System

Abstract

A system is provided.SOLUTION: A system, comprising: means for collecting information regarding sustainable agricultural practices from published AI sources using a generative data model; means for analyzing and providing the collected information in an easy-to-understand form for agricultural producers; means for collecting and analyzing real-time environmental information from different sources; means for generating real-time advice regarding agricultural tasks based on the analyzed environmental information; and means for recording the entire chain from agricultural production to consumption and implementing block chain technology to ensure clarity and reliability.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] Agriculture is an important industry that forms the basis of food production around the world, but in recent years, environmental impact and sustainability have become issues. In particular, there is a demand for the adoption of ecological agricultural practices, but it is currently difficult to efficiently learn such knowledge and methods and apply them to actual farming. Furthermore, consumers are unable to fully verify the methods used to produce the agricultural products they purchase, which creates problems of transparency and reliability. This invention aims to solve these problems and improve the efficiency and transparency of the entire agricultural production process. [Means for solving the problem]

[0005] This invention includes a means for collecting and analyzing information on sustainable agricultural practices from public data sources using a generative AI model and providing it to agricultural producers in an easy-to-understand format. It also includes a means for collecting and analyzing real-time environmental data from various sources and providing real-time advice on agricultural work based on that data. Furthermore, it provides a means for transparently and reliably recording and managing the entire process from agricultural production to consumption using blockchain technology, allowing consumers to check detailed information via QR codes, etc. It also includes providing a marketplace where agricultural producers can network with other producers and share their experiences and best practices. This allows agricultural producers to learn about and efficiently implement the latest ecological agricultural practices, and consumers can choose agricultural products with confidence.

[0006] A "generative AI model" is an artificial intelligence that automatically extracts specific patterns and information from available data and provides predictions and advice based on the results.

[0007] A "data source" is the place or medium from which information is collected, including online research papers, blogs, forums, etc.

[0008] "Ecological agricultural practices" is a general term for methods and techniques for agricultural production that minimize the burden on the environment.

[0009] "Easy-to-understand format" refers to a format that allows the content to be easily grasped even without specialized knowledge.

[0010] "Real-time environmental data" refers to data collected without time delay, such as current weather conditions or soil conditions.

[0011] "Agricultural work advice" refers to guidelines and recommendations for agricultural producers to carry out optimal agricultural work based on collected and analyzed data.

[0012] "Blockchain technology" is a technology that records data on a distributed ledger and prevents tampering in multiple locations.

[0013] A "marketplace" is an online platform where multiple agricultural producers can come together to exchange and share information.

[0014] "Transparency" refers to a state in which operations and data are visible to anyone.

[0015] "Reliability" refers to the property of information or systems being accurate and functioning reliably.

[0016] A "QR code" is a type of two-dimensional barcode that allows information to be easily encoded and read using a dedicated reader. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The invention is a system that integrates generative AI models, environmental monitoring, and blockchain technology to help farmers learn and implement sustainable ecological agricultural practices, while providing consumers with transparency and trust from production to consumption.

[0039] Server Processing

[0040] 1. Information Collection:

[0041] The server launches a crawler to collect information about sustainable agricultural practices from research papers, blogs, forums, etc. on the internet. The collected data is then analyzed using natural language processing (NLP) models to extract relevant information. This information is then stored in a database that can be later accessed by farmers.

[0042] 2. Environmental Data Collection and Analysis:

[0043] The server collects real-time environmental data from various sources, including weather data, soil data, and observation data. This data is analyzed by an AI model, and real-time advice is generated for farmers on optimal farming practices, enabling them to carry out farming work efficiently without missing a beat.

[0044] 3. Blockchain Management:

[0045] The server uses blockchain technology to record and manage all data from the production process to the consumption process. This prevents data tampering and ensures that all processes are managed transparently and reliably. Consumers can scan the QR code to check the production process of the food they purchased in detail.

[0046] Device operation

[0047] 1. Information display:

[0048] Using a device (app or browser), farmers can access the server and view information on ecological farming practices extracted by generative AI and NLP models, allowing them to incorporate the latest knowledge and apply it to their farming practices.

[0049] 2. Receiving Advice:

[0050] The device sends real-time advice to farmers from the server, which is displayed in an easy-to-understand format and suggests optimal farming methods based on weather and soil conditions.

[0051] User operations

[0052] 1. Login and Information Access:

[0053] Farmers can log in to the system and access their accounts to view the collected and analyzed information and advice, and can also use the marketplace feature to network with other farmers and share experiences and best practices.

[0054] 2. Consumer scans the QR code:

[0055] When consumers buy food in stores, they can scan the QR code to learn about the food's production process and whether it was grown sustainably, allowing them to make informed purchasing decisions based on product context.

[0056] Specific examples

[0057] For example, if an agricultural producer is growing cabbage, the server will collect environmental information in real time from satellite data, weather data, soil data, etc., and generate advice such as, "There will be a lot of rain this week, so irrigation is not necessary." This advice is then sent to the agricultural producer via a terminal. The cabbage's production process is also recorded on the blockchain, and consumers can scan a QR code at the store to confirm that "this cabbage was grown using ecological methods."

[0058] In this way, the system of the present invention aims to help farmers adopt efficient and sustainable agricultural practices and provide consumers with transparent and reliable information, thereby contributing to the maturation and expansion of the sustainable agriculture community.

[0059] The processing flow will be explained below.

[0060] Detailed process flowchart

[0061] (Server processing)

[0062] Step 1:

[0063] The server periodically launches a crawler to collect information about sustainable agricultural practices from research papers, blogs, forums, etc. on the Internet. For example, it retrieves articles from specific websites and saves their contents in text format.

[0064] Step 2:

[0065] The server passes the collected text data to a natural language processing (NLP) model to extract relevant information, including article summaries and key keywords, allowing for efficient extraction of necessary insights from large amounts of information.

[0066] Step 3:

[0067] The server then stores the extracted relevant information in a database, making it available for later access by farmers.

[0068] Step 4:

[0069] The server collects environmental data (e.g., weather data, soil data, observation data) in real time from different sources, including using APIs to retrieve weather information.

[0070] Step 5:

[0071] The server passes the collected environmental data to an AI model for analysis, which then generates optimal farming advice for farmers in real time. For example, it suggests the appropriate timing for irrigation based on temperature and humidity data.

[0072] Step 6:

[0073] The server will use blockchain technology to record the entire process from agricultural production to consumption, which will increase transparency and reliability and prevent data tampering.

[0074] Terminal Processing

[0075] Step 1:

[0076] The terminal connects to the database so that farmers can access the system and view the information provided, which displays extracted information about ecological farming practices.

[0077] Step 2:

[0078] The terminals then notify farmers of real-time farming advice received from the server, for example by displaying a message such as "There has been a lot of rain this week, so no irrigation is necessary."

[0079] User processing

[0080] Step 1:

[0081] Users (farmers) log in to the system, access their account and view information. Once successfully logged in, they can use the marketplace feature to network with other farmers and share experiences and best practices.

[0082] Step 2:

[0083] When purchasing food in a store, users (consumers) can scan the QR code to view information about the food's production process and whether it was grown using sustainable methods. For example, a message such as "This cabbage was grown using ecological methods" may be displayed.

[0084] Specific examples

[0085] For example, if an agricultural producer is growing cabbage, the server will collect environmental information in real time from satellite data, weather data, soil data, etc., and generate advice such as, "There will be a lot of rain this week, so irrigation is not necessary." This advice is then sent to the agricultural producer via their device. In addition, the cabbage production process is recorded on the blockchain, and consumers can scan a QR code at the store to confirm that "this cabbage was grown using ecological methods."

[0086] In this way, the system of the present invention assists farmers in adopting efficient and sustainable agricultural practices and provides transparent and reliable information to consumers.

[0087] Example 1

[0088] 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."

[0089] Farmers seeking to implement sustainable agricultural practices face challenges accessing reliable, up-to-date, and effective advice. Consumers demand transparency about the production process of their food, but there are a lack of means to achieve this. Furthermore, farmers lack effective platforms for sharing knowledge and experiences with other farmers.

[0090] 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.

[0091] In this invention, the server includes: means for collecting information on sustainable agricultural practices from publicly available data sources using a generative AI model; means for analyzing the collected information using a natural language processing model to extract relevant information and store it in a database; means for collecting real-time environmental data from different sources and analyzing it using an AI model; means for generating real-time advice on agricultural work based on the analyzed environmental data and notifying the terminal; and means for recording the entire process from agricultural production to consumption using blockchain technology to ensure transparency and reliability. This allows agricultural producers to receive reliable, up-to-date information and advice, and consumers to enjoy transparency into the food production process. It also provides a platform for agricultural producers to share their knowledge and experience with each other.

[0092] "Generative AI model" refers to artificial intelligence technology for automatically collecting and analyzing information about sustainable agricultural practices from publicly available data sources.

[0093] A "natural language processing model" is a computer program that analyzes collected text data and extracts highly relevant information.

[0094] A "database" is a digital storage system for organizing and storing collected information and analysis results.

[0095] "Real-time environmental data" refers to environmental information related to agriculture, such as current weather data and soil data.

[0096] An "AI model" is an artificial intelligence technology that analyzes collected data and generates optimal advice for agricultural producers.

[0097] "Terminal" refers to the device that agricultural producers use to view information and advice, such as a smartphone or computer.

[0098] "Blockchain technology" is a distributed digital ledger technology that prevents data tampering and ensures transparency and reliability.

[0099] A "QR code" is a two-dimensional barcode that consumers can scan with a smartphone or other device to access detailed information.

[0100] MODE FOR CARRYING OUT THE INVENTION

[0101] This invention is a system that integrates generative AI models, environmental monitoring, and blockchain technology, and is designed primarily to meet the needs of agricultural producers and consumers. Regarding the mode for implementing the invention, the roles of the server, terminal, and user are clearly defined, and specific examples are presented.

[0102] Server configuration and functions

[0103] 1. Information gathering

[0104] The server uses a Python crawler (e.g., Beautiful Soup library) to collect information about sustainable agricultural practices from research papers, blogs, forums, etc. on the Internet. The collected data is then analyzed using natural language processing (NLP) models (e.g., NLTK library) to extract relevant information. This information is then stored in a database (e.g., MongoDB) for later access by farmers.

[0105] 2. Environmental data collection and analysis

[0106] The server collects environmental data in real time from weather data APIs (e.g., OpenWeatherMap), sensor devices, soil data APIs, etc. This data is analyzed using AI models (e.g., TensorFlow and Scikit-learn), and based on the analysis results, real-time advice on optimal farming practices is generated for agricultural producers.

[0107] 3. Implementation of Blockchain Technology

[0108] The server uses blockchain technology such as Hyperledger Fabric and Ethereum to record and manage all data from the production process to the consumption process, preventing data tampering and ensuring that all processes are managed transparently and reliably.

[0109] Device configuration and functions

[0110] 1. Information display

[0111] Farmers access the server using a device (app or browser) and can view information about ecological farming practices extracted by generative AI and NLP models through a mobile app built with React Native.

[0112] 2. Receiving Advice

[0113] The device sends real-time advice from the server to farmers via push notifications and emails. Notifications are sent using Firebase Cloud Messaging (FCM), and messages such as "There is a lot of rain this week, so there is no need to irrigate" are displayed.

[0114] User operations

[0115] 1. Login and Information Access

[0116] Agricultural producers log in to the system using an authentication system that uses OAuth 2.0. After logging in, they can not only view the collected and analyzed information and advice on a dashboard, but also use the marketplace function to network with other producers and share best practices.

[0117] 2. QR Code Scan

[0118] Consumers can check the production process and whether the food was grown sustainably by scanning the QR code on the food they purchased with a smartphone QR code reader (e.g., ZXing library). The scan results will display detailed information retrieved from the blockchain.

[0119] Specific examples

[0120] For example, if a farmer is growing cabbage, the server collects satellite, weather, and soil data in real time and generates advice such as, "There will be a lot of rain this week, so irrigation is not necessary." This advice is then sent to the farmer's smartphone via Firebase Cloud Messaging. The cabbage's production process is also recorded on the Ethereum blockchain, and consumers can scan the code at the store using a ZXing QR code scanner to confirm that the cabbage was grown using ecological methods.

[0121] Prompt Sentence Examples

[0122] "Please tell me how to grow cabbage ecologically."

[0123] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0124] Server Processing

[0125] Step 1: Gather information

[0126] Specific operation: The server launches the crawler using Python's Beautiful Soup library.

[0127] Input: A list of URLs for research papers, blogs, and forums on the internet.

[0128] Data processing / calculation: The crawler parses the HTML content and extracts text data, which is then analyzed using a natural language processing model (e.g., NLTK) to extract relevant information.

[0129] Output: The extracted information is stored in a database (e.g. MongoDB).

[0130] Step 2: Environmental data collection

[0131] Specific operation: The server collects real-time data from weather data APIs (e.g., OpenWeatherMap), soil data APIs, and sensor devices.

[0132] Input: API endpoints for weather, soil, and sensor data.

[0133] Data processing / calculation: Analyze the JSON data obtained from the API and extract the necessary information. Data from the soil sensor is sent directly to the server.

[0134] Output: The visualized environmental data is stored in a database.

[0135] Step 3: Data analysis

[0136] Specific operation: Analyze the collected data using an AI model (e.g., TensorFlow or Scikit-learn).

[0137] Input: Stored environmental and intelligence gathering data.

[0138] Data processing / calculation: AI models analyze data based on criteria and detect specific patterns or anomalies.

[0139] Output: The analysis results are saved in a database and used in the next step.

[0140] Step 4: Real-time advice generation

[0141] How it works: The server uses AI models to generate advice based on real-time environmental data.

[0142] Input: Analysis results and current environmental data.

[0143] Data processing / computation: AI models generate advice based on environmental conditions and format it in an easy-to-understand way.

[0144] Output: Advice is sent to the terminal.

[0145] Step 5: Record on the blockchain

[0146] Specific operation: The server uses Hyperledger Fabric or Ethereum to record data on the blockchain.

[0147] Input: All data from the production process to the consumption process.

[0148] Data processing / calculation: Smart contracts are used to record data as transactions, preventing tampering.

[0149] Output: The data recorded on the blockchain is visible to consumers.

[0150] Device operation

[0151] Step 1: Display information

[0152] How it works: Farmers access the server using a mobile app built with React Native.

[0153] Input: Information stored in a database.

[0154] Data processing / calculation: The app retrieves information and displays it in list or dashboard format.

[0155] Output: Agricultural producers can view relevant information on their terminals.

[0156] Step 2: Advice Notification

[0157] Specific operation: The advice is sent to the device using Firebase Cloud Messaging (FCM).

[0158] Input: Server-generated advice data.

[0159] Data processing / calculation: Notification messages are formatted in an easy-to-understand way.

[0160] Output: A notification will be displayed on the farmer's device.

[0161] User operations

[0162] Step 1: Log in and access your information

[0163] How it works: Farmers log in to the system using OAuth 2.0.

[0164] Input: User credentials (email address, password).

[0165] Data processing / computation: Credentials are verified and the user's session begins.

[0166] Output: A dashboard is accessible, allowing you to view the collected and analyzed information.

[0167] Step 2: Scan the QR code

[0168] How it works: Consumers scan the QR code in the store to check the production process.

[0169] Input: QR code attached to the product.

[0170] Data processing / calculation: Scan the code with a QR code reader (e.g., ZXing library) and access the blockchain to retrieve related information.

[0171] Output: Information about the product's production process and sustainable practices is displayed on the consumer's device.

[0172] (Application example 1)

[0173] 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."

[0174] In traditional agricultural systems, implementing sustainable agricultural practices and ensuring transparency have been challenging. In particular, there is a lack of information and real-time environmental advice to help agricultural producers learn and practice ecological methods. Furthermore, there are limited ways for consumers to check detailed information about the production process of the agricultural products they purchase in physical stores. This has made it difficult to gain consumer trust.

[0175] 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.

[0176] In this invention, the server includes: means for collecting information on sustainable agricultural practices from publicly available data sources using a generative AI model; means for analyzing the collected information and providing it in an easy-to-understand format for agricultural producers; means for collecting and analyzing real-time environmental data from different sources; means for generating real-time advice on agricultural work based on the analyzed environmental data; means for implementing distributed ledger technology to record the entire process from agricultural production to consumption and ensure transparency and trust; and means for consumers to scan an identification code attached to a product in a physical store and display detailed production information of the product, the agricultural method used, and environmental data. This makes it easier for agricultural producers to adopt effective and sustainable agricultural practices, and allows consumers to easily check the background information of the products they purchase, thereby improving trust and transparency.

[0177] A "generative AI model" is an artificial intelligence model that learns specific patterns and rules from data and provides generative answers to new data.

[0178] "Sustainable agricultural practices" are agricultural methods that are economically sustainable and socially acceptable while minimizing their impact on the environment.

[0179] "Distributed ledger technology" is a database technology that is shared and managed by multiple participants in a network and is difficult to tamper with, and blockchain is a prime example of this.

[0180] "Real-time environmental data" refers to data that instantly collects and updates current environmental conditions such as weather, soil, and temperature.

[0181] An "identification code" is a symbol or code used to uniquely identify a product or information, and examples of this include barcodes and QR codes.

[0182] An "electronic market" is a virtual market where goods and services are bought and sold through digital platforms such as the Internet.

[0183] "Visual device" means a device that provides a visual interface to a user, including head-mounted displays and smart glasses.

[0184] MODE FOR CARRYING OUT THE INVENTION

[0185] System Program

[0186] In the system of the present invention, the server includes the following means:

[0187] 1. Data collection methods for generative AI models:

[0188] The server uses a generative AI model to collect information about sustainable agricultural practices from multiple publicly available data sources, such as research papers, blogs, and forums, and analyzes it using a natural language processing (NLP) model, such as BERT or GPT-3.

[0189] 2. Information analysis and provision method:

[0190] The collected information is analyzed using generative AI and NLP models and presented in an easy-to-understand format for farmers. This information is stored in a database on a server and can be accessed by farmers via a browser or app.

[0191] 3. Real-time environmental data collection methods:

[0192] The server collects real-time environmental data, such as weather data and soil data, from various sensors and external data sources. For example, weather data can be obtained from the Internet using an API.

[0193] 4. Real-time advice generation methods:

[0194] Based on the collected and analyzed environmental data, generative AI models are used to generate real-time advice on farming practices, enabling farmers to carry out optimal practices at the right time.

[0195] 5. Distributed Ledger Technology Implementation Methods:

[0196] The server uses blockchain technology to record the entire process from agricultural production to consumption. This prevents data tampering and ensures that all processes are managed transparently and reliably. Consumers can scan the identification code to check the details of the food's production process.

[0197] Examples of specific examples and prompts

[0198] For example, when a consumer buys organic tomatoes at a supermarket, they scan an identification code using this system, and the server provides information such as, "The tomatoes were grown using sustainable agricultural practices. Organic fertilizer was used on the farm. Environmental data used includes soil moisture, sunshine hours, and temperature data." This information is recorded and managed using generative AI models and distributed ledger technology.

[0199] The following are examples of specific prompt sentences:

[0200] Please provide us with the latest information on sustainable agricultural practices. Specifically, please share details about your organic fertilizer use and insights into sustainable irrigation techniques.

[0201] explanation

[0202] 1. Hardware and Software:

[0203] The system includes a server, weather sensors, soil sensors, an identification code reader (e.g., a QR code reader), and a browser or smartphone app.

[0204] The server will be equipped with a generative AI model (e.g., GPT-3), an NLP model (e.g., BERT), a database (e.g., MySQL), and blockchain technology (e.g., Ethereum).

[0205] 2. Data processing and calculation:

[0206] Data collected by the server is used by an NLP model to extract relevant information, which is then converted by a generative AI model into optimal advice that is provided to farmers.

[0207] Environmental data collected in real time is analyzed and a generative AI model generates real-time advice on farming practices.

[0208] All data is managed using distributed ledger technology, and consumers can scan an identification code to obtain detailed information about the production process.

[0209] In this way, the invention is a system that makes it easier for farmers to adopt sustainable practices and provides transparent and reliable information to consumers.

[0210] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0211] Flow of the system program that realizes the application example

[0212] Step 1:

[0213] The server uses a generative AI model to collect information on sustainable agricultural practices from multiple publicly available data sources (e.g., research papers, blogs, forums, etc.). The server then launches a crawler to collect text data from the specified data source. The data source URL is required as input, and the collected text data is obtained as output.

[0214] Step 2:

[0215] The server then passes the collected text data to a natural language processing (NLP) model for analysis. The NLP model (e.g., BERT) extracts relevant information about sustainable agricultural practices from the collected text data. The input is the text data, and the output is the extracted relevant information.

[0216] Step 3:

[0217] The server stores the analyzed information in a database. Using a generative AI model, the information is organized and stored in a format that is easy for farmers to understand. The analyzed information is required as input, and the information stored in the database is obtained as output.

[0218] Step 4:

[0219] The server collects real-time environmental data from different sources, for example, weather data and soil data from APIs and sensors. It requires API keys and sensor data as inputs and provides the collected environmental data as output.

[0220] Step 5:

[0221] The server passes the collected environmental data to a generative AI model for analysis and generates real-time advice on farming. Environmental data is required as input, and real-time advice is obtained as output. The generated advice is sent to the terminal.

[0222] Step 6:

[0223] Using a device (smartphone or browser), farmers access the server and view the collected and analyzed information and real-time advice on sustainable agricultural practices. Data from the server is required as input, and displayed information is obtained as output.

[0224] Step 7:

[0225] The server records the entire process from agricultural production to consumption using blockchain technology. For example, using blockchain technology (e.g., Ethereum), each step of agricultural production is recorded as a transaction. Data for each step is required as input, and transaction data recorded on the blockchain is obtained as output.

[0226] Step 8:

[0227] Consumers scan the identification code at a physical store to obtain detailed production information about the product. By scanning the identification code (e.g., QR code) using a terminal, production process information is obtained from the server. The identification code is required as input, and detailed production information about the product is obtained as output.

[0228] At each step of the process, farmers are supported in implementing sustainable agricultural practices, and consumers can be assured of a transparent and reliable source of the products they purchase.

[0229] 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.

[0230] The invention is a system that integrates generative AI models, environmental monitoring, blockchain technology, and an emotion engine to help farmers learn and implement sustainable ecological agricultural practices, while providing consumers with transparency and trust from production to consumption.

[0231] Server Processing

[0232] 1. Information Collection:

[0233] The server launches a crawler to collect information about sustainable agricultural practices from research papers, blogs, forums, etc. on the internet. The collected data is then analyzed using natural language processing (NLP) models to extract relevant information. This information is then stored in a database that can be later accessed by farmers.

[0234] 2. Environmental Data Collection and Analysis:

[0235] The server collects real-time environmental data from different sources, such as weather data, soil data, and observation data. This data is analyzed by an AI model, and optimal farming advice is generated in real time for farmers.

[0236] 3. Emotion engine combination:

[0237] The server also uses an emotion engine to recognize the emotions of farmers and consumers and incorporates that emotional data into its analysis. For example, if it senses that a farmer is under high stress, it can adjust the content and format of advice and provide resources to reduce stress. It also analyzes consumers' purchasing intentions and optimizes information accordingly.

[0238] 4. Blockchain Management:

[0239] The server uses blockchain technology to record and manage all data from the production process to the consumption process. This prevents data tampering and ensures that all processes are managed transparently and reliably. Consumers can scan the QR code to check the production process of the food they purchased in detail.

[0240] Device operation

[0241] 1. Information display:

[0242] Using a device (app or browser), farmers can access the server and view information on ecological farming practices extracted by generative AI and NLP models. Based on emotional data recognized by the emotion engine, information and resources are provided to reduce stress and increase motivation, if needed.

[0243] 2. Receiving Advice:

[0244] The device then sends real-time farming advice to farmers from the server, displaying messages such as "There's been a lot of rain this week, so no irrigation is necessary."

[0245] User operations

[0246] 1. Login and Information Access:

[0247] Farmers can log in to the system, access their accounts, and view information. They can also use the marketplace function to network with other farmers and share experiences and best practices. The emotion engine monitors the farmer's emotional state and provides support information as needed.

[0248] 2. Consumer scans the QR code:

[0249] When consumers buy food in stores, they can scan the QR code to see the food's production history, and the emotion engine can analyze their reactions and provide additional information or promotions based on their purchasing intent.

[0250] Specific examples

[0251] For example, in the case of a cabbage farmer, the server collects environmental information in real time from satellite data, weather data, soil data, etc., and generates advice such as, "There will be a lot of rain this week, so there is no need for irrigation." This advice is notified to the farmer via his device. Furthermore, if the server uses its emotion engine to determine that the farmer's stress level is high, it will also provide a link to a video on how to relax.

[0252] The cabbage's production process is also recorded on the blockchain, and consumers can scan a QR code at the store to confirm that the cabbage was grown using ecological methods. Consumer responses are analyzed by an emotion engine, and interested consumers are provided with further promotional information.

[0253] In this way, the system of the present invention helps farmers adopt efficient and sustainable agricultural practices, provides transparent and reliable information to consumers, and, with the introduction of an emotion engine, enables more personalized assistance and information delivery, improving the user experience.

[0254] The processing flow will be explained below.

[0255] Detailed process flowchart

[0256] (Server processing)

[0257] Step 1:

[0258] The server periodically launches a crawler to collect information about sustainable agricultural practices from online research papers, blogs, forums, etc. For example, it scans multiple websites to retrieve articles and saves their contents in text format.

[0259] Step 2:

[0260] The server passes the collected text data through natural language processing (NLP) models to extract relevant information, including summarizing the text and extracting key keywords, such as summarizing the latest research findings on sustainable agricultural practices.

[0261] Step 3:

[0262] The server stores the extracted relevant information in a database, which allows farmers to access the information later. For example, the database may store summaries of each source.

[0263] Step 4:

[0264] The server collects real-time environmental data from different sources, such as weather data, soil data, and observation data. For example, it obtains information such as current temperature, precipitation, and humidity from a weather API.

[0265] Step 5:

[0266] The server passes the collected environmental data to an AI model for analysis. This allows the AI ​​model to generate optimal farming advice for farmers in real time. For example, based on current weather conditions and soil conditions, the model might generate advice such as "There will be a lot of rain this week, so irrigation is not necessary."

[0267] Step 6:

[0268] The server uses an emotion engine to recognize the emotions of farmers and consumers and incorporates that emotion data into its analysis. For example, if the server senses that a farmer is under high stress, it will adjust the content and format of advice and provide resources to reduce stress.

[0269] Step 7:

[0270] The server uses blockchain technology to record all data from the production process to the consumption process. This prevents data tampering and ensures that all processes are managed transparently and reliably. For example, the production history of agricultural products can be recorded on the blockchain.

[0271] Terminal Processing

[0272] Step 1:

[0273] The terminals connect to the database so that farmers can access the system and view the information provided. This displays information about ecological farming practices extracted by generative AI and NLP models. For example, farmers can view a summary of the latest sustainable farming methods on their terminals.

[0274] Step 2:

[0275] The terminals then notify farmers of real-time farming advice received from the server, for example displaying a message saying, "There's been a lot of rain this week, so no irrigation is necessary."

[0276] Step 3:

[0277] The device displays additional information based on the farmer's emotions as recognized by the emotion engine, for example providing a video link on relaxation techniques to a farmer who is under a lot of stress.

[0278] User processing

[0279] Step 1:

[0280] Users (farmers) log in to the system, access their account, and view information. Once successfully logged in, they can use the marketplace function to network with other farmers and share experiences and best practices. For example, farmers can chat with other farmers and exchange cultivation tips.

[0281] Step 2:

[0282] When purchasing food in a store, users (consumers) can scan the QR code to view information about the food's production process and whether it was grown using sustainable methods. For example, a message such as "This cabbage was grown using ecological methods" may be displayed.

[0283] Step 3:

[0284] The emotion engine analyzes the user's (consumer's) emotions and provides additional information and promotions according to their purchasing intent. For example, consumers who show interest will be shown promotional information about similar sustainable agricultural products.

[0285] Specific examples

[0286] For example, in the case of a cabbage farmer, the server collects environmental information in real time from satellite data, weather data, soil data, etc., and generates advice such as, "There will be a lot of rain this week, so there is no need for irrigation." This advice is notified to the farmer via his device. If the server uses its emotion engine to determine that the farmer's stress level is high, it will also provide a link to a video on how to relax.

[0287] Furthermore, the cabbage's production process is recorded on the blockchain, and consumers can scan a QR code at the store to confirm that the cabbage was grown using ecological methods. Consumer responses are analyzed by an emotion engine, and interested consumers are provided with further promotional information.

[0288] In this way, the system of the present invention helps farmers adopt efficient and sustainable agricultural practices, provides transparent and reliable information to consumers, and, with the introduction of an emotion engine, enables more personalized assistance and information delivery, improving the user experience.

[0289] Example 2

[0290] 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."

[0291] Modern agriculture faces many challenges in terms of profitability and sustainability. Farmers have difficulty efficiently gathering information on the latest sustainable agricultural practices and applying them to their daily operations. They are also required to understand the impact of climate change and environmental factors in real time and take optimal action based on that information. Furthermore, it is important to manage the process from production to consumption in a transparent and reliable manner, which requires the introduction of distributed ledger technology. On the consumer side, ensuring transparency in the production process and providing an environment in which they can purchase with confidence are challenges.

[0292] 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.

[0293] In this invention, the server includes: means for collecting information on sustainable agricultural practices from publicly available data sources using a generative AI model; means for analyzing the collected information and providing it in an easy-to-understand format for agricultural producers; means for collecting and analyzing real-time environmental data from various sources; means for generating real-time advice on agricultural work based on the analyzed environmental data; means for analyzing the emotions of agricultural producers and consumers using an emotion engine and providing personalized support information and purchasing promotion information; and means for implementing distributed ledger technology to record the entire process from agricultural production to consumption and ensure transparency and reliability. This enables agricultural producers to efficiently learn and practice the latest sustainable agricultural practices and to carry out optimal agricultural work based on real-time environmental data. Furthermore, the process from production to consumption can be managed in a transparent and reliable manner, providing consumers with a safe and secure purchasing environment.

[0294] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze large amounts of data and generate information for specific purposes.

[0295] "Publicly available data sources" are sources such as papers, blogs, forums, etc. that are freely accessible on the internet.

[0296] "Sustainable agricultural practices" refer to methods and initiatives for long-term agricultural production while preserving the environment.

[0297] "Real-time environmental data" refers to environmental information that is updated at each point in time, such as weather data and soil data.

[0298] The "emotion engine" is a system that analyzes the user's emotional state and suggests appropriate measures based on the results.

[0299] "Personalized support information" refers to information or advice that is individualized and provided according to the user's emotions and circumstances.

[0300] "Purchase promotion information" refers to promotional and sales information provided to increase consumers' desire to purchase.

[0301] "Distributed ledger technology" is a technology that maintains high transparency and reliability by distributing and recording and managing data across multiple nodes.

[0302] "Networking" refers to agricultural producers exchanging information and cooperating with other producers.

[0303] A "market" is a platform or venue where agricultural producers can come together to exchange information, share experiences and best practices.

[0304] The invention is a system that integrates generative AI models, environmental monitoring, distributed ledger technology, and an emotion engine to help farmers learn and implement sustainable agricultural practices, while providing consumers with transparency and trust from production to consumption.

[0305] Server Processing

[0306] The server first launches a crawler to collect information about sustainable agricultural practices from research papers, blogs, forums, and other sources on the Internet. For example, it uses the keyword "sustainable farming" to gather related information. The acquired data is then analyzed using a natural language processing (NLP) model to extract relevant information. This information is then stored in a database and made accessible to farmers.

[0307] The server then collects real-time environmental data from different sources, such as weather data, soil data, and observation data. This data is analyzed by an AI model to generate optimal farming advice for farmers. For example, real-time advice such as "There will be a lot of rain this week, so irrigation is not necessary" is generated.

[0308] The server also uses an emotion engine to analyze the emotions of agricultural producers and consumers. Based on the results of the emotion analysis, it provides agricultural producers with resources to reduce stress and consumers with purchasing promotion information. For example, if it determines that "Producer A is in a high stress state," it provides a link to a "video on relaxation methods."

[0309] Finally, the server uses distributed ledger technology to record and manage all data from the production process to the consumption process, preventing data tampering and ensuring that all processes are managed transparently and reliably. Consumers can scan the QR code to check the production process of the food they purchased in detail.

[0310] Device operation

[0311] The device (app or browser) accesses the server and displays information about ecological farming practices extracted by generative AI and NLP models. Based on the emotional data recognized by the emotion engine, information and resources are provided to reduce stress and increase motivation, if needed.

[0312] The device also notifies farmers of real-time farming advice sent from the server, such as "There's been a lot of rain this week, so no irrigation is necessary."

[0313] User operations

[0314] Farmers log in to the system, access their accounts, and view information. They can then use the marketplace function to network with other farmers and share experiences and best practices. The emotion engine monitors the farmer's emotional state and provides support information as needed.

[0315] When consumers buy food in stores, they can scan the QR code to see the food's production history, and the emotion engine can analyze their reactions and provide additional information or promotions based on their purchasing intent.

[0316] Specific examples

[0317] For example, in the case of a cabbage farmer, the server collects environmental information in real time from satellite data, weather data, soil data, etc., and generates advice such as, "There will be a lot of rain this week, so irrigation is not necessary." This advice is then sent to the farmer via their device. Furthermore, if the server uses an emotion engine to determine that the farmer's stress level is high, it will also provide a link to a video on how to relax. The cabbage production process is also recorded on distributed ledger technology, and by scanning a QR code at the store, consumers can confirm that the cabbage was grown using ecological methods. The emotion engine analyzes consumer responses, and interested consumers are provided with further promotional information.

[0318] Prompt Sentence Examples

[0319] Examples of prompts to input to a generative AI model include:

[0320] "Collect and summarize the latest research papers on sustainable agricultural practices."

[0321] "Based on current weather data, what is the best way to grow cabbage?"

[0322] "Generate relaxation advice for farmers in high-stress situations."

[0323] "Please explain how distributed ledger technology can be used to manage the production process of the food purchased by consumers."

[0324] The foregoing is a detailed description of the present invention, which allows both farmers and consumers to conveniently and efficiently implement sustainable agricultural practices and access transparent and reliable information.

[0325] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0326] Step 1: Gather information

[0327] Specific operation: The server launches a crawler to collect information about sustainable agricultural practices from research papers, blogs, forums, etc. on the Internet. For example, it uses the keyword "sustainable farming" to collect related information.

[0328] Input: A list of URLs on the Internet.

[0329] Data processing and calculation: The crawler accesses each URL, obtains HTML data, and extracts it as text.

[0330] Output: Collected text data.

[0331] Step 2: Data analysis

[0332] Specific operation: The server sends the collected data to a natural language processing (NLP) model to summarize the sentences and extract important keywords.

[0333] Input: Collected text data.

[0334] Data processing and calculation: NLP models analyze text data, extract relevant information, and generate summaries.

[0335] Output: Summarized information and extracted keywords.

[0336] Step 3: Save to database

[0337] What it does: The server stores the analyzed information in a database, where farmers can access it later.

[0338] Input: Abstracted information and extracted keywords.

[0339] Data processing and calculation: Organizing and storing information in a database.

[0340] Output: The saved information is stored in a database.

[0341] Step 4: Environmental data collection

[0342] Specific operation: The server accesses weather data provision services and soil data provision services to obtain environmental data in real time.

[0343] Input: API for weather data and soil data provision services.

[0344] Data processing and calculation: Obtain data from the API and compile it into an integrated dataset.

[0345] Output: A consolidated environmental dataset.

[0346] Step 5: Environmental data analysis

[0347] Specific operation: The server sends the integrated data to the AI ​​model and generates optimal farming advice.

[0348] Input: Integrated environmental dataset.

[0349] Data processing and calculation: The AI ​​model analyzes the data and generates specific farming advice.

[0350] Output: The generated farming advice.

[0351] Step 6: Collect emotional data

[0352] Specific operation: The server collects app usage data and feedback from farmers and consumers and sends it to the emotion engine.

[0353] Input: App usage data, feedback data.

[0354] Data processing and calculation: The emotion engine analyzes the data and determines the emotional state.

[0355] Output: Action instructions based on emotional state.

[0356] Step 7: Sentiment Data Analysis and Action

[0357] Specific operation: The server determines the appropriate action based on the results of the sentiment analysis.

[0358] Input: Action instructions based on emotional state.

[0359] Data processing and calculation: Provide producers with resources to reduce stress and optimize information to promote purchases for consumers.

[0360] Output: Personalized support and promotional information.

[0361] Step 8: Blockchain Management

[0362] Specific operation: The server records and manages all data from the production process to the consumption process using distributed ledger technology.

[0363] Input: Data from the production and consumption processes.

[0364] Data processing and calculation: Blockchain technology is used to record data one by one into blocks and verify transactions.

[0365] Output: Recorded data and generated QR code.

[0366] These are the specific processing steps of this system, which will enable farmers to efficiently learn and implement sustainable agricultural practices, while also providing consumers with transparency and reliability in the production process.

[0367] (Application example 2)

[0368] 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."

[0369] Today's agricultural producers face difficulties in easily obtaining information on sustainable agricultural practices and adopting highly efficient and ecological methods. Furthermore, there is a lack of systems that provide consumers with transparency and reliability in the production process while providing personalized purchasing support. In particular, there is a need for advanced personalization that reflects environmental data and production information in real time and utilizes sentiment analysis.

[0370] 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.

[0371] In this invention, the server includes: means for collecting information on sustainable agricultural practices from publicly available data sources using a generative AI model; means for analyzing the collected information and providing it in an easy-to-understand format for agricultural producers; means for collecting and analyzing real-time environmental data from various sources; means for generating real-time advice on agricultural work based on the analyzed environmental data; means for recording the entire process from agricultural production to consumption and implementing blockchain technology to ensure transparency and reliability; means for recognizing the emotions of agricultural producers and consumers using a sentiment analysis engine and providing information based on stress reduction and purchasing motivation; and means for showing consumers the production process of agricultural products using QR codes in physical stores and promoting the purchase of ecological products. This enables agricultural producers to learn and practice efficient and sustainable agricultural practices, provides consumers with transparency and reliability from production to consumption, and realizes personalized purchasing support using sentiment analysis.

[0372] A "generative AI model" is a type of artificial intelligence used to collect information from publicly available data sources, analyze that information, and generate it.

[0373] "Real-time environmental data" refers to data used to collect and analyze agriculturally relevant environmental information in real time, such as weather data and soil data.

[0374] "Blockchain technology" is a distributed database used to prevent data tampering and ensure transparency and reliability.

[0375] An "emotion analysis engine" is an algorithm or system that recognizes and analyzes a user's emotions and provides information and advice based on those emotions.

[0376] A "QR code" is a type of two-dimensional barcode that allows information to be easily read.

[0377] "Ecological products" are products that are produced in a sustainable manner with minimal impact on the environment.

[0378] "Agricultural practices" is a general term that refers to the methods, techniques, and procedures used in the agricultural sector.

[0379] "Real-time advice" refers to advice or instructions provided immediately based on data collected in real time.

[0380] "Transparency" refers to a state in which systems and data are made public and anyone can check their contents.

[0381] "Reliability" is a term used to describe a situation in which a system or data is accurate, error-free, and trustworthy.

[0382] "Personalized purchasing support" refers to providing purchasing suggestions and support that are customized to suit each individual consumer's preferences and circumstances.

[0383] To implement this invention, the system is configured as follows: The server uses a generative AI model to collect information on sustainable agricultural practices from publicly available data sources, analyzes the information, and provides it to agricultural producers. The server also collects and analyzes real-time environmental data from multiple sources, and generates and notifies agricultural producers of real-time advice based on the results.

[0384] Furthermore, the server uses blockchain technology to record the entire process from agricultural production to consumption, ensuring transparency and reliability. It uses a sentiment analysis engine to recognize the emotions of agricultural producers and consumers, and provides personalized information based on their stress levels and purchasing intentions. Specifically, QR codes can be used in physical stores to show consumers the production process of agricultural products, encouraging them to purchase eco-friendly products.

[0385] Program Description

[0386] The server system is built using Python and Django, and the front end is configured using React Native. The generative AI model is implemented using TensorFlow, and support for environmental data and sentiment analysis is provided using Google Cloud Natural Language API and OpenCV. PostgreSQL is used as the database, and blockchain technology is implemented using Hyperledger Fabric.

[0387] Specific examples

[0388] Information collection and display:

[0389] 1. The server uses a crawler to collect public information about agriculture from the Internet and analyzes it using an NLP (natural language processing) model.

[0390] 2. The analysis results are stored in a database, and farmers can access this information via a smartphone app.

[0391] Real-time advice:

[0392] 1. The server collects and analyzes environmental data in real time, such as weather data and soil data.

[0393] 2. Based on the analysis results, a message is sent to agricultural producers, for example, "There will be a lot of rain this week, so there is no need for irrigation."

[0394] Emotion analysis:

[0395] 1. When a consumer scans a QR code in a physical store, the server retrieves production process data from the blockchain.

[0396] 2. By analyzing the consumer's facial expressions and voice, we can recognize their stress and purchasing intent and provide them with appropriate purchasing information.

[0397] For example: "These cabbages were grown using ecological methods. They're a more environmentally friendly product. Are you interested?"

[0398] Prompt Sentence Examples

[0399] This cabbage was grown ecologically. Is it produced using sustainable farming methods?

[0400] Yes → The generative AI model will suggest ecological products in the same category.

[0401] No → Get information about other cabbages.

[0402]

[0403] I scanned your face and it looks like you're a little stressed. Would you like some tips on how to relax?

[0404] Yes → Provides videos on how to relax.

[0405] No → Proceed to the next step.

[0406] This will enable farmers to learn and implement efficient and sustainable agricultural practices, provide consumers with transparency and trust from production to consumption, and enable personalized purchasing support using sentiment analysis.

[0407] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0408] Step 1:

[0409] Information collection and analysis

[0410] The server uses a crawler to collect information about sustainable agricultural practices from research papers, blogs, forums, etc. on the Internet. The collected data is then analyzed using natural language processing (NLP) models to extract relevant information.

[0411] Input: Public information on the internet, such as research papers, blogs, forums, etc.

[0412] Data processing: The crawler collects information and analyzes it using NLP models (e.g., text summarization, keyword extraction).

[0413] Output: Information about the analyzed sustainable agricultural practices.

[0414] How it works: The crawler collects public information from the web and performs text analysis using a Python NLP library (e.g., SpaCy).

[0415] Step 2:

[0416] Environmental data collection and analysis

[0417] The server collects environmental data in real time from multiple sources, including weather data, soil data, and observation data, and analyzes it using an AI model.

[0418] Input: Environmental information such as weather data, soil data, and observation data.

[0419] Data processing: Collecting data in real time and analyzing it with AI models (e.g., predicting future weather conditions using predictive models).

[0420] Output: Analyzed environmental data and farming advice derived from the results.

[0421] Specific operation: Collects weather and soil data through APIs and runs analytical models using TensorFlow.

[0422] Step 3:

[0423] Real-time advice generation and notification

[0424] Based on the results of analyzing the environmental data, the server generates real-time farming advice for agricultural producers and notifies them to the terminal.

[0425] Input: Parsed environmental data.

[0426] Data calculations: Based on the analysis results, the AI ​​model generates appropriate advice (e.g., the need for irrigation based on weather conditions).

[0427] Output: Specific farming advice communicated to farmers.

[0428] Specific operation: The analysis results are stored in a database and a push notification is sent to the farmer's device (e.g., smartphone).

[0429] Step 4:

[0430] Sentiment analysis and information provision

[0431] The server uses an emotion analysis engine to recognize the emotions of users (producers and consumers) and provides personalized information and promotions based on stress reduction and purchasing intentions.

[0432] Input: User's facial and voice data.

[0433] Data calculation: Recognize user emotions using a sentiment analysis engine (e.g., OpenCV or Google Cloud Natural Language API) and generate appropriate information based on that data.

[0434] Output: Personalized information and promotions.

[0435] Specific operation: The app uses the smartphone's camera and microphone to capture the user's facial expressions and voice, and displays recommended information and promotions based on the analysis results.

[0436] Step 5:

[0437] Data management using blockchain

[0438] The server uses blockchain technology to record and manage the entire process from agricultural production to consumption, and consumers can access the data in physical stores using a QR code.

[0439] Input: Data on production and distribution processes.

[0440] Data computation: Blockchain technology is used to ensure data integrity and tamper-proofing.

[0441] Output: Transparent production process information displayed by consumers by scanning a QR code.

[0442] How it works: Hyperledger Fabric is used to manage the blockchain network and write and read data. When a consumer scans a QR code with a smartphone app, the server retrieves the corresponding data from the blockchain and displays it.

[0443] 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.

[0444] 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.

[0445] 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.

[0446] [Second embodiment]

[0447] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0448] 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.

[0449] 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).

[0450] 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.

[0451] 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.

[0452] 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).

[0453] 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.

[0454] 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.

[0455] 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.

[0456] 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.

[0457] 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.

[0458] 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."

[0459] The invention is a system that integrates generative AI models, environmental monitoring, and blockchain technology to help farmers learn and implement sustainable ecological agricultural practices, while providing consumers with transparency and trust from production to consumption.

[0460] Server Processing

[0461] 1. Information Collection:

[0462] The server launches a crawler to collect information about sustainable agricultural practices from research papers, blogs, forums, etc. on the internet. The collected data is then analyzed using natural language processing (NLP) models to extract relevant information. This information is then stored in a database that can be later accessed by farmers.

[0463] 2. Environmental Data Collection and Analysis:

[0464] The server collects real-time environmental data from various sources, including weather data, soil data, and observation data. This data is analyzed by an AI model, and real-time advice is generated for farmers on optimal farming practices, enabling them to carry out farming work efficiently without missing a beat.

[0465] 3. Blockchain Management:

[0466] The server uses blockchain technology to record and manage all data from the production process to the consumption process. This prevents data tampering and ensures that all processes are managed transparently and reliably. Consumers can scan the QR code to check the production process of the food they purchased in detail.

[0467] Device operation

[0468] 1. Information display:

[0469] Using a device (app or browser), farmers can access the server and view information on ecological farming practices extracted by generative AI and NLP models, allowing them to incorporate the latest knowledge and apply it to their farming practices.

[0470] 2. Receiving Advice:

[0471] The device sends real-time advice to farmers from the server, which is displayed in an easy-to-understand format and suggests optimal farming methods based on weather and soil conditions.

[0472] User operations

[0473] 1. Login and Information Access:

[0474] Farmers can log in to the system and access their accounts to view the collected and analyzed information and advice, and can also use the marketplace feature to network with other farmers and share experiences and best practices.

[0475] 2. Consumer scans the QR code:

[0476] When consumers buy food in stores, they can scan the QR code to learn about the food's production process and whether it was grown sustainably, allowing them to make informed purchasing decisions based on product context.

[0477] Specific examples

[0478] For example, if an agricultural producer is growing cabbage, the server will collect environmental information in real time from satellite data, weather data, soil data, etc., and generate advice such as, "There will be a lot of rain this week, so irrigation is not necessary." This advice is then sent to the agricultural producer via a terminal. The cabbage's production process is also recorded on the blockchain, and consumers can scan a QR code at the store to confirm that "this cabbage was grown using ecological methods."

[0479] In this way, the system of the present invention aims to help farmers adopt efficient and sustainable agricultural practices and provide consumers with transparent and reliable information, thereby contributing to the maturation and expansion of the sustainable agriculture community.

[0480] The processing flow will be explained below.

[0481] Detailed process flowchart

[0482] (Server processing)

[0483] Step 1:

[0484] The server periodically launches a crawler to collect information about sustainable agricultural practices from research papers, blogs, forums, etc. on the Internet. For example, it retrieves articles from specific websites and saves their contents in text format.

[0485] Step 2:

[0486] The server passes the collected text data to a natural language processing (NLP) model to extract relevant information, including article summaries and key keywords, allowing for efficient extraction of necessary insights from large amounts of information.

[0487] Step 3:

[0488] The server then stores the extracted relevant information in a database, making it available for later access by farmers.

[0489] Step 4:

[0490] The server collects environmental data (e.g., weather data, soil data, observation data) in real time from different sources, including using APIs to retrieve weather information.

[0491] Step 5:

[0492] The server passes the collected environmental data to an AI model for analysis, which then generates optimal farming advice for farmers in real time. For example, it suggests the appropriate timing for irrigation based on temperature and humidity data.

[0493] Step 6:

[0494] The server will use blockchain technology to record the entire process from agricultural production to consumption, which will increase transparency and reliability and prevent data tampering.

[0495] Terminal Processing

[0496] Step 1:

[0497] The terminal connects to the database so that farmers can access the system and view the information provided, which displays extracted information about ecological farming practices.

[0498] Step 2:

[0499] The terminals then notify farmers of real-time farming advice received from the server, for example by displaying a message such as "There has been a lot of rain this week, so no irrigation is necessary."

[0500] User processing

[0501] Step 1:

[0502] Users (farmers) log in to the system, access their account and view information. Once successfully logged in, they can use the marketplace feature to network with other farmers and share experiences and best practices.

[0503] Step 2:

[0504] When purchasing food in a store, users (consumers) can scan the QR code to view information about the food's production process and whether it was grown using sustainable methods. For example, a message such as "This cabbage was grown using ecological methods" may be displayed.

[0505] Specific examples

[0506] For example, if an agricultural producer is growing cabbage, the server will collect environmental information in real time from satellite data, weather data, soil data, etc., and generate advice such as, "There will be a lot of rain this week, so irrigation is not necessary." This advice is then sent to the agricultural producer via their device. In addition, the cabbage production process is recorded on the blockchain, and consumers can scan a QR code at the store to confirm that "this cabbage was grown using ecological methods."

[0507] In this way, the system of the present invention assists farmers in adopting efficient and sustainable agricultural practices and provides transparent and reliable information to consumers.

[0508] Example 1

[0509] 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."

[0510] Farmers seeking to implement sustainable agricultural practices face challenges accessing reliable, up-to-date, and effective advice. Consumers demand transparency about the production process of their food, but there are a lack of means to achieve this. Furthermore, farmers lack effective platforms for sharing knowledge and experiences with other farmers.

[0511] 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.

[0512] In this invention, the server includes: means for collecting information on sustainable agricultural practices from publicly available data sources using a generative AI model; means for analyzing the collected information using a natural language processing model to extract relevant information and store it in a database; means for collecting real-time environmental data from different sources and analyzing it using an AI model; means for generating real-time advice on agricultural work based on the analyzed environmental data and notifying the terminal; and means for recording the entire process from agricultural production to consumption using blockchain technology to ensure transparency and reliability. This allows agricultural producers to receive reliable, up-to-date information and advice, and consumers to enjoy transparency into the food production process. It also provides a platform for agricultural producers to share their knowledge and experience with each other.

[0513] "Generative AI model" refers to artificial intelligence technology for automatically collecting and analyzing information about sustainable agricultural practices from publicly available data sources.

[0514] A "natural language processing model" is a computer program that analyzes collected text data and extracts highly relevant information.

[0515] A "database" is a digital storage system for organizing and storing collected information and analysis results.

[0516] "Real-time environmental data" refers to environmental information related to agriculture, such as current weather data and soil data.

[0517] An "AI model" is an artificial intelligence technology that analyzes collected data and generates optimal advice for agricultural producers.

[0518] "Terminal" refers to the device that agricultural producers use to view information and advice, such as a smartphone or computer.

[0519] "Blockchain technology" is a distributed digital ledger technology that prevents data tampering and ensures transparency and reliability.

[0520] A "QR code" is a two-dimensional barcode that consumers can scan with a smartphone or other device to access detailed information.

[0521] MODE FOR CARRYING OUT THE INVENTION

[0522] This invention is a system that integrates generative AI models, environmental monitoring, and blockchain technology, and is designed primarily to meet the needs of agricultural producers and consumers. Regarding the mode for implementing the invention, the roles of the server, terminal, and user are clearly defined, and specific examples are presented.

[0523] Server configuration and functions

[0524] 1. Information gathering

[0525] The server uses a Python crawler (e.g., Beautiful Soup library) to collect information about sustainable agricultural practices from research papers, blogs, forums, etc. on the Internet. The collected data is then analyzed using natural language processing (NLP) models (e.g., NLTK library) to extract relevant information. This information is then stored in a database (e.g., MongoDB) for later access by farmers.

[0526] 2. Environmental data collection and analysis

[0527] The server collects environmental data in real time from weather data APIs (e.g., OpenWeatherMap), sensor devices, soil data APIs, etc. This data is analyzed using AI models (e.g., TensorFlow and Scikit-learn), and based on the analysis results, real-time advice on optimal farming practices is generated for agricultural producers.

[0528] 3. Implementation of Blockchain Technology

[0529] The server uses blockchain technology such as Hyperledger Fabric and Ethereum to record and manage all data from the production process to the consumption process, preventing data tampering and ensuring that all processes are managed transparently and reliably.

[0530] Device configuration and functions

[0531] 1. Information display

[0532] Farmers access the server using a device (app or browser) and can view information about ecological farming practices extracted by generative AI and NLP models through a mobile app built with React Native.

[0533] 2. Receiving Advice

[0534] The device sends real-time advice from the server to farmers via push notifications and emails. Notifications are sent using Firebase Cloud Messaging (FCM), and messages such as "There is a lot of rain this week, so there is no need to irrigate" are displayed.

[0535] User operations

[0536] 1. Login and Information Access

[0537] Agricultural producers log in to the system using an authentication system that uses OAuth 2.0. After logging in, they can not only view the collected and analyzed information and advice on a dashboard, but also use the marketplace function to network with other producers and share best practices.

[0538] 2. QR Code Scan

[0539] Consumers can check the production process and whether the food was grown sustainably by scanning the QR code on the food they purchased with a smartphone QR code reader (e.g., ZXing library). The scan results will display detailed information retrieved from the blockchain.

[0540] Specific examples

[0541] For example, if a farmer is growing cabbage, the server collects satellite, weather, and soil data in real time and generates advice such as, "There will be a lot of rain this week, so irrigation is not necessary." This advice is then sent to the farmer's smartphone via Firebase Cloud Messaging. The cabbage's production process is also recorded on the Ethereum blockchain, and consumers can scan the code at the store using a ZXing QR code scanner to confirm that the cabbage was grown using ecological methods.

[0542] Prompt Sentence Examples

[0543] "Please tell me how to grow cabbage ecologically."

[0544] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0545] Server Processing

[0546] Step 1: Gather information

[0547] Specific operation: The server launches the crawler using Python's Beautiful Soup library.

[0548] Input: A list of URLs for research papers, blogs, and forums on the internet.

[0549] Data processing / calculation: The crawler parses the HTML content and extracts text data, which is then analyzed using a natural language processing model (e.g., NLTK) to extract relevant information.

[0550] Output: The extracted information is stored in a database (e.g. MongoDB).

[0551] Step 2: Environmental data collection

[0552] Specific operation: The server collects real-time data from weather data APIs (e.g., OpenWeatherMap), soil data APIs, and sensor devices.

[0553] Input: API endpoints for weather, soil, and sensor data.

[0554] Data processing / calculation: Analyze the JSON data obtained from the API and extract the necessary information. Data from the soil sensor is sent directly to the server.

[0555] Output: The visualized environmental data is stored in a database.

[0556] Step 3: Data analysis

[0557] Specific operation: Analyze the collected data using an AI model (e.g., TensorFlow or Scikit-learn).

[0558] Input: Stored environmental and intelligence gathering data.

[0559] Data processing / calculation: AI models analyze data based on criteria and detect specific patterns or anomalies.

[0560] Output: The analysis results are saved in a database and used in the next step.

[0561] Step 4: Real-time advice generation

[0562] How it works: The server uses AI models to generate advice based on real-time environmental data.

[0563] Input: Analysis results and current environmental data.

[0564] Data processing / computation: AI models generate advice based on environmental conditions and format it in an easy-to-understand way.

[0565] Output: Advice is sent to the terminal.

[0566] Step 5: Record on the blockchain

[0567] Specific operation: The server uses Hyperledger Fabric or Ethereum to record data on the blockchain.

[0568] Input: All data from the production process to the consumption process.

[0569] Data processing / calculation: Smart contracts are used to record data as transactions, preventing tampering.

[0570] Output: The data recorded on the blockchain is visible to consumers.

[0571] Device operation

[0572] Step 1: Display information

[0573] How it works: Farmers access the server using a mobile app built with React Native.

[0574] Input: Information stored in a database.

[0575] Data processing / calculation: The app retrieves information and displays it in list or dashboard format.

[0576] Output: Agricultural producers can view relevant information on their terminals.

[0577] Step 2: Advice Notification

[0578] Specific operation: The advice is sent to the device using Firebase Cloud Messaging (FCM).

[0579] Input: Server-generated advice data.

[0580] Data processing / calculation: Notification messages are formatted in an easy-to-understand way.

[0581] Output: A notification will be displayed on the farmer's device.

[0582] User operations

[0583] Step 1: Log in and access your information

[0584] How it works: Farmers log in to the system using OAuth 2.0.

[0585] Input: User credentials (email address, password).

[0586] Data processing / computation: Credentials are verified and the user's session begins.

[0587] Output: A dashboard is accessible, allowing you to view the collected and analyzed information.

[0588] Step 2: Scan the QR code

[0589] How it works: Consumers scan the QR code in the store to check the production process.

[0590] Input: QR code attached to the product.

[0591] Data processing / calculation: Scan the code with a QR code reader (e.g., ZXing library) and access the blockchain to retrieve related information.

[0592] Output: Information about the product's production process and sustainable practices is displayed on the consumer's device.

[0593] (Application example 1)

[0594] 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."

[0595] In traditional agricultural systems, implementing sustainable agricultural practices and ensuring transparency have been challenging. In particular, there is a lack of information and real-time environmental advice to help agricultural producers learn and practice ecological methods. Furthermore, there are limited ways for consumers to check detailed information about the production process of the agricultural products they purchase in physical stores. This has made it difficult to gain consumer trust.

[0596] 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.

[0597] In this invention, the server includes: means for collecting information on sustainable agricultural practices from publicly available data sources using a generative AI model; means for analyzing the collected information and providing it in an easy-to-understand format for agricultural producers; means for collecting and analyzing real-time environmental data from different sources; means for generating real-time advice on agricultural work based on the analyzed environmental data; means for implementing distributed ledger technology to record the entire process from agricultural production to consumption and ensure transparency and trust; and means for consumers to scan an identification code attached to a product in a physical store and display detailed production information of the product, the agricultural method used, and environmental data. This makes it easier for agricultural producers to adopt effective and sustainable agricultural practices, and allows consumers to easily check the background information of the products they purchase, thereby improving trust and transparency.

[0598] A "generative AI model" is an artificial intelligence model that learns specific patterns and rules from data and provides generative answers to new data.

[0599] "Sustainable agricultural practices" are agricultural methods that are economically sustainable and socially acceptable while minimizing their impact on the environment.

[0600] "Distributed ledger technology" is a database technology that is shared and managed by multiple participants in a network and is difficult to tamper with, and blockchain is a prime example of this.

[0601] "Real-time environmental data" refers to data that instantly collects and updates current environmental conditions such as weather, soil, and temperature.

[0602] An "identification code" is a symbol or code used to uniquely identify a product or information, and examples of this include barcodes and QR codes.

[0603] An "electronic market" is a virtual market where goods and services are bought and sold through digital platforms such as the Internet.

[0604] "Visual device" means a device that provides a visual interface to a user, including head-mounted displays and smart glasses.

[0605] MODE FOR CARRYING OUT THE INVENTION

[0606] System Program

[0607] In the system of the present invention, the server includes the following means:

[0608] 1. Data collection methods for generative AI models:

[0609] The server uses a generative AI model to collect information about sustainable agricultural practices from multiple publicly available data sources, such as research papers, blogs, and forums, and analyzes it using a natural language processing (NLP) model, such as BERT or GPT-3.

[0610] 2. Information analysis and provision method:

[0611] The collected information is analyzed using generative AI and NLP models and presented in an easy-to-understand format for farmers. This information is stored in a database on a server and can be accessed by farmers via a browser or app.

[0612] 3. Real-time environmental data collection methods:

[0613] The server collects real-time environmental data, such as weather data and soil data, from various sensors and external data sources. For example, weather data can be obtained from the Internet using an API.

[0614] 4. Real-time advice generation methods:

[0615] Based on the collected and analyzed environmental data, generative AI models are used to generate real-time advice on farming practices, enabling farmers to carry out optimal practices at the right time.

[0616] 5. Distributed Ledger Technology Implementation Methods:

[0617] The server uses blockchain technology to record the entire process from agricultural production to consumption. This prevents data tampering and ensures that all processes are managed transparently and reliably. Consumers can scan the identification code to check the details of the food's production process.

[0618] Examples of specific examples and prompts

[0619] For example, when a consumer buys organic tomatoes at a supermarket, they scan an identification code using this system, and the server provides information such as, "The tomatoes were grown using sustainable agricultural practices. Organic fertilizer was used on the farm. Environmental data used includes soil moisture, sunshine hours, and temperature data." This information is recorded and managed using generative AI models and distributed ledger technology.

[0620] The following are examples of specific prompt sentences:

[0621] Please provide us with the latest information on sustainable agricultural practices. Specifically, please share details about your organic fertilizer use and insights into sustainable irrigation techniques.

[0622] explanation

[0623] 1. Hardware and Software:

[0624] The system includes a server, weather sensors, soil sensors, an identification code reader (e.g., a QR code reader), and a browser or smartphone app.

[0625] The server will be equipped with a generative AI model (e.g., GPT-3), an NLP model (e.g., BERT), a database (e.g., MySQL), and blockchain technology (e.g., Ethereum).

[0626] 2. Data processing and calculation:

[0627] Data collected by the server is used by an NLP model to extract relevant information, which is then converted by a generative AI model into optimal advice that is provided to farmers.

[0628] Environmental data collected in real time is analyzed and a generative AI model generates real-time advice on farming practices.

[0629] All data is managed using distributed ledger technology, and consumers can scan an identification code to obtain detailed information about the production process.

[0630] In this way, the invention is a system that makes it easier for farmers to adopt sustainable practices and provides transparent and reliable information to consumers.

[0631] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0632] Flow of the system program that realizes the application example

[0633] Step 1:

[0634] The server uses a generative AI model to collect information on sustainable agricultural practices from multiple publicly available data sources (e.g., research papers, blogs, forums, etc.). The server then launches a crawler to collect text data from the specified data source. The data source URL is required as input, and the collected text data is obtained as output.

[0635] Step 2:

[0636] The server then passes the collected text data to a natural language processing (NLP) model for analysis. The NLP model (e.g., BERT) extracts relevant information about sustainable agricultural practices from the collected text data. The input is the text data, and the output is the extracted relevant information.

[0637] Step 3:

[0638] The server stores the analyzed information in a database. Using a generative AI model, the information is organized and stored in a format that is easy for farmers to understand. The analyzed information is required as input, and the information stored in the database is obtained as output.

[0639] Step 4:

[0640] The server collects real-time environmental data from different sources, for example, weather data and soil data from APIs and sensors. It requires API keys and sensor data as inputs and provides the collected environmental data as output.

[0641] Step 5:

[0642] The server passes the collected environmental data to a generative AI model for analysis and generates real-time advice on farming. Environmental data is required as input, and real-time advice is obtained as output. The generated advice is sent to the terminal.

[0643] Step 6:

[0644] Using a device (smartphone or browser), farmers access the server and view the collected and analyzed information and real-time advice on sustainable agricultural practices. Data from the server is required as input, and displayed information is obtained as output.

[0645] Step 7:

[0646] The server records the entire process from agricultural production to consumption using blockchain technology. For example, using blockchain technology (e.g., Ethereum), each step of agricultural production is recorded as a transaction. Data for each step is required as input, and transaction data recorded on the blockchain is obtained as output.

[0647] Step 8:

[0648] Consumers scan the identification code at a physical store to obtain detailed production information about the product. By scanning the identification code (e.g., QR code) using a terminal, production process information is obtained from the server. The identification code is required as input, and detailed production information about the product is obtained as output.

[0649] At each step of the process, farmers are supported in implementing sustainable agricultural practices, and consumers can be assured of a transparent and reliable source of the products they purchase.

[0650] 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.

[0651] The invention is a system that integrates generative AI models, environmental monitoring, blockchain technology, and an emotion engine to help farmers learn and implement sustainable ecological agricultural practices, while providing consumers with transparency and trust from production to consumption.

[0652] Server Processing

[0653] 1. Information Collection:

[0654] The server launches a crawler to collect information about sustainable agricultural practices from research papers, blogs, forums, etc. on the internet. The collected data is then analyzed using natural language processing (NLP) models to extract relevant information. This information is then stored in a database that can be later accessed by farmers.

[0655] 2. Environmental Data Collection and Analysis:

[0656] The server collects real-time environmental data from different sources, such as weather data, soil data, and observation data. This data is analyzed by an AI model, and optimal farming advice is generated in real time for farmers.

[0657] 3. Emotion engine combination:

[0658] The server also uses an emotion engine to recognize the emotions of farmers and consumers and incorporates that emotional data into its analysis. For example, if it senses that a farmer is under high stress, it can adjust the content and format of advice and provide resources to reduce stress. It also analyzes consumers' purchasing intentions and optimizes information accordingly.

[0659] 4. Blockchain Management:

[0660] The server uses blockchain technology to record and manage all data from the production process to the consumption process. This prevents data tampering and ensures that all processes are managed transparently and reliably. Consumers can scan the QR code to check the production process of the food they purchased in detail.

[0661] Device operation

[0662] 1. Information display:

[0663] Using a device (app or browser), farmers can access the server and view information on ecological farming practices extracted by generative AI and NLP models. Based on emotional data recognized by the emotion engine, information and resources are provided to reduce stress and increase motivation, if needed.

[0664] 2. Receiving Advice:

[0665] The device then sends real-time farming advice to farmers from the server, displaying messages such as "There's been a lot of rain this week, so no irrigation is necessary."

[0666] User operations

[0667] 1. Login and Information Access:

[0668] Farmers can log in to the system, access their accounts, and view information. They can also use the marketplace function to network with other farmers and share experiences and best practices. The emotion engine monitors the farmer's emotional state and provides support information as needed.

[0669] 2. Consumer scans the QR code:

[0670] When consumers buy food in stores, they can scan the QR code to see the food's production history, and the emotion engine can analyze their reactions and provide additional information or promotions based on their purchasing intent.

[0671] Specific examples

[0672] For example, in the case of a cabbage farmer, the server collects environmental information in real time from satellite data, weather data, soil data, etc., and generates advice such as, "There will be a lot of rain this week, so there is no need for irrigation." This advice is notified to the farmer via his device. Furthermore, if the server uses its emotion engine to determine that the farmer's stress level is high, it will also provide a link to a video on how to relax.

[0673] The cabbage's production process is also recorded on the blockchain, and consumers can scan a QR code at the store to confirm that the cabbage was grown using ecological methods. Consumer responses are analyzed by an emotion engine, and interested consumers are provided with further promotional information.

[0674] In this way, the system of the present invention helps farmers adopt efficient and sustainable agricultural practices, provides transparent and reliable information to consumers, and, with the introduction of an emotion engine, enables more personalized assistance and information delivery, improving the user experience.

[0675] The processing flow will be explained below.

[0676] Detailed process flowchart

[0677] (Server processing)

[0678] Step 1:

[0679] The server periodically launches a crawler to collect information about sustainable agricultural practices from online research papers, blogs, forums, etc. For example, it scans multiple websites to retrieve articles and saves their contents in text format.

[0680] Step 2:

[0681] The server passes the collected text data through natural language processing (NLP) models to extract relevant information, including summarizing the text and extracting key keywords, such as summarizing the latest research findings on sustainable agricultural practices.

[0682] Step 3:

[0683] The server stores the extracted relevant information in a database, which allows farmers to access the information later. For example, the database may store summaries of each source.

[0684] Step 4:

[0685] The server collects real-time environmental data from different sources, such as weather data, soil data, and observation data. For example, it obtains information such as current temperature, precipitation, and humidity from a weather API.

[0686] Step 5:

[0687] The server passes the collected environmental data to an AI model for analysis. This allows the AI ​​model to generate optimal farming advice for farmers in real time. For example, based on current weather conditions and soil conditions, the model might generate advice such as "There will be a lot of rain this week, so irrigation is not necessary."

[0688] Step 6:

[0689] The server uses an emotion engine to recognize the emotions of farmers and consumers and incorporates that emotion data into its analysis. For example, if the server senses that a farmer is under high stress, it will adjust the content and format of advice and provide resources to reduce stress.

[0690] Step 7:

[0691] The server uses blockchain technology to record all data from the production process to the consumption process. This prevents data tampering and ensures that all processes are managed transparently and reliably. For example, the production history of agricultural products can be recorded on the blockchain.

[0692] Terminal Processing

[0693] Step 1:

[0694] The terminals connect to the database so that farmers can access the system and view the information provided. This displays information about ecological farming practices extracted by generative AI and NLP models. For example, farmers can view a summary of the latest sustainable farming methods on their terminals.

[0695] Step 2:

[0696] The terminals then notify farmers of real-time farming advice received from the server, for example displaying a message saying, "There's been a lot of rain this week, so no irrigation is necessary."

[0697] Step 3:

[0698] The device displays additional information based on the farmer's emotions as recognized by the emotion engine, for example providing a video link on relaxation techniques to a farmer who is under a lot of stress.

[0699] User processing

[0700] Step 1:

[0701] Users (farmers) log in to the system, access their account, and view information. Once successfully logged in, they can use the marketplace function to network with other farmers and share experiences and best practices. For example, farmers can chat with other farmers and exchange cultivation tips.

[0702] Step 2:

[0703] When purchasing food in a store, users (consumers) can scan the QR code to view information about the food's production process and whether it was grown using sustainable methods. For example, a message such as "This cabbage was grown using ecological methods" may be displayed.

[0704] Step 3:

[0705] The emotion engine analyzes the user's (consumer's) emotions and provides additional information and promotions according to their purchasing intent. For example, consumers who show interest will be shown promotional information about similar sustainable agricultural products.

[0706] Specific examples

[0707] For example, in the case of a cabbage farmer, the server collects environmental information in real time from satellite data, weather data, soil data, etc., and generates advice such as, "There will be a lot of rain this week, so there is no need for irrigation." This advice is notified to the farmer via his device. If the server uses its emotion engine to determine that the farmer's stress level is high, it will also provide a link to a video on how to relax.

[0708] Furthermore, the cabbage's production process is recorded on the blockchain, and consumers can scan a QR code at the store to confirm that the cabbage was grown using ecological methods. Consumer responses are analyzed by an emotion engine, and interested consumers are provided with further promotional information.

[0709] In this way, the system of the present invention helps farmers adopt efficient and sustainable agricultural practices, provides transparent and reliable information to consumers, and, with the introduction of an emotion engine, enables more personalized assistance and information delivery, improving the user experience.

[0710] Example 2

[0711] 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."

[0712] Modern agriculture faces many challenges in terms of profitability and sustainability. Farmers have difficulty efficiently gathering information on the latest sustainable agricultural practices and applying them to their daily operations. They are also required to understand the impact of climate change and environmental factors in real time and take optimal action based on that information. Furthermore, it is important to manage the process from production to consumption in a transparent and reliable manner, which requires the introduction of distributed ledger technology. On the consumer side, ensuring transparency in the production process and providing an environment in which they can purchase with confidence are challenges.

[0713] 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.

[0714] In this invention, the server includes: means for collecting information on sustainable agricultural practices from publicly available data sources using a generative AI model; means for analyzing the collected information and providing it in an easy-to-understand format for agricultural producers; means for collecting and analyzing real-time environmental data from various sources; means for generating real-time advice on agricultural work based on the analyzed environmental data; means for analyzing the emotions of agricultural producers and consumers using an emotion engine and providing personalized support information and purchasing promotion information; and means for implementing distributed ledger technology to record the entire process from agricultural production to consumption and ensure transparency and reliability. This enables agricultural producers to efficiently learn and practice the latest sustainable agricultural practices and to carry out optimal agricultural work based on real-time environmental data. Furthermore, the process from production to consumption can be managed in a transparent and reliable manner, providing consumers with a safe and secure purchasing environment.

[0715] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze large amounts of data and generate information for specific purposes.

[0716] "Publicly available data sources" are sources such as papers, blogs, forums, etc. that are freely accessible on the internet.

[0717] "Sustainable agricultural practices" refer to methods and initiatives for long-term agricultural production while preserving the environment.

[0718] "Real-time environmental data" refers to environmental information that is updated at each point in time, such as weather data and soil data.

[0719] The "emotion engine" is a system that analyzes the user's emotional state and suggests appropriate measures based on the results.

[0720] "Personalized support information" refers to information or advice that is individualized and provided according to the user's emotions and circumstances.

[0721] "Purchase promotion information" refers to promotional and sales information provided to increase consumers' desire to purchase.

[0722] "Distributed ledger technology" is a technology that maintains high transparency and reliability by distributing and recording and managing data across multiple nodes.

[0723] "Networking" refers to agricultural producers exchanging information and cooperating with other producers.

[0724] A "market" is a platform or venue where agricultural producers can come together to exchange information, share experiences and best practices.

[0725] The invention is a system that integrates generative AI models, environmental monitoring, distributed ledger technology, and an emotion engine to help farmers learn and implement sustainable agricultural practices, while providing consumers with transparency and trust from production to consumption.

[0726] Server Processing

[0727] The server first launches a crawler to collect information about sustainable agricultural practices from research papers, blogs, forums, and other sources on the Internet. For example, it uses the keyword "sustainable farming" to gather related information. The acquired data is then analyzed using a natural language processing (NLP) model to extract relevant information. This information is then stored in a database and made accessible to farmers.

[0728] The server then collects real-time environmental data from different sources, such as weather data, soil data, and observation data. This data is analyzed by an AI model to generate optimal farming advice for farmers. For example, real-time advice such as "There will be a lot of rain this week, so irrigation is not necessary" is generated.

[0729] The server also uses an emotion engine to analyze the emotions of agricultural producers and consumers. Based on the results of the emotion analysis, it provides agricultural producers with resources to reduce stress and consumers with purchasing promotion information. For example, if it determines that "Producer A is in a high stress state," it provides a link to a "video on relaxation methods."

[0730] Finally, the server uses distributed ledger technology to record and manage all data from the production process to the consumption process, preventing data tampering and ensuring that all processes are managed transparently and reliably. Consumers can scan the QR code to check the production process of the food they purchased in detail.

[0731] Device operation

[0732] The device (app or browser) accesses the server and displays information about ecological farming practices extracted by generative AI and NLP models. Based on the emotional data recognized by the emotion engine, information and resources are provided to reduce stress and increase motivation, if needed.

[0733] The device also notifies farmers of real-time farming advice sent from the server, such as "There's been a lot of rain this week, so no irrigation is necessary."

[0734] User operations

[0735] Farmers log in to the system, access their accounts, and view information. They can then use the marketplace function to network with other farmers and share experiences and best practices. The emotion engine monitors the farmer's emotional state and provides support information as needed.

[0736] When consumers buy food in stores, they can scan the QR code to see the food's production history, and the emotion engine can analyze their reactions and provide additional information or promotions based on their purchasing intent.

[0737] Specific examples

[0738] For example, in the case of a cabbage farmer, the server collects environmental information in real time from satellite data, weather data, soil data, etc., and generates advice such as, "There will be a lot of rain this week, so irrigation is not necessary." This advice is then sent to the farmer via their device. Furthermore, if the server uses an emotion engine to determine that the farmer's stress level is high, it will also provide a link to a video on how to relax. The cabbage production process is also recorded on distributed ledger technology, and by scanning a QR code at the store, consumers can confirm that the cabbage was grown using ecological methods. The emotion engine analyzes consumer responses, and interested consumers are provided with further promotional information.

[0739] Prompt Sentence Examples

[0740] Examples of prompts to input to a generative AI model include:

[0741] "Collect and summarize the latest research papers on sustainable agricultural practices."

[0742] "Based on current weather data, what is the best way to grow cabbage?"

[0743] "Generate relaxation advice for farmers in high-stress situations."

[0744] "Please explain how distributed ledger technology can be used to manage the production process of the food purchased by consumers."

[0745] The foregoing is a detailed description of the present invention, which allows both farmers and consumers to conveniently and efficiently implement sustainable agricultural practices and access transparent and reliable information.

[0746] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0747] Step 1: Gather information

[0748] Specific operation: The server launches a crawler to collect information about sustainable agricultural practices from research papers, blogs, forums, etc. on the Internet. For example, it uses the keyword "sustainable farming" to collect related information.

[0749] Input: A list of URLs on the Internet.

[0750] Data processing and calculation: The crawler accesses each URL, obtains HTML data, and extracts it as text.

[0751] Output: Collected text data.

[0752] Step 2: Data analysis

[0753] Specific operation: The server sends the collected data to a natural language processing (NLP) model to summarize the sentences and extract important keywords.

[0754] Input: Collected text data.

[0755] Data processing and calculation: NLP models analyze text data, extract relevant information, and generate summaries.

[0756] Output: Summarized information and extracted keywords.

[0757] Step 3: Save to database

[0758] What it does: The server stores the analyzed information in a database, where farmers can access it later.

[0759] Input: Abstracted information and extracted keywords.

[0760] Data processing and calculation: Organizing and storing information in a database.

[0761] Output: The saved information is stored in a database.

[0762] Step 4: Environmental data collection

[0763] Specific operation: The server accesses weather data provision services and soil data provision services to obtain environmental data in real time.

[0764] Input: API for weather data and soil data provision services.

[0765] Data processing and calculation: Obtain data from the API and compile it into an integrated dataset.

[0766] Output: A consolidated environmental dataset.

[0767] Step 5: Environmental data analysis

[0768] Specific operation: The server sends the integrated data to the AI ​​model and generates optimal farming advice.

[0769] Input: Integrated environmental dataset.

[0770] Data processing and calculation: The AI ​​model analyzes the data and generates specific farming advice.

[0771] Output: The generated farming advice.

[0772] Step 6: Collect emotional data

[0773] Specific operation: The server collects app usage data and feedback from farmers and consumers and sends it to the emotion engine.

[0774] Input: App usage data, feedback data.

[0775] Data processing and calculation: The emotion engine analyzes the data and determines the emotional state.

[0776] Output: Action instructions based on emotional state.

[0777] Step 7: Sentiment Data Analysis and Action

[0778] Specific operation: The server determines the appropriate action based on the results of the sentiment analysis.

[0779] Input: Action instructions based on emotional state.

[0780] Data processing and calculation: Provide producers with resources to reduce stress and optimize information to promote purchases for consumers.

[0781] Output: Personalized support and promotional information.

[0782] Step 8: Blockchain Management

[0783] Specific operation: The server records and manages all data from the production process to the consumption process using distributed ledger technology.

[0784] Input: Data from the production and consumption processes.

[0785] Data processing and calculation: Blockchain technology is used to record data one by one into blocks and verify transactions.

[0786] Output: Recorded data and generated QR code.

[0787] These are the specific processing steps of this system, which will enable farmers to efficiently learn and implement sustainable agricultural practices, while also providing consumers with transparency and reliability in the production process.

[0788] (Application example 2)

[0789] 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."

[0790] Today's agricultural producers face difficulties in easily obtaining information on sustainable agricultural practices and adopting highly efficient and ecological methods. Furthermore, there is a lack of systems that provide consumers with transparency and reliability in the production process while providing personalized purchasing support. In particular, there is a need for advanced personalization that reflects environmental data and production information in real time and utilizes sentiment analysis.

[0791] 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.

[0792] In this invention, the server includes: means for collecting information on sustainable agricultural practices from publicly available data sources using a generative AI model; means for analyzing the collected information and providing it in an easy-to-understand format for agricultural producers; means for collecting and analyzing real-time environmental data from various sources; means for generating real-time advice on agricultural work based on the analyzed environmental data; means for recording the entire process from agricultural production to consumption and implementing blockchain technology to ensure transparency and reliability; means for recognizing the emotions of agricultural producers and consumers using a sentiment analysis engine and providing information based on stress reduction and purchasing motivation; and means for showing consumers the production process of agricultural products using QR codes in physical stores and promoting the purchase of ecological products. This enables agricultural producers to learn and practice efficient and sustainable agricultural practices, provides consumers with transparency and reliability from production to consumption, and realizes personalized purchasing support using sentiment analysis.

[0793] A "generative AI model" is a type of artificial intelligence used to collect information from publicly available data sources, analyze that information, and generate it.

[0794] "Real-time environmental data" refers to data used to collect and analyze agriculturally relevant environmental information in real time, such as weather data and soil data.

[0795] "Blockchain technology" is a distributed database used to prevent data tampering and ensure transparency and reliability.

[0796] An "emotion analysis engine" is an algorithm or system that recognizes and analyzes a user's emotions and provides information and advice based on those emotions.

[0797] A "QR code" is a type of two-dimensional barcode that allows information to be easily read.

[0798] "Ecological products" are products that are produced in a sustainable manner with minimal impact on the environment.

[0799] "Agricultural practices" is a general term that refers to the methods, techniques, and procedures used in the agricultural sector.

[0800] "Real-time advice" refers to advice or instructions provided immediately based on data collected in real time.

[0801] "Transparency" refers to a state in which systems and data are made public and anyone can check their contents.

[0802] "Reliability" is a term used to describe a situation in which a system or data is accurate, error-free, and trustworthy.

[0803] "Personalized purchasing support" refers to providing purchasing suggestions and support that are customized to suit each individual consumer's preferences and circumstances.

[0804] To implement this invention, the system is configured as follows: The server uses a generative AI model to collect information on sustainable agricultural practices from publicly available data sources, analyzes the information, and provides it to agricultural producers. The server also collects and analyzes real-time environmental data from multiple sources, and generates and notifies agricultural producers of real-time advice based on the results.

[0805] Furthermore, the server uses blockchain technology to record the entire process from agricultural production to consumption, ensuring transparency and reliability. It uses a sentiment analysis engine to recognize the emotions of agricultural producers and consumers, and provides personalized information based on their stress levels and purchasing intentions. Specifically, QR codes can be used in physical stores to show consumers the production process of agricultural products, encouraging them to purchase eco-friendly products.

[0806] Program Description

[0807] The server system is built using Python and Django, and the front end is configured using React Native. The generative AI model is implemented using TensorFlow, and support for environmental data and sentiment analysis is provided using Google Cloud Natural Language API and OpenCV. PostgreSQL is used as the database, and blockchain technology is implemented using Hyperledger Fabric.

[0808] Specific examples

[0809] Information collection and display:

[0810] 1. The server uses a crawler to collect public information about agriculture from the Internet and analyzes it using an NLP (natural language processing) model.

[0811] 2. The analysis results are stored in a database, and farmers can access this information via a smartphone app.

[0812] Real-time advice:

[0813] 1. The server collects and analyzes environmental data in real time, such as weather data and soil data.

[0814] 2. Based on the analysis results, a message is sent to agricultural producers, for example, "There will be a lot of rain this week, so there is no need for irrigation."

[0815] Emotion analysis:

[0816] 1. When a consumer scans a QR code in a physical store, the server retrieves production process data from the blockchain.

[0817] 2. By analyzing the consumer's facial expressions and voice, we can recognize their stress and purchasing intent and provide them with appropriate purchasing information.

[0818] For example: "These cabbages were grown using ecological methods. They're a more environmentally friendly product. Are you interested?"

[0819] Prompt Sentence Examples

[0820] This cabbage was grown ecologically. Is it produced using sustainable farming methods?

[0821] Yes → The generative AI model will suggest ecological products in the same category.

[0822] No → Get information about other cabbages.

[0823]

[0824] I scanned your face and it looks like you're a little stressed. Would you like some tips on how to relax?

[0825] Yes → Provides videos on how to relax.

[0826] No → Proceed to the next step.

[0827] This will enable farmers to learn and implement efficient and sustainable agricultural practices, provide consumers with transparency and trust from production to consumption, and enable personalized purchasing support using sentiment analysis.

[0828] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0829] Step 1:

[0830] Information collection and analysis

[0831] The server uses a crawler to collect information about sustainable agricultural practices from research papers, blogs, forums, etc. on the Internet. The collected data is then analyzed using natural language processing (NLP) models to extract relevant information.

[0832] Input: Public information on the internet, such as research papers, blogs, forums, etc.

[0833] Data processing: The crawler collects information and analyzes it using NLP models (e.g., text summarization, keyword extraction).

[0834] Output: Information about the analyzed sustainable agricultural practices.

[0835] How it works: The crawler collects public information from the web and performs text analysis using a Python NLP library (e.g., SpaCy).

[0836] Step 2:

[0837] Environmental data collection and analysis

[0838] The server collects environmental data in real time from multiple sources, including weather data, soil data, and observation data, and analyzes it using an AI model.

[0839] Input: Environmental information such as weather data, soil data, and observation data.

[0840] Data processing: Collecting data in real time and analyzing it with AI models (e.g., predicting future weather conditions using predictive models).

[0841] Output: Analyzed environmental data and farming advice derived from the results.

[0842] Specific operation: Collects weather and soil data through APIs and runs analytical models using TensorFlow.

[0843] Step 3:

[0844] Real-time advice generation and notification

[0845] Based on the results of analyzing the environmental data, the server generates real-time farming advice for agricultural producers and notifies them to the terminal.

[0846] Input: Parsed environmental data.

[0847] Data calculations: Based on the analysis results, the AI ​​model generates appropriate advice (e.g., the need for irrigation based on weather conditions).

[0848] Output: Specific farming advice communicated to farmers.

[0849] Specific operation: The analysis results are stored in a database and a push notification is sent to the farmer's device (e.g., smartphone).

[0850] Step 4:

[0851] Sentiment analysis and information provision

[0852] The server uses an emotion analysis engine to recognize the emotions of users (producers and consumers) and provides personalized information and promotions based on stress reduction and purchasing intentions.

[0853] Input: User's facial and voice data.

[0854] Data calculation: Recognize user emotions using a sentiment analysis engine (e.g., OpenCV or Google Cloud Natural Language API) and generate appropriate information based on that data.

[0855] Output: Personalized information and promotions.

[0856] Specific operation: The app uses the smartphone's camera and microphone to capture the user's facial expressions and voice, and displays recommended information and promotions based on the analysis results.

[0857] Step 5:

[0858] Data management using blockchain

[0859] The server uses blockchain technology to record and manage the entire process from agricultural production to consumption, and consumers can access the data in physical stores using a QR code.

[0860] Input: Data on production and distribution processes.

[0861] Data computation: Blockchain technology is used to ensure data integrity and tamper-proofing.

[0862] Output: Transparent production process information displayed by consumers by scanning a QR code.

[0863] How it works: Hyperledger Fabric is used to manage the blockchain network and write and read data. When a consumer scans a QR code with a smartphone app, the server retrieves the corresponding data from the blockchain and displays it.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] [Third embodiment]

[0868] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0869] 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.

[0870] 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).

[0871] 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.

[0872] 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.

[0873] 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).

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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.

[0879] 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."

[0880] The invention is a system that integrates generative AI models, environmental monitoring, and blockchain technology to help farmers learn and implement sustainable ecological agricultural practices, while providing consumers with transparency and trust from production to consumption.

[0881] Server Processing

[0882] 1. Information Collection:

[0883] The server launches a crawler to collect information about sustainable agricultural practices from research papers, blogs, forums, etc. on the internet. The collected data is then analyzed using natural language processing (NLP) models to extract relevant information. This information is then stored in a database that can be later accessed by farmers.

[0884] 2. Environmental Data Collection and Analysis:

[0885] The server collects real-time environmental data from various sources, including weather data, soil data, and observation data. This data is analyzed by an AI model, and real-time advice is generated for farmers on optimal farming practices, enabling them to carry out farming work efficiently without missing a beat.

[0886] 3. Blockchain Management:

[0887] The server uses blockchain technology to record and manage all data from the production process to the consumption process. This prevents data tampering and ensures that all processes are managed transparently and reliably. Consumers can scan the QR code to check the production process of the food they purchased in detail.

[0888] Device operation

[0889] 1. Information display:

[0890] Using a device (app or browser), farmers can access the server and view information on ecological farming practices extracted by generative AI and NLP models, allowing them to incorporate the latest knowledge and apply it to their farming practices.

[0891] 2. Receiving Advice:

[0892] The device sends real-time advice to farmers from the server, which is displayed in an easy-to-understand format and suggests optimal farming methods based on weather and soil conditions.

[0893] User operations

[0894] 1. Login and Information Access:

[0895] Farmers can log in to the system and access their accounts to view the collected and analyzed information and advice, and can also use the marketplace feature to network with other farmers and share experiences and best practices.

[0896] 2. Consumer scans the QR code:

[0897] When consumers buy food in stores, they can scan the QR code to learn about the food's production process and whether it was grown sustainably, allowing them to make informed purchasing decisions based on product context.

[0898] Specific examples

[0899] For example, if an agricultural producer is growing cabbage, the server will collect environmental information in real time from satellite data, weather data, soil data, etc., and generate advice such as, "There will be a lot of rain this week, so irrigation is not necessary." This advice is then sent to the agricultural producer via a terminal. The cabbage's production process is also recorded on the blockchain, and consumers can scan a QR code at the store to confirm that "this cabbage was grown using ecological methods."

[0900] In this way, the system of the present invention aims to help farmers adopt efficient and sustainable agricultural practices and provide consumers with transparent and reliable information, thereby contributing to the maturation and expansion of the sustainable agriculture community.

[0901] The processing flow will be explained below.

[0902] Detailed process flowchart

[0903] (Server processing)

[0904] Step 1:

[0905] The server periodically launches a crawler to collect information about sustainable agricultural practices from research papers, blogs, forums, etc. on the Internet. For example, it retrieves articles from specific websites and saves their contents in text format.

[0906] Step 2:

[0907] The server passes the collected text data to a natural language processing (NLP) model to extract relevant information, including article summaries and key keywords, allowing for efficient extraction of necessary insights from large amounts of information.

[0908] Step 3:

[0909] The server then stores the extracted relevant information in a database, making it available for later access by farmers.

[0910] Step 4:

[0911] The server collects environmental data (e.g., weather data, soil data, observation data) in real time from different sources, including using APIs to retrieve weather information.

[0912] Step 5:

[0913] The server passes the collected environmental data to an AI model for analysis, which then generates optimal farming advice for farmers in real time. For example, it suggests the appropriate timing for irrigation based on temperature and humidity data.

[0914] Step 6:

[0915] The server will use blockchain technology to record the entire process from agricultural production to consumption, which will increase transparency and reliability and prevent data tampering.

[0916] Terminal Processing

[0917] Step 1:

[0918] The terminal connects to the database so that farmers can access the system and view the information provided, which displays extracted information about ecological farming practices.

[0919] Step 2:

[0920] The terminals then notify farmers of real-time farming advice received from the server, for example by displaying a message such as "There has been a lot of rain this week, so no irrigation is necessary."

[0921] User processing

[0922] Step 1:

[0923] Users (farmers) log in to the system, access their account and view information. Once successfully logged in, they can use the marketplace feature to network with other farmers and share experiences and best practices.

[0924] Step 2:

[0925] When purchasing food in a store, users (consumers) can scan the QR code to view information about the food's production process and whether it was grown using sustainable methods. For example, a message such as "This cabbage was grown using ecological methods" may be displayed.

[0926] Specific examples

[0927] For example, if an agricultural producer is growing cabbage, the server will collect environmental information in real time from satellite data, weather data, soil data, etc., and generate advice such as, "There will be a lot of rain this week, so irrigation is not necessary." This advice is then sent to the agricultural producer via their device. In addition, the cabbage production process is recorded on the blockchain, and consumers can scan a QR code at the store to confirm that "this cabbage was grown using ecological methods."

[0928] In this way, the system of the present invention assists farmers in adopting efficient and sustainable agricultural practices and provides transparent and reliable information to consumers.

[0929] Example 1

[0930] 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."

[0931] Farmers seeking to implement sustainable agricultural practices face challenges accessing reliable, up-to-date, and effective advice. Consumers demand transparency about the production process of their food, but there are a lack of means to achieve this. Furthermore, farmers lack effective platforms for sharing knowledge and experiences with other farmers.

[0932] 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.

[0933] In this invention, the server includes: means for collecting information on sustainable agricultural practices from publicly available data sources using a generative AI model; means for analyzing the collected information using a natural language processing model to extract relevant information and store it in a database; means for collecting real-time environmental data from different sources and analyzing it using an AI model; means for generating real-time advice on agricultural work based on the analyzed environmental data and notifying the terminal; and means for recording the entire process from agricultural production to consumption using blockchain technology to ensure transparency and reliability. This allows agricultural producers to receive reliable, up-to-date information and advice, and consumers to enjoy transparency into the food production process. It also provides a platform for agricultural producers to share their knowledge and experience with each other.

[0934] "Generative AI model" refers to artificial intelligence technology for automatically collecting and analyzing information about sustainable agricultural practices from publicly available data sources.

[0935] A "natural language processing model" is a computer program that analyzes collected text data and extracts highly relevant information.

[0936] A "database" is a digital storage system for organizing and storing collected information and analysis results.

[0937] "Real-time environmental data" refers to environmental information related to agriculture, such as current weather data and soil data.

[0938] An "AI model" is an artificial intelligence technology that analyzes collected data and generates optimal advice for agricultural producers.

[0939] "Terminal" refers to the device that agricultural producers use to view information and advice, such as a smartphone or computer.

[0940] "Blockchain technology" is a distributed digital ledger technology that prevents data tampering and ensures transparency and reliability.

[0941] A "QR code" is a two-dimensional barcode that consumers can scan with a smartphone or other device to access detailed information.

[0942] MODE FOR CARRYING OUT THE INVENTION

[0943] This invention is a system that integrates generative AI models, environmental monitoring, and blockchain technology, and is designed primarily to meet the needs of agricultural producers and consumers. Regarding the mode for implementing the invention, the roles of the server, terminal, and user are clearly defined, and specific examples are presented.

[0944] Server configuration and functions

[0945] 1. Information gathering

[0946] The server uses a Python crawler (e.g., Beautiful Soup library) to collect information about sustainable agricultural practices from research papers, blogs, forums, etc. on the Internet. The collected data is then analyzed using natural language processing (NLP) models (e.g., NLTK library) to extract relevant information. This information is then stored in a database (e.g., MongoDB) for later access by farmers.

[0947] 2. Environmental data collection and analysis

[0948] The server collects environmental data in real time from weather data APIs (e.g., OpenWeatherMap), sensor devices, soil data APIs, etc. This data is analyzed using AI models (e.g., TensorFlow and Scikit-learn), and based on the analysis results, real-time advice on optimal farming practices is generated for agricultural producers.

[0949] 3. Implementation of Blockchain Technology

[0950] The server uses blockchain technology such as Hyperledger Fabric and Ethereum to record and manage all data from the production process to the consumption process, preventing data tampering and ensuring that all processes are managed transparently and reliably.

[0951] Device configuration and functions

[0952] 1. Information display

[0953] Farmers access the server using a device (app or browser) and can view information about ecological farming practices extracted by generative AI and NLP models through a mobile app built with React Native.

[0954] 2. Receiving Advice

[0955] The device sends real-time advice from the server to farmers via push notifications and emails. Notifications are sent using Firebase Cloud Messaging (FCM), and messages such as "There is a lot of rain this week, so there is no need to irrigate" are displayed.

[0956] User operations

[0957] 1. Login and Information Access

[0958] Agricultural producers log in to the system using an authentication system that uses OAuth 2.0. After logging in, they can not only view the collected and analyzed information and advice on a dashboard, but also use the marketplace function to network with other producers and share best practices.

[0959] 2. QR Code Scan

[0960] Consumers can check the production process and whether the food was grown sustainably by scanning the QR code on the food they purchased with a smartphone QR code reader (e.g., ZXing library). The scan results will display detailed information retrieved from the blockchain.

[0961] Specific examples

[0962] For example, if a farmer is growing cabbage, the server collects satellite, weather, and soil data in real time and generates advice such as, "There will be a lot of rain this week, so irrigation is not necessary." This advice is then sent to the farmer's smartphone via Firebase Cloud Messaging. The cabbage's production process is also recorded on the Ethereum blockchain, and consumers can scan the code at the store using a ZXing QR code scanner to confirm that the cabbage was grown using ecological methods.

[0963] Prompt Sentence Examples

[0964] "Please tell me how to grow cabbage ecologically."

[0965] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0966] Server Processing

[0967] Step 1: Gather information

[0968] Specific operation: The server launches the crawler using Python's Beautiful Soup library.

[0969] Input: A list of URLs for research papers, blogs, and forums on the internet.

[0970] Data processing / calculation: The crawler parses the HTML content and extracts text data, which is then analyzed using a natural language processing model (e.g., NLTK) to extract relevant information.

[0971] Output: The extracted information is stored in a database (e.g. MongoDB).

[0972] Step 2: Environmental data collection

[0973] Specific operation: The server collects real-time data from weather data APIs (e.g., OpenWeatherMap), soil data APIs, and sensor devices.

[0974] Input: API endpoints for weather, soil, and sensor data.

[0975] Data processing / calculation: Analyze the JSON data obtained from the API and extract the necessary information. Data from the soil sensor is sent directly to the server.

[0976] Output: The visualized environmental data is stored in a database.

[0977] Step 3: Data analysis

[0978] Specific operation: Analyze the collected data using an AI model (e.g., TensorFlow or Scikit-learn).

[0979] Input: Stored environmental and intelligence gathering data.

[0980] Data processing / calculation: AI models analyze data based on criteria and detect specific patterns or anomalies.

[0981] Output: The analysis results are saved in a database and used in the next step.

[0982] Step 4: Real-time advice generation

[0983] How it works: The server uses AI models to generate advice based on real-time environmental data.

[0984] Input: Analysis results and current environmental data.

[0985] Data processing / computation: AI models generate advice based on environmental conditions and format it in an easy-to-understand way.

[0986] Output: Advice is sent to the terminal.

[0987] Step 5: Record on the blockchain

[0988] Specific operation: The server uses Hyperledger Fabric or Ethereum to record data on the blockchain.

[0989] Input: All data from the production process to the consumption process.

[0990] Data processing / calculation: Smart contracts are used to record data as transactions, preventing tampering.

[0991] Output: The data recorded on the blockchain is visible to consumers.

[0992] Device operation

[0993] Step 1: Display information

[0994] How it works: Farmers access the server using a mobile app built with React Native.

[0995] Input: Information stored in a database.

[0996] Data processing / calculation: The app retrieves information and displays it in list or dashboard format.

[0997] Output: Agricultural producers can view relevant information on their terminals.

[0998] Step 2: Advice Notification

[0999] Specific operation: The advice is sent to the device using Firebase Cloud Messaging (FCM).

[1000] Input: Server-generated advice data.

[1001] Data processing / calculation: Notification messages are formatted in an easy-to-understand way.

[1002] Output: A notification will be displayed on the farmer's device.

[1003] User operations

[1004] Step 1: Log in and access your information

[1005] How it works: Farmers log in to the system using OAuth 2.0.

[1006] Input: User credentials (email address, password).

[1007] Data processing / computation: Credentials are verified and the user's session begins.

[1008] Output: A dashboard is accessible, allowing you to view the collected and analyzed information.

[1009] Step 2: Scan the QR code

[1010] How it works: Consumers scan the QR code in the store to check the production process.

[1011] Input: QR code attached to the product.

[1012] Data processing / calculation: Scan the code with a QR code reader (e.g., ZXing library) and access the blockchain to retrieve related information.

[1013] Output: Information about the product's production process and sustainable practices is displayed on the consumer's device.

[1014] (Application example 1)

[1015] 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."

[1016] In traditional agricultural systems, implementing sustainable agricultural practices and ensuring transparency have been challenging. In particular, there is a lack of information and real-time environmental advice to help agricultural producers learn and practice ecological methods. Furthermore, there are limited ways for consumers to check detailed information about the production process of the agricultural products they purchase in physical stores. This has made it difficult to gain consumer trust.

[1017] 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.

[1018] In this invention, the server includes: means for collecting information on sustainable agricultural practices from publicly available data sources using a generative AI model; means for analyzing the collected information and providing it in an easy-to-understand format for agricultural producers; means for collecting and analyzing real-time environmental data from different sources; means for generating real-time advice on agricultural work based on the analyzed environmental data; means for implementing distributed ledger technology to record the entire process from agricultural production to consumption and ensure transparency and trust; and means for consumers to scan an identification code attached to a product in a physical store and display detailed production information of the product, the agricultural method used, and environmental data. This makes it easier for agricultural producers to adopt effective and sustainable agricultural practices, and allows consumers to easily check the background information of the products they purchase, thereby improving trust and transparency.

[1019] A "generative AI model" is an artificial intelligence model that learns specific patterns and rules from data and provides generative answers to new data.

[1020] "Sustainable agricultural practices" are agricultural methods that are economically sustainable and socially acceptable while minimizing their impact on the environment.

[1021] "Distributed ledger technology" is a database technology that is shared and managed by multiple participants in a network and is difficult to tamper with, and blockchain is a prime example of this.

[1022] "Real-time environmental data" refers to data that instantly collects and updates current environmental conditions such as weather, soil, and temperature.

[1023] An "identification code" is a symbol or code used to uniquely identify a product or information, and examples of this include barcodes and QR codes.

[1024] An "electronic market" is a virtual market where goods and services are bought and sold through digital platforms such as the Internet.

[1025] "Visual device" means a device that provides a visual interface to a user, including head-mounted displays and smart glasses.

[1026] MODE FOR CARRYING OUT THE INVENTION

[1027] System Program

[1028] In the system of the present invention, the server includes the following means:

[1029] 1. Data collection methods for generative AI models:

[1030] The server uses a generative AI model to collect information about sustainable agricultural practices from multiple publicly available data sources, such as research papers, blogs, and forums, and analyzes it using a natural language processing (NLP) model, such as BERT or GPT-3.

[1031] 2. Information analysis and provision method:

[1032] The collected information is analyzed using generative AI and NLP models and presented in an easy-to-understand format for farmers. This information is stored in a database on a server and can be accessed by farmers via a browser or app.

[1033] 3. Real-time environmental data collection methods:

[1034] The server collects real-time environmental data, such as weather data and soil data, from various sensors and external data sources. For example, weather data can be obtained from the Internet using an API.

[1035] 4. Real-time advice generation methods:

[1036] Based on the collected and analyzed environmental data, generative AI models are used to generate real-time advice on farming practices, enabling farmers to carry out optimal practices at the right time.

[1037] 5. Distributed Ledger Technology Implementation Methods:

[1038] The server uses blockchain technology to record the entire process from agricultural production to consumption. This prevents data tampering and ensures that all processes are managed transparently and reliably. Consumers can scan the identification code to check the details of the food's production process.

[1039] Examples of specific examples and prompts

[1040] For example, when a consumer buys organic tomatoes at a supermarket, they scan an identification code using this system, and the server provides information such as, "The tomatoes were grown using sustainable agricultural practices. Organic fertilizer was used on the farm. Environmental data used includes soil moisture, sunshine hours, and temperature data." This information is recorded and managed using generative AI models and distributed ledger technology.

[1041] The following are examples of specific prompt sentences:

[1042] Please provide us with the latest information on sustainable agricultural practices. Specifically, please share details about your organic fertilizer use and insights into sustainable irrigation techniques.

[1043] explanation

[1044] 1. Hardware and Software:

[1045] The system includes a server, weather sensors, soil sensors, an identification code reader (e.g., a QR code reader), and a browser or smartphone app.

[1046] The server will be equipped with a generative AI model (e.g., GPT-3), an NLP model (e.g., BERT), a database (e.g., MySQL), and blockchain technology (e.g., Ethereum).

[1047] 2. Data processing and calculation:

[1048] Data collected by the server is used by an NLP model to extract relevant information, which is then converted by a generative AI model into optimal advice that is provided to farmers.

[1049] Environmental data collected in real time is analyzed and a generative AI model generates real-time advice on farming practices.

[1050] All data is managed using distributed ledger technology, and consumers can scan an identification code to obtain detailed information about the production process.

[1051] In this way, the invention is a system that makes it easier for farmers to adopt sustainable practices and provides transparent and reliable information to consumers.

[1052] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1053] Flow of the system program that realizes the application example

[1054] Step 1:

[1055] The server uses a generative AI model to collect information on sustainable agricultural practices from multiple publicly available data sources (e.g., research papers, blogs, forums, etc.). The server then launches a crawler to collect text data from the specified data source. The data source URL is required as input, and the collected text data is obtained as output.

[1056] Step 2:

[1057] The server then passes the collected text data to a natural language processing (NLP) model for analysis. The NLP model (e.g., BERT) extracts relevant information about sustainable agricultural practices from the collected text data. The input is the text data, and the output is the extracted relevant information.

[1058] Step 3:

[1059] The server stores the analyzed information in a database. Using a generative AI model, the information is organized and stored in a format that is easy for farmers to understand. The analyzed information is required as input, and the information stored in the database is obtained as output.

[1060] Step 4:

[1061] The server collects real-time environmental data from different sources, for example, weather data and soil data from APIs and sensors. It requires API keys and sensor data as inputs and provides the collected environmental data as output.

[1062] Step 5:

[1063] The server passes the collected environmental data to a generative AI model for analysis and generates real-time advice on farming. Environmental data is required as input, and real-time advice is obtained as output. The generated advice is sent to the terminal.

[1064] Step 6:

[1065] Using a device (smartphone or browser), farmers access the server and view the collected and analyzed information and real-time advice on sustainable agricultural practices. Data from the server is required as input, and displayed information is obtained as output.

[1066] Step 7:

[1067] The server records the entire process from agricultural production to consumption using blockchain technology. For example, using blockchain technology (e.g., Ethereum), each step of agricultural production is recorded as a transaction. Data for each step is required as input, and transaction data recorded on the blockchain is obtained as output.

[1068] Step 8:

[1069] Consumers scan the identification code at a physical store to obtain detailed production information about the product. By scanning the identification code (e.g., QR code) using a terminal, production process information is obtained from the server. The identification code is required as input, and detailed production information about the product is obtained as output.

[1070] At each step of the process, farmers are supported in implementing sustainable agricultural practices, and consumers can be assured of a transparent and reliable source of the products they purchase.

[1071] 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.

[1072] The invention is a system that integrates generative AI models, environmental monitoring, blockchain technology, and an emotion engine to help farmers learn and implement sustainable ecological agricultural practices, while providing consumers with transparency and trust from production to consumption.

[1073] Server Processing

[1074] 1. Information Collection:

[1075] The server launches a crawler to collect information about sustainable agricultural practices from research papers, blogs, forums, etc. on the internet. The collected data is then analyzed using natural language processing (NLP) models to extract relevant information. This information is then stored in a database that can be later accessed by farmers.

[1076] 2. Environmental Data Collection and Analysis:

[1077] The server collects real-time environmental data from different sources, such as weather data, soil data, and observation data. This data is analyzed by an AI model, and optimal farming advice is generated in real time for farmers.

[1078] 3. Emotion engine combination:

[1079] The server also uses an emotion engine to recognize the emotions of farmers and consumers and incorporates that emotional data into its analysis. For example, if it senses that a farmer is under high stress, it can adjust the content and format of advice and provide resources to reduce stress. It also analyzes consumers' purchasing intentions and optimizes information accordingly.

[1080] 4. Blockchain Management:

[1081] The server uses blockchain technology to record and manage all data from the production process to the consumption process. This prevents data tampering and ensures that all processes are managed transparently and reliably. Consumers can scan the QR code to check the production process of the food they purchased in detail.

[1082] Device operation

[1083] 1. Information display:

[1084] Using a device (app or browser), farmers can access the server and view information on ecological farming practices extracted by generative AI and NLP models. Based on emotional data recognized by the emotion engine, information and resources are provided to reduce stress and increase motivation, if needed.

[1085] 2. Receiving Advice:

[1086] The device then sends real-time farming advice to farmers from the server, displaying messages such as "There's been a lot of rain this week, so no irrigation is necessary."

[1087] User operations

[1088] 1. Login and Information Access:

[1089] Farmers can log in to the system, access their accounts, and view information. They can also use the marketplace function to network with other farmers and share experiences and best practices. The emotion engine monitors the farmer's emotional state and provides support information as needed.

[1090] 2. Consumer scans the QR code:

[1091] When consumers buy food in stores, they can scan the QR code to see the food's production history, and the emotion engine can analyze their reactions and provide additional information or promotions based on their purchasing intent.

[1092] Specific examples

[1093] For example, in the case of a cabbage farmer, the server collects environmental information in real time from satellite data, weather data, soil data, etc., and generates advice such as, "There will be a lot of rain this week, so there is no need for irrigation." This advice is notified to the farmer via his device. Furthermore, if the server uses its emotion engine to determine that the farmer's stress level is high, it will also provide a link to a video on how to relax.

[1094] The cabbage's production process is also recorded on the blockchain, and consumers can scan a QR code at the store to confirm that the cabbage was grown using ecological methods. Consumer responses are analyzed by an emotion engine, and interested consumers are provided with further promotional information.

[1095] In this way, the system of the present invention helps farmers adopt efficient and sustainable agricultural practices, provides transparent and reliable information to consumers, and, with the introduction of an emotion engine, enables more personalized assistance and information delivery, improving the user experience.

[1096] The processing flow will be explained below.

[1097] Detailed process flowchart

[1098] (Server processing)

[1099] Step 1:

[1100] The server periodically launches a crawler to collect information about sustainable agricultural practices from online research papers, blogs, forums, etc. For example, it scans multiple websites to retrieve articles and saves their contents in text format.

[1101] Step 2:

[1102] The server passes the collected text data through natural language processing (NLP) models to extract relevant information, including summarizing the text and extracting key keywords, such as summarizing the latest research findings on sustainable agricultural practices.

[1103] Step 3:

[1104] The server stores the extracted relevant information in a database, which allows farmers to access the information later. For example, the database may store summaries of each source.

[1105] Step 4:

[1106] The server collects real-time environmental data from different sources, such as weather data, soil data, and observation data. For example, it obtains information such as current temperature, precipitation, and humidity from a weather API.

[1107] Step 5:

[1108] The server passes the collected environmental data to an AI model for analysis. This allows the AI ​​model to generate optimal farming advice for farmers in real time. For example, based on current weather conditions and soil conditions, the model might generate advice such as "There will be a lot of rain this week, so irrigation is not necessary."

[1109] Step 6:

[1110] The server uses an emotion engine to recognize the emotions of farmers and consumers and incorporates that emotion data into its analysis. For example, if the server senses that a farmer is under high stress, it will adjust the content and format of advice and provide resources to reduce stress.

[1111] Step 7:

[1112] The server uses blockchain technology to record all data from the production process to the consumption process. This prevents data tampering and ensures that all processes are managed transparently and reliably. For example, the production history of agricultural products can be recorded on the blockchain.

[1113] Terminal Processing

[1114] Step 1:

[1115] The terminals connect to the database so that farmers can access the system and view the information provided. This displays information about ecological farming practices extracted by generative AI and NLP models. For example, farmers can view a summary of the latest sustainable farming methods on their terminals.

[1116] Step 2:

[1117] The terminals then notify farmers of real-time farming advice received from the server, for example displaying a message saying, "There's been a lot of rain this week, so no irrigation is necessary."

[1118] Step 3:

[1119] The device displays additional information based on the farmer's emotions as recognized by the emotion engine, for example providing a video link on relaxation techniques to a farmer who is under a lot of stress.

[1120] User processing

[1121] Step 1:

[1122] Users (farmers) log in to the system, access their account, and view information. Once successfully logged in, they can use the marketplace function to network with other farmers and share experiences and best practices. For example, farmers can chat with other farmers and exchange cultivation tips.

[1123] Step 2:

[1124] When purchasing food in a store, users (consumers) can scan the QR code to view information about the food's production process and whether it was grown using sustainable methods. For example, a message such as "This cabbage was grown using ecological methods" may be displayed.

[1125] Step 3:

[1126] The emotion engine analyzes the user's (consumer's) emotions and provides additional information and promotions according to their purchasing intent. For example, consumers who show interest will be shown promotional information about similar sustainable agricultural products.

[1127] Specific examples

[1128] For example, in the case of a cabbage farmer, the server collects environmental information in real time from satellite data, weather data, soil data, etc., and generates advice such as, "There will be a lot of rain this week, so there is no need for irrigation." This advice is notified to the farmer via his device. If the server uses its emotion engine to determine that the farmer's stress level is high, it will also provide a link to a video on how to relax.

[1129] Furthermore, the cabbage's production process is recorded on the blockchain, and consumers can scan a QR code at the store to confirm that the cabbage was grown using ecological methods. Consumer responses are analyzed by an emotion engine, and interested consumers are provided with further promotional information.

[1130] In this way, the system of the present invention helps farmers adopt efficient and sustainable agricultural practices, provides transparent and reliable information to consumers, and, with the introduction of an emotion engine, enables more personalized assistance and information delivery, improving the user experience.

[1131] Example 2

[1132] 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."

[1133] Modern agriculture faces many challenges in terms of profitability and sustainability. Farmers have difficulty efficiently gathering information on the latest sustainable agricultural practices and applying them to their daily operations. They are also required to understand the impact of climate change and environmental factors in real time and take optimal action based on that information. Furthermore, it is important to manage the process from production to consumption in a transparent and reliable manner, which requires the introduction of distributed ledger technology. On the consumer side, ensuring transparency in the production process and providing an environment in which they can purchase with confidence are challenges.

[1134] 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.

[1135] In this invention, the server includes: means for collecting information on sustainable agricultural practices from publicly available data sources using a generative AI model; means for analyzing the collected information and providing it in an easy-to-understand format for agricultural producers; means for collecting and analyzing real-time environmental data from various sources; means for generating real-time advice on agricultural work based on the analyzed environmental data; means for analyzing the emotions of agricultural producers and consumers using an emotion engine and providing personalized support information and purchasing promotion information; and means for implementing distributed ledger technology to record the entire process from agricultural production to consumption and ensure transparency and reliability. This enables agricultural producers to efficiently learn and practice the latest sustainable agricultural practices and to carry out optimal agricultural work based on real-time environmental data. Furthermore, the process from production to consumption can be managed in a transparent and reliable manner, providing consumers with a safe and secure purchasing environment.

[1136] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze large amounts of data and generate information for specific purposes.

[1137] "Publicly available data sources" are sources such as papers, blogs, forums, etc. that are freely accessible on the internet.

[1138] "Sustainable agricultural practices" refer to methods and initiatives for long-term agricultural production while preserving the environment.

[1139] "Real-time environmental data" refers to environmental information that is updated at each point in time, such as weather data and soil data.

[1140] The "emotion engine" is a system that analyzes the user's emotional state and suggests appropriate measures based on the results.

[1141] "Personalized support information" refers to information or advice that is individualized and provided according to the user's emotions and circumstances.

[1142] "Purchase promotion information" refers to promotional and sales information provided to increase consumers' desire to purchase.

[1143] "Distributed ledger technology" is a technology that maintains high transparency and reliability by distributing and recording and managing data across multiple nodes.

[1144] "Networking" refers to agricultural producers exchanging information and cooperating with other producers.

[1145] A "market" is a platform or venue where agricultural producers can come together to exchange information, share experiences and best practices.

[1146] The invention is a system that integrates generative AI models, environmental monitoring, distributed ledger technology, and an emotion engine to help farmers learn and implement sustainable agricultural practices, while providing consumers with transparency and trust from production to consumption.

[1147] Server Processing

[1148] The server first launches a crawler to collect information about sustainable agricultural practices from research papers, blogs, forums, and other sources on the Internet. For example, it uses the keyword "sustainable farming" to gather related information. The acquired data is then analyzed using a natural language processing (NLP) model to extract relevant information. This information is then stored in a database and made accessible to farmers.

[1149] The server then collects real-time environmental data from different sources, such as weather data, soil data, and observation data. This data is analyzed by an AI model to generate optimal farming advice for farmers. For example, real-time advice such as "There will be a lot of rain this week, so irrigation is not necessary" is generated.

[1150] The server also uses an emotion engine to analyze the emotions of agricultural producers and consumers. Based on the results of the emotion analysis, it provides agricultural producers with resources to reduce stress and consumers with purchasing promotion information. For example, if it determines that "Producer A is in a high stress state," it provides a link to a "video on relaxation methods."

[1151] Finally, the server uses distributed ledger technology to record and manage all data from the production process to the consumption process, preventing data tampering and ensuring that all processes are managed transparently and reliably. Consumers can scan the QR code to check the production process of the food they purchased in detail.

[1152] Device operation

[1153] The device (app or browser) accesses the server and displays information about ecological farming practices extracted by generative AI and NLP models. Based on the emotional data recognized by the emotion engine, information and resources are provided to reduce stress and increase motivation, if needed.

[1154] The device also notifies farmers of real-time farming advice sent from the server, such as "There's been a lot of rain this week, so no irrigation is necessary."

[1155] User operations

[1156] Farmers log in to the system, access their accounts, and view information. They can then use the marketplace function to network with other farmers and share experiences and best practices. The emotion engine monitors the farmer's emotional state and provides support information as needed.

[1157] When consumers buy food in stores, they can scan the QR code to see the food's production history, and the emotion engine can analyze their reactions and provide additional information or promotions based on their purchasing intent.

[1158] Specific examples

[1159] For example, in the case of a cabbage farmer, the server collects environmental information in real time from satellite data, weather data, soil data, etc., and generates advice such as, "There will be a lot of rain this week, so irrigation is not necessary." This advice is then sent to the farmer via their device. Furthermore, if the server uses an emotion engine to determine that the farmer's stress level is high, it will also provide a link to a video on how to relax. The cabbage production process is also recorded on distributed ledger technology, and by scanning a QR code at the store, consumers can confirm that the cabbage was grown using ecological methods. The emotion engine analyzes consumer responses, and interested consumers are provided with further promotional information.

[1160] Prompt Sentence Examples

[1161] Examples of prompts to input to a generative AI model include:

[1162] "Collect and summarize the latest research papers on sustainable agricultural practices."

[1163] "Based on current weather data, what is the best way to grow cabbage?"

[1164] "Generate relaxation advice for farmers in high-stress situations."

[1165] "Please explain how distributed ledger technology can be used to manage the production process of the food purchased by consumers."

[1166] The foregoing is a detailed description of the present invention, which allows both farmers and consumers to conveniently and efficiently implement sustainable agricultural practices and access transparent and reliable information.

[1167] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1168] Step 1: Gather information

[1169] Specific operation: The server launches a crawler to collect information about sustainable agricultural practices from research papers, blogs, forums, etc. on the Internet. For example, it uses the keyword "sustainable farming" to collect related information.

[1170] Input: A list of URLs on the Internet.

[1171] Data processing and calculation: The crawler accesses each URL, obtains HTML data, and extracts it as text.

[1172] Output: Collected text data.

[1173] Step 2: Data analysis

[1174] Specific operation: The server sends the collected data to a natural language processing (NLP) model to summarize the sentences and extract important keywords.

[1175] Input: Collected text data.

[1176] Data processing and calculation: NLP models analyze text data, extract relevant information, and generate summaries.

[1177] Output: Summarized information and extracted keywords.

[1178] Step 3: Save to database

[1179] What it does: The server stores the analyzed information in a database, where farmers can access it later.

[1180] Input: Abstracted information and extracted keywords.

[1181] Data processing and calculation: Organizing and storing information in a database.

[1182] Output: The saved information is stored in a database.

[1183] Step 4: Environmental data collection

[1184] Specific operation: The server accesses weather data provision services and soil data provision services to obtain environmental data in real time.

[1185] Input: API for weather data and soil data provision services.

[1186] Data processing and calculation: Obtain data from the API and compile it into an integrated dataset.

[1187] Output: A consolidated environmental dataset.

[1188] Step 5: Environmental data analysis

[1189] Specific operation: The server sends the integrated data to the AI ​​model and generates optimal farming advice.

[1190] Input: Integrated environmental dataset.

[1191] Data processing and calculation: The AI ​​model analyzes the data and generates specific farming advice.

[1192] Output: The generated farming advice.

[1193] Step 6: Collect emotional data

[1194] Specific operation: The server collects app usage data and feedback from farmers and consumers and sends it to the emotion engine.

[1195] Input: App usage data, feedback data.

[1196] Data processing and calculation: The emotion engine analyzes the data and determines the emotional state.

[1197] Output: Action instructions based on emotional state.

[1198] Step 7: Sentiment Data Analysis and Action

[1199] Specific operation: The server determines the appropriate action based on the results of the sentiment analysis.

[1200] Input: Action instructions based on emotional state.

[1201] Data processing and calculation: Provide producers with resources to reduce stress and optimize information to promote purchases for consumers.

[1202] Output: Personalized support and promotional information.

[1203] Step 8: Blockchain Management

[1204] Specific operation: The server records and manages all data from the production process to the consumption process using distributed ledger technology.

[1205] Input: Data from the production and consumption processes.

[1206] Data processing and calculation: Blockchain technology is used to record data one by one into blocks and verify transactions.

[1207] Output: Recorded data and generated QR code.

[1208] These are the specific processing steps of this system, which will enable farmers to efficiently learn and implement sustainable agricultural practices, while also providing consumers with transparency and reliability in the production process.

[1209] (Application example 2)

[1210] 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."

[1211] Today's agricultural producers face difficulties in easily obtaining information on sustainable agricultural practices and adopting highly efficient and ecological methods. Furthermore, there is a lack of systems that provide consumers with transparency and reliability in the production process while providing personalized purchasing support. In particular, there is a need for advanced personalization that reflects environmental data and production information in real time and utilizes sentiment analysis.

[1212] 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.

[1213] In this invention, the server includes: means for collecting information on sustainable agricultural practices from publicly available data sources using a generative AI model; means for analyzing the collected information and providing it in an easy-to-understand format for agricultural producers; means for collecting and analyzing real-time environmental data from various sources; means for generating real-time advice on agricultural work based on the analyzed environmental data; means for recording the entire process from agricultural production to consumption and implementing blockchain technology to ensure transparency and reliability; means for recognizing the emotions of agricultural producers and consumers using a sentiment analysis engine and providing information based on stress reduction and purchasing motivation; and means for showing consumers the production process of agricultural products using QR codes in physical stores and promoting the purchase of ecological products. This enables agricultural producers to learn and practice efficient and sustainable agricultural practices, provides consumers with transparency and reliability from production to consumption, and realizes personalized purchasing support using sentiment analysis.

[1214] A "generative AI model" is a type of artificial intelligence used to collect information from publicly available data sources, analyze that information, and generate it.

[1215] "Real-time environmental data" refers to data used to collect and analyze agriculturally relevant environmental information in real time, such as weather data and soil data.

[1216] "Blockchain technology" is a distributed database used to prevent data tampering and ensure transparency and reliability.

[1217] An "emotion analysis engine" is an algorithm or system that recognizes and analyzes a user's emotions and provides information and advice based on those emotions.

[1218] A "QR code" is a type of two-dimensional barcode that allows information to be easily read.

[1219] "Ecological products" are products that are produced in a sustainable manner with minimal impact on the environment.

[1220] "Agricultural practices" is a general term that refers to the methods, techniques, and procedures used in the agricultural sector.

[1221] "Real-time advice" refers to advice or instructions provided immediately based on data collected in real time.

[1222] "Transparency" refers to a state in which systems and data are made public and anyone can check their contents.

[1223] "Reliability" is a term used to describe a situation in which a system or data is accurate, error-free, and trustworthy.

[1224] "Personalized purchasing support" refers to providing purchasing suggestions and support that are customized to suit each individual consumer's preferences and circumstances.

[1225] To implement this invention, the system is configured as follows: The server uses a generative AI model to collect information on sustainable agricultural practices from publicly available data sources, analyzes the information, and provides it to agricultural producers. The server also collects and analyzes real-time environmental data from multiple sources, and generates and notifies agricultural producers of real-time advice based on the results.

[1226] Furthermore, the server uses blockchain technology to record the entire process from agricultural production to consumption, ensuring transparency and reliability. It uses a sentiment analysis engine to recognize the emotions of agricultural producers and consumers, and provides personalized information based on their stress levels and purchasing intentions. Specifically, QR codes can be used in physical stores to show consumers the production process of agricultural products, encouraging them to purchase eco-friendly products.

[1227] Program Description

[1228] The server system is built using Python and Django, and the front end is configured using React Native. The generative AI model is implemented using TensorFlow, and support for environmental data and sentiment analysis is provided using Google Cloud Natural Language API and OpenCV. PostgreSQL is used as the database, and blockchain technology is implemented using Hyperledger Fabric.

[1229] Specific examples

[1230] Information collection and display:

[1231] 1. The server uses a crawler to collect public information about agriculture from the Internet and analyzes it using an NLP (natural language processing) model.

[1232] 2. The analysis results are stored in a database, and farmers can access this information via a smartphone app.

[1233] Real-time advice:

[1234] 1. The server collects and analyzes environmental data in real time, such as weather data and soil data.

[1235] 2. Based on the analysis results, a message is sent to agricultural producers, for example, "There will be a lot of rain this week, so there is no need for irrigation."

[1236] Emotion analysis:

[1237] 1. When a consumer scans a QR code in a physical store, the server retrieves production process data from the blockchain.

[1238] 2. By analyzing the consumer's facial expressions and voice, we can recognize their stress and purchasing intent and provide them with appropriate purchasing information.

[1239] For example: "These cabbages were grown using ecological methods. They're a more environmentally friendly product. Are you interested?"

[1240] Prompt Sentence Examples

[1241] This cabbage was grown ecologically. Is it produced using sustainable farming methods?

[1242] Yes → The generative AI model will suggest ecological products in the same category.

[1243] No → Get information about other cabbages.

[1244]

[1245] I scanned your face and it looks like you're a little stressed. Would you like some tips on how to relax?

[1246] Yes → Provides videos on how to relax.

[1247] No → Proceed to the next step.

[1248] This will enable farmers to learn and implement efficient and sustainable agricultural practices, provide consumers with transparency and trust from production to consumption, and enable personalized purchasing support using sentiment analysis.

[1249] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1250] Step 1:

[1251] Information collection and analysis

[1252] The server uses a crawler to collect information about sustainable agricultural practices from research papers, blogs, forums, etc. on the Internet. The collected data is then analyzed using natural language processing (NLP) models to extract relevant information.

[1253] Input: Public information on the internet, such as research papers, blogs, forums, etc.

[1254] Data processing: The crawler collects information and analyzes it using NLP models (e.g., text summarization, keyword extraction).

[1255] Output: Information about the analyzed sustainable agricultural practices.

[1256] How it works: The crawler collects public information from the web and performs text analysis using a Python NLP library (e.g., SpaCy).

[1257] Step 2:

[1258] Environmental data collection and analysis

[1259] The server collects environmental data in real time from multiple sources, including weather data, soil data, and observation data, and analyzes it using an AI model.

[1260] Input: Environmental information such as weather data, soil data, and observation data.

[1261] Data processing: Collecting data in real time and analyzing it with AI models (e.g., predicting future weather conditions using predictive models).

[1262] Output: Analyzed environmental data and farming advice derived from the results.

[1263] Specific operation: Collects weather and soil data through APIs and runs analytical models using TensorFlow.

[1264] Step 3:

[1265] Real-time advice generation and notification

[1266] Based on the results of analyzing the environmental data, the server generates real-time farming advice for agricultural producers and notifies them to the terminal.

[1267] Input: Parsed environmental data.

[1268] Data calculations: Based on the analysis results, the AI ​​model generates appropriate advice (e.g., the need for irrigation based on weather conditions).

[1269] Output: Specific farming advice communicated to farmers.

[1270] Specific operation: The analysis results are stored in a database and a push notification is sent to the farmer's device (e.g., smartphone).

[1271] Step 4:

[1272] Sentiment analysis and information provision

[1273] The server uses an emotion analysis engine to recognize the emotions of users (producers and consumers) and provides personalized information and promotions based on stress reduction and purchasing intentions.

[1274] Input: User's facial and voice data.

[1275] Data calculation: Recognize user emotions using a sentiment analysis engine (e.g., OpenCV or Google Cloud Natural Language API) and generate appropriate information based on that data.

[1276] Output: Personalized information and promotions.

[1277] Specific operation: The app uses the smartphone's camera and microphone to capture the user's facial expressions and voice, and displays recommended information and promotions based on the analysis results.

[1278] Step 5:

[1279] Data management using blockchain

[1280] The server uses blockchain technology to record and manage the entire process from agricultural production to consumption, and consumers can access the data in physical stores using a QR code.

[1281] Input: Data on production and distribution processes.

[1282] Data computation: Blockchain technology is used to ensure data integrity and tamper-proofing.

[1283] Output: Transparent production process information displayed by consumers by scanning a QR code.

[1284] How it works: Hyperledger Fabric is used to manage the blockchain network and write and read data. When a consumer scans a QR code with a smartphone app, the server retrieves the corresponding data from the blockchain and displays it.

[1285] 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.

[1286] 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.

[1287] 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.

[1288] [Fourth embodiment]

[1289] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1290] 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.

[1291] 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).

[1292] 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.

[1293] 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.

[1294] 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).

[1295] 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.

[1296] 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.

[1297] 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.

[1298] 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.

[1299] 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.

[1300] 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.

[1301] 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."

[1302] The invention is a system that integrates generative AI models, environmental monitoring, and blockchain technology to help farmers learn and implement sustainable ecological agricultural practices, while providing consumers with transparency and trust from production to consumption.

[1303] Server Processing

[1304] 1. Information Collection:

[1305] The server launches a crawler to collect information about sustainable agricultural practices from research papers, blogs, forums, etc. on the internet. The collected data is then analyzed using natural language processing (NLP) models to extract relevant information. This information is then stored in a database that can be later accessed by farmers.

[1306] 2. Environmental Data Collection and Analysis:

[1307] The server collects real-time environmental data from various sources, including weather data, soil data, and observation data. This data is analyzed by an AI model, and real-time advice is generated for farmers on optimal farming practices, enabling them to carry out farming work efficiently without missing a beat.

[1308] 3. Blockchain Management:

[1309] The server uses blockchain technology to record and manage all data from the production process to the consumption process. This prevents data tampering and ensures that all processes are managed transparently and reliably. Consumers can scan the QR code to check the production process of the food they purchased in detail.

[1310] Device operation

[1311] 1. Information display:

[1312] Using a device (app or browser), farmers can access the server and view information on ecological farming practices extracted by generative AI and NLP models, allowing them to incorporate the latest knowledge and apply it to their farming practices.

[1313] 2. Receiving Advice:

[1314] The device sends real-time advice to farmers from the server, which is displayed in an easy-to-understand format and suggests optimal farming methods based on weather and soil conditions.

[1315] User operations

[1316] 1. Login and Information Access:

[1317] Farmers can log in to the system and access their accounts to view the collected and analyzed information and advice, and can also use the marketplace feature to network with other farmers and share experiences and best practices.

[1318] 2. Consumer scans the QR code:

[1319] When consumers buy food in stores, they can scan the QR code to learn about the food's production process and whether it was grown sustainably, allowing them to make informed purchasing decisions based on product context.

[1320] Specific examples

[1321] For example, if an agricultural producer is growing cabbage, the server will collect environmental information in real time from satellite data, weather data, soil data, etc., and generate advice such as, "There will be a lot of rain this week, so irrigation is not necessary." This advice is then sent to the agricultural producer via a terminal. The cabbage's production process is also recorded on the blockchain, and consumers can scan a QR code at the store to confirm that "this cabbage was grown using ecological methods."

[1322] In this way, the system of the present invention aims to help farmers adopt efficient and sustainable agricultural practices and provide consumers with transparent and reliable information, thereby contributing to the maturation and expansion of the sustainable agriculture community.

[1323] The processing flow will be explained below.

[1324] Detailed process flowchart

[1325] (Server processing)

[1326] Step 1:

[1327] The server periodically launches a crawler to collect information about sustainable agricultural practices from research papers, blogs, forums, etc. on the Internet. For example, it retrieves articles from specific websites and saves their contents in text format.

[1328] Step 2:

[1329] The server passes the collected text data to a natural language processing (NLP) model to extract relevant information, including article summaries and key keywords, allowing for efficient extraction of necessary insights from large amounts of information.

[1330] Step 3:

[1331] The server then stores the extracted relevant information in a database, making it available for later access by farmers.

[1332] Step 4:

[1333] The server collects environmental data (e.g., weather data, soil data, observation data) in real time from different sources, including using APIs to retrieve weather information.

[1334] Step 5:

[1335] The server passes the collected environmental data to an AI model for analysis, which then generates optimal farming advice for farmers in real time. For example, it suggests the appropriate timing for irrigation based on temperature and humidity data.

[1336] Step 6:

[1337] The server will use blockchain technology to record the entire process from agricultural production to consumption, which will increase transparency and reliability and prevent data tampering.

[1338] Terminal Processing

[1339] Step 1:

[1340] The terminal connects to the database so that farmers can access the system and view the information provided, which displays extracted information about ecological farming practices.

[1341] Step 2:

[1342] The terminals then notify farmers of real-time farming advice received from the server, for example by displaying a message such as "There has been a lot of rain this week, so no irrigation is necessary."

[1343] User processing

[1344] Step 1:

[1345] Users (farmers) log in to the system, access their account and view information. Once successfully logged in, they can use the marketplace feature to network with other farmers and share experiences and best practices.

[1346] Step 2:

[1347] When purchasing food in a store, users (consumers) can scan the QR code to view information about the food's production process and whether it was grown using sustainable methods. For example, a message such as "This cabbage was grown using ecological methods" may be displayed.

[1348] Specific examples

[1349] For example, if an agricultural producer is growing cabbage, the server will collect environmental information in real time from satellite data, weather data, soil data, etc., and generate advice such as, "There will be a lot of rain this week, so irrigation is not necessary." This advice is then sent to the agricultural producer via their device. In addition, the cabbage production process is recorded on the blockchain, and consumers can scan a QR code at the store to confirm that "this cabbage was grown using ecological methods."

[1350] In this way, the system of the present invention assists farmers in adopting efficient and sustainable agricultural practices and provides transparent and reliable information to consumers.

[1351] Example 1

[1352] 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."

[1353] Farmers seeking to implement sustainable agricultural practices face challenges accessing reliable, up-to-date, and effective advice. Consumers demand transparency about the production process of their food, but there are a lack of means to achieve this. Furthermore, farmers lack effective platforms for sharing knowledge and experiences with other farmers.

[1354] 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.

[1355] In this invention, the server includes: means for collecting information on sustainable agricultural practices from publicly available data sources using a generative AI model; means for analyzing the collected information using a natural language processing model to extract relevant information and store it in a database; means for collecting real-time environmental data from different sources and analyzing it using an AI model; means for generating real-time advice on agricultural work based on the analyzed environmental data and notifying the terminal; and means for recording the entire process from agricultural production to consumption using blockchain technology to ensure transparency and reliability. This allows agricultural producers to receive reliable, up-to-date information and advice, and consumers to enjoy transparency into the food production process. It also provides a platform for agricultural producers to share their knowledge and experience with each other.

[1356] "Generative AI model" refers to artificial intelligence technology for automatically collecting and analyzing information about sustainable agricultural practices from publicly available data sources.

[1357] A "natural language processing model" is a computer program that analyzes collected text data and extracts highly relevant information.

[1358] A "database" is a digital storage system for organizing and storing collected information and analysis results.

[1359] "Real-time environmental data" refers to environmental information related to agriculture, such as current weather data and soil data.

[1360] An "AI model" is an artificial intelligence technology that analyzes collected data and generates optimal advice for agricultural producers.

[1361] "Terminal" refers to the device that agricultural producers use to view information and advice, such as a smartphone or computer.

[1362] "Blockchain technology" is a distributed digital ledger technology that prevents data tampering and ensures transparency and reliability.

[1363] A "QR code" is a two-dimensional barcode that consumers can scan with a smartphone or other device to access detailed information.

[1364] MODE FOR CARRYING OUT THE INVENTION

[1365] This invention is a system that integrates generative AI models, environmental monitoring, and blockchain technology, and is designed primarily to meet the needs of agricultural producers and consumers. Regarding the mode for implementing the invention, the roles of the server, terminal, and user are clearly defined, and specific examples are presented.

[1366] Server configuration and functions

[1367] 1. Information gathering

[1368] The server uses a Python crawler (e.g., Beautiful Soup library) to collect information about sustainable agricultural practices from research papers, blogs, forums, etc. on the Internet. The collected data is then analyzed using natural language processing (NLP) models (e.g., NLTK library) to extract relevant information. This information is then stored in a database (e.g., MongoDB) for later access by farmers.

[1369] 2. Environmental data collection and analysis

[1370] The server collects environmental data in real time from weather data APIs (e.g., OpenWeatherMap), sensor devices, soil data APIs, etc. This data is analyzed using AI models (e.g., TensorFlow and Scikit-learn), and based on the analysis results, real-time advice on optimal farming practices is generated for agricultural producers.

[1371] 3. Implementation of Blockchain Technology

[1372] The server uses blockchain technology such as Hyperledger Fabric and Ethereum to record and manage all data from the production process to the consumption process, preventing data tampering and ensuring that all processes are managed transparently and reliably.

[1373] Device configuration and functions

[1374] 1. Information display

[1375] Farmers access the server using a device (app or browser) and can view information about ecological farming practices extracted by generative AI and NLP models through a mobile app built with React Native.

[1376] 2. Receiving Advice

[1377] The device sends real-time advice from the server to farmers via push notifications and emails. Notifications are sent using Firebase Cloud Messaging (FCM), and messages such as "There is a lot of rain this week, so there is no need to irrigate" are displayed.

[1378] User operations

[1379] 1. Login and Information Access

[1380] Agricultural producers log in to the system using an authentication system that uses OAuth 2.0. After logging in, they can not only view the collected and analyzed information and advice on a dashboard, but also use the marketplace function to network with other producers and share best practices.

[1381] 2. QR Code Scan

[1382] Consumers can check the production process and whether the food was grown sustainably by scanning the QR code on the food they purchased with a smartphone QR code reader (e.g., ZXing library). The scan results will display detailed information retrieved from the blockchain.

[1383] Specific examples

[1384] For example, if a farmer is growing cabbage, the server collects satellite, weather, and soil data in real time and generates advice such as, "There will be a lot of rain this week, so irrigation is not necessary." This advice is then sent to the farmer's smartphone via Firebase Cloud Messaging. The cabbage's production process is also recorded on the Ethereum blockchain, and consumers can scan the code at the store using a ZXing QR code scanner to confirm that the cabbage was grown using ecological methods.

[1385] Prompt Sentence Examples

[1386] "Please tell me how to grow cabbage ecologically."

[1387] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1388] Server Processing

[1389] Step 1: Gather information

[1390] Specific operation: The server launches the crawler using Python's Beautiful Soup library.

[1391] Input: A list of URLs for research papers, blogs, and forums on the internet.

[1392] Data processing / calculation: The crawler parses the HTML content and extracts text data, which is then analyzed using a natural language processing model (e.g., NLTK) to extract relevant information.

[1393] Output: The extracted information is stored in a database (e.g. MongoDB).

[1394] Step 2: Environmental data collection

[1395] Specific operation: The server collects real-time data from weather data APIs (e.g., OpenWeatherMap), soil data APIs, and sensor devices.

[1396] Input: API endpoints for weather, soil, and sensor data.

[1397] Data processing / calculation: Analyze the JSON data obtained from the API and extract the necessary information. Data from the soil sensor is sent directly to the server.

[1398] Output: The visualized environmental data is stored in a database.

[1399] Step 3: Data analysis

[1400] Specific operation: Analyze the collected data using an AI model (e.g., TensorFlow or Scikit-learn).

[1401] Input: Stored environmental and intelligence gathering data.

[1402] Data processing / calculation: AI models analyze data based on criteria and detect specific patterns or anomalies.

[1403] Output: The analysis results are saved in a database and used in the next step.

[1404] Step 4: Real-time advice generation

[1405] How it works: The server uses AI models to generate advice based on real-time environmental data.

[1406] Input: Analysis results and current environmental data.

[1407] Data processing / computation: AI models generate advice based on environmental conditions and format it in an easy-to-understand way.

[1408] Output: Advice is sent to the terminal.

[1409] Step 5: Record on the blockchain

[1410] Specific operation: The server uses Hyperledger Fabric or Ethereum to record data on the blockchain.

[1411] Input: All data from the production process to the consumption process.

[1412] Data processing / calculation: Smart contracts are used to record data as transactions, preventing tampering.

[1413] Output: The data recorded on the blockchain is visible to consumers.

[1414] Device operation

[1415] Step 1: Display information

[1416] How it works: Farmers access the server using a mobile app built with React Native.

[1417] Input: Information stored in a database.

[1418] Data processing / calculation: The app retrieves information and displays it in list or dashboard format.

[1419] Output: Agricultural producers can view relevant information on their terminals.

[1420] Step 2: Advice Notification

[1421] Specific operation: The advice is sent to the device using Firebase Cloud Messaging (FCM).

[1422] Input: Server-generated advice data.

[1423] Data processing / calculation: Notification messages are formatted in an easy-to-understand way.

[1424] Output: A notification will be displayed on the farmer's device.

[1425] User operations

[1426] Step 1: Log in and access your information

[1427] How it works: Farmers log in to the system using OAuth 2.0.

[1428] Input: User credentials (email address, password).

[1429] Data processing / computation: Credentials are verified and the user's session begins.

[1430] Output: A dashboard is accessible, allowing you to view the collected and analyzed information.

[1431] Step 2: Scan the QR code

[1432] How it works: Consumers scan the QR code in the store to check the production process.

[1433] Input: QR code attached to the product.

[1434] Data processing / calculation: Scan the code with a QR code reader (e.g., ZXing library) and access the blockchain to retrieve related information.

[1435] Output: Information about the product's production process and sustainable practices is displayed on the consumer's device.

[1436] (Application example 1)

[1437] 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."

[1438] In traditional agricultural systems, implementing sustainable agricultural practices and ensuring transparency have been challenging. In particular, there is a lack of information and real-time environmental advice to help agricultural producers learn and practice ecological methods. Furthermore, there are limited ways for consumers to check detailed information about the production process of the agricultural products they purchase in physical stores. This has made it difficult to gain consumer trust.

[1439] 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.

[1440] In this invention, the server includes: means for collecting information on sustainable agricultural practices from publicly available data sources using a generative AI model; means for analyzing the collected information and providing it in an easy-to-understand format for agricultural producers; means for collecting and analyzing real-time environmental data from different sources; means for generating real-time advice on agricultural work based on the analyzed environmental data; means for implementing distributed ledger technology to record the entire process from agricultural production to consumption and ensure transparency and trust; and means for consumers to scan an identification code attached to a product in a physical store and display detailed production information of the product, the agricultural method used, and environmental data. This makes it easier for agricultural producers to adopt effective and sustainable agricultural practices, and allows consumers to easily check the background information of the products they purchase, thereby improving trust and transparency.

[1441] A "generative AI model" is an artificial intelligence model that learns specific patterns and rules from data and provides generative answers to new data.

[1442] "Sustainable agricultural practices" are agricultural methods that are economically sustainable and socially acceptable while minimizing their impact on the environment.

[1443] "Distributed ledger technology" is a database technology that is shared and managed by multiple participants in a network and is difficult to tamper with, and blockchain is a prime example of this.

[1444] "Real-time environmental data" refers to data that instantly collects and updates current environmental conditions such as weather, soil, and temperature.

[1445] An "identification code" is a symbol or code used to uniquely identify a product or information, and examples of this include barcodes and QR codes.

[1446] An "electronic market" is a virtual market where goods and services are bought and sold through digital platforms such as the Internet.

[1447] "Visual device" means a device that provides a visual interface to a user, including head-mounted displays and smart glasses.

[1448] MODE FOR CARRYING OUT THE INVENTION

[1449] System Program

[1450] In the system of the present invention, the server includes the following means:

[1451] 1. Data collection methods for generative AI models:

[1452] The server uses a generative AI model to collect information about sustainable agricultural practices from multiple publicly available data sources, such as research papers, blogs, and forums, and analyzes it using a natural language processing (NLP) model, such as BERT or GPT-3.

[1453] 2. Information analysis and provision method:

[1454] The collected information is analyzed using generative AI and NLP models and presented in an easy-to-understand format for farmers. This information is stored in a database on a server and can be accessed by farmers via a browser or app.

[1455] 3. Real-time environmental data collection methods:

[1456] The server collects real-time environmental data, such as weather data and soil data, from various sensors and external data sources. For example, weather data can be obtained from the Internet using an API.

[1457] 4. Real-time advice generation methods:

[1458] Based on the collected and analyzed environmental data, generative AI models are used to generate real-time advice on farming practices, enabling farmers to carry out optimal practices at the right time.

[1459] 5. Distributed Ledger Technology Implementation Methods:

[1460] The server uses blockchain technology to record the entire process from agricultural production to consumption. This prevents data tampering and ensures that all processes are managed transparently and reliably. Consumers can scan the identification code to check the details of the food's production process.

[1461] Examples of specific examples and prompts

[1462] For example, when a consumer buys organic tomatoes at a supermarket, they scan an identification code using this system, and the server provides information such as, "The tomatoes were grown using sustainable agricultural practices. Organic fertilizer was used on the farm. Environmental data used includes soil moisture, sunshine hours, and temperature data." This information is recorded and managed using generative AI models and distributed ledger technology.

[1463] The following are examples of specific prompt sentences:

[1464] Please provide us with the latest information on sustainable agricultural practices. Specifically, please share details about your organic fertilizer use and insights into sustainable irrigation techniques.

[1465] explanation

[1466] 1. Hardware and Software:

[1467] The system includes a server, weather sensors, soil sensors, an identification code reader (e.g., a QR code reader), and a browser or smartphone app.

[1468] The server will be equipped with a generative AI model (e.g., GPT-3), an NLP model (e.g., BERT), a database (e.g., MySQL), and blockchain technology (e.g., Ethereum).

[1469] 2. Data processing and calculation:

[1470] Data collected by the server is used by an NLP model to extract relevant information, which is then converted by a generative AI model into optimal advice that is provided to farmers.

[1471] Environmental data collected in real time is analyzed and a generative AI model generates real-time advice on farming practices.

[1472] All data is managed using distributed ledger technology, and consumers can scan an identification code to obtain detailed information about the production process.

[1473] In this way, the invention is a system that makes it easier for farmers to adopt sustainable practices and provides transparent and reliable information to consumers.

[1474] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1475] Flow of the system program that realizes the application example

[1476] Step 1:

[1477] The server uses a generative AI model to collect information on sustainable agricultural practices from multiple publicly available data sources (e.g., research papers, blogs, forums, etc.). The server then launches a crawler to collect text data from the specified data source. The data source URL is required as input, and the collected text data is obtained as output.

[1478] Step 2:

[1479] The server then passes the collected text data to a natural language processing (NLP) model for analysis. The NLP model (e.g., BERT) extracts relevant information about sustainable agricultural practices from the collected text data. The input is the text data, and the output is the extracted relevant information.

[1480] Step 3:

[1481] The server stores the analyzed information in a database. Using a generative AI model, the information is organized and stored in a format that is easy for farmers to understand. The analyzed information is required as input, and the information stored in the database is obtained as output.

[1482] Step 4:

[1483] The server collects real-time environmental data from different sources, for example, weather data and soil data from APIs and sensors. It requires API keys and sensor data as inputs and provides the collected environmental data as output.

[1484] Step 5:

[1485] The server passes the collected environmental data to a generative AI model for analysis and generates real-time advice on farming. Environmental data is required as input, and real-time advice is obtained as output. The generated advice is sent to the terminal.

[1486] Step 6:

[1487] Using a device (smartphone or browser), farmers access the server and view the collected and analyzed information and real-time advice on sustainable agricultural practices. Data from the server is required as input, and displayed information is obtained as output.

[1488] Step 7:

[1489] The server records the entire process from agricultural production to consumption using blockchain technology. For example, using blockchain technology (e.g., Ethereum), each step of agricultural production is recorded as a transaction. Data for each step is required as input, and transaction data recorded on the blockchain is obtained as output.

[1490] Step 8:

[1491] Consumers scan the identification code at a physical store to obtain detailed production information about the product. By scanning the identification code (e.g., QR code) using a terminal, production process information is obtained from the server. The identification code is required as input, and detailed production information about the product is obtained as output.

[1492] At each step of the process, farmers are supported in implementing sustainable agricultural practices, and consumers can be assured of a transparent and reliable source of the products they purchase.

[1493] 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.

[1494] The invention is a system that integrates generative AI models, environmental monitoring, blockchain technology, and an emotion engine to help farmers learn and implement sustainable ecological agricultural practices, while providing consumers with transparency and trust from production to consumption.

[1495] Server Processing

[1496] 1. Information Collection:

[1497] The server launches a crawler to collect information about sustainable agricultural practices from research papers, blogs, forums, etc. on the internet. The collected data is then analyzed using natural language processing (NLP) models to extract relevant information. This information is then stored in a database that can be later accessed by farmers.

[1498] 2. Environmental Data Collection and Analysis:

[1499] The server collects real-time environmental data from different sources, such as weather data, soil data, and observation data. This data is analyzed by an AI model, and optimal farming advice is generated in real time for farmers.

[1500] 3. Emotion engine combination:

[1501] The server also uses an emotion engine to recognize the emotions of farmers and consumers and incorporates that emotional data into its analysis. For example, if it senses that a farmer is under high stress, it can adjust the content and format of advice and provide resources to reduce stress. It also analyzes consumers' purchasing intentions and optimizes information accordingly.

[1502] 4. Blockchain Management:

[1503] The server uses blockchain technology to record and manage all data from the production process to the consumption process. This prevents data tampering and ensures that all processes are managed transparently and reliably. Consumers can scan the QR code to check the production process of the food they purchased in detail.

[1504] Device operation

[1505] 1. Information display:

[1506] Using a device (app or browser), farmers can access the server and view information on ecological farming practices extracted by generative AI and NLP models. Based on emotional data recognized by the emotion engine, information and resources are provided to reduce stress and increase motivation, if needed.

[1507] 2. Receiving Advice:

[1508] The device then sends real-time farming advice to farmers from the server, displaying messages such as "There's been a lot of rain this week, so no irrigation is necessary."

[1509] User operations

[1510] 1. Login and Information Access:

[1511] Farmers can log in to the system, access their accounts, and view information. They can also use the marketplace function to network with other farmers and share experiences and best practices. The emotion engine monitors the farmer's emotional state and provides support information as needed.

[1512] 2. Consumer scans the QR code:

[1513] When consumers buy food in stores, they can scan the QR code to see the food's production history, and the emotion engine can analyze their reactions and provide additional information or promotions based on their purchasing intent.

[1514] Specific examples

[1515] For example, in the case of a cabbage farmer, the server collects environmental information in real time from satellite data, weather data, soil data, etc., and generates advice such as, "There will be a lot of rain this week, so there is no need for irrigation." This advice is notified to the farmer via his device. Furthermore, if the server uses its emotion engine to determine that the farmer's stress level is high, it will also provide a link to a video on how to relax.

[1516] The cabbage's production process is also recorded on the blockchain, and consumers can scan a QR code at the store to confirm that the cabbage was grown using ecological methods. Consumer responses are analyzed by an emotion engine, and interested consumers are provided with further promotional information.

[1517] In this way, the system of the present invention helps farmers adopt efficient and sustainable agricultural practices, provides transparent and reliable information to consumers, and, with the introduction of an emotion engine, enables more personalized assistance and information delivery, improving the user experience.

[1518] The processing flow will be explained below.

[1519] Detailed process flowchart

[1520] (Server processing)

[1521] Step 1:

[1522] The server periodically launches a crawler to collect information about sustainable agricultural practices from online research papers, blogs, forums, etc. For example, it scans multiple websites to retrieve articles and saves their contents in text format.

[1523] Step 2:

[1524] The server passes the collected text data through natural language processing (NLP) models to extract relevant information, including summarizing the text and extracting key keywords, such as summarizing the latest research findings on sustainable agricultural practices.

[1525] Step 3:

[1526] The server stores the extracted relevant information in a database, which allows farmers to access the information later. For example, the database may store summaries of each source.

[1527] Step 4:

[1528] The server collects real-time environmental data from different sources, such as weather data, soil data, and observation data. For example, it obtains information such as current temperature, precipitation, and humidity from a weather API.

[1529] Step 5:

[1530] The server passes the collected environmental data to an AI model for analysis. This allows the AI ​​model to generate optimal farming advice for farmers in real time. For example, based on current weather conditions and soil conditions, the model might generate advice such as "There will be a lot of rain this week, so irrigation is not necessary."

[1531] Step 6:

[1532] The server uses an emotion engine to recognize the emotions of farmers and consumers and incorporates that emotion data into its analysis. For example, if the server senses that a farmer is under high stress, it will adjust the content and format of advice and provide resources to reduce stress.

[1533] Step 7:

[1534] The server uses blockchain technology to record all data from the production process to the consumption process. This prevents data tampering and ensures that all processes are managed transparently and reliably. For example, the production history of agricultural products can be recorded on the blockchain.

[1535] Terminal Processing

[1536] Step 1:

[1537] The terminals connect to the database so that farmers can access the system and view the information provided. This displays information about ecological farming practices extracted by generative AI and NLP models. For example, farmers can view a summary of the latest sustainable farming methods on their terminals.

[1538] Step 2:

[1539] The terminals then notify farmers of real-time farming advice received from the server, for example displaying a message saying, "There's been a lot of rain this week, so no irrigation is necessary."

[1540] Step 3:

[1541] The device displays additional information based on the farmer's emotions as recognized by the emotion engine, for example providing a video link on relaxation techniques to a farmer who is under a lot of stress.

[1542] User processing

[1543] Step 1:

[1544] Users (farmers) log in to the system, access their account, and view information. Once successfully logged in, they can use the marketplace function to network with other farmers and share experiences and best practices. For example, farmers can chat with other farmers and exchange cultivation tips.

[1545] Step 2:

[1546] When purchasing food in a store, users (consumers) can scan the QR code to view information about the food's production process and whether it was grown using sustainable methods. For example, a message such as "This cabbage was grown using ecological methods" may be displayed.

[1547] Step 3:

[1548] The emotion engine analyzes the user's (consumer's) emotions and provides additional information and promotions according to their purchasing intent. For example, consumers who show interest will be shown promotional information about similar sustainable agricultural products.

[1549] Specific examples

[1550] For example, in the case of a cabbage farmer, the server collects environmental information in real time from satellite data, weather data, soil data, etc., and generates advice such as, "There will be a lot of rain this week, so there is no need for irrigation." This advice is notified to the farmer via his device. If the server uses its emotion engine to determine that the farmer's stress level is high, it will also provide a link to a video on how to relax.

[1551] Furthermore, the cabbage's production process is recorded on the blockchain, and consumers can scan a QR code at the store to confirm that the cabbage was grown using ecological methods. Consumer responses are analyzed by an emotion engine, and interested consumers are provided with further promotional information.

[1552] In this way, the system of the present invention helps farmers adopt efficient and sustainable agricultural practices, provides transparent and reliable information to consumers, and, with the introduction of an emotion engine, enables more personalized assistance and information delivery, improving the user experience.

[1553] Example 2

[1554] 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."

[1555] Modern agriculture faces many challenges in terms of profitability and sustainability. Farmers have difficulty efficiently gathering information on the latest sustainable agricultural practices and applying them to their daily operations. They are also required to understand the impact of climate change and environmental factors in real time and take optimal action based on that information. Furthermore, it is important to manage the process from production to consumption in a transparent and reliable manner, which requires the introduction of distributed ledger technology. On the consumer side, ensuring transparency in the production process and providing an environment in which they can purchase with confidence are challenges.

[1556] 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.

[1557] In this invention, the server includes: means for collecting information on sustainable agricultural practices from publicly available data sources using a generative AI model; means for analyzing the collected information and providing it in an easy-to-understand format for agricultural producers; means for collecting and analyzing real-time environmental data from various sources; means for generating real-time advice on agricultural work based on the analyzed environmental data; means for analyzing the emotions of agricultural producers and consumers using an emotion engine and providing personalized support information and purchasing promotion information; and means for implementing distributed ledger technology to record the entire process from agricultural production to consumption and ensure transparency and reliability. This enables agricultural producers to efficiently learn and practice the latest sustainable agricultural practices and to carry out optimal agricultural work based on real-time environmental data. Furthermore, the process from production to consumption can be managed in a transparent and reliable manner, providing consumers with a safe and secure purchasing environment.

[1558] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze large amounts of data and generate information for specific purposes.

[1559] "Publicly available data sources" are sources such as papers, blogs, forums, etc. that are freely accessible on the internet.

[1560] "Sustainable agricultural practices" refer to methods and initiatives for long-term agricultural production while preserving the environment.

[1561] "Real-time environmental data" refers to environmental information that is updated at each point in time, such as weather data and soil data.

[1562] The "emotion engine" is a system that analyzes the user's emotional state and suggests appropriate measures based on the results.

[1563] "Personalized support information" refers to information or advice that is individualized and provided according to the user's emotions and circumstances.

[1564] "Purchase promotion information" refers to promotional and sales information provided to increase consumers' desire to purchase.

[1565] "Distributed ledger technology" is a technology that maintains high transparency and reliability by distributing and recording and managing data across multiple nodes.

[1566] "Networking" refers to agricultural producers exchanging information and cooperating with other producers.

[1567] A "market" is a platform or venue where agricultural producers can come together to exchange information, share experiences and best practices.

[1568] The invention is a system that integrates generative AI models, environmental monitoring, distributed ledger technology, and an emotion engine to help farmers learn and implement sustainable agricultural practices, while providing consumers with transparency and trust from production to consumption.

[1569] Server Processing

[1570] The server first launches a crawler to collect information about sustainable agricultural practices from research papers, blogs, forums, and other sources on the Internet. For example, it uses the keyword "sustainable farming" to gather related information. The acquired data is then analyzed using a natural language processing (NLP) model to extract relevant information. This information is then stored in a database and made accessible to farmers.

[1571] The server then collects real-time environmental data from different sources, such as weather data, soil data, and observation data. This data is analyzed by an AI model to generate optimal farming advice for farmers. For example, real-time advice such as "There will be a lot of rain this week, so irrigation is not necessary" is generated.

[1572] The server also uses an emotion engine to analyze the emotions of agricultural producers and consumers. Based on the results of the emotion analysis, it provides agricultural producers with resources to reduce stress and consumers with purchasing promotion information. For example, if it determines that "Producer A is in a high stress state," it provides a link to a "video on relaxation methods."

[1573] Finally, the server uses distributed ledger technology to record and manage all data from the production process to the consumption process, preventing data tampering and ensuring that all processes are managed transparently and reliably. Consumers can scan the QR code to check the production process of the food they purchased in detail.

[1574] Device operation

[1575] The device (app or browser) accesses the server and displays information about ecological farming practices extracted by generative AI and NLP models. Based on the emotional data recognized by the emotion engine, information and resources are provided to reduce stress and increase motivation, if needed.

[1576] The device also notifies farmers of real-time farming advice sent from the server, such as "There's been a lot of rain this week, so no irrigation is necessary."

[1577] User operations

[1578] Farmers log in to the system, access their accounts, and view information. They can then use the marketplace function to network with other farmers and share experiences and best practices. The emotion engine monitors the farmer's emotional state and provides support information as needed.

[1579] When consumers buy food in stores, they can scan the QR code to see the food's production history, and the emotion engine can analyze their reactions and provide additional information or promotions based on their purchasing intent.

[1580] Specific examples

[1581] For example, in the case of a cabbage farmer, the server collects environmental information in real time from satellite data, weather data, soil data, etc., and generates advice such as, "There will be a lot of rain this week, so irrigation is not necessary." This advice is then sent to the farmer via their device. Furthermore, if the server uses an emotion engine to determine that the farmer's stress level is high, it will also provide a link to a video on how to relax. The cabbage production process is also recorded on distributed ledger technology, and by scanning a QR code at the store, consumers can confirm that the cabbage was grown using ecological methods. The emotion engine analyzes consumer responses, and interested consumers are provided with further promotional information.

[1582] Prompt Sentence Examples

[1583] Examples of prompts to input to a generative AI model include:

[1584] "Collect and summarize the latest research papers on sustainable agricultural practices."

[1585] "Based on current weather data, what is the best way to grow cabbage?"

[1586] "Generate relaxation advice for farmers in high-stress situations."

[1587] "Please explain how distributed ledger technology can be used to manage the production process of the food purchased by consumers."

[1588] The foregoing is a detailed description of the present invention, which allows both farmers and consumers to conveniently and efficiently implement sustainable agricultural practices and access transparent and reliable information.

[1589] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1590] Step 1: Gather information

[1591] Specific operation: The server launches a crawler to collect information about sustainable agricultural practices from research papers, blogs, forums, etc. on the Internet. For example, it uses the keyword "sustainable farming" to collect related information.

[1592] Input: A list of URLs on the Internet.

[1593] Data processing and calculation: The crawler accesses each URL, obtains HTML data, and extracts it as text.

[1594] Output: Collected text data.

[1595] Step 2: Data analysis

[1596] Specific operation: The server sends the collected data to a natural language processing (NLP) model to summarize the sentences and extract important keywords.

[1597] Input: Collected text data.

[1598] Data processing and calculation: NLP models analyze text data, extract relevant information, and generate summaries.

[1599] Output: Summarized information and extracted keywords.

[1600] Step 3: Save to database

[1601] What it does: The server stores the analyzed information in a database, where farmers can access it later.

[1602] Input: Abstracted information and extracted keywords.

[1603] Data processing and calculation: Organizing and storing information in a database.

[1604] Output: The saved information is stored in a database.

[1605] Step 4: Environmental data collection

[1606] Specific operation: The server accesses weather data provision services and soil data provision services to obtain environmental data in real time.

[1607] Input: API for weather data and soil data provision services.

[1608] Data processing and calculation: Obtain data from the API and compile it into an integrated dataset.

[1609] Output: A consolidated environmental dataset.

[1610] Step 5: Environmental data analysis

[1611] Specific operation: The server sends the integrated data to the AI ​​model and generates optimal farming advice.

[1612] Input: Integrated environmental dataset.

[1613] Data processing and calculation: The AI ​​model analyzes the data and generates specific farming advice.

[1614] Output: The generated farming advice.

[1615] Step 6: Collect emotional data

[1616] Specific operation: The server collects app usage data and feedback from farmers and consumers and sends it to the emotion engine.

[1617] Input: App usage data, feedback data.

[1618] Data processing and calculation: The emotion engine analyzes the data and determines the emotional state.

[1619] Output: Action instructions based on emotional state.

[1620] Step 7: Sentiment Data Analysis and Action

[1621] Specific operation: The server determines the appropriate action based on the results of the sentiment analysis.

[1622] Input: Action instructions based on emotional state.

[1623] Data processing and calculation: Provide producers with resources to reduce stress and optimize information to promote purchases for consumers.

[1624] Output: Personalized support and promotional information.

[1625] Step 8: Blockchain Management

[1626] Specific operation: The server records and manages all data from the production process to the consumption process using distributed ledger technology.

[1627] Input: Data from the production and consumption processes.

[1628] Data processing and calculation: Blockchain technology is used to record data one by one into blocks and verify transactions.

[1629] Output: Recorded data and generated QR code.

[1630] These are the specific processing steps of this system, which will enable farmers to efficiently learn and implement sustainable agricultural practices, while also providing consumers with transparency and reliability in the production process.

[1631] (Application example 2)

[1632] 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."

[1633] Today's agricultural producers face difficulties in easily obtaining information on sustainable agricultural practices and adopting highly efficient and ecological methods. Furthermore, there is a lack of systems that provide consumers with transparency and reliability in the production process while providing personalized purchasing support. In particular, there is a need for advanced personalization that reflects environmental data and production information in real time and utilizes sentiment analysis.

[1634] 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.

[1635] In this invention, the server includes: means for collecting information on sustainable agricultural practices from publicly available data sources using a generative AI model; means for analyzing the collected information and providing it in an easy-to-understand format for agricultural producers; means for collecting and analyzing real-time environmental data from various sources; means for generating real-time advice on agricultural work based on the analyzed environmental data; means for recording the entire process from agricultural production to consumption and implementing blockchain technology to ensure transparency and reliability; means for recognizing the emotions of agricultural producers and consumers using a sentiment analysis engine and providing information based on stress reduction and purchasing motivation; and means for showing consumers the production process of agricultural products using QR codes in physical stores and promoting the purchase of ecological products. This enables agricultural producers to learn and practice efficient and sustainable agricultural practices, provides consumers with transparency and reliability from production to consumption, and realizes personalized purchasing support using sentiment analysis.

[1636] A "generative AI model" is a type of artificial intelligence used to collect information from publicly available data sources, analyze that information, and generate it.

[1637] "Real-time environmental data" refers to data used to collect and analyze agriculturally relevant environmental information in real time, such as weather data and soil data.

[1638] "Blockchain technology" is a distributed database used to prevent data tampering and ensure transparency and reliability.

[1639] An "emotion analysis engine" is an algorithm or system that recognizes and analyzes a user's emotions and provides information and advice based on those emotions.

[1640] A "QR code" is a type of two-dimensional barcode that allows information to be easily read.

[1641] "Ecological products" are products that are produced in a sustainable manner with minimal impact on the environment.

[1642] "Agricultural practices" is a general term that refers to the methods, techniques, and procedures used in the agricultural sector.

[1643] "Real-time advice" refers to advice or instructions provided immediately based on data collected in real time.

[1644] "Transparency" refers to a state in which systems and data are made public and anyone can check their contents.

[1645] "Reliability" is a term used to describe a situation in which a system or data is accurate, error-free, and trustworthy.

[1646] "Personalized purchasing support" refers to providing purchasing suggestions and support that are customized to suit each individual consumer's preferences and circumstances.

[1647] To implement this invention, the system is configured as follows: The server uses a generative AI model to collect information on sustainable agricultural practices from publicly available data sources, analyzes the information, and provides it to agricultural producers. The server also collects and analyzes real-time environmental data from multiple sources, and generates and notifies agricultural producers of real-time advice based on the results.

[1648] Furthermore, the server uses blockchain technology to record the entire process from agricultural production to consumption, ensuring transparency and reliability. It uses a sentiment analysis engine to recognize the emotions of agricultural producers and consumers, and provides personalized information based on their stress levels and purchasing intentions. Specifically, QR codes can be used in physical stores to show consumers the production process of agricultural products, encouraging them to purchase eco-friendly products.

[1649] Program Description

[1650] The server system is built using Python and Django, and the front end is configured using React Native. The generative AI model is implemented using TensorFlow, and support for environmental data and sentiment analysis is provided using Google Cloud Natural Language API and OpenCV. PostgreSQL is used as the database, and blockchain technology is implemented using Hyperledger Fabric.

[1651] Specific examples

[1652] Information collection and display:

[1653] 1. The server uses a crawler to collect public information about agriculture from the Internet and analyzes it using an NLP (natural language processing) model.

[1654] 2. The analysis results are stored in a database, and farmers can access this information via a smartphone app.

[1655] Real-time advice:

[1656] 1. The server collects and analyzes environmental data in real time, such as weather data and soil data.

[1657] 2. Based on the analysis results, a message is sent to agricultural producers, for example, "There will be a lot of rain this week, so there is no need for irrigation."

[1658] Emotion analysis:

[1659] 1. When a consumer scans a QR code in a physical store, the server retrieves production process data from the blockchain.

[1660] 2. By analyzing the consumer's facial expressions and voice, we can recognize their stress and purchasing intent and provide them with appropriate purchasing information.

[1661] For example: "These cabbages were grown using ecological methods. They're a more environmentally friendly product. Are you interested?"

[1662] Prompt Sentence Examples

[1663] This cabbage was grown ecologically. Is it produced using sustainable farming methods?

[1664] Yes → The generative AI model will suggest ecological products in the same category.

[1665] No → Get information about other cabbages.

[1666]

[1667] I scanned your face and it looks like you're a little stressed. Would you like some tips on how to relax?

[1668] Yes → Provides videos on how to relax.

[1669] No → Proceed to the next step.

[1670] This will enable farmers to learn and implement efficient and sustainable agricultural practices, provide consumers with transparency and trust from production to consumption, and enable personalized purchasing support using sentiment analysis.

[1671] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1672] Step 1:

[1673] Information collection and analysis

[1674] The server uses a crawler to collect information about sustainable agricultural practices from research papers, blogs, forums, etc. on the Internet. The collected data is then analyzed using natural language processing (NLP) models to extract relevant information.

[1675] Input: Public information on the internet, such as research papers, blogs, forums, etc.

[1676] Data processing: The crawler collects information and analyzes it using NLP models (e.g., text summarization, keyword extraction).

[1677] Output: Information about the analyzed sustainable agricultural practices.

[1678] How it works: The crawler collects public information from the web and performs text analysis using a Python NLP library (e.g., SpaCy).

[1679] Step 2:

[1680] Environmental data collection and analysis

[1681] The server collects environmental data in real time from multiple sources, including weather data, soil data, and observation data, and analyzes it using an AI model.

[1682] Input: Environmental information such as weather data, soil data, and observation data.

[1683] Data processing: Collecting data in real time and analyzing it with AI models (e.g., predicting future weather conditions using predictive models).

[1684] Output: Analyzed environmental data and farming advice derived from the results.

[1685] Specific operation: Collects weather and soil data through APIs and runs analytical models using TensorFlow.

[1686] Step 3:

[1687] Real-time advice generation and notification

[1688] Based on the results of analyzing the environmental data, the server generates real-time farming advice for agricultural producers and notifies them to the terminal.

[1689] Input: Parsed environmental data.

[1690] Data calculations: Based on the analysis results, the AI ​​model generates appropriate advice (e.g., the need for irrigation based on weather conditions).

[1691] Output: Specific farming advice communicated to farmers.

[1692] Specific operation: The analysis results are stored in a database and a push notification is sent to the farmer's device (e.g., smartphone).

[1693] Step 4:

[1694] Sentiment analysis and information provision

[1695] The server uses an emotion analysis engine to recognize the emotions of users (producers and consumers) and provides personalized information and promotions based on stress reduction and purchasing intentions.

[1696] Input: User's facial and voice data.

[1697] Data calculation: Recognize user emotions using a sentiment analysis engine (e.g., OpenCV or Google Cloud Natural Language API) and generate appropriate information based on that data.

[1698] Output: Personalized information and promotions.

[1699] Specific operation: The app uses the smartphone's camera and microphone to capture the user's facial expressions and voice, and displays recommended information and promotions based on the analysis results.

[1700] Step 5:

[1701] Data management using blockchain

[1702] The server uses blockchain technology to record and manage the entire process from agricultural production to consumption, and consumers can access the data in physical stores using a QR code.

[1703] Input: Data on production and distribution processes.

[1704] Data computation: Blockchain technology is used to ensure data integrity and tamper-proofing.

[1705] Output: Transparent production process information displayed by consumers by scanning a QR code.

[1706] How it works: Hyperledger Fabric is used to manage the blockchain network and write and read data. When a consumer scans a QR code with a smartphone app, the server retrieves the corresponding data from the blockchain and displays it.

[1707] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1708] 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.

[1709] 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 robot 414.

[1710] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1711] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1712] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1713] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1714] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1715] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1716] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1717] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1718] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1719] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1720] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1721] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1722] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1723] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1724] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1725] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1726] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1727] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1728] The following is further disclosed regarding the above embodiment.

[1729] (Claim 1)

[1730] A means of collecting information on sustainable agricultural practices from publicly available data sources using generative AI models; and

[1731] A means to analyze the collected information and provide it in an easy-to-understand format for agricultural producers,

[1732] a means for collecting and analyzing real-time environmental data from different sources;

[1733] means for generating real-time advice on agricultural operations based on the analyzed environmental data;

[1734] Implementing blockchain technology to record the entire process from agricultural production to consumption, ensuring transparency and reliability;

[1735] A system including:

[1736] (Claim 2)

[1737] providing a means for agricultural producers to network with other producers and provide a marketplace for them to share experiences and best practices;

[1738] A means to store the information collected using the generative AI model in a database and make it accessible to agricultural producers;

[1739] The system of claim 1, comprising at least one of:

[1740] (Claim 3)

[1741] Using blockchain technology, consumers can check the production process of agricultural products through QR codes, etc.

[1742] A means of providing agricultural producers with operational advice based on environmental data in real time;

[1743] The system of claim 1, comprising at least one of:

[1744] "Example 1"

[1745] (Claim 1)

[1746] A means of collecting information on sustainable agricultural practices from publicly available data sources using generative AI models; and

[1747] A means of analyzing the collected information using a natural language processing model, extracting highly relevant information, and storing it in a database;

[1748] A means to collect real-time environmental data from different sources and analyze it using AI models;

[1749] A means for generating real-time advice on farm work based on the analyzed environmental data and notifying the advice to a terminal;

[1750] A means to record the entire process from agricultural production to consumption using blockchain technology to ensure transparency and reliability,

[1751] A system including:

[1752] (Claim 2)

[1753] providing a means for agricultural producers to network with other producers and provide a marketplace for them to share experiences and best practices;

[1754] A means to store the information collected using the generative AI model in a database and make it accessible to agricultural producers;

[1755] The system of claim 1, comprising at least one of:

[1756] (Claim 3)

[1757] Using blockchain technology, consumers can check the production process of agricultural products through QR codes, etc.

[1758] A means for generating farming advice based on environmental data for agricultural producers in real time and notifying them to their terminals;

[1759] The system of claim 1, comprising at least one of:

[1760] "Application Example 1"

[1761] (Claim 1)

[1762] A means of collecting information on sustainable agricultural practices from publicly available data sources using generative AI models; and

[1763] A means to analyze the collected information and provide it in an easy-to-understand format for agricultural producers,

[1764] a means for collecting and analyzing real-time environmental data from different sources;

[1765] means for generating real-time advice on agricultural operations based on the analyzed environmental data;

[1766] Implementing distributed ledger technology to record the entire process from agricultural production to consumption, ensuring transparency and reliability;

[1767] A means for consumers to scan the identification code on a product in a physical store to view detailed production information, agricultural methods used, and environmental data for the product;

[1768] A system including:

[1769] (Claim 2)

[1770] providing a means for agricultural producers to network with other producers and share experiences and best practices;

[1771] A means to store the information collected using the generative AI model in a database and make it accessible to agricultural producers;

[1772] The system of claim 1, comprising at least one of:

[1773] (Claim 3)

[1774] Using distributed ledger technology, we will enable consumers to check the production process of agricultural products through identification codes, etc.

[1775] A means of providing agricultural producers with operational advice based on environmental data in real time;

[1776] A means for providing an experience of virtually visiting agricultural producers' farms using visual devices;

[1777] The system of claim 1, comprising at least one of:

[1778] "Example 2: Combining Emotion Engines"

[1779] (Claim 1)

[1780] A means of collecting information on sustainable agricultural practices from publicly available data sources using generative AI models; and

[1781] A means to analyze the collected information and provide it in an easy-to-understand format for agricultural producers,

[1782] a means for collecting and analyzing real-time environmental data from different sources;

[1783] means for generating real-time advice on agricultural operations based on the analyzed environmental data;

[1784] A means for analyzing emotions of agricultural producers and consumers using an emotion engine and providing personalized support information and purchase promotion information;

[1785] Implementing distributed ledger technology to record the entire process from agricultural production to consumption, ensuring transparency and reliability;

[1786] A system including:

[1787] (Claim 2)

[1788] providing a means for agricultural producers to network with other producers and provide a marketplace for sharing experiences and best practices;

[1789] A means to store the information collected using the generative AI model in a database and make it accessible to agricultural producers;

[1790] The system of claim 1, comprising at least one of:

[1791] (Claim 3)

[1792] Using distributed ledger technology, we will enable consumers to check the production process of agricultural pr...

Claims

1. A means of collecting information on sustainable agricultural practices from publicly available data sources using generative AI models; and A means to analyze the collected information and provide it in an easy-to-understand format for agricultural producers, a means for collecting and analyzing real-time environmental data from different sources; means for generating real-time advice on agricultural operations based on the analyzed environmental data; Implementing blockchain technology to record the entire process from agricultural production to consumption, ensuring transparency and reliability; A system including:

2. providing a means for agricultural producers to network with other producers and provide a marketplace for them to share experiences and best practices; A means to store the information collected using the generative AI model in a database and make it accessible to agricultural producers; The system of claim 1 , comprising at least one of:

3. Using blockchain technology, consumers can check the production process of agricultural products through QR codes, etc. A means of providing agricultural producers with operational advice based on environmental data in real time; The system of claim 1 , comprising at least one of:

Images