System

Abstract

A system is provided.SOLUTION: The system includes a means for inputting a category of crops and a target point, a means for collecting environmental data, a means for analyzing the environmental data and generating an optimum crop list and a cultivation schedule, a means for displaying the generated crop list and cultivation schedule, a means for managing and instructing cultivation, and a means for automatically generating contents of a blog and a home page.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] Modern agriculture is facing an aging workforce and a lack of successors, making it difficult for small-scale farmers to find efficient cultivation methods and stable sources of income. In addition, in order to increase the brand power of crops, they must appeal to consumers in an attractive way. However, these challenges are difficult to resolve on an individual basis, and a comprehensive support system is needed. Conventional systems are unable to consistently handle environmental data collection, crop selection, cultivation management, and marketing support, making it difficult to achieve efficient agricultural management. [Means for solving the problem]

[0005] To address these challenges, the present invention provides a means for inputting crop categories and key features, and collects and analyzes environmental data based on this information. Specifically, it acquires climate data, soil data, and sunshine data using environmental sensors and cameras, and also collects data using APIs from weather information providers. The collected data is used to generate an optimal crop list and cultivation schedule. The generated list and schedule are displayed to the user, allowing them to create an efficient cultivation plan. Furthermore, during cultivation, the system connects with devices such as smart scarecrows to provide management and instructions, improving work efficiency in real time. Furthermore, it automatically generates content for blogs and websites using data and photos obtained during the cultivation process, providing a means to promote the appeal of crops to consumers. In this way, the present invention provides comprehensive agricultural support and realizes efficient and profitable farm management.

[0006] "Crop category" refers to the type or classification of the crop being cultivated.

[0007] "Highlights" refer to the user's specific requirements and key features for the crops they cultivate.

[0008] "Environmental data" is a general term for information about the crop cultivation environment, such as climate data, soil data, and sunshine data.

[0009] "Climate data" refers to weather-related information such as temperature, humidity, precipitation, and wind speed.

[0010] "Soil data" refers to information about soil properties such as soil pH, fertility, and moisture content.

[0011] "Sunshine data" refers to information related to sunlight, such as the duration and intensity of sunlight.

[0012] An "environmental sensor" refers to a device for measuring environmental information such as climate data and soil data.

[0013] "Camera" refers to a device for acquiring visual information.

[0014] "Weather information provider API" refers to an application programming interface that exchanges data with external weather information providers.

[0015] "Analysis" refers to the process of analyzing collected data to extract useful information and patterns.

[0016] The "crop list" refers to a list of crops that are recommended for cultivation based on the analysis results.

[0017] A "cultivation schedule" refers to a timetable that plans the processes of sowing, growing, and harvesting crops.

[0018] A "smart scarecrow" refers to a device that automates agricultural management.

[0019] A "blog" refers to a diary-style website published on the Internet.

[0020] "Homepage" refers to a website for publishing information on the Internet.

[0021] "Automatic content generation" refers to the process of creating articles or information automatically through a program.

[0022] "Users" refers to the farmers and managers who use this system.

[0023] "Terminal" refers to a device that a user operates to input data and receive results.

[0024] A "server" refers to a computer system for analyzing and processing data. [Brief explanation of the drawings]

[0025] [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

[0026] 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.

[0027] First, the terms used in the following description will be explained.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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."

[0033] [First embodiment]

[0034] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0035] 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.

[0036] 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).

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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."

[0046] The present invention is a total support system for supporting agricultural activities. This system includes a means for inputting crop categories and key features, a means for collecting environmental data, a means for analyzing the collected data and generating an optimal crop list and cultivation schedule, a means for displaying the generated data to the user, a means for providing real-time management and instructions during cultivation, and a means for delivering automatically generated blog and homepage content to consumers.

[0047] System Overview

[0048] User operation flow

[0049] 1. User enters crop category and highlight points:

[0050] The user inputs the crop category (e.g., leafy vegetables) and key features (e.g., quality-oriented) from the terminal. This information is sent to the system, which then proceeds to the next step.

[0051] 2. The device collects environmental data:

[0052] The system collects data from installed temperature, humidity, and soil sensors, as well as cameras. It also collects climate data using the APIs of weather information providers. This data is sent to a server in real time.

[0053] 3. The server analyzes the data:

[0054] The server uses the received environmental data, crop categories, and key points to generate an optimal crop list, and also analyzes the data to create an annual cultivation schedule.

[0055] 4. The server generates a proposal and sends it to the device:

[0056] The proposed crop list and cultivation schedule are sent to the terminal and displayed to the user, who can then use this information to plan their cultivation.

[0057] 5. Cultivation Management:

[0058] The terminals will link with agricultural devices such as smart scarecrows and automate tasks based on instructions from the server, such as watering and spreading fertilizer.

[0059] 6. Auto-generated content:

[0060] The server automatically generates blog and homepage content based on the collected information and photos, including growth records, harvest status, cooking recipes, and more.

[0061] 7. Consumer Appeal:

[0062] The user can check the generated content and make any necessary edits. After that, the information is disseminated to consumers via websites and social media, promoting the appeal of the crops.

[0063] Program processing details (example)

[0064] Registering categories and highlight points

[0065] The user accesses the terminal, selects "leafy vegetables" and "quality-oriented" and inputs the information.

[0066] The terminal validates the input in real time and sends it to the server.

[0067] Environmental data collection

[0068] The terminal periodically collects data from installed sensors (temperature, humidity, soil type) and cameras.

[0069] The device accesses the weather information provider's API via the Internet to obtain climate data.

[0070] The terminal integrates this data and sends it to the server.

[0071] Data analysis and proposal creation

[0072] Based on the received data, the server uses AI and machine learning algorithms to generate an optimal crop list, suggesting "lettuce" and "spinach," for example.

[0073] The server creates an annual schedule based on the crop list.

[0074] View Suggestions

[0075] The server transmits the generated list and schedule to the terminal.

[0076] The terminal visually displays this information to the user.

[0077] Management during cultivation

[0078] The terminals control agricultural devices such as smart scarecrows based on instructions from the server, for example by setting watering schedules and monitoring temperature and humidity.

[0079] Creating auto-generated content

[0080] The server automatically generates blog posts and homepage content based on the growth records and photos, such as "This month's lettuce growth status" or "Easy spinach recipes."

[0081] Improving brand power and appealing to consumers

[0082] The user finalizes the generated content and corrects it if necessary.

[0083] Users use their homepages and social media to spread content and promote the appeal of their crops.

[0084] In this way, the system of the present invention provides comprehensive support at each stage of agriculture, helping users to cultivate crops efficiently and make them more appealing to consumers.

[0085] The processing flow will be explained below.

[0086] Step 1:

[0087] The user inputs the crop category and highlight points.

[0088] The user accesses the terminal screen and inputs the crop category (e.g., leafy vegetables) and the key points (e.g., emphasis on quality).

[0089] The terminal transmits the input information to the server.

[0090] Step 2:

[0091] The device collects environmental data.

[0092] The terminal collects data from temperature sensors, humidity sensors, soil sensors and cameras installed on-site.

[0093] The device uses the weather information provider's API to obtain local climate data.

[0094] The device consolidates the collected data and sends it to a server.

[0095] Step 3:

[0096] The server analyzes the data and generates a list of possible crops and a schedule.

[0097] The server analyzes the received environmental data, crop categories and highlight points.

[0098] The server generates a list of optimal crops and creates an annual cultivation schedule.

[0099] Step 4:

[0100] The server sends the proposal to the terminal and displays it to the user.

[0101] The server sends the proposed crop list and cultivation schedule to the terminal.

[0102] The terminal visually displays the suggestions to the user.

[0103] Step 5:

[0104] The terminal works in conjunction with agricultural devices such as smart scarecrows to manage the cultivation process.

[0105] The terminal receives instructions from the server and sends them to smart scarecrows and other agricultural devices.

[0106] The smart scarecrow automatically performs necessary farming tasks (e.g. watering, spreading fertilizer).

[0107] The terminal monitors the data in real time and reports any abnormalities to the server.

[0108] Step 6:

[0109] The server automatically generates the content for the blog and homepage.

[0110] The server automatically generates blog and homepage content based on data such as cultivation information, photos, and growth records.

[0111] The server publishes the generated content.

[0112] Step 7:

[0113] Review user-generated content and engage with consumers.

[0114] The user checks the content of the generated blog or homepage and edits it as necessary.

[0115] Users can disseminate information to consumers through their websites and social media, promoting the appeal of their crops.

[0116] As described above, in the present invention, users, terminals, and servers cooperate to realize efficient agricultural activities.

[0117] Example 1

[0118] 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."

[0119] There is a need for systems that can streamline agricultural activities, support the cultivation of high-quality crops, and effectively appeal to consumers. However, conventional agricultural support systems were unable to provide comprehensive support for everything from collecting and analyzing environmental data to managing crop cultivation and disseminating information. This forced farmers to expend a lot of time and money, making it difficult to manage their farms efficiently.

[0120] 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.

[0121] In this invention, the server includes means for inputting crop categories and key features, means for collecting environmental data, means for analyzing the environmental data and generating an optimal crop list and cultivation schedule, means for displaying the generated crop list and cultivation schedule, means for managing and providing instructions during cultivation, means for automatically generating blog and homepage content, and means for providing the automatically generated content to consumers. This makes it possible to comprehensively manage and support a series of processes at each stage of agricultural activities, allowing users to efficiently cultivate high-quality crops and effectively appeal to consumers.

[0122] "Means for inputting crop categories and key points" refers to an interface and device that allows a user to select and input the type of crop they wish to grow and the elements they wish to focus on.

[0123] "Means for collecting environmental data" refers to a system that uses sensors and cameras installed on farmland to obtain information about the environment, such as temperature, humidity, soil quality, and sunlight.

[0124] The "means for analyzing environmental data and generating an optimal crop list and cultivation schedule" refers to algorithms and software that analyze the acquired environmental data and, based on the results, create optimal crop types and cultivation plans.

[0125] The "means for displaying the generated crop list and cultivation schedule" refers to an interface and device for displaying the crop list and cultivation schedule generated by the server on a terminal in a form that can be confirmed by the user.

[0126] The "means for management and instructions during cultivation" is a system for automating the management tasks and instructions required during the cultivation period and controlling smart agricultural equipment.

[0127] "Means for automatically generating blog and homepage content" refers to algorithms and software that allow AI to automatically create articles and content based on cultivation process and harvest information, etc.

[0128] "Means for providing automatically generated content to consumers" refers to a system for posting and distributing content on social media and websites in order to deliver automatically generated articles and information to consumers.

[0129] "Means for acquiring data using environmental sensors and cameras" refers to a system consisting of sensors for collecting environmental data such as temperature, humidity, soil quality, and sunlight, and cameras for acquiring image data.

[0130] "Means for obtaining climate data using APIs of weather information providers" refers to software and programming methods for calling APIs (Application Program Interfaces) to obtain real-time climate data using external weather information services.

[0131] The "means for transmitting environmental data acquired by the terminal to the server" refers to a communication means and protocol for transmitting collected environmental data from the terminal to the server.

[0132] "Means for the server to analyze data using AI algorithms and generate a crop list and cultivation schedule" refers to a system in which the server analyzes collected data using AI (artificial intelligence) and machine learning algorithms, and generates an optimal crop list and annual cultivation schedule based on the results.

[0133] "Means for a terminal to control agricultural devices and perform automated cultivation management" refers to means for a terminal to control agricultural equipment such as irrigation systems and smart scarecrows in order to automate cultivation management.

[0134] "Means for the server to automatically generate content using a generative AI model" refers to algorithms and software for automatically generating blog post and homepage content using an AI (artificial intelligence) model.

[0135] "Means for users to edit and publish content on homepages or SNS" refers to interfaces and devices that allow users to review and edit the content they have created and post it on homepages or SNS for publication.

[0136] This invention is a total support system for agricultural activities. This system collects and analyzes environmental data based on the crop category and key features entered by the user, and generates an optimal crop list and cultivation schedule. It also has the function of automating management and instructions during cultivation and providing the generated content to consumers.

[0137] Overview of program processing

[0138] Enter crop category

[0139] The user starts the device and enters the crop category (e.g., "leafy vegetables") and key features (e.g., "quality-oriented") via a dedicated app or web interface. The device verifies this information in real time and sends it to the server.

[0140] Environmental data collection

[0141] The device collects environmental data from installed environmental sensors (temperature, humidity, soil sensors) and cameras. It also obtains climate data from weather information providers' APIs (e.g., OpenWeatherMap API) via the Internet. This data is integrated and sent to a server.

[0142] Data analysis

[0143] The server generates an optimal crop list using AI algorithms (e.g., "random forest" or "neural network") based on the received environmental data, crop category, and key points. For example, if "leafy vegetables" and "quality-oriented" are selected, "lettuce" and "spinach" will be suggested. The server also generates an annual cultivation schedule based on the crop list.

[0144] View Suggestions

[0145] The server transmits the generated crop list and cultivation schedule to the terminal, which visually displays them to the user.

[0146] Management during cultivation

[0147] Based on instructions from the server, the terminals control agricultural devices such as smart irrigation systems and smart scarecrows, automating tasks such as setting watering and fertilizer application schedules and monitoring temperature and humidity.

[0148] Auto-generated content

[0149] The server uses a generative AI model (e.g., GPT-3 (registered trademark)) to automatically generate blog posts and homepage content based on the collected growth records and photos. The generated content includes specific details such as "This month's lettuce growth status" and "Easy spinach recipes."

[0150] Appeal to consumers

[0151] The user checks the generated content, makes any necessary corrections, and then publishes it on their website or via social media to appeal to consumers.

[0152] Examples of concrete examples and prompts

[0153] Specific examples

[0154] When the user selects "leafy vegetables" and "quality-oriented," the following processing is performed.

[0155] The device collects temperature, humidity, soil type, and camera data, and also uses a weather API to obtain climate data.

[0156] The server analyzes this data, suggests "lettuce" and "spinach," and creates an annual schedule based on that.

[0157] The terminal displays the proposal list and schedule to the user.

[0158] The device uses a smart irrigation system to automate watering and fertilizing.

[0159] The server generates a blog post titled "This month's lettuce growth status" based on the growth record.

[0160] After users check and edit the article, they can share it on social media platforms such as Facebook and Instagram.

[0161] Prompt Sentence Examples

[0162] Please suggest the optimal crop list and cultivation schedule if "leafy vegetables" and "quality-oriented" are selected.

[0163] This invention is a system that comprehensively manages and supports a series of processes at each stage of agriculture, enabling users to efficiently cultivate high-quality crops and effectively appeal to consumers.

[0164] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0165] Step 1:

[0166] Enter crop category and highlight points

[0167] Input: The user starts the device and enters the "crop category" (e.g., "leafy vegetables") and "highlights" (e.g., "quality-oriented") via a dedicated app or web interface.

[0168] How it works: When the user enters this information, the device verifies the integrity of the input data in real time and sends it to the server if there are no errors.

[0169] Output: The input crop category and highlight point information are sent to the server.

[0170] Step 2:

[0171] Environmental data collection

[0172] Input: The device collects data from environmental sensors (temperature sensors, humidity sensors, soil sensors) and cameras installed in the farmland, and obtains climate data from weather information providers' APIs (e.g., "OpenWeatherMap API").

[0173] How it works: The device periodically retrieves data from these sensors and accesses the weather API to get the necessary climate data.

[0174] Output: Collected environmental and climate data is sent to a server.

[0175] Step 3:

[0176] Data analysis

[0177] Input: The server receives data based on environmental data, climate data, crop categories, and eyeball points sent from the terminal.

[0178] How it works: The server analyzes the data using AI algorithms (e.g., "random forest" or "neural network") to generate an optimal crop list. It also creates an annual cultivation schedule based on the crop list. For example, if you select "leafy vegetables" and "quality-oriented," "lettuce" and "spinach" may be suggested.

[0179] Output: An optimal crop list and cultivation schedule is generated.

[0180] Step 4:

[0181] Submitting and Viewing Proposals

[0182] Input: Server-generated crop list and cultivation schedule.

[0183] Operation: The server sends this information to the device. The device visually displays the received suggestions to the user. The user confirms the suggestions.

[0184] Output: The user can check the optimal crop list and cultivation schedule through the terminal.

[0185] Step 5:

[0186] Management during cultivation

[0187] Input: Instruction data from the server and environmental data collected in real time.

[0188] How it works: Based on instructions from the server, the device operates agricultural devices such as smart irrigation systems and smart scarecrows to automate appropriate cultivation management, such as setting schedules for watering and fertilizer application, and monitoring temperature and humidity.

[0189] Output: Automated cultivation management data and warning notifications are displayed on the user's terminal if there are any abnormalities.

[0190] Step 6:

[0191] Auto-generated content

[0192] Input: Collected growth records and photographic data.

[0193] How it works: Based on the growth records and photos, the server uses a generative AI model (e.g., GPT-3) to automatically generate blog posts and homepage content, such as "This month's lettuce growth status" or "Easy spinach recipes."

[0194] Output: Auto-generated blog post and homepage content.

[0195] Step 7:

[0196] Appeal to consumers

[0197] Input: Server-generated content.

[0198] How it works: The user reviews the generated content, edits it as necessary, and then publishes the information on their website or social media to promote the appeal of their crops to consumers.

[0199] Output: The published content can be viewed on websites and social media, making it appealing to consumers.

[0200] (Application example 1)

[0201] 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."

[0202] In modern agriculture and industry, it is important to collect and analyze environmental data in real time. However, while conventional systems can generate optimal crop lists and cultivation schedules in agriculture, they do not support generating optimal work schedules and automatic execution in industry, nor generating work progress reports. As a result, efficient automation of agriculture and industry is difficult to achieve, and managers have difficulty understanding the situation in real time and issuing appropriate instructions.

[0203] 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.

[0204] In this invention, the server includes means for inputting crop categories and key features, means for collecting environmental data, means for analyzing the environmental data and generating an optimal crop list and cultivation schedule, means for displaying the generated crop list and cultivation schedule, means for managing and providing instructions during cultivation, means for automatically generating blog and homepage content, means for collecting work environment data, means for analyzing the data and generating an optimal work schedule and procedure, means for automatically performing work based on the work procedure, and means for automatically generating work progress reports. This enables efficient automation in both agriculture and industry, and enables managers to grasp the situation in real time and provide appropriate instructions.

[0205] "Crop category" refers to a classification that indicates the type of crop that the user intends to cultivate.

[0206] "Eye-catching points" refer to points or characteristics that are particularly important in crop cultivation.

[0207] "Environmental data" refers to data such as temperature, humidity, soil quality, and weather information that affect crops and the working environment.

[0208] "Means of collecting data" refers to the equipment and technology used to acquire data using environmental sensors, cameras, etc.

[0209] "Means of analysis" refers to the algorithms and software used to analyze collected data and extract meaningful information.

[0210] The "optimal crop list" is a list of the most suitable crops proposed based on the analysis results.

[0211] A "cultivation schedule" is an optimal timetable or plan for growing crops.

[0212] The "display means" refers to a display or screen that visually shows the generated information to the user.

[0213] "Means for control and instruction" refers to devices and software for operating and instructing in order to efficiently carry out cultivation work.

[0214] "Auto-generating methods" refers to technologies or algorithms that automatically create content based on collected data and user input.

[0215] "Work environment data" refers to data on environmental conditions such as temperature, humidity, vibration, and sound that affect work in factories and on farmland.

[0216] "Work schedules and procedures" refers to the optimal work timetable and execution procedures.

[0217] "Means for automatically executing work" refers to the technology and devices that allow machines and robots to autonomously perform work based on the generated work procedures.

[0218] A "work progress report" is a report summarizing the progress and results of work that has been performed.

[0219] The system of the present invention is a total support system for efficiently managing and automating specific agricultural and industrial operations. This system is composed of the following main components:

[0220] 1. User enters crop category and highlight points:

[0221] The user inputs the crop category (e.g., leafy vegetables) and key features (e.g., quality-oriented) from the terminal. This information is sent to the system, which then proceeds to the next step.

[0222] 2. The device collects environmental data:

[0223] The device (smartphone, PC, etc.) acquires data in real time from installed temperature sensors, humidity sensors, soil sensors, vibration sensors, sound sensors, and cameras. It also acquires climate data using the API of weather information providers.

[0224] 3. The server analyzes the data:

[0225] The server analyzes the received environmental data, crop categories, and key points using AI and machine learning algorithms (e.g., TENSORFLOW® and PyTorch) to generate an optimal crop list and annual cultivation schedule. It also analyzes similar environmental data for factory operations to generate optimal work schedules and procedures.

[0226] 4. Proposal generation and display:

[0227] The generated crop list and cultivation schedule, as well as the factory work procedures and schedule, are sent to the terminal and displayed to the user, who then uses this information to create specific work and cultivation plans.

[0228] 5. Cultivation and operation management:

[0229] The terminals will work with devices such as smart scarecrows and factory robots to automate tasks based on instructions from the server, such as watering plants, spreading fertilizer, and performing specific assembly and inspection tasks in factories.

[0230] 6. Auto-generated content:

[0231] The server automatically generates blog posts and homepage content based on the collected information and photos, including growth records, work progress reports, harvest status reports, cooking recipes, and work reports.

[0232] 7. Information for consumers and administrators:

[0233] The user then performs a final check of the generated content, corrects it as necessary, and then disseminates the information to consumers via their website or social media. The administrator also checks the generated report and gives appropriate instructions.

[0234] As a concrete example, content can be automatically generated by inputting the following prompt sentence into a generative AI model.

[0235] Generate an optimal work schedule based on sensor data. Use the following data:

[0236] Temperature: 22℃ - 24℃

[0237] Humidity: 60% - 62%

[0238] Vibration: 0.2g - 0.3g

[0239] Sound: 50dB - 55dB

[0240] Please suggest the best working time based on this data.

[0241] In this way, the system of the present invention provides comprehensive support for achieving efficient management and automation in both agriculture and industry.

[0242] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0243] Step 1:

[0244] The user operates the terminal to input the crop category and key features. The user inputs information such as "leafy vegetables" and "quality-oriented" through the terminal interface. This information is sent from the terminal to the server in real time. The input is important data based on the user's cultivation plan and is essential for subsequent processing.

[0245] Step 2:

[0246] The device collects environmental data. It acquires data in real time using environmental sensors (temperature sensor, humidity sensor, soil sensor, vibration sensor, sound sensor) and cameras. It also accesses the API of weather information providers to acquire climate data. All collected data is sent to a server via the internet. Input data is provided by the sensors and API, and output data is stored on the server.

[0247] Step 3:

[0248] The server analyzes the received data based on environmental data, crop categories, and key points. It then uses AI and machine learning algorithms (TensorFlow and PyTorch) to generate an optimal crop list and cultivation schedule. This analysis combines multiple datasets to help select the best crops and generate the best schedule. The data collected in the previous step is used as input, and the optimal crop list and cultivation schedule is generated as output.

[0249] Step 4:

[0250] The server sends the generated crop list and cultivation schedule to the terminal. The terminal displays it to the user in a visually easy-to-understand manner. This display allows the user to easily understand the specific work content and schedule and put it into action. The input is the data generated in step 3, and the output is displayed on the terminal in a form that the user can see.

[0251] Step 5:

[0252] The terminal manages and gives instructions during cultivation. It works in conjunction with smart scarecrows and factory robots to automatically carry out tasks based on instructions from the server. For example, it automatically controls temperature and humidity, waters plants, spreads fertilizer, assembles parts, and performs inspection work. Instructions are given based on data received in real time from the server, and optimal instructions suited to the working environment are issued. The input is instructions from the server, and the output is automated task execution.

[0253] Step 6:

[0254] The server automatically generates blog and homepage content based on the collected information and photos. This includes growth records, work progress, harvest information, cooking recipes, and work reports. A generative AI model is used to generate the content, and prompts are entered to output content in a specified format. The input is the collected information and photos, and the output is content for the user.

[0255] Step 7:

[0256] The user checks the generated content and makes any necessary corrections. The information is then provided to consumers and administrators via the website or social media. The final, checked content is notified and made public to relevant parties, helping to improve brand recognition and ensure transparency in the work process. The input is the generated content, and the output is the content made public on the website or social media.

[0257] 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.

[0258] This invention is a system that comprehensively supports agricultural activities, and includes functions for inputting crop categories and key points, collecting and analyzing environmental data, generating and displaying optimal crop lists and cultivation schedules, managing cultivation in real time, and automatically generating content for blogs and homepages. It also incorporates an emotion engine that recognizes the user's emotions and adjusts the system's suggestions and operations accordingly.

[0259] System Overview

[0260] User operation flow

[0261] 1. User enters crop category and highlight points:

[0262] The user inputs the crop category (e.g., leafy vegetables) and key features (e.g., quality-oriented) through the input screen on the terminal. This information is sent to the system.

[0263] 2. The device collects environmental data:

[0264] The device collects data from temperature, humidity, and soil sensors, as well as a camera, and uses APIs from weather information providers to obtain climate data, which is then sent to a server in real time.

[0265] 3. The server analyzes the data:

[0266] The server analyzes the received environmental data, crop categories, and key points to generate an optimal crop list and create an annual cultivation schedule.

[0267] 4. The server generates a proposal and sends it to the device:

[0268] The server transmits the generated crop list and cultivation schedule to the terminal and displays them visually to the user.

[0269] 5. Use of Emotion Engine:

[0270] To recognize the user's emotions, the emotion engine collects facial and voice data from the user, which is used to analyze whether the user is feeling stressed or satisfied. The server takes this emotional data into account and adjusts the suggestions and cultivation schedule accordingly.

[0271] 6. Cultivation Management:

[0272] The terminals connect to agricultural devices such as smart scarecrows and automate tasks based on instructions from the server, such as watering and fertilizing. They also monitor data in real time and report any abnormalities to the server.

[0273] 7. Auto-generated content:

[0274] The server automatically generates blog and homepage content based on the growth information and photos, including growth records, harvest status, and cooking recipes.

[0275] 8. Consumer Appeal:

[0276] Users can review the generated content and edit it as necessary, then share the information with consumers via their website or social media, effectively promoting the appeal of their crops.

[0277] Program processing details (example)

[0278] Registering categories and highlight points

[0279] The user accesses the terminal, selects "leafy vegetables" and "quality-oriented" and inputs the information.

[0280] The terminal validates the input in real time and sends it to the server.

[0281] Environmental data collection

[0282] The terminal periodically collects data from installed sensors (temperature, humidity, soil type) and cameras.

[0283] The device accesses the weather information provider's API via the Internet to obtain climate data.

[0284] The terminal integrates this data and sends it to the server.

[0285] Data analysis and proposal creation

[0286] Based on the received data, the server uses AI and machine learning algorithms to generate an optimal crop list, suggesting "lettuce" and "spinach," for example.

[0287] The server creates an annual schedule based on the crop list.

[0288] View Suggestions

[0289] The server transmits the generated list and schedule to the terminal.

[0290] The terminal visually displays this information to the user.

[0291] Using the Emotion Engine

[0292] The device inputs the user's facial expressions and voice into an emotion engine and analyzes the user's emotional state in real time.

[0293] Based on the analyzed emotional data, the server adjusts the proposal content and schedule, and suggests actions to reduce the user's stress.

[0294] Management during cultivation

[0295] The device receives instructions from the server and controls smart scarecrows and other agricultural devices, such as setting watering schedules and monitoring temperature and humidity.

[0296] Creating auto-generated content

[0297] The server automatically generates blog posts and homepage content based on the growth records and photos, such as "This month's lettuce growth status" or "Easy spinach recipes."

[0298] Improving brand power and appealing to consumers

[0299] The user finalizes the generated content and corrects it if necessary.

[0300] Users use their homepages and social media to spread content and promote the appeal of their crops.

[0301] In this way, the system of the present invention provides comprehensive and efficient agricultural support through cooperation between the user, terminal, server, and emotion engine. A system that takes the user's emotional state into account enables more personalized suggestions and management, thereby improving user satisfaction.

[0302] The processing flow will be explained below.

[0303] Step 1:

[0304] The user inputs the crop category and highlight points.

[0305] The user inputs the crop category (e.g., leafy vegetables) and key features (e.g., quality-oriented) on the terminal screen.

[0306] The terminal transmits the input information to the server.

[0307] Step 2:

[0308] The device collects environmental data.

[0309] The terminal collects data from temperature sensors, humidity sensors, soil sensors and cameras installed on-site.

[0310] The device uses the weather information provider's API to obtain local climate data.

[0311] The device consolidates the collected data and sends it to a server.

[0312] Step 3:

[0313] The server analyzes the data and generates a list of possible crops and a schedule.

[0314] The server analyzes the received environmental data, crop categories and highlight points.

[0315] The server generates a list of optimal crops and creates an annual cultivation schedule.

[0316] Step 4:

[0317] The server sends the proposal to the terminal and displays it to the user.

[0318] The server sends the proposed crop list and cultivation schedule to the terminal.

[0319] The terminal visually displays the suggestions to the user.

[0320] Step 5:

[0321] The device collects and analyzes the user's emotional data.

[0322] The device uses a camera and microphone to collect the user's facial expressions and voice.

[0323] The device transmits the collected emotion data to the server.

[0324] Step 6:

[0325] The server analyzes the emotional data and adjusts the cultivation schedule and suggestions.

[0326] The server uses an emotion engine to analyze the user's emotional state.

[0327] Based on the analysis results, the server adjusts the suggestions and cultivation schedules, and standardizes actions to reduce user stress.

[0328] Step 7:

[0329] The terminal works in conjunction with agricultural devices such as smart scarecrows to manage the cultivation process.

[0330] The terminal receives instructions from the server and sends them to smart scarecrows and other agricultural devices.

[0331] The smart scarecrow automatically performs necessary farming tasks (e.g. watering, spreading fertilizer).

[0332] The terminal monitors the data in real time and reports any abnormalities to the server.

[0333] Step 8:

[0334] The server automatically generates the content for the blog and homepage.

[0335] The server automatically generates blog and homepage content based on data such as cultivation information, photos, and growth records.

[0336] The server publishes the generated content.

[0337] Step 9:

[0338] Review user-generated content and engage with consumers.

[0339] The user checks the content of the generated blog or homepage and edits it as necessary.

[0340] Users can disseminate information to consumers through their websites and social media, promoting the appeal of their crops.

[0341] In this way, the system of the present invention provides comprehensive and efficient agricultural support through cooperation between the user, terminal, server, and emotion engine. A system that takes the user's emotional state into account enables more personalized suggestions and management, thereby improving user satisfaction.

[0342] Example 2

[0343] 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."

[0344] In conventional agricultural support systems, even if you input the crop category and goals, the system collects and analyzes environmental data, and even proposes optimal cultivation schedules individually, requiring a lot of operation and management. Furthermore, no system takes into account the user's emotional state, which does not reduce stress or improve satisfaction. Furthermore, it is time-consuming to manually generate content for blogs and websites.

[0345] 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.

[0346] In this invention, the server includes a means for inputting crop categories and key features, a means for collecting environmental data, a means for analyzing the environmental data and generating an optimal crop list and cultivation schedule, a means for displaying the generated crop list and cultivation schedule, a means for collecting user emotion data and adjusting the proposal content based on the analysis, a means for managing and instructing during cultivation, a means for automatically generating blog and homepage content, and a means for integrating data in real time and transmitting it to the server. This enables comprehensive and efficient agricultural support, realizes personalized proposals and management that take the user's emotional state into consideration, and improves user satisfaction.

[0347] The "means for inputting the crop category and key points" is a device that provides an interface for the user to input the type of crop and characteristics that the user considers important (for example, quality, yield, etc.).

[0348] "Means for collecting environmental data" refers to sensors and devices for obtaining various environmental data such as temperature, humidity, soil quality, and sunlight.

[0349] The "means for analyzing environmental data and generating optimal crop lists and cultivation schedules" refers to a system that uses AI and machine learning algorithms to create optimal crop lists and cultivation schedules based on collected environmental data.

[0350] The "means for displaying the generated crop list and cultivation schedule" is a device that displays the crop list and annual cultivation schedule generated by the server in an easy-to-understand manner for the user.

[0351] The "means for collecting user emotional data and adjusting the proposed content based on the analysis" refers to a device that analyzes the user's facial expressions and voice and appropriately adjusts the system's proposed content and cultivation schedule based on the user's emotional state.

[0352] The "means for management and instruction during cultivation" is a system that uses smart devices to automatically control tasks such as watering and fertilizer application during the cultivation period.

[0353] The "means for automatically generating content for blogs and homepages" is a system that automatically creates content for blog articles and homepages based on information and photos of the growth process.

[0354] "Means of collecting climate data, soil data and sunlight data" refers to various sensors and information sources for obtaining data on climate, soil conditions, sunlight conditions, etc.

[0355] "Means for acquiring data using environmental sensors and cameras" refers to devices for periodically collecting data using environmental sensors and cameras.

[0356] "Means for obtaining climate data using the API of a weather information provider" refers to a system that obtains the latest weather data through the API of an external service that provides weather information.

[0357] The "means for integrating data in real time and transmitting it to a server" is a device that compiles data obtained from each sensor or information source into a single packet in real time and transmits it to a server.

[0358] "Means for delivering automatically generated blog and homepage content to consumers" refers to a system that allows users to review and edit automatically generated content, publish it via the Internet, and disseminate information to consumers.

[0359] This invention is a system that comprehensively supports agricultural activities, and includes functions for inputting crop categories and key points, collecting and analyzing environmental data, generating and displaying optimal crop lists and cultivation schedules, managing cultivation in real time, and automatically generating content for blogs and homepages. It also incorporates an emotion engine that recognizes the user's emotions and adjusts the system's suggestions and operations accordingly.

[0360] System Overview

[0361] This system mainly consists of four components: the user, the terminal, the server, and the emotion engine. Each component is responsible for the following functions:

[0362] Entering crop categories and highlight points

[0363] The user uses a terminal to input crop categories such as "tomato" or "yield-oriented" and key points, and this information is sent to the server in real time.

[0364] Environmental data collection

[0365] The device collects data from temperature sensors, humidity sensors, soil sensors, and cameras. It also obtains climate data using APIs from weather information providers. This data is integrated and sent to a server.

[0366] Data analysis and crop list generation

[0367] The server uses an AI algorithm to generate the optimal crop list based on the received environmental data, crop category, and key points. This analysis uses machine learning algorithms such as TensorFlow. Specifically, it calculates the optimal environmental conditions for growing tomatoes and generates recommended cultivation methods and lists.

[0368] Creating and displaying cultivation schedules

[0369] The server creates an annual cultivation schedule based on the crop list and sends it to the terminal, which visually displays the schedule and asks the user for confirmation.

[0370] Using the Emotion Engine

[0371] The device uses a camera and microphone to collect the user's facial expressions and voice, which are then analyzed by an emotion engine. This analysis uses, for example, Microsoft® Azure® emotion analysis API. Based on the analysis results, the server adjusts the recommendations and cultivation schedule.

[0372] Management during cultivation

[0373] The terminals control agricultural devices such as smart scarecrows and automatic watering devices based on instructions from the server. They also monitor environmental data in real time and immediately report any abnormalities to the server.

[0374] Auto-generated content

[0375] The server automatically generates blog posts and homepage content based on information and photos of the growing process, such as articles like "This month's tomato growth record" and "Tomato cooking recipes."

[0376] Improving brand power and appealing to consumers

[0377] Users can check the content they generate, edit it as necessary, and then publish it on their website or social media, thus promoting the appeal of their crops to consumers.

[0378] Specific examples

[0379] When a user inputs "tomato" and "emphasis on yield" as evaluation points, the device acquires data in real time from temperature sensors, humidity sensors, soil sensors, cameras, and weather information provider APIs, and sends it to the server. The server analyzes the data and generates an optimal cultivation schedule. It also analyzes the user's emotional state to adjust the suggestions and manage the cultivation. Blog articles and recipes are automatically generated based on growth records and photos, and the user can publish them on their website or social media. This series of steps realizes efficient agricultural support.

[0380] Example prompts for generative AI models

[0381] "Please tell me how to grow tomatoes and what the best growing conditions are. Also, could you give me some advice on how to maximize my yield?"

[0382] In this way, the system of the present invention provides comprehensive agricultural support through collaboration between users, terminals, servers, and emotion engines, enabling personalized suggestions and management, thereby improving user satisfaction and streamlining cultivation activities.

[0383] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0384] Step 1:

[0385] The user inputs the crop category and highlight points.

[0386] Input: The user inputs "tomato" and "yield-oriented" on the input screen of the terminal.

[0387] Action: The user completes the input and presses the submit button.

[0388] Output: The entered information is sent to the server.

[0389] Step 2:

[0390] The device collects environmental data.

[0391] Input: Real-time data from installed temperature, humidity, and soil sensors, and cameras.

[0392] How it works: The device collects data from sensors and cameras every 10 minutes, and also accesses weather information providers' APIs via the internet to obtain climate data.

[0393] Output: The acquired environmental data is stored on the terminal and combined into a single data packet.

[0394] Step 3:

[0395] The device transmits the environmental data to the server.

[0396] Input: Integrated temperature, humidity, soil quality, solar radiation, climate data, and image data.

[0397] Operation: Collected data is sent to the server in real time.

[0398] Output: The server receives the environment data.

[0399] Step 4:

[0400] The server analyzes the data and generates an optimal crop list.

[0401] Inputs: Environmental data, crop categories, eyeball points.

[0402] How it works: The server inputs the received data into a machine learning algorithm (e.g., TensorFlow) for analysis.

[0403] Output: A list of optimal crops will be generated, including the best environmental conditions and cultivation methods for growing tomatoes.

[0404] Step 5:

[0405] The server creates an annual cultivation schedule.

[0406] Input: The generated crop list.

[0407] How it works: The server determines the dates for each step, such as sowing, transplanting, and harvesting, based on recommended cultivation methods.

[0408] Output: An annual cultivation schedule is created.

[0409] Step 6:

[0410] The server sends the crop list and cultivation schedule to the terminal.

[0411] Input: Created crop list and annual cultivation schedule.

[0412] How it works: The server sends the list and schedule in JSON format to the device.

[0413] Output: The device receives and analyzes the data.

[0414] Step 7:

[0415] The terminal displays a list of crops and a cultivation schedule.

[0416] Input: Crop list and cultivation schedule received from the server.

[0417] Action: The device displays a visual representation on the user's screen.

[0418] Output: The user confirms the specific cultivation plan.

[0419] Step 8:

[0420] The device collects the user's facial expressions and voice data and analyzes them using an emotion engine.

[0421] Input: Facial expression and voice data collected by camera and microphone.

[0422] How it works: The device uses an emotion analysis API (e.g., Microsoft Azure) to analyze the user's emotional state in real time.

[0423] Output: The analyzed emotion data is sent to the server.

[0424] Step 9:

[0425] The server adjusts the suggestions based on the analyzed emotion data.

[0426] Input: User emotion data, crop list and cultivation schedule.

[0427] How it works: The server determines the user's stress level and satisfaction level and adjusts the suggestions and schedule accordingly.

[0428] Output: A tailored proposal and schedule are generated.

[0429] Step 10:

[0430] The terminal controls agricultural devices based on instructions from the server.

[0431] Input: Instructions received from the server.

[0432] What it does: The device activates a smart scarecrow, automatic watering system, and fertilizer spreading system.

[0433] Output: Appropriate cultivation practices are implemented.

[0434] Step 11:

[0435] The device monitors environmental data in real time and detects abnormalities.

[0436] Input: Environmental data collected in real time.

[0437] Operation: When the device detects an abnormal value, it sends an alert to the server and notifies the user.

[0438] Output: The server and user recognize the anomaly and take corresponding action.

[0439] Step 12:

[0440] The server automatically generates the content for the blog and homepage.

[0441] Input: Growth records, photo data.

[0442] How it works: The server uses an automated generation algorithm to create content based on the collected data. Examples include "This month's tomato growth record" and "Tomato cooking recipes."

[0443] Output: The generated blog post and homepage content.

[0444] Step 13:

[0445] The user reviews the generated content and makes edits as needed.

[0446] Input: Content data received from the server.

[0447] What it does: A user sees a preview of their blog post or social media post and edits the text and images as needed.

[0448] Output: A revised blog post or social media post.

[0449] Step 14:

[0450] Users publish content on their homepages or social media.

[0451] Input: The modified content.

[0452] What happens: A user presses the submit button to publish an article.

[0453] Output: Information is disseminated to consumers, and the attractiveness of the crop is effectively promoted.

[0454] In this way, the system of the present invention provides comprehensive agricultural support through collaboration between users, terminals, servers, and emotion engines, enabling personalized suggestions and management, thereby improving user satisfaction and streamlining cultivation activities.

[0455] (Application example 2)

[0456] 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."

[0457] The present invention relates to a comprehensive agricultural activity support system and its applications, and in particular to solving the problem of improving user satisfaction and efficiency by suggesting ingredients and recipes based on the user's preferences and emotions. Currently, agricultural activity support systems collect and analyze environmental data in real time, but it is difficult to make personalized suggestions based on the user's preferences and emotions. Furthermore, there is a need for more accurate service provision by combining content generation based on food suggestions and emotion recognition.

[0458] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting crop categories and highlights, a means for collecting environmental data, a means for analyzing the environmental data and generating an optimal crop list and cultivation schedule, a means for displaying the generated crop list and cultivation schedule, a means for managing and providing instructions during cultivation, a means for automatically generating blog and homepage content, a means for suggesting ingredients and recipes based on the user's preferences, and a means for recognizing the user's emotions and adjusting the suggestions accordingly. This enables personalized suggestions based on the user's preferences and emotions, thereby improving user satisfaction and efficiency.

[0459] The "means for inputting crop category and highlight points" is a function that allows the user to input the type and characteristics of a particular crop.

[0460] "Means for collecting environmental data" refers to a function for acquiring environmental information such as temperature, humidity, soil condition, and sunlight.

[0461] The "means for analyzing environmental data and generating an optimal crop list and cultivation schedule" is a function for analyzing collected environmental data and generating the most suitable crops and their cultivation plans.

[0462] The "means for displaying the generated crop list and cultivation schedule" is a function for showing the user the crop list and cultivation schedule created based on the analysis.

[0463] "Means for management and instruction during cultivation" refers to a function for carrying out management tasks and operational instructions required during the cultivation process.

[0464] "Means for automatically generating blog and homepage content" refers to a function for automatically creating web content based on collected data.

[0465] The "means for suggesting ingredients and recipes based on the user's preferences" is a function for recommending appropriate ingredients and cooking recipes in consideration of the user's preferences.

[0466] "Means for recognizing the user's emotions and adjusting suggestions accordingly" refers to a function that analyzes the user's emotions from their facial expressions and voice, and changes the information and suggestions provided accordingly.

[0467] "Means for collecting climate data, soil data, and sunshine data" refers to a function for acquiring climate, soil, and sunshine information in order to obtain detailed information about external environmental conditions.

[0468] "Means for acquiring data using environmental sensors and cameras" refers to a function for collecting environmental information using sensors and cameras.

[0469] "Means for obtaining climate data using APIs of weather information providers" refers to a function for obtaining climate-related data using online weather information services.

[0470] "Means for recommending optimal ingredients and recipes using machine learning algorithms" is a function that uses machine learning technology to select optimal ingredients and recipes for users.

[0471] The "means for analyzing the user's facial expression and voice data using an emotion recognition engine" is a function for determining the user's emotional state using facial expression and voice analysis technology.

[0472] "Means for delivering automatically generated blog and homepage content to consumers" is a feature for creating web content and publishing it online.

[0473] The "means for generating content tailored based on food suggestions" is a function for automatically creating appropriate web content based on food information that matches the user's preferences.

[0474] MODE FOR CARRYING OUT THE INVENTION

[0475] The present invention aims to improve user satisfaction and efficiency by adding a function to suggest ingredients and recipes that take into account the user's preferences and feelings to a comprehensive agricultural activity support system. The system for realizing this invention is composed of the following methods and means.

[0476] System Configuration

[0477] The server includes means for inputting crop categories and highlight points, means for collecting environmental data, means for analyzing the environmental data and generating an optimal crop list and cultivation schedule, means for displaying the generated crop list and cultivation schedule, means for managing and giving instructions during cultivation, means for automatically generating blog and homepage content, means for suggesting ingredients and recipes based on the user's preferences, and means for recognizing the user's emotions and adjusting suggestions accordingly.

[0478] Program processing

[0479] Hardware and Software

[0480] This system uses the following hardware and software:

[0481] Hardware: Smartphone, temperature sensor, humidity sensor

[0482] Software: Python, TensorFlow, cloud AI services, cloud vision services, communication APIs, web frameworks

[0483] Data processing and calculation

[0484] 1. Processing user-entered data:

[0485] The user inputs the crop category and key points into the terminal and sends them to the server. The terminal uses a web framework (e.g., Flask) to process the input data in real time and provide it to the server.

[0486] 2. Environmental Data Collection:

[0487] The device periodically acquires environmental data such as temperature, humidity, soil condition, and sunlight from various sensors and sends it to the server. It also acquires climate data using the API of an online weather information service.

[0488] 3. Data analysis and proposal generation:

[0489] The server uses machine learning algorithms (e.g., TensorFlow) to analyze the collected environmental data. Based on the analysis results, it generates a list of optimal crops and a cultivation schedule, which are then sent to the user's device for display. It also analyzes the user's preferences and past behavioral data to suggest optimal ingredients and recipes.

[0490] 4. Emotion recognition:

[0491] The device uses an emotion recognition engine to analyze the user's facial expressions and voice data to determine their current emotional state. The analysis results are sent to the server, which then adjusts the recommendations and cultivation schedule accordingly.

[0492] 5. Automatic content generation:

[0493] The server automatically generates blog and homepage content based on the collected data, and users can review this content, edit it if necessary, and then publish it online.

[0494] Specific examples

[0495] For example:

[0496] A user uses a smartphone application to request "ideas for tonight's dinner." The application collects current weather data (temperature 25 degrees, humidity 60%) and suggests optimal recipes based on the user's past preferences (e.g., Japanese food). The application also reads the user's emotions from their facial expressions and recommends ingredients with a relaxing effect if the user's stress level is high.

[0497] Here is an example prompt:

[0498] Give me some ideas for dinner tonight. The current weather data shows a temperature of 25°C and humidity of 60%, and the user has often shown a preference for Japanese food in the past. The user also seems to be feeling stressed. Based on this, please suggest the best recipe.

[0499] In this way, the present invention provides a highly personalized function that takes into account the user's preferences and feelings, thereby significantly improving user satisfaction and work efficiency.

[0500] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0501] Program processing flow

[0502] Step 1:

[0503] The user inputs the crop category and key points via a smartphone or other device. The information entered by the user (e.g., crop desired to grow = "tomatoes," key point = "quality-oriented") is sent from the device to the server. Based on this input, the system prepares to perform a personalized analysis.

[0504] Step 2:

[0505] The device collects environmental data using temperature, humidity, and soil sensors, as well as a camera. Data obtained from these sensors and cameras (e.g., temperature 25°C, humidity 60%, soil humidity 40%) is sent to a server in real time. Additionally, weather data is also acquired and integrated using the API of weather information providers.

[0506] Step 3:

[0507] The server analyzes the collected environmental data, crop categories, and key points. It then uses machine learning algorithms (e.g., TensorFlow) to generate an optimal crop list and cultivation schedule. Based on this data analysis, a list is generated that suggests, for example, "tomatoes" and "bell peppers." An annual cultivation schedule is also generated.

[0508] Step 4:

[0509] The server sends the generated crop list and cultivation schedule to the terminal, which visually displays this information to the user, who can then review the proposed list and schedule and make selections or changes as necessary.

[0510] Step 5:

[0511] The device uses an emotion recognition engine to collect the user's facial and voice data and send it to a server. For example, the user can record their facial expressions through a camera or speak using a microphone. This data is analyzed in real time to determine the user's emotional state (e.g., stressed, happy, etc.).

[0512] Step 6:

[0513] The server then adjusts the suggestions and cultivation schedules based on the user's emotional data. For example, if the user is feeling stressed, it may suggest easier, less labor-intensive crops or make gentler changes to the schedule.

[0514] Step 7:

[0515] The terminals interface with smart agricultural devices (e.g., automatic watering devices and temperature control systems) and execute instructions from the server, including real-time data monitoring (e.g., automatic watering when soil moisture drops) and sending alerts (e.g., notifications when temperatures are too high).

[0516] Step 8:

[0517] The server automatically generates blog and homepage content based on the collected growth information and photos. For example, it generates articles such as "Tomato Growth Status This Month" and "Easy Tomato Recipes." The generated content is provided to users, who can edit it as needed before publishing it.

[0518] Step 9:

[0519] Users can then publish the generated content on social media or their homepages to promote their crops to consumers, effectively highlighting their appeal and improving their brand power.

[0520] Through the above steps, the system of the present invention can provide users with comprehensive and personalized agricultural support. This system has an advanced suggestion function that takes into account the user's preferences and emotions, and can improve user satisfaction more than conventional agricultural support systems.

[0521] 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.

[0522] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0523] 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.

[0524] [Second embodiment]

[0525] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0526] 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.

[0527] 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).

[0528] 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.

[0529] 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.

[0530] 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).

[0531] 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.

[0532] 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.

[0533] 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.

[0534] 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.

[0535] 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.

[0536] 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."

[0537] The present invention is a total support system for supporting agricultural activities. This system includes a means for inputting crop categories and key features, a means for collecting environmental data, a means for analyzing the collected data and generating an optimal crop list and cultivation schedule, a means for displaying the generated data to the user, a means for providing real-time management and instructions during cultivation, and a means for delivering automatically generated blog and homepage content to consumers.

[0538] System Overview

[0539] User operation flow

[0540] 1. User enters crop category and highlight points:

[0541] The user inputs the crop category (e.g., leafy vegetables) and key features (e.g., quality-oriented) from the terminal. This information is sent to the system, which then proceeds to the next step.

[0542] 2. The device collects environmental data:

[0543] The system collects data from installed temperature, humidity, and soil sensors, as well as cameras. It also collects climate data using the APIs of weather information providers. This data is sent to a server in real time.

[0544] 3. The server analyzes the data:

[0545] The server uses the received environmental data, crop categories, and key points to generate an optimal crop list, and also analyzes the data to create an annual cultivation schedule.

[0546] 4. The server generates a proposal and sends it to the device:

[0547] The proposed crop list and cultivation schedule are sent to the terminal and displayed to the user, who can then use this information to plan their cultivation.

[0548] 5. Cultivation Management:

[0549] The terminals will link with agricultural devices such as smart scarecrows and automate tasks based on instructions from the server, such as watering and spreading fertilizer.

[0550] 6. Auto-generated content:

[0551] The server automatically generates blog and homepage content based on the collected information and photos, including growth records, harvest status, cooking recipes, and more.

[0552] 7. Consumer Appeal:

[0553] The user can check the generated content and make any necessary edits. After that, the information is disseminated to consumers via websites and social media, promoting the appeal of the crops.

[0554] Program processing details (example)

[0555] Registering categories and highlight points

[0556] The user accesses the terminal, selects "leafy vegetables" and "quality-oriented" and inputs the information.

[0557] The terminal validates the input in real time and sends it to the server.

[0558] Environmental data collection

[0559] The terminal periodically collects data from installed sensors (temperature, humidity, soil type) and cameras.

[0560] The device accesses the weather information provider's API via the Internet to obtain climate data.

[0561] The terminal integrates this data and sends it to the server.

[0562] Data analysis and proposal creation

[0563] Based on the received data, the server uses AI and machine learning algorithms to generate an optimal crop list, suggesting "lettuce" and "spinach," for example.

[0564] The server creates an annual schedule based on the crop list.

[0565] View Suggestions

[0566] The server transmits the generated list and schedule to the terminal.

[0567] The terminal visually displays this information to the user.

[0568] Management during cultivation

[0569] The terminals control agricultural devices such as smart scarecrows based on instructions from the server, for example by setting watering schedules and monitoring temperature and humidity.

[0570] Creating auto-generated content

[0571] The server automatically generates blog posts and homepage content based on the growth records and photos, such as "This month's lettuce growth status" or "Easy spinach recipes."

[0572] Improving brand power and appealing to consumers

[0573] The user finalizes the generated content and corrects it if necessary.

[0574] Users use their homepages and social media to spread content and promote the appeal of their crops.

[0575] In this way, the system of the present invention provides comprehensive support at each stage of agriculture, helping users to cultivate crops efficiently and make them more appealing to consumers.

[0576] The processing flow will be explained below.

[0577] Step 1:

[0578] The user inputs the crop category and highlight points.

[0579] The user accesses the terminal screen and inputs the crop category (e.g., leafy vegetables) and the key points (e.g., emphasis on quality).

[0580] The terminal transmits the input information to the server.

[0581] Step 2:

[0582] The device collects environmental data.

[0583] The terminal collects data from temperature sensors, humidity sensors, soil sensors and cameras installed on-site.

[0584] The device uses the weather information provider's API to obtain local climate data.

[0585] The device consolidates the collected data and sends it to a server.

[0586] Step 3:

[0587] The server analyzes the data and generates a list of possible crops and a schedule.

[0588] The server analyzes the received environmental data, crop categories and highlight points.

[0589] The server generates a list of optimal crops and creates an annual cultivation schedule.

[0590] Step 4:

[0591] The server sends the proposal to the terminal and displays it to the user.

[0592] The server sends the proposed crop list and cultivation schedule to the terminal.

[0593] The terminal visually displays the suggestions to the user.

[0594] Step 5:

[0595] The terminal works in conjunction with agricultural devices such as smart scarecrows to manage the cultivation process.

[0596] The terminal receives instructions from the server and sends them to smart scarecrows and other agricultural devices.

[0597] The smart scarecrow automatically performs necessary farming tasks (e.g. watering, spreading fertilizer).

[0598] The terminal monitors the data in real time and reports any abnormalities to the server.

[0599] Step 6:

[0600] The server automatically generates the content for the blog and homepage.

[0601] The server automatically generates blog and homepage content based on data such as cultivation information, photos, and growth records.

[0602] The server publishes the generated content.

[0603] Step 7:

[0604] Review user-generated content and engage with consumers.

[0605] The user checks the content of the generated blog or homepage and edits it as necessary.

[0606] Users can disseminate information to consumers through their websites and social media, promoting the appeal of their crops.

[0607] As described above, in the present invention, users, terminals, and servers cooperate to realize efficient agricultural activities.

[0608] Example 1

[0609] 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."

[0610] There is a need for systems that can streamline agricultural activities, support the cultivation of high-quality crops, and effectively appeal to consumers. However, conventional agricultural support systems were unable to provide comprehensive support for everything from collecting and analyzing environmental data to managing crop cultivation and disseminating information. This forced farmers to expend a lot of time and money, making it difficult to manage their farms efficiently.

[0611] 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.

[0612] In this invention, the server includes means for inputting crop categories and key features, means for collecting environmental data, means for analyzing the environmental data and generating an optimal crop list and cultivation schedule, means for displaying the generated crop list and cultivation schedule, means for managing and providing instructions during cultivation, means for automatically generating blog and homepage content, and means for providing the automatically generated content to consumers. This makes it possible to comprehensively manage and support a series of processes at each stage of agricultural activities, allowing users to efficiently cultivate high-quality crops and effectively appeal to consumers.

[0613] "Means for inputting crop categories and key points" refers to an interface and device that allows a user to select and input the type of crop they wish to grow and the elements they wish to focus on.

[0614] "Means for collecting environmental data" refers to a system that uses sensors and cameras installed on farmland to obtain information about the environment, such as temperature, humidity, soil quality, and sunlight.

[0615] The "means for analyzing environmental data and generating an optimal crop list and cultivation schedule" refers to algorithms and software that analyze the acquired environmental data and, based on the results, create optimal crop types and cultivation plans.

[0616] The "means for displaying the generated crop list and cultivation schedule" refers to an interface and device for displaying the crop list and cultivation schedule generated by the server on a terminal in a form that can be confirmed by the user.

[0617] The "means for management and instructions during cultivation" is a system for automating the management tasks and instructions required during the cultivation period and controlling smart agricultural equipment.

[0618] "Means for automatically generating blog and homepage content" refers to algorithms and software that allow AI to automatically create articles and content based on cultivation process and harvest information, etc.

[0619] "Means for providing automatically generated content to consumers" refers to a system for posting and distributing content on social media and websites in order to deliver automatically generated articles and information to consumers.

[0620] "Means for acquiring data using environmental sensors and cameras" refers to a system consisting of sensors for collecting environmental data such as temperature, humidity, soil quality, and sunlight, and cameras for acquiring image data.

[0621] "Means for obtaining climate data using APIs of weather information providers" refers to software and programming methods for calling APIs (Application Program Interfaces) to obtain real-time climate data using external weather information services.

[0622] The "means for transmitting environmental data acquired by the terminal to the server" refers to a communication means and protocol for transmitting collected environmental data from the terminal to the server.

[0623] "Means for the server to analyze data using AI algorithms and generate a crop list and cultivation schedule" refers to a system in which the server analyzes collected data using AI (artificial intelligence) and machine learning algorithms, and generates an optimal crop list and annual cultivation schedule based on the results.

[0624] "Means for a terminal to control agricultural devices and perform automated cultivation management" refers to means for a terminal to control agricultural equipment such as irrigation systems and smart scarecrows in order to automate cultivation management.

[0625] "Means for the server to automatically generate content using a generative AI model" refers to algorithms and software for automatically generating blog post and homepage content using an AI (artificial intelligence) model.

[0626] "Means for users to edit and publish content on homepages or SNS" refers to interfaces and devices that allow users to review and edit the content they have created and post it on homepages or SNS for publication.

[0627] This invention is a total support system for agricultural activities. This system collects and analyzes environmental data based on the crop category and key features entered by the user, and generates an optimal crop list and cultivation schedule. It also has the function of automating management and instructions during cultivation and providing the generated content to consumers.

[0628] Overview of program processing

[0629] Enter crop category

[0630] The user starts the device and enters the crop category (e.g., "leafy vegetables") and key features (e.g., "quality-oriented") via a dedicated app or web interface. The device verifies this information in real time and sends it to the server.

[0631] Environmental data collection

[0632] The device collects environmental data from installed environmental sensors (temperature, humidity, soil sensors) and cameras. It also obtains climate data from weather information providers' APIs (e.g., OpenWeatherMap API) via the Internet. This data is integrated and sent to a server.

[0633] Data analysis

[0634] The server generates an optimal crop list using AI algorithms (e.g., "random forest" or "neural network") based on the received environmental data, crop category, and key points. For example, if "leafy vegetables" and "quality-oriented" are selected, "lettuce" and "spinach" will be suggested. The server also generates an annual cultivation schedule based on the crop list.

[0635] View Suggestions

[0636] The server transmits the generated crop list and cultivation schedule to the terminal, which visually displays them to the user.

[0637] Management during cultivation

[0638] Based on instructions from the server, the terminals control agricultural devices such as smart irrigation systems and smart scarecrows, automating tasks such as setting watering and fertilizer application schedules and monitoring temperature and humidity.

[0639] Auto-generated content

[0640] The server uses a generative AI model (e.g., GPT-3) to automatically generate blog posts and homepage content based on the collected growth records and photos. The generated content includes specific details such as "This month's lettuce growth status" and "Easy spinach recipes."

[0641] Appeal to consumers

[0642] The user checks the generated content, makes any necessary corrections, and then publishes it on their website or via social media to appeal to consumers.

[0643] Examples of concrete examples and prompts

[0644] Specific examples

[0645] When the user selects "leafy vegetables" and "quality-oriented," the following processing is performed.

[0646] The device collects temperature, humidity, soil type, and camera data, and also uses a weather API to obtain climate data.

[0647] The server analyzes this data, suggests "lettuce" and "spinach," and creates an annual schedule based on that.

[0648] The terminal displays the proposal list and schedule to the user.

[0649] The device uses a smart irrigation system to automate watering and fertilizing.

[0650] The server generates a blog post titled "This month's lettuce growth status" based on the growth record.

[0651] After users check and edit the article, they can share it on social media platforms such as Facebook and Instagram.

[0652] Prompt Sentence Examples

[0653] Please suggest the optimal crop list and cultivation schedule if "leafy vegetables" and "quality-oriented" are selected.

[0654] This invention is a system that comprehensively manages and supports a series of processes at each stage of agriculture, enabling users to efficiently cultivate high-quality crops and effectively appeal to consumers.

[0655] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0656] Step 1:

[0657] Enter crop category and highlight points

[0658] Input: The user starts the device and enters the "crop category" (e.g., "leafy vegetables") and "highlights" (e.g., "quality-oriented") via a dedicated app or web interface.

[0659] How it works: When the user enters this information, the device verifies the integrity of the input data in real time and sends it to the server if there are no errors.

[0660] Output: The input crop category and highlight point information are sent to the server.

[0661] Step 2:

[0662] Environmental data collection

[0663] Input: The device collects data from environmental sensors (temperature sensors, humidity sensors, soil sensors) and cameras installed in the farmland, and obtains climate data from weather information providers' APIs (e.g., "OpenWeatherMap API").

[0664] How it works: The device periodically retrieves data from these sensors and accesses the weather API to get the necessary climate data.

[0665] Output: Collected environmental and climate data is sent to a server.

[0666] Step 3:

[0667] Data analysis

[0668] Input: The server receives data based on environmental data, climate data, crop categories, and eyeball points sent from the terminal.

[0669] How it works: The server analyzes the data using AI algorithms (e.g., "random forest" or "neural network") to generate an optimal crop list. It also creates an annual cultivation schedule based on the crop list. For example, if you select "leafy vegetables" and "quality-oriented," "lettuce" and "spinach" may be suggested.

[0670] Output: An optimal crop list and cultivation schedule is generated.

[0671] Step 4:

[0672] Submitting and Viewing Proposals

[0673] Input: Server-generated crop list and cultivation schedule.

[0674] Operation: The server sends this information to the device. The device visually displays the received suggestions to the user. The user confirms the suggestions.

[0675] Output: The user can check the optimal crop list and cultivation schedule through the terminal.

[0676] Step 5:

[0677] Management during cultivation

[0678] Input: Instruction data from the server and environmental data collected in real time.

[0679] How it works: Based on instructions from the server, the device operates agricultural devices such as smart irrigation systems and smart scarecrows to automate appropriate cultivation management, such as setting schedules for watering and fertilizer application, and monitoring temperature and humidity.

[0680] Output: Automated cultivation management data and warning notifications are displayed on the user's terminal if there are any abnormalities.

[0681] Step 6:

[0682] Auto-generated content

[0683] Input: Collected growth records and photographic data.

[0684] How it works: Based on the growth records and photos, the server uses a generative AI model (e.g., GPT-3) to automatically generate blog posts and homepage content, such as "This month's lettuce growth status" or "Easy spinach recipes."

[0685] Output: Auto-generated blog post and homepage content.

[0686] Step 7:

[0687] Appeal to consumers

[0688] Input: Server-generated content.

[0689] How it works: The user reviews the generated content, edits it as necessary, and then publishes the information on their website or social media to promote the appeal of their crops to consumers.

[0690] Output: The published content can be viewed on websites and social media, making it appealing to consumers.

[0691] (Application example 1)

[0692] 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."

[0693] In modern agriculture and industry, it is important to collect and analyze environmental data in real time. However, while conventional systems can generate optimal crop lists and cultivation schedules in agriculture, they do not support generating optimal work schedules and automatic execution in industry, nor generating work progress reports. As a result, efficient automation of agriculture and industry is difficult to achieve, and managers have difficulty understanding the situation in real time and issuing appropriate instructions.

[0694] 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.

[0695] In this invention, the server includes means for inputting crop categories and key features, means for collecting environmental data, means for analyzing the environmental data and generating an optimal crop list and cultivation schedule, means for displaying the generated crop list and cultivation schedule, means for managing and providing instructions during cultivation, means for automatically generating blog and homepage content, means for collecting work environment data, means for analyzing the data and generating an optimal work schedule and procedure, means for automatically performing work based on the work procedure, and means for automatically generating work progress reports. This enables efficient automation in both agriculture and industry, and enables managers to grasp the situation in real time and provide appropriate instructions.

[0696] "Crop category" refers to a classification that indicates the type of crop that the user intends to cultivate.

[0697] "Eye-catching points" refer to points or characteristics that are particularly important in crop cultivation.

[0698] "Environmental data" refers to data such as temperature, humidity, soil quality, and weather information that affect crops and the working environment.

[0699] "Means of collecting data" refers to the equipment and technology used to acquire data using environmental sensors, cameras, etc.

[0700] "Means of analysis" refers to the algorithms and software used to analyze collected data and extract meaningful information.

[0701] The "optimal crop list" is a list of the most suitable crops proposed based on the analysis results.

[0702] A "cultivation schedule" is an optimal timetable or plan for growing crops.

[0703] The "display means" refers to a display or screen that visually shows the generated information to the user.

[0704] "Means for control and instruction" refers to devices and software for operating and instructing in order to efficiently carry out cultivation work.

[0705] "Auto-generating methods" refers to technologies or algorithms that automatically create content based on collected data and user input.

[0706] "Work environment data" refers to data on environmental conditions such as temperature, humidity, vibration, and sound that affect work in factories and on farmland.

[0707] "Work schedules and procedures" refers to the optimal work timetable and execution procedures.

[0708] "Means for automatically executing work" refers to the technology and devices that allow machines and robots to autonomously perform work based on the generated work procedures.

[0709] A "work progress report" is a report summarizing the progress and results of work that has been performed.

[0710] The system of the present invention is a total support system for efficiently managing and automating specific agricultural and industrial operations. This system is composed of the following main components:

[0711] 1. User enters crop category and highlight points:

[0712] The user inputs the crop category (e.g., leafy vegetables) and key features (e.g., quality-oriented) from the terminal. This information is sent to the system, which then proceeds to the next step.

[0713] 2. The device collects environmental data:

[0714] The device (smartphone, PC, etc.) acquires data in real time from installed temperature sensors, humidity sensors, soil sensors, vibration sensors, sound sensors, and cameras. It also acquires climate data using the API of weather information providers.

[0715] 3. The server analyzes the data:

[0716] The server analyzes the received environmental data, crop categories, and key points using AI and machine learning algorithms (e.g., TensorFlow and PyTorch) to generate an optimal crop list and annual cultivation schedule. It also analyzes similar environmental data for factory operations to generate optimal work schedules and procedures.

[0717] 4. Proposal generation and display:

[0718] The generated crop list and cultivation schedule, as well as the factory work procedures and schedule, are sent to the terminal and displayed to the user, who then uses this information to create specific work and cultivation plans.

[0719] 5. Cultivation and operation management:

[0720] The terminals will work with devices such as smart scarecrows and factory robots to automate tasks based on instructions from the server, such as watering plants, spreading fertilizer, and performing specific assembly and inspection tasks in factories.

[0721] 6. Auto-generated content:

[0722] The server automatically generates blog posts and homepage content based on the collected information and photos, including growth records, work progress reports, harvest status reports, cooking recipes, and work reports.

[0723] 7. Information for consumers and administrators:

[0724] The user then performs a final check of the generated content, corrects it as necessary, and then disseminates the information to consumers via their website or social media. The administrator also checks the generated report and gives appropriate instructions.

[0725] As a concrete example, content can be automatically generated by inputting the following prompt sentence into a generative AI model.

[0726] Generate an optimal work schedule based on sensor data. Use the following data:

[0727] Temperature: 22℃ - 24℃

[0728] Humidity: 60% - 62%

[0729] Vibration: 0.2g - 0.3g

[0730] Sound: 50dB - 55dB

[0731] Please suggest the best working time based on this data.

[0732] In this way, the system of the present invention provides comprehensive support for achieving efficient management and automation in both agriculture and industry.

[0733] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0734] Step 1:

[0735] The user operates the terminal to input the crop category and key features. The user inputs information such as "leafy vegetables" and "quality-oriented" through the terminal interface. This information is sent from the terminal to the server in real time. The input is important data based on the user's cultivation plan and is essential for subsequent processing.

[0736] Step 2:

[0737] The device collects environmental data. It acquires data in real time using environmental sensors (temperature sensor, humidity sensor, soil sensor, vibration sensor, sound sensor) and cameras. It also accesses the API of weather information providers to acquire climate data. All collected data is sent to a server via the internet. Input data is provided by the sensors and API, and output data is stored on the server.

[0738] Step 3:

[0739] The server analyzes the received data based on environmental data, crop categories, and key points. It then uses AI and machine learning algorithms (TensorFlow and PyTorch) to generate an optimal crop list and cultivation schedule. This analysis combines multiple datasets to help select the best crops and generate the best schedule. The data collected in the previous step is used as input, and the optimal crop list and cultivation schedule is generated as output.

[0740] Step 4:

[0741] The server sends the generated crop list and cultivation schedule to the terminal. The terminal displays it to the user in a visually easy-to-understand manner. This display allows the user to easily understand the specific work content and schedule and put it into action. The input is the data generated in step 3, and the output is displayed on the terminal in a form that the user can see.

[0742] Step 5:

[0743] The terminal manages and gives instructions during cultivation. It works in conjunction with smart scarecrows and factory robots to automatically carry out tasks based on instructions from the server. For example, it automatically controls temperature and humidity, waters plants, spreads fertilizer, assembles parts, and performs inspection work. Instructions are given based on data received in real time from the server, and optimal instructions suited to the working environment are issued. The input is instructions from the server, and the output is automated task execution.

[0744] Step 6:

[0745] The server automatically generates blog and homepage content based on the collected information and photos. This includes growth records, work progress, harvest information, cooking recipes, and work reports. A generative AI model is used to generate the content, and prompts are entered to output content in a specified format. The input is the collected information and photos, and the output is content for the user.

[0746] Step 7:

[0747] The user checks the generated content and makes any necessary corrections. The information is then provided to consumers and administrators via the website or social media. The final, checked content is notified and made public to relevant parties, helping to improve brand recognition and ensure transparency in the work process. The input is the generated content, and the output is the content made public on the website or social media.

[0748] 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.

[0749] This invention is a system that comprehensively supports agricultural activities, and includes functions for inputting crop categories and key points, collecting and analyzing environmental data, generating and displaying optimal crop lists and cultivation schedules, managing cultivation in real time, and automatically generating content for blogs and homepages. It also incorporates an emotion engine that recognizes the user's emotions and adjusts the system's suggestions and operations accordingly.

[0750] System Overview

[0751] User operation flow

[0752] 1. User enters crop category and highlight points:

[0753] The user inputs the crop category (e.g., leafy vegetables) and key features (e.g., quality-oriented) through the input screen on the terminal. This information is sent to the system.

[0754] 2. The device collects environmental data:

[0755] The device collects data from temperature, humidity, and soil sensors, as well as a camera, and uses APIs from weather information providers to obtain climate data, which is then sent to a server in real time.

[0756] 3. The server analyzes the data:

[0757] The server analyzes the received environmental data, crop categories, and key points to generate an optimal crop list and create an annual cultivation schedule.

[0758] 4. The server generates a proposal and sends it to the device:

[0759] The server transmits the generated crop list and cultivation schedule to the terminal and displays them visually to the user.

[0760] 5. Use of Emotion Engine:

[0761] To recognize the user's emotions, the emotion engine collects facial and voice data from the user, which is used to analyze whether the user is feeling stressed or satisfied. The server takes this emotional data into account and adjusts the suggestions and cultivation schedule accordingly.

[0762] 6. Cultivation Management:

[0763] The terminals connect to agricultural devices such as smart scarecrows and automate tasks based on instructions from the server, such as watering and fertilizing. They also monitor data in real time and report any abnormalities to the server.

[0764] 7. Auto-generated content:

[0765] The server automatically generates blog and homepage content based on the growth information and photos, including growth records, harvest status, and cooking recipes.

[0766] 8. Consumer Appeal:

[0767] Users can review the generated content and edit it as necessary, then share the information with consumers via their website or social media, effectively promoting the appeal of their crops.

[0768] Program processing details (example)

[0769] Registering categories and highlight points

[0770] The user accesses the terminal, selects "leafy vegetables" and "quality-oriented" and inputs the information.

[0771] The terminal validates the input in real time and sends it to the server.

[0772] Environmental data collection

[0773] The terminal periodically collects data from installed sensors (temperature, humidity, soil type) and cameras.

[0774] The device accesses the weather information provider's API via the Internet to obtain climate data.

[0775] The terminal integrates this data and sends it to the server.

[0776] Data analysis and proposal creation

[0777] Based on the received data, the server uses AI and machine learning algorithms to generate an optimal crop list, suggesting "lettuce" and "spinach," for example.

[0778] The server creates an annual schedule based on the crop list.

[0779] View Suggestions

[0780] The server transmits the generated list and schedule to the terminal.

[0781] The terminal visually displays this information to the user.

[0782] Using the Emotion Engine

[0783] The device inputs the user's facial expressions and voice into an emotion engine and analyzes the user's emotional state in real time.

[0784] Based on the analyzed emotional data, the server adjusts the proposal content and schedule, and suggests actions to reduce the user's stress.

[0785] Management during cultivation

[0786] The device receives instructions from the server and controls smart scarecrows and other agricultural devices, such as setting watering schedules and monitoring temperature and humidity.

[0787] Creating auto-generated content

[0788] The server automatically generates blog posts and homepage content based on the growth records and photos, such as "This month's lettuce growth status" or "Easy spinach recipes."

[0789] Improving brand power and appealing to consumers

[0790] The user finalizes the generated content and corrects it if necessary.

[0791] Users use their homepages and social media to spread content and promote the appeal of their crops.

[0792] In this way, the system of the present invention provides comprehensive and efficient agricultural support through cooperation between the user, terminal, server, and emotion engine. A system that takes the user's emotional state into account enables more personalized suggestions and management, thereby improving user satisfaction.

[0793] The processing flow will be explained below.

[0794] Step 1:

[0795] The user inputs the crop category and highlight points.

[0796] The user inputs the crop category (e.g., leafy vegetables) and key features (e.g., quality-oriented) on the terminal screen.

[0797] The terminal transmits the input information to the server.

[0798] Step 2:

[0799] The device collects environmental data.

[0800] The terminal collects data from temperature sensors, humidity sensors, soil sensors and cameras installed on-site.

[0801] The device uses the weather information provider's API to obtain local climate data.

[0802] The device consolidates the collected data and sends it to a server.

[0803] Step 3:

[0804] The server analyzes the data and generates a list of possible crops and a schedule.

[0805] The server analyzes the received environmental data, crop categories and highlight points.

[0806] The server generates a list of optimal crops and creates an annual cultivation schedule.

[0807] Step 4:

[0808] The server sends the proposal to the terminal and displays it to the user.

[0809] The server sends the proposed crop list and cultivation schedule to the terminal.

[0810] The terminal visually displays the suggestions to the user.

[0811] Step 5:

[0812] The device collects and analyzes the user's emotional data.

[0813] The device uses a camera and microphone to collect the user's facial expressions and voice.

[0814] The device transmits the collected emotion data to the server.

[0815] Step 6:

[0816] The server analyzes the emotional data and adjusts the cultivation schedule and suggestions.

[0817] The server uses an emotion engine to analyze the user's emotional state.

[0818] Based on the analysis results, the server adjusts the suggestions and cultivation schedules, and standardizes actions to reduce user stress.

[0819] Step 7:

[0820] The terminal works in conjunction with agricultural devices such as smart scarecrows to manage the cultivation process.

[0821] The terminal receives instructions from the server and sends them to smart scarecrows and other agricultural devices.

[0822] The smart scarecrow automatically performs necessary farming tasks (e.g. watering, spreading fertilizer).

[0823] The terminal monitors the data in real time and reports any abnormalities to the server.

[0824] Step 8:

[0825] The server automatically generates the content for the blog and homepage.

[0826] The server automatically generates blog and homepage content based on data such as cultivation information, photos, and growth records.

[0827] The server publishes the generated content.

[0828] Step 9:

[0829] Review user-generated content and engage with consumers.

[0830] The user checks the content of the generated blog or homepage and edits it as necessary.

[0831] Users can disseminate information to consumers through their websites and social media, promoting the appeal of their crops.

[0832] In this way, the system of the present invention provides comprehensive and efficient agricultural support through cooperation between the user, terminal, server, and emotion engine. A system that takes the user's emotional state into account enables more personalized suggestions and management, thereby improving user satisfaction.

[0833] Example 2

[0834] 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."

[0835] In conventional agricultural support systems, even if you input the crop category and goals, the system collects and analyzes environmental data, and even proposes optimal cultivation schedules individually, requiring a lot of operation and management. Furthermore, no system takes into account the user's emotional state, which does not reduce stress or improve satisfaction. Furthermore, it is time-consuming to manually generate content for blogs and websites.

[0836] 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.

[0837] In this invention, the server includes a means for inputting crop categories and key features, a means for collecting environmental data, a means for analyzing the environmental data and generating an optimal crop list and cultivation schedule, a means for displaying the generated crop list and cultivation schedule, a means for collecting user emotion data and adjusting the proposal content based on the analysis, a means for managing and instructing during cultivation, a means for automatically generating blog and homepage content, and a means for integrating data in real time and transmitting it to the server. This enables comprehensive and efficient agricultural support, realizes personalized proposals and management that take the user's emotional state into consideration, and improves user satisfaction.

[0838] The "means for inputting the crop category and key points" is a device that provides an interface for the user to input the type of crop and characteristics that the user considers important (for example, quality, yield, etc.).

[0839] "Means for collecting environmental data" refers to sensors and devices for obtaining various environmental data such as temperature, humidity, soil quality, and sunlight.

[0840] The "means for analyzing environmental data and generating optimal crop lists and cultivation schedules" refers to a system that uses AI and machine learning algorithms to create optimal crop lists and cultivation schedules based on collected environmental data.

[0841] The "means for displaying the generated crop list and cultivation schedule" is a device that displays the crop list and annual cultivation schedule generated by the server in an easy-to-understand manner for the user.

[0842] The "means for collecting user emotional data and adjusting the proposed content based on the analysis" refers to a device that analyzes the user's facial expressions and voice and appropriately adjusts the system's proposed content and cultivation schedule based on the user's emotional state.

[0843] The "means for management and instruction during cultivation" is a system that uses smart devices to automatically control tasks such as watering and fertilizer application during the cultivation period.

[0844] The "means for automatically generating content for blogs and homepages" is a system that automatically creates content for blog articles and homepages based on information and photos of the growth process.

[0845] "Means of collecting climate data, soil data and sunlight data" refers to various sensors and information sources for obtaining data on climate, soil conditions, sunlight conditions, etc.

[0846] "Means for acquiring data using environmental sensors and cameras" refers to devices for periodically collecting data using environmental sensors and cameras.

[0847] "Means for obtaining climate data using the API of a weather information provider" refers to a system that obtains the latest weather data through the API of an external service that provides weather information.

[0848] The "means for integrating data in real time and transmitting it to a server" is a device that compiles data obtained from each sensor or information source into a single packet in real time and transmits it to a server.

[0849] "Means for delivering automatically generated blog and homepage content to consumers" refers to a system that allows users to review and edit automatically generated content, publish it via the Internet, and disseminate information to consumers.

[0850] This invention is a system that comprehensively supports agricultural activities, and includes functions for inputting crop categories and key points, collecting and analyzing environmental data, generating and displaying optimal crop lists and cultivation schedules, managing cultivation in real time, and automatically generating content for blogs and homepages. It also incorporates an emotion engine that recognizes the user's emotions and adjusts the system's suggestions and operations accordingly.

[0851] System Overview

[0852] This system mainly consists of four components: the user, the terminal, the server, and the emotion engine. Each component is responsible for the following functions:

[0853] Entering crop categories and highlight points

[0854] The user uses a terminal to input crop categories such as "tomato" or "yield-oriented" and key points, and this information is sent to the server in real time.

[0855] Environmental data collection

[0856] The device collects data from temperature sensors, humidity sensors, soil sensors, and cameras. It also obtains climate data using APIs from weather information providers. This data is integrated and sent to a server.

[0857] Data analysis and crop list generation

[0858] The server uses an AI algorithm to generate the optimal crop list based on the received environmental data, crop category, and key points. This analysis uses machine learning algorithms such as TensorFlow. Specifically, it calculates the optimal environmental conditions for growing tomatoes and generates recommended cultivation methods and lists.

[0859] Creating and displaying cultivation schedules

[0860] The server creates an annual cultivation schedule based on the crop list and sends it to the terminal, which visually displays the schedule and asks the user for confirmation.

[0861] Using the Emotion Engine

[0862] The device uses a camera and microphone to collect the user's facial expressions and voice, which are then analyzed by an emotion engine. This analysis is performed using, for example, Microsoft Azure's emotion analysis API. Based on the analysis results, the server adjusts the recommendations and cultivation schedule.

[0863] Management during cultivation

[0864] The terminals control agricultural devices such as smart scarecrows and automatic watering devices based on instructions from the server. They also monitor environmental data in real time and immediately report any abnormalities to the server.

[0865] Auto-generated content

[0866] The server automatically generates blog posts and homepage content based on information and photos of the growing process, such as articles like "This month's tomato growth record" and "Tomato cooking recipes."

[0867] Improving brand power and appealing to consumers

[0868] Users can check the content they generate, edit it as necessary, and then publish it on their website or social media, thus promoting the appeal of their crops to consumers.

[0869] Specific examples

[0870] When a user inputs "tomato" and "emphasis on yield" as evaluation points, the device acquires data in real time from temperature sensors, humidity sensors, soil sensors, cameras, and weather information provider APIs, and sends it to the server. The server analyzes the data and generates an optimal cultivation schedule. It also analyzes the user's emotional state to adjust the suggestions and manage the cultivation. Blog articles and recipes are automatically generated based on growth records and photos, and the user can publish them on their website or social media. This series of steps realizes efficient agricultural support.

[0871] Example prompts for generative AI models

[0872] "Please tell me how to grow tomatoes and what the best growing conditions are. Also, could you give me some advice on how to maximize my yield?"

[0873] In this way, the system of the present invention provides comprehensive agricultural support through collaboration between users, terminals, servers, and emotion engines, enabling personalized suggestions and management, thereby improving user satisfaction and streamlining cultivation activities.

[0874] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0875] Step 1:

[0876] The user inputs the crop category and highlight points.

[0877] Input: The user inputs "tomato" and "yield-oriented" on the input screen of the terminal.

[0878] Action: The user completes the input and presses the submit button.

[0879] Output: The entered information is sent to the server.

[0880] Step 2:

[0881] The device collects environmental data.

[0882] Input: Real-time data from installed temperature, humidity, and soil sensors, and cameras.

[0883] How it works: The device collects data from sensors and cameras every 10 minutes, and also accesses weather information providers' APIs via the internet to obtain climate data.

[0884] Output: The acquired environmental data is stored on the terminal and combined into a single data packet.

[0885] Step 3:

[0886] The device transmits the environmental data to the server.

[0887] Input: Integrated temperature, humidity, soil quality, solar radiation, climate data, and image data.

[0888] Operation: Collected data is sent to the server in real time.

[0889] Output: The server receives the environment data.

[0890] Step 4:

[0891] The server analyzes the data and generates an optimal crop list.

[0892] Inputs: Environmental data, crop categories, eyeball points.

[0893] How it works: The server inputs the received data into a machine learning algorithm (e.g., TensorFlow) for analysis.

[0894] Output: A list of optimal crops will be generated, including the best environmental conditions and cultivation methods for growing tomatoes.

[0895] Step 5:

[0896] The server creates an annual cultivation schedule.

[0897] Input: The generated crop list.

[0898] How it works: The server determines the dates for each step, such as sowing, transplanting, and harvesting, based on recommended cultivation methods.

[0899] Output: An annual cultivation schedule is created.

[0900] Step 6:

[0901] The server sends the crop list and cultivation schedule to the terminal.

[0902] Input: Created crop list and annual cultivation schedule.

[0903] How it works: The server sends the list and schedule in JSON format to the device.

[0904] Output: The device receives and analyzes the data.

[0905] Step 7:

[0906] The terminal displays a list of crops and a cultivation schedule.

[0907] Input: Crop list and cultivation schedule received from the server.

[0908] Action: The device displays a visual representation on the user's screen.

[0909] Output: The user confirms the specific cultivation plan.

[0910] Step 8:

[0911] The device collects the user's facial expressions and voice data and analyzes them using an emotion engine.

[0912] Input: Facial expression and voice data collected by camera and microphone.

[0913] How it works: The device uses an emotion analysis API (e.g., Microsoft Azure) to analyze the user's emotional state in real time.

[0914] Output: The analyzed emotion data is sent to the server.

[0915] Step 9:

[0916] The server adjusts the suggestions based on the analyzed emotion data.

[0917] Input: User emotion data, crop list and cultivation schedule.

[0918] How it works: The server determines the user's stress level and satisfaction level and adjusts the suggestions and schedule accordingly.

[0919] Output: A tailored proposal and schedule are generated.

[0920] Step 10:

[0921] The terminal controls agricultural devices based on instructions from the server.

[0922] Input: Instructions received from the server.

[0923] What it does: The device activates a smart scarecrow, automatic watering system, and fertilizer spreading system.

[0924] Output: Appropriate cultivation practices are implemented.

[0925] Step 11:

[0926] The device monitors environmental data in real time and detects abnormalities.

[0927] Input: Environmental data collected in real time.

[0928] Operation: When the device detects an abnormal value, it sends an alert to the server and notifies the user.

[0929] Output: The server and user recognize the anomaly and take corresponding action.

[0930] Step 12:

[0931] The server automatically generates the content for the blog and homepage.

[0932] Input: Growth records, photo data.

[0933] How it works: The server uses an automated generation algorithm to create content based on the collected data. Examples include "This month's tomato growth record" and "Tomato cooking recipes."

[0934] Output: The generated blog post and homepage content.

[0935] Step 13:

[0936] The user reviews the generated content and makes edits as needed.

[0937] Input: Content data received from the server.

[0938] What it does: A user sees a preview of their blog post or social media post and edits the text and images as needed.

[0939] Output: A revised blog post or social media post.

[0940] Step 14:

[0941] Users publish content on their homepages or social media.

[0942] Input: The modified content.

[0943] What happens: A user presses the submit button to publish an article.

[0944] Output: Information is disseminated to consumers, and the attractiveness of the crop is effectively promoted.

[0945] In this way, the system of the present invention provides comprehensive agricultural support through collaboration between users, terminals, servers, and emotion engines, enabling personalized suggestions and management, thereby improving user satisfaction and streamlining cultivation activities.

[0946] (Application example 2)

[0947] 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."

[0948] The present invention relates to a comprehensive agricultural activity support system and its applications, and in particular to solving the problem of improving user satisfaction and efficiency by suggesting ingredients and recipes based on the user's preferences and emotions. Currently, agricultural activity support systems collect and analyze environmental data in real time, but it is difficult to make personalized suggestions based on the user's preferences and emotions. Furthermore, there is a need for more accurate service provision by combining content generation based on food suggestions and emotion recognition.

[0949] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting crop categories and highlights, a means for collecting environmental data, a means for analyzing the environmental data and generating an optimal crop list and cultivation schedule, a means for displaying the generated crop list and cultivation schedule, a means for managing and providing instructions during cultivation, a means for automatically generating blog and homepage content, a means for suggesting ingredients and recipes based on the user's preferences, and a means for recognizing the user's emotions and adjusting the suggestions accordingly. This enables personalized suggestions based on the user's preferences and emotions, thereby improving user satisfaction and efficiency.

[0950] The "means for inputting crop category and highlight points" is a function that allows the user to input the type and characteristics of a particular crop.

[0951] "Means for collecting environmental data" refers to a function for acquiring environmental information such as temperature, humidity, soil condition, and sunlight.

[0952] The "means for analyzing environmental data and generating an optimal crop list and cultivation schedule" is a function for analyzing collected environmental data and generating the most suitable crops and their cultivation plans.

[0953] The "means for displaying the generated crop list and cultivation schedule" is a function for showing the user the crop list and cultivation schedule created based on the analysis.

[0954] "Means for management and instruction during cultivation" refers to a function for carrying out management tasks and operational instructions required during the cultivation process.

[0955] "Means for automatically generating blog and homepage content" refers to a function for automatically creating web content based on collected data.

[0956] The "means for suggesting ingredients and recipes based on the user's preferences" is a function for recommending appropriate ingredients and cooking recipes in consideration of the user's preferences.

[0957] "Means for recognizing the user's emotions and adjusting suggestions accordingly" refers to a function that analyzes the user's emotions from their facial expressions and voice, and changes the information and suggestions provided accordingly.

[0958] "Means for collecting climate data, soil data, and sunshine data" refers to a function for acquiring climate, soil, and sunshine information in order to obtain detailed information about external environmental conditions.

[0959] "Means for acquiring data using environmental sensors and cameras" refers to a function for collecting environmental information using sensors and cameras.

[0960] "Means for obtaining climate data using APIs of weather information providers" refers to a function for obtaining climate-related data using online weather information services.

[0961] "Means for recommending optimal ingredients and recipes using machine learning algorithms" is a function that uses machine learning technology to select optimal ingredients and recipes for users.

[0962] The "means for analyzing the user's facial expression and voice data using an emotion recognition engine" is a function for determining the user's emotional state using facial expression and voice analysis technology.

[0963] "Means for delivering automatically generated blog and homepage content to consumers" is a feature for creating web content and publishing it online.

[0964] The "means for generating content tailored based on food suggestions" is a function for automatically creating appropriate web content based on food information that matches the user's preferences.

[0965] MODE FOR CARRYING OUT THE INVENTION

[0966] The present invention aims to improve user satisfaction and efficiency by adding a function to suggest ingredients and recipes that take into account the user's preferences and feelings to a comprehensive agricultural activity support system. The system for realizing this invention is composed of the following methods and means.

[0967] System Configuration

[0968] The server includes means for inputting crop categories and highlight points, means for collecting environmental data, means for analyzing the environmental data and generating an optimal crop list and cultivation schedule, means for displaying the generated crop list and cultivation schedule, means for managing and giving instructions during cultivation, means for automatically generating blog and homepage content, means for suggesting ingredients and recipes based on the user's preferences, and means for recognizing the user's emotions and adjusting suggestions accordingly.

[0969] Program processing

[0970] Hardware and Software

[0971] This system uses the following hardware and software:

[0972] Hardware: Smartphone, temperature sensor, humidity sensor

[0973] Software: Python, TensorFlow, cloud AI services, cloud vision services, communication APIs, web frameworks

[0974] Data processing and calculation

[0975] 1. Processing user-entered data:

[0976] The user inputs the crop category and key points into the terminal and sends them to the server. The terminal uses a web framework (e.g., Flask) to process the input data in real time and provide it to the server.

[0977] 2. Environmental Data Collection:

[0978] The device periodically acquires environmental data such as temperature, humidity, soil condition, and sunlight from various sensors and sends it to the server. It also acquires climate data using the API of an online weather information service.

[0979] 3. Data analysis and proposal generation:

[0980] The server uses machine learning algorithms (e.g., TensorFlow) to analyze the collected environmental data. Based on the analysis results, it generates a list of optimal crops and a cultivation schedule, which are then sent to the user's device for display. It also analyzes the user's preferences and past behavioral data to suggest optimal ingredients and recipes.

[0981] 4. Emotion recognition:

[0982] The device uses an emotion recognition engine to analyze the user's facial expressions and voice data to determine their current emotional state. The analysis results are sent to the server, which then adjusts the recommendations and cultivation schedule accordingly.

[0983] 5. Automatic content generation:

[0984] The server automatically generates blog and homepage content based on the collected data, and users can review this content, edit it if necessary, and then publish it online.

[0985] Specific examples

[0986] For example:

[0987] A user uses a smartphone application to request "ideas for tonight's dinner." The application collects current weather data (temperature 25 degrees, humidity 60%) and suggests optimal recipes based on the user's past preferences (e.g., Japanese food). The application also reads the user's emotions from their facial expressions and recommends ingredients with a relaxing effect if the user's stress level is high.

[0988] Here is an example prompt:

[0989] Give me some ideas for dinner tonight. The current weather data shows a temperature of 25°C and humidity of 60%, and the user has often shown a preference for Japanese food in the past. The user also seems to be feeling stressed. Based on this, please suggest the best recipe.

[0990] In this way, the present invention provides a highly personalized function that takes into account the user's preferences and feelings, thereby significantly improving user satisfaction and work efficiency.

[0991] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0992] Program processing flow

[0993] Step 1:

[0994] The user inputs the crop category and key points via a smartphone or other device. The information entered by the user (e.g., crop desired to grow = "tomatoes," key point = "quality-oriented") is sent from the device to the server. Based on this input, the system prepares to perform a personalized analysis.

[0995] Step 2:

[0996] The device collects environmental data using temperature, humidity, and soil sensors, as well as a camera. Data obtained from these sensors and cameras (e.g., temperature 25°C, humidity 60%, soil humidity 40%) is sent to a server in real time. Additionally, weather data is also acquired and integrated using the API of weather information providers.

[0997] Step 3:

[0998] The server analyzes the collected environmental data, crop categories, and key points. It then uses machine learning algorithms (e.g., TensorFlow) to generate an optimal crop list and cultivation schedule. Based on this data analysis, a list is generated that suggests, for example, "tomatoes" and "bell peppers." An annual cultivation schedule is also generated.

[0999] Step 4:

[1000] The server sends the generated crop list and cultivation schedule to the terminal, which visually displays this information to the user, who can then review the proposed list and schedule and make selections or changes as necessary.

[1001] Step 5:

[1002] The device uses an emotion recognition engine to collect the user's facial and voice data and send it to a server. For example, the user can record their facial expressions through a camera or speak using a microphone. This data is analyzed in real time to determine the user's emotional state (e.g., stressed, happy, etc.).

[1003] Step 6:

[1004] The server then adjusts the suggestions and cultivation schedules based on the user's emotional data. For example, if the user is feeling stressed, it may suggest easier, less labor-intensive crops or make gentler changes to the schedule.

[1005] Step 7:

[1006] The terminals interface with smart agricultural devices (e.g., automatic watering devices and temperature control systems) and execute instructions from the server, including real-time data monitoring (e.g., automatic watering when soil moisture drops) and sending alerts (e.g., notifications when temperatures are too high).

[1007] Step 8:

[1008] The server automatically generates blog and homepage content based on the collected growth information and photos. For example, it generates articles such as "Tomato Growth Status This Month" and "Easy Tomato Recipes." The generated content is provided to users, who can edit it as needed before publishing it.

[1009] Step 9:

[1010] Users can then publish the generated content on social media or their homepages to promote their crops to consumers, effectively highlighting their appeal and improving their brand power.

[1011] Through the above steps, the system of the present invention can provide users with comprehensive and personalized agricultural support. This system has an advanced suggestion function that takes into account the user's preferences and emotions, and can improve user satisfaction more than conventional agricultural support systems.

[1012] 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.

[1013] 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.

[1014] 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.

[1015] [Third embodiment]

[1016] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1017] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1018] 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).

[1019] 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.

[1020] 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.

[1021] 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).

[1022] 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.

[1023] 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.

[1024] 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.

[1025] 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.

[1026] 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.

[1027] 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."

[1028] The present invention is a total support system for supporting agricultural activities. This system includes a means for inputting crop categories and key features, a means for collecting environmental data, a means for analyzing the collected data and generating an optimal crop list and cultivation schedule, a means for displaying the generated data to the user, a means for providing real-time management and instructions during cultivation, and a means for delivering automatically generated blog and homepage content to consumers.

[1029] System Overview

[1030] User operation flow

[1031] 1. User enters crop category and highlight points:

[1032] The user inputs the crop category (e.g., leafy vegetables) and key features (e.g., quality-oriented) from the terminal. This information is sent to the system, which then proceeds to the next step.

[1033] 2. The device collects environmental data:

[1034] The system collects data from installed temperature, humidity, and soil sensors, as well as cameras. It also collects climate data using the APIs of weather information providers. This data is sent to a server in real time.

[1035] 3. The server analyzes the data:

[1036] The server uses the received environmental data, crop categories, and key points to generate an optimal crop list, and also analyzes the data to create an annual cultivation schedule.

[1037] 4. The server generates a proposal and sends it to the device:

[1038] The proposed crop list and cultivation schedule are sent to the terminal and displayed to the user, who can then use this information to plan their cultivation.

[1039] 5. Cultivation Management:

[1040] The terminals will link with agricultural devices such as smart scarecrows and automate tasks based on instructions from the server, such as watering and spreading fertilizer.

[1041] 6. Auto-generated content:

[1042] The server automatically generates blog and homepage content based on the collected information and photos, including growth records, harvest status, cooking recipes, and more.

[1043] 7. Consumer Appeal:

[1044] The user can check the generated content and make any necessary edits. After that, the information is disseminated to consumers via websites and social media, promoting the appeal of the crops.

[1045] Program processing details (example)

[1046] Registering categories and highlight points

[1047] The user accesses the terminal, selects "leafy vegetables" and "quality-oriented" and inputs the information.

[1048] The terminal validates the input in real time and sends it to the server.

[1049] Environmental data collection

[1050] The terminal periodically collects data from installed sensors (temperature, humidity, soil type) and cameras.

[1051] The device accesses the weather information provider's API via the Internet to obtain climate data.

[1052] The terminal integrates this data and sends it to the server.

[1053] Data analysis and proposal creation

[1054] Based on the received data, the server uses AI and machine learning algorithms to generate an optimal crop list, suggesting "lettuce" and "spinach," for example.

[1055] The server creates an annual schedule based on the crop list.

[1056] View Suggestions

[1057] The server transmits the generated list and schedule to the terminal.

[1058] The terminal visually displays this information to the user.

[1059] Management during cultivation

[1060] The terminals control agricultural devices such as smart scarecrows based on instructions from the server, for example by setting watering schedules and monitoring temperature and humidity.

[1061] Creating auto-generated content

[1062] The server automatically generates blog posts and homepage content based on the growth records and photos, such as "This month's lettuce growth status" or "Easy spinach recipes."

[1063] Improving brand power and appealing to consumers

[1064] The user finalizes the generated content and corrects it if necessary.

[1065] Users use their homepages and social media to spread content and promote the appeal of their crops.

[1066] In this way, the system of the present invention provides comprehensive support at each stage of agriculture, helping users to cultivate crops efficiently and make them more appealing to consumers.

[1067] The processing flow will be explained below.

[1068] Step 1:

[1069] The user inputs the crop category and highlight points.

[1070] The user accesses the terminal screen and inputs the crop category (e.g., leafy vegetables) and the key points (e.g., emphasis on quality).

[1071] The terminal transmits the input information to the server.

[1072] Step 2:

[1073] The device collects environmental data.

[1074] The terminal collects data from temperature sensors, humidity sensors, soil sensors and cameras installed on-site.

[1075] The device uses the weather information provider's API to obtain local climate data.

[1076] The device consolidates the collected data and sends it to a server.

[1077] Step 3:

[1078] The server analyzes the data and generates a list of possible crops and a schedule.

[1079] The server analyzes the received environmental data, crop categories and highlight points.

[1080] The server generates a list of optimal crops and creates an annual cultivation schedule.

[1081] Step 4:

[1082] The server sends the proposal to the terminal and displays it to the user.

[1083] The server sends the proposed crop list and cultivation schedule to the terminal.

[1084] The terminal visually displays the suggestions to the user.

[1085] Step 5:

[1086] The terminal works in conjunction with agricultural devices such as smart scarecrows to manage the cultivation process.

[1087] The terminal receives instructions from the server and sends them to smart scarecrows and other agricultural devices.

[1088] The smart scarecrow automatically performs necessary farming tasks (e.g. watering, spreading fertilizer).

[1089] The terminal monitors the data in real time and reports any abnormalities to the server.

[1090] Step 6:

[1091] The server automatically generates the content for the blog and homepage.

[1092] The server automatically generates blog and homepage content based on data such as cultivation information, photos, and growth records.

[1093] The server publishes the generated content.

[1094] Step 7:

[1095] Review user-generated content and engage with consumers.

[1096] The user checks the content of the generated blog or homepage and edits it as necessary.

[1097] Users can disseminate information to consumers through their websites and social media, promoting the appeal of their crops.

[1098] As described above, in the present invention, users, terminals, and servers cooperate to realize efficient agricultural activities.

[1099] Example 1

[1100] 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."

[1101] There is a need for systems that can streamline agricultural activities, support the cultivation of high-quality crops, and effectively appeal to consumers. However, conventional agricultural support systems were unable to provide comprehensive support for everything from collecting and analyzing environmental data to managing crop cultivation and disseminating information. This forced farmers to expend a lot of time and money, making it difficult to manage their farms efficiently.

[1102] 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.

[1103] In this invention, the server includes means for inputting crop categories and key features, means for collecting environmental data, means for analyzing the environmental data and generating an optimal crop list and cultivation schedule, means for displaying the generated crop list and cultivation schedule, means for managing and providing instructions during cultivation, means for automatically generating blog and homepage content, and means for providing the automatically generated content to consumers. This makes it possible to comprehensively manage and support a series of processes at each stage of agricultural activities, allowing users to efficiently cultivate high-quality crops and effectively appeal to consumers.

[1104] "Means for inputting crop categories and key points" refers to an interface and device that allows a user to select and input the type of crop they wish to grow and the elements they wish to focus on.

[1105] "Means for collecting environmental data" refers to a system that uses sensors and cameras installed on farmland to obtain information about the environment, such as temperature, humidity, soil quality, and sunlight.

[1106] The "means for analyzing environmental data and generating an optimal crop list and cultivation schedule" refers to algorithms and software that analyze the acquired environmental data and, based on the results, create optimal crop types and cultivation plans.

[1107] The "means for displaying the generated crop list and cultivation schedule" refers to an interface and device for displaying the crop list and cultivation schedule generated by the server on a terminal in a form that can be confirmed by the user.

[1108] The "means for management and instructions during cultivation" is a system for automating the management tasks and instructions required during the cultivation period and controlling smart agricultural equipment.

[1109] "Means for automatically generating blog and homepage content" refers to algorithms and software that allow AI to automatically create articles and content based on cultivation process and harvest information, etc.

[1110] "Means for providing automatically generated content to consumers" refers to a system for posting and distributing content on social media and websites in order to deliver automatically generated articles and information to consumers.

[1111] "Means for acquiring data using environmental sensors and cameras" refers to a system consisting of sensors for collecting environmental data such as temperature, humidity, soil quality, and sunlight, and cameras for acquiring image data.

[1112] "Means for obtaining climate data using APIs of weather information providers" refers to software and programming methods for calling APIs (Application Program Interfaces) to obtain real-time climate data using external weather information services.

[1113] The "means for transmitting environmental data acquired by the terminal to the server" refers to a communication means and protocol for transmitting collected environmental data from the terminal to the server.

[1114] "Means for the server to analyze data using AI algorithms and generate a crop list and cultivation schedule" refers to a system in which the server analyzes collected data using AI (artificial intelligence) and machine learning algorithms, and generates an optimal crop list and annual cultivation schedule based on the results.

[1115] "Means for a terminal to control agricultural devices and perform automated cultivation management" refers to means for a terminal to control agricultural equipment such as irrigation systems and smart scarecrows in order to automate cultivation management.

[1116] "Means for the server to automatically generate content using a generative AI model" refers to algorithms and software for automatically generating blog post and homepage content using an AI (artificial intelligence) model.

[1117] "Means for users to edit and publish content on homepages or SNS" refers to interfaces and devices that allow users to review and edit the content they have created and post it on homepages or SNS for publication.

[1118] This invention is a total support system for agricultural activities. This system collects and analyzes environmental data based on the crop category and key features entered by the user, and generates an optimal crop list and cultivation schedule. It also has the function of automating management and instructions during cultivation and providing the generated content to consumers.

[1119] Overview of program processing

[1120] Enter crop category

[1121] The user starts the device and enters the crop category (e.g., "leafy vegetables") and key features (e.g., "quality-oriented") via a dedicated app or web interface. The device verifies this information in real time and sends it to the server.

[1122] Environmental data collection

[1123] The device collects environmental data from installed environmental sensors (temperature, humidity, soil sensors) and cameras. It also obtains climate data from weather information providers' APIs (e.g., OpenWeatherMap API) via the Internet. This data is integrated and sent to a server.

[1124] Data analysis

[1125] The server generates an optimal crop list using AI algorithms (e.g., "random forest" or "neural network") based on the received environmental data, crop category, and key points. For example, if "leafy vegetables" and "quality-oriented" are selected, "lettuce" and "spinach" will be suggested. The server also generates an annual cultivation schedule based on the crop list.

[1126] View Suggestions

[1127] The server transmits the generated crop list and cultivation schedule to the terminal, which visually displays them to the user.

[1128] Management during cultivation

[1129] Based on instructions from the server, the terminals control agricultural devices such as smart irrigation systems and smart scarecrows, automating tasks such as setting watering and fertilizer application schedules and monitoring temperature and humidity.

[1130] Auto-generated content

[1131] The server uses a generative AI model (e.g., GPT-3) to automatically generate blog posts and homepage content based on the collected growth records and photos. The generated content includes specific details such as "This month's lettuce growth status" and "Easy spinach recipes."

[1132] Appeal to consumers

[1133] The user checks the generated content, makes any necessary corrections, and then publishes it on their website or via social media to appeal to consumers.

[1134] Examples of concrete examples and prompts

[1135] Specific examples

[1136] When the user selects "leafy vegetables" and "quality-oriented," the following processing is performed.

[1137] The device collects temperature, humidity, soil type, and camera data, and also uses a weather API to obtain climate data.

[1138] The server analyzes this data, suggests "lettuce" and "spinach," and creates an annual schedule based on that.

[1139] The terminal displays the proposal list and schedule to the user.

[1140] The device uses a smart irrigation system to automate watering and fertilizing.

[1141] The server generates a blog post titled "This month's lettuce growth status" based on the growth record.

[1142] After users check and edit the article, they can share it on social media platforms such as Facebook and Instagram.

[1143] Prompt Sentence Examples

[1144] Please suggest the optimal crop list and cultivation schedule if "leafy vegetables" and "quality-oriented" are selected.

[1145] This invention is a system that comprehensively manages and supports a series of processes at each stage of agriculture, enabling users to efficiently cultivate high-quality crops and effectively appeal to consumers.

[1146] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1147] Step 1:

[1148] Enter crop category and highlight points

[1149] Input: The user starts the device and enters the "crop category" (e.g., "leafy vegetables") and "highlights" (e.g., "quality-oriented") via a dedicated app or web interface.

[1150] How it works: When the user enters this information, the device verifies the integrity of the input data in real time and sends it to the server if there are no errors.

[1151] Output: The input crop category and highlight point information are sent to the server.

[1152] Step 2:

[1153] Environmental data collection

[1154] Input: The device collects data from environmental sensors (temperature sensors, humidity sensors, soil sensors) and cameras installed in the farmland, and obtains climate data from weather information providers' APIs (e.g., "OpenWeatherMap API").

[1155] How it works: The device periodically retrieves data from these sensors and accesses the weather API to get the necessary climate data.

[1156] Output: Collected environmental and climate data is sent to a server.

[1157] Step 3:

[1158] Data analysis

[1159] Input: The server receives data based on environmental data, climate data, crop categories, and eyeball points sent from the terminal.

[1160] How it works: The server analyzes the data using AI algorithms (e.g., "random forest" or "neural network") to generate an optimal crop list. It also creates an annual cultivation schedule based on the crop list. For example, if you select "leafy vegetables" and "quality-oriented," "lettuce" and "spinach" may be suggested.

[1161] Output: An optimal crop list and cultivation schedule is generated.

[1162] Step 4:

[1163] Submitting and Viewing Proposals

[1164] Input: Server-generated crop list and cultivation schedule.

[1165] Operation: The server sends this information to the device. The device visually displays the received suggestions to the user. The user confirms the suggestions.

[1166] Output: The user can check the optimal crop list and cultivation schedule through the terminal.

[1167] Step 5:

[1168] Management during cultivation

[1169] Input: Instruction data from the server and environmental data collected in real time.

[1170] How it works: Based on instructions from the server, the device operates agricultural devices such as smart irrigation systems and smart scarecrows to automate appropriate cultivation management, such as setting schedules for watering and fertilizer application, and monitoring temperature and humidity.

[1171] Output: Automated cultivation management data and warning notifications are displayed on the user's terminal if there are any abnormalities.

[1172] Step 6:

[1173] Auto-generated content

[1174] Input: Collected growth records and photographic data.

[1175] How it works: Based on the growth records and photos, the server uses a generative AI model (e.g., GPT-3) to automatically generate blog posts and homepage content, such as "This month's lettuce growth status" or "Easy spinach recipes."

[1176] Output: Auto-generated blog post and homepage content.

[1177] Step 7:

[1178] Appeal to consumers

[1179] Input: Server-generated content.

[1180] How it works: The user reviews the generated content, edits it as necessary, and then publishes the information on their website or social media to promote the appeal of their crops to consumers.

[1181] Output: The published content can be viewed on websites and social media, making it appealing to consumers.

[1182] (Application example 1)

[1183] 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."

[1184] In modern agriculture and industry, it is important to collect and analyze environmental data in real time. However, while conventional systems can generate optimal crop lists and cultivation schedules in agriculture, they do not support generating optimal work schedules and automatic execution in industry, nor generating work progress reports. As a result, efficient automation of agriculture and industry is difficult to achieve, and managers have difficulty understanding the situation in real time and issuing appropriate instructions.

[1185] 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.

[1186] In this invention, the server includes means for inputting crop categories and key features, means for collecting environmental data, means for analyzing the environmental data and generating an optimal crop list and cultivation schedule, means for displaying the generated crop list and cultivation schedule, means for managing and providing instructions during cultivation, means for automatically generating blog and homepage content, means for collecting work environment data, means for analyzing the data and generating an optimal work schedule and procedure, means for automatically performing work based on the work procedure, and means for automatically generating work progress reports. This enables efficient automation in both agriculture and industry, and enables managers to grasp the situation in real time and provide appropriate instructions.

[1187] "Crop category" refers to a classification that indicates the type of crop that the user intends to cultivate.

[1188] "Eye-catching points" refer to points or characteristics that are particularly important in crop cultivation.

[1189] "Environmental data" refers to data such as temperature, humidity, soil quality, and weather information that affect crops and the working environment.

[1190] "Means of collecting data" refers to the equipment and technology used to acquire data using environmental sensors, cameras, etc.

[1191] "Means of analysis" refers to the algorithms and software used to analyze collected data and extract meaningful information.

[1192] The "optimal crop list" is a list of the most suitable crops proposed based on the analysis results.

[1193] A "cultivation schedule" is an optimal timetable or plan for growing crops.

[1194] The "display means" refers to a display or screen that visually shows the generated information to the user.

[1195] "Means for control and instruction" refers to devices and software for operating and instructing in order to efficiently carry out cultivation work.

[1196] "Auto-generating methods" refers to technologies or algorithms that automatically create content based on collected data and user input.

[1197] "Work environment data" refers to data on environmental conditions such as temperature, humidity, vibration, and sound that affect work in factories and on farmland.

[1198] "Work schedules and procedures" refers to the optimal work timetable and execution procedures.

[1199] "Means for automatically executing work" refers to the technology and devices that allow machines and robots to autonomously perform work based on the generated work procedures.

[1200] A "work progress report" is a report summarizing the progress and results of work that has been performed.

[1201] The system of the present invention is a total support system for efficiently managing and automating specific agricultural and industrial operations. This system is composed of the following main components:

[1202] 1. User enters crop category and highlight points:

[1203] The user inputs the crop category (e.g., leafy vegetables) and key features (e.g., quality-oriented) from the terminal. This information is sent to the system, which then proceeds to the next step.

[1204] 2. The device collects environmental data:

[1205] The device (smartphone, PC, etc.) acquires data in real time from installed temperature sensors, humidity sensors, soil sensors, vibration sensors, sound sensors, and cameras. It also acquires climate data using the API of weather information providers.

[1206] 3. The server analyzes the data:

[1207] The server analyzes the received environmental data, crop categories, and key points using AI and machine learning algorithms (e.g., TensorFlow and PyTorch) to generate an optimal crop list and annual cultivation schedule. It also analyzes similar environmental data for factory operations to generate optimal work schedules and procedures.

[1208] 4. Proposal generation and display:

[1209] The generated crop list and cultivation schedule, as well as the factory work procedures and schedule, are sent to the terminal and displayed to the user, who then uses this information to create specific work and cultivation plans.

[1210] 5. Cultivation and operation management:

[1211] The terminals will work with devices such as smart scarecrows and factory robots to automate tasks based on instructions from the server, such as watering plants, spreading fertilizer, and performing specific assembly and inspection tasks in factories.

[1212] 6. Auto-generated content:

[1213] The server automatically generates blog posts and homepage content based on the collected information and photos, including growth records, work progress reports, harvest status reports, cooking recipes, and work reports.

[1214] 7. Information for consumers and administrators:

[1215] The user then performs a final check of the generated content, corrects it as necessary, and then disseminates the information to consumers via their website or social media. The administrator also checks the generated report and gives appropriate instructions.

[1216] As a concrete example, content can be automatically generated by inputting the following prompt sentence into a generative AI model.

[1217] Generate an optimal work schedule based on sensor data. Use the following data:

[1218] Temperature: 22℃ - 24℃

[1219] Humidity: 60% - 62%

[1220] Vibration: 0.2g - 0.3g

[1221] Sound: 50dB - 55dB

[1222] Please suggest the best working time based on this data.

[1223] In this way, the system of the present invention provides comprehensive support for achieving efficient management and automation in both agriculture and industry.

[1224] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1225] Step 1:

[1226] The user operates the terminal to input the crop category and key features. The user inputs information such as "leafy vegetables" and "quality-oriented" through the terminal interface. This information is sent from the terminal to the server in real time. The input is important data based on the user's cultivation plan and is essential for subsequent processing.

[1227] Step 2:

[1228] The device collects environmental data. It acquires data in real time using environmental sensors (temperature sensor, humidity sensor, soil sensor, vibration sensor, sound sensor) and cameras. It also accesses the API of weather information providers to acquire climate data. All collected data is sent to a server via the internet. Input data is provided by the sensors and API, and output data is stored on the server.

[1229] Step 3:

[1230] The server analyzes the received data based on environmental data, crop categories, and key points. It then uses AI and machine learning algorithms (TensorFlow and PyTorch) to generate an optimal crop list and cultivation schedule. This analysis combines multiple datasets to help select the best crops and generate the best schedule. The data collected in the previous step is used as input, and the optimal crop list and cultivation schedule is generated as output.

[1231] Step 4:

[1232] The server sends the generated crop list and cultivation schedule to the terminal. The terminal displays it to the user in a visually easy-to-understand manner. This display allows the user to easily understand the specific work content and schedule and put it into action. The input is the data generated in step 3, and the output is displayed on the terminal in a form that the user can see.

[1233] Step 5:

[1234] The terminal manages and gives instructions during cultivation. It works in conjunction with smart scarecrows and factory robots to automatically carry out tasks based on instructions from the server. For example, it automatically controls temperature and humidity, waters plants, spreads fertilizer, assembles parts, and performs inspection work. Instructions are given based on data received in real time from the server, and optimal instructions suited to the working environment are issued. The input is instructions from the server, and the output is automated task execution.

[1235] Step 6:

[1236] The server automatically generates blog and homepage content based on the collected information and photos. This includes growth records, work progress, harvest information, cooking recipes, and work reports. A generative AI model is used to generate the content, and prompts are entered to output content in a specified format. The input is the collected information and photos, and the output is content for the user.

[1237] Step 7:

[1238] The user checks the generated content and makes any necessary corrections. The information is then provided to consumers and administrators via the website or social media. The final, checked content is notified and made public to relevant parties, helping to improve brand recognition and ensure transparency in the work process. The input is the generated content, and the output is the content made public on the website or social media.

[1239] 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.

[1240] This invention is a system that comprehensively supports agricultural activities, and includes functions for inputting crop categories and key points, collecting and analyzing environmental data, generating and displaying optimal crop lists and cultivation schedules, managing cultivation in real time, and automatically generating content for blogs and homepages. It also incorporates an emotion engine that recognizes the user's emotions and adjusts the system's suggestions and operations accordingly.

[1241] System Overview

[1242] User operation flow

[1243] 1. User enters crop category and highlight points:

[1244] The user inputs the crop category (e.g., leafy vegetables) and key features (e.g., quality-oriented) through the input screen on the terminal. This information is sent to the system.

[1245] 2. The device collects environmental data:

[1246] The device collects data from temperature, humidity, and soil sensors, as well as a camera, and uses APIs from weather information providers to obtain climate data, which is then sent to a server in real time.

[1247] 3. The server analyzes the data:

[1248] The server analyzes the received environmental data, crop categories, and key points to generate an optimal crop list and create an annual cultivation schedule.

[1249] 4. The server generates a proposal and sends it to the device:

[1250] The server transmits the generated crop list and cultivation schedule to the terminal and displays them visually to the user.

[1251] 5. Use of Emotion Engine:

[1252] To recognize the user's emotions, the emotion engine collects facial and voice data from the user, which is used to analyze whether the user is feeling stressed or satisfied. The server takes this emotional data into account and adjusts the suggestions and cultivation schedule accordingly.

[1253] 6. Cultivation Management:

[1254] The terminals connect to agricultural devices such as smart scarecrows and automate tasks based on instructions from the server, such as watering and fertilizing. They also monitor data in real time and report any abnormalities to the server.

[1255] 7. Auto-generated content:

[1256] The server automatically generates blog and homepage content based on the growth information and photos, including growth records, harvest status, and cooking recipes.

[1257] 8. Consumer Appeal:

[1258] Users can review the generated content and edit it as necessary, then share the information with consumers via their website or social media, effectively promoting the appeal of their crops.

[1259] Program processing details (example)

[1260] Registering categories and highlight points

[1261] The user accesses the terminal, selects "leafy vegetables" and "quality-oriented" and inputs the information.

[1262] The terminal validates the input in real time and sends it to the server.

[1263] Environmental data collection

[1264] The terminal periodically collects data from installed sensors (temperature, humidity, soil type) and cameras.

[1265] The device accesses the weather information provider's API via the Internet to obtain climate data.

[1266] The terminal integrates this data and sends it to the server.

[1267] Data analysis and proposal creation

[1268] Based on the received data, the server uses AI and machine learning algorithms to generate an optimal crop list, suggesting "lettuce" and "spinach," for example.

[1269] The server creates an annual schedule based on the crop list.

[1270] View Suggestions

[1271] The server transmits the generated list and schedule to the terminal.

[1272] The terminal visually displays this information to the user.

[1273] Using the Emotion Engine

[1274] The device inputs the user's facial expressions and voice into an emotion engine and analyzes the user's emotional state in real time.

[1275] Based on the analyzed emotional data, the server adjusts the proposal content and schedule, and suggests actions to reduce the user's stress.

[1276] Management during cultivation

[1277] The device receives instructions from the server and controls smart scarecrows and other agricultural devices, such as setting watering schedules and monitoring temperature and humidity.

[1278] Creating auto-generated content

[1279] The server automatically generates blog posts and homepage content based on the growth records and photos, such as "This month's lettuce growth status" or "Easy spinach recipes."

[1280] Improving brand power and appealing to consumers

[1281] The user finalizes the generated content and corrects it if necessary.

[1282] Users use their homepages and social media to spread content and promote the appeal of their crops.

[1283] In this way, the system of the present invention provides comprehensive and efficient agricultural support through cooperation between the user, terminal, server, and emotion engine. A system that takes the user's emotional state into account enables more personalized suggestions and management, thereby improving user satisfaction.

[1284] The processing flow will be explained below.

[1285] Step 1:

[1286] The user inputs the crop category and highlight points.

[1287] The user inputs the crop category (e.g., leafy vegetables) and key features (e.g., quality-oriented) on the terminal screen.

[1288] The terminal transmits the input information to the server.

[1289] Step 2:

[1290] The device collects environmental data.

[1291] The terminal collects data from temperature sensors, humidity sensors, soil sensors and cameras installed on-site.

[1292] The device uses the weather information provider's API to obtain local climate data.

[1293] The device consolidates the collected data and sends it to a server.

[1294] Step 3:

[1295] The server analyzes the data and generates a list of possible crops and a schedule.

[1296] The server analyzes the received environmental data, crop categories and highlight points.

[1297] The server generates a list of optimal crops and creates an annual cultivation schedule.

[1298] Step 4:

[1299] The server sends the proposal to the terminal and displays it to the user.

[1300] The server sends the proposed crop list and cultivation schedule to the terminal.

[1301] The terminal visually displays the suggestions to the user.

[1302] Step 5:

[1303] The device collects and analyzes the user's emotional data.

[1304] The device uses a camera and microphone to collect the user's facial expressions and voice.

[1305] The device transmits the collected emotion data to the server.

[1306] Step 6:

[1307] The server analyzes the emotional data and adjusts the cultivation schedule and suggestions.

[1308] The server uses an emotion engine to analyze the user's emotional state.

[1309] Based on the analysis results, the server adjusts the suggestions and cultivation schedules, and standardizes actions to reduce user stress.

[1310] Step 7:

[1311] The terminal works in conjunction with agricultural devices such as smart scarecrows to manage the cultivation process.

[1312] The terminal receives instructions from the server and sends them to smart scarecrows and other agricultural devices.

[1313] The smart scarecrow automatically performs necessary farming tasks (e.g. watering, spreading fertilizer).

[1314] The terminal monitors the data in real time and reports any abnormalities to the server.

[1315] Step 8:

[1316] The server automatically generates the content for the blog and homepage.

[1317] The server automatically generates blog and homepage content based on data such as cultivation information, photos, and growth records.

[1318] The server publishes the generated content.

[1319] Step 9:

[1320] Review user-generated content and engage with consumers.

[1321] The user checks the content of the generated blog or homepage and edits it as necessary.

[1322] Users can disseminate information to consumers through their websites and social media, promoting the appeal of their crops.

[1323] In this way, the system of the present invention provides comprehensive and efficient agricultural support through cooperation between the user, terminal, server, and emotion engine. A system that takes the user's emotional state into account enables more personalized suggestions and management, thereby improving user satisfaction.

[1324] Example 2

[1325] 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."

[1326] In conventional agricultural support systems, even if you input the crop category and goals, the system collects and analyzes environmental data, and even proposes optimal cultivation schedules individually, requiring a lot of operation and management. Furthermore, no system takes into account the user's emotional state, which does not reduce stress or improve satisfaction. Furthermore, it is time-consuming to manually generate content for blogs and websites.

[1327] 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.

[1328] In this invention, the server includes a means for inputting crop categories and key features, a means for collecting environmental data, a means for analyzing the environmental data and generating an optimal crop list and cultivation schedule, a means for displaying the generated crop list and cultivation schedule, a means for collecting user emotion data and adjusting the proposal content based on the analysis, a means for managing and instructing during cultivation, a means for automatically generating blog and homepage content, and a means for integrating data in real time and transmitting it to the server. This enables comprehensive and efficient agricultural support, realizes personalized proposals and management that take the user's emotional state into consideration, and improves user satisfaction.

[1329] The "means for inputting the crop category and key points" is a device that provides an interface for the user to input the type of crop and characteristics that the user considers important (for example, quality, yield, etc.).

[1330] "Means for collecting environmental data" refers to sensors and devices for obtaining various environmental data such as temperature, humidity, soil quality, and sunlight.

[1331] The "means for analyzing environmental data and generating optimal crop lists and cultivation schedules" refers to a system that uses AI and machine learning algorithms to create optimal crop lists and cultivation schedules based on collected environmental data.

[1332] The "means for displaying the generated crop list and cultivation schedule" is a device that displays the crop list and annual cultivation schedule generated by the server in an easy-to-understand manner for the user.

[1333] The "means for collecting user emotional data and adjusting the proposed content based on the analysis" refers to a device that analyzes the user's facial expressions and voice and appropriately adjusts the system's proposed content and cultivation schedule based on the user's emotional state.

[1334] The "means for management and instruction during cultivation" is a system that uses smart devices to automatically control tasks such as watering and fertilizer application during the cultivation period.

[1335] The "means for automatically generating content for blogs and homepages" is a system that automatically creates content for blog articles and homepages based on information and photos of the growth process.

[1336] "Means of collecting climate data, soil data and sunlight data" refers to various sensors and information sources for obtaining data on climate, soil conditions, sunlight conditions, etc.

[1337] "Means for acquiring data using environmental sensors and cameras" refers to devices for periodically collecting data using environmental sensors and cameras.

[1338] "Means for obtaining climate data using the API of a weather information provider" refers to a system that obtains the latest weather data through the API of an external service that provides weather information.

[1339] The "means for integrating data in real time and transmitting it to a server" is a device that compiles data obtained from each sensor or information source into a single packet in real time and transmits it to a server.

[1340] "Means for delivering automatically generated blog and homepage content to consumers" refers to a system that allows users to review and edit automatically generated content, publish it via the Internet, and disseminate information to consumers.

[1341] This invention is a system that comprehensively supports agricultural activities, and includes functions for inputting crop categories and key points, collecting and analyzing environmental data, generating and displaying optimal crop lists and cultivation schedules, managing cultivation in real time, and automatically generating content for blogs and homepages. It also incorporates an emotion engine that recognizes the user's emotions and adjusts the system's suggestions and operations accordingly.

[1342] System Overview

[1343] This system mainly consists of four components: the user, the terminal, the server, and the emotion engine. Each component is responsible for the following functions:

[1344] Entering crop categories and highlight points

[1345] The user uses a terminal to input crop categories such as "tomato" or "yield-oriented" and key points, and this information is sent to the server in real time.

[1346] Environmental data collection

[1347] The device collects data from temperature sensors, humidity sensors, soil sensors, and cameras. It also obtains climate data using APIs from weather information providers. This data is integrated and sent to a server.

[1348] Data analysis and crop list generation

[1349] The server uses an AI algorithm to generate the optimal crop list based on the received environmental data, crop category, and key points. This analysis uses machine learning algorithms such as TensorFlow. Specifically, it calculates the optimal environmental conditions for growing tomatoes and generates recommended cultivation methods and lists.

[1350] Creating and displaying cultivation schedules

[1351] The server creates an annual cultivation schedule based on the crop list and sends it to the terminal, which visually displays the schedule and asks the user for confirmation.

[1352] Using the Emotion Engine

[1353] The device uses a camera and microphone to collect the user's facial expressions and voice, which are then analyzed by an emotion engine. This analysis is performed using, for example, Microsoft Azure's emotion analysis API. Based on the analysis results, the server adjusts the recommendations and cultivation schedule.

[1354] Management during cultivation

[1355] The terminals control agricultural devices such as smart scarecrows and automatic watering devices based on instructions from the server. They also monitor environmental data in real time and immediately report any abnormalities to the server.

[1356] Auto-generated content

[1357] The server automatically generates blog posts and homepage content based on information and photos of the growing process, such as articles like "This month's tomato growth record" and "Tomato cooking recipes."

[1358] Improving brand power and appealing to consumers

[1359] Users can check the content they generate, edit it as necessary, and then publish it on their website or social media, thus promoting the appeal of their crops to consumers.

[1360] Specific examples

[1361] When a user inputs "tomato" and "emphasis on yield" as evaluation points, the device acquires data in real time from temperature sensors, humidity sensors, soil sensors, cameras, and weather information provider APIs, and sends it to the server. The server analyzes the data and generates an optimal cultivation schedule. It also analyzes the user's emotional state to adjust the suggestions and manage the cultivation. Blog articles and recipes are automatically generated based on growth records and photos, and the user can publish them on their website or social media. This series of steps realizes efficient agricultural support.

[1362] Example prompts for generative AI models

[1363] "Please tell me how to grow tomatoes and what the best growing conditions are. Also, could you give me some advice on how to maximize my yield?"

[1364] In this way, the system of the present invention provides comprehensive agricultural support through collaboration between users, terminals, servers, and emotion engines, enabling personalized suggestions and management, thereby improving user satisfaction and streamlining cultivation activities.

[1365] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1366] Step 1:

[1367] The user inputs the crop category and highlight points.

[1368] Input: The user inputs "tomato" and "yield-oriented" on the input screen of the terminal.

[1369] Action: The user completes the input and presses the submit button.

[1370] Output: The entered information is sent to the server.

[1371] Step 2:

[1372] The device collects environmental data.

[1373] Input: Real-time data from installed temperature, humidity, and soil sensors, and cameras.

[1374] How it works: The device collects data from sensors and cameras every 10 minutes, and also accesses weather information providers' APIs via the internet to obtain climate data.

[1375] Output: The acquired environmental data is stored on the terminal and combined into a single data packet.

[1376] Step 3:

[1377] The device transmits the environmental data to the server.

[1378] Input: Integrated temperature, humidity, soil quality, solar radiation, climate data, and image data.

[1379] Operation: Collected data is sent to the server in real time.

[1380] Output: The server receives the environment data.

[1381] Step 4:

[1382] The server analyzes the data and generates an optimal crop list.

[1383] Inputs: Environmental data, crop categories, eyeball points.

[1384] How it works: The server inputs the received data into a machine learning algorithm (e.g., TensorFlow) for analysis.

[1385] Output: A list of optimal crops will be generated, including the best environmental conditions and cultivation methods for growing tomatoes.

[1386] Step 5:

[1387] The server creates an annual cultivation schedule.

[1388] Input: The generated crop list.

[1389] How it works: The server determines the dates for each step, such as sowing, transplanting, and harvesting, based on recommended cultivation methods.

[1390] Output: An annual cultivation schedule is created.

[1391] Step 6:

[1392] The server sends the crop list and cultivation schedule to the terminal.

[1393] Input: Created crop list and annual cultivation schedule.

[1394] How it works: The server sends the list and schedule in JSON format to the device.

[1395] Output: The device receives and analyzes the data.

[1396] Step 7:

[1397] The terminal displays a list of crops and a cultivation schedule.

[1398] Input: Crop list and cultivation schedule received from the server.

[1399] Action: The device displays a visual representation on the user's screen.

[1400] Output: The user confirms the specific cultivation plan.

[1401] Step 8:

[1402] The device collects the user's facial expressions and voice data and analyzes them using an emotion engine.

[1403] Input: Facial expression and voice data collected by camera and microphone.

[1404] How it works: The device uses an emotion analysis API (e.g., Microsoft Azure) to analyze the user's emotional state in real time.

[1405] Output: The analyzed emotion data is sent to the server.

[1406] Step 9:

[1407] The server adjusts the suggestions based on the analyzed emotion data.

[1408] Input: User emotion data, crop list and cultivation schedule.

[1409] How it works: The server determines the user's stress level and satisfaction level and adjusts the suggestions and schedule accordingly.

[1410] Output: A tailored proposal and schedule are generated.

[1411] Step 10:

[1412] The terminal controls agricultural devices based on instructions from the server.

[1413] Input: Instructions received from the server.

[1414] What it does: The device activates a smart scarecrow, automatic watering system, and fertilizer spreading system.

[1415] Output: Appropriate cultivation practices are implemented.

[1416] Step 11:

[1417] The device monitors environmental data in real time and detects abnormalities.

[1418] Input: Environmental data collected in real time.

[1419] Operation: When the device detects an abnormal value, it sends an alert to the server and notifies the user.

[1420] Output: The server and user recognize the anomaly and take corresponding action.

[1421] Step 12:

[1422] The server automatically generates the content for the blog and homepage.

[1423] Input: Growth records, photo data.

[1424] How it works: The server uses an automated generation algorithm to create content based on the collected data. Examples include "This month's tomato growth record" and "Tomato cooking recipes."

[1425] Output: The generated blog post and homepage content.

[1426] Step 13:

[1427] The user reviews the generated content and makes edits as needed.

[1428] Input: Content data received from the server.

[1429] What it does: A user sees a preview of their blog post or social media post and edits the text and images as needed.

[1430] Output: A revised blog post or social media post.

[1431] Step 14:

[1432] Users publish content on their homepages or social media.

[1433] Input: The modified content.

[1434] What happens: A user presses the submit button to publish an article.

[1435] Output: Information is disseminated to consumers, and the attractiveness of the crop is effectively promoted.

[1436] In this way, the system of the present invention provides comprehensive agricultural support through collaboration between users, terminals, servers, and emotion engines, enabling personalized suggestions and management, thereby improving user satisfaction and streamlining cultivation activities.

[1437] (Application example 2)

[1438] 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."

[1439] The present invention relates to a comprehensive agricultural activity support system and its applications, and in particular to solving the problem of improving user satisfaction and efficiency by suggesting ingredients and recipes based on the user's preferences and emotions. Currently, agricultural activity support systems collect and analyze environmental data in real time, but it is difficult to make personalized suggestions based on the user's preferences and emotions. Furthermore, there is a need for more accurate service provision by combining content generation based on food suggestions and emotion recognition.

[1440] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting crop categories and highlights, a means for collecting environmental data, a means for analyzing the environmental data and generating an optimal crop list and cultivation schedule, a means for displaying the generated crop list and cultivation schedule, a means for managing and providing instructions during cultivation, a means for automatically generating blog and homepage content, a means for suggesting ingredients and recipes based on the user's preferences, and a means for recognizing the user's emotions and adjusting the suggestions accordingly. This enables personalized suggestions based on the user's preferences and emotions, thereby improving user satisfaction and efficiency.

[1441] The "means for inputting crop category and highlight points" is a function that allows the user to input the type and characteristics of a particular crop.

[1442] "Means for collecting environmental data" refers to a function for acquiring environmental information such as temperature, humidity, soil condition, and sunlight.

[1443] The "means for analyzing environmental data and generating an optimal crop list and cultivation schedule" is a function for analyzing collected environmental data and generating the most suitable crops and their cultivation plans.

[1444] The "means for displaying the generated crop list and cultivation schedule" is a function for showing the user the crop list and cultivation schedule created based on the analysis.

[1445] "Means for management and instruction during cultivation" refers to a function for carrying out management tasks and operational instructions required during the cultivation process.

[1446] "Means for automatically generating blog and homepage content" refers to a function for automatically creating web content based on collected data.

[1447] The "means for suggesting ingredients and recipes based on the user's preferences" is a function for recommending appropriate ingredients and cooking recipes in consideration of the user's preferences.

[1448] "Means for recognizing the user's emotions and adjusting suggestions accordingly" refers to a function that analyzes the user's emotions from their facial expressions and voice, and changes the information and suggestions provided accordingly.

[1449] "Means for collecting climate data, soil data, and sunshine data" refers to a function for acquiring climate, soil, and sunshine information in order to obtain detailed information about external environmental conditions.

[1450] "Means for acquiring data using environmental sensors and cameras" refers to a function for collecting environmental information using sensors and cameras.

[1451] "Means for obtaining climate data using APIs of weather information providers" refers to a function for obtaining climate-related data using online weather information services.

[1452] "Means for recommending optimal ingredients and recipes using machine learning algorithms" is a function that uses machine learning technology to select optimal ingredients and recipes for users.

[1453] The "means for analyzing the user's facial expression and voice data using an emotion recognition engine" is a function for determining the user's emotional state using facial expression and voice analysis technology.

[1454] "Means for delivering automatically generated blog and homepage content to consumers" is a feature for creating web content and publishing it online.

[1455] The "means for generating content tailored based on food suggestions" is a function for automatically creating appropriate web content based on food information that matches the user's preferences.

[1456] MODE FOR CARRYING OUT THE INVENTION

[1457] The present invention aims to improve user satisfaction and efficiency by adding a function to suggest ingredients and recipes that take into account the user's preferences and feelings to a comprehensive agricultural activity support system. The system for realizing this invention is composed of the following methods and means.

[1458] System Configuration

[1459] The server includes means for inputting crop categories and highlight points, means for collecting environmental data, means for analyzing the environmental data and generating an optimal crop list and cultivation schedule, means for displaying the generated crop list and cultivation schedule, means for managing and giving instructions during cultivation, means for automatically generating blog and homepage content, means for suggesting ingredients and recipes based on the user's preferences, and means for recognizing the user's emotions and adjusting suggestions accordingly.

[1460] Program processing

[1461] Hardware and Software

[1462] This system uses the following hardware and software:

[1463] Hardware: Smartphone, temperature sensor, humidity sensor

[1464] Software: Python, TensorFlow, cloud AI services, cloud vision services, communication APIs, web frameworks

[1465] Data processing and calculation

[1466] 1. Processing user-entered data:

[1467] The user inputs the crop category and key points into the terminal and sends them to the server. The terminal uses a web framework (e.g., Flask) to process the input data in real time and provide it to the server.

[1468] 2. Environmental Data Collection:

[1469] The device periodically acquires environmental data such as temperature, humidity, soil condition, and sunlight from various sensors and sends it to the server. It also acquires climate data using the API of an online weather information service.

[1470] 3. Data analysis and proposal generation:

[1471] The server uses machine learning algorithms (e.g., TensorFlow) to analyze the collected environmental data. Based on the analysis results, it generates a list of optimal crops and a cultivation schedule, which are then sent to the user's device for display. It also analyzes the user's preferences and past behavioral data to suggest optimal ingredients and recipes.

[1472] 4. Emotion recognition:

[1473] The device uses an emotion recognition engine to analyze the user's facial expressions and voice data to determine their current emotional state. The analysis results are sent to the server, which then adjusts the recommendations and cultivation schedule accordingly.

[1474] 5. Automatic content generation:

[1475] The server automatically generates blog and homepage content based on the collected data, and users can review this content, edit it if necessary, and then publish it online.

[1476] Specific examples

[1477] For example:

[1478] A user uses a smartphone application to request "ideas for tonight's dinner." The application collects current weather data (temperature 25 degrees, humidity 60%) and suggests optimal recipes based on the user's past preferences (e.g., Japanese food). The application also reads the user's emotions from their facial expressions and recommends ingredients with a relaxing effect if the user's stress level is high.

[1479] Here is an example prompt:

[1480] Give me some ideas for dinner tonight. The current weather data shows a temperature of 25°C and humidity of 60%, and the user has often shown a preference for Japanese food in the past. The user also seems to be feeling stressed. Based on this, please suggest the best recipe.

[1481] In this way, the present invention provides a highly personalized function that takes into account the user's preferences and feelings, thereby significantly improving user satisfaction and work efficiency.

[1482] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1483] Program processing flow

[1484] Step 1:

[1485] The user inputs the crop category and key points via a smartphone or other device. The information entered by the user (e.g., crop desired to grow = "tomatoes," key point = "quality-oriented") is sent from the device to the server. Based on this input, the system prepares to perform a personalized analysis.

[1486] Step 2:

[1487] The device collects environmental data using temperature, humidity, and soil sensors, as well as a camera. Data obtained from these sensors and cameras (e.g., temperature 25°C, humidity 60%, soil humidity 40%) is sent to a server in real time. Additionally, weather data is also acquired and integrated using the API of weather information providers.

[1488] Step 3:

[1489] The server analyzes the collected environmental data, crop categories, and key points. It then uses machine learning algorithms (e.g., TensorFlow) to generate an optimal crop list and cultivation schedule. Based on this data analysis, a list is generated that suggests, for example, "tomatoes" and "bell peppers." An annual cultivation schedule is also generated.

[1490] Step 4:

[1491] The server sends the generated crop list and cultivation schedule to the terminal, which visually displays this information to the user, who can then review the proposed list and schedule and make selections or changes as necessary.

[1492] Step 5:

[1493] The device uses an emotion recognition engine to collect the user's facial and voice data and send it to a server. For example, the user can record their facial expressions through a camera or speak using a microphone. This data is analyzed in real time to determine the user's emotional state (e.g., stressed, happy, etc.).

[1494] Step 6:

[1495] The server then adjusts the suggestions and cultivation schedules based on the user's emotional data. For example, if the user is feeling stressed, it may suggest easier, less labor-intensive crops or make gentler changes to the schedule.

[1496] Step 7:

[1497] The terminals interface with smart agricultural devices (e.g., automatic watering devices and temperature control systems) and execute instructions from the server, including real-time data monitoring (e.g., automatic watering when soil moisture drops) and sending alerts (e.g., notifications when temperatures are too high).

[1498] Step 8:

[1499] The server automatically generates blog and homepage content based on the collected growth information and photos. For example, it generates articles such as "Tomato Growth Status This Month" and "Easy Tomato Recipes." The generated content is provided to users, who can edit it as needed before publishing it.

[1500] Step 9:

[1501] Users can then publish the generated content on social media or their homepages to promote their crops to consumers, effectively highlighting their appeal and improving their brand power.

[1502] Through the above steps, the system of the present invention can provide users with comprehensive and personalized agricultural support. This system has an advanced suggestion function that takes into account the user's preferences and emotions, and can improve user satisfaction more than conventional agricultural support systems.

[1503] 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.

[1504] 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.

[1505] 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.

[1506] [Fourth embodiment]

[1507] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1508] 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.

[1509] 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).

[1510] 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.

[1511] 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.

[1512] 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).

[1513] 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.

[1514] 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.

[1515] 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.

[1516] 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.

[1517] 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.

[1518] 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.

[1519] 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."

[1520] The present invention is a total support system for supporting agricultural activities. This system includes a means for inputting crop categories and key features, a means for collecting environmental data, a means for analyzing the collected data and generating an optimal crop list and cultivation schedule, a means for displaying the generated data to the user, a means for providing real-time management and instructions during cultivation, and a means for delivering automatically generated blog and homepage content to consumers.

[1521] System Overview

[1522] User operation flow

[1523] 1. User enters crop category and highlight points:

[1524] The user inputs the crop category (e.g., leafy vegetables) and key features (e.g., quality-oriented) from the terminal. This information is sent to the system, which then proceeds to the next step.

[1525] 2. The device collects environmental data:

[1526] The system collects data from installed temperature, humidity, and soil sensors, as well as cameras. It also collects climate data using the APIs of weather information providers. This data is sent to a server in real time.

[1527] 3. The server analyzes the data:

[1528] The server uses the received environmental data, crop categories, and key points to generate an optimal crop list, and also analyzes the data to create an annual cultivation schedule.

[1529] 4. The server generates a proposal and sends it to the device:

[1530] The proposed crop list and cultivation schedule are sent to the terminal and displayed to the user, who can then use this information to plan their cultivation.

[1531] 5. Cultivation Management:

[1532] The terminals will link with agricultural devices such as smart scarecrows and automate tasks based on instructions from the server, such as watering and spreading fertilizer.

[1533] 6. Auto-generated content:

[1534] The server automatically generates blog and homepage content based on the collected information and photos, including growth records, harvest status, cooking recipes, and more.

[1535] 7. Consumer Appeal:

[1536] The user can check the generated content and make any necessary edits. After that, the information is disseminated to consumers via websites and social media, promoting the appeal of the crops.

[1537] Program processing details (example)

[1538] Registering categories and highlight points

[1539] The user accesses the terminal, selects "leafy vegetables" and "quality-oriented" and inputs the information.

[1540] The terminal validates the input in real time and sends it to the server.

[1541] Environmental data collection

[1542] The terminal periodically collects data from installed sensors (temperature, humidity, soil type) and cameras.

[1543] The device accesses the weather information provider's API via the Internet to obtain climate data.

[1544] The terminal integrates this data and sends it to the server.

[1545] Data analysis and proposal creation

[1546] Based on the received data, the server uses AI and machine learning algorithms to generate an optimal crop list, suggesting "lettuce" and "spinach," for example.

[1547] The server creates an annual schedule based on the crop list.

[1548] View Suggestions

[1549] The server transmits the generated list and schedule to the terminal.

[1550] The terminal visually displays this information to the user.

[1551] Management during cultivation

[1552] The terminals control agricultural devices such as smart scarecrows based on instructions from the server, for example by setting watering schedules and monitoring temperature and humidity.

[1553] Creating auto-generated content

[1554] The server automatically generates blog posts and homepage content based on the growth records and photos, such as "This month's lettuce growth status" or "Easy spinach recipes."

[1555] Improving brand power and appealing to consumers

[1556] The user finalizes the generated content and corrects it if necessary.

[1557] Users use their homepages and social media to spread content and promote the appeal of their crops.

[1558] In this way, the system of the present invention provides comprehensive support at each stage of agriculture, helping users to cultivate crops efficiently and make them more appealing to consumers.

[1559] The processing flow will be explained below.

[1560] Step 1:

[1561] The user inputs the crop category and highlight points.

[1562] The user accesses the terminal screen and inputs the crop category (e.g., leafy vegetables) and the key points (e.g., emphasis on quality).

[1563] The terminal transmits the input information to the server.

[1564] Step 2:

[1565] The device collects environmental data.

[1566] The terminal collects data from temperature sensors, humidity sensors, soil sensors and cameras installed on-site.

[1567] The device uses the weather information provider's API to obtain local climate data.

[1568] The device consolidates the collected data and sends it to a server.

[1569] Step 3:

[1570] The server analyzes the data and generates a list of possible crops and a schedule.

[1571] The server analyzes the received environmental data, crop categories and highlight points.

[1572] The server generates a list of optimal crops and creates an annual cultivation schedule.

[1573] Step 4:

[1574] The server sends the proposal to the terminal and displays it to the user.

[1575] The server sends the proposed crop list and cultivation schedule to the terminal.

[1576] The terminal visually displays the suggestions to the user.

[1577] Step 5:

[1578] The terminal works in conjunction with agricultural devices such as smart scarecrows to manage the cultivation process.

[1579] The terminal receives instructions from the server and sends them to smart scarecrows and other agricultural devices.

[1580] The smart scarecrow automatically performs necessary farming tasks (e.g. watering, spreading fertilizer).

[1581] The terminal monitors the data in real time and reports any abnormalities to the server.

[1582] Step 6:

[1583] The server automatically generates the content for the blog and homepage.

[1584] The server automatically generates blog and homepage content based on data such as cultivation information, photos, and growth records.

[1585] The server publishes the generated content.

[1586] Step 7:

[1587] Review user-generated content and engage with consumers.

[1588] The user checks the content of the generated blog or homepage and edits it as necessary.

[1589] Users can disseminate information to consumers through their websites and social media, promoting the appeal of their crops.

[1590] As described above, in the present invention, users, terminals, and servers cooperate to realize efficient agricultural activities.

[1591] Example 1

[1592] 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."

[1593] There is a need for systems that can streamline agricultural activities, support the cultivation of high-quality crops, and effectively appeal to consumers. However, conventional agricultural support systems were unable to provide comprehensive support for everything from collecting and analyzing environmental data to managing crop cultivation and disseminating information. This forced farmers to expend a lot of time and money, making it difficult to manage their farms efficiently.

[1594] 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.

[1595] In this invention, the server includes means for inputting crop categories and key features, means for collecting environmental data, means for analyzing the environmental data and generating an optimal crop list and cultivation schedule, means for displaying the generated crop list and cultivation schedule, means for managing and providing instructions during cultivation, means for automatically generating blog and homepage content, and means for providing the automatically generated content to consumers. This makes it possible to comprehensively manage and support a series of processes at each stage of agricultural activities, allowing users to efficiently cultivate high-quality crops and effectively appeal to consumers.

[1596] "Means for inputting crop categories and key points" refers to an interface and device that allows a user to select and input the type of crop they wish to grow and the elements they wish to focus on.

[1597] "Means for collecting environmental data" refers to a system that uses sensors and cameras installed on farmland to obtain information about the environment, such as temperature, humidity, soil quality, and sunlight.

[1598] The "means for analyzing environmental data and generating an optimal crop list and cultivation schedule" refers to algorithms and software that analyze the acquired environmental data and, based on the results, create optimal crop types and cultivation plans.

[1599] The "means for displaying the generated crop list and cultivation schedule" refers to an interface and device for displaying the crop list and cultivation schedule generated by the server on a terminal in a form that can be confirmed by the user.

[1600] The "means for management and instructions during cultivation" is a system for automating the management tasks and instructions required during the cultivation period and controlling smart agricultural equipment.

[1601] "Means for automatically generating blog and homepage content" refers to algorithms and software that allow AI to automatically create articles and content based on cultivation process and harvest information, etc.

[1602] "Means for providing automatically generated content to consumers" refers to a system for posting and distributing content on social media and websites in order to deliver automatically generated articles and information to consumers.

[1603] "Means for acquiring data using environmental sensors and cameras" refers to a system consisting of sensors for collecting environmental data such as temperature, humidity, soil quality, and sunlight, and cameras for acquiring image data.

[1604] "Means for obtaining climate data using APIs of weather information providers" refers to software and programming methods for calling APIs (Application Program Interfaces) to obtain real-time climate data using external weather information services.

[1605] The "means for transmitting environmental data acquired by the terminal to the server" refers to a communication means and protocol for transmitting collected environmental data from the terminal to the server.

[1606] "Means for the server to analyze data using AI algorithms and generate a crop list and cultivation schedule" refers to a system in which the server analyzes collected data using AI (artificial intelligence) and machine learning algorithms, and generates an optimal crop list and annual cultivation schedule based on the results.

[1607] "Means for a terminal to control agricultural devices and perform automated cultivation management" refers to means for a terminal to control agricultural equipment such as irrigation systems and smart scarecrows in order to automate cultivation management.

[1608] "Means for the server to automatically generate content using a generative AI model" refers to algorithms and software for automatically generating blog post and homepage content using an AI (artificial intelligence) model.

[1609] "Means for users to edit and publish content on homepages or SNS" refers to interfaces and devices that allow users to review and edit the content they have created and post it on homepages or SNS for publication.

[1610] This invention is a total support system for agricultural activities. This system collects and analyzes environmental data based on the crop category and key features entered by the user, and generates an optimal crop list and cultivation schedule. It also has the function of automating management and instructions during cultivation and providing the generated content to consumers.

[1611] Overview of program processing

[1612] Enter crop category

[1613] The user starts the device and enters the crop category (e.g., "leafy vegetables") and key features (e.g., "quality-oriented") via a dedicated app or web interface. The device verifies this information in real time and sends it to the server.

[1614] Environmental data collection

[1615] The device collects environmental data from installed environmental sensors (temperature, humidity, soil sensors) and cameras. It also obtains climate data from weather information providers' APIs (e.g., OpenWeatherMap API) via the Internet. This data is integrated and sent to a server.

[1616] Data analysis

[1617] The server generates an optimal crop list using AI algorithms (e.g., "random forest" or "neural network") based on the received environmental data, crop category, and key points. For example, if "leafy vegetables" and "quality-oriented" are selected, "lettuce" and "spinach" will be suggested. The server also generates an annual cultivation schedule based on the crop list.

[1618] View Suggestions

[1619] The server transmits the generated crop list and cultivation schedule to the terminal, which visually displays them to the user.

[1620] Management during cultivation

[1621] Based on instructions from the server, the terminals control agricultural devices such as smart irrigation systems and smart scarecrows, automating tasks such as setting watering and fertilizer application schedules and monitoring temperature and humidity.

[1622] Auto-generated content

[1623] The server uses a generative AI model (e.g., GPT-3) to automatically generate blog posts and homepage content based on the collected growth records and photos. The generated content includes specific details such as "This month's lettuce growth status" and "Easy spinach recipes."

[1624] Appeal to consumers

[1625] The user checks the generated content, makes any necessary corrections, and then publishes it on their website or via social media to appeal to consumers.

[1626] Examples of concrete examples and prompts

[1627] Specific examples

[1628] When the user selects "leafy vegetables" and "quality-oriented," the following processing is performed.

[1629] The device collects temperature, humidity, soil type, and camera data, and also uses a weather API to obtain climate data.

[1630] The server analyzes this data, suggests "lettuce" and "spinach," and creates an annual schedule based on that.

[1631] The terminal displays the proposal list and schedule to the user.

[1632] The device uses a smart irrigation system to automate watering and fertilizing.

[1633] The server generates a blog post titled "This month's lettuce growth status" based on the growth record.

[1634] After users check and edit the article, they can share it on social media platforms such as Facebook and Instagram.

[1635] Prompt Sentence Examples

[1636] Please suggest the optimal crop list and cultivation schedule if "leafy vegetables" and "quality-oriented" are selected.

[1637] This invention is a system that comprehensively manages and supports a series of processes at each stage of agriculture, enabling users to efficiently cultivate high-quality crops and effectively appeal to consumers.

[1638] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1639] Step 1:

[1640] Enter crop category and highlight points

[1641] Input: The user starts the device and enters the "crop category" (e.g., "leafy vegetables") and "highlights" (e.g., "quality-oriented") via a dedicated app or web interface.

[1642] How it works: When the user enters this information, the device verifies the integrity of the input data in real time and sends it to the server if there are no errors.

[1643] Output: The input crop category and highlight point information are sent to the server.

[1644] Step 2:

[1645] Environmental data collection

[1646] Input: The device collects data from environmental sensors (temperature sensors, humidity sensors, soil sensors) and cameras installed in the farmland, and obtains climate data from weather information providers' APIs (e.g., "OpenWeatherMap API").

[1647] How it works: The device periodically retrieves data from these sensors and accesses the weather API to get the necessary climate data.

[1648] Output: Collected environmental and climate data is sent to a server.

[1649] Step 3:

[1650] Data analysis

[1651] Input: The server receives data based on environmental data, climate data, crop categories, and eyeball points sent from the terminal.

[1652] How it works: The server analyzes the data using AI algorithms (e.g., "random forest" or "neural network") to generate an optimal crop list. It also creates an annual cultivation schedule based on the crop list. For example, if you select "leafy vegetables" and "quality-oriented," "lettuce" and "spinach" may be suggested.

[1653] Output: An optimal crop list and cultivation schedule is generated.

[1654] Step 4:

[1655] Submitting and Viewing Proposals

[1656] Input: Server-generated crop list and cultivation schedule.

[1657] Operation: The server sends this information to the device. The device visually displays the received suggestions to the user. The user confirms the suggestions.

[1658] Output: The user can check the optimal crop list and cultivation schedule through the terminal.

[1659] Step 5:

[1660] Management during cultivation

[1661] Input: Instruction data from the server and environmental data collected in real time.

[1662] How it works: Based on instructions from the server, the device operates agricultural devices such as smart irrigation systems and smart scarecrows to automate appropriate cultivation management, such as setting schedules for watering and fertilizer application, and monitoring temperature and humidity.

[1663] Output: Automated cultivation management data and warning notifications are displayed on the user's terminal if there are any abnormalities.

[1664] Step 6:

[1665] Auto-generated content

[1666] Input: Collected growth records and photographic data.

[1667] How it works: Based on the growth records and photos, the server uses a generative AI model (e.g., GPT-3) to automatically generate blog posts and homepage content, such as "This month's lettuce growth status" or "Easy spinach recipes."

[1668] Output: Auto-generated blog post and homepage content.

[1669] Step 7:

[1670] Appeal to consumers

[1671] Input: Server-generated content.

[1672] How it works: The user reviews the generated content, edits it as necessary, and then publishes the information on their website or social media to promote the appeal of their crops to consumers.

[1673] Output: The published content can be viewed on websites and social media, making it appealing to consumers.

[1674] (Application example 1)

[1675] 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."

[1676] In modern agriculture and industry, it is important to collect and analyze environmental data in real time. However, while conventional systems can generate optimal crop lists and cultivation schedules in agriculture, they do not support generating optimal work schedules and automatic execution in industry, nor generating work progress reports. As a result, efficient automation of agriculture and industry is difficult to achieve, and managers have difficulty understanding the situation in real time and issuing appropriate instructions.

[1677] 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.

[1678] In this invention, the server includes means for inputting crop categories and key features, means for collecting environmental data, means for analyzing the environmental data and generating an optimal crop list and cultivation schedule, means for displaying the generated crop list and cultivation schedule, means for managing and providing instructions during cultivation, means for automatically generating blog and homepage content, means for collecting work environment data, means for analyzing the data and generating an optimal work schedule and procedure, means for automatically performing work based on the work procedure, and means for automatically generating work progress reports. This enables efficient automation in both agriculture and industry, and enables managers to grasp the situation in real time and provide appropriate instructions.

[1679] "Crop category" refers to a classification that indicates the type of crop that the user intends to cultivate.

[1680] "Eye-catching points" refer to points or characteristics that are particularly important in crop cultivation.

[1681] "Environmental data" refers to data such as temperature, humidity, soil quality, and weather information that affect crops and the working environment.

[1682] "Means of collecting data" refers to the equipment and technology used to acquire data using environmental sensors, cameras, etc.

[1683] "Means of analysis" refers to the algorithms and software used to analyze collected data and extract meaningful information.

[1684] The "optimal crop list" is a list of the most suitable crops proposed based on the analysis results.

[1685] A "cultivation schedule" is an optimal timetable or plan for growing crops.

[1686] The "display means" refers to a display or screen that visually shows the generated information to the user.

[1687] "Means for control and instruction" refers to devices and software for operating and instructing in order to efficiently carry out cultivation work.

[1688] "Auto-generating methods" refers to technologies or algorithms that automatically create content based on collected data and user input.

[1689] "Work environment data" refers to data on environmental conditions such as temperature, humidity, vibration, and sound that affect work in factories and on farmland.

[1690] "Work schedules and procedures" refers to the optimal work timetable and execution procedures.

[1691] "Means for automatically executing work" refers to the technology and devices that allow machines and robots to autonomously perform work based on the generated work procedures.

[1692] A "work progress report" is a report summarizing the progress and results of work that has been performed.

[1693] The system of the present invention is a total support system for efficiently managing and automating specific agricultural and industrial operations. This system is composed of the following main components:

[1694] 1. User enters crop category and highlight points:

[1695] The user inputs the crop category (e.g., leafy vegetables) and key features (e.g., quality-oriented) from the terminal. This information is sent to the system, which then proceeds to the next step.

[1696] 2. The device collects environmental data:

[1697] The device (smartphone, PC, etc.) acquires data in real time from installed temperature sensors, humidity sensors, soil sensors, vibration sensors, sound sensors, and cameras. It also acquires climate data using the API of weather information providers.

[1698] 3. The server analyzes the data:

[1699] The server analyzes the received environmental data, crop categories, and key points using AI and machine learning algorithms (e.g., TensorFlow and PyTorch) to generate an optimal crop list and annual cultivation schedule. It also analyzes similar environmental data for factory operations to generate optimal work schedules and procedures.

[1700] 4. Proposal generation and display:

[1701] The generated crop list and cultivation schedule, as well as the factory work procedures and schedule, are sent to the terminal and displayed to the user, who then uses this information to create specific work and cultivation plans.

[1702] 5. Cultivation and operation management:

[1703] The terminals will work with devices such as smart scarecrows and factory robots to automate tasks based on instructions from the server, such as watering plants, spreading fertilizer, and performing specific assembly and inspection tasks in factories.

[1704] 6. Auto-generated content:

[1705] The server automatically generates blog posts and homepage content based on the collected information and photos, including growth records, work progress reports, harvest status reports, cooking recipes, and work reports.

[1706] 7. Information for consumers and administrators:

[1707] The user then performs a final check of the generated content, corrects it as necessary, and then disseminates the information to consumers via their website or social media. The administrator also checks the generated report and gives appropriate instructions.

[1708] As a concrete example, content can be automatically generated by inputting the following prompt sentence into a generative AI model.

[1709] Generate an optimal work schedule based on sensor data. Use the following data:

[1710] Temperature: 22℃ - 24℃

[1711] Humidity: 60% - 62%

[1712] Vibration: 0.2g - 0.3g

[1713] Sound: 50dB - 55dB

[1714] Please suggest the best working time based on this data.

[1715] In this way, the system of the present invention provides comprehensive support for achieving efficient management and automation in both agriculture and industry.

[1716] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1717] Step 1:

[1718] The user operates the terminal to input the crop category and key features. The user inputs information such as "leafy vegetables" and "quality-oriented" through the terminal interface. This information is sent from the terminal to the server in real time. The input is important data based on the user's cultivation plan and is essential for subsequent processing.

[1719] Step 2:

[1720] The device collects environmental data. It acquires data in real time using environmental sensors (temperature sensor, humidity sensor, soil sensor, vibration sensor, sound sensor) and cameras. It also accesses the API of weather information providers to acquire climate data. All collected data is sent to a server via the internet. Input data is provided by the sensors and API, and output data is stored on the server.

[1721] Step 3:

[1722] The server analyzes the received data based on environmental data, crop categories, and key points. It then uses AI and machine learning algorithms (TensorFlow and PyTorch) to generate an optimal crop list and cultivation schedule. This analysis combines multiple datasets to help select the best crops and generate the best schedule. The data collected in the previous step is used as input, and the optimal crop list and cultivation schedule is generated as output.

[1723] Step 4:

[1724] The server sends the generated crop list and cultivation schedule to the terminal. The terminal displays it to the user in a visually easy-to-understand manner. This display allows the user to easily understand the specific work content and schedule and put it into action. The input is the data generated in step 3, and the output is displayed on the terminal in a form that the user can see.

[1725] Step 5:

[1726] The terminal manages and gives instructions during cultivation. It works in conjunction with smart scarecrows and factory robots to automatically carry out tasks based on instructions from the server. For example, it automatically controls temperature and humidity, waters plants, spreads fertilizer, assembles parts, and performs inspection work. Instructions are given based on data received in real time from the server, and optimal instructions suited to the working environment are issued. The input is instructions from the server, and the output is automated task execution.

[1727] Step 6:

[1728] The server automatically generates blog and homepage content based on the collected information and photos. This includes growth records, work progress, harvest information, cooking recipes, and work reports. A generative AI model is used to generate the content, and prompts are entered to output content in a specified format. The input is the collected information and photos, and the output is content for the user.

[1729] Step 7:

[1730] The user checks the generated content and makes any necessary corrections. The information is then provided to consumers and administrators via the website or social media. The final, checked content is notified and made public to relevant parties, helping to improve brand recognition and ensure transparency in the work process. The input is the generated content, and the output is the content made public on the website or social media.

[1731] 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.

[1732] This invention is a system that comprehensively supports agricultural activities, and includes functions for inputting crop categories and key points, collecting and analyzing environmental data, generating and displaying optimal crop lists and cultivation schedules, managing cultivation in real time, and automatically generating content for blogs and homepages. It also incorporates an emotion engine that recognizes the user's emotions and adjusts the system's suggestions and operations accordingly.

[1733] System Overview

[1734] User operation flow

[1735] 1. User enters crop category and highlight points:

[1736] The user inputs the crop category (e.g., leafy vegetables) and key features (e.g., quality-oriented) through the input screen on the terminal. This information is sent to the system.

[1737] 2. The device collects environmental data:

[1738] The device collects data from temperature, humidity, and soil sensors, as well as a camera, and uses APIs from weather information providers to obtain climate data, which is then sent to a server in real time.

[1739] 3. The server analyzes the data:

[1740] The server analyzes the received environmental data, crop categories, and key points to generate an optimal crop list and create an annual cultivation schedule.

[1741] 4. The server generates a proposal and sends it to the device:

[1742] The server transmits the generated crop list and cultivation schedule to the terminal and displays them visually to the user.

[1743] 5. Use of Emotion Engine:

[1744] To recognize the user's emotions, the emotion engine collects facial and voice data from the user, which is used to analyze whether the user is feeling stressed or satisfied. The server takes this emotional data into account and adjusts the suggestions and cultivation schedule accordingly.

[1745] 6. Cultivation Management:

[1746] The terminals connect to agricultural devices such as smart scarecrows and automate tasks based on instructions from the server, such as watering and fertilizing. They also monitor data in real time and report any abnormalities to the server.

[1747] 7. Auto-generated content:

[1748] The server automatically generates blog and homepage content based on the growth information and photos, including growth records, harvest status, and cooking recipes.

[1749] 8. Consumer Appeal:

[1750] Users can review the generated content and edit it as necessary, then share the information with consumers via their website or social media, effectively promoting the appeal of their crops.

[1751] Program processing details (example)

[1752] Registering categories and highlight points

[1753] The user accesses the terminal, selects "leafy vegetables" and "quality-oriented" and inputs the information.

[1754] The terminal validates the input in real time and sends it to the server.

[1755] Environmental data collection

[1756] The terminal periodically collects data from installed sensors (temperature, humidity, soil type) and cameras.

[1757] The device accesses the weather information provider's API via the Internet to obtain climate data.

[1758] The terminal integrates this data and sends it to the server.

[1759] Data analysis and proposal creation

[1760] Based on the received data, the server uses AI and machine learning algorithms to generate an optimal crop list, suggesting "lettuce" and "spinach," for example.

[1761] The server creates an annual schedule based on the crop list.

[1762] View Suggestions

[1763] The server transmits the generated list and schedule to the terminal.

[1764] The terminal visually displays this information to the user.

[1765] Using the Emotion Engine

[1766] The device inputs the user's facial expressions and voice into an emotion engine and analyzes the user's emotional state in real time.

[1767] Based on the analyzed emotional data, the server adjusts the proposal content and schedule, and suggests actions to reduce the user's stress.

[1768] Management during cultivation

[1769] The device receives instructions from the server and controls smart scarecrows and other agricultural devices, such as setting watering schedules and monitoring temperature and humidity.

[1770] Creating auto-generated content

[1771] The server automatically generates blog posts and homepage content based on the growth records and photos, such as "This month's lettuce growth status" or "Easy spinach recipes."

[1772] Improving brand power and appealing to consumers

[1773] The user finalizes the generated content and corrects it if necessary.

[1774] Users use their homepages and social media to spread content and promote the appeal of their crops.

[1775] In this way, the system of the present invention provides comprehensive and efficient agricultural support through cooperation between the user, terminal, server, and emotion engine. A system that takes the user's emotional state into account enables more personalized suggestions and management, thereby improving user satisfaction.

[1776] The processing flow will be explained below.

[1777] Step 1:

[1778] The user inputs the crop category and highlight points.

[1779] The user inputs the crop category (e.g., leafy vegetables) and key features (e.g., quality-oriented) on the terminal screen.

[1780] The terminal transmits the input information to the server.

[1781] Step 2:

[1782] The device collects environmental data.

[1783] The terminal collects data from temperature sensors, humidity sensors, soil sensors and cameras installed on-site.

[1784] The device uses the weather information provider's API to obtain local climate data.

[1785] The device consolidates the collected data and sends it to a server.

[1786] Step 3:

[1787] The server analyzes the data and generates a list of possible crops and a schedule.

[1788] The server analyzes the received environmental data, crop categories and highlight points.

[1789] The server generates a list of optimal crops and creates an annual cultivation schedule.

[1790] Step 4:

[1791] The server sends the proposal to the terminal and displays it to the user.

[1792] The server sends the proposed crop list and cultivation schedule to the terminal.

[1793] The terminal visually displays the suggestions to the user.

[1794] Step 5:

[1795] The device collects and analyzes the user's emotional data.

[1796] The device uses a camera and microphone to collect the user's facial expressions and voice.

[1797] The device transmits the collected emotion data to the server.

[1798] Step 6:

[1799] The server analyzes the emotional data and adjusts the cultivation schedule and suggestions.

[1800] The server uses an emotion engine to analyze the user's emotional state.

[1801] Based on the analysis results, the server adjusts the suggestions and cultivation schedules, and standardizes actions to reduce user stress.

[1802] Step 7:

[1803] The terminal works in conjunction with agricultural devices such as smart scarecrows to manage the cultivation process.

[1804] The terminal receives instructions from the server and sends them to smart scarecrows and other agricultural devices.

[1805] The smart scarecrow automatically performs necessary farming tasks (e.g. watering, spreading fertilizer).

[1806] The terminal monitors the data in real time and reports any abnormalities to the server.

[1807] Step 8:

[1808] The server automatically generates the content for the blog and homepage.

[1809] The server automatically generates blog and homepage content based on data such as cultivation information, photos, and growth records.

[1810] The server publishes the generated content.

[1811] Step 9:

[1812] Review user-generated content and engage with consumers.

[1813] The user checks the content of the generated blog or homepage and edits it as necessary.

[1814] Users can disseminate information to consumers through their websites and social media, promoting the appeal of their crops.

[1815] In this way, the system of the present invention provides comprehensive and efficient agricultural support through cooperation between the user, terminal, server, and emotion engine. A system that takes the user's emotional state into account enables more personalized suggestions and management, thereby improving user satisfaction.

[1816] Example 2

[1817] 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."

[1818] In conventional agricultural support systems, even if you input the crop category and goals, the system collects and analyzes environmental data, and even proposes optimal cultivation schedules individually, requiring a lot of operation and management. Furthermore, no system takes into account the user's emotional state, which does not reduce stress or improve satisfaction. Furthermore, it is time-consuming to manually generate content for blogs and websites.

[1819] 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.

[1820] In this invention, the server includes a means for inputting crop categories and key features, a means for collecting environmental data, a means for analyzing the environmental data and generating an optimal crop list and cultivation schedule, a means for displaying the generated crop list and cultivation schedule, a means for collecting user emotion data and adjusting the proposal content based on the analysis, a means for managing and instructing during cultivation, a means for automatically generating blog and homepage content, and a means for integrating data in real time and transmitting it to the server. This enables comprehensive and efficient agricultural support, realizes personalized proposals and management that take the user's emotional state into consideration, and improves user satisfaction.

[1821] The "means for inputting the crop category and key points" is a device that provides an interface for the user to input the type of crop and characteristics that the user considers important (for example, quality, yield, etc.).

[1822] "Means for collecting environmental data" refers to sensors and devices for obtaining various environmental data such as temperature, humidity, soil quality, and sunlight.

[1823] The "means for analyzing environmental data and generating optimal crop lists and cultivation schedules" refers to a system that uses AI and machine learning algorithms to create optimal crop lists and cultivation schedules based on collected environmental data.

[1824] The "means for displaying the generated crop list and cultivation schedule" is a device that displays the crop list and annual cultivation schedule generated by the server in an easy-to-understand manner for the user.

[1825] The "means for collecting user emotional data and adjusting the proposed content based on the analysis" refers to a device that analyzes the user's facial expressions and voice and appropriately adjusts the system's proposed content and cultivation schedule based on the user's emotional state.

[1826] The "means for management and instruction during cultivation" is a system that uses smart devices to automatically control tasks such as watering and fertilizer application during the cultivation period.

[1827] The "means for automatically generating content for blogs and homepages" is a system that automatically creates content for blog articles and homepages based on information and photos of the growth process.

[1828] "Means of collecting climate data, soil data and sunlight data" refers to various sensors and information sources for obtaining data on climate, soil conditions, sunlight conditions, etc.

[1829] "Means for acquiring data using environmental sensors and cameras" refers to devices for periodically collecting data using environmental sensors and cameras.

[1830] "Means for obtaining climate data using the API of a weather information provider" refers to a system that obtains the latest weather data through the API of an external service that provides weather information.

[1831] The "means for integrating data in real time and transmitting it to a server" is a device that compiles data obtained from each sensor or information source into a single packet in real time and transmits it to a server.

[1832] "Means for delivering automatically generated blog and homepage content to consumers" refers to a system that allows users to review and edit automatically generated content, publish it via the Internet, and disseminate information to consumers.

[1833] This invention is a system that comprehensively supports agricultural activities, and includes functions for inputting crop categories and key points, collecting and analyzing environmental data, generating and displaying optimal crop lists and cultivation schedules, managing cultivation in real time, and automatically generating content for blogs and homepages. It also incorporates an emotion engine that recognizes the user's emotions and adjusts the system's suggestions and operations accordingly.

[1834] System Overview

[1835] This system mainly consists of four components: the user, the terminal, the server, and the emotion engine. Each component is responsible for the following functions:

[1836] Entering crop categories and highlight points

[1837] The user uses a terminal to input crop categories such as "tomato" or "yield-oriented" and key points, and this information is sent to the server in real time.

[1838] Environmental data collection

[1839] The device collects data from temperature sensors, humidity sensors, soil sensors, and cameras. It also obtains climate data using APIs from weather information providers. This data is integrated and sent to a server.

[1840] Data analysis and crop list generation

[1841] The server uses an AI algorithm to generate the optimal crop list based on the received environmental data, crop category, and key points. This analysis uses machine learning algorithms such as TensorFlow. Specifically, it calculates the optimal environmental conditions for growing tomatoes and generates recommended cultivation methods and lists.

[1842] Creating and displaying cultivation schedules

[1843] The server creates an annual cultivation schedule based on the crop list and sends it to the terminal, which visually displays the schedule and asks the user for confirmation.

[1844] Using the Emotion Engine

[1845] The device uses a camera and microphone to collect the user's facial expressions and voice, which are then analyzed by an emotion engine. This analysis is performed using, for example, Microsoft Azure's emotion analysis API. Based on the analysis results, the server adjusts the recommendations and cultivation schedule.

[1846] Management during cultivation

[1847] The terminals control agricultural devices such as smart scarecrows and automatic watering devices based on instructions from the server. They also monitor environmental data in real time and immediately report any abnormalities to the server.

[1848] Auto-generated content

[1849] The server automatically generates blog posts and homepage content based on information and photos of the growing process, such as articles like "This month's tomato growth record" and "Tomato cooking recipes."

[1850] Improving brand power and appealing to consumers

[1851] Users can check the content they generate, edit it as necessary, and then publish it on their website or social media, thus promoting the appeal of their crops to consumers.

[1852] Specific examples

[1853] When a user inputs "tomato" and "emphasis on yield" as evaluation points, the device acquires data in real time from temperature sensors, humidity sensors, soil sensors, cameras, and weather information provider APIs, and sends it to the server. The server analyzes the data and generates an optimal cultivation schedule. It also analyzes the user's emotional state to adjust the suggestions and manage the cultivation. Blog articles and recipes are automatically generated based on growth records and photos, and the user can publish them on their website or social media. This series of steps realizes efficient agricultural support.

[1854] Example prompts for generative AI models

[1855] "Please tell me how to grow tomatoes and what the best growing conditions are. Also, could you give me some advice on how to maximize my yield?"

[1856] In this way, the system of the present invention provides comprehensive agricultural support through collaboration between users, terminals, servers, and emotion engines, enabling personalized suggestions and management, thereby improving user satisfaction and streamlining cultivation activities.

[1857] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1858] Step 1:

[1859] The user inputs the crop category and highlight points.

[1860] Input: The user inputs "tomato" and "yield-oriented" on the input screen of the terminal.

[1861] Action: The user completes the input and presses the submit button.

[1862] Output: The entered information is sent to the server.

[1863] Step 2:

[1864] The device collects environmental data.

[1865] Input: Real-time data from installed temperature, humidity, and soil sensors, and cameras.

[1866] How it works: The device collects data from sensors and cameras every 10 minutes, and also accesses weather information providers' APIs via the internet to obtain climate data.

[1867] Output: The acquired environmental data is stored on the terminal and combined into a single data packet.

[1868] Step 3:

[1869] The device transmits the environmental data to the server.

[1870] Input: Integrated temperature, humidity, soil quality, solar radiation, climate data, and image data.

[1871] Operation: Collected data is sent to the server in real time.

[1872] Output: The server receives the environment data.

[1873] Step 4:

[1874] The server analyzes the data and generates an optimal crop list.

[1875] Inputs: Environmental data, crop categories, eyeball points.

[1876] How it works: The server inputs the received data into a machine learning algorithm (e.g., TensorFlow) for analysis.

[1877] Output: A list of optimal crops will be generated, including the best environmental conditions and cultivation methods for growing tomatoes.

[1878] Step 5:

[1879] The server creates an annual cultivation schedule.

[1880] Input: The generated crop list.

[1881] How it works: The server determines the dates for each step, such as sowing, transplanting, and harvesting, based on recommended cultivation methods.

[1882] Output: An annual cultivation schedule is created.

[1883] Step 6:

[1884] The server sends the crop list and cultivation schedule to the terminal.

[1885] Input: Created crop list and annual cultivation schedule.

[1886] How it works: The server sends the list and schedule in JSON format to the device.

[1887] Output: The device receives and analyzes the data.

[1888] Step 7:

[1889] The terminal displays a list of crops and a cultivation schedule.

[1890] Input: Crop list and cultivation schedule received from the server.

[1891] Action: The device displays a visual representation on the user's screen.

[1892] Output: The user confirms the specific cultivation plan.

[1893] Step 8:

[1894] The device collects the user's facial expressions and voice data and analyzes them using an emotion engine.

[1895] Input: Facial expression and voice data collected by camera and microphone.

[1896] How it works: The device uses an emotion analysis API (e.g., Microsoft Azure) to analyze the user's emotional state in real time.

[1897] Output: The analyzed emotion data is sent to the server.

[1898] Step 9:

[1899] The server adjusts the suggestions based on the analyzed emotion data.

[1900] Input: User emotion data, crop list and cultivation schedule.

[1901] How it works: The server determines the user's stress level and satisfaction level and adjusts the suggestions and schedule accordingly.

[1902] Output: A tailored proposal and schedule are generated.

[1903] Step 10:

[1904] The terminal controls agricultural devices based on instructions from the server.

[1905] Input: Instructions received from the server.

[1906] What it does: The device activates a smart scarecrow, automatic watering system, and fertilizer spreading system.

[1907] Output: Appropriate cultivation practices are implemented.

[1908] Step 11:

[1909] The device monitors environmental data in real time and detects abnormalities.

[1910] Input: Environmental data collected in real time.

[1911] Operation: When the device detects an abnormal value, it sends an alert to the server and notifies the user.

[1912] Output: The server and user recognize the anomaly and take corresponding action.

[1913] Step 12:

[1914] The server automatically generates the content for the blog and homepage.

[1915] Input: Growth records, photo data.

[1916] How it works: The server uses an automated generation algorithm to create content based on the collected data. Examples include "This month's tomato growth record" and "Tomato cooking recipes."

[1917] Output: The generated blog post and homepage content.

[1918] Step 13:

[1919] The user reviews the generated content and makes edits as needed.

[1920] Input: Content data received from the server.

[1921] What it does: A user sees a preview of their blog post or social media post and edits the text and images as needed.

[1922] Output: A revised blog post or social media post.

[1923] Step 14:

[1924] Users publish content on their homepages or social media.

[1925] Input: The modified content.

[1926] What happens: A user presses the submit button to publish an article.

[1927] Output: Information is disseminated to consumers, and the attractiveness of the crop is effectively promoted.

[1928] In this way, the system of the present invention provides comprehensive agricultural support through collaboration between users, terminals, servers, and emotion engines, enabling personalized suggestions and management, thereby improving user satisfaction and streamlining cultivation activities.

[1929] (Application example 2)

[1930] 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."

[1931] The present invention relates to a comprehensive agricultural activity support system and its applications, and in particular to solving the problem of improving user satisfaction and efficiency by suggesting ingredients and recipes based on the user's preferences and emotions. Currently, agricultural activity support systems collect and analyze environmental data in real time, but it is difficult to make personalized suggestions based on the user's preferences and emotions. Furthermore, there is a need for more accurate service provision by combining content generation based on food suggestions and emotion recognition.

[1932] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting crop categories and highlights, a means for collecting environmental data, a means for analyzing the environmental data and generating an optimal crop list and cultivation schedule, a means for displaying the generated crop list and cultivation schedule, a means for managing and providing instructions during cultivation, a means for automatically generating blog and homepage content, a means for suggesting ingredients and recipes based on the user's preferences, and a means for recognizing the user's emotions and adjusting the suggestions accordingly. This enables personalized suggestions based on the user's preferences and emotions, thereby improving user satisfaction and efficiency.

[1933] The "means for inputting crop category and highlight points" is a function that allows the user to input the type and characteristics of a particular crop.

[1934] "Means for collecting environmental data" refers to a function for acquiring environmental information such as temperature, humidity, soil condition, and sunlight.

[1935] The "means for analyzing environmental data and generating an optimal crop list and cultivation schedule" is a function for analyzing collected environmental data and generating the most suitable crops and their cultivation plans.

[1936] The "means for displaying the generated crop list and cultivation schedule" is a function for showing the user the crop list and cultivation schedule created based on the analysis.

[1937] "Means for management and instruction during cultivation" refers to a function for carrying out management tasks and operational instructions required during the cultivation process.

[1938] "Means for automatically generating blog and homepage content" refers to a function for automatically creating web content based on collected data.

[1939] The "means for suggesting ingredients and recipes based on the user's preferences" is a function for recommending appropriate ingredients and cooking recipes in consideration of the user's preferences.

[1940] "Means for recognizing the user's emotions and adjusting suggestions accordingly" refers to a function that analyzes the user's emotions from their facial expressions and voice, and changes the information and suggestions provided accordingly.

[1941] "Means for collecting climate data, soil data, and sunshine data" refers to a function for acquiring climate, soil, and sunshine information in order to obtain detailed information about external environmental conditions.

[1942] "Means for acquiring data using environmental sensors and cameras" refers to a function for collecting environmental information using sensors and cameras.

[1943] "Means for obtaining climate data using APIs of weather information providers" refers to a function for obtaining climate-related data using online weather information services.

[1944] "Means for recommending optimal ingredients and recipes using machine learning algorithms" is a function that uses machine learning technology to select optimal ingredients and recipes for users.

[1945] The "means for analyzing the user's facial expression and voice data using an emotion recognition engine" is a function for determining the user's emotional state using facial expression and voice analysis technology.

[1946] "Means for delivering automatically generated blog and homepage content to consumers" is a feature for creating web content and publishing it online.

[1947] The "means for generating content tailored based on food suggestions" is a function for automatically creating appropriate web content based on food information that matches the user's preferences.

[1948] MODE FOR CARRYING OUT THE INVENTION

[1949] The present invention aims to improve user satisfaction and efficiency by adding a function to suggest ingredients and recipes that take into account the user's preferences and feelings to a comprehensive agricultural activity support system. The system for realizing this invention is composed of the following methods and means.

[1950] System Configuration

[1951] The server includes means for inputting crop categories and highlight points, means for collecting environmental data, means for analyzing the environmental data and generating an optimal crop list and cultivation schedule, means for displaying the generated crop list and cultivation schedule, means for managing and giving instructions during cultivation, means for automatically generating blog and homepage content, means for suggesting ingredients and recipes based on the user's preferences, and means for recognizing the user's emotions and adjusting suggestions accordingly.

[1952] Program processing

[1953] Hardware and Software

[1954] This system uses the following hardware and software:

[1955] Hardware: Smartphone, temperature sensor, humidity sensor

[1956] Software: Python, TensorFlow, cloud AI services, cloud vision services, communication APIs, web frameworks

[1957] Data processing and calculation

[1958] 1. Processing user-entered data:

[1959] The user inputs the crop category and key points into the terminal and sends them to the server. The terminal uses a web framework (e.g., Flask) to process the input data in real time and provide it to the server.

[1960] 2. Environmental Data Collection:

[1961] The device periodically acquires environmental data such as temperature, humidity, soil condition, and sunlight from various sensors and sends it to the server. It also acquires climate data using the API of an online weather information service.

[1962] 3. Data analysis and proposal generation:

[1963] The server uses machine learning algorithms (e.g., TensorFlow) to analyze the collected environmental data. Based on the analysis results, it generates a list of optimal crops and a cultivation schedule, which are then sent to the user's device for display. It also analyzes the user's preferences and past behavioral data to suggest optimal ingredients and recipes.

[1964] 4. Emotion recognition:

[1965] The device uses an emotion recognition engine to analyze the user's facial expressions and voice data to determine their current emotional state. The analysis results are sent to the server, which then adjusts the recommendations and cultivation schedule accordingly.

[1966] 5. Automatic content generation:

[1967] The server automatically generates blog and homepage content based on the collected data, and users can review this content, edit it if necessary, and then publish it online.

[1968] Specific examples

[1969] For example:

[1970] A user uses a smartphone application to request "ideas for tonight's dinner." The application collects current weather data (temperature 25 degrees, humidity 60%) and suggests optimal recipes based on the user's past preferences (e.g., Japanese food). The application also reads the user's emotions from their facial expressions and recommends ingredients with a relaxing effect if the user's stress level is high.

[1971] Here is an example prompt:

[1972] Give me some ideas for dinner tonight. The current weather data shows a temperature of 25°C and humidity of 60%, and the user has often shown a preference for Japanese food in the past. The user also seems to be feeling stressed. Based on this, please suggest the best recipe.

[1973] In this way, the present invention provides a highly personalized function that takes into account the user's preferences and feelings, thereby significantly improving user satisfaction and work efficiency.

[1974] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1975] Program processing flow

[1976] Step 1:

[1977] The user inputs the crop category and key points via a smartphone or other device. The information entered by the user (e.g., crop desired to grow = "tomatoes," key point = "quality-oriented") is sent from the device to the server. Based on this input, the system prepares to perform a personalized analysis.

[1978] Step 2:

[1979] The device collects environmental data using temperature, humidity, and soil sensors, as well as a camera. Data obtained from these sensors and cameras (e.g., temperature 25°C, humidity 60%, soil humidity 40%) is sent to a server in real time. Additionally, weather data is also acquired and integrated using the API of weather information providers.

[1980] Step 3:

[1981] The server analyzes the collected environmental data, crop categories, and key points. It then uses machine learning algorithms (e.g., TensorFlow) to generate an optimal crop list and cultivation schedule. Based on this data analysis, a list is generated that suggests, for example, "tomatoes" and "bell peppers." An annual cultivation schedule is also generated.

[1982] Step 4:

[1983] The server sends the generated crop list and cultivation schedule to the terminal, which visually displays this information to the user, who can then review the proposed list and schedule and make selections or changes as necessary.

[1984] Step 5:

[1985] The device uses an emotion recognition engine to collect the user's facial and voice data and send it to a server. For example, the user can record their facial expressions through a camera or speak using a microphone. This data is analyzed in real time to determine the user's emotional state (e.g., stressed, happy, etc.).

[1986] Step 6:

[1987] The server then adjusts the suggestions and cultivation schedules based on the user's emotional data. For example, if the user is feeling stressed, it may suggest easier, less labor-intensive crops or make gentler changes to the schedule.

[1988] Step 7:

[1989] The terminals interface with smart agricultural devices (e.g., automatic watering devices and temperature control systems) and execute instructions from the server, including real-time data monitoring (e.g., automatic watering when soil moisture drops) and sending alerts (e.g., notifications when temperatures are too high).

[1990] Step 8:

[1991] The server automatically generates blog and homepage content based on the collected growth information and photos. For example, it generates articles such as "Tomato Growth Status This Month" and "Easy Tomato Recipes." The generated content is provided to users, who can edit it as needed before publishing it.

[1992] Step 9:

[1993] Users can then publish the generated content on social media or their homepages to promote their crops to consumers, effectively highlighting their appeal and improving their brand power.

[1994] Through the above steps, the system of the present invention can provide users with comprehensive and personalized agricultural support. This system has an advanced suggestion function that takes into account the user's preferences and emotions, and can improve user satisfaction more than conventional agricultural support systems.

[1995] 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.

[1996] 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, analysi...

Claims

1. a means for inputting crop categories and highlight points; a means for collecting environmental data; means for analyzing environmental data and generating an optimal crop list and cultivation schedule; a means for displaying the generated crop list and cultivation schedule; means of control and direction during cultivation; A means for automatically generating blog and homepage content; A system including:

2. means for collecting climate data, soil data and sunshine data; means for acquiring data using environmental sensors and cameras; A means of obtaining climate data using the API of a weather information provider; The system of claim 1 further comprising:

3. The system of claim 1 further comprising means for delivering automatically generated blog and homepage content to consumers.

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