System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting market analysis data using generative artificial intelligence and predicting demand and supply of a crop; means for collecting climate analysis data using generative artificial intelligence and identifying an optimal cultivation area; means for presenting a recommended crop and cultivation area to a user based on the collected market analysis data and climate analysis data; means for receiving an investment decision by the user, selecting farmland, and generating an investment project; and means for harvesting and shipping a crop that has reached a harvest time to a market and distributing an obtained profit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] This invention relates to a system for effectively utilizing abandoned farmland and resolving the shortage of agricultural workers. In particular, as the number of abandoned farmlands increases due to depopulation and labor shortages and the number of agricultural workers continues to decline, there is a need for technology to solve these problems and realize efficient and sustainable agriculture. The current agricultural support system has difficulty responding flexibly to market and climate fluctuations, and there are high hurdles for individual investors to enter the agricultural industry. Furthermore, there is a lack of appropriate know-how and technical support for individuals who wish to experience farming. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing the following means: A means for collecting market analysis data using generative AI and forecasting crop supply and demand; A means for collecting climate analysis data using generative AI and identifying optimal cultivation areas; A means for presenting users with recommended crops and cultivation areas based on the collected market analysis data and climate analysis data; A means for accepting users' investment decisions, selecting farmland, and creating investment projects; A means for harvesting and shipping crops to market when they reach harvest age, and A means for distributing the resulting profits to users. The present invention also includes a means for using the collected data to monitor and manage farmland, and a means for providing users with efficient agricultural techniques and know-how via an online platform. The present invention also includes a means for monitoring farmland conditions in real time using generative AI and generating alerts when abnormalities are detected, and a means for notifying users of the progress of investment projects and profit distributions via a user interface. This enables the effective utilization of abandoned farmland, lowers the barriers to entry for individual investors, and promotes efficient and sustainable agriculture.

[0006] "Generative AI" is a type of AI technology that analyzes massive amounts of data, learns patterns, and automatically makes predictions and suggestions.

[0007] "Market analysis data" refers to data that includes market information such as agricultural product prices, consumption volumes, and import / export volumes, and is used to analyze market trends.

[0008] "Climate analysis data" refers to data that includes meteorological information such as temperature, precipitation, and sunshine hours, and is used to analyze climate conditions.

[0009] "Investment Project" means a project that includes the selection of farmland and cultivation plan generated based on the User's investment decision.

[0010] "Revenue sharing" refers to the distribution of profits from the harvest and sale of cultivated crops to the users who invested.

[0011] "Monitoring" refers to the activity of observing the condition of farmland in real time and detecting abnormalities.

[0012] "Efficient agricultural technology" refers to agricultural methods and know-how that make optimal use of resources and maintain high productivity.

[0013] "Know-how" is a collection of specialized knowledge and technical knowledge related to a particular task or technology.

[0014] An "alert" is a warning message that is sent when an abnormality is detected based on monitored data.

[0015] A "user interface" is a component that includes a screen and input devices that allow a user to interact with a system. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention relates to an agricultural support system that uses generative artificial intelligence to efficiently perform market and climate analysis, identify optimal crops and regions for cultivation, and provide them to users. It also provides an investment platform that makes it easier for individual investors to enter the agricultural industry, and supports the entire process from cultivation, harvesting, sales, and profit distribution.

[0038] Market and climate analysis

[0039] server

[0040] First, generative AI is used to collect market analysis data and climate analysis data. Market analysis data is obtained by scraping data such as crop prices, consumption, and import / export volumes from online market information sites and published statistical reports. Climate analysis data is obtained by collecting weather information such as temperature, precipitation, and sunshine hours through meteorological agencies and APIs. The server analyzes the data collected in this way to forecast crop demand and identify optimal cultivation areas.

[0041] Specific examples

[0042] For example, the server may detect that the market price of tomatoes is on the rise this year, and determine from regional weather data that a certain region is suitable for growing tomatoes. Based on this information, the server may recommend growing tomatoes to the user.

[0043] Investment project creation and management

[0044] User

[0045] The user uses the terminal to review the provided information and select the crops and farmland to invest in. Once the user's investment decision is sent to the server, the server generates an investment project and formulates a management plan for the selected farmland.

[0046] Specific examples

[0047] If a user decides to invest in a tomato cultivation project in a certain region, the server creates a specific cultivation plan based on that information and manages it as a project.

[0048] Farmland monitoring and management

[0049] server

[0050] The server collects real-time data from IoT devices and sensors to monitor the condition of the farmland, collecting data such as soil moisture, temperature, and nutrient levels, and generates an alert to notify the user if an abnormality is detected.

[0051] Specific examples

[0052] For example, if soil moisture falls below an optimum level, the server will automatically activate the irrigation system to provide the appropriate amount of water.

[0053] Providing efficient agricultural technology and know-how

[0054] server

[0055] Based on collected farmland data and market analysis data, the company provides efficient agricultural techniques and know-how via an online platform, allowing users to learn about the latest agricultural techniques.

[0056] User

[0057] Users can apply the provided know-how to their actual work and manage their farmland, and can also respond immediately based on monitoring results and alert information.

[0058] Specific examples

[0059] For example, you can learn the optimal planting spacing and fertilization schedule for tomatoes on the platform and then put it into practice in the field.

[0060] Harvesting, selling, and profit sharing

[0061] server

[0062] When the crops reach the harvesting period, the system plans and executes the harvesting work, delivers the harvested crops to the market, and tally up the sales profits and distribute them to users.

[0063] Terminal

[0064] Users can view the profits distributed through their accounts.

[0065] Specific examples

[0066] For example, if the tomato harvest is complete and 200,000 yen in profits are earned from sales, a portion of that profit will be transferred to the user's account and the user will be notified that the profit distribution has been completed.

[0067] In this way, the present invention utilizes generative artificial intelligence to streamline various agricultural processes and provide an environment in which individual investors can easily enter the agricultural industry.

[0068] The processing flow will be explained below.

[0069] Step 1:

[0070] server:

[0071] Collect market analysis data by scraping agricultural product price information websites and published statistical reports on the Internet to obtain data on crop prices, consumption, import and export volumes, etc.

[0072] Climate analysis data such as temperature, precipitation, and sunshine hours are collected through meteorological agencies and APIs.

[0073] The collected market analysis data and climate analysis data are stored in a database and formatted into the format required for analysis.

[0074] Step 2:

[0075] server:

[0076] Generative artificial intelligence is used to analyze collected market and climate analysis data to forecast crop demand and identify optimal cultivation areas.

[0077] Based on the results of these analyses, information on recommended crops and their growing areas is generated and converted into a format for providing information to users.

[0078] Step 3:

[0079] server:

[0080] Send the analysis results to the application.

[0081] Device:

[0082] The analysis results received from the server are displayed to the user, including recommended crops, growing areas, and reasons for market demand.

[0083] User:

[0084] Based on the information provided, select the crops and farmland to invest in.

[0085] Step 4:

[0086] User:

[0087] Enter the investment amount on the application and confirm your investment decision.

[0088] Device:

[0089] The investment selection results are sent to the server.

[0090] Step 5:

[0091] server:

[0092] Based on the user's investment decision, an investment project is generated. Project information includes the selected farmland, crops, and investment amount.

[0093] Develop a management plan for the farm, setting out the necessary resources and work schedules.

[0094] Step 6:

[0095] server:

[0096] The generated investment project information is sent to the user, providing an overview of the project and its progress.

[0097] Device:

[0098] Display project information in your account and keep users updated on progress.

[0099] Step 7:

[0100] server:

[0101] Through IoT devices and sensors, real-time data on farmland is collected and conditions such as soil moisture, temperature, and nutrient levels are monitored.

[0102] If an abnormality is detected, an alert is immediately generated and the user is notified of the abnormality and the countermeasures to be taken.

[0103] Device:

[0104] An alert notification is displayed to the user, prompting them to take the necessary action.

[0105] Step 8:

[0106] server:

[0107] The online platform provides users with efficient agricultural techniques and know-how, and updates information as new techniques and methods are released.

[0108] User:

[0109] Participants learn agricultural know-how and techniques provided on the platform and apply them to their actual agricultural work.

[0110] Step 9:

[0111] server:

[0112] Plan and execute harvest operations when crops reach harvest stage.

[0113] Ship the harvested crops to market and collect sales data.

[0114] Step 10:

[0115] server:

[0116] Profits from sales are aggregated and distributed to users for each investment project.

[0117] Profit sharing information is notified to users.

[0118] Device:

[0119] Display the refunded profits in the user's account and notify them that the distribution is complete.

[0120] In this way, the present invention provides a system that enables efficient and sustainable agriculture, benefiting both investors and farmers.

[0121] Example 1

[0122] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0123] In the agricultural sector, efficient crop cultivation, market supply and demand forecasting, and identifying optimal cultivation areas based on climatic conditions are extremely important, but conventional methods require the time and effort required to collect and analyze vast amounts of information, which acts as a barrier to individual investors entering the agricultural industry. Furthermore, automating the process from harvesting to sales and profit distribution, and monitoring the condition of farmland in real time, are essential for further efficiency and quality improvement. Furthermore, there is a lack of systems for providing efficient agricultural techniques and know-how. There is a need to solve these problems and create an environment that makes it easier for many people, including individual investors, to enter the agricultural industry.

[0124] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0125] In this invention, the server includes: means for collecting market analysis information using generative artificial intelligence and forecasting crop supply and demand; means for collecting climate analysis information using generative artificial intelligence and identifying optimal cultivation areas; means for presenting users with recommended crops and cultivation areas based on the collected market and climate analysis information; means for accepting users' investment decisions, selecting farmland, and creating investment projects; and means for harvesting and shipping crops to market when the crops reach harvest time and distributing the resulting profits to users. This streamlines the collection and analysis of vast amounts of information, making it easier for individual investors to enter the agricultural industry. Furthermore, the process from harvesting to sales and profit distribution is automated, and real-time monitoring of farmland conditions enables more efficient and higher-quality agriculture. Furthermore, providing efficient agricultural techniques and know-how contributes to improving users' agricultural skills.

[0126] "Generative AI" refers to AI that can generate, analyze, and predict data using machine learning technology.

[0127] "Market analysis information" refers to various market-related data such as crop prices, consumption, and import / export volumes.

[0128] "Climate analysis information" refers to various data related to climate, such as temperature, precipitation, and sunshine hours.

[0129] "User" refers to an individual or organization that uses this system to cultivate crops or invest.

[0130] "Recommended crops" refer to crops that are deemed optimal based on collected data.

[0131] "Cultivation area" refers to the area where a particular crop can be optimally grown.

[0132] "Investment Project" refers to the cultivation plan or farmland management plan in which a user makes an investment.

[0133] "When the crop reaches the harvest stage" refers to the point in time when the crop reaches the maturity set as the cultivation goal.

[0134] "Marketing" refers to supplying harvested crops to the market.

[0135] "Profit sharing" refers to sharing the revenue generated from the sale of crops with users.

[0136] "Real-time monitoring" refers to the continuous monitoring of the condition of agricultural land in an almost immediate manner.

[0137] "Generating an alert" refers to sending a notification when an anomaly is detected.

[0138] "User interface" refers to the screen or input device that allows a user to access the system and check or enter information.

[0139] "Online platform" refers to a software environment for providing information and services to users via the Internet.

[0140] "Agricultural technology" refers to the means and methods for carrying out agriculture efficiently.

[0141] "Know-how" refers to practical knowledge and experience in agriculture.

[0142] This invention relates to an agricultural support system that uses generative artificial intelligence to enable users to efficiently conduct market and climate analysis and identify optimal crops and regions for cultivation. It also provides an investment platform that makes it easier for individual investors to enter the agricultural industry, and supports the entire process from cultivation, harvesting, sales, and profit distribution.

[0143] Hardware and software used

[0144] Hardware

[0145] IoT sensor devices (e.g. soil humidity sensors, temperature sensors)

[0146] Server (e.g. cloud server provider)

[0147] User's device (e.g. smartphone, PC)

[0148] software

[0149] Generative AI models (e.g., GPT-4)

[0150] Scraping tools (e.g. BeautifulSoup, Scrapy)

[0151] Weather information API (e.g., OpenWeatherMap, WeatherAPI)

[0152] System program processing

[0153] server

[0154] The server uses generative artificial intelligence to collect market analysis information. It uses scraping tools (e.g., BeautifulSoup, Scrapy) to obtain data such as crop prices, consumption, and import / export volumes from market information websites and published statistical reports.

[0155] The server also collects climate analysis information such as temperature, precipitation, and sunshine hours through meteorological agency APIs (e.g., OpenWeatherMap, WeatherAPI). Based on the collected data, it performs analysis using a generative artificial intelligence model (e.g., GPT-4). The server identifies the optimal crops and regions to cultivate and makes recommendations to the user.

[0156] User

[0157] The user uses a terminal to check the information provided by the server. Based on the data on recommended crops and cultivation areas, the user selects the crops and farmland to invest in. The selected information is sent to the server, which then generates an investment project and formulates a management plan for the selected farmland.

[0158] server

[0159] The server collects real-time data from the farmland through IoT sensor devices. It collects data such as soil moisture, temperature, and nutrient levels, and analyzes the condition using a generative AI model. If an abnormality is detected, an alert is generated to notify the user. For example, if soil moisture falls below an appropriate value, the server automatically activates the irrigation system to provide the appropriate amount of water.

[0160] server

[0161] Based on the collected farmland data and market analysis information, the server provides efficient agricultural techniques and know-how via an online platform, allowing users to learn about the latest agricultural techniques.

[0162] server

[0163] When the crops reach harvest time, the system plans and executes the harvesting work, and delivers the harvested crops to market. The system tallys up the sales profits and distributes them to users, who can check the distributed profits through their own devices.

[0164] Examples of concrete examples and prompts

[0165] For example, the server may detect that the market price of tomatoes is on the rise this year, and determine from regional weather data that a particular region is suitable for tomato cultivation. Based on this information, the server may recommend tomato cultivation to the user.

[0166] Prompt Sentence Examples

[0167] "Make a forecast based on market prices and climate data to identify the best tomato growing regions for 2023."

[0168] In this way, the system of the present invention makes full use of generative artificial intelligence to streamline various agricultural processes and provide an environment in which individual investors can easily enter the agricultural industry.

[0169] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0170] Step 1: Collect market analysis data

[0171] server

[0172] Market analysis data such as crop prices, consumption, and import / export volumes are collected from market information sites and published statistical reports. The server uses a scraping tool to collect the data and stores it in a database.

[0173] Input: Market information site URL

[0174] Data processing: HTML analysis of web pages, extraction of necessary data

[0175] Output: Market analysis data (price, consumption, import / export volume, etc.)

[0176] Specific actions

[0177] The server scrapes data from multiple market information sites on the internet and stores information on prices, consumption, imports and exports in a database.

[0178] Step 2: Collecting climate analysis data

[0179] server

[0180] Weather analysis data such as temperature, precipitation, and sunshine hours are collected through the weather information API. The server sends the API request and stores the required data in a database.

[0181] Input: Weather information API endpoint, location information

[0182] Data processing: Analyzing JSON responses from API and extracting necessary weather data

[0183] Output: Climate analysis data (temperature, precipitation, sunshine hours, etc.)

[0184] Specific actions

[0185] The server sends a request to the weather information API, analyzes the acquired weather data, and stores it in a database.

[0186] Step 3: Analyze the data

[0187] server

[0188] Collected market and climate analysis data is analyzed using a generative artificial intelligence model (e.g., GPT-4), which forecasts demand and identifies optimal cultivation areas.

[0189] Input: Market analysis data, climate analysis data

[0190] Data calculations: demand forecasting and cultivation area identification using generative artificial intelligence

[0191] Output: List of recommended crops and growing areas

[0192] Specific actions

[0193] The server provides prompts to a generative artificial intelligence model that uses the collected data to predict demand and identify optimal growing areas, storing the results in a database.

[0194] Step 4: Providing recommendations

[0195] server

[0196] Based on the analysis results, the system provides users with information on recommended crops and cultivation areas.

[0197] Input: List of recommended crops and growing areas

[0198] Data processing: Select the necessary information and format it in a user-friendly way

[0199] Output: Recommendations to be presented to the user

[0200] Specific actions

[0201] The server converts the analysis results into a user-friendly format and provides them to users via a web application or mobile app.

[0202] Step 5: Investment decision and project generation

[0203] User

[0204] The user uses the terminal to review the provided information and select the crops and farmland to invest in. The selected information is sent to the server, which then generates an investment project.

[0205] Input: User's investment decision information (crop, cultivation area)

[0206] Data processing: generating investment projects and formulating management plans

[0207] Output: Investment project details

[0208] Specific actions

[0209] Users make investment decisions through a web interface, and the server generates projects and develops management plans based on the information received.

[0210] Step 6: Farm monitoring

[0211] server

[0212] It collects real-time data from farmland through IoT sensor devices, collecting data such as soil moisture, temperature, and nutrient levels, and generates alerts if anomalies are detected.

[0213] Input: IoT sensor data (humidity, temperature, nutrient levels)

[0214] Data calculation: Real-time analysis of data, detection of anomalies

[0215] Output: Alert notification (if necessary)

[0216] Specific actions

[0217] The server collects real-time data from IoT devices and notifies the user if an abnormality is detected.

[0218] Step 7: Providing agricultural technology and know-how

[0219] server

[0220] Based on collected farmland data and market analysis information, we provide efficient agricultural techniques and know-how via an online platform.

[0221] Input: Farmland data, market analysis information

[0222] Data calculation: Generation of agricultural technology and know-how, provision of learning resources

[0223] Output: Learning resources provided to the user

[0224] Specific actions

[0225] The server generates educational materials and guides containing the latest agricultural techniques and know-how and provides them to users through an online platform.

[0226] Step 8: Harvest, sell, and share profits

[0227] server

[0228] When the crops reach the harvesting period, the system plans and executes the harvesting work, delivers the harvested crops to the market, and tally up the sales profits and distribute them to users.

[0229] Input: Harvest data, sales data

[0230] Data calculation: Revenue calculation, profit distribution plan

[0231] Output: Profit distribution information

[0232] Specific actions

[0233] The server plans the harvesting work, ships the harvested crops to market, and distributes the profits earned to users according to their investment amounts. Users can check the distributed profits on their devices.

[0234] In this way, the system of the present invention specifically executes each processing step and provides efficient agricultural support and investment opportunities to users.

[0235] (Application example 1)

[0236] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0237] Conventional agricultural support systems simply perform market and climate analysis, but lack interactive user education, real-time data provision, and detailed guidance on crop cultivation management. As a result, it is difficult for users to properly learn and utilize agricultural techniques, and the provision and management of real-time information regarding investments is insufficient. Therefore, it has been a challenge to provide an environment that makes it easy for individual investors to enter agriculture and allows them to efficiently select and manage cultivated crops.

[0238] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0239] In this invention, the server includes: means for providing market analysis and climate analysis data reports via a smartphone, smart glasses, or head-mounted display; means for providing virtual reality or augmented reality content that allows users to interactively learn agricultural techniques; means for using collected data to monitor and manage farmland; means for providing efficient agricultural techniques and know-how to users via an online platform; means for presenting monitoring data in real time via smart glasses or a head-mounted display and guiding users on necessary actions; means for monitoring farmland conditions in real time using generative artificial intelligence and generating alerts when an abnormality is detected; means for notifying users of the progress of investment projects and revenue distribution through a user interface; and means for providing training in efficient agricultural techniques using virtual reality or augmented reality. This enables users to efficiently learn agricultural techniques while receiving interactive, real-time data and easily grasp the progress of investment projects in real time.

[0240] "Generative AI" is an AI system that automatically generates and analyzes data, and has the ability to process large amounts of data to make predictions and analyses.

[0241] "Market analysis data" refers to data such as market price trends, supply volume, and demand volume, and is information used to analyze market trends for specific crops or products.

[0242] "Climate analysis data" refers to meteorological information such as temperature, precipitation, and sunshine hours, and is used to analyze the climate conditions in a specific region.

[0243] "Smart glasses" are devices that extend vision, displaying information directly in the user's field of vision and allowing them to be operated by the user's gaze or voice input.

[0244] A "head-mounted display" is a display device worn on the user's head, and is a device that visually provides virtual reality and augmented reality content.

[0245] "Virtual reality" is a technology that allows users to immerse themselves in and experience interactive virtual environments generated using computer technology.

[0246] "Augmented reality" is a technology that combines reality and virtuality by overlaying virtual information onto images of the real world.

[0247] An "online platform" is a collection of services that can be accessed via the Internet, and is an interface that facilitates communication between users and the provision of services.

[0248] "Monitoring data" is data collected in real time using sensors and devices, and is information used to monitor the status of a specific environment or system.

[0249] An "investment project" refers to a plan or set of activities undertaken by a user to invest funds in a particular crop or piece of farmland and enjoy the benefits.

[0250] "Interactive agricultural technology learning" is a process in which users actively participate and learn agricultural technology through interactive information exchange.

[0251] This invention provides a system that uses generative artificial intelligence to collect market analysis data and climate analysis data to support individual investors in effectively entering the agricultural industry. A specific embodiment of this system is described below.

[0252] Market and climate analysis

[0253] The server uses generative AI to collect market analysis data and climate analysis data. Market analysis data is obtained by scraping data such as crop prices, consumption, and import / export volumes from online market information sites and published statistical reports. Climate analysis data is obtained by collecting weather information such as temperature, precipitation, and sunshine hours through meteorological agencies and APIs (OpenWeatherMap, WeatherStack). This data is analyzed to forecast crop demand and identify optimal cultivation areas.

[0254] Use of smartphones, smart glasses, and head-mounted displays

[0255] The server provides market and climate analysis data reports via smartphones, smart glasses, and head-mounted displays, allowing users to visually check the information they need in real time and learn about agricultural techniques interactively.

[0256] Investment project creation and management

[0257] The server accepts investment decisions from users, selects farmland, and creates investment projects. For example, if a user decides to invest in tomato cultivation in a specific area, the server uses that information to create a specific cultivation plan and manages it as a project. Furthermore, the server notifies users of the progress of the investment project and the distribution of profits via a smartphone app.

[0258] Farmland monitoring and management

[0259] The server collects real-time data on the farmland through IoT devices (Arduino, Raspberry Pi) and sensors, and monitors its condition. It collects data on soil moisture, temperature, nutrient levels, etc., and generates an alert to notify the user if an abnormality is detected. The server also allows users to view the monitoring data in real time through smart glasses or a head-mounted display.

[0260] Providing efficient agricultural technology and know-how

[0261] The server uses collected farmland data and market analysis data to provide efficient agricultural techniques and know-how via an online platform. Users can use this platform to learn the latest agricultural techniques. For example, they can use virtual reality (VR) and augmented reality (AR) to visually learn optimal planting spacing and fertilization plans for tomatoes using 3D models.

[0262] Harvesting, selling, and profit sharing

[0263] When the crops reach the harvesting season, the server plans and executes the harvesting work, ships the harvested crops to the market, and tally up the sales profits and distribute them to users. Users can check the distributed profits through their own accounts.

[0264] Prompt Sentence Examples

[0265] Please generate a Python script that scrapes market information websites and weather data to forecast demand for specific crops and identify optimal growing areas. Also, please provide examples of back-end and front-end implementations for an app that can display the results on a smartphone.

[0266] In this way, the present invention utilizes generative artificial intelligence to streamline various agricultural processes and provide an environment in which individual investors can easily enter the agricultural industry.

[0267] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0268] Step 1:

[0269] Collecting market analysis data

[0270] The server scrapes and collects market analysis data such as crop prices, consumption, and import / export volumes from market information sites and published statistical reports on the Internet. To do this, a web scraping tool (BeautifulSoup, Selenium, etc.) is used to extract and format data from the target sites. The input is a list of URLs for market information sites, and the output is the collected market analysis data.

[0271] Step 2:

[0272] Climate analysis data collection

[0273] The server collects weather data such as temperature, precipitation, and sunshine hours using meteorological agencies and APIs (OpenWeatherMap, WeatherStack). This data is used to determine the climate conditions in various locations. It analyzes the raw data obtained from the API and formats it as needed. The input is the API request, and the output is the climate analysis data.

[0274] Step 3:

[0275] Data analysis and crop recommendation identification

[0276] The server inputs the collected market analysis data and climate analysis data into a generative artificial intelligence (AI model), which then uses this data to predict crop demand and identify optimal cultivation areas.To do this, Python's Pandas and Numpy libraries are used to preprocess the data and generate prompt statements to be input into the AI ​​model.The inputs are market analysis data and climate analysis data, and the output is crop demand predictions and optimal cultivation areas.

[0277] Step 4:

[0278] Providing market and climate analysis data

[0279] The server provides market and climate analysis data reports via smartphones, smart glasses, and head-mounted displays. To quickly display data that requires real-time updates, React Native (for smartphones) and Unity (for VR / AR) are used. The input is the results of data analysis, and the output is a report displayed on the user's device.

[0280] Step 5:

[0281] Investment project creation and management

[0282] Users select investment crops and cultivation areas based on the data they provide. The server receives the user's selection results, generates specific investment projects, and formulates investment plans. This information is stored on a dedicated management platform for progress management. The input is the user's investment selection information, and the output is the generated investment project.

[0283] Step 6:

[0284] Farm monitoring and real-time notifications

[0285] The server uses IoT devices (Arduino, Raspberry Pi) and sensors to collect farmland data in real time and monitor the situation. If soil moisture, temperature, nutrient levels, etc. are outside of acceptable ranges, the server generates an alert and notifies the user device. The input is real-time data from the IoT device, and the output is a notification to the user.

[0286] Step 7:

[0287] Providing efficient agricultural technology and know-how

[0288] The server uses AI analysis results and collected data to provide efficient agricultural techniques and know-how via an online platform. Users can learn about agricultural techniques interactively using virtual reality and augmented reality. The input is the results of data analysis and educational content, and the output is an interactive learning experience.

[0289] Step 8:

[0290] Managing harvesting, sales and profit sharing

[0291] When the crops reach the harvesting period, the server plans and manages the harvesting work and arranges for shipment to the market. If any sales profits are earned, they are distributed to the users and the profit details are notified to the user devices. The input is harvest and sales data, and the output is a profit distribution notification to the users.

[0292] This allows users to efficiently learn about agricultural techniques while receiving interactive, real-time data, and makes it easier to keep track of the progress of investment projects in real time.

[0293] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0294] This invention is a system that combines generative artificial intelligence with an emotion engine to recognize and analyze user emotions, thereby providing more effective investment and agricultural support. This system collects market analysis data and climate analysis data, forecasts crop demand and supply, and identifies optimal cultivation areas and crops to provide to users. It also accepts user investment decisions, selects farmland, creates investment projects, and manages the process from harvesting to sales and profit distribution. It also uses the emotion engine to analyze user emotions and provide appropriate information and support.

[0295] Market and climate analysis

[0296] server

[0297] First, generative AI is used to collect market analysis data and climate analysis data. Market analysis data is obtained by scraping data such as crop prices, consumption, and import / export volumes from online agricultural crop price information sites and published statistical reports. Climate analysis data is obtained by collecting weather information such as temperature, precipitation, and sunshine hours through meteorological agencies and APIs. The server analyzes the data collected in this way to forecast crop demand and identify optimal cultivation areas.

[0298] Specific examples

[0299] For example, the server may detect that the market price of tomatoes is on the rise this year, and determine from regional weather data that a certain region is suitable for growing tomatoes. Based on this information, the server may recommend growing tomatoes to the user.

[0300] Use of emotion engine

[0301] server

[0302] The emotion engine analyzes the user's emotions in real time by analyzing user input, past activity data, and even biometric information (e.g., facial expressions and tone of voice collected through a camera or microphone) to determine the user's emotional state.

[0303] User

[0304] If the sentiment engine determines that a customer is motivated to invest, the system will provide more detailed investment information and success stories to encourage investment activity. On the other hand, if a customer is in a state of stress or has doubts, the system will provide risk information and supportive messages.

[0305] Specific examples

[0306] If a user expresses interest in growing tomatoes but is nervous about investing, the sentiment engine will detect that and provide information about past success stories and risk mitigation strategies.

[0307] Investment project creation and management

[0308] User

[0309] The user uses the terminal to review the provided information and select the crops and farmland to invest in. Once the user's investment decision is sent to the server, the server generates an investment project and formulates a management plan for the selected farmland.

[0310] Specific examples

[0311] If a user decides to invest in a tomato cultivation project in a certain region, the server creates a specific cultivation plan based on that information and manages it as a project.

[0312] Farmland monitoring and management

[0313] server

[0314] The server collects real-time data from IoT devices and sensors to monitor the condition of the farmland, collecting data such as soil moisture, temperature, and nutrient levels, and generates an alert to notify the user if an abnormality is detected.

[0315] Specific examples

[0316] For example, if soil moisture falls below an optimum level, the server will automatically activate the irrigation system to provide the appropriate amount of water.

[0317] Providing efficient agricultural technology and know-how

[0318] server

[0319] Based on collected farmland data and market analysis data, the company provides efficient agricultural techniques and know-how via an online platform, allowing users to learn about the latest agricultural techniques.

[0320] User

[0321] Users can apply the provided know-how to their actual work and manage their farmland, and can also respond immediately based on monitoring results and alert information.

[0322] Specific examples

[0323] For example, you can learn the optimal planting spacing and fertilization schedule for tomatoes on the platform and then put it into practice in the field.

[0324] Harvesting, selling, and profit sharing

[0325] server

[0326] When the crops reach the harvesting period, the system plans and executes the harvesting work, delivers the harvested crops to the market, and tally up the sales profits and distribute them to users.

[0327] Terminal

[0328] Users can view the profits distributed through their accounts.

[0329] Specific examples

[0330] For example, when the tomato harvest is completed and a profit of 200,000 yen is earned from sales, a portion of that profit is transferred to the user's account and the user is notified that the profit distribution has been completed.

[0331] In this way, the present invention utilizes generative AI and an emotion engine to streamline various agricultural processes and provide an environment in which individual investors can easily enter the agricultural industry. The introduction of an emotion engine makes it possible to provide information and support suited to the user's psychological state, thereby improving the effectiveness of investment activities and agricultural management.

[0332] The processing flow will be explained below.

[0333] Step 1:

[0334] server:

[0335] Market analysis data is scraped from online agricultural crop price information sites and statistical reports, and data such as crop prices, consumption, and import / export volumes is collected.

[0336] It collects climate analysis data such as temperature, precipitation, and sunshine hours from meteorological agency APIs and public weather databases.

[0337] The collected market analysis data and climate analysis data are stored in a database and prepared for data analysis.

[0338] Step 2:

[0339] server:

[0340] Generative artificial intelligence is used to analyze the collected market analysis data and climate analysis data.

[0341] Conduct demand and supply forecasts for crops and identify optimal crops.

[0342] Based on climate analysis data, the optimum cultivation areas for growing crops are identified.

[0343] Based on the analysis results, recommended crops and cultivation areas are determined, and a data format is created to provide the data to users.

[0344] Step 3:

[0345] server:

[0346] The analysis results are sent to a user-facing application.

[0347] Device:

[0348] The analysis results received from the server are displayed to the user, including recommended crops, recommended growing areas, and market demand analysis results.

[0349] The emotion engine analyzes the user's emotions and generates appropriate investment suggestions and support messages.

[0350] Step 4:

[0351] User:

[0352] Based on the analysis results and investment proposals presented, crops and farmland for investment are selected.

[0353] Enter the investment amount in the application and confirm your investment decision.

[0354] Device:

[0355] Data relating to the investment decision is transmitted to a server.

[0356] Step 5:

[0357] server:

[0358] Based on the user's investment decisions, an investment project is generated, including the selected farmland, crops, investment amount, and projected profits.

[0359] Develop a management plan for the farmland and set the required resources and work schedule.

[0360] Using an emotion engine, it analyzes users' emotions and investment motivation, and provides appropriate investment support and information to maintain motivation.

[0361] Specific examples

[0362] If the user decides to invest in tomato cultivation, the server creates a specific cultivation plan and provides the user with predicted profits and risk information.

[0363] Step 6:

[0364] server:

[0365] After the investment project is created, the progress of the project is provided to the user through a user interface.

[0366] Real-time data on farmland is collected through IoT devices and sensors, monitoring soil moisture, temperature, nutrient levels, and more.

[0367] If an abnormality is detected, an alert is generated and notified to the user.

[0368] Device:

[0369] Display project information and alert notifications in your account to prompt you to take necessary action.

[0370] Step 7:

[0371] User:

[0372] Receive alert notifications, take necessary action, and use online platforms to learn efficient farming techniques and know-how.

[0373] Put new technologies and methods into practice locally.

[0374] Specific examples

[0375] After users learn about irrigation methods for tomato cultivation, they can apply that information to actual farm management and ensure proper water supply.

[0376] Step 8:

[0377] server:

[0378] When the crop reaches harvest time, plan and execute the harvesting operations.

[0379] Ship the harvested crops to market and collect sales data.

[0380] Step 9:

[0381] server:

[0382] Profits from sales are tallied and distributed to users.

[0383] Profit sharing information will be notified to users.

[0384] Device:

[0385] Display the distributed profits in the user's account and notify them when the distribution is complete.

[0386] Specific examples

[0387] For example, when the tomato harvest is completed and a profit of 200,000 yen is earned from sales, a portion of that (for example, 50,000 yen) is transferred to the user's account, and the user is notified that the profit distribution has been completed.

[0388] In this way, the present invention utilizes generative artificial intelligence and an emotion engine to streamline a series of processes, from market analysis, climate analysis, investment plan creation, farmland management, harvesting, sales, and profit distribution, and provides an agricultural support system that provides information and support that adapts to the user's emotions.

[0389] Example 2

[0390] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0391] Modern agriculture and investment activities require advanced data analysis and real-time sentiment analysis to be efficient and effective. However, conventional technologies have difficulty integrating agricultural and investment data analysis, making it difficult to provide information and support that takes into account investors' daily emotional states. This has made it difficult for investors to make accurate decisions, often resulting in inefficient agricultural project management.

[0392] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0393] In this invention, the server includes means for collecting market analysis data using generative artificial intelligence and forecasting crop supply and demand, means for collecting climate analysis data using generative artificial intelligence and identifying optimal cultivation areas, means for presenting users with recommended crops and cultivation areas based on the collected market analysis data and climate analysis data, means for analyzing users' emotional data and providing information and support according to their psychological state, means for accepting users' investment decisions, selecting farmland and creating investment projects, and means for harvesting crops that have reached the harvest stage and shipping them to market and distributing the resulting profits to users. This allows users to receive highly accurate information that takes their emotional state into consideration, enabling more accurate and efficient investment and agricultural project management.

[0394] "Generative AI" is a type of AI technology used to collect, analyze, and predict data, specifically the ability to generate new information and patterns using generative models.

[0395] "Market analysis data" refers to data containing market information such as agricultural product prices, consumption, and import / export volumes, and is used to forecast agricultural supply and demand.

[0396] "Climate analysis data" refers to data containing information about the climate, such as temperature, precipitation, and hours of sunshine, that is used to identify optimal conditions for growing crops.

[0397] "Emotional data" refers to data that reflects the user's psychological state, and refers to information collected based on facial expressions, tone of voice, input content, etc.

[0398] "Agricultural land monitoring" is the process of using IoT devices and sensors to monitor agricultural land environmental data (e.g., soil moisture, temperature, and nutrient levels) in real time and responding if anomalies are detected.

[0399] An "online platform" is a collection of systems or services that can be accessed by users via the internet and that serve as a means of providing efficient agricultural technology and know-how.

[0400] An "investment project" is a planning and management framework generated by a user to invest in a specific crop and farmland, including the entire process from harvesting to sales and profit sharing.

[0401] An "alert" is a warning message that is sent to users when an abnormality is detected during agricultural land monitoring, and is intended to encourage a prompt response.

[0402] A "user interface" is the set of means and tools by which a user interacts with a system or service, allowing them to enter and retrieve information and view project status.

[0403] This invention is a system that combines generative artificial intelligence and an emotion engine, and by recognizing and analyzing the user's emotions, it provides effective investment and agricultural support. This system collects and analyzes various data, and not only provides users with optimal agricultural investment information, but also provides support according to the user's psychological state.

[0404] The server uses generative artificial intelligence (e.g., Google Cloud AI) to collect market analysis data. The market analysis data is obtained by scraping data such as crop prices, consumption, and import / export volumes from online agricultural crop price information websites and published statistical reports. Climate analysis data is also collected from meteorological agencies (e.g., the Japan Meteorological Agency) and APIs (e.g., OpenWeather API) to collect weather information such as temperature, precipitation, and sunshine hours.

[0405] The server integrates collected market analysis data and climate analysis data and analyzes the data using a generative AI model. This makes it possible to forecast crop demand and identify optimal cultivation areas. For example, the server may detect that the market price of tomatoes is on the rise this year, and determine from regional weather data that a certain region is suitable for tomato cultivation. Based on this information, it may recommend tomato cultivation to the user.

[0406] The server then uses an emotion engine (e.g., Azure Cognitive Services' Emotion API) to analyze the user's emotions in real time. It analyzes user input, past activity data, and biometric information collected through the camera and microphone, such as facial expressions and tone of voice, to identify the user's emotional state. For example, if a user expresses interest in growing tomatoes but is feeling anxious, the emotion engine will detect this anxiety and provide information about past success stories and risk mitigation measures.

[0407] The user uses a device (e.g., smartphone or PC) to check the provided information and select the crop and farmland in which to invest. When the user's investment decision is sent to the server, the server creates a specific cultivation plan for the selected farmland and crop as a project and manages it. For example, if the user decides to invest in a tomato cultivation project in a certain region, the server creates a specific cultivation plan based on that information and manages it as a project.

[0408] In addition, the server uses IoT devices (e.g., soil sensors, weather stations) to obtain real-time data on the managed farmland and monitor its condition. It collects data such as soil moisture, temperature, and nutrient levels, and generates alerts to notify users if an abnormality is detected. For example, if soil moisture falls below an appropriate level, the server automatically activates the irrigation system to provide the appropriate amount of water.

[0409] The server uses collected farmland data and market analysis data to provide efficient agricultural techniques and know-how via an online platform (e.g., Moodle). Users can use this platform to learn the latest agricultural techniques and apply them to their own work. For example, they can learn the optimal planting spacing and fertilization schedule for tomatoes on the platform and then put it into practice in the field.

[0410] When the crops reach harvest time, the server plans and executes the harvesting work and ships the harvested crops to market. The sales profits are tallied and distributed to users. Through the terminal, users can check their own accounts and confirm the distributed profits. For example, if the tomato harvest is complete and profits from sales are 200,000 yen, a portion of that will be transferred to the user's account and the user will be notified that the profit distribution has been completed.

[0411] In this way, the present invention utilizes generative AI and an emotion engine to streamline various agricultural processes and provide an environment in which individual investors can easily enter the agricultural industry. The introduction of an emotion engine makes it possible to provide information and support suited to the user's psychological state, thereby improving the effectiveness of investment activities and agricultural management.

[0412] Examples of specific prompts include the following:

[0413] 1. "Collect current market prices and consumption of tomatoes"

[0414] 2. "Collect climate data for the past year for a certain region."

[0415] 3. "If a user is interested in growing tomatoes, which areas are suitable for growing them?"

[0416] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0417] Step 1: Collect market analysis data

[0418] server

[0419] The server uses generative artificial intelligence (e.g., Google Cloud AI) to collect market analysis data.

[0420] Input: URL or API endpoint of a crop pricing information site or statistical report.

[0421] Output: Market analysis data such as prices, consumption, import and export volumes of agricultural products.

[0422] Specific operation: The server uses web scraping technology to obtain data from agricultural product price information websites on the Internet. For example, it uses a prompt such as "Collect the current market price and consumption of tomatoes."

[0423] Step 2: Collecting climate analysis data

[0424] server

[0425] The server collects climate analysis data from meteorological agencies (e.g., the Japan Meteorological Agency) or through APIs (e.g., OpenWeatherAPI).

[0426] Input: Weather agency database or API endpoint.

[0427] Output: Climate analysis data such as temperature, precipitation, and sunshine hours.

[0428] Specific operation: The server uses the API to obtain data such as temperature, precipitation, and sunshine hours for the specified area. An example of a prompt is "Collect climate data for the past year for the XX area."

[0429] Step 3: Data analysis and demand forecasting

[0430] server

[0431] The server integrates the collected market analysis data and climate analysis data and analyzes the data using a generative AI model.

[0432] Input: Market analysis data and climate analysis data.

[0433] Output: Demand forecast results and identification of optimal growing areas.

[0434] Specific operation: The server inputs the integrated data into an AI model and performs demand forecasting. For example, based on the collected market price of tomatoes and climate data for a certain region, it predicts that "a certain region is suitable for tomato cultivation."

[0435] Step 4: Analyzing user emotions with the emotion engine

[0436] server

[0437] The server uses an emotion engine (e.g., Azure Cognitive Services' Emotion API) to analyze the user's emotion data in real time.

[0438] Input: User input data, facial expression data, voice data, etc.

[0439] Output: Information about the user's emotional state.

[0440] Specific operation: The server analyzes facial expressions, tone of voice, and input from the user collected through the camera and microphone to determine the user's current emotional state. For example, if a user inputs "I feel anxious" about investing, the server analyzes this and provides appropriate support information.

[0441] Step 5: User investment decision and project creation

[0442] User

[0443] Users use the terminal to make investment decisions based on analysis results and advice.

[0444] Input: Prediction data and sentiment analysis results provided by the server.

[0445] Output: Submission of investment decision.

[0446] Specific operation: The user looks at the analysis results provided (for example, the best region for tomato cultivation), decides on an investment, and sends a message to the server such as "Invest in a tomato cultivation project in region X."

[0447] Step 6: Farm monitoring

[0448] server

[0449] The server collects real-time data on the farmland through IoT devices and sensors and monitors its condition.

[0450] Input: Data from IoT sensors (e.g. soil moisture, temperature, nutrient levels).

[0451] Output: Real-time monitoring data and abnormal alerts.

[0452] Specific operation: The server analyzes data sent from the sensors in real time, and if an abnormality is detected, it automatically starts the irrigation system and sends an alert to the user.

[0453] Step 7: Providing agricultural technology

[0454] server

[0455] Based on the collected data, the server provides efficient agricultural techniques and know-how via an online platform.

[0456] Input: Analyzed farmland data and market data.

[0457] Output: Technical information and know-how on an online platform.

[0458] Specific operation: The server posts information such as "optimal planting intervals for tomatoes" and "fertilization schedules" on the online platform. Users can learn from this information and apply it to their actual farming activities.

[0459] Step 8: Harvest, sell, and share profits

[0460] server

[0461] When the crops reach harvest time, the server plans and executes the harvesting operation, delivers the harvested products to market, and tallys up the sales profits and distribute them to users.

[0462] Inputs: Quantity of crop harvested and sale price.

[0463] Output: Profit sharing status.

[0464] Specific operation: After the harvest is completed, the server automatically calculates the sales profit and transfers it to the user's account. For example, if the tomato harvest is completed and the sales profit is 200,000 yen, it will be distributed to each user and notified.

[0465] (Application example 2)

[0466] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0467] In recent years, the agricultural sector has seen a demand for systems that utilize market and climate analysis data to forecast crop demand and supply and identify optimal cultivation areas. Furthermore, there is a need for methods to optimize agricultural product sales strategies based on consumer preferences and emotions, support investment decisions, and ensure profits. However, existing systems do not adequately analyze users' emotions and preferences, and recommend agricultural products and propose sales strategies that are tailored to the user's psychological state. Therefore, an effective system is needed to increase consumer and investor satisfaction.

[0468] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0469] In this invention, the server includes means for collecting market analysis data using generative artificial intelligence and forecasting crop supply and demand, means for collecting climate analysis data using generative artificial intelligence and identifying optimal cultivation areas, means for presenting recommended crops and cultivation areas to the user based on the collected market analysis data and climate analysis data, emotion engine means for analyzing the user's emotions and providing information and support based on the user's emotional state, means for accepting investment decisions from the user, selecting farmland, and creating investment projects, means for harvesting crops that have reached the harvest season and shipping them to market and distributing the resulting profits to the user, and means for analyzing consumer preferences based on the analysis of the user's emotions and recommending optimal agricultural products. This enables optimization of investment and agricultural product sales taking into account the user's emotions and preferences.

[0470] "Generative AI" refers to artificial intelligence techniques used to collect, analyze, and predict market and climate analysis data.

[0471] "Market analysis data" refers to data that includes market information such as prices, consumption, and import / export volumes of agricultural products.

[0472] "Climate analysis data" refers to data that includes meteorological information such as temperature, precipitation, and hours of sunshine.

[0473] The "emotion engine" is an engine that analyzes the user's emotions and provides information and support based on their psychological state.

[0474] "Site selection" is the process of selecting the most suitable land for an investment project.

[0475] "Investment project generation" is the process of selecting farmland and formulating a cultivation plan based on the user's investment decision, and then launching the project.

[0476] "Harvesting season" is the time when the crop is fully grown and ready for harvest.

[0477] "Profit sharing" is the process of distributing the profits from the sale of marketed crops to users.

[0478] "Agricultural product recommendation" is the act of suggesting the most suitable agricultural products based on consumer preferences and sentiment analysis.

[0479] "Collected Data" refers to various data collected and stored by the system, including market analysis data and climate analysis data.

[0480] An "online platform" is an internet-based system accessible to users that provides efficient agricultural techniques and know-how.

[0481] "Sales strategy proposal" is the act of presenting optimal sales methods and marketing strategies based on the consumer's emotional state through the emotional engine means.

[0482] "Smart devices" are devices with internet connectivity, such as smartphones and tablets, that provide information based on emotion analysis.

[0483] The program of the system that realizes this invention is executed on a smart device such as a smartphone or tablet, and provides optimal information on agricultural products and proposes sales strategies to consumers and investors based on market analysis data and climate analysis data. Specific examples are shown below.

[0484] The server uses generative artificial intelligence to scrape market analysis data from online agricultural product price information websites and published statistical reports. The main data collected includes crop prices, consumption, and import / export volumes. At the same time, climate analysis data is collected from meteorological agencies and through APIs. Climate analysis data includes temperature, precipitation, and sunshine hours. This data is then analyzed within the server to forecast crop demand and identify optimal cultivation areas.

[0485] Next, the emotion engine analyzes the user's input, past activity data, and even biometric information (e.g., facial expressions and tone of voice collected through the camera and microphone) in real time to identify the user's emotional state. Based on the emotion analysis, the user's psychological state is analyzed and information and support are provided based on their willingness to invest or purchase. For example, if a user is interested in growing tomatoes but feels anxious, information about past success stories and risk mitigation measures is provided.

[0486] Users can review the information provided through their smart devices and select the crops and farmland they wish to invest in. Once the user's investment decision is sent to the server, the server selects farmland, generates an investment project, and formulates a management plan for the selected farmland. When the crops reach harvest time, the server plans and executes the harvesting work and ships the harvested crops to market. The sales profits obtained are tallied and distributed to users.

[0487] In addition, the server obtains real-time data from the farmland (e.g., soil moisture, temperature, and nutrient levels) through IoT devices and sensors, and generates alerts and notifies users when an abnormality is detected, allowing them to respond quickly.

[0488] As a concrete example, suppose a user types, "Recently, I've been wondering about the quality of tomatoes." This text is subjected to sentiment analysis, and if the user's positive sentiment is confirmed, the server will suggest tomato cultivation based on market and climate data. Specific examples of suggestions include recommended cultivation areas, investment amounts, and past success stories. This information is provided to the user via their smart device.

[0489] Examples of prompts for the generative AI model in this system include:

[0490] Lately, I've been wondering about the quality of my tomatoes. Question: What's the best crop to buy?

[0491] In this way, the present invention is a system that takes into account the user's emotions and preferences and realizes optimal information provision and sales strategies for agricultural products.

[0492] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0493] Step 1:

[0494] Market analysis data collection and analysis

[0495] The server scrapes data such as crop prices, consumption, and import / export volumes from online agricultural product price information websites and published statistical reports. This data is then collected and used by the internal generative AI to perform market analysis.

[0496] Input: Online agricultural price information sites and statistical reports

[0497] Data processing: scraping, data cleansing, data analysis

[0498] Output: Demand and supply forecast data

[0499] Step 2:

[0500] Climate analysis data collection and analysis

[0501] The server collects weather data such as temperature, precipitation, and sunshine hours through weather agencies and APIs, and uses generative artificial intelligence to identify optimal growing areas.

[0502] Input: Weather data from weather agencies and APIs

[0503] Data processing: API requests, data cleansing, data analysis

[0504] Output: Optimal cultivation area data

[0505] Step 3:

[0506] User sentiment analysis

[0507] The server analyzes the user's input, past activity data, and biometric information (e.g., facial expressions and tone of voice collected through a camera and microphone) in real time to identify their emotional state.

[0508] Input: User text input, biometric information

[0509] Data processing: Sentiment analysis algorithms, text analysis, speech analysis

[0510] Output: User's emotional state data

[0511] Step 4:

[0512] Recommended crops and growing areas

[0513] The server presents users with recommended crops and growing areas based on the collected market and climate analysis data, and uses an emotion engine to provide information according to the user's emotional state.

[0514] Input: Market analysis data, climate analysis data, user emotional state data

[0515] Data processing: data integration, optimization algorithms

[0516] Output: Recommended crops and growing area information

[0517] Step 5:

[0518] Investment decisions and land selection

[0519] The user checks the information provided through the smart device and selects the crops and farmland to invest in. The user's investment decision is sent to the server, which then generates the farmland selection and investment project.

[0520] Input: User's investment decision data

[0521] Data processing: Database updates, project generation algorithms

[0522] Output: Land selection and investment project data

[0523] Step 6:

[0524] Harvesting and profit sharing

[0525] When the crops reach harvest time, the server plans and executes the harvesting operation, delivers the harvested crops to market, and compiles and distributes the sales profits to users.

[0526] Input: Harvest data, sales data

[0527] Data processing: Harvest plan generation, profit aggregation

[0528] Output: Profit distribution data to users

[0529] Step 7:

[0530] Real-time monitoring of farmland

[0531] The server obtains real-time data from the farmland (e.g., soil moisture, temperature, and nutrient levels) through IoT devices and sensors, and generates alerts and notifies users if any abnormalities are detected.

[0532] Input: Real-time data from IoT devices

[0533] Data processing: sensor data analysis, anomaly detection algorithms

[0534] Output: Alert notification data

[0535] Step 8:

[0536] Proposing sales strategies based on consumer preferences and emotions

[0537] The server, through an emotion engine means, proposes a sales strategy based on the consumer's emotional state.

[0538] Input: Consumer emotional state data

[0539] Data processing: emotional data analysis, sales strategy algorithms

[0540] Output: Sales strategy proposal data

[0541] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0542] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0543] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0544] [Second embodiment]

[0545] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0546] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0547] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0548] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0549] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0550] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0551] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0552] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0553] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0554] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0555] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0556] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0557] This invention relates to an agricultural support system that uses generative artificial intelligence to efficiently perform market and climate analysis, identify optimal crops and regions for cultivation, and provide them to users. It also provides an investment platform that makes it easier for individual investors to enter the agricultural industry, and supports the entire process from cultivation, harvesting, sales, and profit distribution.

[0558] Market and climate analysis

[0559] server

[0560] First, generative AI is used to collect market analysis data and climate analysis data. Market analysis data is obtained by scraping data such as crop prices, consumption, and import / export volumes from online market information sites and published statistical reports. Climate analysis data is obtained by collecting weather information such as temperature, precipitation, and sunshine hours through meteorological agencies and APIs. The server analyzes the data collected in this way to forecast crop demand and identify optimal cultivation areas.

[0561] Specific examples

[0562] For example, the server may detect that the market price of tomatoes is on the rise this year, and determine from regional weather data that a certain region is suitable for growing tomatoes. Based on this information, the server may recommend growing tomatoes to the user.

[0563] Investment project creation and management

[0564] User

[0565] The user uses the terminal to review the provided information and select the crops and farmland to invest in. Once the user's investment decision is sent to the server, the server generates an investment project and formulates a management plan for the selected farmland.

[0566] Specific examples

[0567] If a user decides to invest in a tomato cultivation project in a certain region, the server creates a specific cultivation plan based on that information and manages it as a project.

[0568] Farmland monitoring and management

[0569] server

[0570] The server collects real-time data from IoT devices and sensors to monitor the condition of the farmland, collecting data such as soil moisture, temperature, and nutrient levels, and generates an alert to notify the user if an abnormality is detected.

[0571] Specific examples

[0572] For example, if soil moisture falls below an optimum level, the server will automatically activate the irrigation system to provide the appropriate amount of water.

[0573] Providing efficient agricultural technology and know-how

[0574] server

[0575] Based on collected farmland data and market analysis data, the company provides efficient agricultural techniques and know-how via an online platform, allowing users to learn about the latest agricultural techniques.

[0576] User

[0577] Users can apply the provided know-how to their actual work and manage their farmland, and can also respond immediately based on monitoring results and alert information.

[0578] Specific examples

[0579] For example, you can learn the optimal planting spacing and fertilization schedule for tomatoes on the platform and then put it into practice in the field.

[0580] Harvesting, selling, and profit sharing

[0581] server

[0582] When the crops reach the harvesting period, the system plans and executes the harvesting work, delivers the harvested crops to the market, and tally up the sales profits and distribute them to users.

[0583] Terminal

[0584] Users can view the profits distributed through their accounts.

[0585] Specific examples

[0586] For example, if the tomato harvest is complete and 200,000 yen in profits are earned from sales, a portion of that profit will be transferred to the user's account and the user will be notified that the profit distribution has been completed.

[0587] In this way, the present invention utilizes generative artificial intelligence to streamline various agricultural processes and provide an environment in which individual investors can easily enter the agricultural industry.

[0588] The processing flow will be explained below.

[0589] Step 1:

[0590] server:

[0591] Collect market analysis data by scraping agricultural product price information websites and published statistical reports on the Internet to obtain data on crop prices, consumption, import and export volumes, etc.

[0592] Climate analysis data such as temperature, precipitation, and sunshine hours are collected through meteorological agencies and APIs.

[0593] The collected market analysis data and climate analysis data are stored in a database and formatted into the format required for analysis.

[0594] Step 2:

[0595] server:

[0596] Generative artificial intelligence is used to analyze collected market and climate analysis data to forecast crop demand and identify optimal cultivation areas.

[0597] Based on the results of these analyses, information on recommended crops and their growing areas is generated and converted into a format for providing information to users.

[0598] Step 3:

[0599] server:

[0600] Send the analysis results to the application.

[0601] Device:

[0602] The analysis results received from the server are displayed to the user, including recommended crops, growing areas, and reasons for market demand.

[0603] User:

[0604] Based on the information provided, select the crops and farmland to invest in.

[0605] Step 4:

[0606] User:

[0607] Enter the investment amount on the application and confirm your investment decision.

[0608] Device:

[0609] The investment selection results are sent to the server.

[0610] Step 5:

[0611] server:

[0612] Based on the user's investment decision, an investment project is generated. Project information includes the selected farmland, crops, and investment amount.

[0613] Develop a management plan for the farm, setting out the necessary resources and work schedules.

[0614] Step 6:

[0615] server:

[0616] The generated investment project information is sent to the user, providing an overview of the project and its progress.

[0617] Device:

[0618] Display project information in your account and keep users updated on progress.

[0619] Step 7:

[0620] server:

[0621] Through IoT devices and sensors, real-time data on farmland is collected and conditions such as soil moisture, temperature, and nutrient levels are monitored.

[0622] If an abnormality is detected, an alert is immediately generated and the user is notified of the abnormality and the countermeasures to be taken.

[0623] Device:

[0624] An alert notification is displayed to the user, prompting them to take the necessary action.

[0625] Step 8:

[0626] server:

[0627] The online platform provides users with efficient agricultural techniques and know-how, and updates information as new techniques and methods are released.

[0628] User:

[0629] Participants learn agricultural know-how and techniques provided on the platform and apply them to their actual agricultural work.

[0630] Step 9:

[0631] server:

[0632] Plan and execute harvest operations when crops reach harvest stage.

[0633] Ship the harvested crops to market and collect sales data.

[0634] Step 10:

[0635] server:

[0636] Profits from sales are aggregated and distributed to users for each investment project.

[0637] Profit sharing information is notified to users.

[0638] Device:

[0639] Display the refunded profits in the user's account and notify them that the distribution is complete.

[0640] In this way, the present invention provides a system that enables efficient and sustainable agriculture, benefiting both investors and farmers.

[0641] Example 1

[0642] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0643] In the agricultural sector, efficient crop cultivation, market supply and demand forecasting, and identifying optimal cultivation areas based on climatic conditions are extremely important, but conventional methods require the time and effort required to collect and analyze vast amounts of information, which acts as a barrier to individual investors entering the agricultural industry. Furthermore, automating the process from harvesting to sales and profit distribution, and monitoring the condition of farmland in real time, are essential for further efficiency and quality improvement. Furthermore, there is a lack of systems for providing efficient agricultural techniques and know-how. There is a need to solve these problems and create an environment that makes it easier for many people, including individual investors, to enter the agricultural industry.

[0644] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0645] In this invention, the server includes: means for collecting market analysis information using generative artificial intelligence and forecasting crop supply and demand; means for collecting climate analysis information using generative artificial intelligence and identifying optimal cultivation areas; means for presenting users with recommended crops and cultivation areas based on the collected market and climate analysis information; means for accepting users' investment decisions, selecting farmland, and creating investment projects; and means for harvesting and shipping crops to market when the crops reach harvest time and distributing the resulting profits to users. This streamlines the collection and analysis of vast amounts of information, making it easier for individual investors to enter the agricultural industry. Furthermore, the process from harvesting to sales and profit distribution is automated, and real-time monitoring of farmland conditions enables more efficient and higher-quality agriculture. Furthermore, providing efficient agricultural techniques and know-how contributes to improving users' agricultural skills.

[0646] "Generative AI" refers to AI that can generate, analyze, and predict data using machine learning technology.

[0647] "Market analysis information" refers to various market-related data such as crop prices, consumption, and import / export volumes.

[0648] "Climate analysis information" refers to various data related to climate, such as temperature, precipitation, and sunshine hours.

[0649] "User" refers to an individual or organization that uses this system to cultivate crops or invest.

[0650] "Recommended crops" refer to crops that are deemed optimal based on collected data.

[0651] "Cultivation area" refers to the area where a particular crop can be optimally grown.

[0652] "Investment Project" refers to the cultivation plan or farmland management plan in which a user makes an investment.

[0653] "When the crop reaches the harvest stage" refers to the point in time when the crop reaches the maturity set as the cultivation goal.

[0654] "Marketing" refers to supplying harvested crops to the market.

[0655] "Profit sharing" refers to sharing the revenue generated from the sale of crops with users.

[0656] "Real-time monitoring" refers to the continuous monitoring of the condition of agricultural land in an almost immediate manner.

[0657] "Generating an alert" refers to sending a notification when an anomaly is detected.

[0658] "User interface" refers to the screen or input device that allows a user to access the system and check or enter information.

[0659] "Online platform" refers to a software environment for providing information and services to users via the Internet.

[0660] "Agricultural technology" refers to the means and methods for carrying out agriculture efficiently.

[0661] "Know-how" refers to practical knowledge and experience in agriculture.

[0662] This invention relates to an agricultural support system that uses generative artificial intelligence to enable users to efficiently conduct market and climate analysis and identify optimal crops and regions for cultivation. It also provides an investment platform that makes it easier for individual investors to enter the agricultural industry, and supports the entire process from cultivation, harvesting, sales, and profit distribution.

[0663] Hardware and software used

[0664] Hardware

[0665] IoT sensor devices (e.g. soil humidity sensors, temperature sensors)

[0666] Server (e.g. cloud server provider)

[0667] User's device (e.g. smartphone, PC)

[0668] software

[0669] Generative AI models (e.g., GPT-4)

[0670] Scraping tools (e.g. BeautifulSoup, Scrapy)

[0671] Weather information API (e.g., OpenWeatherMap, WeatherAPI)

[0672] System program processing

[0673] server

[0674] The server uses generative artificial intelligence to collect market analysis information. It uses scraping tools (e.g., BeautifulSoup, Scrapy) to obtain data such as crop prices, consumption, and import / export volumes from market information websites and published statistical reports.

[0675] The server also collects climate analysis information such as temperature, precipitation, and sunshine hours through meteorological agency APIs (e.g., OpenWeatherMap, WeatherAPI). Based on the collected data, it performs analysis using a generative artificial intelligence model (e.g., GPT-4). The server identifies the optimal crops and regions to cultivate and makes recommendations to the user.

[0676] User

[0677] The user uses a terminal to check the information provided by the server. Based on the data on recommended crops and cultivation areas, the user selects the crops and farmland to invest in. The selected information is sent to the server, which then generates an investment project and formulates a management plan for the selected farmland.

[0678] server

[0679] The server collects real-time data from the farmland through IoT sensor devices. It collects data such as soil moisture, temperature, and nutrient levels, and analyzes the condition using a generative AI model. If an abnormality is detected, an alert is generated to notify the user. For example, if soil moisture falls below an appropriate value, the server automatically activates the irrigation system to provide the appropriate amount of water.

[0680] server

[0681] Based on the collected farmland data and market analysis information, the server provides efficient agricultural techniques and know-how via an online platform, allowing users to learn about the latest agricultural techniques.

[0682] server

[0683] When the crops reach harvest time, the system plans and executes the harvesting work, and delivers the harvested crops to market. The system tallys up the sales profits and distributes them to users, who can check the distributed profits through their own devices.

[0684] Examples of concrete examples and prompts

[0685] For example, the server may detect that the market price of tomatoes is on the rise this year, and determine from regional weather data that a particular region is suitable for tomato cultivation. Based on this information, the server may recommend tomato cultivation to the user.

[0686] Prompt Sentence Examples

[0687] "Make a forecast based on market prices and climate data to identify the best tomato growing regions for 2023."

[0688] In this way, the system of the present invention makes full use of generative artificial intelligence to streamline various agricultural processes and provide an environment in which individual investors can easily enter the agricultural industry.

[0689] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0690] Step 1: Collect market analysis data

[0691] server

[0692] Market analysis data such as crop prices, consumption, and import / export volumes are collected from market information sites and published statistical reports. The server uses a scraping tool to collect the data and stores it in a database.

[0693] Input: Market information site URL

[0694] Data processing: HTML analysis of web pages, extraction of necessary data

[0695] Output: Market analysis data (price, consumption, import / export volume, etc.)

[0696] Specific actions

[0697] The server scrapes data from multiple market information sites on the internet and stores information on prices, consumption, imports and exports in a database.

[0698] Step 2: Collecting climate analysis data

[0699] server

[0700] Weather analysis data such as temperature, precipitation, and sunshine hours are collected through the weather information API. The server sends the API request and stores the required data in a database.

[0701] Input: Weather information API endpoint, location information

[0702] Data processing: Analyzing JSON responses from API and extracting necessary weather data

[0703] Output: Climate analysis data (temperature, precipitation, sunshine hours, etc.)

[0704] Specific actions

[0705] The server sends a request to the weather information API, analyzes the acquired weather data, and stores it in a database.

[0706] Step 3: Analyze the data

[0707] server

[0708] Collected market and climate analysis data is analyzed using a generative artificial intelligence model (e.g., GPT-4), which forecasts demand and identifies optimal cultivation areas.

[0709] Input: Market analysis data, climate analysis data

[0710] Data calculations: demand forecasting and cultivation area identification using generative artificial intelligence

[0711] Output: List of recommended crops and growing areas

[0712] Specific actions

[0713] The server provides prompts to a generative artificial intelligence model that uses the collected data to predict demand and identify optimal growing areas, storing the results in a database.

[0714] Step 4: Providing recommendations

[0715] server

[0716] Based on the analysis results, the system provides users with information on recommended crops and cultivation areas.

[0717] Input: List of recommended crops and growing areas

[0718] Data processing: Select the necessary information and format it in a user-friendly way

[0719] Output: Recommendations to be presented to the user

[0720] Specific actions

[0721] The server converts the analysis results into a user-friendly format and provides them to users via a web application or mobile app.

[0722] Step 5: Investment decision and project generation

[0723] User

[0724] The user uses the terminal to review the provided information and select the crops and farmland to invest in. The selected information is sent to the server, which then generates an investment project.

[0725] Input: User's investment decision information (crop, cultivation area)

[0726] Data processing: generating investment projects and formulating management plans

[0727] Output: Investment project details

[0728] Specific actions

[0729] Users make investment decisions through a web interface, and the server generates projects and develops management plans based on the information received.

[0730] Step 6: Farm monitoring

[0731] server

[0732] It collects real-time data from farmland through IoT sensor devices, collecting data such as soil moisture, temperature, and nutrient levels, and generates alerts if anomalies are detected.

[0733] Input: IoT sensor data (humidity, temperature, nutrient levels)

[0734] Data calculation: Real-time analysis of data, detection of anomalies

[0735] Output: Alert notification (if necessary)

[0736] Specific actions

[0737] The server collects real-time data from IoT devices and notifies the user if an abnormality is detected.

[0738] Step 7: Providing agricultural technology and know-how

[0739] server

[0740] Based on collected farmland data and market analysis information, we provide efficient agricultural techniques and know-how via an online platform.

[0741] Input: Farmland data, market analysis information

[0742] Data calculation: Generation of agricultural technology and know-how, provision of learning resources

[0743] Output: Learning resources provided to the user

[0744] Specific actions

[0745] The server generates educational materials and guides containing the latest agricultural techniques and know-how and provides them to users through an online platform.

[0746] Step 8: Harvest, sell, and share profits

[0747] server

[0748] When the crops reach the harvesting period, the system plans and executes the harvesting work, delivers the harvested crops to the market, and tally up the sales profits and distribute them to users.

[0749] Input: Harvest data, sales data

[0750] Data calculation: Revenue calculation, profit distribution plan

[0751] Output: Profit distribution information

[0752] Specific actions

[0753] The server plans the harvesting work, ships the harvested crops to market, and distributes the profits earned to users according to their investment amounts. Users can check the distributed profits on their devices.

[0754] In this way, the system of the present invention specifically executes each processing step and provides efficient agricultural support and investment opportunities to users.

[0755] (Application example 1)

[0756] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0757] Conventional agricultural support systems simply perform market and climate analysis, but lack interactive user education, real-time data provision, and detailed guidance on crop cultivation management. As a result, it is difficult for users to properly learn and utilize agricultural techniques, and the provision and management of real-time information regarding investments is insufficient. Therefore, it has been a challenge to provide an environment that makes it easy for individual investors to enter agriculture and allows them to efficiently select and manage cultivated crops.

[0758] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0759] In this invention, the server includes: means for providing market analysis and climate analysis data reports via a smartphone, smart glasses, or head-mounted display; means for providing virtual reality or augmented reality content that allows users to interactively learn agricultural techniques; means for using collected data to monitor and manage farmland; means for providing efficient agricultural techniques and know-how to users via an online platform; means for presenting monitoring data in real time via smart glasses or a head-mounted display and guiding users on necessary actions; means for monitoring farmland conditions in real time using generative artificial intelligence and generating alerts when an abnormality is detected; means for notifying users of the progress of investment projects and revenue distribution through a user interface; and means for providing training in efficient agricultural techniques using virtual reality or augmented reality. This enables users to efficiently learn agricultural techniques while receiving interactive, real-time data and easily grasp the progress of investment projects in real time.

[0760] "Generative AI" is an AI system that automatically generates and analyzes data, and has the ability to process large amounts of data to make predictions and analyses.

[0761] "Market analysis data" refers to data such as market price trends, supply volume, and demand volume, and is information used to analyze market trends for specific crops or products.

[0762] "Climate analysis data" refers to meteorological information such as temperature, precipitation, and sunshine hours, and is used to analyze the climate conditions in a specific region.

[0763] "Smart glasses" are devices that extend vision, displaying information directly in the user's field of vision and allowing them to be operated by the user's gaze or voice input.

[0764] A "head-mounted display" is a display device worn on the user's head, and is a device that visually provides virtual reality and augmented reality content.

[0765] "Virtual reality" is a technology that allows users to immerse themselves in and experience interactive virtual environments generated using computer technology.

[0766] "Augmented reality" is a technology that combines reality and virtuality by overlaying virtual information onto images of the real world.

[0767] An "online platform" is a collection of services that can be accessed via the Internet, and is an interface that facilitates communication between users and the provision of services.

[0768] "Monitoring data" is data collected in real time using sensors and devices, and is information used to monitor the status of a specific environment or system.

[0769] An "investment project" refers to a plan or set of activities undertaken by a user to invest funds in a particular crop or piece of farmland and enjoy the benefits.

[0770] "Interactive agricultural technology learning" is a process in which users actively participate and learn agricultural technology through interactive information exchange.

[0771] This invention provides a system that uses generative artificial intelligence to collect market analysis data and climate analysis data to support individual investors in effectively entering the agricultural industry. A specific embodiment of this system is described below.

[0772] Market and climate analysis

[0773] The server uses generative AI to collect market analysis data and climate analysis data. Market analysis data is obtained by scraping data such as crop prices, consumption, and import / export volumes from online market information sites and published statistical reports. Climate analysis data is obtained by collecting weather information such as temperature, precipitation, and sunshine hours through meteorological agencies and APIs (OpenWeatherMap, WeatherStack). This data is analyzed to forecast crop demand and identify optimal cultivation areas.

[0774] Use of smartphones, smart glasses, and head-mounted displays

[0775] The server provides market and climate analysis data reports via smartphones, smart glasses, and head-mounted displays, allowing users to visually check the information they need in real time and learn about agricultural techniques interactively.

[0776] Investment project creation and management

[0777] The server accepts investment decisions from users, selects farmland, and creates investment projects. For example, if a user decides to invest in tomato cultivation in a specific area, the server uses that information to create a specific cultivation plan and manages it as a project. Furthermore, the server notifies users of the progress of the investment project and the distribution of profits via a smartphone app.

[0778] Farmland monitoring and management

[0779] The server collects real-time data on the farmland through IoT devices (Arduino, Raspberry Pi) and sensors, and monitors its condition. It collects data on soil moisture, temperature, nutrient levels, etc., and generates an alert to notify the user if an abnormality is detected. The server also allows users to view the monitoring data in real time through smart glasses or a head-mounted display.

[0780] Providing efficient agricultural technology and know-how

[0781] The server uses collected farmland data and market analysis data to provide efficient agricultural techniques and know-how via an online platform. Users can use this platform to learn the latest agricultural techniques. For example, they can use virtual reality (VR) and augmented reality (AR) to visually learn optimal planting spacing and fertilization plans for tomatoes using 3D models.

[0782] Harvesting, selling, and profit sharing

[0783] When the crops reach the harvesting season, the server plans and executes the harvesting work, ships the harvested crops to the market, and tally up the sales profits and distribute them to users. Users can check the distributed profits through their own accounts.

[0784] Prompt Sentence Examples

[0785] Please generate a Python script that scrapes market information websites and weather data to forecast demand for specific crops and identify optimal growing areas. Also, please provide examples of back-end and front-end implementations for an app that can display the results on a smartphone.

[0786] In this way, the present invention utilizes generative artificial intelligence to streamline various agricultural processes and provide an environment in which individual investors can easily enter the agricultural industry.

[0787] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0788] Step 1:

[0789] Collecting market analysis data

[0790] The server scrapes and collects market analysis data such as crop prices, consumption, and import / export volumes from market information sites and published statistical reports on the Internet. To do this, a web scraping tool (BeautifulSoup, Selenium, etc.) is used to extract and format data from the target sites. The input is a list of URLs for market information sites, and the output is the collected market analysis data.

[0791] Step 2:

[0792] Climate analysis data collection

[0793] The server collects weather data such as temperature, precipitation, and sunshine hours using meteorological agencies and APIs (OpenWeatherMap, WeatherStack). This data is used to determine the climate conditions in various locations. It analyzes the raw data obtained from the API and formats it as needed. The input is the API request, and the output is the climate analysis data.

[0794] Step 3:

[0795] Data analysis and crop recommendation identification

[0796] The server inputs the collected market analysis data and climate analysis data into a generative artificial intelligence (AI model), which then uses this data to predict crop demand and identify optimal cultivation areas.To do this, Python's Pandas and Numpy libraries are used to preprocess the data and generate prompt statements to be input into the AI ​​model.The inputs are market analysis data and climate analysis data, and the output is crop demand predictions and optimal cultivation areas.

[0797] Step 4:

[0798] Providing market and climate analysis data

[0799] The server provides market and climate analysis data reports via smartphones, smart glasses, and head-mounted displays. To quickly display data that requires real-time updates, React Native (for smartphones) and Unity (for VR / AR) are used. The input is the results of data analysis, and the output is a report displayed on the user's device.

[0800] Step 5:

[0801] Investment project creation and management

[0802] Users select investment crops and cultivation areas based on the data they provide. The server receives the user's selection results, generates specific investment projects, and formulates investment plans. This information is stored on a dedicated management platform for progress management. The input is the user's investment selection information, and the output is the generated investment project.

[0803] Step 6:

[0804] Farm monitoring and real-time notifications

[0805] The server uses IoT devices (Arduino, Raspberry Pi) and sensors to collect farmland data in real time and monitor the situation. If soil moisture, temperature, nutrient levels, etc. are outside of acceptable ranges, the server generates an alert and notifies the user device. The input is real-time data from the IoT device, and the output is a notification to the user.

[0806] Step 7:

[0807] Providing efficient agricultural technology and know-how

[0808] The server uses AI analysis results and collected data to provide efficient agricultural techniques and know-how via an online platform. Users can learn about agricultural techniques interactively using virtual reality and augmented reality. The input is the results of data analysis and educational content, and the output is an interactive learning experience.

[0809] Step 8:

[0810] Managing harvesting, sales and profit sharing

[0811] When the crops reach the harvesting period, the server plans and manages the harvesting work and arranges for shipment to the market. If any sales profits are earned, they are distributed to the users and the profit details are notified to the user devices. The input is harvest and sales data, and the output is a profit distribution notification to the users.

[0812] This allows users to efficiently learn about agricultural techniques while receiving interactive, real-time data, and makes it easier to keep track of the progress of investment projects in real time.

[0813] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0814] This invention is a system that combines generative artificial intelligence with an emotion engine to recognize and analyze user emotions, thereby providing more effective investment and agricultural support. This system collects market analysis data and climate analysis data, forecasts crop demand and supply, and identifies optimal cultivation areas and crops to provide to users. It also accepts user investment decisions, selects farmland, creates investment projects, and manages the process from harvesting to sales and profit distribution. It also uses the emotion engine to analyze user emotions and provide appropriate information and support.

[0815] Market and climate analysis

[0816] server

[0817] First, generative AI is used to collect market analysis data and climate analysis data. Market analysis data is obtained by scraping data such as crop prices, consumption, and import / export volumes from online agricultural crop price information sites and published statistical reports. Climate analysis data is obtained by collecting weather information such as temperature, precipitation, and sunshine hours through meteorological agencies and APIs. The server analyzes the data collected in this way to forecast crop demand and identify optimal cultivation areas.

[0818] Specific examples

[0819] For example, the server may detect that the market price of tomatoes is on the rise this year, and determine from regional weather data that a certain region is suitable for growing tomatoes. Based on this information, the server may recommend growing tomatoes to the user.

[0820] Use of emotion engine

[0821] server

[0822] The emotion engine analyzes the user's emotions in real time by analyzing user input, past activity data, and even biometric information (e.g., facial expressions and tone of voice collected through a camera or microphone) to determine the user's emotional state.

[0823] User

[0824] If the sentiment engine determines that a customer is motivated to invest, the system will provide more detailed investment information and success stories to encourage investment activity. On the other hand, if a customer is in a state of stress or has doubts, the system will provide risk information and supportive messages.

[0825] Specific examples

[0826] If a user expresses interest in growing tomatoes but is nervous about investing, the sentiment engine will detect that and provide information about past success stories and risk mitigation strategies.

[0827] Investment project creation and management

[0828] User

[0829] The user uses the terminal to review the provided information and select the crops and farmland to invest in. Once the user's investment decision is sent to the server, the server generates an investment project and formulates a management plan for the selected farmland.

[0830] Specific examples

[0831] If a user decides to invest in a tomato cultivation project in a certain region, the server creates a specific cultivation plan based on that information and manages it as a project.

[0832] Farmland monitoring and management

[0833] server

[0834] The server collects real-time data from IoT devices and sensors to monitor the condition of the farmland, collecting data such as soil moisture, temperature, and nutrient levels, and generates an alert to notify the user if an abnormality is detected.

[0835] Specific examples

[0836] For example, if soil moisture falls below an optimum level, the server will automatically activate the irrigation system to provide the appropriate amount of water.

[0837] Providing efficient agricultural technology and know-how

[0838] server

[0839] Based on collected farmland data and market analysis data, the company provides efficient agricultural techniques and know-how via an online platform, allowing users to learn about the latest agricultural techniques.

[0840] User

[0841] Users can apply the provided know-how to their actual work and manage their farmland, and can also respond immediately based on monitoring results and alert information.

[0842] Specific examples

[0843] For example, you can learn the optimal planting spacing and fertilization schedule for tomatoes on the platform and then put it into practice in the field.

[0844] Harvesting, selling, and profit sharing

[0845] server

[0846] When the crops reach the harvesting period, the system plans and executes the harvesting work, delivers the harvested crops to the market, and tally up the sales profits and distribute them to users.

[0847] Terminal

[0848] Users can view the profits distributed through their accounts.

[0849] Specific examples

[0850] For example, when the tomato harvest is completed and a profit of 200,000 yen is earned from sales, a portion of that profit is transferred to the user's account and the user is notified that the profit distribution has been completed.

[0851] In this way, the present invention utilizes generative AI and an emotion engine to streamline various agricultural processes and provide an environment in which individual investors can easily enter the agricultural industry. The introduction of an emotion engine makes it possible to provide information and support suited to the user's psychological state, thereby improving the effectiveness of investment activities and agricultural management.

[0852] The processing flow will be explained below.

[0853] Step 1:

[0854] server:

[0855] Market analysis data is scraped from online agricultural crop price information sites and statistical reports, and data such as crop prices, consumption, and import / export volumes is collected.

[0856] It collects climate analysis data such as temperature, precipitation, and sunshine hours from meteorological agency APIs and public weather databases.

[0857] The collected market analysis data and climate analysis data are stored in a database and prepared for data analysis.

[0858] Step 2:

[0859] server:

[0860] Generative artificial intelligence is used to analyze the collected market analysis data and climate analysis data.

[0861] Conduct demand and supply forecasts for crops and identify optimal crops.

[0862] Based on climate analysis data, the optimum cultivation areas for growing crops are identified.

[0863] Based on the analysis results, recommended crops and cultivation areas are determined, and a data format is created to provide the data to users.

[0864] Step 3:

[0865] server:

[0866] The analysis results are sent to a user-facing application.

[0867] Device:

[0868] The analysis results received from the server are displayed to the user, including recommended crops, recommended growing areas, and market demand analysis results.

[0869] The emotion engine analyzes the user's emotions and generates appropriate investment suggestions and support messages.

[0870] Step 4:

[0871] User:

[0872] Based on the analysis results and investment proposals presented, crops and farmland for investment are selected.

[0873] Enter the investment amount in the application and confirm your investment decision.

[0874] Device:

[0875] Data relating to the investment decision is transmitted to a server.

[0876] Step 5:

[0877] server:

[0878] Based on the user's investment decisions, an investment project is generated, including the selected farmland, crops, investment amount, and projected profits.

[0879] Develop a management plan for the farmland and set the required resources and work schedule.

[0880] Using an emotion engine, it analyzes users' emotions and investment motivation, and provides appropriate investment support and information to maintain motivation.

[0881] Specific examples

[0882] If the user decides to invest in tomato cultivation, the server creates a specific cultivation plan and provides the user with predicted profits and risk information.

[0883] Step 6:

[0884] server:

[0885] After the investment project is created, the progress of the project is provided to the user through a user interface.

[0886] Real-time data on farmland is collected through IoT devices and sensors, monitoring soil moisture, temperature, nutrient levels, and more.

[0887] If an abnormality is detected, an alert is generated and notified to the user.

[0888] Device:

[0889] Display project information and alert notifications in your account to prompt you to take necessary action.

[0890] Step 7:

[0891] User:

[0892] Receive alert notifications, take necessary action, and use online platforms to learn efficient farming techniques and know-how.

[0893] Put new technologies and methods into practice locally.

[0894] Specific examples

[0895] After users learn about irrigation methods for tomato cultivation, they can apply that information to actual farm management and ensure proper water supply.

[0896] Step 8:

[0897] server:

[0898] When the crop reaches harvest time, plan and execute the harvesting operations.

[0899] Ship the harvested crops to market and collect sales data.

[0900] Step 9:

[0901] server:

[0902] Profits from sales are tallied and distributed to users.

[0903] Profit sharing information will be notified to users.

[0904] Device:

[0905] Display the distributed profits in the user's account and notify them when the distribution is complete.

[0906] Specific examples

[0907] For example, when the tomato harvest is completed and a profit of 200,000 yen is earned from sales, a portion of that (for example, 50,000 yen) is transferred to the user's account, and the user is notified that the profit distribution has been completed.

[0908] In this way, the present invention utilizes generative artificial intelligence and an emotion engine to streamline a series of processes, from market analysis, climate analysis, investment plan creation, farmland management, harvesting, sales, and profit distribution, and provides an agricultural support system that provides information and support that adapts to the user's emotions.

[0909] Example 2

[0910] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0911] Modern agriculture and investment activities require advanced data analysis and real-time sentiment analysis to be efficient and effective. However, conventional technologies have difficulty integrating agricultural and investment data analysis, making it difficult to provide information and support that takes into account investors' daily emotional states. This has made it difficult for investors to make accurate decisions, often resulting in inefficient agricultural project management.

[0912] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0913] In this invention, the server includes means for collecting market analysis data using generative artificial intelligence and forecasting crop supply and demand, means for collecting climate analysis data using generative artificial intelligence and identifying optimal cultivation areas, means for presenting users with recommended crops and cultivation areas based on the collected market analysis data and climate analysis data, means for analyzing users' emotional data and providing information and support according to their psychological state, means for accepting users' investment decisions, selecting farmland and creating investment projects, and means for harvesting crops that have reached the harvest stage and shipping them to market and distributing the resulting profits to users. This allows users to receive highly accurate information that takes their emotional state into consideration, enabling more accurate and efficient investment and agricultural project management.

[0914] "Generative AI" is a type of AI technology used to collect, analyze, and predict data, specifically the ability to generate new information and patterns using generative models.

[0915] "Market analysis data" refers to data containing market information such as agricultural product prices, consumption, and import / export volumes, and is used to forecast agricultural supply and demand.

[0916] "Climate analysis data" refers to data containing information about the climate, such as temperature, precipitation, and hours of sunshine, that is used to identify optimal conditions for growing crops.

[0917] "Emotional data" refers to data that reflects the user's psychological state, and refers to information collected based on facial expressions, tone of voice, input content, etc.

[0918] "Agricultural land monitoring" is the process of using IoT devices and sensors to monitor agricultural land environmental data (e.g., soil moisture, temperature, and nutrient levels) in real time and responding if anomalies are detected.

[0919] An "online platform" is a collection of systems or services that can be accessed by users via the internet and that serve as a means of providing efficient agricultural technology and know-how.

[0920] An "investment project" is a planning and management framework generated by a user to invest in a specific crop and farmland, including the entire process from harvesting to sales and profit sharing.

[0921] An "alert" is a warning message that is sent to users when an abnormality is detected during agricultural land monitoring, and is intended to encourage a prompt response.

[0922] A "user interface" is the set of means and tools by which a user interacts with a system or service, allowing them to enter and retrieve information and view project status.

[0923] This invention is a system that combines generative artificial intelligence and an emotion engine, and by recognizing and analyzing the user's emotions, it provides effective investment and agricultural support. This system collects and analyzes various data, and not only provides users with optimal agricultural investment information, but also provides support according to the user's psychological state.

[0924] The server uses generative artificial intelligence (e.g., Google Cloud AI) to collect market analysis data. The market analysis data is obtained by scraping data such as crop prices, consumption, and import / export volumes from online agricultural crop price information websites and published statistical reports. Climate analysis data is also collected from meteorological agencies (e.g., the Japan Meteorological Agency) and APIs (e.g., OpenWeather API) to collect weather information such as temperature, precipitation, and sunshine hours.

[0925] The server integrates collected market analysis data and climate analysis data and analyzes the data using a generative AI model. This makes it possible to forecast crop demand and identify optimal cultivation areas. For example, the server may detect that the market price of tomatoes is on the rise this year, and determine from regional weather data that a certain region is suitable for tomato cultivation. Based on this information, it may recommend tomato cultivation to the user.

[0926] The server then uses an emotion engine (e.g., Azure Cognitive Services' Emotion API) to analyze the user's emotions in real time. It analyzes user input, past activity data, and biometric information collected through the camera and microphone, such as facial expressions and tone of voice, to identify the user's emotional state. For example, if a user expresses interest in growing tomatoes but is feeling anxious, the emotion engine will detect this anxiety and provide information about past success stories and risk mitigation measures.

[0927] The user uses a device (e.g., smartphone or PC) to check the provided information and select the crop and farmland in which to invest. When the user's investment decision is sent to the server, the server creates a specific cultivation plan for the selected farmland and crop as a project and manages it. For example, if the user decides to invest in a tomato cultivation project in a certain region, the server creates a specific cultivation plan based on that information and manages it as a project.

[0928] In addition, the server uses IoT devices (e.g., soil sensors, weather stations) to obtain real-time data on the managed farmland and monitor its condition. It collects data such as soil moisture, temperature, and nutrient levels, and generates alerts to notify users if an abnormality is detected. For example, if soil moisture falls below an appropriate level, the server automatically activates the irrigation system to provide the appropriate amount of water.

[0929] The server uses collected farmland data and market analysis data to provide efficient agricultural techniques and know-how via an online platform (e.g., Moodle). Users can use this platform to learn the latest agricultural techniques and apply them to their own work. For example, they can learn the optimal planting spacing and fertilization schedule for tomatoes on the platform and then put it into practice in the field.

[0930] When the crops reach harvest time, the server plans and executes the harvesting work and ships the harvested crops to market. The sales profits are tallied and distributed to users. Through the terminal, users can check their own accounts and confirm the distributed profits. For example, if the tomato harvest is complete and profits from sales are 200,000 yen, a portion of that will be transferred to the user's account and the user will be notified that the profit distribution has been completed.

[0931] In this way, the present invention utilizes generative AI and an emotion engine to streamline various agricultural processes and provide an environment in which individual investors can easily enter the agricultural industry. The introduction of an emotion engine makes it possible to provide information and support suited to the user's psychological state, thereby improving the effectiveness of investment activities and agricultural management.

[0932] Examples of specific prompts include the following:

[0933] 1. "Collect current market prices and consumption of tomatoes"

[0934] 2. "Collect climate data for the past year for a certain region."

[0935] 3. "If a user is interested in growing tomatoes, which areas are suitable for growing them?"

[0936] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0937] Step 1: Collect market analysis data

[0938] server

[0939] The server uses generative artificial intelligence (e.g., Google Cloud AI) to collect market analysis data.

[0940] Input: URL or API endpoint of a crop pricing information site or statistical report.

[0941] Output: Market analysis data such as prices, consumption, import and export volumes of agricultural products.

[0942] Specific operation: The server uses web scraping technology to obtain data from agricultural product price information websites on the Internet. For example, it uses a prompt such as "Collect the current market price and consumption of tomatoes."

[0943] Step 2: Collecting climate analysis data

[0944] server

[0945] The server collects climate analysis data from meteorological agencies (e.g., the Japan Meteorological Agency) or through APIs (e.g., OpenWeatherAPI).

[0946] Input: Weather agency database or API endpoint.

[0947] Output: Climate analysis data such as temperature, precipitation, and sunshine hours.

[0948] Specific operation: The server uses the API to obtain data such as temperature, precipitation, and sunshine hours for the specified area. An example of a prompt is "Collect climate data for the past year for the XX area."

[0949] Step 3: Data analysis and demand forecasting

[0950] server

[0951] The server integrates the collected market analysis data and climate analysis data and analyzes the data using a generative AI model.

[0952] Input: Market analysis data and climate analysis data.

[0953] Output: Demand forecast results and identification of optimal growing areas.

[0954] Specific operation: The server inputs the integrated data into an AI model and performs demand forecasting. For example, based on the collected market price of tomatoes and climate data for a certain region, it predicts that "a certain region is suitable for tomato cultivation."

[0955] Step 4: Analyzing user emotions with the emotion engine

[0956] server

[0957] The server uses an emotion engine (e.g., Azure Cognitive Services' Emotion API) to analyze the user's emotion data in real time.

[0958] Input: User input data, facial expression data, voice data, etc.

[0959] Output: Information about the user's emotional state.

[0960] Specific operation: The server analyzes facial expressions, tone of voice, and input from the user collected through the camera and microphone to determine the user's current emotional state. For example, if a user inputs "I feel anxious" about investing, the server analyzes this and provides appropriate support information.

[0961] Step 5: User investment decision and project creation

[0962] User

[0963] Users use the terminal to make investment decisions based on analysis results and advice.

[0964] Input: Prediction data and sentiment analysis results provided by the server.

[0965] Output: Submission of investment decision.

[0966] Specific operation: The user looks at the analysis results provided (for example, the best region for tomato cultivation), decides on an investment, and sends a message to the server such as "Invest in a tomato cultivation project in region X."

[0967] Step 6: Farm monitoring

[0968] server

[0969] The server collects real-time data on the farmland through IoT devices and sensors and monitors its condition.

[0970] Input: Data from IoT sensors (e.g. soil moisture, temperature, nutrient levels).

[0971] Output: Real-time monitoring data and abnormal alerts.

[0972] Specific operation: The server analyzes data sent from the sensors in real time, and if an abnormality is detected, it automatically starts the irrigation system and sends an alert to the user.

[0973] Step 7: Providing agricultural technology

[0974] server

[0975] Based on the collected data, the server provides efficient agricultural techniques and know-how via an online platform.

[0976] Input: Analyzed farmland data and market data.

[0977] Output: Technical information and know-how on an online platform.

[0978] Specific operation: The server posts information such as "optimal planting intervals for tomatoes" and "fertilization schedules" on the online platform. Users can learn from this information and apply it to their actual farming activities.

[0979] Step 8: Harvest, sell, and share profits

[0980] server

[0981] When the crops reach harvest time, the server plans and executes the harvesting operation, delivers the harvested products to market, and tallys up the sales profits and distribute them to users.

[0982] Inputs: Quantity of crop harvested and sale price.

[0983] Output: Profit sharing status.

[0984] Specific operation: After the harvest is completed, the server automatically calculates the sales profit and transfers it to the user's account. For example, if the tomato harvest is completed and the sales profit is 200,000 yen, it will be distributed to each user and notified.

[0985] (Application example 2)

[0986] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0987] In recent years, the agricultural sector has seen a demand for systems that utilize market and climate analysis data to forecast crop demand and supply and identify optimal cultivation areas. Furthermore, there is a need for methods to optimize agricultural product sales strategies based on consumer preferences and emotions, support investment decisions, and ensure profits. However, existing systems do not adequately analyze users' emotions and preferences, and recommend agricultural products and propose sales strategies that are tailored to the user's psychological state. Therefore, an effective system is needed to increase consumer and investor satisfaction.

[0988] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0989] In this invention, the server includes means for collecting market analysis data using generative artificial intelligence and forecasting crop supply and demand, means for collecting climate analysis data using generative artificial intelligence and identifying optimal cultivation areas, means for presenting recommended crops and cultivation areas to the user based on the collected market analysis data and climate analysis data, emotion engine means for analyzing the user's emotions and providing information and support based on the user's emotional state, means for accepting investment decisions from the user, selecting farmland, and creating investment projects, means for harvesting crops that have reached the harvest season and shipping them to market and distributing the resulting profits to the user, and means for analyzing consumer preferences based on the analysis of the user's emotions and recommending optimal agricultural products. This enables optimization of investment and agricultural product sales taking into account the user's emotions and preferences.

[0990] "Generative AI" refers to artificial intelligence techniques used to collect, analyze, and predict market and climate analysis data.

[0991] "Market analysis data" refers to data that includes market information such as prices, consumption, and import / export volumes of agricultural products.

[0992] "Climate analysis data" refers to data that includes meteorological information such as temperature, precipitation, and hours of sunshine.

[0993] The "emotion engine" is an engine that analyzes the user's emotions and provides information and support based on their psychological state.

[0994] "Site selection" is the process of selecting the most suitable land for an investment project.

[0995] "Investment project generation" is the process of selecting farmland and formulating a cultivation plan based on the user's investment decision, and then launching the project.

[0996] "Harvesting season" is the time when the crop is fully grown and ready for harvest.

[0997] "Profit sharing" is the process of distributing the profits from the sale of marketed crops to users.

[0998] "Agricultural product recommendation" is the act of suggesting the most suitable agricultural products based on consumer preferences and sentiment analysis.

[0999] "Collected Data" refers to various data collected and stored by the system, including market analysis data and climate analysis data.

[1000] An "online platform" is an internet-based system accessible to users that provides efficient agricultural techniques and know-how.

[1001] "Sales strategy proposal" is the act of presenting optimal sales methods and marketing strategies based on the consumer's emotional state through the emotional engine means.

[1002] "Smart devices" are devices with internet connectivity, such as smartphones and tablets, that provide information based on emotion analysis.

[1003] The program of the system that realizes this invention is executed on a smart device such as a smartphone or tablet, and provides optimal information on agricultural products and proposes sales strategies to consumers and investors based on market analysis data and climate analysis data. Specific examples are shown below.

[1004] The server uses generative artificial intelligence to scrape market analysis data from online agricultural product price information websites and published statistical reports. The main data collected includes crop prices, consumption, and import / export volumes. At the same time, climate analysis data is collected from meteorological agencies and through APIs. Climate analysis data includes temperature, precipitation, and sunshine hours. This data is then analyzed within the server to forecast crop demand and identify optimal cultivation areas.

[1005] Next, the emotion engine analyzes the user's input, past activity data, and even biometric information (e.g., facial expressions and tone of voice collected through the camera and microphone) in real time to identify the user's emotional state. Based on the emotion analysis, the user's psychological state is analyzed and information and support are provided based on their willingness to invest or purchase. For example, if a user is interested in growing tomatoes but feels anxious, information about past success stories and risk mitigation measures is provided.

[1006] Users can review the information provided through their smart devices and select the crops and farmland they wish to invest in. Once the user's investment decision is sent to the server, the server selects farmland, generates an investment project, and formulates a management plan for the selected farmland. When the crops reach harvest time, the server plans and executes the harvesting work and ships the harvested crops to market. The sales profits obtained are tallied and distributed to users.

[1007] In addition, the server obtains real-time data from the farmland (e.g., soil moisture, temperature, and nutrient levels) through IoT devices and sensors, and generates alerts and notifies users when an abnormality is detected, allowing them to respond quickly.

[1008] As a concrete example, suppose a user types, "Recently, I've been wondering about the quality of tomatoes." This text is subjected to sentiment analysis, and if the user's positive sentiment is confirmed, the server will suggest tomato cultivation based on market and climate data. Specific examples of suggestions include recommended cultivation areas, investment amounts, and past success stories. This information is provided to the user via their smart device.

[1009] Examples of prompts for the generative AI model in this system include:

[1010] Lately, I've been wondering about the quality of my tomatoes. Question: What's the best crop to buy?

[1011] In this way, the present invention is a system that takes into account the user's emotions and preferences and realizes optimal information provision and sales strategies for agricultural products.

[1012] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1013] Step 1:

[1014] Market analysis data collection and analysis

[1015] The server scrapes data such as crop prices, consumption, and import / export volumes from online agricultural product price information websites and published statistical reports. This data is then collected and used by the internal generative AI to perform market analysis.

[1016] Input: Online agricultural price information sites and statistical reports

[1017] Data processing: scraping, data cleansing, data analysis

[1018] Output: Demand and supply forecast data

[1019] Step 2:

[1020] Climate analysis data collection and analysis

[1021] The server collects weather data such as temperature, precipitation, and sunshine hours through weather agencies and APIs, and uses generative artificial intelligence to identify optimal growing areas.

[1022] Input: Weather data from weather agencies and APIs

[1023] Data processing: API requests, data cleansing, data analysis

[1024] Output: Optimal cultivation area data

[1025] Step 3:

[1026] User sentiment analysis

[1027] The server analyzes the user's input, past activity data, and biometric information (e.g., facial expressions and tone of voice collected through a camera and microphone) in real time to identify their emotional state.

[1028] Input: User text input, biometric information

[1029] Data processing: Sentiment analysis algorithms, text analysis, speech analysis

[1030] Output: User's emotional state data

[1031] Step 4:

[1032] Recommended crops and growing areas

[1033] The server presents users with recommended crops and growing areas based on the collected market and climate analysis data, and uses an emotion engine to provide information according to the user's emotional state.

[1034] Input: Market analysis data, climate analysis data, user emotional state data

[1035] Data processing: data integration, optimization algorithms

[1036] Output: Recommended crops and growing area information

[1037] Step 5:

[1038] Investment decisions and land selection

[1039] The user checks the information provided through the smart device and selects the crops and farmland to invest in. The user's investment decision is sent to the server, which then generates the farmland selection and investment project.

[1040] Input: User's investment decision data

[1041] Data processing: Database updates, project generation algorithms

[1042] Output: Land selection and investment project data

[1043] Step 6:

[1044] Harvesting and profit sharing

[1045] When the crops reach harvest time, the server plans and executes the harvesting operation, delivers the harvested crops to market, and compiles and distributes the sales profits to users.

[1046] Input: Harvest data, sales data

[1047] Data processing: Harvest plan generation, profit aggregation

[1048] Output: Profit distribution data to users

[1049] Step 7:

[1050] Real-time monitoring of farmland

[1051] The server obtains real-time data from the farmland (e.g., soil moisture, temperature, and nutrient levels) through IoT devices and sensors, and generates alerts and notifies users if any abnormalities are detected.

[1052] Input: Real-time data from IoT devices

[1053] Data processing: sensor data analysis, anomaly detection algorithms

[1054] Output: Alert notification data

[1055] Step 8:

[1056] Proposing sales strategies based on consumer preferences and emotions

[1057] The server, through an emotion engine means, proposes a sales strategy based on the consumer's emotional state.

[1058] Input: Consumer emotional state data

[1059] Data processing: emotional data analysis, sales strategy algorithms

[1060] Output: Sales strategy proposal data

[1061] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1062] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1063] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1064] [Third embodiment]

[1065] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1066] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1067] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1068] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1069] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1070] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1071] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1072] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1073] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1074] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1075] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1076] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1077] This invention relates to an agricultural support system that uses generative artificial intelligence to efficiently perform market and climate analysis, identify optimal crops and regions for cultivation, and provide them to users. It also provides an investment platform that makes it easier for individual investors to enter the agricultural industry, and supports the entire process from cultivation, harvesting, sales, and profit distribution.

[1078] Market and climate analysis

[1079] server

[1080] First, generative AI is used to collect market analysis data and climate analysis data. Market analysis data is obtained by scraping data such as crop prices, consumption, and import / export volumes from online market information sites and published statistical reports. Climate analysis data is obtained by collecting weather information such as temperature, precipitation, and sunshine hours through meteorological agencies and APIs. The server analyzes the data collected in this way to forecast crop demand and identify optimal cultivation areas.

[1081] Specific examples

[1082] For example, the server may detect that the market price of tomatoes is on the rise this year, and determine from regional weather data that a certain region is suitable for growing tomatoes. Based on this information, the server may recommend growing tomatoes to the user.

[1083] Investment project creation and management

[1084] User

[1085] The user uses the terminal to review the provided information and select the crops and farmland to invest in. Once the user's investment decision is sent to the server, the server generates an investment project and formulates a management plan for the selected farmland.

[1086] Specific examples

[1087] If a user decides to invest in a tomato cultivation project in a certain region, the server creates a specific cultivation plan based on that information and manages it as a project.

[1088] Farmland monitoring and management

[1089] server

[1090] The server collects real-time data from IoT devices and sensors to monitor the condition of the farmland, collecting data such as soil moisture, temperature, and nutrient levels, and generates an alert to notify the user if an abnormality is detected.

[1091] Specific examples

[1092] For example, if soil moisture falls below an optimum level, the server will automatically activate the irrigation system to provide the appropriate amount of water.

[1093] Providing efficient agricultural technology and know-how

[1094] server

[1095] Based on collected farmland data and market analysis data, the company provides efficient agricultural techniques and know-how via an online platform, allowing users to learn about the latest agricultural techniques.

[1096] User

[1097] Users can apply the provided know-how to their actual work and manage their farmland, and can also respond immediately based on monitoring results and alert information.

[1098] Specific examples

[1099] For example, you can learn the optimal planting spacing and fertilization schedule for tomatoes on the platform and then put it into practice in the field.

[1100] Harvesting, selling, and profit sharing

[1101] server

[1102] When the crops reach the harvesting period, the system plans and executes the harvesting work, delivers the harvested crops to the market, and tally up the sales profits and distribute them to users.

[1103] Terminal

[1104] Users can view the profits distributed through their accounts.

[1105] Specific examples

[1106] For example, if the tomato harvest is complete and 200,000 yen in profits are earned from sales, a portion of that profit will be transferred to the user's account and the user will be notified that the profit distribution has been completed.

[1107] In this way, the present invention utilizes generative artificial intelligence to streamline various agricultural processes and provide an environment in which individual investors can easily enter the agricultural industry.

[1108] The processing flow will be explained below.

[1109] Step 1:

[1110] server:

[1111] Collect market analysis data by scraping agricultural product price information websites and published statistical reports on the Internet to obtain data on crop prices, consumption, import and export volumes, etc.

[1112] Climate analysis data such as temperature, precipitation, and sunshine hours are collected through meteorological agencies and APIs.

[1113] The collected market analysis data and climate analysis data are stored in a database and formatted into the format required for analysis.

[1114] Step 2:

[1115] server:

[1116] Generative artificial intelligence is used to analyze collected market and climate analysis data to forecast crop demand and identify optimal cultivation areas.

[1117] Based on the results of these analyses, information on recommended crops and their growing areas is generated and converted into a format for providing information to users.

[1118] Step 3:

[1119] server:

[1120] Send the analysis results to the application.

[1121] Device:

[1122] The analysis results received from the server are displayed to the user, including recommended crops, growing areas, and reasons for market demand.

[1123] User:

[1124] Based on the information provided, select the crops and farmland to invest in.

[1125] Step 4:

[1126] User:

[1127] Enter the investment amount on the application and confirm your investment decision.

[1128] Device:

[1129] The investment selection results are sent to the server.

[1130] Step 5:

[1131] server:

[1132] Based on the user's investment decision, an investment project is generated. Project information includes the selected farmland, crops, and investment amount.

[1133] Develop a management plan for the farm, setting out the necessary resources and work schedules.

[1134] Step 6:

[1135] server:

[1136] The generated investment project information is sent to the user, providing an overview of the project and its progress.

[1137] Device:

[1138] Display project information in your account and keep users updated on progress.

[1139] Step 7:

[1140] server:

[1141] Through IoT devices and sensors, real-time data on farmland is collected and conditions such as soil moisture, temperature, and nutrient levels are monitored.

[1142] If an abnormality is detected, an alert is immediately generated and the user is notified of the abnormality and the countermeasures to be taken.

[1143] Device:

[1144] An alert notification is displayed to the user, prompting them to take the necessary action.

[1145] Step 8:

[1146] server:

[1147] The online platform provides users with efficient agricultural techniques and know-how, and updates information as new techniques and methods are released.

[1148] User:

[1149] Participants learn agricultural know-how and techniques provided on the platform and apply them to their actual agricultural work.

[1150] Step 9:

[1151] server:

[1152] Plan and execute harvest operations when crops reach harvest stage.

[1153] Ship the harvested crops to market and collect sales data.

[1154] Step 10:

[1155] server:

[1156] Profits from sales are aggregated and distributed to users for each investment project.

[1157] Profit sharing information is notified to users.

[1158] Device:

[1159] Display the refunded profits in the user's account and notify them that the distribution is complete.

[1160] In this way, the present invention provides a system that enables efficient and sustainable agriculture, benefiting both investors and farmers.

[1161] Example 1

[1162] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1163] In the agricultural sector, efficient crop cultivation, market supply and demand forecasting, and identifying optimal cultivation areas based on climatic conditions are extremely important, but conventional methods require the time and effort required to collect and analyze vast amounts of information, which acts as a barrier to individual investors entering the agricultural industry. Furthermore, automating the process from harvesting to sales and profit distribution, and monitoring the condition of farmland in real time, are essential for further efficiency and quality improvement. Furthermore, there is a lack of systems for providing efficient agricultural techniques and know-how. There is a need to solve these problems and create an environment that makes it easier for many people, including individual investors, to enter the agricultural industry.

[1164] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1165] In this invention, the server includes: means for collecting market analysis information using generative artificial intelligence and forecasting crop supply and demand; means for collecting climate analysis information using generative artificial intelligence and identifying optimal cultivation areas; means for presenting users with recommended crops and cultivation areas based on the collected market and climate analysis information; means for accepting users' investment decisions, selecting farmland, and creating investment projects; and means for harvesting and shipping crops to market when the crops reach harvest time and distributing the resulting profits to users. This streamlines the collection and analysis of vast amounts of information, making it easier for individual investors to enter the agricultural industry. Furthermore, the process from harvesting to sales and profit distribution is automated, and real-time monitoring of farmland conditions enables more efficient and higher-quality agriculture. Furthermore, providing efficient agricultural techniques and know-how contributes to improving users' agricultural skills.

[1166] "Generative AI" refers to AI that can generate, analyze, and predict data using machine learning technology.

[1167] "Market analysis information" refers to various market-related data such as crop prices, consumption, and import / export volumes.

[1168] "Climate analysis information" refers to various data related to climate, such as temperature, precipitation, and sunshine hours.

[1169] "User" refers to an individual or organization that uses this system to cultivate crops or invest.

[1170] "Recommended crops" refer to crops that are deemed optimal based on collected data.

[1171] "Cultivation area" refers to the area where a particular crop can be optimally grown.

[1172] "Investment Project" refers to the cultivation plan or farmland management plan in which a user makes an investment.

[1173] "When the crop reaches the harvest stage" refers to the point in time when the crop reaches the maturity set as the cultivation goal.

[1174] "Marketing" refers to supplying harvested crops to the market.

[1175] "Profit sharing" refers to sharing the revenue generated from the sale of crops with users.

[1176] "Real-time monitoring" refers to the continuous monitoring of the condition of agricultural land in an almost immediate manner.

[1177] "Generating an alert" refers to sending a notification when an anomaly is detected.

[1178] "User interface" refers to the screen or input device that allows a user to access the system and check or enter information.

[1179] "Online platform" refers to a software environment for providing information and services to users via the Internet.

[1180] "Agricultural technology" refers to the means and methods for carrying out agriculture efficiently.

[1181] "Know-how" refers to practical knowledge and experience in agriculture.

[1182] This invention relates to an agricultural support system that uses generative artificial intelligence to enable users to efficiently conduct market and climate analysis and identify optimal crops and regions for cultivation. It also provides an investment platform that makes it easier for individual investors to enter the agricultural industry, and supports the entire process from cultivation, harvesting, sales, and profit distribution.

[1183] Hardware and software used

[1184] Hardware

[1185] IoT sensor devices (e.g. soil humidity sensors, temperature sensors)

[1186] Server (e.g. cloud server provider)

[1187] User's device (e.g. smartphone, PC)

[1188] software

[1189] Generative AI models (e.g., GPT-4)

[1190] Scraping tools (e.g. BeautifulSoup, Scrapy)

[1191] Weather information API (e.g., OpenWeatherMap, WeatherAPI)

[1192] System program processing

[1193] server

[1194] The server uses generative artificial intelligence to collect market analysis information. It uses scraping tools (e.g., BeautifulSoup, Scrapy) to obtain data such as crop prices, consumption, and import / export volumes from market information websites and published statistical reports.

[1195] The server also collects climate analysis information such as temperature, precipitation, and sunshine hours through meteorological agency APIs (e.g., OpenWeatherMap, WeatherAPI). Based on the collected data, it performs analysis using a generative artificial intelligence model (e.g., GPT-4). The server identifies the optimal crops and regions to cultivate and makes recommendations to the user.

[1196] User

[1197] The user uses a terminal to check the information provided by the server. Based on the data on recommended crops and cultivation areas, the user selects the crops and farmland to invest in. The selected information is sent to the server, which then generates an investment project and formulates a management plan for the selected farmland.

[1198] server

[1199] The server collects real-time data from the farmland through IoT sensor devices. It collects data such as soil moisture, temperature, and nutrient levels, and analyzes the condition using a generative AI model. If an abnormality is detected, an alert is generated to notify the user. For example, if soil moisture falls below an appropriate value, the server automatically activates the irrigation system to provide the appropriate amount of water.

[1200] server

[1201] Based on the collected farmland data and market analysis information, the server provides efficient agricultural techniques and know-how via an online platform, allowing users to learn about the latest agricultural techniques.

[1202] server

[1203] When the crops reach harvest time, the system plans and executes the harvesting work, and delivers the harvested crops to market. The system tallys up the sales profits and distributes them to users, who can check the distributed profits through their own devices.

[1204] Examples of concrete examples and prompts

[1205] For example, the server may detect that the market price of tomatoes is on the rise this year, and determine from regional weather data that a particular region is suitable for tomato cultivation. Based on this information, the server may recommend tomato cultivation to the user.

[1206] Prompt Sentence Examples

[1207] "Make a forecast based on market prices and climate data to identify the best tomato growing regions for 2023."

[1208] In this way, the system of the present invention makes full use of generative artificial intelligence to streamline various agricultural processes and provide an environment in which individual investors can easily enter the agricultural industry.

[1209] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1210] Step 1: Collect market analysis data

[1211] server

[1212] Market analysis data such as crop prices, consumption, and import / export volumes are collected from market information sites and published statistical reports. The server uses a scraping tool to collect the data and stores it in a database.

[1213] Input: Market information site URL

[1214] Data processing: HTML analysis of web pages, extraction of necessary data

[1215] Output: Market analysis data (price, consumption, import / export volume, etc.)

[1216] Specific actions

[1217] The server scrapes data from multiple market information sites on the internet and stores information on prices, consumption, imports and exports in a database.

[1218] Step 2: Collecting climate analysis data

[1219] server

[1220] Weather analysis data such as temperature, precipitation, and sunshine hours are collected through the weather information API. The server sends the API request and stores the required data in a database.

[1221] Input: Weather information API endpoint, location information

[1222] Data processing: Analyzing JSON responses from API and extracting necessary weather data

[1223] Output: Climate analysis data (temperature, precipitation, sunshine hours, etc.)

[1224] Specific actions

[1225] The server sends a request to the weather information API, analyzes the acquired weather data, and stores it in a database.

[1226] Step 3: Analyze the data

[1227] server

[1228] Collected market and climate analysis data is analyzed using a generative artificial intelligence model (e.g., GPT-4), which forecasts demand and identifies optimal cultivation areas.

[1229] Input: Market analysis data, climate analysis data

[1230] Data calculations: demand forecasting and cultivation area identification using generative artificial intelligence

[1231] Output: List of recommended crops and growing areas

[1232] Specific actions

[1233] The server provides prompts to a generative artificial intelligence model that uses the collected data to predict demand and identify optimal growing areas, storing the results in a database.

[1234] Step 4: Providing recommendations

[1235] server

[1236] Based on the analysis results, the system provides users with information on recommended crops and cultivation areas.

[1237] Input: List of recommended crops and growing areas

[1238] Data processing: Select the necessary information and format it in a user-friendly way

[1239] Output: Recommendations to be presented to the user

[1240] Specific actions

[1241] The server converts the analysis results into a user-friendly format and provides them to users via a web application or mobile app.

[1242] Step 5: Investment decision and project generation

[1243] User

[1244] The user uses the terminal to review the provided information and select the crops and farmland to invest in. The selected information is sent to the server, which then generates an investment project.

[1245] Input: User's investment decision information (crop, cultivation area)

[1246] Data processing: generating investment projects and formulating management plans

[1247] Output: Investment project details

[1248] Specific actions

[1249] Users make investment decisions through a web interface, and the server generates projects and develops management plans based on the information received.

[1250] Step 6: Farm monitoring

[1251] server

[1252] It collects real-time data from farmland through IoT sensor devices, collecting data such as soil moisture, temperature, and nutrient levels, and generates alerts if anomalies are detected.

[1253] Input: IoT sensor data (humidity, temperature, nutrient levels)

[1254] Data calculation: Real-time analysis of data, detection of anomalies

[1255] Output: Alert notification (if necessary)

[1256] Specific actions

[1257] The server collects real-time data from IoT devices and notifies the user if an abnormality is detected.

[1258] Step 7: Providing agricultural technology and know-how

[1259] server

[1260] Based on collected farmland data and market analysis information, we provide efficient agricultural techniques and know-how via an online platform.

[1261] Input: Farmland data, market analysis information

[1262] Data calculation: Generation of agricultural technology and know-how, provision of learning resources

[1263] Output: Learning resources provided to the user

[1264] Specific actions

[1265] The server generates educational materials and guides containing the latest agricultural techniques and know-how and provides them to users through an online platform.

[1266] Step 8: Harvest, sell, and share profits

[1267] server

[1268] When the crops reach the harvesting period, the system plans and executes the harvesting work, delivers the harvested crops to the market, and tally up the sales profits and distribute them to users.

[1269] Input: Harvest data, sales data

[1270] Data calculation: Revenue calculation, profit distribution plan

[1271] Output: Profit distribution information

[1272] Specific actions

[1273] The server plans the harvesting work, ships the harvested crops to market, and distributes the profits earned to users according to their investment amounts. Users can check the distributed profits on their devices.

[1274] In this way, the system of the present invention specifically executes each processing step and provides efficient agricultural support and investment opportunities to users.

[1275] (Application example 1)

[1276] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1277] Conventional agricultural support systems simply perform market and climate analysis, but lack interactive user education, real-time data provision, and detailed guidance on crop cultivation management. As a result, it is difficult for users to properly learn and utilize agricultural techniques, and the provision and management of real-time information regarding investments is insufficient. Therefore, it has been a challenge to provide an environment that makes it easy for individual investors to enter agriculture and allows them to efficiently select and manage cultivated crops.

[1278] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1279] In this invention, the server includes: means for providing market analysis and climate analysis data reports via a smartphone, smart glasses, or head-mounted display; means for providing virtual reality or augmented reality content that allows users to interactively learn agricultural techniques; means for using collected data to monitor and manage farmland; means for providing efficient agricultural techniques and know-how to users via an online platform; means for presenting monitoring data in real time via smart glasses or a head-mounted display and guiding users on necessary actions; means for monitoring farmland conditions in real time using generative artificial intelligence and generating alerts when an abnormality is detected; means for notifying users of the progress of investment projects and revenue distribution through a user interface; and means for providing training in efficient agricultural techniques using virtual reality or augmented reality. This enables users to efficiently learn agricultural techniques while receiving interactive, real-time data and easily grasp the progress of investment projects in real time.

[1280] "Generative AI" is an AI system that automatically generates and analyzes data, and has the ability to process large amounts of data to make predictions and analyses.

[1281] "Market analysis data" refers to data such as market price trends, supply volume, and demand volume, and is information used to analyze market trends for specific crops or products.

[1282] "Climate analysis data" refers to meteorological information such as temperature, precipitation, and sunshine hours, and is used to analyze the climate conditions in a specific region.

[1283] "Smart glasses" are devices that extend vision, displaying information directly in the user's field of vision and allowing them to be operated by the user's gaze or voice input.

[1284] A "head-mounted display" is a display device worn on the user's head, and is a device that visually provides virtual reality and augmented reality content.

[1285] "Virtual reality" is a technology that allows users to immerse themselves in and experience interactive virtual environments generated using computer technology.

[1286] "Augmented reality" is a technology that combines reality and virtuality by overlaying virtual information onto images of the real world.

[1287] An "online platform" is a collection of services that can be accessed via the Internet, and is an interface that facilitates communication between users and the provision of services.

[1288] "Monitoring data" is data collected in real time using sensors and devices, and is information used to monitor the status of a specific environment or system.

[1289] An "investment project" refers to a plan or set of activities undertaken by a user to invest funds in a particular crop or piece of farmland and enjoy the benefits.

[1290] "Interactive agricultural technology learning" is a process in which users actively participate and learn agricultural technology through interactive information exchange.

[1291] This invention provides a system that uses generative artificial intelligence to collect market analysis data and climate analysis data to support individual investors in effectively entering the agricultural industry. A specific embodiment of this system is described below.

[1292] Market and climate analysis

[1293] The server uses generative AI to collect market analysis data and climate analysis data. Market analysis data is obtained by scraping data such as crop prices, consumption, and import / export volumes from online market information sites and published statistical reports. Climate analysis data is obtained by collecting weather information such as temperature, precipitation, and sunshine hours through meteorological agencies and APIs (OpenWeatherMap, WeatherStack). This data is analyzed to forecast crop demand and identify optimal cultivation areas.

[1294] Use of smartphones, smart glasses, and head-mounted displays

[1295] The server provides market and climate analysis data reports via smartphones, smart glasses, and head-mounted displays, allowing users to visually check the information they need in real time and learn about agricultural techniques interactively.

[1296] Investment project creation and management

[1297] The server accepts investment decisions from users, selects farmland, and creates investment projects. For example, if a user decides to invest in tomato cultivation in a specific area, the server uses that information to create a specific cultivation plan and manages it as a project. Furthermore, the server notifies users of the progress of the investment project and the distribution of profits via a smartphone app.

[1298] Farmland monitoring and management

[1299] The server collects real-time data on the farmland through IoT devices (Arduino, Raspberry Pi) and sensors, and monitors its condition. It collects data on soil moisture, temperature, nutrient levels, etc., and generates an alert to notify the user if an abnormality is detected. The server also allows users to view the monitoring data in real time through smart glasses or a head-mounted display.

[1300] Providing efficient agricultural technology and know-how

[1301] The server uses collected farmland data and market analysis data to provide efficient agricultural techniques and know-how via an online platform. Users can use this platform to learn the latest agricultural techniques. For example, they can use virtual reality (VR) and augmented reality (AR) to visually learn optimal planting spacing and fertilization plans for tomatoes using 3D models.

[1302] Harvesting, selling, and profit sharing

[1303] When the crops reach the harvesting season, the server plans and executes the harvesting work, ships the harvested crops to the market, and tally up the sales profits and distribute them to users. Users can check the distributed profits through their own accounts.

[1304] Prompt Sentence Examples

[1305] Please generate a Python script that scrapes market information websites and weather data to forecast demand for specific crops and identify optimal growing areas. Also, please provide examples of back-end and front-end implementations for an app that can display the results on a smartphone.

[1306] In this way, the present invention utilizes generative artificial intelligence to streamline various agricultural processes and provide an environment in which individual investors can easily enter the agricultural industry.

[1307] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1308] Step 1:

[1309] Collecting market analysis data

[1310] The server scrapes and collects market analysis data such as crop prices, consumption, and import / export volumes from market information sites and published statistical reports on the Internet. To do this, a web scraping tool (BeautifulSoup, Selenium, etc.) is used to extract and format data from the target sites. The input is a list of URLs for market information sites, and the output is the collected market analysis data.

[1311] Step 2:

[1312] Climate analysis data collection

[1313] The server collects weather data such as temperature, precipitation, and sunshine hours using meteorological agencies and APIs (OpenWeatherMap, WeatherStack). This data is used to determine the climate conditions in various locations. It analyzes the raw data obtained from the API and formats it as needed. The input is the API request, and the output is the climate analysis data.

[1314] Step 3:

[1315] Data analysis and crop recommendation identification

[1316] The server inputs the collected market analysis data and climate analysis data into a generative artificial intelligence (AI model), which then uses this data to predict crop demand and identify optimal cultivation areas.To do this, Python's Pandas and Numpy libraries are used to preprocess the data and generate prompt statements to be input into the AI ​​model.The inputs are market analysis data and climate analysis data, and the output is crop demand predictions and optimal cultivation areas.

[1317] Step 4:

[1318] Providing market and climate analysis data

[1319] The server provides market and climate analysis data reports via smartphones, smart glasses, and head-mounted displays. To quickly display data that requires real-time updates, React Native (for smartphones) and Unity (for VR / AR) are used. The input is the results of data analysis, and the output is a report displayed on the user's device.

[1320] Step 5:

[1321] Investment project creation and management

[1322] Users select investment crops and cultivation areas based on the data they provide. The server receives the user's selection results, generates specific investment projects, and formulates investment plans. This information is stored on a dedicated management platform for progress management. The input is the user's investment selection information, and the output is the generated investment project.

[1323] Step 6:

[1324] Farm monitoring and real-time notifications

[1325] The server uses IoT devices (Arduino, Raspberry Pi) and sensors to collect farmland data in real time and monitor the situation. If soil moisture, temperature, nutrient levels, etc. are outside of acceptable ranges, the server generates an alert and notifies the user device. The input is real-time data from the IoT device, and the output is a notification to the user.

[1326] Step 7:

[1327] Providing efficient agricultural technology and know-how

[1328] The server uses AI analysis results and collected data to provide efficient agricultural techniques and know-how via an online platform. Users can learn about agricultural techniques interactively using virtual reality and augmented reality. The input is the results of data analysis and educational content, and the output is an interactive learning experience.

[1329] Step 8:

[1330] Managing harvesting, sales and profit sharing

[1331] When the crops reach the harvesting period, the server plans and manages the harvesting work and arranges for shipment to the market. If any sales profits are earned, they are distributed to the users and the profit details are notified to the user devices. The input is harvest and sales data, and the output is a profit distribution notification to the users.

[1332] This allows users to efficiently learn about agricultural techniques while receiving interactive, real-time data, and makes it easier to keep track of the progress of investment projects in real time.

[1333] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1334] This invention is a system that combines generative artificial intelligence with an emotion engine to recognize and analyze user emotions, thereby providing more effective investment and agricultural support. This system collects market analysis data and climate analysis data, forecasts crop demand and supply, and identifies optimal cultivation areas and crops to provide to users. It also accepts user investment decisions, selects farmland, creates investment projects, and manages the process from harvesting to sales and profit distribution. It also uses the emotion engine to analyze user emotions and provide appropriate information and support.

[1335] Market and climate analysis

[1336] server

[1337] First, generative AI is used to collect market analysis data and climate analysis data. Market analysis data is obtained by scraping data such as crop prices, consumption, and import / export volumes from online agricultural crop price information sites and published statistical reports. Climate analysis data is obtained by collecting weather information such as temperature, precipitation, and sunshine hours through meteorological agencies and APIs. The server analyzes the data collected in this way to forecast crop demand and identify optimal cultivation areas.

[1338] Specific examples

[1339] For example, the server may detect that the market price of tomatoes is on the rise this year, and determine from regional weather data that a certain region is suitable for growing tomatoes. Based on this information, the server may recommend growing tomatoes to the user.

[1340] Use of emotion engine

[1341] server

[1342] The emotion engine analyzes the user's emotions in real time by analyzing user input, past activity data, and even biometric information (e.g., facial expressions and tone of voice collected through a camera or microphone) to determine the user's emotional state.

[1343] User

[1344] If the sentiment engine determines that a customer is motivated to invest, the system will provide more detailed investment information and success stories to encourage investment activity. On the other hand, if a customer is in a state of stress or has doubts, the system will provide risk information and supportive messages.

[1345] Specific examples

[1346] If a user expresses interest in growing tomatoes but is nervous about investing, the sentiment engine will detect that and provide information about past success stories and risk mitigation strategies.

[1347] Investment project creation and management

[1348] User

[1349] The user uses the terminal to review the provided information and select the crops and farmland to invest in. Once the user's investment decision is sent to the server, the server generates an investment project and formulates a management plan for the selected farmland.

[1350] Specific examples

[1351] If a user decides to invest in a tomato cultivation project in a certain region, the server creates a specific cultivation plan based on that information and manages it as a project.

[1352] Farmland monitoring and management

[1353] server

[1354] The server collects real-time data from IoT devices and sensors to monitor the condition of the farmland, collecting data such as soil moisture, temperature, and nutrient levels, and generates an alert to notify the user if an abnormality is detected.

[1355] Specific examples

[1356] For example, if soil moisture falls below an optimum level, the server will automatically activate the irrigation system to provide the appropriate amount of water.

[1357] Providing efficient agricultural technology and know-how

[1358] server

[1359] Based on collected farmland data and market analysis data, the company provides efficient agricultural techniques and know-how via an online platform, allowing users to learn about the latest agricultural techniques.

[1360] User

[1361] Users can apply the provided know-how to their actual work and manage their farmland, and can also respond immediately based on monitoring results and alert information.

[1362] Specific examples

[1363] For example, you can learn the optimal planting spacing and fertilization schedule for tomatoes on the platform and then put it into practice in the field.

[1364] Harvesting, selling, and profit sharing

[1365] server

[1366] When the crops reach the harvesting period, the system plans and executes the harvesting work, delivers the harvested crops to the market, and tally up the sales profits and distribute them to users.

[1367] Terminal

[1368] Users can view the profits distributed through their accounts.

[1369] Specific examples

[1370] For example, when the tomato harvest is completed and a profit of 200,000 yen is earned from sales, a portion of that profit is transferred to the user's account and the user is notified that the profit distribution has been completed.

[1371] In this way, the present invention utilizes generative AI and an emotion engine to streamline various agricultural processes and provide an environment in which individual investors can easily enter the agricultural industry. The introduction of an emotion engine makes it possible to provide information and support suited to the user's psychological state, thereby improving the effectiveness of investment activities and agricultural management.

[1372] The processing flow will be explained below.

[1373] Step 1:

[1374] server:

[1375] Market analysis data is scraped from online agricultural crop price information sites and statistical reports, and data such as crop prices, consumption, and import / export volumes is collected.

[1376] It collects climate analysis data such as temperature, precipitation, and sunshine hours from meteorological agency APIs and public weather databases.

[1377] The collected market analysis data and climate analysis data are stored in a database and prepared for data analysis.

[1378] Step 2:

[1379] server:

[1380] Generative artificial intelligence is used to analyze the collected market analysis data and climate analysis data.

[1381] Conduct demand and supply forecasts for crops and identify optimal crops.

[1382] Based on climate analysis data, the optimum cultivation areas for growing crops are identified.

[1383] Based on the analysis results, recommended crops and cultivation areas are determined, and a data format is created to provide the data to users.

[1384] Step 3:

[1385] server:

[1386] The analysis results are sent to a user-facing application.

[1387] Device:

[1388] The analysis results received from the server are displayed to the user, including recommended crops, recommended growing areas, and market demand analysis results.

[1389] The emotion engine analyzes the user's emotions and generates appropriate investment suggestions and support messages.

[1390] Step 4:

[1391] User:

[1392] Based on the analysis results and investment proposals presented, crops and farmland for investment are selected.

[1393] Enter the investment amount in the application and confirm your investment decision.

[1394] Device:

[1395] Data relating to the investment decision is transmitted to a server.

[1396] Step 5:

[1397] server:

[1398] Based on the user's investment decisions, an investment project is generated, including the selected farmland, crops, investment amount, and projected profits.

[1399] Develop a management plan for the farmland and set the required resources and work schedule.

[1400] Using an emotion engine, it analyzes users' emotions and investment motivation, and provides appropriate investment support and information to maintain motivation.

[1401] Specific examples

[1402] If the user decides to invest in tomato cultivation, the server creates a specific cultivation plan and provides the user with predicted profits and risk information.

[1403] Step 6:

[1404] server:

[1405] After the investment project is created, the progress of the project is provided to the user through a user interface.

[1406] Real-time data on farmland is collected through IoT devices and sensors, monitoring soil moisture, temperature, nutrient levels, and more.

[1407] If an abnormality is detected, an alert is generated and notified to the user.

[1408] Device:

[1409] Display project information and alert notifications in your account to prompt you to take necessary action.

[1410] Step 7:

[1411] User:

[1412] Receive alert notifications, take necessary action, and use online platforms to learn efficient farming techniques and know-how.

[1413] Put new technologies and methods into practice locally.

[1414] Specific examples

[1415] After users learn about irrigation methods for tomato cultivation, they can apply that information to actual farm management and ensure proper water supply.

[1416] Step 8:

[1417] server:

[1418] When the crop reaches harvest time, plan and execute the harvesting operations.

[1419] Ship the harvested crops to market and collect sales data.

[1420] Step 9:

[1421] server:

[1422] Profits from sales are tallied and distributed to users.

[1423] Profit sharing information will be notified to users.

[1424] Device:

[1425] Display the distributed profits in the user's account and notify them when the distribution is complete.

[1426] Specific examples

[1427] For example, when the tomato harvest is completed and a profit of 200,000 yen is earned from sales, a portion of that (for example, 50,000 yen) is transferred to the user's account, and the user is notified that the profit distribution has been completed.

[1428] In this way, the present invention utilizes generative artificial intelligence and an emotion engine to streamline a series of processes, from market analysis, climate analysis, investment plan creation, farmland management, harvesting, sales, and profit distribution, and provides an agricultural support system that provides information and support that adapts to the user's emotions.

[1429] Example 2

[1430] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1431] Modern agriculture and investment activities require advanced data analysis and real-time sentiment analysis to be efficient and effective. However, conventional technologies have difficulty integrating agricultural and investment data analysis, making it difficult to provide information and support that takes into account investors' daily emotional states. This has made it difficult for investors to make accurate decisions, often resulting in inefficient agricultural project management.

[1432] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1433] In this invention, the server includes means for collecting market analysis data using generative artificial intelligence and forecasting crop supply and demand, means for collecting climate analysis data using generative artificial intelligence and identifying optimal cultivation areas, means for presenting users with recommended crops and cultivation areas based on the collected market analysis data and climate analysis data, means for analyzing users' emotional data and providing information and support according to their psychological state, means for accepting users' investment decisions, selecting farmland and creating investment projects, and means for harvesting crops that have reached the harvest stage and shipping them to market and distributing the resulting profits to users. This allows users to receive highly accurate information that takes their emotional state into consideration, enabling more accurate and efficient investment and agricultural project management.

[1434] "Generative AI" is a type of AI technology used to collect, analyze, and predict data, specifically the ability to generate new information and patterns using generative models.

[1435] "Market analysis data" refers to data containing market information such as agricultural product prices, consumption, and import / export volumes, and is used to forecast agricultural supply and demand.

[1436] "Climate analysis data" refers to data containing information about the climate, such as temperature, precipitation, and hours of sunshine, that is used to identify optimal conditions for growing crops.

[1437] "Emotional data" refers to data that reflects the user's psychological state, and refers to information collected based on facial expressions, tone of voice, input content, etc.

[1438] "Agricultural land monitoring" is the process of using IoT devices and sensors to monitor agricultural land environmental data (e.g., soil moisture, temperature, and nutrient levels) in real time and responding if anomalies are detected.

[1439] An "online platform" is a collection of systems or services that can be accessed by users via the internet and that serve as a means of providing efficient agricultural technology and know-how.

[1440] An "investment project" is a planning and management framework generated by a user to invest in a specific crop and farmland, including the entire process from harvesting to sales and profit sharing.

[1441] An "alert" is a warning message that is sent to users when an abnormality is detected during agricultural land monitoring, and is intended to encourage a prompt response.

[1442] A "user interface" is the set of means and tools by which a user interacts with a system or service, allowing them to enter and retrieve information and view project status.

[1443] This invention is a system that combines generative artificial intelligence and an emotion engine, and by recognizing and analyzing the user's emotions, it provides effective investment and agricultural support. This system collects and analyzes various data, and not only provides users with optimal agricultural investment information, but also provides support according to the user's psychological state.

[1444] The server uses generative artificial intelligence (e.g., Google Cloud AI) to collect market analysis data. The market analysis data is obtained by scraping data such as crop prices, consumption, and import / export volumes from online agricultural crop price information websites and published statistical reports. Climate analysis data is also collected from meteorological agencies (e.g., the Japan Meteorological Agency) and APIs (e.g., OpenWeather API) to collect weather information such as temperature, precipitation, and sunshine hours.

[1445] The server integrates collected market analysis data and climate analysis data and analyzes the data using a generative AI model. This makes it possible to forecast crop demand and identify optimal cultivation areas. For example, the server may detect that the market price of tomatoes is on the rise this year, and determine from regional weather data that a certain region is suitable for tomato cultivation. Based on this information, it may recommend tomato cultivation to the user.

[1446] The server then uses an emotion engine (e.g., Azure Cognitive Services' Emotion API) to analyze the user's emotions in real time. It analyzes user input, past activity data, and biometric information collected through the camera and microphone, such as facial expressions and tone of voice, to identify the user's emotional state. For example, if a user expresses interest in growing tomatoes but is feeling anxious, the emotion engine will detect this anxiety and provide information about past success stories and risk mitigation measures.

[1447] The user uses a device (e.g., smartphone or PC) to check the provided information and select the crop and farmland in which to invest. When the user's investment decision is sent to the server, the server creates a specific cultivation plan for the selected farmland and crop as a project and manages it. For example, if the user decides to invest in a tomato cultivation project in a certain region, the server creates a specific cultivation plan based on that information and manages it as a project.

[1448] In addition, the server uses IoT devices (e.g., soil sensors, weather stations) to obtain real-time data on the managed farmland and monitor its condition. It collects data such as soil moisture, temperature, and nutrient levels, and generates alerts to notify users if an abnormality is detected. For example, if soil moisture falls below an appropriate level, the server automatically activates the irrigation system to provide the appropriate amount of water.

[1449] The server uses collected farmland data and market analysis data to provide efficient agricultural techniques and know-how via an online platform (e.g., Moodle). Users can use this platform to learn the latest agricultural techniques and apply them to their own work. For example, they can learn the optimal planting spacing and fertilization schedule for tomatoes on the platform and then put it into practice in the field.

[1450] When the crops reach harvest time, the server plans and executes the harvesting work and ships the harvested crops to market. The sales profits are tallied and distributed to users. Through the terminal, users can check their own accounts and confirm the distributed profits. For example, if the tomato harvest is complete and profits from sales are 200,000 yen, a portion of that will be transferred to the user's account and the user will be notified that the profit distribution has been completed.

[1451] In this way, the present invention utilizes generative AI and an emotion engine to streamline various agricultural processes and provide an environment in which individual investors can easily enter the agricultural industry. The introduction of an emotion engine makes it possible to provide information and support suited to the user's psychological state, thereby improving the effectiveness of investment activities and agricultural management.

[1452] Examples of specific prompts include the following:

[1453] 1. "Collect current market prices and consumption of tomatoes"

[1454] 2. "Collect climate data for the past year for a certain region."

[1455] 3. "If a user is interested in growing tomatoes, which areas are suitable for growing them?"

[1456] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1457] Step 1: Collect market analysis data

[1458] server

[1459] The server uses generative artificial intelligence (e.g., Google Cloud AI) to collect market analysis data.

[1460] Input: URL or API endpoint of a crop pricing information site or statistical report.

[1461] Output: Market analysis data such as prices, consumption, import and export volumes of agricultural products.

[1462] Specific operation: The server uses web scraping technology to obtain data from agricultural product price information websites on the Internet. For example, it uses a prompt such as "Collect the current market price and consumption of tomatoes."

[1463] Step 2: Collecting climate analysis data

[1464] server

[1465] The server collects climate analysis data from meteorological agencies (e.g., the Japan Meteorological Agency) or through APIs (e.g., OpenWeatherAPI).

[1466] Input: Weather agency database or API endpoint.

[1467] Output: Climate analysis data such as temperature, precipitation, and sunshine hours.

[1468] Specific operation: The server uses the API to obtain data such as temperature, precipitation, and sunshine hours for the specified area. An example of a prompt is "Collect climate data for the past year for the XX area."

[1469] Step 3: Data analysis and demand forecasting

[1470] server

[1471] The server integrates the collected market analysis data and climate analysis data and analyzes the data using a generative AI model.

[1472] Input: Market analysis data and climate analysis data.

[1473] Output: Demand forecast results and identification of optimal growing areas.

[1474] Specific operation: The server inputs the integrated data into an AI model and performs demand forecasting. For example, based on the collected market price of tomatoes and climate data for a certain region, it predicts that "a certain region is suitable for tomato cultivation."

[1475] Step 4: Analyzing user emotions with the emotion engine

[1476] server

[1477] The server uses an emotion engine (e.g., Azure Cognitive Services' Emotion API) to analyze the user's emotion data in real time.

[1478] Input: User input data, facial expression data, voice data, etc.

[1479] Output: Information about the user's emotional state.

[1480] Specific operation: The server analyzes facial expressions, tone of voice, and input from the user collected through the camera and microphone to determine the user's current emotional state. For example, if a user inputs "I feel anxious" about investing, the server analyzes this and provides appropriate support information.

[1481] Step 5: User investment decision and project creation

[1482] User

[1483] Users use the terminal to make investment decisions based on analysis results and advice.

[1484] Input: Prediction data and sentiment analysis results provided by the server.

[1485] Output: Submission of investment decision.

[1486] Specific operation: The user looks at the analysis results provided (for example, the best region for tomato cultivation), decides on an investment, and sends a message to the server such as "Invest in a tomato cultivation project in region X."

[1487] Step 6: Farm monitoring

[1488] server

[1489] The server collects real-time data on the farmland through IoT devices and sensors and monitors its condition.

[1490] Input: Data from IoT sensors (e.g. soil moisture, temperature, nutrient levels).

[1491] Output: Real-time monitoring data and abnormal alerts.

[1492] Specific operation: The server analyzes data sent from the sensors in real time, and if an abnormality is detected, it automatically starts the irrigation system and sends an alert to the user.

[1493] Step 7: Providing agricultural technology

[1494] server

[1495] Based on the collected data, the server provides efficient agricultural techniques and know-how via an online platform.

[1496] Input: Analyzed farmland data and market data.

[1497] Output: Technical information and know-how on an online platform.

[1498] Specific operation: The server posts information such as "optimal planting intervals for tomatoes" and "fertilization schedules" on the online platform. Users can learn from this information and apply it to their actual farming activities.

[1499] Step 8: Harvest, sell, and share profits

[1500] server

[1501] When the crops reach harvest time, the server plans and executes the harvesting operation, delivers the harvested products to market, and tallys up the sales profits and distribute them to users.

[1502] Inputs: Quantity of crop harvested and sale price.

[1503] Output: Profit sharing status.

[1504] Specific operation: After the harvest is completed, the server automatically calculates the sales profit and transfers it to the user's account. For example, if the tomato harvest is completed and the sales profit is 200,000 yen, it will be distributed to each user and notified.

[1505] (Application example 2)

[1506] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1507] In recent years, the agricultural sector has seen a demand for systems that utilize market and climate analysis data to forecast crop demand and supply and identify optimal cultivation areas. Furthermore, there is a need for methods to optimize agricultural product sales strategies based on consumer preferences and emotions, support investment decisions, and ensure profits. However, existing systems do not adequately analyze users' emotions and preferences, and recommend agricultural products and propose sales strategies that are tailored to the user's psychological state. Therefore, an effective system is needed to increase consumer and investor satisfaction.

[1508] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1509] In this invention, the server includes means for collecting market analysis data using generative artificial intelligence and forecasting crop supply and demand, means for collecting climate analysis data using generative artificial intelligence and identifying optimal cultivation areas, means for presenting recommended crops and cultivation areas to the user based on the collected market analysis data and climate analysis data, emotion engine means for analyzing the user's emotions and providing information and support based on the user's emotional state, means for accepting investment decisions from the user, selecting farmland, and creating investment projects, means for harvesting crops that have reached the harvest season and shipping them to market and distributing the resulting profits to the user, and means for analyzing consumer preferences based on the analysis of the user's emotions and recommending optimal agricultural products. This enables optimization of investment and agricultural product sales taking into account the user's emotions and preferences.

[1510] "Generative AI" refers to artificial intelligence techniques used to collect, analyze, and predict market and climate analysis data.

[1511] "Market analysis data" refers to data that includes market information such as prices, consumption, and import / export volumes of agricultural products.

[1512] "Climate analysis data" refers to data that includes meteorological information such as temperature, precipitation, and hours of sunshine.

[1513] The "emotion engine" is an engine that analyzes the user's emotions and provides information and support based on their psychological state.

[1514] "Site selection" is the process of selecting the most suitable land for an investment project.

[1515] "Investment project generation" is the process of selecting farmland and formulating a cultivation plan based on the user's investment decision, and then launching the project.

[1516] "Harvesting season" is the time when the crop is fully grown and ready for harvest.

[1517] "Profit sharing" is the process of distributing the profits from the sale of marketed crops to users.

[1518] "Agricultural product recommendation" is the act of suggesting the most suitable agricultural products based on consumer preferences and sentiment analysis.

[1519] "Collected Data" refers to various data collected and stored by the system, including market analysis data and climate analysis data.

[1520] An "online platform" is an internet-based system accessible to users that provides efficient agricultural techniques and know-how.

[1521] "Sales strategy proposal" is the act of presenting optimal sales methods and marketing strategies based on the consumer's emotional state through the emotional engine means.

[1522] "Smart devices" are devices with internet connectivity, such as smartphones and tablets, that provide information based on emotion analysis.

[1523] The program of the system that realizes this invention is executed on a smart device such as a smartphone or tablet, and provides optimal information on agricultural products and proposes sales strategies to consumers and investors based on market analysis data and climate analysis data. Specific examples are shown below.

[1524] The server uses generative artificial intelligence to scrape market analysis data from online agricultural product price information websites and published statistical reports. The main data collected includes crop prices, consumption, and import / export volumes. At the same time, climate analysis data is collected from meteorological agencies and through APIs. Climate analysis data includes temperature, precipitation, and sunshine hours. This data is then analyzed within the server to forecast crop demand and identify optimal cultivation areas.

[1525] Next, the emotion engine analyzes the user's input, past activity data, and even biometric information (e.g., facial expressions and tone of voice collected through the camera and microphone) in real time to identify the user's emotional state. Based on the emotion analysis, the user's psychological state is analyzed and information and support are provided based on their willingness to invest or purchase. For example, if a user is interested in growing tomatoes but feels anxious, information about past success stories and risk mitigation measures is provided.

[1526] Users can review the information provided through their smart devices and select the crops and farmland they wish to invest in. Once the user's investment decision is sent to the server, the server selects farmland, generates an investment project, and formulates a management plan for the selected farmland. When the crops reach harvest time, the server plans and executes the harvesting work and ships the harvested crops to market. The sales profits obtained are tallied and distributed to users.

[1527] In addition, the server obtains real-time data from the farmland (e.g., soil moisture, temperature, and nutrient levels) through IoT devices and sensors, and generates alerts and notifies users when an abnormality is detected, allowing them to respond quickly.

[1528] As a concrete example, suppose a user types, "Recently, I've been wondering about the quality of tomatoes." This text is subjected to sentiment analysis, and if the user's positive sentiment is confirmed, the server will suggest tomato cultivation based on market and climate data. Specific examples of suggestions include recommended cultivation areas, investment amounts, and past success stories. This information is provided to the user via their smart device.

[1529] Examples of prompts for the generative AI model in this system include:

[1530] Lately, I've been wondering about the quality of my tomatoes. Question: What's the best crop to buy?

[1531] In this way, the present invention is a system that takes into account the user's emotions and preferences and realizes optimal information provision and sales strategies for agricultural products.

[1532] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1533] Step 1:

[1534] Market analysis data collection and analysis

[1535] The server scrapes data such as crop prices, consumption, and import / export volumes from online agricultural product price information websites and published statistical reports. This data is then collected and used by the internal generative AI to perform market analysis.

[1536] Input: Online agricultural price information sites and statistical reports

[1537] Data processing: scraping, data cleansing, data analysis

[1538] Output: Demand and supply forecast data

[1539] Step 2:

[1540] Climate analysis data collection and analysis

[1541] The server collects weather data such as temperature, precipitation, and sunshine hours through weather agencies and APIs, and uses generative artificial intelligence to identify optimal growing areas.

[1542] Input: Weather data from weather agencies and APIs

[1543] Data processing: API requests, data cleansing, data analysis

[1544] Output: Optimal cultivation area data

[1545] Step 3:

[1546] User sentiment analysis

[1547] The server analyzes the user's input, past activity data, and biometric information (e.g., facial expressions and tone of voice collected through a camera and microphone) in real time to identify their emotional state.

[1548] Input: User text input, biometric information

[1549] Data processing: Sentiment analysis algorithms, text analysis, speech analysis

[1550] Output: User's emotional state data

[1551] Step 4:

[1552] Recommended crops and growing areas

[1553] The server presents users with recommended crops and growing areas based on the collected market and climate analysis data, and uses an emotion engine to provide information according to the user's emotional state.

[1554] Input: Market analysis data, climate analysis data, user emotional state data

[1555] Data processing: data integration, optimization algorithms

[1556] Output: Recommended crops and growing area information

[1557] Step 5:

[1558] Investment decisions and land selection

[1559] The user checks the information provided through the smart device and selects the crops and farmland to invest in. The user's investment decision is sent to the server, which then generates the farmland selection and investment project.

[1560] Input: User's investment decision data

[1561] Data processing: Database updates, project generation algorithms

[1562] Output: Land selection and investment project data

[1563] Step 6:

[1564] Harvesting and profit sharing

[1565] When the crops reach harvest time, the server plans and executes the harvesting operation, delivers the harvested crops to market, and compiles and distributes the sales profits to users.

[1566] Input: Harvest data, sales data

[1567] Data processing: Harvest plan generation, profit aggregation

[1568] Output: Profit distribution data to users

[1569] Step 7:

[1570] Real-time monitoring of farmland

[1571] The server obtains real-time data from the farmland (e.g., soil moisture, temperature, and nutrient levels) through IoT devices and sensors, and generates alerts and notifies users if any abnormalities are detected.

[1572] Input: Real-time data from IoT devices

[1573] Data processing: sensor data analysis, anomaly detection algorithms

[1574] Output: Alert notification data

[1575] Step 8:

[1576] Proposing sales strategies based on consumer preferences and emotions

[1577] The server, through an emotion engine means, proposes a sales strategy based on the consumer's emotional state.

[1578] Input: Consumer emotional state data

[1579] Data processing: emotional data analysis, sales strategy algorithms

[1580] Output: Sales strategy proposal data

[1581] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1582] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1583] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1584] [Fourth embodiment]

[1585] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1586] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1587] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1588] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1589] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1590] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1591] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1592] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1593] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1594] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1595] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1596] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1597] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1598] This invention relates to an agricultural support system that uses generative artificial intelligence to efficiently perform market and climate analysis, identify optimal crops and regions for cultivation, and provide them to users. It also provides an investment platform that makes it easier for individual investors to enter the agricultural industry, and supports the entire process from cultivation, harvesting, sales, and profit distribution.

[1599] Market and climate analysis

[1600] server

[1601] First, generative AI is used to collect market analysis data and climate analysis data. Market analysis data is obtained by scraping data such as crop prices, consumption, and import / export volumes from online market information sites and published statistical reports. Climate analysis data is obtained by collecting weather information such as temperature, precipitation, and sunshine hours through meteorological agencies and APIs. The server analyzes the data collected in this way to forecast crop demand and identify optimal cultivation areas.

[1602] Specific examples

[1603] For example, the server may detect that the market price of tomatoes is on the rise this year, and determine from regional weather data that a certain region is suitable for growing tomatoes. Based on this information, the server may recommend growing tomatoes to the user.

[1604] Investment project creation and management

[1605] User

[1606] The user uses the terminal to review the provided information and select the crops and farmland to invest in. Once the user's investment decision is sent to the server, the server generates an investment project and formulates a management plan for the selected farmland.

[1607] Specific examples

[1608] If a user decides to invest in a tomato cultivation project in a certain region, the server creates a specific cultivation plan based on that information and manages it as a project.

[1609] Farmland monitoring and management

[1610] server

[1611] The server collects real-time data from IoT devices and sensors to monitor the condition of the farmland, collecting data such as soil moisture, temperature, and nutrient levels, and generates an alert to notify the user if an abnormality is detected.

[1612] Specific examples

[1613] For example, if soil moisture falls below an optimum level, the server will automatically activate the irrigation system to provide the appropriate amount of water.

[1614] Providing efficient agricultural technology and know-how

[1615] server

[1616] Based on collected farmland data and market analysis data, the company provides efficient agricultural techniques and know-how via an online platform, allowing users to learn about the latest agricultural techniques.

[1617] User

[1618] Users can apply the provided know-how to their actual work and manage their farmland, and can also respond immediately based on monitoring results and alert information.

[1619] Specific examples

[1620] For example, you can learn the optimal planting spacing and fertilization schedule for tomatoes on the platform and then put it into practice in the field.

[1621] Harvesting, selling, and profit sharing

[1622] server

[1623] When the crops reach the harvesting period, the system plans and executes the harvesting work, delivers the harvested crops to the market, and tally up the sales profits and distribute them to users.

[1624] Terminal

[1625] Users can view the profits distributed through their accounts.

[1626] Specific examples

[1627] For example, if the tomato harvest is complete and 200,000 yen in profits are earned from sales, a portion of that profit will be transferred to the user's account and the user will be notified that the profit distribution has been completed.

[1628] In this way, the present invention utilizes generative artificial intelligence to streamline various agricultural processes and provide an environment in which individual investors can easily enter the agricultural industry.

[1629] The processing flow will be explained below.

[1630] Step 1:

[1631] server:

[1632] Collect market analysis data by scraping agricultural product price information websites and published statistical reports on the Internet to obtain data on crop prices, consumption, import and export volumes, etc.

[1633] Climate analysis data such as temperature, precipitation, and sunshine hours are collected through meteorological agencies and APIs.

[1634] The collected market analysis data and climate analysis data are stored in a database and formatted into the format required for analysis.

[1635] Step 2:

[1636] server:

[1637] Generative artificial intelligence is used to analyze collected market and climate analysis data to forecast crop demand and identify optimal cultivation areas.

[1638] Based on the results of these analyses, information on recommended crops and their growing areas is generated and converted into a format for providing information to users.

[1639] Step 3:

[1640] server:

[1641] Send the analysis results to the application.

[1642] Device:

[1643] The analysis results received from the server are displayed to the user, including recommended crops, growing areas, and reasons for market demand.

[1644] User:

[1645] Based on the information provided, select the crops and farmland to invest in.

[1646] Step 4:

[1647] User:

[1648] Enter the investment amount on the application and confirm your investment decision.

[1649] Device:

[1650] The investment selection results are sent to the server.

[1651] Step 5:

[1652] server:

[1653] Based on the user's investment decision, an investment project is generated. Project information includes the selected farmland, crops, and investment amount.

[1654] Develop a management plan for the farm, setting out the necessary resources and work schedules.

[1655] Step 6:

[1656] server:

[1657] The generated investment project information is sent to the user, providing an overview of the project and its progress.

[1658] Device:

[1659] Display project information in your account and keep users updated on progress.

[1660] Step 7:

[1661] server:

[1662] Through IoT devices and sensors, real-time data on farmland is collected and conditions such as soil moisture, temperature, and nutrient levels are monitored.

[1663] If an abnormality is detected, an alert is immediately generated and the user is notified of the abnormality and the countermeasures to be taken.

[1664] Device:

[1665] An alert notification is displayed to the user, prompting them to take the necessary action.

[1666] Step 8:

[1667] server:

[1668] The online platform provides users with efficient agricultural techniques and know-how, and updates information as new techniques and methods are released.

[1669] User:

[1670] Participants learn agricultural know-how and techniques provided on the platform and apply them to their actual agricultural work.

[1671] Step 9:

[1672] server:

[1673] Plan and execute harvest operations when crops reach harvest stage.

[1674] Ship the harvested crops to market and collect sales data.

[1675] Step 10:

[1676] server:

[1677] Profits from sales are aggregated and distributed to users for each investment project.

[1678] Profit sharing information is notified to users.

[1679] Device:

[1680] Display the refunded profits in the user's account and notify them that the distribution is complete.

[1681] In this way, the present invention provides a system that enables efficient and sustainable agriculture, benefiting both investors and farmers.

[1682] Example 1

[1683] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1684] In the agricultural sector, efficient crop cultivation, market supply and demand forecasting, and identifying optimal cultivation areas based on climatic conditions are extremely important, but conventional methods require the time and effort required to collect and analyze vast amounts of information, which acts as a barrier to individual investors entering the agricultural industry. Furthermore, automating the process from harvesting to sales and profit distribution, and monitoring the condition of farmland in real time, are essential for further efficiency and quality improvement. Furthermore, there is a lack of systems for providing efficient agricultural techniques and know-how. There is a need to solve these problems and create an environment that makes it easier for many people, including individual investors, to enter the agricultural industry.

[1685] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1686] In this invention, the server includes: means for collecting market analysis information using generative artificial intelligence and forecasting crop supply and demand; means for collecting climate analysis information using generative artificial intelligence and identifying optimal cultivation areas; means for presenting users with recommended crops and cultivation areas based on the collected market and climate analysis information; means for accepting users' investment decisions, selecting farmland, and creating investment projects; and means for harvesting and shipping crops to market when the crops reach harvest time and distributing the resulting profits to users. This streamlines the collection and analysis of vast amounts of information, making it easier for individual investors to enter the agricultural industry. Furthermore, the process from harvesting to sales and profit distribution is automated, and real-time monitoring of farmland conditions enables more efficient and higher-quality agriculture. Furthermore, providing efficient agricultural techniques and know-how contributes to improving users' agricultural skills.

[1687] "Generative AI" refers to AI that can generate, analyze, and predict data using machine learning technology.

[1688] "Market analysis information" refers to various market-related data such as crop prices, consumption, and import / export volumes.

[1689] "Climate analysis information" refers to various data related to climate, such as temperature, precipitation, and sunshine hours.

[1690] "User" refers to an individual or organization that uses this system to cultivate crops or invest.

[1691] "Recommended crops" refer to crops that are deemed optimal based on collected data.

[1692] "Cultivation area" refers to the area where a particular crop can be optimally grown.

[1693] "Investment Project" refers to the cultivation plan or farmland management plan in which a user makes an investment.

[1694] "When the crop reaches the harvest stage" refers to the point in time when the crop reaches the maturity set as the cultivation goal.

[1695] "Marketing" refers to supplying harvested crops to the market.

[1696] "Profit sharing" refers to sharing the revenue generated from the sale of crops with users.

[1697] "Real-time monitoring" refers to the continuous monitoring of the condition of agricultural land in an almost immediate manner.

[1698] "Generating an alert" refers to sending a notification when an anomaly is detected.

[1699] "User interface" refers to the screen or input device that allows a user to access the system and check or enter information.

[1700] "Online platform" refers to a software environment for providing information and services to users via the Internet.

[1701] "Agricultural technology" refers to the means and methods for carrying out agriculture efficiently.

[1702] "Know-how" refers to practical knowledge and experience in agriculture.

[1703] This invention relates to an agricultural support system that uses generative artificial intelligence to enable users to efficiently conduct market and climate analysis and identify optimal crops and regions for cultivation. It also provides an investment platform that makes it easier for individual investors to enter the agricultural industry, and supports the entire process from cultivation, harvesting, sales, and profit distribution.

[1704] Hardware and software used

[1705] Hardware

[1706] IoT sensor devices (e.g. soil humidity sensors, temperature sensors)

[1707] Server (e.g. cloud server provider)

[1708] User's device (e.g. smartphone, PC)

[1709] software

[1710] Generative AI models (e.g., GPT-4)

[1711] Scraping tools (e.g. BeautifulSoup, Scrapy)

[1712] Weather information API (e.g., OpenWeatherMap, WeatherAPI)

[1713] System program processing

[1714] server

[1715] The server uses generative artificial intelligence to collect market analysis information. It uses scraping tools (e.g., BeautifulSoup, Scrapy) to obtain data such as crop prices, consumption, and import / export volumes from market information websites and published statistical reports.

[1716] The server also collects climate analysis information such as temperature, precipitation, and sunshine hours through meteorological agency APIs (e.g., OpenWeatherMap, WeatherAPI). Based on the collected data, it performs analysis using a generative artificial intelligence model (e.g., GPT-4). The server identifies the optimal crops and regions to cultivate and makes recommendations to the user.

[1717] User

[1718] The user uses a terminal to check the information provided by the server. Based on the data on recommended crops and cultivation areas, the user selects the crops and farmland to invest in. The selected information is sent to the server, which then generates an investment project and formulates a management plan for the selected farmland.

[1719] server

[1720] The server collects real-time data from the farmland through IoT sensor devices. It collects data such as soil moisture, temperature, and nutrient levels, and analyzes the condition using a generative AI model. If an abnormality is detected, an alert is generated to notify the user. For example, if soil moisture falls below an appropriate value, the server automatically activates the irrigation system to provide the appropriate amount of water.

[1721] server

[1722] Based on the collected farmland data and market analysis information, the server provides efficient agricultural techniques and know-how via an online platform, allowing users to learn about the latest agricultural techniques.

[1723] server

[1724] When the crops reach harvest time, the system plans and executes the harvesting work, and delivers the harvested crops to market. The system tallys up the sales profits and distributes them to users, who can check the distributed profits through their own devices.

[1725] Examples of concrete examples and prompts

[1726] For example, the server may detect that the market price of tomatoes is on the rise this year, and determine from regional weather data that a particular region is suitable for tomato cultivation. Based on this information, the server may recommend tomato cultivation to the user.

[1727] Prompt Sentence Examples

[1728] "Make a forecast based on market prices and climate data to identify the best tomato growing regions for 2023."

[1729] In this way, the system of the present invention makes full use of generative artificial intelligence to streamline various agricultural processes and provide an environment in which individual investors can easily enter the agricultural industry.

[1730] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1731] Step 1: Collect market analysis data

[1732] server

[1733] Market analysis data such as crop prices, consumption, and import / export volumes are collected from market information sites and published statistical reports. The server uses a scraping tool to collect the data and stores it in a database.

[1734] Input: Market information site URL

[1735] Data processing: HTML analysis of web pages, extraction of necessary data

[1736] Output: Market analysis data (price, consumption, import / export volume, etc.)

[1737] Specific actions

[1738] The server scrapes data from multiple market information sites on the internet and stores information on prices, consumption, imports and exports in a database.

[1739] Step 2: Collecting climate analysis data

[1740] server

[1741] Weather analysis data such as temperature, precipitation, and sunshine hours are collected through the weather information API. The server sends the API request and stores the required data in a database.

[1742] Input: Weather information API endpoint, location information

[1743] Data processing: Analyzing JSON responses from API and extracting necessary weather data

[1744] Output: Climate analysis data (temperature, precipitation, sunshine hours, etc.)

[1745] Specific actions

[1746] The server sends a request to the weather information API, analyzes the acquired weather data, and stores it in a database.

[1747] Step 3: Analyze the data

[1748] server

[1749] Collected market and climate analysis data is analyzed using a generative artificial intelligence model (e.g., GPT-4), which forecasts demand and identifies optimal cultivation areas.

[1750] Input: Market analysis data, climate analysis data

[1751] Data calculations: demand forecasting and cultivation area identification using generative artificial intelligence

[1752] Output: List of recommended crops and growing areas

[1753] Specific actions

[1754] The server provides prompts to a generative artificial intelligence model that uses the collected data to predict demand and identify optimal growing areas, storing the results in a database.

[1755] Step 4: Providing recommendations

[1756] server

[1757] Based on the analysis results, the system provides users with information on recommended crops and cultivation areas.

[1758] Input: List of recommended crops and growing areas

[1759] Data processing: Select the necessary information and format it in a user-friendly way

[1760] Output: Recommendations to be presented to the user

[1761] Specific actions

[1762] The server converts the analysis results into a user-friendly format and provides them to users via a web application or mobile app.

[1763] Step 5: Investment decision and project generation

[1764] User

[1765] The user uses the terminal to review the provided information and select the crops and farmland to invest in. The selected information is sent to the server, which then generates an investment project.

[1766] Input: User's investment decision information (crop, cultivation area)

[1767] Data processing: generating investment projects and formulating management plans

[1768] Output: Investment project details

[1769] Specific actions

[1770] Users make investment decisions through a web interface, and the server generates projects and develops management plans based on the information received.

[1771] Step 6: Farm monitoring

[1772] server

[1773] It collects real-time data from farmland through IoT sensor devices, collecting data such as soil moisture, temperature, and nutrient levels, and generates alerts if anomalies are detected.

[1774] Input: IoT sensor data (humidity, temperature, nutrient levels)

[1775] Data calculation: Real-time analysis of data, detection of anomalies

[1776] Output: Alert notification (if necessary)

[1777] Specific actions

[1778] The server collects real-time data from IoT devices and notifies the user if an abnormality is detected.

[1779] Step 7: Providing agricultural technology and know-how

[1780] server

[1781] Based on collected farmland data and market analysis information, we provide efficient agricultural techniques and know-how via an online platform.

[1782] Input: Farmland data, market analysis information

[1783] Data calculation: Generation of agricultural technology and know-how, provision of learning resources

[1784] Output: Learning resources provided to the user

[1785] Specific actions

[1786] The server generates educational materials and guides containing the latest agricultural techniques and know-how and provides them to users through an online platform.

[1787] Step 8: Harvest, sell, and share profits

[1788] server

[1789] When the crops reach the harvesting period, the system plans and executes the harvesting work, delivers the harvested crops to the market, and tally up the sales profits and distribute them to users.

[1790] Input: Harvest data, sales data

[1791] Data calculation: Revenue calculation, profit distribution plan

[1792] Output: Profit distribution information

[1793] Specific actions

[1794] The server plans the harvesting work, ships the harvested crops to market, and distributes the profits earned to users according to their investment amounts. Users can check the distributed profits on their devices.

[1795] In this way, the system of the present invention specifically executes each processing step and provides efficient agricultural support and investment opportunities to users.

[1796] (Application example 1)

[1797] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1798] Conventional agricultural support systems simply perform market and climate analysis, but lack interactive user education, real-time data provision, and detailed guidance on crop cultivation management. As a result, it is difficult for users to properly learn and utilize agricultural techniques, and the provision and management of real-time information regarding investments is insufficient. Therefore, it has been a challenge to provide an environment that makes it easy for individual investors to enter agriculture and allows them to efficiently select and manage cultivated crops.

[1799] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1800] In this invention, the server includes: means for providing market analysis and climate analysis data reports via a smartphone, smart glasses, or head-mounted display; means for providing virtual reality or augmented reality content that allows users to interactively learn agricultural techniques; means for using collected data to monitor and manage farmland; means for providing efficient agricultural techniques and know-how to users via an online platform; means for presenting monitoring data in real time via smart glasses or a head-mounted display and guiding users on necessary actions; means for monitoring farmland conditions in real time using generative artificial intelligence and generating alerts when an abnormality is detected; means for notifying users of the progress of investment projects and revenue distribution through a user interface; and means for providing training in efficient agricultural techniques using virtual reality or augmented reality. This enables users to efficiently learn agricultural techniques while receiving interactive, real-time data and easily grasp the progress of investment projects in real time.

[1801] "Generative AI" is an AI system that automatically generates and analyzes data, and has the ability to process large amounts of data to make predictions and analyses.

[1802] "Market analysis data" refers to data such as market price trends, supply volume, and demand volume, and is information used to analyze market trends for specific crops or products.

[1803] "Climate analysis data" refers to meteorological information such as temperature, precipitation, and sunshine hours, and is used to analyze the climate conditions in a specific region.

[1804] "Smart glasses" are devices that extend vision, displaying information directly in the user's field of vision and allowing them to be operated by the user's gaze or voice input.

[1805] A "head-mounted display" is a display device worn on the user's head, and is a device that visually provides virtual reality and augmented reality content.

[1806] "Virtual reality" is a technology that allows users to immerse themselves in and experience interactive virtual environments generated using computer technology.

[1807] "Augmented reality" is a technology that combines reality and virtuality by overlaying virtual information onto images of the real world.

[1808] An "online platform" is a collection of services that can be accessed via the Internet, and is an interface that facilitates communication between users and the provision of services.

[1809] "Monitoring data" is data collected in real time using sensors and devices, and is information used to monitor the status of a specific environment or system.

[1810] An "investment project" refers to a plan or set of activities undertaken by a user to invest funds in a particular crop or piece of farmland and enjoy the benefits.

[1811] "Interactive agricultural technology learning" is a process in which users actively participate and learn agricultural technology through interactive information exchange.

[1812] This invention provides a system that uses generative artificial intelligence to collect market analysis data and climate analysis data to support individual investors in effectively entering the agricultural industry. A specific embodiment of this system is described below.

[1813] Market and climate analysis

[1814] The server uses generative AI to collect market analysis data and climate analysis data. Market analysis data is obtained by scraping data such as crop prices, consumption, and import / export volumes from online market information sites and published statistical reports. Climate analysis data is obtained by collecting weather information such as temperature, precipitation, and sunshine hours through meteorological agencies and APIs (OpenWeatherMap, WeatherStack). This data is analyzed to forecast crop demand and identify optimal cultivation areas.

[1815] Use of smartphones, smart glasses, and head-mounted displays

[1816] The server provides market and climate analysis data reports via smartphones, smart glasses, and head-mounted displays, allowing users to visually check the information they need in real time and learn about agricultural techniques interactively.

[1817] Investment project creation and management

[1818] The server accepts investment decisions from users, selects farmland, and creates investment projects. For example, if a user decides to invest in tomato cultivation in a specific area, the server uses that information to create a specific cultivation plan and manages it as a project. Furthermore, the server notifies users of the progress of the investment project and the distribution of profits via a smartphone app.

[1819] Farmland monitoring and management

[1820] The server collects real-time data on the farmland through IoT devices (Arduino, Raspberry Pi) and sensors, and monitors its condition. It collects data on soil moisture, temperature, nutrient levels, etc., and generates an alert to notify the user if an abnormality is detected. The server also allows users to view the monitoring data in real time through smart glasses or a head-mounted display.

[1821] Providing efficient agricultural technology and know-how

[1822] The server uses collected farmland data and market analysis data to provide efficient agricultural techniques and know-how via an online platform. Users can use this platform to learn the latest agricultural techniques. For example, they can use virtual reality (VR) and augmented reality (AR) to visually learn optimal planting spacing and fertilization plans for tomatoes using 3D models.

[1823] Harvesting, selling, and profit sharing

[1824] When the crops reach the harvesting season, the server plans and executes the harvesting work, ships the harvested crops to the market, and tally up the sales profits and distribute them to users. Users can check the distributed profits through their own accounts.

[1825] Prompt Sentence Examples

[1826] Please generate a Python script that scrapes market information websites and weather data to forecast demand for specific crops and identify optimal growing areas. Also, please provide examples of back-end and front-end implementations for an app that can display the results on a smartphone.

[1827] In this way, the present invention utilizes generative artificial intelligence to streamline various agricultural processes and provide an environment in which individual investors can easily enter the agricultural industry.

[1828] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1829] Step 1:

[1830] Collecting market analysis data

[1831] The server scrapes and collects market analysis data such as crop prices, consumption, and import / export volumes from market information sites and published statistical reports on the Internet. To do this, a web scraping tool (BeautifulSoup, Selenium, etc.) is used to extract and format data from the target sites. The input is a list of URLs for market information sites, and the output is the collected market analysis data.

[1832] Step 2:

[1833] Climate analysis data collection

[1834] The server collects weather data such as temperature, precipitation, and sunshine hours using meteorological agencies and APIs (OpenWeatherMap, WeatherStack). This data is used to determine the climate conditions in various locations. It analyzes the raw data obtained from the API and formats it as needed. The input is the API request, and the output is the climate analysis data.

[1835] Step 3:

[1836] Data analysis and crop recommendation identification

[1837] The server inputs the collected market analysis data and climate analysis data into a generative artificial intelligence (AI model), which then uses this data to predict crop demand and identify optimal cultivation areas.To do this, Python's Pandas and Numpy libraries are used to preprocess the data and generate prompt statements to be input into the AI ​​model.The inputs are market analysis data and climate analysis data, and the output is crop demand predictions and optimal cultivation areas.

[1838] Step 4:

[1839] Providing market and climate analysis data

[1840] The server provides market and climate analysis data reports via smartphones, smart glasses, and head-mounted displays. To quickly display data that requires real-time updates, React Native (for smartphones) and Unity (for VR / AR) are used. The input is the results of data analysis, and the output is a report displayed on the user's device.

[1841] Step 5:

[1842] Investment project creation and management

[1843] Users select investment crops and cultivation areas based on the data they provide. The server receives the user's selection results, generates specific investment projects, and formulates investment plans. This information is stored on a dedicated management platform for progress management. The input is the user's investment selection information, and the output is the generated investment project.

[1844] Step 6:

[1845] Farm monitoring and real-time notifications

[1846] The server uses IoT devices (Arduino, Raspberry Pi) and sensors to collect farmland data in real time and monitor the situation. If soil moisture, temperature, nutrient levels, etc. are outside of acceptable ranges, the server generates an alert and notifies the user device. The input is real-time data from the IoT device, and the output is a notification to the user.

[1847] Step 7:

[1848] Providing efficient agricultural technology and know-how

[1849] The server uses AI analysis results and collected data to provide efficient agricultural techniques and know-how via an online platform. Users can learn about agricultural techniques interactively using virtual reality and augmented reality. The input is the results of data analysis and educational content, and the output is an interactive learning experience.

[1850] Step 8:

[1851] Managing harvesting, sales and profit sharing

[1852] When the crops reach the harvesting period, the server plans and manages the harvesting work and arranges for shipment to the market. If any sales profits are earned, they are distributed to the users and the profit details are notified to the user devices. The input is harvest and sales data, and the output is a profit distribution notification to the users.

[1853] This allows users to efficiently learn about agricultural techniques while receiving interactive, real-time data, and makes it easier to keep track of the progress of investment projects in real time.

[1854] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1855] This invention is a system that combines generative artificial intelligence with an emotion engine to recognize and analyze user emotions, thereby providing more effective investment and agricultural support. This system collects market analysis data and climate analysis data, forecasts crop demand and supply, and identifies optimal cultivation areas and crops to provide to users. It also accepts user investment decisions, selects farmland, creates investment projects, and manages the process from harvesting to sales and profit distribution. It also uses the emotion engine to analyze user emotions and provide appropriate information and support.

[1856] Market and climate analysis

[1857] server

[1858] First, generative AI is used to collect market analysis data and climate analysis data. Market analysis data is obtained by scraping data such as crop prices, consumption, and import / export volumes from online agricultural crop price information sites and published statistical reports. Climate analysis data is obtained by collecting weather information such as temperature, precipitation, and sunshine hours through meteorological agencies and APIs. The server analyzes the data collected in this way to forecast crop demand and identify optimal cultivation areas.

[1859] Specific examples

[1860] For example, the server may detect that the market price of tomatoes is on the rise this year, and determine from regional weather data that a certain region is suitable for growing tomatoes. Based on this information, the server may recommend growing tomatoes to the user.

[1861] Use of emotion engine

[1862] server

[1863] The emotion engine analyzes the user's emotions in real time by analyzing user input, past activity data, and even biometric information (e.g., facial expressions and tone of voice collected through a camera or microphone) to determine the user's emotional state.

[1864] User

[1865] If the sentiment engine determines that a customer is motivated to invest, the system will provide more detailed investment information and success stories to encourage investment activity. On the other hand, if a customer is in a state of stress or has doubts, the system will provide risk information and supportive messages.

[1866] Specific examples

[1867] If a user expresses interest in growing tomatoes but is nervous about investing, the sentiment engine will detect that and provide information about past success stories and risk mitigation strategies.

[1868] Investment project creation and management

[1869] User

[1870] The user uses the terminal to review the provided information and select the crops and farmland to invest in. Once the user's investment decision is sent to the server, the server generates an investment project and formulates a management plan for the selected farmland.

[1871] Specific examples

[1872] If a user decides to invest in a tomato cultivation project in a certain region, the server creates a specific cultivation plan based on that information and manages it as a project.

[1873] Farmland monitoring and management

[1874] server

[1875] The server collects real-time data from IoT devices and sensors to monitor the condition of the farmland, collecting data such as soil moisture, temperature, and nutrient levels, and generates an alert to notify the user if an abnormality is detected.

[1876] Specific examples

[1877] For example, if soil moisture falls below an optimum level, the server will automatically activate the irrigation system to provide the appropriate amount of water.

[1878] Providing efficient agricultural technology and know-how

[1879] server

[1880] Based on collected farmland data and market analysis data, the company provides efficient agricultural techniques and know-how via an online platform, allowing users to learn about the latest agricultural techniques.

[1881] User

[1882] Users can apply the provided know-how to their actual work and manage their farmland, and can also respond immediately based on monitoring results and alert information.

[1883] Specific examples

[1884] For example, you can learn the optimal planting spacing and fertilization schedule for tomatoes on the platform and then put it into practice in the field.

[1885] Harvesting, selling, and profit sharing

[1886] server

[1887] When the crops reach the harvesting period, the system plans and executes the harvesting work, delivers the harvested crops to the market, and tally up the sales profits and distribute them to users.

[1888] Terminal

[1889] Users can view the profits distributed through their accounts.

[1890] Specific examples

[1891] For example, when the tomato harvest is completed and a profit of 200,000 yen is earned from sales, a portion of that profit is transferred to the user's account and the user is notified that the profit distribution has been completed.

[1892] In this way, the present invention utilizes generative AI and an emotion engine to streamline various agricultural processes and provide an environment in which individual investors can easily enter the agricultural industry. The introduction of an emotion engine makes it possible to provide information and support suited to the user's psychological state, thereby improving the effectiveness of investment activities and agricultural management.

[1893] The processing flow will be explained below.

[1894] Step 1:

[1895] server:

[1896] Market analysis data is scraped from online agricultural crop price information sites and statistical reports, and data such as crop prices, consumption, and import / export volumes is collected.

[1897] It collects climate analysis data such as temperature, precipitation, and sunshine hours from meteorological agency APIs and public weather databases.

[1898] The collected market analysis data and climate analysis data are stored in a database and prepared for data analysis.

[1899] Step 2:

[1900] server:

[1901] Generative artificial intelligence is used to analyze the collected market analysis data and climate analysis data.

[1902] Conduct demand and supply forecasts for crops and identify optimal crops.

[1903] Based on climate analysis data, the optimum cultivation areas for growing crops are identified.

[1904] Based on the analysis results, recommended crops and cultivation areas are determined, and a data format is created to provide the data to users.

[1905] Step 3:

[1906] server:

[1907] The analysis results are sent to a user-facing application.

[1908] Device:

[1909] The analysis results received from the server are displayed to the user, including recommended crops, recommended growing areas, and market demand analysis results.

[1910] The emotion engine analyzes the user's emotions and generates appropriate investment suggestions and support messages.

[1911] Step 4:

[1912] User:

[1913] Based on the analysis results and investment proposals presented, crops and farmland for investment are selected.

[1914] Enter the investment amount in the application and confirm your investment decision.

[1915] Device:

[1916] Data relating to the investment decision is transmitted to a server.

[1917] Step 5:

[1918] server:

[1919] Based on the user's investment decisions, an investment project is generated, including the selected farmland, crops, investment amount, and projected profits.

[1920] Develop a management plan for the farmland and set the required resources and work schedule.

[1921] Using an emotion engine, it analyzes users' emotions and investment motivation, and provides appropriate investment support and information to maintain motivation.

[1922] Specific examples

[1923] If the user decides to invest in tomato cultivation, the server creates a specific cultivation plan and provides the user with predicted profits and risk information.

[1924] Step 6:

[1925] server:

[1926] After the investment project is created, the progress of the project is provided to the user through a user interface.

[1927] Real-time data on farmland is collected through IoT devices and sensors, monitoring soil moisture, temperature, nutrient levels, and more.

[1928] If an abnormality is detected, an alert is generated and notified to the user.

[1929] Device:

[1930] Display project information and alert notifications in your account to prompt you to take necessary action.

[1931] Step 7:

[1932] User:

[1933] Receive alert notifications, take necessary action, and use online platforms to learn efficient farming techniques and know-how.

[1934] Put new technologies and methods into practice locally.

[1935] Specific examples

[1936] After users learn about irrigation methods for tomato cultivation, they can apply that information to actual farm management and ensure proper water supply.

[1937] Step 8:

[1938] server:

[1939] When the crop reaches harvest time, plan and execute the harvesting operations.

[1940] Ship the harvested crops to market and collect sales data.

[1941] Step 9:

[1942] server:

[1943] Profits from sales are tallied and distributed to users.

[1944] Profit sharing information will be notified to users.

[1945] Device:

[1946] Display the distributed profits in the user's account and notify them when the distribution is complete.

[1947] Specific examples

[1948] For example, when the tomato harvest is completed and a profit of 200,000 yen is earned from sales, a portion of that (for example, 50,000 yen) is transferred to the user's account, and the user is notified that the profit distribution has been completed.

[1949] In this way, the present invention utilizes generative artificial intelligence and an emotion engine to streamline a series of processes, from market analysis, climate analysis, investment plan creation, farmland management, harvesting, sales, and profit distribution, and provides an agricultural support system that provides information and support that adapts to the user's emotions.

[1950] Example 2

[1951] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1952] Modern agriculture and investment activities require advanced data analysis and real-time sentiment analysis to be efficient and effective. However, conventional technologies have difficulty integrating agricultural and investment data analysis, making it difficult to provide information and support that takes into account investors' daily emotional states. This has made it difficult for investors to make accurate decisions, often resulting in inefficient agricultural project management.

[1953] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1954] In this invention, the server includes means for collecting market analysis data using generative artificial intelligence and forecasting crop supply and demand, means for collecting climate analysis data using generative artificial intelligence and identifying optimal cultivation areas, means for presenting users with recommended crops and cultivation areas based on the collected market analysis data and climate analysis data, means for analyzing users' emotional data and providing information and support according to their psychological state, means for accepting users' investment decisions, selecting farmland and creating investment projects, and means for harvesting crops that have reached the harvest stage and shipping them to market and distributing the resulting profits to users. This allows users to receive highly accurate information that takes their emotional state into consideration, enabling more accurate and efficient investment and agricultural project management.

[1955] "Generative AI" is a type of AI technology used to collect, analyze, and predict data, specifically the ability to generate new information and patterns using generative models.

[1956] "Market analysis data" refers to data containing market information such as agricultural product prices, consumption, and import / export volumes, and is used to forecast agricultural supply and demand.

[1957] "Climate analysis data" refers to data containing information about the climate, such as temperature, precipitation, and hours of sunshine, that is used to identify optimal conditions for growing crops.

[1958] "Emotional data" refers to data that reflects the user's psychological state, and refers to information collected based on facial expressions, tone of voice, input content, etc.

[1959] "Agricultural land monitoring" is the process of using IoT devices and sensors to monitor agricultural land environmental data (e.g., soil moisture, temperature, and nutrient levels) in real time and responding if anomalies are detected.

[1960] An "online platform" is a collection of systems or services that can be accessed by users via the internet and that serve as a means of providing efficient agricultural technology and know-how.

[1961] An "investment project" is a planning and management framework generated by a user to invest in a specific crop and farmland, including the entire process from harvesting to sales and profit sharing.

[1962] An "alert" is a warning message that is sent to users when an abnormality is detected during agricultural land monitoring, and is intended to encourage a prompt response.

[1963] A "user interface" is the set of means and tools by which a user interacts with a system or service, allowing them to enter and retrieve information and view project status.

[1964] This invention is a system that combines generative artificial intelligence and an emotion engine, and by recognizing and analyzing the user's emotions, it provides effective investment and agricultural support. This system collects and analyzes various data, and not only provides users with optimal agricultural investment information, but also provides support according to the user's psychological state.

[1965] The server uses generative artificial intelligence (e.g., Google Cloud AI) to collect market analysis data. The market analysis data is obtained by scraping data such as crop prices, consumption, and import / export volumes from online agricultural crop price information websites and published statistical reports. Climate analysis data is also collected from meteorological agencies (e.g., the Japan Meteorological Agency) and APIs (e.g., OpenWeather API) to collect weather information such as temperature, precipitation, and sunshine hours.

[1966] The server integrates collected market analysis data and climate analysis data and analyzes the data using a generative AI model. This makes it possible to forecast crop demand and identify optimal cultivation areas. For example, the server may detect that the market price of tomatoes is on the rise this year, and determine from regional weather data that a certain region is suitable for tomato cultivation. Based on this information, it may recommend tomato cultivation to the user.

[1967] The server then uses an emotion engine (e.g., Azure Cognitive Services' Emotion API) to analyze the user's emotions in real time. It analyzes user input, past activity data, and biometric information collected through the camera and microphone, such as facial expressions and tone of voice, to identify the user's emotional state. For example, if a user expresses interest in growing tomatoes but is feeling anxious, the emotion engine will detect this anxiety and provide information about past success stories and risk mitigation measures.

[1968] The user uses a device (e.g., smartphone or PC) to check the provided information and select the crop and farmland in which to invest. When the user's investment decision is sent to the server, the server creates a specific cultivation plan for the selected farmland and crop as a project and manages it. For example, if the user decides to invest in a tomato cultivation project in a certain region, the server creates a specific cultivation plan based on that information and manages it as a project.

[1969] In addition, the server uses IoT devices (e.g., soil sensors, weather stations) to obtain real-time data on the managed farmland and monitor its condition. It collects data such as soil moisture, temperature, and nutrient levels, and generates alerts to notify users if an abnormality is detected. For example, if soil moisture falls below an appropriate level, the server automatically activates the irrigation system to provide the appropriate amount of water.

[1970] The server uses collected farmland data and market analysis data to provide efficient agricultural techniques and know-how via an online platform (e.g., Moodle). Users can use this platform to learn the latest agricultural techniques and apply them to their own work. For example, they can learn the optimal planting spacing and fertilization schedule for tomatoes on the platform and then put it into practice in the field.

[1971] When the crops reach harvest time, the server plans and executes the harvesting work and ships the harvested crops to market. The sales profits are tallied and distributed to users. Through the terminal, users can check their own accounts and confirm the distributed profits. For example, if the tomato harvest is complete and profits from sales are 200,000 yen, a portion of that will be transferred to the user's account and the user will be notified that the profit distribution has been completed.

[1972] In this way, the present invention utilizes generative AI and an emotion engine to streamline various agricultural processes and provide an environment in which individual investors can easily enter the agricultural industry. The introduction of an emotion engine makes it possible to provide information and support suited to the user's psychological state, thereby improving the effectiveness of investment activities and agricultural management.

[1973] Examples of specific prompts include the following:

[1974] 1. "Collect current market prices and consumption of tomatoes"

[1975] 2. "Collect climate data for the past year for a certain region."

[1976] 3. "If a user is interested in growing tomatoes, which areas are suitable for growing them?"

[1977] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1978] Step 1: Collect market analysis data

[1979] server

[1980] The server uses generative artificial intelligence (e.g., Google Cloud AI) to collect market analysis data.

[1981] Input: URL or API endpoint of a crop pricing information site or statistical report.

[1982] Output: Market analysis data such as prices, consumption, import and export volumes of agricultural products.

[1983] Specific operation: The server uses web scraping technology to obtain data from agricultural product price information websites on the Internet. For example, it uses a prompt such as "Collect the current market price and consumption of tomatoes."

[1984] Step 2: Collecting climate analysis data

[1985] server

[1986] The server collects climate analysis data from meteorological agencies (e.g., the Japan Meteorological Agency) or through APIs (e.g., OpenWeatherAPI).

[1987] Input: Weather agency database or API endpoint.

[1988] Output: Climate analysis data such as temperature, precipitation, and sunshine hours.

[1989] Specific operation: The server uses the API to obtain data such as temperature, precipitation, and sunshine hours for the specified area. An example of a prompt is "Collect climate data for the past year for the XX area."

[1990] Step 3: Data analysis and demand forecasting

[1991] server

[1992] The server integrates the collected market analysis data and climate analysis data and analyzes the data using a generative AI model.

[1993] Input: Market analysis data and climate analysis data.

[1994] Output: Demand forecast results and identification of optimal growing areas.

[1995] Specific operation: The server inputs the integrated data into an AI model and performs demand forecasting. For example, based on the collected market price of tomatoes and climate data for a certain region, it predicts that "a certain region is suitable for tomato cultivation."

[1996] Step 4: Analyzing user emotions with the emotion engine

[1997] server

[1998] The server uses an emotion engine (e.g., Azure Cognitive Services' Emotion API) to analyze the user's emotion data in real time.

[1999] Input: User input data, facial expression data, voice data, etc.

[2000] Output: Information about the user's emotional state.

[2001] Specific operation: The server analyzes facial expressions, tone of voice, and input from the user collected through the camera and microphone to determine the user's current emotional state. For example, if a user inputs "I feel anxious" about investing, the server analyzes this and provides appropriate support information.

[2002] Step 5: User investment decision and project creation

[2003] User

[2004] Users use the terminal to make investment decisions based on analysis results and advice.

[2005] Input: Prediction data ...

Claims

1. A means of using generative artificial intelligence to collect market analysis data and forecast crop demand and supply; A means of using generative artificial intelligence to collect climate analysis data and identify optimal growing areas; and A means for presenting recommended crops and growing areas to users based on the collected market analysis data and climate analysis data; A means for accepting investment decisions by users, selecting farmland and generating investment projects; A means for harvesting and shipping crops to market when they reach harvest time and distributing the profits gained to users; A system including:

2. means for using the collected data to carry out agricultural land monitoring and management; A means to provide users with efficient agricultural technology and know-how through an online platform; The system of claim 1 further comprising:

3. A means of using generative artificial intelligence to monitor the condition of farmland in real time and generate alerts when an abnormality is detected; A means of communicating the progress of investment projects and revenue distribution through a user interface; The system of claim 1 , comprising:

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