optimal sales strategy in the market, along with the harvest yield prediction data.

[0261] (Application Example 1)

[0262] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0263] To effectively utilize neglected farmland and achieve sustainable agricultural production, it is necessary to accurately analyze geographical information and effectively match the needs of local communities and businesses. Furthermore, automation using autonomous machinery is required to simultaneously achieve increased efficiency in agricultural activities and reduced environmental impact.

[0264] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0265] This invention includes a server that processes geographic information obtained from multiple sources to identify neglected farmland, proposes appropriate crops for the identified farmland based on local community needs, and matches the requirements of businesses and local communities to plan agricultural activities. This makes it possible to monitor and coordinate the production process of farmland using autonomous machinery and support sustainable agricultural activities.

[0266] "Means for processing geographic information" refers to technologies that analyze geographically related data obtained from multiple sources to understand the characteristics of a particular land or region.

[0267] "Abandoned farmland" refers to agricultural land that is not properly managed or utilized, and is land where the potential for agricultural production lies dormant.

[0268] "Means for proposing crops to cultivate" refers to a system for selecting the most suitable seeds and crops based on the needs of the local community and climatic conditions.

[0269] "Methods for matching the needs of businesses and local communities" refers to an approach that maximizes the benefits for both parties by comparing a company's resource provision capabilities with the local agricultural demand.

[0270] An "autonomous machine" refers to a robot or device that can complete a task automatically without requiring external intervention.

[0271] An "automated work profile tailored to the characteristics of farmland" is a plan that automatically generates guidelines for machines to perform optimal tasks based on the conditions of each individual plot of land.

[0272] "Report data" refers to information that summarizes the results of activities, visualizes progress and achievements, and is presented in an easy-to-understand document.

[0273] This invention is implemented in a system in which a server, terminal, and user work together.

[0274] The server plays a crucial role in processing geographic information obtained from multiple sources. Specifically, the server collects and analyzes satellite imagery and weather data, and uses generative AI models to identify neglected farmland. The server also handles the process of suggesting appropriate crops to cultivate, taking into account demand data gathered from local communities and the resource availability of businesses. For this purpose, software technologies such as Python and machine learning algorithms are utilized.

[0275] Users receive cultivation plans and sales strategy proposals from the system using a terminal. These terminals include tablets and smartphones. Furthermore, users participate in agricultural production activities using autonomous machinery, receiving and executing work profiles tailored to the characteristics of their land from the system.

[0276] For example, if abandoned farmland is found in a specific area of ​​a region, the server will recommend corn as the most suitable crop for that land. Based on this, the user can download the suggested profile to an autonomous tractor and automate tasks such as sowing and fertilizing, enabling efficient agricultural activities.

[0277] As an example of a prompt sentence, if a scenario is set as "Based on the soil conditions and climate data of this farmland, identify the crops that can grow and propose an optimal cultivation plan.", the generative AI can generate specific cultivation proposals and profiles based on this request.

[0278] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0279] Step 1:

[0280] The server collects geospatial information data from multiple information sources. The input is satellite images and weather data, and these are used to access the database and convert them into an analyzable format. The output is the analyzed data that can be used for subsequent processing. Python scripts and data cleansing techniques are used for this conversion process.

[0281] Step 2:

[0282] The server uses the generative AI model to identify abandoned farmland from the collected geospatial information. The input is the analyzed data obtained in Step 1, and by performing data processing with the AI model, the locations of unused farmland are identified. The output is a list of location information of the identified abandoned farmland.

[0283] Step 3:

[0284] The server proposes appropriate cultivated crops for the identified abandoned farmland based on the demand information of the region. The input is the location information of the abandoned farmland and the demand data from the local community. Conditional evaluation is performed with the generative AI model to select appropriate crops. The output is a list of recommended cultivated crops.

[0285] Step 4:

[0286] The terminal receives the crop recommendation information from the server and notifies the user. The input is the crop list from the server, and it is visually presented to the user through the notification application on the terminal. The output is a crop information message recognizable by the user.

[0287] Step 5:

[0288] The user uses the terminal to generate an automatic operation profile based on the recommended cultivated crops and send it to the autonomous machine. The input is the cultivated crop information displayed on the terminal, and the dedicated application creates the operation profile. The output is the operation instruction data received by the autonomous machine.

[0289] Step 6:

[0290] The user operates the autonomous machine to monitor the production activities on the farmland and make adjustments as needed. The input is the operation instruction data received by the machine, which is reflected in the actual agricultural operations. The output is the status report of the farmland where the work has been completed.

[0291] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0292] To implement this invention, an emotion engine is incorporated into the system to enable feedback and service provision based on the user's emotions. First, the server collects and analyzes geographical information data and performs a process of identifying abandoned farmland. Next, in order to propose cultivated crops according to the needs of the local community, the user's emotion data is obtained through the terminal.

[0293] Based on this emotion data, the server optimizes the plan for agricultural activities. The emotion engine analyzes the user's feedback and reactions and detects what emotions the user has towards the proposed cultivated crops and business plans. Based on this information, the server adjusts the matching algorithm and modifies the plan so that the needs of the enterprise and the local community are more in line.

[0294] Furthermore, the analysis results from the emotion engine are provided to companies via the terminal. This allows companies to improve their communication strategies and strengthen their relationships with local communities. For example, if a proposed crop in a certain area elicits a positive response from residents, it can serve as justification for promoting a cultivation plan for that crop.

[0295] As a concrete example, when a local community receives a proposal to cultivate a certain crop on abandoned farmland, the system senses the user's reaction via a terminal and analyzes that data on a server. Based on these results, the server, taking into account the output of the emotion engine, selects the proposal that is most supported by community members and concretizes a collaboration plan with a company. This process increases the likelihood of success for agricultural projects and provides effective solutions to local communities.

[0296] The following describes the processing flow.

[0297] Step 1:

[0298] The server retrieves the necessary data from the geographic information database and uses a generative model to identify unused farmland. This initiates the analysis to understand the status of abandoned farmland.

[0299] Step 2:

[0300] The terminal displays information about abandoned farmland provided by the server to the user. The user uses this information to prepare for on-site surveys and provide feedback.

[0301] Step 3:

[0302] Users provide feedback on the proposed farmland use plan. The device detects the user's emotions from their facial expressions and voice, and collects emotional data.

[0303] Step 4:

[0304] The server analyzes the collected emotional data and adjusts the agricultural plan according to the user's emotions. For example, crop proposals with a high number of positive reactions are prioritized.

[0305] Step 5:

[0306] The server uses the analysis results by the emotion engine to optimize the cultivated crops corresponding to the needs of the local community. Also, the adjusted plan is submitted to the company as feedback.

[0307] Step 6:

[0308] The terminal supports deepening the communication between the company and the local community by providing this feedback to the company's staff.

[0309] Step 7:

[0310] Based on the information from the server and the terminal, the user proceeds with the preparation for the start of agricultural activities through the cooperation of the local area and the company. Strengthen the cooperation through communication and promote sustainable agricultural use.

[0311] (Example 2)

[0312] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0313] The present invention aims to promote the use of fallow land and activate the local area by efficiently matching the needs of the local community and the company through the collection and analysis of geographical information and the utilization of the user's emotional data, and creating an optimal plan for agricultural activities. Also, the purpose is to strengthen the cooperation between the company and the local community through the improvement of the communication strategy that reflects the opinions of the residents.

[0314] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following respective means.

[0315] This invention includes a server that processes geographic information obtained from multiple sources to identify fallow land, an emotion engine that obtains emotional data from users via a terminal and analyzes that data to optimize agricultural activity plans based on the user's emotions, and a means for providing the analysis results to companies to improve communication strategies. This makes it possible to plan agricultural projects that accurately reflect the needs of local communities.

[0316] "Information sources" refer to the foundational elements that provide the data a system needs, such as geographical information and user data.

[0317] "Geographic information" is a general term for data that indicates geographical conditions, such as the location and condition of abandoned land.

[0318] "Fallow land" refers to farmland that is currently not in use or has been left unattended.

[0319] A "device" is an electronic device used by a user to input emotional data, and includes, for example, smartphones and tablets.

[0320] "Emotional data" refers to emotional information expressed through text and ratings that users provide to the system.

[0321] An "emotion engine" is a software system that has algorithms for analyzing emotional data and classifying user emotions.

[0322] "Matching" refers to the process of creating an optimal agricultural plan by combining the needs and requirements of businesses and local communities.

[0323] "Communication strategy" refers to the plans and methodologies that companies use to build relationships with local communities and effectively communicate information.

[0324] In a mode for carrying out the invention, this system provides a means for planning optimal agricultural activities through the collection of geographic information data and the analysis of user sentiment data. Details are provided below.

[0325] The server uses Geographic Information System (GIS) software to collect and analyze local geographic data. By utilizing remote sensing technology and satellite imagery analysis, it can automatically identify neglected farmland. Image analysis algorithms visualize the data on a map, making it easily accessible to administrators.

[0326] The device plays a role in acquiring emotional data from users. Specifically, a mobile application is used, and users input feedback on suggestions and questions related to cultivation. Emotional data is collected in various formats, including text comments, emojis, and rating scores.

[0327] The server uses an emotion engine to analyze the collected emotion data. It employs natural language processing (NLP) techniques to analyze user text data and classify emotions as positive, negative, or neutral. Specifically, by using a natural language processing engine, a type of generative AI model, the emotion data can be numerically evaluated.

[0328] Furthermore, the server dynamically adjusts the matching algorithm between businesses and local communities based on the results of sentiment analysis. By prioritizing agricultural products supported by local residents and optimizing cultivation plans, it can benefit both residents and businesses.

[0329] For example, if the server detects positive reactions from residents to a proposed lavender cultivation plan in a certain area, it can use this result to provide the company with suggested countermeasures to help them decide whether to proceed with the cultivation plan. An example of a prompt message a user might access is, "What kind of emotional reactions are residents having to the proposed crop cultivation plan?" In response to this prompt, the system can provide detailed analysis results.

[0330] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0331] Step 1:

[0332] The server uses Geographic Information System (GIS) software to collect satellite imagery and remote sensing data. Based on this input data, it applies image analysis algorithms to identify fallow land. Specifically, it analyzes the pixel data of the images to identify vegetation and land-use patterns. The output of this analysis is the geographic location information of the identified fallow land.

[0333] Step 2:

[0334] The device collects sentiment data from users through a mobile application. Users input their opinions and feedback on cultivation suggestions. This input includes text comments, rating scores, and emojis. The device sends this information to a server. Specifically, it processes user input in the form of a form or questionnaire. The output is obtained as collected sentiment data.

[0335] Step 3:

[0336] The server analyzes the received sentiment data using an emotion engine. This analysis uses a generative AI model to perform linguistic analysis of user feedback and process the data to quantify emotional tendencies. Specifically, it utilizes natural language processing techniques to classify the sentiment in the text data into one of three categories: "positive," "negative," or "neutral." The output of this step is the analyzed sentiment score.

[0337] Step 4:

[0338] The server adjusts the matching algorithm based on the results of sentiment data analysis. Specifically, it receives input that re-evaluates the priorities of agricultural products that companies propose to local communities and selects the crops with the highest sentiment scores. The algorithm uses this data to dynamically update the plan and optimize the proposals. The output is a list of selected crops and the optimized plan.

[0339] Step 5:

[0340] The server provides companies with optimized cultivation plans and sentiment score analysis results via terminals. Based on this information, companies develop and improve their communication strategies with local communities. Specifically, they create presentation materials for their proposals and use them for promotions to facilitate dialogue with the local community. The output is provided as feedback reports and improvement suggestions for the companies.

[0341] (Application Example 2)

[0342] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0343] In modern society, the continued neglect of farmland in local communities leads to the waste of agricultural resources and hinders the revitalization of local communities. Furthermore, aligning the needs of businesses and local communities is not easy, and building appropriate cooperative relationships is a challenge. In addition, there is a need to realize effective commercial strategies by utilizing user sentiment data in agricultural planning.

[0344] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0345] This invention includes a server that processes spatial data obtained from multiple sources to identify neglected farmland, proposes appropriate plants for cultivation based on the needs of the local community for the identified farmland, and analyzes people's emotional information to optimize commercial activity plans based on their reactions to the proposed plants and plans. This enables the optimization of farmland utilization, the establishment of smooth cooperative relationships between companies and local communities, and the formulation of effective commercial strategies based on user emotions.

[0346] "Spatial data" refers to datasets that include geographical information, providing details about the location of farmland and its surrounding environment.

[0347] "Agricultural land" refers to land used for agricultural purposes, specifically areas where cultivation and harvesting take place.

[0348] "Cultivated plants" refer to plants grown on farmland, specifically varieties selected according to local demand.

[0349] "Emotional information" refers to information that indicates a user's emotional state and reactions, and is the data that is subject to analysis.

[0350] A "commercial activity plan" is a plan for developing sales and promotional strategies for agricultural products.

[0351] A "virtual exhibition space" is a space virtually created through a digital system, serving as a platform for promoting agricultural products and other goods.

[0352] To implement this invention, a server, a user's terminal, and a cloud-based service must work together. First, the server utilizes a geographic information system (GIS) to analyze spatial data. This makes it possible to identify neglected farmland and suggest plants to cultivate based on the needs of the local community.

[0353] Next, the user's device collects emotional information from the user through an application installed on their smartphone or tablet. Using the device's camera and microphone, the system analyzes the user's emotions from their facial expressions and tone of voice. Machine learning libraries such as TensorFlow are used to analyze the user's emotions in real time. The collected emotional information is sent to a server and incorporated into a process to optimize the planning of commercial activities.

[0354] The server processes data using cloud services such as Amazon Web Services (AWS) and suggests suitable plants for cultivation. It then provides users with promotions and suggestions using generative AI models through a virtual exhibition space. Visually appealing content can be created using 3D modeling software such as Unity.

[0355] As a concrete example, when a user shows interest in a crop in a virtual exhibition space, the server provides relevant content based on analyzed sentiment information. For instance, if a user shows a positive reaction to a particular vegetable, the server will then present recipes and nutritional information using that vegetable.

[0356] An example of a prompt might be, "Please tell me how to develop an optimal farmland use plan and present relevant content based on user sentiment data."

[0357] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0358] Step 1:

[0359] The server uses GIS to process spatial data and identify abandoned farmland. Geographic information and map data are provided as input, and the output is the location information and characteristic data of the farmland. In this identification process, software equipped with algorithms within the geographic information system is executed to generate coordinate data for the abandoned farmland.

[0360] Step 2:

[0361] The server suggests suitable plants for specific farmland based on local community needs. Input includes local demand data and farmland characteristics data, and output is a list of recommended plants. This includes querying market research databases and selecting the most suitable plants considering local consumption trends.

[0362] Step 3:

[0363] The user's device collects emotional data. The input consists of video and audio data from the device's camera and microphone, and the output is the analyzed emotional information sent to the server. This process utilizes facial recognition AI models and voice analysis algorithms (e.g., TensorFlow) to perform real-time emotional analysis.

[0364] Step 4:

[0365] The server optimizes commercial activity plans based on the received emotional information. Inputs include emotional data and local community demand data, and the output generates the most effective market strategy for the target user. This optimization uses an AI-powered data analysis platform to recognize patterns and adjust promotional strategies.

[0366] Step 5:

[0367] The server presents users with content generated using AI models through a virtual exhibition space. Optimized market strategy and visual content data are used as input, and visually appealing information is displayed to the user as output. 3D content creation software such as Unity is used here, enabling attractive exhibitions.

[0368] 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.

[0369] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0370] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0371] [Third Embodiment]

[0372] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0373] 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.

[0374] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0375] 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.

[0376] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0377] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0378] 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.

[0379] 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.

[0380] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0381] The 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.

[0382] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0383] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0384] To implement this invention, it is necessary to build a system in which a server, a terminal, and a user work together to realize the function. In this system, the server first accesses a geographic information database to identify and analyze abandoned farmland. Specifically, the server collects satellite images and weather data and uses a generative model to detect candidates for abandoned farmland.

[0385] Next, the system collects information on local community demand and available resources from businesses via terminals, and performs matching that suits the conditions of both parties. Based on this information, the server proposes appropriate crops to cultivate, plans agricultural activities, and facilitates efficient collaboration. In addition, the server simulates the amount of CO2 absorbed by crops planned for farmland to contribute to the environment, and generates data that can be linked to the company's environmental targets.

[0386] Based on the information obtained through this system, users proceed with their farmland implementation plans and carry out specific agricultural activities. Furthermore, the server makes harvest forecasts for surplus crops, uses terminals to formulate market sales strategies, and evaluates the feasibility of selling to the government or relevant organizations.

[0387] As a concrete example, if a crop is identified as being planned for cultivation in a particular area, the server analyzes the soil and climate conditions of the abandoned farmland and generates a list of optimal crops. The terminal then notifies local specialty product vendors of this information, and the user coordinates the entire process from cultivation to sales. This makes it possible to revitalize the local economy through the effective use of abandoned farmland and contribute to achieving the environmental goals of companies.

[0388] The following describes the processing flow.

[0389] Step 1:

[0390] The server collects satellite imagery, weather data, and land use data from geographic information databases. This provides the basic data needed to easily identify areas that could become abandoned farmland.

[0391] Step 2:

[0392] The server inputs the collected geographic information data into a generative model to identify abandoned farmland. The model extracts land that is likely to be unused and records its location.

[0393] Step 3:

[0394] The terminal displays map data of abandoned farmland obtained from the server to the user. The user then uses this information to conduct on-site surveys and determine which farmlands are usable.

[0395] Step 4:

[0396] The server analyzes local specialties and community demand information to generate suitable crop candidates. This makes it easier to plan agricultural activities tailored to the region.

[0397] Step 5:

[0398] The terminal compares crop candidates generated by the server with databases of local restaurants and businesses, matching the needs of both parties. Based on this information, the user explores specific collaborations with businesses.

[0399] Step 6:

[0400] The server predicts CO2 absorption by crops and generates data that aligns with the company's environmental goals. Companies can use this data to help plan sustainable activities.

[0401] Step 7:

[0402] The server calculates the amount of surplus crop based on harvest forecast data and notifies the user via a terminal. The user uses this information to formulate a sales strategy for the surplus crop. The goal is to maximize its use, including considering sales to the government and related organizations.

[0403] (Example 1)

[0404] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0405] In modern society, a significant problem is that much farmland is left unmanaged and neglected. While such neglected land should be utilized as a valuable local economic resource, efficient utilization methods have not yet been established. Furthermore, businesses need to effectively manage CO2 absorption through agricultural activities to achieve environmental targets. A system is needed to address these challenges and achieve both the effective use of neglected farmland and environmental contribution.

[0406] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0407] This invention includes a server that collects geographic information from multiple sources and detects abandoned farmland, a device that performs climate and soil analysis on the identified farmland and presents suitable crops using a generating AI model, and a device that integrates demand information collected from local communities and businesses through terminals and performs matching. This makes it possible to support the efficient utilization of abandoned farmland and the achievement of environmental goals by businesses.

[0408] "Information source" refers to the function or system that provides the data, and includes infrastructure that provides geographic information and weather data.

[0409] "Geographic information" refers to data related to a specific region or location, including location information, topography, and land use information.

[0410] "Abandoned farmland" refers to land that is not properly managed and is not being used for agricultural production.

[0411] A "generative AI model" refers to a framework or algorithm used to analyze and generate data using artificial intelligence, and represents a technology used for analysis and prediction.

[0412] "Climate conditions" refer to environmental factors that affect agriculture in a specific region, such as temperature, precipitation, and humidity.

[0413] Soil analysis is a method for investigating the physical and chemical properties of land, and it is a process for obtaining information that is useful for optimizing agricultural production.

[0414] "Matching" refers to the process of effectively connecting elements with different needs and conditions, aiming to integrate supply and demand.

[0415] "CO2 absorption" is an indicator that shows how much carbon dioxide a particular activity removes from the atmosphere, and it serves as an evaluation criterion for reducing environmental impact.

[0416] To implement this invention, the entire system must have a structure in which servers, terminals, and users cooperate. The following describes each component and its role.

[0417] The server is the core of the system, collecting and analyzing information. Specifically, it accesses geographic information databases, obtains satellite image data from Google Earth Engine, and collects weather data through the Japan Meteorological Agency API. Based on the collected information, the server uses a generative AI model to detect potential abandoned farmland. The generative AI model is implemented using, for example, a common AI framework, and prompts such as "Please perform an analysis to identify abandoned farmland in this area and suggest suitable crops" are used for analysis.

[0418] The terminals play a role in collecting information on demand and available resources from local communities and businesses. Smartphones and tablets are used as terminals, and information is entered via a dedicated mobile application. This information is sent to a server and used in the matching process.

[0419] Users perform specific agricultural activities based on information provided via their devices. They prepare, cultivate, and manage farmland according to crop cultivation plans proposed by the server. They also develop market strategies based on predictive information from the server (e.g., tomato harvest forecasts).

[0420] As a concrete example, if the server analyzes the climate and soil of a specific area and determines that tomatoes are suitable, it will notify local residents via their terminals. An example of a prompt message in this case would be, "Generate a list of crops best suited for producing local specialty products and propose a tomato cultivation plan to local residents."

[0421] This system enables the efficient use of abandoned farmland, revitalizes the local economy, and supports companies in achieving their environmental goals.

[0422] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0423] Step 1:

[0424] The server connects to a geographic information database and retrieves satellite image data and weather data. The input includes a request for geographic information for a specified region. The server retrieves satellite imagery from Google Earth Engine and weather data from the Japan Meteorological Agency API, and formats these into datasets for analysis. As output, the server generates datasets of geographic and weather information necessary for analysis.

[0425] Step 2:

[0426] The server sends a prompt to the generating AI model to detect abandoned farmland. The specific inputs include the dataset generated in step 1 and the prompt, "Identify and analyze abandoned farmland." The server inputs these into the generating AI model, which identifies candidate areas of abandoned farmland. As output, the server generates information about the location and characteristics of the identified farmland.

[0427] Step 3:

[0428] The terminal collects demand information and available resource information from local communities and businesses. The input here is the demand and resource information entered by local residents and businesses into the application. Specifically, the user enters the information using a smartphone or tablet app and presses the send button, transmitting the data from the terminal to the server. As output, the server receives a dataset of integrated demand and resource information.

[0429] Step 4:

[0430] The server uses the information received from the terminal to suggest crops to cultivate using a generative AI model. The input includes the demand and supply information obtained in step 3, and the prompt message "Please suggest the optimal crops to cultivate." The server analyzes the information using the generative AI model and generates a list of suitable crops. As output, the server outputs a list of recommended crops and sends it to the terminal.

[0431] Step 5:

[0432] Based on suggestions from the server, the user begins preparing the farmland and starting cultivation activities. Specific inputs include a list of crops to cultivate and a regional farming schedule provided by the server. The user uses this information to arrange necessary resources and plan cultivation activities. The output is the prepared farmland and the planned planting schedule for the crops.

[0433] Step 6:

[0434] The server predicts the harvest season and develops a market strategy. The input consists of a cultivation schedule and a harvest prediction prompt message generated by an AI model: "Predict the harvest yield and formulate a sales strategy." As output, the server generates and sends to the terminal a plan for the optimal sales strategy in the market, along with the harvest yield prediction data.

[0435] (Application Example 1)

[0436] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0437] To effectively utilize neglected farmland and achieve sustainable agricultural production, it is necessary to accurately analyze geographical information and effectively match the needs of local communities and businesses. Furthermore, automation using autonomous machinery is required to simultaneously achieve increased efficiency in agricultural activities and reduced environmental impact.

[0438] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0439] This invention includes a server that processes geographic information obtained from multiple sources to identify neglected farmland, proposes appropriate crops for the identified farmland based on local community needs, and matches the requirements of businesses and local communities to plan agricultural activities. This makes it possible to monitor and coordinate the production process of farmland using autonomous machinery and support sustainable agricultural activities.

[0440] "Means for processing geographic information" refers to technologies that analyze geographically related data obtained from multiple sources to understand the characteristics of a particular land or region.

[0441] "Abandoned farmland" refers to agricultural land that is not properly managed or utilized, and is land where the potential for agricultural production lies dormant.

[0442] "Means for proposing crops to cultivate" refers to a system for selecting the most suitable seeds and crops based on the needs of the local community and climatic conditions.

[0443] "Methods for matching the needs of businesses and local communities" refers to an approach that maximizes the benefits for both parties by comparing a company's resource provision capabilities with the local agricultural demand.

[0444] An "autonomous machine" refers to a robot or device that can complete a task automatically without requiring external intervention.

[0445] An "automated work profile tailored to the characteristics of farmland" is a plan that automatically generates guidelines for machines to perform optimal tasks based on the conditions of each individual plot of land.

[0446] "Report data" refers to information that summarizes the results of activities, visualizes progress and achievements, and is presented in an easy-to-understand document.

[0447] This invention is implemented in a system in which a server, terminal, and user work together.

[0448] The server plays a crucial role in processing geographic information obtained from multiple sources. Specifically, the server collects and analyzes satellite imagery and weather data, and uses generative AI models to identify neglected farmland. The server also handles the process of suggesting appropriate crops to cultivate, taking into account demand data gathered from local communities and the resource availability of businesses. For this purpose, software technologies such as Python and machine learning algorithms are utilized.

[0449] Users receive cultivation plans and sales strategy proposals from the system using a terminal. These terminals include tablets and smartphones. Furthermore, users participate in agricultural production activities using autonomous machinery, receiving and executing work profiles tailored to the characteristics of their land from the system.

[0450] For example, if abandoned farmland is found in a specific area of ​​a region, the server will recommend corn as the most suitable crop for that land. Based on this, the user can download the suggested profile to an autonomous tractor and automate tasks such as sowing and fertilizing, enabling efficient agricultural activities.

[0451] As an example of a prompt, if you set a scenario such as, "Based on the soil conditions and climate data of this farmland, identify crops that can be grown and propose an optimal cultivation plan," the generated AI can then generate specific cultivation suggestions and profiles based on that request.

[0452] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0453] Step 1:

[0454] The server collects geographic information data from multiple sources. Inputs include satellite imagery and weather data, which are used to access databases and convert them into an analyzable format. Output is analyzed data ready for subsequent processing. This conversion process utilizes Python scripts and data cleansing techniques.

[0455] Step 2:

[0456] The server uses a generative AI model to identify abandoned farmland from collected geographic information. The input is the analyzed data obtained in step 1, and the AI ​​model processes the data to identify the locations of unused farmland. The output is a list of the locations of the identified abandoned farmland.

[0457] Step 3:

[0458] The server suggests appropriate crops for identified abandoned farmland based on local demand information. Inputs include the location of the abandoned farmland and demand data from the local community. A generative AI model evaluates the conditions and selects appropriate crops. The output is a list of recommended crops.

[0459] Step 4:

[0460] The terminal receives crop recommendation information from the server and notifies the user. The input is a list of crops from the server, which is visually presented to the user through a notification application on the terminal. The output is a crop information message that the user can recognize.

[0461] Step 5:

[0462] The user uses a terminal to generate an automated work profile based on the recommended crops to be grown and sends it to the autonomous machine. The input is the crop information displayed on the terminal, and a dedicated app creates the work profile. The output is the operation instruction data received by the autonomous machine.

[0463] Step 6:

[0464] The user operates the autonomous machine to monitor production activities on the farmland and make adjustments as needed. The input is the operation instruction data received by the machine, which is then reflected in the actual agricultural work. The output is a report of the farmland's condition after the work has been completed.

[0465] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0466] To implement this invention, an emotion engine is incorporated into the system to enable the provision of feedback and services based on the user's emotions. First, the server collects and analyzes geographic information data and performs a process to identify abandoned farmland. Next, it acquires the user's emotion data through the terminal in order to suggest crops to cultivate that meet the needs of the local community.

[0467] Based on this sentiment data, the server optimizes agricultural activity plans. The sentiment engine analyzes user feedback and reactions to detect how users feel about proposed crops and business plans. Based on this information, the server adjusts the matching algorithm and modifies the plans to better match the needs of businesses and local communities.

[0468] Furthermore, the analysis results from the emotion engine are provided to companies via the terminal. This allows companies to improve their communication strategies and strengthen their relationships with local communities. For example, if a proposed crop in a certain area elicits a positive response from residents, it can serve as justification for promoting a cultivation plan for that crop.

[0469] As a concrete example, when a local community receives a proposal to cultivate a certain crop on abandoned farmland, the system senses the user's reaction via a terminal and analyzes that data on a server. Based on these results, the server, taking into account the output of the emotion engine, selects the proposal that is most supported by community members and concretizes a collaboration plan with a company. This process increases the likelihood of success for agricultural projects and provides effective solutions to local communities.

[0470] The following describes the processing flow.

[0471] Step 1:

[0472] The server retrieves the necessary data from the geographic information database and uses a generative model to identify unused farmland. This initiates the analysis to understand the status of abandoned farmland.

[0473] Step 2:

[0474] The terminal displays information about abandoned farmland provided by the server to the user. The user uses this information to prepare for on-site surveys and provide feedback.

[0475] Step 3:

[0476] Users provide feedback on the proposed farmland use plan. The device detects the user's emotions from their facial expressions and voice, and collects emotional data.

[0477] Step 4:

[0478] The server analyzes collected sentiment data and adjusts farming plans according to the user's emotions. For example, it prioritizes crop proposals that receive many positive responses.

[0479] Step 5:

[0480] The server uses analysis results from its emotion engine to optimize crop cultivation to meet the needs of the local community. It also submits the adjusted plan as feedback to the company.

[0481] Step 6:

[0482] The device helps deepen communication between companies and local communities by providing this feedback to company representatives.

[0483] Step 7:

[0484] Based on information from servers and terminals, users will prepare to launch agricultural activities in cooperation with local communities and businesses. This will strengthen communication and promote sustainable agricultural use.

[0485] (Example 2)

[0486] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0487] This invention aims to promote the use of fallow land and revitalize local communities by efficiently matching the needs of local communities and businesses through the collection and analysis of geographic information and the utilization of user sentiment data, thereby creating optimal agricultural activity plans. Furthermore, it aims to strengthen collaboration between businesses and local communities through improved communication strategies that reflect the opinions of residents.

[0488] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0489] This invention includes a server that processes geographic information obtained from multiple sources to identify fallow land, an emotion engine that obtains emotional data from users via a terminal and analyzes that data to optimize agricultural activity plans based on the user's emotions, and a means for providing the analysis results to companies to improve communication strategies. This makes it possible to plan agricultural projects that accurately reflect the needs of local communities.

[0490] "Information sources" refer to the foundational elements that provide the data a system needs, such as geographical information and user data.

[0491] "Geographic information" is a general term for data that indicates geographical conditions, such as the location and condition of abandoned land.

[0492] "Fallow land" refers to farmland that is currently not in use or has been left unattended.

[0493] A "device" is an electronic device used by a user to input emotional data, and includes, for example, smartphones and tablets.

[0494] "Emotional data" refers to emotional information expressed through text and ratings that users provide to the system.

[0495] An "emotion engine" is a software system that has algorithms for analyzing emotional data and classifying user emotions.

[0496] "Matching" refers to the process of creating an optimal agricultural plan by combining the needs and requirements of businesses and local communities.

[0497] "Communication strategy" refers to the plans and methodologies that companies use to build relationships with local communities and effectively communicate information.

[0498] In a mode for carrying out the invention, this system provides a means for planning optimal agricultural activities through the collection of geographic information data and the analysis of user sentiment data. Details are provided below.

[0499] The server uses Geographic Information System (GIS) software to collect and analyze local geographic data. By utilizing remote sensing technology and satellite imagery analysis, it can automatically identify neglected farmland. Image analysis algorithms visualize the data on a map, making it easily accessible to administrators.

[0500] The device plays a role in acquiring emotional data from users. Specifically, a mobile application is used, and users input feedback on suggestions and questions related to cultivation. Emotional data is collected in various formats, including text comments, emojis, and rating scores.

[0501] The server uses an emotion engine to analyze the collected emotion data. It employs natural language processing (NLP) techniques to analyze user text data and classify emotions as positive, negative, or neutral. Specifically, by using a natural language processing engine, a type of generative AI model, the emotion data can be numerically evaluated.

[0502] Furthermore, the server dynamically adjusts the matching algorithm between businesses and local communities based on the results of sentiment analysis. By prioritizing agricultural products supported by local residents and optimizing cultivation plans, it can benefit both residents and businesses.

[0503] For example, if the server detects positive reactions from residents to a proposed lavender cultivation plan in a certain area, it can use this result to provide the company with suggested countermeasures to help them decide whether to proceed with the cultivation plan. An example of a prompt message a user might access is, "What kind of emotional reactions are residents having to the proposed crop cultivation plan?" In response to this prompt, the system can provide detailed analysis results.

[0504] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0505] Step 1:

[0506] The server uses Geographic Information System (GIS) software to collect satellite imagery and remote sensing data. Based on this input data, it applies image analysis algorithms to identify fallow land. Specifically, it analyzes the pixel data of the images to identify vegetation and land-use patterns. The output of this analysis is the geographic location information of the identified fallow land.

[0507] Step 2:

[0508] The device collects sentiment data from users through a mobile application. Users input their opinions and feedback on cultivation suggestions. This input includes text comments, rating scores, and emojis. The device sends this information to a server. Specifically, it processes user input in the form of a form or questionnaire. The output is obtained as collected sentiment data.

[0509] Step 3:

[0510] The server analyzes the received sentiment data using an emotion engine. This analysis uses a generative AI model to perform linguistic analysis of user feedback and process the data to quantify emotional tendencies. Specifically, it utilizes natural language processing techniques to classify the sentiment in the text data into one of three categories: "positive," "negative," or "neutral." The output of this step is the analyzed sentiment score.

[0511] Step 4:

[0512] The server adjusts the matching algorithm based on the results of sentiment data analysis. Specifically, it receives input that re-evaluates the priorities of agricultural products that companies propose to local communities and selects the crops with the highest sentiment scores. The algorithm uses this data to dynamically update the plan and optimize the proposals. The output is a list of selected crops and the optimized plan.

[0513] Step 5:

[0514] The server provides companies with optimized cultivation plans and sentiment score analysis results via terminals. Based on this information, companies develop and improve their communication strategies with local communities. Specifically, they create presentation materials for their proposals and use them for promotions to facilitate dialogue with the local community. The output is provided as feedback reports and improvement suggestions for the companies.

[0515] (Application Example 2)

[0516] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0517] In modern society, the continued neglect of farmland in local communities leads to the waste of agricultural resources and hinders the revitalization of local communities. Furthermore, aligning the needs of businesses and local communities is not easy, and building appropriate cooperative relationships is a challenge. In addition, there is a need to realize effective commercial strategies by utilizing user sentiment data in agricultural planning.

[0518] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0519] This invention includes a server that processes spatial data obtained from multiple sources to identify neglected farmland, proposes appropriate plants for cultivation based on the needs of the local community for the identified farmland, and analyzes people's emotional information to optimize commercial activity plans based on their reactions to the proposed plants and plans. This enables the optimization of farmland utilization, the establishment of smooth cooperative relationships between companies and local communities, and the formulation of effective commercial strategies based on user emotions.

[0520] "Spatial data" refers to datasets that include geographical information, providing details about the location of farmland and its surrounding environment.

[0521] "Agricultural land" refers to land used for agricultural purposes, specifically areas where cultivation and harvesting take place.

[0522] "Cultivated plants" refer to plants grown on farmland, specifically varieties selected according to local demand.

[0523] "Emotional information" refers to information that indicates a user's emotional state and reactions, and is the data that is subject to analysis.

[0524] A "commercial activity plan" is a plan for developing sales and promotional strategies for agricultural products.

[0525] A "virtual exhibition space" is a space virtually created through a digital system, serving as a platform for promoting agricultural products and other goods.

[0526] To implement this invention, a server, a user's terminal, and a cloud-based service must work together. First, the server utilizes a geographic information system (GIS) to analyze spatial data. This makes it possible to identify neglected farmland and suggest plants to cultivate based on the needs of the local community.

[0527] Next, the user's device collects emotional information from the user through an application installed on their smartphone or tablet. Using the device's camera and microphone, the system analyzes the user's emotions from their facial expressions and tone of voice. Machine learning libraries such as TensorFlow are used to analyze the user's emotions in real time. The collected emotional information is sent to a server and incorporated into a process to optimize the planning of commercial activities.

[0528] The server processes data using cloud services such as Amazon Web Services (AWS) and suggests suitable plants for cultivation. It then provides users with promotions and suggestions using generative AI models through a virtual exhibition space. Visually appealing content can be created using 3D modeling software such as Unity.

[0529] As a concrete example, when a user shows interest in a crop in a virtual exhibition space, the server provides relevant content based on analyzed sentiment information. For instance, if a user shows a positive reaction to a particular vegetable, the server will then present recipes and nutritional information using that vegetable.

[0530] An example of a prompt might be, "Please tell me how to develop an optimal farmland use plan and present relevant content based on user sentiment data."

[0531] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0532] Step 1:

[0533] The server uses GIS to process spatial data and identify abandoned farmland. Geographic information and map data are provided as input, and the output is the location information and characteristic data of the farmland. In this identification process, software equipped with algorithms within the geographic information system is executed to generate coordinate data for the abandoned farmland.

[0534] Step 2:

[0535] The server suggests suitable plants for specific farmland based on local community needs. Input includes local demand data and farmland characteristics data, and output is a list of recommended plants. This includes querying market research databases and selecting the most suitable plants considering local consumption trends.

[0536] Step 3:

[0537] The user's device collects emotional data. The input consists of video and audio data from the device's camera and microphone, and the output is the analyzed emotional information sent to the server. This process utilizes facial recognition AI models and voice analysis algorithms (e.g., TensorFlow) to perform real-time emotional analysis.

[0538] Step 4:

[0539] The server optimizes commercial activity plans based on the received emotional information. Inputs include emotional data and local community demand data, and the output generates the most effective market strategy for the target user. This optimization uses an AI-powered data analysis platform to recognize patterns and adjust promotional strategies.

[0540] Step 5:

[0541] The server presents users with content generated using AI models through a virtual exhibition space. Optimized market strategy and visual content data are used as input, and visually appealing information is displayed to the user as output. 3D content creation software such as Unity is used here, enabling attractive exhibitions.

[0542] 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.

[0543] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0544] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0545] [Fourth Embodiment]

[0546] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0547] 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.

[0548] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0549] 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.

[0550] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0551] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0552] 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.

[0553] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0554] 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.

[0555] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0556] The 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.

[0557] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0558] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0559] To implement this invention, it is necessary to build a system in which a server, a terminal, and a user work together to realize the function. In this system, the server first accesses a geographic information database to identify and analyze abandoned farmland. Specifically, the server collects satellite images and weather data and uses a generative model to detect candidates for abandoned farmland.

[0560] Next, the system collects information on local community demand and available resources from businesses via terminals, and performs matching that suits the conditions of both parties. Based on this information, the server proposes appropriate crops to cultivate, plans agricultural activities, and facilitates efficient collaboration. In addition, the server simulates the amount of CO2 absorbed by crops planned for farmland to contribute to the environment, and generates data that can be linked to the company's environmental targets.

[0561] Based on the information obtained through this system, users proceed with their farmland implementation plans and carry out specific agricultural activities. Furthermore, the server makes harvest forecasts for surplus crops, uses terminals to formulate market sales strategies, and evaluates the feasibility of selling to the government or relevant organizations.

[0562] As a concrete example, if a crop is identified as being planned for cultivation in a particular area, the server analyzes the soil and climate conditions of the abandoned farmland and generates a list of optimal crops. The terminal then notifies local specialty product vendors of this information, and the user coordinates the entire process from cultivation to sales. This makes it possible to revitalize the local economy through the effective use of abandoned farmland and contribute to achieving the environmental goals of companies.

[0563] The following describes the processing flow.

[0564] Step 1:

[0565] The server collects satellite imagery, weather data, and land use data from geographic information databases. This provides the basic data needed to easily identify areas that could become abandoned farmland.

[0566] Step 2:

[0567] The server inputs the collected geographic information data into a generative model to identify abandoned farmland. The model extracts land that is likely to be unused and records its location.

[0568] Step 3:

[0569] The terminal displays map data of abandoned farmland obtained from the server to the user. The user then uses this information to conduct on-site surveys and determine which farmlands are usable.

[0570] Step 4:

[0571] The server analyzes local specialties and community demand information to generate suitable crop candidates. This makes it easier to plan agricultural activities tailored to the region.

[0572] Step 5:

[0573] The terminal compares crop candidates generated by the server with databases of local restaurants and businesses, matching the needs of both parties. Based on this information, the user explores specific collaborations with businesses.

[0574] Step 6:

[0575] The server predicts CO2 absorption by crops and generates data that aligns with the company's environmental goals. Companies can use this data to help plan sustainable activities.

[0576] Step 7:

[0577] The server calculates the amount of surplus crop based on harvest forecast data and notifies the user via a terminal. The user uses this information to formulate a sales strategy for the surplus crop. The goal is to maximize its use, including considering sales to the government and related organizations.

[0578] (Example 1)

[0579] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0580] In modern society, a significant problem is that much farmland is left unmanaged and neglected. While such neglected land should be utilized as a valuable local economic resource, efficient utilization methods have not yet been established. Furthermore, businesses need to effectively manage CO2 absorption through agricultural activities to achieve environmental targets. A system is needed to address these challenges and achieve both the effective use of neglected farmland and environmental contribution.

[0581] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0582] This invention includes a server that collects geographic information from multiple sources and detects abandoned farmland, a device that performs climate and soil analysis on the identified farmland and presents suitable crops using a generating AI model, and a device that integrates demand information collected from local communities and businesses through terminals and performs matching. This makes it possible to support the efficient utilization of abandoned farmland and the achievement of environmental goals by businesses.

[0583] "Information source" refers to the function or system that provides the data, and includes infrastructure that provides geographic information and weather data.

[0584] "Geographic information" refers to data related to a specific region or location, including location information, topography, and land use information.

[0585] "Abandoned farmland" refers to land that is not properly managed and is not being used for agricultural production.

[0586] A "generative AI model" refers to a framework or algorithm used to analyze and generate data using artificial intelligence, and represents a technology used for analysis and prediction.

[0587] "Climate conditions" refer to environmental factors that affect agriculture in a specific region, such as temperature, precipitation, and humidity.

[0588] Soil analysis is a method for investigating the physical and chemical properties of land, and it is a process for obtaining information that is useful for optimizing agricultural production.

[0589] "Matching" refers to the process of effectively connecting elements with different needs and conditions, aiming to integrate supply and demand.

[0590] "CO2 absorption" is an indicator that shows how much carbon dioxide a particular activity removes from the atmosphere, and it serves as an evaluation criterion for reducing environmental impact.

[0591] To implement this invention, the entire system must have a structure in which servers, terminals, and users cooperate. The following describes each component and its role.

[0592] The server is the core of the system, collecting and analyzing information. Specifically, it accesses geographic information databases, obtains satellite image data from Google Earth Engine, and collects weather data through the Japan Meteorological Agency API. Based on the collected information, the server uses a generative AI model to detect potential abandoned farmland. The generative AI model is implemented using, for example, a common AI framework, and prompts such as "Please perform an analysis to identify abandoned farmland in this area and suggest suitable crops" are used for analysis.

[0593] The terminals play a role in collecting information on demand and available resources from local communities and businesses. Smartphones and tablets are used as terminals, and information is entered via a dedicated mobile application. This information is sent to a server and used in the matching process.

[0594] Users perform specific agricultural activities based on information provided via their devices. They prepare, cultivate, and manage farmland according to crop cultivation plans proposed by the server. They also develop market strategies based on predictive information from the server (e.g., tomato harvest forecasts).

[0595] As a concrete example, if the server analyzes the climate and soil of a specific area and determines that tomatoes are suitable, it will notify local residents via their terminals. An example of a prompt message in this case would be, "Generate a list of crops best suited for producing local specialty products and propose a tomato cultivation plan to local residents."

[0596] This system enables the efficient use of abandoned farmland, revitalizes the local economy, and supports companies in achieving their environmental goals.

[0597] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0598] Step 1:

[0599] The server connects to a geographic information database and retrieves satellite image data and weather data. The input includes a request for geographic information for a specified region. The server retrieves satellite imagery from Google Earth Engine and weather data from the Japan Meteorological Agency API, and formats these into datasets for analysis. As output, the server generates datasets of geographic and weather information necessary for analysis.

[0600] Step 2:

[0601] The server sends a prompt to the generating AI model to detect abandoned farmland. The specific inputs include the dataset generated in step 1 and the prompt, "Identify and analyze abandoned farmland." The server inputs these into the generating AI model, which identifies candidate areas of abandoned farmland. As output, the server generates information about the location and characteristics of the identified farmland.

[0602] Step 3:

[0603] The terminal collects demand information and available resource information from local communities and businesses. The input here is the demand and resource information entered by local residents and businesses into the application. Specifically, the user enters the information using a smartphone or tablet app and presses the send button, transmitting the data from the terminal to the server. As output, the server receives a dataset of integrated demand and resource information.

[0604] Step 4:

[0605] The server uses the information received from the terminal to suggest crops to cultivate using a generative AI model. The input includes the demand and supply information obtained in step 3, and the prompt message "Please suggest the optimal crops to cultivate." The server analyzes the information using the generative AI model and generates a list of suitable crops. As output, the server outputs a list of recommended crops and sends it to the terminal.

[0606] Step 5:

[0607] Based on suggestions from the server, the user begins preparing the farmland and starting cultivation activities. Specific inputs include a list of crops to cultivate and a regional farming schedule provided by the server. The user uses this information to arrange necessary resources and plan cultivation activities. The output is the prepared farmland and the planned planting schedule for the crops.

[0608] Step 6:

[0609] The server predicts the harvest season and develops a market strategy. The input consists of a cultivation schedule and a harvest prediction prompt message generated by an AI model: "Predict the harvest yield and formulate a sales strategy." As output, the server generates and sends to the terminal a plan for the optimal sales strategy in the market, along with the harvest yield prediction data.

[0610] (Application Example 1)

[0611] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0612] To effectively utilize neglected farmland and achieve sustainable agricultural production, it is necessary to accurately analyze geographical information and effectively match the needs of local communities and businesses. Furthermore, automation using autonomous machinery is required to simultaneously achieve increased efficiency in agricultural activities and reduced environmental impact.

[0613] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0614] This invention includes a server that processes geographic information obtained from multiple sources to identify neglected farmland, proposes appropriate crops for the identified farmland based on local community needs, and matches the requirements of businesses and local communities to plan agricultural activities. This makes it possible to monitor and coordinate the production process of farmland using autonomous machinery and support sustainable agricultural activities.

[0615] "Means for processing geographic information" refers to technologies that analyze geographically related data obtained from multiple sources to understand the characteristics of a particular land or region.

[0616] "Abandoned farmland" refers to agricultural land that is not properly managed or utilized, and is land where the potential for agricultural production lies dormant.

[0617] "Means for proposing crops to cultivate" refers to a system for selecting the most suitable seeds and crops based on the needs of the local community and climatic conditions.

[0618] "Methods for matching the needs of businesses and local communities" refers to an approach that maximizes the benefits for both parties by comparing a company's resource provision capabilities with the local agricultural demand.

[0619] An "autonomous machine" refers to a robot or device that can complete a task automatically without requiring external intervention.

[0620] An "automated work profile tailored to the characteristics of farmland" is a plan that automatically generates guidelines for machines to perform optimal tasks based on the conditions of each individual plot of land.

[0621] "Report data" refers to information that summarizes the results of activities, visualizes progress and achievements, and is presented in an easy-to-understand document.

[0622] This invention is implemented in a system in which a server, terminal, and user work together.

[0623] The server plays a crucial role in processing geographic information obtained from multiple sources. Specifically, the server collects and analyzes satellite imagery and weather data, and uses generative AI models to identify neglected farmland. The server also handles the process of suggesting appropriate crops to cultivate, taking into account demand data gathered from local communities and the resource availability of businesses. For this purpose, software technologies such as Python and machine learning algorithms are utilized.

[0624] Users receive cultivation plans and sales strategy proposals from the system using a terminal. These terminals include tablets and smartphones. Furthermore, users participate in agricultural production activities using autonomous machinery, receiving and executing work profiles tailored to the characteristics of their land from the system.

[0625] For example, if abandoned farmland is found in a specific area of ​​a region, the server will recommend corn as the most suitable crop for that land. Based on this, the user can download the suggested profile to an autonomous tractor and automate tasks such as sowing and fertilizing, enabling efficient agricultural activities.

[0626] As an example of a prompt, if you set a scenario such as, "Based on the soil conditions and climate data of this farmland, identify crops that can be grown and propose an optimal cultivation plan," the generated AI can then generate specific cultivation suggestions and profiles based on that request.

[0627] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0628] Step 1:

[0629] The server collects geographic information data from multiple sources. Inputs include satellite imagery and weather data, which are used to access databases and convert them into an analyzable format. Output is analyzed data ready for subsequent processing. This conversion process utilizes Python scripts and data cleansing techniques.

[0630] Step 2:

[0631] The server uses a generative AI model to identify abandoned farmland from collected geographic information. The input is the analyzed data obtained in step 1, and the AI ​​model processes the data to identify the locations of unused farmland. The output is a list of the locations of the identified abandoned farmland.

[0632] Step 3:

[0633] The server suggests appropriate crops for identified abandoned farmland based on local demand information. Inputs include the location of the abandoned farmland and demand data from the local community. A generative AI model evaluates the conditions and selects appropriate crops. The output is a list of recommended crops.

[0634] Step 4:

[0635] The terminal receives crop recommendation information from the server and notifies the user. The input is a list of crops from the server, which is visually presented to the user through a notification application on the terminal. The output is a crop information message that the user can recognize.

[0636] Step 5:

[0637] The user uses a terminal to generate an automated work profile based on the recommended crops to be grown and sends it to the autonomous machine. The input is the crop information displayed on the terminal, and a dedicated app creates the work profile. The output is the operation instruction data received by the autonomous machine.

[0638] Step 6:

[0639] The user operates the autonomous machine to monitor production activities on the farmland and make adjustments as needed. The input is the operation instruction data received by the machine, which is then reflected in the actual agricultural work. The output is a report of the farmland's condition after the work has been completed.

[0640] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0641] To implement this invention, an emotion engine is incorporated into the system to enable the provision of feedback and services based on the user's emotions. First, the server collects and analyzes geographic information data and performs a process to identify abandoned farmland. Next, it acquires the user's emotion data through the terminal in order to suggest crops to cultivate that meet the needs of the local community.

[0642] Based on this sentiment data, the server optimizes agricultural activity plans. The sentiment engine analyzes user feedback and reactions to detect how users feel about proposed crops and business plans. Based on this information, the server adjusts the matching algorithm and modifies the plans to better match the needs of businesses and local communities.

[0643] Furthermore, the analysis results from the emotion engine are provided to companies via the terminal. This allows companies to improve their communication strategies and strengthen their relationships with local communities. For example, if a proposed crop in a certain area elicits a positive response from residents, it can serve as justification for promoting a cultivation plan for that crop.

[0644] As a concrete example, when a local community receives a proposal to cultivate a certain crop on abandoned farmland, the system senses the user's reaction via a terminal and analyzes that data on a server. Based on these results, the server, taking into account the output of the emotion engine, selects the proposal that is most supported by community members and concretizes a collaboration plan with a company. This process increases the likelihood of success for agricultural projects and provides effective solutions to local communities.

[0645] The following describes the processing flow.

[0646] Step 1:

[0647] The server retrieves the necessary data from the geographic information database and uses a generative model to identify unused farmland. This initiates the analysis to understand the status of abandoned farmland.

[0648] Step 2:

[0649] The terminal displays information about abandoned farmland provided by the server to the user. The user uses this information to prepare for on-site surveys and provide feedback.

[0650] Step 3:

[0651] Users provide feedback on the proposed farmland use plan. The device detects the user's emotions from their facial expressions and voice, and collects emotional data.

[0652] Step 4:

[0653] The server analyzes collected sentiment data and adjusts farming plans according to the user's emotions. For example, it prioritizes crop proposals that receive many positive responses.

[0654] Step 5:

[0655] The server uses analysis results from its emotion engine to optimize crop cultivation to meet the needs of the local community. It also submits the adjusted plan as feedback to the company.

[0656] Step 6:

[0657] The device helps deepen communication between companies and local communities by providing this feedback to company representatives.

[0658] Step 7:

[0659] Based on information from servers and terminals, users will prepare to launch agricultural activities in cooperation with local communities and businesses. This will strengthen communication and promote sustainable agricultural use.

[0660] (Example 2)

[0661] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0662] This invention aims to promote the use of fallow land and revitalize local communities by efficiently matching the needs of local communities and businesses through the collection and analysis of geographic information and the utilization of user sentiment data, thereby creating optimal agricultural activity plans. Furthermore, it aims to strengthen collaboration between businesses and local communities through improved communication strategies that reflect the opinions of residents.

[0663] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0664] This invention includes a server that processes geographic information obtained from multiple sources to identify fallow land, an emotion engine that obtains emotional data from users via a terminal and analyzes that data to optimize agricultural activity plans based on the user's emotions, and a means for providing the analysis results to companies to improve communication strategies. This makes it possible to plan agricultural projects that accurately reflect the needs of local communities.

[0665] "Information sources" refer to the foundational elements that provide the data a system needs, such as geographical information and user data.

[0666] "Geographic information" is a general term for data that indicates geographical conditions, such as the location and condition of abandoned land.

[0667] "Fallow land" refers to farmland that is currently not in use or has been left unattended.

[0668] A "device" is an electronic device used by a user to input emotional data, and includes, for example, smartphones and tablets.

[0669] "Emotional data" refers to emotional information expressed through text and ratings that users provide to the system.

[0670] An "emotion engine" is a software system that has algorithms for analyzing emotional data and classifying user emotions.

[0671] "Matching" refers to the process of creating an optimal agricultural plan by combining the needs and requirements of businesses and local communities.

[0672] "Communication strategy" refers to the plans and methodologies that companies use to build relationships with local communities and effectively communicate information.

[0673] In a mode for carrying out the invention, this system provides a means for planning optimal agricultural activities through the collection of geographic information data and the analysis of user sentiment data. Details are provided below.

[0674] The server uses Geographic Information System (GIS) software to collect and analyze local geographic data. By utilizing remote sensing technology and satellite imagery analysis, it can automatically identify neglected farmland. Image analysis algorithms visualize the data on a map, making it easily accessible to administrators.

[0675] The device plays a role in acquiring emotional data from users. Specifically, a mobile application is used, and users input feedback on suggestions and questions related to cultivation. Emotional data is collected in various formats, including text comments, emojis, and rating scores.

[0676] The server uses an emotion engine to analyze the collected emotion data. It employs natural language processing (NLP) techniques to analyze user text data and classify emotions as positive, negative, or neutral. Specifically, by using a natural language processing engine, a type of generative AI model, the emotion data can be numerically evaluated.

[0677] Furthermore, the server dynamically adjusts the matching algorithm between businesses and local communities based on the results of sentiment analysis. By prioritizing agricultural products supported by local residents and optimizing cultivation plans, it can benefit both residents and businesses.

[0678] For example, if the server detects positive reactions from residents to a proposed lavender cultivation plan in a certain area, it can use this result to provide the company with suggested countermeasures to help them decide whether to proceed with the cultivation plan. An example of a prompt message a user might access is, "What kind of emotional reactions are residents having to the proposed crop cultivation plan?" In response to this prompt, the system can provide detailed analysis results.

[0679] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0680] Step 1:

[0681] The server uses Geographic Information System (GIS) software to collect satellite imagery and remote sensing data. Based on this input data, it applies image analysis algorithms to identify fallow land. Specifically, it analyzes the pixel data of the images to identify vegetation and land-use patterns. The output of this analysis is the geographic location information of the identified fallow land.

[0682] Step 2:

[0683] The device collects sentiment data from users through a mobile application. Users input their opinions and feedback on cultivation suggestions. This input includes text comments, rating scores, and emojis. The device sends this information to a server. Specifically, it processes user input in the form of a form or questionnaire. The output is obtained as collected sentiment data.

[0684] Step 3:

[0685] The server analyzes the received sentiment data using an emotion engine. This analysis uses a generative AI model to perform linguistic analysis of user feedback and process the data to quantify emotional tendencies. Specifically, it utilizes natural language processing techniques to classify the sentiment in the text data into one of three categories: "positive," "negative," or "neutral." The output of this step is the analyzed sentiment score.

[0686] Step 4:

[0687] The server adjusts the matching algorithm based on the results of sentiment data analysis. Specifically, it receives input that re-evaluates the priorities of agricultural products that companies propose to local communities and selects the crops with the highest sentiment scores. The algorithm uses this data to dynamically update the plan and optimize the proposals. The output is a list of selected crops and the optimized plan.

[0688] Step 5:

[0689] The server provides companies with optimized cultivation plans and sentiment score analysis results via terminals. Based on this information, companies develop and improve their communication strategies with local communities. Specifically, they create presentation materials for their proposals and use them for promotions to facilitate dialogue with the local community. The output is provided as feedback reports and improvement suggestions for the companies.

[0690] (Application Example 2)

[0691] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0692] In modern society, the continued neglect of farmland in local communities leads to the waste of agricultural resources and hinders the revitalization of local communities. Furthermore, aligning the needs of businesses and local communities is not easy, and building appropriate cooperative relationships is a challenge. In addition, there is a need to realize effective commercial strategies by utilizing user sentiment data in agricultural planning.

[0693] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0694] This invention includes a server that processes spatial data obtained from multiple sources to identify neglected farmland, proposes appropriate plants for cultivation based on the needs of the local community for the identified farmland, and analyzes people's emotional information to optimize commercial activity plans based on their reactions to the proposed plants and plans. This enables the optimization of farmland utilization, the establishment of smooth cooperative relationships between companies and local communities, and the formulation of effective commercial strategies based on user emotions.

[0695] "Spatial data" refers to datasets that include geographical information, providing details about the location of farmland and its surrounding environment.

[0696] "Agricultural land" refers to land used for agricultural purposes, specifically areas where cultivation and harvesting take place.

[0697] "Cultivated plants" refer to plants grown on farmland, specifically varieties selected according to local demand.

[0698] "Emotional information" refers to information that indicates a user's emotional state and reactions, and is the data that is subject to analysis.

[0699] A "commercial activity plan" is a plan for developing sales and promotional strategies for agricultural products.

[0700] A "virtual exhibition space" is a space virtually created through a digital system, serving as a platform for promoting agricultural products and other goods.

[0701] To implement this invention, a server, a user's terminal, and a cloud-based service must work together. First, the server utilizes a geographic information system (GIS) to analyze spatial data. This makes it possible to identify neglected farmland and suggest plants to cultivate based on the needs of the local community.

[0702] Next, the user's device collects emotional information from the user through an application installed on their smartphone or tablet. Using the device's camera and microphone, the system analyzes the user's emotions from their facial expressions and tone of voice. Machine learning libraries such as TensorFlow are used to analyze the user's emotions in real time. The collected emotional information is sent to a server and incorporated into a process to optimize the planning of commercial activities.

[0703] The server processes data using cloud services such as Amazon Web Services (AWS) and suggests suitable plants for cultivation. It then provides users with promotions and suggestions using generative AI models through a virtual exhibition space. Visually appealing content can be created using 3D modeling software such as Unity.

[0704] As a concrete example, when a user shows interest in a crop in a virtual exhibition space, the server provides relevant content based on analyzed sentiment information. For instance, if a user shows a positive reaction to a particular vegetable, the server will then present recipes and nutritional information using that vegetable.

[0705] An example of a prompt might be, "Please tell me how to develop an optimal farmland use plan and present relevant content based on user sentiment data."

[0706] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0707] Step 1:

[0708] The server uses GIS to process spatial data and identify abandoned farmland. Geographic information and map data are provided as input, and the output is the location information and characteristic data of the farmland. In this identification process, software equipped with algorithms within the geographic information system is executed to generate coordinate data for the abandoned farmland.

[0709] Step 2:

[0710] The server suggests suitable plants for specific farmland based on local community needs. Input includes local demand data and farmland characteristics data, and output is a list of recommended plants. This includes querying market research databases and selecting the most suitable plants considering local consumption trends.

[0711] Step 3:

[0712] The user's device collects emotional data. The input consists of video and audio data from the device's camera and microphone, and the output is the analyzed emotional information sent to the server. This process utilizes facial recognition AI models and voice analysis algorithms (e.g., TensorFlow) to perform real-time emotional analysis.

[0713] Step 4:

[0714] The server optimizes commercial activity plans based on the received emotional information. Inputs include emotional data and local community demand data, and the output generates the most effective market strategy for the target user. This optimization uses an AI-powered data analysis platform to recognize patterns and adjust promotional strategies.

[0715] Step 5:

[0716] The server presents users with content generated using AI models through a virtual exhibition space. Optimized market strategy and visual content data are used as input, and visually appealing information is displayed to the user as output. 3D content creation software such as Unity is used here, enabling attractive exhibitions.

[0717] 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.

[0718] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0719] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0720] 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.

[0721] Figure 9 shows an 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.

[0722] 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.

[0723] 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.

[0724] 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, motorcycles, etc., 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, for example, based 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.

[0725] 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."

[0726] 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.

[0727] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0728] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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 the like 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.

[0737] 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.

[0738] The following is further disclosed regarding the embodiments described above.

[0739] (Claim 1)

[0740] A means of processing geographic information obtained from multiple sources to identify abandoned farmland,

[0741] A means of proposing appropriate crops to cultivate for identified farmland based on the needs of the local community,

[0742] A means of matching the needs of businesses and local communities, and formulating plans for agricultural activities,

[0743] A means of generating environmental contribution data and linking it to a company's CO2 absorption target,

[0744] Means for creating and providing to the government or relevant agencies a sales strategy for the surplus crops generated,

[0745] A system that includes this.

[0746] (Claim 2)

[0747] The system according to claim 1, which uses a generative model for processing geographic information.

[0748] (Claim 3)

[0749] The system according to claim 1, which automatically evaluates a company's environmental targets and the amount of CO2 absorbed by crop cultivation on farmland.

[0750] "Example 1"

[0751] (Claim 1)

[0752] A device for collecting geographic information obtained from multiple sources and detecting abandoned farmland,

[0753] A device that analyzes the climate conditions and soil of identified farmland and uses an AI model to suggest suitable crops for cultivation,

[0754] A device that integrates demand information collected from local communities and businesses via terminals using a server and performs matching,

[0755] A device that simulates the amount of CO2 absorbed by cultivated crops and generates data to support companies in achieving their environmental goals,

[0756] The equipment provides harvest forecasts, develops market sales strategies based on the surplus crops produced, and offers them to customers.

[0757] A system that includes this.

[0758] (Claim 2)

[0759] The system according to claim 1, which analyzes displayed information using a generative AI model.

[0760] (Claim 3)

[0761] The system according to claim 1, which provides automated processing including detection of abandoned farmland and planning of cultivation.

[0762] "Application Example 1"

[0763] (Claim 1)

[0764] A means of processing geographic information obtained from multiple sources to identify abandoned farmland,

[0765] A means of proposing appropriate crops to cultivate for identified farmland based on the needs of the local community,

[0766] A means of matching the needs of businesses and local communities, and formulating plans for agricultural activities,

[0767] A means of generating environmental contribution data and linking it to a company's CO2 absorption target,

[0768] A means of creating and providing to relevant organizations a sales strategy for the surplus crops generated,

[0769] A means of monitoring and adjusting the production process of agricultural land using autonomous machinery,

[0770] A means of generating automated work profiles tailored to the characteristics of farmland by utilizing digital data of abandoned farmland,

[0771] A means of communicating automatically generated reporting data to companies and promoting sustainable activities,

[0772] A system that includes this.

[0773] (Claim 2)

[0774] The system according to claim 1, which uses a generative model for processing geographic information.

[0775] (Claim 3)

[0776] The system according to claim 1, which automatically evaluates a company's environmental targets and the amount of CO2 absorbed by crop cultivation on farmland.

[0777] "Example 2 of combining an emotion engine"

[0778] (Claim 1)

[0779] A means of processing geographic information obtained from multiple sources to identify fallow land,

[0780] A means of proposing appropriate agricultural products to specific farmland based on the needs of the local community,

[0781] A means for optimizing agricultural activity planning based on the user's emotions, using an emotion engine that acquires emotional data from the user via a terminal and analyzes that data,

[0782] A means of matching the needs of businesses and local communities, and formulating plans for agricultural activities,

[0783] Providing companies with analysis results as a means to improve their communication strategies,

[0784] A system that includes this.

[0785] (Claim 2)

[0786] The system according to claim 1, which uses a generative model for processing geographic information and analyzing sentiment data.

[0787] (Claim 3)

[0788] The system according to claim 1, which evaluates a company's environmental objectives and the environmental impact of crop cultivation on farmland, and adjusts strategies based on the response of the local community.

[0789] "Application example 2 when combining with an emotional engine"

[0790] (Claim 1)

[0791] A means of processing spatial data obtained from multiple sources to identify abandoned farmland,

[0792] A means of proposing appropriate plants to cultivate for specific farmland based on the needs of the local community,

[0793] A means of analyzing people's emotional information and optimizing commercial activity plans based on their reactions to proposed cultivated plants and plans,

[0794] A means of creating a sales policy for the surplus crops generated and providing that information to the competent authorities,

[0795] A method for enhancing promotional effectiveness using emotional data in a virtual exhibition space,

[0796] A system that includes this.

[0797] (Claim 2)

[0798] The system according to claim 1, which analyzes user feedback using an emotion analysis engine.

[0799] (Claim 3)

[0800] The system according to claim 1, which integrates emotion recognition and spatial data in a commercial space to optimize the effectiveness of a proposal in real time. [Explanation of symbols]

[0801] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of processing geographic information obtained from multiple sources to identify abandoned farmland, A means of proposing appropriate crops to cultivate for identified farmland based on the needs of the local community, A means of matching the needs of businesses and local communities, and formulating plans for agricultural activities, A means of generating environmental contribution data and linking it to a company's CO2 absorption target, Means for creating and providing to the government or relevant agencies a sales strategy for the surplus crops generated, A system that includes this.

2. The system according to claim 1, which uses a generative model for processing geographic information.

3. The system according to claim 1, which automatically evaluates a company's environmental targets and the amount of CO2 absorbed by crop cultivation on farmland.

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