System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting weather data; means for acquiring a cultivation condition of a plant; means for analyzing the collected weather data and the cultivation condition; means for generating an optimal cultivation plan based on an analysis result; means for displaying the generated cultivation plan; means for notifying a user of the generated cultivation plan; means for receiving feedback from the user; and means for improving analysis accuracy based on the feedback.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's busy lifestyles, it is extremely difficult for beginners starting home gardens or those with limited knowledge of weather and cultivation to create effective cultivation plans. Furthermore, knowledge and skills are required to quickly respond to weather fluctuations, but it is difficult to cover all of them. This can lead to variations in yield and quality in home gardens, potentially making home gardening unenjoyable for those with little cultivation experience. Therefore, a system is needed that allows even beginners to enjoy home gardening with peace of mind and achieve stable harvests. [Means for solving the problem]

[0005] The present invention is a system that includes means for collecting weather data, means for acquiring plant cultivation conditions, means for analyzing the collected weather data and cultivation conditions, means for generating an optimal cultivation plan based on the analysis results, means for displaying the generated cultivation plan, means for notifying the user of the generated cultivation plan, means for receiving feedback from the user, and means for improving the accuracy of the analysis based on the feedback. In particular, by using artificial intelligence to analyze the weather data and cultivation conditions, the system provides an optimal cultivation plan in real time in response to changes in the weather, enabling effective home gardening even without specialized knowledge. Furthermore, by improving the accuracy of the analysis based on user feedback and providing continuously improved plans, even first-time users can expect high yields and quality.

[0006] "Weather Data" means information about the weather in a particular area, such as temperature, precipitation, humidity, wind speed, and hours of sunshine.

[0007] "Plant cultivation conditions" refers to the environmental conditions necessary for optimal growth of a particular plant, including temperature, humidity, hours of sunlight, soil type, amount of water, etc.

[0008] A "collection means" is a device or program that obtains information from an external source and processes it into a usable form.

[0009] "Analysis tools" are the algorithms and software used to process collected data and extract meaningful information and patterns.

[0010] "Generative means" refers to mechanisms or programs for creating new data or plans based on the results of analysis.

[0011] The "display means" refers to a device or interface that allows the user to visually confirm the generated information and plans.

[0012] "Notification means" refers to the communication methods and programs used to notify users of generated information and important updates.

[0013] "Means for receiving feedback" refers to a method or device for collecting opinions and execution results from users and incorporating them into the system.

[0014] "Means for improving analytical accuracy" refers to methods or programs for improving existing analytical algorithms or models based on collected feedback or new data.

[0015] "Artificial intelligence" refers to technologies and algorithms that analyze large amounts of data, automatically recognize patterns, and make predictions and optimizations. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0024] [First embodiment]

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

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

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

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

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

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

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

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

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

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

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0037] This invention is a system that helps beginners who are starting a home vegetable garden or people with limited knowledge of weather and cultivation to create effective cultivation plans. This system is composed of three main components: a server, a terminal, and a user.

[0038] server

[0039] The server is responsible for data processing and management for the entire system. It obtains the latest weather information from an external weather data API, filters it by region, and stores it in a database. It also obtains the cultivation conditions for the plants selected by the user from the database and inputs them into the AI ​​model along with the weather data for analysis. It generates an optimal cultivation plan based on the analysis results and saves this plan by associating it with the user's account. It also has a function to retrain the AI ​​model based on user feedback to improve analysis accuracy.

[0040] Terminal

[0041] The terminal functions as an interface with the user. Users access the system through their smartphone, PC, or other device and enter the location of their home garden and the type of plants they wish to cultivate. The terminal displays the cultivation plan and management method generated by the server and sends notifications to the user. Important information, such as sudden changes in the weather, is also immediately communicated to the user via push notifications. User feedback is also sent to the server via the terminal.

[0042] User

[0043] Users are the operators of home gardens and the ultimate users of this system. They access the system using their own devices and set up their home gardens. By providing optimal cultivation plans based on weather forecasts and cultivation conditions, even beginners can easily manage their home gardens effectively. Users actually cultivate plants and learn how to manage them and how to deal with unexpected weather conditions. Furthermore, they can register successes and problems in cultivation as feedback in the system, contributing to improving the system's accuracy.

[0044] Specific examples

[0045] For example, consider the case of a beginner home gardener named Tanaka who is trying to grow tomatoes in his garden.

[0046] 1. The user (Mr. Tanaka) logs in to the system using his / her smartphone. He / she enters his / her address (Shinjuku-ku, Tokyo) and the plant he / she wants to grow (tomato).

[0047] 2. The server retrieves the latest weather forecast from an external weather data API, filters the weather data for the area where Tanaka's vegetable garden is located, and saves it in a database.

[0048] 3. The server retrieves tomato cultivation conditions (optimum temperature: 20-30°C, sunlight duration: 8 hours or more, etc.) from the database and inputs them into the AI ​​model along with weather data.

[0049] 4. Based on weather data from Tanaka's garden and the tomato growing conditions, the AI ​​model calculates the optimal sowing and harvest times, and also suggests measures to deal with sudden weather changes (e.g., using shade nets).

[0050] 5. The server saves the generated cultivation plan to Tanaka's account and displays it on his device. Important information is also sent to Tanaka's smartphone as a push notification.

[0051] 6. The user (Mr. Tanaka) sows tomato seeds according to the proposed cultivation plan and performs the necessary maintenance procedures. If the use of shade nets is required during cultivation, he receives a notification on his device and takes immediate action.

[0052] 7. After harvesting, Tanaka will register feedback in the system, which will improve the accuracy of future cultivation plans.

[0053] As mentioned above, this system is designed to enable even beginners to effectively manage their home garden and enjoy growing a variety of vegetables.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] Users log in to the system using their smartphone or computer, enter the address or GPS information of the location where they want to set up their vegetable garden, and select the type of plants they want to grow.

[0057] Step 2:

[0058] The device sends the input information to the server, including the user's location and the type of plant selected.

[0059] Step 3:

[0060] The server accesses an external weather data API to obtain the latest weather data (temperature, precipitation, humidity, wind speed, sunshine hours, etc.) for the specified area.

[0061] Step 4:

[0062] The server saves the weather data it obtains, filters it by region, and stores it in a database.

[0063] Step 5:

[0064] The server retrieves the cultivation conditions (optimum temperature, sunlight hours, amount of water, etc.) for the plant (e.g., tomato) selected by the user from the database.

[0065] Step 6:

[0066] The weather data and plant cultivation conditions acquired by the server are input into the AI ​​model, which analyzes this data and calculates the following:

[0067] Optimal sowing and harvesting times

[0068] Daily care methods (e.g., irrigation timing, fertilizer application timing)

[0069] Step 7:

[0070] The server generates a specific cultivation plan based on the analysis results, which includes the necessary management procedures and precautions for the user.

[0071] Step 8:

[0072] The server stores the generated cultivation plan in association with the user's account.

[0073] Step 9:

[0074] When the user opens the app, the device displays the latest cultivation plan and notifies them of important updates and precautions in real time.

[0075] Step 10:

[0076] The user cultivates plants according to the proposed cultivation plan and carries out the management procedures. Problems and successes that arise during the actual cultivation process are registered in the system as feedback.

[0077] Step 11:

[0078] The server receives feedback from users and uses it to retrain the AI ​​model to improve analysis accuracy.

[0079] Step 12:

[0080] The server periodically obtains new weather data, updates cultivation plans in real time based on this data, and proposes countermeasures for sudden weather changes.

[0081] Example 1

[0082] 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."

[0083] Providing effective cultivation plans to beginner home gardeners and those with limited knowledge of weather and cultivation is a challenge. Current systems cannot provide appropriate measures in real time based on sudden changes in weather or individual plant characteristics, and lack a way to use user feedback to improve the system's accuracy. As a result, it is difficult to significantly reduce the time it takes beginners to find appropriate cultivation methods.

[0084] 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.

[0085] In this invention, the server includes: means for inputting location information and information about the plant being cultivated from the user; means for collecting weather data from an external weather data API; means for acquiring plant cultivation conditions from a database; means for inputting the collected weather data and cultivation conditions into an AI model and analyzing them; means for generating an optimal cultivation plan based on the analysis results; means for saving the generated cultivation plan in the user's account; means for displaying the generated cultivation plan on the user's device; means for notifying the user of the generated cultivation plan via push notification; means for collecting feedback from the user; and means for retraining the AI ​​model based on the collected feedback to improve analysis accuracy. This allows the user to quickly respond to changes in the weather and implement an effective cultivation plan. Furthermore, by utilizing feedback, the system's analysis accuracy can be continuously improved, allowing for the provision of more accurate cultivation plans.

[0086] "User" refers to a person who uses the system to manage a home garden.

[0087] "Location information" refers to information about the area where the user's home garden is located.

[0088] "Information about the plant to be cultivated" refers to information about the type and characteristics of the plant that the user is trying to cultivate.

[0089] "External Weather Data API" refers to an external system interface that provides weather information such as weather forecasts.

[0090] "Weather Data" refers to information regarding weather conditions such as temperature, precipitation, wind speed, and humidity.

[0091] "Plant growing conditions" refers to the environmental conditions necessary for a particular plant to grow.

[0092] A "database" is a system that organizes and stores information so that it can be retrieved and used when needed.

[0093] An "artificial intelligence model" refers to a machine learning algorithm that analyzes large amounts of data to generate useful information.

[0094] "Analysis" refers to the process of deriving specific results or conclusions from collected data.

[0095] A "cultivation plan" refers to specific procedures and schedules for cultivating plants.

[0096] "User account" refers to a mechanism for identifying an individual user within the system and managing information about that user.

[0097] "Terminal" refers to a device (e.g., a smartphone or PC) through which a user interfaces with the system.

[0098] "Push notifications" refers to a feature that notifies users of new information or important messages in real time.

[0099] "Feedback" refers to a user reporting to the system the successes and problems they experience while using the system.

[0100] "Retraining" refers to the process of improving the performance of an artificial intelligence model using new data collected.

[0101] This invention is a system that helps beginners who are starting a home vegetable garden or people with limited knowledge of weather and cultivation to create effective cultivation plans. The system is composed of three main components: a server, a terminal, and a user.

[0102] server

[0103] The server is responsible for data processing and management of the entire system. The server obtains the latest weather information using an external weather data API (e.g., OpenWeatherAPI). The obtained weather data is filtered by region and stored in a database (e.g., MySQL (registered trademark)).

[0104] Next, the server retrieves the plant's cultivation conditions from the database. For example, for tomatoes, these include information such as the optimum temperature, sunlight hours, and humidity. This data, along with weather data, is input into an artificial intelligence model (e.g., TENSORFLOW (registered trademark)) for analysis. The AI ​​model generates an optimal cultivation plan based on the collected data. This cultivation plan includes information such as the sowing time, harvest time, and weather-related measures (e.g., the use of shade nets).

[0105] The generated cultivation plan is stored in the user's account, and is then sent from the server to the user's terminal so that it can be displayed on the terminal.

[0106] Furthermore, the server has the function of collecting feedback from users. The feedback provided by users to the system (e.g., actual harvest results, effectiveness of management methods, etc.) is used to retrain the AI ​​model, thereby improving the accuracy of analysis in the future.

[0107] Terminal

[0108] The terminal functions as an interface with the user. The terminal provides an interface for the user to input information about the location of the home garden and the plants they want to cultivate. The input data is sent from the terminal to the server.

[0109] The generated cultivation plan is displayed to the user via their device. Important information, such as sudden changes in the weather, is also immediately communicated to the user via push notifications. Feedback obtained from users as they use the system is also sent to the server via their device.

[0110] User

[0111] The user is the manager of the home garden and the final user of the system. The user accesses the system using their own device and sets up the home garden. They effectively manage the home garden according to the optimal cultivation plan based on the weather forecast and cultivation conditions provided. Through actual cultivation, the user follows the management procedures suggested by the system and performs the necessary tasks.

[0112] Specific examples

[0113] For example, consider a user who wants to grow tomatoes in his or her garden.

[0114] 1. Users log in to the system using their smartphone and enter information about their address and the plant they want to grow.

[0115] 2. The server retrieves the latest weather forecast from an external weather data API, filters the weather data for the area where the user's home garden is located, and saves it in a database.

[0116] 3. The server retrieves the tomato cultivation conditions from the database and inputs them into the AI ​​model along with weather data.

[0117] 4. The AI ​​model will suggest optimal sowing and harvest times, as well as strategies for dealing with weather fluctuations.

[0118] 5. The server saves the generated cultivation plan in the user's account, and the device displays it. Important information is also immediately communicated to the user via push notifications.

[0119] Prompt Sentence Examples

[0120] "What steps should I take to create a system for beginners who want to start a home vegetable garden?"

[0121] "How do I create the best growing plan for tomatoes in a particular area?"

[0122] This system allows even beginners to effectively manage their home gardens and enjoy growing a variety of plants.

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

[0124] Step 1:

[0125] A user logs into the system using their own terminal and inputs the location information of their home garden and information about the plants they are growing.

[0126] Input: User's location information and information about the plant they want to grow (e.g., Shinjuku-ku, Tokyo, tomato)

[0127] Output: The data format in which the input information is sent to the server (e.g., JSON format)

[0128] Specific actions: The user opens the smartphone app, enters the address and the tomato, and presses the send button.

[0129] Step 2:

[0130] The server sends a request to an external weather data API to retrieve the latest weather data and store it in a database.

[0131] Input: User's location information (e.g., Shinjuku-ku, Tokyo)

[0132] Output: Weather data for the specified area is saved in a database (e.g. temperature, precipitation, humidity)

[0133] Specific operation: The server generates a URL like "https: / / api.openweathermap.org / data / 2.5 / weather?q=Shinjuku&appid=YOUR_API_KEY" and sends a request to the API. The retrieved weather data is stored in a MySQL database.

[0134] Step 3:

[0135] The server retrieves the cultivation conditions for the plants to be cultivated from the database, and inputs them along with weather data into the AI ​​model for analysis.

[0136] Input: User-entered plant information (e.g., tomatoes) and stored weather data

[0137] Output: Analysis results from the AI ​​model (e.g., optimal sowing time, harvest time, and measures to deal with weather fluctuations)

[0138] How it works: The server retrieves data by executing the SQL query "SELECT FROM crop_conditions WHERE crop_name = 'tomato'". The retrieved weather data and growing conditions are then input into the TensorFlow model for analysis.

[0139] Step 4:

[0140] The server generates an optimal cultivation plan based on the analysis results obtained from the AI ​​model and saves it in the user's account.

[0141] Input: Analysis results by AI model

[0142] Output: Generated cultivation plan (e.g. sowing in early April, harvesting in mid-July, use of shade net)

[0143] Specific operation: The server generates a cultivation plan based on the analysis results and saves it by executing the SQL query "INSERT INTO user_plans (user_id, plan_data) VALUES (user_id, 'Cultivation plan here')".

[0144] Step 5:

[0145] The terminal displays the generated cultivation plan to the user and, if necessary, communicates important information via push notifications.

[0146] Input: Generated cultivation plan and other important information (e.g., measures to deal with sudden weather changes)

[0147] Output: Cultivation plan and push notification displayed on the user's smartphone screen

[0148] Specific operation: The terminal application displays the cultivation plan obtained from the server on the screen and sends push notifications as necessary.

[0149] Step 6:

[0150] The user manages their home garden according to the cultivation plan proposed by the system and provides feedback to the system.

[0151] Input: User's cultivation record and observations (e.g., actual yield, effectiveness of management methods)

[0152] Output: Feedback information is sent to the server and stored in the system.

[0153] Specific operations: The user enters the post-harvest results and any observations made during management through the application and presses the send button.

[0154] Step 7:

[0155] The server uses user feedback to retrain the AI ​​model and improve the accuracy of future analyses.

[0156] Input: User feedback data

[0157] Output: Retrained AI model

[0158] What it does: The server adds new feedback data to the dataset and retrains the AI ​​model using TensorFlow. The retrained model is saved and used for the next analysis.

[0159] (Application example 1)

[0160] 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."

[0161] Creating an effective gardening plan is a significant challenge for beginners and those with limited knowledge of weather and cultivation. Furthermore, physical stores often lack support for selecting appropriate gardening supplies, leaving users unsure of what to choose. Furthermore, insufficient information is provided to quickly respond to changing weather data, potentially reducing the success rate of cultivation. Therefore, the present invention aims to solve these problems and provide a system that supports even beginners in effectively managing their gardens.

[0162] 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.

[0163] In this invention, the server includes means for collecting weather data, means for acquiring plant cultivation conditions, means for analyzing the collected weather data and cultivation conditions, means for generating an optimal cultivation plan based on the analysis results, means for displaying the generated cultivation plan, means for notifying the user of the generated cultivation plan, means for receiving feedback from the user, means for improving the accuracy of the analysis based on the feedback, means for acquiring the user's location information in the physical store and navigating to the appropriate product shelves, means for suggesting optimal products based on the user's cultivation history and weather data, and means for displaying detailed information about the suggested products and providing on-site instructions. This allows even beginners to create an appropriate cultivation plan and easily select the necessary home gardening supplies in the physical store. Furthermore, it is possible to quickly respond to changing weather data in real time, thereby improving the success rate of cultivation.

[0164] "Means for collecting weather data" refers to devices or systems that obtain the latest weather information from external weather data APIs, filter it by region, and store it in a database.

[0165] The "means for acquiring the cultivation conditions of the plant" refers to a mechanism for acquiring the cultivation conditions of the plant selected by the user from the database and analyzing this information.

[0166] "Means for analyzing collected weather data and cultivation conditions" refers to AI models and algorithms used to conduct analysis based on weather data and plant cultivation conditions.

[0167] The "means for generating an optimal cultivation plan" refers to a system for creating an optimal cultivation plan for the user based on the analysis results.

[0168] The "means for displaying the generated cultivation plan" refers to an interface or tool for displaying the generated cultivation plan on the user's terminal.

[0169] "Means for notifying the user of the generated cultivation plan" refers to a mechanism for immediately transmitting the cultivation plan and important information to the user via push notifications, etc.

[0170] "Means for receiving feedback from users" refers to interfaces or functions that allow users to send feedback to the system.

[0171] "Means for improving analysis accuracy" refers to a system for retraining AI models based on user feedback to improve the accuracy of analysis results.

[0172] "Means for obtaining user location information within a physical store" refers to location information technology or systems for identifying the user's current location within a physical store.

[0173] "A means for navigating shelves of appropriate products" refers to a navigation system that guides users to shelves of products they are looking for in a physical store.

[0174] "Means for suggesting optimal products based on the user's cultivation history and weather data" refers to AI models and algorithms that recommend appropriate home gardening products based on the user's past cultivation history and current weather data.

[0175] The "means for displaying detailed information about the proposed product" refers to a technology for displaying detailed information about the proposed product in real time through the user's smart glasses or the like.

[0176] The "means for providing guidance on how to use the product" refers to a system that displays and provides guidance on how to use the product and precautions for handling the product that the user is considering purchasing.

[0177] The present invention is a system that helps beginners and users with limited knowledge of home gardening to create effective cultivation plans. This system is mainly composed of a server, a terminal, and a user, and implements the invention according to the following steps.

[0178] server

[0179] The server is responsible for data processing and management of the entire system. Specifically, it performs the following processes:

[0180] 1. Weather data collection: Obtain the latest weather information from an external weather data API, filter it by region, and store it in a database.

[0181] 2. Obtaining cultivation conditions: Obtain the cultivation conditions (optimum temperature, required sunlight hours, etc.) of the plant selected by the user from the database.

[0182] 3. Data analysis: The collected weather data and plant cultivation conditions are input into an AI model for analysis, and an optimal cultivation plan is generated. This analysis uses artificial intelligence to analyze the weather data and cultivation conditions.

[0183] 4. Generate cultivation plan: Generate an optimal cultivation plan for the user based on the analysis results and save this plan in association with the user's account.

[0184] 5. Leveraging feedback: Retraining the AI ​​model based on user feedback to improve analysis accuracy.

[0185] Terminal

[0186] The terminal functions as an interface with the user and provides the following functions:

[0187] 1. Setup and input: Users access the system via their smartphone or computer and input the location of their home garden and the type of plants they wish to grow.

[0188] 2. Information display: The system displays the cultivation plan and management method generated by the server and sends notifications to the user. Important information, such as sudden changes in the weather, is also immediately communicated to the user via push notifications.

[0189] 3. Product Recommendations: The system acquires the user's location information in a physical store, navigates to the appropriate product shelves, and recommends the most suitable product based on the user's cultivation history and weather data, and displays detailed product information.

[0190] User

[0191] The user is the operator of the home garden and the final user of this system. The user will use the system as follows:

[0192] 1. Setup and operation: Access the system using your smartphone or computer and set up your home garden.

[0193] 2. Implementing a cultivation plan: By providing optimal cultivation plans based on weather forecasts and growing conditions, even beginners can easily manage their home vegetable gardens effectively.

[0194] 3. Product Selection: Smart glasses can be used in brick-and-mortar stores to easily find and purchase the right home gardening products.

[0195] 4. Providing feedback: Registering successes and problems in cultivation as feedback to the system and contributing to improving the system's accuracy.

[0196] Examples and prompts

[0197] For example, if a user wants to grow tomatoes in their garden at home, they can use the system as follows:

[0198] 1. User: Logs into the system using a smartphone and enters his / her address and the plant (tomato) he / she wants to grow.

[0199] 2. Server: Obtains the latest weather forecast from an external weather data API, filters the weather data for the area where the user's home garden is located, and stores it in a database.

[0200] 3. Server: Retrieves tomato cultivation conditions from the database and inputs them into the AI ​​model along with weather data.

[0201] 4. Analysis: Based on the weather data of the user's garden and the growing conditions of the tomatoes, the AI ​​model calculates the optimal sowing and harvesting times, and also suggests countermeasures for sudden weather changes.

[0202] 5. Server: The generated cultivation plan is saved in the user's account, displayed on the device, and important information is sent to the user's smartphone as a push notification.

[0203] 6. User: Sows tomato seeds according to the proposed cultivation plan and performs the necessary maintenance procedures. When visiting a physical store, the user uses the smart glasses to select the necessary home gardening supplies.

[0204] 7. Feedback: After harvesting, users can provide feedback to the system, improving the accuracy of future cultivation plans.

[0205] Example prompt sentence:

[0206] "Recommend the best tools and plants for your home garden based on the user's growing history and current weather data. Plant: Tomato. Growing conditions: Optimal temperature 20-30°C, 8+ hours of sunshine. Current weather data: Temperature 25°C, 5 hours of sunshine."

[0207] "When a user walks towards a shelf in a physical store, compare their current location (35.6895, 139.6917) with the shelf's location (35.6894, 139.6916) and let them know if they're approaching."

[0208] In this way, the system makes it easy for even beginners to obtain advanced cultivation information and quickly and accurately select the necessary home gardening supplies in a physical store.

[0209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0210] Step 1:

[0211] The server retrieves the latest weather information from an external weather data API. The input is weather data from the API, and the output is weather data filtered by region. Specifically, it sends an HTTP request to the API endpoint, receives a JSON response, parses it, and stores it in a database.

[0212] Step 2:

[0213] The server retrieves the cultivation conditions of the plant selected by the user from the database. The input is the information about the plant selected by the user, and the output is the cultivation conditions of the corresponding plant. Specifically, the server executes a database query based on the plant information entered by the user to retrieve the cultivation conditions.

[0214] Step 3:

[0215] The server inputs weather data and plant cultivation conditions into the AI ​​model for analysis. The input is weather data and cultivation conditions, and the output is an optimal cultivation plan. Specifically, the weather data and cultivation conditions are input into the generative AI model, and the model calculates the optimal cultivation plan.

[0216] Step 4:

[0217] The server associates the generated cultivation plan with the user's account and saves it. The input is the generated cultivation plan, and the output is the cultivation plan saved in the user's account. Specifically, the cultivation plan data is linked to the user's account and saved in the database.

[0218] Step 5:

[0219] The terminal displays the cultivation plan and management method generated from the server. The input is the cultivation plan from the server, and the output is the cultivation plan displayed on the terminal's display. Specifically, the terminal receives data from the server and displays it on the user interface.

[0220] Step 6:

[0221] The device immediately notifies the user of important information, such as sudden changes in the weather, via push notifications. The input is notification data from the server, and the output is a push notification sent to the user. Specifically, the device's notification function is used to immediately convey emergency information to the user.

[0222] Step 7:

[0223] The device acquires the user's location information within the physical store and navigates to the appropriate product shelves. The input is the user's location information, and the output is navigation information. Specifically, it uses the in-store location information system to identify the user's current location, calculates the optimal route, and guides the user there.

[0224] Step 8:

[0225] The device suggests optimal products based on the user's cultivation history and weather data. The input is the user's cultivation history and weather data, and the output is a list of suggested products. Specifically, the AI ​​model selects products based on this data and displays the results to the user.

[0226] Step 9:

[0227] The terminal displays detailed information about the suggested product and provides on-the-spot instructions on how to handle it. The input is product information, and the output is the display of detailed information. Specifically, the terminal displays product information and provides additional instructions to the user.

[0228] Step 10:

[0229] The server retrains the AI ​​model based on user feedback to improve analysis accuracy. The input is user feedback, and the output is the retrained AI model. Specifically, the server collects feedback data, updates the training dataset for the AI ​​model, and retrains it.

[0230] 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.

[0231] This invention is a system that helps beginners who are starting home gardening or people with limited knowledge of weather and cultivation to create effective cultivation plans. This system combines weather data, cultivation conditions, and user emotional information to provide a more human-friendly home gardening experience. This system is composed of a server, terminals, users, and an emotion engine.

[0232] server

[0233] The server is responsible for data processing and management for the entire system. It obtains the latest weather information from an external weather data API, filters it by region, and stores it in a database. It also obtains the cultivation conditions for the plants selected by the user from the database and inputs them into an AI model along with the weather data for analysis. Based on the analysis results, it generates an optimal cultivation plan and saves it in association with the user's account. It also has a function that uses an emotion engine that recognizes the user's emotions to evaluate the user's emotional state and stress level and adjust the cultivation plan accordingly.

[0234] Terminal

[0235] The device functions as an interface with the user. Users access the system through devices such as smartphones or PCs and input the location of their home garden and the type of plants they wish to cultivate. The device displays the cultivation plan and management method generated by the server and sends notifications to the user. It also has the function of collecting emotional information from the user's facial expressions and voice and sending it to the server. Important information and notifications regarding sudden weather changes are immediately communicated to the user via push notifications.

[0236] User

[0237] The user is the manager of a home garden and the ultimate user of this system. The user accesses the system using their own device and sets up the home garden. By providing an optimal cultivation plan based on weather forecasts and cultivation conditions, even beginners can easily manage their home garden effectively. The user actually cultivates plants and checks management methods and countermeasures for sudden weather events. Furthermore, the user can register successes and problems in cultivation as feedback in the system, contributing to improving the system's accuracy. In addition, suggestions and reminders are displayed based on the user's emotional state, providing a better experience.

[0238] Emotion Engine

[0239] The emotion engine is an engine for collecting and analyzing the user's emotional information. This allows the system to recognize the user's stress level and motivation, and adjust cultivation plans and management methods according to the user's emotional state. For example, if the system determines that the user is under high stress, it will suggest work reductions and send encouraging messages. It will also display reminders to boost the user's motivation (for example, "Look forward to today's harvest!").

[0240] Specific examples

[0241] For example, consider the case of a beginner home gardener named Suzuki who is trying to grow tomatoes in his garden.

[0242] 1. The user (Mr. Suzuki) logs in to the system using his / her smartphone. He / she enters his / her address (Shinjuku-ku, Tokyo) and the plant he / she wants to grow (tomato).

[0243] 2. The terminal sends the entered information to the server.

[0244] 3. The server retrieves the latest weather forecast from an external weather data API, filters the weather data for the area where Suzuki's vegetable garden is located, and saves it.

[0245] 4. The server retrieves the tomato cultivation conditions (optimum temperature: 20-30°C, sunlight duration: 8 hours or more) from the database and inputs them into the AI ​​model along with weather data.

[0246] 5. An AI model analyzes this data to calculate optimal sowing and harvest times, and also suggests countermeasures for sudden weather changes.

[0247] 6. The cultivation plan generated by the server is saved in Suzuki's account and displayed on his device. Important information is also sent to Suzuki's smartphone via push notification.

[0248] 7. The user (Mr. Suzuki) follows the proposed cultivation plan, sows tomato seeds, and performs the necessary management procedures.

[0249] 8. The device collects emotional information from Suzuki's facial expressions and voice and sends this to the server.

[0250] 9. The server uses an emotion engine to assess Suzuki's stress level and motivation, and generates and displays suggestions and messages appropriate to the situation.

[0251] 10. As Suzuki continues to cultivate, he will register his feedback in the system, and the server will use this data to improve the analytical accuracy of the AI ​​model.

[0252] As described above, this system is designed to enable even beginners to effectively manage their home gardens and enjoy growing a wide variety of vegetables. Furthermore, by taking into account the user's emotional state, it provides a better experience and supports their motivation to continue growing.

[0253] The processing flow will be explained below.

[0254] Step 1:

[0255] Users log in to the system using their smartphone or computer, enter the address or GPS information of the location where they want to set up their vegetable garden, and select the type of plants they want to grow.

[0256] Step 2:

[0257] The terminal sends the entered user information (location, type of plant) to the server.

[0258] Step 3:

[0259] The server accesses an external weather data API to obtain the latest weather data (temperature, precipitation, humidity, wind speed, sunshine hours, etc.) for the specified area.

[0260] Step 4:

[0261] The server saves the weather data it obtains, filters it by region, and stores it in a database.

[0262] Step 5:

[0263] The server retrieves the cultivation conditions (optimum temperature range, required sunlight, amount of water, etc.) of the plant (e.g., tomato) selected by the user from the database.

[0264] Step 6:

[0265] The server inputs weather data and plant cultivation condition data collected into the AI ​​model, which then analyzes the optimal cultivation plan, calculating the optimal sowing time, harvest time, and daily maintenance methods (e.g., when to irrigate and when to use fertilizer).

[0266] Step 7:

[0267] The server generates a specific cultivation plan based on the analysis results and stores this plan in association with the user's account.

[0268] Step 8:

[0269] The server transmits the generated cultivation plan information to the terminal.

[0270] Step 9:

[0271] When the user opens the app, the device displays the latest cultivation plan, and push notifications are sent to users immediately with important updates and suggestions for dealing with sudden weather changes.

[0272] Step 10:

[0273] The user cultivates plants according to the proposed cultivation plan, performs daily management procedures, and registers problems and successes that occur during the actual cultivation process as feedback to the system.

[0274] Step 11:

[0275] The device collects emotional information from the user's facial expressions and voice and sends it to the server. This emotional information includes the user's stress level and motivation state.

[0276] Step 12:

[0277] The server uses an emotion engine to analyze the user's emotional state. If the stress level is high, it will suggest work reduction (e.g., automating irrigation work), and if motivation is low, it will generate encouraging messages and appropriate reminders (e.g., "Look forward to today's harvest!").

[0278] Step 13:

[0279] The server sends suggestions and messages generated based on the analysis results of the emotion engine to the terminal, which then displays them to the user.

[0280] Step 14:

[0281] The server retrains the AI ​​model based on user feedback and emotional information to improve analysis accuracy. This process is continuous and is reflected in future cultivation plans.

[0282] Step 15:

[0283] The server periodically retrieves new weather data and constantly updates the cultivation plan in response to changes in the weather. It also proposes appropriate countermeasures in real time in response to sudden weather changes.

[0284] Example 2

[0285] 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."

[0286] Conventional home gardening support systems can create cultivation plans based on weather data and cultivation conditions, but they do not take into account the user's emotional state when making suggestions or adjustments, which can increase the user's mental stress. Furthermore, the system lacks the functionality to receive feedback and improve analysis accuracy, making it difficult to use effectively. Furthermore, in many cases, the system does not provide measures to respond to real-time weather changes, making it difficult to respond to sudden weather fluctuations.

[0287] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting weather data, means for acquiring plant cultivation conditions, means for analyzing the collected weather data and cultivation conditions, means for generating an optimal cultivation plan based on the analysis results, means for displaying the generated cultivation plan, means for notifying the user of the generated cultivation plan, means for collecting user emotion information, means for adjusting the cultivation plan based on the emotion information, means for receiving feedback from the user, and means for improving analysis accuracy based on the feedback. This makes it possible to adjust the cultivation plan appropriately according to the user's emotional state and improve the accuracy of the system by utilizing the feedback.

[0288] "Weather Data" refers collectively to information regarding past, present, and future weather conditions in a particular geographic area.

[0289] "Plant growing conditions" refers to information about environmental factors such as temperature, humidity, and hours of sunlight required for optimal growth of a particular plant.

[0290] "Means for analysis" refers to methods and devices for combining and evaluating weather data and plant cultivation conditions to create optimal cultivation plans.

[0291] A "cultivation plan" is a detailed description of the actions and timing required to effectively grow a particular plant.

[0292] The "display means" refers to a method or device that allows a user to visually confirm the generated cultivation plan and related information.

[0293] The "notification means" refers to a method or device for instantly notifying the user of the generated cultivation plan or important information.

[0294] "Emotion information" is data relating to changes in the user's mental state and emotions, and includes information obtained from facial expressions and voice.

[0295] "Feedback" refers to information based on evaluations, opinions, and experiences that users provide to the system.

[0296] "Means for improving analytical accuracy" refers to methods or devices that use collected feedback to improve the performance of a system or AI model.

[0297] The present invention is a system that helps beginners who are starting a home garden or users with limited knowledge of weather and cultivation to create effective cultivation plans. This system combines weather data, cultivation conditions, and user emotional information to provide a more human-friendly home gardening experience. Specific embodiments of the present invention are described below.

[0298] Server Operation

[0299] The server performs the following actions:

[0300] 1. Weather data collection: The server uses external weather data APIs (e.g., OpenWeatherMap or Weather.com) to obtain the latest weather information. The data is filtered by region and stored in a database.

[0301] 2. Obtaining cultivation conditions: The server obtains the cultivation conditions (optimum temperature, humidity, sunlight hours, etc.) for a specific plant (e.g., tomato) from the database.

[0302] 3. Data analysis: Collected weather data and cultivation conditions are input into an AI model (using TensorFlow or PyTorch, for example) for analysis, which then calculates the optimal sowing and harvest times, as well as management methods.

[0303] 4. Emotional information analysis: The server uses an emotion engine (e.g., Microsoft® Azure® Text Analytics API or IBM Watson® Tone Analyzer) to analyze the emotional information collected from the user and evaluate the user's stress level and motivation. Based on this information, the cultivation plan is adjusted accordingly.

[0304] Device behavior

[0305] The terminal provides the following features:

[0306] 1. User input: The user accesses the system using a device such as a smartphone or PC and enters the location of their home garden and the type of plants they wish to grow.

[0307] 2. Display of cultivation plan: Displays the cultivation plan and management method generated from the server. Important information is immediately notified to the user via push notification.

[0308] 3. Collecting emotional information: The device collects the user's facial expressions and voice through the smartphone's camera and microphone, and transmits the emotional information to the server.

[0309] User operations

[0310] Users use the system in the following ways:

[0311] 1. Setting up a home garden: Users log in to the system using their own devices and enter the location of their home garden and the type of plants they want to grow.

[0312] 2. Implementing the cultivation plan: By providing the optimal cultivation plan based on weather forecasts and cultivation conditions, the user can actually manage their home garden and cultivate plants. The user will proceed with the work by following the provided management method.

[0313] 3. Providing feedback: By providing feedback to the system about problems and successes experienced during cultivation, the system's accuracy can be improved. This allows the server to improve the analytical accuracy of the AI ​​model.

[0314] Specific examples

[0315] For example, consider a case where a user who is a beginner at home gardening is trying to grow tomatoes in his or her garden.

[0316] Specifically, follow these steps:

[0317] 1. A user logs into the system using their smartphone, inputting their address (e.g., a specific area) and the plant they want to grow (tomatoes).

[0318] 2. The server retrieves the latest weather forecast from an external weather data API, filters the weather data for the area where the home garden is located, and saves it.

[0319] 3. The server retrieves tomato cultivation conditions (e.g., optimal temperature, hours of sunlight) from the database and inputs them into the AI ​​model for analysis.

[0320] 4. The server generates a cultivation plan (e.g., optimal sowing and harvesting times) and saves it in the user's account, which is then displayed on the device. Important information is also immediately sent via push notifications.

[0321] 5. The device collects emotional information from the user's facial expressions and voice and sends it to the server.

[0322] 6. The server uses an emotion engine to analyze the emotional information, and if the user is feeling stressed, it suggests appropriate measures or generates an encouraging message.

[0323] Prompt Sentence Examples

[0324] Here are some examples of prompts for an AI model:

[0325] Describe the process for the following home garden support system:

[0326] 1. A user accesses the system using a smartphone to create a tomato cultivation plan.

[0327] 2. The server retrieves the latest weather information from an external weather data API and filters it by region.

[0328] 3. The server inputs weather information and cultivation conditions into the AI ​​model, analyzes them, and generates an optimal cultivation plan.

[0329] 4. The device collects the user's emotional information (facial expressions and voice) and sends it to the server.

[0330] 5. The emotion engine analyzes the user's emotional state, and the server adjusts the cultivation plan based on the feedback.

[0331] The detailed prompts make each step of the system easy to understand and are designed to help even beginners manage their vegetable gardens efficiently and receive support tailored to their emotional state.

[0332] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0333] Step 1: Collecting User Input Data

[0334] Users log in to the system using their smartphone or computer and enter the location of their garden and the type of plant they want to grow, including the specific address and plant name.

[0335] Input: User's address (e.g., specific area), plant they want to grow (e.g., tomatoes)

[0336] Output: The entered data is sent from the terminal to the server.

[0337] Specific operation: The user enters "Shinjuku-ku, Tokyo" into their smartphone and selects "tomato" as the plant they want to grow.

[0338] Step 2: Obtaining and filtering weather data

[0339] The server uses external weather data APIs (e.g., OpenWeatherMap or Weather.com) to retrieve the latest weather information, filter it by region, and store it in a database.

[0340] Input: User's location

[0341] Output: Weather data filtered by region is saved in a database.

[0342] Specific operation: The server calls the OpenWeatherMap API to obtain the latest weather data for Shinjuku Ward, Tokyo, filters it, and saves it in a database.

[0343] Step 3: Obtaining cultivation conditions

[0344] The server retrieves the cultivation conditions (optimum temperature, humidity, sunlight hours, etc.) for a particular plant (e.g., tomato) from the database.

[0345] Input: User-selected plant species

[0346] Output: Optimal growing conditions for plants (e.g., optimal temperature, humidity, and sunlight hours for tomatoes)

[0347] Specific operation: The server retrieves the cultivation conditions for "tomatoes" from the database and prepares the information.

[0348] Step 4: Analyze the data

[0349] The server inputs the collected weather data and cultivation conditions into an AI model (using TensorFlow or PyTorch, for example) for analysis, which then calculates the optimal sowing and harvest times, as well as management methods.

[0350] Input: Weather data, cultivation condition data

[0351] Output: Optimal cultivation plan (e.g. sowing time, harvest time, management method)

[0352] Specific operation: The server inputs weather data and tomato cultivation conditions into the AI ​​model and generates an optimal cultivation plan.

[0353] Step 5: Notification and display of cultivation plans

[0354] The server saves the generated cultivation plan in the user's account and notifies the user via the terminal.

[0355] Input: Generated cultivation plan

[0356] Output: Cultivation plans saved in the user's account, notifications displayed on the device

[0357] Specific operation: The server saves the generated cultivation plan in the user's account and displays it on the device. Important events are immediately notified via push notifications.

[0358] Step 6: Collecting emotional information

[0359] The device collects the user's facial expressions and voice through the smartphone's camera and microphone, and transmits the emotional information to a server.

[0360] Input: User's facial expression data, voice data

[0361] Output: Collected emotion information is sent to the server.

[0362] Specific operation: The device collects the user's emotional information through the camera and microphone and sends it to the server.

[0363] Step 7: Analyze and regulate emotional information

[0364] The server uses an emotion engine to analyze the user's emotional state and stress level and adjusts the cultivation plan as needed.

[0365] Input: User's emotional information

[0366] Output: Coordinated cultivation plan, encouraging messages and suggestions

[0367] Specific operation: The server uses an emotion engine to analyze the emotion data, and if the user is feeling stressed, it generates and displays suggestions to reduce the work or encouraging messages.

[0368] Step 8: Improve the system through feedback

[0369] Users provide feedback to the system about any problems or successes they encounter during cultivation, and the server accumulates this information to improve the analytical accuracy of the AI ​​model.

[0370] Input: User feedback

[0371] Output: AI model with improved analytical accuracy

[0372] Specific behavior: The user inputs specific feedback into the system (e.g., "The tomato leaves have turned yellow"), which the server stores and uses to train the AI ​​model.

[0373] In this way, each processing step of the system supports the user's home gardening activities in multiple ways, making it easy for even beginners to manage their home garden. Furthermore, by providing appropriate support according to the user's emotional state, the system provides a better experience and supports the user's motivation to continue gardening.

[0374] (Application example 2)

[0375] 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."

[0376] Previous home gardening support systems focused on creating cultivation plans using weather data and information on cultivation conditions, but no systems took into account the user's emotional state or motivation. This often led to beginners and busy users giving up on cultivation midway. Furthermore, there was a lack of a way to effectively utilize fresh ingredients from home gardens for food delivery services.

[0377] 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 means for collecting weather data, means for acquiring plant cultivation conditions, means for analyzing the collected weather data and cultivation conditions, means for generating an optimal cultivation plan based on the analysis results, means for displaying the generated cultivation plan, means for notifying the user of the generated cultivation plan, means for analyzing the user's emotional state, means for displaying suggestions and reminders to the user based on the analyzed emotional state, means for receiving feedback from the user, and means for improving the analysis accuracy based on the feedback. This makes it possible to manage a home vegetable garden taking into account the user's emotional state and motivation, allowing for continuous cultivation without frustration and also enabling the user to effectively use fresh ingredients from the home vegetable garden for food delivery services and earn income.

[0378] "Weather data" refers to meteorological information such as current weather, forecasts, temperature, humidity, and precipitation obtained from APIs accessible via the Internet.

[0379] "Plant growing conditions" are the environmental conditions, such as temperature, humidity, hours of sunlight, and soil type and quality, that are necessary for a particular plant to grow optimally.

[0380] "Means for generating optimal cultivation plans based on analysis results" refers to a means for using collected weather data and plant cultivation conditions to create plans for optimal sowing times, harvesting times, management methods, etc. using AI models and algorithms.

[0381] The "means for displaying the generated cultivation plan" is a function for visually displaying the cultivation plan created on the server on the user's terminal (smartphone or PC).

[0382] The "means for notifying the user of the generated cultivation plan" is a function for notifying the user of the generated cultivation plan and any changes thereto via push notification, email, etc.

[0383] "Means for analyzing the user's emotional state" refers to AI models and algorithms that collect and analyze the user's facial and voice data to assess the user's stress level and motivation.

[0384] The "means for displaying suggestions and reminders to the user based on the analyzed emotional state" is a function that displays suggestions for reducing cultivation work, encouraging messages, and reminders according to the user's emotional state.

[0385] The "means for receiving feedback from users" is a function that allows users to input information about successes and problems with cultivation and send it to the system.

[0386] "Means for improving analytical accuracy based on feedback" refers to a learning process that incorporates feedback data from users into the AI ​​model to generate more accurate cultivation plans.

[0387] The present invention is a system that enables beginners and people with limited knowledge of weather and cultivation to effectively manage their home gardens while providing fresh ingredients through a food delivery service. The system includes a server, a terminal, a user, and an emotion engine.

[0388] server

[0389] The server is responsible for data processing and management for the entire system. It obtains the latest weather information from an external weather data API, filters it by region, and stores it in a database. The cultivation conditions for plants selected by the user are also stored in the database. The server uses this data for analysis and generates an optimal cultivation plan. It also has the function of evaluating the user's emotional state using an emotion engine and adjusting the cultivation plan and suggestions based on this. Specific software used includes the Python library requests, scikit-learn (for the AI ​​model), and OpenCV (for emotion analysis).

[0390] Terminal

[0391] The terminal functions as an interface with the user. The user accesses the system via their smartphone and inputs the location of their home garden and the type of plant they wish to cultivate. The terminal displays the cultivation plan and management method generated by the server and sends notifications to the user as needed. The terminal also collects the user's facial expressions and voice via a camera and microphone and sends this to the server. As a concrete example, if a user wants to cultivate tomatoes, they enter their address and the plant they wish to cultivate.

[0392] User

[0393] The user is the manager of a home garden and the ultimate user of this system. The user accesses the system to set up the garden and cultivates plants according to an optimal cultivation plan based on weather forecasts and cultivation conditions provided by the server. The user also provides emotional information during cultivation, which the system uses to provide optimal support. The user also provides feedback on successes and problems with cultivation, contributing to improving the system's analysis accuracy.

[0394] Emotion Engine

[0395] The emotion engine is an engine for collecting and analyzing the user's emotional information. It analyzes the user's stress level and motivation and adjusts cultivation plans and suggestions accordingly. For example, if the system determines that the user is under high stress, it will suggest ways to reduce work and send encouraging messages. It will also display reminder notifications such as "Look forward to today's harvest!" This helps users continue to enjoy their home garden.

[0396] Specific examples

[0397] For example, consider a user trying to grow tomatoes.

[0398] 1. Users log in to the system using their smartphone and enter their address and the plant they want to grow.

[0399] 2. The server retrieves weather forecasts from an external weather data API, filters the weather data for the user's location, and saves it.

[0400] 3. The server retrieves tomato cultivation conditions from the database and inputs them into the AI ​​model along with weather data for analysis.

[0401] 4. The AI ​​model calculates the optimal sowing and harvesting times and also suggests countermeasures for sudden weather changes.

[0402] 5. The server saves the generated cultivation plan to the user's account and displays it on the device. Important information is also sent to the user via push notifications.

[0403] 6. The user follows the proposed cultivation plan, sows tomato seeds and performs the necessary management procedures.

[0404] 7. The device collects emotional information from the user's facial expressions and voice and sends it to the server.

[0405] 8. The server uses an emotion engine to assess the user's stress level and motivation, and generates and displays suggestions and messages appropriate to the situation.

[0406] 9. As users continue to grow crops, they register feedback with the system, and the server uses this data to improve the analysis accuracy of the AI ​​model.

[0407] For example, a prompt sentence to be input to a generative AI model is:

[0408] "Draw the best plan for growing tomatoes in Shinjuku Ward. Please take current weather data into consideration."

[0409] There is.

[0410] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0411] Step 1:

[0412] The server accesses an external weather data API to collect the latest weather data for the specified region. In this step, the server obtains current weather information, forecast, temperature, humidity, precipitation, etc. from the weather data API (e.g., WeatherAPI), filters it by region, and stores it in a database. The input is region information, and the output is the filtered weather data.

[0413] Step 2:

[0414] The terminal receives input from the user. The user accesses the system via their smartphone and inputs the location of their home garden and the type of plant they wish to cultivate. The input is the address and type of plant, and the output is to send this information to the server.

[0415] Step 3:

[0416] The server retrieves the plant's cultivation conditions from a database. These conditions include temperature, humidity, sunlight hours, soil type, etc., for optimal plant growth. The input is the plant type, and the output is the cultivation conditions for that plant.

[0417] Step 4:

[0418] The server inputs weather data and cultivation conditions into an AI model to generate an optimal cultivation plan. The AI ​​model (for example, scikit-learn's RandomForestRegressor) analyzes this data and calculates the optimal sowing time, harvest time, management procedures, etc. The input is weather data and cultivation conditions, and the output is the generated cultivation plan.

[0419] Step 5:

[0420] The server saves the generated cultivation plan in the user's account and displays it on the terminal. The terminal visually presents the cultivation plan to the user via a smartphone or PC. The input is the generated cultivation plan, and the output is a display of the plan.

[0421] Step 6:

[0422] The device sends the generated cultivation plan and important notifications to the user. Push notifications and emails are used to notify the user of sudden weather changes and important management procedures. The input is the generated cultivation plan and important notification content, and the output is the notification to the user.

[0423] Step 7:

[0424] The device collects the user's facial expressions and voice and sends the emotional information to the server. The device uses a camera and microphone to collect the user's facial expressions and voice. The input is the user's facial expressions and voice, and the output is digital data of emotional information.

[0425] Step 8:

[0426] The server uses an emotion engine to analyze the user's emotional state. The collected facial and voice data is input into an emotion analysis algorithm (e.g., OpenCV) to evaluate the user's stress level and motivation. The input is digital data of emotional information, and the output is the evaluation result of the emotional state.

[0427] Step 9:

[0428] The server displays suggestions and reminders to the user based on the emotional state analyzed. If the user's stress level is high, it generates work reduction suggestions and encouraging messages and displays them on the device. The input is the evaluation result of the emotional state, and the output is the suggestion or reminder message.

[0429] Step 10:

[0430] As users continue cultivating, they register their feedback in the system. They input their successes and problems in cultivation via their smartphones and send this to the server. The input is the user's feedback, and the output is the feedback data stored on the server.

[0431] Step 11:

[0432] The server improves the analytical accuracy of the AI ​​model based on the feedback. Feedback data from users is incorporated into the AI ​​model, and by re-learning, a more accurate cultivation plan is generated. The input is the user's feedback data, and the output is an improved AI model.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] [Second embodiment]

[0437] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0438] 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.

[0439] 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).

[0440] 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.

[0441] 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.

[0442] 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).

[0443] 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. 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.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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."

[0449] This invention is a system that helps beginners who are starting a home vegetable garden or people with limited knowledge of weather and cultivation to create effective cultivation plans. This system is composed of three main components: a server, a terminal, and a user.

[0450] server

[0451] The server is responsible for data processing and management for the entire system. It obtains the latest weather information from an external weather data API, filters it by region, and stores it in a database. It also obtains the cultivation conditions for the plants selected by the user from the database and inputs them into the AI ​​model along with the weather data for analysis. It generates an optimal cultivation plan based on the analysis results and saves this plan by associating it with the user's account. It also has a function to retrain the AI ​​model based on user feedback to improve analysis accuracy.

[0452] Terminal

[0453] The terminal functions as an interface with the user. Users access the system through their smartphone, PC, or other device and enter the location of their home garden and the type of plants they wish to cultivate. The terminal displays the cultivation plan and management method generated by the server and sends notifications to the user. Important information, such as sudden changes in the weather, is also immediately communicated to the user via push notifications. User feedback is also sent to the server via the terminal.

[0454] User

[0455] Users are the operators of home gardens and the ultimate users of this system. They access the system using their own devices and set up their home gardens. By providing optimal cultivation plans based on weather forecasts and cultivation conditions, even beginners can easily manage their home gardens effectively. Users actually cultivate plants and learn how to manage them and how to deal with unexpected weather conditions. Furthermore, they can register successes and problems in cultivation as feedback in the system, contributing to improving the system's accuracy.

[0456] Specific examples

[0457] For example, consider the case of a beginner home gardener named Tanaka who is trying to grow tomatoes in his garden.

[0458] 1. The user (Mr. Tanaka) logs in to the system using his / her smartphone. He / she enters his / her address (Shinjuku-ku, Tokyo) and the plant he / she wants to grow (tomato).

[0459] 2. The server retrieves the latest weather forecast from an external weather data API, filters the weather data for the area where Tanaka's vegetable garden is located, and saves it in a database.

[0460] 3. The server retrieves tomato cultivation conditions (optimum temperature: 20-30°C, sunlight duration: 8 hours or more, etc.) from the database and inputs them into the AI ​​model along with weather data.

[0461] 4. Based on weather data from Tanaka's garden and the tomato growing conditions, the AI ​​model calculates the optimal sowing and harvest times, and also suggests measures to deal with sudden weather changes (e.g., using shade nets).

[0462] 5. The server saves the generated cultivation plan to Tanaka's account and displays it on his device. Important information is also sent to Tanaka's smartphone as a push notification.

[0463] 6. The user (Mr. Tanaka) sows tomato seeds according to the proposed cultivation plan and performs the necessary maintenance procedures. If the use of shade nets is required during cultivation, he receives a notification on his device and takes immediate action.

[0464] 7. After harvesting, Tanaka will register feedback in the system, which will improve the accuracy of future cultivation plans.

[0465] As mentioned above, this system is designed to enable even beginners to effectively manage their home garden and enjoy growing a variety of vegetables.

[0466] The processing flow will be explained below.

[0467] Step 1:

[0468] Users log in to the system using their smartphone or computer, enter the address or GPS information of the location where they want to set up their vegetable garden, and select the type of plants they want to grow.

[0469] Step 2:

[0470] The device sends the input information to the server, including the user's location and the type of plant selected.

[0471] Step 3:

[0472] The server accesses an external weather data API to obtain the latest weather data (temperature, precipitation, humidity, wind speed, sunshine hours, etc.) for the specified area.

[0473] Step 4:

[0474] The server saves the weather data it obtains, filters it by region, and stores it in a database.

[0475] Step 5:

[0476] The server retrieves the cultivation conditions (optimum temperature, sunlight hours, amount of water, etc.) for the plant (e.g., tomato) selected by the user from the database.

[0477] Step 6:

[0478] The weather data and plant cultivation conditions acquired by the server are input into the AI ​​model, which analyzes this data and calculates the following:

[0479] Optimal sowing and harvesting times

[0480] Daily care methods (e.g., irrigation timing, fertilizer application timing)

[0481] Step 7:

[0482] The server generates a specific cultivation plan based on the analysis results, which includes the necessary management procedures and precautions for the user.

[0483] Step 8:

[0484] The server stores the generated cultivation plan in association with the user's account.

[0485] Step 9:

[0486] When the user opens the app, the device displays the latest cultivation plan and notifies them of important updates and precautions in real time.

[0487] Step 10:

[0488] The user cultivates plants according to the proposed cultivation plan and carries out the management procedures. Problems and successes that arise during the actual cultivation process are registered in the system as feedback.

[0489] Step 11:

[0490] The server receives feedback from users and uses it to retrain the AI ​​model to improve analysis accuracy.

[0491] Step 12:

[0492] The server periodically obtains new weather data, updates cultivation plans in real time based on this data, and proposes countermeasures for sudden weather changes.

[0493] Example 1

[0494] 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."

[0495] Providing effective cultivation plans to beginner home gardeners and those with limited knowledge of weather and cultivation is a challenge. Current systems cannot provide appropriate measures in real time based on sudden changes in weather or individual plant characteristics, and lack a way to use user feedback to improve the system's accuracy. As a result, it is difficult to significantly reduce the time it takes beginners to find appropriate cultivation methods.

[0496] 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.

[0497] In this invention, the server includes: means for inputting location information and information about the plant being cultivated from the user; means for collecting weather data from an external weather data API; means for acquiring plant cultivation conditions from a database; means for inputting the collected weather data and cultivation conditions into an AI model and analyzing them; means for generating an optimal cultivation plan based on the analysis results; means for saving the generated cultivation plan in the user's account; means for displaying the generated cultivation plan on the user's device; means for notifying the user of the generated cultivation plan via push notification; means for collecting feedback from the user; and means for retraining the AI ​​model based on the collected feedback to improve analysis accuracy. This allows the user to quickly respond to changes in the weather and implement an effective cultivation plan. Furthermore, by utilizing feedback, the system's analysis accuracy can be continuously improved, allowing for the provision of more accurate cultivation plans.

[0498] "User" refers to a person who uses the system to manage a home garden.

[0499] "Location information" refers to information about the area where the user's home garden is located.

[0500] "Information about the plant to be cultivated" refers to information about the type and characteristics of the plant that the user is trying to cultivate.

[0501] "External Weather Data API" refers to an external system interface that provides weather information such as weather forecasts.

[0502] "Weather Data" refers to information regarding weather conditions such as temperature, precipitation, wind speed, and humidity.

[0503] "Plant growing conditions" refers to the environmental conditions necessary for a particular plant to grow.

[0504] A "database" is a system that organizes and stores information so that it can be retrieved and used when needed.

[0505] An "artificial intelligence model" refers to a machine learning algorithm that analyzes large amounts of data to generate useful information.

[0506] "Analysis" refers to the process of deriving specific results or conclusions from collected data.

[0507] A "cultivation plan" refers to specific procedures and schedules for cultivating plants.

[0508] "User account" refers to a mechanism for identifying an individual user within the system and managing information about that user.

[0509] "Terminal" refers to a device (e.g., a smartphone or PC) through which a user interfaces with the system.

[0510] "Push notifications" refers to a feature that notifies users of new information or important messages in real time.

[0511] "Feedback" refers to a user reporting to the system the successes and problems they experience while using the system.

[0512] "Retraining" refers to the process of improving the performance of an artificial intelligence model using new data collected.

[0513] This invention is a system that helps beginners who are starting a home vegetable garden or people with limited knowledge of weather and cultivation to create effective cultivation plans. The system is composed of three main components: a server, a terminal, and a user.

[0514] server

[0515] The server is responsible for data processing and management of the entire system. The server obtains the latest weather information using an external weather data API (e.g., OpenWeatherAPI). The obtained weather data is filtered by region and stored in a database (e.g., MySQL).

[0516] Next, the server retrieves the plant's cultivation conditions from a database. For example, for tomatoes, these include information such as the optimum temperature, sunlight hours, and humidity. This data, along with weather data, is input into an artificial intelligence model (e.g., TensorFlow) for analysis. The AI ​​model generates an optimal cultivation plan based on the collected data. This cultivation plan includes information such as the sowing time, harvest time, and weather-related measures (e.g., the use of shade nets).

[0517] The generated cultivation plan is stored in the user's account, and is then sent from the server to the user's terminal so that it can be displayed on the terminal.

[0518] Furthermore, the server has the function of collecting feedback from users. The feedback provided by users to the system (e.g., actual harvest results, effectiveness of management methods, etc.) is used to retrain the AI ​​model, thereby improving the accuracy of analysis in the future.

[0519] Terminal

[0520] The terminal functions as an interface with the user. The terminal provides an interface for the user to input information about the location of the home garden and the plants they want to cultivate. The input data is sent from the terminal to the server.

[0521] The generated cultivation plan is displayed to the user via their device. Important information, such as sudden changes in the weather, is also immediately communicated to the user via push notifications. Feedback obtained from users as they use the system is also sent to the server via their device.

[0522] User

[0523] The user is the manager of the home garden and the final user of the system. The user accesses the system using their own device and sets up the home garden. They effectively manage the home garden according to the optimal cultivation plan based on the weather forecast and cultivation conditions provided. Through actual cultivation, the user follows the management procedures suggested by the system and performs the necessary tasks.

[0524] Specific examples

[0525] For example, consider a user who wants to grow tomatoes in his or her garden.

[0526] 1. Users log in to the system using their smartphone and enter information about their address and the plant they want to grow.

[0527] 2. The server retrieves the latest weather forecast from an external weather data API, filters the weather data for the area where the user's home garden is located, and saves it in a database.

[0528] 3. The server retrieves the tomato cultivation conditions from the database and inputs them into the AI ​​model along with weather data.

[0529] 4. The AI ​​model will suggest optimal sowing and harvest times, as well as strategies for dealing with weather fluctuations.

[0530] 5. The server saves the generated cultivation plan in the user's account, and the device displays it. Important information is also immediately communicated to the user via push notifications.

[0531] Prompt Sentence Examples

[0532] "What steps should I take to create a system for beginners who want to start a home vegetable garden?"

[0533] "How do I create the best growing plan for tomatoes in a particular area?"

[0534] This system allows even beginners to effectively manage their home gardens and enjoy growing a variety of plants.

[0535] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0536] Step 1:

[0537] A user logs into the system using their own terminal and inputs the location information of their home garden and information about the plants they are growing.

[0538] Input: User's location information and information about the plant they want to grow (e.g., Shinjuku-ku, Tokyo, tomato)

[0539] Output: The data format in which the input information is sent to the server (e.g., JSON format)

[0540] Specific actions: The user opens the smartphone app, enters the address and the tomato, and presses the send button.

[0541] Step 2:

[0542] The server sends a request to an external weather data API to retrieve the latest weather data and store it in a database.

[0543] Input: User's location information (e.g., Shinjuku-ku, Tokyo)

[0544] Output: Weather data for the specified area is saved in a database (e.g. temperature, precipitation, humidity)

[0545] Specific operation: The server generates a URL like "https: / / api.openweathermap.org / data / 2.5 / weather?q=Shinjuku&appid=YOUR_API_KEY" and sends a request to the API. The retrieved weather data is stored in a MySQL database.

[0546] Step 3:

[0547] The server retrieves the cultivation conditions for the plants to be cultivated from the database, and inputs them along with weather data into the AI ​​model for analysis.

[0548] Input: User-entered plant information (e.g., tomatoes) and stored weather data

[0549] Output: Analysis results from the AI ​​model (e.g., optimal sowing time, harvest time, and measures to deal with weather fluctuations)

[0550] How it works: The server retrieves data by executing the SQL query "SELECT FROM crop_conditions WHERE crop_name = 'tomato'". The retrieved weather data and growing conditions are then input into the TensorFlow model for analysis.

[0551] Step 4:

[0552] The server generates an optimal cultivation plan based on the analysis results obtained from the AI ​​model and saves it in the user's account.

[0553] Input: Analysis results by AI model

[0554] Output: Generated cultivation plan (e.g. sowing in early April, harvesting in mid-July, use of shade net)

[0555] Specific operation: The server generates a cultivation plan based on the analysis results and saves it by executing the SQL query "INSERT INTO user_plans (user_id, plan_data) VALUES (user_id, 'Cultivation plan here')".

[0556] Step 5:

[0557] The terminal displays the generated cultivation plan to the user and, if necessary, communicates important information via push notifications.

[0558] Input: Generated cultivation plan and other important information (e.g., measures to deal with sudden weather changes)

[0559] Output: Cultivation plan and push notification displayed on the user's smartphone screen

[0560] Specific operation: The terminal application displays the cultivation plan obtained from the server on the screen and sends push notifications as necessary.

[0561] Step 6:

[0562] The user manages their home garden according to the cultivation plan proposed by the system and provides feedback to the system.

[0563] Input: User's cultivation record and observations (e.g., actual yield, effectiveness of management methods)

[0564] Output: Feedback information is sent to the server and stored in the system.

[0565] Specific operations: The user enters the post-harvest results and any observations made during management through the application and presses the send button.

[0566] Step 7:

[0567] The server uses user feedback to retrain the AI ​​model and improve the accuracy of future analyses.

[0568] Input: User feedback data

[0569] Output: Retrained AI model

[0570] What it does: The server adds new feedback data to the dataset and retrains the AI ​​model using TensorFlow. The retrained model is saved and used for the next analysis.

[0571] (Application example 1)

[0572] 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."

[0573] Creating an effective gardening plan is a significant challenge for beginners and those with limited knowledge of weather and cultivation. Furthermore, physical stores often lack support for selecting appropriate gardening supplies, leaving users unsure of what to choose. Furthermore, insufficient information is provided to quickly respond to changing weather data, potentially reducing the success rate of cultivation. Therefore, the present invention aims to solve these problems and provide a system that supports even beginners in effectively managing their gardens.

[0574] 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.

[0575] In this invention, the server includes means for collecting weather data, means for acquiring plant cultivation conditions, means for analyzing the collected weather data and cultivation conditions, means for generating an optimal cultivation plan based on the analysis results, means for displaying the generated cultivation plan, means for notifying the user of the generated cultivation plan, means for receiving feedback from the user, means for improving the accuracy of the analysis based on the feedback, means for acquiring the user's location information in the physical store and navigating to the appropriate product shelves, means for suggesting optimal products based on the user's cultivation history and weather data, and means for displaying detailed information about the suggested products and providing on-site instructions. This allows even beginners to create an appropriate cultivation plan and easily select the necessary home gardening supplies in the physical store. Furthermore, it is possible to quickly respond to changing weather data in real time, thereby improving the success rate of cultivation.

[0576] "Means for collecting weather data" refers to devices or systems that obtain the latest weather information from external weather data APIs, filter it by region, and store it in a database.

[0577] The "means for acquiring the cultivation conditions of the plant" refers to a mechanism for acquiring the cultivation conditions of the plant selected by the user from the database and analyzing this information.

[0578] "Means for analyzing collected weather data and cultivation conditions" refers to AI models and algorithms used to conduct analysis based on weather data and plant cultivation conditions.

[0579] The "means for generating an optimal cultivation plan" refers to a system for creating an optimal cultivation plan for the user based on the analysis results.

[0580] The "means for displaying the generated cultivation plan" refers to an interface or tool for displaying the generated cultivation plan on the user's terminal.

[0581] "Means for notifying the user of the generated cultivation plan" refers to a mechanism for immediately transmitting the cultivation plan and important information to the user via push notifications, etc.

[0582] "Means for receiving feedback from users" refers to interfaces or functions that allow users to send feedback to the system.

[0583] "Means for improving analysis accuracy" refers to a system for retraining AI models based on user feedback to improve the accuracy of analysis results.

[0584] "Means for obtaining user location information within a physical store" refers to location information technology or systems for identifying the user's current location within a physical store.

[0585] "A means for navigating shelves of appropriate products" refers to a navigation system that guides users to shelves of products they are looking for in a physical store.

[0586] "Means for suggesting optimal products based on the user's cultivation history and weather data" refers to AI models and algorithms that recommend appropriate home gardening products based on the user's past cultivation history and current weather data.

[0587] The "means for displaying detailed information about the proposed product" refers to a technology for displaying detailed information about the proposed product in real time through the user's smart glasses or the like.

[0588] The "means for providing guidance on how to use the product" refers to a system that displays and provides guidance on how to use the product and precautions for handling the product that the user is considering purchasing.

[0589] The present invention is a system that helps beginners and users with limited knowledge of home gardening to create effective cultivation plans. This system is mainly composed of a server, a terminal, and a user, and implements the invention according to the following steps.

[0590] server

[0591] The server is responsible for data processing and management of the entire system. Specifically, it performs the following processes:

[0592] 1. Weather data collection: Obtain the latest weather information from an external weather data API, filter it by region, and store it in a database.

[0593] 2. Obtaining cultivation conditions: Obtain the cultivation conditions (optimum temperature, required sunlight hours, etc.) of the plant selected by the user from the database.

[0594] 3. Data analysis: The collected weather data and plant cultivation conditions are input into an AI model for analysis, and an optimal cultivation plan is generated. This analysis uses artificial intelligence to analyze the weather data and cultivation conditions.

[0595] 4. Generate cultivation plan: Generate an optimal cultivation plan for the user based on the analysis results and save this plan in association with the user's account.

[0596] 5. Leveraging feedback: Retraining the AI ​​model based on user feedback to improve analysis accuracy.

[0597] Terminal

[0598] The terminal functions as an interface with the user and provides the following functions:

[0599] 1. Setup and input: Users access the system via their smartphone or computer and input the location of their home garden and the type of plants they wish to grow.

[0600] 2. Information display: The system displays the cultivation plan and management method generated by the server and sends notifications to the user. Important information, such as sudden changes in the weather, is also immediately communicated to the user via push notifications.

[0601] 3. Product Recommendations: The system acquires the user's location information in a physical store, navigates to the appropriate product shelves, and recommends the most suitable product based on the user's cultivation history and weather data, and displays detailed product information.

[0602] User

[0603] The user is the operator of the home garden and the final user of this system. The user will use the system as follows:

[0604] 1. Setup and operation: Access the system using your smartphone or computer and set up your home garden.

[0605] 2. Implementing a cultivation plan: By providing optimal cultivation plans based on weather forecasts and growing conditions, even beginners can easily manage their home vegetable gardens effectively.

[0606] 3. Product Selection: Smart glasses can be used in brick-and-mortar stores to easily find and purchase the right home gardening products.

[0607] 4. Providing feedback: Registering successes and problems in cultivation as feedback to the system and contributing to improving the system's accuracy.

[0608] Examples and prompts

[0609] For example, if a user wants to grow tomatoes in their garden at home, they can use the system as follows:

[0610] 1. User: Logs into the system using a smartphone and enters his / her address and the plant (tomato) he / she wants to grow.

[0611] 2. Server: Obtains the latest weather forecast from an external weather data API, filters the weather data for the area where the user's home garden is located, and stores it in a database.

[0612] 3. Server: Retrieves tomato cultivation conditions from the database and inputs them into the AI ​​model along with weather data.

[0613] 4. Analysis: Based on the weather data of the user's garden and the growing conditions of the tomatoes, the AI ​​model calculates the optimal sowing and harvesting times, and also suggests countermeasures for sudden weather changes.

[0614] 5. Server: The generated cultivation plan is saved in the user's account, displayed on the device, and important information is sent to the user's smartphone as a push notification.

[0615] 6. User: Sows tomato seeds according to the proposed cultivation plan and performs the necessary maintenance procedures. When visiting a physical store, the user uses the smart glasses to select the necessary home gardening supplies.

[0616] 7. Feedback: After harvesting, users can provide feedback to the system, improving the accuracy of future cultivation plans.

[0617] Example prompt sentence:

[0618] "Recommend the best tools and plants for your home garden based on the user's growing history and current weather data. Plant: Tomato. Growing conditions: Optimal temperature 20-30°C, 8+ hours of sunshine. Current weather data: Temperature 25°C, 5 hours of sunshine."

[0619] "When a user walks towards a shelf in a physical store, compare their current location (35.6895, 139.6917) with the shelf's location (35.6894, 139.6916) and let them know if they're approaching."

[0620] In this way, the system makes it easy for even beginners to obtain advanced cultivation information and quickly and accurately select the necessary home gardening supplies in a physical store.

[0621] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0622] Step 1:

[0623] The server retrieves the latest weather information from an external weather data API. The input is weather data from the API, and the output is weather data filtered by region. Specifically, it sends an HTTP request to the API endpoint, receives a JSON response, parses it, and stores it in a database.

[0624] Step 2:

[0625] The server retrieves the cultivation conditions of the plant selected by the user from the database. The input is the information about the plant selected by the user, and the output is the cultivation conditions of the corresponding plant. Specifically, the server executes a database query based on the plant information entered by the user to retrieve the cultivation conditions.

[0626] Step 3:

[0627] The server inputs weather data and plant cultivation conditions into the AI ​​model for analysis. The input is weather data and cultivation conditions, and the output is an optimal cultivation plan. Specifically, the weather data and cultivation conditions are input into the generative AI model, and the model calculates the optimal cultivation plan.

[0628] Step 4:

[0629] The server associates the generated cultivation plan with the user's account and saves it. The input is the generated cultivation plan, and the output is the cultivation plan saved in the user's account. Specifically, the cultivation plan data is linked to the user's account and saved in the database.

[0630] Step 5:

[0631] The terminal displays the cultivation plan and management method generated from the server. The input is the cultivation plan from the server, and the output is the cultivation plan displayed on the terminal's display. Specifically, the terminal receives data from the server and displays it on the user interface.

[0632] Step 6:

[0633] The device immediately notifies the user of important information, such as sudden changes in the weather, via push notifications. The input is notification data from the server, and the output is a push notification sent to the user. Specifically, the device's notification function is used to immediately convey emergency information to the user.

[0634] Step 7:

[0635] The device acquires the user's location information within the physical store and navigates to the appropriate product shelves. The input is the user's location information, and the output is navigation information. Specifically, it uses the in-store location information system to identify the user's current location, calculates the optimal route, and guides the user there.

[0636] Step 8:

[0637] The device suggests optimal products based on the user's cultivation history and weather data. The input is the user's cultivation history and weather data, and the output is a list of suggested products. Specifically, the AI ​​model selects products based on this data and displays the results to the user.

[0638] Step 9:

[0639] The terminal displays detailed information about the suggested product and provides on-the-spot instructions on how to handle it. The input is product information, and the output is the display of detailed information. Specifically, the terminal displays product information and provides additional instructions to the user.

[0640] Step 10:

[0641] The server retrains the AI ​​model based on user feedback to improve analysis accuracy. The input is user feedback, and the output is the retrained AI model. Specifically, the server collects feedback data, updates the training dataset for the AI ​​model, and retrains it.

[0642] 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.

[0643] This invention is a system that helps beginners who are starting home gardening or people with limited knowledge of weather and cultivation to create effective cultivation plans. This system combines weather data, cultivation conditions, and user emotional information to provide a more human-friendly home gardening experience. This system is composed of a server, terminals, users, and an emotion engine.

[0644] server

[0645] The server is responsible for data processing and management for the entire system. It obtains the latest weather information from an external weather data API, filters it by region, and stores it in a database. It also obtains the cultivation conditions for the plants selected by the user from the database and inputs them into an AI model along with the weather data for analysis. Based on the analysis results, it generates an optimal cultivation plan and saves it in association with the user's account. It also has a function that uses an emotion engine that recognizes the user's emotions to evaluate the user's emotional state and stress level and adjust the cultivation plan accordingly.

[0646] Terminal

[0647] The device functions as an interface with the user. Users access the system through devices such as smartphones or PCs and input the location of their home garden and the type of plants they wish to cultivate. The device displays the cultivation plan and management method generated by the server and sends notifications to the user. It also has the function of collecting emotional information from the user's facial expressions and voice and sending it to the server. Important information and notifications regarding sudden weather changes are immediately communicated to the user via push notifications.

[0648] User

[0649] The user is the manager of a home garden and the ultimate user of this system. The user accesses the system using their own device and sets up the home garden. By providing an optimal cultivation plan based on weather forecasts and cultivation conditions, even beginners can easily manage their home garden effectively. The user actually cultivates plants and checks management methods and countermeasures for sudden weather events. Furthermore, the user can register successes and problems in cultivation as feedback in the system, contributing to improving the system's accuracy. In addition, suggestions and reminders are displayed based on the user's emotional state, providing a better experience.

[0650] Emotion Engine

[0651] The emotion engine is an engine for collecting and analyzing the user's emotional information. This allows the system to recognize the user's stress level and motivation, and adjust cultivation plans and management methods according to the user's emotional state. For example, if the system determines that the user is under high stress, it will suggest work reductions and send encouraging messages. It will also display reminders to boost the user's motivation (for example, "Look forward to today's harvest!").

[0652] Specific examples

[0653] For example, consider the case of a beginner home gardener named Suzuki who is trying to grow tomatoes in his garden.

[0654] 1. The user (Mr. Suzuki) logs in to the system using his / her smartphone. He / she enters his / her address (Shinjuku-ku, Tokyo) and the plant he / she wants to grow (tomato).

[0655] 2. The terminal sends the entered information to the server.

[0656] 3. The server retrieves the latest weather forecast from an external weather data API, filters the weather data for the area where Suzuki's vegetable garden is located, and saves it.

[0657] 4. The server retrieves the tomato cultivation conditions (optimum temperature: 20-30°C, sunlight duration: 8 hours or more) from the database and inputs them into the AI ​​model along with weather data.

[0658] 5. An AI model analyzes this data to calculate optimal sowing and harvest times, and also suggests countermeasures for sudden weather changes.

[0659] 6. The cultivation plan generated by the server is saved in Suzuki's account and displayed on his device. Important information is also sent to Suzuki's smartphone via push notification.

[0660] 7. The user (Mr. Suzuki) follows the proposed cultivation plan, sows tomato seeds, and performs the necessary management procedures.

[0661] 8. The device collects emotional information from Suzuki's facial expressions and voice and sends this to the server.

[0662] 9. The server uses an emotion engine to assess Suzuki's stress level and motivation, and generates and displays suggestions and messages appropriate to the situation.

[0663] 10. As Suzuki continues to cultivate, he will register his feedback in the system, and the server will use this data to improve the analytical accuracy of the AI ​​model.

[0664] As described above, this system is designed to enable even beginners to effectively manage their home gardens and enjoy growing a wide variety of vegetables. Furthermore, by taking into account the user's emotional state, it provides a better experience and supports their motivation to continue growing.

[0665] The processing flow will be explained below.

[0666] Step 1:

[0667] Users log in to the system using their smartphone or computer, enter the address or GPS information of the location where they want to set up their vegetable garden, and select the type of plants they want to grow.

[0668] Step 2:

[0669] The terminal sends the entered user information (location, type of plant) to the server.

[0670] Step 3:

[0671] The server accesses an external weather data API to obtain the latest weather data (temperature, precipitation, humidity, wind speed, sunshine hours, etc.) for the specified area.

[0672] Step 4:

[0673] The server saves the weather data it obtains, filters it by region, and stores it in a database.

[0674] Step 5:

[0675] The server retrieves the cultivation conditions (optimum temperature range, required sunlight, amount of water, etc.) of the plant (e.g., tomato) selected by the user from the database.

[0676] Step 6:

[0677] The server inputs weather data and plant cultivation condition data collected into the AI ​​model, which then analyzes the optimal cultivation plan, calculating the optimal sowing time, harvest time, and daily maintenance methods (e.g., when to irrigate and when to use fertilizer).

[0678] Step 7:

[0679] The server generates a specific cultivation plan based on the analysis results and stores this plan in association with the user's account.

[0680] Step 8:

[0681] The server transmits the generated cultivation plan information to the terminal.

[0682] Step 9:

[0683] When the user opens the app, the device displays the latest cultivation plan, and push notifications are sent to users immediately with important updates and suggestions for dealing with sudden weather changes.

[0684] Step 10:

[0685] The user cultivates plants according to the proposed cultivation plan, performs daily management procedures, and registers problems and successes that occur during the actual cultivation process as feedback to the system.

[0686] Step 11:

[0687] The device collects emotional information from the user's facial expressions and voice and sends it to the server. This emotional information includes the user's stress level and motivation state.

[0688] Step 12:

[0689] The server uses an emotion engine to analyze the user's emotional state. If the stress level is high, it will suggest work reduction (e.g., automating irrigation work), and if motivation is low, it will generate encouraging messages and appropriate reminders (e.g., "Look forward to today's harvest!").

[0690] Step 13:

[0691] The server sends suggestions and messages generated based on the analysis results of the emotion engine to the terminal, which then displays them to the user.

[0692] Step 14:

[0693] The server retrains the AI ​​model based on user feedback and emotional information to improve analysis accuracy. This process is continuous and is reflected in future cultivation plans.

[0694] Step 15:

[0695] The server periodically retrieves new weather data and constantly updates the cultivation plan in response to changes in the weather. It also proposes appropriate countermeasures in real time in response to sudden weather changes.

[0696] Example 2

[0697] 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."

[0698] Conventional home gardening support systems can create cultivation plans based on weather data and cultivation conditions, but they do not take into account the user's emotional state when making suggestions or adjustments, which can increase the user's mental stress. Furthermore, the system lacks the functionality to receive feedback and improve analysis accuracy, making it difficult to use effectively. Furthermore, in many cases, the system does not provide measures to respond to real-time weather changes, making it difficult to respond to sudden weather fluctuations.

[0699] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting weather data, means for acquiring plant cultivation conditions, means for analyzing the collected weather data and cultivation conditions, means for generating an optimal cultivation plan based on the analysis results, means for displaying the generated cultivation plan, means for notifying the user of the generated cultivation plan, means for collecting user emotion information, means for adjusting the cultivation plan based on the emotion information, means for receiving feedback from the user, and means for improving analysis accuracy based on the feedback. This makes it possible to adjust the cultivation plan appropriately according to the user's emotional state and improve the accuracy of the system by utilizing the feedback.

[0700] "Weather Data" refers collectively to information regarding past, present, and future weather conditions in a particular geographic area.

[0701] "Plant growing conditions" refers to information about environmental factors such as temperature, humidity, and hours of sunlight required for optimal growth of a particular plant.

[0702] "Means for analysis" refers to methods and devices for combining and evaluating weather data and plant cultivation conditions to create optimal cultivation plans.

[0703] A "cultivation plan" is a detailed description of the actions and timing required to effectively grow a particular plant.

[0704] The "display means" refers to a method or device that allows a user to visually confirm the generated cultivation plan and related information.

[0705] The "notification means" refers to a method or device for instantly notifying the user of the generated cultivation plan or important information.

[0706] "Emotion information" is data relating to changes in the user's mental state and emotions, and includes information obtained from facial expressions and voice.

[0707] "Feedback" refers to information based on evaluations, opinions, and experiences that users provide to the system.

[0708] "Means for improving analytical accuracy" refers to methods or devices that use collected feedback to improve the performance of a system or AI model.

[0709] The present invention is a system that helps beginners who are starting a home garden or users with limited knowledge of weather and cultivation to create effective cultivation plans. This system combines weather data, cultivation conditions, and user emotional information to provide a more human-friendly home gardening experience. Specific embodiments of the present invention are described below.

[0710] Server Operation

[0711] The server performs the following actions:

[0712] 1. Weather data collection: The server uses external weather data APIs (e.g., OpenWeatherMap or Weather.com) to obtain the latest weather information. The data is filtered by region and stored in a database.

[0713] 2. Obtaining cultivation conditions: The server obtains the cultivation conditions (optimum temperature, humidity, sunlight hours, etc.) for a specific plant (e.g., tomato) from the database.

[0714] 3. Data analysis: Collected weather data and cultivation conditions are input into an AI model (using TensorFlow or PyTorch, for example) for analysis, which then calculates the optimal sowing and harvest times, as well as management methods.

[0715] 4. Emotional information analysis: The server uses an emotion engine (e.g., Microsoft Azure's Text Analytics API or IBM Watson's Tone Analyzer) to analyze the emotional information collected from the user and evaluate the user's stress level and motivation. Based on this information, the cultivation plan is adjusted accordingly.

[0716] Device behavior

[0717] The terminal provides the following features:

[0718] 1. User input: The user accesses the system using a device such as a smartphone or PC and enters the location of their home garden and the type of plants they wish to grow.

[0719] 2. Display of cultivation plan: Displays the cultivation plan and management method generated from the server. Important information is immediately notified to the user via push notification.

[0720] 3. Collecting emotional information: The device collects the user's facial expressions and voice through the smartphone's camera and microphone, and transmits the emotional information to the server.

[0721] User operations

[0722] Users use the system in the following ways:

[0723] 1. Setting up a home garden: Users log in to the system using their own devices and enter the location of their home garden and the type of plants they want to grow.

[0724] 2. Implementing the cultivation plan: By providing the optimal cultivation plan based on weather forecasts and cultivation conditions, the user can actually manage their home garden and cultivate plants. The user will proceed with the work by following the provided management method.

[0725] 3. Providing feedback: By providing feedback to the system about problems and successes experienced during cultivation, the system's accuracy can be improved. This allows the server to improve the analytical accuracy of the AI ​​model.

[0726] Specific examples

[0727] For example, consider a case where a user who is a beginner at home gardening is trying to grow tomatoes in his or her garden.

[0728] Specifically, follow these steps:

[0729] 1. A user logs into the system using their smartphone, inputting their address (e.g., a specific area) and the plant they want to grow (tomatoes).

[0730] 2. The server retrieves the latest weather forecast from an external weather data API, filters the weather data for the area where the home garden is located, and saves it.

[0731] 3. The server retrieves tomato cultivation conditions (e.g., optimal temperature, hours of sunlight) from the database and inputs them into the AI ​​model for analysis.

[0732] 4. The server generates a cultivation plan (e.g., optimal sowing and harvesting times) and saves it in the user's account, which is then displayed on the device. Important information is also immediately sent via push notifications.

[0733] 5. The device collects emotional information from the user's facial expressions and voice and sends it to the server.

[0734] 6. The server uses an emotion engine to analyze the emotional information, and if the user is feeling stressed, it suggests appropriate measures or generates an encouraging message.

[0735] Prompt Sentence Examples

[0736] Here are some examples of prompts for an AI model:

[0737] Describe the process for the following home garden support system:

[0738] 1. A user accesses the system using a smartphone to create a tomato cultivation plan.

[0739] 2. The server retrieves the latest weather information from an external weather data API and filters it by region.

[0740] 3. The server inputs weather information and cultivation conditions into the AI ​​model, analyzes them, and generates an optimal cultivation plan.

[0741] 4. The device collects the user's emotional information (facial expressions and voice) and sends it to the server.

[0742] 5. The emotion engine analyzes the user's emotional state, and the server adjusts the cultivation plan based on the feedback.

[0743] The detailed prompts make each step of the system easy to understand and are designed to help even beginners manage their vegetable gardens efficiently and receive support tailored to their emotional state.

[0744] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0745] Step 1: Collecting User Input Data

[0746] Users log in to the system using their smartphone or computer and enter the location of their garden and the type of plant they want to grow, including the specific address and plant name.

[0747] Input: User's address (e.g., specific area), plant they want to grow (e.g., tomatoes)

[0748] Output: The entered data is sent from the terminal to the server.

[0749] Specific operation: The user enters "Shinjuku-ku, Tokyo" into their smartphone and selects "tomato" as the plant they want to grow.

[0750] Step 2: Obtaining and filtering weather data

[0751] The server uses external weather data APIs (e.g., OpenWeatherMap or Weather.com) to retrieve the latest weather information, filter it by region, and store it in a database.

[0752] Input: User's location

[0753] Output: Weather data filtered by region is saved in a database.

[0754] Specific operation: The server calls the OpenWeatherMap API to obtain the latest weather data for Shinjuku Ward, Tokyo, filters it, and saves it in a database.

[0755] Step 3: Obtaining cultivation conditions

[0756] The server retrieves the cultivation conditions (optimum temperature, humidity, sunlight hours, etc.) for a particular plant (e.g., tomato) from the database.

[0757] Input: User-selected plant species

[0758] Output: Optimal growing conditions for plants (e.g., optimal temperature, humidity, and sunlight hours for tomatoes)

[0759] Specific operation: The server retrieves the cultivation conditions for "tomatoes" from the database and prepares the information.

[0760] Step 4: Analyze the data

[0761] The server inputs the collected weather data and cultivation conditions into an AI model (using TensorFlow or PyTorch, for example) for analysis, which then calculates the optimal sowing and harvest times, as well as management methods.

[0762] Input: Weather data, cultivation condition data

[0763] Output: Optimal cultivation plan (e.g. sowing time, harvest time, management method)

[0764] Specific operation: The server inputs weather data and tomato cultivation conditions into the AI ​​model and generates an optimal cultivation plan.

[0765] Step 5: Notification and display of cultivation plans

[0766] The server saves the generated cultivation plan in the user's account and notifies the user via the terminal.

[0767] Input: Generated cultivation plan

[0768] Output: Cultivation plans saved in the user's account, notifications displayed on the device

[0769] Specific operation: The server saves the generated cultivation plan in the user's account and displays it on the device. Important events are immediately notified via push notifications.

[0770] Step 6: Collecting emotional information

[0771] The device collects the user's facial expressions and voice through the smartphone's camera and microphone, and transmits the emotional information to a server.

[0772] Input: User's facial expression data, voice data

[0773] Output: Collected emotion information is sent to the server.

[0774] Specific operation: The device collects the user's emotional information through the camera and microphone and sends it to the server.

[0775] Step 7: Analyze and regulate emotional information

[0776] The server uses an emotion engine to analyze the user's emotional state and stress level and adjusts the cultivation plan as needed.

[0777] Input: User's emotional information

[0778] Output: Coordinated cultivation plan, encouraging messages and suggestions

[0779] Specific operation: The server uses an emotion engine to analyze the emotion data, and if the user is feeling stressed, it generates and displays suggestions to reduce the work or encouraging messages.

[0780] Step 8: Improve the system through feedback

[0781] Users provide feedback to the system about any problems or successes they encounter during cultivation, and the server accumulates this information to improve the analytical accuracy of the AI ​​model.

[0782] Input: User feedback

[0783] Output: AI model with improved analytical accuracy

[0784] Specific behavior: The user inputs specific feedback into the system (e.g., "The tomato leaves have turned yellow"), which the server stores and uses to train the AI ​​model.

[0785] In this way, each processing step of the system supports the user's home gardening activities in multiple ways, making it easy for even beginners to manage their home garden. Furthermore, by providing appropriate support according to the user's emotional state, the system provides a better experience and supports the user's motivation to continue gardening.

[0786] (Application example 2)

[0787] 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."

[0788] Previous home gardening support systems focused on creating cultivation plans using weather data and information on cultivation conditions, but no systems took into account the user's emotional state or motivation. This often led to beginners and busy users giving up on cultivation midway. Furthermore, there was a lack of a way to effectively utilize fresh ingredients from home gardens for food delivery services.

[0789] 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 means for collecting weather data, means for acquiring plant cultivation conditions, means for analyzing the collected weather data and cultivation conditions, means for generating an optimal cultivation plan based on the analysis results, means for displaying the generated cultivation plan, means for notifying the user of the generated cultivation plan, means for analyzing the user's emotional state, means for displaying suggestions and reminders to the user based on the analyzed emotional state, means for receiving feedback from the user, and means for improving the analysis accuracy based on the feedback. This makes it possible to manage a home vegetable garden taking into account the user's emotional state and motivation, allowing for continuous cultivation without frustration and also enabling the user to effectively use fresh ingredients from the home vegetable garden for food delivery services and earn income.

[0790] "Weather data" refers to meteorological information such as current weather, forecasts, temperature, humidity, and precipitation obtained from APIs accessible via the Internet.

[0791] "Plant growing conditions" are the environmental conditions, such as temperature, humidity, hours of sunlight, and soil type and quality, that are necessary for a particular plant to grow optimally.

[0792] "Means for generating optimal cultivation plans based on analysis results" refers to a means for using collected weather data and plant cultivation conditions to create plans for optimal sowing times, harvesting times, management methods, etc. using AI models and algorithms.

[0793] The "means for displaying the generated cultivation plan" is a function for visually displaying the cultivation plan created on the server on the user's terminal (smartphone or PC).

[0794] The "means for notifying the user of the generated cultivation plan" is a function for notifying the user of the generated cultivation plan and any changes thereto via push notification, email, etc.

[0795] "Means for analyzing the user's emotional state" refers to AI models and algorithms that collect and analyze the user's facial and voice data to assess the user's stress level and motivation.

[0796] The "means for displaying suggestions and reminders to the user based on the analyzed emotional state" is a function that displays suggestions for reducing cultivation work, encouraging messages, and reminders according to the user's emotional state.

[0797] The "means for receiving feedback from users" is a function that allows users to input information about successes and problems with cultivation and send it to the system.

[0798] "Means for improving analytical accuracy based on feedback" refers to a learning process that incorporates feedback data from users into the AI ​​model to generate more accurate cultivation plans.

[0799] The present invention is a system that enables beginners and people with limited knowledge of weather and cultivation to effectively manage their home gardens while providing fresh ingredients through a food delivery service. The system includes a server, a terminal, a user, and an emotion engine.

[0800] server

[0801] The server is responsible for data processing and management for the entire system. It obtains the latest weather information from an external weather data API, filters it by region, and stores it in a database. The cultivation conditions for plants selected by the user are also stored in the database. The server uses this data for analysis and generates an optimal cultivation plan. It also has the function of evaluating the user's emotional state using an emotion engine and adjusting the cultivation plan and suggestions based on this. Specific software used includes the Python library requests, scikit-learn (for the AI ​​model), and OpenCV (for emotion analysis).

[0802] Terminal

[0803] The terminal functions as an interface with the user. The user accesses the system via their smartphone and inputs the location of their home garden and the type of plant they wish to cultivate. The terminal displays the cultivation plan and management method generated by the server and sends notifications to the user as needed. The terminal also collects the user's facial expressions and voice via a camera and microphone and sends this to the server. As a concrete example, if a user wants to cultivate tomatoes, they enter their address and the plant they wish to cultivate.

[0804] User

[0805] The user is the manager of a home garden and the ultimate user of this system. The user accesses the system to set up the garden and cultivates plants according to an optimal cultivation plan based on weather forecasts and cultivation conditions provided by the server. The user also provides emotional information during cultivation, which the system uses to provide optimal support. The user also provides feedback on successes and problems with cultivation, contributing to improving the system's analysis accuracy.

[0806] Emotion Engine

[0807] The emotion engine is an engine for collecting and analyzing the user's emotional information. It analyzes the user's stress level and motivation and adjusts cultivation plans and suggestions accordingly. For example, if the system determines that the user is under high stress, it will suggest ways to reduce work and send encouraging messages. It will also display reminder notifications such as "Look forward to today's harvest!" This helps users continue to enjoy their home garden.

[0808] Specific examples

[0809] For example, consider a user trying to grow tomatoes.

[0810] 1. Users log in to the system using their smartphone and enter their address and the plant they want to grow.

[0811] 2. The server retrieves weather forecasts from an external weather data API, filters the weather data for the user's location, and saves it.

[0812] 3. The server retrieves tomato cultivation conditions from the database and inputs them into the AI ​​model along with weather data for analysis.

[0813] 4. The AI ​​model calculates the optimal sowing and harvesting times and also suggests countermeasures for sudden weather changes.

[0814] 5. The server saves the generated cultivation plan to the user's account and displays it on the device. Important information is also sent to the user via push notifications.

[0815] 6. The user follows the proposed cultivation plan, sows tomato seeds and performs the necessary management procedures.

[0816] 7. The device collects emotional information from the user's facial expressions and voice and sends it to the server.

[0817] 8. The server uses an emotion engine to assess the user's stress level and motivation, and generates and displays suggestions and messages appropriate to the situation.

[0818] 9. As users continue to grow crops, they register feedback with the system, and the server uses this data to improve the analysis accuracy of the AI ​​model.

[0819] For example, a prompt sentence to be input to a generative AI model is:

[0820] "Draw the best plan for growing tomatoes in Shinjuku Ward. Please take current weather data into consideration."

[0821] There is.

[0822] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0823] Step 1:

[0824] The server accesses an external weather data API to collect the latest weather data for the specified region. In this step, the server obtains current weather information, forecast, temperature, humidity, precipitation, etc. from the weather data API (e.g., WeatherAPI), filters it by region, and stores it in a database. The input is region information, and the output is the filtered weather data.

[0825] Step 2:

[0826] The terminal receives input from the user. The user accesses the system via their smartphone and inputs the location of their home garden and the type of plant they wish to cultivate. The input is the address and type of plant, and the output is to send this information to the server.

[0827] Step 3:

[0828] The server retrieves the plant's cultivation conditions from a database. These conditions include temperature, humidity, sunlight hours, soil type, etc., for optimal plant growth. The input is the plant type, and the output is the cultivation conditions for that plant.

[0829] Step 4:

[0830] The server inputs weather data and cultivation conditions into an AI model to generate an optimal cultivation plan. The AI ​​model (for example, scikit-learn's RandomForestRegressor) analyzes this data and calculates the optimal sowing time, harvest time, management procedures, etc. The input is weather data and cultivation conditions, and the output is the generated cultivation plan.

[0831] Step 5:

[0832] The server saves the generated cultivation plan in the user's account and displays it on the terminal. The terminal visually presents the cultivation plan to the user via a smartphone or PC. The input is the generated cultivation plan, and the output is a display of the plan.

[0833] Step 6:

[0834] The device sends the generated cultivation plan and important notifications to the user. Push notifications and emails are used to notify the user of sudden weather changes and important management procedures. The input is the generated cultivation plan and important notification content, and the output is the notification to the user.

[0835] Step 7:

[0836] The device collects the user's facial expressions and voice and sends the emotional information to the server. The device uses a camera and microphone to collect the user's facial expressions and voice. The input is the user's facial expressions and voice, and the output is digital data of emotional information.

[0837] Step 8:

[0838] The server uses an emotion engine to analyze the user's emotional state. The collected facial and voice data is input into an emotion analysis algorithm (e.g., OpenCV) to evaluate the user's stress level and motivation. The input is digital data of emotional information, and the output is the evaluation result of the emotional state.

[0839] Step 9:

[0840] The server displays suggestions and reminders to the user based on the emotional state analyzed. If the user's stress level is high, it generates work reduction suggestions and encouraging messages and displays them on the device. The input is the evaluation result of the emotional state, and the output is the suggestion or reminder message.

[0841] Step 10:

[0842] As users continue cultivating, they register their feedback in the system. They input their successes and problems in cultivation via their smartphones and send this to the server. The input is the user's feedback, and the output is the feedback data stored on the server.

[0843] Step 11:

[0844] The server improves the analytical accuracy of the AI ​​model based on the feedback. Feedback data from users is incorporated into the AI ​​model, and by re-learning, a more accurate cultivation plan is generated. The input is the user's feedback data, and the output is an improved AI model.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] [Third embodiment]

[0849] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0850] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0851] 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).

[0852] 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.

[0853] 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.

[0854] 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).

[0855] 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. 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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."

[0861] This invention is a system that helps beginners who are starting a home vegetable garden or people with limited knowledge of weather and cultivation to create effective cultivation plans. This system is composed of three main components: a server, a terminal, and a user.

[0862] server

[0863] The server is responsible for data processing and management for the entire system. It obtains the latest weather information from an external weather data API, filters it by region, and stores it in a database. It also obtains the cultivation conditions for the plants selected by the user from the database and inputs them into the AI ​​model along with the weather data for analysis. It generates an optimal cultivation plan based on the analysis results and saves this plan by associating it with the user's account. It also has a function to retrain the AI ​​model based on user feedback to improve analysis accuracy.

[0864] Terminal

[0865] The terminal functions as an interface with the user. Users access the system through their smartphone, PC, or other device and enter the location of their home garden and the type of plants they wish to cultivate. The terminal displays the cultivation plan and management method generated by the server and sends notifications to the user. Important information, such as sudden changes in the weather, is also immediately communicated to the user via push notifications. User feedback is also sent to the server via the terminal.

[0866] User

[0867] Users are the operators of home gardens and the ultimate users of this system. They access the system using their own devices and set up their home gardens. By providing optimal cultivation plans based on weather forecasts and cultivation conditions, even beginners can easily manage their home gardens effectively. Users actually cultivate plants and learn how to manage them and how to deal with unexpected weather conditions. Furthermore, they can register successes and problems in cultivation as feedback in the system, contributing to improving the system's accuracy.

[0868] Specific examples

[0869] For example, consider the case of a beginner home gardener named Tanaka who is trying to grow tomatoes in his garden.

[0870] 1. The user (Mr. Tanaka) logs in to the system using his / her smartphone. He / she enters his / her address (Shinjuku-ku, Tokyo) and the plant he / she wants to grow (tomato).

[0871] 2. The server retrieves the latest weather forecast from an external weather data API, filters the weather data for the area where Tanaka's vegetable garden is located, and saves it in a database.

[0872] 3. The server retrieves tomato cultivation conditions (optimum temperature: 20-30°C, sunlight duration: 8 hours or more, etc.) from the database and inputs them into the AI ​​model along with weather data.

[0873] 4. Based on weather data from Tanaka's garden and the tomato growing conditions, the AI ​​model calculates the optimal sowing and harvest times, and also suggests measures to deal with sudden weather changes (e.g., using shade nets).

[0874] 5. The server saves the generated cultivation plan to Tanaka's account and displays it on his device. Important information is also sent to Tanaka's smartphone as a push notification.

[0875] 6. The user (Mr. Tanaka) sows tomato seeds according to the proposed cultivation plan and performs the necessary maintenance procedures. If the use of shade nets is required during cultivation, he receives a notification on his device and takes immediate action.

[0876] 7. After harvesting, Tanaka will register feedback in the system, which will improve the accuracy of future cultivation plans.

[0877] As mentioned above, this system is designed to enable even beginners to effectively manage their home garden and enjoy growing a variety of vegetables.

[0878] The processing flow will be explained below.

[0879] Step 1:

[0880] Users log in to the system using their smartphone or computer, enter the address or GPS information of the location where they want to set up their vegetable garden, and select the type of plants they want to grow.

[0881] Step 2:

[0882] The device sends the input information to the server, including the user's location and the type of plant selected.

[0883] Step 3:

[0884] The server accesses an external weather data API to obtain the latest weather data (temperature, precipitation, humidity, wind speed, sunshine hours, etc.) for the specified area.

[0885] Step 4:

[0886] The server saves the weather data it obtains, filters it by region, and stores it in a database.

[0887] Step 5:

[0888] The server retrieves the cultivation conditions (optimum temperature, sunlight hours, amount of water, etc.) for the plant (e.g., tomato) selected by the user from the database.

[0889] Step 6:

[0890] The weather data and plant cultivation conditions acquired by the server are input into the AI ​​model, which analyzes this data and calculates the following:

[0891] Optimal sowing and harvesting times

[0892] Daily care methods (e.g., irrigation timing, fertilizer application timing)

[0893] Step 7:

[0894] The server generates a specific cultivation plan based on the analysis results, which includes the necessary management procedures and precautions for the user.

[0895] Step 8:

[0896] The server stores the generated cultivation plan in association with the user's account.

[0897] Step 9:

[0898] When the user opens the app, the device displays the latest cultivation plan and notifies them of important updates and precautions in real time.

[0899] Step 10:

[0900] The user cultivates plants according to the proposed cultivation plan and carries out the management procedures. Problems and successes that arise during the actual cultivation process are registered in the system as feedback.

[0901] Step 11:

[0902] The server receives feedback from users and uses it to retrain the AI ​​model to improve analysis accuracy.

[0903] Step 12:

[0904] The server periodically obtains new weather data, updates cultivation plans in real time based on this data, and proposes countermeasures for sudden weather changes.

[0905] Example 1

[0906] 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."

[0907] Providing effective cultivation plans to beginner home gardeners and those with limited knowledge of weather and cultivation is a challenge. Current systems cannot provide appropriate measures in real time based on sudden changes in weather or individual plant characteristics, and lack a way to use user feedback to improve the system's accuracy. As a result, it is difficult to significantly reduce the time it takes beginners to find appropriate cultivation methods.

[0908] 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.

[0909] In this invention, the server includes: means for inputting location information and information about the plant being cultivated from the user; means for collecting weather data from an external weather data API; means for acquiring plant cultivation conditions from a database; means for inputting the collected weather data and cultivation conditions into an AI model and analyzing them; means for generating an optimal cultivation plan based on the analysis results; means for saving the generated cultivation plan in the user's account; means for displaying the generated cultivation plan on the user's device; means for notifying the user of the generated cultivation plan via push notification; means for collecting feedback from the user; and means for retraining the AI ​​model based on the collected feedback to improve analysis accuracy. This allows the user to quickly respond to changes in the weather and implement an effective cultivation plan. Furthermore, by utilizing feedback, the system's analysis accuracy can be continuously improved, allowing for the provision of more accurate cultivation plans.

[0910] "User" refers to a person who uses the system to manage a home garden.

[0911] "Location information" refers to information about the area where the user's home garden is located.

[0912] "Information about the plant to be cultivated" refers to information about the type and characteristics of the plant that the user is trying to cultivate.

[0913] "External Weather Data API" refers to an external system interface that provides weather information such as weather forecasts.

[0914] "Weather Data" refers to information regarding weather conditions such as temperature, precipitation, wind speed, and humidity.

[0915] "Plant growing conditions" refers to the environmental conditions necessary for a particular plant to grow.

[0916] A "database" is a system that organizes and stores information so that it can be retrieved and used when needed.

[0917] An "artificial intelligence model" refers to a machine learning algorithm that analyzes large amounts of data to generate useful information.

[0918] "Analysis" refers to the process of deriving specific results or conclusions from collected data.

[0919] A "cultivation plan" refers to specific procedures and schedules for cultivating plants.

[0920] "User account" refers to a mechanism for identifying an individual user within the system and managing information about that user.

[0921] "Terminal" refers to a device (e.g., a smartphone or PC) through which a user interfaces with the system.

[0922] "Push notifications" refers to a feature that notifies users of new information or important messages in real time.

[0923] "Feedback" refers to a user reporting to the system the successes and problems they experience while using the system.

[0924] "Retraining" refers to the process of improving the performance of an artificial intelligence model using new data collected.

[0925] This invention is a system that helps beginners who are starting a home vegetable garden or people with limited knowledge of weather and cultivation to create effective cultivation plans. The system is composed of three main components: a server, a terminal, and a user.

[0926] server

[0927] The server is responsible for data processing and management of the entire system. The server obtains the latest weather information using an external weather data API (e.g., OpenWeatherAPI). The obtained weather data is filtered by region and stored in a database (e.g., MySQL).

[0928] Next, the server retrieves the plant's cultivation conditions from a database. For example, for tomatoes, these include information such as the optimum temperature, sunlight hours, and humidity. This data, along with weather data, is input into an artificial intelligence model (e.g., TensorFlow) for analysis. The AI ​​model generates an optimal cultivation plan based on the collected data. This cultivation plan includes information such as the sowing time, harvest time, and weather-related measures (e.g., the use of shade nets).

[0929] The generated cultivation plan is stored in the user's account, and is then sent from the server to the user's terminal so that it can be displayed on the terminal.

[0930] Furthermore, the server has the function of collecting feedback from users. The feedback provided by users to the system (e.g., actual harvest results, effectiveness of management methods, etc.) is used to retrain the AI ​​model, thereby improving the accuracy of analysis in the future.

[0931] Terminal

[0932] The terminal functions as an interface with the user. The terminal provides an interface for the user to input information about the location of the home garden and the plants they want to cultivate. The input data is sent from the terminal to the server.

[0933] The generated cultivation plan is displayed to the user via their device. Important information, such as sudden changes in the weather, is also immediately communicated to the user via push notifications. Feedback obtained from users as they use the system is also sent to the server via their device.

[0934] User

[0935] The user is the manager of the home garden and the final user of the system. The user accesses the system using their own device and sets up the home garden. They effectively manage the home garden according to the optimal cultivation plan based on the weather forecast and cultivation conditions provided. Through actual cultivation, the user follows the management procedures suggested by the system and performs the necessary tasks.

[0936] Specific examples

[0937] For example, consider a user who wants to grow tomatoes in his or her garden.

[0938] 1. Users log in to the system using their smartphone and enter information about their address and the plant they want to grow.

[0939] 2. The server retrieves the latest weather forecast from an external weather data API, filters the weather data for the area where the user's home garden is located, and saves it in a database.

[0940] 3. The server retrieves the tomato cultivation conditions from the database and inputs them into the AI ​​model along with weather data.

[0941] 4. The AI ​​model will suggest optimal sowing and harvest times, as well as strategies for dealing with weather fluctuations.

[0942] 5. The server saves the generated cultivation plan in the user's account, and the device displays it. Important information is also immediately communicated to the user via push notifications.

[0943] Prompt Sentence Examples

[0944] "What steps should I take to create a system for beginners who want to start a home vegetable garden?"

[0945] "How do I create the best growing plan for tomatoes in a particular area?"

[0946] This system allows even beginners to effectively manage their home gardens and enjoy growing a variety of plants.

[0947] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0948] Step 1:

[0949] A user logs into the system using their own terminal and inputs the location information of their home garden and information about the plants they are growing.

[0950] Input: User's location information and information about the plant they want to grow (e.g., Shinjuku-ku, Tokyo, tomato)

[0951] Output: The data format in which the input information is sent to the server (e.g., JSON format)

[0952] Specific actions: The user opens the smartphone app, enters the address and the tomato, and presses the send button.

[0953] Step 2:

[0954] The server sends a request to an external weather data API to retrieve the latest weather data and store it in a database.

[0955] Input: User's location information (e.g., Shinjuku-ku, Tokyo)

[0956] Output: Weather data for the specified area is saved in a database (e.g. temperature, precipitation, humidity)

[0957] Specific operation: The server generates a URL like "https: / / api.openweathermap.org / data / 2.5 / weather?q=Shinjuku&appid=YOUR_API_KEY" and sends a request to the API. The retrieved weather data is stored in a MySQL database.

[0958] Step 3:

[0959] The server retrieves the cultivation conditions for the plants to be cultivated from the database, and inputs them along with weather data into the AI ​​model for analysis.

[0960] Input: User-entered plant information (e.g., tomatoes) and stored weather data

[0961] Output: Analysis results from the AI ​​model (e.g., optimal sowing time, harvest time, and measures to deal with weather fluctuations)

[0962] How it works: The server retrieves data by executing the SQL query "SELECT FROM crop_conditions WHERE crop_name = 'tomato'". The retrieved weather data and growing conditions are then input into the TensorFlow model for analysis.

[0963] Step 4:

[0964] The server generates an optimal cultivation plan based on the analysis results obtained from the AI ​​model and saves it in the user's account.

[0965] Input: Analysis results by AI model

[0966] Output: Generated cultivation plan (e.g. sowing in early April, harvesting in mid-July, use of shade net)

[0967] Specific operation: The server generates a cultivation plan based on the analysis results and saves it by executing the SQL query "INSERT INTO user_plans (user_id, plan_data) VALUES (user_id, 'Cultivation plan here')".

[0968] Step 5:

[0969] The terminal displays the generated cultivation plan to the user and, if necessary, communicates important information via push notifications.

[0970] Input: Generated cultivation plan and other important information (e.g., measures to deal with sudden weather changes)

[0971] Output: Cultivation plan and push notification displayed on the user's smartphone screen

[0972] Specific operation: The terminal application displays the cultivation plan obtained from the server on the screen and sends push notifications as necessary.

[0973] Step 6:

[0974] The user manages their home garden according to the cultivation plan proposed by the system and provides feedback to the system.

[0975] Input: User's cultivation record and observations (e.g., actual yield, effectiveness of management methods)

[0976] Output: Feedback information is sent to the server and stored in the system.

[0977] Specific operations: The user enters the post-harvest results and any observations made during management through the application and presses the send button.

[0978] Step 7:

[0979] The server uses user feedback to retrain the AI ​​model and improve the accuracy of future analyses.

[0980] Input: User feedback data

[0981] Output: Retrained AI model

[0982] What it does: The server adds new feedback data to the dataset and retrains the AI ​​model using TensorFlow. The retrained model is saved and used for the next analysis.

[0983] (Application example 1)

[0984] 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."

[0985] Creating an effective gardening plan is a significant challenge for beginners and those with limited knowledge of weather and cultivation. Furthermore, physical stores often lack support for selecting appropriate gardening supplies, leaving users unsure of what to choose. Furthermore, insufficient information is provided to quickly respond to changing weather data, potentially reducing the success rate of cultivation. Therefore, the present invention aims to solve these problems and provide a system that supports even beginners in effectively managing their gardens.

[0986] 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.

[0987] In this invention, the server includes means for collecting weather data, means for acquiring plant cultivation conditions, means for analyzing the collected weather data and cultivation conditions, means for generating an optimal cultivation plan based on the analysis results, means for displaying the generated cultivation plan, means for notifying the user of the generated cultivation plan, means for receiving feedback from the user, means for improving the accuracy of the analysis based on the feedback, means for acquiring the user's location information in the physical store and navigating to the appropriate product shelves, means for suggesting optimal products based on the user's cultivation history and weather data, and means for displaying detailed information about the suggested products and providing on-site instructions. This allows even beginners to create an appropriate cultivation plan and easily select the necessary home gardening supplies in the physical store. Furthermore, it is possible to quickly respond to changing weather data in real time, thereby improving the success rate of cultivation.

[0988] "Means for collecting weather data" refers to devices or systems that obtain the latest weather information from external weather data APIs, filter it by region, and store it in a database.

[0989] The "means for acquiring the cultivation conditions of the plant" refers to a mechanism for acquiring the cultivation conditions of the plant selected by the user from the database and analyzing this information.

[0990] "Means for analyzing collected weather data and cultivation conditions" refers to AI models and algorithms used to conduct analysis based on weather data and plant cultivation conditions.

[0991] The "means for generating an optimal cultivation plan" refers to a system for creating an optimal cultivation plan for the user based on the analysis results.

[0992] The "means for displaying the generated cultivation plan" refers to an interface or tool for displaying the generated cultivation plan on the user's terminal.

[0993] "Means for notifying the user of the generated cultivation plan" refers to a mechanism for immediately transmitting the cultivation plan and important information to the user via push notifications, etc.

[0994] "Means for receiving feedback from users" refers to interfaces or functions that allow users to send feedback to the system.

[0995] "Means for improving analysis accuracy" refers to a system for retraining AI models based on user feedback to improve the accuracy of analysis results.

[0996] "Means for obtaining user location information within a physical store" refers to location information technology or systems for identifying the user's current location within a physical store.

[0997] "A means for navigating shelves of appropriate products" refers to a navigation system that guides users to shelves of products they are looking for in a physical store.

[0998] "Means for suggesting optimal products based on the user's cultivation history and weather data" refers to AI models and algorithms that recommend appropriate home gardening products based on the user's past cultivation history and current weather data.

[0999] The "means for displaying detailed information about the proposed product" refers to a technology for displaying detailed information about the proposed product in real time through the user's smart glasses or the like.

[1000] The "means for providing guidance on how to use the product" refers to a system that displays and provides guidance on how to use the product and precautions for handling the product that the user is considering purchasing.

[1001] The present invention is a system that helps beginners and users with limited knowledge of home gardening to create effective cultivation plans. This system is mainly composed of a server, a terminal, and a user, and implements the invention according to the following steps.

[1002] server

[1003] The server is responsible for data processing and management of the entire system. Specifically, it performs the following processes:

[1004] 1. Weather data collection: Obtain the latest weather information from an external weather data API, filter it by region, and store it in a database.

[1005] 2. Obtaining cultivation conditions: Obtain the cultivation conditions (optimum temperature, required sunlight hours, etc.) of the plant selected by the user from the database.

[1006] 3. Data analysis: The collected weather data and plant cultivation conditions are input into an AI model for analysis, and an optimal cultivation plan is generated. This analysis uses artificial intelligence to analyze the weather data and cultivation conditions.

[1007] 4. Generate cultivation plan: Generate an optimal cultivation plan for the user based on the analysis results and save this plan in association with the user's account.

[1008] 5. Leveraging feedback: Retraining the AI ​​model based on user feedback to improve analysis accuracy.

[1009] Terminal

[1010] The terminal functions as an interface with the user and provides the following functions:

[1011] 1. Setup and input: Users access the system via their smartphone or computer and input the location of their home garden and the type of plants they wish to grow.

[1012] 2. Information display: The system displays the cultivation plan and management method generated by the server and sends notifications to the user. Important information, such as sudden changes in the weather, is also immediately communicated to the user via push notifications.

[1013] 3. Product Recommendations: The system acquires the user's location information in a physical store, navigates to the appropriate product shelves, and recommends the most suitable product based on the user's cultivation history and weather data, and displays detailed product information.

[1014] User

[1015] The user is the operator of the home garden and the final user of this system. The user will use the system as follows:

[1016] 1. Setup and operation: Access the system using your smartphone or computer and set up your home garden.

[1017] 2. Implementing a cultivation plan: By providing optimal cultivation plans based on weather forecasts and growing conditions, even beginners can easily manage their home vegetable gardens effectively.

[1018] 3. Product Selection: Smart glasses can be used in brick-and-mortar stores to easily find and purchase the right home gardening products.

[1019] 4. Providing feedback: Registering successes and problems in cultivation as feedback to the system and contributing to improving the system's accuracy.

[1020] Examples and prompts

[1021] For example, if a user wants to grow tomatoes in their garden at home, they can use the system as follows:

[1022] 1. User: Logs into the system using a smartphone and enters his / her address and the plant (tomato) he / she wants to grow.

[1023] 2. Server: Obtains the latest weather forecast from an external weather data API, filters the weather data for the area where the user's home garden is located, and stores it in a database.

[1024] 3. Server: Retrieves tomato cultivation conditions from the database and inputs them into the AI ​​model along with weather data.

[1025] 4. Analysis: Based on the weather data of the user's garden and the growing conditions of the tomatoes, the AI ​​model calculates the optimal sowing and harvesting times, and also suggests countermeasures for sudden weather changes.

[1026] 5. Server: The generated cultivation plan is saved in the user's account, displayed on the device, and important information is sent to the user's smartphone as a push notification.

[1027] 6. User: Sows tomato seeds according to the proposed cultivation plan and performs the necessary maintenance procedures. When visiting a physical store, the user uses the smart glasses to select the necessary home gardening supplies.

[1028] 7. Feedback: After harvesting, users can provide feedback to the system, improving the accuracy of future cultivation plans.

[1029] Example prompt sentence:

[1030] "Recommend the best tools and plants for your home garden based on the user's growing history and current weather data. Plant: Tomato. Growing conditions: Optimal temperature 20-30°C, 8+ hours of sunshine. Current weather data: Temperature 25°C, 5 hours of sunshine."

[1031] "When a user walks towards a shelf in a physical store, compare their current location (35.6895, 139.6917) with the shelf's location (35.6894, 139.6916) and let them know if they're approaching."

[1032] In this way, the system makes it easy for even beginners to obtain advanced cultivation information and quickly and accurately select the necessary home gardening supplies in a physical store.

[1033] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1034] Step 1:

[1035] The server retrieves the latest weather information from an external weather data API. The input is weather data from the API, and the output is weather data filtered by region. Specifically, it sends an HTTP request to the API endpoint, receives a JSON response, parses it, and stores it in a database.

[1036] Step 2:

[1037] The server retrieves the cultivation conditions of the plant selected by the user from the database. The input is the information about the plant selected by the user, and the output is the cultivation conditions of the corresponding plant. Specifically, the server executes a database query based on the plant information entered by the user to retrieve the cultivation conditions.

[1038] Step 3:

[1039] The server inputs weather data and plant cultivation conditions into the AI ​​model for analysis. The input is weather data and cultivation conditions, and the output is an optimal cultivation plan. Specifically, the weather data and cultivation conditions are input into the generative AI model, and the model calculates the optimal cultivation plan.

[1040] Step 4:

[1041] The server associates the generated cultivation plan with the user's account and saves it. The input is the generated cultivation plan, and the output is the cultivation plan saved in the user's account. Specifically, the cultivation plan data is linked to the user's account and saved in the database.

[1042] Step 5:

[1043] The terminal displays the cultivation plan and management method generated from the server. The input is the cultivation plan from the server, and the output is the cultivation plan displayed on the terminal's display. Specifically, the terminal receives data from the server and displays it on the user interface.

[1044] Step 6:

[1045] The device immediately notifies the user of important information, such as sudden changes in the weather, via push notifications. The input is notification data from the server, and the output is a push notification sent to the user. Specifically, the device's notification function is used to immediately convey emergency information to the user.

[1046] Step 7:

[1047] The device acquires the user's location information within the physical store and navigates to the appropriate product shelves. The input is the user's location information, and the output is navigation information. Specifically, it uses the in-store location information system to identify the user's current location, calculates the optimal route, and guides the user there.

[1048] Step 8:

[1049] The device suggests optimal products based on the user's cultivation history and weather data. The input is the user's cultivation history and weather data, and the output is a list of suggested products. Specifically, the AI ​​model selects products based on this data and displays the results to the user.

[1050] Step 9:

[1051] The terminal displays detailed information about the suggested product and provides on-the-spot instructions on how to handle it. The input is product information, and the output is the display of detailed information. Specifically, the terminal displays product information and provides additional instructions to the user.

[1052] Step 10:

[1053] The server retrains the AI ​​model based on user feedback to improve analysis accuracy. The input is user feedback, and the output is the retrained AI model. Specifically, the server collects feedback data, updates the training dataset for the AI ​​model, and retrains it.

[1054] 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.

[1055] This invention is a system that helps beginners who are starting home gardening or people with limited knowledge of weather and cultivation to create effective cultivation plans. This system combines weather data, cultivation conditions, and user emotional information to provide a more human-friendly home gardening experience. This system is composed of a server, terminals, users, and an emotion engine.

[1056] server

[1057] The server is responsible for data processing and management for the entire system. It obtains the latest weather information from an external weather data API, filters it by region, and stores it in a database. It also obtains the cultivation conditions for the plants selected by the user from the database and inputs them into an AI model along with the weather data for analysis. Based on the analysis results, it generates an optimal cultivation plan and saves it in association with the user's account. It also has a function that uses an emotion engine that recognizes the user's emotions to evaluate the user's emotional state and stress level and adjust the cultivation plan accordingly.

[1058] Terminal

[1059] The device functions as an interface with the user. Users access the system through devices such as smartphones or PCs and input the location of their home garden and the type of plants they wish to cultivate. The device displays the cultivation plan and management method generated by the server and sends notifications to the user. It also has the function of collecting emotional information from the user's facial expressions and voice and sending it to the server. Important information and notifications regarding sudden weather changes are immediately communicated to the user via push notifications.

[1060] User

[1061] The user is the manager of a home garden and the ultimate user of this system. The user accesses the system using their own device and sets up the home garden. By providing an optimal cultivation plan based on weather forecasts and cultivation conditions, even beginners can easily manage their home garden effectively. The user actually cultivates plants and checks management methods and countermeasures for sudden weather events. Furthermore, the user can register successes and problems in cultivation as feedback in the system, contributing to improving the system's accuracy. In addition, suggestions and reminders are displayed based on the user's emotional state, providing a better experience.

[1062] Emotion Engine

[1063] The emotion engine is an engine for collecting and analyzing the user's emotional information. This allows the system to recognize the user's stress level and motivation, and adjust cultivation plans and management methods according to the user's emotional state. For example, if the system determines that the user is under high stress, it will suggest work reductions and send encouraging messages. It will also display reminders to boost the user's motivation (for example, "Look forward to today's harvest!").

[1064] Specific examples

[1065] For example, consider the case of a beginner home gardener named Suzuki who is trying to grow tomatoes in his garden.

[1066] 1. The user (Mr. Suzuki) logs in to the system using his / her smartphone. He / she enters his / her address (Shinjuku-ku, Tokyo) and the plant he / she wants to grow (tomato).

[1067] 2. The terminal sends the entered information to the server.

[1068] 3. The server retrieves the latest weather forecast from an external weather data API, filters the weather data for the area where Suzuki's vegetable garden is located, and saves it.

[1069] 4. The server retrieves the tomato cultivation conditions (optimum temperature: 20-30°C, sunlight duration: 8 hours or more) from the database and inputs them into the AI ​​model along with weather data.

[1070] 5. An AI model analyzes this data to calculate optimal sowing and harvest times, and also suggests countermeasures for sudden weather changes.

[1071] 6. The cultivation plan generated by the server is saved in Suzuki's account and displayed on his device. Important information is also sent to Suzuki's smartphone via push notification.

[1072] 7. The user (Mr. Suzuki) follows the proposed cultivation plan, sows tomato seeds, and performs the necessary management procedures.

[1073] 8. The device collects emotional information from Suzuki's facial expressions and voice and sends this to the server.

[1074] 9. The server uses an emotion engine to assess Suzuki's stress level and motivation, and generates and displays suggestions and messages appropriate to the situation.

[1075] 10. As Suzuki continues to cultivate, he will register his feedback in the system, and the server will use this data to improve the analytical accuracy of the AI ​​model.

[1076] As described above, this system is designed to enable even beginners to effectively manage their home gardens and enjoy growing a wide variety of vegetables. Furthermore, by taking into account the user's emotional state, it provides a better experience and supports their motivation to continue growing.

[1077] The processing flow will be explained below.

[1078] Step 1:

[1079] Users log in to the system using their smartphone or computer, enter the address or GPS information of the location where they want to set up their vegetable garden, and select the type of plants they want to grow.

[1080] Step 2:

[1081] The terminal sends the entered user information (location, type of plant) to the server.

[1082] Step 3:

[1083] The server accesses an external weather data API to obtain the latest weather data (temperature, precipitation, humidity, wind speed, sunshine hours, etc.) for the specified area.

[1084] Step 4:

[1085] The server saves the weather data it obtains, filters it by region, and stores it in a database.

[1086] Step 5:

[1087] The server retrieves the cultivation conditions (optimum temperature range, required sunlight, amount of water, etc.) of the plant (e.g., tomato) selected by the user from the database.

[1088] Step 6:

[1089] The server inputs weather data and plant cultivation condition data collected into the AI ​​model, which then analyzes the optimal cultivation plan, calculating the optimal sowing time, harvest time, and daily maintenance methods (e.g., when to irrigate and when to use fertilizer).

[1090] Step 7:

[1091] The server generates a specific cultivation plan based on the analysis results and stores this plan in association with the user's account.

[1092] Step 8:

[1093] The server transmits the generated cultivation plan information to the terminal.

[1094] Step 9:

[1095] When the user opens the app, the device displays the latest cultivation plan, and push notifications are sent to users immediately with important updates and suggestions for dealing with sudden weather changes.

[1096] Step 10:

[1097] The user cultivates plants according to the proposed cultivation plan, performs daily management procedures, and registers problems and successes that occur during the actual cultivation process as feedback to the system.

[1098] Step 11:

[1099] The device collects emotional information from the user's facial expressions and voice and sends it to the server. This emotional information includes the user's stress level and motivation state.

[1100] Step 12:

[1101] The server uses an emotion engine to analyze the user's emotional state. If the stress level is high, it will suggest work reduction (e.g., automating irrigation work), and if motivation is low, it will generate encouraging messages and appropriate reminders (e.g., "Look forward to today's harvest!").

[1102] Step 13:

[1103] The server sends suggestions and messages generated based on the analysis results of the emotion engine to the terminal, which then displays them to the user.

[1104] Step 14:

[1105] The server retrains the AI ​​model based on user feedback and emotional information to improve analysis accuracy. This process is continuous and is reflected in future cultivation plans.

[1106] Step 15:

[1107] The server periodically retrieves new weather data and constantly updates the cultivation plan in response to changes in the weather. It also proposes appropriate countermeasures in real time in response to sudden weather changes.

[1108] Example 2

[1109] 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."

[1110] Conventional home gardening support systems can create cultivation plans based on weather data and cultivation conditions, but they do not take into account the user's emotional state when making suggestions or adjustments, which can increase the user's mental stress. Furthermore, the system lacks the functionality to receive feedback and improve analysis accuracy, making it difficult to use effectively. Furthermore, in many cases, the system does not provide measures to respond to real-time weather changes, making it difficult to respond to sudden weather fluctuations.

[1111] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting weather data, means for acquiring plant cultivation conditions, means for analyzing the collected weather data and cultivation conditions, means for generating an optimal cultivation plan based on the analysis results, means for displaying the generated cultivation plan, means for notifying the user of the generated cultivation plan, means for collecting user emotion information, means for adjusting the cultivation plan based on the emotion information, means for receiving feedback from the user, and means for improving analysis accuracy based on the feedback. This makes it possible to adjust the cultivation plan appropriately according to the user's emotional state and improve the accuracy of the system by utilizing the feedback.

[1112] "Weather Data" refers collectively to information regarding past, present, and future weather conditions in a particular geographic area.

[1113] "Plant growing conditions" refers to information about environmental factors such as temperature, humidity, and hours of sunlight required for optimal growth of a particular plant.

[1114] "Means for analysis" refers to methods and devices for combining and evaluating weather data and plant cultivation conditions to create optimal cultivation plans.

[1115] A "cultivation plan" is a detailed description of the actions and timing required to effectively grow a particular plant.

[1116] The "display means" refers to a method or device that allows a user to visually confirm the generated cultivation plan and related information.

[1117] The "notification means" refers to a method or device for instantly notifying the user of the generated cultivation plan or important information.

[1118] "Emotion information" is data relating to changes in the user's mental state and emotions, and includes information obtained from facial expressions and voice.

[1119] "Feedback" refers to information based on evaluations, opinions, and experiences that users provide to the system.

[1120] "Means for improving analytical accuracy" refers to methods or devices that use collected feedback to improve the performance of a system or AI model.

[1121] The present invention is a system that helps beginners who are starting a home garden or users with limited knowledge of weather and cultivation to create effective cultivation plans. This system combines weather data, cultivation conditions, and user emotional information to provide a more human-friendly home gardening experience. Specific embodiments of the present invention are described below.

[1122] Server Operation

[1123] The server performs the following actions:

[1124] 1. Weather data collection: The server uses external weather data APIs (e.g., OpenWeatherMap or Weather.com) to obtain the latest weather information. The data is filtered by region and stored in a database.

[1125] 2. Obtaining cultivation conditions: The server obtains the cultivation conditions (optimum temperature, humidity, sunlight hours, etc.) for a specific plant (e.g., tomato) from the database.

[1126] 3. Data analysis: Collected weather data and cultivation conditions are input into an AI model (using TensorFlow or PyTorch, for example) for analysis, which then calculates the optimal sowing and harvest times, as well as management methods.

[1127] 4. Emotional information analysis: The server uses an emotion engine (e.g., Microsoft Azure's Text Analytics API or IBM Watson's Tone Analyzer) to analyze the emotional information collected from the user and evaluate the user's stress level and motivation. Based on this information, the cultivation plan is adjusted accordingly.

[1128] Device behavior

[1129] The terminal provides the following features:

[1130] 1. User input: The user accesses the system using a device such as a smartphone or PC and enters the location of their home garden and the type of plants they wish to grow.

[1131] 2. Display of cultivation plan: Displays the cultivation plan and management method generated from the server. Important information is immediately notified to the user via push notification.

[1132] 3. Collecting emotional information: The device collects the user's facial expressions and voice through the smartphone's camera and microphone, and transmits the emotional information to the server.

[1133] User operations

[1134] Users use the system in the following ways:

[1135] 1. Setting up a home garden: Users log in to the system using their own devices and enter the location of their home garden and the type of plants they want to grow.

[1136] 2. Implementing the cultivation plan: By providing the optimal cultivation plan based on weather forecasts and cultivation conditions, the user can actually manage their home garden and cultivate plants. The user will proceed with the work by following the provided management method.

[1137] 3. Providing feedback: By providing feedback to the system about problems and successes experienced during cultivation, the system's accuracy can be improved. This allows the server to improve the analytical accuracy of the AI ​​model.

[1138] Specific examples

[1139] For example, consider a case where a user who is a beginner at home gardening is trying to grow tomatoes in his or her garden.

[1140] Specifically, follow these steps:

[1141] 1. A user logs into the system using their smartphone, inputting their address (e.g., a specific area) and the plant they want to grow (tomatoes).

[1142] 2. The server retrieves the latest weather forecast from an external weather data API, filters the weather data for the area where the home garden is located, and saves it.

[1143] 3. The server retrieves tomato cultivation conditions (e.g., optimal temperature, hours of sunlight) from the database and inputs them into the AI ​​model for analysis.

[1144] 4. The server generates a cultivation plan (e.g., optimal sowing and harvesting times) and saves it in the user's account, which is then displayed on the device. Important information is also immediately sent via push notifications.

[1145] 5. The device collects emotional information from the user's facial expressions and voice and sends it to the server.

[1146] 6. The server uses an emotion engine to analyze the emotional information, and if the user is feeling stressed, it suggests appropriate measures or generates an encouraging message.

[1147] Prompt Sentence Examples

[1148] Here are some examples of prompts for an AI model:

[1149] Describe the process for the following home garden support system:

[1150] 1. A user accesses the system using a smartphone to create a tomato cultivation plan.

[1151] 2. The server retrieves the latest weather information from an external weather data API and filters it by region.

[1152] 3. The server inputs weather information and cultivation conditions into the AI ​​model, analyzes them, and generates an optimal cultivation plan.

[1153] 4. The device collects the user's emotional information (facial expressions and voice) and sends it to the server.

[1154] 5. The emotion engine analyzes the user's emotional state, and the server adjusts the cultivation plan based on the feedback.

[1155] The detailed prompts make each step of the system easy to understand and are designed to help even beginners manage their vegetable gardens efficiently and receive support tailored to their emotional state.

[1156] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1157] Step 1: Collecting User Input Data

[1158] Users log in to the system using their smartphone or computer and enter the location of their garden and the type of plant they want to grow, including the specific address and plant name.

[1159] Input: User's address (e.g., specific area), plant they want to grow (e.g., tomatoes)

[1160] Output: The entered data is sent from the terminal to the server.

[1161] Specific operation: The user enters "Shinjuku-ku, Tokyo" into their smartphone and selects "tomato" as the plant they want to grow.

[1162] Step 2: Obtaining and filtering weather data

[1163] The server uses external weather data APIs (e.g., OpenWeatherMap or Weather.com) to retrieve the latest weather information, filter it by region, and store it in a database.

[1164] Input: User's location

[1165] Output: Weather data filtered by region is saved in a database.

[1166] Specific operation: The server calls the OpenWeatherMap API to obtain the latest weather data for Shinjuku Ward, Tokyo, filters it, and saves it in a database.

[1167] Step 3: Obtaining cultivation conditions

[1168] The server retrieves the cultivation conditions (optimum temperature, humidity, sunlight hours, etc.) for a particular plant (e.g., tomato) from the database.

[1169] Input: User-selected plant species

[1170] Output: Optimal growing conditions for plants (e.g., optimal temperature, humidity, and sunlight hours for tomatoes)

[1171] Specific operation: The server retrieves the cultivation conditions for "tomatoes" from the database and prepares the information.

[1172] Step 4: Analyze the data

[1173] The server inputs the collected weather data and cultivation conditions into an AI model (using TensorFlow or PyTorch, for example) for analysis, which then calculates the optimal sowing and harvest times, as well as management methods.

[1174] Input: Weather data, cultivation condition data

[1175] Output: Optimal cultivation plan (e.g. sowing time, harvest time, management method)

[1176] Specific operation: The server inputs weather data and tomato cultivation conditions into the AI ​​model and generates an optimal cultivation plan.

[1177] Step 5: Notification and display of cultivation plans

[1178] The server saves the generated cultivation plan in the user's account and notifies the user via the terminal.

[1179] Input: Generated cultivation plan

[1180] Output: Cultivation plans saved in the user's account, notifications displayed on the device

[1181] Specific operation: The server saves the generated cultivation plan in the user's account and displays it on the device. Important events are immediately notified via push notifications.

[1182] Step 6: Collecting emotional information

[1183] The device collects the user's facial expressions and voice through the smartphone's camera and microphone, and transmits the emotional information to a server.

[1184] Input: User's facial expression data, voice data

[1185] Output: Collected emotion information is sent to the server.

[1186] Specific operation: The device collects the user's emotional information through the camera and microphone and sends it to the server.

[1187] Step 7: Analyze and regulate emotional information

[1188] The server uses an emotion engine to analyze the user's emotional state and stress level and adjusts the cultivation plan as needed.

[1189] Input: User's emotional information

[1190] Output: Coordinated cultivation plan, encouraging messages and suggestions

[1191] Specific operation: The server uses an emotion engine to analyze the emotion data, and if the user is feeling stressed, it generates and displays suggestions to reduce the work or encouraging messages.

[1192] Step 8: Improve the system through feedback

[1193] Users provide feedback to the system about any problems or successes they encounter during cultivation, and the server accumulates this information to improve the analytical accuracy of the AI ​​model.

[1194] Input: User feedback

[1195] Output: AI model with improved analytical accuracy

[1196] Specific behavior: The user inputs specific feedback into the system (e.g., "The tomato leaves have turned yellow"), which the server stores and uses to train the AI ​​model.

[1197] In this way, each processing step of the system supports the user's home gardening activities in multiple ways, making it easy for even beginners to manage their home garden. Furthermore, by providing appropriate support according to the user's emotional state, the system provides a better experience and supports the user's motivation to continue gardening.

[1198] (Application example 2)

[1199] 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."

[1200] Previous home gardening support systems focused on creating cultivation plans using weather data and information on cultivation conditions, but no systems took into account the user's emotional state or motivation. This often led to beginners and busy users giving up on cultivation midway. Furthermore, there was a lack of a way to effectively utilize fresh ingredients from home gardens for food delivery services.

[1201] 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 means for collecting weather data, means for acquiring plant cultivation conditions, means for analyzing the collected weather data and cultivation conditions, means for generating an optimal cultivation plan based on the analysis results, means for displaying the generated cultivation plan, means for notifying the user of the generated cultivation plan, means for analyzing the user's emotional state, means for displaying suggestions and reminders to the user based on the analyzed emotional state, means for receiving feedback from the user, and means for improving the analysis accuracy based on the feedback. This makes it possible to manage a home vegetable garden taking into account the user's emotional state and motivation, allowing for continuous cultivation without frustration and also enabling the user to effectively use fresh ingredients from the home vegetable garden for food delivery services and earn income.

[1202] "Weather data" refers to meteorological information such as current weather, forecasts, temperature, humidity, and precipitation obtained from APIs accessible via the Internet.

[1203] "Plant growing conditions" are the environmental conditions, such as temperature, humidity, hours of sunlight, and soil type and quality, that are necessary for a particular plant to grow optimally.

[1204] "Means for generating optimal cultivation plans based on analysis results" refers to a means for using collected weather data and plant cultivation conditions to create plans for optimal sowing times, harvesting times, management methods, etc. using AI models and algorithms.

[1205] The "means for displaying the generated cultivation plan" is a function for visually displaying the cultivation plan created on the server on the user's terminal (smartphone or PC).

[1206] The "means for notifying the user of the generated cultivation plan" is a function for notifying the user of the generated cultivation plan and any changes thereto via push notification, email, etc.

[1207] "Means for analyzing the user's emotional state" refers to AI models and algorithms that collect and analyze the user's facial and voice data to assess the user's stress level and motivation.

[1208] The "means for displaying suggestions and reminders to the user based on the analyzed emotional state" is a function that displays suggestions for reducing cultivation work, encouraging messages, and reminders according to the user's emotional state.

[1209] The "means for receiving feedback from users" is a function that allows users to input information about successes and problems with cultivation and send it to the system.

[1210] "Means for improving analytical accuracy based on feedback" refers to a learning process that incorporates feedback data from users into the AI ​​model to generate more accurate cultivation plans.

[1211] The present invention is a system that enables beginners and people with limited knowledge of weather and cultivation to effectively manage their home gardens while providing fresh ingredients through a food delivery service. The system includes a server, a terminal, a user, and an emotion engine.

[1212] server

[1213] The server is responsible for data processing and management for the entire system. It obtains the latest weather information from an external weather data API, filters it by region, and stores it in a database. The cultivation conditions for plants selected by the user are also stored in the database. The server uses this data for analysis and generates an optimal cultivation plan. It also has the function of evaluating the user's emotional state using an emotion engine and adjusting the cultivation plan and suggestions based on this. Specific software used includes the Python library requests, scikit-learn (for the AI ​​model), and OpenCV (for emotion analysis).

[1214] Terminal

[1215] The terminal functions as an interface with the user. The user accesses the system via their smartphone and inputs the location of their home garden and the type of plant they wish to cultivate. The terminal displays the cultivation plan and management method generated by the server and sends notifications to the user as needed. The terminal also collects the user's facial expressions and voice via a camera and microphone and sends this to the server. As a concrete example, if a user wants to cultivate tomatoes, they enter their address and the plant they wish to cultivate.

[1216] User

[1217] The user is the manager of a home garden and the ultimate user of this system. The user accesses the system to set up the garden and cultivates plants according to an optimal cultivation plan based on weather forecasts and cultivation conditions provided by the server. The user also provides emotional information during cultivation, which the system uses to provide optimal support. The user also provides feedback on successes and problems with cultivation, contributing to improving the system's analysis accuracy.

[1218] Emotion Engine

[1219] The emotion engine is an engine for collecting and analyzing the user's emotional information. It analyzes the user's stress level and motivation and adjusts cultivation plans and suggestions accordingly. For example, if the system determines that the user is under high stress, it will suggest ways to reduce work and send encouraging messages. It will also display reminder notifications such as "Look forward to today's harvest!" This helps users continue to enjoy their home garden.

[1220] Specific examples

[1221] For example, consider a user trying to grow tomatoes.

[1222] 1. Users log in to the system using their smartphone and enter their address and the plant they want to grow.

[1223] 2. The server retrieves weather forecasts from an external weather data API, filters the weather data for the user's location, and saves it.

[1224] 3. The server retrieves tomato cultivation conditions from the database and inputs them into the AI ​​model along with weather data for analysis.

[1225] 4. The AI ​​model calculates the optimal sowing and harvesting times and also suggests countermeasures for sudden weather changes.

[1226] 5. The server saves the generated cultivation plan to the user's account and displays it on the device. Important information is also sent to the user via push notifications.

[1227] 6. The user follows the proposed cultivation plan, sows tomato seeds and performs the necessary management procedures.

[1228] 7. The device collects emotional information from the user's facial expressions and voice and sends it to the server.

[1229] 8. The server uses an emotion engine to assess the user's stress level and motivation, and generates and displays suggestions and messages appropriate to the situation.

[1230] 9. As users continue to grow crops, they register feedback with the system, and the server uses this data to improve the analysis accuracy of the AI ​​model.

[1231] For example, a prompt sentence to be input to a generative AI model is:

[1232] "Draw the best plan for growing tomatoes in Shinjuku Ward. Please take current weather data into consideration."

[1233] There is.

[1234] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1235] Step 1:

[1236] The server accesses an external weather data API to collect the latest weather data for the specified region. In this step, the server obtains current weather information, forecast, temperature, humidity, precipitation, etc. from the weather data API (e.g., WeatherAPI), filters it by region, and stores it in a database. The input is region information, and the output is the filtered weather data.

[1237] Step 2:

[1238] The terminal receives input from the user. The user accesses the system via their smartphone and inputs the location of their home garden and the type of plant they wish to cultivate. The input is the address and type of plant, and the output is to send this information to the server.

[1239] Step 3:

[1240] The server retrieves the plant's cultivation conditions from a database. These conditions include temperature, humidity, sunlight hours, soil type, etc., for optimal plant growth. The input is the plant type, and the output is the cultivation conditions for that plant.

[1241] Step 4:

[1242] The server inputs weather data and cultivation conditions into an AI model to generate an optimal cultivation plan. The AI ​​model (for example, scikit-learn's RandomForestRegressor) analyzes this data and calculates the optimal sowing time, harvest time, management procedures, etc. The input is weather data and cultivation conditions, and the output is the generated cultivation plan.

[1243] Step 5:

[1244] The server saves the generated cultivation plan in the user's account and displays it on the terminal. The terminal visually presents the cultivation plan to the user via a smartphone or PC. The input is the generated cultivation plan, and the output is a display of the plan.

[1245] Step 6:

[1246] The device sends the generated cultivation plan and important notifications to the user. Push notifications and emails are used to notify the user of sudden weather changes and important management procedures. The input is the generated cultivation plan and important notification content, and the output is the notification to the user.

[1247] Step 7:

[1248] The device collects the user's facial expressions and voice and sends the emotional information to the server. The device uses a camera and microphone to collect the user's facial expressions and voice. The input is the user's facial expressions and voice, and the output is digital data of emotional information.

[1249] Step 8:

[1250] The server uses an emotion engine to analyze the user's emotional state. The collected facial and voice data is input into an emotion analysis algorithm (e.g., OpenCV) to evaluate the user's stress level and motivation. The input is digital data of emotional information, and the output is the evaluation result of the emotional state.

[1251] Step 9:

[1252] The server displays suggestions and reminders to the user based on the emotional state analyzed. If the user's stress level is high, it generates work reduction suggestions and encouraging messages and displays them on the device. The input is the evaluation result of the emotional state, and the output is the suggestion or reminder message.

[1253] Step 10:

[1254] As users continue cultivating, they register their feedback in the system. They input their successes and problems in cultivation via their smartphones and send this to the server. The input is the user's feedback, and the output is the feedback data stored on the server.

[1255] Step 11:

[1256] The server improves the analytical accuracy of the AI ​​model based on the feedback. Feedback data from users is incorporated into the AI ​​model, and by re-learning, a more accurate cultivation plan is generated. The input is the user's feedback data, and the output is an improved AI model.

[1257] 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.

[1258] 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.

[1259] 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.

[1260] [Fourth embodiment]

[1261] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1262] 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.

[1263] 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).

[1264] 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.

[1265] 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.

[1266] 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).

[1267] 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. 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.

[1268] 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.

[1269] 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.

[1270] 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.

[1271] 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.

[1272] 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.

[1273] 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."

[1274] This invention is a system that helps beginners who are starting a home vegetable garden or people with limited knowledge of weather and cultivation to create effective cultivation plans. This system is composed of three main components: a server, a terminal, and a user.

[1275] server

[1276] The server is responsible for data processing and management for the entire system. It obtains the latest weather information from an external weather data API, filters it by region, and stores it in a database. It also obtains the cultivation conditions for the plants selected by the user from the database and inputs them into the AI ​​model along with the weather data for analysis. It generates an optimal cultivation plan based on the analysis results and saves this plan by associating it with the user's account. It also has a function to retrain the AI ​​model based on user feedback to improve analysis accuracy.

[1277] Terminal

[1278] The terminal functions as an interface with the user. Users access the system through their smartphone, PC, or other device and enter the location of their home garden and the type of plants they wish to cultivate. The terminal displays the cultivation plan and management method generated by the server and sends notifications to the user. Important information, such as sudden changes in the weather, is also immediately communicated to the user via push notifications. User feedback is also sent to the server via the terminal.

[1279] User

[1280] Users are the operators of home gardens and the ultimate users of this system. They access the system using their own devices and set up their home gardens. By providing optimal cultivation plans based on weather forecasts and cultivation conditions, even beginners can easily manage their home gardens effectively. Users actually cultivate plants and learn how to manage them and how to deal with unexpected weather conditions. Furthermore, they can register successes and problems in cultivation as feedback in the system, contributing to improving the system's accuracy.

[1281] Specific examples

[1282] For example, consider the case of a beginner home gardener named Tanaka who is trying to grow tomatoes in his garden.

[1283] 1. The user (Mr. Tanaka) logs in to the system using his / her smartphone. He / she enters his / her address (Shinjuku-ku, Tokyo) and the plant he / she wants to grow (tomato).

[1284] 2. The server retrieves the latest weather forecast from an external weather data API, filters the weather data for the area where Tanaka's vegetable garden is located, and saves it in a database.

[1285] 3. The server retrieves tomato cultivation conditions (optimum temperature: 20-30°C, sunlight duration: 8 hours or more, etc.) from the database and inputs them into the AI ​​model along with weather data.

[1286] 4. Based on weather data from Tanaka's garden and the tomato growing conditions, the AI ​​model calculates the optimal sowing and harvest times, and also suggests measures to deal with sudden weather changes (e.g., using shade nets).

[1287] 5. The server saves the generated cultivation plan to Tanaka's account and displays it on his device. Important information is also sent to Tanaka's smartphone as a push notification.

[1288] 6. The user (Mr. Tanaka) sows tomato seeds according to the proposed cultivation plan and performs the necessary maintenance procedures. If the use of shade nets is required during cultivation, he receives a notification on his device and takes immediate action.

[1289] 7. After harvesting, Tanaka will register feedback in the system, which will improve the accuracy of future cultivation plans.

[1290] As mentioned above, this system is designed to enable even beginners to effectively manage their home garden and enjoy growing a variety of vegetables.

[1291] The processing flow will be explained below.

[1292] Step 1:

[1293] Users log in to the system using their smartphone or computer, enter the address or GPS information of the location where they want to set up their vegetable garden, and select the type of plants they want to grow.

[1294] Step 2:

[1295] The device sends the input information to the server, including the user's location and the type of plant selected.

[1296] Step 3:

[1297] The server accesses an external weather data API to obtain the latest weather data (temperature, precipitation, humidity, wind speed, sunshine hours, etc.) for the specified area.

[1298] Step 4:

[1299] The server saves the weather data it obtains, filters it by region, and stores it in a database.

[1300] Step 5:

[1301] The server retrieves the cultivation conditions (optimum temperature, sunlight hours, amount of water, etc.) for the plant (e.g., tomato) selected by the user from the database.

[1302] Step 6:

[1303] The weather data and plant cultivation conditions acquired by the server are input into the AI ​​model, which analyzes this data and calculates the following:

[1304] Optimal sowing and harvesting times

[1305] Daily care methods (e.g., irrigation timing, fertilizer application timing)

[1306] Step 7:

[1307] The server generates a specific cultivation plan based on the analysis results, which includes the necessary management procedures and precautions for the user.

[1308] Step 8:

[1309] The server stores the generated cultivation plan in association with the user's account.

[1310] Step 9:

[1311] When the user opens the app, the device displays the latest cultivation plan and notifies them of important updates and precautions in real time.

[1312] Step 10:

[1313] The user cultivates plants according to the proposed cultivation plan and carries out the management procedures. Problems and successes that arise during the actual cultivation process are registered in the system as feedback.

[1314] Step 11:

[1315] The server receives feedback from users and uses it to retrain the AI ​​model to improve analysis accuracy.

[1316] Step 12:

[1317] The server periodically obtains new weather data, updates cultivation plans in real time based on this data, and proposes countermeasures for sudden weather changes.

[1318] Example 1

[1319] 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."

[1320] Providing effective cultivation plans to beginner home gardeners and those with limited knowledge of weather and cultivation is a challenge. Current systems cannot provide appropriate measures in real time based on sudden changes in weather or individual plant characteristics, and lack a way to use user feedback to improve the system's accuracy. As a result, it is difficult to significantly reduce the time it takes beginners to find appropriate cultivation methods.

[1321] 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.

[1322] In this invention, the server includes: means for inputting location information and information about the plant being cultivated from the user; means for collecting weather data from an external weather data API; means for acquiring plant cultivation conditions from a database; means for inputting the collected weather data and cultivation conditions into an AI model and analyzing them; means for generating an optimal cultivation plan based on the analysis results; means for saving the generated cultivation plan in the user's account; means for displaying the generated cultivation plan on the user's device; means for notifying the user of the generated cultivation plan via push notification; means for collecting feedback from the user; and means for retraining the AI ​​model based on the collected feedback to improve analysis accuracy. This allows the user to quickly respond to changes in the weather and implement an effective cultivation plan. Furthermore, by utilizing feedback, the system's analysis accuracy can be continuously improved, allowing for the provision of more accurate cultivation plans.

[1323] "User" refers to a person who uses the system to manage a home garden.

[1324] "Location information" refers to information about the area where the user's home garden is located.

[1325] "Information about the plant to be cultivated" refers to information about the type and characteristics of the plant that the user is trying to cultivate.

[1326] "External Weather Data API" refers to an external system interface that provides weather information such as weather forecasts.

[1327] "Weather Data" refers to information regarding weather conditions such as temperature, precipitation, wind speed, and humidity.

[1328] "Plant growing conditions" refers to the environmental conditions necessary for a particular plant to grow.

[1329] A "database" is a system that organizes and stores information so that it can be retrieved and used when needed.

[1330] An "artificial intelligence model" refers to a machine learning algorithm that analyzes large amounts of data to generate useful information.

[1331] "Analysis" refers to the process of deriving specific results or conclusions from collected data.

[1332] A "cultivation plan" refers to specific procedures and schedules for cultivating plants.

[1333] "User account" refers to a mechanism for identifying an individual user within the system and managing information about that user.

[1334] "Terminal" refers to a device (e.g., a smartphone or PC) through which a user interfaces with the system.

[1335] "Push notifications" refers to a feature that notifies users of new information or important messages in real time.

[1336] "Feedback" refers to a user reporting to the system the successes and problems they experience while using the system.

[1337] "Retraining" refers to the process of improving the performance of an artificial intelligence model using new data collected.

[1338] This invention is a system that helps beginners who are starting a home vegetable garden or people with limited knowledge of weather and cultivation to create effective cultivation plans. The system is composed of three main components: a server, a terminal, and a user.

[1339] server

[1340] The server is responsible for data processing and management of the entire system. The server obtains the latest weather information using an external weather data API (e.g., OpenWeatherAPI). The obtained weather data is filtered by region and stored in a database (e.g., MySQL).

[1341] Next, the server retrieves the plant's cultivation conditions from a database. For example, for tomatoes, these include information such as the optimum temperature, sunlight hours, and humidity. This data, along with weather data, is input into an artificial intelligence model (e.g., TensorFlow) for analysis. The AI ​​model generates an optimal cultivation plan based on the collected data. This cultivation plan includes information such as the sowing time, harvest time, and weather-related measures (e.g., the use of shade nets).

[1342] The generated cultivation plan is stored in the user's account, and is then sent from the server to the user's terminal so that it can be displayed on the terminal.

[1343] Furthermore, the server has the function of collecting feedback from users. The feedback provided by users to the system (e.g., actual harvest results, effectiveness of management methods, etc.) is used to retrain the AI ​​model, thereby improving the accuracy of analysis in the future.

[1344] Terminal

[1345] The terminal functions as an interface with the user. The terminal provides an interface for the user to input information about the location of the home garden and the plants they want to cultivate. The input data is sent from the terminal to the server.

[1346] The generated cultivation plan is displayed to the user via their device. Important information, such as sudden changes in the weather, is also immediately communicated to the user via push notifications. Feedback obtained from users as they use the system is also sent to the server via their device.

[1347] User

[1348] The user is the manager of the home garden and the final user of the system. The user accesses the system using their own device and sets up the home garden. They effectively manage the home garden according to the optimal cultivation plan based on the weather forecast and cultivation conditions provided. Through actual cultivation, the user follows the management procedures suggested by the system and performs the necessary tasks.

[1349] Specific examples

[1350] For example, consider a user who wants to grow tomatoes in his or her garden.

[1351] 1. Users log in to the system using their smartphone and enter information about their address and the plant they want to grow.

[1352] 2. The server retrieves the latest weather forecast from an external weather data API, filters the weather data for the area where the user's home garden is located, and saves it in a database.

[1353] 3. The server retrieves the tomato cultivation conditions from the database and inputs them into the AI ​​model along with weather data.

[1354] 4. The AI ​​model will suggest optimal sowing and harvest times, as well as strategies for dealing with weather fluctuations.

[1355] 5. The server saves the generated cultivation plan in the user's account, and the device displays it. Important information is also immediately communicated to the user via push notifications.

[1356] Prompt Sentence Examples

[1357] "What steps should I take to create a system for beginners who want to start a home vegetable garden?"

[1358] "How do I create the best growing plan for tomatoes in a particular area?"

[1359] This system allows even beginners to effectively manage their home gardens and enjoy growing a variety of plants.

[1360] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1361] Step 1:

[1362] A user logs into the system using their own terminal and inputs the location information of their home garden and information about the plants they are growing.

[1363] Input: User's location information and information about the plant they want to grow (e.g., Shinjuku-ku, Tokyo, tomato)

[1364] Output: The data format in which the input information is sent to the server (e.g., JSON format)

[1365] Specific actions: The user opens the smartphone app, enters the address and the tomato, and presses the send button.

[1366] Step 2:

[1367] The server sends a request to an external weather data API to retrieve the latest weather data and store it in a database.

[1368] Input: User's location information (e.g., Shinjuku-ku, Tokyo)

[1369] Output: Weather data for the specified area is saved in a database (e.g. temperature, precipitation, humidity)

[1370] Specific operation: The server generates a URL like "https: / / api.openweathermap.org / data / 2.5 / weather?q=Shinjuku&appid=YOUR_API_KEY" and sends a request to the API. The retrieved weather data is stored in a MySQL database.

[1371] Step 3:

[1372] The server retrieves the cultivation conditions for the plants to be cultivated from the database, and inputs them along with weather data into the AI ​​model for analysis.

[1373] Input: User-entered plant information (e.g., tomatoes) and stored weather data

[1374] Output: Analysis results from the AI ​​model (e.g., optimal sowing time, harvest time, and measures to deal with weather fluctuations)

[1375] How it works: The server retrieves data by executing the SQL query "SELECT FROM crop_conditions WHERE crop_name = 'tomato'". The retrieved weather data and growing conditions are then input into the TensorFlow model for analysis.

[1376] Step 4:

[1377] The server generates an optimal cultivation plan based on the analysis results obtained from the AI ​​model and saves it in the user's account.

[1378] Input: Analysis results by AI model

[1379] Output: Generated cultivation plan (e.g. sowing in early April, harvesting in mid-July, use of shade net)

[1380] Specific operation: The server generates a cultivation plan based on the analysis results and saves it by executing the SQL query "INSERT INTO user_plans (user_id, plan_data) VALUES (user_id, 'Cultivation plan here')".

[1381] Step 5:

[1382] The terminal displays the generated cultivation plan to the user and, if necessary, communicates important information via push notifications.

[1383] Input: Generated cultivation plan and other important information (e.g., measures to deal with sudden weather changes)

[1384] Output: Cultivation plan and push notification displayed on the user's smartphone screen

[1385] Specific operation: The terminal application displays the cultivation plan obtained from the server on the screen and sends push notifications as necessary.

[1386] Step 6:

[1387] The user manages their home garden according to the cultivation plan proposed by the system and provides feedback to the system.

[1388] Input: User's cultivation record and observations (e.g., actual yield, effectiveness of management methods)

[1389] Output: Feedback information is sent to the server and stored in the system.

[1390] Specific operations: The user enters the post-harvest results and any observations made during management through the application and presses the send button.

[1391] Step 7:

[1392] The server uses user feedback to retrain the AI ​​model and improve the accuracy of future analyses.

[1393] Input: User feedback data

[1394] Output: Retrained AI model

[1395] What it does: The server adds new feedback data to the dataset and retrains the AI ​​model using TensorFlow. The retrained model is saved and used for the next analysis.

[1396] (Application example 1)

[1397] 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."

[1398] Creating an effective gardening plan is a significant challenge for beginners and those with limited knowledge of weather and cultivation. Furthermore, physical stores often lack support for selecting appropriate gardening supplies, leaving users unsure of what to choose. Furthermore, insufficient information is provided to quickly respond to changing weather data, potentially reducing the success rate of cultivation. Therefore, the present invention aims to solve these problems and provide a system that supports even beginners in effectively managing their gardens.

[1399] 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.

[1400] In this invention, the server includes means for collecting weather data, means for acquiring plant cultivation conditions, means for analyzing the collected weather data and cultivation conditions, means for generating an optimal cultivation plan based on the analysis results, means for displaying the generated cultivation plan, means for notifying the user of the generated cultivation plan, means for receiving feedback from the user, means for improving the accuracy of the analysis based on the feedback, means for acquiring the user's location information in the physical store and navigating to the appropriate product shelves, means for suggesting optimal products based on the user's cultivation history and weather data, and means for displaying detailed information about the suggested products and providing on-site instructions. This allows even beginners to create an appropriate cultivation plan and easily select the necessary home gardening supplies in the physical store. Furthermore, it is possible to quickly respond to changing weather data in real time, thereby improving the success rate of cultivation.

[1401] "Means for collecting weather data" refers to devices or systems that obtain the latest weather information from external weather data APIs, filter it by region, and store it in a database.

[1402] The "means for acquiring the cultivation conditions of the plant" refers to a mechanism for acquiring the cultivation conditions of the plant selected by the user from the database and analyzing this information.

[1403] "Means for analyzing collected weather data and cultivation conditions" refers to AI models and algorithms used to conduct analysis based on weather data and plant cultivation conditions.

[1404] The "means for generating an optimal cultivation plan" refers to a system for creating an optimal cultivation plan for the user based on the analysis results.

[1405] The "means for displaying the generated cultivation plan" refers to an interface or tool for displaying the generated cultivation plan on the user's terminal.

[1406] "Means for notifying the user of the generated cultivation plan" refers to a mechanism for immediately transmitting the cultivation plan and important information to the user via push notifications, etc.

[1407] "Means for receiving feedback from users" refers to interfaces or functions that allow users to send feedback to the system.

[1408] "Means for improving analysis accuracy" refers to a system for retraining AI models based on user feedback to improve the accuracy of analysis results.

[1409] "Means for obtaining user location information within a physical store" refers to location information technology or systems for identifying the user's current location within a physical store.

[1410] "A means for navigating shelves of appropriate products" refers to a navigation system that guides users to shelves of products they are looking for in a physical store.

[1411] "Means for suggesting optimal products based on the user's cultivation history and weather data" refers to AI models and algorithms that recommend appropriate home gardening products based on the user's past cultivation history and current weather data.

[1412] The "means for displaying detailed information about the proposed product" refers to a technology for displaying detailed information about the proposed product in real time through the user's smart glasses or the like.

[1413] The "means for providing guidance on how to use the product" refers to a system that displays and provides guidance on how to use the product and precautions for handling the product that the user is considering purchasing.

[1414] The present invention is a system that helps beginners and users with limited knowledge of home gardening to create effective cultivation plans. This system is mainly composed of a server, a terminal, and a user, and implements the invention according to the following steps.

[1415] server

[1416] The server is responsible for data processing and management of the entire system. Specifically, it performs the following processes:

[1417] 1. Weather data collection: Obtain the latest weather information from an external weather data API, filter it by region, and store it in a database.

[1418] 2. Obtaining cultivation conditions: Obtain the cultivation conditions (optimum temperature, required sunlight hours, etc.) of the plant selected by the user from the database.

[1419] 3. Data analysis: The collected weather data and plant cultivation conditions are input into an AI model for analysis, and an optimal cultivation plan is generated. This analysis uses artificial intelligence to analyze the weather data and cultivation conditions.

[1420] 4. Generate cultivation plan: Generate an optimal cultivation plan for the user based on the analysis results and save this plan in association with the user's account.

[1421] 5. Leveraging feedback: Retraining the AI ​​model based on user feedback to improve analysis accuracy.

[1422] Terminal

[1423] The terminal functions as an interface with the user and provides the following functions:

[1424] 1. Setup and input: Users access the system via their smartphone or computer and input the location of their home garden and the type of plants they wish to grow.

[1425] 2. Information display: The system displays the cultivation plan and management method generated by the server and sends notifications to the user. Important information, such as sudden changes in the weather, is also immediately communicated to the user via push notifications.

[1426] 3. Product Recommendations: The system acquires the user's location information in a physical store, navigates to the appropriate product shelves, and recommends the most suitable product based on the user's cultivation history and weather data, and displays detailed product information.

[1427] User

[1428] The user is the operator of the home garden and the final user of this system. The user will use the system as follows:

[1429] 1. Setup and operation: Access the system using your smartphone or computer and set up your home garden.

[1430] 2. Implementing a cultivation plan: By providing optimal cultivation plans based on weather forecasts and growing conditions, even beginners can easily manage their home vegetable gardens effectively.

[1431] 3. Product Selection: Smart glasses can be used in brick-and-mortar stores to easily find and purchase the right home gardening products.

[1432] 4. Providing feedback: Registering successes and problems in cultivation as feedback to the system and contributing to improving the system's accuracy.

[1433] Examples and prompts

[1434] For example, if a user wants to grow tomatoes in their garden at home, they can use the system as follows:

[1435] 1. User: Logs into the system using a smartphone and enters his / her address and the plant (tomato) he / she wants to grow.

[1436] 2. Server: Obtains the latest weather forecast from an external weather data API, filters the weather data for the area where the user's home garden is located, and stores it in a database.

[1437] 3. Server: Retrieves tomato cultivation conditions from the database and inputs them into the AI ​​model along with weather data.

[1438] 4. Analysis: Based on the weather data of the user's garden and the growing conditions of the tomatoes, the AI ​​model calculates the optimal sowing and harvesting times, and also suggests countermeasures for sudden weather changes.

[1439] 5. Server: The generated cultivation plan is saved in the user's account, displayed on the device, and important information is sent to the user's smartphone as a push notification.

[1440] 6. User: Sows tomato seeds according to the proposed cultivation plan and performs the necessary maintenance procedures. When visiting a physical store, the user uses the smart glasses to select the necessary home gardening supplies.

[1441] 7. Feedback: After harvesting, users can provide feedback to the system, improving the accuracy of future cultivation plans.

[1442] Example prompt sentence:

[1443] "Recommend the best tools and plants for your home garden based on the user's growing history and current weather data. Plant: Tomato. Growing conditions: Optimal temperature 20-30°C, 8+ hours of sunshine. Current weather data: Temperature 25°C, 5 hours of sunshine."

[1444] "When a user walks towards a shelf in a physical store, compare their current location (35.6895, 139.6917) with the shelf's location (35.6894, 139.6916) and let them know if they're approaching."

[1445] In this way, the system makes it easy for even beginners to obtain advanced cultivation information and quickly and accurately select the necessary home gardening supplies in a physical store.

[1446] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1447] Step 1:

[1448] The server retrieves the latest weather information from an external weather data API. The input is weather data from the API, and the output is weather data filtered by region. Specifically, it sends an HTTP request to the API endpoint, receives a JSON response, parses it, and stores it in a database.

[1449] Step 2:

[1450] The server retrieves the cultivation conditions of the plant selected by the user from the database. The input is the information about the plant selected by the user, and the output is the cultivation conditions of the corresponding plant. Specifically, the server executes a database query based on the plant information entered by the user to retrieve the cultivation conditions.

[1451] Step 3:

[1452] The server inputs weather data and plant cultivation conditions into the AI ​​model for analysis. The input is weather data and cultivation conditions, and the output is an optimal cultivation plan. Specifically, the weather data and cultivation conditions are input into the generative AI model, and the model calculates the optimal cultivation plan.

[1453] Step 4:

[1454] The server associates the generated cultivation plan with the user's account and saves it. The input is the generated cultivation plan, and the output is the cultivation plan saved in the user's account. Specifically, the cultivation plan data is linked to the user's account and saved in the database.

[1455] Step 5:

[1456] The terminal displays the cultivation plan and management method generated from the server. The input is the cultivation plan from the server, and the output is the cultivation plan displayed on the terminal's display. Specifically, the terminal receives data from the server and displays it on the user interface.

[1457] Step 6:

[1458] The device immediately notifies the user of important information, such as sudden changes in the weather, via push notifications. The input is notification data from the server, and the output is a push notification sent to the user. Specifically, the device's notification function is used to immediately convey emergency information to the user.

[1459] Step 7:

[1460] The device acquires the user's location information within the physical store and navigates to the appropriate product shelves. The input is the user's location information, and the output is navigation information. Specifically, it uses the in-store location information system to identify the user's current location, calculates the optimal route, and guides the user there.

[1461] Step 8:

[1462] The device suggests optimal products based on the user's cultivation history and weather data. The input is the user's cultivation history and weather data, and the output is a list of suggested products. Specifically, the AI ​​model selects products based on this data and displays the results to the user.

[1463] Step 9:

[1464] The terminal displays detailed information about the suggested product and provides on-the-spot instructions on how to handle it. The input is product information, and the output is the display of detailed information. Specifically, the terminal displays product information and provides additional instructions to the user.

[1465] Step 10:

[1466] The server retrains the AI ​​model based on user feedback to improve analysis accuracy. The input is user feedback, and the output is the retrained AI model. Specifically, the server collects feedback data, updates the training dataset for the AI ​​model, and retrains it.

[1467] 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.

[1468] This invention is a system that helps beginners who are starting home gardening or people with limited knowledge of weather and cultivation to create effective cultivation plans. This system combines weather data, cultivation conditions, and user emotional information to provide a more human-friendly home gardening experience. This system is composed of a server, terminals, users, and an emotion engine.

[1469] server

[1470] The server is responsible for data processing and management for the entire system. It obtains the latest weather information from an external weather data API, filters it by region, and stores it in a database. It also obtains the cultivation conditions for the plants selected by the user from the database and inputs them into an AI model along with the weather data for analysis. Based on the analysis results, it generates an optimal cultivation plan and saves it in association with the user's account. It also has a function that uses an emotion engine that recognizes the user's emotions to evaluate the user's emotional state and stress level and adjust the cultivation plan accordingly.

[1471] Terminal

[1472] The device functions as an interface with the user. Users access the system through devices such as smartphones or PCs and input the location of their home garden and the type of plants they wish to cultivate. The device displays the cultivation plan and management method generated by the server and sends notifications to the user. It also has the function of collecting emotional information from the user's facial expressions and voice and sending it to the server. Important information and notifications regarding sudden weather changes are immediately communicated to the user via push notifications.

[1473] User

[1474] The user is the manager of a home garden and the ultimate user of this system. The user accesses the system using their own device and sets up the home garden. By providing an optimal cultivation plan based on weather forecasts and cultivation conditions, even beginners can easily manage their home garden effectively. The user actually cultivates plants and checks management methods and countermeasures for sudden weather events. Furthermore, the user can register successes and problems in cultivation as feedback in the system, contributing to improving the system's accuracy. In addition, suggestions and reminders are displayed based on the user's emotional state, providing a better experience.

[1475] Emotion Engine

[1476] The emotion engine is an engine for collecting and analyzing the user's emotional information. This allows the system to recognize the user's stress level and motivation, and adjust cultivation plans and management methods according to the user's emotional state. For example, if the system determines that the user is under high stress, it will suggest work reductions and send encouraging messages. It will also display reminders to boost the user's motivation (for example, "Look forward to today's harvest!").

[1477] Specific examples

[1478] For example, consider the case of a beginner home gardener named Suzuki who is trying to grow tomatoes in his garden.

[1479] 1. The user (Mr. Suzuki) logs in to the system using his / her smartphone. He / she enters his / her address (Shinjuku-ku, Tokyo) and the plant he / she wants to grow (tomato).

[1480] 2. The terminal sends the entered information to the server.

[1481] 3. The server retrieves the latest weather forecast from an external weather data API, filters the weather data for the area where Suzuki's vegetable garden is located, and saves it.

[1482] 4. The server retrieves the tomato cultivation conditions (optimum temperature: 20-30°C, sunlight duration: 8 hours or more) from the database and inputs them into the AI ​​model along with weather data.

[1483] 5. An AI model analyzes this data to calculate optimal sowing and harvest times, and also suggests countermeasures for sudden weather changes.

[1484] 6. The cultivation plan generated by the server is saved in Suzuki's account and displayed on his device. Important information is also sent to Suzuki's smartphone via push notification.

[1485] 7. The user (Mr. Suzuki) follows the proposed cultivation plan, sows tomato seeds, and performs the necessary management procedures.

[1486] 8. The device collects emotional information from Suzuki's facial expressions and voice and sends this to the server.

[1487] 9. The server uses an emotion engine to assess Suzuki's stress level and motivation, and generates and displays suggestions and messages appropriate to the situation.

[1488] 10. As Suzuki continues to cultivate, he will register his feedback in the system, and the server will use this data to improve the analytical accuracy of the AI ​​model.

[1489] As described above, this system is designed to enable even beginners to effectively manage their home gardens and enjoy growing a wide variety of vegetables. Furthermore, by taking into account the user's emotional state, it provides a better experience and supports their motivation to continue growing.

[1490] The processing flow will be explained below.

[1491] Step 1:

[1492] Users log in to the system using their smartphone or computer, enter the address or GPS information of the location where they want to set up their vegetable garden, and select the type of plants they want to grow.

[1493] Step 2:

[1494] The terminal sends the entered user information (location, type of plant) to the server.

[1495] Step 3:

[1496] The server accesses an external weather data API to obtain the latest weather data (temperature, precipitation, humidity, wind speed, sunshine hours, etc.) for the specified area.

[1497] Step 4:

[1498] The server saves the weather data it obtains, filters it by region, and stores it in a database.

[1499] Step 5:

[1500] The server retrieves the cultivation conditions (optimum temperature range, required sunlight, amount of water, etc.) of the plant (e.g., tomato) selected by the user from the database.

[1501] Step 6:

[1502] The server inputs weather data and plant cultivation condition data collected into the AI ​​model, which then analyzes the optimal cultivation plan, calculating the optimal sowing time, harvest time, and daily maintenance methods (e.g., when to irrigate and when to use fertilizer).

[1503] Step 7:

[1504] The server generates a specific cultivation plan based on the analysis results and stores this plan in association with the user's account.

[1505] Step 8:

[1506] The server transmits the generated cultivation plan information to the terminal.

[1507] Step 9:

[1508] When the user opens the app, the device displays the latest cultivation plan, and push notifications are sent to users immediately with important updates and suggestions for dealing with sudden weather changes.

[1509] Step 10:

[1510] The user cultivates plants according to the proposed cultivation plan, performs daily management procedures, and registers problems and successes that occur during the actual cultivation process as feedback to the system.

[1511] Step 11:

[1512] The device collects emotional information from the user's facial expressions and voice and sends it to the server. This emotional information includes the user's stress level and motivation state.

[1513] Step 12:

[1514] The server uses an emotion engine to analyze the user's emotional state. If the stress level is high, it will suggest work reduction (e.g., automating irrigation work), and if motivation is low, it will generate encouraging messages and appropriate reminders (e.g., "Look forward to today's harvest!").

[1515] Step 13:

[1516] The server sends suggestions and messages generated based on the analysis results of the emotion engine to the terminal, which then displays them to the user.

[1517] Step 14:

[1518] The server retrains the AI ​​model based on user feedback and emotional information to improve analysis accuracy. This process is continuous and is reflected in future cultivation plans.

[1519] Step 15:

[1520] The server periodically retrieves new weather data and constantly updates the cultivation plan in response to changes in the weather. It also proposes appropriate countermeasures in real time in response to sudden weather changes.

[1521] Example 2

[1522] 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."

[1523] Conventional home gardening support systems can create cultivation plans based on weather data and cultivation conditions, but they do not take into account the user's emotional state when making suggestions or adjustments, which can increase the user's mental stress. Furthermore, the system lacks the functionality to receive feedback and improve analysis accuracy, making it difficult to use effectively. Furthermore, in many cases, the system does not provide measures to respond to real-time weather changes, making it difficult to respond to sudden weather fluctuations.

[1524] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting weather data, means for acquiring plant cultivation conditions, means for analyzing the collected weather data and cultivation conditions, means for generating an optimal cultivation plan based on the analysis results, means for displaying the generated cultivation plan, means for notifying the user of the generated cultivation plan, means for collecting user emotion information, means for adjusting the cultivation plan based on the emotion information, means for receiving feedback from the user, and means for improving analysis accuracy based on the feedback. This makes it possible to adjust the cultivation plan appropriately according to the user's emotional state and improve the accuracy of the system by utilizing the feedback.

[1525] "Weather Data" refers collectively to information regarding past, present, and future weather conditions in a particular geographic area.

[1526] "Plant growing conditions" refers to information about environmental factors such as temperature, humidity, and hours of sunlight required for optimal growth of a particular plant.

[1527] "Means for analysis" refers to methods and devices for combining and evaluating weather data and plant cultivation conditions to create optimal cultivation plans.

[1528] A "cultivation plan" is a detailed description of the actions and timing required to effectively grow a particular plant.

[1529] The "display means" refers to a method or device that allows a user to visually confirm the generated cultivation plan and related information.

[1530] The "notification means" refers to a method or device for instantly notifying the user of the generated cultivation plan or important information.

[1531] "Emotion information" is data relating to changes in the user's mental state and emotions, and includes information obtained from facial expressions and voice.

[1532] "Feedback" refers to information based on evaluations, opinions, and experiences that users provide to the system.

[1533] "Means for improving analytical accuracy" refers to methods or devices that use collected feedback to improve the performance of a system or AI model.

[1534] The present invention is a system that helps beginners who are starting a home garden or users with limited knowledge of weather and cultivation to create effective cultivation plans. This system combines weather data, cultivation conditions, and user emotional information to provide a more human-friendly home gardening experience. Specific embodiments of the present invention are described below.

[1535] Server Operation

[1536] The server performs the following actions:

[1537] 1. Weather data collection: The server uses external weather data APIs (e.g., OpenWeatherMap or Weather.com) to obtain the latest weather information. The data is filtered by region and stored in a database.

[1538] 2. Obtaining cultivation conditions: The server obtains the cultivation conditions (optimum temperature, humidity, sunlight hours, etc.) for a specific plant (e.g., tomato) from the database.

[1539] 3. Data analysis: Collected weather data and cultivation conditions are input into an AI model (using TensorFlow or PyTorch, for example) for analysis, which then calculates the optimal sowing and harvest times, as well as management methods.

[1540] 4. Emotional information analysis: The server uses an emotion engine (e.g., Microsoft Azure's Text Analytics API or IBM Watson's Tone Analyzer) to analyze the emotional information collected from the user and evaluate the user's stress level and motivation. Based on this information, the cultivation plan is adjusted accordingly.

[1541] Device behavior

[1542] The terminal provides the following features:

[1543] 1. User input: The user accesses the system using a device such as a smartphone or PC and enters the location of their home garden and the type of plants they wish to grow.

[1544] 2. Display of cultivation plan: Displays the cultivation plan and management method generated from the server. Important information is immediately notified to the user via push notification.

[1545] 3. Collecting emotional information: The device collects the user's facial expressions and voice through the smartphone's camera and microphone, and transmits the emotional information to the server.

[1546] User operations

[1547] Users use the system in the following ways:

[1548] 1. Setting up a home garden: Users log in to the system using their own devices and enter the location of their home garden and the type of plants they want to grow.

[1549] 2. Implementing the cultivation plan: By providing the optimal cultivation plan based on weather forecasts and cultivation conditions, the user can actually manage their home garden and cultivate plants. The user will proceed with the work by following the provided management method.

[1550] 3. Providing feedback: By providing feedback to the system about problems and successes experienced during cultivation, the system's accuracy can be improved. This allows the server to improve the analytical accuracy of the AI ​​model.

[1551] Specific examples

[1552] For example, consider a case where a user who is a beginner at home gardening is trying to grow tomatoes in his or her garden.

[1553] Specifically, follow these steps:

[1554] 1. A user logs into the system using their smartphone, inputting their address (e.g., a specific area) and the plant they want to grow (tomatoes).

[1555] 2. The server retrieves the latest weather forecast from an external weather data API, filters the weather data for the area where the home garden is located, and saves it.

[1556] 3. The server retrieves tomato cultivation conditions (e.g., optimal temperature, hours of sunlight) from the database and inputs them into the AI ​​model for analysis.

[1557] 4. The server generates a cultivation plan (e.g., optimal sowing and harvesting times) and saves it in the user's account, which is then displayed on the device. Important information is also immediately sent via push notifications.

[1558] 5. The device collects emotional information from the user's facial expressions and voice and sends it to the server.

[1559] 6. The server uses an emotion engine to analyze the emotional information, and if the user is feeling stressed, it suggests appropriate measures or generates an encouraging message.

[1560] Prompt Sentence Examples

[1561] Here are some examples of prompts for an AI model:

[1562] Describe the process for the following home garden support system:

[1563] 1. A user accesses the system using a smartphone to create a tomato cultivation plan.

[1564] 2. The server retrieves the latest weather information from an external weather data API and filters it by region.

[1565] 3. The server inputs weather information and cultivation conditions into the AI ​​model, analyzes them, and generates an optimal cultivation plan.

[1566] 4. The device collects the user's emotional information (facial expressions and voice) and sends it to the server.

[1567] 5. The emotion engine analyzes the user's emotional state, and the server adjusts the cultivation plan based on the feedback.

[1568] The detailed prompts make each step of the system easy to understand and are designed to help even beginners manage their vegetable gardens efficiently and receive support tailored to their emotional state.

[1569] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1570] Step 1: Collecting User Input Data

[1571] Users log in to the system using their smartphone or computer and enter the location of their garden and the type of plant they want to grow, including the specific address and plant name.

[1572] Input: User's address (e.g., specific area), plant they want to grow (e.g., tomatoes)

[1573] Output: The entered data is sent from the terminal to the server.

[1574] Specific operation: The user enters "Shinjuku-ku, Tokyo" into their smartphone and selects "tomato" as the plant they want to grow.

[1575] Step 2: Obtaining and filtering weather data

[1576] The server uses external weather data APIs (e.g., OpenWeatherMap or Weather.com) to retrieve the latest weather information, filter it by region, and store it in a database.

[1577] Input: User's location

[1578] Output: Weather data filtered by region is saved in a database.

[1579] Specific operation: The server calls the OpenWeatherMap API to obtain the latest weather data for Shinjuku Ward, Tokyo, filters it, and saves it in a database.

[1580] Step 3: Obtaining cultivation conditions

[1581] The server retrieves the cultivation conditions (optimum temperature, humidity, sunlight hours, etc.) for a particular plant (e.g., tomato) from the database.

[1582] Input: User-selected plant species

[1583] Output: Optimal growing conditions for plants (e.g., optimal temperature, humidity, and sunlight hours for tomatoes)

[1584] Specific operation: The server retrieves the cultivation conditions for "tomatoes" from the database and prepares the information.

[1585] Step 4: Analyze the data

[1586] The server inputs the collected weather data and cultivation conditions into an AI model (using TensorFlow or PyTorch, for example) for analysis, which then calculates the optimal sowing and harvest times, as well as management methods.

[1587] Input: Weather data, cultivation condition data

[1588] Output: Optimal cultivation plan (e.g. sowing time, harvest time, management method)

[1589] Specific operation: The server inputs weather data and tomato cultivation conditions into the AI ​​model and generates an optimal cultivation plan.

[1590] Step 5: Notification and display of cultivation plans

[1591] The server saves the generated cultivation plan in the user's account and notifies the user via the terminal.

[1592] Input: Generated cultivation plan

[1593] Output: Cultivation plans saved in the user's account, notifications displayed on the device

[1594] Specific operation: The server saves the generated cultivation plan in the user's account and displays it on the device. Important events are immediately notified via push notifications.

[1595] Step 6: Collecting emotional information

[1596] The device collects the user's facial expressions and voice through the smartphone's camera and microphone, and transmits the emotional information to a server.

[1597] Input: User's facial expression data, voice data

[1598] Output: Collected emotion information is sent to the server.

[1599] Specific operation: The device collects the user's emotional information through the camera and microphone and sends it to the server.

[1600] Step 7: Analyze and regulate emotional information

[1601] The server uses an emotion engine to analyze the user's emotional state and stress level and adjusts the cultivation plan as needed.

[1602] Input: User's emotional information

[1603] Output: Coordinated cultivation plan, encouraging messages and suggestions

[1604] Specific operation: The server uses an emotion engine to analyze the emotion data, and if the user is feeling stressed, it generates and displays suggestions to reduce the work or encouraging messages.

[1605] Step 8: Improve the system through feedback

[1606] Users provide feedback to the system about any problems or successes they encounter during cultivation, and the server accumulates this information to improve the analytical accuracy of the AI ​​model.

[1607] Input: User feedback

[1608] Output: AI model with improved analytical accuracy

[1609] Specific behavior: The user inputs specific feedback into the system (e.g., "The tomato leaves have turned yellow"), which the server stores and uses to train the AI ​​model.

[1610] In this way, each processing step of the system supports the user's home gardening activities in multiple ways, making it easy for even beginners to manage their home garden. Furthermore, by providing appropriate support according to the user's emotional state, the system provides a better experience and supports the user's motivation to continue gardening.

[1611] (Application example 2)

[1612] 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."

[1613] Previous home gardening support systems focused on creating cultivation plans using weather data and information on cultivation conditions, but no systems took into account the user's emotional state or motivation. This often led to beginners and busy users giving up on cultivation midway. Furthermore, there was a lack of a way to effectively utilize fresh ingredients from home gardens for food delivery services.

[1614] 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 means for collecting weather data, means for acquiring plant cultivation conditions, means for analyzing the collected weather data and cultivation conditions, means for generating an optimal cultivation plan based on the analysis results, means for displaying the generated cultivation plan, means for notifying the user of the generated cultivation plan, means for analyzing the user's emotional state, means for displaying suggestions and reminders to the user based on the analyzed emotional state, means for receiving feedback from the user, and means for improving the analysis accuracy based on the feedback. This makes it possible to manage a home vegetable garden taking into account the user's emotional state and motivation, allowing for continuous cultivation without frustration and also enabling the user to effectively use fresh ingredients from the home vegetable garden for food delivery services and earn income.

[1615] "Weather data" refers to meteorological information such as current weather, forecasts, temperature, humidity, and precipitation obtained from APIs accessible via the Internet.

[1616] "Plant growing conditions" are the environmental conditions, such as temperature, humidity, hours of sunlight, and soil type and quality, that are necessary for a particular plant to grow optimally.

[1617] "Means for generating optimal cultivation plans based on analysis results" refers to a means for using collected weather data and plant cultivation conditions to create plans for optimal sowing times, harvesting times, management methods, etc. using AI models and algorithms.

[1618] The "means for displaying the generated cultivation plan" is a function for visually displaying the cultivation plan created on the server on the user's terminal (smartphone or PC).

[1619] The "means for notifying the user of the generated cultivation plan" is a function for notifying the user of the generated cultivation plan and any changes thereto via push notification, email, etc.

[1620] "Means for analyzing the user's emotional state" refers to AI models and algorithms that collect and analyze the user's facial and voice data to assess the user's stress level and motivation.

[1621] The "means for displaying suggestions and reminders to the user based on the analyzed emotional state" is a function that displays suggestions for reducing cultivation work, encouraging messages, and reminders according to the user's emotional state.

[1622] The "means for receiving feedback from users" is a function that allows users to input information about successes and problems with cultivation and send it to the system.

[1623] "Means for improving analytical accuracy based on feedback" refers to a learning process that incorporates feedback data from users into the AI ​​model to generate more accurate cultivation plans.

[1624] The present invention is a system that enables beginners and people with limited knowledge of weather and cultivation to effectively manage their home gardens while providing fresh ingredients through a food delivery service. The system includes a server, a terminal, a user, and an emotion engine.

[1625] server

[1626] The server is responsible for data processing and management for the entire system. It obtains the latest weather information from an external weather data API, filters it by region, and stores it in a database. The cultivation conditions for plants selected by the user are also stored in the database. The server uses this data for analysis and generates an optimal cultivation plan. It also has the function of evaluating the user's emotional state using an emotion engine and adjusting the cultivation plan and suggestions based on this. Specific software used includes the Python library requests, scikit-learn (for the AI ​​model), and OpenCV (for emotion analysis).

[1627] Terminal

[1628] The terminal functions as an interface with the user. The user accesses the system via their smartphone and inputs the location of their home garden and the type of plant they wish to cultivate. The terminal displays the cultivation plan and management method generated by the server and sends notifications to the user as needed. The terminal also collects the user's facial expressions and voice via a camera and microphone and sends this to the server. As a concrete example, if a user wants to cultivate tomatoes, they enter their address and the plant they wish to cultivate.

[1629] User

[1630] The user is the manager of a home garden and the ultimate user of this system. The user accesses the system to set up the garden and cultivates plants according to an optimal cultivation plan based on weather forecasts and cultivation conditions provided by the server. The user also provides emotional information during cultivation, which the system uses to provide optimal support. The user also provides feedback on successes and problems with cultivation, contributing to improving the system's analysis accuracy.

[1631] Emotion Engine

[1632] The emotion engine is an engine for collecting and analyzing the user's emotional information. It analyzes the user's stress level and motivation and adjusts cultivation plans and suggestions accordingly. For example, if the system determines that the user is under high stress, it will suggest ways to reduce work and send encouraging messages. It will also display reminder notifications such as "Look forward to today's harvest!" This helps users continue to enjoy their home garden.

[1633] Specific examples

[1634] For example, consider a user trying to grow tomatoes.

[1635] 1. Users log in to the system using their smartphone and enter their address and the plant they want to grow.

[1636] 2. The server retrieves weather forecasts from an external weather data API, filters the weather data for the user's location, and saves it.

[1637] 3. The server retrieves tomato cultivation conditions from the database and inputs them into the AI ​​model along with weather data for analysis.

[1638] 4. The AI ​​model calculates the optimal sowing and harvesting times and also suggests countermeasures for sudden weather changes.

[1639] 5. The server saves the generated cultivation plan to the user's account and displays it on the device. Important information is also sent to the user via push notifications.

[1640] 6. The user follows the proposed cultivation plan, sows tomato seeds and performs the necessary management procedures.

[1641] 7. The device collects emotional information from the user's facial expressions and voice and sends it to the server.

[1642] 8. The server uses an emotion engine to assess the user's stress level and motivation, and generates and displays suggestions and messages appropriate to the situation.

[1643] 9. As users continue to grow crops, they register feedback with the system, and the server uses this data to improve the analysis accuracy of the AI ​​model.

[1644] For example, a prompt sentence to be input to a generative AI model is:

[1645] "Draw the best plan for growing tomatoes in Shinjuku Ward. Please take current weather data into consideration."

[1646] There is.

[1647] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1648] Step 1:

[1649] The server accesses an external weather data API to collect the latest weather data for the specified region. In this step, the server obtains current weather information, forecast, temperature, humidity, precipitation, etc. from the weather data API (e.g., WeatherAPI), filters it by region, and stores it in a database. The input is region information, and the output is the filtered weather data.

[1650] Step 2:

[1651] The terminal receives input from the user. The user accesses the system via their smartphone and inputs the location of their home garden and the type of plant they wish to cultivate. The input is the address and type of plant, and the output is to send this information to the server.

[1652] Step 3:

[1653] The server retrieves the plant's cultivation conditions from a database. These conditions include temperature, humidity, sunlight hours, soil type, etc., for optimal plant growth. The input is the plant type, and the output is the cultivation conditions for that plant.

[1654] Step 4:

[1655] The server inputs weather data and cultivation conditions into an AI model to generate an optimal cultivation plan. The AI ​​model (for example, scikit-learn's RandomForestRegressor) analyzes this data and calculates the optimal sowing time, harvest time, management procedures, etc. The input is weather data and cultivation conditions, and the output is the generated cultivation plan.

[1656] Step 5:

[1657] The server saves the generated cultivation plan in the user's account and displays it on the terminal. The terminal visually presents the cultivation plan to the user via a smartphone or PC. The input is the generated cultivation plan, and the output is a display of the plan.

[1658] Step 6:

[1659] The device sends the generated cultivation plan and important notifications to the user. Push notifications and emails are used to notify the user of sudden weather changes and important management procedures. The input is the generated cultivation plan and important notification content, and the output is the notification to the user.

[1660] Step 7:

[1661] The device collects the user's facial expressions and voice and sends the emotional information to the server. The device uses a camera and microphone to collect the user's facial expressions and voice. The input is the user's facial expressions and voice, and the output is digital data of emotional information.

[1662] Step 8:

[1663] The server uses an emotion engine to analyze the user's emotional state. The collected facial and voice data is input into an emotion analysis algorithm (e.g., OpenCV) to evaluate the user's stress level and motivation. The input is digital data of emotional information, and the output is the evaluation result of the emotional state.

[1664] Step 9:

[1665] The server displays suggestions and reminders to the us...

Claims

1. a means for collecting weather data; A means for acquiring plant cultivation conditions; a means for analyzing the collected weather data and cultivation conditions; A means for generating an optimal cultivation plan based on the analysis results; A means for displaying the generated cultivation plan; a means for notifying a user of the generated cultivation plan; a means for receiving feedback from users; A system that includes a means to improve analysis accuracy based on feedback.

2. The system according to claim 1, wherein the system proposes measures in response to changes in weather data in real time.

3. 10. The system of claim 1, wherein artificial intelligence is used to analyze weather data and cultivation conditions.

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