System

Abstract

A system is provided.SOLUTION: A system, comprising: means for collecting environmental data with a sensor; means for transmitting the collected data to a server; means for analyzing the transmitted data; and means for providing a notification to a user based on the analysis.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] Efficient crop management and yield optimization are important challenges in modern agriculture. Conventional methods make it difficult to properly understand crop conditions and environmental conditions, which can lead to excessive watering and fertilizer use, as well as a lack of appropriate pest management. As a result, crop quality and yield decline, and agricultural sustainability is undermined. Farmers, both small and large, in particular, lack effective means of collecting and analyzing data and receiving appropriate instructions for action. Systems that can solve these problems and achieve efficient agricultural management are needed. [Means for solving the problem]

[0005] The present invention provides an agricultural support system that collects environmental data using sensors, transmits the data to a server for analysis, and provides notifications to users based on the analysis results. Specifically, the system includes a means for collecting environmental data (e.g., soil humidity, temperature, and pH value) using sensors and a means for transmitting the collected data to a server. The server analyzes the data using a machine learning model to predict, for example, the next watering timing. The analysis results are notified to the user's device, allowing the user to take appropriate action based on the notification. The present invention enables efficient crop management and yield optimization, promoting sustainable agriculture.

[0006] A "sensor" is a device for collecting environmental data.

[0007] A "server" is a computer system that receives and analyzes collected data.

[0008] "User" means an individual or legal entity engaged in farming who takes action based on notifications provided by the system.

[0009] "Environmental data" refers to information that indicates the state of the crop and its surrounding environment, including, for example, humidity, temperature, and pH value.

[0010] "Send" is the act of sending data from a terminal to a server.

[0011] "Analysis" is the process in which the server processes the data it receives using machine learning models and other methods to generate insights.

[0012] "Notification" is a message to inform the user of the analysis results, specifically indicating when to water the plants.

[0013] A "machine learning model" is an algorithm used to analyze data and make predictions or classifications. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

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

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

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

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0029] 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.

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

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The present invention relates to an agricultural support system that includes a sensor, a server, and a user terminal. An embodiment of this system will be described in detail below.

[0036] System Overview

[0037] The agricultural support system consists of a consistent process in which sensors collect environmental data, send the data to a server, the server analyzes the data, and notifies the user based on the analysis results.

[0038] Data collection and transmission (terminal)

[0039] Sensors are installed in farmland to measure environmental data such as soil humidity, temperature, and pH in real time. The device temporarily stores these measurement data in its local memory and periodically packets the data and transmits it to a server. For example, a soil humidity sensor measures the humidity level and transmits the data to a server via Wi-Fi.

[0040] Data analysis and insight generation (server)

[0041] The server receives the environmental data sent from the device and stores it in a database. It then analyzes the data using machine learning models to generate insights such as the health of the crop, the best time to water it, and the risk of pest infestation. For example, the server analyzes humidity data and predicts that the next watering will be at 10 a.m. tomorrow.

[0042] Providing results and feedback (user)

[0043] The insights generated by the server are sent to the user's device as notifications. The user's smartphone or tablet receives the notifications and displays them visually. This allows the user to take appropriate action based on the notification content. For example, a user may receive a notification that "the next watering time is tomorrow at 10:00 AM" and water the plants at that time.

[0044] Specific examples

[0045] Multiple sensors installed in a certain field measure the humidity, temperature, and pH value of the soil in real time. Data indicating that the humidity is 40% is instantly sent to a server. The server analyzes this humidity data using a machine learning model to predict the optimal timing for watering. As a result of the prediction, an insight is generated, such as "The next watering time is tomorrow at 10:00 AM." This insight is then sent to the user's smartphone, allowing the user to water the plants accordingly.

[0046] This invention allows farmers to utilize real-time, highly accurate data and the insights derived from that data to practice efficient and sustainable agriculture. By taking appropriate action based on the data, they can maintain the health of their crops and optimize yields.

[0047] The processing flow will be explained below.

[0048] Step 1:

[0049] The device collects environmental data: specifically, sensors measure the moisture, temperature, and pH of the farmland soil.

[0050] Step 2:

[0051] The device temporarily stores the collected environmental data in its local memory. Specifically, the humidity data measured by the sensor is recorded in the device's memory.

[0052] Step 3:

[0053] The device packetizes the temporarily stored data and transmits it to the server. Specifically, it forms a packet containing humidity data and transmits it to the server via Wi-Fi.

[0054] Step 4:

[0055] The server receives the data packet sent from the terminal. Specifically, the server receives the data for humidity 40%.

[0056] Step 5:

[0057] The server stores the received data in a database. Specifically, the server stores the humidity data in the database.

[0058] Step 6:

[0059] The server inputs the data into a machine learning model for analysis, specifically using humidity data to predict when the next watering should be done.

[0060] Step 7:

[0061] The server organizes the analysis results and generates a message to notify the user. Specifically, it creates a notification message saying, "The next watering will be tomorrow at 10:00 AM."

[0062] Step 8:

[0063] The server sends the generated notification message to the user's device. Specifically, it sends a notification packet to the user's smartphone via the Internet.

[0064] Step 9:

[0065] The user's device receives the notification message sent from the server. Specifically, the user's smartphone receives the notification "The next watering will be at 10:00 AM tomorrow."

[0066] Step 10:

[0067] The user's device visually displays the received notification, specifically as a pop-up on the smartphone screen.

[0068] Step 11:

[0069] The user takes appropriate action based on the notification, specifically, the user acknowledges the notification and waters the crops at 10:00 AM the next day.

[0070] Example 1

[0071] 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."

[0072] Conventional agricultural support systems have struggled to acquire and effectively analyze farmland environmental data in real time and notify users of appropriate actions. Furthermore, there was a lack of technology to accurately predict the health of crops and the optimal timing for watering. This made it difficult to practice efficient agriculture and optimize crop yields.

[0073] 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.

[0074] In this invention, the server includes means for collecting environmental data using sensors, means for transmitting the collected data to the server via a network, means for storing the transmitted data in a database, means for acquiring the stored data and analyzing it using a machine learning model, and means for generating crop health conditions and optimal watering timings as analysis results and providing notifications to users. This enables high-precision data analysis in real time, allows appropriate instructions to be provided to users, and realizes efficient and sustainable agriculture.

[0075] A "sensor" is a device for measuring environmental data, specifically, acquiring environmental parameters such as soil humidity, temperature, and acidity in real time.

[0076] "Means for transmitting via a network" refers to a method for transmitting data collected by a sensor to a server using a communication protocol, and includes communication technologies such as Wi-Fi and wired connections.

[0077] A "database" is a system for structuring and storing collected environmental data, and includes relational databases and NoSQL databases.

[0078] A "machine learning model" is an algorithm that learns patterns and regularities from large amounts of data and makes predictions and classifications based on new data. Specifically, this includes generative AI models.

[0079] "Analysis means" refers to a processing method for processing stored data using machine learning models or the like to extract necessary insights.

[0080] "Insights" are useful information obtained through data analysis, such as the health of crops and the optimal time to water them, that can encourage users to take action.

[0081] "Notification means" refers to a method for transmitting analysis results to a user device, including push notifications to smartphones and tablets.

[0082] The present invention relates to an agricultural support system that includes a sensor, a server, and a user terminal. An embodiment of this system will be described in detail below.

[0083] System Overview

[0084] The agricultural support system consists of a consistent process in which environmental data is collected by sensors, the data is sent to a server via a network, the server analyzes the data, and notifications are provided to users based on the analysis results.

[0085] Data collection and transmission (terminal)

[0086] Sensors that measure the humidity, temperature, and pH value of the soil are connected to the device. The sensors measure environmental data in real time, and the measured data is temporarily stored in the device's local memory. The data obtained from the sensors is periodically packetized and sent to a server over the network via a Wi-Fi module. For example, if the humidity sensor measures the soil humidity to be 40%, this data is stored on the device. The device then packetizes this humidity data and sends it to the server via Wi-Fi.

[0087] Data analysis and insight generation (server)

[0088] The server receives the environmental data sent from the device and stores it in a database. The server then analyzes the stored data using a generative AI model. Specifically, it uses machine learning libraries such as TensorFlow to generate insights such as the health of the crops, the optimal timing for watering, and the risk of pest infestation. For example, a prediction might be generated, such as "The next watering will be at 10:00 AM tomorrow."

[0089] Providing results and feedback (user)

[0090] The insights generated by the server are sent to the user's device as notifications. The user's smartphone or tablet receives the notifications and displays them visually. This allows the user to take appropriate action based on the notification content. For example, a user may receive a notification that "the next watering time is tomorrow at 10:00 AM" and water the plants at that time.

[0091] Specific examples

[0092] Multiple sensors installed in a farm field measure the soil's humidity, temperature, and pH value in real time. Data indicating a humidity level of 40% is instantly sent to a server. The server analyzes this humidity data using a generative AI model to predict the optimal watering timing. As a result of the prediction, an insight is generated, such as "The next watering time is tomorrow at 10:00 AM." This insight is sent to the user's smartphone, allowing them to water accordingly. This allows users to achieve efficient and sustainable agriculture thanks to high-precision data analysis and the insights based on its results.

[0093] As a result, this system provides farmers with real-time data collection and analysis results, helping them optimize yields while maintaining the health of their crops.

[0094] Prompt Sentence Examples

[0095] "Analyze data collected from humidity sensors to predict the optimal time for the next watering."

[0096] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0097] Step 1: Data collection (device)

[0098] A sensor connected to the device measures the humidity, temperature, and pH value of the soil. The sensor collects this environmental data in real time and temporarily stores the data in the device's local memory. For example, if the sensor measures the soil humidity to be 40%, the data is recorded in memory. The input of this step is the environmental data from the sensor, and the output is the data stored in the device's local memory.

[0099] Step 2: Send data (terminal)

[0100] The device periodically packetizes the measurement data stored in its local memory. The packetized data is then sent to the server over the network via the Wi-Fi module. Specifically, the HTTP protocol is used to encrypt the data packets and transmit them securely. The input of this step is the data stored in its local memory, and the output is the data sent to the server.

[0101] Step 3: Data reception and storage (server)

[0102] The server receives data packets sent from the device. The received data is stored in a database. For example, environmental data is structured and stored in a database such as MongoDB. The input of this step is the data packets sent from the device, and the output is the data stored in the database.

[0103] Step 4: Data analysis (server)

[0104] The server retrieves the stored data from the database. The retrieved data is then analyzed using a generative AI model. For example, TensorFlow can be used to analyze humidity data and make predictions such as "The next watering will be tomorrow at 10 AM." The input for this step is the environmental data stored in the database, and the output is the analyzed insights.

[0105] Step 5: Insight generation and formatting (server)

[0106] The generated insights are formatted as notification data for users. Specifically, the notification data is prepared in JSON or text format. The input of this step is the insights obtained as a result of the analysis, and the output is the formatted notification data.

[0107] Step 6: Result notification (server)

[0108] The server sends the formatted notification data to the user's device. A notification service such as FCM (Firebase Cloud Messaging) is used to notify the user in real time. The input of this step is the formatted notification data, and the output is the notification sent to the user's device.

[0109] Step 7: Receiving and Displaying Notifications (User)

[0110] The user's smartphone or tablet receives the notification and displays it visually. The user checks the notification and performs farm work based on the notification content. For example, the user checks the notification that "the next watering is tomorrow at 10:00 AM" and waters the fields at that time. The input of this step is the notification data sent from the server, and the output is the notification displayed on the user's device.

[0111] (Application example 1)

[0112] 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."

[0113] Conventional food delivery services lack the means to monitor the freshness and quality of fresh food in real time, which means that the quality of the ingredients received by users may not always be optimal. Furthermore, there is no mechanism for notifying users of the optimal pickup time, making it difficult to guarantee the quality of ingredients, which may result in reduced user satisfaction. Therefore, there is a need for a system that can appropriately manage the pickup timing and quality of fresh food.

[0114] 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.

[0115] In this invention, the server includes means for collecting environmental data using a sensor, means for transmitting the collected data to the server, means for analyzing the transmitted data, means for providing a notification to a user based on the analysis results, means for the collected data to include the temperature, humidity, and gas concentration of the fresh food, means for the analysis means to generate insights for determining the freshness of the fresh food in real time, and means for notifying the user of the optimal time to receive the fresh food. This makes it possible to monitor the quality of the fresh food in real time and notify the user of the optimal time to receive the fresh food.

[0116] A "sensor" is a device that collects environmental data, and in this invention measures the temperature, humidity, and gas concentration of fresh food.

[0117] A "server" is a computer system that analyzes collected environmental data and provides notifications to users based on the results of that analysis.

[0118] "Data transmission means" refers to devices or software that have the function of transmitting environmental data collected by sensors to a server.

[0119] "Analysis means" refers to software and algorithms used to analyze the environmental data sent to the server, specifically machine learning models.

[0120] "Notification providing means" refers to a device or software that has the function of notifying the user's terminal of the analysis results.

[0121] "Environmental data" refers to data such as temperature, humidity, and gas concentration of fresh food.

[0122] A "machine learning model" is an algorithm that analyzes environmental data and generates insights such as the freshness of perishable foods and the optimal pickup time.

[0123] "Insights" are key pieces of information generated by analytics about the freshness of perishables and optimal pickup times.

[0124] "Optimal pick-up time" refers to the ideal time for a user to receive fresh food at its best quality.

[0125] MODE FOR CARRYING OUT THE INVENTION

[0126] The present invention is a system for monitoring the quality and freshness of fresh food in real time and notifying the user of the optimal time for collection. Specific embodiments for carrying out the present invention will be described below.

[0127] System Configuration

[0128] This system consists of sensors, a server, and user terminals.

[0129] Sensor

[0130] Multiple sensors, including temperature sensors, humidity sensors, and ethylene sensors, are installed inside the cooler boxes that store fresh food. Each sensor collects environmental data around the fresh food in real time. For example, the temperature sensor measures the temperature around the food, the humidity sensor measures humidity, and the ethylene sensor measures gas concentration.

[0131] server

[0132] The collected environmental data is sent to a server via the device. The server stores the data in a database and analyzes it using a machine learning model. The analysis results generate insights into the freshness of perishable foods and the optimal pickup time. The machine learning model predicts food deterioration and loss of freshness based on past data and patterns.

[0133] User terminal

[0134] The insights generated by the server are sent as notifications to the user's smartphone or tablet, where they are received and visually displayed. Based on these notifications, the user can receive fresh food at the optimal time.

[0135] Specific examples

[0136] A cooler box used by a certain delivery service is equipped with a temperature sensor, a humidity sensor, and an ethylene sensor. These sensors collect their respective environmental data in real time and send it to a server via Wi-Fi. For example, if the temperature sensor detects 12°C, the humidity sensor detects 85%, and the ethylene sensor detects 0.06 ppm, the server analyzes this data and generates insights such as, "The temperature is outside the appropriate range. The humidity is outside the appropriate range. The ethylene level indicates a decrease in freshness." This insight is then sent to the user's smartphone with a message stating, "The freshness of your perishable food is decreasing. We recommend that you pick it up immediately."

[0137] Hardware and software used

[0138] The hardware used includes a temperature sensor, humidity sensor, and ethylene sensor. The software uses Python scripts to collect and transmit data, and machine learning models to analyze the data on a cloud server. An API is also required to send notifications to user devices.

[0139] Prompt Sentence Examples

[0140] Examples of prompts to give to a generative AI model include:

[0141] Design an application that monitors the quality of agricultural products in a cooler box in real time and notifies the user before the quality deteriorates. Use the following sensor data (temperature, humidity, ethylene concentration) to set the notification conditions and implement it in Python.

[0142] Suitable temperature range: 5℃ to 10℃

[0143] Suitable humidity range: 60% to 80%

[0144] Ethylene concentration limit: 0.05 ppm

[0145] As a result, the present invention provides an environment in which users can always receive fresh food of the highest quality.

[0146] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0147] Step 1:

[0148] Data collection with sensors

[0149] Sensors collect environmental data in the cooler box in real time. The inputs are temperature, humidity, and ethylene concentration, and each sensor measures these data. The collected data is temporarily stored in local memory. The output is the measured values ​​of temperature, humidity, and gas concentration.

[0150] Step 2:

[0151] Data transmission

[0152] The device sends the collected environmental data to the server. As input, it uses the temperature, humidity, and gas concentration data collected in step 1. The data is periodically packetized and sent to the server via Wi-Fi. The output is the data packets received by the server.

[0153] Step 3:

[0154] Data analysis

[0155] The server analyzes the received data. It uses temperature, humidity, and gas concentration data as input. A machine learning model analyzes this data based on the data stored in the database. Insights are generated as a result of the analysis if a deterioration in freshness or quality is predicted. The output is insights about the freshness and quality of the food.

[0156] Step 4:

[0157] Insight generation

[0158] The server generates insights to notify the user based on the analysis results. The analysis results obtained in step 3 are used as input. For example, insights such as "The temperature is out of the normal range. The humidity is out of the normal range. The ethylene level indicates a decrease in freshness" are generated. The output is the notification content.

[0159] Step 5:

[0160] Send notifications

[0161] The insights generated by the server are notified to the user's device. The notification content generated in step 4 is used as input. The notification is sent to the user's smartphone or tablet via the API. Specifically, a visual notification such as "Fresh food is losing its freshness. We recommend that you pick it up immediately" is displayed. The output is a notification that arrives on the user's device.

[0162] Step 6:

[0163] User response

[0164] The user checks the notification sent to their smartphone or tablet. The notification sent in step 5 is used as input. The user can receive fresh food at the optimal time according to the received notification. The output is the result of receiving the food in the best quality.

[0165] 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.

[0166] The present invention relates to an agricultural support system that is composed of sensors, a server, and a user terminal, and further combines it with an emotion engine that recognizes the user's emotions. An embodiment of this system will be described in detail below.

[0167] System Overview

[0168] The agricultural support system is comprised of a series of consistent processes: sensors collect environmental data, the data is sent to a server, the server analyzes the data, and notifications are sent to the user based on the analysis results. It also incorporates an emotion engine that recognizes the user's emotional state and adjusts the content and frequency of notifications accordingly.

[0169] Data collection and transmission (terminal)

[0170] Sensors are installed in farmland to measure environmental data such as soil humidity, temperature, and pH in real time. The device temporarily stores these measurement data in its local memory and periodically packets the data and transmits it to a server. For example, a soil humidity sensor measures the humidity level and transmits the data to a server via Wi-Fi.

[0171] Data analysis and insight generation (server)

[0172] The server receives the environmental data sent from the device and stores it in a database. It then analyzes the data using machine learning models to generate insights such as the health of the crop, the best time to water it, and the risk of pest infestation. For example, the server analyzes humidity data and predicts that the next watering will be at 10 a.m. tomorrow.

[0173] Emotion engine operation (server)

[0174] The server is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's voice and facial expressions to detect their emotional state. Based on the results, the server can adjust the content and frequency of notifications. For example, if the user is feeling stressed, the server can reduce the frequency of notifications or send notifications with relaxation suggestions.

[0175] Providing results and feedback (user)

[0176] Notifications based on the insights generated by the server and the results of the emotion engine are sent to the user's device. The user's smartphone or tablet receives the notification and displays it visually. This allows the user to take appropriate action based on the content of the notification. For example, a user may receive a notification in the form of an emotion-sensitive message saying, "The next watering time is tomorrow at 10:00 AM," and water the plants at that time.

[0177] Specific examples

[0178] Multiple sensors installed in a farm field measure the soil's humidity, temperature, and pH in real time. Data indicating a humidity level of 40% is instantly sent to a server. The server then analyzes this humidity data using a machine learning model to predict the optimal timing for watering. The resulting prediction generates an insight: "The next watering will be at 10:00 AM tomorrow." Meanwhile, the server's emotion engine detects that the user is currently feeling stressed. In this case, a notification is sent stating, "The next watering will be at 10:00 AM tomorrow. Please take your time and relax." The user's smartphone receives this notification, confirms its contents, and waters the crops at 10:00 AM the following day.

[0179] This invention allows farmers to receive highly accurate data obtained in real time, insights based on that data, and notifications that take into account the user's emotional state, enabling them to not only practice efficient and sustainable agriculture but also to take into consideration the user's mental health.

[0180] The processing flow will be explained below.

[0181] Step 1:

[0182] The device collects environmental data: specifically, sensors measure the moisture, temperature, and pH of the farmland soil.

[0183] Step 2:

[0184] The device temporarily stores the collected environmental data in its local memory. Specifically, the humidity data measured by the sensor is recorded in the device's memory.

[0185] Step 3:

[0186] The device packetizes the temporarily stored data and transmits it to the server. Specifically, it forms a packet containing humidity data and transmits it to the server via Wi-Fi.

[0187] Step 4:

[0188] The server receives the data packet sent from the terminal. Specifically, the server receives the data for humidity 40%.

[0189] Step 5:

[0190] The server stores the received data in a database. Specifically, the server stores the humidity data in the database.

[0191] Step 6:

[0192] The server inputs the data into a machine learning model for analysis, specifically using humidity data to predict when the next watering should be done.

[0193] Step 7:

[0194] The server collects emotional data from users, specifically voice and facial expression data from their smartphones or wearable devices.

[0195] Step 8:

[0196] The server's emotion engine analyzes the collected emotion data to detect the user's emotional state, specifically detecting stress and fatigue from voice data.

[0197] Step 9:

[0198] The server then compiles the analysis results and generates a message that adjusts the content and frequency of notifications based on the user's emotional state. For example, "The next watering is tomorrow at 10 AM" might be changed to "The next watering is tomorrow at 10 AM, but please also take time to relax."

[0199] Step 10:

[0200] The server sends the generated notification message to the user's device. Specifically, it sends a notification packet to the user's smartphone via the Internet.

[0201] Step 11:

[0202] The user's device receives the notification message sent from the server. Specifically, the user's smartphone receives a notification saying, "The next watering will be at 10:00 AM tomorrow, but please take time to relax."

[0203] Step 12:

[0204] The user's device visually displays the received notification, specifically as a pop-up on the smartphone screen.

[0205] Step 13:

[0206] The user takes appropriate action based on the notification. Specifically, the user acknowledges the notification, waters the crops at 10:00 AM the next day, and schedules time to relax.

[0207] Example 2

[0208] 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."

[0209] Current agricultural support systems achieve the basic functions of collecting and analyzing environmental data and providing notifications to users, but lack the ability to consider the user's emotional state. As a result, there are concerns that notifications sent without taking the user's mental health into consideration could increase stress and reduce work efficiency. The present invention aims to solve this problem by providing an agricultural support system that adjusts the content and frequency of notifications according to the user's emotional state.

[0210] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting environmental data by a sensor, means for transmitting the collected data to the server, means for analyzing the transmitted data, means for recognizing the emotional state of the user, and means for adjusting the content and frequency of notifications based on the emotional state. This makes it possible to provide notifications according to the emotional state of the user.

[0211] A "sensor" is a device that measures environmental data in real time and collects necessary information.

[0212] "Server" means a central processing unit that receives and analyzes data sent from the terminal and provides appropriate notifications to the user.

[0213] A "user" is a person or organization that uses the agricultural support system to carry out agricultural work.

[0214] "Environmental data" refers to information about factors that affect crop growth, such as soil moisture, temperature, and pH value in agricultural land.

[0215] The "emotion engine" is a system that analyzes the user's voice and facial expression data to detect their emotional state.

[0216] A "machine learning model" is an algorithm for analyzing and predicting data, and is a technology that improves accuracy by learning specific patterns.

[0217] "Insights" refers to useful information and suggestions generated from analyzed data.

[0218] "Notification content" is the content of the information message sent from the server to the user.

[0219] "Notification frequency" refers to the interval between notifications sent from the server to the user.

[0220] A "user terminal" is a device that a user uses to receive and operate information, and includes smartphones, tablets, and the like.

[0221] The present invention relates to an agricultural support system that is composed of sensors, a server, and a user terminal, and further combines it with an emotion engine that recognizes the user's emotions. An embodiment of this system will be described in detail below.

[0222] System Configuration

[0223] The agricultural support system consists of the following main components:

[0224] 1. Sensors

[0225] The sensors are installed in farmland to measure environmental data such as soil humidity, temperature, and pH value in real time. Specific sensors used include soil humidity sensors, temperature sensors, and pH sensors.

[0226] 2. User Device

[0227] A user terminal is a mobile device such as a smartphone or tablet that is used to receive and visually display notifications.

[0228] 3. Server

[0229] The server receives data sent from the sensors and stores it in a database. It also analyzes the data using machine learning models to generate insights. It also has an emotion engine that recognizes user emotions.

[0230] Data collection and transmission

[0231] Users install various sensors on their farmland. The sensors measure environmental data in real time and temporarily store the data on a terminal. The terminal then packets the data and transmits it to a server at regular intervals.

[0232] Data analysis and insight generation

[0233] The server receives the environmental data sent from the devices and stores it in a database. The server then analyzes the data using machine learning models to generate insights such as the health of the crops, optimal watering times, and the risk of pest infestation.

[0234] Emotion Engine Operation

[0235] The server is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's voice and facial expression data to detect their emotional state. Based on the results, the server can adjust the content and frequency of notifications.

[0236] Providing results and feedback

[0237] Notifications based on the insights generated by the server and the results of the emotion engine are sent to the user's device, which receives and visually displays the notifications, allowing the user to take appropriate action based on the content of the notifications.

[0238] Specific examples

[0239] Multiple sensors installed in a certain field measure the humidity, temperature, and pH value of the soil in real time. For example, data indicating that the humidity is 40% is instantly sent to a server. The server analyzes this humidity data using a machine learning model to predict the optimal timing for watering. The resulting prediction is an insight such as, "The next watering time is tomorrow at 10:00 AM."

[0240] Meanwhile, the server's emotion engine detects that the user is currently feeling stressed. In this case, a notification is sent with the message, "The next watering will be at 10:00 AM tomorrow, so please take your time and relax." The user's smartphone receives this notification, confirms its contents, and waters the plants at 10:00 AM the next day.

[0241] Prompt Sentence Examples

[0242] When is the next time to water?

[0243] "What is the current soil moisture percentage?"

[0244] "Analyze the user's emotional state."

[0245] This invention allows farmers to receive highly accurate data obtained in real time, insights based on that data, and notifications that take into account the user's emotional state, enabling them to not only practice efficient and sustainable agriculture but also to take into consideration the user's mental health.

[0246] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0247] Step 1: Data collection

[0248] The sensors installed on the terminal measure the environmental data of the farmland in real time. Specifically, the soil moisture sensor measures the soil moisture, the temperature sensor measures the soil and ambient temperature, and the pH sensor measures the soil pH value. All of this data is temporarily stored in the local memory.

[0249] Input: Soil moisture, temperature, pH value

[0250] Data processing: Sensors measure environmental data in real time

[0251] Output: Environmental data stored in local memory

[0252] Step 2: Send data

[0253] The device periodically packets the environmental data stored in its local memory and transmits it to the server via the Wi-Fi module. Specifically, the device packets the collected data every five minutes, for example.

[0254] Input: Environmental data stored in local memory

[0255] Data processing: Packetizing environmental data

[0256] Output: Data packet sent to the server

[0257] Step 3: Receiving and storing data

[0258] The server receives data packets sent from the terminal and stores them in a database, which is based on SQL and allows for fast access.

[0259] Input: Data packets sent from the device

[0260] Data processing: Store data packets in a database

[0261] Output: Environmental data stored in a database

[0262] Step 4: Data analysis

[0263] The server then inputs the environmental data stored in the database into a machine learning model for analysis, which predicts things like the health of the crops, optimal watering times, and the risk of pest infestation.

[0264] Input: Environmental data stored in a database

[0265] Data processing: Data analysis using machine learning models

[0266] Output: Predicted insight (e.g., next watering timing)

[0267] Step 5: Emotion Recognition

[0268] The emotion engine installed on the server analyzes the user's voice and facial expression data to detect their emotional state, identifying whether they are stressed or relaxed.

[0269] Input: User's voice data, facial expression data

[0270] Data processing: Emotional state analysis using emotion engine

[0271] Output: The user's current emotional state

[0272] Step 6: Notification Generation and Coordination

[0273] The server adjusts the content and frequency of notifications based on the analyzed insights and the user's emotional state detected by the emotion engine. For example, if the user is feeling stressed, the server reduces the frequency of notifications and adds a message encouraging relaxation.

[0274] Input: predicted insights, user emotional state

[0275] Data processing: Adjustment of notification content and frequency

[0276] Output: The adjusted notification message

[0277] Step 7: Sending and displaying notifications

[0278] The server sends the generated notification to the user terminal, which receives and visually displays the notification, allowing the user to check the notification content and take appropriate action.

[0279] Input: The adjusted notification message

[0280] Data processing: Sending notifications and receiving / displaying them on the device

[0281] Output: A visual notification displayed on the user's device.

[0282] Step 8: Get user feedback

[0283] The user inputs the results and impressions of the actions taken based on the notification into the device and sends them to the server, which then receives the feedback and reflects it in future analyses and notifications.

[0284] Input: User feedback

[0285] Data processing: collecting and storing feedback

[0286] Output: Feedback data that can be used to improve the accuracy of future analyses

[0287] (Application example 2)

[0288] 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."

[0289] Conventional systems that integrate emotion analysis are often specialized for agricultural support or individual applications, and have the problem of lacking effectiveness in situations that require instantaneous responses in physical stores. In addition, there are no tools to understand the emotional state of customers and provide appropriate customer service, making it difficult for store employees to provide appropriate customer service.

[0290] 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 a sensor, means for transmitting the collected data to the server, and means for analyzing the transmitted data. This makes it possible to recognize the user's emotional state and provide notifications including appropriate customer service methods and product recommendations based on the analysis results. This makes it possible to grasp the customer's emotional state in real time and provide appropriate customer service methods and products in a timely manner.

[0291] A "sensor" is a device for collecting environmental data.

[0292] A "server" is a central processing unit that analyzes collected data and provides notifications based on the analysis results.

[0293] "User" means an individual or group that uses the system.

[0294] "Emotional state" refers to a user's mental or emotional state.

[0295] An "emotion engine" is software that analyzes the user's voice and facial expressions to recognize their emotional state.

[0296] A "machine learning model" is a type of algorithm used in data analysis that learns from past data to make predictions and classifications.

[0297] A "notification" is a message or alert that provides analysis results or other information to a user.

[0298] "Smart glasses" are wearable devices that incorporate a camera and display and support the user's visual and voice input.

[0299] "Environmental Data" means measured information about specific environmental conditions, such as humidity, temperature, pH value, etc.

[0300] "Analysis" is the process of processing collected data to derive useful information and insights.

[0301] "Customer service methods" refer to the way customers are treated and services are provided.

[0302] "Product recommendation" is the act of suggesting appropriate products based on a customer's needs and emotional state.

[0303] This invention is a system for supporting customer service in brick-and-mortar stores, and is composed of sensors, a server, and a user's terminal. The system transmits environmental data collected by the sensors to the server, which analyzes the data and provides notifications to the user. The system also incorporates an emotion engine that recognizes the user's emotional state and adjusts the content and frequency of notifications based on the analysis results.

[0304] System Overview

[0305] This system sends environmental data collected by sensors and the user's emotional state to a server, and provides notifications to the user based on the analysis results. For example, when a store clerk wearing smart glasses serves a customer, the smart glasses' camera and microphone collect the customer's facial expressions and voice, analyze the data in real time, and provide appropriate notifications.

[0306] Data collection (terminal)

[0307] The smart glasses' camera and microphone are used to capture the customer's facial expressions and voice in real time. For example, the camera recognizes the customer's smile, and the microphone analyzes the customer's level of satisfaction from the tone of their voice. This data is temporarily stored in the device's local memory and periodically packetized and sent to a server.

[0308] Data analysis (server)

[0309] The server receives the environmental and emotional data sent from the device and stores it in a database. It then analyzes the data using machine learning models to generate insights into the customer's emotional state, appropriate customer service methods, and recommended products. For example, the server analyzes data on a customer's smile and recognizes that the customer is happy, and then notifies the customer of the appropriate customer service method.

[0310] Emotion engine operation (server)

[0311] The server is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's voice and facial expressions to detect their emotional state. Based on the results, the server can adjust the content and frequency of notifications. For example, if a customer looks dissatisfied, the server sends a notification to the store clerk saying, "The customer is dissatisfied. Please respond quickly."

[0312] Providing results and feedback (user)

[0313] Notifications based on the insights generated by the server and the results of the emotion engine are sent to the user's smart glasses. The notification content is displayed on the smart glasses' display, allowing the store clerk to provide appropriate customer service and product recommendations based on the notification content. For example, a store clerk may receive a notification saying, "The customer is happy. Please recommend these shoes," and then suggest a product to the customer based on that notification.

[0314] Specific examples

[0315] In a physical store, a salesperson wears smart glasses to serve customers. The smart glasses' camera captures the customer's facial expressions, and the microphone picks up the customer's voice. When the customer shows interest in a product and smiles, the server analyzes the data and displays a notification on the smart glasses saying, "The customer is pleased. Please suggest a new product."

[0316] Prompt Sentence Examples

[0317] "Please explain an application of smart glasses that analyzes the facial expressions and voices of customers in a physical store to provide customer service support. Please give specific examples of what kind of notification is displayed to the store clerk when the customer is happy."

[0318] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0319] Program processing flow

[0320] Step 1:

[0321] The device (smart glasses) captures the customer's facial expressions and voice in real time through a camera and microphone. The camera input is the customer's facial image, and the microphone input is the customer's voice. This data is temporarily stored in local memory.

[0322] Step 2:

[0323] The device packetizes the customer's facial expression and voice data stored in its local memory and transmits them to the server via Wi-Fi. The input here is the data stored in the local memory, and the output is the packetized data.

[0324] Step 3:

[0325] The server receives data packets sent from the terminals and stores them in a database. The input is the received data packets, and the output is the data stored in the database.

[0326] Step 4:

[0327] The server analyzes the stored data using an emotion engine. The emotion engine analyzes the customer's emotional state from their facial expression data and their voice tone from their voice data. The input is the customer's facial expression data and voice data stored in the database, and the output is the customer's emotional state.

[0328] Step 5:

[0329] The server uses a machine learning model to generate customer service methods and product recommendations that correspond to the customer's emotional state. The input is the customer's emotional state, and the output is customer service methods and product recommendations. Appropriate notification content is generated based on the analysis results.

[0330] Step 6:

[0331] The server sends the generated notification content to the device (smart glasses). The input is the generated notification content, and the output is the notification to the device.

[0332] Step 7:

[0333] The terminal (smart glasses) displays the notification content sent from the server on its display, visually notifying the store clerk. The input is the notification content sent from the server, and the output is the notification displayed on the smart glasses' display. This allows the store clerk to take appropriate action depending on the customer's emotional state.

[0334] Specific examples of operation

[0335] Step 1: When the customer enters the field of view of the smart glasses, the camera automatically captures their facial expressions and the microphone records their voice.

[0336] Step 2: Facial expression data (e.g., an image of the customer's smile) and voice data (e.g., the customer's interesting tone of voice) are packetized.

[0337] Step 3: The server receives these data packets and stores them in a database along with the customer ID.

[0338] Step 4: The server uses the emotion engine to analyze the emotional state of "customer is satisfied."

[0339] Step 5: The server uses the machine learning model to generate a notification suggesting a new product for this customer.

[0340] Step 6: The server sends the generated notification "Please suggest a new product" to the terminal.

[0341] Step 7: The smart glasses display will display a notification saying, "Customer is happy. Please recommend these shoes."

[0342] 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.

[0343] 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.

[0344] 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.

[0345] [Second embodiment]

[0346] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0347] 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.

[0348] 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).

[0349] 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.

[0350] 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.

[0351] 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).

[0352] 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.

[0353] 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.

[0354] 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.

[0355] 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.

[0356] In the smart glasses 214, 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.

[0357] 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."

[0358] The present invention relates to an agricultural support system that includes a sensor, a server, and a user terminal. An embodiment of this system will be described in detail below.

[0359] System Overview

[0360] The agricultural support system consists of a consistent process in which sensors collect environmental data, send the data to a server, the server analyzes the data, and notifies the user based on the analysis results.

[0361] Data collection and transmission (terminal)

[0362] Sensors are installed in farmland to measure environmental data such as soil humidity, temperature, and pH in real time. The device temporarily stores these measurement data in its local memory and periodically packets the data and transmits it to a server. For example, a soil humidity sensor measures the humidity level and transmits the data to a server via Wi-Fi.

[0363] Data analysis and insight generation (server)

[0364] The server receives the environmental data sent from the device and stores it in a database. It then analyzes the data using machine learning models to generate insights such as the health of the crop, the best time to water it, and the risk of pest infestation. For example, the server analyzes humidity data and predicts that the next watering will be at 10 a.m. tomorrow.

[0365] Providing results and feedback (user)

[0366] The insights generated by the server are sent to the user's device as notifications. The user's smartphone or tablet receives the notifications and displays them visually. This allows the user to take appropriate action based on the notification content. For example, a user may receive a notification that "the next watering time is tomorrow at 10:00 AM" and water the plants at that time.

[0367] Specific examples

[0368] Multiple sensors installed in a certain field measure the humidity, temperature, and pH value of the soil in real time. Data indicating that the humidity is 40% is instantly sent to a server. The server analyzes this humidity data using a machine learning model to predict the optimal timing for watering. As a result of the prediction, an insight is generated, such as "The next watering time is tomorrow at 10:00 AM." This insight is then sent to the user's smartphone, allowing the user to water the plants accordingly.

[0369] This invention allows farmers to utilize real-time, highly accurate data and the insights derived from that data to practice efficient and sustainable agriculture. By taking appropriate action based on the data, they can maintain the health of their crops and optimize yields.

[0370] The processing flow will be explained below.

[0371] Step 1:

[0372] The device collects environmental data: specifically, sensors measure the moisture, temperature, and pH of the farmland soil.

[0373] Step 2:

[0374] The device temporarily stores the collected environmental data in its local memory. Specifically, the humidity data measured by the sensor is recorded in the device's memory.

[0375] Step 3:

[0376] The device packetizes the temporarily stored data and transmits it to the server. Specifically, it forms a packet containing humidity data and transmits it to the server via Wi-Fi.

[0377] Step 4:

[0378] The server receives the data packet sent from the terminal. Specifically, the server receives the data for humidity 40%.

[0379] Step 5:

[0380] The server stores the received data in a database. Specifically, the server stores the humidity data in the database.

[0381] Step 6:

[0382] The server inputs the data into a machine learning model for analysis, specifically using humidity data to predict when the next watering should be done.

[0383] Step 7:

[0384] The server organizes the analysis results and generates a message to notify the user. Specifically, it creates a notification message saying, "The next watering will be tomorrow at 10:00 AM."

[0385] Step 8:

[0386] The server sends the generated notification message to the user's device. Specifically, it sends a notification packet to the user's smartphone via the Internet.

[0387] Step 9:

[0388] The user's device receives the notification message sent from the server. Specifically, the user's smartphone receives the notification "The next watering will be at 10:00 AM tomorrow."

[0389] Step 10:

[0390] The user's device visually displays the received notification, specifically as a pop-up on the smartphone screen.

[0391] Step 11:

[0392] The user takes appropriate action based on the notification, specifically, the user acknowledges the notification and waters the crops at 10:00 AM the next day.

[0393] Example 1

[0394] 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."

[0395] Conventional agricultural support systems have struggled to acquire and effectively analyze farmland environmental data in real time and notify users of appropriate actions. Furthermore, there was a lack of technology to accurately predict the health of crops and the optimal timing for watering. This made it difficult to practice efficient agriculture and optimize crop yields.

[0396] 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.

[0397] In this invention, the server includes means for collecting environmental data using sensors, means for transmitting the collected data to the server via a network, means for storing the transmitted data in a database, means for acquiring the stored data and analyzing it using a machine learning model, and means for generating crop health conditions and optimal watering timings as analysis results and providing notifications to users. This enables high-precision data analysis in real time, allows appropriate instructions to be provided to users, and realizes efficient and sustainable agriculture.

[0398] A "sensor" is a device for measuring environmental data, specifically, acquiring environmental parameters such as soil humidity, temperature, and acidity in real time.

[0399] "Means for transmitting via a network" refers to a method for transmitting data collected by a sensor to a server using a communication protocol, and includes communication technologies such as Wi-Fi and wired connections.

[0400] A "database" is a system for structuring and storing collected environmental data, and includes relational databases and NoSQL databases.

[0401] A "machine learning model" is an algorithm that learns patterns and regularities from large amounts of data and makes predictions and classifications based on new data. Specifically, this includes generative AI models.

[0402] "Analysis means" refers to a processing method for processing stored data using machine learning models or the like to extract necessary insights.

[0403] "Insights" are useful information obtained through data analysis, such as the health of crops and the optimal time to water them, that can encourage users to take action.

[0404] "Notification means" refers to a method for transmitting analysis results to a user device, including push notifications to smartphones and tablets.

[0405] The present invention relates to an agricultural support system that includes a sensor, a server, and a user terminal. An embodiment of this system will be described in detail below.

[0406] System Overview

[0407] The agricultural support system consists of a consistent process in which environmental data is collected by sensors, the data is sent to a server via a network, the server analyzes the data, and notifications are provided to users based on the analysis results.

[0408] Data collection and transmission (terminal)

[0409] Sensors that measure the humidity, temperature, and pH value of the soil are connected to the device. The sensors measure environmental data in real time, and the measured data is temporarily stored in the device's local memory. The data obtained from the sensors is periodically packetized and sent to a server over the network via a Wi-Fi module. For example, if the humidity sensor measures the soil humidity to be 40%, this data is stored on the device. The device then packetizes this humidity data and sends it to the server via Wi-Fi.

[0410] Data analysis and insight generation (server)

[0411] The server receives the environmental data sent from the device and stores it in a database. The server then analyzes the stored data using a generative AI model. Specifically, it uses machine learning libraries such as TensorFlow to generate insights such as the health of the crops, the optimal timing for watering, and the risk of pest infestation. For example, a prediction might be generated, such as "The next watering will be at 10:00 AM tomorrow."

[0412] Providing results and feedback (user)

[0413] The insights generated by the server are sent to the user's device as notifications. The user's smartphone or tablet receives the notifications and displays them visually. This allows the user to take appropriate action based on the notification content. For example, a user may receive a notification that "the next watering time is tomorrow at 10:00 AM" and water the plants at that time.

[0414] Specific examples

[0415] Multiple sensors installed in a farm field measure the soil's humidity, temperature, and pH value in real time. Data indicating a humidity level of 40% is instantly sent to a server. The server analyzes this humidity data using a generative AI model to predict the optimal watering timing. As a result of the prediction, an insight is generated, such as "The next watering time is tomorrow at 10:00 AM." This insight is sent to the user's smartphone, allowing them to water accordingly. This allows users to achieve efficient and sustainable agriculture thanks to high-precision data analysis and the insights based on its results.

[0416] As a result, this system provides farmers with real-time data collection and analysis results, helping them optimize yields while maintaining the health of their crops.

[0417] Prompt Sentence Examples

[0418] "Analyze data collected from humidity sensors to predict the optimal time for the next watering."

[0419] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0420] Step 1: Data collection (device)

[0421] A sensor connected to the device measures the humidity, temperature, and pH value of the soil. The sensor collects this environmental data in real time and temporarily stores the data in the device's local memory. For example, if the sensor measures the soil humidity to be 40%, the data is recorded in memory. The input of this step is the environmental data from the sensor, and the output is the data stored in the device's local memory.

[0422] Step 2: Send data (terminal)

[0423] The device periodically packetizes the measurement data stored in its local memory. The packetized data is then sent to the server over the network via the Wi-Fi module. Specifically, the HTTP protocol is used to encrypt the data packets and transmit them securely. The input of this step is the data stored in its local memory, and the output is the data sent to the server.

[0424] Step 3: Data reception and storage (server)

[0425] The server receives data packets sent from the device. The received data is stored in a database. For example, environmental data is structured and stored in a database such as MongoDB. The input of this step is the data packets sent from the device, and the output is the data stored in the database.

[0426] Step 4: Data analysis (server)

[0427] The server retrieves the stored data from the database. The retrieved data is then analyzed using a generative AI model. For example, TensorFlow can be used to analyze humidity data and make predictions such as "The next watering will be tomorrow at 10 AM." The input for this step is the environmental data stored in the database, and the output is the analyzed insights.

[0428] Step 5: Insight generation and formatting (server)

[0429] The generated insights are formatted as notification data for users. Specifically, the notification data is prepared in JSON or text format. The input of this step is the insights obtained as a result of the analysis, and the output is the formatted notification data.

[0430] Step 6: Result notification (server)

[0431] The server sends the formatted notification data to the user's device. A notification service such as FCM (Firebase Cloud Messaging) is used to notify the user in real time. The input of this step is the formatted notification data, and the output is the notification sent to the user's device.

[0432] Step 7: Receiving and Displaying Notifications (User)

[0433] The user's smartphone or tablet receives the notification and displays it visually. The user checks the notification and performs farm work based on the notification content. For example, the user checks the notification that "the next watering is tomorrow at 10:00 AM" and waters the fields at that time. The input of this step is the notification data sent from the server, and the output is the notification displayed on the user's device.

[0434] (Application example 1)

[0435] 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."

[0436] Conventional food delivery services lack the means to monitor the freshness and quality of fresh food in real time, which means that the quality of the ingredients received by users may not always be optimal. Furthermore, there is no mechanism for notifying users of the optimal pickup time, making it difficult to guarantee the quality of ingredients, which may result in reduced user satisfaction. Therefore, there is a need for a system that can appropriately manage the pickup timing and quality of fresh food.

[0437] 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.

[0438] In this invention, the server includes means for collecting environmental data using a sensor, means for transmitting the collected data to the server, means for analyzing the transmitted data, means for providing a notification to a user based on the analysis results, means for the collected data to include the temperature, humidity, and gas concentration of the fresh food, means for the analysis means to generate insights for determining the freshness of the fresh food in real time, and means for notifying the user of the optimal time to receive the fresh food. This makes it possible to monitor the quality of the fresh food in real time and notify the user of the optimal time to receive the fresh food.

[0439] A "sensor" is a device that collects environmental data, and in this invention measures the temperature, humidity, and gas concentration of fresh food.

[0440] A "server" is a computer system that analyzes collected environmental data and provides notifications to users based on the results of that analysis.

[0441] "Data transmission means" refers to devices or software that have the function of transmitting environmental data collected by sensors to a server.

[0442] "Analysis means" refers to software and algorithms used to analyze the environmental data sent to the server, specifically machine learning models.

[0443] "Notification providing means" refers to a device or software that has the function of notifying the user's terminal of the analysis results.

[0444] "Environmental data" refers to data such as temperature, humidity, and gas concentration of fresh food.

[0445] A "machine learning model" is an algorithm that analyzes environmental data and generates insights such as the freshness of perishable foods and the optimal pickup time.

[0446] "Insights" are key pieces of information generated by analytics about the freshness of perishables and optimal pickup times.

[0447] "Optimal pick-up time" refers to the ideal time for a user to receive fresh food at its best quality.

[0448] MODE FOR CARRYING OUT THE INVENTION

[0449] The present invention is a system for monitoring the quality and freshness of fresh food in real time and notifying the user of the optimal time for collection. Specific embodiments for carrying out the present invention will be described below.

[0450] System Configuration

[0451] This system consists of sensors, a server, and user terminals.

[0452] Sensor

[0453] Multiple sensors, including temperature sensors, humidity sensors, and ethylene sensors, are installed inside the cooler boxes that store fresh food. Each sensor collects environmental data around the fresh food in real time. For example, the temperature sensor measures the temperature around the food, the humidity sensor measures humidity, and the ethylene sensor measures gas concentration.

[0454] server

[0455] The collected environmental data is sent to a server via the device. The server stores the data in a database and analyzes it using a machine learning model. The analysis results generate insights into the freshness of perishable foods and the optimal pickup time. The machine learning model predicts food deterioration and loss of freshness based on past data and patterns.

[0456] User terminal

[0457] The insights generated by the server are sent as notifications to the user's smartphone or tablet, where they are received and visually displayed. Based on these notifications, the user can receive fresh food at the optimal time.

[0458] Specific examples

[0459] A cooler box used by a certain delivery service is equipped with a temperature sensor, a humidity sensor, and an ethylene sensor. These sensors collect their respective environmental data in real time and send it to a server via Wi-Fi. For example, if the temperature sensor detects 12°C, the humidity sensor detects 85%, and the ethylene sensor detects 0.06 ppm, the server analyzes this data and generates insights such as, "The temperature is outside the appropriate range. The humidity is outside the appropriate range. The ethylene level indicates a decrease in freshness." This insight is then sent to the user's smartphone with a message stating, "The freshness of your perishable food is decreasing. We recommend that you pick it up immediately."

[0460] Hardware and software used

[0461] The hardware used includes a temperature sensor, humidity sensor, and ethylene sensor. The software uses Python scripts to collect and transmit data, and machine learning models to analyze the data on a cloud server. An API is also required to send notifications to user devices.

[0462] Prompt Sentence Examples

[0463] Examples of prompts to give to a generative AI model include:

[0464] Design an application that monitors the quality of agricultural products in a cooler box in real time and notifies the user before the quality deteriorates. Use the following sensor data (temperature, humidity, ethylene concentration) to set the notification conditions and implement it in Python.

[0465] Suitable temperature range: 5℃ to 10℃

[0466] Suitable humidity range: 60% to 80%

[0467] Ethylene concentration limit: 0.05 ppm

[0468] As a result, the present invention provides an environment in which users can always receive fresh food of the highest quality.

[0469] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0470] Step 1:

[0471] Data collection with sensors

[0472] Sensors collect environmental data in the cooler box in real time. The inputs are temperature, humidity, and ethylene concentration, and each sensor measures these data. The collected data is temporarily stored in local memory. The output is the measured values ​​of temperature, humidity, and gas concentration.

[0473] Step 2:

[0474] Data transmission

[0475] The device sends the collected environmental data to the server. As input, it uses the temperature, humidity, and gas concentration data collected in step 1. The data is periodically packetized and sent to the server via Wi-Fi. The output is the data packets received by the server.

[0476] Step 3:

[0477] Data analysis

[0478] The server analyzes the received data. It uses temperature, humidity, and gas concentration data as input. A machine learning model analyzes this data based on the data stored in the database. Insights are generated as a result of the analysis if a deterioration in freshness or quality is predicted. The output is insights about the freshness and quality of the food.

[0479] Step 4:

[0480] Insight generation

[0481] The server generates insights to notify the user based on the analysis results. The analysis results obtained in step 3 are used as input. For example, insights such as "The temperature is out of the normal range. The humidity is out of the normal range. The ethylene level indicates a decrease in freshness" are generated. The output is the notification content.

[0482] Step 5:

[0483] Send notifications

[0484] The insights generated by the server are notified to the user's device. The notification content generated in step 4 is used as input. The notification is sent to the user's smartphone or tablet via the API. Specifically, a visual notification such as "Fresh food is losing its freshness. We recommend that you pick it up immediately" is displayed. The output is a notification that arrives on the user's device.

[0485] Step 6:

[0486] User response

[0487] The user checks the notification sent to their smartphone or tablet. The notification sent in step 5 is used as input. The user can receive fresh food at the optimal time according to the received notification. The output is the result of receiving the food in the best quality.

[0488] 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.

[0489] The present invention relates to an agricultural support system that is composed of sensors, a server, and a user terminal, and further combines it with an emotion engine that recognizes the user's emotions. An embodiment of this system will be described in detail below.

[0490] System Overview

[0491] The agricultural support system is comprised of a series of consistent processes: sensors collect environmental data, the data is sent to a server, the server analyzes the data, and notifications are sent to the user based on the analysis results. It also incorporates an emotion engine that recognizes the user's emotional state and adjusts the content and frequency of notifications accordingly.

[0492] Data collection and transmission (terminal)

[0493] Sensors are installed in farmland to measure environmental data such as soil humidity, temperature, and pH in real time. The device temporarily stores these measurement data in its local memory and periodically packets the data and transmits it to a server. For example, a soil humidity sensor measures the humidity level and transmits the data to a server via Wi-Fi.

[0494] Data analysis and insight generation (server)

[0495] The server receives the environmental data sent from the device and stores it in a database. It then analyzes the data using machine learning models to generate insights such as the health of the crop, the best time to water it, and the risk of pest infestation. For example, the server analyzes humidity data and predicts that the next watering will be at 10 a.m. tomorrow.

[0496] Emotion engine operation (server)

[0497] The server is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's voice and facial expressions to detect their emotional state. Based on the results, the server can adjust the content and frequency of notifications. For example, if the user is feeling stressed, the server can reduce the frequency of notifications or send notifications with relaxation suggestions.

[0498] Providing results and feedback (user)

[0499] Notifications based on the insights generated by the server and the results of the emotion engine are sent to the user's device. The user's smartphone or tablet receives the notification and displays it visually. This allows the user to take appropriate action based on the content of the notification. For example, a user may receive a notification in the form of an emotion-sensitive message saying, "The next watering time is tomorrow at 10:00 AM," and water the plants at that time.

[0500] Specific examples

[0501] Multiple sensors installed in a farm field measure the soil's humidity, temperature, and pH in real time. Data indicating a humidity level of 40% is instantly sent to a server. The server then analyzes this humidity data using a machine learning model to predict the optimal timing for watering. The resulting prediction generates an insight: "The next watering will be at 10:00 AM tomorrow." Meanwhile, the server's emotion engine detects that the user is currently feeling stressed. In this case, a notification is sent stating, "The next watering will be at 10:00 AM tomorrow. Please take your time and relax." The user's smartphone receives this notification, confirms its contents, and waters the crops at 10:00 AM the following day.

[0502] This invention allows farmers to receive highly accurate data obtained in real time, insights based on that data, and notifications that take into account the user's emotional state, enabling them to not only practice efficient and sustainable agriculture but also to take into consideration the user's mental health.

[0503] The processing flow will be explained below.

[0504] Step 1:

[0505] The device collects environmental data: specifically, sensors measure the moisture, temperature, and pH of the farmland soil.

[0506] Step 2:

[0507] The device temporarily stores the collected environmental data in its local memory. Specifically, the humidity data measured by the sensor is recorded in the device's memory.

[0508] Step 3:

[0509] The device packetizes the temporarily stored data and transmits it to the server. Specifically, it forms a packet containing humidity data and transmits it to the server via Wi-Fi.

[0510] Step 4:

[0511] The server receives the data packet sent from the terminal. Specifically, the server receives the data for humidity 40%.

[0512] Step 5:

[0513] The server stores the received data in a database. Specifically, the server stores the humidity data in the database.

[0514] Step 6:

[0515] The server inputs the data into a machine learning model for analysis, specifically using humidity data to predict when the next watering should be done.

[0516] Step 7:

[0517] The server collects emotional data from users, specifically voice and facial expression data from their smartphones or wearable devices.

[0518] Step 8:

[0519] The server's emotion engine analyzes the collected emotion data to detect the user's emotional state, specifically detecting stress and fatigue from voice data.

[0520] Step 9:

[0521] The server then compiles the analysis results and generates a message that adjusts the content and frequency of notifications based on the user's emotional state. For example, "The next watering is tomorrow at 10 AM" might be changed to "The next watering is tomorrow at 10 AM, but please also take time to relax."

[0522] Step 10:

[0523] The server sends the generated notification message to the user's device. Specifically, it sends a notification packet to the user's smartphone via the Internet.

[0524] Step 11:

[0525] The user's device receives the notification message sent from the server. Specifically, the user's smartphone receives a notification saying, "The next watering will be at 10:00 AM tomorrow, but please take time to relax."

[0526] Step 12:

[0527] The user's device visually displays the received notification, specifically as a pop-up on the smartphone screen.

[0528] Step 13:

[0529] The user takes appropriate action based on the notification. Specifically, the user acknowledges the notification, waters the crops at 10:00 AM the next day, and schedules time to relax.

[0530] Example 2

[0531] 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."

[0532] Current agricultural support systems achieve the basic functions of collecting and analyzing environmental data and providing notifications to users, but lack the ability to consider the user's emotional state. As a result, there are concerns that notifications sent without taking the user's mental health into consideration could increase stress and reduce work efficiency. The present invention aims to solve this problem by providing an agricultural support system that adjusts the content and frequency of notifications according to the user's emotional state.

[0533] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting environmental data by a sensor, means for transmitting the collected data to the server, means for analyzing the transmitted data, means for recognizing the emotional state of the user, and means for adjusting the content and frequency of notifications based on the emotional state. This makes it possible to provide notifications according to the emotional state of the user.

[0534] A "sensor" is a device that measures environmental data in real time and collects necessary information.

[0535] "Server" means a central processing unit that receives and analyzes data sent from the terminal and provides appropriate notifications to the user.

[0536] A "user" is a person or organization that uses the agricultural support system to carry out agricultural work.

[0537] "Environmental data" refers to information about factors that affect crop growth, such as soil moisture, temperature, and pH value in agricultural land.

[0538] The "emotion engine" is a system that analyzes the user's voice and facial expression data to detect their emotional state.

[0539] A "machine learning model" is an algorithm for analyzing and predicting data, and is a technology that improves accuracy by learning specific patterns.

[0540] "Insights" refers to useful information and suggestions generated from analyzed data.

[0541] "Notification content" is the content of the information message sent from the server to the user.

[0542] "Notification frequency" refers to the interval between notifications sent from the server to the user.

[0543] A "user terminal" is a device that a user uses to receive and operate information, and includes smartphones, tablets, and the like.

[0544] The present invention relates to an agricultural support system that is composed of sensors, a server, and a user terminal, and further combines it with an emotion engine that recognizes the user's emotions. An embodiment of this system will be described in detail below.

[0545] System Configuration

[0546] The agricultural support system consists of the following main components:

[0547] 1. Sensors

[0548] The sensors are installed in farmland to measure environmental data such as soil humidity, temperature, and pH value in real time. Specific sensors used include soil humidity sensors, temperature sensors, and pH sensors.

[0549] 2. User Device

[0550] A user terminal is a mobile device such as a smartphone or tablet that is used to receive and visually display notifications.

[0551] 3. Server

[0552] The server receives data sent from the sensors and stores it in a database. It also analyzes the data using machine learning models to generate insights. It also has an emotion engine that recognizes user emotions.

[0553] Data collection and transmission

[0554] Users install various sensors on their farmland. The sensors measure environmental data in real time and temporarily store the data on a terminal. The terminal then packets the data and transmits it to a server at regular intervals.

[0555] Data analysis and insight generation

[0556] The server receives the environmental data sent from the devices and stores it in a database. The server then analyzes the data using machine learning models to generate insights such as the health of the crops, optimal watering times, and the risk of pest infestation.

[0557] Emotion Engine Operation

[0558] The server is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's voice and facial expression data to detect their emotional state. Based on the results, the server can adjust the content and frequency of notifications.

[0559] Providing results and feedback

[0560] Notifications based on the insights generated by the server and the results of the emotion engine are sent to the user's device, which receives and visually displays the notifications, allowing the user to take appropriate action based on the content of the notifications.

[0561] Specific examples

[0562] Multiple sensors installed in a certain field measure the humidity, temperature, and pH value of the soil in real time. For example, data indicating that the humidity is 40% is instantly sent to a server. The server analyzes this humidity data using a machine learning model to predict the optimal timing for watering. The resulting prediction is an insight such as, "The next watering time is tomorrow at 10:00 AM."

[0563] Meanwhile, the server's emotion engine detects that the user is currently feeling stressed. In this case, a notification is sent with the message, "The next watering will be at 10:00 AM tomorrow, so please take your time and relax." The user's smartphone receives this notification, confirms its contents, and waters the plants at 10:00 AM the next day.

[0564] Prompt Sentence Examples

[0565] When is the next time to water?

[0566] "What is the current soil moisture percentage?"

[0567] "Analyze the user's emotional state."

[0568] This invention allows farmers to receive highly accurate data obtained in real time, insights based on that data, and notifications that take into account the user's emotional state, enabling them to not only practice efficient and sustainable agriculture but also to take into consideration the user's mental health.

[0569] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0570] Step 1: Data collection

[0571] The sensors installed on the terminal measure the environmental data of the farmland in real time. Specifically, the soil moisture sensor measures the soil moisture, the temperature sensor measures the soil and ambient temperature, and the pH sensor measures the soil pH value. All of this data is temporarily stored in the local memory.

[0572] Input: Soil moisture, temperature, pH value

[0573] Data processing: Sensors measure environmental data in real time

[0574] Output: Environmental data stored in local memory

[0575] Step 2: Send data

[0576] The device periodically packets the environmental data stored in its local memory and transmits it to the server via the Wi-Fi module. Specifically, the device packets the collected data every five minutes, for example.

[0577] Input: Environmental data stored in local memory

[0578] Data processing: Packetizing environmental data

[0579] Output: Data packet sent to the server

[0580] Step 3: Receiving and storing data

[0581] The server receives data packets sent from the terminal and stores them in a database, which is based on SQL and allows for fast access.

[0582] Input: Data packets sent from the device

[0583] Data processing: Store data packets in a database

[0584] Output: Environmental data stored in a database

[0585] Step 4: Data analysis

[0586] The server then inputs the environmental data stored in the database into a machine learning model for analysis, which predicts things like the health of the crops, optimal watering times, and the risk of pest infestation.

[0587] Input: Environmental data stored in a database

[0588] Data processing: Data analysis using machine learning models

[0589] Output: Predicted insight (e.g., next watering timing)

[0590] Step 5: Emotion Recognition

[0591] The emotion engine installed on the server analyzes the user's voice and facial expression data to detect their emotional state, identifying whether they are stressed or relaxed.

[0592] Input: User's voice data, facial expression data

[0593] Data processing: Emotional state analysis using emotion engine

[0594] Output: The user's current emotional state

[0595] Step 6: Notification Generation and Coordination

[0596] The server adjusts the content and frequency of notifications based on the analyzed insights and the user's emotional state detected by the emotion engine. For example, if the user is feeling stressed, the server reduces the frequency of notifications and adds a message encouraging relaxation.

[0597] Input: predicted insights, user emotional state

[0598] Data processing: Adjustment of notification content and frequency

[0599] Output: The adjusted notification message

[0600] Step 7: Sending and displaying notifications

[0601] The server sends the generated notification to the user terminal, which receives and visually displays the notification, allowing the user to check the notification content and take appropriate action.

[0602] Input: The adjusted notification message

[0603] Data processing: Sending notifications and receiving / displaying them on the device

[0604] Output: A visual notification displayed on the user's device.

[0605] Step 8: Get user feedback

[0606] The user inputs the results and impressions of the actions taken based on the notification into the device and sends them to the server, which then receives the feedback and reflects it in future analyses and notifications.

[0607] Input: User feedback

[0608] Data processing: collecting and storing feedback

[0609] Output: Feedback data that can be used to improve the accuracy of future analyses

[0610] (Application example 2)

[0611] 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."

[0612] Conventional systems that integrate emotion analysis are often specialized for agricultural support or individual applications, and have the problem of lacking effectiveness in situations that require instantaneous responses in physical stores. In addition, there are no tools to understand the emotional state of customers and provide appropriate customer service, making it difficult for store employees to provide appropriate customer service.

[0613] 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 a sensor, means for transmitting the collected data to the server, and means for analyzing the transmitted data. This makes it possible to recognize the user's emotional state and provide notifications including appropriate customer service methods and product recommendations based on the analysis results. This makes it possible to grasp the customer's emotional state in real time and provide appropriate customer service methods and products in a timely manner.

[0614] A "sensor" is a device for collecting environmental data.

[0615] A "server" is a central processing unit that analyzes collected data and provides notifications based on the analysis results.

[0616] "User" means an individual or group that uses the system.

[0617] "Emotional state" refers to a user's mental or emotional state.

[0618] An "emotion engine" is software that analyzes the user's voice and facial expressions to recognize their emotional state.

[0619] A "machine learning model" is a type of algorithm used in data analysis that learns from past data to make predictions and classifications.

[0620] A "notification" is a message or alert that provides analysis results or other information to a user.

[0621] "Smart glasses" are wearable devices that incorporate a camera and display and support the user's visual and voice input.

[0622] "Environmental Data" means measured information about specific environmental conditions, such as humidity, temperature, pH value, etc.

[0623] "Analysis" is the process of processing collected data to derive useful information and insights.

[0624] "Customer service methods" refer to the way customers are treated and services are provided.

[0625] "Product recommendation" is the act of suggesting appropriate products based on a customer's needs and emotional state.

[0626] This invention is a system for supporting customer service in brick-and-mortar stores, and is composed of sensors, a server, and a user's terminal. The system transmits environmental data collected by the sensors to the server, which analyzes the data and provides notifications to the user. The system also incorporates an emotion engine that recognizes the user's emotional state and adjusts the content and frequency of notifications based on the analysis results.

[0627] System Overview

[0628] This system sends environmental data collected by sensors and the user's emotional state to a server, and provides notifications to the user based on the analysis results. For example, when a store clerk wearing smart glasses serves a customer, the smart glasses' camera and microphone collect the customer's facial expressions and voice, analyze the data in real time, and provide appropriate notifications.

[0629] Data collection (terminal)

[0630] The smart glasses' camera and microphone are used to capture the customer's facial expressions and voice in real time. For example, the camera recognizes the customer's smile, and the microphone analyzes the customer's level of satisfaction from the tone of their voice. This data is temporarily stored in the device's local memory and periodically packetized and sent to a server.

[0631] Data analysis (server)

[0632] The server receives the environmental and emotional data sent from the device and stores it in a database. It then analyzes the data using machine learning models to generate insights into the customer's emotional state, appropriate customer service methods, and recommended products. For example, the server analyzes data on a customer's smile and recognizes that the customer is happy, and then notifies the customer of the appropriate customer service method.

[0633] Emotion engine operation (server)

[0634] The server is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's voice and facial expressions to detect their emotional state. Based on the results, the server can adjust the content and frequency of notifications. For example, if a customer looks dissatisfied, the server sends a notification to the store clerk saying, "The customer is dissatisfied. Please respond quickly."

[0635] Providing results and feedback (user)

[0636] Notifications based on the insights generated by the server and the results of the emotion engine are sent to the user's smart glasses. The notification content is displayed on the smart glasses' display, allowing the store clerk to provide appropriate customer service and product recommendations based on the notification content. For example, a store clerk may receive a notification saying, "The customer is happy. Please recommend these shoes," and then suggest a product to the customer based on that notification.

[0637] Specific examples

[0638] In a physical store, a salesperson wears smart glasses to serve customers. The smart glasses' camera captures the customer's facial expressions, and the microphone picks up the customer's voice. When the customer shows interest in a product and smiles, the server analyzes the data and displays a notification on the smart glasses saying, "The customer is pleased. Please suggest a new product."

[0639] Prompt Sentence Examples

[0640] "Please explain an application of smart glasses that analyzes the facial expressions and voices of customers in a physical store to provide customer service support. Please give specific examples of what kind of notification is displayed to the store clerk when the customer is happy."

[0641] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0642] Program processing flow

[0643] Step 1:

[0644] The device (smart glasses) captures the customer's facial expressions and voice in real time through a camera and microphone. The camera input is the customer's facial image, and the microphone input is the customer's voice. This data is temporarily stored in local memory.

[0645] Step 2:

[0646] The device packetizes the customer's facial expression and voice data stored in its local memory and transmits them to the server via Wi-Fi. The input here is the data stored in the local memory, and the output is the packetized data.

[0647] Step 3:

[0648] The server receives data packets sent from the terminals and stores them in a database. The input is the received data packets, and the output is the data stored in the database.

[0649] Step 4:

[0650] The server analyzes the stored data using an emotion engine. The emotion engine analyzes the customer's emotional state from their facial expression data and their voice tone from their voice data. The input is the customer's facial expression data and voice data stored in the database, and the output is the customer's emotional state.

[0651] Step 5:

[0652] The server uses a machine learning model to generate customer service methods and product recommendations that correspond to the customer's emotional state. The input is the customer's emotional state, and the output is customer service methods and product recommendations. Appropriate notification content is generated based on the analysis results.

[0653] Step 6:

[0654] The server sends the generated notification content to the device (smart glasses). The input is the generated notification content, and the output is the notification to the device.

[0655] Step 7:

[0656] The terminal (smart glasses) displays the notification content sent from the server on its display, visually notifying the store clerk. The input is the notification content sent from the server, and the output is the notification displayed on the smart glasses' display. This allows the store clerk to take appropriate action depending on the customer's emotional state.

[0657] Specific examples of operation

[0658] Step 1: When the customer enters the field of view of the smart glasses, the camera automatically captures their facial expressions and the microphone records their voice.

[0659] Step 2: Facial expression data (e.g., an image of the customer's smile) and voice data (e.g., the customer's interesting tone of voice) are packetized.

[0660] Step 3: The server receives these data packets and stores them in a database along with the customer ID.

[0661] Step 4: The server uses the emotion engine to analyze the emotional state of "customer is satisfied."

[0662] Step 5: The server uses the machine learning model to generate a notification suggesting a new product for this customer.

[0663] Step 6: The server sends the generated notification "Please suggest a new product" to the terminal.

[0664] Step 7: The smart glasses display will display a notification saying, "Customer is happy. Please recommend these shoes."

[0665] 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.

[0666] 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.

[0667] 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.

[0668] [Third embodiment]

[0669] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0670] 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.

[0671] 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).

[0672] 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.

[0673] 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.

[0674] 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).

[0675] 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.

[0676] 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.

[0677] 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.

[0678] 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.

[0679] 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.

[0680] 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."

[0681] The present invention relates to an agricultural support system that includes a sensor, a server, and a user terminal. An embodiment of this system will be described in detail below.

[0682] System Overview

[0683] The agricultural support system consists of a consistent process in which sensors collect environmental data, send the data to a server, the server analyzes the data, and notifies the user based on the analysis results.

[0684] Data collection and transmission (terminal)

[0685] Sensors are installed in farmland to measure environmental data such as soil humidity, temperature, and pH in real time. The device temporarily stores these measurement data in its local memory and periodically packets the data and transmits it to a server. For example, a soil humidity sensor measures the humidity level and transmits the data to a server via Wi-Fi.

[0686] Data analysis and insight generation (server)

[0687] The server receives the environmental data sent from the device and stores it in a database. It then analyzes the data using machine learning models to generate insights such as the health of the crop, the best time to water it, and the risk of pest infestation. For example, the server analyzes humidity data and predicts that the next watering will be at 10 a.m. tomorrow.

[0688] Providing results and feedback (user)

[0689] The insights generated by the server are sent to the user's device as notifications. The user's smartphone or tablet receives the notifications and displays them visually. This allows the user to take appropriate action based on the notification content. For example, a user may receive a notification that "the next watering time is tomorrow at 10:00 AM" and water the plants at that time.

[0690] Specific examples

[0691] Multiple sensors installed in a certain field measure the humidity, temperature, and pH value of the soil in real time. Data indicating that the humidity is 40% is instantly sent to a server. The server analyzes this humidity data using a machine learning model to predict the optimal timing for watering. As a result of the prediction, an insight is generated, such as "The next watering time is tomorrow at 10:00 AM." This insight is then sent to the user's smartphone, allowing the user to water the plants accordingly.

[0692] This invention allows farmers to utilize real-time, highly accurate data and the insights derived from that data to practice efficient and sustainable agriculture. By taking appropriate action based on the data, they can maintain the health of their crops and optimize yields.

[0693] The processing flow will be explained below.

[0694] Step 1:

[0695] The device collects environmental data: specifically, sensors measure the moisture, temperature, and pH of the farmland soil.

[0696] Step 2:

[0697] The device temporarily stores the collected environmental data in its local memory. Specifically, the humidity data measured by the sensor is recorded in the device's memory.

[0698] Step 3:

[0699] The device packetizes the temporarily stored data and transmits it to the server. Specifically, it forms a packet containing humidity data and transmits it to the server via Wi-Fi.

[0700] Step 4:

[0701] The server receives the data packet sent from the terminal. Specifically, the server receives the data for humidity 40%.

[0702] Step 5:

[0703] The server stores the received data in a database. Specifically, the server stores the humidity data in the database.

[0704] Step 6:

[0705] The server inputs the data into a machine learning model for analysis, specifically using humidity data to predict when the next watering should be done.

[0706] Step 7:

[0707] The server organizes the analysis results and generates a message to notify the user. Specifically, it creates a notification message saying, "The next watering will be tomorrow at 10:00 AM."

[0708] Step 8:

[0709] The server sends the generated notification message to the user's device. Specifically, it sends a notification packet to the user's smartphone via the Internet.

[0710] Step 9:

[0711] The user's device receives the notification message sent from the server. Specifically, the user's smartphone receives the notification "The next watering will be at 10:00 AM tomorrow."

[0712] Step 10:

[0713] The user's device visually displays the received notification, specifically as a pop-up on the smartphone screen.

[0714] Step 11:

[0715] The user takes appropriate action based on the notification, specifically, the user acknowledges the notification and waters the crops at 10:00 AM the next day.

[0716] Example 1

[0717] 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."

[0718] Conventional agricultural support systems have struggled to acquire and effectively analyze farmland environmental data in real time and notify users of appropriate actions. Furthermore, there was a lack of technology to accurately predict the health of crops and the optimal timing for watering. This made it difficult to practice efficient agriculture and optimize crop yields.

[0719] 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.

[0720] In this invention, the server includes means for collecting environmental data using sensors, means for transmitting the collected data to the server via a network, means for storing the transmitted data in a database, means for acquiring the stored data and analyzing it using a machine learning model, and means for generating crop health conditions and optimal watering timings as analysis results and providing notifications to users. This enables high-precision data analysis in real time, allows appropriate instructions to be provided to users, and realizes efficient and sustainable agriculture.

[0721] A "sensor" is a device for measuring environmental data, specifically, acquiring environmental parameters such as soil humidity, temperature, and acidity in real time.

[0722] "Means for transmitting via a network" refers to a method for transmitting data collected by a sensor to a server using a communication protocol, and includes communication technologies such as Wi-Fi and wired connections.

[0723] A "database" is a system for structuring and storing collected environmental data, and includes relational databases and NoSQL databases.

[0724] A "machine learning model" is an algorithm that learns patterns and regularities from large amounts of data and makes predictions and classifications based on new data. Specifically, this includes generative AI models.

[0725] "Analysis means" refers to a processing method for processing stored data using machine learning models or the like to extract necessary insights.

[0726] "Insights" are useful information obtained through data analysis, such as the health of crops and the optimal time to water them, that can encourage users to take action.

[0727] "Notification means" refers to a method for transmitting analysis results to a user device, including push notifications to smartphones and tablets.

[0728] The present invention relates to an agricultural support system that includes a sensor, a server, and a user terminal. An embodiment of this system will be described in detail below.

[0729] System Overview

[0730] The agricultural support system consists of a consistent process in which environmental data is collected by sensors, the data is sent to a server via a network, the server analyzes the data, and notifications are provided to users based on the analysis results.

[0731] Data collection and transmission (terminal)

[0732] Sensors that measure the humidity, temperature, and pH value of the soil are connected to the device. The sensors measure environmental data in real time, and the measured data is temporarily stored in the device's local memory. The data obtained from the sensors is periodically packetized and sent to a server over the network via a Wi-Fi module. For example, if the humidity sensor measures the soil humidity to be 40%, this data is stored on the device. The device then packetizes this humidity data and sends it to the server via Wi-Fi.

[0733] Data analysis and insight generation (server)

[0734] The server receives the environmental data sent from the device and stores it in a database. The server then analyzes the stored data using a generative AI model. Specifically, it uses machine learning libraries such as TensorFlow to generate insights such as the health of the crops, the optimal timing for watering, and the risk of pest infestation. For example, a prediction might be generated, such as "The next watering will be at 10:00 AM tomorrow."

[0735] Providing results and feedback (user)

[0736] The insights generated by the server are sent to the user's device as notifications. The user's smartphone or tablet receives the notifications and displays them visually. This allows the user to take appropriate action based on the notification content. For example, a user may receive a notification that "the next watering time is tomorrow at 10:00 AM" and water the plants at that time.

[0737] Specific examples

[0738] Multiple sensors installed in a farm field measure the soil's humidity, temperature, and pH value in real time. Data indicating a humidity level of 40% is instantly sent to a server. The server analyzes this humidity data using a generative AI model to predict the optimal watering timing. As a result of the prediction, an insight is generated, such as "The next watering time is tomorrow at 10:00 AM." This insight is sent to the user's smartphone, allowing them to water accordingly. This allows users to achieve efficient and sustainable agriculture thanks to high-precision data analysis and the insights based on its results.

[0739] As a result, this system provides farmers with real-time data collection and analysis results, helping them optimize yields while maintaining the health of their crops.

[0740] Prompt Sentence Examples

[0741] "Analyze data collected from humidity sensors to predict the optimal time for the next watering."

[0742] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0743] Step 1: Data collection (device)

[0744] A sensor connected to the device measures the humidity, temperature, and pH value of the soil. The sensor collects this environmental data in real time and temporarily stores the data in the device's local memory. For example, if the sensor measures the soil humidity to be 40%, the data is recorded in memory. The input of this step is the environmental data from the sensor, and the output is the data stored in the device's local memory.

[0745] Step 2: Send data (terminal)

[0746] The device periodically packetizes the measurement data stored in its local memory. The packetized data is then sent to the server over the network via the Wi-Fi module. Specifically, the HTTP protocol is used to encrypt the data packets and transmit them securely. The input of this step is the data stored in its local memory, and the output is the data sent to the server.

[0747] Step 3: Data reception and storage (server)

[0748] The server receives data packets sent from the device. The received data is stored in a database. For example, environmental data is structured and stored in a database such as MongoDB. The input of this step is the data packets sent from the device, and the output is the data stored in the database.

[0749] Step 4: Data analysis (server)

[0750] The server retrieves the stored data from the database. The retrieved data is then analyzed using a generative AI model. For example, TensorFlow can be used to analyze humidity data and make predictions such as "The next watering will be tomorrow at 10 AM." The input for this step is the environmental data stored in the database, and the output is the analyzed insights.

[0751] Step 5: Insight generation and formatting (server)

[0752] The generated insights are formatted as notification data for users. Specifically, the notification data is prepared in JSON or text format. The input of this step is the insights obtained as a result of the analysis, and the output is the formatted notification data.

[0753] Step 6: Result notification (server)

[0754] The server sends the formatted notification data to the user's device. A notification service such as FCM (Firebase Cloud Messaging) is used to notify the user in real time. The input of this step is the formatted notification data, and the output is the notification sent to the user's device.

[0755] Step 7: Receiving and Displaying Notifications (User)

[0756] The user's smartphone or tablet receives the notification and displays it visually. The user checks the notification and performs farm work based on the notification content. For example, the user checks the notification that "the next watering is tomorrow at 10:00 AM" and waters the fields at that time. The input of this step is the notification data sent from the server, and the output is the notification displayed on the user's device.

[0757] (Application example 1)

[0758] 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."

[0759] Conventional food delivery services lack the means to monitor the freshness and quality of fresh food in real time, which means that the quality of the ingredients received by users may not always be optimal. Furthermore, there is no mechanism for notifying users of the optimal pickup time, making it difficult to guarantee the quality of ingredients, which may result in reduced user satisfaction. Therefore, there is a need for a system that can appropriately manage the pickup timing and quality of fresh food.

[0760] 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.

[0761] In this invention, the server includes means for collecting environmental data using a sensor, means for transmitting the collected data to the server, means for analyzing the transmitted data, means for providing a notification to a user based on the analysis results, means for the collected data to include the temperature, humidity, and gas concentration of the fresh food, means for the analysis means to generate insights for determining the freshness of the fresh food in real time, and means for notifying the user of the optimal time to receive the fresh food. This makes it possible to monitor the quality of the fresh food in real time and notify the user of the optimal time to receive the fresh food.

[0762] A "sensor" is a device that collects environmental data, and in this invention measures the temperature, humidity, and gas concentration of fresh food.

[0763] A "server" is a computer system that analyzes collected environmental data and provides notifications to users based on the results of that analysis.

[0764] "Data transmission means" refers to devices or software that have the function of transmitting environmental data collected by sensors to a server.

[0765] "Analysis means" refers to software and algorithms used to analyze the environmental data sent to the server, specifically machine learning models.

[0766] "Notification providing means" refers to a device or software that has the function of notifying the user's terminal of the analysis results.

[0767] "Environmental data" refers to data such as temperature, humidity, and gas concentration of fresh food.

[0768] A "machine learning model" is an algorithm that analyzes environmental data and generates insights such as the freshness of perishable foods and the optimal pickup time.

[0769] "Insights" are key pieces of information generated by analytics about the freshness of perishables and optimal pickup times.

[0770] "Optimal pick-up time" refers to the ideal time for a user to receive fresh food at its best quality.

[0771] MODE FOR CARRYING OUT THE INVENTION

[0772] The present invention is a system for monitoring the quality and freshness of fresh food in real time and notifying the user of the optimal time for collection. Specific embodiments for carrying out the present invention will be described below.

[0773] System Configuration

[0774] This system consists of sensors, a server, and user terminals.

[0775] Sensor

[0776] Multiple sensors, including temperature sensors, humidity sensors, and ethylene sensors, are installed inside the cooler boxes that store fresh food. Each sensor collects environmental data around the fresh food in real time. For example, the temperature sensor measures the temperature around the food, the humidity sensor measures humidity, and the ethylene sensor measures gas concentration.

[0777] server

[0778] The collected environmental data is sent to a server via the device. The server stores the data in a database and analyzes it using a machine learning model. The analysis results generate insights into the freshness of perishable foods and the optimal pickup time. The machine learning model predicts food deterioration and loss of freshness based on past data and patterns.

[0779] User terminal

[0780] The insights generated by the server are sent as notifications to the user's smartphone or tablet, where they are received and visually displayed. Based on these notifications, the user can receive fresh food at the optimal time.

[0781] Specific examples

[0782] A cooler box used by a certain delivery service is equipped with a temperature sensor, a humidity sensor, and an ethylene sensor. These sensors collect their respective environmental data in real time and send it to a server via Wi-Fi. For example, if the temperature sensor detects 12°C, the humidity sensor detects 85%, and the ethylene sensor detects 0.06 ppm, the server analyzes this data and generates insights such as, "The temperature is outside the appropriate range. The humidity is outside the appropriate range. The ethylene level indicates a decrease in freshness." This insight is then sent to the user's smartphone with a message stating, "The freshness of your perishable food is decreasing. We recommend that you pick it up immediately."

[0783] Hardware and software used

[0784] The hardware used includes a temperature sensor, humidity sensor, and ethylene sensor. The software uses Python scripts to collect and transmit data, and machine learning models to analyze the data on a cloud server. An API is also required to send notifications to user devices.

[0785] Prompt Sentence Examples

[0786] Examples of prompts to give to a generative AI model include:

[0787] Design an application that monitors the quality of agricultural products in a cooler box in real time and notifies the user before the quality deteriorates. Use the following sensor data (temperature, humidity, ethylene concentration) to set the notification conditions and implement it in Python.

[0788] Suitable temperature range: 5℃ to 10℃

[0789] Suitable humidity range: 60% to 80%

[0790] Ethylene concentration limit: 0.05 ppm

[0791] As a result, the present invention provides an environment in which users can always receive fresh food of the highest quality.

[0792] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0793] Step 1:

[0794] Data collection with sensors

[0795] Sensors collect environmental data in the cooler box in real time. The inputs are temperature, humidity, and ethylene concentration, and each sensor measures these data. The collected data is temporarily stored in local memory. The output is the measured values ​​of temperature, humidity, and gas concentration.

[0796] Step 2:

[0797] Data transmission

[0798] The device sends the collected environmental data to the server. As input, it uses the temperature, humidity, and gas concentration data collected in step 1. The data is periodically packetized and sent to the server via Wi-Fi. The output is the data packets received by the server.

[0799] Step 3:

[0800] Data analysis

[0801] The server analyzes the received data. It uses temperature, humidity, and gas concentration data as input. A machine learning model analyzes this data based on the data stored in the database. Insights are generated as a result of the analysis if a deterioration in freshness or quality is predicted. The output is insights about the freshness and quality of the food.

[0802] Step 4:

[0803] Insight generation

[0804] The server generates insights to notify the user based on the analysis results. The analysis results obtained in step 3 are used as input. For example, insights such as "The temperature is out of the normal range. The humidity is out of the normal range. The ethylene level indicates a decrease in freshness" are generated. The output is the notification content.

[0805] Step 5:

[0806] Send notifications

[0807] The insights generated by the server are notified to the user's device. The notification content generated in step 4 is used as input. The notification is sent to the user's smartphone or tablet via the API. Specifically, a visual notification such as "Fresh food is losing its freshness. We recommend that you pick it up immediately" is displayed. The output is a notification that arrives on the user's device.

[0808] Step 6:

[0809] User response

[0810] The user checks the notification sent to their smartphone or tablet. The notification sent in step 5 is used as input. The user can receive fresh food at the optimal time according to the received notification. The output is the result of receiving the food in the best quality.

[0811] 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.

[0812] The present invention relates to an agricultural support system that is composed of sensors, a server, and a user terminal, and further combines it with an emotion engine that recognizes the user's emotions. An embodiment of this system will be described in detail below.

[0813] System Overview

[0814] The agricultural support system is comprised of a series of consistent processes: sensors collect environmental data, the data is sent to a server, the server analyzes the data, and notifications are sent to the user based on the analysis results. It also incorporates an emotion engine that recognizes the user's emotional state and adjusts the content and frequency of notifications accordingly.

[0815] Data collection and transmission (terminal)

[0816] Sensors are installed in farmland to measure environmental data such as soil humidity, temperature, and pH in real time. The device temporarily stores these measurement data in its local memory and periodically packets the data and transmits it to a server. For example, a soil humidity sensor measures the humidity level and transmits the data to a server via Wi-Fi.

[0817] Data analysis and insight generation (server)

[0818] The server receives the environmental data sent from the device and stores it in a database. It then analyzes the data using machine learning models to generate insights such as the health of the crop, the best time to water it, and the risk of pest infestation. For example, the server analyzes humidity data and predicts that the next watering will be at 10 a.m. tomorrow.

[0819] Emotion engine operation (server)

[0820] The server is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's voice and facial expressions to detect their emotional state. Based on the results, the server can adjust the content and frequency of notifications. For example, if the user is feeling stressed, the server can reduce the frequency of notifications or send notifications with relaxation suggestions.

[0821] Providing results and feedback (user)

[0822] Notifications based on the insights generated by the server and the results of the emotion engine are sent to the user's device. The user's smartphone or tablet receives the notification and displays it visually. This allows the user to take appropriate action based on the content of the notification. For example, a user may receive a notification in the form of an emotion-sensitive message saying, "The next watering time is tomorrow at 10:00 AM," and water the plants at that time.

[0823] Specific examples

[0824] Multiple sensors installed in a farm field measure the soil's humidity, temperature, and pH in real time. Data indicating a humidity level of 40% is instantly sent to a server. The server then analyzes this humidity data using a machine learning model to predict the optimal timing for watering. The resulting prediction generates an insight: "The next watering will be at 10:00 AM tomorrow." Meanwhile, the server's emotion engine detects that the user is currently feeling stressed. In this case, a notification is sent stating, "The next watering will be at 10:00 AM tomorrow. Please take your time and relax." The user's smartphone receives this notification, confirms its contents, and waters the crops at 10:00 AM the following day.

[0825] This invention allows farmers to receive highly accurate data obtained in real time, insights based on that data, and notifications that take into account the user's emotional state, enabling them to not only practice efficient and sustainable agriculture but also to take into consideration the user's mental health.

[0826] The processing flow will be explained below.

[0827] Step 1:

[0828] The device collects environmental data: specifically, sensors measure the moisture, temperature, and pH of the farmland soil.

[0829] Step 2:

[0830] The device temporarily stores the collected environmental data in its local memory. Specifically, the humidity data measured by the sensor is recorded in the device's memory.

[0831] Step 3:

[0832] The device packetizes the temporarily stored data and transmits it to the server. Specifically, it forms a packet containing humidity data and transmits it to the server via Wi-Fi.

[0833] Step 4:

[0834] The server receives the data packet sent from the terminal. Specifically, the server receives the data for humidity 40%.

[0835] Step 5:

[0836] The server stores the received data in a database. Specifically, the server stores the humidity data in the database.

[0837] Step 6:

[0838] The server inputs the data into a machine learning model for analysis, specifically using humidity data to predict when the next watering should be done.

[0839] Step 7:

[0840] The server collects emotional data from users, specifically voice and facial expression data from their smartphones or wearable devices.

[0841] Step 8:

[0842] The server's emotion engine analyzes the collected emotion data to detect the user's emotional state, specifically detecting stress and fatigue from voice data.

[0843] Step 9:

[0844] The server then compiles the analysis results and generates a message that adjusts the content and frequency of notifications based on the user's emotional state. For example, "The next watering is tomorrow at 10 AM" might be changed to "The next watering is tomorrow at 10 AM, but please also take time to relax."

[0845] Step 10:

[0846] The server sends the generated notification message to the user's device. Specifically, it sends a notification packet to the user's smartphone via the Internet.

[0847] Step 11:

[0848] The user's device receives the notification message sent from the server. Specifically, the user's smartphone receives a notification saying, "The next watering will be at 10:00 AM tomorrow, but please take time to relax."

[0849] Step 12:

[0850] The user's device visually displays the received notification, specifically as a pop-up on the smartphone screen.

[0851] Step 13:

[0852] The user takes appropriate action based on the notification. Specifically, the user acknowledges the notification, waters the crops at 10:00 AM the next day, and schedules time to relax.

[0853] Example 2

[0854] 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."

[0855] Current agricultural support systems achieve the basic functions of collecting and analyzing environmental data and providing notifications to users, but lack the ability to consider the user's emotional state. As a result, there are concerns that notifications sent without taking the user's mental health into consideration could increase stress and reduce work efficiency. The present invention aims to solve this problem by providing an agricultural support system that adjusts the content and frequency of notifications according to the user's emotional state.

[0856] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting environmental data by a sensor, means for transmitting the collected data to the server, means for analyzing the transmitted data, means for recognizing the emotional state of the user, and means for adjusting the content and frequency of notifications based on the emotional state. This makes it possible to provide notifications according to the emotional state of the user.

[0857] A "sensor" is a device that measures environmental data in real time and collects necessary information.

[0858] "Server" means a central processing unit that receives and analyzes data sent from the terminal and provides appropriate notifications to the user.

[0859] A "user" is a person or organization that uses the agricultural support system to carry out agricultural work.

[0860] "Environmental data" refers to information about factors that affect crop growth, such as soil moisture, temperature, and pH value in agricultural land.

[0861] The "emotion engine" is a system that analyzes the user's voice and facial expression data to detect their emotional state.

[0862] A "machine learning model" is an algorithm for analyzing and predicting data, and is a technology that improves accuracy by learning specific patterns.

[0863] "Insights" refers to useful information and suggestions generated from analyzed data.

[0864] "Notification content" is the content of the information message sent from the server to the user.

[0865] "Notification frequency" refers to the interval between notifications sent from the server to the user.

[0866] A "user terminal" is a device that a user uses to receive and operate information, and includes smartphones, tablets, and the like.

[0867] The present invention relates to an agricultural support system that is composed of sensors, a server, and a user terminal, and further combines it with an emotion engine that recognizes the user's emotions. An embodiment of this system will be described in detail below.

[0868] System Configuration

[0869] The agricultural support system consists of the following main components:

[0870] 1. Sensors

[0871] The sensors are installed in farmland to measure environmental data such as soil humidity, temperature, and pH value in real time. Specific sensors used include soil humidity sensors, temperature sensors, and pH sensors.

[0872] 2. User Device

[0873] A user terminal is a mobile device such as a smartphone or tablet that is used to receive and visually display notifications.

[0874] 3. Server

[0875] The server receives data sent from the sensors and stores it in a database. It also analyzes the data using machine learning models to generate insights. It also has an emotion engine that recognizes user emotions.

[0876] Data collection and transmission

[0877] Users install various sensors on their farmland. The sensors measure environmental data in real time and temporarily store the data on a terminal. The terminal then packets the data and transmits it to a server at regular intervals.

[0878] Data analysis and insight generation

[0879] The server receives the environmental data sent from the devices and stores it in a database. The server then analyzes the data using machine learning models to generate insights such as the health of the crops, optimal watering times, and the risk of pest infestation.

[0880] Emotion Engine Operation

[0881] The server is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's voice and facial expression data to detect their emotional state. Based on the results, the server can adjust the content and frequency of notifications.

[0882] Providing results and feedback

[0883] Notifications based on the insights generated by the server and the results of the emotion engine are sent to the user's device, which receives and visually displays the notifications, allowing the user to take appropriate action based on the content of the notifications.

[0884] Specific examples

[0885] Multiple sensors installed in a certain field measure the humidity, temperature, and pH value of the soil in real time. For example, data indicating that the humidity is 40% is instantly sent to a server. The server analyzes this humidity data using a machine learning model to predict the optimal timing for watering. The resulting prediction is an insight such as, "The next watering time is tomorrow at 10:00 AM."

[0886] Meanwhile, the server's emotion engine detects that the user is currently feeling stressed. In this case, a notification is sent with the message, "The next watering will be at 10:00 AM tomorrow, so please take your time and relax." The user's smartphone receives this notification, confirms its contents, and waters the plants at 10:00 AM the next day.

[0887] Prompt Sentence Examples

[0888] When is the next time to water?

[0889] "What is the current soil moisture percentage?"

[0890] "Analyze the user's emotional state."

[0891] This invention allows farmers to receive highly accurate data obtained in real time, insights based on that data, and notifications that take into account the user's emotional state, enabling them to not only practice efficient and sustainable agriculture but also to take into consideration the user's mental health.

[0892] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0893] Step 1: Data collection

[0894] The sensors installed on the terminal measure the environmental data of the farmland in real time. Specifically, the soil moisture sensor measures the soil moisture, the temperature sensor measures the soil and ambient temperature, and the pH sensor measures the soil pH value. All of this data is temporarily stored in the local memory.

[0895] Input: Soil moisture, temperature, pH value

[0896] Data processing: Sensors measure environmental data in real time

[0897] Output: Environmental data stored in local memory

[0898] Step 2: Send data

[0899] The device periodically packets the environmental data stored in its local memory and transmits it to the server via the Wi-Fi module. Specifically, the device packets the collected data every five minutes, for example.

[0900] Input: Environmental data stored in local memory

[0901] Data processing: Packetizing environmental data

[0902] Output: Data packet sent to the server

[0903] Step 3: Receiving and storing data

[0904] The server receives data packets sent from the terminal and stores them in a database, which is based on SQL and allows for fast access.

[0905] Input: Data packets sent from the device

[0906] Data processing: Store data packets in a database

[0907] Output: Environmental data stored in a database

[0908] Step 4: Data analysis

[0909] The server then inputs the environmental data stored in the database into a machine learning model for analysis, which predicts things like the health of the crops, optimal watering times, and the risk of pest infestation.

[0910] Input: Environmental data stored in a database

[0911] Data processing: Data analysis using machine learning models

[0912] Output: Predicted insight (e.g., next watering timing)

[0913] Step 5: Emotion Recognition

[0914] The emotion engine installed on the server analyzes the user's voice and facial expression data to detect their emotional state, identifying whether they are stressed or relaxed.

[0915] Input: User's voice data, facial expression data

[0916] Data processing: Emotional state analysis using emotion engine

[0917] Output: The user's current emotional state

[0918] Step 6: Notification Generation and Coordination

[0919] The server adjusts the content and frequency of notifications based on the analyzed insights and the user's emotional state detected by the emotion engine. For example, if the user is feeling stressed, the server reduces the frequency of notifications and adds a message encouraging relaxation.

[0920] Input: predicted insights, user emotional state

[0921] Data processing: Adjustment of notification content and frequency

[0922] Output: The adjusted notification message

[0923] Step 7: Sending and displaying notifications

[0924] The server sends the generated notification to the user terminal, which receives and visually displays the notification, allowing the user to check the notification content and take appropriate action.

[0925] Input: The adjusted notification message

[0926] Data processing: Sending notifications and receiving / displaying them on the device

[0927] Output: A visual notification displayed on the user's device.

[0928] Step 8: Get user feedback

[0929] The user inputs the results and impressions of the actions taken based on the notification into the device and sends them to the server, which then receives the feedback and reflects it in future analyses and notifications.

[0930] Input: User feedback

[0931] Data processing: collecting and storing feedback

[0932] Output: Feedback data that can be used to improve the accuracy of future analyses

[0933] (Application example 2)

[0934] 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."

[0935] Conventional systems that integrate emotion analysis are often specialized for agricultural support or individual applications, and have the problem of lacking effectiveness in situations that require instantaneous responses in physical stores. In addition, there are no tools to understand the emotional state of customers and provide appropriate customer service, making it difficult for store employees to provide appropriate customer service.

[0936] 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 a sensor, means for transmitting the collected data to the server, and means for analyzing the transmitted data. This makes it possible to recognize the user's emotional state and provide notifications including appropriate customer service methods and product recommendations based on the analysis results. This makes it possible to grasp the customer's emotional state in real time and provide appropriate customer service methods and products in a timely manner.

[0937] A "sensor" is a device for collecting environmental data.

[0938] A "server" is a central processing unit that analyzes collected data and provides notifications based on the analysis results.

[0939] "User" means an individual or group that uses the system.

[0940] "Emotional state" refers to a user's mental or emotional state.

[0941] An "emotion engine" is software that analyzes the user's voice and facial expressions to recognize their emotional state.

[0942] A "machine learning model" is a type of algorithm used in data analysis that learns from past data to make predictions and classifications.

[0943] A "notification" is a message or alert that provides analysis results or other information to a user.

[0944] "Smart glasses" are wearable devices that incorporate a camera and display and support the user's visual and voice input.

[0945] "Environmental Data" means measured information about specific environmental conditions, such as humidity, temperature, pH value, etc.

[0946] "Analysis" is the process of processing collected data to derive useful information and insights.

[0947] "Customer service methods" refer to the way customers are treated and services are provided.

[0948] "Product recommendation" is the act of suggesting appropriate products based on a customer's needs and emotional state.

[0949] This invention is a system for supporting customer service in brick-and-mortar stores, and is composed of sensors, a server, and a user's terminal. The system transmits environmental data collected by the sensors to the server, which analyzes the data and provides notifications to the user. The system also incorporates an emotion engine that recognizes the user's emotional state and adjusts the content and frequency of notifications based on the analysis results.

[0950] System Overview

[0951] This system sends environmental data collected by sensors and the user's emotional state to a server, and provides notifications to the user based on the analysis results. For example, when a store clerk wearing smart glasses serves a customer, the smart glasses' camera and microphone collect the customer's facial expressions and voice, analyze the data in real time, and provide appropriate notifications.

[0952] Data collection (terminal)

[0953] The smart glasses' camera and microphone are used to capture the customer's facial expressions and voice in real time. For example, the camera recognizes the customer's smile, and the microphone analyzes the customer's level of satisfaction from the tone of their voice. This data is temporarily stored in the device's local memory and periodically packetized and sent to a server.

[0954] Data analysis (server)

[0955] The server receives the environmental and emotional data sent from the device and stores it in a database. It then analyzes the data using machine learning models to generate insights into the customer's emotional state, appropriate customer service methods, and recommended products. For example, the server analyzes data on a customer's smile and recognizes that the customer is happy, and then notifies the customer of the appropriate customer service method.

[0956] Emotion engine operation (server)

[0957] The server is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's voice and facial expressions to detect their emotional state. Based on the results, the server can adjust the content and frequency of notifications. For example, if a customer looks dissatisfied, the server sends a notification to the store clerk saying, "The customer is dissatisfied. Please respond quickly."

[0958] Providing results and feedback (user)

[0959] Notifications based on the insights generated by the server and the results of the emotion engine are sent to the user's smart glasses. The notification content is displayed on the smart glasses' display, allowing the store clerk to provide appropriate customer service and product recommendations based on the notification content. For example, a store clerk may receive a notification saying, "The customer is happy. Please recommend these shoes," and then suggest a product to the customer based on that notification.

[0960] Specific examples

[0961] In a physical store, a salesperson wears smart glasses to serve customers. The smart glasses' camera captures the customer's facial expressions, and the microphone picks up the customer's voice. When the customer shows interest in a product and smiles, the server analyzes the data and displays a notification on the smart glasses saying, "The customer is pleased. Please suggest a new product."

[0962] Prompt Sentence Examples

[0963] "Please explain an application of smart glasses that analyzes the facial expressions and voices of customers in a physical store to provide customer service support. Please give specific examples of what kind of notification is displayed to the store clerk when the customer is happy."

[0964] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0965] Program processing flow

[0966] Step 1:

[0967] The device (smart glasses) captures the customer's facial expressions and voice in real time through a camera and microphone. The camera input is the customer's facial image, and the microphone input is the customer's voice. This data is temporarily stored in local memory.

[0968] Step 2:

[0969] The device packetizes the customer's facial expression and voice data stored in its local memory and transmits them to the server via Wi-Fi. The input here is the data stored in the local memory, and the output is the packetized data.

[0970] Step 3:

[0971] The server receives data packets sent from the terminals and stores them in a database. The input is the received data packets, and the output is the data stored in the database.

[0972] Step 4:

[0973] The server analyzes the stored data using an emotion engine. The emotion engine analyzes the customer's emotional state from their facial expression data and their voice tone from their voice data. The input is the customer's facial expression data and voice data stored in the database, and the output is the customer's emotional state.

[0974] Step 5:

[0975] The server uses a machine learning model to generate customer service methods and product recommendations that correspond to the customer's emotional state. The input is the customer's emotional state, and the output is customer service methods and product recommendations. Appropriate notification content is generated based on the analysis results.

[0976] Step 6:

[0977] The server sends the generated notification content to the device (smart glasses). The input is the generated notification content, and the output is the notification to the device.

[0978] Step 7:

[0979] The terminal (smart glasses) displays the notification content sent from the server on its display, visually notifying the store clerk. The input is the notification content sent from the server, and the output is the notification displayed on the smart glasses' display. This allows the store clerk to take appropriate action depending on the customer's emotional state.

[0980] Specific examples of operation

[0981] Step 1: When the customer enters the field of view of the smart glasses, the camera automatically captures their facial expressions and the microphone records their voice.

[0982] Step 2: Facial expression data (e.g., an image of the customer's smile) and voice data (e.g., the customer's interesting tone of voice) are packetized.

[0983] Step 3: The server receives these data packets and stores them in a database along with the customer ID.

[0984] Step 4: The server uses the emotion engine to analyze the emotional state of "customer is satisfied."

[0985] Step 5: The server uses the machine learning model to generate a notification suggesting a new product for this customer.

[0986] Step 6: The server sends the generated notification "Please suggest a new product" to the terminal.

[0987] Step 7: The smart glasses display will display a notification saying, "Customer is happy. Please recommend these shoes."

[0988] 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.

[0989] 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.

[0990] 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.

[0991] [Fourth embodiment]

[0992] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0993] 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.

[0994] 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).

[0995] 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.

[0996] 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.

[0997] 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).

[0998] 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.

[0999] 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.

[1000] 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.

[1001] 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.

[1002] 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.

[1003] 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.

[1004] 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."

[1005] The present invention relates to an agricultural support system that includes a sensor, a server, and a user terminal. An embodiment of this system will be described in detail below.

[1006] System Overview

[1007] The agricultural support system consists of a consistent process in which sensors collect environmental data, send the data to a server, the server analyzes the data, and notifies the user based on the analysis results.

[1008] Data collection and transmission (terminal)

[1009] Sensors are installed in farmland to measure environmental data such as soil humidity, temperature, and pH in real time. The device temporarily stores these measurement data in its local memory and periodically packets the data and transmits it to a server. For example, a soil humidity sensor measures the humidity level and transmits the data to a server via Wi-Fi.

[1010] Data analysis and insight generation (server)

[1011] The server receives the environmental data sent from the device and stores it in a database. It then analyzes the data using machine learning models to generate insights such as the health of the crop, the best time to water it, and the risk of pest infestation. For example, the server analyzes humidity data and predicts that the next watering will be at 10 a.m. tomorrow.

[1012] Providing results and feedback (user)

[1013] The insights generated by the server are sent to the user's device as notifications. The user's smartphone or tablet receives the notifications and displays them visually. This allows the user to take appropriate action based on the notification content. For example, a user may receive a notification that "the next watering time is tomorrow at 10:00 AM" and water the plants at that time.

[1014] Specific examples

[1015] Multiple sensors installed in a certain field measure the humidity, temperature, and pH value of the soil in real time. Data indicating that the humidity is 40% is instantly sent to a server. The server analyzes this humidity data using a machine learning model to predict the optimal timing for watering. As a result of the prediction, an insight is generated, such as "The next watering time is tomorrow at 10:00 AM." This insight is then sent to the user's smartphone, allowing the user to water the plants accordingly.

[1016] This invention allows farmers to utilize real-time, highly accurate data and the insights derived from that data to practice efficient and sustainable agriculture. By taking appropriate action based on the data, they can maintain the health of their crops and optimize yields.

[1017] The processing flow will be explained below.

[1018] Step 1:

[1019] The device collects environmental data: specifically, sensors measure the moisture, temperature, and pH of the farmland soil.

[1020] Step 2:

[1021] The device temporarily stores the collected environmental data in its local memory. Specifically, the humidity data measured by the sensor is recorded in the device's memory.

[1022] Step 3:

[1023] The device packetizes the temporarily stored data and transmits it to the server. Specifically, it forms a packet containing humidity data and transmits it to the server via Wi-Fi.

[1024] Step 4:

[1025] The server receives the data packet sent from the terminal. Specifically, the server receives the data for humidity 40%.

[1026] Step 5:

[1027] The server stores the received data in a database. Specifically, the server stores the humidity data in the database.

[1028] Step 6:

[1029] The server inputs the data into a machine learning model for analysis, specifically using humidity data to predict when the next watering should be done.

[1030] Step 7:

[1031] The server organizes the analysis results and generates a message to notify the user. Specifically, it creates a notification message saying, "The next watering will be tomorrow at 10:00 AM."

[1032] Step 8:

[1033] The server sends the generated notification message to the user's device. Specifically, it sends a notification packet to the user's smartphone via the Internet.

[1034] Step 9:

[1035] The user's device receives the notification message sent from the server. Specifically, the user's smartphone receives the notification "The next watering will be at 10:00 AM tomorrow."

[1036] Step 10:

[1037] The user's device visually displays the received notification, specifically as a pop-up on the smartphone screen.

[1038] Step 11:

[1039] The user takes appropriate action based on the notification, specifically, the user acknowledges the notification and waters the crops at 10:00 AM the next day.

[1040] Example 1

[1041] 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."

[1042] Conventional agricultural support systems have struggled to acquire and effectively analyze farmland environmental data in real time and notify users of appropriate actions. Furthermore, there was a lack of technology to accurately predict the health of crops and the optimal timing for watering. This made it difficult to practice efficient agriculture and optimize crop yields.

[1043] 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.

[1044] In this invention, the server includes means for collecting environmental data using sensors, means for transmitting the collected data to the server via a network, means for storing the transmitted data in a database, means for acquiring the stored data and analyzing it using a machine learning model, and means for generating crop health conditions and optimal watering timings as analysis results and providing notifications to users. This enables high-precision data analysis in real time, allows appropriate instructions to be provided to users, and realizes efficient and sustainable agriculture.

[1045] A "sensor" is a device for measuring environmental data, specifically, acquiring environmental parameters such as soil humidity, temperature, and acidity in real time.

[1046] "Means for transmitting via a network" refers to a method for transmitting data collected by a sensor to a server using a communication protocol, and includes communication technologies such as Wi-Fi and wired connections.

[1047] A "database" is a system for structuring and storing collected environmental data, and includes relational databases and NoSQL databases.

[1048] A "machine learning model" is an algorithm that learns patterns and regularities from large amounts of data and makes predictions and classifications based on new data. Specifically, this includes generative AI models.

[1049] "Analysis means" refers to a processing method for processing stored data using machine learning models or the like to extract necessary insights.

[1050] "Insights" are useful information obtained through data analysis, such as the health of crops and the optimal time to water them, that can encourage users to take action.

[1051] "Notification means" refers to a method for transmitting analysis results to a user device, including push notifications to smartphones and tablets.

[1052] The present invention relates to an agricultural support system that includes a sensor, a server, and a user terminal. An embodiment of this system will be described in detail below.

[1053] System Overview

[1054] The agricultural support system consists of a consistent process in which environmental data is collected by sensors, the data is sent to a server via a network, the server analyzes the data, and notifications are provided to users based on the analysis results.

[1055] Data collection and transmission (terminal)

[1056] Sensors that measure the humidity, temperature, and pH value of the soil are connected to the device. The sensors measure environmental data in real time, and the measured data is temporarily stored in the device's local memory. The data obtained from the sensors is periodically packetized and sent to a server over the network via a Wi-Fi module. For example, if the humidity sensor measures the soil humidity to be 40%, this data is stored on the device. The device then packetizes this humidity data and sends it to the server via Wi-Fi.

[1057] Data analysis and insight generation (server)

[1058] The server receives the environmental data sent from the device and stores it in a database. The server then analyzes the stored data using a generative AI model. Specifically, it uses machine learning libraries such as TensorFlow to generate insights such as the health of the crops, the optimal timing for watering, and the risk of pest infestation. For example, a prediction might be generated, such as "The next watering will be at 10:00 AM tomorrow."

[1059] Providing results and feedback (user)

[1060] The insights generated by the server are sent to the user's device as notifications. The user's smartphone or tablet receives the notifications and displays them visually. This allows the user to take appropriate action based on the notification content. For example, a user may receive a notification that "the next watering time is tomorrow at 10:00 AM" and water the plants at that time.

[1061] Specific examples

[1062] Multiple sensors installed in a farm field measure the soil's humidity, temperature, and pH value in real time. Data indicating a humidity level of 40% is instantly sent to a server. The server analyzes this humidity data using a generative AI model to predict the optimal watering timing. As a result of the prediction, an insight is generated, such as "The next watering time is tomorrow at 10:00 AM." This insight is sent to the user's smartphone, allowing them to water accordingly. This allows users to achieve efficient and sustainable agriculture thanks to high-precision data analysis and the insights based on its results.

[1063] As a result, this system provides farmers with real-time data collection and analysis results, helping them optimize yields while maintaining the health of their crops.

[1064] Prompt Sentence Examples

[1065] "Analyze data collected from humidity sensors to predict the optimal time for the next watering."

[1066] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1067] Step 1: Data collection (device)

[1068] A sensor connected to the device measures the humidity, temperature, and pH value of the soil. The sensor collects this environmental data in real time and temporarily stores the data in the device's local memory. For example, if the sensor measures the soil humidity to be 40%, the data is recorded in memory. The input of this step is the environmental data from the sensor, and the output is the data stored in the device's local memory.

[1069] Step 2: Send data (terminal)

[1070] The device periodically packetizes the measurement data stored in its local memory. The packetized data is then sent to the server over the network via the Wi-Fi module. Specifically, the HTTP protocol is used to encrypt the data packets and transmit them securely. The input of this step is the data stored in its local memory, and the output is the data sent to the server.

[1071] Step 3: Data reception and storage (server)

[1072] The server receives data packets sent from the device. The received data is stored in a database. For example, environmental data is structured and stored in a database such as MongoDB. The input of this step is the data packets sent from the device, and the output is the data stored in the database.

[1073] Step 4: Data analysis (server)

[1074] The server retrieves the stored data from the database. The retrieved data is then analyzed using a generative AI model. For example, TensorFlow can be used to analyze humidity data and make predictions such as "The next watering will be tomorrow at 10 AM." The input for this step is the environmental data stored in the database, and the output is the analyzed insights.

[1075] Step 5: Insight generation and formatting (server)

[1076] The generated insights are formatted as notification data for users. Specifically, the notification data is prepared in JSON or text format. The input of this step is the insights obtained as a result of the analysis, and the output is the formatted notification data.

[1077] Step 6: Result notification (server)

[1078] The server sends the formatted notification data to the user's device. A notification service such as FCM (Firebase Cloud Messaging) is used to notify the user in real time. The input of this step is the formatted notification data, and the output is the notification sent to the user's device.

[1079] Step 7: Receiving and Displaying Notifications (User)

[1080] The user's smartphone or tablet receives the notification and displays it visually. The user checks the notification and performs farm work based on the notification content. For example, the user checks the notification that "the next watering is tomorrow at 10:00 AM" and waters the fields at that time. The input of this step is the notification data sent from the server, and the output is the notification displayed on the user's device.

[1081] (Application example 1)

[1082] 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."

[1083] Conventional food delivery services lack the means to monitor the freshness and quality of fresh food in real time, which means that the quality of the ingredients received by users may not always be optimal. Furthermore, there is no mechanism for notifying users of the optimal pickup time, making it difficult to guarantee the quality of ingredients, which may result in reduced user satisfaction. Therefore, there is a need for a system that can appropriately manage the pickup timing and quality of fresh food.

[1084] 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.

[1085] In this invention, the server includes means for collecting environmental data using a sensor, means for transmitting the collected data to the server, means for analyzing the transmitted data, means for providing a notification to a user based on the analysis results, means for the collected data to include the temperature, humidity, and gas concentration of the fresh food, means for the analysis means to generate insights for determining the freshness of the fresh food in real time, and means for notifying the user of the optimal time to receive the fresh food. This makes it possible to monitor the quality of the fresh food in real time and notify the user of the optimal time to receive the fresh food.

[1086] A "sensor" is a device that collects environmental data, and in this invention measures the temperature, humidity, and gas concentration of fresh food.

[1087] A "server" is a computer system that analyzes collected environmental data and provides notifications to users based on the results of that analysis.

[1088] "Data transmission means" refers to devices or software that have the function of transmitting environmental data collected by sensors to a server.

[1089] "Analysis means" refers to software and algorithms used to analyze the environmental data sent to the server, specifically machine learning models.

[1090] "Notification providing means" refers to a device or software that has the function of notifying the user's terminal of the analysis results.

[1091] "Environmental data" refers to data such as temperature, humidity, and gas concentration of fresh food.

[1092] A "machine learning model" is an algorithm that analyzes environmental data and generates insights such as the freshness of perishable foods and the optimal pickup time.

[1093] "Insights" are key pieces of information generated by analytics about the freshness of perishables and optimal pickup times.

[1094] "Optimal pick-up time" refers to the ideal time for a user to receive fresh food at its best quality.

[1095] MODE FOR CARRYING OUT THE INVENTION

[1096] The present invention is a system for monitoring the quality and freshness of fresh food in real time and notifying the user of the optimal time for collection. Specific embodiments for carrying out the present invention will be described below.

[1097] System Configuration

[1098] This system consists of sensors, a server, and user terminals.

[1099] Sensor

[1100] Multiple sensors, including temperature sensors, humidity sensors, and ethylene sensors, are installed inside the cooler boxes that store fresh food. Each sensor collects environmental data around the fresh food in real time. For example, the temperature sensor measures the temperature around the food, the humidity sensor measures humidity, and the ethylene sensor measures gas concentration.

[1101] server

[1102] The collected environmental data is sent to a server via the device. The server stores the data in a database and analyzes it using a machine learning model. The analysis results generate insights into the freshness of perishable foods and the optimal pickup time. The machine learning model predicts food deterioration and loss of freshness based on past data and patterns.

[1103] User terminal

[1104] The insights generated by the server are sent as notifications to the user's smartphone or tablet, where they are received and visually displayed. Based on these notifications, the user can receive fresh food at the optimal time.

[1105] Specific examples

[1106] A cooler box used by a certain delivery service is equipped with a temperature sensor, a humidity sensor, and an ethylene sensor. These sensors collect their respective environmental data in real time and send it to a server via Wi-Fi. For example, if the temperature sensor detects 12°C, the humidity sensor detects 85%, and the ethylene sensor detects 0.06 ppm, the server analyzes this data and generates insights such as, "The temperature is outside the appropriate range. The humidity is outside the appropriate range. The ethylene level indicates a decrease in freshness." This insight is then sent to the user's smartphone with a message stating, "The freshness of your perishable food is decreasing. We recommend that you pick it up immediately."

[1107] Hardware and software used

[1108] The hardware used includes a temperature sensor, humidity sensor, and ethylene sensor. The software uses Python scripts to collect and transmit data, and machine learning models to analyze the data on a cloud server. An API is also required to send notifications to user devices.

[1109] Prompt Sentence Examples

[1110] Examples of prompts to give to a generative AI model include:

[1111] Design an application that monitors the quality of agricultural products in a cooler box in real time and notifies the user before the quality deteriorates. Use the following sensor data (temperature, humidity, ethylene concentration) to set the notification conditions and implement it in Python.

[1112] Suitable temperature range: 5℃ to 10℃

[1113] Suitable humidity range: 60% to 80%

[1114] Ethylene concentration limit: 0.05 ppm

[1115] As a result, the present invention provides an environment in which users can always receive fresh food of the highest quality.

[1116] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1117] Step 1:

[1118] Data collection with sensors

[1119] Sensors collect environmental data in the cooler box in real time. The inputs are temperature, humidity, and ethylene concentration, and each sensor measures these data. The collected data is temporarily stored in local memory. The output is the measured values ​​of temperature, humidity, and gas concentration.

[1120] Step 2:

[1121] Data transmission

[1122] The device sends the collected environmental data to the server. As input, it uses the temperature, humidity, and gas concentration data collected in step 1. The data is periodically packetized and sent to the server via Wi-Fi. The output is the data packets received by the server.

[1123] Step 3:

[1124] Data analysis

[1125] The server analyzes the received data. It uses temperature, humidity, and gas concentration data as input. A machine learning model analyzes this data based on the data stored in the database. Insights are generated as a result of the analysis if a deterioration in freshness or quality is predicted. The output is insights about the freshness and quality of the food.

[1126] Step 4:

[1127] Insight generation

[1128] The server generates insights to notify the user based on the analysis results. The analysis results obtained in step 3 are used as input. For example, insights such as "The temperature is out of the normal range. The humidity is out of the normal range. The ethylene level indicates a decrease in freshness" are generated. The output is the notification content.

[1129] Step 5:

[1130] Send notifications

[1131] The insights generated by the server are notified to the user's device. The notification content generated in step 4 is used as input. The notification is sent to the user's smartphone or tablet via the API. Specifically, a visual notification such as "Fresh food is losing its freshness. We recommend that you pick it up immediately" is displayed. The output is a notification that arrives on the user's device.

[1132] Step 6:

[1133] User response

[1134] The user checks the notification sent to their smartphone or tablet. The notification sent in step 5 is used as input. The user can receive fresh food at the optimal time according to the received notification. The output is the result of receiving the food in the best quality.

[1135] 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.

[1136] The present invention relates to an agricultural support system that is composed of sensors, a server, and a user terminal, and further combines it with an emotion engine that recognizes the user's emotions. An embodiment of this system will be described in detail below.

[1137] System Overview

[1138] The agricultural support system is comprised of a series of consistent processes: sensors collect environmental data, the data is sent to a server, the server analyzes the data, and notifications are sent to the user based on the analysis results. It also incorporates an emotion engine that recognizes the user's emotional state and adjusts the content and frequency of notifications accordingly.

[1139] Data collection and transmission (terminal)

[1140] Sensors are installed in farmland to measure environmental data such as soil humidity, temperature, and pH in real time. The device temporarily stores these measurement data in its local memory and periodically packets the data and transmits it to a server. For example, a soil humidity sensor measures the humidity level and transmits the data to a server via Wi-Fi.

[1141] Data analysis and insight generation (server)

[1142] The server receives the environmental data sent from the device and stores it in a database. It then analyzes the data using machine learning models to generate insights such as the health of the crop, the best time to water it, and the risk of pest infestation. For example, the server analyzes humidity data and predicts that the next watering will be at 10 a.m. tomorrow.

[1143] Emotion engine operation (server)

[1144] The server is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's voice and facial expressions to detect their emotional state. Based on the results, the server can adjust the content and frequency of notifications. For example, if the user is feeling stressed, the server can reduce the frequency of notifications or send notifications with relaxation suggestions.

[1145] Providing results and feedback (user)

[1146] Notifications based on the insights generated by the server and the results of the emotion engine are sent to the user's device. The user's smartphone or tablet receives the notification and displays it visually. This allows the user to take appropriate action based on the content of the notification. For example, a user may receive a notification in the form of an emotion-sensitive message saying, "The next watering time is tomorrow at 10:00 AM," and water the plants at that time.

[1147] Specific examples

[1148] Multiple sensors installed in a farm field measure the soil's humidity, temperature, and pH in real time. Data indicating a humidity level of 40% is instantly sent to a server. The server then analyzes this humidity data using a machine learning model to predict the optimal timing for watering. The resulting prediction generates an insight: "The next watering will be at 10:00 AM tomorrow." Meanwhile, the server's emotion engine detects that the user is currently feeling stressed. In this case, a notification is sent stating, "The next watering will be at 10:00 AM tomorrow. Please take your time and relax." The user's smartphone receives this notification, confirms its contents, and waters the crops at 10:00 AM the following day.

[1149] This invention allows farmers to receive highly accurate data obtained in real time, insights based on that data, and notifications that take into account the user's emotional state, enabling them to not only practice efficient and sustainable agriculture but also to take into consideration the user's mental health.

[1150] The processing flow will be explained below.

[1151] Step 1:

[1152] The device collects environmental data: specifically, sensors measure the moisture, temperature, and pH of the farmland soil.

[1153] Step 2:

[1154] The device temporarily stores the collected environmental data in its local memory. Specifically, the humidity data measured by the sensor is recorded in the device's memory.

[1155] Step 3:

[1156] The device packetizes the temporarily stored data and transmits it to the server. Specifically, it forms a packet containing humidity data and transmits it to the server via Wi-Fi.

[1157] Step 4:

[1158] The server receives the data packet sent from the terminal. Specifically, the server receives the data for humidity 40%.

[1159] Step 5:

[1160] The server stores the received data in a database. Specifically, the server stores the humidity data in the database.

[1161] Step 6:

[1162] The server inputs the data into a machine learning model for analysis, specifically using humidity data to predict when the next watering should be done.

[1163] Step 7:

[1164] The server collects emotional data from users, specifically voice and facial expression data from their smartphones or wearable devices.

[1165] Step 8:

[1166] The server's emotion engine analyzes the collected emotion data to detect the user's emotional state, specifically detecting stress and fatigue from voice data.

[1167] Step 9:

[1168] The server then compiles the analysis results and generates a message that adjusts the content and frequency of notifications based on the user's emotional state. For example, "The next watering is tomorrow at 10 AM" might be changed to "The next watering is tomorrow at 10 AM, but please also take time to relax."

[1169] Step 10:

[1170] The server sends the generated notification message to the user's device. Specifically, it sends a notification packet to the user's smartphone via the Internet.

[1171] Step 11:

[1172] The user's device receives the notification message sent from the server. Specifically, the user's smartphone receives a notification saying, "The next watering will be at 10:00 AM tomorrow, but please take time to relax."

[1173] Step 12:

[1174] The user's device visually displays the received notification, specifically as a pop-up on the smartphone screen.

[1175] Step 13:

[1176] The user takes appropriate action based on the notification. Specifically, the user acknowledges the notification, waters the crops at 10:00 AM the next day, and schedules time to relax.

[1177] Example 2

[1178] 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."

[1179] Current agricultural support systems achieve the basic functions of collecting and analyzing environmental data and providing notifications to users, but lack the ability to consider the user's emotional state. As a result, there are concerns that notifications sent without taking the user's mental health into consideration could increase stress and reduce work efficiency. The present invention aims to solve this problem by providing an agricultural support system that adjusts the content and frequency of notifications according to the user's emotional state.

[1180] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting environmental data by a sensor, means for transmitting the collected data to the server, means for analyzing the transmitted data, means for recognizing the emotional state of the user, and means for adjusting the content and frequency of notifications based on the emotional state. This makes it possible to provide notifications according to the emotional state of the user.

[1181] A "sensor" is a device that measures environmental data in real time and collects necessary information.

[1182] "Server" means a central processing unit that receives and analyzes data sent from the terminal and provides appropriate notifications to the user.

[1183] A "user" is a person or organization that uses the agricultural support system to carry out agricultural work.

[1184] "Environmental data" refers to information about factors that affect crop growth, such as soil moisture, temperature, and pH value in agricultural land.

[1185] The "emotion engine" is a system that analyzes the user's voice and facial expression data to detect their emotional state.

[1186] A "machine learning model" is an algorithm for analyzing and predicting data, and is a technology that improves accuracy by learning specific patterns.

[1187] "Insights" refers to useful information and suggestions generated from analyzed data.

[1188] "Notification content" is the content of the information message sent from the server to the user.

[1189] "Notification frequency" refers to the interval between notifications sent from the server to the user.

[1190] A "user terminal" is a device that a user uses to receive and operate information, and includes smartphones, tablets, and the like.

[1191] The present invention relates to an agricultural support system that is composed of sensors, a server, and a user terminal, and further combines it with an emotion engine that recognizes the user's emotions. An embodiment of this system will be described in detail below.

[1192] System Configuration

[1193] The agricultural support system consists of the following main components:

[1194] 1. Sensors

[1195] The sensors are installed in farmland to measure environmental data such as soil humidity, temperature, and pH value in real time. Specific sensors used include soil humidity sensors, temperature sensors, and pH sensors.

[1196] 2. User Device

[1197] A user terminal is a mobile device such as a smartphone or tablet that is used to receive and visually display notifications.

[1198] 3. Server

[1199] The server receives data sent from the sensors and stores it in a database. It also analyzes the data using machine learning models to generate insights. It also has an emotion engine that recognizes user emotions.

[1200] Data collection and transmission

[1201] Users install various sensors on their farmland. The sensors measure environmental data in real time and temporarily store the data on a terminal. The terminal then packets the data and transmits it to a server at regular intervals.

[1202] Data analysis and insight generation

[1203] The server receives the environmental data sent from the devices and stores it in a database. The server then analyzes the data using machine learning models to generate insights such as the health of the crops, optimal watering times, and the risk of pest infestation.

[1204] Emotion Engine Operation

[1205] The server is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's voice and facial expression data to detect their emotional state. Based on the results, the server can adjust the content and frequency of notifications.

[1206] Providing results and feedback

[1207] Notifications based on the insights generated by the server and the results of the emotion engine are sent to the user's device, which receives and visually displays the notifications, allowing the user to take appropriate action based on the content of the notifications.

[1208] Specific examples

[1209] Multiple sensors installed in a certain field measure the humidity, temperature, and pH value of the soil in real time. For example, data indicating that the humidity is 40% is instantly sent to a server. The server analyzes this humidity data using a machine learning model to predict the optimal timing for watering. The resulting prediction is an insight such as, "The next watering time is tomorrow at 10:00 AM."

[1210] Meanwhile, the server's emotion engine detects that the user is currently feeling stressed. In this case, a notification is sent with the message, "The next watering will be at 10:00 AM tomorrow, so please take your time and relax." The user's smartphone receives this notification, confirms its contents, and waters the plants at 10:00 AM the next day.

[1211] Prompt Sentence Examples

[1212] When is the next time to water?

[1213] "What is the current soil moisture percentage?"

[1214] "Analyze the user's emotional state."

[1215] This invention allows farmers to receive highly accurate data obtained in real time, insights based on that data, and notifications that take into account the user's emotional state, enabling them to not only practice efficient and sustainable agriculture but also to take into consideration the user's mental health.

[1216] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1217] Step 1: Data collection

[1218] The sensors installed on the terminal measure the environmental data of the farmland in real time. Specifically, the soil moisture sensor measures the soil moisture, the temperature sensor measures the soil and ambient temperature, and the pH sensor measures the soil pH value. All of this data is temporarily stored in the local memory.

[1219] Input: Soil moisture, temperature, pH value

[1220] Data processing: Sensors measure environmental data in real time

[1221] Output: Environmental data stored in local memory

[1222] Step 2: Send data

[1223] The device periodically packets the environmental data stored in its local memory and transmits it to the server via the Wi-Fi module. Specifically, the device packets the collected data every five minutes, for example.

[1224] Input: Environmental data stored in local memory

[1225] Data processing: Packetizing environmental data

[1226] Output: Data packet sent to the server

[1227] Step 3: Receiving and storing data

[1228] The server receives data packets sent from the terminal and stores them in a database, which is based on SQL and allows for fast access.

[1229] Input: Data packets sent from the device

[1230] Data processing: Store data packets in a database

[1231] Output: Environmental data stored in a database

[1232] Step 4: Data analysis

[1233] The server then inputs the environmental data stored in the database into a machine learning model for analysis, which predicts things like the health of the crops, optimal watering times, and the risk of pest infestation.

[1234] Input: Environmental data stored in a database

[1235] Data processing: Data analysis using machine learning models

[1236] Output: Predicted insight (e.g., next watering timing)

[1237] Step 5: Emotion Recognition

[1238] The emotion engine installed on the server analyzes the user's voice and facial expression data to detect their emotional state, identifying whether they are stressed or relaxed.

[1239] Input: User's voice data, facial expression data

[1240] Data processing: Emotional state analysis using emotion engine

[1241] Output: The user's current emotional state

[1242] Step 6: Notification Generation and Coordination

[1243] The server adjusts the content and frequency of notifications based on the analyzed insights and the user's emotional state detected by the emotion engine. For example, if the user is feeling stressed, the server reduces the frequency of notifications and adds a message encouraging relaxation.

[1244] Input: predicted insights, user emotional state

[1245] Data processing: Adjustment of notification content and frequency

[1246] Output: The adjusted notification message

[1247] Step 7: Sending and displaying notifications

[1248] The server sends the generated notification to the user terminal, which receives and visually displays the notification, allowing the user to check the notification content and take appropriate action.

[1249] Input: The adjusted notification message

[1250] Data processing: Sending notifications and receiving / displaying them on the device

[1251] Output: A visual notification displayed on the user's device.

[1252] Step 8: Get user feedback

[1253] The user inputs the results and impressions of the actions taken based on the notification into the device and sends them to the server, which then receives the feedback and reflects it in future analyses and notifications.

[1254] Input: User feedback

[1255] Data processing: collecting and storing feedback

[1256] Output: Feedback data that can be used to improve the accuracy of future analyses

[1257] (Application example 2)

[1258] 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."

[1259] Conventional systems that integrate emotion analysis are often specialized for agricultural support or individual applications, and have the problem of lacking effectiveness in situations that require instantaneous responses in physical stores. In addition, there are no tools to understand the emotional state of customers and provide appropriate customer service, making it difficult for store employees to provide appropriate customer service.

[1260] 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 a sensor, means for transmitting the collected data to the server, and means for analyzing the transmitted data. This makes it possible to recognize the user's emotional state and provide notifications including appropriate customer service methods and product recommendations based on the analysis results. This makes it possible to grasp the customer's emotional state in real time and provide appropriate customer service methods and products in a timely manner.

[1261] A "sensor" is a device for collecting environmental data.

[1262] A "server" is a central processing unit that analyzes collected data and provides notifications based on the analysis results.

[1263] "User" means an individual or group that uses the system.

[1264] "Emotional state" refers to a user's mental or emotional state.

[1265] An "emotion engine" is software that analyzes the user's voice and facial expressions to recognize their emotional state.

[1266] A "machine learning model" is a type of algorithm used in data analysis that learns from past data to make predictions and classifications.

[1267] A "notification" is a message or alert that provides analysis results or other information to a user.

[1268] "Smart glasses" are wearable devices that incorporate a camera and display and support the user's visual and voice input.

[1269] "Environmental Data" means measured information about specific environmental conditions, such as humidity, temperature, pH value, etc.

[1270] "Analysis" is the process of processing collected data to derive useful information and insights.

[1271] "Customer service methods" refer to the way customers are treated and services are provided.

[1272] "Product recommendation" is the act of suggesting appropriate products based on a customer's needs and emotional state.

[1273] This invention is a system for supporting customer service in brick-and-mortar stores, and is composed of sensors, a server, and a user's terminal. The system transmits environmental data collected by the sensors to the server, which analyzes the data and provides notifications to the user. The system also incorporates an emotion engine that recognizes the user's emotional state and adjusts the content and frequency of notifications based on the analysis results.

[1274] System Overview

[1275] This system sends environmental data collected by sensors and the user's emotional state to a server, and provides notifications to the user based on the analysis results. For example, when a store clerk wearing smart glasses serves a customer, the smart glasses' camera and microphone collect the customer's facial expressions and voice, analyze the data in real time, and provide appropriate notifications.

[1276] Data collection (terminal)

[1277] The smart glasses' camera and microphone are used to capture the customer's facial expressions and voice in real time. For example, the camera recognizes the customer's smile, and the microphone analyzes the customer's level of satisfaction from the tone of their voice. This data is temporarily stored in the device's local memory and periodically packetized and sent to a server.

[1278] Data analysis (server)

[1279] The server receives the environmental and emotional data sent from the device and stores it in a database. It then analyzes the data using machine learning models to generate insights into the customer's emotional state, appropriate customer service methods, and recommended products. For example, the server analyzes data on a customer's smile and recognizes that the customer is happy, and then notifies the customer of the appropriate customer service method.

[1280] Emotion engine operation (server)

[1281] The server is equipped with an emotion engine that recognizes the user's emotions. This emotion engine analyzes the user's voice and facial expressions to detect their emotional state. Based on the results, the server can adjust the content and frequency of notifications. For example, if a customer looks dissatisfied, the server sends a notification to the store clerk saying, "The customer is dissatisfied. Please respond quickly."

[1282] Providing results and feedback (user)

[1283] Notifications based on the insights generated by the server and the results of the emotion engine are sent to the user's smart glasses. The notification content is displayed on the smart glasses' display, allowing the store clerk to provide appropriate customer service and product recommendations based on the notification content. For example, a store clerk may receive a notification saying, "The customer is happy. Please recommend these shoes," and then suggest a product to the customer based on that notification.

[1284] Specific examples

[1285] In a physical store, a salesperson wears smart glasses to serve customers. The smart glasses' camera captures the customer's facial expressions, and the microphone picks up the customer's voice. When the customer shows interest in a product and smiles, the server analyzes the data and displays a notification on the smart glasses saying, "The customer is pleased. Please suggest a new product."

[1286] Prompt Sentence Examples

[1287] "Please explain an application of smart glasses that analyzes the facial expressions and voices of customers in a physical store to provide customer service support. Please give specific examples of what kind of notification is displayed to the store clerk when the customer is happy."

[1288] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1289] Program processing flow

[1290] Step 1:

[1291] The device (smart glasses) captures the customer's facial expressions and voice in real time through a camera and microphone. The camera input is the customer's facial image, and the microphone input is the customer's voice. This data is temporarily stored in local memory.

[1292] Step 2:

[1293] The device packetizes the customer's facial expression and voice data stored in its local memory and transmits them to the server via Wi-Fi. The input here is the data stored in the local memory, and the output is the packetized data.

[1294] Step 3:

[1295] The server receives data packets sent from the terminals and stores them in a database. The input is the received data packets, and the output is the data stored in the database.

[1296] Step 4:

[1297] The server analyzes the stored data using an emotion engine. The emotion engine analyzes the customer's emotional state from their facial expression data and their voice tone from their voice data. The input is the customer's facial expression data and voice data stored in the database, and the output is the customer's emotional state.

[1298] Step 5:

[1299] The server uses a machine learning model to generate customer service methods and product recommendations that correspond to the customer's emotional state. The input is the customer's emotional state, and the output is customer service methods and product recommendations. Appropriate notification content is generated based on the analysis results.

[1300] Step 6:

[1301] The server sends the generated notification content to the device (smart glasses). The input is the generated notification content, and the output is the notification to the device.

[1302] Step 7:

[1303] The terminal (smart glasses) displays the notification content sent from the server on its display, visually notifying the store clerk. The input is the notification content sent from the server, and the output is the notification displayed on the smart glasses' display. This allows the store clerk to take appropriate action depending on the customer's emotional state.

[1304] Specific examples of operation

[1305] Step 1: When the customer enters the field of view of the smart glasses, the camera automatically captures their facial expressions and the microphone records their voice.

[1306] Step 2: Facial expression data (e.g., an image of the customer's smile) and voice data (e.g., the customer's interesting tone of voice) are packetized.

[1307] Step 3: The server receives these data packets and stores them in a database along with the customer ID.

[1308] Step 4: The server uses the emotion engine to analyze the emotional state of "customer is satisfied."

[1309] Step 5: The server uses the machine learning model to generate a notification suggesting a new product for this customer.

[1310] Step 6: The server sends the generated notification "Please suggest a new product" to the terminal.

[1311] Step 7: The smart glasses display will display a notification saying, "Customer is happy. Please recommend these shoes."

[1312] 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.

[1313] 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.

[1314] 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.

[1315] 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.

[1316] 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.

[1317] 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.

[1318] 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).

[1319] 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.

[1320] 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."

[1321] 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.

[1322] 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).

[1323] 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.

[1324] 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.

[1325] 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.

[1326] 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.

[1327] 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.

[1328] 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.

[1329] 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.

[1330] 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.

[1331] 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.

[1332] 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.

[1333] The following is further disclosed regarding the above embodiment.

[1334] (Claim 1)

[1335] means for collecting environmental data with sensors;

[1336] means for transmitting the collected data to a server;

[1337] means for analyzing the transmitted data;

[1338] means for providing a notification to a user based on the analysis results;

[1339] A system including:

[1340] (Claim 2)

[1341] The system according to claim 1, wherein the analysis means predicts the next watering timing using a machine learning model.

[1342] (Claim 3)

[1343] 10. The system of claim 1, wherein the sensor means measures humidity, temperature, and pH value.

[1344] "Example 1"

[1345] (Claim 1)

[1346] means for collecting environmental data with sensors;

[1347] means for transmitting the collected data to a server via a network;

[1348] means for storing the transmitted data in a database;

[1349] means for acquiring the stored data and analyzing it using a machine learning model;

[1350] means for generating a crop health status and an optimal watering timing as a result of the analysis and providing a notification to a user;

[1351] A system including:

[1352] (Claim 2)

[1353] The system according to claim 1, wherein the analysis means predicts the next watering timing using a generative AI model.

[1354] (Claim 3)

[1355] 10. The system of claim 1, wherein said sensor means measures soil moisture, temperature, and acidity.

[1356] "Application Example 1"

[1357] (Claim 1)

[1358] means for collecting environmental data with sensors;

[1359] means for transmitting the collected data to a server;

[1360] means for analyzing the transmitted data;

[1361] means for providing a notification to a user based on the analysis results;

[1362] The collected data includes temperature, humidity, and gas concentration of the fresh food;

[1363] The analysis means generates insights for determining the freshness of perishable foods in real time;

[1364] means for notifying the user of the optimum time for receiving the fresh food;

[1365] A system including:

[1366] (Claim 2)

[1367] The system according to claim 1, wherein the analysis means predicts the next watering timing using a machine learning model.

[1368] (Claim 3)

[1369] 10. The system of claim 1, wherein said sensor means measures humidity, temperature, and gas concentration.

[1370] "Example 2: Combining Emotion Engines"

[1371] (Claim 1)

[1372] means for collecting environmental data with sensors;

[1373] means for transmitting the collected data to a server;

[1374] means for analyzing the transmitted data;

[1375] means for providing a notification to a user based on the analysis results;

[1376] means for recognizing the emotional state of a user;

[1377] means for adjusting notification content and frequency based on said emotional state;

[1378] A system including:

[1379] (Claim 2)

[1380] The system according to claim 1, wherein the analysis means predicts the next watering timing using a machine learning model.

[1381] (Claim 3)

[1382] 10. The system of claim 1, wherein the sensor means measures humidity, temperature, and pH value.

[1383] (Claim 4)

[1384] 2. The system of claim 1, wherein the notification providing means transmits a notification to a user terminal and visually displays the notification.

[1385] (Claim 5)

[1386] 2. The system of claim 1, wherein the emotion recognition means detects the user's emotional state by analyzing speech and facial expression data.

[1387] "Application example 2 when combining emotion engines"

[1388] (Claim 1)

[1389] means for collecting environmental data with sensors;

[1390] means for transmitting the collected data to a server;

[1391] means for analyzing the transmitted data;

[1392] means for providing a notification to a user based on the analysis results;

[1393] means for recognizing the emotional state of a user;

[1394] means for adjusting notification content and frequency based on said emotional state;

[1395] A system including:

[1396] (Claim 2)

[1397] The system according to claim 1, wherein the analysis means is a means for predicting the next timing of watering using a machine learning model.

[1398] (Claim 3)

[1399] 2. The system of claim 1, wherein the sensor means is a means for measuring humidity, temperature, and pH value.

[1400] (Claim 4)

[1401] 2. The system of claim 1, wherein the means for recognizing the user's emotional state comprises means for analyzing voice and facial expressions.

[1402] (Claim 5)

[1403] 2. The system according to claim 1, wherein the means for adjusting the content and frequency of the notification is a means for presenting appropriate customer service methods and recommended products according to the emotional state of the customer. [Explanation of symbols]

[1404] 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 with sensors; means for transmitting the collected data to a server; means for analyzing the transmitted data; means for providing a notification to a user based on the analysis results; A system including:

2. The system according to claim 1 , wherein the analysis means predicts the next watering timing using a machine learning model.

3. 2. The system of claim 1, wherein said sensor means measures humidity, temperature, and pH.

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