System

Abstract

A system is provided.SOLUTION: Means for collecting geographical data and weather data of an agricultural land, means for managing the collected data on a cloud, means for collecting past production data and market data, means for making a supply and demand prediction and a production plan using artificial intelligence, means for transmitting an instruction to an automatic agricultural machine to execute seeding, watering, fertilization, and harvesting, means for collecting and analyzing data from the automatic agricultural machine and a sensor in real time, means for performing inventory management of harvested agricultural products, means for selling the agricultural products by an online sales system, means for receiving an order from a consumer and updating an inventory, and means for automatically performing delivery arrangement; A system comprising: means for collecting and analyzing sales data and providing feedback for subsequent production planning.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] In recent years, a decline in food self-sufficiency, a decrease in the workforce due to aging, and an increase in abandoned farmland have become serious social issues. These problems make it difficult to achieve sustainable agriculture and a stable food supply. Furthermore, efficient agricultural management is difficult, and the process from agricultural production to sales requires a great deal of effort and cost. Furthermore, it is difficult to improve the accuracy of production forecasts and inventory management, making production efficiency and market responsiveness issues. To solve these issues, the present invention integrates agricultural automation with an online sales system to achieve an efficient and stable food supply. [Means for solving the problem]

[0005] The present invention solves the above problems by providing the following means: A means for collecting geographical data and meteorological data on farmland and managing it on the cloud. A means for collecting past production data and market data and using artificial intelligence to create supply and demand forecasts and production plans. A means for sending instructions to automated agricultural machinery to sow seeds, water, fertilize, and harvest. A means for collecting and analyzing data from the automated agricultural machinery and sensors in real time and for inventory management of harvested crops. A means for selling agricultural products through an online sales system, a means for receiving orders from consumers and updating inventory, and a means for automatically arranging deliveries. Finally, a means for collecting and analyzing sales data and providing feedback to the next production plan is provided, thereby building a system that achieves efficient agricultural management and a stable food supply.

[0006] "Geographic data of agricultural land" refers to geographic information such as the location, area, topography, and soil type of agricultural land.

[0007] "Weather data" refers to information about the weather around a particular farmland, such as temperature, precipitation, humidity, and wind speed.

[0008] "Managing on the cloud" means storing and managing collected data on a server accessible via the Internet.

[0009] "Historical production data" refers to historical information about agriculture, such as previous crop types, yields, agricultural inputs used, and pest and disease occurrence.

[0010] "Market data" refers to information about commercial transactions, such as market prices of agricultural products, the balance between supply and demand, and consumer preferences.

[0011] "Using artificial intelligence to forecast supply and demand and create production plans" refers to using AI technology to predict future supply and demand based on past production data and market data, and creating appropriate production plans.

[0012] "Sending instructions to an automated agricultural machine" refers to electronically sending specific instructions to a remotely controlled machine to perform a task.

[0013] "Sowing, watering, fertilizing, and harvesting" refers to a machine automatically performing a series of agricultural tasks required to grow crops.

[0014] "Data from sensors" refers to information such as temperature, humidity, soil condition, and crop growth status measured by sensors installed on agricultural machinery and in the field.

[0015] "Collecting and analyzing data from automated agricultural machinery and sensors in real time" refers to collecting and analyzing data in real time to instantly grasp the agricultural situation.

[0016] "Performing inventory management" refers to recording and managing the quantity, quality, storage condition, etc. of harvested agricultural products.

[0017] "Online sales system" refers to a system for selling agricultural products via the Internet.

[0018] "Receive orders from consumers and update inventory" means that the system receives purchase requests from customers and immediately updates inventory information.

[0019] "Automatically arranging delivery" means that when an order is received, the system automatically issues instructions to the delivery company and proceeds with the shipping procedures.

[0020] "Collecting and analyzing sales data and feeding it back into the next production plan" means collecting and analyzing sales results and revenue data, and using that data to improve the next production plan. [Brief explanation of the drawings]

[0021] [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

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] 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).

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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."

[0042] This invention is a system that collects and manages geographical and meteorological data for farmland, uses AI to create production plans, and realizes automated farm work and online sales. Specific examples of the operation of each element are shown below.

[0043] Collection and management of farmland information

[0044] First, the user uses a device (such as a smartphone or PC) to input geographic data such as the location, area, and soil type of the farmland. The server then collects weather data in real time from a weather data provider and stores it in the cloud. This allows basic information about the farmland to be stored in a database.

[0045] Collection of historical production and market data

[0046] The server collects historical production data (e.g., yields, agricultural inputs used, pest and disease occurrence) and market data (e.g., market prices, supply and demand balance, consumer preferences) and stores these in a database.

[0047] Production planning

[0048] The server uses AI to predict supply and demand for the next season based on collected farmland information, weather data, past production data, and market data. Based on the results, it creates a production plan (for example, sowing timing, planting area, fertilizer to be used, etc.) and notifies the user via their device so that they can check it.

[0049] Automated farming

[0050] The server sends specific work instructions to automated agricultural machinery (e.g., robots or drones). For example, the automated agricultural machinery begins work in time for sowing seeds, and sensors send progress updates to the server in real time. The server monitors this and sends additional instructions for watering, fertilizing, and weeding as needed. The timing of harvesting is also automatically determined based on crop growth data, and the terminal (automated agricultural machinery) carries out the harvesting work.

[0051] Inventory management and online sales

[0052] Harvested produce is managed in inventory by a server. Based on supply and demand forecasts, the server creates an optimal online sales strategy and updates inventory information in real time to the online sales system. When a user purchases produce online, the server receives the order and automatically updates the inventory. It also sends instructions to delivery companies and automatically arranges for delivery. The server also collects sales data and feeds it back into the next production plan.

[0053] Specific examples

[0054] Here's an example of how it works on a farm. After a user registers a new piece of farmland and enters its location and soil type, the server begins collecting local weather data. Using past yield data, the server predicts the supply and demand of tomatoes for the next season and determines the timing of sowing. The server then sends instructions to the automated farm machinery to sow the tomatoes. Sensors monitor the soil moisture, and if it dries out, the server automatically sends instructions to water the soil. At harvest time, the server sends harvesting instructions to the automated farm machinery, and the harvested tomatoes are immediately registered in the inventory management system. Consumers then purchase the tomatoes online, and the server automatically arranges for delivery. The sales data is reflected in the next season's tomato production plan.

[0055] As described above, the present invention realizes agricultural efficiency and a stable food supply.

[0056] The processing flow will be explained below.

[0057] Step 1:

[0058] Users use a device (smartphone or PC) to input geographical data such as the location, area, and soil type of farmland into the system.

[0059] Step 2:

[0060] The server collects real-time weather data for the farmland (e.g., temperature, precipitation, humidity, wind speed, etc.) from a weather data provider and stores it on the cloud.

[0061] Step 3:

[0062] The server performs an initial data analysis based on the farmland information entered by the user and the collected weather data, confirms the basic information about the farmland, and stores it in a database.

[0063] Step 4:

[0064] The server collects past production data (e.g., crop type, yield, pest and disease occurrence status, etc.) and market data (e.g., market price, balance of supply and demand, consumer preferences, etc.) and stores them in a database.

[0065] Step 5:

[0066] Based on farmland information, weather data, past production data, and market data collected by the server, artificial intelligence (AI) is used to predict supply and demand for the next season. Based on the results of this prediction, a production plan (sowing timing, planting area, fertilizer to be used, etc.) is created.

[0067] Step 6:

[0068] The server notifies the terminal of the production plan it has drawn up, and the user checks this plan, modifies it as necessary, and gives final approval.

[0069] Step 7:

[0070] The server sends instructions to automated agricultural machinery (e.g., robots and drones), including tasks such as sowing seeds, watering, fertilizing, weeding, and harvesting.

[0071] Step 8:

[0072] The terminal (automated agricultural machinery) automatically carries out agricultural work according to the received instructions. Sensors attached to the agricultural machinery send work progress and environmental data to a server in real time.

[0073] Step 9:

[0074] The server analyzes real-time data from the sensors and sends additional work instructions (e.g., additional watering or fertilization) to the device as needed.

[0075] Step 10:

[0076] The server determines the timing of harvesting based on the crop growth data, and when the time comes, it sends harvesting instructions to the automated farm machinery.

[0077] Step 11:

[0078] The terminal (automated farm machine) performs the harvesting work and sends the data to the server, which updates the inventory based on the harvest data.

[0079] Step 12:

[0080] Based on the inventory data, the server develops an online sales strategy taking into account supply and demand forecasts and market data, and reflects inventory information on the sales site in real time.

[0081] Step 13:

[0082] When a user purchases produce from an online sales site, the server receives the order and automatically updates the inventory.

[0083] Step 14:

[0084] The server issues instructions to the delivery company and automatically arranges delivery. Order details and delivery information are managed in real time and notified to the consumer.

[0085] Step 15:

[0086] The server collects sales data and analyzes it to provide feedback for the next production plan. Users can check this feedback data on their devices and use it to plan the next production season.

[0087] In this way, a system will be built that automates the entire agricultural process, ensuring efficient operations and a stable food supply.

[0088] Example 1

[0089] 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."

[0090] Efficient production planning and management remains a major challenge in modern agriculture. Specifically, it is difficult to accurately collect and manage geographic and meteorological data on farmland, and to forecast supply and demand and formulate production plans based on past production and market data. Other challenges include maximizing work efficiency with automated farm machinery, using sensors to monitor and adjust farm work in real time, and managing post-harvest inventory and quickly managing online sales.

[0091] 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.

[0092] In this invention, the server includes: means for collecting geographical data and meteorological data of farmland; means for managing the collected data on the cloud; means for collecting past production data and market data; means for using artificial intelligence to forecast supply and demand and create production plans; means for sending instructions to automated agricultural machinery to sow, water, fertilize, and harvest; means for collecting and analyzing data from the automated agricultural machinery and sensors in real time; means for managing inventory of harvested crops; means for selling agricultural products through an online sales system; means for receiving orders from consumers and updating inventory; means for automatically arranging deliveries; means for collecting and analyzing sales data and feeding it back into the next production plan; means for using artificial intelligence to analyze meteorological data, farmland information, and market data and for notifying the users of the timing of sowing, planting area, and type and amount of materials to be used in real time; and means for monitoring soil moisture using data from sensors and automatically sending watering instructions when the soil becomes dry. This enables more efficient and automated agricultural work, enabling stable crop production and rapid market supply.

[0093] "Agricultural land geographic data" refers to all geographic information including agricultural land location, area, soil type, etc.

[0094] "Weather data" refers to data related to weather, such as temperature, precipitation, and wind speed.

[0095] "Cloud" refers to data storage or computing resources provided over the internet.

[0096] "Past production data" refers to data such as yields from previous cultivation on farmland, agricultural materials used, and the occurrence of pests and diseases.

[0097] "Market data" refers to various data about the market, such as market prices, the balance of supply and demand, and consumer preferences.

[0098] "Artificial intelligence" refers to a system that uses machine learning and data analysis techniques to make specific predictions and judgments from data.

[0099] "Supply and demand forecast" refers to predicting the future balance between supply and demand.

[0100] A "production plan" refers to a specific plan for agricultural work, such as the timing of sowing seeds, the area to be planted, and the types and amounts of fertilizers and pesticides to be used.

[0101] "Automated agricultural machinery" refers to machines such as robots and drones that perform agricultural work automatically according to a program.

[0102] A "sensor" refers to a device that measures environmental changes, such as soil humidity and temperature, in real time and acquires data.

[0103] "Inventory management" refers to managing the quantity and condition of harvested agricultural products.

[0104] "Online sales system" refers to a system for selling products via the Internet.

[0105] "Shipping arrangements" refers to managing the shipping of ordered products.

[0106] "Sales Data" means data related to sales, such as the quantity, price, and purchaser information of the items sold.

[0107] This invention is a system that collects and manages geographical and meteorological data for farmland, uses artificial intelligence to create production plans, and realizes automated farm work and online sales. Specific examples of the operation of each element are shown below.

[0108] Collection and management of farmland information

[0109] First, the user uses a terminal (e.g., a smartphone or personal computer) to input geographic data such as the location, area, and soil type of the farmland. The server then collects weather data in real time from a weather data provider (e.g., a general weather data API) and stores it in the cloud. This operation accumulates basic information about the farmland in a database. The specific hardware and software used include a smart device as the terminal and a cloud platform as the server.

[0110] Collection of historical production and market data

[0111] The server collects past production data (e.g., yields, agricultural inputs used, pest and disease occurrence status) and market data (e.g., price information, supply and demand balance, consumer preferences), and stores this data in a cloud database. Data collection methods used include APIs and agricultural information management systems.

[0112] Production planning

[0113] The server uses an artificial intelligence model to predict supply and demand for the next season based on collected farmland information, weather data, past production data, and market data. Based on the prediction results, it creates a production plan (for example, sowing timing, planting area, and type and amount of fertilizer to be used). These plans are sent to the terminal and can be checked by the user. The specific software used is a machine learning framework built in Python (such as TensorFlow or PyTorch).

[0114] Automated farming

[0115] The server sends specific work instructions to the automated agricultural machinery (e.g., robots or drones). For example, the automated agricultural machinery starts work when it's time to sow seeds, and sends its progress information to the server in real time via sensors. Sensing technology and IoT device management systems (such as AWS IoT Core) are used. The automated agricultural machinery follows the server's instructions to perform tasks such as sowing seeds, watering, fertilizing, and harvesting.

[0116] Inventory management and online sales

[0117] Harvested produce is managed in inventory by a server. Based on supply and demand forecasts, the server creates an optimal online sales strategy and updates inventory information in real time to the online sales system (a typical e-commerce platform). When a user purchases produce online, the server receives the order and automatically updates the inventory. The server then sends instructions to the delivery company to automatically arrange delivery. Sales data is fed back into the next season's production plan.

[0118] Specific examples

[0119] Here's an example of how it works on a farm. After a user registers a new piece of farmland and enters its location and soil type, the server begins collecting local weather data. Based on past yield data, the server predicts the supply and demand of tomatoes for the next season and determines the timing of sowing. The server then sends instructions to the automated farm machinery to sow the tomatoes. Sensors monitor the moisture content of the soil, and if it becomes too dry, the server automatically sends instructions to water the soil. When it's time to harvest, the server sends harvesting instructions to the automated farm machinery, and the harvested tomatoes are immediately registered in the inventory management system. Consumers then purchase the tomatoes online, and the server automatically arranges for delivery. The sales data is reflected in the next season's tomato production plan.

[0120] Prompt Sentence Examples

[0121] Below are examples of prompts to use with the generative AI model:

[0122] "Please explain the functions of the system that inputs geographical and meteorological data of farmland and uses AI to create production plans. Please provide a detailed description, including the specific data collection method, automated farm machinery used, and online sales process."

[0123] Through the above process, the present invention realizes agricultural efficiency and a stable food supply.

[0124] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0125] Step 1:

[0126] The user inputs the geographic data of the farmland.

[0127] The user uses a device to input geographic data such as the location, area, and soil type of the farmland. The input data includes the location of the farmland on a map, its area, and soil quality. This input data is sent to the server, which receives the data and stores it in a cloud database. Specific operations include mapping the location of the farmland on a map on the application screen and registering information such as the area and soil type through an input form.

[0128] Step 2:

[0129] The server collects and manages weather data

[0130] The server collects weather data in real time from a weather data provider service. Specifically, it periodically obtains data such as temperature, precipitation, and wind speed using the weather data provider service's API. The obtained data is stored in a cloud database. This process involves sending an API request and writing the weather data obtained in response to the request into the database. This weather data plays an important role in subsequent production planning.

[0131] Step 3:

[0132] The server collects historical production and market data

[0133] The server collects past production data and market data. Past production data inputs include yields, agricultural inputs used, and pest and disease occurrence status. Market data inputs include price information, supply and demand balance, and consumer preferences. The server periodically obtains this data from various data sources and stores it in a cloud database. Specifically, the data is collected using the APIs of agricultural management systems and market data providers.

[0134] Step 4:

[0135] The server analyzes the data and makes supply and demand predictions

[0136] The server integrates collected farmland information, weather data, past production data, and market data, and uses an artificial intelligence model to predict supply and demand for the next season. All collected data is provided as input to the AI ​​model, and supply and demand forecast results are obtained as output. During this process, data analysis is performed using machine learning frameworks developed in Python (such as TensorFlow and PyTorch). Specific operations include data preprocessing, feature selection, model training, and validation.

[0137] Step 5:

[0138] The server creates a production plan and notifies the terminal

[0139] The server creates a production plan based on the predictions of the AI ​​model. The plan includes the timing of sowing seeds, the area to be planted, and the type and amount of fertilizer and pesticide to be used. These plans are notified to the terminal. Specifically, the created plan is sent to the user using a messaging system (for example, push notification or email notification).

[0140] Step 6:

[0141] The server operates the automated farm machinery and starts farming.

[0142] The server sends specific operational instructions to the automated agricultural machinery. For example, it instructs the timing of sowing seeds, fertilizing, watering, and harvesting. Input data includes production plans and real-time weather and soil data, and the output is the agricultural machinery performing its tasks. Specific operations include controlling GPIO and various sensors using an IoT device management system.

[0143] Step 7:

[0144] The server analyzes the data from the sensors in real time and adds necessary instructions.

[0145] The server collects real-time data from sensors (e.g., soil moisture sensors) and sends additional instructions to the automated farming equipment depending on the situation. The input data includes real-time data from the sensors, and the output sends instructions to the farming equipment, for example, to water. It also performs constant monitoring using real-time data analysis tools (e.g., Apache Kafka and Spark Streaming).

[0146] Step 8:

[0147] The server manages post-harvest inventory

[0148] The harvested produce is inventory-managed by the server. The input includes harvested quantity and quality data, and the output updates the inventory database. Specific operations include writing to the database and monitoring the inventory status.

[0149] Step 9:

[0150] The server executes the online sale

[0151] The server updates the online sales system with inventory information in real time and accepts orders from consumers. The input data includes updated inventory and order information, and the output is order processing and inventory updates. Specific operations include data integration with the e-commerce platform, order confirmation, and payment processing.

[0152] Step 10:

[0153] The server automatically arranges delivery

[0154] Delivery arrangements are made automatically by the server. Input data includes the consumer's order information and delivery address information, and output includes instructions to the delivery company. Specifically, this includes setting up an efficient delivery route through API integration with the delivery system and completing the delivery arrangements.

[0155] (Application example 1)

[0156] 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."

[0157] In agricultural and industrial production, uncertainty in production plans and a lack of automation are obstacles to efficient production. It is also difficult to accurately predict the supply and demand balance of agricultural crops and factory-produced goods and to formulate optimal production plans based on that. Furthermore, the lack of automation in inventory management, sales, and delivery has resulted in labor surpluses and shortages, preventing stable food self-sufficiency and product supply. It is necessary to solve these issues and achieve efficient and stable agricultural and industrial production.

[0158] 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.

[0159] In this invention, the server includes means for collecting geographical data and meteorological data of the farmland, means for managing the collected data on the cloud, means for collecting past production data and market data, means for using artificial intelligence to make supply and demand forecasts and production plans, means for sending instructions to automated agricultural machinery to perform sowing, watering, fertilizing, and harvesting, means for collecting and analyzing data from the automated agricultural machinery and sensors in real time, means for managing inventory of harvested crops, means for selling agricultural products through an online sales system, means for receiving orders from consumers and updating inventory, means for automatically making delivery arrangements, means for collecting and analyzing sales data and feeding it back to the next production plan, means for collecting and managing location information and equipment data within the factory, means for collecting work environment data within the factory in real time, means for making production plans using past production data and demand data, means for sending specific work instructions to automated robots and having them perform production work, and means for automatically managing inventory and shipping within the factory. This will reduce uncertainty in production plans in agricultural and industrial production, enabling efficient production and stable supply.

[0160] "Geographic data of agricultural land" refers to data on the geographic characteristics of agricultural land, such as its location, area, and soil type.

[0161] "Weather data" refers to data related to climate, such as temperature, precipitation, humidity, and wind speed.

[0162] "Managing on the cloud" means storing collected data in a data center connected via the Internet and making it accessible.

[0163] "Past production data" refers to data on past crop yields, agricultural materials used, pest and disease occurrence, and so on.

[0164] "Market data" refers to data on market trends such as market prices, the balance between supply and demand, and consumer preferences.

[0165] "Artificial intelligence" is a technology that uses machine learning and data analysis techniques to derive knowledge and patterns from data.

[0166] "Supply and demand forecasting" is the prediction of future demand and supply based on collected data.

[0167] "Production planning" involves planning the production volume and timing of crops and industrial products, as well as the materials to be used.

[0168] "Automated agricultural machinery" refers to machines such as robots and drones that can perform agricultural work automatically.

[0169] "Real-time collection and analysis" means collecting data immediately and analyzing it on the spot.

[0170] "Inventory management" is the tracking and management of harvested agricultural crops and manufactured industrial products.

[0171] An "online sales system" is a system for selling products over the Internet.

[0172] "Updating inventory upon receiving an order from a consumer" means updating inventory data based on the information when a consumer places an order through the online sales system.

[0173] "Automatically arranging delivery" means automatically sending delivery instructions to a delivery company based on a consumer's order.

[0174] "Location information within a factory" is information relating to the locations of equipment and machines within a factory.

[0175] "Facility data" refers to data related to the functions and status of machines and equipment within a factory.

[0176] "Work environment data" refers to data related to the work environment in a factory, such as temperature, humidity, and sound level.

[0177] "Planning a production plan" means planning the production processes and schedules within a factory.

[0178] An "automatic robot" is a robot that can autonomously operate machinery and perform production tasks.

[0179] "Automated shipping" means automatically packaging products and preparing them for shipping.

[0180] The present invention provides a system that improves the efficiency of production management systems in farmland and factories and provides specific means for realizing stable supply. Specific embodiments of the system are described below.

[0181] System Overview

[0182] This system consists of a server, terminals, users, automated agricultural machinery, and automated robots. The server collects, manages, analyzes, sends instructions, and manages inventory. Terminals are devices that users use to input and check data, and include smartphones and PCs. The automated agricultural machinery and automated robots are machines that perform specific tasks.

[0183] Program Overview

[0184] The server first collects geographic and meteorological data for the farmland. Geographic data includes the location, area, and soil type of the farmland. Meteorological data includes temperature, precipitation, humidity, and wind speed. This data is stored in the cloud, along with past production data and market data. Market data includes market prices, supply and demand balance, and consumer preferences.

[0185] Based on this data, the server uses artificial intelligence (AI) to predict supply and demand for the next season and create a production plan. This plan includes sowing, watering, fertilizing, harvesting timing, and materials to be used. The plan is then sent to the device so that the user can check and modify it.

[0186] The server then sends instructions to the autonomous farming equipment to perform the tasks, and data from the autonomous farming equipment and sensors is sent to the server in real time for analysis, which then sends additional instructions on watering and fertilizing as needed.

[0187] Harvested produce is managed by the server and reflected in the online sales system. When a consumer purchases produce online, the server receives the order, updates the inventory, and automatically arranges delivery. Sales data is also collected and fed back into the next production plan.

[0188] Within the factory, a server collects and manages the factory's location information and equipment data. Work environment data (temperature, humidity, sound level, etc.) is also collected in real time. Production plans are created using past production data and demand data, and specific work instructions are sent to automated robots. Inventory management and shipping within the factory are also automated.

[0189] Hardware and Software Use

[0190] Hardware: Servers, smartphones, PCs, automated farm machinery, automated robots, sensors.

[0191] Software: Cloud database, AI models (machine learning algorithms), online sales systems, inventory management systems, communication protocols.

[0192] Specific examples

[0193] As an example of how a new farm is operated, let's say a new piece of farmland is registered. When a user uses a device to enter the location information and soil type of the farmland, the server begins collecting local weather data. Using past yield data, the server predicts the supply and demand of tomatoes for the next season and decides when to sow seeds. The server then sends instructions to the automated farm machinery to sow the tomatoes. Sensors monitor the moisture content of the soil, and if it becomes too dry, the server automatically sends instructions to water the soil. When it's time to harvest, the server sends harvesting instructions to the automated farm machinery, and the harvested tomatoes are immediately registered in the inventory management system. Consumers then purchase the tomatoes online, and the server automatically arranges for delivery. The sales data is reflected in the next season's tomato production plan.

[0194] Prompt Sentence Examples

[0195] Collect location information, equipment data, and real-time environmental data within the factory, integrate past production data with demand data, and create a production plan that predicts supply and demand for the next season. Then, design a system that sends specific work instructions to robots based on the generated production plan. Store the collected data in an SQLite database and use an AI model to predict supply and demand.

[0196] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0197] Step 1:

[0198] The user inputs geographic data of the farmland (location, area, soil type) using a terminal. The terminal sends this data to the server, which receives the input data and stores it in a database on the cloud.

[0199] Step 2:

[0200] The server collects real-time weather data (temperature, precipitation, humidity, wind speed) from a weather data provider. The collected weather data is stored in a database on the cloud. This allows the server to obtain basic environmental information about the farmland.

[0201] Step 3:

[0202] The server collects past production data (yields, agricultural inputs used, pest and disease occurrence status) and market data (market prices, supply and demand balance, consumer preferences) and stores them in a database, allowing the server to understand past agricultural activities and market trends.

[0203] Step 4:

[0204] The server uses collected farmland information, weather data, past production data, and market data to predict supply and demand for the next season using a generative AI model. The model uses this data as input, performs data calculations, and outputs supply and demand forecast results. Based on these forecast results, the server creates a production plan (sowing timing, planting area, fertilizer to be used, etc.) and notifies the terminal.

[0205] Step 5:

[0206] The user checks the production plan on the terminal and modifies it as necessary. When the user makes modifications, the modified production plan is sent from the terminal to the server. The server saves the updated production plan in a database on the cloud.

[0207] Step 6:

[0208] The server sends specific work instructions (such as sowing seeds, watering, fertilizing, and harvesting) to the automated farming machine. The automated farming machine carries out the work according to these instructions and sends its progress and environmental data to the server in real time.

[0209] Step 7:

[0210] The server collects and analyzes data from automated farm machinery and sensors in real time. The server uses the collected data to send additional instructions for watering and fertilizing as needed. The data is stored in a database on the cloud.

[0211] Step 8:

[0212] Harvested produce is managed by a server. Inventory data is stored in a cloud database and updated in real time in the online sales system. The server monitors inventory status and develops optimal sales strategies.

[0213] Step 9:

[0214] When a consumer purchases produce through the online sales system, the server receives the order, updates inventory data, and automatically sends delivery instructions to the delivery company.

[0215] Step 10:

[0216] The server collects and analyzes sales data. The sales data is analyzed by a generative AI model and fed back into the next season's production plan. The collected and analyzed data is stored in a database on the cloud.

[0217] Step 11:

[0218] The server collects and manages factory location information and equipment data, and the collected data is stored in a database on the cloud.

[0219] Step 12:

[0220] The server collects real-time data on the working environment within the factory (temperature, humidity, sound level, etc.) and stores it in a database on the cloud. The collected data is used to assist in production planning.

[0221] Step 13:

[0222] The server uses past production and demand data to generate demand and supply forecasts using a generative AI model, and creates a production plan, which is then stored in a cloud database.

[0223] Step 14:

[0224] The server sends specific work instructions to the automated robot, which then carries out production tasks based on the instructions and reports its status to the server in real time.

[0225] Step 15:

[0226] The server manages inventory in the factory and automatically handles shipping. Inventory and shipping data is stored in a cloud database and updated in real time on the online sales system.

[0227] 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.

[0228] This system combines a system that collects and manages geographical and meteorological data on farmland, uses AI to create production plans, and realizes automated farm work and online sales, with an emotion engine that recognizes user emotions. Specific examples of the operation of each element are shown below.

[0229] Collection and management of farmland information

[0230] First, the user uses a device (such as a smartphone or PC) to input geographic data such as the location, area, and soil type of the farmland. The server then collects weather data in real time from a weather data provider and stores it in the cloud. This allows basic information about the farmland to be stored in a database.

[0231] Collection of historical production and market data

[0232] The server collects historical production data (e.g., yields, agricultural inputs used, pest and disease occurrence) and market data (e.g., market prices, supply and demand balance, consumer preferences) and stores these in a database.

[0233] Production planning

[0234] The server uses AI to forecast supply and demand for the next season based on collected farmland information, weather data, past production data, and market data. Based on the results, it creates a production plan (for example, sowing timing, planting area, fertilizer to be used, etc.) and notifies the user via their device so that they can check it.

[0235] Automated farming

[0236] The server sends specific work instructions to automated agricultural machinery (e.g., robots or drones). For example, the automated agricultural machinery begins work in time for sowing seeds, and sensors send progress updates to the server in real time. The server monitors this and sends additional instructions for watering, fertilizing, and weeding as needed. The timing of harvesting is also automatically determined based on crop growth data, and the terminal (automated agricultural machinery) carries out the harvesting work.

[0237] Inventory management and online sales

[0238] Harvested produce is managed in inventory by a server. Based on supply and demand forecasts, the server creates an optimal online sales strategy and updates inventory information in real time to the online sales system. When a user purchases produce online, the server receives the order and automatically updates the inventory. It also sends instructions to delivery companies and automatically arranges for delivery. The server also collects sales data and feeds it back into the next production plan.

[0239] Use of emotion engine

[0240] The emotion engine is used to evaluate user feedback and purchasing intent in real time, and the server adjusts and optimizes production plans and sales strategies based on this emotion data.

[0241] Specific examples

[0242] Here's an example of how it works on a farm. After a user registers a new piece of farmland and enters its location and soil type, the server begins collecting local weather data. Using past yield data, the server predicts the supply and demand of tomatoes for the next season and determines the timing of sowing. The server then sends instructions to the automated farm machinery to sow the tomatoes. Sensors monitor the soil moisture, and if it dries out, the server automatically sends instructions to water the soil. At harvest time, the server sends harvesting instructions to the automated farm machinery, and the harvested tomatoes are immediately registered in the inventory management system. Consumers then purchase the tomatoes online, and the server automatically arranges for delivery. The sales data is reflected in the next season's tomato production plan.

[0243] Furthermore, when a user purchases tomatoes from an online sales site, the emotion engine evaluates the user's purchasing motivation and satisfaction in real time, and the server adjusts inventory and delivery arrangements based on this data. For example, if user satisfaction is high, that data can be reflected in the next season's production plan, and the plan can be adjusted to produce more tomatoes. Conversely, if satisfaction is low, the cause can be analyzed and improvements can be made to improve the quality of agricultural products and service.

[0244] In this way, the present invention is a system that can realize agricultural efficiency and a stable food supply, while also increasing user satisfaction.

[0245] The processing flow will be explained below.

[0246] Step 1:

[0247] Users use a device (smartphone or PC) to input geographical data such as the location, area, and soil type of farmland into the system.

[0248] Step 2:

[0249] The server collects real-time weather data for the farmland (e.g., temperature, precipitation, humidity, wind speed, etc.) from a weather data provider and stores it on the cloud.

[0250] Step 3:

[0251] The server performs an initial data analysis based on the farmland information entered by the user and the collected weather data, and stores the basic information about the farmland in a database.

[0252] Step 4:

[0253] The server collects past production data (e.g., crop type, yield, pest and disease occurrence status, etc.) and market data (e.g., market price, balance of supply and demand, consumer preferences, etc.) and stores them in a database.

[0254] Step 5:

[0255] Based on farmland information, weather data, past production data, and market data collected by the server, artificial intelligence (AI) is used to predict supply and demand for the next season. Based on the results of this prediction, a production plan (sowing timing, planting area, fertilizer to be used, etc.) is created.

[0256] Step 6:

[0257] The server notifies the terminal of the production plan it has drawn up, and the user checks this plan, modifies it as necessary, and gives final approval.

[0258] Step 7:

[0259] The server sends instructions to automated agricultural machinery (e.g., robots and drones), including tasks such as sowing seeds, watering, fertilizing, weeding, and harvesting.

[0260] Step 8:

[0261] The terminal (automated agricultural machinery) automatically carries out agricultural work according to the received instructions. Sensors attached to the agricultural machinery send work progress and environmental data to a server in real time.

[0262] Step 9:

[0263] The server analyzes real-time data from the sensors and sends additional work instructions (e.g., additional watering or fertilization) to the device as needed.

[0264] Step 10:

[0265] The server determines the timing of harvesting based on the crop growth data, and when the time comes, it sends harvesting instructions to the automated farm machinery.

[0266] Step 11:

[0267] The terminal (automated farm machine) performs the harvesting work and sends the data to the server, which updates the inventory based on the harvest data.

[0268] Step 12:

[0269] Based on the inventory data, the server develops an online sales strategy taking into account supply and demand forecasts and market data, and reflects inventory information on the sales site in real time.

[0270] Step 13:

[0271] When a user purchases produce from an online sales site, the server receives the order and automatically updates the inventory.

[0272] Step 14:

[0273] The server issues instructions to the delivery company and automatically arranges delivery. Order details and delivery information are managed in real time and notified to the consumer.

[0274] Step 15:

[0275] The emotion engine evaluates users' feedback and purchasing intentions in real time, and the server adjusts inventory and delivery arrangements based on that data. It also reflects factors that lead to high satisfaction in production plans for the next season.

[0276] Step 16:

[0277] The server collects sales data and analyzes it to provide feedback for the next production plan. Users can check this feedback data on their devices and use it to plan the next production season.

[0278] In this way, a system will be built that automates the entire agricultural process, ensuring efficient operations and a stable food supply.

[0279] Example 2

[0280] 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."

[0281] In conventional agricultural systems, the collection and management of geographical data and meteorological data for farmland was often done manually, making efficient data management difficult. Furthermore, there was a lack of systems for integrating and analyzing past production data and market data to forecast supply and demand and formulate production plans. Furthermore, there were challenges with operating automated agricultural machinery, collecting and analyzing data in real time, and swiftly managing inventory and arranging deliveries in response to consumer orders.

[0282] 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. In this invention, the server includes means for collecting geographic data such as location information, area, and soil type of farmland, means for collecting weather data in real time from a weather data providing service, means for managing the collected geographic data and weather data on the cloud, means for collecting production data on past yields, agricultural materials used, and the occurrence of pests and diseases, and market data on market prices, the balance of supply and demand, and consumer preferences, means for using artificial intelligence to predict supply and demand and formulate production plans based on the collected data, means for sending instructions to automated agricultural machinery to sow seeds, water, fertilize, and harvest, means for collecting and analyzing data such as work progress and soil humidity from the automated agricultural machinery and sensors in real time, means for managing inventory of harvested crops, means for selling agricultural products through an online sales system, means for receiving orders from consumers and updating inventory, means for automatically arranging deliveries, collecting and analyzing sales data, and providing feedback to the next production plan, and an emotion engine for evaluating user feedback and purchasing intentions in real time and adjusting production plans and sales strategies. This will lead to more efficient agriculture, a stable food supply, and improved consumer satisfaction.

[0283] "Farmland location information, area, and soil type" is data that indicates the exact location of the farmland, the size of the farmland, and the type of soil on the farmland.

[0284] "Weather Data Service" means an external data source that provides real-time weather information (temperature, humidity, precipitation, wind speed, etc.).

[0285] "Managing on the cloud" means storing data on a remote server via the Internet and accessing and analyzing it.

[0286] "Production data on past yields, agricultural materials used, and pest and disease occurrence status" refers to data showing the yield of crops grown in the past, the types and amounts of fertilizers and pesticides used, as well as the types of pests and diseases that have occurred and their impacts.

[0287] "Market data on market prices, supply and demand balance, and consumer preferences" refers to data showing the market trading prices of agricultural products, the state of supply and demand in the market, and consumer purchasing behavior and preferences.

[0288] "Using artificial intelligence to forecast supply and demand and create production plans" means using AI technology to predict the future balance of supply and demand and to create effective production schedules based on that.

[0289] "Automated agricultural machinery" refers to mechanical devices such as robots and drones that are used to perform automated agricultural work.

[0290] "Data from sensors such as work progress and soil moisture" refers to measurements such as the current progress of work and the amount of moisture in the soil detected by sensors.

[0291] "Inventory management" is the process of tracking and efficiently managing harvested produce, including its quantity, storage location, and freshness.

[0292] An "online sales system" is an e-commerce platform for selling products to consumers over the Internet.

[0293] The "emotion engine that evaluates user feedback and purchasing intent in real time" is an analytical tool that analyzes users' opinions and purchasing behavior regarding products and services, and evaluates their emotions and satisfaction in real time.

[0294] This invention is a system that collects and manages geographical and meteorological data on farmland, uses AI to create production plans, and realizes automated farming and online sales. Furthermore, by combining it with an emotion engine that recognizes user emotions, it aims to improve agricultural efficiency and consumer satisfaction.

[0295] In this system, users first use a terminal to input geographic data such as the location, area, and soil type of their farmland. The terminal can be a smartphone or a PC. This data is sent to a server and stored in a database. The server then collects weather data in real time from a weather data provider (for example, a weather API) and stores it in a database on the cloud.

[0296] The server then collects past production data (e.g., yields, agricultural inputs used, pest and disease occurrence) and market data (market prices, supply and demand balance, consumer preferences) and stores them in a database. This allows the server to use artificial intelligence to create supply and demand forecasts and production plans based on all the collected data. The generative AI model is used to determine the next season's supply and demand forecast and crop growth schedule. This plan is then sent to the device and can be viewed by the user.

[0297] The server then sends specific work instructions to the automated farming machinery (e.g., robots or drones). The automated farming machinery follows the server's instructions to sow seeds, water, fertilize, weed, and harvest. Data such as the progress of work and soil moisture is collected in real time by sensors and sent to the server. The server analyzes this data and sends additional instructions to the automated farming machinery as needed.

[0298] Once the harvest is complete, the harvested produce is managed as inventory by the server. The server uses the inventory data to sell the produce through an online sales system and updates inventory information in real time. When a user purchases produce online, the server receives the order and automatically updates the inventory. It also sends instructions to the delivery company, which then automatically arranges for delivery. At the same time, sales data is fed back into the next production plan.

[0299] Furthermore, an emotion engine will be used to evaluate user feedback and purchasing intent in real time, and the server will then adjust and optimize production plans and sales strategies based on this emotion data.

[0300] Specific examples

[0301] For example, in a farm operation, when a user registers a new piece of farmland and enters its location and soil type, the server immediately begins collecting local weather data. Using past yield data, the server predicts the supply and demand of tomatoes for the next season and determines the timing of sowing. The server sends instructions to the automated farming machinery, such as "Start sowing tomato seeds on one hectare of farmland at 10:00 AM tomorrow," and the automated farming machinery begins work at the specified time. When it is time to harvest, the server sends instructions to the automated farming machinery, such as "Start harvesting at 8:00 AM tomorrow," and the harvested tomatoes are immediately registered in the inventory management system.

[0302] Prompt Sentence Examples

[0303] "Based on the geographical and meteorological data of the farmland, you will create a production plan for the next season. Specifically, you will determine the timing for sowing tomatoes and generate instructions to send to the automated farm machinery."

[0304] In this way, the present invention can improve agricultural efficiency and ensure a stable food supply while also increasing user satisfaction.

[0305] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0306] Step 1:

[0307] A user uses a device to input geographic data such as the location, area, and soil type of the farmland. This input data includes GPS information, the size of the farmland, and soil analysis results. The input data is sent to a server and stored in a database. For example, a user inputs the GPS coordinates of the farmland and the pH value of its soil.

[0308] Step 2:

[0309] The server collects weather data (temperature, humidity, wind speed, precipitation, etc.) in real time from a weather data provider (e.g., weather API). The collected data is stored in a database on the cloud. This collection process is performed automatically and periodically. For example, the server calls the weather API every day at 6:00 AM to obtain the latest weather data.

[0310] Step 3:

[0311] The server collects past production data (e.g., yields, agricultural inputs used, pest and disease occurrence status) and market data (market prices, supply and demand balance, consumer preferences) and stores them in a database. This data is obtained from agricultural statistics databases and market information services. For example, tomato yield data and market price data for the past five years are collected.

[0312] Step 4:

[0313] The server uses a generative AI model based on collected geographical data, weather data, past production data, and market data to predict supply and demand for the next season. It then creates a production plan (sowing timing, planting area, fertilizer to be used, etc.) based on the results of the AI ​​analysis. These plans are sent to the terminal and can be checked by the user. For example, a plan may be generated that states, "Since demand for tomatoes is predicted to increase next season, tomatoes should be sown on two hectares of farmland."

[0314] Step 5:

[0315] The server sends specific work instructions to the automated farm machinery (robots and drones). For example, an instruction might be sent saying, "Start sowing tomato seeds on one hectare of farmland at 10:00 AM tomorrow." The automated farm machinery starts work at the specified time, and sensors detect its progress in real time. This progress data is sent to the server, and additional instructions are sent as needed.

[0316] Step 6:

[0317] The server collects and analyzes data from automated farm machinery and sensors in real time and updates work instructions as needed. For example, sensors monitor soil moisture, and if the moisture level drops, the server automatically sends instructions to water the fields.

[0318] Step 7:

[0319] Once the harvest is complete, the server manages the inventory of the harvested crops. Data such as the quantity of harvested crops and their storage location is saved in a database. For example, when 50 boxes of harvested tomatoes are stored in a warehouse, that information is immediately registered in the database.

[0320] Step 8:

[0321] The server sells agricultural products in an online sales system. Based on inventory data, sales information for agricultural products is updated in the online sales system. When a consumer purchases a product online, the order status is sent to the server, and inventory information is automatically updated. For example, when a consumer purchases 10 boxes of tomatoes, the order information is sent to the server, and 10 boxes are subtracted from the database.

[0322] Step 9:

[0323] The server automatically arranges delivery. Once an order is confirmed in the online sales system, the server sends delivery instructions to the delivery company and the delivery arrangements proceed. For example, delivery arrangements are made for 10 boxes of purchased tomatoes, and the delivery status is updated on the server in real time.

[0324] Step 10:

[0325] The server collects and analyzes sales data and provides feedback to the next production plan, improving the accuracy of the production plan. For example, tomato sales data and user feedback can be analyzed to optimize the next season's production volume and sales methods.

[0326] Step 11:

[0327] The emotion engine evaluates user feedback and purchasing intent in real time. The server adjusts and optimizes production plans and sales strategies based on this emotion data. For example, if a user evaluates their satisfaction as "very satisfied," the server adjusts plans to increase production for the next season.

[0328] As described above, through each step of this system, we aim to achieve agricultural efficiency, a stable food supply, and improved consumer satisfaction.

[0329] (Application example 2)

[0330] 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."

[0331] Modern agriculture requires efficient production planning that responds to fluctuations in weather conditions and market demand. While combining automated farming with online sales enables more efficient farm management, conventional systems have difficulty assessing consumer sentiment and purchasing intent in real time and reflecting this information in production plans and sales strategies. Furthermore, new methods for improving the customer experience in brick-and-mortar stores are also needed. Therefore, the objective of this invention is to realize efficient and flexible production planning, optimization of sales strategies, and an improvement in the customer experience in brick-and-mortar stores.

[0332] 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.

[0333] In this invention, the server includes means for collecting geographical data and meteorological data of farmland, means for managing the collected data on the cloud, means for collecting past production data and market data, means for using artificial intelligence to forecast supply and demand and develop production plans, means for sending instructions to automated agricultural machinery to sow, water, fertilize, and harvest, means for collecting and analyzing data from the automated agricultural machinery and sensors in real time, means for inventory management of harvested crops, means for selling agricultural products through an online sales system, means for receiving orders from consumers and updating inventory, means for automatically arranging deliveries, means for collecting and analyzing sales data and providing feedback to the next production plan, means for providing a virtual tour of the farm in a physical store using augmented reality technology, means for analyzing user emotions to evaluate purchasing intent, and means for adjusting inventory and sales strategies based on user emotion data. This enables efficient and flexible production planning, optimization of sales strategies, and an improvement in customer experience in physical stores.

[0334] "Geographical data of agricultural land" refers to basic information about agricultural land, such as its location, area, and soil type.

[0335] "Weather data" is information about weather conditions that affect agricultural activities, such as temperature, precipitation, humidity, and wind speed.

[0336] "Cloud management" refers to the practice of storing collected data on remote servers on the Internet for access and analysis.

[0337] "Past production data" refers to information about past agricultural activities, such as previous crop yields, agricultural inputs used, and pest and disease occurrence.

[0338] "Market data" refers to information about the market, such as market prices, the balance of supply and demand, and consumer preferences.

[0339] "Using artificial intelligence to forecast supply and demand and develop production plans" means using AI technology to predict future supply and demand and develop efficient production plans.

[0340] "Autonomous agricultural machinery" refers to agricultural machinery that operates autonomously and performs agricultural tasks such as sowing seeds, watering, fertilizing, and harvesting.

[0341] "Real-time collection and analysis" means that data is collected immediately and analyzed on the spot.

[0342] "Inventory management" means managing the quantity and condition of harvested agricultural products, and understanding and updating the inventory status.

[0343] An "online sales system" is a system for selling agricultural products via the Internet.

[0344] "Automatically arranging delivery" means that upon receiving an order from a consumer, delivery procedures are automatically carried out.

[0345] "Providing virtual farm tours within physical stores using augmented reality technology" means using AR technology to provide virtual farm tours within physical stores.

[0346] "Analyzing user emotions and assessing purchasing intent" means utilizing an emotion engine to analyze user emotions and assess purchasing intent.

[0347] "Adjusting inventory and sales strategies based on emotional data" means optimizing inventory status and sales strategies based on collected emotional data.

[0348] This system collects and manages geographic and meteorological data for farmland, uses AI to create production plans, and automates farm work and online sales. Furthermore, it recognizes and evaluates user emotions in real time and reflects them in production plans and sales strategies, improving the customer experience.

[0349] System Program

[0350] The server collects geographical and meteorological data for the farmland and manages it on the cloud. This uses remote server functions via the internet and sensors for data collection. The server also collects past production data and market data, which it analyzes using AI (artificial intelligence) to create supply and demand forecasts and production plans. This AI uses, for example, a "generative AI model."

[0351] Data collection and analysis

[0352] The server then sends instructions to the automated farming machines based on the collected data. These instructions include instructions for sowing, watering, fertilizing, and harvesting. The automated farming machines then perform each task based on the instructions and transmit their progress to the server in real time via sensors. The server then analyzes this information and sends additional instructions as needed.

[0353] Inventory management and online sales

[0354] Harvested produce is managed by a server and provided to consumers through an online sales system. The online sales system reflects real-time inventory information and updates inventory upon receiving orders from consumers. The server also automatically arranges delivery and feeds sales data back into the next production plan.

[0355] Improving customer experience in physical stores

[0356] To enhance customer experience in physical stores, the server uses augmented reality (AR) technology to provide virtual farm tours. The system uses smartphones, smart glasses, and head-mounted displays. When a user starts a farm tour in a physical store using these devices, the server provides an interactive farm tour through AR.

[0357] Use of emotion engine

[0358] The server also includes an emotion engine that analyzes user emotions and assesses their willingness to purchase and satisfaction in real time. Emotional data is automatically collected and analyzed. Based on this data, the server adjusts and optimizes inventory and sales strategies.

[0359] Specific examples

[0360] Here's an example of how this works on a farm. After a user registers a new piece of farmland and enters its location and soil type, the server begins collecting local weather data. Using past production data, the server predicts the supply and demand of tomatoes for the next season and determines the timing of sowing. The server then sends instructions to the automated farm machinery to sow the tomatoes. Sensors then monitor the soil's moisture content, and if it becomes too dry, the server automatically sends instructions to water the soil. At harvest time, the server sends harvesting instructions to the automated farm machinery, and the harvested tomatoes are immediately registered in an inventory management system. Consumers then purchase the tomatoes online, and the server automatically arranges for delivery. The sales data is reflected in the next season's tomato production plan.

[0361] Prompt Sentence Examples

[0362] Below are some example prompts to input to a generative AI model:

[0363] Write code to create real-time production plans based on farmland and weather data from the server.

[0364] A program that stores farmland information and weather data in the cloud and uses AI to make production plans and forecast supply and demand. It is designed to send work instructions to automated farm machinery and optimize harvests in conjunction with an online purchasing system. It also uses an emotion engine to evaluate users' purchasing intent in real time and reflect this in online sales strategies. Specific library names and technologies used must also be clearly stated.

[0365] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0366] Step 1:

[0367] User enters farmland information

[0368] Users use a device (smartphone or PC) to input the location, area, and soil type of their farmland. This input data is sent to a server and stored in the cloud. The server then uses this data to accumulate basic information about the farmland in a database.

[0369] Step 2:

[0370] Weather data collection

[0371] The server collects real-time weather data from a weather data provider. The collected data includes temperature, precipitation, humidity, wind speed, etc., and is stored in the cloud. The server integrates and manages this weather data with farmland data.

[0372] Step 3:

[0373] Collection of historical production and market data

[0374] The server collects and stores historical production data (yields, agricultural inputs used, pest and disease occurrence) and market data (market prices, supply and demand balance, consumer preferences) on the cloud. This data is used to understand past agricultural activities and market trends.

[0375] Step 4:

[0376] Production planning

[0377] The server uses a generative AI model to forecast supply and demand for the next season based on collected farmland information, weather data, past production data, and market data. Based on the results, it creates a production plan (sowing timing, planting area, fertilizer to be used, etc.) and notifies the terminal. The user can check this notification and make adjustments as necessary.

[0378] Step 5:

[0379] Automated farming

[0380] The server sends specific instructions (such as sowing, watering, fertilizing, and harvesting) to the automated farming machines. The automated farming machines carry out the farming tasks based on these instructions and transmit their progress in real time via sensors to the server. The server analyzes this data and sends additional instructions as needed.

[0381] Step 6:

[0382] Post-harvest inventory management and online sales

[0383] Harvested produce is managed in inventory by a server. The server creates an optimal online sales strategy based on supply and demand forecasts and updates inventory information in the online sales system in real time. When a user purchases produce online, the server receives the order, updates inventory, and automatically arranges delivery.

[0384] Step 7:

[0385] Offering augmented reality tours of brick-and-mortar stores

[0386] The server uses augmented reality technology to provide a virtual tour of the farm within a physical store. Once the user starts the tour using smart glasses or a head-mounted display, the server provides an interactive guide through AR technology.

[0387] Step 8:

[0388] Evaluating users' purchasing intentions using an emotion engine

[0389] The server uses an emotion engine to assess users' willingness to buy and their satisfaction in real time, analyzing emotional data collected during the user's in-store and online purchase process. The server then adjusts inventory and sales strategies based on this data.

[0390] 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.

[0391] 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.

[0392] 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.

[0393] [Second embodiment]

[0394] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0395] 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.

[0396] 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).

[0397] 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.

[0398] 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.

[0399] 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).

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] 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."

[0406] This invention is a system that collects and manages geographical and meteorological data for farmland, uses AI to create production plans, and realizes automated farm work and online sales. Specific examples of the operation of each element are shown below.

[0407] Collection and management of farmland information

[0408] First, the user uses a device (such as a smartphone or PC) to input geographic data such as the location, area, and soil type of the farmland. The server then collects weather data in real time from a weather data provider and stores it in the cloud. This allows basic information about the farmland to be stored in a database.

[0409] Collection of historical production and market data

[0410] The server collects historical production data (e.g., yields, agricultural inputs used, pest and disease occurrence) and market data (e.g., market prices, supply and demand balance, consumer preferences) and stores these in a database.

[0411] Production planning

[0412] The server uses AI to predict supply and demand for the next season based on collected farmland information, weather data, past production data, and market data. Based on the results, it creates a production plan (for example, sowing timing, planting area, fertilizer to be used, etc.) and notifies the user via their device so that they can check it.

[0413] Automated farming

[0414] The server sends specific work instructions to automated agricultural machinery (e.g., robots or drones). For example, the automated agricultural machinery begins work in time for sowing seeds, and sensors send progress updates to the server in real time. The server monitors this and sends additional instructions for watering, fertilizing, and weeding as needed. The timing of harvesting is also automatically determined based on crop growth data, and the terminal (automated agricultural machinery) carries out the harvesting work.

[0415] Inventory management and online sales

[0416] Harvested produce is managed in inventory by a server. Based on supply and demand forecasts, the server creates an optimal online sales strategy and updates inventory information in real time to the online sales system. When a user purchases produce online, the server receives the order and automatically updates the inventory. It also sends instructions to delivery companies and automatically arranges for delivery. The server also collects sales data and feeds it back into the next production plan.

[0417] Specific examples

[0418] Here's an example of how it works on a farm. After a user registers a new piece of farmland and enters its location and soil type, the server begins collecting local weather data. Using past yield data, the server predicts the supply and demand of tomatoes for the next season and determines the timing of sowing. The server then sends instructions to the automated farm machinery to sow the tomatoes. Sensors monitor the soil moisture, and if it dries out, the server automatically sends instructions to water the soil. At harvest time, the server sends harvesting instructions to the automated farm machinery, and the harvested tomatoes are immediately registered in the inventory management system. Consumers then purchase the tomatoes online, and the server automatically arranges for delivery. The sales data is reflected in the next season's tomato production plan.

[0419] As described above, the present invention realizes agricultural efficiency and a stable food supply.

[0420] The processing flow will be explained below.

[0421] Step 1:

[0422] Users use a device (smartphone or PC) to input geographical data such as the location, area, and soil type of farmland into the system.

[0423] Step 2:

[0424] The server collects real-time weather data for the farmland (e.g., temperature, precipitation, humidity, wind speed, etc.) from a weather data provider and stores it on the cloud.

[0425] Step 3:

[0426] The server performs an initial data analysis based on the farmland information entered by the user and the collected weather data, confirms the basic information about the farmland, and stores it in a database.

[0427] Step 4:

[0428] The server collects past production data (e.g., crop type, yield, pest and disease occurrence status, etc.) and market data (e.g., market price, balance of supply and demand, consumer preferences, etc.) and stores them in a database.

[0429] Step 5:

[0430] Based on farmland information, weather data, past production data, and market data collected by the server, artificial intelligence (AI) is used to predict supply and demand for the next season. Based on the results of this prediction, a production plan (sowing timing, planting area, fertilizer to be used, etc.) is created.

[0431] Step 6:

[0432] The server notifies the terminal of the production plan it has drawn up, and the user checks this plan, modifies it as necessary, and gives final approval.

[0433] Step 7:

[0434] The server sends instructions to automated agricultural machinery (e.g., robots and drones), including tasks such as sowing seeds, watering, fertilizing, weeding, and harvesting.

[0435] Step 8:

[0436] The terminal (automated agricultural machinery) automatically carries out agricultural work according to the received instructions. Sensors attached to the agricultural machinery send work progress and environmental data to a server in real time.

[0437] Step 9:

[0438] The server analyzes real-time data from the sensors and sends additional work instructions (e.g., additional watering or fertilization) to the device as needed.

[0439] Step 10:

[0440] The server determines the timing of harvesting based on the crop growth data, and when the time comes, it sends harvesting instructions to the automated farm machinery.

[0441] Step 11:

[0442] The terminal (automated farm machine) performs the harvesting work and sends the data to the server, which updates the inventory based on the harvest data.

[0443] Step 12:

[0444] Based on the inventory data, the server develops an online sales strategy taking into account supply and demand forecasts and market data, and reflects inventory information on the sales site in real time.

[0445] Step 13:

[0446] When a user purchases produce from an online sales site, the server receives the order and automatically updates the inventory.

[0447] Step 14:

[0448] The server issues instructions to the delivery company and automatically arranges delivery. Order details and delivery information are managed in real time and notified to the consumer.

[0449] Step 15:

[0450] The server collects sales data and analyzes it to provide feedback for the next production plan. Users can check this feedback data on their devices and use it to plan the next production season.

[0451] In this way, a system will be built that automates the entire agricultural process, ensuring efficient operations and a stable food supply.

[0452] Example 1

[0453] 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."

[0454] Efficient production planning and management remains a major challenge in modern agriculture. Specifically, it is difficult to accurately collect and manage geographic and meteorological data on farmland, and to forecast supply and demand and formulate production plans based on past production and market data. Other challenges include maximizing work efficiency with automated farm machinery, using sensors to monitor and adjust farm work in real time, and managing post-harvest inventory and quickly managing online sales.

[0455] 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.

[0456] In this invention, the server includes: means for collecting geographical data and meteorological data of farmland; means for managing the collected data on the cloud; means for collecting past production data and market data; means for using artificial intelligence to forecast supply and demand and create production plans; means for sending instructions to automated agricultural machinery to sow, water, fertilize, and harvest; means for collecting and analyzing data from the automated agricultural machinery and sensors in real time; means for managing inventory of harvested crops; means for selling agricultural products through an online sales system; means for receiving orders from consumers and updating inventory; means for automatically arranging deliveries; means for collecting and analyzing sales data and feeding it back into the next production plan; means for using artificial intelligence to analyze meteorological data, farmland information, and market data and for notifying the users of the timing of sowing, planting area, and type and amount of materials to be used in real time; and means for monitoring soil moisture using data from sensors and automatically sending watering instructions when the soil becomes dry. This enables more efficient and automated agricultural work, enabling stable crop production and rapid market supply.

[0457] "Agricultural land geographic data" refers to all geographic information including agricultural land location, area, soil type, etc.

[0458] "Weather data" refers to data related to weather, such as temperature, precipitation, and wind speed.

[0459] "Cloud" refers to data storage or computing resources provided over the internet.

[0460] "Past production data" refers to data such as yields from previous cultivation on farmland, agricultural materials used, and the occurrence of pests and diseases.

[0461] "Market data" refers to various data about the market, such as market prices, the balance of supply and demand, and consumer preferences.

[0462] "Artificial intelligence" refers to a system that uses machine learning and data analysis techniques to make specific predictions and judgments from data.

[0463] "Supply and demand forecast" refers to predicting the future balance between supply and demand.

[0464] A "production plan" refers to a specific plan for agricultural work, such as the timing of sowing seeds, the area to be planted, and the types and amounts of fertilizers and pesticides to be used.

[0465] "Automated agricultural machinery" refers to machines such as robots and drones that perform agricultural work automatically according to a program.

[0466] A "sensor" refers to a device that measures environmental changes, such as soil humidity and temperature, in real time and acquires data.

[0467] "Inventory management" refers to managing the quantity and condition of harvested agricultural products.

[0468] "Online sales system" refers to a system for selling products via the Internet.

[0469] "Shipping arrangements" refers to managing the shipping of ordered products.

[0470] "Sales Data" means data related to sales, such as the quantity, price, and purchaser information of the items sold.

[0471] This invention is a system that collects and manages geographical and meteorological data for farmland, uses artificial intelligence to create production plans, and realizes automated farm work and online sales. Specific examples of the operation of each element are shown below.

[0472] Collection and management of farmland information

[0473] First, the user uses a terminal (e.g., a smartphone or personal computer) to input geographic data such as the location, area, and soil type of the farmland. The server then collects weather data in real time from a weather data provider (e.g., a general weather data API) and stores it in the cloud. This operation accumulates basic information about the farmland in a database. The specific hardware and software used include a smart device as the terminal and a cloud platform as the server.

[0474] Collection of historical production and market data

[0475] The server collects past production data (e.g., yields, agricultural inputs used, pest and disease occurrence status) and market data (e.g., price information, supply and demand balance, consumer preferences), and stores this data in a cloud database. Data collection methods used include APIs and agricultural information management systems.

[0476] Production planning

[0477] The server uses an artificial intelligence model to predict supply and demand for the next season based on collected farmland information, weather data, past production data, and market data. Based on the prediction results, it creates a production plan (for example, sowing timing, planting area, and type and amount of fertilizer to be used). These plans are sent to the terminal and can be checked by the user. The specific software used is a machine learning framework built in Python (such as TensorFlow or PyTorch).

[0478] Automated farming

[0479] The server sends specific work instructions to the automated agricultural machinery (e.g., robots or drones). For example, the automated agricultural machinery starts work when it's time to sow seeds, and sends its progress information to the server in real time via sensors. Sensing technology and IoT device management systems (such as AWS IoT Core) are used. The automated agricultural machinery follows the server's instructions to perform tasks such as sowing seeds, watering, fertilizing, and harvesting.

[0480] Inventory management and online sales

[0481] Harvested produce is managed in inventory by a server. Based on supply and demand forecasts, the server creates an optimal online sales strategy and updates inventory information in real time to the online sales system (a typical e-commerce platform). When a user purchases produce online, the server receives the order and automatically updates the inventory. The server then sends instructions to the delivery company to automatically arrange delivery. Sales data is fed back into the next season's production plan.

[0482] Specific examples

[0483] Here's an example of how it works on a farm. After a user registers a new piece of farmland and enters its location and soil type, the server begins collecting local weather data. Based on past yield data, the server predicts the supply and demand of tomatoes for the next season and determines the timing of sowing. The server then sends instructions to the automated farm machinery to sow the tomatoes. Sensors monitor the moisture content of the soil, and if it becomes too dry, the server automatically sends instructions to water the soil. When it's time to harvest, the server sends harvesting instructions to the automated farm machinery, and the harvested tomatoes are immediately registered in the inventory management system. Consumers then purchase the tomatoes online, and the server automatically arranges for delivery. The sales data is reflected in the next season's tomato production plan.

[0484] Prompt Sentence Examples

[0485] Below are examples of prompts to use with the generative AI model:

[0486] "Please explain the functions of the system that inputs geographical and meteorological data of farmland and uses AI to create production plans. Please provide a detailed description, including the specific data collection method, automated farm machinery used, and online sales process."

[0487] Through the above process, the present invention realizes agricultural efficiency and a stable food supply.

[0488] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0489] Step 1:

[0490] The user inputs the geographic data of the farmland.

[0491] The user uses a device to input geographic data such as the location, area, and soil type of the farmland. The input data includes the location of the farmland on a map, its area, and soil quality. This input data is sent to the server, which receives the data and stores it in a cloud database. Specific operations include mapping the location of the farmland on a map on the application screen and registering information such as the area and soil type through an input form.

[0492] Step 2:

[0493] The server collects and manages weather data

[0494] The server collects weather data in real time from a weather data provider service. Specifically, it periodically obtains data such as temperature, precipitation, and wind speed using the weather data provider service's API. The obtained data is stored in a cloud database. This process involves sending an API request and writing the weather data obtained in response to the request into the database. This weather data plays an important role in subsequent production planning.

[0495] Step 3:

[0496] The server collects historical production and market data

[0497] The server collects past production data and market data. Past production data inputs include yields, agricultural inputs used, and pest and disease occurrence status. Market data inputs include price information, supply and demand balance, and consumer preferences. The server periodically obtains this data from various data sources and stores it in a cloud database. Specifically, the data is collected using the APIs of agricultural management systems and market data providers.

[0498] Step 4:

[0499] The server analyzes the data and makes supply and demand predictions

[0500] The server integrates collected farmland information, weather data, past production data, and market data, and uses an artificial intelligence model to predict supply and demand for the next season. All collected data is provided as input to the AI ​​model, and supply and demand forecast results are obtained as output. During this process, data analysis is performed using machine learning frameworks developed in Python (such as TensorFlow and PyTorch). Specific operations include data preprocessing, feature selection, model training, and validation.

[0501] Step 5:

[0502] The server creates a production plan and notifies the terminal

[0503] The server creates a production plan based on the predictions of the AI ​​model. The plan includes the timing of sowing seeds, the area to be planted, and the type and amount of fertilizer and pesticide to be used. These plans are notified to the terminal. Specifically, the created plan is sent to the user using a messaging system (for example, push notification or email notification).

[0504] Step 6:

[0505] The server operates the automated farm machinery and starts farming.

[0506] The server sends specific operational instructions to the automated agricultural machinery. For example, it instructs the timing of sowing seeds, fertilizing, watering, and harvesting. Input data includes production plans and real-time weather and soil data, and the output is the agricultural machinery performing its tasks. Specific operations include controlling GPIO and various sensors using an IoT device management system.

[0507] Step 7:

[0508] The server analyzes the data from the sensors in real time and adds necessary instructions.

[0509] The server collects real-time data from sensors (e.g., soil moisture sensors) and sends additional instructions to the automated farming equipment depending on the situation. The input data includes real-time data from the sensors, and the output sends instructions to the farming equipment, for example, to water. It also performs constant monitoring using real-time data analysis tools (e.g., Apache Kafka and Spark Streaming).

[0510] Step 8:

[0511] The server manages post-harvest inventory

[0512] The harvested produce is inventory-managed by the server. The input includes harvested quantity and quality data, and the output updates the inventory database. Specific operations include writing to the database and monitoring the inventory status.

[0513] Step 9:

[0514] The server executes the online sale

[0515] The server updates the online sales system with inventory information in real time and accepts orders from consumers. The input data includes updated inventory and order information, and the output is order processing and inventory updates. Specific operations include data integration with the e-commerce platform, order confirmation, and payment processing.

[0516] Step 10:

[0517] The server automatically arranges delivery

[0518] Delivery arrangements are made automatically by the server. Input data includes the consumer's order information and delivery address information, and output includes instructions to the delivery company. Specifically, this includes setting up an efficient delivery route through API integration with the delivery system and completing the delivery arrangements.

[0519] (Application example 1)

[0520] 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."

[0521] In agricultural and industrial production, uncertainty in production plans and a lack of automation are obstacles to efficient production. It is also difficult to accurately predict the supply and demand balance of agricultural crops and factory-produced goods and to formulate optimal production plans based on that. Furthermore, the lack of automation in inventory management, sales, and delivery has resulted in labor surpluses and shortages, preventing stable food self-sufficiency and product supply. It is necessary to solve these issues and achieve efficient and stable agricultural and industrial production.

[0522] 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.

[0523] In this invention, the server includes means for collecting geographical data and meteorological data of the farmland, means for managing the collected data on the cloud, means for collecting past production data and market data, means for using artificial intelligence to make supply and demand forecasts and production plans, means for sending instructions to automated agricultural machinery to perform sowing, watering, fertilizing, and harvesting, means for collecting and analyzing data from the automated agricultural machinery and sensors in real time, means for managing inventory of harvested crops, means for selling agricultural products through an online sales system, means for receiving orders from consumers and updating inventory, means for automatically making delivery arrangements, means for collecting and analyzing sales data and feeding it back to the next production plan, means for collecting and managing location information and equipment data within the factory, means for collecting work environment data within the factory in real time, means for making production plans using past production data and demand data, means for sending specific work instructions to automated robots and having them perform production work, and means for automatically managing inventory and shipping within the factory. This will reduce uncertainty in production plans in agricultural and industrial production, enabling efficient production and stable supply.

[0524] "Geographic data of agricultural land" refers to data on the geographic characteristics of agricultural land, such as its location, area, and soil type.

[0525] "Weather data" refers to data related to climate, such as temperature, precipitation, humidity, and wind speed.

[0526] "Managing on the cloud" means storing collected data in a data center connected via the Internet and making it accessible.

[0527] "Past production data" refers to data on past crop yields, agricultural materials used, pest and disease occurrence, and so on.

[0528] "Market data" refers to data on market trends such as market prices, the balance between supply and demand, and consumer preferences.

[0529] "Artificial intelligence" is a technology that uses machine learning and data analysis techniques to derive knowledge and patterns from data.

[0530] "Supply and demand forecasting" is the prediction of future demand and supply based on collected data.

[0531] "Production planning" involves planning the production volume and timing of crops and industrial products, as well as the materials to be used.

[0532] "Automated agricultural machinery" refers to machines such as robots and drones that can perform agricultural work automatically.

[0533] "Real-time collection and analysis" means collecting data immediately and analyzing it on the spot.

[0534] "Inventory management" is the tracking and management of harvested agricultural crops and manufactured industrial products.

[0535] An "online sales system" is a system for selling products over the Internet.

[0536] "Updating inventory upon receiving an order from a consumer" means updating inventory data based on the information when a consumer places an order through the online sales system.

[0537] "Automatically arranging delivery" means automatically sending delivery instructions to a delivery company based on a consumer's order.

[0538] "Location information within a factory" is information relating to the locations of equipment and machines within a factory.

[0539] "Facility data" refers to data related to the functions and status of machines and equipment within a factory.

[0540] "Work environment data" refers to data related to the work environment in a factory, such as temperature, humidity, and sound level.

[0541] "Planning a production plan" means planning the production processes and schedules within a factory.

[0542] An "automatic robot" is a robot that can autonomously operate machinery and perform production tasks.

[0543] "Automated shipping" means automatically packaging products and preparing them for shipping.

[0544] The present invention provides a system that improves the efficiency of production management systems in farmland and factories and provides specific means for realizing stable supply. Specific embodiments of the system are described below.

[0545] System Overview

[0546] This system consists of a server, terminals, users, automated agricultural machinery, and automated robots. The server collects, manages, analyzes, sends instructions, and manages inventory. Terminals are devices that users use to input and check data, and include smartphones and PCs. The automated agricultural machinery and automated robots are machines that perform specific tasks.

[0547] Program Overview

[0548] The server first collects geographic and meteorological data for the farmland. Geographic data includes the location, area, and soil type of the farmland. Meteorological data includes temperature, precipitation, humidity, and wind speed. This data is stored in the cloud, along with past production data and market data. Market data includes market prices, supply and demand balance, and consumer preferences.

[0549] Based on this data, the server uses artificial intelligence (AI) to predict supply and demand for the next season and create a production plan. This plan includes sowing, watering, fertilizing, harvesting timing, and materials to be used. The plan is then sent to the device so that the user can check and modify it.

[0550] The server then sends instructions to the autonomous farming equipment to perform the tasks, and data from the autonomous farming equipment and sensors is sent to the server in real time for analysis, which then sends additional instructions on watering and fertilizing as needed.

[0551] Harvested produce is managed by the server and reflected in the online sales system. When a consumer purchases produce online, the server receives the order, updates the inventory, and automatically arranges delivery. Sales data is also collected and fed back into the next production plan.

[0552] Within the factory, a server collects and manages the factory's location information and equipment data. Work environment data (temperature, humidity, sound level, etc.) is also collected in real time. Production plans are created using past production data and demand data, and specific work instructions are sent to automated robots. Inventory management and shipping within the factory are also automated.

[0553] Hardware and Software Use

[0554] Hardware: Servers, smartphones, PCs, automated farm machinery, automated robots, sensors.

[0555] Software: Cloud database, AI models (machine learning algorithms), online sales systems, inventory management systems, communication protocols.

[0556] Specific examples

[0557] As an example of how a new farm is operated, let's say a new piece of farmland is registered. When a user uses a device to enter the location information and soil type of the farmland, the server begins collecting local weather data. Using past yield data, the server predicts the supply and demand of tomatoes for the next season and decides when to sow seeds. The server then sends instructions to the automated farm machinery to sow the tomatoes. Sensors monitor the moisture content of the soil, and if it becomes too dry, the server automatically sends instructions to water the soil. When it's time to harvest, the server sends harvesting instructions to the automated farm machinery, and the harvested tomatoes are immediately registered in the inventory management system. Consumers then purchase the tomatoes online, and the server automatically arranges for delivery. The sales data is reflected in the next season's tomato production plan.

[0558] Prompt Sentence Examples

[0559] Collect location information, equipment data, and real-time environmental data within the factory, integrate past production data with demand data, and create a production plan that predicts supply and demand for the next season. Then, design a system that sends specific work instructions to robots based on the generated production plan. Store the collected data in an SQLite database and use an AI model to predict supply and demand.

[0560] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0561] Step 1:

[0562] The user inputs geographic data of the farmland (location, area, soil type) using a terminal. The terminal sends this data to the server, which receives the input data and stores it in a database on the cloud.

[0563] Step 2:

[0564] The server collects real-time weather data (temperature, precipitation, humidity, wind speed) from a weather data provider. The collected weather data is stored in a database on the cloud. This allows the server to obtain basic environmental information about the farmland.

[0565] Step 3:

[0566] The server collects past production data (yields, agricultural inputs used, pest and disease occurrence status) and market data (market prices, supply and demand balance, consumer preferences) and stores them in a database, allowing the server to understand past agricultural activities and market trends.

[0567] Step 4:

[0568] The server uses collected farmland information, weather data, past production data, and market data to predict supply and demand for the next season using a generative AI model. The model uses this data as input, performs data calculations, and outputs supply and demand forecast results. Based on these forecast results, the server creates a production plan (sowing timing, planting area, fertilizer to be used, etc.) and notifies the terminal.

[0569] Step 5:

[0570] The user checks the production plan on the terminal and modifies it as necessary. When the user makes modifications, the modified production plan is sent from the terminal to the server. The server saves the updated production plan in a database on the cloud.

[0571] Step 6:

[0572] The server sends specific work instructions (such as sowing seeds, watering, fertilizing, and harvesting) to the automated farming machine. The automated farming machine carries out the work according to these instructions and sends its progress and environmental data to the server in real time.

[0573] Step 7:

[0574] The server collects and analyzes data from automated farm machinery and sensors in real time. The server uses the collected data to send additional instructions for watering and fertilizing as needed. The data is stored in a database on the cloud.

[0575] Step 8:

[0576] Harvested produce is managed by a server. Inventory data is stored in a cloud database and updated in real time in the online sales system. The server monitors inventory status and develops optimal sales strategies.

[0577] Step 9:

[0578] When a consumer purchases produce through the online sales system, the server receives the order, updates inventory data, and automatically sends delivery instructions to the delivery company.

[0579] Step 10:

[0580] The server collects and analyzes sales data. The sales data is analyzed by a generative AI model and fed back into the next season's production plan. The collected and analyzed data is stored in a database on the cloud.

[0581] Step 11:

[0582] The server collects and manages factory location information and equipment data, and the collected data is stored in a database on the cloud.

[0583] Step 12:

[0584] The server collects real-time data on the working environment within the factory (temperature, humidity, sound level, etc.) and stores it in a database on the cloud. The collected data is used to assist in production planning.

[0585] Step 13:

[0586] The server uses past production and demand data to generate demand and supply forecasts using a generative AI model, and creates a production plan, which is then stored in a cloud database.

[0587] Step 14:

[0588] The server sends specific work instructions to the automated robot, which then carries out production tasks based on the instructions and reports its status to the server in real time.

[0589] Step 15:

[0590] The server manages inventory in the factory and automatically handles shipping. Inventory and shipping data is stored in a cloud database and updated in real time on the online sales system.

[0591] 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.

[0592] This system combines a system that collects and manages geographical and meteorological data on farmland, uses AI to create production plans, and realizes automated farm work and online sales, with an emotion engine that recognizes user emotions. Specific examples of the operation of each element are shown below.

[0593] Collection and management of farmland information

[0594] First, the user uses a device (such as a smartphone or PC) to input geographic data such as the location, area, and soil type of the farmland. The server then collects weather data in real time from a weather data provider and stores it in the cloud. This allows basic information about the farmland to be stored in a database.

[0595] Collection of historical production and market data

[0596] The server collects historical production data (e.g., yields, agricultural inputs used, pest and disease occurrence) and market data (e.g., market prices, supply and demand balance, consumer preferences) and stores these in a database.

[0597] Production planning

[0598] The server uses AI to forecast supply and demand for the next season based on collected farmland information, weather data, past production data, and market data. Based on the results, it creates a production plan (for example, sowing timing, planting area, fertilizer to be used, etc.) and notifies the user via their device so that they can check it.

[0599] Automated farming

[0600] The server sends specific work instructions to automated agricultural machinery (e.g., robots or drones). For example, the automated agricultural machinery begins work in time for sowing seeds, and sensors send progress updates to the server in real time. The server monitors this and sends additional instructions for watering, fertilizing, and weeding as needed. The timing of harvesting is also automatically determined based on crop growth data, and the terminal (automated agricultural machinery) carries out the harvesting work.

[0601] Inventory management and online sales

[0602] Harvested produce is managed in inventory by a server. Based on supply and demand forecasts, the server creates an optimal online sales strategy and updates inventory information in real time to the online sales system. When a user purchases produce online, the server receives the order and automatically updates the inventory. It also sends instructions to delivery companies and automatically arranges for delivery. The server also collects sales data and feeds it back into the next production plan.

[0603] Use of emotion engine

[0604] The emotion engine is used to evaluate user feedback and purchasing intent in real time, and the server adjusts and optimizes production plans and sales strategies based on this emotion data.

[0605] Specific examples

[0606] Here's an example of how it works on a farm. After a user registers a new piece of farmland and enters its location and soil type, the server begins collecting local weather data. Using past yield data, the server predicts the supply and demand of tomatoes for the next season and determines the timing of sowing. The server then sends instructions to the automated farm machinery to sow the tomatoes. Sensors monitor the soil moisture, and if it dries out, the server automatically sends instructions to water the soil. At harvest time, the server sends harvesting instructions to the automated farm machinery, and the harvested tomatoes are immediately registered in the inventory management system. Consumers then purchase the tomatoes online, and the server automatically arranges for delivery. The sales data is reflected in the next season's tomato production plan.

[0607] Furthermore, when a user purchases tomatoes from an online sales site, the emotion engine evaluates the user's purchasing motivation and satisfaction in real time, and the server adjusts inventory and delivery arrangements based on this data. For example, if user satisfaction is high, that data can be reflected in the next season's production plan, and the plan can be adjusted to produce more tomatoes. Conversely, if satisfaction is low, the cause can be analyzed and improvements can be made to improve the quality of agricultural products and service.

[0608] In this way, the present invention is a system that can realize agricultural efficiency and a stable food supply, while also increasing user satisfaction.

[0609] The processing flow will be explained below.

[0610] Step 1:

[0611] Users use a device (smartphone or PC) to input geographical data such as the location, area, and soil type of farmland into the system.

[0612] Step 2:

[0613] The server collects real-time weather data for the farmland (e.g., temperature, precipitation, humidity, wind speed, etc.) from a weather data provider and stores it on the cloud.

[0614] Step 3:

[0615] The server performs an initial data analysis based on the farmland information entered by the user and the collected weather data, and stores the basic information about the farmland in a database.

[0616] Step 4:

[0617] The server collects past production data (e.g., crop type, yield, pest and disease occurrence status, etc.) and market data (e.g., market price, balance of supply and demand, consumer preferences, etc.) and stores them in a database.

[0618] Step 5:

[0619] Based on farmland information, weather data, past production data, and market data collected by the server, artificial intelligence (AI) is used to predict supply and demand for the next season. Based on the results of this prediction, a production plan (sowing timing, planting area, fertilizer to be used, etc.) is created.

[0620] Step 6:

[0621] The server notifies the terminal of the production plan it has drawn up, and the user checks this plan, modifies it as necessary, and gives final approval.

[0622] Step 7:

[0623] The server sends instructions to automated agricultural machinery (e.g., robots and drones), including tasks such as sowing seeds, watering, fertilizing, weeding, and harvesting.

[0624] Step 8:

[0625] The terminal (automated agricultural machinery) automatically carries out agricultural work according to the received instructions. Sensors attached to the agricultural machinery send work progress and environmental data to a server in real time.

[0626] Step 9:

[0627] The server analyzes real-time data from the sensors and sends additional work instructions (e.g., additional watering or fertilization) to the device as needed.

[0628] Step 10:

[0629] The server determines the timing of harvesting based on the crop growth data, and when the time comes, it sends harvesting instructions to the automated farm machinery.

[0630] Step 11:

[0631] The terminal (automated farm machine) performs the harvesting work and sends the data to the server, which updates the inventory based on the harvest data.

[0632] Step 12:

[0633] Based on the inventory data, the server develops an online sales strategy taking into account supply and demand forecasts and market data, and reflects inventory information on the sales site in real time.

[0634] Step 13:

[0635] When a user purchases produce from an online sales site, the server receives the order and automatically updates the inventory.

[0636] Step 14:

[0637] The server issues instructions to the delivery company and automatically arranges delivery. Order details and delivery information are managed in real time and notified to the consumer.

[0638] Step 15:

[0639] The emotion engine evaluates users' feedback and purchasing intentions in real time, and the server adjusts inventory and delivery arrangements based on that data. It also reflects factors that lead to high satisfaction in production plans for the next season.

[0640] Step 16:

[0641] The server collects sales data and analyzes it to provide feedback for the next production plan. Users can check this feedback data on their devices and use it to plan the next production season.

[0642] In this way, a system will be built that automates the entire agricultural process, ensuring efficient operations and a stable food supply.

[0643] Example 2

[0644] 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."

[0645] In conventional agricultural systems, the collection and management of geographical data and meteorological data for farmland was often done manually, making efficient data management difficult. Furthermore, there was a lack of systems for integrating and analyzing past production data and market data to forecast supply and demand and formulate production plans. Furthermore, there were challenges with operating automated agricultural machinery, collecting and analyzing data in real time, and swiftly managing inventory and arranging deliveries in response to consumer orders.

[0646] 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. In this invention, the server includes means for collecting geographic data such as location information, area, and soil type of farmland, means for collecting weather data in real time from a weather data providing service, means for managing the collected geographic data and weather data on the cloud, means for collecting production data on past yields, agricultural materials used, and the occurrence of pests and diseases, and market data on market prices, the balance of supply and demand, and consumer preferences, means for using artificial intelligence to predict supply and demand and formulate production plans based on the collected data, means for sending instructions to automated agricultural machinery to sow seeds, water, fertilize, and harvest, means for collecting and analyzing data such as work progress and soil humidity from the automated agricultural machinery and sensors in real time, means for managing inventory of harvested crops, means for selling agricultural products through an online sales system, means for receiving orders from consumers and updating inventory, means for automatically arranging deliveries, collecting and analyzing sales data, and providing feedback to the next production plan, and an emotion engine for evaluating user feedback and purchasing intentions in real time and adjusting production plans and sales strategies. This will lead to more efficient agriculture, a stable food supply, and improved consumer satisfaction.

[0647] "Farmland location information, area, and soil type" is data that indicates the exact location of the farmland, the size of the farmland, and the type of soil on the farmland.

[0648] "Weather Data Service" means an external data source that provides real-time weather information (temperature, humidity, precipitation, wind speed, etc.).

[0649] "Managing on the cloud" means storing data on a remote server via the Internet and accessing and analyzing it.

[0650] "Production data on past yields, agricultural materials used, and pest and disease occurrence status" refers to data showing the yield of crops grown in the past, the types and amounts of fertilizers and pesticides used, as well as the types of pests and diseases that have occurred and their impacts.

[0651] "Market data on market prices, supply and demand balance, and consumer preferences" refers to data showing the market trading prices of agricultural products, the state of supply and demand in the market, and consumer purchasing behavior and preferences.

[0652] "Using artificial intelligence to forecast supply and demand and create production plans" means using AI technology to predict the future balance of supply and demand and to create effective production schedules based on that.

[0653] "Automated agricultural machinery" refers to mechanical devices such as robots and drones that are used to perform automated agricultural work.

[0654] "Data from sensors such as work progress and soil moisture" refers to measurements such as the current progress of work and the amount of moisture in the soil detected by sensors.

[0655] "Inventory management" is the process of tracking and efficiently managing harvested produce, including its quantity, storage location, and freshness.

[0656] An "online sales system" is an e-commerce platform for selling products to consumers over the Internet.

[0657] The "emotion engine that evaluates user feedback and purchasing intent in real time" is an analytical tool that analyzes users' opinions and purchasing behavior regarding products and services, and evaluates their emotions and satisfaction in real time.

[0658] This invention is a system that collects and manages geographical and meteorological data on farmland, uses AI to create production plans, and realizes automated farming and online sales. Furthermore, by combining it with an emotion engine that recognizes user emotions, it aims to improve agricultural efficiency and consumer satisfaction.

[0659] In this system, users first use a terminal to input geographic data such as the location, area, and soil type of their farmland. The terminal can be a smartphone or a PC. This data is sent to a server and stored in a database. The server then collects weather data in real time from a weather data provider (for example, a weather API) and stores it in a database on the cloud.

[0660] The server then collects past production data (e.g., yields, agricultural inputs used, pest and disease occurrence) and market data (market prices, supply and demand balance, consumer preferences) and stores them in a database. This allows the server to use artificial intelligence to create supply and demand forecasts and production plans based on all the collected data. The generative AI model is used to determine the next season's supply and demand forecast and crop growth schedule. This plan is then sent to the device and can be viewed by the user.

[0661] The server then sends specific work instructions to the automated farming machinery (e.g., robots or drones). The automated farming machinery follows the server's instructions to sow seeds, water, fertilize, weed, and harvest. Data such as the progress of work and soil moisture is collected in real time by sensors and sent to the server. The server analyzes this data and sends additional instructions to the automated farming machinery as needed.

[0662] Once the harvest is complete, the harvested produce is managed as inventory by the server. The server uses the inventory data to sell the produce through an online sales system and updates inventory information in real time. When a user purchases produce online, the server receives the order and automatically updates the inventory. It also sends instructions to the delivery company, which then automatically arranges for delivery. At the same time, sales data is fed back into the next production plan.

[0663] Furthermore, an emotion engine will be used to evaluate user feedback and purchasing intent in real time, and the server will then adjust and optimize production plans and sales strategies based on this emotion data.

[0664] Specific examples

[0665] For example, in a farm operation, when a user registers a new piece of farmland and enters its location and soil type, the server immediately begins collecting local weather data. Using past yield data, the server predicts the supply and demand of tomatoes for the next season and determines the timing of sowing. The server sends instructions to the automated farming machinery, such as "Start sowing tomato seeds on one hectare of farmland at 10:00 AM tomorrow," and the automated farming machinery begins work at the specified time. When it is time to harvest, the server sends instructions to the automated farming machinery, such as "Start harvesting at 8:00 AM tomorrow," and the harvested tomatoes are immediately registered in the inventory management system.

[0666] Prompt Sentence Examples

[0667] "Based on the geographical and meteorological data of the farmland, you will create a production plan for the next season. Specifically, you will determine the timing for sowing tomatoes and generate instructions to send to the automated farm machinery."

[0668] In this way, the present invention can improve agricultural efficiency and ensure a stable food supply while also increasing user satisfaction.

[0669] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0670] Step 1:

[0671] A user uses a device to input geographic data such as the location, area, and soil type of the farmland. This input data includes GPS information, the size of the farmland, and soil analysis results. The input data is sent to a server and stored in a database. For example, a user inputs the GPS coordinates of the farmland and the pH value of its soil.

[0672] Step 2:

[0673] The server collects weather data (temperature, humidity, wind speed, precipitation, etc.) in real time from a weather data provider (e.g., weather API). The collected data is stored in a database on the cloud. This collection process is performed automatically and periodically. For example, the server calls the weather API every day at 6:00 AM to obtain the latest weather data.

[0674] Step 3:

[0675] The server collects past production data (e.g., yields, agricultural inputs used, pest and disease occurrence status) and market data (market prices, supply and demand balance, consumer preferences) and stores them in a database. This data is obtained from agricultural statistics databases and market information services. For example, tomato yield data and market price data for the past five years are collected.

[0676] Step 4:

[0677] The server uses a generative AI model based on collected geographical data, weather data, past production data, and market data to predict supply and demand for the next season. It then creates a production plan (sowing timing, planting area, fertilizer to be used, etc.) based on the results of the AI ​​analysis. These plans are sent to the terminal and can be checked by the user. For example, a plan may be generated that states, "Since demand for tomatoes is predicted to increase next season, tomatoes should be sown on two hectares of farmland."

[0678] Step 5:

[0679] The server sends specific work instructions to the automated farm machinery (robots and drones). For example, an instruction might be sent saying, "Start sowing tomato seeds on one hectare of farmland at 10:00 AM tomorrow." The automated farm machinery starts work at the specified time, and sensors detect its progress in real time. This progress data is sent to the server, and additional instructions are sent as needed.

[0680] Step 6:

[0681] The server collects and analyzes data from automated farm machinery and sensors in real time and updates work instructions as needed. For example, sensors monitor soil moisture, and if the moisture level drops, the server automatically sends instructions to water the fields.

[0682] Step 7:

[0683] Once the harvest is complete, the server manages the inventory of the harvested crops. Data such as the quantity of harvested crops and their storage location is saved in a database. For example, when 50 boxes of harvested tomatoes are stored in a warehouse, that information is immediately registered in the database.

[0684] Step 8:

[0685] The server sells agricultural products in an online sales system. Based on inventory data, sales information for agricultural products is updated in the online sales system. When a consumer purchases a product online, the order status is sent to the server, and inventory information is automatically updated. For example, when a consumer purchases 10 boxes of tomatoes, the order information is sent to the server, and 10 boxes are subtracted from the database.

[0686] Step 9:

[0687] The server automatically arranges delivery. Once an order is confirmed in the online sales system, the server sends delivery instructions to the delivery company and the delivery arrangements proceed. For example, delivery arrangements are made for 10 boxes of purchased tomatoes, and the delivery status is updated on the server in real time.

[0688] Step 10:

[0689] The server collects and analyzes sales data and provides feedback to the next production plan, improving the accuracy of the production plan. For example, tomato sales data and user feedback can be analyzed to optimize the next season's production volume and sales methods.

[0690] Step 11:

[0691] The emotion engine evaluates user feedback and purchasing intent in real time. The server adjusts and optimizes production plans and sales strategies based on this emotion data. For example, if a user evaluates their satisfaction as "very satisfied," the server adjusts plans to increase production for the next season.

[0692] As described above, through each step of this system, we aim to achieve agricultural efficiency, a stable food supply, and improved consumer satisfaction.

[0693] (Application example 2)

[0694] 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."

[0695] Modern agriculture requires efficient production planning that responds to fluctuations in weather conditions and market demand. While combining automated farming with online sales enables more efficient farm management, conventional systems have difficulty assessing consumer sentiment and purchasing intent in real time and reflecting this information in production plans and sales strategies. Furthermore, new methods for improving the customer experience in brick-and-mortar stores are also needed. Therefore, the objective of this invention is to realize efficient and flexible production planning, optimization of sales strategies, and an improvement in the customer experience in brick-and-mortar stores.

[0696] 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.

[0697] In this invention, the server includes means for collecting geographical data and meteorological data of farmland, means for managing the collected data on the cloud, means for collecting past production data and market data, means for using artificial intelligence to forecast supply and demand and develop production plans, means for sending instructions to automated agricultural machinery to sow, water, fertilize, and harvest, means for collecting and analyzing data from the automated agricultural machinery and sensors in real time, means for inventory management of harvested crops, means for selling agricultural products through an online sales system, means for receiving orders from consumers and updating inventory, means for automatically arranging deliveries, means for collecting and analyzing sales data and providing feedback to the next production plan, means for providing a virtual tour of the farm in a physical store using augmented reality technology, means for analyzing user emotions to evaluate purchasing intent, and means for adjusting inventory and sales strategies based on user emotion data. This enables efficient and flexible production planning, optimization of sales strategies, and an improvement in customer experience in physical stores.

[0698] "Geographical data of agricultural land" refers to basic information about agricultural land, such as its location, area, and soil type.

[0699] "Weather data" is information about weather conditions that affect agricultural activities, such as temperature, precipitation, humidity, and wind speed.

[0700] "Cloud management" refers to the practice of storing collected data on remote servers on the Internet for access and analysis.

[0701] "Past production data" refers to information about past agricultural activities, such as previous crop yields, agricultural inputs used, and pest and disease occurrence.

[0702] "Market data" refers to information about the market, such as market prices, the balance of supply and demand, and consumer preferences.

[0703] "Using artificial intelligence to forecast supply and demand and develop production plans" means using AI technology to predict future supply and demand and develop efficient production plans.

[0704] "Autonomous agricultural machinery" refers to agricultural machinery that operates autonomously and performs agricultural tasks such as sowing seeds, watering, fertilizing, and harvesting.

[0705] "Real-time collection and analysis" means that data is collected immediately and analyzed on the spot.

[0706] "Inventory management" means managing the quantity and condition of harvested agricultural products, and understanding and updating the inventory status.

[0707] An "online sales system" is a system for selling agricultural products via the Internet.

[0708] "Automatically arranging delivery" means that upon receiving an order from a consumer, delivery procedures are automatically carried out.

[0709] "Providing virtual farm tours within physical stores using augmented reality technology" means using AR technology to provide virtual farm tours within physical stores.

[0710] "Analyzing user emotions and assessing purchasing intent" means utilizing an emotion engine to analyze user emotions and assess purchasing intent.

[0711] "Adjusting inventory and sales strategies based on emotional data" means optimizing inventory status and sales strategies based on collected emotional data.

[0712] This system collects and manages geographic and meteorological data for farmland, uses AI to create production plans, and automates farm work and online sales. Furthermore, it recognizes and evaluates user emotions in real time and reflects them in production plans and sales strategies, improving the customer experience.

[0713] System Program

[0714] The server collects geographical and meteorological data for the farmland and manages it on the cloud. This uses remote server functions via the internet and sensors for data collection. The server also collects past production data and market data, which it analyzes using AI (artificial intelligence) to create supply and demand forecasts and production plans. This AI uses, for example, a "generative AI model."

[0715] Data collection and analysis

[0716] The server then sends instructions to the automated farming machines based on the collected data. These instructions include instructions for sowing, watering, fertilizing, and harvesting. The automated farming machines then perform each task based on the instructions and transmit their progress to the server in real time via sensors. The server then analyzes this information and sends additional instructions as needed.

[0717] Inventory management and online sales

[0718] Harvested produce is managed by a server and provided to consumers through an online sales system. The online sales system reflects real-time inventory information and updates inventory upon receiving orders from consumers. The server also automatically arranges delivery and feeds sales data back into the next production plan.

[0719] Improving customer experience in physical stores

[0720] To enhance customer experience in physical stores, the server uses augmented reality (AR) technology to provide virtual farm tours. The system uses smartphones, smart glasses, and head-mounted displays. When a user starts a farm tour in a physical store using these devices, the server provides an interactive farm tour through AR.

[0721] Use of emotion engine

[0722] The server also includes an emotion engine that analyzes user emotions and assesses their willingness to purchase and satisfaction in real time. Emotional data is automatically collected and analyzed. Based on this data, the server adjusts and optimizes inventory and sales strategies.

[0723] Specific examples

[0724] Here's an example of how this works on a farm. After a user registers a new piece of farmland and enters its location and soil type, the server begins collecting local weather data. Using past production data, the server predicts the supply and demand of tomatoes for the next season and determines the timing of sowing. The server then sends instructions to the automated farm machinery to sow the tomatoes. Sensors then monitor the soil's moisture content, and if it becomes too dry, the server automatically sends instructions to water the soil. At harvest time, the server sends harvesting instructions to the automated farm machinery, and the harvested tomatoes are immediately registered in an inventory management system. Consumers then purchase the tomatoes online, and the server automatically arranges for delivery. The sales data is reflected in the next season's tomato production plan.

[0725] Prompt Sentence Examples

[0726] Below are some example prompts to input to a generative AI model:

[0727] Write code to create real-time production plans based on farmland and weather data from the server.

[0728] A program that stores farmland information and weather data in the cloud and uses AI to make production plans and forecast supply and demand. It is designed to send work instructions to automated farm machinery and optimize harvests in conjunction with an online purchasing system. It also uses an emotion engine to evaluate users' purchasing intent in real time and reflect this in online sales strategies. Specific library names and technologies used must also be clearly stated.

[0729] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0730] Step 1:

[0731] User enters farmland information

[0732] Users use a device (smartphone or PC) to input the location, area, and soil type of their farmland. This input data is sent to a server and stored in the cloud. The server then uses this data to accumulate basic information about the farmland in a database.

[0733] Step 2:

[0734] Weather data collection

[0735] The server collects real-time weather data from a weather data provider. The collected data includes temperature, precipitation, humidity, wind speed, etc., and is stored in the cloud. The server integrates and manages this weather data with farmland data.

[0736] Step 3:

[0737] Collection of historical production and market data

[0738] The server collects and stores historical production data (yields, agricultural inputs used, pest and disease occurrence) and market data (market prices, supply and demand balance, consumer preferences) on the cloud. This data is used to understand past agricultural activities and market trends.

[0739] Step 4:

[0740] Production planning

[0741] The server uses a generative AI model to forecast supply and demand for the next season based on collected farmland information, weather data, past production data, and market data. Based on the results, it creates a production plan (sowing timing, planting area, fertilizer to be used, etc.) and notifies the terminal. The user can check this notification and make adjustments as necessary.

[0742] Step 5:

[0743] Automated farming

[0744] The server sends specific instructions (such as sowing, watering, fertilizing, and harvesting) to the automated farming machines. The automated farming machines carry out the farming tasks based on these instructions and transmit their progress in real time via sensors to the server. The server analyzes this data and sends additional instructions as needed.

[0745] Step 6:

[0746] Post-harvest inventory management and online sales

[0747] Harvested produce is managed in inventory by a server. The server creates an optimal online sales strategy based on supply and demand forecasts and updates inventory information in the online sales system in real time. When a user purchases produce online, the server receives the order, updates inventory, and automatically arranges delivery.

[0748] Step 7:

[0749] Offering augmented reality tours of brick-and-mortar stores

[0750] The server uses augmented reality technology to provide a virtual tour of the farm within a physical store. Once the user starts the tour using smart glasses or a head-mounted display, the server provides an interactive guide through AR technology.

[0751] Step 8:

[0752] Evaluating users' purchasing intentions using an emotion engine

[0753] The server uses an emotion engine to assess users' willingness to buy and their satisfaction in real time, analyzing emotional data collected during the user's in-store and online purchase process. The server then adjusts inventory and sales strategies based on this data.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] [Third embodiment]

[0758] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0759] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0760] 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).

[0761] 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.

[0762] 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.

[0763] 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).

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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."

[0770] This invention is a system that collects and manages geographical and meteorological data for farmland, uses AI to create production plans, and realizes automated farm work and online sales. Specific examples of the operation of each element are shown below.

[0771] Collection and management of farmland information

[0772] First, the user uses a device (such as a smartphone or PC) to input geographic data such as the location, area, and soil type of the farmland. The server then collects weather data in real time from a weather data provider and stores it in the cloud. This allows basic information about the farmland to be stored in a database.

[0773] Collection of historical production and market data

[0774] The server collects historical production data (e.g., yields, agricultural inputs used, pest and disease occurrence) and market data (e.g., market prices, supply and demand balance, consumer preferences) and stores these in a database.

[0775] Production planning

[0776] The server uses AI to predict supply and demand for the next season based on collected farmland information, weather data, past production data, and market data. Based on the results, it creates a production plan (for example, sowing timing, planting area, fertilizer to be used, etc.) and notifies the user via their device so that they can check it.

[0777] Automated farming

[0778] The server sends specific work instructions to automated agricultural machinery (e.g., robots or drones). For example, the automated agricultural machinery begins work in time for sowing seeds, and sensors send progress updates to the server in real time. The server monitors this and sends additional instructions for watering, fertilizing, and weeding as needed. The timing of harvesting is also automatically determined based on crop growth data, and the terminal (automated agricultural machinery) carries out the harvesting work.

[0779] Inventory management and online sales

[0780] Harvested produce is managed in inventory by a server. Based on supply and demand forecasts, the server creates an optimal online sales strategy and updates inventory information in real time to the online sales system. When a user purchases produce online, the server receives the order and automatically updates the inventory. It also sends instructions to delivery companies and automatically arranges for delivery. The server also collects sales data and feeds it back into the next production plan.

[0781] Specific examples

[0782] Here's an example of how it works on a farm. After a user registers a new piece of farmland and enters its location and soil type, the server begins collecting local weather data. Using past yield data, the server predicts the supply and demand of tomatoes for the next season and determines the timing of sowing. The server then sends instructions to the automated farm machinery to sow the tomatoes. Sensors monitor the soil moisture, and if it dries out, the server automatically sends instructions to water the soil. At harvest time, the server sends harvesting instructions to the automated farm machinery, and the harvested tomatoes are immediately registered in the inventory management system. Consumers then purchase the tomatoes online, and the server automatically arranges for delivery. The sales data is reflected in the next season's tomato production plan.

[0783] As described above, the present invention realizes agricultural efficiency and a stable food supply.

[0784] The processing flow will be explained below.

[0785] Step 1:

[0786] Users use a device (smartphone or PC) to input geographical data such as the location, area, and soil type of farmland into the system.

[0787] Step 2:

[0788] The server collects real-time weather data for the farmland (e.g., temperature, precipitation, humidity, wind speed, etc.) from a weather data provider and stores it on the cloud.

[0789] Step 3:

[0790] The server performs an initial data analysis based on the farmland information entered by the user and the collected weather data, confirms the basic information about the farmland, and stores it in a database.

[0791] Step 4:

[0792] The server collects past production data (e.g., crop type, yield, pest and disease occurrence status, etc.) and market data (e.g., market price, balance of supply and demand, consumer preferences, etc.) and stores them in a database.

[0793] Step 5:

[0794] Based on farmland information, weather data, past production data, and market data collected by the server, artificial intelligence (AI) is used to predict supply and demand for the next season. Based on the results of this prediction, a production plan (sowing timing, planting area, fertilizer to be used, etc.) is created.

[0795] Step 6:

[0796] The server notifies the terminal of the production plan it has drawn up, and the user checks this plan, modifies it as necessary, and gives final approval.

[0797] Step 7:

[0798] The server sends instructions to automated agricultural machinery (e.g., robots and drones), including tasks such as sowing seeds, watering, fertilizing, weeding, and harvesting.

[0799] Step 8:

[0800] The terminal (automated agricultural machinery) automatically carries out agricultural work according to the received instructions. Sensors attached to the agricultural machinery send work progress and environmental data to a server in real time.

[0801] Step 9:

[0802] The server analyzes real-time data from the sensors and sends additional work instructions (e.g., additional watering or fertilization) to the device as needed.

[0803] Step 10:

[0804] The server determines the timing of harvesting based on the crop growth data, and when the time comes, it sends harvesting instructions to the automated farm machinery.

[0805] Step 11:

[0806] The terminal (automated farm machine) performs the harvesting work and sends the data to the server, which updates the inventory based on the harvest data.

[0807] Step 12:

[0808] Based on the inventory data, the server develops an online sales strategy taking into account supply and demand forecasts and market data, and reflects inventory information on the sales site in real time.

[0809] Step 13:

[0810] When a user purchases produce from an online sales site, the server receives the order and automatically updates the inventory.

[0811] Step 14:

[0812] The server issues instructions to the delivery company and automatically arranges delivery. Order details and delivery information are managed in real time and notified to the consumer.

[0813] Step 15:

[0814] The server collects sales data and analyzes it to provide feedback for the next production plan. Users can check this feedback data on their devices and use it to plan the next production season.

[0815] In this way, a system will be built that automates the entire agricultural process, ensuring efficient operations and a stable food supply.

[0816] Example 1

[0817] 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."

[0818] Efficient production planning and management remains a major challenge in modern agriculture. Specifically, it is difficult to accurately collect and manage geographic and meteorological data on farmland, and to forecast supply and demand and formulate production plans based on past production and market data. Other challenges include maximizing work efficiency with automated farm machinery, using sensors to monitor and adjust farm work in real time, and managing post-harvest inventory and quickly managing online sales.

[0819] 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.

[0820] In this invention, the server includes: means for collecting geographical data and meteorological data of farmland; means for managing the collected data on the cloud; means for collecting past production data and market data; means for using artificial intelligence to forecast supply and demand and create production plans; means for sending instructions to automated agricultural machinery to sow, water, fertilize, and harvest; means for collecting and analyzing data from the automated agricultural machinery and sensors in real time; means for managing inventory of harvested crops; means for selling agricultural products through an online sales system; means for receiving orders from consumers and updating inventory; means for automatically arranging deliveries; means for collecting and analyzing sales data and feeding it back into the next production plan; means for using artificial intelligence to analyze meteorological data, farmland information, and market data and for notifying the users of the timing of sowing, planting area, and type and amount of materials to be used in real time; and means for monitoring soil moisture using data from sensors and automatically sending watering instructions when the soil becomes dry. This enables more efficient and automated agricultural work, enabling stable crop production and rapid market supply.

[0821] "Agricultural land geographic data" refers to all geographic information including agricultural land location, area, soil type, etc.

[0822] "Weather data" refers to data related to weather, such as temperature, precipitation, and wind speed.

[0823] "Cloud" refers to data storage or computing resources provided over the internet.

[0824] "Past production data" refers to data such as yields from previous cultivation on farmland, agricultural materials used, and the occurrence of pests and diseases.

[0825] "Market data" refers to various data about the market, such as market prices, the balance of supply and demand, and consumer preferences.

[0826] "Artificial intelligence" refers to a system that uses machine learning and data analysis techniques to make specific predictions and judgments from data.

[0827] "Supply and demand forecast" refers to predicting the future balance between supply and demand.

[0828] A "production plan" refers to a specific plan for agricultural work, such as the timing of sowing seeds, the area to be planted, and the types and amounts of fertilizers and pesticides to be used.

[0829] "Automated agricultural machinery" refers to machines such as robots and drones that perform agricultural work automatically according to a program.

[0830] A "sensor" refers to a device that measures environmental changes, such as soil humidity and temperature, in real time and acquires data.

[0831] "Inventory management" refers to managing the quantity and condition of harvested agricultural products.

[0832] "Online sales system" refers to a system for selling products via the Internet.

[0833] "Shipping arrangements" refers to managing the shipping of ordered products.

[0834] "Sales Data" means data related to sales, such as the quantity, price, and purchaser information of the items sold.

[0835] This invention is a system that collects and manages geographical and meteorological data for farmland, uses artificial intelligence to create production plans, and realizes automated farm work and online sales. Specific examples of the operation of each element are shown below.

[0836] Collection and management of farmland information

[0837] First, the user uses a terminal (e.g., a smartphone or personal computer) to input geographic data such as the location, area, and soil type of the farmland. The server then collects weather data in real time from a weather data provider (e.g., a general weather data API) and stores it in the cloud. This operation accumulates basic information about the farmland in a database. The specific hardware and software used include a smart device as the terminal and a cloud platform as the server.

[0838] Collection of historical production and market data

[0839] The server collects past production data (e.g., yields, agricultural inputs used, pest and disease occurrence status) and market data (e.g., price information, supply and demand balance, consumer preferences), and stores this data in a cloud database. Data collection methods used include APIs and agricultural information management systems.

[0840] Production planning

[0841] The server uses an artificial intelligence model to predict supply and demand for the next season based on collected farmland information, weather data, past production data, and market data. Based on the prediction results, it creates a production plan (for example, sowing timing, planting area, and type and amount of fertilizer to be used). These plans are sent to the terminal and can be checked by the user. The specific software used is a machine learning framework built in Python (such as TensorFlow or PyTorch).

[0842] Automated farming

[0843] The server sends specific work instructions to the automated agricultural machinery (e.g., robots or drones). For example, the automated agricultural machinery starts work when it's time to sow seeds, and sends its progress information to the server in real time via sensors. Sensing technology and IoT device management systems (such as AWS IoT Core) are used. The automated agricultural machinery follows the server's instructions to perform tasks such as sowing seeds, watering, fertilizing, and harvesting.

[0844] Inventory management and online sales

[0845] Harvested produce is managed in inventory by a server. Based on supply and demand forecasts, the server creates an optimal online sales strategy and updates inventory information in real time to the online sales system (a typical e-commerce platform). When a user purchases produce online, the server receives the order and automatically updates the inventory. The server then sends instructions to the delivery company to automatically arrange delivery. Sales data is fed back into the next season's production plan.

[0846] Specific examples

[0847] Here's an example of how it works on a farm. After a user registers a new piece of farmland and enters its location and soil type, the server begins collecting local weather data. Based on past yield data, the server predicts the supply and demand of tomatoes for the next season and determines the timing of sowing. The server then sends instructions to the automated farm machinery to sow the tomatoes. Sensors monitor the moisture content of the soil, and if it becomes too dry, the server automatically sends instructions to water the soil. When it's time to harvest, the server sends harvesting instructions to the automated farm machinery, and the harvested tomatoes are immediately registered in the inventory management system. Consumers then purchase the tomatoes online, and the server automatically arranges for delivery. The sales data is reflected in the next season's tomato production plan.

[0848] Prompt Sentence Examples

[0849] Below are examples of prompts to use with the generative AI model:

[0850] "Please explain the functions of the system that inputs geographical and meteorological data of farmland and uses AI to create production plans. Please provide a detailed description, including the specific data collection method, automated farm machinery used, and online sales process."

[0851] Through the above process, the present invention realizes agricultural efficiency and a stable food supply.

[0852] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0853] Step 1:

[0854] The user inputs the geographic data of the farmland.

[0855] The user uses a device to input geographic data such as the location, area, and soil type of the farmland. The input data includes the location of the farmland on a map, its area, and soil quality. This input data is sent to the server, which receives the data and stores it in a cloud database. Specific operations include mapping the location of the farmland on a map on the application screen and registering information such as the area and soil type through an input form.

[0856] Step 2:

[0857] The server collects and manages weather data

[0858] The server collects weather data in real time from a weather data provider service. Specifically, it periodically obtains data such as temperature, precipitation, and wind speed using the weather data provider service's API. The obtained data is stored in a cloud database. This process involves sending an API request and writing the weather data obtained in response to the request into the database. This weather data plays an important role in subsequent production planning.

[0859] Step 3:

[0860] The server collects historical production and market data

[0861] The server collects past production data and market data. Past production data inputs include yields, agricultural inputs used, and pest and disease occurrence status. Market data inputs include price information, supply and demand balance, and consumer preferences. The server periodically obtains this data from various data sources and stores it in a cloud database. Specifically, the data is collected using the APIs of agricultural management systems and market data providers.

[0862] Step 4:

[0863] The server analyzes the data and makes supply and demand predictions

[0864] The server integrates collected farmland information, weather data, past production data, and market data, and uses an artificial intelligence model to predict supply and demand for the next season. All collected data is provided as input to the AI ​​model, and supply and demand forecast results are obtained as output. During this process, data analysis is performed using machine learning frameworks developed in Python (such as TensorFlow and PyTorch). Specific operations include data preprocessing, feature selection, model training, and validation.

[0865] Step 5:

[0866] The server creates a production plan and notifies the terminal

[0867] The server creates a production plan based on the predictions of the AI ​​model. The plan includes the timing of sowing seeds, the area to be planted, and the type and amount of fertilizer and pesticide to be used. These plans are notified to the terminal. Specifically, the created plan is sent to the user using a messaging system (for example, push notification or email notification).

[0868] Step 6:

[0869] The server operates the automated farm machinery and starts farming.

[0870] The server sends specific operational instructions to the automated agricultural machinery. For example, it instructs the timing of sowing seeds, fertilizing, watering, and harvesting. Input data includes production plans and real-time weather and soil data, and the output is the agricultural machinery performing its tasks. Specific operations include controlling GPIO and various sensors using an IoT device management system.

[0871] Step 7:

[0872] The server analyzes the data from the sensors in real time and adds necessary instructions.

[0873] The server collects real-time data from sensors (e.g., soil moisture sensors) and sends additional instructions to the automated farming equipment depending on the situation. The input data includes real-time data from the sensors, and the output sends instructions to the farming equipment, for example, to water. It also performs constant monitoring using real-time data analysis tools (e.g., Apache Kafka and Spark Streaming).

[0874] Step 8:

[0875] The server manages post-harvest inventory

[0876] The harvested produce is inventory-managed by the server. The input includes harvested quantity and quality data, and the output updates the inventory database. Specific operations include writing to the database and monitoring the inventory status.

[0877] Step 9:

[0878] The server executes the online sale

[0879] The server updates the online sales system with inventory information in real time and accepts orders from consumers. The input data includes updated inventory and order information, and the output is order processing and inventory updates. Specific operations include data integration with the e-commerce platform, order confirmation, and payment processing.

[0880] Step 10:

[0881] The server automatically arranges delivery

[0882] Delivery arrangements are made automatically by the server. Input data includes the consumer's order information and delivery address information, and output includes instructions to the delivery company. Specifically, this includes setting up an efficient delivery route through API integration with the delivery system and completing the delivery arrangements.

[0883] (Application example 1)

[0884] 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."

[0885] In agricultural and industrial production, uncertainty in production plans and a lack of automation are obstacles to efficient production. It is also difficult to accurately predict the supply and demand balance of agricultural crops and factory-produced goods and to formulate optimal production plans based on that. Furthermore, the lack of automation in inventory management, sales, and delivery has resulted in labor surpluses and shortages, preventing stable food self-sufficiency and product supply. It is necessary to solve these issues and achieve efficient and stable agricultural and industrial production.

[0886] 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.

[0887] In this invention, the server includes means for collecting geographical data and meteorological data of the farmland, means for managing the collected data on the cloud, means for collecting past production data and market data, means for using artificial intelligence to make supply and demand forecasts and production plans, means for sending instructions to automated agricultural machinery to perform sowing, watering, fertilizing, and harvesting, means for collecting and analyzing data from the automated agricultural machinery and sensors in real time, means for managing inventory of harvested crops, means for selling agricultural products through an online sales system, means for receiving orders from consumers and updating inventory, means for automatically making delivery arrangements, means for collecting and analyzing sales data and feeding it back to the next production plan, means for collecting and managing location information and equipment data within the factory, means for collecting work environment data within the factory in real time, means for making production plans using past production data and demand data, means for sending specific work instructions to automated robots and having them perform production work, and means for automatically managing inventory and shipping within the factory. This will reduce uncertainty in production plans in agricultural and industrial production, enabling efficient production and stable supply.

[0888] "Geographic data of agricultural land" refers to data on the geographic characteristics of agricultural land, such as its location, area, and soil type.

[0889] "Weather data" refers to data related to climate, such as temperature, precipitation, humidity, and wind speed.

[0890] "Managing on the cloud" means storing collected data in a data center connected via the Internet and making it accessible.

[0891] "Past production data" refers to data on past crop yields, agricultural materials used, pest and disease occurrence, and so on.

[0892] "Market data" refers to data on market trends such as market prices, the balance between supply and demand, and consumer preferences.

[0893] "Artificial intelligence" is a technology that uses machine learning and data analysis techniques to derive knowledge and patterns from data.

[0894] "Supply and demand forecasting" is the prediction of future demand and supply based on collected data.

[0895] "Production planning" involves planning the production volume and timing of crops and industrial products, as well as the materials to be used.

[0896] "Automated agricultural machinery" refers to machines such as robots and drones that can perform agricultural work automatically.

[0897] "Real-time collection and analysis" means collecting data immediately and analyzing it on the spot.

[0898] "Inventory management" is the tracking and management of harvested agricultural crops and manufactured industrial products.

[0899] An "online sales system" is a system for selling products over the Internet.

[0900] "Updating inventory upon receiving an order from a consumer" means updating inventory data based on the information when a consumer places an order through the online sales system.

[0901] "Automatically arranging delivery" means automatically sending delivery instructions to a delivery company based on a consumer's order.

[0902] "Location information within a factory" is information relating to the locations of equipment and machines within a factory.

[0903] "Facility data" refers to data related to the functions and status of machines and equipment within a factory.

[0904] "Work environment data" refers to data related to the work environment in a factory, such as temperature, humidity, and sound level.

[0905] "Planning a production plan" means planning the production processes and schedules within a factory.

[0906] An "automatic robot" is a robot that can autonomously operate machinery and perform production tasks.

[0907] "Automated shipping" means automatically packaging products and preparing them for shipping.

[0908] The present invention provides a system that improves the efficiency of production management systems in farmland and factories and provides specific means for realizing stable supply. Specific embodiments of the system are described below.

[0909] System Overview

[0910] This system consists of a server, terminals, users, automated agricultural machinery, and automated robots. The server collects, manages, analyzes, sends instructions, and manages inventory. Terminals are devices that users use to input and check data, and include smartphones and PCs. The automated agricultural machinery and automated robots are machines that perform specific tasks.

[0911] Program Overview

[0912] The server first collects geographic and meteorological data for the farmland. Geographic data includes the location, area, and soil type of the farmland. Meteorological data includes temperature, precipitation, humidity, and wind speed. This data is stored in the cloud, along with past production data and market data. Market data includes market prices, supply and demand balance, and consumer preferences.

[0913] Based on this data, the server uses artificial intelligence (AI) to predict supply and demand for the next season and create a production plan. This plan includes sowing, watering, fertilizing, harvesting timing, and materials to be used. The plan is then sent to the device so that the user can check and modify it.

[0914] The server then sends instructions to the autonomous farming equipment to perform the tasks, and data from the autonomous farming equipment and sensors is sent to the server in real time for analysis, which then sends additional instructions on watering and fertilizing as needed.

[0915] Harvested produce is managed by the server and reflected in the online sales system. When a consumer purchases produce online, the server receives the order, updates the inventory, and automatically arranges delivery. Sales data is also collected and fed back into the next production plan.

[0916] Within the factory, a server collects and manages the factory's location information and equipment data. Work environment data (temperature, humidity, sound level, etc.) is also collected in real time. Production plans are created using past production data and demand data, and specific work instructions are sent to automated robots. Inventory management and shipping within the factory are also automated.

[0917] Hardware and Software Use

[0918] Hardware: Servers, smartphones, PCs, automated farm machinery, automated robots, sensors.

[0919] Software: Cloud database, AI models (machine learning algorithms), online sales systems, inventory management systems, communication protocols.

[0920] Specific examples

[0921] As an example of how a new farm is operated, let's say a new piece of farmland is registered. When a user uses a device to enter the location information and soil type of the farmland, the server begins collecting local weather data. Using past yield data, the server predicts the supply and demand of tomatoes for the next season and decides when to sow seeds. The server then sends instructions to the automated farm machinery to sow the tomatoes. Sensors monitor the moisture content of the soil, and if it becomes too dry, the server automatically sends instructions to water the soil. When it's time to harvest, the server sends harvesting instructions to the automated farm machinery, and the harvested tomatoes are immediately registered in the inventory management system. Consumers then purchase the tomatoes online, and the server automatically arranges for delivery. The sales data is reflected in the next season's tomato production plan.

[0922] Prompt Sentence Examples

[0923] Collect location information, equipment data, and real-time environmental data within the factory, integrate past production data with demand data, and create a production plan that predicts supply and demand for the next season. Then, design a system that sends specific work instructions to robots based on the generated production plan. Store the collected data in an SQLite database and use an AI model to predict supply and demand.

[0924] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0925] Step 1:

[0926] The user inputs geographic data of the farmland (location, area, soil type) using a terminal. The terminal sends this data to the server, which receives the input data and stores it in a database on the cloud.

[0927] Step 2:

[0928] The server collects real-time weather data (temperature, precipitation, humidity, wind speed) from a weather data provider. The collected weather data is stored in a database on the cloud. This allows the server to obtain basic environmental information about the farmland.

[0929] Step 3:

[0930] The server collects past production data (yields, agricultural inputs used, pest and disease occurrence status) and market data (market prices, supply and demand balance, consumer preferences) and stores them in a database, allowing the server to understand past agricultural activities and market trends.

[0931] Step 4:

[0932] The server uses collected farmland information, weather data, past production data, and market data to predict supply and demand for the next season using a generative AI model. The model uses this data as input, performs data calculations, and outputs supply and demand forecast results. Based on these forecast results, the server creates a production plan (sowing timing, planting area, fertilizer to be used, etc.) and notifies the terminal.

[0933] Step 5:

[0934] The user checks the production plan on the terminal and modifies it as necessary. When the user makes modifications, the modified production plan is sent from the terminal to the server. The server saves the updated production plan in a database on the cloud.

[0935] Step 6:

[0936] The server sends specific work instructions (such as sowing seeds, watering, fertilizing, and harvesting) to the automated farming machine. The automated farming machine carries out the work according to these instructions and sends its progress and environmental data to the server in real time.

[0937] Step 7:

[0938] The server collects and analyzes data from automated farm machinery and sensors in real time. The server uses the collected data to send additional instructions for watering and fertilizing as needed. The data is stored in a database on the cloud.

[0939] Step 8:

[0940] Harvested produce is managed by a server. Inventory data is stored in a cloud database and updated in real time in the online sales system. The server monitors inventory status and develops optimal sales strategies.

[0941] Step 9:

[0942] When a consumer purchases produce through the online sales system, the server receives the order, updates inventory data, and automatically sends delivery instructions to the delivery company.

[0943] Step 10:

[0944] The server collects and analyzes sales data. The sales data is analyzed by a generative AI model and fed back into the next season's production plan. The collected and analyzed data is stored in a database on the cloud.

[0945] Step 11:

[0946] The server collects and manages factory location information and equipment data, and the collected data is stored in a database on the cloud.

[0947] Step 12:

[0948] The server collects real-time data on the working environment within the factory (temperature, humidity, sound level, etc.) and stores it in a database on the cloud. The collected data is used to assist in production planning.

[0949] Step 13:

[0950] The server uses past production and demand data to generate demand and supply forecasts using a generative AI model, and creates a production plan, which is then stored in a cloud database.

[0951] Step 14:

[0952] The server sends specific work instructions to the automated robot, which then carries out production tasks based on the instructions and reports its status to the server in real time.

[0953] Step 15:

[0954] The server manages inventory in the factory and automatically handles shipping. Inventory and shipping data is stored in a cloud database and updated in real time on the online sales system.

[0955] 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.

[0956] This system combines a system that collects and manages geographical and meteorological data on farmland, uses AI to create production plans, and realizes automated farm work and online sales, with an emotion engine that recognizes user emotions. Specific examples of the operation of each element are shown below.

[0957] Collection and management of farmland information

[0958] First, the user uses a device (such as a smartphone or PC) to input geographic data such as the location, area, and soil type of the farmland. The server then collects weather data in real time from a weather data provider and stores it in the cloud. This allows basic information about the farmland to be stored in a database.

[0959] Collection of historical production and market data

[0960] The server collects historical production data (e.g., yields, agricultural inputs used, pest and disease occurrence) and market data (e.g., market prices, supply and demand balance, consumer preferences) and stores these in a database.

[0961] Production planning

[0962] The server uses AI to forecast supply and demand for the next season based on collected farmland information, weather data, past production data, and market data. Based on the results, it creates a production plan (for example, sowing timing, planting area, fertilizer to be used, etc.) and notifies the user via their device so that they can check it.

[0963] Automated farming

[0964] The server sends specific work instructions to automated agricultural machinery (e.g., robots or drones). For example, the automated agricultural machinery begins work in time for sowing seeds, and sensors send progress updates to the server in real time. The server monitors this and sends additional instructions for watering, fertilizing, and weeding as needed. The timing of harvesting is also automatically determined based on crop growth data, and the terminal (automated agricultural machinery) carries out the harvesting work.

[0965] Inventory management and online sales

[0966] Harvested produce is managed in inventory by a server. Based on supply and demand forecasts, the server creates an optimal online sales strategy and updates inventory information in real time to the online sales system. When a user purchases produce online, the server receives the order and automatically updates the inventory. It also sends instructions to delivery companies and automatically arranges for delivery. The server also collects sales data and feeds it back into the next production plan.

[0967] Use of emotion engine

[0968] The emotion engine is used to evaluate user feedback and purchasing intent in real time, and the server adjusts and optimizes production plans and sales strategies based on this emotion data.

[0969] Specific examples

[0970] Here's an example of how it works on a farm. After a user registers a new piece of farmland and enters its location and soil type, the server begins collecting local weather data. Using past yield data, the server predicts the supply and demand of tomatoes for the next season and determines the timing of sowing. The server then sends instructions to the automated farm machinery to sow the tomatoes. Sensors monitor the soil moisture, and if it dries out, the server automatically sends instructions to water the soil. At harvest time, the server sends harvesting instructions to the automated farm machinery, and the harvested tomatoes are immediately registered in the inventory management system. Consumers then purchase the tomatoes online, and the server automatically arranges for delivery. The sales data is reflected in the next season's tomato production plan.

[0971] Furthermore, when a user purchases tomatoes from an online sales site, the emotion engine evaluates the user's purchasing motivation and satisfaction in real time, and the server adjusts inventory and delivery arrangements based on this data. For example, if user satisfaction is high, that data can be reflected in the next season's production plan, and the plan can be adjusted to produce more tomatoes. Conversely, if satisfaction is low, the cause can be analyzed and improvements can be made to improve the quality of agricultural products and service.

[0972] In this way, the present invention is a system that can realize agricultural efficiency and a stable food supply, while also increasing user satisfaction.

[0973] The processing flow will be explained below.

[0974] Step 1:

[0975] Users use a device (smartphone or PC) to input geographical data such as the location, area, and soil type of farmland into the system.

[0976] Step 2:

[0977] The server collects real-time weather data for the farmland (e.g., temperature, precipitation, humidity, wind speed, etc.) from a weather data provider and stores it on the cloud.

[0978] Step 3:

[0979] The server performs an initial data analysis based on the farmland information entered by the user and the collected weather data, and stores the basic information about the farmland in a database.

[0980] Step 4:

[0981] The server collects past production data (e.g., crop type, yield, pest and disease occurrence status, etc.) and market data (e.g., market price, balance of supply and demand, consumer preferences, etc.) and stores them in a database.

[0982] Step 5:

[0983] Based on farmland information, weather data, past production data, and market data collected by the server, artificial intelligence (AI) is used to predict supply and demand for the next season. Based on the results of this prediction, a production plan (sowing timing, planting area, fertilizer to be used, etc.) is created.

[0984] Step 6:

[0985] The server notifies the terminal of the production plan it has drawn up, and the user checks this plan, modifies it as necessary, and gives final approval.

[0986] Step 7:

[0987] The server sends instructions to automated agricultural machinery (e.g., robots and drones), including tasks such as sowing seeds, watering, fertilizing, weeding, and harvesting.

[0988] Step 8:

[0989] The terminal (automated agricultural machinery) automatically carries out agricultural work according to the received instructions. Sensors attached to the agricultural machinery send work progress and environmental data to a server in real time.

[0990] Step 9:

[0991] The server analyzes real-time data from the sensors and sends additional work instructions (e.g., additional watering or fertilization) to the device as needed.

[0992] Step 10:

[0993] The server determines the timing of harvesting based on the crop growth data, and when the time comes, it sends harvesting instructions to the automated farm machinery.

[0994] Step 11:

[0995] The terminal (automated farm machine) performs the harvesting work and sends the data to the server, which updates the inventory based on the harvest data.

[0996] Step 12:

[0997] Based on the inventory data, the server develops an online sales strategy taking into account supply and demand forecasts and market data, and reflects inventory information on the sales site in real time.

[0998] Step 13:

[0999] When a user purchases produce from an online sales site, the server receives the order and automatically updates the inventory.

[1000] Step 14:

[1001] The server issues instructions to the delivery company and automatically arranges delivery. Order details and delivery information are managed in real time and notified to the consumer.

[1002] Step 15:

[1003] The emotion engine evaluates users' feedback and purchasing intentions in real time, and the server adjusts inventory and delivery arrangements based on that data. It also reflects factors that lead to high satisfaction in production plans for the next season.

[1004] Step 16:

[1005] The server collects sales data and analyzes it to provide feedback for the next production plan. Users can check this feedback data on their devices and use it to plan the next production season.

[1006] In this way, a system will be built that automates the entire agricultural process, ensuring efficient operations and a stable food supply.

[1007] Example 2

[1008] 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."

[1009] In conventional agricultural systems, the collection and management of geographical data and meteorological data for farmland was often done manually, making efficient data management difficult. Furthermore, there was a lack of systems for integrating and analyzing past production data and market data to forecast supply and demand and formulate production plans. Furthermore, there were challenges with operating automated agricultural machinery, collecting and analyzing data in real time, and swiftly managing inventory and arranging deliveries in response to consumer orders.

[1010] 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. In this invention, the server includes means for collecting geographic data such as location information, area, and soil type of farmland, means for collecting weather data in real time from a weather data providing service, means for managing the collected geographic data and weather data on the cloud, means for collecting production data on past yields, agricultural materials used, and the occurrence of pests and diseases, and market data on market prices, the balance of supply and demand, and consumer preferences, means for using artificial intelligence to predict supply and demand and formulate production plans based on the collected data, means for sending instructions to automated agricultural machinery to sow seeds, water, fertilize, and harvest, means for collecting and analyzing data such as work progress and soil humidity from the automated agricultural machinery and sensors in real time, means for managing inventory of harvested crops, means for selling agricultural products through an online sales system, means for receiving orders from consumers and updating inventory, means for automatically arranging deliveries, collecting and analyzing sales data, and providing feedback to the next production plan, and an emotion engine for evaluating user feedback and purchasing intentions in real time and adjusting production plans and sales strategies. This will lead to more efficient agriculture, a stable food supply, and improved consumer satisfaction.

[1011] "Farmland location information, area, and soil type" is data that indicates the exact location of the farmland, the size of the farmland, and the type of soil on the farmland.

[1012] "Weather Data Service" means an external data source that provides real-time weather information (temperature, humidity, precipitation, wind speed, etc.).

[1013] "Managing on the cloud" means storing data on a remote server via the Internet and accessing and analyzing it.

[1014] "Production data on past yields, agricultural materials used, and pest and disease occurrence status" refers to data showing the yield of crops grown in the past, the types and amounts of fertilizers and pesticides used, as well as the types of pests and diseases that have occurred and their impacts.

[1015] "Market data on market prices, supply and demand balance, and consumer preferences" refers to data showing the market trading prices of agricultural products, the state of supply and demand in the market, and consumer purchasing behavior and preferences.

[1016] "Using artificial intelligence to forecast supply and demand and create production plans" means using AI technology to predict the future balance of supply and demand and to create effective production schedules based on that.

[1017] "Automated agricultural machinery" refers to mechanical devices such as robots and drones that are used to perform automated agricultural work.

[1018] "Data from sensors such as work progress and soil moisture" refers to measurements such as the current progress of work and the amount of moisture in the soil detected by sensors.

[1019] "Inventory management" is the process of tracking and efficiently managing harvested produce, including its quantity, storage location, and freshness.

[1020] An "online sales system" is an e-commerce platform for selling products to consumers over the Internet.

[1021] The "emotion engine that evaluates user feedback and purchasing intent in real time" is an analytical tool that analyzes users' opinions and purchasing behavior regarding products and services, and evaluates their emotions and satisfaction in real time.

[1022] This invention is a system that collects and manages geographical and meteorological data on farmland, uses AI to create production plans, and realizes automated farming and online sales. Furthermore, by combining it with an emotion engine that recognizes user emotions, it aims to improve agricultural efficiency and consumer satisfaction.

[1023] In this system, users first use a terminal to input geographic data such as the location, area, and soil type of their farmland. The terminal can be a smartphone or a PC. This data is sent to a server and stored in a database. The server then collects weather data in real time from a weather data provider (for example, a weather API) and stores it in a database on the cloud.

[1024] The server then collects past production data (e.g., yields, agricultural inputs used, pest and disease occurrence) and market data (market prices, supply and demand balance, consumer preferences) and stores them in a database. This allows the server to use artificial intelligence to create supply and demand forecasts and production plans based on all the collected data. The generative AI model is used to determine the next season's supply and demand forecast and crop growth schedule. This plan is then sent to the device and can be viewed by the user.

[1025] The server then sends specific work instructions to the automated farming machinery (e.g., robots or drones). The automated farming machinery follows the server's instructions to sow seeds, water, fertilize, weed, and harvest. Data such as the progress of work and soil moisture is collected in real time by sensors and sent to the server. The server analyzes this data and sends additional instructions to the automated farming machinery as needed.

[1026] Once the harvest is complete, the harvested produce is managed as inventory by the server. The server uses the inventory data to sell the produce through an online sales system and updates inventory information in real time. When a user purchases produce online, the server receives the order and automatically updates the inventory. It also sends instructions to the delivery company, which then automatically arranges for delivery. At the same time, sales data is fed back into the next production plan.

[1027] Furthermore, an emotion engine will be used to evaluate user feedback and purchasing intent in real time, and the server will then adjust and optimize production plans and sales strategies based on this emotion data.

[1028] Specific examples

[1029] For example, in a farm operation, when a user registers a new piece of farmland and enters its location and soil type, the server immediately begins collecting local weather data. Using past yield data, the server predicts the supply and demand of tomatoes for the next season and determines the timing of sowing. The server sends instructions to the automated farming machinery, such as "Start sowing tomato seeds on one hectare of farmland at 10:00 AM tomorrow," and the automated farming machinery begins work at the specified time. When it is time to harvest, the server sends instructions to the automated farming machinery, such as "Start harvesting at 8:00 AM tomorrow," and the harvested tomatoes are immediately registered in the inventory management system.

[1030] Prompt Sentence Examples

[1031] "Based on the geographical and meteorological data of the farmland, you will create a production plan for the next season. Specifically, you will determine the timing for sowing tomatoes and generate instructions to send to the automated farm machinery."

[1032] In this way, the present invention can improve agricultural efficiency and ensure a stable food supply while also increasing user satisfaction.

[1033] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1034] Step 1:

[1035] A user uses a device to input geographic data such as the location, area, and soil type of the farmland. This input data includes GPS information, the size of the farmland, and soil analysis results. The input data is sent to a server and stored in a database. For example, a user inputs the GPS coordinates of the farmland and the pH value of its soil.

[1036] Step 2:

[1037] The server collects weather data (temperature, humidity, wind speed, precipitation, etc.) in real time from a weather data provider (e.g., weather API). The collected data is stored in a database on the cloud. This collection process is performed automatically and periodically. For example, the server calls the weather API every day at 6:00 AM to obtain the latest weather data.

[1038] Step 3:

[1039] The server collects past production data (e.g., yields, agricultural inputs used, pest and disease occurrence status) and market data (market prices, supply and demand balance, consumer preferences) and stores them in a database. This data is obtained from agricultural statistics databases and market information services. For example, tomato yield data and market price data for the past five years are collected.

[1040] Step 4:

[1041] The server uses a generative AI model based on collected geographical data, weather data, past production data, and market data to predict supply and demand for the next season. It then creates a production plan (sowing timing, planting area, fertilizer to be used, etc.) based on the results of the AI ​​analysis. These plans are sent to the terminal and can be checked by the user. For example, a plan may be generated that states, "Since demand for tomatoes is predicted to increase next season, tomatoes should be sown on two hectares of farmland."

[1042] Step 5:

[1043] The server sends specific work instructions to the automated farm machinery (robots and drones). For example, an instruction might be sent saying, "Start sowing tomato seeds on one hectare of farmland at 10:00 AM tomorrow." The automated farm machinery starts work at the specified time, and sensors detect its progress in real time. This progress data is sent to the server, and additional instructions are sent as needed.

[1044] Step 6:

[1045] The server collects and analyzes data from automated farm machinery and sensors in real time and updates work instructions as needed. For example, sensors monitor soil moisture, and if the moisture level drops, the server automatically sends instructions to water the fields.

[1046] Step 7:

[1047] Once the harvest is complete, the server manages the inventory of the harvested crops. Data such as the quantity of harvested crops and their storage location is saved in a database. For example, when 50 boxes of harvested tomatoes are stored in a warehouse, that information is immediately registered in the database.

[1048] Step 8:

[1049] The server sells agricultural products in an online sales system. Based on inventory data, sales information for agricultural products is updated in the online sales system. When a consumer purchases a product online, the order status is sent to the server, and inventory information is automatically updated. For example, when a consumer purchases 10 boxes of tomatoes, the order information is sent to the server, and 10 boxes are subtracted from the database.

[1050] Step 9:

[1051] The server automatically arranges delivery. Once an order is confirmed in the online sales system, the server sends delivery instructions to the delivery company and the delivery arrangements proceed. For example, delivery arrangements are made for 10 boxes of purchased tomatoes, and the delivery status is updated on the server in real time.

[1052] Step 10:

[1053] The server collects and analyzes sales data and provides feedback to the next production plan, improving the accuracy of the production plan. For example, tomato sales data and user feedback can be analyzed to optimize the next season's production volume and sales methods.

[1054] Step 11:

[1055] The emotion engine evaluates user feedback and purchasing intent in real time. The server adjusts and optimizes production plans and sales strategies based on this emotion data. For example, if a user evaluates their satisfaction as "very satisfied," the server adjusts plans to increase production for the next season.

[1056] As described above, through each step of this system, we aim to achieve agricultural efficiency, a stable food supply, and improved consumer satisfaction.

[1057] (Application example 2)

[1058] 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."

[1059] Modern agriculture requires efficient production planning that responds to fluctuations in weather conditions and market demand. While combining automated farming with online sales enables more efficient farm management, conventional systems have difficulty assessing consumer sentiment and purchasing intent in real time and reflecting this information in production plans and sales strategies. Furthermore, new methods for improving the customer experience in brick-and-mortar stores are also needed. Therefore, the objective of this invention is to realize efficient and flexible production planning, optimization of sales strategies, and an improvement in the customer experience in brick-and-mortar stores.

[1060] 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.

[1061] In this invention, the server includes means for collecting geographical data and meteorological data of farmland, means for managing the collected data on the cloud, means for collecting past production data and market data, means for using artificial intelligence to forecast supply and demand and develop production plans, means for sending instructions to automated agricultural machinery to sow, water, fertilize, and harvest, means for collecting and analyzing data from the automated agricultural machinery and sensors in real time, means for inventory management of harvested crops, means for selling agricultural products through an online sales system, means for receiving orders from consumers and updating inventory, means for automatically arranging deliveries, means for collecting and analyzing sales data and providing feedback to the next production plan, means for providing a virtual tour of the farm in a physical store using augmented reality technology, means for analyzing user emotions to evaluate purchasing intent, and means for adjusting inventory and sales strategies based on user emotion data. This enables efficient and flexible production planning, optimization of sales strategies, and an improvement in customer experience in physical stores.

[1062] "Geographical data of agricultural land" refers to basic information about agricultural land, such as its location, area, and soil type.

[1063] "Weather data" is information about weather conditions that affect agricultural activities, such as temperature, precipitation, humidity, and wind speed.

[1064] "Cloud management" refers to the practice of storing collected data on remote servers on the Internet for access and analysis.

[1065] "Past production data" refers to information about past agricultural activities, such as previous crop yields, agricultural inputs used, and pest and disease occurrence.

[1066] "Market data" refers to information about the market, such as market prices, the balance of supply and demand, and consumer preferences.

[1067] "Using artificial intelligence to forecast supply and demand and develop production plans" means using AI technology to predict future supply and demand and develop efficient production plans.

[1068] "Autonomous agricultural machinery" refers to agricultural machinery that operates autonomously and performs agricultural tasks such as sowing seeds, watering, fertilizing, and harvesting.

[1069] "Real-time collection and analysis" means that data is collected immediately and analyzed on the spot.

[1070] "Inventory management" means managing the quantity and condition of harvested agricultural products, and understanding and updating the inventory status.

[1071] An "online sales system" is a system for selling agricultural products via the Internet.

[1072] "Automatically arranging delivery" means that upon receiving an order from a consumer, delivery procedures are automatically carried out.

[1073] "Providing virtual farm tours within physical stores using augmented reality technology" means using AR technology to provide virtual farm tours within physical stores.

[1074] "Analyzing user emotions and assessing purchasing intent" means utilizing an emotion engine to analyze user emotions and assess purchasing intent.

[1075] "Adjusting inventory and sales strategies based on emotional data" means optimizing inventory status and sales strategies based on collected emotional data.

[1076] This system collects and manages geographic and meteorological data for farmland, uses AI to create production plans, and automates farm work and online sales. Furthermore, it recognizes and evaluates user emotions in real time and reflects them in production plans and sales strategies, improving the customer experience.

[1077] System Program

[1078] The server collects geographical and meteorological data for the farmland and manages it on the cloud. This uses remote server functions via the internet and sensors for data collection. The server also collects past production data and market data, which it analyzes using AI (artificial intelligence) to create supply and demand forecasts and production plans. This AI uses, for example, a "generative AI model."

[1079] Data collection and analysis

[1080] The server then sends instructions to the automated farming machines based on the collected data. These instructions include instructions for sowing, watering, fertilizing, and harvesting. The automated farming machines then perform each task based on the instructions and transmit their progress to the server in real time via sensors. The server then analyzes this information and sends additional instructions as needed.

[1081] Inventory management and online sales

[1082] Harvested produce is managed by a server and provided to consumers through an online sales system. The online sales system reflects real-time inventory information and updates inventory upon receiving orders from consumers. The server also automatically arranges delivery and feeds sales data back into the next production plan.

[1083] Improving customer experience in physical stores

[1084] To enhance customer experience in physical stores, the server uses augmented reality (AR) technology to provide virtual farm tours. The system uses smartphones, smart glasses, and head-mounted displays. When a user starts a farm tour in a physical store using these devices, the server provides an interactive farm tour through AR.

[1085] Use of emotion engine

[1086] The server also includes an emotion engine that analyzes user emotions and assesses their willingness to purchase and satisfaction in real time. Emotional data is automatically collected and analyzed. Based on this data, the server adjusts and optimizes inventory and sales strategies.

[1087] Specific examples

[1088] Here's an example of how this works on a farm. After a user registers a new piece of farmland and enters its location and soil type, the server begins collecting local weather data. Using past production data, the server predicts the supply and demand of tomatoes for the next season and determines the timing of sowing. The server then sends instructions to the automated farm machinery to sow the tomatoes. Sensors then monitor the soil's moisture content, and if it becomes too dry, the server automatically sends instructions to water the soil. At harvest time, the server sends harvesting instructions to the automated farm machinery, and the harvested tomatoes are immediately registered in an inventory management system. Consumers then purchase the tomatoes online, and the server automatically arranges for delivery. The sales data is reflected in the next season's tomato production plan.

[1089] Prompt Sentence Examples

[1090] Below are some example prompts to input to a generative AI model:

[1091] Write code to create real-time production plans based on farmland and weather data from the server.

[1092] A program that stores farmland information and weather data in the cloud and uses AI to make production plans and forecast supply and demand. It is designed to send work instructions to automated farm machinery and optimize harvests in conjunction with an online purchasing system. It also uses an emotion engine to evaluate users' purchasing intent in real time and reflect this in online sales strategies. Specific library names and technologies used must also be clearly stated.

[1093] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1094] Step 1:

[1095] User enters farmland information

[1096] Users use a device (smartphone or PC) to input the location, area, and soil type of their farmland. This input data is sent to a server and stored in the cloud. The server then uses this data to accumulate basic information about the farmland in a database.

[1097] Step 2:

[1098] Weather data collection

[1099] The server collects real-time weather data from a weather data provider. The collected data includes temperature, precipitation, humidity, wind speed, etc., and is stored in the cloud. The server integrates and manages this weather data with farmland data.

[1100] Step 3:

[1101] Collection of historical production and market data

[1102] The server collects and stores historical production data (yields, agricultural inputs used, pest and disease occurrence) and market data (market prices, supply and demand balance, consumer preferences) on the cloud. This data is used to understand past agricultural activities and market trends.

[1103] Step 4:

[1104] Production planning

[1105] The server uses a generative AI model to forecast supply and demand for the next season based on collected farmland information, weather data, past production data, and market data. Based on the results, it creates a production plan (sowing timing, planting area, fertilizer to be used, etc.) and notifies the terminal. The user can check this notification and make adjustments as necessary.

[1106] Step 5:

[1107] Automated farming

[1108] The server sends specific instructions (such as sowing, watering, fertilizing, and harvesting) to the automated farming machines. The automated farming machines carry out the farming tasks based on these instructions and transmit their progress in real time via sensors to the server. The server analyzes this data and sends additional instructions as needed.

[1109] Step 6:

[1110] Post-harvest inventory management and online sales

[1111] Harvested produce is managed in inventory by a server. The server creates an optimal online sales strategy based on supply and demand forecasts and updates inventory information in the online sales system in real time. When a user purchases produce online, the server receives the order, updates inventory, and automatically arranges delivery.

[1112] Step 7:

[1113] Offering augmented reality tours of brick-and-mortar stores

[1114] The server uses augmented reality technology to provide a virtual tour of the farm within a physical store. Once the user starts the tour using smart glasses or a head-mounted display, the server provides an interactive guide through AR technology.

[1115] Step 8:

[1116] Evaluating users' purchasing intentions using an emotion engine

[1117] The server uses an emotion engine to assess users' willingness to buy and their satisfaction in real time, analyzing emotional data collected during the user's in-store and online purchase process. The server then adjusts inventory and sales strategies based on this data.

[1118] 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.

[1119] 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.

[1120] 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.

[1121] [Fourth embodiment]

[1122] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1123] 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.

[1124] 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).

[1125] 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.

[1126] 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.

[1127] 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).

[1128] 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.

[1129] 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.

[1130] 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.

[1131] 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.

[1132] 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.

[1133] 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.

[1134] 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."

[1135] This invention is a system that collects and manages geographical and meteorological data for farmland, uses AI to create production plans, and realizes automated farm work and online sales. Specific examples of the operation of each element are shown below.

[1136] Collection and management of farmland information

[1137] First, the user uses a device (such as a smartphone or PC) to input geographic data such as the location, area, and soil type of the farmland. The server then collects weather data in real time from a weather data provider and stores it in the cloud. This allows basic information about the farmland to be stored in a database.

[1138] Collection of historical production and market data

[1139] The server collects historical production data (e.g., yields, agricultural inputs used, pest and disease occurrence) and market data (e.g., market prices, supply and demand balance, consumer preferences) and stores these in a database.

[1140] Production planning

[1141] The server uses AI to predict supply and demand for the next season based on collected farmland information, weather data, past production data, and market data. Based on the results, it creates a production plan (for example, sowing timing, planting area, fertilizer to be used, etc.) and notifies the user via their device so that they can check it.

[1142] Automated farming

[1143] The server sends specific work instructions to automated agricultural machinery (e.g., robots or drones). For example, the automated agricultural machinery begins work in time for sowing seeds, and sensors send progress updates to the server in real time. The server monitors this and sends additional instructions for watering, fertilizing, and weeding as needed. The timing of harvesting is also automatically determined based on crop growth data, and the terminal (automated agricultural machinery) carries out the harvesting work.

[1144] Inventory management and online sales

[1145] Harvested produce is managed in inventory by a server. Based on supply and demand forecasts, the server creates an optimal online sales strategy and updates inventory information in real time to the online sales system. When a user purchases produce online, the server receives the order and automatically updates the inventory. It also sends instructions to delivery companies and automatically arranges for delivery. The server also collects sales data and feeds it back into the next production plan.

[1146] Specific examples

[1147] Here's an example of how it works on a farm. After a user registers a new piece of farmland and enters its location and soil type, the server begins collecting local weather data. Using past yield data, the server predicts the supply and demand of tomatoes for the next season and determines the timing of sowing. The server then sends instructions to the automated farm machinery to sow the tomatoes. Sensors monitor the soil moisture, and if it dries out, the server automatically sends instructions to water the soil. At harvest time, the server sends harvesting instructions to the automated farm machinery, and the harvested tomatoes are immediately registered in the inventory management system. Consumers then purchase the tomatoes online, and the server automatically arranges for delivery. The sales data is reflected in the next season's tomato production plan.

[1148] As described above, the present invention realizes agricultural efficiency and a stable food supply.

[1149] The processing flow will be explained below.

[1150] Step 1:

[1151] Users use a device (smartphone or PC) to input geographical data such as the location, area, and soil type of farmland into the system.

[1152] Step 2:

[1153] The server collects real-time weather data for the farmland (e.g., temperature, precipitation, humidity, wind speed, etc.) from a weather data provider and stores it on the cloud.

[1154] Step 3:

[1155] The server performs an initial data analysis based on the farmland information entered by the user and the collected weather data, confirms the basic information about the farmland, and stores it in a database.

[1156] Step 4:

[1157] The server collects past production data (e.g., crop type, yield, pest and disease occurrence status, etc.) and market data (e.g., market price, balance of supply and demand, consumer preferences, etc.) and stores them in a database.

[1158] Step 5:

[1159] Based on farmland information, weather data, past production data, and market data collected by the server, artificial intelligence (AI) is used to predict supply and demand for the next season. Based on the results of this prediction, a production plan (sowing timing, planting area, fertilizer to be used, etc.) is created.

[1160] Step 6:

[1161] The server notifies the terminal of the production plan it has drawn up, and the user checks this plan, modifies it as necessary, and gives final approval.

[1162] Step 7:

[1163] The server sends instructions to automated agricultural machinery (e.g., robots and drones), including tasks such as sowing seeds, watering, fertilizing, weeding, and harvesting.

[1164] Step 8:

[1165] The terminal (automated agricultural machinery) automatically carries out agricultural work according to the received instructions. Sensors attached to the agricultural machinery send work progress and environmental data to a server in real time.

[1166] Step 9:

[1167] The server analyzes real-time data from the sensors and sends additional work instructions (e.g., additional watering or fertilization) to the device as needed.

[1168] Step 10:

[1169] The server determines the timing of harvesting based on the crop growth data, and when the time comes, it sends harvesting instructions to the automated farm machinery.

[1170] Step 11:

[1171] The terminal (automated farm machine) performs the harvesting work and sends the data to the server, which updates the inventory based on the harvest data.

[1172] Step 12:

[1173] Based on the inventory data, the server develops an online sales strategy taking into account supply and demand forecasts and market data, and reflects inventory information on the sales site in real time.

[1174] Step 13:

[1175] When a user purchases produce from an online sales site, the server receives the order and automatically updates the inventory.

[1176] Step 14:

[1177] The server issues instructions to the delivery company and automatically arranges delivery. Order details and delivery information are managed in real time and notified to the consumer.

[1178] Step 15:

[1179] The server collects sales data and analyzes it to provide feedback for the next production plan. Users can check this feedback data on their devices and use it to plan the next production season.

[1180] In this way, a system will be built that automates the entire agricultural process, ensuring efficient operations and a stable food supply.

[1181] Example 1

[1182] 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."

[1183] Efficient production planning and management remains a major challenge in modern agriculture. Specifically, it is difficult to accurately collect and manage geographic and meteorological data on farmland, and to forecast supply and demand and formulate production plans based on past production and market data. Other challenges include maximizing work efficiency with automated farm machinery, using sensors to monitor and adjust farm work in real time, and managing post-harvest inventory and quickly managing online sales.

[1184] 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.

[1185] In this invention, the server includes: means for collecting geographical data and meteorological data of farmland; means for managing the collected data on the cloud; means for collecting past production data and market data; means for using artificial intelligence to forecast supply and demand and create production plans; means for sending instructions to automated agricultural machinery to sow, water, fertilize, and harvest; means for collecting and analyzing data from the automated agricultural machinery and sensors in real time; means for managing inventory of harvested crops; means for selling agricultural products through an online sales system; means for receiving orders from consumers and updating inventory; means for automatically arranging deliveries; means for collecting and analyzing sales data and feeding it back into the next production plan; means for using artificial intelligence to analyze meteorological data, farmland information, and market data and for notifying the users of the timing of sowing, planting area, and type and amount of materials to be used in real time; and means for monitoring soil moisture using data from sensors and automatically sending watering instructions when the soil becomes dry. This enables more efficient and automated agricultural work, enabling stable crop production and rapid market supply.

[1186] "Agricultural land geographic data" refers to all geographic information including agricultural land location, area, soil type, etc.

[1187] "Weather data" refers to data related to weather, such as temperature, precipitation, and wind speed.

[1188] "Cloud" refers to data storage or computing resources provided over the internet.

[1189] "Past production data" refers to data such as yields from previous cultivation on farmland, agricultural materials used, and the occurrence of pests and diseases.

[1190] "Market data" refers to various data about the market, such as market prices, the balance of supply and demand, and consumer preferences.

[1191] "Artificial intelligence" refers to a system that uses machine learning and data analysis techniques to make specific predictions and judgments from data.

[1192] "Supply and demand forecast" refers to predicting the future balance between supply and demand.

[1193] A "production plan" refers to a specific plan for agricultural work, such as the timing of sowing seeds, the area to be planted, and the types and amounts of fertilizers and pesticides to be used.

[1194] "Automated agricultural machinery" refers to machines such as robots and drones that perform agricultural work automatically according to a program.

[1195] A "sensor" refers to a device that measures environmental changes, such as soil humidity and temperature, in real time and acquires data.

[1196] "Inventory management" refers to managing the quantity and condition of harvested agricultural products.

[1197] "Online sales system" refers to a system for selling products via the Internet.

[1198] "Shipping arrangements" refers to managing the shipping of ordered products.

[1199] "Sales Data" means data related to sales, such as the quantity, price, and purchaser information of the items sold.

[1200] This invention is a system that collects and manages geographical and meteorological data for farmland, uses artificial intelligence to create production plans, and realizes automated farm work and online sales. Specific examples of the operation of each element are shown below.

[1201] Collection and management of farmland information

[1202] First, the user uses a terminal (e.g., a smartphone or personal computer) to input geographic data such as the location, area, and soil type of the farmland. The server then collects weather data in real time from a weather data provider (e.g., a general weather data API) and stores it in the cloud. This operation accumulates basic information about the farmland in a database. The specific hardware and software used include a smart device as the terminal and a cloud platform as the server.

[1203] Collection of historical production and market data

[1204] The server collects past production data (e.g., yields, agricultural inputs used, pest and disease occurrence status) and market data (e.g., price information, supply and demand balance, consumer preferences), and stores this data in a cloud database. Data collection methods used include APIs and agricultural information management systems.

[1205] Production planning

[1206] The server uses an artificial intelligence model to predict supply and demand for the next season based on collected farmland information, weather data, past production data, and market data. Based on the prediction results, it creates a production plan (for example, sowing timing, planting area, and type and amount of fertilizer to be used). These plans are sent to the terminal and can be checked by the user. The specific software used is a machine learning framework built in Python (such as TensorFlow or PyTorch).

[1207] Automated farming

[1208] The server sends specific work instructions to the automated agricultural machinery (e.g., robots or drones). For example, the automated agricultural machinery starts work when it's time to sow seeds, and sends its progress information to the server in real time via sensors. Sensing technology and IoT device management systems (such as AWS IoT Core) are used. The automated agricultural machinery follows the server's instructions to perform tasks such as sowing seeds, watering, fertilizing, and harvesting.

[1209] Inventory management and online sales

[1210] Harvested produce is managed in inventory by a server. Based on supply and demand forecasts, the server creates an optimal online sales strategy and updates inventory information in real time to the online sales system (a typical e-commerce platform). When a user purchases produce online, the server receives the order and automatically updates the inventory. The server then sends instructions to the delivery company to automatically arrange delivery. Sales data is fed back into the next season's production plan.

[1211] Specific examples

[1212] Here's an example of how it works on a farm. After a user registers a new piece of farmland and enters its location and soil type, the server begins collecting local weather data. Based on past yield data, the server predicts the supply and demand of tomatoes for the next season and determines the timing of sowing. The server then sends instructions to the automated farm machinery to sow the tomatoes. Sensors monitor the moisture content of the soil, and if it becomes too dry, the server automatically sends instructions to water the soil. When it's time to harvest, the server sends harvesting instructions to the automated farm machinery, and the harvested tomatoes are immediately registered in the inventory management system. Consumers then purchase the tomatoes online, and the server automatically arranges for delivery. The sales data is reflected in the next season's tomato production plan.

[1213] Prompt Sentence Examples

[1214] Below are examples of prompts to use with the generative AI model:

[1215] "Please explain the functions of the system that inputs geographical and meteorological data of farmland and uses AI to create production plans. Please provide a detailed description, including the specific data collection method, automated farm machinery used, and online sales process."

[1216] Through the above process, the present invention realizes agricultural efficiency and a stable food supply.

[1217] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1218] Step 1:

[1219] The user inputs the geographic data of the farmland.

[1220] The user uses a device to input geographic data such as the location, area, and soil type of the farmland. The input data includes the location of the farmland on a map, its area, and soil quality. This input data is sent to the server, which receives the data and stores it in a cloud database. Specific operations include mapping the location of the farmland on a map on the application screen and registering information such as the area and soil type through an input form.

[1221] Step 2:

[1222] The server collects and manages weather data

[1223] The server collects weather data in real time from a weather data provider service. Specifically, it periodically obtains data such as temperature, precipitation, and wind speed using the weather data provider service's API. The obtained data is stored in a cloud database. This process involves sending an API request and writing the weather data obtained in response to the request into the database. This weather data plays an important role in subsequent production planning.

[1224] Step 3:

[1225] The server collects historical production and market data

[1226] The server collects past production data and market data. Past production data inputs include yields, agricultural inputs used, and pest and disease occurrence status. Market data inputs include price information, supply and demand balance, and consumer preferences. The server periodically obtains this data from various data sources and stores it in a cloud database. Specifically, the data is collected using the APIs of agricultural management systems and market data providers.

[1227] Step 4:

[1228] The server analyzes the data and makes supply and demand predictions

[1229] The server integrates collected farmland information, weather data, past production data, and market data, and uses an artificial intelligence model to predict supply and demand for the next season. All collected data is provided as input to the AI ​​model, and supply and demand forecast results are obtained as output. During this process, data analysis is performed using machine learning frameworks developed in Python (such as TensorFlow and PyTorch). Specific operations include data preprocessing, feature selection, model training, and validation.

[1230] Step 5:

[1231] The server creates a production plan and notifies the terminal

[1232] The server creates a production plan based on the predictions of the AI ​​model. The plan includes the timing of sowing seeds, the area to be planted, and the type and amount of fertilizer and pesticide to be used. These plans are notified to the terminal. Specifically, the created plan is sent to the user using a messaging system (for example, push notification or email notification).

[1233] Step 6:

[1234] The server operates the automated farm machinery and starts farming.

[1235] The server sends specific operational instructions to the automated agricultural machinery. For example, it instructs the timing of sowing seeds, fertilizing, watering, and harvesting. Input data includes production plans and real-time weather and soil data, and the output is the agricultural machinery performing its tasks. Specific operations include controlling GPIO and various sensors using an IoT device management system.

[1236] Step 7:

[1237] The server analyzes the data from the sensors in real time and adds necessary instructions.

[1238] The server collects real-time data from sensors (e.g., soil moisture sensors) and sends additional instructions to the automated farming equipment depending on the situation. The input data includes real-time data from the sensors, and the output sends instructions to the farming equipment, for example, to water. It also performs constant monitoring using real-time data analysis tools (e.g., Apache Kafka and Spark Streaming).

[1239] Step 8:

[1240] The server manages post-harvest inventory

[1241] The harvested produce is inventory-managed by the server. The input includes harvested quantity and quality data, and the output updates the inventory database. Specific operations include writing to the database and monitoring the inventory status.

[1242] Step 9:

[1243] The server executes the online sale

[1244] The server updates the online sales system with inventory information in real time and accepts orders from consumers. The input data includes updated inventory and order information, and the output is order processing and inventory updates. Specific operations include data integration with the e-commerce platform, order confirmation, and payment processing.

[1245] Step 10:

[1246] The server automatically arranges delivery

[1247] Delivery arrangements are made automatically by the server. Input data includes the consumer's order information and delivery address information, and output includes instructions to the delivery company. Specifically, this includes setting up an efficient delivery route through API integration with the delivery system and completing the delivery arrangements.

[1248] (Application example 1)

[1249] 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."

[1250] In agricultural and industrial production, uncertainty in production plans and a lack of automation are obstacles to efficient production. It is also difficult to accurately predict the supply and demand balance of agricultural crops and factory-produced goods and to formulate optimal production plans based on that. Furthermore, the lack of automation in inventory management, sales, and delivery has resulted in labor surpluses and shortages, preventing stable food self-sufficiency and product supply. It is necessary to solve these issues and achieve efficient and stable agricultural and industrial production.

[1251] 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.

[1252] In this invention, the server includes means for collecting geographical data and meteorological data of the farmland, means for managing the collected data on the cloud, means for collecting past production data and market data, means for using artificial intelligence to make supply and demand forecasts and production plans, means for sending instructions to automated agricultural machinery to perform sowing, watering, fertilizing, and harvesting, means for collecting and analyzing data from the automated agricultural machinery and sensors in real time, means for managing inventory of harvested crops, means for selling agricultural products through an online sales system, means for receiving orders from consumers and updating inventory, means for automatically making delivery arrangements, means for collecting and analyzing sales data and feeding it back to the next production plan, means for collecting and managing location information and equipment data within the factory, means for collecting work environment data within the factory in real time, means for making production plans using past production data and demand data, means for sending specific work instructions to automated robots and having them perform production work, and means for automatically managing inventory and shipping within the factory. This will reduce uncertainty in production plans in agricultural and industrial production, enabling efficient production and stable supply.

[1253] "Geographic data of agricultural land" refers to data on the geographic characteristics of agricultural land, such as its location, area, and soil type.

[1254] "Weather data" refers to data related to climate, such as temperature, precipitation, humidity, and wind speed.

[1255] "Managing on the cloud" means storing collected data in a data center connected via the Internet and making it accessible.

[1256] "Past production data" refers to data on past crop yields, agricultural materials used, pest and disease occurrence, and so on.

[1257] "Market data" refers to data on market trends such as market prices, the balance between supply and demand, and consumer preferences.

[1258] "Artificial intelligence" is a technology that uses machine learning and data analysis techniques to derive knowledge and patterns from data.

[1259] "Supply and demand forecasting" is the prediction of future demand and supply based on collected data.

[1260] "Production planning" involves planning the production volume and timing of crops and industrial products, as well as the materials to be used.

[1261] "Automated agricultural machinery" refers to machines such as robots and drones that can perform agricultural work automatically.

[1262] "Real-time collection and analysis" means collecting data immediately and analyzing it on the spot.

[1263] "Inventory management" is the tracking and management of harvested agricultural crops and manufactured industrial products.

[1264] An "online sales system" is a system for selling products over the Internet.

[1265] "Updating inventory upon receiving an order from a consumer" means updating inventory data based on the information when a consumer places an order through the online sales system.

[1266] "Automatically arranging delivery" means automatically sending delivery instructions to a delivery company based on a consumer's order.

[1267] "Location information within a factory" is information relating to the locations of equipment and machines within a factory.

[1268] "Facility data" refers to data related to the functions and status of machines and equipment within a factory.

[1269] "Work environment data" refers to data related to the work environment in a factory, such as temperature, humidity, and sound level.

[1270] "Planning a production plan" means planning the production processes and schedules within a factory.

[1271] An "automatic robot" is a robot that can autonomously operate machinery and perform production tasks.

[1272] "Automated shipping" means automatically packaging products and preparing them for shipping.

[1273] The present invention provides a system that improves the efficiency of production management systems in farmland and factories and provides specific means for realizing stable supply. Specific embodiments of the system are described below.

[1274] System Overview

[1275] This system consists of a server, terminals, users, automated agricultural machinery, and automated robots. The server collects, manages, analyzes, sends instructions, and manages inventory. Terminals are devices that users use to input and check data, and include smartphones and PCs. The automated agricultural machinery and automated robots are machines that perform specific tasks.

[1276] Program Overview

[1277] The server first collects geographic and meteorological data for the farmland. Geographic data includes the location, area, and soil type of the farmland. Meteorological data includes temperature, precipitation, humidity, and wind speed. This data is stored in the cloud, along with past production data and market data. Market data includes market prices, supply and demand balance, and consumer preferences.

[1278] Based on this data, the server uses artificial intelligence (AI) to predict supply and demand for the next season and create a production plan. This plan includes sowing, watering, fertilizing, harvesting timing, and materials to be used. The plan is then sent to the device so that the user can check and modify it.

[1279] The server then sends instructions to the autonomous farming equipment to perform the tasks, and data from the autonomous farming equipment and sensors is sent to the server in real time for analysis, which then sends additional instructions on watering and fertilizing as needed.

[1280] Harvested produce is managed by the server and reflected in the online sales system. When a consumer purchases produce online, the server receives the order, updates the inventory, and automatically arranges delivery. Sales data is also collected and fed back into the next production plan.

[1281] Within the factory, a server collects and manages the factory's location information and equipment data. Work environment data (temperature, humidity, sound level, etc.) is also collected in real time. Production plans are created using past production data and demand data, and specific work instructions are sent to automated robots. Inventory management and shipping within the factory are also automated.

[1282] Hardware and Software Use

[1283] Hardware: Servers, smartphones, PCs, automated farm machinery, automated robots, sensors.

[1284] Software: Cloud database, AI models (machine learning algorithms), online sales systems, inventory management systems, communication protocols.

[1285] Specific examples

[1286] As an example of how a new farm is operated, let's say a new piece of farmland is registered. When a user uses a device to enter the location information and soil type of the farmland, the server begins collecting local weather data. Using past yield data, the server predicts the supply and demand of tomatoes for the next season and decides when to sow seeds. The server then sends instructions to the automated farm machinery to sow the tomatoes. Sensors monitor the moisture content of the soil, and if it becomes too dry, the server automatically sends instructions to water the soil. When it's time to harvest, the server sends harvesting instructions to the automated farm machinery, and the harvested tomatoes are immediately registered in the inventory management system. Consumers then purchase the tomatoes online, and the server automatically arranges for delivery. The sales data is reflected in the next season's tomato production plan.

[1287] Prompt Sentence Examples

[1288] Collect location information, equipment data, and real-time environmental data within the factory, integrate past production data with demand data, and create a production plan that predicts supply and demand for the next season. Then, design a system that sends specific work instructions to robots based on the generated production plan. Store the collected data in an SQLite database and use an AI model to predict supply and demand.

[1289] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1290] Step 1:

[1291] The user inputs geographic data of the farmland (location, area, soil type) using a terminal. The terminal sends this data to the server, which receives the input data and stores it in a database on the cloud.

[1292] Step 2:

[1293] The server collects real-time weather data (temperature, precipitation, humidity, wind speed) from a weather data provider. The collected weather data is stored in a database on the cloud. This allows the server to obtain basic environmental information about the farmland.

[1294] Step 3:

[1295] The server collects past production data (yields, agricultural inputs used, pest and disease occurrence status) and market data (market prices, supply and demand balance, consumer preferences) and stores them in a database, allowing the server to understand past agricultural activities and market trends.

[1296] Step 4:

[1297] The server uses collected farmland information, weather data, past production data, and market data to predict supply and demand for the next season using a generative AI model. The model uses this data as input, performs data calculations, and outputs supply and demand forecast results. Based on these forecast results, the server creates a production plan (sowing timing, planting area, fertilizer to be used, etc.) and notifies the terminal.

[1298] Step 5:

[1299] The user checks the production plan on the terminal and modifies it as necessary. When the user makes modifications, the modified production plan is sent from the terminal to the server. The server saves the updated production plan in a database on the cloud.

[1300] Step 6:

[1301] The server sends specific work instructions (such as sowing seeds, watering, fertilizing, and harvesting) to the automated farming machine. The automated farming machine carries out the work according to these instructions and sends its progress and environmental data to the server in real time.

[1302] Step 7:

[1303] The server collects and analyzes data from automated farm machinery and sensors in real time. The server uses the collected data to send additional instructions for watering and fertilizing as needed. The data is stored in a database on the cloud.

[1304] Step 8:

[1305] Harvested produce is managed by a server. Inventory data is stored in a cloud database and updated in real time in the online sales system. The server monitors inventory status and develops optimal sales strategies.

[1306] Step 9:

[1307] When a consumer purchases produce through the online sales system, the server receives the order, updates inventory data, and automatically sends delivery instructions to the delivery company.

[1308] Step 10:

[1309] The server collects and analyzes sales data. The sales data is analyzed by a generative AI model and fed back into the next season's production plan. The collected and analyzed data is stored in a database on the cloud.

[1310] Step 11:

[1311] The server collects and manages factory location information and equipment data, and the collected data is stored in a database on the cloud.

[1312] Step 12:

[1313] The server collects real-time data on the working environment within the factory (temperature, humidity, sound level, etc.) and stores it in a database on the cloud. The collected data is used to assist in production planning.

[1314] Step 13:

[1315] The server uses past production and demand data to generate demand and supply forecasts using a generative AI model, and creates a production plan, which is then stored in a cloud database.

[1316] Step 14:

[1317] The server sends specific work instructions to the automated robot, which then carries out production tasks based on the instructions and reports its status to the server in real time.

[1318] Step 15:

[1319] The server manages inventory in the factory and automatically handles shipping. Inventory and shipping data is stored in a cloud database and updated in real time on the online sales system.

[1320] 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.

[1321] This system combines a system that collects and manages geographical and meteorological data on farmland, uses AI to create production plans, and realizes automated farm work and online sales, with an emotion engine that recognizes user emotions. Specific examples of the operation of each element are shown below.

[1322] Collection and management of farmland information

[1323] First, the user uses a device (such as a smartphone or PC) to input geographic data such as the location, area, and soil type of the farmland. The server then collects weather data in real time from a weather data provider and stores it in the cloud. This allows basic information about the farmland to be stored in a database.

[1324] Collection of historical production and market data

[1325] The server collects historical production data (e.g., yields, agricultural inputs used, pest and disease occurrence) and market data (e.g., market prices, supply and demand balance, consumer preferences) and stores these in a database.

[1326] Production planning

[1327] The server uses AI to forecast supply and demand for the next season based on collected farmland information, weather data, past production data, and market data. Based on the results, it creates a production plan (for example, sowing timing, planting area, fertilizer to be used, etc.) and notifies the user via their device so that they can check it.

[1328] Automated farming

[1329] The server sends specific work instructions to automated agricultural machinery (e.g., robots or drones). For example, the automated agricultural machinery begins work in time for sowing seeds, and sensors send progress updates to the server in real time. The server monitors this and sends additional instructions for watering, fertilizing, and weeding as needed. The timing of harvesting is also automatically determined based on crop growth data, and the terminal (automated agricultural machinery) carries out the harvesting work.

[1330] Inventory management and online sales

[1331] Harvested produce is managed in inventory by a server. Based on supply and demand forecasts, the server creates an optimal online sales strategy and updates inventory information in real time to the online sales system. When a user purchases produce online, the server receives the order and automatically updates the inventory. It also sends instructions to delivery companies and automatically arranges for delivery. The server also collects sales data and feeds it back into the next production plan.

[1332] Use of emotion engine

[1333] The emotion engine is used to evaluate user feedback and purchasing intent in real time, and the server adjusts and optimizes production plans and sales strategies based on this emotion data.

[1334] Specific examples

[1335] Here's an example of how it works on a farm. After a user registers a new piece of farmland and enters its location and soil type, the server begins collecting local weather data. Using past yield data, the server predicts the supply and demand of tomatoes for the next season and determines the timing of sowing. The server then sends instructions to the automated farm machinery to sow the tomatoes. Sensors monitor the soil moisture, and if it dries out, the server automatically sends instructions to water the soil. At harvest time, the server sends harvesting instructions to the automated farm machinery, and the harvested tomatoes are immediately registered in the inventory management system. Consumers then purchase the tomatoes online, and the server automatically arranges for delivery. The sales data is reflected in the next season's tomato production plan.

[1336] Furthermore, when a user purchases tomatoes from an online sales site, the emotion engine evaluates the user's purchasing motivation and satisfaction in real time, and the server adjusts inventory and delivery arrangements based on this data. For example, if user satisfaction is high, that data can be reflected in the next season's production plan, and the plan can be adjusted to produce more tomatoes. Conversely, if satisfaction is low, the cause can be analyzed and improvements can be made to improve the quality of agricultural products and service.

[1337] In this way, the present invention is a system that can realize agricultural efficiency and a stable food supply, while also increasing user satisfaction.

[1338] The processing flow will be explained below.

[1339] Step 1:

[1340] Users use a device (smartphone or PC) to input geographical data such as the location, area, and soil type of farmland into the system.

[1341] Step 2:

[1342] The server collects real-time weather data for the farmland (e.g., temperature, precipitation, humidity, wind speed, etc.) from a weather data provider and stores it on the cloud.

[1343] Step 3:

[1344] The server performs an initial data analysis based on the farmland information entered by the user and the collected weather data, and stores the basic information about the farmland in a database.

[1345] Step 4:

[1346] The server collects past production data (e.g., crop type, yield, pest and disease occurrence status, etc.) and market data (e.g., market price, balance of supply and demand, consumer preferences, etc.) and stores them in a database.

[1347] Step 5:

[1348] Based on farmland information, weather data, past production data, and market data collected by the server, artificial intelligence (AI) is used to predict supply and demand for the next season. Based on the results of this prediction, a production plan (sowing timing, planting area, fertilizer to be used, etc.) is created.

[1349] Step 6:

[1350] The server notifies the terminal of the production plan it has drawn up, and the user checks this plan, modifies it as necessary, and gives final approval.

[1351] Step 7:

[1352] The server sends instructions to automated agricultural machinery (e.g., robots and drones), including tasks such as sowing seeds, watering, fertilizing, weeding, and harvesting.

[1353] Step 8:

[1354] The terminal (automated agricultural machinery) automatically carries out agricultural work according to the received instructions. Sensors attached to the agricultural machinery send work progress and environmental data to a server in real time.

[1355] Step 9:

[1356] The server analyzes real-time data from the sensors and sends additional work instructions (e.g., additional watering or fertilization) to the device as needed.

[1357] Step 10:

[1358] The server determines the timing of harvesting based on the crop growth data, and when the time comes, it sends harvesting instructions to the automated farm machinery.

[1359] Step 11:

[1360] The terminal (automated farm machine) performs the harvesting work and sends the data to the server, which updates the inventory based on the harvest data.

[1361] Step 12:

[1362] Based on the inventory data, the server develops an online sales strategy taking into account supply and demand forecasts and market data, and reflects inventory information on the sales site in real time.

[1363] Step 13:

[1364] When a user purchases produce from an online sales site, the server receives the order and automatically updates the inventory.

[1365] Step 14:

[1366] The server issues instructions to the delivery company and automatically arranges delivery. Order details and delivery information are managed in real time and notified to the consumer.

[1367] Step 15:

[1368] The emotion engine evaluates users' feedback and purchasing intentions in real time, and the server adjusts inventory and delivery arrangements based on that data. It also reflects factors that lead to high satisfaction in production plans for the next season.

[1369] Step 16:

[1370] The server collects sales data and analyzes it to provide feedback for the next production plan. Users can check this feedback data on their devices and use it to plan the next production season.

[1371] In this way, a system will be built that automates the entire agricultural process, ensuring efficient operations and a stable food supply.

[1372] Example 2

[1373] 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."

[1374] In conventional agricultural systems, the collection and management of geographical data and meteorological data for farmland was often done manually, making efficient data management difficult. Furthermore, there was a lack of systems for integrating and analyzing past production data and market data to forecast supply and demand and formulate production plans. Furthermore, there were challenges with operating automated agricultural machinery, collecting and analyzing data in real time, and swiftly managing inventory and arranging deliveries in response to consumer orders.

[1375] 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. In this invention, the server includes means for collecting geographic data such as location information, area, and soil type of farmland, means for collecting weather data in real time from a weather data providing service, means for managing the collected geographic data and weather data on the cloud, means for collecting production data on past yields, agricultural materials used, and the occurrence of pests and diseases, and market data on market prices, the balance of supply and demand, and consumer preferences, means for using artificial intelligence to predict supply and demand and formulate production plans based on the collected data, means for sending instructions to automated agricultural machinery to sow seeds, water, fertilize, and harvest, means for collecting and analyzing data such as work progress and soil humidity from the automated agricultural machinery and sensors in real time, means for managing inventory of harvested crops, means for selling agricultural products through an online sales system, means for receiving orders from consumers and updating inventory, means for automatically arranging deliveries, collecting and analyzing sales data, and providing feedback to the next production plan, and an emotion engine for evaluating user feedback and purchasing intentions in real time and adjusting production plans and sales strategies. This will lead to more efficient agriculture, a stable food supply, and improved consumer satisfaction.

[1376] "Farmland location information, area, and soil type" is data that indicates the exact location of the farmland, the size of the farmland, and the type of soil on the farmland.

[1377] "Weather Data Service" means an external data source that provides real-time weather information (temperature, humidity, precipitation, wind speed, etc.).

[1378] "Managing on the cloud" means storing data on a remote server via the Internet and accessing and analyzing it.

[1379] "Production data on past yields, agricultural materials used, and pest and disease occurrence status" refers to data showing the yield of crops grown in the past, the types and amounts of fertilizers and pesticides used, as well as the types of pests and diseases that have occurred and their impacts.

[1380] "Market data on market prices, supply and demand balance, and consumer preferences" refers to data showing the market trading prices of agricultural products, the state of supply and demand in the market, and consumer purchasing behavior and preferences.

[1381] "Using artificial intelligence to forecast supply and demand and create production plans" means using AI technology to predict the future balance of supply and demand and to create effective production schedules based on that.

[1382] "Automated agricultural machinery" refers to mechanical devices such as robots and drones that are used to perform automated agricultural work.

[1383] "Data from sensors such as work progress and soil moisture" refers to measurements such as the current progress of work and the amount of moisture in the soil detected by sensors.

[1384] "Inventory management" is the process of tracking and efficiently managing harvested produce, including its quantity, storage location, and freshness.

[1385] An "online sales system" is an e-commerce platform for selling products to consumers over the Internet.

[1386] The "emotion engine that evaluates user feedback and purchasing intent in real time" is an analytical tool that analyzes users' opinions and purchasing behavior regarding products and services, and evaluates their emotions and satisfaction in real time.

[1387] This invention is a system that collects and manages geographical and meteorological data on farmland, uses AI to create production plans, and realizes automated farming and online sales. Furthermore, by combining it with an emotion engine that recognizes user emotions, it aims to improve agricultural efficiency and consumer satisfaction.

[1388] In this system, users first use a terminal to input geographic data such as the location, area, and soil type of their farmland. The terminal can be a smartphone or a PC. This data is sent to a server and stored in a database. The server then collects weather data in real time from a weather data provider (for example, a weather API) and stores it in a database on the cloud.

[1389] The server then collects past production data (e.g., yields, agricultural inputs used, pest and disease occurrence) and market data (market prices, supply and demand balance, consumer preferences) and stores them in a database. This allows the server to use artificial intelligence to create supply and demand forecasts and production plans based on all the collected data. The generative AI model is used to determine the next season's supply and demand forecast and crop growth schedule. This plan is then sent to the device and can be viewed by the user.

[1390] The server then sends specific work instructions to the automated farming machinery (e.g., robots or drones). The automated farming machinery follows the server's instructions to sow seeds, water, fertilize, weed, and harvest. Data such as the progress of work and soil moisture is collected in real time by sensors and sent to the server. The server analyzes this data and sends additional instructions to the automated farming machinery as needed.

[1391] Once the harvest is complete, the harvested produce is managed as inventory by the server. The server uses the inventory data to sell the produce through an online sales system and updates inventory information in real time. When a user purchases produce online, the server receives the order and automatically updates the inventory. It also sends instructions to the delivery company, which then automatically arranges for delivery. At the same time, sales data is fed back into the next production plan.

[1392] Furthermore, an emotion engine will be used to evaluate user feedback and purchasing intent in real time, and the server will then adjust and optimize production plans and sales strategies based on this emotion data.

[1393] Specific examples

[1394] For example, in a farm operation, when a user registers a new piece of farmland and enters its location and soil type, the server immediately begins collecting local weather data. Using past yield data, the server predicts the supply and demand of tomatoes for the next season and determines the timing of sowing. The server sends instructions to the automated farming machinery, such as "Start sowing tomato seeds on one hectare of farmland at 10:00 AM tomorrow," and the automated farming machinery begins work at the specified time. When it is time to harvest, the server sends instructions to the automated farming machinery, such as "Start harvesting at 8:00 AM tomorrow," and the harvested tomatoes are immediately registered in the inventory management system.

[1395] Prompt Sentence Examples

[1396] "Based on the geographical and meteorological data of the farmland, you will create a production plan for the next season. Specifically, you will determine the timing for sowing tomatoes and generate instructions to send to the automated farm machinery."

[1397] In this way, the present invention can improve agricultural efficiency and ensure a stable food supply while also increasing user satisfaction.

[1398] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1399] Step 1:

[1400] A user uses a device to input geographic data such as the location, area, and soil type of the farmland. This input data includes GPS information, the size of the farmland, and soil analysis results. The input data is sent to a server and stored in a database. For example, a user inputs the GPS coordinates of the farmland and the pH value of its soil.

[1401] Step 2:

[1402] The server collects weather data (temperature, humidity, wind speed, precipitation, etc.) in real time from a weather data provider (e.g., weather API). The collected data is stored in a database on the cloud. This collection process is performed automatically and periodically. For example, the server calls the weather API every day at 6:00 AM to obtain the latest weather data.

[1403] Step 3:

[1404] The server collects past production data (e.g., yields, agricultural inputs used, pest and disease occurrence status) and market data (market prices, supply and demand balance, consumer preferences) and stores them in a database. This data is obtained from agricultural statistics databases and market information services. For example, tomato yield data and market price data for the past five years are collected.

[1405] Step 4:

[1406] The server uses a generative AI model based on collected geographical data, weather data, past production data, and market data to predict supply and demand for the next season. It then creates a production plan (sowing timing, planting area, fertilizer to be used, etc.) based on the results of the AI ​​analysis. These plans are sent to the terminal and can be checked by the user. For example, a plan may be generated that states, "Since demand for tomatoes is predicted to increase next season, tomatoes should be sown on two hectares of farmland."

[1407] Step 5:

[1408] The server sends specific work instructions to the automated farm machinery (robots and drones). For example, an instruction might be sent saying, "Start sowing tomato seeds on one hectare of farmland at 10:00 AM tomorrow." The automated farm machinery starts work at the specified time, and sensors detect its progress in real time. This progress data is sent to the server, and additional instructions are sent as needed.

[1409] Step 6:

[1410] The server collects and analyzes data from automated farm machinery and sensors in real time and updates work instructions as needed. For example, sensors monitor soil moisture, and if the moisture level drops, the server automatically sends instructions to water the fields.

[1411] Step 7:

[1412] Once the harvest is complete, the server manages the inventory of the harvested crops. Data such as the quantity of harvested crops and their storage location is saved in a database. For example, when 50 boxes of harvested tomatoes are stored in a warehouse, that information is immediately registered in the database.

[1413] Step 8:

[1414] The server sells agricultural products in an online sales system. Based on inventory data, sales information for agricultural products is updated in the online sales system. When a consumer purchases a product online, the order status is sent to the server, and inventory information is automatically updated. For example, when a consumer purchases 10 boxes of tomatoes, the order information is sent to the server, and 10 boxes are subtracted from the database.

[1415] Step 9:

[1416] The server automatically arranges delivery. Once an order is confirmed in the online sales system, the server sends delivery instructions to the delivery company and the delivery arrangements proceed. For example, delivery arrangements are made for 10 boxes of purchased tomatoes, and the delivery status is updated on the server in real time.

[1417] Step 10:

[1418] The server collects and analyzes sales data and provides feedback to the next production plan, improving the accuracy of the production plan. For example, tomato sales data and user feedback can be analyzed to optimize the next season's production volume and sales methods.

[1419] Step 11:

[1420] The emotion engine evaluates user feedback and purchasing intent in real time. The server adjusts and optimizes production plans and sales strategies based on this emotion data. For example, if a user evaluates their satisfaction as "very satisfied," the server adjusts plans to increase production for the next season.

[1421] As described above, through each step of this system, we aim to achieve agricultural efficiency, a stable food supply, and improved consumer satisfaction.

[1422] (Application example 2)

[1423] 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 robot 414 will be referred to as a "terminal."

[1424] Modern agriculture requires efficient production planning that responds to fluctuations in weather conditions and market demand. While combining automated farming with online sales enables more efficient farm management, conventional systems have difficulty assessing consumer sentiment and purchasing intent in real time and reflecting this information in production plans and sales strategies. Furthermore, new methods for improving the customer experience in brick-and-mortar stores are also needed. Therefore, the objective of this invention is to realize efficient and flexible production planning, optimization of sales strategies, and an improvement in the customer experience in brick-and-mortar stores.

[1425] 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.

[1426] In this invention, the server includes means for collecting geographical data and meteorological data of farmland, means for managing the collected data on the cloud, means for collecting past production data and market data, means for using artificial intelligence to forecast supply and demand and develop production plans, means for sending instructions to automated agricultural machinery to sow, water, fertilize, and harvest, means for collecting and analyzing data from the automated agricultural machinery and sensors in real time, means for inventory management of harvested crops, means for selling agricultural products through an online sales system, means for receiving orders from consumers and updating inventory, means for automatically arranging deliveries, mean...

Claims

1. a means for collecting geographic and meteorological data for the farmland; A means of managing the collected data on the cloud; a means of collecting historical production and market data; A means for using artificial intelligence to make supply and demand forecasts and production plans; means for sending instructions to the automated farm machinery to perform sowing, watering, fertilizing, and harvesting; A means of collecting and analyzing data from automated farm machinery and sensors in real time; a means for inventory management of harvested agricultural products; a means of selling agricultural products through an online sales system; a means for receiving orders from consumers and updating inventory; A means of automating delivery arrangements; A system that includes a means of collecting and analyzing sales data and providing feedback to the next production plan.

2. 2. The system according to claim 1, which utilizes unused land and achieves stable food self-sufficiency through the above means.

3. 2. The system according to claim 1, further comprising means for updating inventory information in real time and providing agricultural products to consumers promptly in online sales.

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