System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting environmental data using a sensor; means for transmitting the environmental data to a server; data analysis means using a generative AI for analyzing the environmental data; means for automatically controlling agricultural equipment based on the data analysis means; and means for notifying a user of an analysis result and control information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The agricultural sector faces serious problems such as an aging population and a lack of successors, making it difficult for new talent to enter the field of traditional agriculture, which relies on experience and intuition. Furthermore, adapting to weather conditions and environmental fluctuations requires advanced knowledge and experience, which hinders improvements in agricultural efficiency. To solve these issues, there is a need for automated environmental data collection and analysis technology that can be easily used even by inexperienced workers. [Means for solving the problem]

[0005] This invention provides a means for collecting environmental data using sensors and transmitting that data to a server. It also incorporates an algorithm that uses generative AI to analyze the collected data and predict the optimal growing conditions for plants. It builds a system that includes a means for automatically controlling agricultural equipment based on the analysis results. Additionally, this system notifies the user of the analysis results and control information, and provides an interface that allows the user to change settings and issue additional instructions. This enables even inexperienced workers to achieve planned and efficient farming without relying on intuition or experience.

[0006] A "sensor" is a device that detects the physical state of the environment and outputs it as digital or analog data.

[0007] "Environmental data" refers to information related to the agricultural environment, such as temperature, humidity, water content, and hours of sunlight.

[0008] A "server" is a central computer that stores and processes data and communicates with other devices.

[0009] "Generative AI" refers to artificial intelligence technology that analyzes collected data and generates optimal results for a specific purpose.

[0010] "Data analysis means" refers to a technique or mechanism for analyzing the provided data and extracting useful information.

[0011] "Agricultural equipment" refers to machines and devices used to assist or automate agricultural operations, such as watering systems and fertilizer spreading equipment.

[0012] "Control means" refers to the technology or mechanism that directs and controls the operation of equipment or systems based on the results of data analysis.

[0013] "User" refers to a person who can operate this system and change settings or give additional instructions.

[0014] An "interface" is a means or medium by which a user inputs instructions to a system or checks information.

[0015] "Notification means" refers to a technology or mechanism for informing the user of the analysis results and control information of the generating AI. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0024] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0037] ---

[0038] MODE FOR CARRYING OUT THE INVENTION

[0039] This invention is a system aimed at automating and streamlining agriculture, and includes sensors, a generation AI server, a control terminal, and a user interface. This system uses sensors to collect environmental data and transmits it to the server. The generation AI in the server analyzes the data and appropriately controls agricultural equipment based on the results. The user is also notified of the analysis results and control information, allowing them to change system settings as needed.

[0040] System Overview

[0041] First, sensors (temperature, humidity, moisture content, etc.) are installed in the field to collect environmental data in real time. The collected data is then sent to a terminal, where it is temporarily stored.

[0042] The device then periodically uploads the collected data to a server, which stores the data in a database and instructs the AI ​​to analyze it. The AI ​​then uses the environmental data to predict optimal conditions for plant growth.

[0043] The server then generates the necessary control instructions based on the analysis results of the AI ​​and sends them to the terminal, which then executes the received control instructions to control agricultural equipment such as watering systems and fertilizer spreaders.

[0044] Furthermore, the server notifies the user of the analysis results and executed control information via a user interface. The user can check the information using a dedicated application or web portal, and change settings or issue additional instructions as necessary.

[0045] Specific examples of program processing

[0046] Example 1: Temperature and humidity control

[0047] The device measures the temperature and humidity of the farmland every hour through temperature and humidity sensors. For example, at 10:00 a.m., the temperature is measured as 25°C and the humidity as 60%.

[0048] The device stores this data in local storage and uploads it to the server at noon.

[0049] The server instructs the AI ​​to analyze new data, and the AI ​​predicts optimal growing conditions based on data from the past seven days. For example, it generates instructions such as "Because the temperature will be high tomorrow, start the watering system at 2 p.m."

[0050] The server sends this instruction to the terminal, which then automatically starts the watering system at 2:00 p.m. based on the instruction.

[0051] The server also notifies the user of the analysis results and control information of the generating AI, and the user confirms on the smartphone app that "watering started at 2 p.m."

[0052] Example 2: Optimizing fertilizer application

[0053] The device uses a soil moisture sensor to measure the moisture content of the soil. For example, at 8:00 AM, the moisture content is measured as 40%.

[0054] The device uploads the data to a server, which then analyzes it using a generative AI. The generative AI analyzes past data and concludes that fertilizer application should be stopped until the next rain falls.

[0055] Based on the analysis results, the server generates a stop instruction for the fertilizer spreading device and sends it to the terminal.

[0056] The terminal receives this instruction and immediately stops the operation of the fertilizer spreading device.

[0057] The server notifies the user of this operation, and the user confirms in the application that "fertilizer spreading has been stopped."

[0058] As described above, this system combines sensors, generation AI, servers, terminals, and a user interface to automate agricultural work and enable it to be carried out efficiently and systematically. The types of sensors and devices used, analysis algorithms, and control functions can be customized as appropriate to suit the specific agricultural environment and user needs.

[0059] The processing flow will be explained below.

[0060] ---

[0061] Step 1:

[0062] The device measures environmental data using the temperature sensor, humidity sensor, and moisture sensor. For example, at 10:00 AM, the temperature sensor measures 25°C, the humidity sensor measures 60%, and the moisture sensor measures 40%.

[0063] Step 2:

[0064] The device stores the measured environmental data in local storage. The stored data includes the measurement value and a timestamp (e.g., 10:00 AM, October 10, 2023).

[0065] Step 3:

[0066] The device transmits the environmental data stored in the local storage to the server at regular intervals (e.g., every hour). The transmitted data includes a series of measurement values ​​and corresponding timestamps.

[0067] Step 4:

[0068] The server receives the environmental data sent from the device and stores it in a database, including the sensor measurements and timestamps.

[0069] Step 5:

[0070] Every time new data is stored in the database, the server instructs the AI ​​to analyze the data. When the instruction is issued, past environmental data is also provided to the AI.

[0071] Step 6:

[0072] The generative AI analyzes the provided environmental data and predicts the optimal growing conditions for the plant, generating specific actions as a result (e.g., "Increase watering for the next three days").

[0073] Step 7:

[0074] The server receives the analysis results provided by the generation AI and generates specific control instructions for controlling various agricultural equipment based on those results, such as "Start the watering system at 8:00 a.m. tomorrow and stop it at 10:00 a.m."

[0075] Step 8:

[0076] The server transmits the generated control instruction to the terminal.

[0077] Step 9:

[0078] The terminal executes the control instructions received from the server, controlling agricultural equipment such as watering systems and fertilizer spreading devices based on the specified time.

[0079] Step 10:

[0080] The server then sends notifications to the user about the results of the generative AI's analysis and the execution of control instructions, for example, via a smartphone app or web portal.

[0081] Step 11:

[0082] Users receive notifications and can view the current status of the system and analysis results through an application or web portal.

[0083] Step 12:

[0084] Users can change settings or add commands to the system as needed through the application or web portal, for example, manually changing the fertilizer application schedule.

[0085] The above is a detailed description of the processing flow of this system. In this way, a system is realized in which data collected from sensors is analyzed by generative AI, reflected in the automatic control of agricultural equipment, and feedback is also provided to users.

[0086] Example 1

[0087] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0088] In conventional agricultural systems, the collection and analysis of environmental data is often done manually, making efficient agricultural management difficult. Furthermore, controlling agricultural equipment and changing its settings requires direct user intervention, which is time-consuming and labor-intensive. Furthermore, analysis results and control information are not immediately communicated to users, which can delay appropriate responses.

[0089] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0090] In this invention, the server includes means for collecting environmental data using sensors, means for temporarily storing the environmental data in a terminal and transmitting it to the server at regular intervals, means for storing the environmental data in a database and performing data analysis using a generative AI model, means for automatically controlling agricultural equipment based on the data analysis means, means for notifying a user of the analysis results and control information, and means for providing an interface for the user to check the notification content and change control settings as necessary. This enables efficient collection and analysis of environmental data, automatic control of agricultural equipment, and immediate user notification and setting changes.

[0091] A "sensor" is a device for measuring environmental data (e.g., temperature, humidity, water content, etc.).

[0092] A "terminal" is a device that has the function of temporarily storing environmental data collected from sensors and transmitting it to a server.

[0093] The "server" is a device that receives environmental data sent from the terminal, stores it in a database, analyzes the data using a generative AI model, and generates control instructions based on the analysis results.

[0094] A "database" is a collection of information that allows the server to systematically store and manage environmental data received.

[0095] A "generative AI model" is an artificial intelligence algorithm that analyzes environmental data and generates optimal control instructions for agricultural equipment.

[0096] "Data analysis" is the process of analyzing collected environmental data using a generative AI model and generating optimal control instructions for agricultural equipment based on the results.

[0097] "Agricultural equipment" refers to devices that automate and streamline agricultural work, such as watering systems and fertilizer spreading devices.

[0098] An "interface" is a means by which a user changes system settings or issues additional instructions, and includes dedicated applications and web portals.

[0099] "Notification" is a means for conveying analysis results and control information to the user.

[0100] MODE FOR CARRYING OUT THE INVENTION

[0101] This invention is a system for achieving automation and efficiency in agriculture, and includes sensors, a generation AI server, a control terminal, and a user interface. The system uses sensors to collect environmental data on farmland and transmits the data to the server. The generation AI in the server analyzes the data and generates appropriate control instructions for agricultural equipment based on the results. The user is notified of the analysis results and control information, and can change the system settings as needed.

[0102] System Overview

[0103] First, environmental data is collected in real time using sensors (e.g., temperature, humidity, and moisture sensors) installed in the farmland. The collected data is temporarily stored on the device.

[0104] The device then periodically uploads the collected data to a server, which stores the data in a database and analyzes it using a generative AI model. Based on past data, the generative AI predicts the optimal conditions for plant growth.

[0105] The server then generates the necessary control instructions based on the analysis results of the AI ​​and sends them to the terminal, which then executes the received control instructions to control agricultural equipment such as watering systems and fertilizer spreaders.

[0106] Furthermore, the server notifies the user of the analysis results and executed control information via a user interface. The user can check the information using a dedicated application or web portal, and change settings or issue additional instructions as necessary.

[0107] Hardware and software used

[0108] Sensors: Temperature sensors, humidity sensors, moisture sensors, etc. Examples include the DHT22 sensor and the soil moisture sensor (YL-69).

[0109] Terminal: A microcontroller such as a Raspberry Pi or Arduino.

[0110] Server: A high-performance server for running the generative AI. For example, we use an AWS (registered trademark) EC2 instance.

[0111] Generative AI models: Use deep learning frameworks such as PyTorch and TENSORFLOW®.

[0112] Database: Possible databases include MySQL (registered trademark) and PostgreSQL.

[0113] User interface: Smartphone app (iOS, ANDROID (registered trademark)) or web portal (front-end frameworks such as React.js or Angular).

[0114] Specific examples

[0115] Example 1: Temperature and humidity control

[0116] The device uses a temperature sensor (e.g., DHT22) and a humidity sensor to measure the temperature and humidity of the field every hour. For example, at 10:00 AM, the temperature is measured as 25°C and the humidity is measured as 60%.

[0117] The device stores this data in local storage (e.g., SQLite database), converts it to CSV format, and sends it to the server at noon.

[0118] The server stores the received data in a MySQL database and analyzes it using a generative AI model. Based on data from the past seven days, the generative AI generates an analysis result such as "Tomorrow's temperature will be high, so start the watering system at 2:00 PM."

[0119] The server sends this instruction in JSON format to the terminal, which then turns on the watering system via a relay circuit at 2:00 PM.

[0120] The server pushes the analysis results and control information to the user's smartphone app, and the user confirms that "watering started at 2:00 p.m."

[0121] Example 2: Optimizing fertilizer application

[0122] The device measures the soil moisture content using a soil moisture sensor (e.g., YL-69). For example, at 8:00 AM, the moisture content is measured as 40%.

[0123] The device uploads the data to the server at noon, where it is formatted in CSV format and sent via an HTTP POST request.

[0124] The server stores the data in a database and analyzes it using a generative AI model, which analyzes past data and concludes that fertilizer application should be suspended until the next rain.

[0125] Based on the analysis results, the server sends a stop command to the terminal in JSON format to the fertilizer spreading device.

[0126] The terminal receives this instruction and immediately stops the operation of the fertilizer spreading device via the relay circuit.

[0127] The server notifies the user of this operation using Firebase Cloud Messaging, and the user confirms in a dedicated app that "fertilizer spreading has been stopped."

[0128] This system will improve the efficiency and automation of agricultural work, allowing users to easily monitor and control the condition of their farmland.

[0129] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0130] Step 1: Collecting data with sensors

[0131] The terminal collects environmental data in real time using various sensors (temperature sensors, humidity sensors, moisture sensors, etc.) installed on the farmland.

[0132] Input: Real-time environmental data from sensors (temperature, humidity, moisture content).

[0133] How it works: For example, the temperature sensor (DHT22) measures the temperature and humidity every hour. This data is instantly stored in the device's memory.

[0134] Output: Collected environmental data (e.g., temperature 25°C, humidity 60% at 10 AM).

[0135] Step 2: Temporarily save data

[0136] The device temporarily stores the collected environmental data in local storage (e.g., SQLite database) within the device.

[0137] Input: Environmental data collected from sensors (temperature, humidity, moisture content).

[0138] Operation: The device saves the collected data in a specified format (e.g., CSV format) in local storage.

[0139] Output: Environment data saved to local storage.

[0140] Step 3: Send data

[0141] The device uploads the stored data to the server at regular intervals.

[0142] Input: Environment data stored in local storage.

[0143] Operation: At noon, the device converts the saved data into CSV format and sends it to the server using an HTTP POST request.

[0144] Output: The environment data sent to the server.

[0145] Step 4: Save your data

[0146] The server stores the received environmental data in a database (e.g., MySQL).

[0147] Input: Environmental data sent from the device.

[0148] Operation: The server receives the incoming data and stores it in the database appropriately.

[0149] Output: Environmental data stored in a database.

[0150] Step 5: Data analysis

[0151] The server analyzes the received data using a generative AI model.

[0152] Input: Environmental data stored in a database.

[0153] How it works: An analysis script runs periodically, calling a generative AI model (e.g., using TensorFlow) to perform analysis based on the past 7 days of data.

[0154] Output: Analysis results from the generative AI model (optimal growing conditions).

[0155] Step 6: Generate control instructions

[0156] Based on the analysis results of the generation AI, the server generates the necessary control instructions and sends them to the terminal.

[0157] Input: Analysis results from the generative AI model.

[0158] Operation: The server receives the results of the generation AI, generates control instructions (e.g., start the watering system at 2 p.m.), and sends them to the terminal in JSON format.

[0159] Output: Control instructions sent to the terminal.

[0160] Step 7: Controlling agricultural equipment

[0161] The terminal controls the agricultural equipment based on the received control instructions.

[0162] Input: Control instructions received from the server (JSON format).

[0163] Operation: The terminal operates the watering system and fertilizer application device through the relay circuit based on the received instruction. For example, turn on the watering system through the relay circuit at 2:00 PM.

[0164] Output: Controlled agricultural equipment (e.g. turning on a watering system).

[0165] Step 8: User Notifications and Configuration Changes

[0166] The server notifies the user of the analysis results and control information through a user interface.

[0167] Input: Analysis results of the generative AI model and the device's control execution log.

[0168] How it works: The server uses Firebase Cloud Messaging (FCM) to push the analysis results and control information to the user's smartphone app or web portal.

[0169] Output: A notification that appears on the user's smartphone or web portal (e.g., "Watering started at 2 PM").

[0170] Based on these notifications, users can change settings or issue additional instructions as needed using a dedicated application or web portal. This series of processes enables efficient and planned automated control of agricultural work.

[0171] (Application example 1)

[0172] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0173] Modern agriculture demands automation and efficiency, but conventional systems lack the ability to collect and analyze environmental data, making it difficult to control agricultural equipment and manage quality.Furthermore, food delivery services face many challenges in managing the quality of food during delivery and creating optimal delivery routes, and there has been no way to efficiently and comprehensively manage these issues.

[0174] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0175] In this invention, the server includes means for collecting environmental data using sensors, means for transmitting the environmental data to the server, means for analyzing the environmental data using a generation AI, means for automatically controlling agricultural equipment based on the data analysis means, means for notifying the user of the analysis results and control information, means for monitoring the temperature and humidity of items being delivered in real time, means for generating an optimal delivery route taking into account traffic conditions and order priority, and means for collecting and analyzing user feedback on delivery quality. This will advance the automation and efficiency of agriculture, while also enabling integrated quality control during food delivery and optimal delivery planning.

[0176] A "sensor" is a device for collecting environmental data, and includes a temperature sensor, a humidity sensor, and the like.

[0177] "Environmental data" refers to various information collected, such as temperature, humidity, and soil moisture content, that is necessary for agricultural operations and food delivery management.

[0178] The "server" is a centralized computer system that receives environmental data and analyzes it using generative AI.

[0179] "Generative AI" is a system that uses artificial intelligence to analyze collected data and generate optimal control instructions and predictions.

[0180] "Data analysis means" refers to methods or devices that use generative AI to analyze environmental data and derive the necessary control instructions and predictions.

[0181] "Agricultural equipment" refers to devices or machines that are automatically controlled to improve the efficiency of agricultural work, including, for example, watering systems and fertilizer spreading devices.

[0182] The "means for notifying the user" refers to a system or method for notifying the user of analysis results and control information through a user interface.

[0183] "Means for real-time monitoring of temperature and humidity of goods during delivery" refers to devices or systems that continuously monitor the temperature and humidity of the environment in which food or goods are kept during delivery and collect necessary information.

[0184] "Means for generating optimal delivery routes" refers to devices or algorithms that calculate efficient delivery routes in real time, taking into account traffic conditions and order priorities.

[0185] "Means for collecting and analyzing user feedback regarding delivery quality" refers to a system that collects opinions and reviews regarding quality from delivery personnel and customers, and analyzes that information using generative AI, etc.

[0186] MODE FOR CARRYING OUT THE INVENTION

[0187] This invention is a system aimed at automating and streamlining agriculture and controlling food quality in food delivery services. It includes sensors, a generation AI server, a control terminal, and a user interface. The system uses sensors to collect environmental data and transmits it to the server. The generation AI on the server analyzes the data and appropriately controls agricultural equipment based on the results. Users are also notified of the analysis results and control information, allowing them to change system settings as needed.

[0188] System Overview

[0189] First, sensors (for temperature, humidity, soil moisture, etc.) are installed in farmland and delivery vehicles to collect environmental data in real time. The collected data is sent to a terminal, which temporarily stores it. Next, the terminal uploads the collected data to a server at regular intervals. The server stores the received data in a database and instructs the generation AI to analyze the data. The generation AI predicts the optimal conditions for plant growth based on the environmental data. The server also monitors the temperature and humidity during delivery in real time and generates the optimal delivery route.

[0190] Based on the analysis results of the generation AI, the server generates the necessary control instructions and sends them to the terminal. The terminal then executes the received control instructions to control agricultural equipment such as watering systems and fertilizer spreaders. It also notifies the user of necessary measures based on the environmental conditions of the delivery vehicle during delivery. The server then notifies the user of the analysis results and information on the executed controls via a user interface. The user can check this information using a dedicated application or web portal and change settings or issue additional instructions as necessary.

[0191] Program Processing Details

[0192] Hardware and software used

[0193] Hardware: smartphones, sensors

[0194] Software: Python, Flask

[0195] Program Processing Overview

[0196] 1. Collect data from sensors: Collect data from temperature and humidity sensors and send the data to the device.

[0197] 2. Temporary storage and uploading of data: The device temporarily stores the collected data and uploads it to the server at regular intervals.

[0198] 3. Data analysis on the server: The server analyzes the data using generative AI and generates optimal control instructions.

[0199] 4. Execution of control instructions: The terminal executes the control instructions received from the server and controls the agricultural equipment.

[0200] 5. Notification to the user: The server notifies the user of the analysis results and control information of the generated AI through the user interface.

[0201] Specific examples

[0202] Temperature and humidity control: During delivery, the delivery person's smartphone will notify them that the temperature has exceeded 35 degrees Celsius and that they should take measures to lower the temperature in the cooler. The delivery person will then follow the instructions and adjust the temperature in the cooler.

[0203] Optimal delivery route generation: When a customer places an order, the app will notify them, "Currently, the delivery is congested. Please select Route B as the optimal route."

[0204] Prompt Sentence Examples

[0205] "Generate the optimal route from current location A to destination B, taking into account traffic conditions and order priorities."

[0206] This series of systems will simultaneously achieve efficiency and quality control in both agricultural work and food delivery services. Users will also be able to flexibly respond by changing the system settings and issuing additional instructions.

[0207] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0208] Step 1:

[0209] Collecting data from sensors

[0210] Input: Sensors (Temperature sensor, Humidity sensor, etc.)

[0211] How it works: The sensors measure the temperature and humidity in real time on farmland or inside delivery vehicles. For example, the temperature sensor measures 25°C and the humidity sensor measures 60%.

[0212] Output: Collected environmental data

[0213] Step 2:

[0214] Temporary storage and upload of data

[0215] Input: Collected environmental dates

[0216] Specific operation: The device temporarily stores the collected data in local storage. At regular intervals (e.g., every hour), the device uploads the stored data to the server.

[0217] Output: Environment data sent to the server

[0218] Step 3:

[0219] Data analysis on the server

[0220] Input: Environmental data uploaded to the server

[0221] Specific operation: The server stores the received environmental data in a database. It then uses the generation AI to analyze it, including past data. For example, if the temperature is too high, it generates instructions such as "Start automatic watering at 2:00 PM."

[0222] Output: Analysis results and control instructions

[0223] Step 4:

[0224] Sending and executing control instructions

[0225] Input: Generated analysis results and control instructions

[0226] Specific operation: The server sends the generated control instructions to the terminal. The terminal automatically controls the corresponding agricultural equipment (e.g., watering system or fertilizer spraying device) based on the received control instructions.

[0227] Output: Operation of agricultural equipment according to the executed control instructions

[0228] Step 5:

[0229] User Notification and Feedback

[0230] Input: Analysis results and control information

[0231] Specific operation: The server notifies the user of the analysis results and executed control information through the user interface. The user checks the notifications on a dedicated smartphone app or web portal and changes system settings or issues additional instructions as necessary. For example, the user confirms that "watering started at 2:00 PM."

[0232] Output: Notification and feedback to the user

[0233] In this way, each step is processed continuously and automatically, realizing a series of processes from data collection from sensors to analysis by generative AI and control of control devices, enabling efficient environmental management and quality maintenance in agriculture and food delivery.

[0234] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0235] ---

[0236] MODE FOR CARRYING OUT THE INVENTION

[0237] This invention is a system that aims to automate and streamline agriculture by combining sensors, generative AI, servers, terminals, a user interface, and an emotion engine. This system includes components that realize environmental data collection, data analysis, agricultural equipment control, user notifications, and user emotion recognition functions.

[0238] System Overview

[0239] First, various sensors (temperature sensors, humidity sensors, moisture sensors, etc.) are placed at the installation location and environmental data is collected in real time using these sensors. The collected data is sent to the device, which then temporarily stores the data in local storage.

[0240] The device then periodically uploads the collected data to a server, which receives it and stores it in a database. The generative AI on the server analyzes the collected data and uses it to predict the optimal conditions for plant growth.

[0241] Based on the analysis results of the generative AI, the server generates specific control instructions and sends them to the terminal, which then follows the instructions to actually control agricultural equipment such as watering systems and fertilizer spreaders.

[0242] Furthermore, the analysis results and control instructions of the generative AI are notified to the user via a user interface from the server. The user receives these notifications on a dedicated application or web portal, where they can check the current status of the system and the analysis results. The user can also change settings and issue additional instructions to the system through the interface.

[0243] Adding an Emotion Engine

[0244] One of the features of the present invention is that it includes an emotion engine that recognizes the user's emotion. The emotion engine analyzes the user's emotion from, for example, their voice or facial expression, and recognizes their emotional state.

[0245] The emotion engine can tailor notification content based on the emotion it recognizes. For example, if a user is feeling stressed, the notification could have a softer tone or include a positive, encouraging message. The emotion engine can also suggest changes to system settings based on the user's emotion. For example, if a user expresses anxiety, the system could provide more detailed explanations or guidance.

[0246] Specific examples of program processing

[0247] Example 1: Temperature and humidity control and user emotion recognition

[0248] The device measures the temperature and humidity at 10:00 AM to be 25°C and 60%.

[0249] The device stores this data in local storage and sends it to the server at noon.

[0250] The server instructs the generating AI to analyze the data, and the generating AI makes a prediction such as, "Since high temperatures are expected tomorrow, watering will begin at 2 p.m."

[0251] The server sends this instruction to the terminal, which then automatically starts the watering system at 2:00 p.m.

[0252] The server notifies the user of the analysis results and control information of the generative AI, and the emotion engine analyzes the user's emotions.

[0253] If the emotion engine recognizes that the user is stressed, it will change the notification content to a calmer message.

[0254] Example 2: Optimizing fertilizer application and suggesting setting changes to users

[0255] At 8:00 a.m., the device measures the moisture content to 40% using the moisture sensor and sends this data to the server.

[0256] The server uses a generative AI to generate analysis results such as "stop spreading fertilizer until the next rain comes."

[0257] The server sends this instruction to the terminal, and the terminal stops the operation of the fertilizer spreading device.

[0258] The server notifies the user of the analysis results and control information, and the emotion engine analyzes the user's emotions.

[0259] If the emotion engine recognizes that the user is expressing anxiety, the system will suggest "providing more detailed instructions or additional help."

[0260] The above is a specific embodiment of this system. By incorporating an emotion engine, user-friendly support becomes possible, allowing agricultural work to be carried out efficiently and safely.

[0261] The processing flow will be explained below.

[0262] ---

[0263] Example 1: Temperature and humidity control and user emotion recognition

[0264] Step 1:

[0265] The device uses temperature and humidity sensors to measure environmental data every hour. For example, at 10:00 AM, the temperature is 25°C and the humidity is 60%.

[0266] Step 2:

[0267] The device stores the measured data in local storage, which includes the measurement value and a timestamp (e.g., October 10, 2023, 10:00 AM).

[0268] Step 3:

[0269] The device sends the stored environmental data to the server every hour, for example, uploading the data from 10:00 AM at 11:00 AM.

[0270] Step 4:

[0271] The server stores the received environmental data in a database, which includes the measured values ​​and timestamps.

[0272] Step 5:

[0273] Every time new data is stored, the server instructs the generation AI to analyze the data, which then analyzes past data as well.

[0274] Step 6:

[0275] The generative AI predicts the optimal growing conditions for plants based on the provided environmental data, generating instructions such as "Start the watering system tomorrow at 2:00 PM."

[0276] Step 7:

[0277] Based on the analysis results provided by the generation AI, the server generates specific control instructions and sends them to the terminal.

[0278] Step 8:

[0279] The device executes the control instructions received from the server, for example, starting the watering system at 2:00 PM.

[0280] Step 9:

[0281] The server notifies the user of the analysis results and control information of the generated AI via a smartphone app.

[0282] Step 10:

[0283] The emotion engine analyzes the user's facial expressions and voice when checking notifications to recognize their emotional state.

[0284] Step 11:

[0285] The server adjusts the notification content appropriately based on the emotions recognized by the emotion engine. For example, if the user is feeling stressed, the tone of the notification will be softened.

[0286] Example 2: Optimizing fertilizer application and suggesting setting changes to users

[0287] Step 1:

[0288] The device uses a moisture sensor to measure the moisture content of the soil. For example, at 8:00 AM, it measures the moisture content to be 40%.

[0289] Step 2:

[0290] The device stores the measured data in local storage, which includes the measurement value and a timestamp.

[0291] Step 3:

[0292] The device uploads the saved data to the server at regular intervals (for example, sending data to the server at 8:00 when the device is opened).

[0293] Step 4:

[0294] The server stores the received data in a database, which includes the measurement value and a timestamp.

[0295] Step 5:

[0296] Every time new data is stored, the server instructs the generation AI to analyze the data, which then analyzes past data as well.

[0297] Step 6:

[0298] The generative AI predicts the optimal conditions for fertilizer application based on the provided environmental data, generating instructions such as "stop fertilizer application until the next rain."

[0299] Step 7:

[0300] Based on the analysis results provided by the generation AI, the server generates specific control instructions and sends them to the terminal.

[0301] Step 8:

[0302] The terminal executes the control instruction received from the server, for example, stopping the operation of the fertilizer spreading device.

[0303] Step 9:

[0304] The server notifies the user of the analysis results and control information of the generated AI via a smartphone app.

[0305] Step 10:

[0306] The emotion engine analyzes the user's facial expressions and voice when checking notifications to recognize their emotional state.

[0307] Step 11:

[0308] If the emotion engine recognizes that the user is expressing anxiety, the server will suggest system configuration changes or further guidance, such as providing additional help information or instructions.

[0309] The above are the specific processing steps for implementing an agricultural automation system incorporating an emotion engine. This system simultaneously improves the efficiency of agricultural work and the user experience.

[0310] Example 2

[0311] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0312] In modern agriculture, collecting and analyzing environmental data to optimize plant growth conditions is important, but doing so manually takes time and effort. Furthermore, there is a need to support smoother agricultural work by providing optimal notifications based on the user's emotional state, but existing systems lack notifications and suggestions that take the user's emotions into account. This makes it difficult to reduce user stress and provide a sense of security, making it difficult to achieve the goals of improving agricultural efficiency and automation.

[0313] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0314] In this invention, the server includes means for collecting environmental data using sensors, means for transmitting the environmental data to the server, means for analyzing the environmental data using a generative AI, means for automatically controlling agricultural equipment, means for notifying the user of the analysis results and control information, means for analyzing the user's emotional state using an emotion engine and adjusting the notification content, and means for proposing changes to system settings based on the analysis of the emotion engine. This not only enables efficient collection and analysis of environmental data and automatic control of agricultural equipment, but also provides appropriate notifications and suggestions according to the user's emotional state, enabling user-friendly support for agricultural work.

[0315] A "sensor" is a device for detecting environmental data such as temperature, humidity, and moisture content.

[0316] "Environmental data" refers to information related to plant growth conditions, such as temperature, humidity, and water content.

[0317] "Server" is a computer system for storing and analyzing collected environmental data.

[0318] "Generative AI" is artificial intelligence that uses machine learning algorithms to analyze environmental data and make predictions.

[0319] "Data analysis means" refers to a method of using generative AI to analyze environmental data and predict optimal growing conditions for plants.

[0320] "Agricultural equipment" refers to equipment that automatically aids plant growth, such as watering systems and fertilizer spreading devices.

[0321] "Control means" refers to a method for automatically operating agricultural equipment based on data analysis means.

[0322] The "notification means" is a method for notifying the user of the analysis results and control information.

[0323] A "user interface" is a means by which a user changes settings or gives additional instructions to the system.

[0324] The "emotion engine" is a program that analyzes the user's voice and facial expressions to recognize their emotional state.

[0325] "Adjusting notification content" is a method of changing notification content according to the user's emotional state analyzed by the emotion engine.

[0326] "Settings change suggestion" is a method of suggesting system setting changes to the user based on the emotion engine's analysis.

[0327] MODE FOR CARRYING OUT THE INVENTION

[0328] This invention is a system that aims to automate and streamline agriculture by combining sensors, generative AI, servers, terminals, a user interface, and an emotion engine. This system includes components that realize environmental data collection, data analysis, agricultural equipment control, user notifications, and user emotion recognition functions.

[0329] Environmental data collection

[0330] First, the user places temperature sensors, humidity sensors, moisture content sensors, etc. on the farmland. These sensors can be powered by solar energy or batteries. The device collects environmental data from these sensors in real time and temporarily stores it in local storage. For example, at 9:00 AM, the temperature sensor measures 24°C and the humidity sensor measures 65%. This data is stored on the device and uploaded to the server at regular intervals (e.g., every day at noon).

[0331] Data analysis

[0332] The server stores the received environmental data in a database using a database management system such as SQL. A generative AI model (e.g., built using TensorFlow) on the server analyzes the stored data and predicts the optimal conditions for plant growth. For example, it may make a prediction such as, "Since high temperatures are expected tomorrow, watering should begin at 2 p.m."

[0333] Agricultural equipment control

[0334] The server creates specific control instructions based on the analysis results of the generation AI. For example, it generates an instruction to "operate the watering system at 2:00 PM." This is sent to the terminal, which then controls the agricultural equipment according to the instruction. Specifically, the watering system valve is opened at 2:00 PM and watering begins.

[0335] User Notifications and Emotion Recognition

[0336] The server generates a notification message based on the analysis results of the generation AI and the control information. For example, it creates a notification that reads, "The sprinkler system will start up tomorrow at 2:00 PM." This is then sent to the user through the user interface. Notifications are sent in real time using a dedicated application or web portal. The emotion engine analyzes the user's voice and facial expressions to recognize their emotional state. For example, if the user is feeling stressed, the content of the notification can be adjusted based on that information. For example, a gentle message such as, "It's okay, the system is operating optimally" can be sent. The emotion engine's analysis results can also suggest changes to system settings. For example, if the user expresses anxiety, the server can notify them by saying, "We will provide more detailed instructions or additional help."

[0337] Specific examples

[0338] Specific examples are shown below.

[0339] Example 1: Temperature and humidity control and user emotion recognition

[0340] The device uses a temperature sensor (e.g., SHT31) and a humidity sensor to measure a temperature of 25°C and humidity of 60% at 10:00 AM.

[0341] The device saves this data locally in CSV format and sends it to the server at noon (e.g., data_2023-10-25.csv).

[0342] The server uses generative AI (e.g., built using TensorFlow) to predict, "Tomorrow is expected to be hot, so watering should begin at 2 p.m."

[0343] The server sends a control instruction to the terminal to "activate the watering system at 2:00 p.m."

[0344] The terminal will open the valve on the sprinkler system at 2:00 p.m. and begin sprinkling water.

[0345] The server notifies the result and analyzes the user's emotions using an emotion engine (e.g., OpenCV and Emotion API).

[0346] If the emotion engine recognizes that the user is feeling stressed, it will notify them with a calming message such as, "It's okay, the system is working optimally."

[0347] Example 2: Optimizing fertilizer application and suggesting settings changes to users

[0348] At 8:00 a.m., the device measures the moisture content to 40% using a moisture sensor (e.g., TDR-100) and sends this data to the server.

[0349] The server uses generative AI to generate analysis results such as "Stop spreading fertilizer until the next rain comes."

[0350] The server sends an instruction to the terminal to "stop the operation of the fertilizer spreading device."

[0351] The terminal stops the fertilizer spreading device.

[0352] The server notifies the user of the analysis results and control information, and the emotion engine analyzes the user's emotions.

[0353] If the emotion engine recognizes that the user is expressing anxiety, it will send a notification offering more detailed instructions or additional help.

[0354] As a result, the present invention can significantly improve the efficiency of agricultural work and also provide emotional support to users.

[0355] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0356] Step 1:

[0357] Users place temperature, humidity, and moisture sensors in their fields. These sensors run on solar energy or batteries and collect environmental data in real time. The input is sensor placement and activation, and the output is that the sensors are ready to collect environmental data.

[0358] Step 2:

[0359] The terminal collects environmental data such as temperature, humidity, and moisture content in real time from the installed sensors. For example, at 9:00 AM, the terminal obtains data of 24°C from the temperature sensor and 65% from the humidity sensor. The input is the data signal from the sensor, and the output is the collected numerical data.

[0360] Step 3:

[0361] The terminal temporarily stores the collected environmental data in local storage. For example, the data is saved in a CSV file format with a timestamp (e.g., data_2023-10-25.csv). The input is the collected environmental data, and the output is the data saved in local storage.

[0362] Step 4:

[0363] The device periodically uploads data stored in its local storage to the server (e.g., at noon every day). The uploaded data includes the sensor ID and a timestamp. The input is the data in the local storage, and the output is the completion of data transfer to the server.

[0364] Step 5:

[0365] The server stores the uploaded environmental data in a database. A database management system (e.g., SQL) is used to ensure accurate data storage. The input is the data uploaded from the device, and the output is the environmental data stored in the database.

[0366] Step 6:

[0367] The server analyzes the environmental data stored in the database using a generative AI model (e.g., using TensorFlow). The generative AI model predicts the optimal growing conditions for plants based on the accumulated data. For example, it makes a prediction such as, "Since high temperatures are expected tomorrow, watering should begin at 2 p.m." The input is the environmental data in the database, and the output is the analysis results (optimal growing conditions).

[0368] Step 7:

[0369] The server generates specific control instructions based on the analysis results of the generation AI. For example, it generates an instruction to "turn on the watering system at 2:00 PM." The input is the analysis results of the generation AI, and the output is a specific control instruction.

[0370] Step 8:

[0371] The server sends the generated control instructions to the terminal. The instructions include a timestamp and scope of application. The input is the control instruction, and the output is the completion of sending the instruction to the terminal.

[0372] Step 9:

[0373] The terminal controls the agricultural equipment according to the control instructions it receives. Specifically, it opens the valve of the watering system to start watering at 2:00 p.m. The input is a control instruction from the server, and the output is the start of equipment operation.

[0374] Step 10:

[0375] The server generates a notification message based on the analysis results and control information of the generation AI. For example, it generates a notification such as "The sprinkler system will start up tomorrow at 2:00 PM." The input is the analysis results and control information, and the output is the notification message.

[0376] Step 11:

[0377] The server generates notifications and sends them to the user through a user interface, either a dedicated application or a web portal. The input is the notification message, and the output is the completion of notification delivery to the user.

[0378] Step 12:

[0379] The emotion engine analyzes the voice and facial expressions of the user who received the notification to recognize their emotional state. For example, it determines whether the user is feeling stressed. The input is the user's voice and facial expression data, and the output is the analysis result (emotional state).

[0380] Step 13:

[0381] The server reflects the analysis results of the emotion engine and adjusts the notification content. If the user is feeling stressed, the notification content will be calmer and an encouraging message will be added. The input is the analysis results of the emotion engine, and the output is the adjusted notification message.

[0382] Step 14:

[0383] The server proposes changes to system settings based on the analysis results of the emotion engine. For example, if the user expresses anxiety, the server will notify the user by suggesting that they should "provide more detailed instructions or additional help." The input is the analysis results of the emotion engine, and the output is a notification of the proposed changes to the settings.

[0384] (Application example 2)

[0385] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0386] In recent years, there has been a demand for automation and efficiency in agriculture, and optimizing agricultural work based on the collection and analysis of environmental data has become a major challenge. However, existing systems have difficulty collecting environmental data in real time, and they have not yet achieved the control of autonomous vehicles based on the analysis results. There is also a lack of systems that can recognize user emotions and provide appropriate feedback to improve the user experience. There is a need to build a comprehensive system to solve these issues.

[0387] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting environmental data using sensors, means for transmitting the environmental data to the server, and data analysis means using a generation AI to analyze the environmental data. This makes it possible to collect environmental data in real time, control an autonomous vehicle based on the analysis results, and recognize the user's emotions to adjust the content of notifications.

[0388] "Environmental data" refers to physical data such as temperature, humidity, and moisture content collected at agricultural sites.

[0389] "Generative AI" refers to artificial intelligence that analyzes collected environmental data, predicts the optimal conditions for plant growth, and generates automatic control instructions.

[0390] An "autonomous vehicle" refers to a vehicle used as agricultural equipment, equipped with sensors, that autonomously performs agricultural tasks based on the analysis results of generative AI.

[0391] An "emotion engine" refers to a system that has the ability to analyze information such as the user's voice and facial expressions and recognize the user's emotional state.

[0392] "User interface" refers to the means by which a user interacts with a system, and refers to the screens and applications that notify analysis results and control information, and allow settings to be changed or additional instructions to be given.

[0393] This invention is a system that collects environmental data, analyzes the data, controls agricultural equipment and autonomous vehicles, notifies users, and recognizes their emotions. This system is composed of a combination of sensors, generative AI, a server, a terminal, a user interface, and an emotion engine.

[0394] First, multiple sensors (temperature sensors, humidity sensors, moisture sensors, etc.) are installed in the autonomous vehicle to collect environmental data in real time. The collected data is temporarily stored on a terminal inside the vehicle. This data is then sent to a server at regular intervals.

[0395] The server stores the received data in a database and analyzes it using generative AI, which uses machine learning frameworks such as TensorFlow and PyTorch to predict the optimal conditions for plant growth. Based on the results of this analysis, the server generates specific control instructions and sends them to the terminal. The terminal then follows these instructions and the autonomous vehicle performs agricultural tasks such as watering, fertilizing, and soil improvement.

[0396] Furthermore, the analysis results and control instructions are notified to the user through a user interface provided on an application created using React Native or Flutter (registered trademark), and the user can change settings and issue additional instructions to the system through the application.

[0397] The system also incorporates an emotion engine that analyzes the user's emotions using IBM Watson® Tone Analyzer, among other tools. If the user is feeling stressed, the emotion engine adjusts the notification content to include a calmer tone or positive, encouraging messages. If the user expresses anxiety, the system provides more detailed explanations and guidance.

[0398] Specific examples

[0399] Temperature and humidity control and user emotion recognition:

[0400] The device measures the temperature and humidity at 10:00 a.m. using the temperature and humidity sensors to find a temperature of 25°C and a humidity of 60%. The collected data is stored in the device's local storage at 10:00 a.m. and sent to the server at noon.

[0401] The server instructs the generating AI to analyze the data, and the generating AI makes a prediction such as, "Since high temperatures are expected tomorrow, watering will begin at 2 p.m."

[0402] The server sends this instruction to the terminal, and the terminal automatically starts the watering system at 2:00 pm. The server notifies the user of the analysis results and control information, and the emotion engine analyzes the user's emotions.

[0403] If the emotion engine recognizes that the user is stressed, it will change the notification content to a calmer message.

[0404] Prompt Sentence Examples

[0405] Prompt: "Predict the temperature and humidity for the next day and analyze whether watering is necessary starting at 2 PM. Also, notify the user with a calming message if they are feeling stressed."

[0406] Application scenario: "Tomorrow's temperature is expected to exceed 30°C, so start watering for 10 minutes at 2pm. If the user is stressed, display a message saying, 'Don't worry, everything is managed automatically.'"

[0407] The above is a specific embodiment of the present invention. This system can collect environmental data in real time, control an autonomous vehicle based on the analysis results, and recognize the user's emotions and provide feedback.

[0408] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0409] Step 1:

[0410] Sensors are used to collect environmental data.

[0411] Input: Data from temperature, humidity, and moisture sensors installed in the autonomous vehicle.

[0412] Specific operation: The device measures the temperature at 25°C and humidity at 60% at 10:00 AM. The data from the sensor is sent to the device in real time and temporarily stored in local storage.

[0413] Step 2:

[0414] The environmental data is transmitted to a server.

[0415] Input: Environmental data collected from sensors (e.g., temperature 25°C, humidity 60%).

[0416] Specific operation: The terminal uploads the collected data to the server at regular intervals. For example, the collected data is stored in local storage at 10:00 AM and sent to the server at noon.

[0417] Step 3:

[0418] Data analysis is performed using a generation AI that analyzes the environmental data.

[0419] Input: The environment data sent to the server.

[0420] How it works: The server analyzes the data using a generative AI model (such as TensorFlow or PyTorch). Based on the collected data, the generative AI generates a prediction, such as "Tomorrow is expected to be hot, so watering will begin at 2 PM."

[0421] Step 4:

[0422] Based on the data analysis means, agricultural equipment is automatically controlled.

[0423] Input: Control instructions generated by the generation AI.

[0424] Specific operation: The server generates specific control instructions (e.g., an instruction to start watering) based on the analysis results and sends the instructions to the terminal. The terminal follows the received instructions and automatically starts the watering system at 2:00 PM.

[0425] Step 5:

[0426] The analysis results and control information are notified to the user.

[0427] Input: Control instructions and analysis results generated by the server.

[0428] Specific operation: The server notifies the user of the generated analysis results and control information through a user interface (e.g., a smartphone app using React Native or Flutter).

[0429] Step 6:

[0430] Recognize user emotions and tailor notifications accordingly.

[0431] Input: Voice data and facial expression data sent by the server.

[0432] How it works: The emotion engine (e.g., IBM Watson Tone Analyzer) analyzes the user's emotions from their voice and facial expressions collected through the user interface. If the system recognizes that the user is feeling stressed, it adjusts the notification content to be gentler and include encouraging messages.

[0433] Step 7:

[0434] The user changes settings or gives additional instructions to the system.

[0435] Input: User interface operations.

[0436] Specific operation: Users can change settings and provide additional instructions to the system through a smartphone app. The system receives the instructions and reflects the changes.

[0437] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0438] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0439] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0440] [Second embodiment]

[0441] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0442] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0443] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0444] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0445] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0446] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0447] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0448] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0449] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0450] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0451] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0452] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0453] ---

[0454] MODE FOR CARRYING OUT THE INVENTION

[0455] This invention is a system aimed at automating and streamlining agriculture, and includes sensors, a generation AI server, a control terminal, and a user interface. This system uses sensors to collect environmental data and transmits it to the server. The generation AI in the server analyzes the data and appropriately controls agricultural equipment based on the results. The user is also notified of the analysis results and control information, allowing them to change system settings as needed.

[0456] System Overview

[0457] First, sensors (temperature, humidity, moisture content, etc.) are installed in the field to collect environmental data in real time. The collected data is then sent to a terminal, where it is temporarily stored.

[0458] The device then periodically uploads the collected data to a server, which stores the data in a database and instructs the AI ​​to analyze it. The AI ​​then uses the environmental data to predict optimal conditions for plant growth.

[0459] The server then generates the necessary control instructions based on the analysis results of the AI ​​and sends them to the terminal, which then executes the received control instructions to control agricultural equipment such as watering systems and fertilizer spreaders.

[0460] Furthermore, the server notifies the user of the analysis results and executed control information via a user interface. The user can check the information using a dedicated application or web portal, and change settings or issue additional instructions as necessary.

[0461] Specific examples of program processing

[0462] Example 1: Temperature and humidity control

[0463] The device measures the temperature and humidity of the farmland every hour through temperature and humidity sensors. For example, at 10:00 a.m., the temperature is measured as 25°C and the humidity as 60%.

[0464] The device stores this data in local storage and uploads it to the server at noon.

[0465] The server instructs the AI ​​to analyze new data, and the AI ​​predicts optimal growing conditions based on data from the past seven days. For example, it generates instructions such as "Because the temperature will be high tomorrow, start the watering system at 2 p.m."

[0466] The server sends this instruction to the terminal, which then automatically starts the watering system at 2:00 p.m. based on the instruction.

[0467] The server also notifies the user of the analysis results and control information of the generating AI, and the user confirms on the smartphone app that "watering started at 2 p.m."

[0468] Example 2: Optimizing fertilizer application

[0469] The device uses a soil moisture sensor to measure the moisture content of the soil. For example, at 8:00 AM, the moisture content is measured as 40%.

[0470] The device uploads the data to a server, which then analyzes it using a generative AI. The generative AI analyzes past data and concludes that fertilizer application should be stopped until the next rain falls.

[0471] Based on the analysis results, the server generates a stop instruction for the fertilizer spreading device and sends it to the terminal.

[0472] The terminal receives this instruction and immediately stops the operation of the fertilizer spreading device.

[0473] The server notifies the user of this operation, and the user confirms in the application that "fertilizer spreading has been stopped."

[0474] As described above, this system combines sensors, generation AI, servers, terminals, and a user interface to automate agricultural work and enable it to be carried out efficiently and systematically. The types of sensors and devices used, analysis algorithms, and control functions can be customized as appropriate to suit the specific agricultural environment and user needs.

[0475] The processing flow will be explained below.

[0476] ---

[0477] Step 1:

[0478] The device measures environmental data using the temperature sensor, humidity sensor, and moisture sensor. For example, at 10:00 AM, the temperature sensor measures 25°C, the humidity sensor measures 60%, and the moisture sensor measures 40%.

[0479] Step 2:

[0480] The device stores the measured environmental data in local storage. The stored data includes the measurement value and a timestamp (e.g., 10:00 AM, October 10, 2023).

[0481] Step 3:

[0482] The device transmits the environmental data stored in the local storage to the server at regular intervals (e.g., every hour). The transmitted data includes a series of measurement values ​​and corresponding timestamps.

[0483] Step 4:

[0484] The server receives the environmental data sent from the device and stores it in a database, including the sensor measurements and timestamps.

[0485] Step 5:

[0486] Every time new data is stored in the database, the server instructs the AI ​​to analyze the data. When the instruction is issued, past environmental data is also provided to the AI.

[0487] Step 6:

[0488] The generative AI analyzes the provided environmental data and predicts the optimal growing conditions for the plant, generating specific actions as a result (e.g., "Increase watering for the next three days").

[0489] Step 7:

[0490] The server receives the analysis results provided by the generation AI and generates specific control instructions for controlling various agricultural equipment based on those results, such as "Start the watering system at 8:00 a.m. tomorrow and stop it at 10:00 a.m."

[0491] Step 8:

[0492] The server transmits the generated control instruction to the terminal.

[0493] Step 9:

[0494] The terminal executes the control instructions received from the server, controlling agricultural equipment such as watering systems and fertilizer spreading devices based on the specified time.

[0495] Step 10:

[0496] The server then sends notifications to the user about the results of the generative AI's analysis and the execution of control instructions, for example, via a smartphone app or web portal.

[0497] Step 11:

[0498] Users receive notifications and can view the current status of the system and analysis results through an application or web portal.

[0499] Step 12:

[0500] Users can change settings or add commands to the system as needed through the application or web portal, for example, manually changing the fertilizer application schedule.

[0501] The above is a detailed description of the processing flow of this system. In this way, a system is realized in which data collected from sensors is analyzed by generative AI, reflected in the automatic control of agricultural equipment, and feedback is also provided to users.

[0502] Example 1

[0503] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0504] In conventional agricultural systems, the collection and analysis of environmental data is often done manually, making efficient agricultural management difficult. Furthermore, controlling agricultural equipment and changing its settings requires direct user intervention, which is time-consuming and labor-intensive. Furthermore, analysis results and control information are not immediately communicated to users, which can delay appropriate responses.

[0505] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0506] In this invention, the server includes means for collecting environmental data using sensors, means for temporarily storing the environmental data in a terminal and transmitting it to the server at regular intervals, means for storing the environmental data in a database and performing data analysis using a generative AI model, means for automatically controlling agricultural equipment based on the data analysis means, means for notifying a user of the analysis results and control information, and means for providing an interface for the user to check the notification content and change control settings as necessary. This enables efficient collection and analysis of environmental data, automatic control of agricultural equipment, and immediate user notification and setting changes.

[0507] A "sensor" is a device for measuring environmental data (e.g., temperature, humidity, water content, etc.).

[0508] A "terminal" is a device that has the function of temporarily storing environmental data collected from sensors and transmitting it to a server.

[0509] The "server" is a device that receives environmental data sent from the terminal, stores it in a database, analyzes the data using a generative AI model, and generates control instructions based on the analysis results.

[0510] A "database" is a collection of information that allows the server to systematically store and manage environmental data received.

[0511] A "generative AI model" is an artificial intelligence algorithm that analyzes environmental data and generates optimal control instructions for agricultural equipment.

[0512] "Data analysis" is the process of analyzing collected environmental data using a generative AI model and generating optimal control instructions for agricultural equipment based on the results.

[0513] "Agricultural equipment" refers to devices that automate and streamline agricultural work, such as watering systems and fertilizer spreading devices.

[0514] An "interface" is a means by which a user changes system settings or issues additional instructions, and includes dedicated applications and web portals.

[0515] "Notification" is a means for conveying analysis results and control information to the user.

[0516] MODE FOR CARRYING OUT THE INVENTION

[0517] This invention is a system for achieving automation and efficiency in agriculture, and includes sensors, a generation AI server, a control terminal, and a user interface. The system uses sensors to collect environmental data on farmland and transmits the data to the server. The generation AI in the server analyzes the data and generates appropriate control instructions for agricultural equipment based on the results. The user is notified of the analysis results and control information, and can change the system settings as needed.

[0518] System Overview

[0519] First, environmental data is collected in real time using sensors (e.g., temperature, humidity, and moisture sensors) installed in the farmland. The collected data is temporarily stored on the device.

[0520] The device then periodically uploads the collected data to a server, which stores the data in a database and analyzes it using a generative AI model. Based on past data, the generative AI predicts the optimal conditions for plant growth.

[0521] The server then generates the necessary control instructions based on the analysis results of the AI ​​and sends them to the terminal, which then executes the received control instructions to control agricultural equipment such as watering systems and fertilizer spreaders.

[0522] Furthermore, the server notifies the user of the analysis results and executed control information via a user interface. The user can check the information using a dedicated application or web portal, and change settings or issue additional instructions as necessary.

[0523] Hardware and software used

[0524] Sensors: Temperature sensors, humidity sensors, moisture sensors, etc. Examples include the DHT22 sensor and the soil moisture sensor (YL-69).

[0525] Terminal: A microcontroller such as a Raspberry Pi or Arduino.

[0526] Server: A high-performance server for running the generative AI. For example, we will use an AWS EC2 instance.

[0527] Generative AI models: Use deep learning frameworks such as PyTorch and TensorFlow.

[0528] Database: Possible databases are MySQL and PostgreSQL.

[0529] User interface: smartphone apps (iOS, Android) or web portals (front-end frameworks such as React.js or Angular).

[0530] Specific examples

[0531] Example 1: Temperature and humidity control

[0532] The device uses a temperature sensor (e.g., DHT22) and a humidity sensor to measure the temperature and humidity of the field every hour. For example, at 10:00 AM, the temperature is measured as 25°C and the humidity is measured as 60%.

[0533] The device stores this data in local storage (e.g., SQLite database), converts it to CSV format, and sends it to the server at noon.

[0534] The server stores the received data in a MySQL database and analyzes it using a generative AI model. Based on data from the past seven days, the generative AI generates an analysis result such as "Tomorrow's temperature will be high, so start the watering system at 2:00 PM."

[0535] The server sends this instruction in JSON format to the terminal, which then turns on the watering system via a relay circuit at 2:00 PM.

[0536] The server pushes the analysis results and control information to the user's smartphone app, and the user confirms that "watering started at 2:00 p.m."

[0537] Example 2: Optimizing fertilizer application

[0538] The device measures the soil moisture content using a soil moisture sensor (e.g., YL-69). For example, at 8:00 AM, the moisture content is measured as 40%.

[0539] The device uploads the data to the server at noon, where it is formatted in CSV format and sent via an HTTP POST request.

[0540] The server stores the data in a database and analyzes it using a generative AI model, which analyzes past data and concludes that fertilizer application should be suspended until the next rain.

[0541] Based on the analysis results, the server sends a stop command to the terminal in JSON format to the fertilizer spreading device.

[0542] The terminal receives this instruction and immediately stops the operation of the fertilizer spreading device via the relay circuit.

[0543] The server notifies the user of this operation using Firebase Cloud Messaging, and the user confirms in a dedicated app that "fertilizer spreading has been stopped."

[0544] This system will improve the efficiency and automation of agricultural work, allowing users to easily monitor and control the condition of their farmland.

[0545] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0546] Step 1: Collecting data with sensors

[0547] The terminal collects environmental data in real time using various sensors (temperature sensors, humidity sensors, moisture sensors, etc.) installed on the farmland.

[0548] Input: Real-time environmental data from sensors (temperature, humidity, moisture content).

[0549] How it works: For example, the temperature sensor (DHT22) measures the temperature and humidity every hour. This data is instantly stored in the device's memory.

[0550] Output: Collected environmental data (e.g., temperature 25°C, humidity 60% at 10 AM).

[0551] Step 2: Temporarily save data

[0552] The device temporarily stores the collected environmental data in local storage (e.g., SQLite database) within the device.

[0553] Input: Environmental data collected from sensors (temperature, humidity, moisture content).

[0554] Operation: The device saves the collected data in a specified format (e.g., CSV format) in local storage.

[0555] Output: Environment data saved to local storage.

[0556] Step 3: Send data

[0557] The device uploads the stored data to the server at regular intervals.

[0558] Input: Environment data stored in local storage.

[0559] Operation: At noon, the device converts the saved data into CSV format and sends it to the server using an HTTP POST request.

[0560] Output: The environment data sent to the server.

[0561] Step 4: Save your data

[0562] The server stores the received environmental data in a database (e.g., MySQL).

[0563] Input: Environmental data sent from the device.

[0564] Operation: The server receives the incoming data and stores it in the database appropriately.

[0565] Output: Environmental data stored in a database.

[0566] Step 5: Data analysis

[0567] The server analyzes the received data using a generative AI model.

[0568] Input: Environmental data stored in a database.

[0569] How it works: An analysis script runs periodically, calling a generative AI model (e.g., using TensorFlow) to perform analysis based on the past 7 days of data.

[0570] Output: Analysis results from the generative AI model (optimal growing conditions).

[0571] Step 6: Generate control instructions

[0572] Based on the analysis results of the generation AI, the server generates the necessary control instructions and sends them to the terminal.

[0573] Input: Analysis results from the generative AI model.

[0574] Operation: The server receives the results of the generation AI, generates control instructions (e.g., start the watering system at 2 p.m.), and sends them to the terminal in JSON format.

[0575] Output: Control instructions sent to the terminal.

[0576] Step 7: Controlling agricultural equipment

[0577] The terminal controls the agricultural equipment based on the received control instructions.

[0578] Input: Control instructions received from the server (JSON format).

[0579] Operation: The terminal operates the watering system and fertilizer application device through the relay circuit based on the received instruction. For example, turn on the watering system through the relay circuit at 2:00 PM.

[0580] Output: Controlled agricultural equipment (e.g. turning on a watering system).

[0581] Step 8: User Notifications and Configuration Changes

[0582] The server notifies the user of the analysis results and control information through a user interface.

[0583] Input: Analysis results of the generative AI model and the device's control execution log.

[0584] How it works: The server uses Firebase Cloud Messaging (FCM) to push the analysis results and control information to the user's smartphone app or web portal.

[0585] Output: A notification that appears on the user's smartphone or web portal (e.g., "Watering started at 2 PM").

[0586] Based on these notifications, users can change settings or issue additional instructions as needed using a dedicated application or web portal. This series of processes enables efficient and planned automated control of agricultural work.

[0587] (Application example 1)

[0588] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0589] Modern agriculture demands automation and efficiency, but conventional systems lack the ability to collect and analyze environmental data, making it difficult to control agricultural equipment and manage quality.Furthermore, food delivery services face many challenges in managing the quality of food during delivery and creating optimal delivery routes, and there has been no way to efficiently and comprehensively manage these issues.

[0590] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0591] In this invention, the server includes means for collecting environmental data using sensors, means for transmitting the environmental data to the server, means for analyzing the environmental data using a generation AI, means for automatically controlling agricultural equipment based on the data analysis means, means for notifying the user of the analysis results and control information, means for monitoring the temperature and humidity of items being delivered in real time, means for generating an optimal delivery route taking into account traffic conditions and order priority, and means for collecting and analyzing user feedback on delivery quality. This will advance the automation and efficiency of agriculture, while also enabling integrated quality control during food delivery and optimal delivery planning.

[0592] A "sensor" is a device for collecting environmental data, and includes a temperature sensor, a humidity sensor, and the like.

[0593] "Environmental data" refers to various information collected, such as temperature, humidity, and soil moisture content, that is necessary for agricultural operations and food delivery management.

[0594] The "server" is a centralized computer system that receives environmental data and analyzes it using generative AI.

[0595] "Generative AI" is a system that uses artificial intelligence to analyze collected data and generate optimal control instructions and predictions.

[0596] "Data analysis means" refers to methods or devices that use generative AI to analyze environmental data and derive the necessary control instructions and predictions.

[0597] "Agricultural equipment" refers to devices or machines that are automatically controlled to improve the efficiency of agricultural work, including, for example, watering systems and fertilizer spreading devices.

[0598] The "means for notifying the user" refers to a system or method for notifying the user of analysis results and control information through a user interface.

[0599] "Means for real-time monitoring of temperature and humidity of goods during delivery" refers to devices or systems that continuously monitor the temperature and humidity of the environment in which food or goods are kept during delivery and collect necessary information.

[0600] "Means for generating optimal delivery routes" refers to devices or algorithms that calculate efficient delivery routes in real time, taking into account traffic conditions and order priorities.

[0601] "Means for collecting and analyzing user feedback regarding delivery quality" refers to a system that collects opinions and reviews regarding quality from delivery personnel and customers, and analyzes that information using generative AI, etc.

[0602] MODE FOR CARRYING OUT THE INVENTION

[0603] This invention is a system aimed at automating and streamlining agriculture and controlling food quality in food delivery services. It includes sensors, a generation AI server, a control terminal, and a user interface. The system uses sensors to collect environmental data and transmits it to the server. The generation AI on the server analyzes the data and appropriately controls agricultural equipment based on the results. Users are also notified of the analysis results and control information, allowing them to change system settings as needed.

[0604] System Overview

[0605] First, sensors (for temperature, humidity, soil moisture, etc.) are installed in farmland and delivery vehicles to collect environmental data in real time. The collected data is sent to a terminal, which temporarily stores it. Next, the terminal uploads the collected data to a server at regular intervals. The server stores the received data in a database and instructs the generation AI to analyze the data. The generation AI predicts the optimal conditions for plant growth based on the environmental data. The server also monitors the temperature and humidity during delivery in real time and generates the optimal delivery route.

[0606] Based on the analysis results of the generation AI, the server generates the necessary control instructions and sends them to the terminal. The terminal then executes the received control instructions to control agricultural equipment such as watering systems and fertilizer spreaders. It also notifies the user of necessary measures based on the environmental conditions of the delivery vehicle during delivery. The server then notifies the user of the analysis results and information on the executed controls via a user interface. The user can check this information using a dedicated application or web portal and change settings or issue additional instructions as necessary.

[0607] Program Processing Details

[0608] Hardware and software used

[0609] Hardware: smartphones, sensors

[0610] Software: Python, Flask

[0611] Program Processing Overview

[0612] 1. Collect data from sensors: Collect data from temperature and humidity sensors and send the data to the device.

[0613] 2. Temporary storage and uploading of data: The device temporarily stores the collected data and uploads it to the server at regular intervals.

[0614] 3. Data analysis on the server: The server analyzes the data using generative AI and generates optimal control instructions.

[0615] 4. Execution of control instructions: The terminal executes the control instructions received from the server and controls the agricultural equipment.

[0616] 5. Notification to the user: The server notifies the user of the analysis results and control information of the generated AI through the user interface.

[0617] Specific examples

[0618] Temperature and humidity control: During delivery, the delivery person's smartphone will notify them that the temperature has exceeded 35 degrees Celsius and that they should take measures to lower the temperature in the cooler. The delivery person will then follow the instructions and adjust the temperature in the cooler.

[0619] Optimal delivery route generation: When a customer places an order, the app will notify them, "Currently, the delivery is congested. Please select Route B as the optimal route."

[0620] Prompt Sentence Examples

[0621] "Generate the optimal route from current location A to destination B, taking into account traffic conditions and order priorities."

[0622] This series of systems will simultaneously achieve efficiency and quality control in both agricultural work and food delivery services. Users will also be able to flexibly respond by changing the system settings and issuing additional instructions.

[0623] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0624] Step 1:

[0625] Collecting data from sensors

[0626] Input: Sensors (Temperature sensor, Humidity sensor, etc.)

[0627] How it works: The sensors measure the temperature and humidity in real time on farmland or inside delivery vehicles. For example, the temperature sensor measures 25°C and the humidity sensor measures 60%.

[0628] Output: Collected environmental data

[0629] Step 2:

[0630] Temporary storage and upload of data

[0631] Input: Collected environmental dates

[0632] Specific operation: The device temporarily stores the collected data in local storage. At regular intervals (e.g., every hour), the device uploads the stored data to the server.

[0633] Output: Environment data sent to the server

[0634] Step 3:

[0635] Data analysis on the server

[0636] Input: Environmental data uploaded to the server

[0637] Specific operation: The server stores the received environmental data in a database. It then uses the generation AI to analyze it, including past data. For example, if the temperature is too high, it generates instructions such as "Start automatic watering at 2:00 PM."

[0638] Output: Analysis results and control instructions

[0639] Step 4:

[0640] Sending and executing control instructions

[0641] Input: Generated analysis results and control instructions

[0642] Specific operation: The server sends the generated control instructions to the terminal. The terminal automatically controls the corresponding agricultural equipment (e.g., watering system or fertilizer spraying device) based on the received control instructions.

[0643] Output: Operation of agricultural equipment according to the executed control instructions

[0644] Step 5:

[0645] User Notification and Feedback

[0646] Input: Analysis results and control information

[0647] Specific operation: The server notifies the user of the analysis results and executed control information through the user interface. The user checks the notifications on a dedicated smartphone app or web portal and changes system settings or issues additional instructions as necessary. For example, the user confirms that "watering started at 2:00 PM."

[0648] Output: Notification and feedback to the user

[0649] In this way, each step is processed continuously and automatically, realizing a series of processes from data collection from sensors to analysis by generative AI and control of control devices, enabling efficient environmental management and quality maintenance in agriculture and food delivery.

[0650] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0651] ---

[0652] MODE FOR CARRYING OUT THE INVENTION

[0653] This invention is a system that aims to automate and streamline agriculture by combining sensors, generative AI, servers, terminals, a user interface, and an emotion engine. This system includes components that realize environmental data collection, data analysis, agricultural equipment control, user notifications, and user emotion recognition functions.

[0654] System Overview

[0655] First, various sensors (temperature sensors, humidity sensors, moisture sensors, etc.) are placed at the installation location and environmental data is collected in real time using these sensors. The collected data is sent to the device, which then temporarily stores the data in local storage.

[0656] The device then periodically uploads the collected data to a server, which receives it and stores it in a database. The generative AI on the server analyzes the collected data and uses it to predict the optimal conditions for plant growth.

[0657] Based on the analysis results of the generative AI, the server generates specific control instructions and sends them to the terminal, which then follows the instructions to actually control agricultural equipment such as watering systems and fertilizer spreaders.

[0658] Furthermore, the analysis results and control instructions of the generative AI are notified to the user via a user interface from the server. The user receives these notifications on a dedicated application or web portal, where they can check the current status of the system and the analysis results. The user can also change settings and issue additional instructions to the system through the interface.

[0659] Adding an Emotion Engine

[0660] One of the features of the present invention is that it includes an emotion engine that recognizes the user's emotion. The emotion engine analyzes the user's emotion from, for example, their voice or facial expression, and recognizes their emotional state.

[0661] The emotion engine can tailor notification content based on the emotion it recognizes. For example, if a user is feeling stressed, the notification could have a softer tone or include a positive, encouraging message. The emotion engine can also suggest changes to system settings based on the user's emotion. For example, if a user expresses anxiety, the system could provide more detailed explanations or guidance.

[0662] Specific examples of program processing

[0663] Example 1: Temperature and humidity control and user emotion recognition

[0664] The device measures the temperature and humidity at 10:00 AM to be 25°C and 60%.

[0665] The device stores this data in local storage and sends it to the server at noon.

[0666] The server instructs the generating AI to analyze the data, and the generating AI makes a prediction such as, "Since high temperatures are expected tomorrow, watering will begin at 2 p.m."

[0667] The server sends this instruction to the terminal, which then automatically starts the watering system at 2:00 p.m.

[0668] The server notifies the user of the analysis results and control information of the generative AI, and the emotion engine analyzes the user's emotions.

[0669] If the emotion engine recognizes that the user is stressed, it will change the notification content to a calmer message.

[0670] Example 2: Optimizing fertilizer application and suggesting setting changes to users

[0671] At 8:00 a.m., the device measures the moisture content to 40% using the moisture sensor and sends this data to the server.

[0672] The server uses a generative AI to generate analysis results such as "stop spreading fertilizer until the next rain comes."

[0673] The server sends this instruction to the terminal, and the terminal stops the operation of the fertilizer spreading device.

[0674] The server notifies the user of the analysis results and control information, and the emotion engine analyzes the user's emotions.

[0675] If the emotion engine recognizes that the user is expressing anxiety, the system will suggest "providing more detailed instructions or additional help."

[0676] The above is a specific embodiment of this system. By incorporating an emotion engine, user-friendly support becomes possible, allowing agricultural work to be carried out efficiently and safely.

[0677] The processing flow will be explained below.

[0678] ---

[0679] Example 1: Temperature and humidity control and user emotion recognition

[0680] Step 1:

[0681] The device uses temperature and humidity sensors to measure environmental data every hour. For example, at 10:00 AM, the temperature is 25°C and the humidity is 60%.

[0682] Step 2:

[0683] The device stores the measured data in local storage, which includes the measurement value and a timestamp (e.g., October 10, 2023, 10:00 AM).

[0684] Step 3:

[0685] The device sends the stored environmental data to the server every hour, for example, uploading the data from 10:00 AM at 11:00 AM.

[0686] Step 4:

[0687] The server stores the received environmental data in a database, which includes the measured values ​​and timestamps.

[0688] Step 5:

[0689] Every time new data is stored, the server instructs the generation AI to analyze the data, which then analyzes past data as well.

[0690] Step 6:

[0691] The generative AI predicts the optimal growing conditions for plants based on the provided environmental data, generating instructions such as "Start the watering system tomorrow at 2:00 PM."

[0692] Step 7:

[0693] Based on the analysis results provided by the generation AI, the server generates specific control instructions and sends them to the terminal.

[0694] Step 8:

[0695] The device executes the control instructions received from the server, for example, starting the watering system at 2:00 PM.

[0696] Step 9:

[0697] The server notifies the user of the analysis results and control information of the generated AI via a smartphone app.

[0698] Step 10:

[0699] The emotion engine analyzes the user's facial expressions and voice when checking notifications to recognize their emotional state.

[0700] Step 11:

[0701] The server adjusts the notification content appropriately based on the emotions recognized by the emotion engine. For example, if the user is feeling stressed, the tone of the notification will be softened.

[0702] Example 2: Optimizing fertilizer application and suggesting setting changes to users

[0703] Step 1:

[0704] The device uses a moisture sensor to measure the moisture content of the soil. For example, at 8:00 AM, it measures the moisture content to be 40%.

[0705] Step 2:

[0706] The device stores the measured data in local storage, which includes the measurement value and a timestamp.

[0707] Step 3:

[0708] The device uploads the saved data to the server at regular intervals (for example, sending data to the server at 8:00 when the device is opened).

[0709] Step 4:

[0710] The server stores the received data in a database, which includes the measurement value and a timestamp.

[0711] Step 5:

[0712] Every time new data is stored, the server instructs the generation AI to analyze the data, which then analyzes past data as well.

[0713] Step 6:

[0714] The generative AI predicts the optimal conditions for fertilizer application based on the provided environmental data, generating instructions such as "stop fertilizer application until the next rain."

[0715] Step 7:

[0716] Based on the analysis results provided by the generation AI, the server generates specific control instructions and sends them to the terminal.

[0717] Step 8:

[0718] The terminal executes the control instruction received from the server, for example, stopping the operation of the fertilizer spreading device.

[0719] Step 9:

[0720] The server notifies the user of the analysis results and control information of the generated AI via a smartphone app.

[0721] Step 10:

[0722] The emotion engine analyzes the user's facial expressions and voice when checking notifications to recognize their emotional state.

[0723] Step 11:

[0724] If the emotion engine recognizes that the user is expressing anxiety, the server will suggest system configuration changes or further guidance, such as providing additional help information or instructions.

[0725] The above are the specific processing steps for implementing an agricultural automation system incorporating an emotion engine. This system simultaneously improves the efficiency of agricultural work and the user experience.

[0726] Example 2

[0727] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0728] In modern agriculture, collecting and analyzing environmental data to optimize plant growth conditions is important, but doing so manually takes time and effort. Furthermore, there is a need to support smoother agricultural work by providing optimal notifications based on the user's emotional state, but existing systems lack notifications and suggestions that take the user's emotions into account. This makes it difficult to reduce user stress and provide a sense of security, making it difficult to achieve the goals of improving agricultural efficiency and automation.

[0729] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0730] In this invention, the server includes means for collecting environmental data using sensors, means for transmitting the environmental data to the server, means for analyzing the environmental data using a generative AI, means for automatically controlling agricultural equipment, means for notifying the user of the analysis results and control information, means for analyzing the user's emotional state using an emotion engine and adjusting the notification content, and means for proposing changes to system settings based on the analysis of the emotion engine. This not only enables efficient collection and analysis of environmental data and automatic control of agricultural equipment, but also provides appropriate notifications and suggestions according to the user's emotional state, enabling user-friendly support for agricultural work.

[0731] A "sensor" is a device for detecting environmental data such as temperature, humidity, and moisture content.

[0732] "Environmental data" refers to information related to plant growth conditions, such as temperature, humidity, and water content.

[0733] "Server" is a computer system for storing and analyzing collected environmental data.

[0734] "Generative AI" is artificial intelligence that uses machine learning algorithms to analyze environmental data and make predictions.

[0735] "Data analysis means" refers to a method of using generative AI to analyze environmental data and predict optimal growing conditions for plants.

[0736] "Agricultural equipment" refers to equipment that automatically aids plant growth, such as watering systems and fertilizer spreading devices.

[0737] "Control means" refers to a method for automatically operating agricultural equipment based on data analysis means.

[0738] The "notification means" is a method for notifying the user of the analysis results and control information.

[0739] A "user interface" is a means by which a user changes settings or gives additional instructions to the system.

[0740] The "emotion engine" is a program that analyzes the user's voice and facial expressions to recognize their emotional state.

[0741] "Adjusting notification content" is a method of changing notification content according to the user's emotional state analyzed by the emotion engine.

[0742] "Settings change suggestion" is a method of suggesting system setting changes to the user based on the emotion engine's analysis.

[0743] MODE FOR CARRYING OUT THE INVENTION

[0744] This invention is a system that aims to automate and streamline agriculture by combining sensors, generative AI, servers, terminals, a user interface, and an emotion engine. This system includes components that realize environmental data collection, data analysis, agricultural equipment control, user notifications, and user emotion recognition functions.

[0745] Environmental data collection

[0746] First, the user places temperature sensors, humidity sensors, moisture content sensors, etc. on the farmland. These sensors can be powered by solar energy or batteries. The device collects environmental data from these sensors in real time and temporarily stores it in local storage. For example, at 9:00 AM, the temperature sensor measures 24°C and the humidity sensor measures 65%. This data is stored on the device and uploaded to the server at regular intervals (e.g., every day at noon).

[0747] Data analysis

[0748] The server stores the received environmental data in a database using a database management system such as SQL. A generative AI model (e.g., built using TensorFlow) on the server analyzes the stored data and predicts the optimal conditions for plant growth. For example, it may make a prediction such as, "Since high temperatures are expected tomorrow, watering should begin at 2 p.m."

[0749] Agricultural equipment control

[0750] The server creates specific control instructions based on the analysis results of the generation AI. For example, it generates an instruction to "operate the watering system at 2:00 PM." This is sent to the terminal, which then controls the agricultural equipment according to the instruction. Specifically, the watering system valve is opened at 2:00 PM and watering begins.

[0751] User Notifications and Emotion Recognition

[0752] The server generates a notification message based on the analysis results of the generation AI and the control information. For example, it creates a notification that reads, "The sprinkler system will start up tomorrow at 2:00 PM." This is then sent to the user through the user interface. Notifications are sent in real time using a dedicated application or web portal. The emotion engine analyzes the user's voice and facial expressions to recognize their emotional state. For example, if the user is feeling stressed, the content of the notification can be adjusted based on that information. For example, a gentle message such as, "It's okay, the system is operating optimally" can be sent. The emotion engine's analysis results can also suggest changes to system settings. For example, if the user expresses anxiety, the server can notify them by saying, "We will provide more detailed instructions or additional help."

[0753] Specific examples

[0754] Specific examples are shown below.

[0755] Example 1: Temperature and humidity control and user emotion recognition

[0756] The device uses a temperature sensor (e.g., SHT31) and a humidity sensor to measure a temperature of 25°C and humidity of 60% at 10:00 AM.

[0757] The device saves this data locally in CSV format and sends it to the server at noon (e.g., data_2023-10-25.csv).

[0758] The server uses generative AI (e.g., built using TensorFlow) to predict, "Tomorrow is expected to be hot, so watering should begin at 2 p.m."

[0759] The server sends a control instruction to the terminal to "activate the watering system at 2:00 p.m."

[0760] The terminal will open the valve on the sprinkler system at 2:00 p.m. and begin sprinkling water.

[0761] The server notifies the result and analyzes the user's emotions using an emotion engine (e.g., OpenCV and Emotion API).

[0762] If the emotion engine recognizes that the user is feeling stressed, it will notify them with a calming message such as, "It's okay, the system is working optimally."

[0763] Example 2: Optimizing fertilizer application and suggesting settings changes to users

[0764] At 8:00 a.m., the device measures the moisture content to 40% using a moisture sensor (e.g., TDR-100) and sends this data to the server.

[0765] The server uses generative AI to generate analysis results such as "Stop spreading fertilizer until the next rain comes."

[0766] The server sends an instruction to the terminal to "stop the operation of the fertilizer spreading device."

[0767] The terminal stops the fertilizer spreading device.

[0768] The server notifies the user of the analysis results and control information, and the emotion engine analyzes the user's emotions.

[0769] If the emotion engine recognizes that the user is expressing anxiety, it will send a notification offering more detailed instructions or additional help.

[0770] As a result, the present invention can significantly improve the efficiency of agricultural work and also provide emotional support to users.

[0771] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0772] Step 1:

[0773] Users place temperature, humidity, and moisture sensors in their fields. These sensors run on solar energy or batteries and collect environmental data in real time. The input is sensor placement and activation, and the output is that the sensors are ready to collect environmental data.

[0774] Step 2:

[0775] The terminal collects environmental data such as temperature, humidity, and moisture content in real time from the installed sensors. For example, at 9:00 AM, the terminal obtains data of 24°C from the temperature sensor and 65% from the humidity sensor. The input is the data signal from the sensor, and the output is the collected numerical data.

[0776] Step 3:

[0777] The terminal temporarily stores the collected environmental data in local storage. For example, the data is saved in a CSV file format with a timestamp (e.g., data_2023-10-25.csv). The input is the collected environmental data, and the output is the data saved in local storage.

[0778] Step 4:

[0779] The device periodically uploads data stored in its local storage to the server (e.g., at noon every day). The uploaded data includes the sensor ID and a timestamp. The input is the data in the local storage, and the output is the completion of data transfer to the server.

[0780] Step 5:

[0781] The server stores the uploaded environmental data in a database. A database management system (e.g., SQL) is used to ensure accurate data storage. The input is the data uploaded from the device, and the output is the environmental data stored in the database.

[0782] Step 6:

[0783] The server analyzes the environmental data stored in the database using a generative AI model (e.g., using TensorFlow). The generative AI model predicts the optimal growing conditions for plants based on the accumulated data. For example, it makes a prediction such as, "Since high temperatures are expected tomorrow, watering should begin at 2 p.m." The input is the environmental data in the database, and the output is the analysis results (optimal growing conditions).

[0784] Step 7:

[0785] The server generates specific control instructions based on the analysis results of the generation AI. For example, it generates an instruction to "turn on the watering system at 2:00 PM." The input is the analysis results of the generation AI, and the output is a specific control instruction.

[0786] Step 8:

[0787] The server sends the generated control instructions to the terminal. The instructions include a timestamp and scope of application. The input is the control instruction, and the output is the completion of sending the instruction to the terminal.

[0788] Step 9:

[0789] The terminal controls the agricultural equipment according to the control instructions it receives. Specifically, it opens the valve of the watering system to start watering at 2:00 p.m. The input is a control instruction from the server, and the output is the start of equipment operation.

[0790] Step 10:

[0791] The server generates a notification message based on the analysis results and control information of the generation AI. For example, it generates a notification such as "The sprinkler system will start up tomorrow at 2:00 PM." The input is the analysis results and control information, and the output is the notification message.

[0792] Step 11:

[0793] The server generates notifications and sends them to the user through a user interface, either a dedicated application or a web portal. The input is the notification message, and the output is the completion of notification delivery to the user.

[0794] Step 12:

[0795] The emotion engine analyzes the voice and facial expressions of the user who received the notification to recognize their emotional state. For example, it determines whether the user is feeling stressed. The input is the user's voice and facial expression data, and the output is the analysis result (emotional state).

[0796] Step 13:

[0797] The server reflects the analysis results of the emotion engine and adjusts the notification content. If the user is feeling stressed, the notification content will be calmer and an encouraging message will be added. The input is the analysis results of the emotion engine, and the output is the adjusted notification message.

[0798] Step 14:

[0799] The server proposes changes to system settings based on the analysis results of the emotion engine. For example, if the user expresses anxiety, the server will notify the user by suggesting that they should "provide more detailed instructions or additional help." The input is the analysis results of the emotion engine, and the output is a notification of the proposed changes to the settings.

[0800] (Application example 2)

[0801] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0802] In recent years, there has been a demand for automation and efficiency in agriculture, and optimizing agricultural work based on the collection and analysis of environmental data has become a major challenge. However, existing systems have difficulty collecting environmental data in real time, and they have not yet achieved the control of autonomous vehicles based on the analysis results. There is also a lack of systems that can recognize user emotions and provide appropriate feedback to improve the user experience. There is a need to build a comprehensive system to solve these issues.

[0803] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting environmental data using sensors, means for transmitting the environmental data to the server, and data analysis means using a generation AI to analyze the environmental data. This makes it possible to collect environmental data in real time, control an autonomous vehicle based on the analysis results, and recognize the user's emotions to adjust the content of notifications.

[0804] "Environmental data" refers to physical data such as temperature, humidity, and moisture content collected at agricultural sites.

[0805] "Generative AI" refers to artificial intelligence that analyzes collected environmental data, predicts the optimal conditions for plant growth, and generates automatic control instructions.

[0806] An "autonomous vehicle" refers to a vehicle used as agricultural equipment, equipped with sensors, that autonomously performs agricultural tasks based on the analysis results of generative AI.

[0807] An "emotion engine" refers to a system that has the ability to analyze information such as the user's voice and facial expressions and recognize the user's emotional state.

[0808] "User interface" refers to the means by which a user interacts with a system, and refers to the screens and applications that notify analysis results and control information, and allow settings to be changed or additional instructions to be given.

[0809] This invention is a system that collects environmental data, analyzes the data, controls agricultural equipment and autonomous vehicles, notifies users, and recognizes their emotions. This system is composed of a combination of sensors, generative AI, a server, a terminal, a user interface, and an emotion engine.

[0810] First, multiple sensors (temperature sensors, humidity sensors, moisture sensors, etc.) are installed in the autonomous vehicle to collect environmental data in real time. The collected data is temporarily stored on a terminal inside the vehicle. This data is then sent to a server at regular intervals.

[0811] The server stores the received data in a database and analyzes it using generative AI, which uses machine learning frameworks such as TensorFlow and PyTorch to predict the optimal conditions for plant growth. Based on the results of this analysis, the server generates specific control instructions and sends them to the terminal. The terminal then follows these instructions and the autonomous vehicle performs agricultural tasks such as watering, fertilizing, and soil improvement.

[0812] Furthermore, the analysis results and control instructions are notified to the user through a user interface provided on an application created using React Native or Flutter, and the user can change settings and issue additional instructions to the system through the application.

[0813] The system also incorporates an emotion engine that analyzes the user's emotions using tools such as IBM Watson Tone Analyzer. If the user is feeling stressed, the emotion engine adjusts the notification content to include a calmer tone or positive, encouraging messages. If the user expresses anxiety, the system provides more detailed explanations and guidance.

[0814] Specific examples

[0815] Temperature and humidity control and user emotion recognition:

[0816] The device measures the temperature and humidity at 10:00 a.m. using the temperature and humidity sensors to find a temperature of 25°C and a humidity of 60%. The collected data is stored in the device's local storage at 10:00 a.m. and sent to the server at noon.

[0817] The server instructs the generating AI to analyze the data, and the generating AI makes a prediction such as, "Since high temperatures are expected tomorrow, watering will begin at 2 p.m."

[0818] The server sends this instruction to the terminal, and the terminal automatically starts the watering system at 2:00 pm. The server notifies the user of the analysis results and control information, and the emotion engine analyzes the user's emotions.

[0819] If the emotion engine recognizes that the user is stressed, it will change the notification content to a calmer message.

[0820] Prompt Sentence Examples

[0821] Prompt: "Predict the temperature and humidity for the next day and analyze whether watering is necessary starting at 2 PM. Also, notify the user with a calming message if they are feeling stressed."

[0822] Application scenario: "Tomorrow's temperature is expected to exceed 30°C, so start watering for 10 minutes at 2pm. If the user is stressed, display a message saying, 'Don't worry, everything is managed automatically.'"

[0823] The above is a specific embodiment of the present invention. This system can collect environmental data in real time, control an autonomous vehicle based on the analysis results, and recognize the user's emotions and provide feedback.

[0824] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0825] Step 1:

[0826] Sensors are used to collect environmental data.

[0827] Input: Data from temperature, humidity, and moisture sensors installed in the autonomous vehicle.

[0828] Specific operation: The device measures the temperature at 25°C and humidity at 60% at 10:00 AM. The data from the sensor is sent to the device in real time and temporarily stored in local storage.

[0829] Step 2:

[0830] The environmental data is transmitted to a server.

[0831] Input: Environmental data collected from sensors (e.g., temperature 25°C, humidity 60%).

[0832] Specific operation: The terminal uploads the collected data to the server at regular intervals. For example, the collected data is stored in local storage at 10:00 AM and sent to the server at noon.

[0833] Step 3:

[0834] Data analysis is performed using a generation AI that analyzes the environmental data.

[0835] Input: The environment data sent to the server.

[0836] How it works: The server analyzes the data using a generative AI model (such as TensorFlow or PyTorch). Based on the collected data, the generative AI generates a prediction, such as "Tomorrow is expected to be hot, so watering will begin at 2 PM."

[0837] Step 4:

[0838] Based on the data analysis means, agricultural equipment is automatically controlled.

[0839] Input: Control instructions generated by the generation AI.

[0840] Specific operation: The server generates specific control instructions (e.g., an instruction to start watering) based on the analysis results and sends the instructions to the terminal. The terminal follows the received instructions and automatically starts the watering system at 2:00 PM.

[0841] Step 5:

[0842] The analysis results and control information are notified to the user.

[0843] Input: Control instructions and analysis results generated by the server.

[0844] Specific operation: The server notifies the user of the generated analysis results and control information through a user interface (e.g., a smartphone app using React Native or Flutter).

[0845] Step 6:

[0846] Recognize user emotions and tailor notifications accordingly.

[0847] Input: Voice data and facial expression data sent by the server.

[0848] How it works: The emotion engine (e.g., IBM Watson Tone Analyzer) analyzes the user's emotions from their voice and facial expressions collected through the user interface. If the system recognizes that the user is feeling stressed, it adjusts the notification content to be gentler and include encouraging messages.

[0849] Step 7:

[0850] The user changes settings or gives additional instructions to the system.

[0851] Input: User interface operations.

[0852] Specific operation: Users can change settings and provide additional instructions to the system through a smartphone app. The system receives the instructions and reflects the changes.

[0853] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0854] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0855] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0856] [Third embodiment]

[0857] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0858] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0859] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0860] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0861] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0862] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0863] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0864] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0865] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0866] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0867] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0868] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0869] ---

[0870] MODE FOR CARRYING OUT THE INVENTION

[0871] This invention is a system aimed at automating and streamlining agriculture, and includes sensors, a generation AI server, a control terminal, and a user interface. This system uses sensors to collect environmental data and transmits it to the server. The generation AI in the server analyzes the data and appropriately controls agricultural equipment based on the results. The user is also notified of the analysis results and control information, allowing them to change system settings as needed.

[0872] System Overview

[0873] First, sensors (temperature, humidity, moisture content, etc.) are installed in the field to collect environmental data in real time. The collected data is then sent to a terminal, where it is temporarily stored.

[0874] The device then periodically uploads the collected data to a server, which stores the data in a database and instructs the AI ​​to analyze it. The AI ​​then uses the environmental data to predict optimal conditions for plant growth.

[0875] The server then generates the necessary control instructions based on the analysis results of the AI ​​and sends them to the terminal, which then executes the received control instructions to control agricultural equipment such as watering systems and fertilizer spreaders.

[0876] Furthermore, the server notifies the user of the analysis results and executed control information via a user interface. The user can check the information using a dedicated application or web portal, and change settings or issue additional instructions as necessary.

[0877] Specific examples of program processing

[0878] Example 1: Temperature and humidity control

[0879] The device measures the temperature and humidity of the farmland every hour through temperature and humidity sensors. For example, at 10:00 a.m., the temperature is measured as 25°C and the humidity as 60%.

[0880] The device stores this data in local storage and uploads it to the server at noon.

[0881] The server instructs the AI ​​to analyze new data, and the AI ​​predicts optimal growing conditions based on data from the past seven days. For example, it generates instructions such as "Because the temperature will be high tomorrow, start the watering system at 2 p.m."

[0882] The server sends this instruction to the terminal, which then automatically starts the watering system at 2:00 p.m. based on the instruction.

[0883] The server also notifies the user of the analysis results and control information of the generating AI, and the user confirms on the smartphone app that "watering started at 2 p.m."

[0884] Example 2: Optimizing fertilizer application

[0885] The device uses a soil moisture sensor to measure the moisture content of the soil. For example, at 8:00 AM, the moisture content is measured as 40%.

[0886] The device uploads the data to a server, which then analyzes it using a generative AI. The generative AI analyzes past data and concludes that fertilizer application should be stopped until the next rain falls.

[0887] Based on the analysis results, the server generates a stop instruction for the fertilizer spreading device and sends it to the terminal.

[0888] The terminal receives this instruction and immediately stops the operation of the fertilizer spreading device.

[0889] The server notifies the user of this operation, and the user confirms in the application that "fertilizer spreading has been stopped."

[0890] As described above, this system combines sensors, generation AI, servers, terminals, and a user interface to automate agricultural work and enable it to be carried out efficiently and systematically. The types of sensors and devices used, analysis algorithms, and control functions can be customized as appropriate to suit the specific agricultural environment and user needs.

[0891] The processing flow will be explained below.

[0892] ---

[0893] Step 1:

[0894] The device measures environmental data using the temperature sensor, humidity sensor, and moisture sensor. For example, at 10:00 AM, the temperature sensor measures 25°C, the humidity sensor measures 60%, and the moisture sensor measures 40%.

[0895] Step 2:

[0896] The device stores the measured environmental data in local storage. The stored data includes the measurement value and a timestamp (e.g., 10:00 AM, October 10, 2023).

[0897] Step 3:

[0898] The device transmits the environmental data stored in the local storage to the server at regular intervals (e.g., every hour). The transmitted data includes a series of measurement values ​​and corresponding timestamps.

[0899] Step 4:

[0900] The server receives the environmental data sent from the device and stores it in a database, including the sensor measurements and timestamps.

[0901] Step 5:

[0902] Every time new data is stored in the database, the server instructs the AI ​​to analyze the data. When the instruction is issued, past environmental data is also provided to the AI.

[0903] Step 6:

[0904] The generative AI analyzes the provided environmental data and predicts the optimal growing conditions for the plant, generating specific actions as a result (e.g., "Increase watering for the next three days").

[0905] Step 7:

[0906] The server receives the analysis results provided by the generation AI and generates specific control instructions for controlling various agricultural equipment based on those results, such as "Start the watering system at 8:00 a.m. tomorrow and stop it at 10:00 a.m."

[0907] Step 8:

[0908] The server transmits the generated control instruction to the terminal.

[0909] Step 9:

[0910] The terminal executes the control instructions received from the server, controlling agricultural equipment such as watering systems and fertilizer spreading devices based on the specified time.

[0911] Step 10:

[0912] The server then sends notifications to the user about the results of the generative AI's analysis and the execution of control instructions, for example, via a smartphone app or web portal.

[0913] Step 11:

[0914] Users receive notifications and can view the current status of the system and analysis results through an application or web portal.

[0915] Step 12:

[0916] Users can change settings or add commands to the system as needed through the application or web portal, for example, manually changing the fertilizer application schedule.

[0917] The above is a detailed description of the processing flow of this system. In this way, a system is realized in which data collected from sensors is analyzed by generative AI, reflected in the automatic control of agricultural equipment, and feedback is also provided to users.

[0918] Example 1

[0919] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0920] In conventional agricultural systems, the collection and analysis of environmental data is often done manually, making efficient agricultural management difficult. Furthermore, controlling agricultural equipment and changing its settings requires direct user intervention, which is time-consuming and labor-intensive. Furthermore, analysis results and control information are not immediately communicated to users, which can delay appropriate responses.

[0921] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0922] In this invention, the server includes means for collecting environmental data using sensors, means for temporarily storing the environmental data in a terminal and transmitting it to the server at regular intervals, means for storing the environmental data in a database and performing data analysis using a generative AI model, means for automatically controlling agricultural equipment based on the data analysis means, means for notifying a user of the analysis results and control information, and means for providing an interface for the user to check the notification content and change control settings as necessary. This enables efficient collection and analysis of environmental data, automatic control of agricultural equipment, and immediate user notification and setting changes.

[0923] A "sensor" is a device for measuring environmental data (e.g., temperature, humidity, water content, etc.).

[0924] A "terminal" is a device that has the function of temporarily storing environmental data collected from sensors and transmitting it to a server.

[0925] The "server" is a device that receives environmental data sent from the terminal, stores it in a database, analyzes the data using a generative AI model, and generates control instructions based on the analysis results.

[0926] A "database" is a collection of information that allows the server to systematically store and manage environmental data received.

[0927] A "generative AI model" is an artificial intelligence algorithm that analyzes environmental data and generates optimal control instructions for agricultural equipment.

[0928] "Data analysis" is the process of analyzing collected environmental data using a generative AI model and generating optimal control instructions for agricultural equipment based on the results.

[0929] "Agricultural equipment" refers to devices that automate and streamline agricultural work, such as watering systems and fertilizer spreading devices.

[0930] An "interface" is a means by which a user changes system settings or issues additional instructions, and includes dedicated applications and web portals.

[0931] "Notification" is a means for conveying analysis results and control information to the user.

[0932] MODE FOR CARRYING OUT THE INVENTION

[0933] This invention is a system for achieving automation and efficiency in agriculture, and includes sensors, a generation AI server, a control terminal, and a user interface. The system uses sensors to collect environmental data on farmland and transmits the data to the server. The generation AI in the server analyzes the data and generates appropriate control instructions for agricultural equipment based on the results. The user is notified of the analysis results and control information, and can change the system settings as needed.

[0934] System Overview

[0935] First, environmental data is collected in real time using sensors (e.g., temperature, humidity, and moisture sensors) installed in the farmland. The collected data is temporarily stored on the device.

[0936] The device then periodically uploads the collected data to a server, which stores the data in a database and analyzes it using a generative AI model. Based on past data, the generative AI predicts the optimal conditions for plant growth.

[0937] The server then generates the necessary control instructions based on the analysis results of the AI ​​and sends them to the terminal, which then executes the received control instructions to control agricultural equipment such as watering systems and fertilizer spreaders.

[0938] Furthermore, the server notifies the user of the analysis results and executed control information via a user interface. The user can check the information using a dedicated application or web portal, and change settings or issue additional instructions as necessary.

[0939] Hardware and software used

[0940] Sensors: Temperature sensors, humidity sensors, moisture sensors, etc. Examples include the DHT22 sensor and the soil moisture sensor (YL-69).

[0941] Terminal: A microcontroller such as a Raspberry Pi or Arduino.

[0942] Server: A high-performance server for running the generative AI. For example, we will use an AWS EC2 instance.

[0943] Generative AI models: Use deep learning frameworks such as PyTorch and TensorFlow.

[0944] Database: Possible databases are MySQL and PostgreSQL.

[0945] User interface: smartphone apps (iOS, Android) or web portals (front-end frameworks such as React.js or Angular).

[0946] Specific examples

[0947] Example 1: Temperature and humidity control

[0948] The device uses a temperature sensor (e.g., DHT22) and a humidity sensor to measure the temperature and humidity of the field every hour. For example, at 10:00 AM, the temperature is measured as 25°C and the humidity is measured as 60%.

[0949] The device stores this data in local storage (e.g., SQLite database), converts it to CSV format, and sends it to the server at noon.

[0950] The server stores the received data in a MySQL database and analyzes it using a generative AI model. Based on data from the past seven days, the generative AI generates an analysis result such as "Tomorrow's temperature will be high, so start the watering system at 2:00 PM."

[0951] The server sends this instruction in JSON format to the terminal, which then turns on the watering system via a relay circuit at 2:00 PM.

[0952] The server pushes the analysis results and control information to the user's smartphone app, and the user confirms that "watering started at 2:00 p.m."

[0953] Example 2: Optimizing fertilizer application

[0954] The device measures the soil moisture content using a soil moisture sensor (e.g., YL-69). For example, at 8:00 AM, the moisture content is measured as 40%.

[0955] The device uploads the data to the server at noon, where it is formatted in CSV format and sent via an HTTP POST request.

[0956] The server stores the data in a database and analyzes it using a generative AI model, which analyzes past data and concludes that fertilizer application should be suspended until the next rain.

[0957] Based on the analysis results, the server sends a stop command to the terminal in JSON format to the fertilizer spreading device.

[0958] The terminal receives this instruction and immediately stops the operation of the fertilizer spreading device via the relay circuit.

[0959] The server notifies the user of this operation using Firebase Cloud Messaging, and the user confirms in a dedicated app that "fertilizer spreading has been stopped."

[0960] This system will improve the efficiency and automation of agricultural work, allowing users to easily monitor and control the condition of their farmland.

[0961] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0962] Step 1: Collecting data with sensors

[0963] The terminal collects environmental data in real time using various sensors (temperature sensors, humidity sensors, moisture sensors, etc.) installed on the farmland.

[0964] Input: Real-time environmental data from sensors (temperature, humidity, moisture content).

[0965] How it works: For example, the temperature sensor (DHT22) measures the temperature and humidity every hour. This data is instantly stored in the device's memory.

[0966] Output: Collected environmental data (e.g., temperature 25°C, humidity 60% at 10 AM).

[0967] Step 2: Temporarily save data

[0968] The device temporarily stores the collected environmental data in local storage (e.g., SQLite database) within the device.

[0969] Input: Environmental data collected from sensors (temperature, humidity, moisture content).

[0970] Operation: The device saves the collected data in a specified format (e.g., CSV format) in local storage.

[0971] Output: Environment data saved to local storage.

[0972] Step 3: Send data

[0973] The device uploads the stored data to the server at regular intervals.

[0974] Input: Environment data stored in local storage.

[0975] Operation: At noon, the device converts the saved data into CSV format and sends it to the server using an HTTP POST request.

[0976] Output: The environment data sent to the server.

[0977] Step 4: Save your data

[0978] The server stores the received environmental data in a database (e.g., MySQL).

[0979] Input: Environmental data sent from the device.

[0980] Operation: The server receives the incoming data and stores it in the database appropriately.

[0981] Output: Environmental data stored in a database.

[0982] Step 5: Data analysis

[0983] The server analyzes the received data using a generative AI model.

[0984] Input: Environmental data stored in a database.

[0985] How it works: An analysis script runs periodically, calling a generative AI model (e.g., using TensorFlow) to perform analysis based on the past 7 days of data.

[0986] Output: Analysis results from the generative AI model (optimal growing conditions).

[0987] Step 6: Generate control instructions

[0988] Based on the analysis results of the generation AI, the server generates the necessary control instructions and sends them to the terminal.

[0989] Input: Analysis results from the generative AI model.

[0990] Operation: The server receives the results of the generation AI, generates control instructions (e.g., start the watering system at 2 p.m.), and sends them to the terminal in JSON format.

[0991] Output: Control instructions sent to the terminal.

[0992] Step 7: Controlling agricultural equipment

[0993] The terminal controls the agricultural equipment based on the received control instructions.

[0994] Input: Control instructions received from the server (JSON format).

[0995] Operation: The terminal operates the watering system and fertilizer application device through the relay circuit based on the received instruction. For example, turn on the watering system through the relay circuit at 2:00 PM.

[0996] Output: Controlled agricultural equipment (e.g. turning on a watering system).

[0997] Step 8: User Notifications and Configuration Changes

[0998] The server notifies the user of the analysis results and control information through a user interface.

[0999] Input: Analysis results of the generative AI model and the device's control execution log.

[1000] How it works: The server uses Firebase Cloud Messaging (FCM) to push the analysis results and control information to the user's smartphone app or web portal.

[1001] Output: A notification that appears on the user's smartphone or web portal (e.g., "Watering started at 2 PM").

[1002] Based on these notifications, users can change settings or issue additional instructions as needed using a dedicated application or web portal. This series of processes enables efficient and planned automated control of agricultural work.

[1003] (Application example 1)

[1004] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1005] Modern agriculture demands automation and efficiency, but conventional systems lack the ability to collect and analyze environmental data, making it difficult to control agricultural equipment and manage quality.Furthermore, food delivery services face many challenges in managing the quality of food during delivery and creating optimal delivery routes, and there has been no way to efficiently and comprehensively manage these issues.

[1006] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1007] In this invention, the server includes means for collecting environmental data using sensors, means for transmitting the environmental data to the server, means for analyzing the environmental data using a generation AI, means for automatically controlling agricultural equipment based on the data analysis means, means for notifying the user of the analysis results and control information, means for monitoring the temperature and humidity of items being delivered in real time, means for generating an optimal delivery route taking into account traffic conditions and order priority, and means for collecting and analyzing user feedback on delivery quality. This will advance the automation and efficiency of agriculture, while also enabling integrated quality control during food delivery and optimal delivery planning.

[1008] A "sensor" is a device for collecting environmental data, and includes a temperature sensor, a humidity sensor, and the like.

[1009] "Environmental data" refers to various information collected, such as temperature, humidity, and soil moisture content, that is necessary for agricultural operations and food delivery management.

[1010] The "server" is a centralized computer system that receives environmental data and analyzes it using generative AI.

[1011] "Generative AI" is a system that uses artificial intelligence to analyze collected data and generate optimal control instructions and predictions.

[1012] "Data analysis means" refers to methods or devices that use generative AI to analyze environmental data and derive the necessary control instructions and predictions.

[1013] "Agricultural equipment" refers to devices or machines that are automatically controlled to improve the efficiency of agricultural work, including, for example, watering systems and fertilizer spreading devices.

[1014] The "means for notifying the user" refers to a system or method for notifying the user of analysis results and control information through a user interface.

[1015] "Means for real-time monitoring of temperature and humidity of goods during delivery" refers to devices or systems that continuously monitor the temperature and humidity of the environment in which food or goods are kept during delivery and collect necessary information.

[1016] "Means for generating optimal delivery routes" refers to devices or algorithms that calculate efficient delivery routes in real time, taking into account traffic conditions and order priorities.

[1017] "Means for collecting and analyzing user feedback regarding delivery quality" refers to a system that collects opinions and reviews regarding quality from delivery personnel and customers, and analyzes that information using generative AI, etc.

[1018] MODE FOR CARRYING OUT THE INVENTION

[1019] This invention is a system aimed at automating and streamlining agriculture and controlling food quality in food delivery services. It includes sensors, a generation AI server, a control terminal, and a user interface. The system uses sensors to collect environmental data and transmits it to the server. The generation AI on the server analyzes the data and appropriately controls agricultural equipment based on the results. Users are also notified of the analysis results and control information, allowing them to change system settings as needed.

[1020] System Overview

[1021] First, sensors (for temperature, humidity, soil moisture, etc.) are installed in farmland and delivery vehicles to collect environmental data in real time. The collected data is sent to a terminal, which temporarily stores it. Next, the terminal uploads the collected data to a server at regular intervals. The server stores the received data in a database and instructs the generation AI to analyze the data. The generation AI predicts the optimal conditions for plant growth based on the environmental data. The server also monitors the temperature and humidity during delivery in real time and generates the optimal delivery route.

[1022] Based on the analysis results of the generation AI, the server generates the necessary control instructions and sends them to the terminal. The terminal then executes the received control instructions to control agricultural equipment such as watering systems and fertilizer spreaders. It also notifies the user of necessary measures based on the environmental conditions of the delivery vehicle during delivery. The server then notifies the user of the analysis results and information on the executed controls via a user interface. The user can check this information using a dedicated application or web portal and change settings or issue additional instructions as necessary.

[1023] Program Processing Details

[1024] Hardware and software used

[1025] Hardware: smartphones, sensors

[1026] Software: Python, Flask

[1027] Program Processing Overview

[1028] 1. Collect data from sensors: Collect data from temperature and humidity sensors and send the data to the device.

[1029] 2. Temporary storage and uploading of data: The device temporarily stores the collected data and uploads it to the server at regular intervals.

[1030] 3. Data analysis on the server: The server analyzes the data using generative AI and generates optimal control instructions.

[1031] 4. Execution of control instructions: The terminal executes the control instructions received from the server and controls the agricultural equipment.

[1032] 5. Notification to the user: The server notifies the user of the analysis results and control information of the generated AI through the user interface.

[1033] Specific examples

[1034] Temperature and humidity control: During delivery, the delivery person's smartphone will notify them that the temperature has exceeded 35 degrees Celsius and that they should take measures to lower the temperature in the cooler. The delivery person will then follow the instructions and adjust the temperature in the cooler.

[1035] Optimal delivery route generation: When a customer places an order, the app will notify them, "Currently, the delivery is congested. Please select Route B as the optimal route."

[1036] Prompt Sentence Examples

[1037] "Generate the optimal route from current location A to destination B, taking into account traffic conditions and order priorities."

[1038] This series of systems will simultaneously achieve efficiency and quality control in both agricultural work and food delivery services. Users will also be able to flexibly respond by changing the system settings and issuing additional instructions.

[1039] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1040] Step 1:

[1041] Collecting data from sensors

[1042] Input: Sensors (Temperature sensor, Humidity sensor, etc.)

[1043] How it works: The sensors measure the temperature and humidity in real time on farmland or inside delivery vehicles. For example, the temperature sensor measures 25°C and the humidity sensor measures 60%.

[1044] Output: Collected environmental data

[1045] Step 2:

[1046] Temporary storage and upload of data

[1047] Input: Collected environmental dates

[1048] Specific operation: The device temporarily stores the collected data in local storage. At regular intervals (e.g., every hour), the device uploads the stored data to the server.

[1049] Output: Environment data sent to the server

[1050] Step 3:

[1051] Data analysis on the server

[1052] Input: Environmental data uploaded to the server

[1053] Specific operation: The server stores the received environmental data in a database. It then uses the generation AI to analyze it, including past data. For example, if the temperature is too high, it generates instructions such as "Start automatic watering at 2:00 PM."

[1054] Output: Analysis results and control instructions

[1055] Step 4:

[1056] Sending and executing control instructions

[1057] Input: Generated analysis results and control instructions

[1058] Specific operation: The server sends the generated control instructions to the terminal. The terminal automatically controls the corresponding agricultural equipment (e.g., watering system or fertilizer spraying device) based on the received control instructions.

[1059] Output: Operation of agricultural equipment according to the executed control instructions

[1060] Step 5:

[1061] User Notification and Feedback

[1062] Input: Analysis results and control information

[1063] Specific operation: The server notifies the user of the analysis results and executed control information through the user interface. The user checks the notifications on a dedicated smartphone app or web portal and changes system settings or issues additional instructions as necessary. For example, the user confirms that "watering started at 2:00 PM."

[1064] Output: Notification and feedback to the user

[1065] In this way, each step is processed continuously and automatically, realizing a series of processes from data collection from sensors to analysis by generative AI and control of control devices, enabling efficient environmental management and quality maintenance in agriculture and food delivery.

[1066] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1067] ---

[1068] MODE FOR CARRYING OUT THE INVENTION

[1069] This invention is a system that aims to automate and streamline agriculture by combining sensors, generative AI, servers, terminals, a user interface, and an emotion engine. This system includes components that realize environmental data collection, data analysis, agricultural equipment control, user notifications, and user emotion recognition functions.

[1070] System Overview

[1071] First, various sensors (temperature sensors, humidity sensors, moisture sensors, etc.) are placed at the installation location and environmental data is collected in real time using these sensors. The collected data is sent to the device, which then temporarily stores the data in local storage.

[1072] The device then periodically uploads the collected data to a server, which receives it and stores it in a database. The generative AI on the server analyzes the collected data and uses it to predict the optimal conditions for plant growth.

[1073] Based on the analysis results of the generative AI, the server generates specific control instructions and sends them to the terminal, which then follows the instructions to actually control agricultural equipment such as watering systems and fertilizer spreaders.

[1074] Furthermore, the analysis results and control instructions of the generative AI are notified to the user via a user interface from the server. The user receives these notifications on a dedicated application or web portal, where they can check the current status of the system and the analysis results. The user can also change settings and issue additional instructions to the system through the interface.

[1075] Adding an Emotion Engine

[1076] One of the features of the present invention is that it includes an emotion engine that recognizes the user's emotion. The emotion engine analyzes the user's emotion from, for example, their voice or facial expression, and recognizes their emotional state.

[1077] The emotion engine can tailor notification content based on the emotion it recognizes. For example, if a user is feeling stressed, the notification could have a softer tone or include a positive, encouraging message. The emotion engine can also suggest changes to system settings based on the user's emotion. For example, if a user expresses anxiety, the system could provide more detailed explanations or guidance.

[1078] Specific examples of program processing

[1079] Example 1: Temperature and humidity control and user emotion recognition

[1080] The device measures the temperature and humidity at 10:00 AM to be 25°C and 60%.

[1081] The device stores this data in local storage and sends it to the server at noon.

[1082] The server instructs the generating AI to analyze the data, and the generating AI makes a prediction such as, "Since high temperatures are expected tomorrow, watering will begin at 2 p.m."

[1083] The server sends this instruction to the terminal, which then automatically starts the watering system at 2:00 p.m.

[1084] The server notifies the user of the analysis results and control information of the generative AI, and the emotion engine analyzes the user's emotions.

[1085] If the emotion engine recognizes that the user is stressed, it will change the notification content to a calmer message.

[1086] Example 2: Optimizing fertilizer application and suggesting setting changes to users

[1087] At 8:00 a.m., the device measures the moisture content to 40% using the moisture sensor and sends this data to the server.

[1088] The server uses a generative AI to generate analysis results such as "stop spreading fertilizer until the next rain comes."

[1089] The server sends this instruction to the terminal, and the terminal stops the operation of the fertilizer spreading device.

[1090] The server notifies the user of the analysis results and control information, and the emotion engine analyzes the user's emotions.

[1091] If the emotion engine recognizes that the user is expressing anxiety, the system will suggest "providing more detailed instructions or additional help."

[1092] The above is a specific embodiment of this system. By incorporating an emotion engine, user-friendly support becomes possible, allowing agricultural work to be carried out efficiently and safely.

[1093] The processing flow will be explained below.

[1094] ---

[1095] Example 1: Temperature and humidity control and user emotion recognition

[1096] Step 1:

[1097] The device uses temperature and humidity sensors to measure environmental data every hour. For example, at 10:00 AM, the temperature is 25°C and the humidity is 60%.

[1098] Step 2:

[1099] The device stores the measured data in local storage, which includes the measurement value and a timestamp (e.g., October 10, 2023, 10:00 AM).

[1100] Step 3:

[1101] The device sends the stored environmental data to the server every hour, for example, uploading the data from 10:00 AM at 11:00 AM.

[1102] Step 4:

[1103] The server stores the received environmental data in a database, which includes the measured values ​​and timestamps.

[1104] Step 5:

[1105] Every time new data is stored, the server instructs the generation AI to analyze the data, which then analyzes past data as well.

[1106] Step 6:

[1107] The generative AI predicts the optimal growing conditions for plants based on the provided environmental data, generating instructions such as "Start the watering system tomorrow at 2:00 PM."

[1108] Step 7:

[1109] Based on the analysis results provided by the generation AI, the server generates specific control instructions and sends them to the terminal.

[1110] Step 8:

[1111] The device executes the control instructions received from the server, for example, starting the watering system at 2:00 PM.

[1112] Step 9:

[1113] The server notifies the user of the analysis results and control information of the generated AI via a smartphone app.

[1114] Step 10:

[1115] The emotion engine analyzes the user's facial expressions and voice when checking notifications to recognize their emotional state.

[1116] Step 11:

[1117] The server adjusts the notification content appropriately based on the emotions recognized by the emotion engine. For example, if the user is feeling stressed, the tone of the notification will be softened.

[1118] Example 2: Optimizing fertilizer application and suggesting setting changes to users

[1119] Step 1:

[1120] The device uses a moisture sensor to measure the moisture content of the soil. For example, at 8:00 AM, it measures the moisture content to be 40%.

[1121] Step 2:

[1122] The device stores the measured data in local storage, which includes the measurement value and a timestamp.

[1123] Step 3:

[1124] The device uploads the saved data to the server at regular intervals (for example, sending data to the server at 8:00 when the device is opened).

[1125] Step 4:

[1126] The server stores the received data in a database, which includes the measurement value and a timestamp.

[1127] Step 5:

[1128] Every time new data is stored, the server instructs the generation AI to analyze the data, which then analyzes past data as well.

[1129] Step 6:

[1130] The generative AI predicts the optimal conditions for fertilizer application based on the provided environmental data, generating instructions such as "stop fertilizer application until the next rain."

[1131] Step 7:

[1132] Based on the analysis results provided by the generation AI, the server generates specific control instructions and sends them to the terminal.

[1133] Step 8:

[1134] The terminal executes the control instruction received from the server, for example, stopping the operation of the fertilizer spreading device.

[1135] Step 9:

[1136] The server notifies the user of the analysis results and control information of the generated AI via a smartphone app.

[1137] Step 10:

[1138] The emotion engine analyzes the user's facial expressions and voice when checking notifications to recognize their emotional state.

[1139] Step 11:

[1140] If the emotion engine recognizes that the user is expressing anxiety, the server will suggest system configuration changes or further guidance, such as providing additional help information or instructions.

[1141] The above are the specific processing steps for implementing an agricultural automation system incorporating an emotion engine. This system simultaneously improves the efficiency of agricultural work and the user experience.

[1142] Example 2

[1143] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1144] In modern agriculture, collecting and analyzing environmental data to optimize plant growth conditions is important, but doing so manually takes time and effort. Furthermore, there is a need to support smoother agricultural work by providing optimal notifications based on the user's emotional state, but existing systems lack notifications and suggestions that take the user's emotions into account. This makes it difficult to reduce user stress and provide a sense of security, making it difficult to achieve the goals of improving agricultural efficiency and automation.

[1145] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1146] In this invention, the server includes means for collecting environmental data using sensors, means for transmitting the environmental data to the server, means for analyzing the environmental data using a generative AI, means for automatically controlling agricultural equipment, means for notifying the user of the analysis results and control information, means for analyzing the user's emotional state using an emotion engine and adjusting the notification content, and means for proposing changes to system settings based on the analysis of the emotion engine. This not only enables efficient collection and analysis of environmental data and automatic control of agricultural equipment, but also provides appropriate notifications and suggestions according to the user's emotional state, enabling user-friendly support for agricultural work.

[1147] A "sensor" is a device for detecting environmental data such as temperature, humidity, and moisture content.

[1148] "Environmental data" refers to information related to plant growth conditions, such as temperature, humidity, and water content.

[1149] "Server" is a computer system for storing and analyzing collected environmental data.

[1150] "Generative AI" is artificial intelligence that uses machine learning algorithms to analyze environmental data and make predictions.

[1151] "Data analysis means" refers to a method of using generative AI to analyze environmental data and predict optimal growing conditions for plants.

[1152] "Agricultural equipment" refers to equipment that automatically aids plant growth, such as watering systems and fertilizer spreading devices.

[1153] "Control means" refers to a method for automatically operating agricultural equipment based on data analysis means.

[1154] The "notification means" is a method for notifying the user of the analysis results and control information.

[1155] A "user interface" is a means by which a user changes settings or gives additional instructions to the system.

[1156] The "emotion engine" is a program that analyzes the user's voice and facial expressions to recognize their emotional state.

[1157] "Adjusting notification content" is a method of changing notification content according to the user's emotional state analyzed by the emotion engine.

[1158] "Settings change suggestion" is a method of suggesting system setting changes to the user based on the emotion engine's analysis.

[1159] MODE FOR CARRYING OUT THE INVENTION

[1160] This invention is a system that aims to automate and streamline agriculture by combining sensors, generative AI, servers, terminals, a user interface, and an emotion engine. This system includes components that realize environmental data collection, data analysis, agricultural equipment control, user notifications, and user emotion recognition functions.

[1161] Environmental data collection

[1162] First, the user places temperature sensors, humidity sensors, moisture content sensors, etc. on the farmland. These sensors can be powered by solar energy or batteries. The device collects environmental data from these sensors in real time and temporarily stores it in local storage. For example, at 9:00 AM, the temperature sensor measures 24°C and the humidity sensor measures 65%. This data is stored on the device and uploaded to the server at regular intervals (e.g., every day at noon).

[1163] Data analysis

[1164] The server stores the received environmental data in a database using a database management system such as SQL. A generative AI model (e.g., built using TensorFlow) on the server analyzes the stored data and predicts the optimal conditions for plant growth. For example, it may make a prediction such as, "Since high temperatures are expected tomorrow, watering should begin at 2 p.m."

[1165] Agricultural equipment control

[1166] The server creates specific control instructions based on the analysis results of the generation AI. For example, it generates an instruction to "operate the watering system at 2:00 PM." This is sent to the terminal, which then controls the agricultural equipment according to the instruction. Specifically, the watering system valve is opened at 2:00 PM and watering begins.

[1167] User Notifications and Emotion Recognition

[1168] The server generates a notification message based on the analysis results of the generation AI and the control information. For example, it creates a notification that reads, "The sprinkler system will start up tomorrow at 2:00 PM." This is then sent to the user through the user interface. Notifications are sent in real time using a dedicated application or web portal. The emotion engine analyzes the user's voice and facial expressions to recognize their emotional state. For example, if the user is feeling stressed, the content of the notification can be adjusted based on that information. For example, a gentle message such as, "It's okay, the system is operating optimally" can be sent. The emotion engine's analysis results can also suggest changes to system settings. For example, if the user expresses anxiety, the server can notify them by saying, "We will provide more detailed instructions or additional help."

[1169] Specific examples

[1170] Specific examples are shown below.

[1171] Example 1: Temperature and humidity control and user emotion recognition

[1172] The device uses a temperature sensor (e.g., SHT31) and a humidity sensor to measure a temperature of 25°C and humidity of 60% at 10:00 AM.

[1173] The device saves this data locally in CSV format and sends it to the server at noon (e.g., data_2023-10-25.csv).

[1174] The server uses generative AI (e.g., built using TensorFlow) to predict, "Tomorrow is expected to be hot, so watering should begin at 2 p.m."

[1175] The server sends a control instruction to the terminal to "activate the watering system at 2:00 p.m."

[1176] The terminal will open the valve on the sprinkler system at 2:00 p.m. and begin sprinkling water.

[1177] The server notifies the result and analyzes the user's emotions using an emotion engine (e.g., OpenCV and Emotion API).

[1178] If the emotion engine recognizes that the user is feeling stressed, it will notify them with a calming message such as, "It's okay, the system is working optimally."

[1179] Example 2: Optimizing fertilizer application and suggesting settings changes to users

[1180] At 8:00 a.m., the device measures the moisture content to 40% using a moisture sensor (e.g., TDR-100) and sends this data to the server.

[1181] The server uses generative AI to generate analysis results such as "Stop spreading fertilizer until the next rain comes."

[1182] The server sends an instruction to the terminal to "stop the operation of the fertilizer spreading device."

[1183] The terminal stops the fertilizer spreading device.

[1184] The server notifies the user of the analysis results and control information, and the emotion engine analyzes the user's emotions.

[1185] If the emotion engine recognizes that the user is expressing anxiety, it will send a notification offering more detailed instructions or additional help.

[1186] As a result, the present invention can significantly improve the efficiency of agricultural work and also provide emotional support to users.

[1187] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1188] Step 1:

[1189] Users place temperature, humidity, and moisture sensors in their fields. These sensors run on solar energy or batteries and collect environmental data in real time. The input is sensor placement and activation, and the output is that the sensors are ready to collect environmental data.

[1190] Step 2:

[1191] The terminal collects environmental data such as temperature, humidity, and moisture content in real time from the installed sensors. For example, at 9:00 AM, the terminal obtains data of 24°C from the temperature sensor and 65% from the humidity sensor. The input is the data signal from the sensor, and the output is the collected numerical data.

[1192] Step 3:

[1193] The terminal temporarily stores the collected environmental data in local storage. For example, the data is saved in a CSV file format with a timestamp (e.g., data_2023-10-25.csv). The input is the collected environmental data, and the output is the data saved in local storage.

[1194] Step 4:

[1195] The device periodically uploads data stored in its local storage to the server (e.g., at noon every day). The uploaded data includes the sensor ID and a timestamp. The input is the data in the local storage, and the output is the completion of data transfer to the server.

[1196] Step 5:

[1197] The server stores the uploaded environmental data in a database. A database management system (e.g., SQL) is used to ensure accurate data storage. The input is the data uploaded from the device, and the output is the environmental data stored in the database.

[1198] Step 6:

[1199] The server analyzes the environmental data stored in the database using a generative AI model (e.g., using TensorFlow). The generative AI model predicts the optimal growing conditions for plants based on the accumulated data. For example, it makes a prediction such as, "Since high temperatures are expected tomorrow, watering should begin at 2 p.m." The input is the environmental data in the database, and the output is the analysis results (optimal growing conditions).

[1200] Step 7:

[1201] The server generates specific control instructions based on the analysis results of the generation AI. For example, it generates an instruction to "turn on the watering system at 2:00 PM." The input is the analysis results of the generation AI, and the output is a specific control instruction.

[1202] Step 8:

[1203] The server sends the generated control instructions to the terminal. The instructions include a timestamp and scope of application. The input is the control instruction, and the output is the completion of sending the instruction to the terminal.

[1204] Step 9:

[1205] The terminal controls the agricultural equipment according to the control instructions it receives. Specifically, it opens the valve of the watering system to start watering at 2:00 p.m. The input is a control instruction from the server, and the output is the start of equipment operation.

[1206] Step 10:

[1207] The server generates a notification message based on the analysis results and control information of the generation AI. For example, it generates a notification such as "The sprinkler system will start up tomorrow at 2:00 PM." The input is the analysis results and control information, and the output is the notification message.

[1208] Step 11:

[1209] The server generates notifications and sends them to the user through a user interface, either a dedicated application or a web portal. The input is the notification message, and the output is the completion of notification delivery to the user.

[1210] Step 12:

[1211] The emotion engine analyzes the voice and facial expressions of the user who received the notification to recognize their emotional state. For example, it determines whether the user is feeling stressed. The input is the user's voice and facial expression data, and the output is the analysis result (emotional state).

[1212] Step 13:

[1213] The server reflects the analysis results of the emotion engine and adjusts the notification content. If the user is feeling stressed, the notification content will be calmer and an encouraging message will be added. The input is the analysis results of the emotion engine, and the output is the adjusted notification message.

[1214] Step 14:

[1215] The server proposes changes to system settings based on the analysis results of the emotion engine. For example, if the user expresses anxiety, the server will notify the user by suggesting that they should "provide more detailed instructions or additional help." The input is the analysis results of the emotion engine, and the output is a notification of the proposed changes to the settings.

[1216] (Application example 2)

[1217] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1218] In recent years, there has been a demand for automation and efficiency in agriculture, and optimizing agricultural work based on the collection and analysis of environmental data has become a major challenge. However, existing systems have difficulty collecting environmental data in real time, and they have not yet achieved the control of autonomous vehicles based on the analysis results. There is also a lack of systems that can recognize user emotions and provide appropriate feedback to improve the user experience. There is a need to build a comprehensive system to solve these issues.

[1219] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting environmental data using sensors, means for transmitting the environmental data to the server, and data analysis means using a generation AI to analyze the environmental data. This makes it possible to collect environmental data in real time, control an autonomous vehicle based on the analysis results, and recognize the user's emotions to adjust the content of notifications.

[1220] "Environmental data" refers to physical data such as temperature, humidity, and moisture content collected at agricultural sites.

[1221] "Generative AI" refers to artificial intelligence that analyzes collected environmental data, predicts the optimal conditions for plant growth, and generates automatic control instructions.

[1222] An "autonomous vehicle" refers to a vehicle used as agricultural equipment, equipped with sensors, that autonomously performs agricultural tasks based on the analysis results of generative AI.

[1223] An "emotion engine" refers to a system that has the ability to analyze information such as the user's voice and facial expressions and recognize the user's emotional state.

[1224] "User interface" refers to the means by which a user interacts with a system, and refers to the screens and applications that notify analysis results and control information, and allow settings to be changed or additional instructions to be given.

[1225] This invention is a system that collects environmental data, analyzes the data, controls agricultural equipment and autonomous vehicles, notifies users, and recognizes their emotions. This system is composed of a combination of sensors, generative AI, a server, a terminal, a user interface, and an emotion engine.

[1226] First, multiple sensors (temperature sensors, humidity sensors, moisture sensors, etc.) are installed in the autonomous vehicle to collect environmental data in real time. The collected data is temporarily stored on a terminal inside the vehicle. This data is then sent to a server at regular intervals.

[1227] The server stores the received data in a database and analyzes it using generative AI, which uses machine learning frameworks such as TensorFlow and PyTorch to predict the optimal conditions for plant growth. Based on the results of this analysis, the server generates specific control instructions and sends them to the terminal. The terminal then follows these instructions and the autonomous vehicle performs agricultural tasks such as watering, fertilizing, and soil improvement.

[1228] Furthermore, the analysis results and control instructions are notified to the user through a user interface provided on an application created using React Native or Flutter, and the user can change settings and issue additional instructions to the system through the application.

[1229] The system also incorporates an emotion engine that analyzes the user's emotions using tools such as IBM Watson Tone Analyzer. If the user is feeling stressed, the emotion engine adjusts the notification content to include a calmer tone or positive, encouraging messages. If the user expresses anxiety, the system provides more detailed explanations and guidance.

[1230] Specific examples

[1231] Temperature and humidity control and user emotion recognition:

[1232] The device measures the temperature and humidity at 10:00 a.m. using the temperature and humidity sensors to find a temperature of 25°C and a humidity of 60%. The collected data is stored in the device's local storage at 10:00 a.m. and sent to the server at noon.

[1233] The server instructs the generating AI to analyze the data, and the generating AI makes a prediction such as, "Since high temperatures are expected tomorrow, watering will begin at 2 p.m."

[1234] The server sends this instruction to the terminal, and the terminal automatically starts the watering system at 2:00 pm. The server notifies the user of the analysis results and control information, and the emotion engine analyzes the user's emotions.

[1235] If the emotion engine recognizes that the user is stressed, it will change the notification content to a calmer message.

[1236] Prompt Sentence Examples

[1237] Prompt: "Predict the temperature and humidity for the next day and analyze whether watering is necessary starting at 2 PM. Also, notify the user with a calming message if they are feeling stressed."

[1238] Application scenario: "Tomorrow's temperature is expected to exceed 30°C, so start watering for 10 minutes at 2pm. If the user is stressed, display a message saying, 'Don't worry, everything is managed automatically.'"

[1239] The above is a specific embodiment of the present invention. This system can collect environmental data in real time, control an autonomous vehicle based on the analysis results, and recognize the user's emotions and provide feedback.

[1240] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1241] Step 1:

[1242] Sensors are used to collect environmental data.

[1243] Input: Data from temperature, humidity, and moisture sensors installed in the autonomous vehicle.

[1244] Specific operation: The device measures the temperature at 25°C and humidity at 60% at 10:00 AM. The data from the sensor is sent to the device in real time and temporarily stored in local storage.

[1245] Step 2:

[1246] The environmental data is transmitted to a server.

[1247] Input: Environmental data collected from sensors (e.g., temperature 25°C, humidity 60%).

[1248] Specific operation: The terminal uploads the collected data to the server at regular intervals. For example, the collected data is stored in local storage at 10:00 AM and sent to the server at noon.

[1249] Step 3:

[1250] Data analysis is performed using a generation AI that analyzes the environmental data.

[1251] Input: The environment data sent to the server.

[1252] How it works: The server analyzes the data using a generative AI model (such as TensorFlow or PyTorch). Based on the collected data, the generative AI generates a prediction, such as "Tomorrow is expected to be hot, so watering will begin at 2 PM."

[1253] Step 4:

[1254] Based on the data analysis means, agricultural equipment is automatically controlled.

[1255] Input: Control instructions generated by the generation AI.

[1256] Specific operation: The server generates specific control instructions (e.g., an instruction to start watering) based on the analysis results and sends the instructions to the terminal. The terminal follows the received instructions and automatically starts the watering system at 2:00 PM.

[1257] Step 5:

[1258] The analysis results and control information are notified to the user.

[1259] Input: Control instructions and analysis results generated by the server.

[1260] Specific operation: The server notifies the user of the generated analysis results and control information through a user interface (e.g., a smartphone app using React Native or Flutter).

[1261] Step 6:

[1262] Recognize user emotions and tailor notifications accordingly.

[1263] Input: Voice data and facial expression data sent by the server.

[1264] How it works: The emotion engine (e.g., IBM Watson Tone Analyzer) analyzes the user's emotions from their voice and facial expressions collected through the user interface. If the system recognizes that the user is feeling stressed, it adjusts the notification content to be gentler and include encouraging messages.

[1265] Step 7:

[1266] The user changes settings or gives additional instructions to the system.

[1267] Input: User interface operations.

[1268] Specific operation: Users can change settings and provide additional instructions to the system through a smartphone app. The system receives the instructions and reflects the changes.

[1269] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1270] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1271] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1272] [Fourth embodiment]

[1273] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1274] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1275] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1276] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1277] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1278] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1279] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1280] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1281] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1282] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1283] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1284] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1285] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1286] ---

[1287] MODE FOR CARRYING OUT THE INVENTION

[1288] This invention is a system aimed at automating and streamlining agriculture, and includes sensors, a generation AI server, a control terminal, and a user interface. This system uses sensors to collect environmental data and transmits it to the server. The generation AI in the server analyzes the data and appropriately controls agricultural equipment based on the results. The user is also notified of the analysis results and control information, allowing them to change system settings as needed.

[1289] System Overview

[1290] First, sensors (temperature, humidity, moisture content, etc.) are installed in the field to collect environmental data in real time. The collected data is then sent to a terminal, where it is temporarily stored.

[1291] The device then periodically uploads the collected data to a server, which stores the data in a database and instructs the AI ​​to analyze it. The AI ​​then uses the environmental data to predict optimal conditions for plant growth.

[1292] The server then generates the necessary control instructions based on the analysis results of the AI ​​and sends them to the terminal, which then executes the received control instructions to control agricultural equipment such as watering systems and fertilizer spreaders.

[1293] Furthermore, the server notifies the user of the analysis results and executed control information via a user interface. The user can check the information using a dedicated application or web portal, and change settings or issue additional instructions as necessary.

[1294] Specific examples of program processing

[1295] Example 1: Temperature and humidity control

[1296] The device measures the temperature and humidity of the farmland every hour through temperature and humidity sensors. For example, at 10:00 a.m., the temperature is measured as 25°C and the humidity as 60%.

[1297] The device stores this data in local storage and uploads it to the server at noon.

[1298] The server instructs the AI ​​to analyze new data, and the AI ​​predicts optimal growing conditions based on data from the past seven days. For example, it generates instructions such as "Because the temperature will be high tomorrow, start the watering system at 2 p.m."

[1299] The server sends this instruction to the terminal, which then automatically starts the watering system at 2:00 p.m. based on the instruction.

[1300] The server also notifies the user of the analysis results and control information of the generating AI, and the user confirms on the smartphone app that "watering started at 2 p.m."

[1301] Example 2: Optimizing fertilizer application

[1302] The device uses a soil moisture sensor to measure the moisture content of the soil. For example, at 8:00 AM, the moisture content is measured as 40%.

[1303] The device uploads the data to a server, which then analyzes it using a generative AI. The generative AI analyzes past data and concludes that fertilizer application should be stopped until the next rain falls.

[1304] Based on the analysis results, the server generates a stop instruction for the fertilizer spreading device and sends it to the terminal.

[1305] The terminal receives this instruction and immediately stops the operation of the fertilizer spreading device.

[1306] The server notifies the user of this operation, and the user confirms in the application that "fertilizer spreading has been stopped."

[1307] As described above, this system combines sensors, generation AI, servers, terminals, and a user interface to automate agricultural work and enable it to be carried out efficiently and systematically. The types of sensors and devices used, analysis algorithms, and control functions can be customized as appropriate to suit the specific agricultural environment and user needs.

[1308] The processing flow will be explained below.

[1309] ---

[1310] Step 1:

[1311] The device measures environmental data using the temperature sensor, humidity sensor, and moisture sensor. For example, at 10:00 AM, the temperature sensor measures 25°C, the humidity sensor measures 60%, and the moisture sensor measures 40%.

[1312] Step 2:

[1313] The device stores the measured environmental data in local storage. The stored data includes the measurement value and a timestamp (e.g., 10:00 AM, October 10, 2023).

[1314] Step 3:

[1315] The device transmits the environmental data stored in the local storage to the server at regular intervals (e.g., every hour). The transmitted data includes a series of measurement values ​​and corresponding timestamps.

[1316] Step 4:

[1317] The server receives the environmental data sent from the device and stores it in a database, including the sensor measurements and timestamps.

[1318] Step 5:

[1319] Every time new data is stored in the database, the server instructs the AI ​​to analyze the data. When the instruction is issued, past environmental data is also provided to the AI.

[1320] Step 6:

[1321] The generative AI analyzes the provided environmental data and predicts the optimal growing conditions for the plant, generating specific actions as a result (e.g., "Increase watering for the next three days").

[1322] Step 7:

[1323] The server receives the analysis results provided by the generation AI and generates specific control instructions for controlling various agricultural equipment based on those results, such as "Start the watering system at 8:00 a.m. tomorrow and stop it at 10:00 a.m."

[1324] Step 8:

[1325] The server transmits the generated control instruction to the terminal.

[1326] Step 9:

[1327] The terminal executes the control instructions received from the server, controlling agricultural equipment such as watering systems and fertilizer spreading devices based on the specified time.

[1328] Step 10:

[1329] The server then sends notifications to the user about the results of the generative AI's analysis and the execution of control instructions, for example, via a smartphone app or web portal.

[1330] Step 11:

[1331] Users receive notifications and can view the current status of the system and analysis results through an application or web portal.

[1332] Step 12:

[1333] Users can change settings or add commands to the system as needed through the application or web portal, for example, manually changing the fertilizer application schedule.

[1334] The above is a detailed description of the processing flow of this system. In this way, a system is realized in which data collected from sensors is analyzed by generative AI, reflected in the automatic control of agricultural equipment, and feedback is also provided to users.

[1335] Example 1

[1336] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1337] In conventional agricultural systems, the collection and analysis of environmental data is often done manually, making efficient agricultural management difficult. Furthermore, controlling agricultural equipment and changing its settings requires direct user intervention, which is time-consuming and labor-intensive. Furthermore, analysis results and control information are not immediately communicated to users, which can delay appropriate responses.

[1338] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1339] In this invention, the server includes means for collecting environmental data using sensors, means for temporarily storing the environmental data in a terminal and transmitting it to the server at regular intervals, means for storing the environmental data in a database and performing data analysis using a generative AI model, means for automatically controlling agricultural equipment based on the data analysis means, means for notifying a user of the analysis results and control information, and means for providing an interface for the user to check the notification content and change control settings as necessary. This enables efficient collection and analysis of environmental data, automatic control of agricultural equipment, and immediate user notification and setting changes.

[1340] A "sensor" is a device for measuring environmental data (e.g., temperature, humidity, water content, etc.).

[1341] A "terminal" is a device that has the function of temporarily storing environmental data collected from sensors and transmitting it to a server.

[1342] The "server" is a device that receives environmental data sent from the terminal, stores it in a database, analyzes the data using a generative AI model, and generates control instructions based on the analysis results.

[1343] A "database" is a collection of information that allows the server to systematically store and manage environmental data received.

[1344] A "generative AI model" is an artificial intelligence algorithm that analyzes environmental data and generates optimal control instructions for agricultural equipment.

[1345] "Data analysis" is the process of analyzing collected environmental data using a generative AI model and generating optimal control instructions for agricultural equipment based on the results.

[1346] "Agricultural equipment" refers to devices that automate and streamline agricultural work, such as watering systems and fertilizer spreading devices.

[1347] An "interface" is a means by which a user changes system settings or issues additional instructions, and includes dedicated applications and web portals.

[1348] "Notification" is a means for conveying analysis results and control information to the user.

[1349] MODE FOR CARRYING OUT THE INVENTION

[1350] This invention is a system for achieving automation and efficiency in agriculture, and includes sensors, a generation AI server, a control terminal, and a user interface. The system uses sensors to collect environmental data on farmland and transmits the data to the server. The generation AI in the server analyzes the data and generates appropriate control instructions for agricultural equipment based on the results. The user is notified of the analysis results and control information, and can change the system settings as needed.

[1351] System Overview

[1352] First, environmental data is collected in real time using sensors (e.g., temperature, humidity, and moisture sensors) installed in the farmland. The collected data is temporarily stored on the device.

[1353] The device then periodically uploads the collected data to a server, which stores the data in a database and analyzes it using a generative AI model. Based on past data, the generative AI predicts the optimal conditions for plant growth.

[1354] The server then generates the necessary control instructions based on the analysis results of the AI ​​and sends them to the terminal, which then executes the received control instructions to control agricultural equipment such as watering systems and fertilizer spreaders.

[1355] Furthermore, the server notifies the user of the analysis results and executed control information via a user interface. The user can check the information using a dedicated application or web portal, and change settings or issue additional instructions as necessary.

[1356] Hardware and software used

[1357] Sensors: Temperature sensors, humidity sensors, moisture sensors, etc. Examples include the DHT22 sensor and the soil moisture sensor (YL-69).

[1358] Terminal: A microcontroller such as a Raspberry Pi or Arduino.

[1359] Server: A high-performance server for running the generative AI. For example, we will use an AWS EC2 instance.

[1360] Generative AI models: Use deep learning frameworks such as PyTorch and TensorFlow.

[1361] Database: Possible databases are MySQL and PostgreSQL.

[1362] User interface: smartphone apps (iOS, Android) or web portals (front-end frameworks such as React.js or Angular).

[1363] Specific examples

[1364] Example 1: Temperature and humidity control

[1365] The device uses a temperature sensor (e.g., DHT22) and a humidity sensor to measure the temperature and humidity of the field every hour. For example, at 10:00 AM, the temperature is measured as 25°C and the humidity is measured as 60%.

[1366] The device stores this data in local storage (e.g., SQLite database), converts it to CSV format, and sends it to the server at noon.

[1367] The server stores the received data in a MySQL database and analyzes it using a generative AI model. Based on data from the past seven days, the generative AI generates an analysis result such as "Tomorrow's temperature will be high, so start the watering system at 2:00 PM."

[1368] The server sends this instruction in JSON format to the terminal, which then turns on the watering system via a relay circuit at 2:00 PM.

[1369] The server pushes the analysis results and control information to the user's smartphone app, and the user confirms that "watering started at 2:00 p.m."

[1370] Example 2: Optimizing fertilizer application

[1371] The device measures the soil moisture content using a soil moisture sensor (e.g., YL-69). For example, at 8:00 AM, the moisture content is measured as 40%.

[1372] The device uploads the data to the server at noon, where it is formatted in CSV format and sent via an HTTP POST request.

[1373] The server stores the data in a database and analyzes it using a generative AI model, which analyzes past data and concludes that fertilizer application should be suspended until the next rain.

[1374] Based on the analysis results, the server sends a stop command to the terminal in JSON format to the fertilizer spreading device.

[1375] The terminal receives this instruction and immediately stops the operation of the fertilizer spreading device via the relay circuit.

[1376] The server notifies the user of this operation using Firebase Cloud Messaging, and the user confirms in a dedicated app that "fertilizer spreading has been stopped."

[1377] This system will improve the efficiency and automation of agricultural work, allowing users to easily monitor and control the condition of their farmland.

[1378] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1379] Step 1: Collecting data with sensors

[1380] The terminal collects environmental data in real time using various sensors (temperature sensors, humidity sensors, moisture sensors, etc.) installed on the farmland.

[1381] Input: Real-time environmental data from sensors (temperature, humidity, moisture content).

[1382] How it works: For example, the temperature sensor (DHT22) measures the temperature and humidity every hour. This data is instantly stored in the device's memory.

[1383] Output: Collected environmental data (e.g., temperature 25°C, humidity 60% at 10 AM).

[1384] Step 2: Temporarily save data

[1385] The device temporarily stores the collected environmental data in local storage (e.g., SQLite database) within the device.

[1386] Input: Environmental data collected from sensors (temperature, humidity, moisture content).

[1387] Operation: The device saves the collected data in a specified format (e.g., CSV format) in local storage.

[1388] Output: Environment data saved to local storage.

[1389] Step 3: Send data

[1390] The device uploads the stored data to the server at regular intervals.

[1391] Input: Environment data stored in local storage.

[1392] Operation: At noon, the device converts the saved data into CSV format and sends it to the server using an HTTP POST request.

[1393] Output: The environment data sent to the server.

[1394] Step 4: Save your data

[1395] The server stores the received environmental data in a database (e.g., MySQL).

[1396] Input: Environmental data sent from the device.

[1397] Operation: The server receives the incoming data and stores it in the database appropriately.

[1398] Output: Environmental data stored in a database.

[1399] Step 5: Data analysis

[1400] The server analyzes the received data using a generative AI model.

[1401] Input: Environmental data stored in a database.

[1402] How it works: An analysis script runs periodically, calling a generative AI model (e.g., using TensorFlow) to perform analysis based on the past 7 days of data.

[1403] Output: Analysis results from the generative AI model (optimal growing conditions).

[1404] Step 6: Generate control instructions

[1405] Based on the analysis results of the generation AI, the server generates the necessary control instructions and sends them to the terminal.

[1406] Input: Analysis results from the generative AI model.

[1407] Operation: The server receives the results of the generation AI, generates control instructions (e.g., start the watering system at 2 p.m.), and sends them to the terminal in JSON format.

[1408] Output: Control instructions sent to the terminal.

[1409] Step 7: Controlling agricultural equipment

[1410] The terminal controls the agricultural equipment based on the received control instructions.

[1411] Input: Control instructions received from the server (JSON format).

[1412] Operation: The terminal operates the watering system and fertilizer application device through the relay circuit based on the received instruction. For example, turn on the watering system through the relay circuit at 2:00 PM.

[1413] Output: Controlled agricultural equipment (e.g. turning on a watering system).

[1414] Step 8: User Notifications and Configuration Changes

[1415] The server notifies the user of the analysis results and control information through a user interface.

[1416] Input: Analysis results of the generative AI model and the device's control execution log.

[1417] How it works: The server uses Firebase Cloud Messaging (FCM) to push the analysis results and control information to the user's smartphone app or web portal.

[1418] Output: A notification that appears on the user's smartphone or web portal (e.g., "Watering started at 2 PM").

[1419] Based on these notifications, users can change settings or issue additional instructions as needed using a dedicated application or web portal. This series of processes enables efficient and planned automated control of agricultural work.

[1420] (Application example 1)

[1421] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1422] Modern agriculture demands automation and efficiency, but conventional systems lack the ability to collect and analyze environmental data, making it difficult to control agricultural equipment and manage quality.Furthermore, food delivery services face many challenges in managing the quality of food during delivery and creating optimal delivery routes, and there has been no way to efficiently and comprehensively manage these issues.

[1423] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1424] In this invention, the server includes means for collecting environmental data using sensors, means for transmitting the environmental data to the server, means for analyzing the environmental data using a generation AI, means for automatically controlling agricultural equipment based on the data analysis means, means for notifying the user of the analysis results and control information, means for monitoring the temperature and humidity of items being delivered in real time, means for generating an optimal delivery route taking into account traffic conditions and order priority, and means for collecting and analyzing user feedback on delivery quality. This will advance the automation and efficiency of agriculture, while also enabling integrated quality control during food delivery and optimal delivery planning.

[1425] A "sensor" is a device for collecting environmental data, and includes a temperature sensor, a humidity sensor, and the like.

[1426] "Environmental data" refers to various information collected, such as temperature, humidity, and soil moisture content, that is necessary for agricultural operations and food delivery management.

[1427] The "server" is a centralized computer system that receives environmental data and analyzes it using generative AI.

[1428] "Generative AI" is a system that uses artificial intelligence to analyze collected data and generate optimal control instructions and predictions.

[1429] "Data analysis means" refers to methods or devices that use generative AI to analyze environmental data and derive the necessary control instructions and predictions.

[1430] "Agricultural equipment" refers to devices or machines that are automatically controlled to improve the efficiency of agricultural work, including, for example, watering systems and fertilizer spreading devices.

[1431] The "means for notifying the user" refers to a system or method for notifying the user of analysis results and control information through a user interface.

[1432] "Means for real-time monitoring of temperature and humidity of goods during delivery" refers to devices or systems that continuously monitor the temperature and humidity of the environment in which food or goods are kept during delivery and collect necessary information.

[1433] "Means for generating optimal delivery routes" refers to devices or algorithms that calculate efficient delivery routes in real time, taking into account traffic conditions and order priorities.

[1434] "Means for collecting and analyzing user feedback regarding delivery quality" refers to a system that collects opinions and reviews regarding quality from delivery personnel and customers, and analyzes that information using generative AI, etc.

[1435] MODE FOR CARRYING OUT THE INVENTION

[1436] This invention is a system aimed at automating and streamlining agriculture and controlling food quality in food delivery services. It includes sensors, a generation AI server, a control terminal, and a user interface. The system uses sensors to collect environmental data and transmits it to the server. The generation AI on the server analyzes the data and appropriately controls agricultural equipment based on the results. Users are also notified of the analysis results and control information, allowing them to change system settings as needed.

[1437] System Overview

[1438] First, sensors (for temperature, humidity, soil moisture, etc.) are installed in farmland and delivery vehicles to collect environmental data in real time. The collected data is sent to a terminal, which temporarily stores it. Next, the terminal uploads the collected data to a server at regular intervals. The server stores the received data in a database and instructs the generation AI to analyze the data. The generation AI predicts the optimal conditions for plant growth based on the environmental data. The server also monitors the temperature and humidity during delivery in real time and generates the optimal delivery route.

[1439] Based on the analysis results of the generation AI, the server generates the necessary control instructions and sends them to the terminal. The terminal then executes the received control instructions to control agricultural equipment such as watering systems and fertilizer spreaders. It also notifies the user of necessary measures based on the environmental conditions of the delivery vehicle during delivery. The server then notifies the user of the analysis results and information on the executed controls via a user interface. The user can check this information using a dedicated application or web portal and change settings or issue additional instructions as necessary.

[1440] Program Processing Details

[1441] Hardware and software used

[1442] Hardware: smartphones, sensors

[1443] Software: Python, Flask

[1444] Program Processing Overview

[1445] 1. Collect data from sensors: Collect data from temperature and humidity sensors and send the data to the device.

[1446] 2. Temporary storage and uploading of data: The device temporarily stores the collected data and uploads it to the server at regular intervals.

[1447] 3. Data analysis on the server: The server analyzes the data using generative AI and generates optimal control instructions.

[1448] 4. Execution of control instructions: The terminal executes the control instructions received from the server and controls the agricultural equipment.

[1449] 5. Notification to the user: The server notifies the user of the analysis results and control information of the generated AI through the user interface.

[1450] Specific examples

[1451] Temperature and humidity control: During delivery, the delivery person's smartphone will notify them that the temperature has exceeded 35 degrees Celsius and that they should take measures to lower the temperature in the cooler. The delivery person will then follow the instructions and adjust the temperature in the cooler.

[1452] Optimal delivery route generation: When a customer places an order, the app will notify them, "Currently, the delivery is congested. Please select Route B as the optimal route."

[1453] Prompt Sentence Examples

[1454] "Generate the optimal route from current location A to destination B, taking into account traffic conditions and order priorities."

[1455] This series of systems will simultaneously achieve efficiency and quality control in both agricultural work and food delivery services. Users will also be able to flexibly respond by changing the system settings and issuing additional instructions.

[1456] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1457] Step 1:

[1458] Collecting data from sensors

[1459] Input: Sensors (Temperature sensor, Humidity sensor, etc.)

[1460] How it works: The sensors measure the temperature and humidity in real time on farmland or inside delivery vehicles. For example, the temperature sensor measures 25°C and the humidity sensor measures 60%.

[1461] Output: Collected environmental data

[1462] Step 2:

[1463] Temporary storage and upload of data

[1464] Input: Collected environmental dates

[1465] Specific operation: The device temporarily stores the collected data in local storage. At regular intervals (e.g., every hour), the device uploads the stored data to the server.

[1466] Output: Environment data sent to the server

[1467] Step 3:

[1468] Data analysis on the server

[1469] Input: Environmental data uploaded to the server

[1470] Specific operation: The server stores the received environmental data in a database. It then uses the generation AI to analyze it, including past data. For example, if the temperature is too high, it generates instructions such as "Start automatic watering at 2:00 PM."

[1471] Output: Analysis results and control instructions

[1472] Step 4:

[1473] Sending and executing control instructions

[1474] Input: Generated analysis results and control instructions

[1475] Specific operation: The server sends the generated control instructions to the terminal. The terminal automatically controls the corresponding agricultural equipment (e.g., watering system or fertilizer spraying device) based on the received control instructions.

[1476] Output: Operation of agricultural equipment according to the executed control instructions

[1477] Step 5:

[1478] User Notification and Feedback

[1479] Input: Analysis results and control information

[1480] Specific operation: The server notifies the user of the analysis results and executed control information through the user interface. The user checks the notifications on a dedicated smartphone app or web portal and changes system settings or issues additional instructions as necessary. For example, the user confirms that "watering started at 2:00 PM."

[1481] Output: Notification and feedback to the user

[1482] In this way, each step is processed continuously and automatically, realizing a series of processes from data collection from sensors to analysis by generative AI and control of control devices, enabling efficient environmental management and quality maintenance in agriculture and food delivery.

[1483] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1484] ---

[1485] MODE FOR CARRYING OUT THE INVENTION

[1486] This invention is a system that aims to automate and streamline agriculture by combining sensors, generative AI, servers, terminals, a user interface, and an emotion engine. This system includes components that realize environmental data collection, data analysis, agricultural equipment control, user notifications, and user emotion recognition functions.

[1487] System Overview

[1488] First, various sensors (temperature sensors, humidity sensors, moisture sensors, etc.) are placed at the installation location and environmental data is collected in real time using these sensors. The collected data is sent to the device, which then temporarily stores the data in local storage.

[1489] The device then periodically uploads the collected data to a server, which receives it and stores it in a database. The generative AI on the server analyzes the collected data and uses it to predict the optimal conditions for plant growth.

[1490] Based on the analysis results of the generative AI, the server generates specific control instructions and sends them to the terminal, which then follows the instructions to actually control agricultural equipment such as watering systems and fertilizer spreaders.

[1491] Furthermore, the analysis results and control instructions of the generative AI are notified to the user via a user interface from the server. The user receives these notifications on a dedicated application or web portal, where they can check the current status of the system and the analysis results. The user can also change settings and issue additional instructions to the system through the interface.

[1492] Adding an Emotion Engine

[1493] One of the features of the present invention is that it includes an emotion engine that recognizes the user's emotion. The emotion engine analyzes the user's emotion from, for example, their voice or facial expression, and recognizes their emotional state.

[1494] The emotion engine can tailor notification content based on the emotion it recognizes. For example, if a user is feeling stressed, the notification could have a softer tone or include a positive, encouraging message. The emotion engine can also suggest changes to system settings based on the user's emotion. For example, if a user expresses anxiety, the system could provide more detailed explanations or guidance.

[1495] Specific examples of program processing

[1496] Example 1: Temperature and humidity control and user emotion recognition

[1497] The device measures the temperature and humidity at 10:00 AM to be 25°C and 60%.

[1498] The device stores this data in local storage and sends it to the server at noon.

[1499] The server instructs the generating AI to analyze the data, and the generating AI makes a prediction such as, "Since high temperatures are expected tomorrow, watering will begin at 2 p.m."

[1500] The server sends this instruction to the terminal, which then automatically starts the watering system at 2:00 p.m.

[1501] The server notifies the user of the analysis results and control information of the generative AI, and the emotion engine analyzes the user's emotions.

[1502] If the emotion engine recognizes that the user is stressed, it will change the notification content to a calmer message.

[1503] Example 2: Optimizing fertilizer application and suggesting setting changes to users

[1504] At 8:00 a.m., the device measures the moisture content to 40% using the moisture sensor and sends this data to the server.

[1505] The server uses a generative AI to generate analysis results such as "stop spreading fertilizer until the next rain comes."

[1506] The server sends this instruction to the terminal, and the terminal stops the operation of the fertilizer spreading device.

[1507] The server notifies the user of the analysis results and control information, and the emotion engine analyzes the user's emotions.

[1508] If the emotion engine recognizes that the user is expressing anxiety, the system will suggest "providing more detailed instructions or additional help."

[1509] The above is a specific embodiment of this system. By incorporating an emotion engine, user-friendly support becomes possible, allowing agricultural work to be carried out efficiently and safely.

[1510] The processing flow will be explained below.

[1511] ---

[1512] Example 1: Temperature and humidity control and user emotion recognition

[1513] Step 1:

[1514] The device uses temperature and humidity sensors to measure environmental data every hour. For example, at 10:00 AM, the temperature is 25°C and the humidity is 60%.

[1515] Step 2:

[1516] The device stores the measured data in local storage, which includes the measurement value and a timestamp (e.g., October 10, 2023, 10:00 AM).

[1517] Step 3:

[1518] The device sends the stored environmental data to the server every hour, for example, uploading the data from 10:00 AM at 11:00 AM.

[1519] Step 4:

[1520] The server stores the received environmental data in a database, which includes the measured values ​​and timestamps.

[1521] Step 5:

[1522] Every time new data is stored, the server instructs the generation AI to analyze the data, which then analyzes past data as well.

[1523] Step 6:

[1524] The generative AI predicts the optimal growing conditions for plants based on the provided environmental data, generating instructions such as "Start the watering system tomorrow at 2:00 PM."

[1525] Step 7:

[1526] Based on the analysis results provided by the generation AI, the server generates specific control instructions and sends them to the terminal.

[1527] Step 8:

[1528] The device executes the control instructions received from the server, for example, starting the watering system at 2:00 PM.

[1529] Step 9:

[1530] The server notifies the user of the analysis results and control information of the generated AI via a smartphone app.

[1531] Step 10:

[1532] The emotion engine analyzes the user's facial expressions and voice when checking notifications to recognize their emotional state.

[1533] Step 11:

[1534] The server adjusts the notification content appropriately based on the emotions recognized by the emotion engine. For example, if the user is feeling stressed, the tone of the notification will be softened.

[1535] Example 2: Optimizing fertilizer application and suggesting setting changes to users

[1536] Step 1:

[1537] The device uses a moisture sensor to measure the moisture content of the soil. For example, at 8:00 AM, it measures the moisture content to be 40%.

[1538] Step 2:

[1539] The device stores the measured data in local storage, which includes the measurement value and a timestamp.

[1540] Step 3:

[1541] The device uploads the saved data to the server at regular intervals (for example, sending data to the server at 8:00 when the device is opened).

[1542] Step 4:

[1543] The server stores the received data in a database, which includes the measurement value and a timestamp.

[1544] Step 5:

[1545] Every time new data is stored, the server instructs the generation AI to analyze the data, which then analyzes past data as well.

[1546] Step 6:

[1547] The generative AI predicts the optimal conditions for fertilizer application based on the provided environmental data, generating instructions such as "stop fertilizer application until the next rain."

[1548] Step 7:

[1549] Based on the analysis results provided by the generation AI, the server generates specific control instructions and sends them to the terminal.

[1550] Step 8:

[1551] The terminal executes the control instruction received from the server, for example, stopping the operation of the fertilizer spreading device.

[1552] Step 9:

[1553] The server notifies the user of the analysis results and control information of the generated AI via a smartphone app.

[1554] Step 10:

[1555] The emotion engine analyzes the user's facial expressions and voice when checking notifications to recognize their emotional state.

[1556] Step 11:

[1557] If the emotion engine recognizes that the user is expressing anxiety, the server will suggest system configuration changes or further guidance, such as providing additional help information or instructions.

[1558] The above are the specific processing steps for implementing an agricultural automation system incorporating an emotion engine. This system simultaneously improves the efficiency of agricultural work and the user experience.

[1559] Example 2

[1560] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1561] In modern agriculture, collecting and analyzing environmental data to optimize plant growth conditions is important, but doing so manually takes time and effort. Furthermore, there is a need to support smoother agricultural work by providing optimal notifications based on the user's emotional state, but existing systems lack notifications and suggestions that take the user's emotions into account. This makes it difficult to reduce user stress and provide a sense of security, making it difficult to achieve the goals of improving agricultural efficiency and automation.

[1562] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1563] In this invention, the server includes means for collecting environmental data using sensors, means for transmitting the environmental data to the server, means for analyzing the environmental data using a generative AI, means for automatically controlling agricultural equipment, means for notifying the user of the analysis results and control information, means for analyzing the user's emotional state using an emotion engine and adjusting the notification content, and means for proposing changes to system settings based on the analysis of the emotion engine. This not only enables efficient collection and analysis of environmental data and automatic control of agricultural equipment, but also provides appropriate notifications and suggestions according to the user's emotional state, enabling user-friendly support for agricultural work.

[1564] A "sensor" is a device for detecting environmental data such as temperature, humidity, and moisture content.

[1565] "Environmental data" refers to information related to plant growth conditions, such as temperature, humidity, and water content.

[1566] "Server" is a computer system for storing and analyzing collected environmental data.

[1567] "Generative AI" is artificial intelligence that uses machine learning algorithms to analyze environmental data and make predictions.

[1568] "Data analysis means" refers to a method of using generative AI to analyze environmental data and predict optimal growing conditions for plants.

[1569] "Agricultural equipment" refers to equipment that automatically aids plant growth, such as watering systems and fertilizer spreading devices.

[1570] "Control means" refers to a method for automatically operating agricultural equipment based on data analysis means.

[1571] The "notification means" is a method for notifying the user of the analysis results and control information.

[1572] A "user interface" is a means by which a user changes settings or gives additional instructions to the system.

[1573] The "emotion engine" is a program that analyzes the user's voice and facial expressions to recognize their emotional state.

[1574] "Adjusting notification content" is a method of changing notification content according to the user's emotional state analyzed by the emotion engine.

[1575] "Settings change suggestion" is a method of suggesting system setting changes to the user based on the emotion engine's analysis.

[1576] MODE FOR CARRYING OUT THE INVENTION

[1577] This invention is a system that aims to automate and streamline agriculture by combining sensors, generative AI, servers, terminals, a user interface, and an emotion engine. This system includes components that realize environmental data collection, data analysis, agricultural equipment control, user notifications, and user emotion recognition functions.

[1578] Environmental data collection

[1579] First, the user places temperature sensors, humidity sensors, moisture content sensors, etc. on the farmland. These sensors can be powered by solar energy or batteries. The device collects environmental data from these sensors in real time and temporarily stores it in local storage. For example, at 9:00 AM, the temperature sensor measures 24°C and the humidity sensor measures 65%. This data is stored on the device and uploaded to the server at regular intervals (e.g., every day at noon).

[1580] Data analysis

[1581] The server stores the received environmental data in a database using a database management system such as SQL. A generative AI model (e.g., built using TensorFlow) on the server analyzes the stored data and predicts the optimal conditions for plant growth. For example, it may make a prediction such as, "Since high temperatures are expected tomorrow, watering should begin at 2 p.m."

[1582] Agricultural equipment control

[1583] The server creates specific control instructions based on the analysis results of the generation AI. For example, it generates an instruction to "operate the watering system at 2:00 PM." This is sent to the terminal, which then controls the agricultural equipment according to the instruction. Specifically, the watering system valve is opened at 2:00 PM and watering begins.

[1584] User Notifications and Emotion Recognition

[1585] The server generates a notification message based on the analysis results of the generation AI and the control information. For example, it creates a notification that reads, "The sprinkler system will start up tomorrow at 2:00 PM." This is then sent to the user through the user interface. Notifications are sent in real time using a dedicated application or web portal. The emotion engine analyzes the user's voice and facial expressions to recognize their emotional state. For example, if the user is feeling stressed, the content of the notification can be adjusted based on that information. For example, a gentle message such as, "It's okay, the system is operating optimally" can be sent. The emotion engine's analysis results can also suggest changes to system settings. For example, if the user expresses anxiety, the server can notify them by saying, "We will provide more detailed instructions or additional help."

[1586] Specific examples

[1587] Specific examples are shown below.

[1588] Example 1: Temperature and humidity control and user emotion recognition

[1589] The device uses a temperature sensor (e.g., SHT31) and a humidity sensor to measure a temperature of 25°C and humidity of 60% at 10:00 AM.

[1590] The device saves this data locally in CSV format and sends it to the server at noon (e.g., data_2023-10-25.csv).

[1591] The server uses generative AI (e.g., built using TensorFlow) to predict, "Tomorrow is expected to be hot, so watering should begin at 2 p.m."

[1592] The server sends a control instruction to the terminal to "activate the watering system at 2:00 p.m."

[1593] The terminal will open the valve on the sprinkler system at 2:00 p.m. and begin sprinkling water.

[1594] The server notifies the result and analyzes the user's emotions using an emotion engine (e.g., OpenCV and Emotion API).

[1595] If the emotion engine recognizes that the user is feeling stressed, it will notify them with a calming message such as, "It's okay, the system is working optimally."

[1596] Example 2: Optimizing fertilizer application and suggesting settings changes to users

[1597] At 8:00 a.m., the device measures the moisture content to 40% using a moisture sensor (e.g., TDR-100) and sends this data to the server.

[1598] The server uses generative AI to generate analysis results such as "Stop spreading fertilizer until the next rain comes."

[1599] The server sends an instruction to the terminal to "stop the operation of the fertilizer spreading device."

[1600] The terminal stops the fertilizer spreading device.

[1601] The server notifies the user of the analysis results and control information, and the emotion engine analyzes the user's emotions.

[1602] If the emotion engine recognizes that the user is expressing anxiety, it will send a notification offering more detailed instructions or additional help.

[1603] As a result, the present invention can significantly improve the efficiency of agricultural work and also provide emotional support to users.

[1604] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1605] Step 1:

[1606] Users place temperature, humidity, and moisture sensors in their fields. These sensors run on solar energy or batteries and collect environmental data in real time. The input is sensor placement and activation, and the output is that the sensors are ready to collect environmental data.

[1607] Step 2:

[1608] The terminal collects environmental data such as temperature, humidity, and moisture content in real time from the installed sensors. For example, at 9:00 AM, the terminal obtains data of 24°C from the temperature sensor and 65% from the humidity sensor. The input is the data signal from the sensor, and the output is the collected numerical data.

[1609] Step 3:

[1610] The terminal temporarily stores the collected environmental data in local storage. For example, the data is saved in a CSV file format with a timestamp (e.g., data_2023-10-25.csv). The input is the collected environmental data, and the output is the data saved in local storage.

[1611] Step 4:

[1612] The device periodically uploads data stored in its local storage to the server (e.g., at noon every day). The uploaded data includes the sensor ID and a timestamp. The input is the data in the local storage, and the output is the completion of data transfer to the server.

[1613] Step 5:

[1614] The server stores the uploaded environmental data in a database. A database management system (e.g., SQL) is used to ensure accurate data storage. The input is the data uploaded from the device, and the output is the environmental data stored in the database.

[1615] Step 6:

[1616] The server analyzes the environmental data stored in the database using a generative AI model (e.g., using TensorFlow). The generative AI model predicts the optimal growing conditions for plants based on the accumulated data. For example, it makes a prediction such as, "Since high temperatures are expected tomorrow, watering should begin at 2 p.m." The input is the environmental data in the database, and the output is the analysis results (optimal growing conditions).

[1617] Step 7:

[1618] The server generates specific control instructions based on the analysis results of the generation AI. For example, it generates an instruction to "turn on the watering system at 2:00 PM." The input is the analysis results of the generation AI, and the output is a specific control instruction.

[1619] Step 8:

[1620] The server sends the generated control instructions to the terminal. The instructions include a timestamp and scope of application. The input is the control instruction, and the output is the completion of sending the instruction to the terminal.

[1621] Step 9:

[1622] The terminal controls the agricultural equipment according to the control instructions it receives. Specifically, it opens the valve of the watering system to start watering at 2:00 p.m. The input is a control instruction from the server, and the output is the start of equipment operation.

[1623] Step 10:

[1624] The server generates a notification message based on the analysis results and control information of the generation AI. For example, it generates a notification such as "The sprinkler system will start up tomorrow at 2:00 PM." The input is the analysis results and control information, and the output is the notification message.

[1625] Step 11:

[1626] The server generates notifications and sends them to the user through a user interface, either a dedicated application or a web portal. The input is the notification message, and the output is the completion of notification delivery to the user.

[1627] Step 12:

[1628] The emotion engine analyzes the voice and facial expressions of the user who received the notification to recognize their emotional state. For example, it determines whether the user is feeling stressed. The input is the user's voice and facial expression data, and the output is the analysis result (emotional state).

[1629] Step 13:

[1630] The server reflects the analysis results of the emotion engine and adjusts the notification content. If the user is feeling stressed, the notification content will be calmer and an encouraging message will be added. The input is the analysis results of the emotion engine, and the output is the adjusted notification message.

[1631] Step 14:

[1632] The server proposes changes to system settings based on the analysis results of the emotion engine. For example, if the user expresses anxiety, the server will notify the user by suggesting that they should "provide more detailed instructions or additional help." The input is the analysis results of the emotion engine, and the output is a notification of the proposed changes to the settings.

[1633] (Application example 2)

[1634] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1635] In recent years, there has been a demand for automation and efficiency in agriculture, and optimizing agricultural work based on the collection and analysis of environmental data has become a major challenge. However, existing systems have difficulty collecting environmental data in real time, and they have not yet achieved the control of autonomous vehicles based on the analysis results. There is also a lack of systems that can recognize user emotions and provide appropriate feedback to improve the user experience. There is a need to build a comprehensive system to solve these issues.

[1636] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting environmental data using sensors, means for transmitting the environmental data to the server, and data analysis means using a generation AI to analyze the environmental data. This makes it possible to collect environmental data in real time, control an autonomous vehicle based on the analysis results, and recognize the user's emotions to adjust the content of notifications.

[1637] "Environmental data" refers to physical data such as temperature, humidity, and moisture content collected at agricultural sites.

[1638] "Generative AI" refers to artificial intelligence that analyzes collected environmental data, predicts the optimal conditions for plant growth, and generates automatic control instructions.

[1639] An "autonomous vehicle" refers to a vehicle used as agricultural equipment, equipped with sensors, that autonomously performs agricultural tasks based on the analysis results of generative AI.

[1640] An "emotion engine" refers to a system that has the ability to analyze information such as the user's voice and facial expressions and recognize the user's emotional state.

[1641] "User interface" refers to the means by which a user interacts with a system, and refers to the screens and applications that notify analysis results and control information, and allow settings to be changed or additional instructions to be given.

[1642] This invention is a system that collects environmental data, analyzes the data, controls agricultural equipment and autonomous vehicles, notifies users, and recognizes their emotions. This system is composed of a combination of sensors, generative AI, a server, a terminal, a user interface, and an emotion engine.

[1643] First, multiple sensors (temperature sensors, humidity sensors, moisture sensors, etc.) are installed in the autonomous vehicle to collect environmental data in real time. The collected data is temporarily stored on a terminal inside the vehicle. This data is then sent to a server at regular intervals.

[1644] The server stores the received data in a database and analyzes it using generative AI, which uses machine learning frameworks such as TensorFlow and PyTorch to predict the optimal conditions for plant growth. Based on the results of this analysis, the server generates specific control instructions and sends them to the terminal. The terminal then follows these instructions and the autonomous vehicle performs agricultural tasks such as watering, fertilizing, and soil improvement.

[1645] Furthermore, the analysis results and control instructions are notified to the user through a user interface provided on an application created using React Native or Flutter, and the user can change settings and issue additional instructions to the system through the application.

[1646] The system also incorporates an emotion engine that analyzes the user's emotions using tools such as IBM Watson Tone Analyzer. If the user is feeling stressed, the emotion engine adjusts the notification content to include a calmer tone or positive, encouraging messages. If the user expresses anxiety, the system provides more detailed explanations and guidance.

[1647] Specific examples

[1648] Temperature and humidity control and user emotion recognition:

[1649] The device measures the temperature and humidity at 10:00 a.m. using the temperature and humidity sensors to find a temperature of 25°C and a humidity of 60%. The collected data is stored in the device's local storage at 10:00 a.m. and sent to the server at noon.

[1650] The server instructs the generating AI to analyze the data, and the generating AI makes a prediction such as, "Since high temperatures are expected tomorrow, watering will begin at 2 p.m."

[1651] The server sends this instruction to the terminal, and the terminal automatically starts the watering system at 2:00 pm. The server notifies the user of the analysis results and control information, and the emotion engine analyzes the user's emotions.

[1652] If the emotion engine recognizes that the user is stressed, it will change the notification content to a calmer message.

[1653] Prompt Sentence Examples

[1654] Prompt: "Predict the temperature and humidity for the next day and analyze whether watering is necessary starting at 2 PM. Also, notify the user with a calming message if they are feeling stressed."

[1655] Application scenario: "Tomorrow's temperature is expected to exceed 30°C, so start watering for 10 minutes at 2pm. If the user is stressed, display a message saying, 'Don't worry, everything is managed automatically.'"

[1656] The above is a specific embodiment of the present invention. This system can collect environmental data in real time, control an autonomous vehicle based on the analysis results, and recognize the user's emotions and provide feedback.

[1657] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1658] Step 1:

[1659] Sensors are used to collect environmental data.

[1660] Input: Data from temperature, humidity, and moisture sensors installed in the autonomous vehicle.

[1661] Specific operation: The device measures the temperature at 25°C and humidity at 60% at 10:00 AM. The data from the sensor is sent to the device in real time and temporarily stored in local storage.

[1662] Step 2:

[1663] The environmental data is transmitted to a server.

[1664] Input: Environmental data collected from sensors (e.g., temperature 25°C, humidity 60%).

[1665] Specific operation: The terminal uploads the collected data to the server at regular intervals. For example, the collected data is stored in local storage at 10:00 AM and sent to the server at noon.

[1666] Step 3:

[1667] Data analysis is performed using a generation AI that analyzes the environmental data.

[1668] Input: The environment data sent to the server.

[1669] How it works: The server analyzes the data using a generative AI model (such as TensorFlow or PyTorch). Based on the collected data, the generative AI generates a prediction, such as "Tomorrow is expected to be hot, so watering will begin at 2 PM."

[1670] Step 4:

[1671] Based on the data analysis means, agricultural equipment is automatically controlled.

[1672] Input: Control instructions generated by the generation AI.

[1673] Specific operation: The server generates specific control instructions (e.g., an instruction to start watering) based on the analysis results and sends the instructions to the terminal. The terminal follows the received instructions and automatically starts the watering system at 2:00 PM.

[1674] Step 5:

[1675] The analysis results and control information are notified to the user.

[1676] Input: Control instructions and analysis results generated by the server.

[1677] Specific operation: The server notifies the user of the generated analysis results and control information through a user interface (e.g., a smartphone app using React Native or Flutter).

[1678] Step 6:

[1679] Recognize user emotions and tailor notifications accordingly.

[1680] Input: Voice data and facial expression data sent by the server.

[1681] How it works: The emotion engine (e.g., IBM Watson Tone Analyzer) analyzes the user's emotions from their voice and facial expressions collected through the user interface. If the system recognizes that the user is feeling stressed, it adjusts the notification content to be gentler and include encouraging messages.

[1682] Step 7:

[1683] The user changes settings or gives additional instructions to the system.

[1684] Input: User interface operations.

[1685] Specific operation: Users can change settings and provide additional instructions to the system through a smartphone app. The system receives the instructions and reflects the changes.

[1686] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1687] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1688] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1689] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1690] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1691] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1692] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1693] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1694] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1695] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1696] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1697] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1698] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1699] 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.

[1700] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1701] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1702] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1703] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1704] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1705] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1706] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1707] The following is further disclosed regarding the above embodiment.

[1708] (Claim 1)

[1709] means for collecting environmental data using sensors;

[1710] means for transmitting the environmental data to a server;

[1711] a data analysis means using a generation AI to analyze the environmental data;

[1712] means for automatically controlling agricultural equipment based on the data analysis means;

[1713] means for notifying a user of the analysis results and control information;

[1714] A system including:

[1715] (Claim 2)

[1716] 10. The system of claim 1, wherein the generative AI includes an algorithm that predicts optimal growing conditions for a plant.

[1717] (Claim 3)

[1718] 10. The system of claim 1, further comprising an interface for a user to change settings or provide additional instructions to the system.

[1719] "Example 1"

[1720] (Claim 1)

[1721] means for collecting environmental data using sensors;

[1722] means for temporarily storing the environmental data in a terminal and transmitting the data to a server at regular intervals;

[1723] a means for storing the environmental data in a database and performing data analysis using a generative AI model;

[1724] means for automatically controlling agricultural equipment based on the data analysis means;

[1725] means for notifying a user of the analysis results and control information;

[1726] A means to provide an interface for users to review notifications and change control settings as needed.

[1727] A system including:

[1728] (Claim 2)

[1729] 10. The system of claim 1, wherein the generative AI model includes an algorithm that uses historical data to predict optimal growing conditions for a plant.

[1730] (Claim 3)

[1731] 10. The system of claim 1, wherein a user can change system settings or provide additional instructions through an interface.

[1732] "Application Example 1"

[1733] (Claim 1)

[1734] means for collecting environmental data using sensors;

[1735] means for transmitting the environmental data to a server;

[1736] a data analysis means using a generation AI to analyze the environmental data;

[1737] means for automatically controlling agricultural equipment based on the data analysis means;

[1738] means for notifying a user of the analysis results and control information;

[1739] a means for real-time monitoring of temperature and humidity of the goods during delivery;

[1740] A means of generating optimal delivery routes taking into account traffic conditions and order priorities;

[1741] A means of collecting and analyzing user feedback on delivery quality

[1742] A system including:

[1743] (Claim 2)

[1744] 10. The system of claim 1, wherein the generative AI includes an algorithm that predicts optimal growing conditions for a plant.

[1745] (Claim 3)

[1746] 10. The system of claim 1, further comprising an interface for a user to change settings or provide additional instructions to the system.

[1747] "Example 2: Combining Emotion Engines"

[1748] (Claim 1)

[1749] means for collecting environmental data using sensors;

[1750] means for transmitting the environmental data to a server;

[1751] a data analysis means using a generation AI to analyze the environmental data;

[1752] means for automatically controlling agricultural equipment based on the data analysis means;

[1753] means for notifying a user of the analysis results and control information;

[1754] a means for analyzing the emotional state of a user using an emotion engine and adjusting the content of the notification;

[1755] Based on the analysis of the emotion engine, we will propose a method to change the system settings.

[1756] A system including:

[1757] (Claim 2)

[1758] 10. The system of claim 1, wherein the generative AI includes an algorithm that predicts optimal growing conditions for a plant.

[1759] (Claim 3)

[1760] 10. The system of claim 1, further comprising an interface for a user to change settings or provide additional instructions to the system.

[1761] "Application example 2 when combining emotion engines"

[1762] (Claim 1)

[1763] means for collecting environmental data using sensors;

[1764] means for transmitting the environmental data to a server;

[1765] a data analysis means using a generation AI to analyze the environmental data;

[1766] means for automatically controlling agricultural equipment based on the data analysis means;

[1767] means for notifying a user of the analysis results and control information;

[1768] a means for controlling an autonomous vehicle;

[1769] a means for collecting environmental data from sensors installed in the autonomous vehicle and controlling the autonomous vehicle based on the analysis results;

[1770] A system that includes an emotion engine that recognizes user emotions and adjusts notification content

[1771] A system including:

[1772] (Claim 2)

[1773] 10. The system of claim 1, wherein the generative AI includes an algorithm that predicts optimal growing conditions for a plant.

[1774] (Claim 3)

[1775] 10. The system of claim 1, further comprising an interface for a user to change settings or provide additional instructions to the system. [Explanation of symbols]

[1776] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for collecting environmental data using sensors; means for transmitting the environmental data to a server; a data analysis means using a generation AI to analyze the environmental data; means for automatically controlling agricultural equipment based on the data analysis means; means for notifying a user of the analysis results and control information; A system including:

2. The system of claim 1 , wherein the generating AI includes an algorithm that predicts optimal growing conditions for a plant.

3. 10. The system of claim 1, further comprising an interface for a user to change settings or give additional instructions to the system.

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