system

Abstract

Provide a system. 【Solution means】 Means for acquiring and analyzing meteorological data, Means for automatically generating a business plan based on the analyzed meteorological data, Means for notifying the generated business plan to the user and receiving the user's input, Means for recording the input from the user and applying feedback to the plan, Means for improving the accuracy of the business plan using feedback information, Means for real-time monitoring of the health status of plants in urban greening management, Means for scheduling optimal watering and fertilization based on weather prediction data and plant information, A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, agriculture faces problems such as a decline in domestic agricultural productivity due to a shortage of labor force caused by aging, an increase in abandoned farmland, and competition with foreign agricultural products. In addition, existing agricultural AI technologies require high introduction costs and technical knowledge for their implementation, and thus have not reached a level that can be easily used by the general public. As a result, the sustainability of agriculture is feared, and it has become difficult for new participants to enter the agricultural field.

Means for Solving the Problems

[0005] This invention provides a means for automatically generating optimal farming plans using an AI-based system that automatically acquires and analyzes weather information. This allows the system to notify users of specific farming plans tailored to weather and crop conditions, compensating for users' lack of experience and knowledge in agriculture. Furthermore, by collecting user feedback in real time and continuously analyzing and learning from it within the system, the accuracy of farming plans can be continuously improved. In this way, this invention simultaneously increases the number of agricultural participants and improves the efficiency of agriculture, enabling the creation of a sustainable agricultural environment.

[0006] "Weather information" refers to data related to the state of the Earth's surface and the atmosphere, such as air temperature, humidity, precipitation, wind speed, and atmospheric pressure.

[0007] "Analysis" refers to the process of processing acquired data and extracting useful information from it.

[0008] "A farming plan" refers to a plan that determines the content and schedule of tasks necessary for crop growth.

[0009] "User" refers to the entity that operates the system to manage agricultural work, and generally includes agricultural workers and individuals participating in agriculture.

[0010] "Feedback" refers to information about execution results and areas for improvement that users provide to the system, which enables the system's performance to improve.

[0011] A "terminal device" is a device that a user directly operates, and generally includes smartphones, tablets, computers, and other similar devices.

[0012] "Location information" refers to data that indicates the location of a specific device or user using GPS functionality or network data.

[0013] An "AI model" refers to a mathematical model that uses artificial intelligence technology to perform pattern recognition, prediction, and decision-making. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when combined with an emotion engine.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the 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.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0028] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention utilizes an AI-powered management system to improve the efficiency of agricultural work and increase participation. Through the interaction of servers, terminal devices, and users, this system automates agriculture and creates an environment where anyone can easily participate.

[0036] The server periodically collects weather data from weather information services. This includes temperature, precipitation, and humidity, and is used to understand detailed weather conditions for each region. This information is stored in a database and prepared for analysis. The server analyzes the weather data and automatically creates a farming plan suitable for each crop. This plan indicates what tasks should be performed and when.

[0037] The terminal device, such as a smartphone or tablet, is used by the user to review the work plan and execute the work procedures. The terminal device is notified of the work plan obtained from the server, and the user can decide on the next action based on that plan. The terminal device also collects data from the user regarding the progress of the work and the growth status, and sends this feedback back to the server. This allows the system to continuously improve the accuracy of the farm work plan.

[0038] Users perform actual farm work based on the notified work plan. After completing the work, they input feedback on the results and points to note using a terminal device. This feedback includes changes in growth status, actual yield, and crop response to weather changes.

[0039] For example, if rainfall is predicted for a certain area next week, the server will determine that watering plans need to be adjusted based on that information and will notify the terminal device accordingly. The user can then use the notification to adjust the timing of fertilizer and pesticide application to match the specified timing. In this way, the present invention supports real-time decision-making in agriculture and improves work efficiency and productivity by enabling optimal agricultural activities.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server periodically accesses the API of a weather information service to obtain the latest data on the ground and atmosphere. This data includes temperature, precipitation, humidity, and wind speed. The server stores this data in a database.

[0043] Step 2:

[0044] The server analyzes collected weather data and uses AI models to predict weather patterns. This allows it to determine how these patterns will affect crop growth and develop appropriate farming plans. For example, it can adjust irrigation plans based on rainfall forecasts.

[0045] Step 3:

[0046] The device receives the latest farming plan sent from the server. The received plan is displayed as a notification on the user's smartphone or tablet. The user reviews the notification and understands the detailed steps and recommended timing for each task.

[0047] Step 4:

[0048] Users perform farming tasks according to notifications on their devices. These tasks include applying appropriate fertilizers and pesticides, and adjusting the amount and timing of watering. Once the tasks are completed, users input the results and any observations through their devices and send the information to the server.

[0049] Step 5:

[0050] The server collects all feedback information sent by users and stores it as a new dataset. Based on this data, the AI ​​model is updated to improve the accuracy of farm work plans for subsequent cycles.

[0051] Step 6:

[0052] The terminal also receives the next farming plan created by the server using the updated AI model and notifies the user again. By repeating this process, the system is continuously optimized to support the user's farming activities.

[0053] (Example 1)

[0054] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0055] Modern agriculture requires rapid and appropriate responses to weather changes and crop growth conditions. However, traditional methods cannot fully utilize weather data, hindering the improvement of farm work plans. Furthermore, efficiently incorporating work results and feedback to improve productivity is also difficult.

[0056] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0057] In this invention, the server includes information processing means for collecting and analyzing weather information, information processing means for automatically generating a farm work plan based on the analyzed weather information, and means for notifying the user of the generated farm work plan via a terminal device and receiving information on the user's work progress. This enables the provision of accurate farm work plans utilizing weather data and continuous improvement of the plan's accuracy based on feedback.

[0058] "Information processing means" refers to a program or system designed to receive, analyze, generate, and transmit data.

[0059] "Weather information" refers to data that indicates atmospheric conditions for a specific region and time, such as temperature, precipitation, and humidity.

[0060] A "farm work plan" refers to detailed guidelines on the timing and methods of carrying out farm work, which are determined based on weather information and the growth status of crops.

[0061] "Terminal device" refers to a digital device used by a user to receive and input information, and includes, for example, smartphones and tablets.

[0062] A "user" refers to an individual or organization that uses this system to perform agricultural tasks and provides feedback.

[0063] "Feedback" refers to information provided by users based on the results and observations of their work, and is used as data to improve and adjust the system.

[0064] A "machine learning model" refers to a set of algorithms and mathematical formulas used to learn patterns through data analysis and perform predictions and classifications.

[0065] This invention relates to a system for improving agricultural efficiency using AI technology. The following details the configuration for implementing this system.

[0066] The server uses programming languages ​​such as Python and machine learning libraries (e.g., TENSORFLOW® and PyTorch) to collect and analyze weather data. Specifically, the server periodically calls the API of a weather information service to obtain regional weather data in digital format. The acquired data is then stored in a database and analyzed through information processing tools. This analysis helps to understand data trends and fluctuations, and automatically generates optimal farming plans for each crop. This plan utilizes a generative AI model to optimize the timing of tasks such as watering and fertilizing.

[0067] The terminal device functions as a smartphone or tablet based on the ANDROID® or iOS platform, providing an intuitive user interface for users to review and execute farm work plans. The terminal notifies the user of the farm work plan received from the server and provides sequential instructions. The user then performs the actual work based on these instructions and inputs feedback on the progress of the work and the condition of the crops through the terminal.

[0068] Users perform actual farm work according to the generated plan and send feedback information to the server using a terminal device. The server then analyzes the received feedback and adjusts the AI ​​model to continuously improve the efficiency and accuracy of farm work.

[0069] For example, when rainfall is predicted for a particular area, the server re-evaluates the watering plan based on that information, makes necessary adjustments, and then notifies the terminal device. Upon receiving this notification, the user can then appropriately change the timing of fertilizer and pesticide application. This system provides users with an environment where they can conduct appropriate agricultural activities in real time.

[0070] An example of a prompt message is, "Adjust the work plan based on next week's rainfall forecast and notify me of the optimal timing for watering and fertilizing." This prompt message prompts the AI ​​model to generate a specific action plan and provide clear instructions to the user.

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The server periodically retrieves the latest weather data from the weather information service's API. This requires specifying a region and time period as input. The retrieved data, such as temperature, precipitation, and humidity, is stored in the server's database in preparation for analysis. Specifically, it uses a Python library to issue API requests, parses the received JSON response, and saves it.

[0074] Step 2:

[0075] The server analyzes weather data obtained from a database using machine learning models. It analyzes trends and anomalies in the input data and predicts future weather patterns. Based on these analysis results, it automatically generates farming plans suitable for each crop. Using the generating AI model, it determines specific tasks, such as "when to refrain from watering in preparation for the next rainfall." The output is a farming plan.

[0076] Step 3:

[0077] The server sends the generated farming plan to the terminal device as a push notification. The plan included in the notification is converted into a format that the user can easily understand. For example, JSON data is converted into the user's language and presented as easy-to-understand instructions. Since the notification is sent in real time, timely instructions can be provided.

[0078] Step 4:

[0079] The terminal device displays the farm work plan received from the server on the user interface. The user then performs the farm work based on this plan. Input is notification content from the server, which is then analyzed and displayed. Considering user convenience, the interface is user-friendly and provides clear instructions.

[0080] Step 5:

[0081] Users input the results and progress of their farm work through a terminal device. The terminal device collects feedback from users and sends it to a server. This feedback includes the completion status of the work, the observed state of the crops, and the impact of weather. The input feedback is organized and compiled into data for transmission to the server.

[0082] Step 6:

[0083] The server analyzes feedback collected from users and uses it to refine the machine learning model. This analysis aims to improve the accuracy and applicability of farm work plans. By analyzing the feedback received as input and incorporating it into the training dataset, it is reflected in the next plan generation. As an output, it becomes possible to generate improved farm work plans.

[0084] (Application Example 1)

[0085] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0086] Managing green spaces in urban environments requires considerable effort and expertise to constantly monitor plant health and provide appropriate watering and fertilization. Failure to respond quickly to changes in weather conditions can result in poor plant growth and increased management costs. Therefore, there is a need for systems that monitor weather data and plant health in real time to provide optimal management.

[0087] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0088] In this invention, the server includes means for acquiring and analyzing weather data, means for automatically generating a work plan based on the analyzed weather data, and means for monitoring the health status of plants in urban greening management in real time. This enables users to manage green spaces efficiently and effectively based on the latest information at all times.

[0089] "Meteorological data" refers to environmental information such as temperature, precipitation, and humidity, and is a factor that affects plant growth in urban green space management.

[0090] "Means of analysis" refers to the process or apparatus for performing calculations and analyses based on acquired meteorological data and plant information, and then using the results to formulate a management plan.

[0091] "Means for automatically generating business plans" refers to a process or mechanism that automatically creates specific action plans for green space management based on analyzed data.

[0092] "Means of notifying users and accepting user input" refers to an interface or method for informing users of the generated business plan and receiving feedback and additional information from them.

[0093] "Feedback information" refers to information provided by users, including reports on actual work conditions and the health of plants.

[0094] "Means for monitoring the health status of plants in urban greening management in real time" refers to a system or method that uses sensors and monitoring technologies to constantly understand the growth status of plants in urban areas.

[0095] "Means of scheduling optimal watering and fertilization" refers to methods or devices that plan the application of water and fertilizer at the optimal timing and amount for plants, based on real-time weather data and plant conditions.

[0096] The system of this invention consists of three main components: a server, a terminal, and a user. The server utilizes a weather data API to periodically acquire weather information. The collected data includes temperature, precipitation, humidity, etc., and is used for analysis to automatically generate an optimal work plan for green space management. The programming language used here is Python, and the management plan is created based on the analysis results.

[0097] The work plan generated by the server is sent to mobile devices (smartphones and tablets) via the internet. This notification includes recommended watering and fertilizing schedules based on the real-time monitoring of plant health. The device receives feedback from the user and sends this information to the server, contributing to improving the accuracy of the plan.

[0098] Users perform urban green space management tasks based on the work plan notified via their terminal. For example, if the weather forecast predicts 30mm of rain on Saturday, they can adjust their approach accordingly, such as fertilizing on Friday and refraining from irrigating on Saturday.

[0099] Furthermore, the server incorporates features that utilize a generative AI model, allowing users to input prompts to further optimize their next work plan using real-time data and feedback. An example of a prompt would be, "Please provide an optimal green space management plan based on weather forecasts and plant growth data."

[0100] In this way, the system of the present invention provides a solution for efficiently managing plants in urban areas and contributes to maintaining a sustainable urban environment.

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The server periodically retrieves weather data from an API of a weather information service. The input data includes temperature, precipitation, and humidity obtained from the API, and the output is this raw data stored in a database. Specifically, Python is used to send requests to the API, parse the retrieved JSON data, extract the necessary items, and store them in the database (SQLite).

[0104] Step 2:

[0105] The server analyzes stored weather data to automatically generate work plans. The input is weather data from a database, and the output is a detailed management plan including schedules and work content. This process uses a data analysis library (e.g., Pandas) to calculate the optimal timing for watering and fertilizing based on weather conditions. Furthermore, the generated plan is refined based on prompts obtained using a generation AI model.

[0106] Step 3:

[0107] The server generates a work plan and sends it to the terminal as a notification. The input is the work plan created on the server, and the output is a notification message sent to the user's mobile device. Specifically, a push notification is sent to the user's device via web server software (e.g., Flask), and the notification content includes the next week's fertilization schedule and recommended watering times.

[0108] Step 4:

[0109] The user receives a notification and takes action based on the plan from their device. The input is the plan displayed on the device, and the output is the actual result of watering or fertilizing. The user reviews the notification and adjusts the date and amount of fertilizer based on the weather.

[0110] Step 5:

[0111] The user sends the results of their work and feedback from their device to the server. The input is the user's work result report, and the output is feedback information stored on the server. Specifically, the user inputs the condition of the plants after the work is done and any observations they made, and sends this information to the server via a device app.

[0112] Step 6:

[0113] The server analyzes the feedback it receives and uses it to improve the accuracy of future business plans. The input is user feedback information, and the output is data that will be used when updating the plan next time. The server analyzes the feedback, stores it in a database, and incorporates the insights gained using a generative AI model into the next plan.

[0114] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0115] This invention is a system that, in addition to improving agricultural efficiency, takes into account the user's emotional state to provide a more comfortable and effective farming experience. The main components of the system are a server, terminal devices, and an emotion engine. Each of these components works in conjunction to support agricultural activities.

[0116] The server retrieves weather data from a weather information service and automatically generates farm work plans using an AI model. These plans include watering schedules based on soil moisture and temperature, and pesticide application plans based on pest infestation risk. The server also considers data from an emotion engine and can adjust specific plans to suit the user's emotions.

[0117] The terminal device receives farm work plans transmitted from the server. Furthermore, the terminal device incorporates an emotion engine that acquires user voice and facial expression data and evaluates emotions in real time. The emotion engine analyzes the acquired emotional data to assess the user's stress level and motivation. Based on this evaluation, it also provides activities and recommended work procedures that contribute to relaxation and motivation maintenance, in addition to the conventional work plan.

[0118] Users receive notifications of farm work plans on their devices and perform the suggested tasks. They also input their experiences and observations into their devices, providing feedback to the server. This allows the system to improve the accuracy of farm work plans while also addressing users' emotional needs.

[0119] For example, if the emotional engine detects fatigue or stress as a result of the user performing harvesting work for a long period of time, the device will immediately suggest a short break and, if necessary, encourage a shift to lighter work. In this way, the present invention can improve the efficiency and effectiveness of agricultural activities while taking into account the user's physical and mental health.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] The server periodically retrieves the latest weather data from a weather information service API. After retrieving the data, it uses an AI model to predict weather changes and generates an optimal farming plan based on that data.

[0123] Step 2:

[0124] The terminal receives farm work plans sent from the server and notifies the user. The terminal selects the most suitable information display format for the user and provides specific work procedures and timings.

[0125] Step 3:

[0126] The device uses a built-in emotion engine to analyze the user's voice and image data and evaluate the user's emotional state in real time. This evaluation includes factors such as stress level, satisfaction level, and fatigue level.

[0127] Step 4:

[0128] As the user performs farm work, the device continuously collects emotional data using an emotion engine. If the device predicts that the user's workload will become too heavy, it sends a notification suggesting switching to a simpler task or taking a break.

[0129] Step 5:

[0130] Users input the results of their farm work and their impressions into a device to provide feedback. This includes comments on crop growth, work progress, and the appropriateness of suggestions.

[0131] Step 6:

[0132] The server continuously updates its AI model based on user feedback and evaluations from its emotion engine, using this data to create more accurate farming plans for future use. This iterative process ensures the system improves with each use.

[0133] (Example 2)

[0134] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0135] Conventional agricultural systems can automatically generate work plans based on weather conditions, but they lack a system that adjusts the work plan to take into account the user's emotions and stress levels. As a result, uniform work is recommended regardless of the user's physical and mental health, leading to the problem of increased burden on the user.

[0136] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0137] In this invention, the server includes means for acquiring and analyzing weather information, means for evaluating the user's emotional state, and means for adjusting the farm work plan based on the evaluated emotional state. This makes it possible to adjust the work plan according to the user's emotional state, thereby improving the efficiency and comfort of agricultural activities while taking into account the user's physical and mental health.

[0138] "Weather information" refers to data related to the weather, including information such as temperature, humidity, precipitation, and wind speed.

[0139] "Analysis" refers to the process of interpreting acquired data and organizing it into meaningful information.

[0140] A "farm work plan" is a plan that automatically generates procedures and schedules for crop cultivation and harvesting, taking into account weather conditions and other factors.

[0141] "Means of receiving user input" refers to methods of collecting information and opinions from users through terminals or other interfaces.

[0142] "Feedback" refers to the process of reviewing and improving the system's operation and plan based on user input.

[0143] "Methods for improving accuracy" refer to ways of using user feedback to enhance the accuracy and efficiency of plans and systems.

[0144] "Emotional state" refers to the user's psychological and physiological state, and includes indicators such as stress and motivation.

[0145] "Methods of evaluation" refer to methods of analyzing and judging a user's emotional state based on data such as voice and facial expressions.

[0146] "Means of adjustment" refers to the process of modifying existing plans and schedules to suit the user's situation based on the analysis results.

[0147] This invention provides a more efficient and user-friendly agricultural system by considering weather information and the user's emotional state. Its main components include a server, terminal devices, and an emotion engine. These components work together to comprehensively support agricultural operations.

[0148] The server obtains weather data from weather information service providers via APIs. This requires a commonly used API interface and an internet connection. The server uses generative AI models (e.g., using TensorFlow or PyTorch) to automatically generate farm work plans based on the acquired weather data. These farm work plans include optimal watering schedules and pesticide application plans tailored to pest outbreak risks. This plan is further adjusted based on the user's emotional state. Emotional data is obtained from the emotional engine of the terminal device.

[0149] The terminal device receives the farm work plan transmitted from the server. The terminal has a built-in camera and microphone, which capture the user's voice and facial expressions. The emotion engine evaluates the user's state using an emotion recognition API (e.g., Google Cloud's Sentiment Analysis API). The evaluated emotion data is analyzed as stress levels and motivation indicators and immediately sent to the server. Based on this data, the server adjusts the farm work plan and provides the user with a customized proposal.

[0150] Users review farm work plans through their terminals and carry out the suggested tasks. The terminals function as direct guides, providing visual or audio instructions for each task. Furthermore, real-time feedback of the user's experience and observations is sent to the server, contributing to improved accuracy in future plans.

[0151] For example, if the emotion engine detects stress after a long period of harvesting, the device may send a notification such as, "We recommend a 5-minute break." In this way, it becomes possible to support efficient agricultural work while considering the user's health condition.

[0152] Examples of prompts include, "Please conduct a risk analysis for the next harvest," and "Please suggest activities that will help reduce stress."

[0153] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0154] Step 1:

[0155] The server makes API requests to weather information services to retrieve the latest weather data. Inputs are API keys and parameters, while outputs include weather data such as temperature, humidity, and precipitation. The retrieved JSON data is parsed to extract necessary information and store it in an internal database. This data is then used by an AI model to create the basis for agricultural work plans.

[0156] Step 2:

[0157] The terminal receives farm work plans transmitted from the server. The received data is planning information, including specific work schedules and procedures. The terminal's built-in camera and microphone collect the user's facial expressions and voice. The user's biometric data is used as input and is sent to an emotion recognition API for real-time analysis to evaluate the user's emotional state (e.g., stress level).

[0158] Step 3:

[0159] The server adjusts the farm work plan based on the user's emotional data received from the terminal. The input is the user's emotional state data, and the output is the adjusted work plan. A generative AI model is used to adjust the work plan according to the emotional data, making customizations such as including breaks when stress levels are high.

[0160] Step 4:

[0161] The terminal receives the coordinated work plan from the server and notifies the user. The input is the coordinated work plan, and the output is work guides and notifications for the user. The terminal provides audio or visual guidance to help the user proceed with the work according to the plan.

[0162] Step 5:

[0163] Users input daily work reports and feedback on plans via their devices. This input feedback, in text or voice format, is received by the server and stored in a database. The feedback is then incorporated into future work plans and used to improve the accuracy of the generating AI model.

[0164] (Application Example 2)

[0165] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0166] Conventional agricultural work systems failed to adjust work plans to take into account the user's emotions and physical condition, making it difficult to provide an efficient and comfortable working environment. Furthermore, there was a lack of means to properly incorporate feedback and improve the accuracy of agricultural work plans. In addition, the lack of real-time suggestions based on the individual user's condition could lead to the accumulation of stress and fatigue.

[0167] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0168] In this invention, the server includes a device for acquiring and analyzing weather data, a device for automatically generating an agricultural work plan based on the analyzed weather data, and a device for evaluating the user's emotional state. This makes it possible to dynamically provide an optimal agricultural work plan that takes into account the emotional state of each individual user, as well as to create an effective work environment that takes into account the user's physical and mental health.

[0169] "Weather data" refers to information about the weather and is an essential element for formulating work plans in agriculture.

[0170] "Analysis" refers to the process of analyzing collected data and using it to improve the efficiency and optimization of agricultural work.

[0171] An "agricultural work plan" refers to a set of procedures and schedules that are formulated in advance to effectively carry out agricultural activities.

[0172] "User" refers to agricultural workers or operators who use this system.

[0173] "Feedback" refers to opinions and results obtained from users, and is information used to improve and adjust future plans.

[0174] "Emotional state" refers to the user's psychological and emotional condition, and includes elements such as stress and motivation.

[0175] "Stress level" refers to the degree of mental burden felt by the user, and it serves as a criterion for adjusting agricultural work plans.

[0176] "Device" is a general term for the equipment and programs used to realize various functions within a system.

[0177] The system of this invention aims to improve the efficiency of agricultural work and adjust work plans based on the user's emotional state. Specifically, a server, terminal devices, and an emotion engine work in conjunction.

[0178] The server acquires weather data in real time from weather information services via the internet. Based on the acquired data, an AI model automatically generates an agricultural work plan. This plan includes work procedures that correspond to specific weather conditions. For example, if dry weather conditions are predicted, an appropriate watering schedule will be suggested.

[0179] The terminal device is equipped with an emotion engine that analyzes the user's voice and facial expressions in real time. Based on the emotion data obtained from this analysis, the server dynamically adjusts the work plan. For example, if the system determines that the user is in a high-stress state, it suggests a short break to reduce the user's burden.

[0180] As a concrete example, in a farm where vegetable harvesting is performed, the user is analyzed through an emotion engine. When the weather is sunny and a long period of work is predicted, the system displays a notification on the device prompting hydration. Furthermore, based on the assessment of the user's emotional state, it can suggest playing music with a relaxing effect.

[0181] An example of a prompt message is: "Generate the optimal farm work plan based on recent weather data and the user's emotional state. Please provide suggestions that particularly focus on reducing the user's stress." This prompt prompts the AI ​​model to optimize the work plan.

[0182] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0183] Step 1:

[0184] The server retrieves weather data from weather information services. This allows it to understand current and future weather conditions. Inputs include regional information and date / time, while outputs include weather information such as temperature, precipitation, and humidity. For data processing, this data is converted to a standard format to prepare it for subsequent analysis.

[0185] Step 2:

[0186] The server inputs acquired weather data into an AI model and automatically generates agricultural work plans. The input is weather data, and the output is a specific agricultural work plan. In terms of data calculation, the AI ​​model calculates the optimal work procedures and schedules based on temperature and precipitation.

[0187] Step 3:

[0188] The device acquires the user's voice and facial expression data and analyzes it using an emotion engine. The input is real-time data of the user, and the output is emotional state such as stress level and motivation. As part of the data processing, the voice and image data are converted into features for emotion identification.

[0189] Step 4:

[0190] The server adjusts the agricultural work plan using data from the emotion engine. The input is the user's emotional state, and the output is the adjusted work plan. In terms of data calculation, it optimizes the order and content of tasks according to the user's emotional state and suggests breaks or light work as needed.

[0191] Step 5:

[0192] The terminal notifies the user of a customized agricultural work plan and accepts feedback. The input is the customized work plan, and the output is a notification message presented to the user. Specifically, a notification is displayed on a mobile device, allowing the user to review it and then provide any necessary feedback.

[0193] Step 6:

[0194] The server incorporates user feedback and uses it to improve future work plans. Input is feedback information, and output is an updated work plan or base model. Data processing involves analyzing the feedback content and reflecting it in adjusting the parameters of the AI ​​model.

[0195] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0196] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0197] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0198] [Second Embodiment]

[0199] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0200] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0201] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0202] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0203] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0204] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0205] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0206] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0207] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0208] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0209] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0210] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0211] This invention utilizes an AI-powered management system to improve the efficiency of agricultural work and increase participation. Through the interaction of servers, terminal devices, and users, this system automates agriculture and creates an environment where anyone can easily participate.

[0212] The server periodically collects weather data from weather information services. This includes temperature, precipitation, and humidity, and is used to understand detailed weather conditions for each region. This information is stored in a database and prepared for analysis. The server analyzes the weather data and automatically creates a farming plan suitable for each crop. This plan indicates what tasks should be performed and when.

[0213] The terminal device, such as a smartphone or tablet, is used by the user to review the work plan and execute the work procedures. The terminal device is notified of the work plan obtained from the server, and the user can decide on the next action based on that plan. The terminal device also collects data from the user regarding the progress of the work and the growth status, and sends this feedback back to the server. This allows the system to continuously improve the accuracy of the farm work plan.

[0214] Users perform actual farm work based on the notified work plan. After completing the work, they input feedback on the results and points to note using a terminal device. This feedback includes changes in growth status, actual yield, and crop response to weather changes.

[0215] For example, if rainfall is predicted for a certain area next week, the server will determine that watering plans need to be adjusted based on that information and will notify the terminal device accordingly. The user can then use the notification to adjust the timing of fertilizer and pesticide application to match the specified timing. In this way, the present invention supports real-time decision-making in agriculture and improves work efficiency and productivity by enabling optimal agricultural activities.

[0216] The following describes the processing flow.

[0217] Step 1:

[0218] The server periodically accesses the API of a weather information service to obtain the latest data on the ground and atmosphere. This data includes temperature, precipitation, humidity, and wind speed. The server stores this data in a database.

[0219] Step 2:

[0220] The server analyzes collected weather data and uses AI models to predict weather patterns. This allows it to determine how these patterns will affect crop growth and develop appropriate farming plans. For example, it can adjust irrigation plans based on rainfall forecasts.

[0221] Step 3:

[0222] The device receives the latest farming plan sent from the server. The received plan is displayed as a notification on the user's smartphone or tablet. The user reviews the notification and understands the detailed steps and recommended timing for each task.

[0223] Step 4:

[0224] Users perform farming tasks according to notifications on their devices. These tasks include applying appropriate fertilizers and pesticides, and adjusting the amount and timing of watering. Once the tasks are completed, users input the results and any observations through their devices and send the information to the server.

[0225] Step 5:

[0226] The server collects all feedback information sent by users and stores it as a new dataset. Based on this data, the AI ​​model is updated to improve the accuracy of farm work plans for subsequent cycles.

[0227] Step 6:

[0228] The terminal also receives the next farming plan created by the server using the updated AI model and notifies the user again. By repeating this process, the system is continuously optimized to support the user's farming activities.

[0229] (Example 1)

[0230] Next, we will describe Example 1. 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."

[0231] Modern agriculture requires rapid and appropriate responses to weather changes and crop growth conditions. However, traditional methods cannot fully utilize weather data, hindering the improvement of farm work plans. Furthermore, efficiently incorporating work results and feedback to improve productivity is also difficult.

[0232] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0233] In this invention, the server includes information processing means for collecting and analyzing weather information, information processing means for automatically generating a farm work plan based on the analyzed weather information, and means for notifying the user of the generated farm work plan via a terminal device and receiving information on the user's work progress. This enables the provision of accurate farm work plans utilizing weather data and continuous improvement of the plan's accuracy based on feedback.

[0234] "Information processing means" refers to a program or system designed to receive, analyze, generate, and transmit data.

[0235] "Weather information" refers to data that indicates atmospheric conditions for a specific region and time, such as temperature, precipitation, and humidity.

[0236] A "farm work plan" refers to detailed guidelines on the timing and methods of carrying out farm work, which are determined based on weather information and the growth status of crops.

[0237] "Terminal device" refers to a digital device used by a user to receive and input information, and includes, for example, smartphones and tablets.

[0238] A "user" refers to an individual or organization that uses this system to perform agricultural tasks and provides feedback.

[0239] "Feedback" refers to information provided by users based on the results and observations of their work, and is used as data to improve and adjust the system.

[0240] A "machine learning model" refers to a set of algorithms and mathematical formulas used to learn patterns through data analysis and perform predictions and classifications.

[0241] This invention relates to a system for improving agricultural efficiency using AI technology. The following details the configuration for implementing this system.

[0242] The server uses programming languages ​​such as Python and machine learning libraries (e.g., TensorFlow and PyTorch) to collect and analyze weather data. Specifically, the server periodically calls the API of a weather information service to obtain regional weather data in digital format. The acquired data is then stored in a database and analyzed through information processing tools. This analysis helps to understand data trends and fluctuations, and automatically generates optimal farming plans for each crop. This plan utilizes a generative AI model to optimize the timing of tasks such as watering and fertilizing.

[0243] The terminal device functions as a smartphone or tablet based on the Android or iOS platform, providing an intuitive user interface for users to review and execute farm work plans. The terminal notifies the user of the farm work plan received from the server and provides sequential instructions. Based on this, the user performs the actual work and inputs feedback on the progress of the work and the condition of the crops through the terminal.

[0244] Users perform actual farm work according to the generated plan and send feedback information to the server using a terminal device. The server then analyzes the received feedback and adjusts the AI ​​model to continuously improve the efficiency and accuracy of farm work.

[0245] For example, when rainfall is predicted for a particular area, the server re-evaluates the watering plan based on that information, makes necessary adjustments, and then notifies the terminal device. Upon receiving this notification, the user can then appropriately change the timing of fertilizer and pesticide application. This system provides users with an environment where they can conduct appropriate agricultural activities in real time.

[0246] An example of a prompt message is, "Adjust the work plan based on next week's rainfall forecast and notify me of the optimal timing for watering and fertilizing." This prompt message prompts the AI ​​model to generate a specific action plan and provide clear instructions to the user.

[0247] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0248] Step 1:

[0249] The server periodically retrieves the latest weather data from the weather information service's API. This requires specifying a region and time period as input. The retrieved data, such as temperature, precipitation, and humidity, is stored in the server's database in preparation for analysis. Specifically, it uses a Python library to issue API requests, parses the received JSON response, and saves it.

[0250] Step 2:

[0251] The server analyzes weather data obtained from a database using machine learning models. It analyzes trends and anomalies in the input data and predicts future weather patterns. Based on these analysis results, it automatically generates farming plans suitable for each crop. Using the generating AI model, it determines specific tasks, such as "when to refrain from watering in preparation for the next rainfall." The output is a farming plan.

[0252] Step 3:

[0253] The server sends the generated farming plan to the terminal device as a push notification. The plan included in the notification is converted into a format that the user can easily understand. For example, JSON data is converted into the user's language and presented as easy-to-understand instructions. Since the notification is sent in real time, timely instructions can be provided.

[0254] Step 4:

[0255] The terminal device displays the farm work plan received from the server on the user interface. The user then performs the farm work based on this plan. Input is notification content from the server, which is then analyzed and displayed. Considering user convenience, the interface is user-friendly and provides clear instructions.

[0256] Step 5:

[0257] Users input the results and progress of their farm work through a terminal device. The terminal device collects feedback from users and sends it to a server. This feedback includes the completion status of the work, the observed state of the crops, and the impact of weather. The input feedback is organized and compiled into data for transmission to the server.

[0258] Step 6:

[0259] The server analyzes feedback collected from users and uses it to refine the machine learning model. This analysis aims to improve the accuracy and applicability of farm work plans. By analyzing the feedback received as input and incorporating it into the training dataset, it is reflected in the next plan generation. As an output, it becomes possible to generate improved farm work plans.

[0260] (Application Example 1)

[0261] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0262] Managing green spaces in urban environments requires considerable effort and expertise to constantly monitor plant health and provide appropriate watering and fertilization. Failure to respond quickly to changes in weather conditions can result in poor plant growth and increased management costs. Therefore, there is a need for systems that monitor weather data and plant health in real time to provide optimal management.

[0263] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0264] In this invention, the server includes means for acquiring and analyzing weather data, means for automatically generating a work plan based on the analyzed weather data, and means for monitoring the health status of plants in urban greening management in real time. This enables users to manage green spaces efficiently and effectively based on the latest information at all times.

[0265] "Meteorological data" refers to environmental information such as temperature, precipitation, and humidity, and is a factor that affects plant growth in urban green space management.

[0266] "Means of analysis" refers to the process or apparatus for performing calculations and analyses based on acquired meteorological data and plant information, and then using the results to formulate a management plan.

[0267] "Means for automatically generating business plans" refers to a process or mechanism that automatically creates specific action plans for green space management based on analyzed data.

[0268] "Means of notifying users and accepting user input" refers to an interface or method for informing users of the generated business plan and receiving feedback and additional information from them.

[0269] "Feedback information" refers to information provided by users, including reports on actual work conditions and the health of plants.

[0270] "Means for monitoring the health status of plants in urban greening management in real time" refers to a system or method that uses sensors and monitoring technologies to constantly understand the growth status of plants in urban areas.

[0271] "Means of scheduling optimal watering and fertilization" refers to methods or devices that plan the application of water and fertilizer at the optimal timing and amount for plants, based on real-time weather data and plant conditions.

[0272] The system of this invention consists of three main components: a server, a terminal, and a user. The server utilizes a weather data API to periodically acquire weather information. The collected data includes temperature, precipitation, humidity, etc., and is used for analysis to automatically generate an optimal work plan for green space management. The programming language used here is Python, and the management plan is created based on the analysis results.

[0273] The work plan generated by the server is sent to mobile devices (smartphones and tablets) via the internet. This notification includes recommended watering and fertilizing schedules based on the real-time monitoring of plant health. The device receives feedback from the user and sends this information to the server, contributing to improving the accuracy of the plan.

[0274] Users perform urban green space management tasks based on the work plan notified via their terminal. For example, if the weather forecast predicts 30mm of rain on Saturday, they can adjust their approach accordingly, such as fertilizing on Friday and refraining from irrigating on Saturday.

[0275] Furthermore, the server incorporates features that utilize a generative AI model, allowing users to input prompts to further optimize their next work plan using real-time data and feedback. An example of a prompt would be, "Please provide an optimal green space management plan based on weather forecasts and plant growth data."

[0276] In this way, the system of the present invention provides a solution for efficiently managing plants in urban areas and contributes to maintaining a sustainable urban environment.

[0277] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0278] Step 1:

[0279] The server periodically retrieves weather data from the API of the weather information providing service. The input data is the temperature, precipitation, humidity, etc. obtained from the API, and the output is that these raw data are stored in the database. Specifically, Python is used to send requests to the API, analyze the obtained JSON-formatted data, extract the necessary items, and store them in the database (SQLite).

[0280] Step 2:

[0281] The server analyzes the stored weather data and automatically generates a business plan. The input is the weather data in the database, and the output is the schedule and work content as a specific management plan. For this process, a data analysis library (such as Pandas) is used to calculate the optimal timing of watering and fertilization according to the weather conditions. Furthermore, the generated plan is improved based on the prompts obtained by utilizing the generative AI model.

[0282] Step 3:

[0283] The server sends the generated business plan to the terminal and notifies. The input is the business plan created by the server, and the output is the notification message to the user's mobile terminal. Specifically, through web server software (such as Flask), push notifications are sent to the user's terminal, and the notification content includes the fertilization schedule for the next week and the recommended timing of watering.

[0284] Step 4:

[0285] The user receives the notification and executes actions based on the plan from the terminal. The input is the plan displayed on the terminal, and the output is the result of the actual watering and fertilization. The user checks the notification content and adjusts the date and fertilization amount based on the weather.

[0286] Step 5:

[0287] The user sends the results of their work and feedback from their device to the server. The input is the user's work result report, and the output is feedback information stored on the server. Specifically, the user inputs the condition of the plants after the work is done and any observations they made, and sends this information to the server via a device app.

[0288] Step 6:

[0289] The server analyzes the feedback it receives and uses it to improve the accuracy of future business plans. The input is user feedback information, and the output is data that will be used when updating the plan next time. The server analyzes the feedback, stores it in a database, and incorporates the insights gained using a generative AI model into the next plan.

[0290] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0291] This invention is a system that, in addition to improving agricultural efficiency, takes into account the user's emotional state to provide a more comfortable and effective farming experience. The main components of the system are a server, terminal devices, and an emotion engine. Each of these components works in conjunction to support agricultural activities.

[0292] The server retrieves weather data from a weather information service and automatically generates farm work plans using an AI model. These plans include watering schedules based on soil moisture and temperature, and pesticide application plans based on pest infestation risk. The server also considers data from an emotion engine and can adjust specific plans to suit the user's emotions.

[0293] The terminal device receives farm work plans transmitted from the server. Furthermore, the terminal device incorporates an emotion engine that acquires user voice and facial expression data and evaluates emotions in real time. The emotion engine analyzes the acquired emotional data to assess the user's stress level and motivation. Based on this evaluation, it also provides activities and recommended work procedures that contribute to relaxation and motivation maintenance, in addition to the conventional work plan.

[0294] Users receive notifications of farm work plans on their devices and perform the suggested tasks. They also input their experiences and observations into their devices, providing feedback to the server. This allows the system to improve the accuracy of farm work plans while also addressing users' emotional needs.

[0295] For example, if the emotional engine detects fatigue or stress as a result of the user performing harvesting work for a long period of time, the device will immediately suggest a short break and, if necessary, encourage a shift to lighter work. In this way, the present invention can improve the efficiency and effectiveness of agricultural activities while taking into account the user's physical and mental health.

[0296] The following describes the processing flow.

[0297] Step 1:

[0298] The server periodically retrieves the latest weather data from a weather information service API. After retrieving the data, it uses an AI model to predict weather changes and generates an optimal farming plan based on that data.

[0299] Step 2:

[0300] The terminal receives farm work plans sent from the server and notifies the user. The terminal selects the most suitable information display format for the user and provides specific work procedures and timings.

[0301] Step 3:

[0302] The terminal uses the built-in emotion engine to analyze the user's voice and image data and evaluate the user's emotional state in real time. This evaluation includes stress level, satisfaction, fatigue level, etc.

[0303] Step 4:

[0304] During the process of the user performing agricultural work, the terminal continuously acquires emotion data using the emotion engine. When it is predicted that the user's burden will increase, the terminal sends a notification proposing a switch to a simple task or a break.

[0305] Step 5:

[0306] The user inputs the results of the agricultural work performed and the feelings experienced into the terminal to provide feedback. This includes the growth state of the crops, the progress of the work, and opinions on the validity of the proposals, etc.

[0307] Step 6:

[0308] The server continuously updates the AI model as data for making the subsequent agricultural work plan more accurate based on the user's feedback and the evaluation by the emotion engine. By circulating this process, the system is improved every time it is used.

[0309] (Example 2)

[0310] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0311] In the conventional agricultural system, it is possible to automatically generate a work plan based on weather conditions, but there is no system that adjusts the work plan considering the user's emotions and stress state. As a result, there is a problem that the burden on the user increases because the same work is recommended regardless of the user's physical and mental health.

[0312] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0313] In this invention, the server includes means for acquiring and analyzing weather information, means for evaluating the user's emotional state, and means for adjusting the farm work plan based on the evaluated emotional state. This makes it possible to adjust the work plan according to the user's emotional state, thereby improving the efficiency and comfort of agricultural activities while taking into account the user's physical and mental health.

[0314] "Weather information" refers to data related to the weather, including information such as temperature, humidity, precipitation, and wind speed.

[0315] "Analysis" refers to the process of interpreting acquired data and organizing it into meaningful information.

[0316] A "farm work plan" is a plan that automatically generates procedures and schedules for crop cultivation and harvesting, taking into account weather conditions and other factors.

[0317] "Means of receiving user input" refers to methods of collecting information and opinions from users through terminals or other interfaces.

[0318] "Feedback" refers to the process of reviewing and improving the system's operation and plan based on user input.

[0319] "Methods for improving accuracy" refer to ways of using user feedback to enhance the accuracy and efficiency of plans and systems.

[0320] "Emotional state" refers to the user's psychological and physiological state, and includes indicators such as stress and motivation.

[0321] "Methods of evaluation" refer to methods of analyzing and judging a user's emotional state based on data such as voice and facial expressions.

[0322] "Means of adjustment" refers to the process of modifying existing plans and schedules to suit the user's situation based on the analysis results.

[0323] This invention provides a more efficient and user-friendly agricultural system by considering weather information and the user's emotional state. Its main components include a server, terminal devices, and an emotion engine. These components work together to comprehensively support agricultural operations.

[0324] The server obtains weather data from weather information service providers via APIs. This requires a commonly used API interface and an internet connection. The server uses generative AI models (e.g., using TensorFlow or PyTorch) to automatically generate farm work plans based on the acquired weather data. These farm work plans include optimal watering schedules and pesticide application plans tailored to pest outbreak risks. This plan is further adjusted based on the user's emotional state. Emotional data is obtained from the emotional engine of the terminal device.

[0325] The terminal device receives the farm work plan transmitted from the server. The terminal has a built-in camera and microphone, which capture the user's voice and facial expressions. The emotion engine evaluates the user's state using an emotion recognition API (e.g., Google Cloud's Sentiment Analysis API). The evaluated emotion data is analyzed as stress levels and motivation indicators and immediately sent to the server. Based on this data, the server adjusts the farm work plan and provides the user with a customized proposal.

[0326] Users review farm work plans through their terminals and carry out the suggested tasks. The terminals function as direct guides, providing visual or audio instructions for each task. Furthermore, real-time feedback of the user's experience and observations is sent to the server, contributing to improved accuracy in future plans.

[0327] For example, if the emotion engine detects stress after a long period of harvesting, the device may send a notification such as, "We recommend a 5-minute break." In this way, it becomes possible to support efficient agricultural work while considering the user's health condition.

[0328] Examples of prompts include, "Please conduct a risk analysis for the next harvest," and "Please suggest activities that will help reduce stress."

[0329] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0330] Step 1:

[0331] The server makes API requests to weather information services to retrieve the latest weather data. Inputs are API keys and parameters, while outputs include weather data such as temperature, humidity, and precipitation. The retrieved JSON data is parsed to extract necessary information and store it in an internal database. This data is then used by an AI model to create the basis for agricultural work plans.

[0332] Step 2:

[0333] The terminal receives farm work plans transmitted from the server. The received data is planning information, including specific work schedules and procedures. The terminal's built-in camera and microphone collect the user's facial expressions and voice. The user's biometric data is used as input and is sent to an emotion recognition API for real-time analysis to evaluate the user's emotional state (e.g., stress level).

[0334] Step 3:

[0335] The server adjusts the farm work plan based on the user's emotional data received from the terminal. The input is the user's emotional state data, and the output is the adjusted work plan. A generative AI model is used to adjust the work plan according to the emotional data, making customizations such as including breaks when stress levels are high.

[0336] Step 4:

[0337] The terminal receives the coordinated work plan from the server and notifies the user. The input is the coordinated work plan, and the output is work guides and notifications for the user. The terminal provides audio or visual guidance to help the user proceed with the work according to the plan.

[0338] Step 5:

[0339] Users input daily work reports and feedback on plans via their devices. This input feedback, in text or voice format, is received by the server and stored in a database. The feedback is then incorporated into future work plans and used to improve the accuracy of the generating AI model.

[0340] (Application Example 2)

[0341] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0342] Conventional agricultural work systems failed to adjust work plans to take into account the user's emotions and physical condition, making it difficult to provide an efficient and comfortable working environment. Furthermore, there was a lack of means to properly incorporate feedback and improve the accuracy of agricultural work plans. In addition, the lack of real-time suggestions based on the individual user's condition could lead to the accumulation of stress and fatigue.

[0343] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0344] In this invention, the server includes a device for acquiring and analyzing weather data, a device for automatically generating an agricultural work plan based on the analyzed weather data, and a device for evaluating the user's emotional state. This makes it possible to dynamically provide an optimal agricultural work plan that takes into account the emotional state of each individual user, as well as to create an effective work environment that takes into account the user's physical and mental health.

[0345] "Weather data" refers to information about the weather and is an essential element for formulating work plans in agriculture.

[0346] "Analysis" refers to the process of analyzing collected data and using it to improve the efficiency and optimization of agricultural work.

[0347] An "agricultural work plan" refers to a set of procedures and schedules that are formulated in advance to effectively carry out agricultural activities.

[0348] "User" refers to agricultural workers or operators who use this system.

[0349] "Feedback" refers to opinions and results obtained from users, and is information used to improve and adjust future plans.

[0350] "Emotional state" refers to the user's psychological and emotional condition, and includes elements such as stress and motivation.

[0351] "Stress level" refers to the degree of mental burden felt by the user, and it serves as a criterion for adjusting agricultural work plans.

[0352] "Device" is a general term for the equipment and programs used to realize various functions within a system.

[0353] The system of this invention aims to improve the efficiency of agricultural work and adjust work plans based on the user's emotional state. Specifically, a server, terminal devices, and an emotion engine work in conjunction.

[0354] The server acquires weather data in real time from weather information services via the internet. Based on the acquired data, an AI model automatically generates an agricultural work plan. This plan includes work procedures that correspond to specific weather conditions. For example, if dry weather conditions are predicted, an appropriate watering schedule will be suggested.

[0355] The terminal device is equipped with an emotion engine that analyzes the user's voice and facial expressions in real time. Based on the emotion data obtained from this analysis, the server dynamically adjusts the work plan. For example, if the system determines that the user is in a high-stress state, it suggests a short break to reduce the user's burden.

[0356] As a concrete example, in a farm where vegetable harvesting is performed, the user is analyzed through an emotion engine. When the weather is sunny and a long period of work is predicted, the system displays a notification on the device prompting hydration. Furthermore, based on the assessment of the user's emotional state, it can suggest playing music with a relaxing effect.

[0357] An example of a prompt message is: "Generate the optimal farm work plan based on recent weather data and the user's emotional state. Please provide suggestions that particularly focus on reducing the user's stress." This prompt prompts the AI ​​model to optimize the work plan.

[0358] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0359] Step 1:

[0360] The server retrieves weather data from weather information services. This allows it to understand current and future weather conditions. Inputs include regional information and date / time, while outputs include weather information such as temperature, precipitation, and humidity. For data processing, this data is converted to a standard format to prepare it for subsequent analysis.

[0361] Step 2:

[0362] The server inputs acquired weather data into an AI model and automatically generates agricultural work plans. The input is weather data, and the output is a specific agricultural work plan. In terms of data calculation, the AI ​​model calculates the optimal work procedures and schedules based on temperature and precipitation.

[0363] Step 3:

[0364] The device acquires the user's voice and facial expression data and analyzes it using an emotion engine. The input is real-time data of the user, and the output is emotional state such as stress level and motivation. As part of the data processing, the voice and image data are converted into features for emotion identification.

[0365] Step 4:

[0366] The server adjusts the agricultural work plan using data from the emotion engine. The input is the user's emotional state, and the output is the adjusted work plan. In terms of data calculation, it optimizes the order and content of tasks according to the user's emotional state and suggests breaks or light work as needed.

[0367] Step 5:

[0368] The terminal notifies the user of a customized agricultural work plan and accepts feedback. The input is the customized work plan, and the output is a notification message presented to the user. Specifically, a notification is displayed on a mobile device, allowing the user to review it and then provide any necessary feedback.

[0369] Step 6:

[0370] The server incorporates user feedback and uses it to improve future work plans. Input is feedback information, and output is an updated work plan or base model. Data processing involves analyzing the feedback content and reflecting it in adjusting the parameters of the AI ​​model.

[0371] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0372] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0373] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0374] [Third Embodiment]

[0375] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0376] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0377] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0378] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0379] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0380] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0381] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0382] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0383] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0384] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0385] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0386] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0387] This invention utilizes an AI-powered management system to improve the efficiency of agricultural work and increase participation. Through the interaction of servers, terminal devices, and users, this system automates agriculture and creates an environment where anyone can easily participate.

[0388] The server periodically collects weather data from weather information services. This includes temperature, precipitation, and humidity, and is used to understand detailed weather conditions for each region. This information is stored in a database and prepared for analysis. The server analyzes the weather data and automatically creates a farming plan suitable for each crop. This plan indicates what tasks should be performed and when.

[0389] The terminal device, such as a smartphone or tablet, is used by the user to review the work plan and execute the work procedures. The terminal device is notified of the work plan obtained from the server, and the user can decide on the next action based on that plan. The terminal device also collects data from the user regarding the progress of the work and the growth status, and sends this feedback back to the server. This allows the system to continuously improve the accuracy of the farm work plan.

[0390] Users perform actual farm work based on the notified work plan. After completing the work, they input feedback on the results and points to note using a terminal device. This feedback includes changes in growth status, actual yield, and crop response to weather changes.

[0391] For example, if rainfall is predicted for a certain area next week, the server will determine that watering plans need to be adjusted based on that information and will notify the terminal device accordingly. The user can then use the notification to adjust the timing of fertilizer and pesticide application to match the specified timing. In this way, the present invention supports real-time decision-making in agriculture and improves work efficiency and productivity by enabling optimal agricultural activities.

[0392] The following describes the processing flow.

[0393] Step 1:

[0394] The server periodically accesses the API of a weather information service to obtain the latest data on the ground and atmosphere. This data includes temperature, precipitation, humidity, and wind speed. The server stores this data in a database.

[0395] Step 2:

[0396] The server analyzes collected weather data and uses AI models to predict weather patterns. This allows it to determine how these patterns will affect crop growth and develop appropriate farming plans. For example, it can adjust irrigation plans based on rainfall forecasts.

[0397] Step 3:

[0398] The device receives the latest farming plan sent from the server. The received plan is displayed as a notification on the user's smartphone or tablet. The user reviews the notification and understands the detailed steps and recommended timing for each task.

[0399] Step 4:

[0400] Users perform farming tasks according to notifications on their devices. These tasks include applying appropriate fertilizers and pesticides, and adjusting the amount and timing of watering. Once the tasks are completed, users input the results and any observations through their devices and send the information to the server.

[0401] Step 5:

[0402] The server collects all feedback information sent by users and stores it as a new dataset. Based on this data, the AI ​​model is updated to improve the accuracy of farm work plans for subsequent cycles.

[0403] Step 6:

[0404] The terminal also receives the next farming plan created by the server using the updated AI model and notifies the user again. By repeating this process, the system is continuously optimized to support the user's farming activities.

[0405] (Example 1)

[0406] Next, we will describe Example 1. 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."

[0407] Modern agriculture requires rapid and appropriate responses to weather changes and crop growth conditions. However, traditional methods cannot fully utilize weather data, hindering the improvement of farm work plans. Furthermore, efficiently incorporating work results and feedback to improve productivity is also difficult.

[0408] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0409] In this invention, the server includes information processing means for collecting and analyzing weather information, information processing means for automatically generating a farm work plan based on the analyzed weather information, and means for notifying the user of the generated farm work plan via a terminal device and receiving information on the user's work progress. This enables the provision of accurate farm work plans utilizing weather data and continuous improvement of the plan's accuracy based on feedback.

[0410] "Information processing means" refers to a program or system designed to receive, analyze, generate, and transmit data.

[0411] "Weather information" refers to data that indicates atmospheric conditions for a specific region and time, such as temperature, precipitation, and humidity.

[0412] A "farm work plan" refers to detailed guidelines on the timing and methods of carrying out farm work, which are determined based on weather information and the growth status of crops.

[0413] "Terminal device" refers to a digital device used by a user to receive and input information, and includes, for example, smartphones and tablets.

[0414] A "user" refers to an individual or organization that uses this system to perform agricultural tasks and provides feedback.

[0415] "Feedback" refers to information provided by users based on the results and observations of their work, and is used as data to improve and adjust the system.

[0416] A "machine learning model" refers to a set of algorithms and mathematical formulas used to learn patterns through data analysis and perform predictions and classifications.

[0417] This invention relates to a system for improving agricultural efficiency using AI technology. The following details the configuration for implementing this system.

[0418] The server uses programming languages ​​such as Python and machine learning libraries (e.g., TensorFlow and PyTorch) to collect and analyze weather data. Specifically, the server periodically calls the API of a weather information service to obtain regional weather data in digital format. The acquired data is then stored in a database and analyzed through information processing tools. This analysis helps to understand data trends and fluctuations, and automatically generates optimal farming plans for each crop. This plan utilizes a generative AI model to optimize the timing of tasks such as watering and fertilizing.

[0419] The terminal device functions as a smartphone or tablet based on the Android or iOS platform, providing an intuitive user interface for users to review and execute farm work plans. The terminal notifies the user of the farm work plan received from the server and provides sequential instructions. Based on this, the user performs the actual work and inputs feedback on the progress of the work and the condition of the crops through the terminal.

[0420] Users perform actual farm work according to the generated plan and send feedback information to the server using a terminal device. The server then analyzes the received feedback and adjusts the AI ​​model to continuously improve the efficiency and accuracy of farm work.

[0421] For example, when rainfall is predicted for a particular area, the server re-evaluates the watering plan based on that information, makes necessary adjustments, and then notifies the terminal device. Upon receiving this notification, the user can then appropriately change the timing of fertilizer and pesticide application. This system provides users with an environment where they can conduct appropriate agricultural activities in real time.

[0422] An example of a prompt message is, "Adjust the work plan based on next week's rainfall forecast and notify me of the optimal timing for watering and fertilizing." This prompt message prompts the AI ​​model to generate a specific action plan and provide clear instructions to the user.

[0423] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0424] Step 1:

[0425] The server periodically retrieves the latest weather data from the weather information service's API. This requires specifying a region and time period as input. The retrieved data, such as temperature, precipitation, and humidity, is stored in the server's database in preparation for analysis. Specifically, it uses a Python library to issue API requests, parses the received JSON response, and saves it.

[0426] Step 2:

[0427] The server analyzes weather data obtained from a database using machine learning models. It analyzes trends and anomalies in the input data and predicts future weather patterns. Based on these analysis results, it automatically generates farming plans suitable for each crop. Using the generating AI model, it determines specific tasks, such as "when to refrain from watering in preparation for the next rainfall." The output is a farming plan.

[0428] Step 3:

[0429] The server sends the generated farming plan to the terminal device as a push notification. The plan included in the notification is converted into a format that the user can easily understand. For example, JSON data is converted into the user's language and presented as easy-to-understand instructions. Since the notification is sent in real time, timely instructions can be provided.

[0430] Step 4:

[0431] The terminal device displays the farm work plan received from the server on the user interface. The user then performs the farm work based on this plan. Input is notification content from the server, which is then analyzed and displayed. Considering user convenience, the interface is user-friendly and provides clear instructions.

[0432] Step 5:

[0433] Users input the results and progress of their farm work through a terminal device. The terminal device collects feedback from users and sends it to a server. This feedback includes the completion status of the work, the observed state of the crops, and the impact of weather. The input feedback is organized and compiled into data for transmission to the server.

[0434] Step 6:

[0435] The server analyzes feedback collected from users and uses it to refine the machine learning model. This analysis aims to improve the accuracy and applicability of farm work plans. By analyzing the feedback received as input and incorporating it into the training dataset, it is reflected in the next plan generation. As an output, it becomes possible to generate improved farm work plans.

[0436] (Application Example 1)

[0437] Next, we will explain Application Example 1. In the following explanation, 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."

[0438] Managing green spaces in urban environments requires considerable effort and expertise to constantly monitor plant health and provide appropriate watering and fertilization. Failure to respond quickly to changes in weather conditions can result in poor plant growth and increased management costs. Therefore, there is a need for systems that monitor weather data and plant health in real time to provide optimal management.

[0439] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0440] In this invention, the server includes means for acquiring and analyzing weather data, means for automatically generating a work plan based on the analyzed weather data, and means for monitoring the health status of plants in urban greening management in real time. This enables users to manage green spaces efficiently and effectively based on the latest information at all times.

[0441] "Meteorological data" refers to environmental information such as temperature, precipitation, and humidity, and is a factor that affects plant growth in urban green space management.

[0442] "Means of analysis" refers to the process or apparatus for performing calculations and analyses based on acquired meteorological data and plant information, and then using the results to formulate a management plan.

[0443] "Means for automatically generating business plans" refers to a process or mechanism that automatically creates specific action plans for green space management based on analyzed data.

[0444] "Means of notifying users and accepting user input" refers to an interface or method for informing users of the generated business plan and receiving feedback and additional information from them.

[0445] "Feedback information" refers to information provided by users, including reports on actual work conditions and the health of plants.

[0446] "Means for monitoring the health status of plants in urban greening management in real time" refers to a system or method that uses sensors and monitoring technologies to constantly understand the growth status of plants in urban areas.

[0447] "Means of scheduling optimal watering and fertilization" refers to methods or devices that plan the application of water and fertilizer at the optimal timing and amount for plants, based on real-time weather data and plant conditions.

[0448] The system of this invention consists of three main components: a server, a terminal, and a user. The server utilizes a weather data API to periodically acquire weather information. The collected data includes temperature, precipitation, humidity, etc., and is used for analysis to automatically generate an optimal work plan for green space management. The programming language used here is Python, and the management plan is created based on the analysis results.

[0449] The work plan generated by the server is sent to mobile devices (smartphones and tablets) via the internet. This notification includes recommended watering and fertilizing schedules based on the real-time monitoring of plant health. The device receives feedback from the user and sends this information to the server, contributing to improving the accuracy of the plan.

[0450] Users perform urban green space management tasks based on the work plan notified via their terminal. For example, if the weather forecast predicts 30mm of rain on Saturday, they can adjust their approach accordingly, such as fertilizing on Friday and refraining from irrigating on Saturday.

[0451] Furthermore, the server incorporates features that utilize a generative AI model, allowing users to input prompts to further optimize their next work plan using real-time data and feedback. An example of a prompt would be, "Please provide an optimal green space management plan based on weather forecasts and plant growth data."

[0452] In this way, the system of the present invention provides a solution for efficiently managing plants in urban areas and contributes to maintaining a sustainable urban environment.

[0453] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0454] Step 1:

[0455] The server periodically retrieves weather data from an API of a weather information service. The input data includes temperature, precipitation, and humidity obtained from the API, and the output is this raw data stored in a database. Specifically, Python is used to send requests to the API, parse the retrieved JSON data, extract the necessary items, and store them in the database (SQLite).

[0456] Step 2:

[0457] The server analyzes stored weather data to automatically generate work plans. The input is weather data from a database, and the output is a detailed management plan including schedules and work content. This process uses a data analysis library (e.g., Pandas) to calculate the optimal timing for watering and fertilizing based on weather conditions. Furthermore, the generated plan is refined based on prompts obtained using a generation AI model.

[0458] Step 3:

[0459] The server generates a work plan and sends it to the terminal as a notification. The input is the work plan created on the server, and the output is a notification message sent to the user's mobile device. Specifically, a push notification is sent to the user's device via web server software (e.g., Flask), and the notification content includes the next week's fertilization schedule and recommended watering times.

[0460] Step 4:

[0461] The user receives a notification and takes action based on the plan from their device. The input is the plan displayed on the device, and the output is the actual result of watering or fertilizing. The user reviews the notification and adjusts the date and amount of fertilizer based on the weather.

[0462] Step 5:

[0463] The user sends the results of their work and feedback from their device to the server. The input is the user's work result report, and the output is feedback information stored on the server. Specifically, the user inputs the condition of the plants after the work is done and any observations they made, and sends this information to the server via a device app.

[0464] Step 6:

[0465] The server analyzes the feedback it receives and uses it to improve the accuracy of future business plans. The input is user feedback information, and the output is data that will be used when updating the plan next time. The server analyzes the feedback, stores it in a database, and incorporates the insights gained using a generative AI model into the next plan.

[0466] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0467] This invention is a system that, in addition to improving agricultural efficiency, takes into account the user's emotional state to provide a more comfortable and effective farming experience. The main components of the system are a server, terminal devices, and an emotion engine. Each of these components works in conjunction to support agricultural activities.

[0468] The server retrieves weather data from a weather information service and automatically generates farm work plans using an AI model. These plans include watering schedules based on soil moisture and temperature, and pesticide application plans based on pest infestation risk. The server also considers data from an emotion engine and can adjust specific plans to suit the user's emotions.

[0469] The terminal device receives farm work plans transmitted from the server. Furthermore, the terminal device incorporates an emotion engine that acquires user voice and facial expression data and evaluates emotions in real time. The emotion engine analyzes the acquired emotional data to assess the user's stress level and motivation. Based on this evaluation, it also provides activities and recommended work procedures that contribute to relaxation and motivation maintenance, in addition to the conventional work plan.

[0470] Users receive notifications of farm work plans on their devices and perform the suggested tasks. They also input their experiences and observations into their devices, providing feedback to the server. This allows the system to improve the accuracy of farm work plans while also addressing users' emotional needs.

[0471] For example, if the emotional engine detects fatigue or stress as a result of the user performing harvesting work for a long period of time, the device will immediately suggest a short break and, if necessary, encourage a shift to lighter work. In this way, the present invention can improve the efficiency and effectiveness of agricultural activities while taking into account the user's physical and mental health.

[0472] The following describes the processing flow.

[0473] Step 1:

[0474] The server periodically retrieves the latest weather data from a weather information service API. After retrieving the data, it uses an AI model to predict weather changes and generates an optimal farming plan based on that data.

[0475] Step 2:

[0476] The terminal receives farm work plans sent from the server and notifies the user. The terminal selects the most suitable information display format for the user and provides specific work procedures and timings.

[0477] Step 3:

[0478] The device uses a built-in emotion engine to analyze the user's voice and image data and evaluate the user's emotional state in real time. This evaluation includes factors such as stress level, satisfaction level, and fatigue level.

[0479] Step 4:

[0480] As the user performs farm work, the device continuously collects emotional data using an emotion engine. If the device predicts that the user's workload will become too heavy, it sends a notification suggesting switching to a simpler task or taking a break.

[0481] Step 5:

[0482] Users input the results of their farm work and their impressions into a device to provide feedback. This includes comments on crop growth, work progress, and the appropriateness of suggestions.

[0483] Step 6:

[0484] The server continuously updates its AI model based on user feedback and evaluations from its emotion engine, using this data to create more accurate farming plans for future use. This iterative process ensures the system improves with each use.

[0485] (Example 2)

[0486] Next, we will describe Example 2. 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."

[0487] Conventional agricultural systems can automatically generate work plans based on weather conditions, but they lack a system that adjusts the work plan to take into account the user's emotions and stress levels. As a result, uniform work is recommended regardless of the user's physical and mental health, leading to the problem of increased burden on the user.

[0488] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0489] In this invention, the server includes means for acquiring and analyzing weather information, means for evaluating the user's emotional state, and means for adjusting the farm work plan based on the evaluated emotional state. This makes it possible to adjust the work plan according to the user's emotional state, thereby improving the efficiency and comfort of agricultural activities while taking into account the user's physical and mental health.

[0490] "Weather information" refers to data related to the weather, including information such as temperature, humidity, precipitation, and wind speed.

[0491] "Analysis" refers to the process of interpreting acquired data and organizing it into meaningful information.

[0492] A "farm work plan" is a plan that automatically generates procedures and schedules for crop cultivation and harvesting, taking into account weather conditions and other factors.

[0493] "Means of receiving user input" refers to methods of collecting information and opinions from users through terminals or other interfaces.

[0494] "Feedback" refers to the process of reviewing and improving the system's operation and plan based on user input.

[0495] "Methods for improving accuracy" refer to ways of using user feedback to enhance the accuracy and efficiency of plans and systems.

[0496] "Emotional state" refers to the user's psychological and physiological state, and includes indicators such as stress and motivation.

[0497] "Methods of evaluation" refer to methods of analyzing and judging a user's emotional state based on data such as voice and facial expressions.

[0498] "Means of adjustment" refers to the process of modifying existing plans and schedules to suit the user's situation based on the analysis results.

[0499] This invention provides a more efficient and user-friendly agricultural system by considering weather information and the user's emotional state. Its main components include a server, terminal devices, and an emotion engine. These components work together to comprehensively support agricultural operations.

[0500] The server obtains weather data from weather information service providers via APIs. This requires a commonly used API interface and an internet connection. The server uses generative AI models (e.g., using TensorFlow or PyTorch) to automatically generate farm work plans based on the acquired weather data. These farm work plans include optimal watering schedules and pesticide application plans tailored to pest outbreak risks. This plan is further adjusted based on the user's emotional state. Emotional data is obtained from the emotional engine of the terminal device.

[0501] The terminal device receives the farm work plan transmitted from the server. The terminal has a built-in camera and microphone, which capture the user's voice and facial expressions. The emotion engine evaluates the user's state using an emotion recognition API (e.g., Google Cloud's Sentiment Analysis API). The evaluated emotion data is analyzed as stress levels and motivation indicators and immediately sent to the server. Based on this data, the server adjusts the farm work plan and provides the user with a customized proposal.

[0502] Users review farm work plans through their terminals and carry out the suggested tasks. The terminals function as direct guides, providing visual or audio instructions for each task. Furthermore, real-time feedback of the user's experience and observations is sent to the server, contributing to improved accuracy in future plans.

[0503] For example, if the emotion engine detects stress after a long period of harvesting, the device may send a notification such as, "We recommend a 5-minute break." In this way, it becomes possible to support efficient agricultural work while considering the user's health condition.

[0504] Examples of prompts include, "Please conduct a risk analysis for the next harvest," and "Please suggest activities that will help reduce stress."

[0505] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0506] Step 1:

[0507] The server makes API requests to weather information services to retrieve the latest weather data. Inputs are API keys and parameters, while outputs include weather data such as temperature, humidity, and precipitation. The retrieved JSON data is parsed to extract necessary information and store it in an internal database. This data is then used by an AI model to create the basis for agricultural work plans.

[0508] Step 2:

[0509] The terminal receives farm work plans transmitted from the server. The received data is planning information, including specific work schedules and procedures. The terminal's built-in camera and microphone collect the user's facial expressions and voice. The user's biometric data is used as input and is sent to an emotion recognition API for real-time analysis to evaluate the user's emotional state (e.g., stress level).

[0510] Step 3:

[0511] The server adjusts the farm work plan based on the user's emotional data received from the terminal. The input is the user's emotional state data, and the output is the adjusted work plan. A generative AI model is used to adjust the work plan according to the emotional data, making customizations such as including breaks when stress levels are high.

[0512] Step 4:

[0513] The terminal receives the coordinated work plan from the server and notifies the user. The input is the coordinated work plan, and the output is work guides and notifications for the user. The terminal provides audio or visual guidance to help the user proceed with the work according to the plan.

[0514] Step 5:

[0515] Users input daily work reports and feedback on plans via their devices. This input feedback, in text or voice format, is received by the server and stored in a database. The feedback is then incorporated into future work plans and used to improve the accuracy of the generating AI model.

[0516] (Application Example 2)

[0517] Next, we will explain application example 2. In the following explanation, 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."

[0518] Conventional agricultural work systems failed to adjust work plans to take into account the user's emotions and physical condition, making it difficult to provide an efficient and comfortable working environment. Furthermore, there was a lack of means to properly incorporate feedback and improve the accuracy of agricultural work plans. In addition, the lack of real-time suggestions based on the individual user's condition could lead to the accumulation of stress and fatigue.

[0519] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0520] In this invention, the server includes a device for acquiring and analyzing weather data, a device for automatically generating an agricultural work plan based on the analyzed weather data, and a device for evaluating the user's emotional state. This makes it possible to dynamically provide an optimal agricultural work plan that takes into account the emotional state of each individual user, as well as to create an effective work environment that takes into account the user's physical and mental health.

[0521] "Weather data" refers to information about the weather and is an essential element for formulating work plans in agriculture.

[0522] "Analysis" refers to the process of analyzing collected data and using it to improve the efficiency and optimization of agricultural work.

[0523] An "agricultural work plan" refers to a set of procedures and schedules that are formulated in advance to effectively carry out agricultural activities.

[0524] "User" refers to agricultural workers or operators who use this system.

[0525] "Feedback" refers to opinions and results obtained from users, and is information used to improve and adjust future plans.

[0526] "Emotional state" refers to the user's psychological and emotional condition, and includes elements such as stress and motivation.

[0527] "Stress level" refers to the degree of mental burden felt by the user, and it serves as a criterion for adjusting agricultural work plans.

[0528] "Device" is a general term for the equipment and programs used to realize various functions within a system.

[0529] The system of this invention aims to improve the efficiency of agricultural work and adjust work plans based on the user's emotional state. Specifically, a server, terminal devices, and an emotion engine work in conjunction.

[0530] The server acquires weather data in real time from weather information services via the internet. Based on the acquired data, an AI model automatically generates an agricultural work plan. This plan includes work procedures that correspond to specific weather conditions. For example, if dry weather conditions are predicted, an appropriate watering schedule will be suggested.

[0531] The terminal device is equipped with an emotion engine that analyzes the user's voice and facial expressions in real time. Based on the emotion data obtained from this analysis, the server dynamically adjusts the work plan. For example, if the system determines that the user is in a high-stress state, it suggests a short break to reduce the user's burden.

[0532] As a concrete example, in a farm where vegetable harvesting is performed, the user is analyzed through an emotion engine. When the weather is sunny and a long period of work is predicted, the system displays a notification on the device prompting hydration. Furthermore, based on the assessment of the user's emotional state, it can suggest playing music with a relaxing effect.

[0533] An example of a prompt message is: "Generate the optimal farm work plan based on recent weather data and the user's emotional state. Please provide suggestions that particularly focus on reducing the user's stress." This prompt prompts the AI ​​model to optimize the work plan.

[0534] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0535] Step 1:

[0536] The server retrieves weather data from weather information services. This allows it to understand current and future weather conditions. Inputs include regional information and date / time, while outputs include weather information such as temperature, precipitation, and humidity. For data processing, this data is converted to a standard format to prepare it for subsequent analysis.

[0537] Step 2:

[0538] The server inputs acquired weather data into an AI model and automatically generates agricultural work plans. The input is weather data, and the output is a specific agricultural work plan. In terms of data calculation, the AI ​​model calculates the optimal work procedures and schedules based on temperature and precipitation.

[0539] Step 3:

[0540] The device acquires the user's voice and facial expression data and analyzes it using an emotion engine. The input is real-time data of the user, and the output is emotional state such as stress level and motivation. As part of the data processing, the voice and image data are converted into features for emotion identification.

[0541] Step 4:

[0542] The server adjusts the agricultural work plan using data from the emotion engine. The input is the user's emotional state, and the output is the adjusted work plan. In terms of data calculation, it optimizes the order and content of tasks according to the user's emotional state and suggests breaks or light work as needed.

[0543] Step 5:

[0544] The terminal notifies the user of a customized agricultural work plan and accepts feedback. The input is the customized work plan, and the output is a notification message presented to the user. Specifically, a notification is displayed on a mobile device, allowing the user to review it and then provide any necessary feedback.

[0545] Step 6:

[0546] The server incorporates user feedback and uses it to improve future work plans. Input is feedback information, and output is an updated work plan or base model. Data processing involves analyzing the feedback content and reflecting it in adjusting the parameters of the AI ​​model.

[0547] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0548] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0549] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0550] [Fourth Embodiment]

[0551] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0552] As shown in Figure 7, the 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.

[0553] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0554] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0555] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0556] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0557] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0558] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0559] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0560] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0561] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0562] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0563] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0564] This invention utilizes an AI-powered management system to improve the efficiency of agricultural work and increase participation. Through the interaction of servers, terminal devices, and users, this system automates agriculture and creates an environment where anyone can easily participate.

[0565] The server periodically collects weather data from weather information services. This includes temperature, precipitation, and humidity, and is used to understand detailed weather conditions for each region. This information is stored in a database and prepared for analysis. The server analyzes the weather data and automatically creates a farming plan suitable for each crop. This plan indicates what tasks should be performed and when.

[0566] The terminal device, such as a smartphone or tablet, is used by the user to review the work plan and execute the work procedures. The terminal device is notified of the work plan obtained from the server, and the user can decide on the next action based on that plan. The terminal device also collects data from the user regarding the progress of the work and the growth status, and sends this feedback back to the server. This allows the system to continuously improve the accuracy of the farm work plan.

[0567] Users perform actual farm work based on the notified work plan. After completing the work, they input feedback on the results and points to note using a terminal device. This feedback includes changes in growth status, actual yield, and crop response to weather changes.

[0568] For example, if rainfall is predicted for a certain area next week, the server will determine that watering plans need to be adjusted based on that information and will notify the terminal device accordingly. The user can then use the notification to adjust the timing of fertilizer and pesticide application to match the specified timing. In this way, the present invention supports real-time decision-making in agriculture and improves work efficiency and productivity by enabling optimal agricultural activities.

[0569] The following describes the processing flow.

[0570] Step 1:

[0571] The server periodically accesses the API of a weather information service to obtain the latest data on the ground and atmosphere. This data includes temperature, precipitation, humidity, and wind speed. The server stores this data in a database.

[0572] Step 2:

[0573] The server analyzes collected weather data and uses AI models to predict weather patterns. This allows it to determine how these patterns will affect crop growth and develop appropriate farming plans. For example, it can adjust irrigation plans based on rainfall forecasts.

[0574] Step 3:

[0575] The device receives the latest farming plan sent from the server. The received plan is displayed as a notification on the user's smartphone or tablet. The user reviews the notification and understands the detailed steps and recommended timing for each task.

[0576] Step 4:

[0577] Users perform farming tasks according to notifications on their devices. These tasks include applying appropriate fertilizers and pesticides, and adjusting the amount and timing of watering. Once the tasks are completed, users input the results and any observations through their devices and send the information to the server.

[0578] Step 5:

[0579] The server collects all feedback information sent by users and stores it as a new dataset. Based on this data, the AI ​​model is updated to improve the accuracy of farm work plans for subsequent cycles.

[0580] Step 6:

[0581] The terminal also receives the next farming plan created by the server using the updated AI model and notifies the user again. By repeating this process, the system is continuously optimized to support the user's farming activities.

[0582] (Example 1)

[0583] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0584] Modern agriculture requires rapid and appropriate responses to weather changes and crop growth conditions. However, traditional methods cannot fully utilize weather data, hindering the improvement of farm work plans. Furthermore, efficiently incorporating work results and feedback to improve productivity is also difficult.

[0585] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0586] In this invention, the server includes information processing means for collecting and analyzing weather information, information processing means for automatically generating a farm work plan based on the analyzed weather information, and means for notifying the user of the generated farm work plan via a terminal device and receiving information on the user's work progress. This enables the provision of accurate farm work plans utilizing weather data and continuous improvement of the plan's accuracy based on feedback.

[0587] "Information processing means" refers to a program or system designed to receive, analyze, generate, and transmit data.

[0588] "Weather information" refers to data that indicates atmospheric conditions for a specific region and time, such as temperature, precipitation, and humidity.

[0589] A "farm work plan" refers to detailed guidelines on the timing and methods of carrying out farm work, which are determined based on weather information and the growth status of crops.

[0590] "Terminal device" refers to a digital device used by a user to receive and input information, and includes, for example, smartphones and tablets.

[0591] A "user" refers to an individual or organization that uses this system to perform agricultural tasks and provides feedback.

[0592] "Feedback" refers to information provided by users based on the results and observations of their work, and is used as data to improve and adjust the system.

[0593] A "machine learning model" refers to a set of algorithms and mathematical formulas used to learn patterns through data analysis and perform predictions and classifications.

[0594] This invention relates to a system for improving agricultural efficiency using AI technology. The following details the configuration for implementing this system.

[0595] The server uses programming languages ​​such as Python and machine learning libraries (e.g., TensorFlow and PyTorch) to collect and analyze weather data. Specifically, the server periodically calls the API of a weather information service to obtain regional weather data in digital format. The acquired data is then stored in a database and analyzed through information processing tools. This analysis helps to understand data trends and fluctuations, and automatically generates optimal farming plans for each crop. This plan utilizes a generative AI model to optimize the timing of tasks such as watering and fertilizing.

[0596] The terminal device functions as a smartphone or tablet based on the Android or iOS platform, providing an intuitive user interface for users to review and execute farm work plans. The terminal notifies the user of the farm work plan received from the server and provides sequential instructions. Based on this, the user performs the actual work and inputs feedback on the progress of the work and the condition of the crops through the terminal.

[0597] Users perform actual farm work according to the generated plan and send feedback information to the server using a terminal device. The server then analyzes the received feedback and adjusts the AI ​​model to continuously improve the efficiency and accuracy of farm work.

[0598] For example, when rainfall is predicted for a particular area, the server re-evaluates the watering plan based on that information, makes necessary adjustments, and then notifies the terminal device. Upon receiving this notification, the user can then appropriately change the timing of fertilizer and pesticide application. This system provides users with an environment where they can conduct appropriate agricultural activities in real time.

[0599] An example of a prompt message is, "Adjust the work plan based on next week's rainfall forecast and notify me of the optimal timing for watering and fertilizing." This prompt message prompts the AI ​​model to generate a specific action plan and provide clear instructions to the user.

[0600] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0601] Step 1:

[0602] The server periodically retrieves the latest weather data from the weather information service's API. This requires specifying a region and time period as input. The retrieved data, such as temperature, precipitation, and humidity, is stored in the server's database in preparation for analysis. Specifically, it uses a Python library to issue API requests, parses the received JSON response, and saves it.

[0603] Step 2:

[0604] The server analyzes weather data obtained from a database using machine learning models. It analyzes trends and anomalies in the input data and predicts future weather patterns. Based on these analysis results, it automatically generates farming plans suitable for each crop. Using the generating AI model, it determines specific tasks, such as "when to refrain from watering in preparation for the next rainfall." The output is a farming plan.

[0605] Step 3:

[0606] The server sends the generated farming plan to the terminal device as a push notification. The plan included in the notification is converted into a format that the user can easily understand. For example, JSON data is converted into the user's language and presented as easy-to-understand instructions. Since the notification is sent in real time, timely instructions can be provided.

[0607] Step 4:

[0608] The terminal device displays the farm work plan received from the server on the user interface. The user then performs the farm work based on this plan. Input is notification content from the server, which is then analyzed and displayed. Considering user convenience, the interface is user-friendly and provides clear instructions.

[0609] Step 5:

[0610] Users input the results and progress of their farm work through a terminal device. The terminal device collects feedback from users and sends it to a server. This feedback includes the completion status of the work, the observed state of the crops, and the impact of weather. The input feedback is organized and compiled into data for transmission to the server.

[0611] Step 6:

[0612] The server analyzes feedback collected from users and uses it to refine the machine learning model. This analysis aims to improve the accuracy and applicability of farm work plans. By analyzing the feedback received as input and incorporating it into the training dataset, it is reflected in the next plan generation. As an output, it becomes possible to generate improved farm work plans.

[0613] (Application Example 1)

[0614] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0615] Managing green spaces in urban environments requires considerable effort and expertise to constantly monitor plant health and provide appropriate watering and fertilization. Failure to respond quickly to changes in weather conditions can result in poor plant growth and increased management costs. Therefore, there is a need for systems that monitor weather data and plant health in real time to provide optimal management.

[0616] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0617] In this invention, the server includes means for acquiring and analyzing weather data, means for automatically generating a work plan based on the analyzed weather data, and means for monitoring the health status of plants in urban greening management in real time. This enables users to manage green spaces efficiently and effectively based on the latest information at all times.

[0618] "Meteorological data" refers to environmental information such as temperature, precipitation, and humidity, and is a factor that affects plant growth in urban green space management.

[0619] "Means of analysis" refers to the process or apparatus for performing calculations and analyses based on acquired meteorological data and plant information, and then using the results to formulate a management plan.

[0620] "Means for automatically generating business plans" refers to a process or mechanism that automatically creates specific action plans for green space management based on analyzed data.

[0621] "Means of notifying users and accepting user input" refers to an interface or method for informing users of the generated business plan and receiving feedback and additional information from them.

[0622] "Feedback information" refers to information provided by users, including reports on actual work conditions and the health of plants.

[0623] "Means for monitoring the health status of plants in urban greening management in real time" refers to a system or method that uses sensors and monitoring technologies to constantly understand the growth status of plants in urban areas.

[0624] "Means of scheduling optimal watering and fertilization" refers to methods or devices that plan the application of water and fertilizer at the optimal timing and amount for plants, based on real-time weather data and plant conditions.

[0625] The system of this invention consists of three main components: a server, a terminal, and a user. The server utilizes a weather data API to periodically acquire weather information. The collected data includes temperature, precipitation, humidity, etc., and is used for analysis to automatically generate an optimal work plan for green space management. The programming language used here is Python, and the management plan is created based on the analysis results.

[0626] The work plan generated by the server is sent to mobile devices (smartphones and tablets) via the internet. This notification includes recommended watering and fertilizing schedules based on the real-time monitoring of plant health. The device receives feedback from the user and sends this information to the server, contributing to improving the accuracy of the plan.

[0627] Users perform urban green space management tasks based on the work plan notified via their terminal. For example, if the weather forecast predicts 30mm of rain on Saturday, they can adjust their approach accordingly, such as fertilizing on Friday and refraining from irrigating on Saturday.

[0628] Furthermore, the server incorporates features that utilize a generative AI model, allowing users to input prompts to further optimize their next work plan using real-time data and feedback. An example of a prompt would be, "Please provide an optimal green space management plan based on weather forecasts and plant growth data."

[0629] In this way, the system of the present invention provides a solution for efficiently managing plants in urban areas and contributes to maintaining a sustainable urban environment.

[0630] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0631] Step 1:

[0632] The server periodically retrieves weather data from an API of a weather information service. The input data includes temperature, precipitation, and humidity obtained from the API, and the output is this raw data stored in a database. Specifically, Python is used to send requests to the API, parse the retrieved JSON data, extract the necessary items, and store them in the database (SQLite).

[0633] Step 2:

[0634] The server analyzes stored weather data to automatically generate work plans. The input is weather data from a database, and the output is a detailed management plan including schedules and work content. This process uses a data analysis library (e.g., Pandas) to calculate the optimal timing for watering and fertilizing based on weather conditions. Furthermore, the generated plan is refined based on prompts obtained using a generation AI model.

[0635] Step 3:

[0636] The server generates a work plan and sends it to the terminal as a notification. The input is the work plan created on the server, and the output is a notification message sent to the user's mobile device. Specifically, a push notification is sent to the user's device via web server software (e.g., Flask), and the notification content includes the next week's fertilization schedule and recommended watering times.

[0637] Step 4:

[0638] The user receives a notification and takes action based on the plan from their device. The input is the plan displayed on the device, and the output is the actual result of watering or fertilizing. The user reviews the notification and adjusts the date and amount of fertilizer based on the weather.

[0639] Step 5:

[0640] The user sends the results of their work and feedback from their device to the server. The input is the user's work result report, and the output is feedback information stored on the server. Specifically, the user inputs the condition of the plants after the work is done and any observations they made, and sends this information to the server via a device app.

[0641] Step 6:

[0642] The server analyzes the feedback it receives and uses it to improve the accuracy of future business plans. The input is user feedback information, and the output is data that will be used when updating the plan next time. The server analyzes the feedback, stores it in a database, and incorporates the insights gained using a generative AI model into the next plan.

[0643] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0644] This invention is a system that, in addition to improving agricultural efficiency, takes into account the user's emotional state to provide a more comfortable and effective farming experience. The main components of the system are a server, terminal devices, and an emotion engine. Each of these components works in conjunction to support agricultural activities.

[0645] The server retrieves weather data from a weather information service and automatically generates farm work plans using an AI model. These plans include watering schedules based on soil moisture and temperature, and pesticide application plans based on pest infestation risk. The server also considers data from an emotion engine and can adjust specific plans to suit the user's emotions.

[0646] The terminal device receives farm work plans transmitted from the server. Furthermore, the terminal device incorporates an emotion engine that acquires user voice and facial expression data and evaluates emotions in real time. The emotion engine analyzes the acquired emotional data to assess the user's stress level and motivation. Based on this evaluation, it also provides activities and recommended work procedures that contribute to relaxation and motivation maintenance, in addition to the conventional work plan.

[0647] Users receive notifications of farm work plans on their devices and perform the suggested tasks. They also input their experiences and observations into their devices, providing feedback to the server. This allows the system to improve the accuracy of farm work plans while also addressing users' emotional needs.

[0648] For example, if the emotional engine detects fatigue or stress as a result of the user performing harvesting work for a long period of time, the device will immediately suggest a short break and, if necessary, encourage a shift to lighter work. In this way, the present invention can improve the efficiency and effectiveness of agricultural activities while taking into account the user's physical and mental health.

[0649] The following describes the processing flow.

[0650] Step 1:

[0651] The server periodically retrieves the latest weather data from a weather information service API. After retrieving the data, it uses an AI model to predict weather changes and generates an optimal farming plan based on that data.

[0652] Step 2:

[0653] The terminal receives farm work plans sent from the server and notifies the user. The terminal selects the most suitable information display format for the user and provides specific work procedures and timings.

[0654] Step 3:

[0655] The device uses a built-in emotion engine to analyze the user's voice and image data and evaluate the user's emotional state in real time. This evaluation includes factors such as stress level, satisfaction level, and fatigue level.

[0656] Step 4:

[0657] As the user performs farm work, the device continuously collects emotional data using an emotion engine. If the device predicts that the user's workload will become too heavy, it sends a notification suggesting switching to a simpler task or taking a break.

[0658] Step 5:

[0659] Users input the results of their farm work and their impressions into a device to provide feedback. This includes comments on crop growth, work progress, and the appropriateness of suggestions.

[0660] Step 6:

[0661] The server continuously updates its AI model based on user feedback and evaluations from its emotion engine, using this data to create more accurate farming plans for future use. This iterative process ensures the system improves with each use.

[0662] (Example 2)

[0663] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0664] Conventional agricultural systems can automatically generate work plans based on weather conditions, but they lack a system that adjusts the work plan to take into account the user's emotions and stress levels. As a result, uniform work is recommended regardless of the user's physical and mental health, leading to the problem of increased burden on the user.

[0665] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0666] In this invention, the server includes means for acquiring and analyzing weather information, means for evaluating the user's emotional state, and means for adjusting the farm work plan based on the evaluated emotional state. This makes it possible to adjust the work plan according to the user's emotional state, thereby improving the efficiency and comfort of agricultural activities while taking into account the user's physical and mental health.

[0667] "Weather information" refers to data related to the weather, including information such as temperature, humidity, precipitation, and wind speed.

[0668] "Analysis" refers to the process of interpreting acquired data and organizing it into meaningful information.

[0669] A "farm work plan" is a plan that automatically generates procedures and schedules for crop cultivation and harvesting, taking into account weather conditions and other factors.

[0670] "Means of receiving user input" refers to methods of collecting information and opinions from users through terminals or other interfaces.

[0671] "Feedback" refers to the process of reviewing and improving the system's operation and plan based on user input.

[0672] "Methods for improving accuracy" refer to ways of using user feedback to enhance the accuracy and efficiency of plans and systems.

[0673] "Emotional state" refers to the user's psychological and physiological state, and includes indicators such as stress and motivation.

[0674] "Methods of evaluation" refer to methods of analyzing and judging a user's emotional state based on data such as voice and facial expressions.

[0675] "Means of adjustment" refers to the process of modifying existing plans and schedules to suit the user's situation based on the analysis results.

[0676] This invention provides a more efficient and user-friendly agricultural system by considering weather information and the user's emotional state. Its main components include a server, terminal devices, and an emotion engine. These components work together to comprehensively support agricultural operations.

[0677] The server obtains weather data from weather information service providers via APIs. This requires a commonly used API interface and an internet connection. The server uses generative AI models (e.g., using TensorFlow or PyTorch) to automatically generate farm work plans based on the acquired weather data. These farm work plans include optimal watering schedules and pesticide application plans tailored to pest outbreak risks. This plan is further adjusted based on the user's emotional state. Emotional data is obtained from the emotional engine of the terminal device.

[0678] The terminal device receives the farm work plan transmitted from the server. The terminal has a built-in camera and microphone, which capture the user's voice and facial expressions. The emotion engine evaluates the user's state using an emotion recognition API (e.g., Google Cloud's Sentiment Analysis API). The evaluated emotion data is analyzed as stress levels and motivation indicators and immediately sent to the server. Based on this data, the server adjusts the farm work plan and provides the user with a customized proposal.

[0679] Users review farm work plans through their terminals and carry out the suggested tasks. The terminals function as direct guides, providing visual or audio instructions for each task. Furthermore, real-time feedback of the user's experience and observations is sent to the server, contributing to improved accuracy in future plans.

[0680] For example, if the emotion engine detects stress after a long period of harvesting, the device may send a notification such as, "We recommend a 5-minute break." In this way, it becomes possible to support efficient agricultural work while considering the user's health condition.

[0681] Examples of prompts include, "Please conduct a risk analysis for the next harvest," and "Please suggest activities that will help reduce stress."

[0682] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0683] Step 1:

[0684] The server makes API requests to weather information services to retrieve the latest weather data. Inputs are API keys and parameters, while outputs include weather data such as temperature, humidity, and precipitation. The retrieved JSON data is parsed to extract necessary information and store it in an internal database. This data is then used by an AI model to create the basis for agricultural work plans.

[0685] Step 2:

[0686] The terminal receives farm work plans transmitted from the server. The received data is planning information, including specific work schedules and procedures. The terminal's built-in camera and microphone collect the user's facial expressions and voice. The user's biometric data is used as input and is sent to an emotion recognition API for real-time analysis to evaluate the user's emotional state (e.g., stress level).

[0687] Step 3:

[0688] The server adjusts the farm work plan based on the user's emotional data received from the terminal. The input is the user's emotional state data, and the output is the adjusted work plan. A generative AI model is used to adjust the work plan according to the emotional data, making customizations such as including breaks when stress levels are high.

[0689] Step 4:

[0690] The terminal receives the coordinated work plan from the server and notifies the user. The input is the coordinated work plan, and the output is work guides and notifications for the user. The terminal provides audio or visual guidance to help the user proceed with the work according to the plan.

[0691] Step 5:

[0692] Users input daily work reports and feedback on plans via their devices. This input feedback, in text or voice format, is received by the server and stored in a database. The feedback is then incorporated into future work plans and used to improve the accuracy of the generating AI model.

[0693] (Application Example 2)

[0694] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0695] Conventional agricultural work systems failed to adjust work plans to take into account the user's emotions and physical condition, making it difficult to provide an efficient and comfortable working environment. Furthermore, there was a lack of means to properly incorporate feedback and improve the accuracy of agricultural work plans. In addition, the lack of real-time suggestions based on the individual user's condition could lead to the accumulation of stress and fatigue.

[0696] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0697] In this invention, the server includes a device for acquiring and analyzing weather data, a device for automatically generating an agricultural work plan based on the analyzed weather data, and a device for evaluating the user's emotional state. This makes it possible to dynamically provide an optimal agricultural work plan that takes into account the emotional state of each individual user, as well as to create an effective work environment that takes into account the user's physical and mental health.

[0698] "Weather data" refers to information about the weather and is an essential element for formulating work plans in agriculture.

[0699] "Analysis" refers to the process of analyzing collected data and using it to improve the efficiency and optimization of agricultural work.

[0700] An "agricultural work plan" refers to a set of procedures and schedules that are formulated in advance to effectively carry out agricultural activities.

[0701] "User" refers to agricultural workers or operators who use this system.

[0702] "Feedback" refers to opinions and results obtained from users, and is information used to improve and adjust future plans.

[0703] "Emotional state" refers to the user's psychological and emotional condition, and includes elements such as stress and motivation.

[0704] "Stress level" refers to the degree of mental burden felt by the user, and it serves as a criterion for adjusting agricultural work plans.

[0705] "Device" is a general term for the equipment and programs used to realize various functions within a system.

[0706] The system of this invention aims to improve the efficiency of agricultural work and adjust work plans based on the user's emotional state. Specifically, a server, terminal devices, and an emotion engine work in conjunction.

[0707] The server acquires weather data in real time from weather information services via the internet. Based on the acquired data, an AI model automatically generates an agricultural work plan. This plan includes work procedures that correspond to specific weather conditions. For example, if dry weather conditions are predicted, an appropriate watering schedule will be suggested.

[0708] The terminal device is equipped with an emotion engine that analyzes the user's voice and facial expressions in real time. Based on the emotion data obtained from this analysis, the server dynamically adjusts the work plan. For example, if the system determines that the user is in a high-stress state, it suggests a short break to reduce the user's burden.

[0709] As a concrete example, in a farm where vegetable harvesting is performed, the user is analyzed through an emotion engine. When the weather is sunny and a long period of work is predicted, the system displays a notification on the device prompting hydration. Furthermore, based on the assessment of the user's emotional state, it can suggest playing music with a relaxing effect.

[0710] An example of a prompt message is: "Generate the optimal farm work plan based on recent weather data and the user's emotional state. Please provide suggestions that particularly focus on reducing the user's stress." This prompt prompts the AI ​​model to optimize the work plan.

[0711] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0712] Step 1:

[0713] The server retrieves weather data from weather information services. This allows it to understand current and future weather conditions. Inputs include regional information and date / time, while outputs include weather information such as temperature, precipitation, and humidity. For data processing, this data is converted to a standard format to prepare it for subsequent analysis.

[0714] Step 2:

[0715] The server inputs acquired weather data into an AI model and automatically generates agricultural work plans. The input is weather data, and the output is a specific agricultural work plan. In terms of data calculation, the AI ​​model calculates the optimal work procedures and schedules based on temperature and precipitation.

[0716] Step 3:

[0717] The device acquires the user's voice and facial expression data and analyzes it using an emotion engine. The input is real-time data of the user, and the output is emotional state such as stress level and motivation. As part of the data processing, the voice and image data are converted into features for emotion identification.

[0718] Step 4:

[0719] The server adjusts the agricultural work plan using data from the emotion engine. The input is the user's emotional state, and the output is the adjusted work plan. In terms of data calculation, it optimizes the order and content of tasks according to the user's emotional state and suggests breaks or light work as needed.

[0720] Step 5:

[0721] The terminal notifies the user of a customized agricultural work plan and accepts feedback. The input is the customized work plan, and the output is a notification message presented to the user. Specifically, a notification is displayed on a mobile device, allowing the user to review it and then provide any necessary feedback.

[0722] Step 6:

[0723] The server incorporates user feedback and uses it to improve future work plans. Input is feedback information, and output is an updated work plan or base model. Data processing involves analyzing the feedback content and reflecting it in adjusting the parameters of the AI ​​model.

[0724] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0725] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0726] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0727] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0728] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0729] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0730] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0731] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0732] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0733] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0734] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0735] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0736] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0737] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0738] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0739] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0740] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0741] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0742] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0743] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0744] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0745] The following is further disclosed regarding the embodiments described above.

[0746] (Claim 1)

[0747] Means for acquiring and analyzing weather information,

[0748] A means for automatically generating farm work plans based on analyzed weather information,

[0749] A means of notifying users of the farm work plan and accepting user input,

[0750] A means of recording user input and applying feedback to the plan,

[0751] A means of improving the accuracy of agricultural work plans using feedback information,

[0752] A system that includes this.

[0753] (Claim 2)

[0754] The system according to claim 1, further comprising means for guiding a user through specific procedures for agricultural work using a terminal device.

[0755] (Claim 3)

[0756] The system according to claim 1, wherein the terminal device includes means for selecting local weather information using the user's location information.

[0757] "Example 1"

[0758] (Claim 1)

[0759] Information processing means for collecting and analyzing weather information,

[0760] An information processing means that automatically generates an agricultural work plan based on analyzed weather information,

[0761] A means for notifying the user of the generated farm work plan via a terminal device and receiving information on the user's work progress,

[0762] A means of recording work progress information from users and applying feedback to the plan to improve the applicability of agricultural work plans,

[0763] A means of continuously improving the accuracy of agricultural work plans by adjusting machine learning models using feedback information,

[0764] A system that includes this.

[0765] (Claim 2)

[0766] The system according to claim 1, further comprising a terminal device for instructing a user on specific procedures for agricultural work.

[0767] (Claim 3)

[0768] The system according to claim 1, wherein the terminal device includes means for selecting region-specific weather information using the user's location information.

[0769] "Application Example 1"

[0770] (Claim 1)

[0771] Means for acquiring and analyzing weather data,

[0772] A means of automatically generating a business plan based on analyzed weather data,

[0773] A means of notifying users of the generated business plan and accepting user input,

[0774] A means of recording user input and applying feedback to the plan,

[0775] A means of improving the accuracy of business plans using feedback information,

[0776] A means of monitoring the health status of plants in urban greening management in real time,

[0777] A method for scheduling optimal watering and fertilization based on weather forecast data and plant information,

[0778] A system that includes this.

[0779] (Claim 2)

[0780] The system according to claim 1, further comprising means for guiding users through specific work procedures using a mobile terminal.

[0781] (Claim 3)

[0782] The system according to claim 1, wherein the mobile terminal includes means for selecting local weather data using the user's location information.

[0783] "Example 2 of combining an emotion engine"

[0784] (Claim 1)

[0785] Means for acquiring and analyzing weather information,

[0786] A means for automatically generating farm work plans based on analyzed weather information,

[0787] A means of notifying users of the farm work plan and accepting user input,

[0788] A means of recording user input and applying feedback to the plan,

[0789] A means of improving the accuracy of agricultural work plans using feedback information,

[0790] A means of evaluating the user's emotional state,

[0791] A means of adjusting the farm work plan based on the evaluated emotional state,

[0792] A system that includes this.

[0793] (Claim 2)

[0794] The system according to claim 1, further comprising means for guiding a user through specific procedures for agricultural work using a terminal device.

[0795] (Claim 3)

[0796] The system according to claim 1, wherein the terminal device includes means for collecting and analyzing emotional states in real time.

[0797] "Application example 2 when combining with an emotional engine"

[0798] (Claim 1)

[0799] A device for acquiring and analyzing weather data,

[0800] A device that automatically generates agricultural work plans based on analyzed weather data,

[0801] A device that notifies users of agricultural work plans and accepts user input,

[0802] A device that records user input and applies improvements to the plan,

[0803] A device that improves the accuracy of agricultural work plans using improvement information,

[0804] A device for evaluating the emotional state of the user,

[0805] A device that adjusts and proposes plans based on emotional state,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, further comprising a device that uses a terminal device to guide the user through the specific procedures of agricultural work. The system according to claim 1, further comprising a device that suggests breaks or light work according to the user's emotional state.

[0809] (Claim 3)

[0810] The system according to claim 1, wherein the terminal device includes a device that selects local weather data using the user's location information. The system according to claim 1, further including a device that makes suggestions according to the stress level using the emotional state. [Explanation of Symbols]

[0811] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for acquiring and analyzing weather data, A means of automatically generating a business plan based on analyzed weather data, A means of notifying users of the generated business plan and accepting user input, A means of recording user input and applying feedback to the plan, A means of improving the accuracy of business plans using feedback information, A means of monitoring the health status of plants in urban greening management in real time, A method for scheduling optimal watering and fertilization based on weather forecast data and plant information, A system that includes this.

2. The system according to claim 1, further comprising means for guiding users through specific work procedures using a mobile terminal.

3. The system according to claim 1, wherein the mobile terminal includes means for selecting local weather data using the user's location information.

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