System

Abstract

A system is provided.SOLUTION: The system includes a sensor means for measuring the humidity of soil, a sensor means for measuring temperature, a sensor means for measuring sunshine hours, a server means for collecting and analyzing data acquired from the respective sensor means, a control means for controlling watering and other environmental adjustment on the basis of a data analysis result, and an interface means for allowing a user to confirm a growth state and harvest timing.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In conventional home coffee cultivation, environmental control, such as soil humidity, ambient temperature, and sunlight hours, is a major burden for beginners. Manually adjusting the environment is not only labor-intensive, but incorrect timing and amount can have a negative impact on coffee quality. Furthermore, determining growth conditions and optimal harvest times requires experience and specialized knowledge, making it difficult for many households to easily grow high-quality coffee beans. There is a need to provide a system that solves these issues and allows anyone to easily grow high-quality coffee at home. [Means for solving the problem]

[0005] In this invention, sensor means for measuring soil humidity, sensor means for measuring temperature, and sensor means for measuring sunshine hours are installed, and server means is provided for collecting and analyzing data obtained from these sensors. Furthermore, control means is provided for automatically performing environmental control such as watering and temperature adjustment based on the results of the data analysis by the server means. Furthermore, users can check the growth status and harvest timing through interface means such as a smartphone app. This makes it easy for even beginners to cultivate high-quality coffee beans.

[0006] "Sensor means for measuring soil moisture" is a sensor device that quantitatively measures the moisture level of soil in coffee cultivation.

[0007] The "sensor means for measuring temperature" is a sensor device for measuring the temperature of the cultivation environment and acquiring the data.

[0008] The "sensor means for measuring the duration of sunlight" is a sensor device for measuring the duration of sunlight and the intensity of light in the cultivation environment.

[0009] The "server means for collecting and analyzing data acquired from each sensor means" refers to a server device that receives data transmitted from each sensor, analyzes the data, and generates appropriate environmental control instructions.

[0010] The "control means for controlling watering and other environmental adjustments based on the results of data analysis" is a device that automatically performs environmental control such as water supply and temperature adjustment based on instructions from the server.

[0011] The "interface means for users to check the growth status and harvest timing" is an interface device that allows users to check the coffee growth status and optimal harvest timing through a smartphone app, web application, etc. [Brief explanation of the drawings]

[0012] [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

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] 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).

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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."

[0033] The personal automated coffee garden system of this invention provides the optimal coffee bean cultivation environment by utilizing various sensors, a server that collects and analyzes data, and a user interface. The system monitors soil humidity, temperature, and sunlight hours, and automatically adjusts the environment using an AI algorithm. The specific functions and operations of each element are described below.

[0034] Sensor means for measuring soil moisture

[0035] The device contains a soil moisture sensor that is inserted into the soil and records the soil's moisture content in real time.

[0036] Sensor means for measuring temperature

[0037] The terminal is equipped with a temperature sensor that measures the temperature of the entire cultivation environment and transmits the data to the terminal.

[0038] Sensor means for measuring sunshine duration

[0039] The device is equipped with a light sensor that measures the intensity of ambient light and records the duration of daylight.

[0040] Server means for collecting and analyzing data obtained from each sensor means

[0041] Data from these sensors is periodically sent via the device to a server, which analyzes the data and uses data models to optimize growth conditions and the environment.

[0042] Example: The server analyzes soil moisture data and, if it determines that the moisture content is below the optimum range, it knows that watering is necessary.

[0043] Control measures to control watering and other environmental adjustments based on data analysis results

[0044] Based on the analysis results, the server sends specific instructions for environmental adjustments to the terminal, which controls the watering device, temperature control device, lighting device, etc.

[0045] Example: The server determines that the humidity is low and sends the command to the terminal to "add 100ml of water to the soil." The terminal automatically opens the nozzle and dispenses 100ml of water.

[0046] An interface for users to check growth status and harvest timing

[0047] Users can check the growth status of coffee beans and resource conditions in real time through a smartphone app or web application.

[0048] Example: A user opens the app and sees that the current soil moisture is 45%, the temperature is 22°C, and there are 8 hours of sunshine. They also receive a notification from the server that the best time to harvest is in 3 days.

[0049] This system allows users to easily manage the detailed environment necessary for coffee bean growth. AI and automatic control instantly respond to environmental changes and provide the optimal growing environment, making it easy to grow high-quality coffee at home.

[0050] The processing flow will be explained below.

[0051] Step 1:

[0052] A user installs the smartphone app and creates an account. The user enters their name, email address, and password and taps the register button.

[0053] Step 2:

[0054] The server receives the information entered by the user and stores it in the database. If the save is successful, it notifies the user with a message that the account was created successfully.

[0055] Step 3:

[0056] The device acquires data using sensors such as soil humidity, temperature, and sunlight duration. For example, the humidity sensor acquires soil humidity in real time, and the temperature sensor measures the ambient temperature.

[0057] Step 4:

[0058] The device periodically transmits the acquired data to the server. The device transmits the data from the sensor to the server via Wi-Fi.

[0059] Step 5:

[0060] The server analyzes the data received from the device and uses AI algorithms to optimize the environment, for example determining if soil moisture is below the optimal range.

[0061] Step 6:

[0062] Based on the analysis results, the server instructs the device to take specific actions such as watering or adjusting the temperature. For example, it might send an instruction to the device to "add 100ml of water to the soil."

[0063] Step 7:

[0064] The device receives instructions from the server and executes specific actions, such as operating the watering nozzle to add 100ml of water to the soil.

[0065] Step 8:

[0066] The server continuously monitors the growth status and uses AI algorithms to predict when to harvest, for example by comparing the growth rate with past data to determine whether it is appropriate.

[0067] Step 9:

[0068] When the time for harvesting approaches, the server sends a notification to the user via the app, informing the user that "it's time for harvesting."

[0069] Step 10:

[0070] The user opens the smartphone app and checks the growth dashboard for current growth status and harvest readiness information, such as the current soil moisture of 50%, the temperature of 23°C, and the remaining three days until harvest.

[0071] Example 1

[0072] 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."

[0073] To easily grow high-quality coffee beans at home, it is necessary to accurately manage and appropriately adjust environmental factors such as soil humidity, temperature, and sunlight hours. However, these management tasks are time-consuming and require specialized knowledge, making them difficult for many users. In addition, systems that monitor environmental conditions in real time and automatically adjust them as needed are not widely available. This means that there is a risk that the quality of the coffee beans will decline if the system is unable to respond appropriately to environmental changes.

[0074] 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.

[0075] In this invention, the server includes sensor means for measuring soil humidity, sensor means for measuring temperature, sensor means for measuring sunshine hours, data processing means for collecting and analyzing data acquired from each sensor means, control means for controlling watering and other environmental adjustments based on the results of data analysis, and display means for the user to check the growth status and harvest timing. This allows the user to grasp the growth status in real time and provide an optimal growing environment through automatic control by AI, making it possible to easily cultivate high-quality coffee beans.

[0076] A "sensor for measuring soil moisture" is a device that is inserted into the soil to measure the moisture content of the soil in real time.

[0077] A "sensor for measuring temperature" is a device for measuring the temperature of the entire cultivation environment and collecting that data.

[0078] A "sensor for measuring sunshine duration" is a device for measuring the intensity of ambient light and recording the sunshine duration.

[0079] "Data processing means" refers to a system that analyzes collected data and understands the optimization of the environment and growth status.

[0080] The "control means" is a system that controls watering devices, temperature control devices, lighting devices, etc. based on the analysis results of the data processing means.

[0081] The "display means" is an interface that allows the user to check the growth status and harvest timing, and includes smartphone applications and web applications.

[0082] The personal automated coffee garden system of this invention provides an optimal coffee bean cultivation environment by utilizing various sensors, a server that collects and analyzes data, and a user interface. The system monitors soil humidity, temperature, and sunlight hours, and automatically adjusts the environment using an AI algorithm. The specific functions and operations of each element are described below.

[0083] Data collection by sensor means

[0084] The device is equipped with the following types of sensors:

[0085] 1. Soil Moisture Sensor: Measures soil moisture content in real time. It is inserted into the soil and collects data every hour.

[0086] 2. Temperature sensor: Measures the temperature of the entire cultivation environment. Temperature data is collected every hour.

[0087] 3. Light sensor: Measures the intensity of ambient light and records the hours of sunlight.

[0088] Data collection and analysis by the server

[0089] The data collected from each sensor is periodically sent to a server via the device, where it is analyzed using AI algorithms and data models to optimize the plant's growth and environment.

[0090] Environmental adjustments based on data analysis

[0091] Based on the analysis results, the server sends instructions to the terminal to adjust the environment through the following control means.

[0092] Watering device: When soil moisture is low, it will send watering instructions and automatically supply water.

[0093] Thermostat: Controls heating and cooling systems when temperatures go outside set ranges.

[0094] Lighting equipment: If there is a lack of sunlight, turn on artificial lighting.

[0095] User interface means

[0096] Users can check the growth status of coffee beans and the status of resources in real time through a smartphone app or web application, and can also receive notifications from the server.

[0097] Examples:

[0098] When a user opens the app, they can see that the current soil humidity is 35%, the temperature is 22°C, and there are 8 hours of sunshine.

[0099] The server sends a notification to the user saying, "Best time to harvest in 3 days."

[0100] This system allows users to easily manage the detailed environment necessary for coffee bean growth. AI and automatic control instantly respond to environmental changes and provide the optimal growing environment, making it easy to grow high-quality coffee at home.

[0101] Example prompt sentence:

[0102] "This automated coffee garden system monitors soil moisture, temperature, and sunlight hours in real time, and uses AI algorithms to optimize the environment. Data is collected from sensors, sent to a server for analysis, and automatically takes action such as watering and adjusting lighting when necessary. Users can check growth status and environmental data using a smartphone app or web application. How can we improve this system to grow coffee beans more efficiently?"

[0103] In this way, users can enjoy advanced environmental management using AI while reducing tedious work, making it easy to grow high-quality coffee beans at home.

[0104] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0105] Step 1:

[0106] The device measures the soil moisture using a soil moisture sensor, which is inserted into the soil and collects moisture data as input every hour.

[0107] Specific behavior:

[0108] The soil moisture sensor works to detect the moisture content of the soil.

[0109] The collected humidity data (e.g. 30%) is obtained.

[0110] Input: Current soil moisture value

[0111] Output: Humidity data (e.g. 30%)

[0112] Step 2:

[0113] The device uses a temperature sensor to measure the temperature of the entire growing environment, and temperature data is also collected every hour.

[0114] Specific behavior:

[0115] The temperature sensor is activated to detect the temperature of the entire environment.

[0116] The collected temperature data (e.g., 25°C) is obtained.

[0117] Input: Current ambient temperature

[0118] Output: Temperature data (e.g. 25°C)

[0119] Step 3:

[0120] The device uses a light sensor to measure the intensity of ambient light and record the duration of daylight, which also collects data every hour.

[0121] Specific behavior:

[0122] The light sensor is activated and detects the intensity of the ambient light.

[0123] The collected light data (e.g., 2000 lux) is obtained.

[0124] Input: Ambient light intensity

[0125] Output: Light data (e.g. 2000 lux)

[0126] Step 4:

[0127] The device sends all the data it collects to a server, either via Wi-Fi or a wired connection.

[0128] Specific behavior:

[0129] Humidity data, temperature data, and light data are combined into one data packet.

[0130] The collected data packets are sent to the server.

[0131] Input: Humidity data, temperature data, light data

[0132] Output: Data packets to the server

[0133] Step 5:

[0134] The server stores the received data in a database and begins analyzing it, using machine learning models and statistical analysis.

[0135] Specific behavior:

[0136] The received data is stored in a database in chronological order.

[0137] The saved data is input into an AI algorithm to analyze the optimal cultivation environment.

[0138] Input: Received data packet

[0139] Output: Analysis results (e.g., low humidity, watering required)

[0140] Step 6:

[0141] Based on the analysis results, the server generates appropriate environmental adjustment instructions and sends them to the device, including instructions for watering, adjusting the temperature, and controlling lighting.

[0142] Specific behavior:

[0143] Based on the analysis results, necessary adjustments are determined.

[0144] Generate specific instructions (e.g., add 100ml of water to the soil) and send them to the device.

[0145] Input: Analysis results

[0146] Output: Instructions for adjusting the environment to the terminal

[0147] Step 7:

[0148] The terminal then adjusts the environment based on the received instructions, automatically activating watering devices, temperature control devices, lighting devices, etc.

[0149] Specific behavior:

[0150] When it receives a watering command, it opens the nozzle and delivers the specified amount of water.

[0151] If a temperature adjustment command is received, the heating or cooling device will be activated.

[0152] If a lighting instruction is received, the lighting device is turned on.

[0153] Input: Environmental adjustment instructions

[0154] Output: Performing environmental controls (e.g. water supply, temperature control, lighting)

[0155] Step 8:

[0156] Users can check data in real time via a smartphone app or web application, and can also receive notifications from the server to check growth status and harvest timing.

[0157] Specific behavior:

[0158] Open the app and check the current humidity, temperature, sunshine hours, and more.

[0159] Receive notifications from the server (e.g., Best time to harvest in 3 days).

[0160] Input: Data request from app or web

[0161] Output: Real-time data, notification information

[0162] Through the above processing steps, users can easily cultivate high-quality coffee beans and quickly respond to environmental changes.

[0163] (Application example 1)

[0164] 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."

[0165] With conventional coffee bean cultivation systems, it is difficult to maintain an appropriate cultivation environment at home or in a store, and ensuring an optimal growing environment requires a lot of effort and time. Furthermore, there are few ways to quickly respond to changes in the cultivation environment, resulting in inconsistent quality of the cultivated coffee beans. Furthermore, the coffee brewing method is heavily dependent on the skill and experience of the barista, making it difficult to provide coffee of consistent quality.

[0166] 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.

[0167] In this invention, the server includes a detector for measuring soil humidity, a detector for measuring temperature, a detector for measuring sunshine hours, a calculation unit for collecting and analyzing data acquired from each detector, an operation unit for controlling watering and other environmental adjustments based on the data analysis results, a communication unit for a user to check the growth status and harvest timing, and a unit for acquiring environmental data using smart glasses, analyzing the data on a cloud server, and displaying instructions for optimal environmental adjustments based on the analysis results. This allows for quick response to environmental changes and provides an optimal cultivation environment. Furthermore, by monitoring data in real time through the smart glasses and receiving appropriate instructions, baristas can provide coffee of consistent quality.

[0168] The "detection means for measuring soil moisture" is a device for measuring the amount of moisture contained in soil in real time and acquiring the data.

[0169] The "detection means for measuring temperature" is a device for measuring the temperature of the environment and acquiring the data.

[0170] A "detection means for measuring the hours of sunlight" is a device for measuring the intensity of ambient light and recording the hours of sunlight.

[0171] The "computing means for collecting and analyzing data obtained from each detecting means" is a computer system or server for receiving and analyzing data transmitted from a plurality of sensors.

[0172] "Operational means for controlling watering and other environmental adjustments based on data analysis results" refers to equipment or devices that automatically perform watering, temperature adjustment, and lighting adjustment based on analysis results.

[0173] "Communication means for users to check the growth status and harvest timing" refers to an interface that allows users to check the growth status, environmental data, harvest timing, etc. via a smartphone or web application.

[0174] "Smart glasses" are wearable devices that display data to the wearer in real time and provide instructions based on the analysis results.

[0175] A "cloud server" is a remote server accessible via the Internet, and is a computer system that analyzes and processes data and manages storage.

[0176] The system for implementing the present invention is configured by combining a plurality of detecting means, calculating means, operating means, communication means, and smart glasses. Specific embodiments are described below.

[0177] First, the detecting means for measuring soil humidity, the detecting means for measuring temperature, and the detecting means for measuring sunshine hours are devices that measure soil and environmental temperatures and light intensity in real time and acquire the data, which are then transmitted to a cloud server via wireless communication.

[0178] The cloud server uses software written in programming languages ​​such as Python and Java to analyze the data it receives. It uses AI models to learn and evaluate patterns in the data and generate instructions for optimal environmental adjustments. For example, if the soil moisture level drops, it will determine that more watering is needed. Or, if the room temperature is too high, it will indicate that cooling equipment should be activated.

[0179] The smart glasses are wearable devices that display instructions from a cloud server to baristas in real time. The smart glasses are equipped with an internet connection and display data in conjunction with the cloud server. This allows baristas to instantly check environmental data and optimal behavioral instructions.

[0180] Users can connect to the cloud server via their smartphone or web application and check the growth status of their coffee beans and the cultivation environment in real time. The smartphone app and web application are built using web technologies such as HTML, CSS, and JavaScript, allowing users to easily monitor the cultivation environment and make adjustments as needed.

[0181] As a concrete example, the operation of the barista assistance system can be confirmed by inputting the following prompt sentence into the generative AI model.

[0182] Example prompt:

[0183] "Please explain how the smart glasses-based coffee barista support system works. This system analyzes data on soil humidity, environmental temperature, and sunlight hours on a cloud server, and displays optimal instructions in real time through the smart glasses."

[0184] This clarifies the mechanism of the system, which allows users and baristas to easily obtain environmental data and receive optimal instructions for action based on the analysis results.

[0185] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0186] Step 1:

[0187] Data collection from each detection method

[0188] The device acquires data in real time using sensing means that measure soil humidity, environmental temperature, and sunlight duration. The acquired data includes moisture content data from the soil humidity sensor, air temperature data from the temperature sensor, and light intensity data from the light sensor. The input data is the raw data from each sensor, and the output is a compiled environmental dataset.

[0189] Step 2:

[0190] Sending data to the cloud server

[0191] The device transmits the collected environmental data to a cloud server via wireless communication. In this process, the device assembles the data acquired from each sensor into a single data packet and sends it to the cloud server via the Internet. The input is the environmental dataset, and the output is the data transmitted to the cloud server.

[0192] Step 3:

[0193] Data analysis using a cloud server

[0194] The cloud server analyzes the received environmental data. Using programming languages ​​such as Python and Java, the server runs an AI model and performs analysis based on the data. Here, it evaluates whether the conditions are suitable for growth based on data on soil moisture, temperature, and light intensity. The input data is the environmental dataset sent from the device, and the analysis results are output.

[0195] Step 4:

[0196] Generate optimal environmental adjustment instructions

[0197] The cloud server generates instructions for optimal environmental adjustment based on the results of data analysis. For example, if the soil humidity is low, it will issue a "water" instruction, and if the temperature is too high, it will issue a "cool" instruction. The input is the analysis results, and the output is specific instructions for environmental adjustment.

[0198] Step 5:

[0199] Displaying instructions on smart glasses

[0200] The cloud server sends the generated environmental adjustment instructions to the smart glasses. The smart glasses display these instructions to the barista in real time. Here, processing is performed to display the content of the instructions on the display. The input is the environmental adjustment instructions from the cloud server, and the output is the instruction content displayed on the smart glasses' display.

[0201] Step 6:

[0202] User confirmation of data

[0203] Users can access the cloud server using a smartphone or web application to check the collected environmental data and analysis results. Here, the process of extracting the information necessary for the user to check the data on the screen is carried out. The input is the environmental data and analysis results stored on the cloud server, and the output is the data displayed on the user's screen.

[0204] 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.

[0205] The personal automated coffee garden system of the present invention provides an optimal coffee bean growing environment by including sensors that monitor soil moisture, temperature, and sunlight hours, a server that collects and analyzes data, a user interface, and an emotion engine that recognizes the user's emotions and adjusts the environment accordingly.

[0206] Sensor means for measuring soil moisture

[0207] The device contains a soil moisture sensor that is inserted into the soil to record the moisture content in real time and transmit that data to the device.

[0208] Sensor means for measuring temperature

[0209] The terminal is equipped with a temperature sensor that measures the temperature of the cultivation environment and transmits the data to the terminal.

[0210] Sensor means for measuring sunshine duration

[0211] The device is equipped with a light sensor that measures the intensity of ambient light and records the duration of daylight.

[0212] Server means for collecting and analyzing data obtained from each sensor means

[0213] Data from these sensors is periodically sent to a server via the device, which then analyzes the data and uses AI algorithms to optimize growth conditions and the environment.

[0214] Example: The server analyzes soil moisture data and, if it determines that the moisture content is below the optimum range, it knows that watering is necessary.

[0215] Control measures to control watering and other environmental adjustments based on data analysis results

[0216] Based on the analysis results, the server sends specific instructions for environmental adjustments to the terminal, which controls the watering device, temperature control device, lighting device, etc.

[0217] Example: The server determines that the humidity is low and sends the command to the terminal to "add 100ml of water to the soil." The terminal automatically opens the nozzle and dispenses 100ml of water.

[0218] An interface for users to check growth status and harvest timing

[0219] Users can check the growth status of coffee beans and resource conditions in real time through a smartphone app or web application.

[0220] Example: A user opens the app and sees that the current soil moisture is 45%, the temperature is 22°C, and there are 8 hours of sunshine. They also receive a notification from the server that the best time to harvest is in 3 days.

[0221] Emotion engine that recognizes user emotions

[0222] An emotion engine is installed on the device or the user's smartphone. This engine uses a camera and microphone to recognize emotions from the user's facial expressions and voice, and sends the data to a server.

[0223] Example: When a user uses an app to check their growth status, the camera analyzes their facial expressions and if it determines that they are tired, the server reduces notifications detailing their growth status and provides only concise information.

[0224] Automatic adjustment of the environment by the emotion engine

[0225] The server receives and analyzes data from the emotion engine and proposes or implements automatic adjustments to the environment based on the user's emotional state, allowing the user to enjoy coffee cultivation without stress.

[0226] Example: If the emotion engine recognizes that the user is relaxed, it will provide detailed information on the best time to harvest the coffee to enjoy its aroma.

[0227] This system not only allows users to easily grow high-quality coffee beans, but also provides more personalized support through an emotion engine. The combination of AI, automatic control, and user emotion recognition further enhances the growing experience.

[0228] The processing flow will be explained below.

[0229] Step 1:

[0230] A user installs the smartphone app and creates an account. The user enters their name, email address, and password and taps the register button.

[0231] Step 2:

[0232] The server receives the information entered by the user and stores it in the database. If the save is successful, it notifies the user with a message that the account was created successfully.

[0233] Step 3:

[0234] The device acquires data using sensors such as soil humidity, temperature, and sunlight duration. For example, the humidity sensor acquires soil humidity in real time, and the temperature sensor measures the ambient temperature.

[0235] Step 4:

[0236] The device periodically transmits the acquired data to the server. The device transmits the data from the sensor to the server via Wi-Fi.

[0237] Step 5:

[0238] The server analyzes the data received from the device and uses AI algorithms to optimize the environment, for example determining if soil moisture is below the optimal range.

[0239] Step 6:

[0240] Based on the analysis results, the server instructs the device to take specific actions such as watering or adjusting the temperature. For example, it might send an instruction to the device to "add 100ml of water to the soil."

[0241] Step 7:

[0242] The device receives instructions from the server and executes specific actions, such as operating the watering nozzle to add 100ml of water to the soil.

[0243] Step 8:

[0244] An emotion engine installed in the device or the user's smartphone analyzes the user's facial expressions and voice to recognize emotions. For example, a camera captures the user's facial expressions, and voice recognition technology analyzes the tone of the user's voice.

[0245] Step 9:

[0246] The emotion engine sends the emotion data it recognizes to the server. For example, if it determines that the user is relaxed, the data is sent to the server.

[0247] Step 10:

[0248] The server analyzes the emotional data and adjusts notifications and environmental settings according to the user's emotional state. For example, if the server determines that the user is tired, it will simplify notifications and automatically adjust the frequency of watering plants.

[0249] Step 11:

[0250] The server sends notifications to the user about the growth status and harvest timing, for example, "The best time to harvest is in 3 days."

[0251] Step 12:

[0252] The user opens the smartphone app to check information about the plant's growth and harvest timing, such as the current soil humidity of 50%, the temperature of 23°C, and the remaining three days until harvest.

[0253] Through these steps, users can easily grow high-quality coffee and receive personalized support using emotion recognition technology.

[0254] Example 2

[0255] 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."

[0256] In recent years, the demand for individual coffee bean cultivation has increased, but effective environmental control and management are difficult, especially for beginners. Furthermore, in many cases, it becomes difficult to continue cultivation due to stress or busy schedules. For this reason, there is a demand for a system that allows anyone to easily maintain an optimal cultivation environment and grow coffee beans without stress.

[0257] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0258] In this invention, the server includes sensor means for measuring soil humidity, sensor means for measuring temperature, sensor means for measuring sunshine hours, a data processing device for collecting and analyzing data obtained from each sensor means, an environmental control device for controlling watering and other environmental adjustments based on the results of the data analysis, a user interface device for allowing the user to check the growth status and harvest timing, an emotion recognition device for recognizing the user's emotions and transmitting that data to the data processing device, and a system for causing the environmental control device to suggest or execute environmental adjustments based on data from the emotion recognition device. This makes it possible for anyone to easily maintain an optimal cultivation environment and grow coffee beans without stress.

[0259] 1. "Sensor means for measuring soil moisture" means a device for measuring and collecting data on the moisture content of soil in real time.

[0260] 2. "Sensor means for measuring temperature" means a device for accurately measuring the temperature within a growing environment and collecting that data.

[0261] 3. "Sensor means for measuring daylight hours" means a device for measuring light intensity and daylight hours in a growing environment and collecting such data.

[0262] 4. "Data Processing Device" means a computer system or device for collecting and analyzing data obtained from each sensor means.

[0263] 5. An "environmental control device" is a device that performs environmental adjustments such as watering, temperature control, and lighting control based on the analysis results of the server.

[0264] 6. "User interface device" refers to a display device or application that allows the user to check the system status, the growth status of coffee beans, harvest timing, etc.

[0265] 7. An "emotion recognition device" is a device that analyzes a user's facial expressions and voice to recognize their emotional state.

[0266] 8. "System for proposing or executing environmental adjustment" means a system that makes suggestions or automatic environmental adjustments to optimize the cultivation environment based on data from an emotion recognition device.

[0267] The automated coffee garden system for individuals of the present invention includes multiple sensors, a data processing device, an environmental control device, a user interface device, and an emotion recognition device, which optimize the coffee bean cultivation environment and allow users to enjoy stress-free cultivation.

[0268] Sensor means

[0269] The device is equipped with a soil moisture sensor, a temperature sensor, and a light sensor. The soil moisture sensor measures the moisture content of the soil, the temperature sensor measures the temperature in the cultivation environment, and the light sensor measures the ambient light intensity and sunlight duration. The data collected by these sensors is sent to the device.

[0270] Data Processing Unit

[0271] The server periodically collects and automatically analyzes sensor data sent from the device. The server is equipped with AI algorithms that analyze data such as humidity, temperature, and sunlight duration to generate specific instructions for maintaining an optimal growing environment. For example, if the server determines that the soil humidity is below the optimal range, it will send instructions to the environmental control device to water the plants.

[0272] Environmental Control Device

[0273] The terminal receives instructions from the server and adjusts the cultivation environment. This includes watering devices, temperature control devices, lighting devices, etc., and automatically controls these to optimize the environment. For example, if the server determines that the humidity is low and sends the command to the terminal to "add 100ml of water to the soil," the terminal will open the nozzle and supply 100ml of water to the soil.

[0274] User Interface Device

[0275] Users can use a smartphone app or web application to check the growth status of coffee beans and the condition of resources in real time. The application displays data on soil humidity, temperature, and sunlight hours, and receives notifications from the server with information such as growth status and optimal harvest timing. For example, when a user opens the app, the current soil humidity is 35%, the temperature is 22°C, and the sunlight hours are 8 hours, and the user receives a notification that "the optimal time for harvest is in 3 days."

[0276] emotion recognition device

[0277] An emotion recognition device is installed on the device or the user's smartphone. This device uses a camera and microphone to analyze the user's facial expressions and voice to recognize their emotional state. The recognized emotion data is sent to a server, which then provides information and adjusts the environment based on the analysis results, adapting to the user's emotions. For example, if the emotion recognition device determines that the user is tired, the server will reduce detailed notifications and provide only concise information.

[0278] Environmental adjustment based on emotion analysis

[0279] The server receives data from the emotion recognition device and suggests or automatically adjusts the cultivation environment based on the user's emotional state, allowing the user to relax and enjoy cultivation. For example, if the emotion engine recognizes that the user is relaxed, it will notify the user in detail of the optimal harvesting time to enjoy the aroma of coffee.

[0280] Prompt Sentence Examples

[0281] Please explain in detail each step of the automated personal coffee garden system. In each step, which of the server, device, or user is primarily responsible? What specific actions are performed?

[0282] This system allows users to easily grow high-quality coffee beans and enjoy stress-free cultivation with the support of the emotion engine.

[0283] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0284] Step 1:

[0285] Data collection by sensors

[0286] The device collects environmental data through soil humidity, temperature, and light sensors, which measure soil humidity, temperature, and sunlight hours, respectively, and transmits the collected data to the device.

[0287] Input: Sensor data for soil moisture, temperature, and sunshine hours

[0288] Output: Collected environmental data (e.g. soil humidity 35%, temperature 22°C, sunshine duration 8 hours)

[0289] Specific operation: The soil humidity sensor measures humidity at 35%, the temperature sensor measures 22°C, and the light sensor records 8 hours of sunlight.

[0290] Step 2:

[0291] Sending data to the server

[0292] The device transmits the collected environmental data to the server at regular intervals, using a network protocol.

[0293] Input: Collected environmental data

[0294] Output: Environment data sent to the server

[0295] Specific operation: The device uploads current soil humidity, temperature, and sunshine hours data to the server every 5 minutes using the HTTP protocol.

[0296] Step 3:

[0297] Data analysis

[0298] The server analyzes the received environmental data using AI algorithms and generates specific instructions to maintain an optimal cultivation environment.

[0299] Input: Environment data sent to the server

[0300] Output: Specific instructions for environmental adjustments as a result of data analysis (e.g., amount of watering, temperature adjustment, sunlight adjustment, etc.)

[0301] Specific operation: The server receives data that the humidity is 35%, and the AI ​​algorithm determines that this is below the optimal humidity of 40-60%, and generates the instruction to "add 100ml of water to the soil."

[0302] Step 4:

[0303] Sending environmental control instructions

[0304] The server sends an instruction to adjust the environment to the terminal based on the analysis result.

[0305] Input: Environmental adjustment instructions

[0306] Output: Environmental adjustment instructions sent to the device

[0307] Specific operation: The server sends the instruction "Add 100ml of water to the soil" to the terminal.

[0308] Step 5:

[0309] Automatic environment adjustment

[0310] The terminal adjusts the environment based on the received instructions, automatically controlling watering devices, temperature control devices, lighting devices, etc.

[0311] Input: Environment adjustment instructions from the server

[0312] Output: Environmental adjustment made (e.g., 100ml of water added)

[0313] Specific operation: The device opens the nozzle and automatically dispenses 100ml of water into the soil.

[0314] Step 6:

[0315] User interface display of information

[0316] Users can check environmental data and growth status through a smartphone app or web application.

[0317] Input: Latest data and notifications from the server

[0318] Output: Environmental data and notifications displayed in the application

[0319] Specific behavior: When the user opens the app, the current soil humidity is displayed as 35%, the temperature is 22°C, and there are 8 hours of sunshine, and a notification is displayed saying, "The best time to harvest is in 3 days."

[0320] Step 7:

[0321] Emotion Recognition and Data Transmission

[0322] An emotion recognition device installed on the terminal or the user's smartphone analyzes the user's facial expressions and voice to obtain emotion data, which is then sent to a server.

[0323] Input: User's facial expression and voice data

[0324] Output: Emotion data sent to the server

[0325] Specific operation: While the user is using the app, the smartphone camera reads their facial expressions, the emotion recognition device determines that they are "tired," and sends that data to the server.

[0326] Step 8:

[0327] Environmental adjustment based on emotion analysis

[0328] The server receives the emotional data and makes environmental adjustment suggestions or implementations based on the user's emotional state.

[0329] Input: Emotion data, environmental data

[0330] Output: Suggest or implement environmental adjustments based on emotions (e.g., simplified notifications, detailed notifications on optimal harvest timing)

[0331] Specific behavior: The server receives data that the user is tired and reduces detailed notifications, providing only concise information. If the server determines that the user is relaxed, it notifies the user in detail about the best time to harvest the coffee so that they can enjoy the aroma.

[0332] This series of processing steps allows users to enjoy growing coffee beans stress-free while automatically maintaining the optimal cultivation environment.

[0333] (Application example 2)

[0334] 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."

[0335] Currently, there are many tools and technologies on the market for personal coffee bean cultivation, but no system exists that can maintain an optimal cultivation environment while providing support tailored to the user's emotions. By incorporating emotion recognition, a more personalized experience can be provided, reducing the burden and stress on users while providing an environment for cultivating high-quality coffee beans. However, achieving this requires a system that integrates advanced data collection, analysis, and emotion recognition technologies.

[0336] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0337] In this invention, the server includes sensor means for measuring soil humidity, sensor means for measuring environmental temperature, sensor means for measuring sunshine hours, means for collecting and analyzing data obtained from each sensor means, means for controlling watering and other environmental adjustments based on the results of the data analysis, interface means for the user to check the growth status and harvest timing, means for recognizing the user's emotional state using an emotion engine and transmitting that data to the server, and means for adjusting the cultivation environment and robot operation based on the user's emotional state. This not only enables the user to easily cultivate high-quality coffee beans while maintaining an optimal cultivation environment, but also enables stress-free cultivation work because environmental adjustments and notifications are made based on the user's emotions.

[0338] "Sensor means" refers to devices used to measure soil moisture, environmental temperature, and hours of sunlight.

[0339] The "server means" is a computer system for collecting and analyzing data acquired from each sensor means.

[0340] The "control means" is a device that performs watering and other environmental adjustments based on the data analyzed by the server means.

[0341] "Interface means" refers to the connection means and screen that the user uses to check the growth status of the coffee beans and the timing of harvest.

[0342] An "emotion engine" is a software or hardware component that recognizes a user's emotional state through a camera or microphone and analyzes the data.

[0343] The "recognition means" is a function that uses an emotion engine to recognize the user's emotional state and transmits that data to the server.

[0344] The "cultivation environment adjusting means" is a means for automatically adjusting the cultivation environment and the operation of the robot based on the recognized emotional data of the user.

[0345] "Data collection" is the process of aggregating data acquired by sensor means and transmitting it to server means.

[0346] "High-quality coffee beans" are coffee seeds that meet the quality standards required by users when cultivating them.

[0347] "Reducing stress" refers to reducing the mental burden felt by the user when performing cultivation work.

[0348] The personal automated coffee garden system of the present invention provides an optimal coffee bean cultivation environment by integrating multiple sensors, a server that collects and analyzes data, a user interface, and an emotion engine that recognizes the user's emotions. The system of the present invention is composed of the following components:

[0349] Sensor means

[0350] The system is equipped with sensors to measure soil moisture, ambient temperature, and sunlight duration. These sensors work as follows:

[0351] Soil moisture sensor: Inserted into the soil, it measures the humidity in real time and transmits the data to the device.

[0352] Temperature sensor: Measures the temperature of the cultivation environment and sends the data to the terminal.

[0353] Light sensor: Measures the intensity of ambient light, records the hours of daylight, and sends this data to your device.

[0354] Data collection and analysis server

[0355] The server periodically collects and analyzes the data sent from the sensors. The analysis process includes the following steps:

[0356] Data collection: Collect and store data from each sensor.

[0357] Data analysis: The collected data is analyzed using an AI algorithm to calculate the optimal cultivation conditions.

[0358] Control measures for environmental regulation

[0359] Based on the results of the server's analysis, the following environmental adjustments are made:

[0360] Watering device: Provides the specified amount of water if the soil moisture level is below the specified level.

[0361] Temperature control device: Adjusts the temperature of the growing environment to the optimum range.

[0362] Lighting system: Adjust lighting to provide the required hours of sunlight.

[0363] User Interface

[0364] Users can check the following information in real time through a smartphone app or web application:

[0365] Growing conditions: Current soil moisture, temperature and sunshine data.

[0366] Harvest timing: Notification of optimal harvest time.

[0367] Emotion Engine

[0368] The emotion engine recognizes the user's emotional state using a camera and microphone and sends that data to the server, which then adjusts the cultivation environment and notifications accordingly.

[0369] Specific examples

[0370] Consider the following specific use case:

[0371] 1. Data collection: The soil moisture sensor detects 40% humidity, the temperature sensor records 22°C, and the light sensor records 8 hours of sunlight.

[0372] 2. Data analysis: The server analyzes this data and determines that the humidity is below the optimum range, indicating that watering is necessary.

[0373] 3. Environmental adjustment: The server sends an instruction to the watering device to add 100 ml of water, which the device executes.

[0374] 4. Emotion recognition: When the user checks the growth status on their smartphone, the camera analyzes the user's facial expression and recognizes that they are relaxed.

[0375] 5. Notification adjustment: The emotion engine provides detailed harvest timing notifications according to the user's relaxation state.

[0376] Prompt Sentence Examples

[0377] "Generates prompts based on the user's emotional state to determine if watering is necessary within the next 30 minutes."

[0378] "If the current soil moisture is not within the optimal range, generate a prompt with instructions on how much water to water."

[0379] The system of the present invention not only allows users to easily cultivate high-quality coffee beans, but also allows them to enjoy the cultivation process without feeling stressed thanks to the emotion-based environmental adjustment function.

[0380] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0381] Step 1: Collecting data with sensors

[0382] The device receives soil humidity data from the soil humidity sensor, ambient temperature data from the temperature sensor, and sunlight duration data from the light sensor. These sensors perform measurements periodically and generate data in real time.

[0383] Input: soil humidity, ambient temperature, sunshine hours

[0384] Output: Measured data for each sensor (e.g. soil humidity 40%, ambient temperature 22°C, sunshine duration 8 hours)

[0385] Step 2: Send data to the server

[0386] The device sends the sensor data acquired in step 1 to the server. The data is sent automatically at regular intervals.

[0387] Input: Data from each sensor

[0388] Output: The data sent.

[0389] Step 3: Data collection by the server

[0390] The server receives the data sent from the terminal and stores it in a database.

[0391] Input: Data sent from the terminal

[0392] Output: Saved data

[0393] Step 4: Data analysis

[0394] The server analyzes the collected data using AI algorithms to assess whether each environmental parameter is within an optimal range, for example, checking whether soil moisture is within the appropriate range (40-60%).

[0395] Input: Saved data

[0396] Output: Optimal conditions determined (e.g. soil moisture is low, watering is required)

[0397] Step 5: Generate environmental adjustment instructions

[0398] The server generates instructions for necessary environmental adjustments based on the results of data analysis, such as watering instructions if humidity is low.

[0399] Input: Data analysis results

[0400] Output: Environmental adjustment instructions (e.g., water 100ml)

[0401] Step 6: Send instructions to the device

[0402] The server transmits the generated environment adjustment instruction to the terminal.

[0403] Input: Environmental adjustment instructions

[0404] Output: Instructions sent

[0405] Step 7: Perform environmental adjustments

[0406] The terminal controls watering devices, temperature control devices, lighting devices, etc. based on instructions from the server, and performs the necessary environmental adjustments.

[0407] Input: Environment adjustment instructions from the server

[0408] Output: Environmental adjustment performed (e.g., 100ml watering)

[0409] Step 8: Collect emotion data

[0410] The device uses a camera and microphone to recognize the user's emotional state and collect that data.

[0411] Input: User's facial expression, voice

[0412] Output: Recognized emotion data (e.g., user is relaxed)

[0413] Step 9: Send emotion data to the server

[0414] The terminal transmits the collected emotion data to the server.

[0415] Input: Recognized emotion data

[0416] Output: Sent emotion data

[0417] Step 10: Sentiment Data Analysis

[0418] The server analyzes the received emotional data and assesses the user's current emotional state.

[0419] Input: Emotion data sent

[0420] Output: Evaluation of the emotional state (e.g., whether the user is relaxed)

[0421] Step 11: Adjust notification content

[0422] The server tailors the notifications to the user based on the evaluation of the user's emotional state, for example providing more detailed notifications if the user is relaxed.

[0423] Input: Emotional state assessment result

[0424] Output: Adjusted notification content (e.g. detailed notification of harvest time)

[0425] Step 12: Notify the user interface

[0426] The server sends the adjusted notification content to the user interface for the user to review.

[0427] Input: Adjusted notification content

[0428] Output: Information displayed on a user interface (e.g., detailed harvest time display on a smartphone app)

[0429] This processing step not only enables the user to maintain an optimal coffee bean cultivation environment at all times, but also provides appropriate information according to the user's emotional state, allowing for a comfortable cultivation experience.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] [Second embodiment]

[0434] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0435] 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.

[0436] 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).

[0437] 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.

[0438] 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.

[0439] 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).

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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.

[0444] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0445] 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."

[0446] The personal automated coffee garden system of this invention provides the optimal coffee bean cultivation environment by utilizing various sensors, a server that collects and analyzes data, and a user interface. The system monitors soil humidity, temperature, and sunlight hours, and automatically adjusts the environment using an AI algorithm. The specific functions and operations of each element are described below.

[0447] Sensor means for measuring soil moisture

[0448] The device contains a soil moisture sensor that is inserted into the soil and records the soil's moisture content in real time.

[0449] Sensor means for measuring temperature

[0450] The terminal is equipped with a temperature sensor that measures the temperature of the entire cultivation environment and transmits the data to the terminal.

[0451] Sensor means for measuring sunshine duration

[0452] The device is equipped with a light sensor that measures the intensity of ambient light and records the duration of daylight.

[0453] Server means for collecting and analyzing data obtained from each sensor means

[0454] Data from these sensors is periodically sent via the device to a server, which analyzes the data and uses data models to optimize growth conditions and the environment.

[0455] Example: The server analyzes soil moisture data and, if it determines that the moisture content is below the optimum range, it knows that watering is necessary.

[0456] Control measures to control watering and other environmental adjustments based on data analysis results

[0457] Based on the analysis results, the server sends specific instructions for environmental adjustments to the terminal, which controls the watering device, temperature control device, lighting device, etc.

[0458] Example: The server determines that the humidity is low and sends the command to the terminal to "add 100ml of water to the soil." The terminal automatically opens the nozzle and dispenses 100ml of water.

[0459] An interface for users to check growth status and harvest timing

[0460] Users can check the growth status of coffee beans and resource conditions in real time through a smartphone app or web application.

[0461] Example: A user opens the app and sees that the current soil moisture is 45%, the temperature is 22°C, and there are 8 hours of sunshine. They also receive a notification from the server that the best time to harvest is in 3 days.

[0462] This system allows users to easily manage the detailed environment necessary for coffee bean growth. AI and automatic control instantly respond to environmental changes and provide the optimal growing environment, making it easy to grow high-quality coffee at home.

[0463] The processing flow will be explained below.

[0464] Step 1:

[0465] A user installs the smartphone app and creates an account. The user enters their name, email address, and password and taps the register button.

[0466] Step 2:

[0467] The server receives the information entered by the user and stores it in the database. If the save is successful, it notifies the user with a message that the account was created successfully.

[0468] Step 3:

[0469] The device acquires data using sensors such as soil humidity, temperature, and sunlight duration. For example, the humidity sensor acquires soil humidity in real time, and the temperature sensor measures the ambient temperature.

[0470] Step 4:

[0471] The device periodically transmits the acquired data to the server. The device transmits the data from the sensor to the server via Wi-Fi.

[0472] Step 5:

[0473] The server analyzes the data received from the device and uses AI algorithms to optimize the environment, for example determining if soil moisture is below the optimal range.

[0474] Step 6:

[0475] Based on the analysis results, the server instructs the device to take specific actions such as watering or adjusting the temperature. For example, it might send an instruction to the device to "add 100ml of water to the soil."

[0476] Step 7:

[0477] The device receives instructions from the server and executes specific actions, such as operating the watering nozzle to add 100ml of water to the soil.

[0478] Step 8:

[0479] The server continuously monitors the growth status and uses AI algorithms to predict when to harvest, for example by comparing the growth rate with past data to determine whether it is appropriate.

[0480] Step 9:

[0481] When the time for harvesting approaches, the server sends a notification to the user via the app, informing the user that "it's time for harvesting."

[0482] Step 10:

[0483] The user opens the smartphone app and checks the growth dashboard for current growth status and harvest readiness information, such as the current soil moisture of 50%, the temperature of 23°C, and the remaining three days until harvest.

[0484] Example 1

[0485] 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."

[0486] To easily grow high-quality coffee beans at home, it is necessary to accurately manage and appropriately adjust environmental factors such as soil humidity, temperature, and sunlight hours. However, these management tasks are time-consuming and require specialized knowledge, making them difficult for many users. In addition, systems that monitor environmental conditions in real time and automatically adjust them as needed are not widely available. This means that there is a risk that the quality of the coffee beans will decline if the system is unable to respond appropriately to environmental changes.

[0487] 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.

[0488] In this invention, the server includes sensor means for measuring soil humidity, sensor means for measuring temperature, sensor means for measuring sunshine hours, data processing means for collecting and analyzing data acquired from each sensor means, control means for controlling watering and other environmental adjustments based on the results of data analysis, and display means for the user to check the growth status and harvest timing. This allows the user to grasp the growth status in real time and provide an optimal growing environment through automatic control by AI, making it possible to easily cultivate high-quality coffee beans.

[0489] A "sensor for measuring soil moisture" is a device that is inserted into the soil to measure the moisture content of the soil in real time.

[0490] A "sensor for measuring temperature" is a device for measuring the temperature of the entire cultivation environment and collecting that data.

[0491] A "sensor for measuring sunshine duration" is a device for measuring the intensity of ambient light and recording the sunshine duration.

[0492] "Data processing means" refers to a system that analyzes collected data and understands the optimization of the environment and growth status.

[0493] The "control means" is a system that controls watering devices, temperature control devices, lighting devices, etc. based on the analysis results of the data processing means.

[0494] The "display means" is an interface that allows the user to check the growth status and harvest timing, and includes smartphone applications and web applications.

[0495] The personal automated coffee garden system of this invention provides an optimal coffee bean cultivation environment by utilizing various sensors, a server that collects and analyzes data, and a user interface. The system monitors soil humidity, temperature, and sunlight hours, and automatically adjusts the environment using an AI algorithm. The specific functions and operations of each element are described below.

[0496] Data collection by sensor means

[0497] The device is equipped with the following types of sensors:

[0498] 1. Soil Moisture Sensor: Measures soil moisture content in real time. It is inserted into the soil and collects data every hour.

[0499] 2. Temperature sensor: Measures the temperature of the entire cultivation environment. Temperature data is collected every hour.

[0500] 3. Light sensor: Measures the intensity of ambient light and records the hours of sunlight.

[0501] Data collection and analysis by the server

[0502] The data collected from each sensor is periodically sent to a server via the device, where it is analyzed using AI algorithms and data models to optimize the plant's growth and environment.

[0503] Environmental adjustments based on data analysis

[0504] Based on the analysis results, the server sends instructions to the terminal to adjust the environment through the following control means.

[0505] Watering device: When soil moisture is low, it will send watering instructions and automatically supply water.

[0506] Thermostat: Controls heating and cooling systems when temperatures go outside set ranges.

[0507] Lighting equipment: If there is a lack of sunlight, turn on artificial lighting.

[0508] User interface means

[0509] Users can check the growth status of coffee beans and the status of resources in real time through a smartphone app or web application, and can also receive notifications from the server.

[0510] Examples:

[0511] When a user opens the app, they can see that the current soil humidity is 35%, the temperature is 22°C, and there are 8 hours of sunshine.

[0512] The server sends a notification to the user saying, "Best time to harvest in 3 days."

[0513] This system allows users to easily manage the detailed environment necessary for coffee bean growth. AI and automatic control instantly respond to environmental changes and provide the optimal growing environment, making it easy to grow high-quality coffee at home.

[0514] Example prompt sentence:

[0515] "This automated coffee garden system monitors soil moisture, temperature, and sunlight hours in real time, and uses AI algorithms to optimize the environment. Data is collected from sensors, sent to a server for analysis, and automatically takes action such as watering and adjusting lighting when necessary. Users can check growth status and environmental data using a smartphone app or web application. How can we improve this system to grow coffee beans more efficiently?"

[0516] In this way, users can enjoy advanced environmental management using AI while reducing tedious work, making it easy to grow high-quality coffee beans at home.

[0517] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0518] Step 1:

[0519] The device measures the soil moisture using a soil moisture sensor, which is inserted into the soil and collects moisture data as input every hour.

[0520] Specific behavior:

[0521] The soil moisture sensor works to detect the moisture content of the soil.

[0522] The collected humidity data (e.g. 30%) is obtained.

[0523] Input: Current soil moisture value

[0524] Output: Humidity data (e.g. 30%)

[0525] Step 2:

[0526] The device uses a temperature sensor to measure the temperature of the entire growing environment, and temperature data is also collected every hour.

[0527] Specific behavior:

[0528] The temperature sensor is activated to detect the temperature of the entire environment.

[0529] The collected temperature data (e.g., 25°C) is obtained.

[0530] Input: Current ambient temperature

[0531] Output: Temperature data (e.g. 25°C)

[0532] Step 3:

[0533] The device uses a light sensor to measure the intensity of ambient light and record the duration of daylight, which also collects data every hour.

[0534] Specific behavior:

[0535] The light sensor is activated and detects the intensity of the ambient light.

[0536] The collected light data (e.g., 2000 lux) is obtained.

[0537] Input: Ambient light intensity

[0538] Output: Light data (e.g. 2000 lux)

[0539] Step 4:

[0540] The device sends all the data it collects to a server, either via Wi-Fi or a wired connection.

[0541] Specific behavior:

[0542] Humidity data, temperature data, and light data are combined into one data packet.

[0543] The collected data packets are sent to the server.

[0544] Input: Humidity data, temperature data, light data

[0545] Output: Data packets to the server

[0546] Step 5:

[0547] The server stores the received data in a database and begins analyzing it, using machine learning models and statistical analysis.

[0548] Specific behavior:

[0549] The received data is stored in a database in chronological order.

[0550] The saved data is input into an AI algorithm to analyze the optimal cultivation environment.

[0551] Input: Received data packet

[0552] Output: Analysis results (e.g., low humidity, watering required)

[0553] Step 6:

[0554] Based on the analysis results, the server generates appropriate environmental adjustment instructions and sends them to the device, including instructions for watering, adjusting the temperature, and controlling lighting.

[0555] Specific behavior:

[0556] Based on the analysis results, necessary adjustments are determined.

[0557] Generate specific instructions (e.g., add 100ml of water to the soil) and send them to the device.

[0558] Input: Analysis results

[0559] Output: Instructions for adjusting the environment to the terminal

[0560] Step 7:

[0561] The terminal then adjusts the environment based on the received instructions, automatically activating watering devices, temperature control devices, lighting devices, etc.

[0562] Specific behavior:

[0563] When it receives a watering command, it opens the nozzle and delivers the specified amount of water.

[0564] If a temperature adjustment command is received, the heating or cooling device will be activated.

[0565] If a lighting instruction is received, the lighting device is turned on.

[0566] Input: Environmental adjustment instructions

[0567] Output: Performing environmental controls (e.g. water supply, temperature control, lighting)

[0568] Step 8:

[0569] Users can check data in real time via a smartphone app or web application, and can also receive notifications from the server to check growth status and harvest timing.

[0570] Specific behavior:

[0571] Open the app and check the current humidity, temperature, sunshine hours, and more.

[0572] Receive notifications from the server (e.g., Best time to harvest in 3 days).

[0573] Input: Data request from app or web

[0574] Output: Real-time data, notification information

[0575] Through the above processing steps, users can easily cultivate high-quality coffee beans and quickly respond to environmental changes.

[0576] (Application example 1)

[0577] 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."

[0578] With conventional coffee bean cultivation systems, it is difficult to maintain an appropriate cultivation environment at home or in a store, and ensuring an optimal growing environment requires a lot of effort and time. Furthermore, there are few ways to quickly respond to changes in the cultivation environment, resulting in inconsistent quality of the cultivated coffee beans. Furthermore, the coffee brewing method is heavily dependent on the skill and experience of the barista, making it difficult to provide coffee of consistent quality.

[0579] 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.

[0580] In this invention, the server includes a detector for measuring soil humidity, a detector for measuring temperature, a detector for measuring sunshine hours, a calculation unit for collecting and analyzing data acquired from each detector, an operation unit for controlling watering and other environmental adjustments based on the data analysis results, a communication unit for a user to check the growth status and harvest timing, and a unit for acquiring environmental data using smart glasses, analyzing the data on a cloud server, and displaying instructions for optimal environmental adjustments based on the analysis results. This allows for quick response to environmental changes and provides an optimal cultivation environment. Furthermore, by monitoring data in real time through the smart glasses and receiving appropriate instructions, baristas can provide coffee of consistent quality.

[0581] The "detection means for measuring soil moisture" is a device for measuring the amount of moisture contained in soil in real time and acquiring the data.

[0582] The "detection means for measuring temperature" is a device for measuring the temperature of the environment and acquiring the data.

[0583] A "detection means for measuring the hours of sunlight" is a device for measuring the intensity of ambient light and recording the hours of sunlight.

[0584] The "computing means for collecting and analyzing data obtained from each detecting means" is a computer system or server for receiving and analyzing data transmitted from a plurality of sensors.

[0585] "Operational means for controlling watering and other environmental adjustments based on data analysis results" refers to equipment or devices that automatically perform watering, temperature adjustment, and lighting adjustment based on analysis results.

[0586] "Communication means for users to check the growth status and harvest timing" refers to an interface that allows users to check the growth status, environmental data, harvest timing, etc. via a smartphone or web application.

[0587] "Smart glasses" are wearable devices that display data to the wearer in real time and provide instructions based on the analysis results.

[0588] A "cloud server" is a remote server accessible via the Internet, and is a computer system that analyzes and processes data and manages storage.

[0589] The system for implementing the present invention is configured by combining a plurality of detecting means, calculating means, operating means, communication means, and smart glasses. Specific embodiments are described below.

[0590] First, the detecting means for measuring soil humidity, the detecting means for measuring temperature, and the detecting means for measuring sunshine hours are devices that measure soil and environmental temperatures and light intensity in real time and acquire the data, which are then transmitted to a cloud server via wireless communication.

[0591] The cloud server uses software written in programming languages ​​such as Python and Java to analyze the data it receives. It uses AI models to learn and evaluate patterns in the data and generate instructions for optimal environmental adjustments. For example, if the soil moisture level drops, it will determine that more watering is needed. Or, if the room temperature is too high, it will indicate that cooling equipment should be activated.

[0592] The smart glasses are wearable devices that display instructions from a cloud server to baristas in real time. The smart glasses are equipped with an internet connection and display data in conjunction with the cloud server. This allows baristas to instantly check environmental data and optimal behavioral instructions.

[0593] Users can connect to the cloud server via their smartphone or web application and check the growth status of their coffee beans and the cultivation environment in real time. The smartphone app and web application are built using web technologies such as HTML, CSS, and JavaScript, allowing users to easily monitor the cultivation environment and make adjustments as needed.

[0594] As a concrete example, the operation of the barista assistance system can be confirmed by inputting the following prompt sentence into the generative AI model.

[0595] Example prompt:

[0596] "Please explain how the smart glasses-based coffee barista support system works. This system analyzes data on soil humidity, environmental temperature, and sunlight hours on a cloud server, and displays optimal instructions in real time through the smart glasses."

[0597] This clarifies the mechanism of the system, which allows users and baristas to easily obtain environmental data and receive optimal instructions for action based on the analysis results.

[0598] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0599] Step 1:

[0600] Data collection from each detection method

[0601] The device acquires data in real time using sensing means that measure soil humidity, environmental temperature, and sunlight duration. The acquired data includes moisture content data from the soil humidity sensor, air temperature data from the temperature sensor, and light intensity data from the light sensor. The input data is the raw data from each sensor, and the output is a compiled environmental dataset.

[0602] Step 2:

[0603] Sending data to the cloud server

[0604] The device transmits the collected environmental data to a cloud server via wireless communication. In this process, the device assembles the data acquired from each sensor into a single data packet and sends it to the cloud server via the Internet. The input is the environmental dataset, and the output is the data transmitted to the cloud server.

[0605] Step 3:

[0606] Data analysis using a cloud server

[0607] The cloud server analyzes the received environmental data. Using programming languages ​​such as Python and Java, the server runs an AI model and performs analysis based on the data. Here, it evaluates whether the conditions are suitable for growth based on data on soil moisture, temperature, and light intensity. The input data is the environmental dataset sent from the device, and the analysis results are output.

[0608] Step 4:

[0609] Generate optimal environmental adjustment instructions

[0610] The cloud server generates instructions for optimal environmental adjustment based on the results of data analysis. For example, if the soil humidity is low, it will issue a "water" instruction, and if the temperature is too high, it will issue a "cool" instruction. The input is the analysis results, and the output is specific instructions for environmental adjustment.

[0611] Step 5:

[0612] Displaying instructions on smart glasses

[0613] The cloud server sends the generated environmental adjustment instructions to the smart glasses. The smart glasses display these instructions to the barista in real time. Here, processing is performed to display the content of the instructions on the display. The input is the environmental adjustment instructions from the cloud server, and the output is the instruction content displayed on the smart glasses' display.

[0614] Step 6:

[0615] User confirmation of data

[0616] Users can access the cloud server using a smartphone or web application to check the collected environmental data and analysis results. Here, the process of extracting the information necessary for the user to check the data on the screen is carried out. The input is the environmental data and analysis results stored on the cloud server, and the output is the data displayed on the user's screen.

[0617] 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.

[0618] The personal automated coffee garden system of the present invention provides an optimal coffee bean growing environment by including sensors that monitor soil moisture, temperature, and sunlight hours, a server that collects and analyzes data, a user interface, and an emotion engine that recognizes the user's emotions and adjusts the environment accordingly.

[0619] Sensor means for measuring soil moisture

[0620] The device contains a soil moisture sensor that is inserted into the soil to record the moisture content in real time and transmit that data to the device.

[0621] Sensor means for measuring temperature

[0622] The terminal is equipped with a temperature sensor that measures the temperature of the cultivation environment and transmits the data to the terminal.

[0623] Sensor means for measuring sunshine duration

[0624] The device is equipped with a light sensor that measures the intensity of ambient light and records the duration of daylight.

[0625] Server means for collecting and analyzing data obtained from each sensor means

[0626] Data from these sensors is periodically sent to a server via the device, which then analyzes the data and uses AI algorithms to optimize growth conditions and the environment.

[0627] Example: The server analyzes soil moisture data and, if it determines that the moisture content is below the optimum range, it knows that watering is necessary.

[0628] Control measures to control watering and other environmental adjustments based on data analysis results

[0629] Based on the analysis results, the server sends specific instructions for environmental adjustments to the terminal, which controls the watering device, temperature control device, lighting device, etc.

[0630] Example: The server determines that the humidity is low and sends the command to the terminal to "add 100ml of water to the soil." The terminal automatically opens the nozzle and dispenses 100ml of water.

[0631] An interface for users to check growth status and harvest timing

[0632] Users can check the growth status of coffee beans and resource conditions in real time through a smartphone app or web application.

[0633] Example: A user opens the app and sees that the current soil moisture is 45%, the temperature is 22°C, and there are 8 hours of sunshine. They also receive a notification from the server that the best time to harvest is in 3 days.

[0634] Emotion engine that recognizes user emotions

[0635] An emotion engine is installed on the device or the user's smartphone. This engine uses a camera and microphone to recognize emotions from the user's facial expressions and voice, and sends the data to a server.

[0636] Example: When a user uses an app to check their growth status, the camera analyzes their facial expressions and if it determines that they are tired, the server reduces notifications detailing their growth status and provides only concise information.

[0637] Automatic adjustment of the environment by the emotion engine

[0638] The server receives and analyzes data from the emotion engine and proposes or implements automatic adjustments to the environment based on the user's emotional state, allowing the user to enjoy coffee cultivation without stress.

[0639] Example: If the emotion engine recognizes that the user is relaxed, it will provide detailed information on the best time to harvest the coffee to enjoy its aroma.

[0640] This system not only allows users to easily grow high-quality coffee beans, but also provides more personalized support through an emotion engine. The combination of AI, automatic control, and user emotion recognition further enhances the growing experience.

[0641] The processing flow will be explained below.

[0642] Step 1:

[0643] A user installs the smartphone app and creates an account. The user enters their name, email address, and password and taps the register button.

[0644] Step 2:

[0645] The server receives the information entered by the user and stores it in the database. If the save is successful, it notifies the user with a message that the account was created successfully.

[0646] Step 3:

[0647] The device acquires data using sensors such as soil humidity, temperature, and sunlight duration. For example, the humidity sensor acquires soil humidity in real time, and the temperature sensor measures the ambient temperature.

[0648] Step 4:

[0649] The device periodically transmits the acquired data to the server. The device transmits the data from the sensor to the server via Wi-Fi.

[0650] Step 5:

[0651] The server analyzes the data received from the device and uses AI algorithms to optimize the environment, for example determining if soil moisture is below the optimal range.

[0652] Step 6:

[0653] Based on the analysis results, the server instructs the device to take specific actions such as watering or adjusting the temperature. For example, it might send an instruction to the device to "add 100ml of water to the soil."

[0654] Step 7:

[0655] The device receives instructions from the server and executes specific actions, such as operating the watering nozzle to add 100ml of water to the soil.

[0656] Step 8:

[0657] An emotion engine installed in the device or the user's smartphone analyzes the user's facial expressions and voice to recognize emotions. For example, a camera captures the user's facial expressions, and voice recognition technology analyzes the tone of the user's voice.

[0658] Step 9:

[0659] The emotion engine sends the emotion data it recognizes to the server. For example, if it determines that the user is relaxed, the data is sent to the server.

[0660] Step 10:

[0661] The server analyzes the emotional data and adjusts notifications and environmental settings according to the user's emotional state. For example, if the server determines that the user is tired, it will simplify notifications and automatically adjust the frequency of watering plants.

[0662] Step 11:

[0663] The server sends notifications to the user about the growth status and harvest timing, for example, "The best time to harvest is in 3 days."

[0664] Step 12:

[0665] The user opens the smartphone app to check information about the plant's growth and harvest timing, such as the current soil humidity of 50%, the temperature of 23°C, and the remaining three days until harvest.

[0666] Through these steps, users can easily grow high-quality coffee and receive personalized support using emotion recognition technology.

[0667] Example 2

[0668] 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."

[0669] In recent years, the demand for individual coffee bean cultivation has increased, but effective environmental control and management are difficult, especially for beginners. Furthermore, in many cases, it becomes difficult to continue cultivation due to stress or busy schedules. For this reason, there is a demand for a system that allows anyone to easily maintain an optimal cultivation environment and grow coffee beans without stress.

[0670] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0671] In this invention, the server includes sensor means for measuring soil humidity, sensor means for measuring temperature, sensor means for measuring sunshine hours, a data processing device for collecting and analyzing data obtained from each sensor means, an environmental control device for controlling watering and other environmental adjustments based on the results of the data analysis, a user interface device for allowing the user to check the growth status and harvest timing, an emotion recognition device for recognizing the user's emotions and transmitting that data to the data processing device, and a system for causing the environmental control device to suggest or execute environmental adjustments based on data from the emotion recognition device. This makes it possible for anyone to easily maintain an optimal cultivation environment and grow coffee beans without stress.

[0672] 1. "Sensor means for measuring soil moisture" means a device for measuring and collecting data on the moisture content of soil in real time.

[0673] 2. "Sensor means for measuring temperature" means a device for accurately measuring the temperature within a growing environment and collecting that data.

[0674] 3. "Sensor means for measuring daylight hours" means a device for measuring light intensity and daylight hours in a growing environment and collecting such data.

[0675] 4. "Data Processing Device" means a computer system or device for collecting and analyzing data obtained from each sensor means.

[0676] 5. An "environmental control device" is a device that performs environmental adjustments such as watering, temperature control, and lighting control based on the analysis results of the server.

[0677] 6. "User interface device" refers to a display device or application that allows the user to check the system status, the growth status of coffee beans, harvest timing, etc.

[0678] 7. An "emotion recognition device" is a device that analyzes a user's facial expressions and voice to recognize their emotional state.

[0679] 8. "System for proposing or executing environmental adjustment" means a system that makes suggestions or automatic environmental adjustments to optimize the cultivation environment based on data from an emotion recognition device.

[0680] The automated coffee garden system for individuals of the present invention includes multiple sensors, a data processing device, an environmental control device, a user interface device, and an emotion recognition device, which optimize the coffee bean cultivation environment and allow users to enjoy stress-free cultivation.

[0681] Sensor means

[0682] The device is equipped with a soil moisture sensor, a temperature sensor, and a light sensor. The soil moisture sensor measures the moisture content of the soil, the temperature sensor measures the temperature in the cultivation environment, and the light sensor measures the ambient light intensity and sunlight duration. The data collected by these sensors is sent to the device.

[0683] Data Processing Unit

[0684] The server periodically collects and automatically analyzes sensor data sent from the device. The server is equipped with AI algorithms that analyze data such as humidity, temperature, and sunlight duration to generate specific instructions for maintaining an optimal growing environment. For example, if the server determines that the soil humidity is below the optimal range, it will send instructions to the environmental control device to water the plants.

[0685] Environmental Control Device

[0686] The terminal receives instructions from the server and adjusts the cultivation environment. This includes watering devices, temperature control devices, lighting devices, etc., and automatically controls these to optimize the environment. For example, if the server determines that the humidity is low and sends the command to the terminal to "add 100ml of water to the soil," the terminal will open the nozzle and supply 100ml of water to the soil.

[0687] User Interface Device

[0688] Users can use a smartphone app or web application to check the growth status of coffee beans and the condition of resources in real time. The application displays data on soil humidity, temperature, and sunlight hours, and receives notifications from the server with information such as growth status and optimal harvest timing. For example, when a user opens the app, the current soil humidity is 35%, the temperature is 22°C, and the sunlight hours are 8 hours, and the user receives a notification that "the optimal time for harvest is in 3 days."

[0689] emotion recognition device

[0690] An emotion recognition device is installed on the device or the user's smartphone. This device uses a camera and microphone to analyze the user's facial expressions and voice to recognize their emotional state. The recognized emotion data is sent to a server, which then provides information and adjusts the environment based on the analysis results, adapting to the user's emotions. For example, if the emotion recognition device determines that the user is tired, the server will reduce detailed notifications and provide only concise information.

[0691] Environmental adjustment based on emotion analysis

[0692] The server receives data from the emotion recognition device and suggests or automatically adjusts the cultivation environment based on the user's emotional state, allowing the user to relax and enjoy cultivation. For example, if the emotion engine recognizes that the user is relaxed, it will notify the user in detail of the optimal harvesting time to enjoy the aroma of coffee.

[0693] Prompt Sentence Examples

[0694] Please explain in detail each step of the automated personal coffee garden system. In each step, which of the server, device, or user is primarily responsible? What specific actions are performed?

[0695] This system allows users to easily grow high-quality coffee beans and enjoy stress-free cultivation with the support of the emotion engine.

[0696] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0697] Step 1:

[0698] Data collection by sensors

[0699] The device collects environmental data through soil humidity, temperature, and light sensors, which measure soil humidity, temperature, and sunlight hours, respectively, and transmits the collected data to the device.

[0700] Input: Sensor data for soil moisture, temperature, and sunshine hours

[0701] Output: Collected environmental data (e.g. soil humidity 35%, temperature 22°C, sunshine duration 8 hours)

[0702] Specific operation: The soil humidity sensor measures humidity at 35%, the temperature sensor measures 22°C, and the light sensor records 8 hours of sunlight.

[0703] Step 2:

[0704] Sending data to the server

[0705] The device transmits the collected environmental data to the server at regular intervals, using a network protocol.

[0706] Input: Collected environmental data

[0707] Output: Environment data sent to the server

[0708] Specific operation: The device uploads current soil humidity, temperature, and sunshine hours data to the server every 5 minutes using the HTTP protocol.

[0709] Step 3:

[0710] Data analysis

[0711] The server analyzes the received environmental data using AI algorithms and generates specific instructions to maintain an optimal cultivation environment.

[0712] Input: Environment data sent to the server

[0713] Output: Specific instructions for environmental adjustments as a result of data analysis (e.g., amount of watering, temperature adjustment, sunlight adjustment, etc.)

[0714] Specific operation: The server receives data that the humidity is 35%, and the AI ​​algorithm determines that this is below the optimal humidity of 40-60%, and generates the instruction to "add 100ml of water to the soil."

[0715] Step 4:

[0716] Sending environmental control instructions

[0717] The server sends an instruction to adjust the environment to the terminal based on the analysis result.

[0718] Input: Environmental adjustment instructions

[0719] Output: Environmental adjustment instructions sent to the device

[0720] Specific operation: The server sends the instruction "Add 100ml of water to the soil" to the terminal.

[0721] Step 5:

[0722] Automatic environment adjustment

[0723] The terminal adjusts the environment based on the received instructions, automatically controlling watering devices, temperature control devices, lighting devices, etc.

[0724] Input: Environment adjustment instructions from the server

[0725] Output: Environmental adjustment made (e.g., 100ml of water added)

[0726] Specific operation: The device opens the nozzle and automatically dispenses 100ml of water into the soil.

[0727] Step 6:

[0728] User interface display of information

[0729] Users can check environmental data and growth status through a smartphone app or web application.

[0730] Input: Latest data and notifications from the server

[0731] Output: Environmental data and notifications displayed in the application

[0732] Specific behavior: When the user opens the app, the current soil humidity is displayed as 35%, the temperature is 22°C, and there are 8 hours of sunshine, and a notification is displayed saying, "The best time to harvest is in 3 days."

[0733] Step 7:

[0734] Emotion Recognition and Data Transmission

[0735] An emotion recognition device installed on the terminal or the user's smartphone analyzes the user's facial expressions and voice to obtain emotion data, which is then sent to a server.

[0736] Input: User's facial expression and voice data

[0737] Output: Emotion data sent to the server

[0738] Specific operation: While the user is using the app, the smartphone camera reads their facial expressions, the emotion recognition device determines that they are "tired," and sends that data to the server.

[0739] Step 8:

[0740] Environmental adjustment based on emotion analysis

[0741] The server receives the emotional data and makes environmental adjustment suggestions or implementations based on the user's emotional state.

[0742] Input: Emotion data, environmental data

[0743] Output: Suggest or implement environmental adjustments based on emotions (e.g., simplified notifications, detailed notifications on optimal harvest timing)

[0744] Specific behavior: The server receives data that the user is tired and reduces detailed notifications, providing only concise information. If the server determines that the user is relaxed, it notifies the user in detail about the best time to harvest the coffee so that they can enjoy the aroma.

[0745] This series of processing steps allows users to enjoy growing coffee beans stress-free while automatically maintaining the optimal cultivation environment.

[0746] (Application example 2)

[0747] 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."

[0748] Currently, there are many tools and technologies on the market for personal coffee bean cultivation, but no system exists that can maintain an optimal cultivation environment while providing support tailored to the user's emotions. By incorporating emotion recognition, a more personalized experience can be provided, reducing the burden and stress on users while providing an environment for cultivating high-quality coffee beans. However, achieving this requires a system that integrates advanced data collection, analysis, and emotion recognition technologies.

[0749] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0750] In this invention, the server includes sensor means for measuring soil humidity, sensor means for measuring environmental temperature, sensor means for measuring sunshine hours, means for collecting and analyzing data obtained from each sensor means, means for controlling watering and other environmental adjustments based on the results of the data analysis, interface means for the user to check the growth status and harvest timing, means for recognizing the user's emotional state using an emotion engine and transmitting that data to the server, and means for adjusting the cultivation environment and robot operation based on the user's emotional state. This not only enables the user to easily cultivate high-quality coffee beans while maintaining an optimal cultivation environment, but also enables stress-free cultivation work because environmental adjustments and notifications are made based on the user's emotions.

[0751] "Sensor means" refers to devices used to measure soil moisture, environmental temperature, and hours of sunlight.

[0752] The "server means" is a computer system for collecting and analyzing data acquired from each sensor means.

[0753] The "control means" is a device that performs watering and other environmental adjustments based on the data analyzed by the server means.

[0754] "Interface means" refers to the connection means and screen that the user uses to check the growth status of the coffee beans and the timing of harvest.

[0755] An "emotion engine" is a software or hardware component that recognizes a user's emotional state through a camera or microphone and analyzes the data.

[0756] The "recognition means" is a function that uses an emotion engine to recognize the user's emotional state and transmits that data to the server.

[0757] The "cultivation environment adjusting means" is a means for automatically adjusting the cultivation environment and the operation of the robot based on the recognized emotional data of the user.

[0758] "Data collection" is the process of aggregating data acquired by sensor means and transmitting it to server means.

[0759] "High-quality coffee beans" are coffee seeds that meet the quality standards required by users when cultivating them.

[0760] "Reducing stress" refers to reducing the mental burden felt by the user when performing cultivation work.

[0761] The personal automated coffee garden system of the present invention provides an optimal coffee bean cultivation environment by integrating multiple sensors, a server that collects and analyzes data, a user interface, and an emotion engine that recognizes the user's emotions. The system of the present invention is composed of the following components:

[0762] Sensor means

[0763] The system is equipped with sensors to measure soil moisture, ambient temperature, and sunlight duration. These sensors work as follows:

[0764] Soil moisture sensor: Inserted into the soil, it measures the humidity in real time and transmits the data to the device.

[0765] Temperature sensor: Measures the temperature of the cultivation environment and sends the data to the terminal.

[0766] Light sensor: Measures the intensity of ambient light, records the hours of daylight, and sends this data to your device.

[0767] Data collection and analysis server

[0768] The server periodically collects and analyzes the data sent from the sensors. The analysis process includes the following steps:

[0769] Data collection: Collect and store data from each sensor.

[0770] Data analysis: The collected data is analyzed using an AI algorithm to calculate the optimal cultivation conditions.

[0771] Control measures for environmental regulation

[0772] Based on the results of the server's analysis, the following environmental adjustments are made:

[0773] Watering device: Provides the specified amount of water if the soil moisture level is below the specified level.

[0774] Temperature control device: Adjusts the temperature of the growing environment to the optimum range.

[0775] Lighting system: Adjust lighting to provide the required hours of sunlight.

[0776] User Interface

[0777] Users can check the following information in real time through a smartphone app or web application:

[0778] Growing conditions: Current soil moisture, temperature and sunshine data.

[0779] Harvest timing: Notification of optimal harvest time.

[0780] Emotion Engine

[0781] The emotion engine recognizes the user's emotional state using a camera and microphone and sends that data to the server, which then adjusts the cultivation environment and notifications accordingly.

[0782] Specific examples

[0783] Consider the following specific use case:

[0784] 1. Data collection: The soil moisture sensor detects 40% humidity, the temperature sensor records 22°C, and the light sensor records 8 hours of sunlight.

[0785] 2. Data analysis: The server analyzes this data and determines that the humidity is below the optimum range, indicating that watering is necessary.

[0786] 3. Environmental adjustment: The server sends an instruction to the watering device to add 100 ml of water, which the device executes.

[0787] 4. Emotion recognition: When the user checks the growth status on their smartphone, the camera analyzes the user's facial expression and recognizes that they are relaxed.

[0788] 5. Notification adjustment: The emotion engine provides detailed harvest timing notifications according to the user's relaxation state.

[0789] Prompt Sentence Examples

[0790] "Generates prompts based on the user's emotional state to determine if watering is necessary within the next 30 minutes."

[0791] "If the current soil moisture is not within the optimal range, generate a prompt with instructions on how much water to water."

[0792] The system of the present invention not only allows users to easily cultivate high-quality coffee beans, but also allows them to enjoy the cultivation process without feeling stressed thanks to the emotion-based environmental adjustment function.

[0793] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0794] Step 1: Collecting data with sensors

[0795] The device receives soil humidity data from the soil humidity sensor, ambient temperature data from the temperature sensor, and sunlight duration data from the light sensor. These sensors perform measurements periodically and generate data in real time.

[0796] Input: soil humidity, ambient temperature, sunshine hours

[0797] Output: Measured data for each sensor (e.g. soil humidity 40%, ambient temperature 22°C, sunshine duration 8 hours)

[0798] Step 2: Send data to the server

[0799] The device sends the sensor data acquired in step 1 to the server. The data is sent automatically at regular intervals.

[0800] Input: Data from each sensor

[0801] Output: The data sent.

[0802] Step 3: Data collection by the server

[0803] The server receives the data sent from the terminal and stores it in a database.

[0804] Input: Data sent from the terminal

[0805] Output: Saved data

[0806] Step 4: Data analysis

[0807] The server analyzes the collected data using AI algorithms to assess whether each environmental parameter is within an optimal range, for example, checking whether soil moisture is within the appropriate range (40-60%).

[0808] Input: Saved data

[0809] Output: Optimal conditions determined (e.g. soil moisture is low, watering is required)

[0810] Step 5: Generate environmental adjustment instructions

[0811] The server generates instructions for necessary environmental adjustments based on the results of data analysis, such as watering instructions if humidity is low.

[0812] Input: Data analysis results

[0813] Output: Environmental adjustment instructions (e.g., water 100ml)

[0814] Step 6: Send instructions to the device

[0815] The server transmits the generated environment adjustment instruction to the terminal.

[0816] Input: Environmental adjustment instructions

[0817] Output: Instructions sent

[0818] Step 7: Perform environmental adjustments

[0819] The terminal controls watering devices, temperature control devices, lighting devices, etc. based on instructions from the server, and performs the necessary environmental adjustments.

[0820] Input: Environment adjustment instructions from the server

[0821] Output: Environmental adjustment performed (e.g., 100ml watering)

[0822] Step 8: Collect emotion data

[0823] The device uses a camera and microphone to recognize the user's emotional state and collect that data.

[0824] Input: User's facial expression, voice

[0825] Output: Recognized emotion data (e.g., user is relaxed)

[0826] Step 9: Send emotion data to the server

[0827] The terminal transmits the collected emotion data to the server.

[0828] Input: Recognized emotion data

[0829] Output: Sent emotion data

[0830] Step 10: Sentiment Data Analysis

[0831] The server analyzes the received emotional data and assesses the user's current emotional state.

[0832] Input: Emotion data sent

[0833] Output: Evaluation of the emotional state (e.g., whether the user is relaxed)

[0834] Step 11: Adjust notification content

[0835] The server tailors the notifications to the user based on the evaluation of the user's emotional state, for example providing more detailed notifications if the user is relaxed.

[0836] Input: Emotional state assessment result

[0837] Output: Adjusted notification content (e.g. detailed notification of harvest time)

[0838] Step 12: Notify the user interface

[0839] The server sends the adjusted notification content to the user interface for the user to review.

[0840] Input: Adjusted notification content

[0841] Output: Information displayed on a user interface (e.g., detailed harvest time display on a smartphone app)

[0842] This processing step not only enables the user to maintain an optimal coffee bean cultivation environment at all times, but also provides appropriate information according to the user's emotional state, allowing for a comfortable cultivation experience.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] [Third embodiment]

[0847] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0848] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0849] 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).

[0850] 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.

[0851] 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.

[0852] 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).

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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."

[0859] The personal automated coffee garden system of this invention provides the optimal coffee bean cultivation environment by utilizing various sensors, a server that collects and analyzes data, and a user interface. The system monitors soil humidity, temperature, and sunlight hours, and automatically adjusts the environment using an AI algorithm. The specific functions and operations of each element are described below.

[0860] Sensor means for measuring soil moisture

[0861] The device contains a soil moisture sensor that is inserted into the soil and records the soil's moisture content in real time.

[0862] Sensor means for measuring temperature

[0863] The terminal is equipped with a temperature sensor that measures the temperature of the entire cultivation environment and transmits the data to the terminal.

[0864] Sensor means for measuring sunshine duration

[0865] The device is equipped with a light sensor that measures the intensity of ambient light and records the duration of daylight.

[0866] Server means for collecting and analyzing data obtained from each sensor means

[0867] Data from these sensors is periodically sent via the device to a server, which analyzes the data and uses data models to optimize growth conditions and the environment.

[0868] Example: The server analyzes soil moisture data and, if it determines that the moisture content is below the optimum range, it knows that watering is necessary.

[0869] Control measures to control watering and other environmental adjustments based on data analysis results

[0870] Based on the analysis results, the server sends specific instructions for environmental adjustments to the terminal, which controls the watering device, temperature control device, lighting device, etc.

[0871] Example: The server determines that the humidity is low and sends the command to the terminal to "add 100ml of water to the soil." The terminal automatically opens the nozzle and dispenses 100ml of water.

[0872] An interface for users to check growth status and harvest timing

[0873] Users can check the growth status of coffee beans and resource conditions in real time through a smartphone app or web application.

[0874] Example: A user opens the app and sees that the current soil moisture is 45%, the temperature is 22°C, and there are 8 hours of sunshine. They also receive a notification from the server that the best time to harvest is in 3 days.

[0875] This system allows users to easily manage the detailed environment necessary for coffee bean growth. AI and automatic control instantly respond to environmental changes and provide the optimal growing environment, making it easy to grow high-quality coffee at home.

[0876] The processing flow will be explained below.

[0877] Step 1:

[0878] A user installs the smartphone app and creates an account. The user enters their name, email address, and password and taps the register button.

[0879] Step 2:

[0880] The server receives the information entered by the user and stores it in the database. If the save is successful, it notifies the user with a message that the account was created successfully.

[0881] Step 3:

[0882] The device acquires data using sensors such as soil humidity, temperature, and sunlight duration. For example, the humidity sensor acquires soil humidity in real time, and the temperature sensor measures the ambient temperature.

[0883] Step 4:

[0884] The device periodically transmits the acquired data to the server. The device transmits the data from the sensor to the server via Wi-Fi.

[0885] Step 5:

[0886] The server analyzes the data received from the device and uses AI algorithms to optimize the environment, for example determining if soil moisture is below the optimal range.

[0887] Step 6:

[0888] Based on the analysis results, the server instructs the device to take specific actions such as watering or adjusting the temperature. For example, it might send an instruction to the device to "add 100ml of water to the soil."

[0889] Step 7:

[0890] The device receives instructions from the server and executes specific actions, such as operating the watering nozzle to add 100ml of water to the soil.

[0891] Step 8:

[0892] The server continuously monitors the growth status and uses AI algorithms to predict when to harvest, for example by comparing the growth rate with past data to determine whether it is appropriate.

[0893] Step 9:

[0894] When the time for harvesting approaches, the server sends a notification to the user via the app, informing the user that "it's time for harvesting."

[0895] Step 10:

[0896] The user opens the smartphone app and checks the growth dashboard for current growth status and harvest readiness information, such as the current soil moisture of 50%, the temperature of 23°C, and the remaining three days until harvest.

[0897] Example 1

[0898] 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."

[0899] To easily grow high-quality coffee beans at home, it is necessary to accurately manage and appropriately adjust environmental factors such as soil humidity, temperature, and sunlight hours. However, these management tasks are time-consuming and require specialized knowledge, making them difficult for many users. In addition, systems that monitor environmental conditions in real time and automatically adjust them as needed are not widely available. This means that there is a risk that the quality of the coffee beans will decline if the system is unable to respond appropriately to environmental changes.

[0900] 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.

[0901] In this invention, the server includes sensor means for measuring soil humidity, sensor means for measuring temperature, sensor means for measuring sunshine hours, data processing means for collecting and analyzing data acquired from each sensor means, control means for controlling watering and other environmental adjustments based on the results of data analysis, and display means for the user to check the growth status and harvest timing. This allows the user to grasp the growth status in real time and provide an optimal growing environment through automatic control by AI, making it possible to easily cultivate high-quality coffee beans.

[0902] A "sensor for measuring soil moisture" is a device that is inserted into the soil to measure the moisture content of the soil in real time.

[0903] A "sensor for measuring temperature" is a device for measuring the temperature of the entire cultivation environment and collecting that data.

[0904] A "sensor for measuring sunshine duration" is a device for measuring the intensity of ambient light and recording the sunshine duration.

[0905] "Data processing means" refers to a system that analyzes collected data and understands the optimization of the environment and growth status.

[0906] The "control means" is a system that controls watering devices, temperature control devices, lighting devices, etc. based on the analysis results of the data processing means.

[0907] The "display means" is an interface that allows the user to check the growth status and harvest timing, and includes smartphone applications and web applications.

[0908] The personal automated coffee garden system of this invention provides an optimal coffee bean cultivation environment by utilizing various sensors, a server that collects and analyzes data, and a user interface. The system monitors soil humidity, temperature, and sunlight hours, and automatically adjusts the environment using an AI algorithm. The specific functions and operations of each element are described below.

[0909] Data collection by sensor means

[0910] The device is equipped with the following types of sensors:

[0911] 1. Soil Moisture Sensor: Measures soil moisture content in real time. It is inserted into the soil and collects data every hour.

[0912] 2. Temperature sensor: Measures the temperature of the entire cultivation environment. Temperature data is collected every hour.

[0913] 3. Light sensor: Measures the intensity of ambient light and records the hours of sunlight.

[0914] Data collection and analysis by the server

[0915] The data collected from each sensor is periodically sent to a server via the device, where it is analyzed using AI algorithms and data models to optimize the plant's growth and environment.

[0916] Environmental adjustments based on data analysis

[0917] Based on the analysis results, the server sends instructions to the terminal to adjust the environment through the following control means.

[0918] Watering device: When soil moisture is low, it will send watering instructions and automatically supply water.

[0919] Thermostat: Controls heating and cooling systems when temperatures go outside set ranges.

[0920] Lighting equipment: If there is a lack of sunlight, turn on artificial lighting.

[0921] User interface means

[0922] Users can check the growth status of coffee beans and the status of resources in real time through a smartphone app or web application, and can also receive notifications from the server.

[0923] Examples:

[0924] When a user opens the app, they can see that the current soil humidity is 35%, the temperature is 22°C, and there are 8 hours of sunshine.

[0925] The server sends a notification to the user saying, "Best time to harvest in 3 days."

[0926] This system allows users to easily manage the detailed environment necessary for coffee bean growth. AI and automatic control instantly respond to environmental changes and provide the optimal growing environment, making it easy to grow high-quality coffee at home.

[0927] Example prompt sentence:

[0928] "This automated coffee garden system monitors soil moisture, temperature, and sunlight hours in real time, and uses AI algorithms to optimize the environment. Data is collected from sensors, sent to a server for analysis, and automatically takes action such as watering and adjusting lighting when necessary. Users can check growth status and environmental data using a smartphone app or web application. How can we improve this system to grow coffee beans more efficiently?"

[0929] In this way, users can enjoy advanced environmental management using AI while reducing tedious work, making it easy to grow high-quality coffee beans at home.

[0930] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0931] Step 1:

[0932] The device measures the soil moisture using a soil moisture sensor, which is inserted into the soil and collects moisture data as input every hour.

[0933] Specific behavior:

[0934] The soil moisture sensor works to detect the moisture content of the soil.

[0935] The collected humidity data (e.g. 30%) is obtained.

[0936] Input: Current soil moisture value

[0937] Output: Humidity data (e.g. 30%)

[0938] Step 2:

[0939] The device uses a temperature sensor to measure the temperature of the entire growing environment, and temperature data is also collected every hour.

[0940] Specific behavior:

[0941] The temperature sensor is activated to detect the temperature of the entire environment.

[0942] The collected temperature data (e.g., 25°C) is obtained.

[0943] Input: Current ambient temperature

[0944] Output: Temperature data (e.g. 25°C)

[0945] Step 3:

[0946] The device uses a light sensor to measure the intensity of ambient light and record the duration of daylight, which also collects data every hour.

[0947] Specific behavior:

[0948] The light sensor is activated and detects the intensity of the ambient light.

[0949] The collected light data (e.g., 2000 lux) is obtained.

[0950] Input: Ambient light intensity

[0951] Output: Light data (e.g. 2000 lux)

[0952] Step 4:

[0953] The device sends all the data it collects to a server, either via Wi-Fi or a wired connection.

[0954] Specific behavior:

[0955] Humidity data, temperature data, and light data are combined into one data packet.

[0956] The collected data packets are sent to the server.

[0957] Input: Humidity data, temperature data, light data

[0958] Output: Data packets to the server

[0959] Step 5:

[0960] The server stores the received data in a database and begins analyzing it, using machine learning models and statistical analysis.

[0961] Specific behavior:

[0962] The received data is stored in a database in chronological order.

[0963] The saved data is input into an AI algorithm to analyze the optimal cultivation environment.

[0964] Input: Received data packet

[0965] Output: Analysis results (e.g., low humidity, watering required)

[0966] Step 6:

[0967] Based on the analysis results, the server generates appropriate environmental adjustment instructions and sends them to the device, including instructions for watering, adjusting the temperature, and controlling lighting.

[0968] Specific behavior:

[0969] Based on the analysis results, necessary adjustments are determined.

[0970] Generate specific instructions (e.g., add 100ml of water to the soil) and send them to the device.

[0971] Input: Analysis results

[0972] Output: Instructions for adjusting the environment to the terminal

[0973] Step 7:

[0974] The terminal then adjusts the environment based on the received instructions, automatically activating watering devices, temperature control devices, lighting devices, etc.

[0975] Specific behavior:

[0976] When it receives a watering command, it opens the nozzle and delivers the specified amount of water.

[0977] If a temperature adjustment command is received, the heating or cooling device will be activated.

[0978] If a lighting instruction is received, the lighting device is turned on.

[0979] Input: Environmental adjustment instructions

[0980] Output: Performing environmental controls (e.g. water supply, temperature control, lighting)

[0981] Step 8:

[0982] Users can check data in real time via a smartphone app or web application, and can also receive notifications from the server to check growth status and harvest timing.

[0983] Specific behavior:

[0984] Open the app and check the current humidity, temperature, sunshine hours, and more.

[0985] Receive notifications from the server (e.g., Best time to harvest in 3 days).

[0986] Input: Data request from app or web

[0987] Output: Real-time data, notification information

[0988] Through the above processing steps, users can easily cultivate high-quality coffee beans and quickly respond to environmental changes.

[0989] (Application example 1)

[0990] 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."

[0991] With conventional coffee bean cultivation systems, it is difficult to maintain an appropriate cultivation environment at home or in a store, and ensuring an optimal growing environment requires a lot of effort and time. Furthermore, there are few ways to quickly respond to changes in the cultivation environment, resulting in inconsistent quality of the cultivated coffee beans. Furthermore, the coffee brewing method is heavily dependent on the skill and experience of the barista, making it difficult to provide coffee of consistent quality.

[0992] 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.

[0993] In this invention, the server includes a detector for measuring soil humidity, a detector for measuring temperature, a detector for measuring sunshine hours, a calculation unit for collecting and analyzing data acquired from each detector, an operation unit for controlling watering and other environmental adjustments based on the data analysis results, a communication unit for a user to check the growth status and harvest timing, and a unit for acquiring environmental data using smart glasses, analyzing the data on a cloud server, and displaying instructions for optimal environmental adjustments based on the analysis results. This allows for quick response to environmental changes and provides an optimal cultivation environment. Furthermore, by monitoring data in real time through the smart glasses and receiving appropriate instructions, baristas can provide coffee of consistent quality.

[0994] The "detection means for measuring soil moisture" is a device for measuring the amount of moisture contained in soil in real time and acquiring the data.

[0995] The "detection means for measuring temperature" is a device for measuring the temperature of the environment and acquiring the data.

[0996] A "detection means for measuring the hours of sunlight" is a device for measuring the intensity of ambient light and recording the hours of sunlight.

[0997] The "computing means for collecting and analyzing data obtained from each detecting means" is a computer system or server for receiving and analyzing data transmitted from a plurality of sensors.

[0998] "Operational means for controlling watering and other environmental adjustments based on data analysis results" refers to equipment or devices that automatically perform watering, temperature adjustment, and lighting adjustment based on analysis results.

[0999] "Communication means for users to check the growth status and harvest timing" refers to an interface that allows users to check the growth status, environmental data, harvest timing, etc. via a smartphone or web application.

[1000] "Smart glasses" are wearable devices that display data to the wearer in real time and provide instructions based on the analysis results.

[1001] A "cloud server" is a remote server accessible via the Internet, and is a computer system that analyzes and processes data and manages storage.

[1002] The system for implementing the present invention is configured by combining a plurality of detecting means, calculating means, operating means, communication means, and smart glasses. Specific embodiments are described below.

[1003] First, the detecting means for measuring soil humidity, the detecting means for measuring temperature, and the detecting means for measuring sunshine hours are devices that measure soil and environmental temperatures and light intensity in real time and acquire the data, which are then transmitted to a cloud server via wireless communication.

[1004] The cloud server uses software written in programming languages ​​such as Python and Java to analyze the data it receives. It uses AI models to learn and evaluate patterns in the data and generate instructions for optimal environmental adjustments. For example, if the soil moisture level drops, it will determine that more watering is needed. Or, if the room temperature is too high, it will indicate that cooling equipment should be activated.

[1005] The smart glasses are wearable devices that display instructions from a cloud server to baristas in real time. The smart glasses are equipped with an internet connection and display data in conjunction with the cloud server. This allows baristas to instantly check environmental data and optimal behavioral instructions.

[1006] Users can connect to the cloud server via their smartphone or web application and check the growth status of their coffee beans and the cultivation environment in real time. The smartphone app and web application are built using web technologies such as HTML, CSS, and JavaScript, allowing users to easily monitor the cultivation environment and make adjustments as needed.

[1007] As a concrete example, the operation of the barista assistance system can be confirmed by inputting the following prompt sentence into the generative AI model.

[1008] Example prompt:

[1009] "Please explain how the smart glasses-based coffee barista support system works. This system analyzes data on soil humidity, environmental temperature, and sunlight hours on a cloud server, and displays optimal instructions in real time through the smart glasses."

[1010] This clarifies the mechanism of the system, which allows users and baristas to easily obtain environmental data and receive optimal instructions for action based on the analysis results.

[1011] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1012] Step 1:

[1013] Data collection from each detection method

[1014] The device acquires data in real time using sensing means that measure soil humidity, environmental temperature, and sunlight duration. The acquired data includes moisture content data from the soil humidity sensor, air temperature data from the temperature sensor, and light intensity data from the light sensor. The input data is the raw data from each sensor, and the output is a compiled environmental dataset.

[1015] Step 2:

[1016] Sending data to the cloud server

[1017] The device transmits the collected environmental data to a cloud server via wireless communication. In this process, the device assembles the data acquired from each sensor into a single data packet and sends it to the cloud server via the Internet. The input is the environmental dataset, and the output is the data transmitted to the cloud server.

[1018] Step 3:

[1019] Data analysis using a cloud server

[1020] The cloud server analyzes the received environmental data. Using programming languages ​​such as Python and Java, the server runs an AI model and performs analysis based on the data. Here, it evaluates whether the conditions are suitable for growth based on data on soil moisture, temperature, and light intensity. The input data is the environmental dataset sent from the device, and the analysis results are output.

[1021] Step 4:

[1022] Generate optimal environmental adjustment instructions

[1023] The cloud server generates instructions for optimal environmental adjustment based on the results of data analysis. For example, if the soil humidity is low, it will issue a "water" instruction, and if the temperature is too high, it will issue a "cool" instruction. The input is the analysis results, and the output is specific instructions for environmental adjustment.

[1024] Step 5:

[1025] Displaying instructions on smart glasses

[1026] The cloud server sends the generated environmental adjustment instructions to the smart glasses. The smart glasses display these instructions to the barista in real time. Here, processing is performed to display the content of the instructions on the display. The input is the environmental adjustment instructions from the cloud server, and the output is the instruction content displayed on the smart glasses' display.

[1027] Step 6:

[1028] User confirmation of data

[1029] Users can access the cloud server using a smartphone or web application to check the collected environmental data and analysis results. Here, the process of extracting the information necessary for the user to check the data on the screen is carried out. The input is the environmental data and analysis results stored on the cloud server, and the output is the data displayed on the user's screen.

[1030] 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.

[1031] The personal automated coffee garden system of the present invention provides an optimal coffee bean growing environment by including sensors that monitor soil moisture, temperature, and sunlight hours, a server that collects and analyzes data, a user interface, and an emotion engine that recognizes the user's emotions and adjusts the environment accordingly.

[1032] Sensor means for measuring soil moisture

[1033] The device contains a soil moisture sensor that is inserted into the soil to record the moisture content in real time and transmit that data to the device.

[1034] Sensor means for measuring temperature

[1035] The terminal is equipped with a temperature sensor that measures the temperature of the cultivation environment and transmits the data to the terminal.

[1036] Sensor means for measuring sunshine duration

[1037] The device is equipped with a light sensor that measures the intensity of ambient light and records the duration of daylight.

[1038] Server means for collecting and analyzing data obtained from each sensor means

[1039] Data from these sensors is periodically sent to a server via the device, which then analyzes the data and uses AI algorithms to optimize growth conditions and the environment.

[1040] Example: The server analyzes soil moisture data and, if it determines that the moisture content is below the optimum range, it knows that watering is necessary.

[1041] Control measures to control watering and other environmental adjustments based on data analysis results

[1042] Based on the analysis results, the server sends specific instructions for environmental adjustments to the terminal, which controls the watering device, temperature control device, lighting device, etc.

[1043] Example: The server determines that the humidity is low and sends the command to the terminal to "add 100ml of water to the soil." The terminal automatically opens the nozzle and dispenses 100ml of water.

[1044] An interface for users to check growth status and harvest timing

[1045] Users can check the growth status of coffee beans and resource conditions in real time through a smartphone app or web application.

[1046] Example: A user opens the app and sees that the current soil moisture is 45%, the temperature is 22°C, and there are 8 hours of sunshine. They also receive a notification from the server that the best time to harvest is in 3 days.

[1047] Emotion engine that recognizes user emotions

[1048] An emotion engine is installed on the device or the user's smartphone. This engine uses a camera and microphone to recognize emotions from the user's facial expressions and voice, and sends the data to a server.

[1049] Example: When a user uses an app to check their growth status, the camera analyzes their facial expressions and if it determines that they are tired, the server reduces notifications detailing their growth status and provides only concise information.

[1050] Automatic adjustment of the environment by the emotion engine

[1051] The server receives and analyzes data from the emotion engine and proposes or implements automatic adjustments to the environment based on the user's emotional state, allowing the user to enjoy coffee cultivation without stress.

[1052] Example: If the emotion engine recognizes that the user is relaxed, it will provide detailed information on the best time to harvest the coffee to enjoy its aroma.

[1053] This system not only allows users to easily grow high-quality coffee beans, but also provides more personalized support through an emotion engine. The combination of AI, automatic control, and user emotion recognition further enhances the growing experience.

[1054] The processing flow will be explained below.

[1055] Step 1:

[1056] A user installs the smartphone app and creates an account. The user enters their name, email address, and password and taps the register button.

[1057] Step 2:

[1058] The server receives the information entered by the user and stores it in the database. If the save is successful, it notifies the user with a message that the account was created successfully.

[1059] Step 3:

[1060] The device acquires data using sensors such as soil humidity, temperature, and sunlight duration. For example, the humidity sensor acquires soil humidity in real time, and the temperature sensor measures the ambient temperature.

[1061] Step 4:

[1062] The device periodically transmits the acquired data to the server. The device transmits the data from the sensor to the server via Wi-Fi.

[1063] Step 5:

[1064] The server analyzes the data received from the device and uses AI algorithms to optimize the environment, for example determining if soil moisture is below the optimal range.

[1065] Step 6:

[1066] Based on the analysis results, the server instructs the device to take specific actions such as watering or adjusting the temperature. For example, it might send an instruction to the device to "add 100ml of water to the soil."

[1067] Step 7:

[1068] The device receives instructions from the server and executes specific actions, such as operating the watering nozzle to add 100ml of water to the soil.

[1069] Step 8:

[1070] An emotion engine installed in the device or the user's smartphone analyzes the user's facial expressions and voice to recognize emotions. For example, a camera captures the user's facial expressions, and voice recognition technology analyzes the tone of the user's voice.

[1071] Step 9:

[1072] The emotion engine sends the emotion data it recognizes to the server. For example, if it determines that the user is relaxed, the data is sent to the server.

[1073] Step 10:

[1074] The server analyzes the emotional data and adjusts notifications and environmental settings according to the user's emotional state. For example, if the server determines that the user is tired, it will simplify notifications and automatically adjust the frequency of watering plants.

[1075] Step 11:

[1076] The server sends notifications to the user about the growth status and harvest timing, for example, "The best time to harvest is in 3 days."

[1077] Step 12:

[1078] The user opens the smartphone app to check information about the plant's growth and harvest timing, such as the current soil humidity of 50%, the temperature of 23°C, and the remaining three days until harvest.

[1079] Through these steps, users can easily grow high-quality coffee and receive personalized support using emotion recognition technology.

[1080] Example 2

[1081] 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."

[1082] In recent years, the demand for individual coffee bean cultivation has increased, but effective environmental control and management are difficult, especially for beginners. Furthermore, in many cases, it becomes difficult to continue cultivation due to stress or busy schedules. For this reason, there is a demand for a system that allows anyone to easily maintain an optimal cultivation environment and grow coffee beans without stress.

[1083] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1084] In this invention, the server includes sensor means for measuring soil humidity, sensor means for measuring temperature, sensor means for measuring sunshine hours, a data processing device for collecting and analyzing data obtained from each sensor means, an environmental control device for controlling watering and other environmental adjustments based on the results of the data analysis, a user interface device for allowing the user to check the growth status and harvest timing, an emotion recognition device for recognizing the user's emotions and transmitting that data to the data processing device, and a system for causing the environmental control device to suggest or execute environmental adjustments based on data from the emotion recognition device. This makes it possible for anyone to easily maintain an optimal cultivation environment and grow coffee beans without stress.

[1085] 1. "Sensor means for measuring soil moisture" means a device for measuring and collecting data on the moisture content of soil in real time.

[1086] 2. "Sensor means for measuring temperature" means a device for accurately measuring the temperature within a growing environment and collecting that data.

[1087] 3. "Sensor means for measuring daylight hours" means a device for measuring light intensity and daylight hours in a growing environment and collecting such data.

[1088] 4. "Data Processing Device" means a computer system or device for collecting and analyzing data obtained from each sensor means.

[1089] 5. An "environmental control device" is a device that performs environmental adjustments such as watering, temperature control, and lighting control based on the analysis results of the server.

[1090] 6. "User interface device" refers to a display device or application that allows the user to check the system status, the growth status of coffee beans, harvest timing, etc.

[1091] 7. An "emotion recognition device" is a device that analyzes a user's facial expressions and voice to recognize their emotional state.

[1092] 8. "System for proposing or executing environmental adjustment" means a system that makes suggestions or automatic environmental adjustments to optimize the cultivation environment based on data from an emotion recognition device.

[1093] The automated coffee garden system for individuals of the present invention includes multiple sensors, a data processing device, an environmental control device, a user interface device, and an emotion recognition device, which optimize the coffee bean cultivation environment and allow users to enjoy stress-free cultivation.

[1094] Sensor means

[1095] The device is equipped with a soil moisture sensor, a temperature sensor, and a light sensor. The soil moisture sensor measures the moisture content of the soil, the temperature sensor measures the temperature in the cultivation environment, and the light sensor measures the ambient light intensity and sunlight duration. The data collected by these sensors is sent to the device.

[1096] Data Processing Unit

[1097] The server periodically collects and automatically analyzes sensor data sent from the device. The server is equipped with AI algorithms that analyze data such as humidity, temperature, and sunlight duration to generate specific instructions for maintaining an optimal growing environment. For example, if the server determines that the soil humidity is below the optimal range, it will send instructions to the environmental control device to water the plants.

[1098] Environmental Control Device

[1099] The terminal receives instructions from the server and adjusts the cultivation environment. This includes watering devices, temperature control devices, lighting devices, etc., and automatically controls these to optimize the environment. For example, if the server determines that the humidity is low and sends the command to the terminal to "add 100ml of water to the soil," the terminal will open the nozzle and supply 100ml of water to the soil.

[1100] User Interface Device

[1101] Users can use a smartphone app or web application to check the growth status of coffee beans and the condition of resources in real time. The application displays data on soil humidity, temperature, and sunlight hours, and receives notifications from the server with information such as growth status and optimal harvest timing. For example, when a user opens the app, the current soil humidity is 35%, the temperature is 22°C, and the sunlight hours are 8 hours, and the user receives a notification that "the optimal time for harvest is in 3 days."

[1102] emotion recognition device

[1103] An emotion recognition device is installed on the device or the user's smartphone. This device uses a camera and microphone to analyze the user's facial expressions and voice to recognize their emotional state. The recognized emotion data is sent to a server, which then provides information and adjusts the environment based on the analysis results, adapting to the user's emotions. For example, if the emotion recognition device determines that the user is tired, the server will reduce detailed notifications and provide only concise information.

[1104] Environmental adjustment based on emotion analysis

[1105] The server receives data from the emotion recognition device and suggests or automatically adjusts the cultivation environment based on the user's emotional state, allowing the user to relax and enjoy cultivation. For example, if the emotion engine recognizes that the user is relaxed, it will notify the user in detail of the optimal harvesting time to enjoy the aroma of coffee.

[1106] Prompt Sentence Examples

[1107] Please explain in detail each step of the automated personal coffee garden system. In each step, which of the server, device, or user is primarily responsible? What specific actions are performed?

[1108] This system allows users to easily grow high-quality coffee beans and enjoy stress-free cultivation with the support of the emotion engine.

[1109] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1110] Step 1:

[1111] Data collection by sensors

[1112] The device collects environmental data through soil humidity, temperature, and light sensors, which measure soil humidity, temperature, and sunlight hours, respectively, and transmits the collected data to the device.

[1113] Input: Sensor data for soil moisture, temperature, and sunshine hours

[1114] Output: Collected environmental data (e.g. soil humidity 35%, temperature 22°C, sunshine duration 8 hours)

[1115] Specific operation: The soil humidity sensor measures humidity at 35%, the temperature sensor measures 22°C, and the light sensor records 8 hours of sunlight.

[1116] Step 2:

[1117] Sending data to the server

[1118] The device transmits the collected environmental data to the server at regular intervals, using a network protocol.

[1119] Input: Collected environmental data

[1120] Output: Environment data sent to the server

[1121] Specific operation: The device uploads current soil humidity, temperature, and sunshine hours data to the server every 5 minutes using the HTTP protocol.

[1122] Step 3:

[1123] Data analysis

[1124] The server analyzes the received environmental data using AI algorithms and generates specific instructions to maintain an optimal cultivation environment.

[1125] Input: Environment data sent to the server

[1126] Output: Specific instructions for environmental adjustments as a result of data analysis (e.g., amount of watering, temperature adjustment, sunlight adjustment, etc.)

[1127] Specific operation: The server receives data that the humidity is 35%, and the AI ​​algorithm determines that this is below the optimal humidity of 40-60%, and generates the instruction to "add 100ml of water to the soil."

[1128] Step 4:

[1129] Sending environmental control instructions

[1130] The server sends an instruction to adjust the environment to the terminal based on the analysis result.

[1131] Input: Environmental adjustment instructions

[1132] Output: Environmental adjustment instructions sent to the device

[1133] Specific operation: The server sends the instruction "Add 100ml of water to the soil" to the terminal.

[1134] Step 5:

[1135] Automatic environment adjustment

[1136] The terminal adjusts the environment based on the received instructions, automatically controlling watering devices, temperature control devices, lighting devices, etc.

[1137] Input: Environment adjustment instructions from the server

[1138] Output: Environmental adjustment made (e.g., 100ml of water added)

[1139] Specific operation: The device opens the nozzle and automatically dispenses 100ml of water into the soil.

[1140] Step 6:

[1141] User interface display of information

[1142] Users can check environmental data and growth status through a smartphone app or web application.

[1143] Input: Latest data and notifications from the server

[1144] Output: Environmental data and notifications displayed in the application

[1145] Specific behavior: When the user opens the app, the current soil humidity is displayed as 35%, the temperature is 22°C, and there are 8 hours of sunshine, and a notification is displayed saying, "The best time to harvest is in 3 days."

[1146] Step 7:

[1147] Emotion Recognition and Data Transmission

[1148] An emotion recognition device installed on the terminal or the user's smartphone analyzes the user's facial expressions and voice to obtain emotion data, which is then sent to a server.

[1149] Input: User's facial expression and voice data

[1150] Output: Emotion data sent to the server

[1151] Specific operation: While the user is using the app, the smartphone camera reads their facial expressions, the emotion recognition device determines that they are "tired," and sends that data to the server.

[1152] Step 8:

[1153] Environmental adjustment based on emotion analysis

[1154] The server receives the emotional data and makes environmental adjustment suggestions or implementations based on the user's emotional state.

[1155] Input: Emotion data, environmental data

[1156] Output: Suggest or implement environmental adjustments based on emotions (e.g., simplified notifications, detailed notifications on optimal harvest timing)

[1157] Specific behavior: The server receives data that the user is tired and reduces detailed notifications, providing only concise information. If the server determines that the user is relaxed, it notifies the user in detail about the best time to harvest the coffee so that they can enjoy the aroma.

[1158] This series of processing steps allows users to enjoy growing coffee beans stress-free while automatically maintaining the optimal cultivation environment.

[1159] (Application example 2)

[1160] 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."

[1161] Currently, there are many tools and technologies on the market for personal coffee bean cultivation, but no system exists that can maintain an optimal cultivation environment while providing support tailored to the user's emotions. By incorporating emotion recognition, a more personalized experience can be provided, reducing the burden and stress on users while providing an environment for cultivating high-quality coffee beans. However, achieving this requires a system that integrates advanced data collection, analysis, and emotion recognition technologies.

[1162] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1163] In this invention, the server includes sensor means for measuring soil humidity, sensor means for measuring environmental temperature, sensor means for measuring sunshine hours, means for collecting and analyzing data obtained from each sensor means, means for controlling watering and other environmental adjustments based on the results of the data analysis, interface means for the user to check the growth status and harvest timing, means for recognizing the user's emotional state using an emotion engine and transmitting that data to the server, and means for adjusting the cultivation environment and robot operation based on the user's emotional state. This not only enables the user to easily cultivate high-quality coffee beans while maintaining an optimal cultivation environment, but also enables stress-free cultivation work because environmental adjustments and notifications are made based on the user's emotions.

[1164] "Sensor means" refers to devices used to measure soil moisture, environmental temperature, and hours of sunlight.

[1165] The "server means" is a computer system for collecting and analyzing data acquired from each sensor means.

[1166] The "control means" is a device that performs watering and other environmental adjustments based on the data analyzed by the server means.

[1167] "Interface means" refers to the connection means and screen that the user uses to check the growth status of the coffee beans and the timing of harvest.

[1168] An "emotion engine" is a software or hardware component that recognizes a user's emotional state through a camera or microphone and analyzes the data.

[1169] The "recognition means" is a function that uses an emotion engine to recognize the user's emotional state and transmits that data to the server.

[1170] The "cultivation environment adjusting means" is a means for automatically adjusting the cultivation environment and the operation of the robot based on the recognized emotional data of the user.

[1171] "Data collection" is the process of aggregating data acquired by sensor means and transmitting it to server means.

[1172] "High-quality coffee beans" are coffee seeds that meet the quality standards required by users when cultivating them.

[1173] "Reducing stress" refers to reducing the mental burden felt by the user when performing cultivation work.

[1174] The personal automated coffee garden system of the present invention provides an optimal coffee bean cultivation environment by integrating multiple sensors, a server that collects and analyzes data, a user interface, and an emotion engine that recognizes the user's emotions. The system of the present invention is composed of the following components:

[1175] Sensor means

[1176] The system is equipped with sensors to measure soil moisture, ambient temperature, and sunlight duration. These sensors work as follows:

[1177] Soil moisture sensor: Inserted into the soil, it measures the humidity in real time and transmits the data to the device.

[1178] Temperature sensor: Measures the temperature of the cultivation environment and sends the data to the terminal.

[1179] Light sensor: Measures the intensity of ambient light, records the hours of daylight, and sends this data to your device.

[1180] Data collection and analysis server

[1181] The server periodically collects and analyzes the data sent from the sensors. The analysis process includes the following steps:

[1182] Data collection: Collect and store data from each sensor.

[1183] Data analysis: The collected data is analyzed using an AI algorithm to calculate the optimal cultivation conditions.

[1184] Control measures for environmental regulation

[1185] Based on the results of the server's analysis, the following environmental adjustments are made:

[1186] Watering device: Provides the specified amount of water if the soil moisture level is below the specified level.

[1187] Temperature control device: Adjusts the temperature of the growing environment to the optimum range.

[1188] Lighting system: Adjust lighting to provide the required hours of sunlight.

[1189] User Interface

[1190] Users can check the following information in real time through a smartphone app or web application:

[1191] Growing conditions: Current soil moisture, temperature and sunshine data.

[1192] Harvest timing: Notification of optimal harvest time.

[1193] Emotion Engine

[1194] The emotion engine recognizes the user's emotional state using a camera and microphone and sends that data to the server, which then adjusts the cultivation environment and notifications accordingly.

[1195] Specific examples

[1196] Consider the following specific use case:

[1197] 1. Data collection: The soil moisture sensor detects 40% humidity, the temperature sensor records 22°C, and the light sensor records 8 hours of sunlight.

[1198] 2. Data analysis: The server analyzes this data and determines that the humidity is below the optimum range, indicating that watering is necessary.

[1199] 3. Environmental adjustment: The server sends an instruction to the watering device to add 100 ml of water, which the device executes.

[1200] 4. Emotion recognition: When the user checks the growth status on their smartphone, the camera analyzes the user's facial expression and recognizes that they are relaxed.

[1201] 5. Notification adjustment: The emotion engine provides detailed harvest timing notifications according to the user's relaxation state.

[1202] Prompt Sentence Examples

[1203] "Generates prompts based on the user's emotional state to determine if watering is necessary within the next 30 minutes."

[1204] "If the current soil moisture is not within the optimal range, generate a prompt with instructions on how much water to water."

[1205] The system of the present invention not only allows users to easily cultivate high-quality coffee beans, but also allows them to enjoy the cultivation process without feeling stressed thanks to the emotion-based environmental adjustment function.

[1206] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1207] Step 1: Collecting data with sensors

[1208] The device receives soil humidity data from the soil humidity sensor, ambient temperature data from the temperature sensor, and sunlight duration data from the light sensor. These sensors perform measurements periodically and generate data in real time.

[1209] Input: soil humidity, ambient temperature, sunshine hours

[1210] Output: Measured data for each sensor (e.g. soil humidity 40%, ambient temperature 22°C, sunshine duration 8 hours)

[1211] Step 2: Send data to the server

[1212] The device sends the sensor data acquired in step 1 to the server. The data is sent automatically at regular intervals.

[1213] Input: Data from each sensor

[1214] Output: The data sent.

[1215] Step 3: Data collection by the server

[1216] The server receives the data sent from the terminal and stores it in a database.

[1217] Input: Data sent from the terminal

[1218] Output: Saved data

[1219] Step 4: Data analysis

[1220] The server analyzes the collected data using AI algorithms to assess whether each environmental parameter is within an optimal range, for example, checking whether soil moisture is within the appropriate range (40-60%).

[1221] Input: Saved data

[1222] Output: Optimal conditions determined (e.g. soil moisture is low, watering is required)

[1223] Step 5: Generate environmental adjustment instructions

[1224] The server generates instructions for necessary environmental adjustments based on the results of data analysis, such as watering instructions if humidity is low.

[1225] Input: Data analysis results

[1226] Output: Environmental adjustment instructions (e.g., water 100ml)

[1227] Step 6: Send instructions to the device

[1228] The server transmits the generated environment adjustment instruction to the terminal.

[1229] Input: Environmental adjustment instructions

[1230] Output: Instructions sent

[1231] Step 7: Perform environmental adjustments

[1232] The terminal controls watering devices, temperature control devices, lighting devices, etc. based on instructions from the server, and performs the necessary environmental adjustments.

[1233] Input: Environment adjustment instructions from the server

[1234] Output: Environmental adjustment performed (e.g., 100ml watering)

[1235] Step 8: Collect emotion data

[1236] The device uses a camera and microphone to recognize the user's emotional state and collect that data.

[1237] Input: User's facial expression, voice

[1238] Output: Recognized emotion data (e.g., user is relaxed)

[1239] Step 9: Send emotion data to the server

[1240] The terminal transmits the collected emotion data to the server.

[1241] Input: Recognized emotion data

[1242] Output: Sent emotion data

[1243] Step 10: Sentiment Data Analysis

[1244] The server analyzes the received emotional data and assesses the user's current emotional state.

[1245] Input: Emotion data sent

[1246] Output: Evaluation of the emotional state (e.g., whether the user is relaxed)

[1247] Step 11: Adjust notification content

[1248] The server tailors the notifications to the user based on the evaluation of the user's emotional state, for example providing more detailed notifications if the user is relaxed.

[1249] Input: Emotional state assessment result

[1250] Output: Adjusted notification content (e.g. detailed notification of harvest time)

[1251] Step 12: Notify the user interface

[1252] The server sends the adjusted notification content to the user interface for the user to review.

[1253] Input: Adjusted notification content

[1254] Output: Information displayed on a user interface (e.g., detailed harvest time display on a smartphone app)

[1255] This processing step not only enables the user to maintain an optimal coffee bean cultivation environment at all times, but also provides appropriate information according to the user's emotional state, allowing for a comfortable cultivation experience.

[1256] 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.

[1257] 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.

[1258] 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.

[1259] [Fourth embodiment]

[1260] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1261] 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.

[1262] 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).

[1263] 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.

[1264] 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.

[1265] 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).

[1266] 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.

[1267] 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.

[1268] 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.

[1269] 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.

[1270] 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.

[1271] 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.

[1272] 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."

[1273] The personal automated coffee garden system of this invention provides the optimal coffee bean cultivation environment by utilizing various sensors, a server that collects and analyzes data, and a user interface. The system monitors soil humidity, temperature, and sunlight hours, and automatically adjusts the environment using an AI algorithm. The specific functions and operations of each element are described below.

[1274] Sensor means for measuring soil moisture

[1275] The device contains a soil moisture sensor that is inserted into the soil and records the soil's moisture content in real time.

[1276] Sensor means for measuring temperature

[1277] The terminal is equipped with a temperature sensor that measures the temperature of the entire cultivation environment and transmits the data to the terminal.

[1278] Sensor means for measuring sunshine duration

[1279] The device is equipped with a light sensor that measures the intensity of ambient light and records the duration of daylight.

[1280] Server means for collecting and analyzing data obtained from each sensor means

[1281] Data from these sensors is periodically sent via the device to a server, which analyzes the data and uses data models to optimize growth conditions and the environment.

[1282] Example: The server analyzes soil moisture data and, if it determines that the moisture content is below the optimum range, it knows that watering is necessary.

[1283] Control measures to control watering and other environmental adjustments based on data analysis results

[1284] Based on the analysis results, the server sends specific instructions for environmental adjustments to the terminal, which controls the watering device, temperature control device, lighting device, etc.

[1285] Example: The server determines that the humidity is low and sends the command to the terminal to "add 100ml of water to the soil." The terminal automatically opens the nozzle and dispenses 100ml of water.

[1286] An interface for users to check growth status and harvest timing

[1287] Users can check the growth status of coffee beans and resource conditions in real time through a smartphone app or web application.

[1288] Example: A user opens the app and sees that the current soil moisture is 45%, the temperature is 22°C, and there are 8 hours of sunshine. They also receive a notification from the server that the best time to harvest is in 3 days.

[1289] This system allows users to easily manage the detailed environment necessary for coffee bean growth. AI and automatic control instantly respond to environmental changes and provide the optimal growing environment, making it easy to grow high-quality coffee at home.

[1290] The processing flow will be explained below.

[1291] Step 1:

[1292] A user installs the smartphone app and creates an account. The user enters their name, email address, and password and taps the register button.

[1293] Step 2:

[1294] The server receives the information entered by the user and stores it in the database. If the save is successful, it notifies the user with a message that the account was created successfully.

[1295] Step 3:

[1296] The device acquires data using sensors such as soil humidity, temperature, and sunlight duration. For example, the humidity sensor acquires soil humidity in real time, and the temperature sensor measures the ambient temperature.

[1297] Step 4:

[1298] The device periodically transmits the acquired data to the server. The device transmits the data from the sensor to the server via Wi-Fi.

[1299] Step 5:

[1300] The server analyzes the data received from the device and uses AI algorithms to optimize the environment, for example determining if soil moisture is below the optimal range.

[1301] Step 6:

[1302] Based on the analysis results, the server instructs the device to take specific actions such as watering or adjusting the temperature. For example, it might send an instruction to the device to "add 100ml of water to the soil."

[1303] Step 7:

[1304] The device receives instructions from the server and executes specific actions, such as operating the watering nozzle to add 100ml of water to the soil.

[1305] Step 8:

[1306] The server continuously monitors the growth status and uses AI algorithms to predict when to harvest, for example by comparing the growth rate with past data to determine whether it is appropriate.

[1307] Step 9:

[1308] When the time for harvesting approaches, the server sends a notification to the user via the app, informing the user that "it's time for harvesting."

[1309] Step 10:

[1310] The user opens the smartphone app and checks the growth dashboard for current growth status and harvest readiness information, such as the current soil moisture of 50%, the temperature of 23°C, and the remaining three days until harvest.

[1311] Example 1

[1312] 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."

[1313] To easily grow high-quality coffee beans at home, it is necessary to accurately manage and appropriately adjust environmental factors such as soil humidity, temperature, and sunlight hours. However, these management tasks are time-consuming and require specialized knowledge, making them difficult for many users. In addition, systems that monitor environmental conditions in real time and automatically adjust them as needed are not widely available. This means that there is a risk that the quality of the coffee beans will decline if the system is unable to respond appropriately to environmental changes.

[1314] 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.

[1315] In this invention, the server includes sensor means for measuring soil humidity, sensor means for measuring temperature, sensor means for measuring sunshine hours, data processing means for collecting and analyzing data acquired from each sensor means, control means for controlling watering and other environmental adjustments based on the results of data analysis, and display means for the user to check the growth status and harvest timing. This allows the user to grasp the growth status in real time and provide an optimal growing environment through automatic control by AI, making it possible to easily cultivate high-quality coffee beans.

[1316] A "sensor for measuring soil moisture" is a device that is inserted into the soil to measure the moisture content of the soil in real time.

[1317] A "sensor for measuring temperature" is a device for measuring the temperature of the entire cultivation environment and collecting that data.

[1318] A "sensor for measuring sunshine duration" is a device for measuring the intensity of ambient light and recording the sunshine duration.

[1319] "Data processing means" refers to a system that analyzes collected data and understands the optimization of the environment and growth status.

[1320] The "control means" is a system that controls watering devices, temperature control devices, lighting devices, etc. based on the analysis results of the data processing means.

[1321] The "display means" is an interface that allows the user to check the growth status and harvest timing, and includes smartphone applications and web applications.

[1322] The personal automated coffee garden system of this invention provides an optimal coffee bean cultivation environment by utilizing various sensors, a server that collects and analyzes data, and a user interface. The system monitors soil humidity, temperature, and sunlight hours, and automatically adjusts the environment using an AI algorithm. The specific functions and operations of each element are described below.

[1323] Data collection by sensor means

[1324] The device is equipped with the following types of sensors:

[1325] 1. Soil Moisture Sensor: Measures soil moisture content in real time. It is inserted into the soil and collects data every hour.

[1326] 2. Temperature sensor: Measures the temperature of the entire cultivation environment. Temperature data is collected every hour.

[1327] 3. Light sensor: Measures the intensity of ambient light and records the hours of sunlight.

[1328] Data collection and analysis by the server

[1329] The data collected from each sensor is periodically sent to a server via the device, where it is analyzed using AI algorithms and data models to optimize the plant's growth and environment.

[1330] Environmental adjustments based on data analysis

[1331] Based on the analysis results, the server sends instructions to the terminal to adjust the environment through the following control means.

[1332] Watering device: When soil moisture is low, it will send watering instructions and automatically supply water.

[1333] Thermostat: Controls heating and cooling systems when temperatures go outside set ranges.

[1334] Lighting equipment: If there is a lack of sunlight, turn on artificial lighting.

[1335] User interface means

[1336] Users can check the growth status of coffee beans and the status of resources in real time through a smartphone app or web application, and can also receive notifications from the server.

[1337] Examples:

[1338] When a user opens the app, they can see that the current soil humidity is 35%, the temperature is 22°C, and there are 8 hours of sunshine.

[1339] The server sends a notification to the user saying, "Best time to harvest in 3 days."

[1340] This system allows users to easily manage the detailed environment necessary for coffee bean growth. AI and automatic control instantly respond to environmental changes and provide the optimal growing environment, making it easy to grow high-quality coffee at home.

[1341] Example prompt sentence:

[1342] "This automated coffee garden system monitors soil moisture, temperature, and sunlight hours in real time, and uses AI algorithms to optimize the environment. Data is collected from sensors, sent to a server for analysis, and automatically takes action such as watering and adjusting lighting when necessary. Users can check growth status and environmental data using a smartphone app or web application. How can we improve this system to grow coffee beans more efficiently?"

[1343] In this way, users can enjoy advanced environmental management using AI while reducing tedious work, making it easy to grow high-quality coffee beans at home.

[1344] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1345] Step 1:

[1346] The device measures the soil moisture using a soil moisture sensor, which is inserted into the soil and collects moisture data as input every hour.

[1347] Specific behavior:

[1348] The soil moisture sensor works to detect the moisture content of the soil.

[1349] The collected humidity data (e.g. 30%) is obtained.

[1350] Input: Current soil moisture value

[1351] Output: Humidity data (e.g. 30%)

[1352] Step 2:

[1353] The device uses a temperature sensor to measure the temperature of the entire growing environment, and temperature data is also collected every hour.

[1354] Specific behavior:

[1355] The temperature sensor is activated to detect the temperature of the entire environment.

[1356] The collected temperature data (e.g., 25°C) is obtained.

[1357] Input: Current ambient temperature

[1358] Output: Temperature data (e.g. 25°C)

[1359] Step 3:

[1360] The device uses a light sensor to measure the intensity of ambient light and record the duration of daylight, which also collects data every hour.

[1361] Specific behavior:

[1362] The light sensor is activated and detects the intensity of the ambient light.

[1363] The collected light data (e.g., 2000 lux) is obtained.

[1364] Input: Ambient light intensity

[1365] Output: Light data (e.g. 2000 lux)

[1366] Step 4:

[1367] The device sends all the data it collects to a server, either via Wi-Fi or a wired connection.

[1368] Specific behavior:

[1369] Humidity data, temperature data, and light data are combined into one data packet.

[1370] The collected data packets are sent to the server.

[1371] Input: Humidity data, temperature data, light data

[1372] Output: Data packets to the server

[1373] Step 5:

[1374] The server stores the received data in a database and begins analyzing it, using machine learning models and statistical analysis.

[1375] Specific behavior:

[1376] The received data is stored in a database in chronological order.

[1377] The saved data is input into an AI algorithm to analyze the optimal cultivation environment.

[1378] Input: Received data packet

[1379] Output: Analysis results (e.g., low humidity, watering required)

[1380] Step 6:

[1381] Based on the analysis results, the server generates appropriate environmental adjustment instructions and sends them to the device, including instructions for watering, adjusting the temperature, and controlling lighting.

[1382] Specific behavior:

[1383] Based on the analysis results, necessary adjustments are determined.

[1384] Generate specific instructions (e.g., add 100ml of water to the soil) and send them to the device.

[1385] Input: Analysis results

[1386] Output: Instructions for adjusting the environment to the terminal

[1387] Step 7:

[1388] The terminal then adjusts the environment based on the received instructions, automatically activating watering devices, temperature control devices, lighting devices, etc.

[1389] Specific behavior:

[1390] When it receives a watering command, it opens the nozzle and delivers the specified amount of water.

[1391] If a temperature adjustment command is received, the heating or cooling device will be activated.

[1392] If a lighting instruction is received, the lighting device is turned on.

[1393] Input: Environmental adjustment instructions

[1394] Output: Performing environmental controls (e.g. water supply, temperature control, lighting)

[1395] Step 8:

[1396] Users can check data in real time via a smartphone app or web application, and can also receive notifications from the server to check growth status and harvest timing.

[1397] Specific behavior:

[1398] Open the app and check the current humidity, temperature, sunshine hours, and more.

[1399] Receive notifications from the server (e.g., Best time to harvest in 3 days).

[1400] Input: Data request from app or web

[1401] Output: Real-time data, notification information

[1402] Through the above processing steps, users can easily cultivate high-quality coffee beans and quickly respond to environmental changes.

[1403] (Application example 1)

[1404] 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."

[1405] With conventional coffee bean cultivation systems, it is difficult to maintain an appropriate cultivation environment at home or in a store, and ensuring an optimal growing environment requires a lot of effort and time. Furthermore, there are few ways to quickly respond to changes in the cultivation environment, resulting in inconsistent quality of the cultivated coffee beans. Furthermore, the coffee brewing method is heavily dependent on the skill and experience of the barista, making it difficult to provide coffee of consistent quality.

[1406] 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.

[1407] In this invention, the server includes a detector for measuring soil humidity, a detector for measuring temperature, a detector for measuring sunshine hours, a calculation unit for collecting and analyzing data acquired from each detector, an operation unit for controlling watering and other environmental adjustments based on the data analysis results, a communication unit for a user to check the growth status and harvest timing, and a unit for acquiring environmental data using smart glasses, analyzing the data on a cloud server, and displaying instructions for optimal environmental adjustments based on the analysis results. This allows for quick response to environmental changes and provides an optimal cultivation environment. Furthermore, by monitoring data in real time through the smart glasses and receiving appropriate instructions, baristas can provide coffee of consistent quality.

[1408] The "detection means for measuring soil moisture" is a device for measuring the amount of moisture contained in soil in real time and acquiring the data.

[1409] The "detection means for measuring temperature" is a device for measuring the temperature of the environment and acquiring the data.

[1410] A "detection means for measuring the hours of sunlight" is a device for measuring the intensity of ambient light and recording the hours of sunlight.

[1411] The "computing means for collecting and analyzing data obtained from each detecting means" is a computer system or server for receiving and analyzing data transmitted from a plurality of sensors.

[1412] "Operational means for controlling watering and other environmental adjustments based on data analysis results" refers to equipment or devices that automatically perform watering, temperature adjustment, and lighting adjustment based on analysis results.

[1413] "Communication means for users to check the growth status and harvest timing" refers to an interface that allows users to check the growth status, environmental data, harvest timing, etc. via a smartphone or web application.

[1414] "Smart glasses" are wearable devices that display data to the wearer in real time and provide instructions based on the analysis results.

[1415] A "cloud server" is a remote server accessible via the Internet, and is a computer system that analyzes and processes data and manages storage.

[1416] The system for implementing the present invention is configured by combining a plurality of detecting means, calculating means, operating means, communication means, and smart glasses. Specific embodiments are described below.

[1417] First, the detecting means for measuring soil humidity, the detecting means for measuring temperature, and the detecting means for measuring sunshine hours are devices that measure soil and environmental temperatures and light intensity in real time and acquire the data, which are then transmitted to a cloud server via wireless communication.

[1418] The cloud server uses software written in programming languages ​​such as Python and Java to analyze the data it receives. It uses AI models to learn and evaluate patterns in the data and generate instructions for optimal environmental adjustments. For example, if the soil moisture level drops, it will determine that more watering is needed. Or, if the room temperature is too high, it will indicate that cooling equipment should be activated.

[1419] The smart glasses are wearable devices that display instructions from a cloud server to baristas in real time. The smart glasses are equipped with an internet connection and display data in conjunction with the cloud server. This allows baristas to instantly check environmental data and optimal behavioral instructions.

[1420] Users can connect to the cloud server via their smartphone or web application and check the growth status of their coffee beans and the cultivation environment in real time. The smartphone app and web application are built using web technologies such as HTML, CSS, and JavaScript, allowing users to easily monitor the cultivation environment and make adjustments as needed.

[1421] As a concrete example, the operation of the barista assistance system can be confirmed by inputting the following prompt sentence into the generative AI model.

[1422] Example prompt:

[1423] "Please explain how the smart glasses-based coffee barista support system works. This system analyzes data on soil humidity, environmental temperature, and sunlight hours on a cloud server, and displays optimal instructions in real time through the smart glasses."

[1424] This clarifies the mechanism of the system, which allows users and baristas to easily obtain environmental data and receive optimal instructions for action based on the analysis results.

[1425] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1426] Step 1:

[1427] Data collection from each detection method

[1428] The device acquires data in real time using sensing means that measure soil humidity, environmental temperature, and sunlight duration. The acquired data includes moisture content data from the soil humidity sensor, air temperature data from the temperature sensor, and light intensity data from the light sensor. The input data is the raw data from each sensor, and the output is a compiled environmental dataset.

[1429] Step 2:

[1430] Sending data to the cloud server

[1431] The device transmits the collected environmental data to a cloud server via wireless communication. In this process, the device assembles the data acquired from each sensor into a single data packet and sends it to the cloud server via the Internet. The input is the environmental dataset, and the output is the data transmitted to the cloud server.

[1432] Step 3:

[1433] Data analysis using a cloud server

[1434] The cloud server analyzes the received environmental data. Using programming languages ​​such as Python and Java, the server runs an AI model and performs analysis based on the data. Here, it evaluates whether the conditions are suitable for growth based on data on soil moisture, temperature, and light intensity. The input data is the environmental dataset sent from the device, and the analysis results are output.

[1435] Step 4:

[1436] Generate optimal environmental adjustment instructions

[1437] The cloud server generates instructions for optimal environmental adjustment based on the results of data analysis. For example, if the soil humidity is low, it will issue a "water" instruction, and if the temperature is too high, it will issue a "cool" instruction. The input is the analysis results, and the output is specific instructions for environmental adjustment.

[1438] Step 5:

[1439] Displaying instructions on smart glasses

[1440] The cloud server sends the generated environmental adjustment instructions to the smart glasses. The smart glasses display these instructions to the barista in real time. Here, processing is performed to display the content of the instructions on the display. The input is the environmental adjustment instructions from the cloud server, and the output is the instruction content displayed on the smart glasses' display.

[1441] Step 6:

[1442] User confirmation of data

[1443] Users can access the cloud server using a smartphone or web application to check the collected environmental data and analysis results. Here, the process of extracting the information necessary for the user to check the data on the screen is carried out. The input is the environmental data and analysis results stored on the cloud server, and the output is the data displayed on the user's screen.

[1444] 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.

[1445] The personal automated coffee garden system of the present invention provides an optimal coffee bean growing environment by including sensors that monitor soil moisture, temperature, and sunlight hours, a server that collects and analyzes data, a user interface, and an emotion engine that recognizes the user's emotions and adjusts the environment accordingly.

[1446] Sensor means for measuring soil moisture

[1447] The device contains a soil moisture sensor that is inserted into the soil to record the moisture content in real time and transmit that data to the device.

[1448] Sensor means for measuring temperature

[1449] The terminal is equipped with a temperature sensor that measures the temperature of the cultivation environment and transmits the data to the terminal.

[1450] Sensor means for measuring sunshine duration

[1451] The device is equipped with a light sensor that measures the intensity of ambient light and records the duration of daylight.

[1452] Server means for collecting and analyzing data obtained from each sensor means

[1453] Data from these sensors is periodically sent to a server via the device, which then analyzes the data and uses AI algorithms to optimize growth conditions and the environment.

[1454] Example: The server analyzes soil moisture data and, if it determines that the moisture content is below the optimum range, it knows that watering is necessary.

[1455] Control measures to control watering and other environmental adjustments based on data analysis results

[1456] Based on the analysis results, the server sends specific instructions for environmental adjustments to the terminal, which controls the watering device, temperature control device, lighting device, etc.

[1457] Example: The server determines that the humidity is low and sends the command to the terminal to "add 100ml of water to the soil." The terminal automatically opens the nozzle and dispenses 100ml of water.

[1458] An interface for users to check growth status and harvest timing

[1459] Users can check the growth status of coffee beans and resource conditions in real time through a smartphone app or web application.

[1460] Example: A user opens the app and sees that the current soil moisture is 45%, the temperature is 22°C, and there are 8 hours of sunshine. They also receive a notification from the server that the best time to harvest is in 3 days.

[1461] Emotion engine that recognizes user emotions

[1462] An emotion engine is installed on the device or the user's smartphone. This engine uses a camera and microphone to recognize emotions from the user's facial expressions and voice, and sends the data to a server.

[1463] Example: When a user uses an app to check their growth status, the camera analyzes their facial expressions and if it determines that they are tired, the server reduces notifications detailing their growth status and provides only concise information.

[1464] Automatic adjustment of the environment by the emotion engine

[1465] The server receives and analyzes data from the emotion engine and proposes or implements automatic adjustments to the environment based on the user's emotional state, allowing the user to enjoy coffee cultivation without stress.

[1466] Example: If the emotion engine recognizes that the user is relaxed, it will provide detailed information on the best time to harvest the coffee to enjoy its aroma.

[1467] This system not only allows users to easily grow high-quality coffee beans, but also provides more personalized support through an emotion engine. The combination of AI, automatic control, and user emotion recognition further enhances the growing experience.

[1468] The processing flow will be explained below.

[1469] Step 1:

[1470] A user installs the smartphone app and creates an account. The user enters their name, email address, and password and taps the register button.

[1471] Step 2:

[1472] The server receives the information entered by the user and stores it in the database. If the save is successful, it notifies the user with a message that the account was created successfully.

[1473] Step 3:

[1474] The device acquires data using sensors such as soil humidity, temperature, and sunlight duration. For example, the humidity sensor acquires soil humidity in real time, and the temperature sensor measures the ambient temperature.

[1475] Step 4:

[1476] The device periodically transmits the acquired data to the server. The device transmits the data from the sensor to the server via Wi-Fi.

[1477] Step 5:

[1478] The server analyzes the data received from the device and uses AI algorithms to optimize the environment, for example determining if soil moisture is below the optimal range.

[1479] Step 6:

[1480] Based on the analysis results, the server instructs the device to take specific actions such as watering or adjusting the temperature. For example, it might send an instruction to the device to "add 100ml of water to the soil."

[1481] Step 7:

[1482] The device receives instructions from the server and executes specific actions, such as operating the watering nozzle to add 100ml of water to the soil.

[1483] Step 8:

[1484] An emotion engine installed in the device or the user's smartphone analyzes the user's facial expressions and voice to recognize emotions. For example, a camera captures the user's facial expressions, and voice recognition technology analyzes the tone of the user's voice.

[1485] Step 9:

[1486] The emotion engine sends the emotion data it recognizes to the server. For example, if it determines that the user is relaxed, the data is sent to the server.

[1487] Step 10:

[1488] The server analyzes the emotional data and adjusts notifications and environmental settings according to the user's emotional state. For example, if the server determines that the user is tired, it will simplify notifications and automatically adjust the frequency of watering plants.

[1489] Step 11:

[1490] The server sends notifications to the user about the growth status and harvest timing, for example, "The best time to harvest is in 3 days."

[1491] Step 12:

[1492] The user opens the smartphone app to check information about the plant's growth and harvest timing, such as the current soil humidity of 50%, the temperature of 23°C, and the remaining three days until harvest.

[1493] Through these steps, users can easily grow high-quality coffee and receive personalized support using emotion recognition technology.

[1494] Example 2

[1495] 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."

[1496] In recent years, the demand for individual coffee bean cultivation has increased, but effective environmental control and management are difficult, especially for beginners. Furthermore, in many cases, it becomes difficult to continue cultivation due to stress or busy schedules. For this reason, there is a demand for a system that allows anyone to easily maintain an optimal cultivation environment and grow coffee beans without stress.

[1497] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1498] In this invention, the server includes sensor means for measuring soil humidity, sensor means for measuring temperature, sensor means for measuring sunshine hours, a data processing device for collecting and analyzing data obtained from each sensor means, an environmental control device for controlling watering and other environmental adjustments based on the results of the data analysis, a user interface device for allowing the user to check the growth status and harvest timing, an emotion recognition device for recognizing the user's emotions and transmitting that data to the data processing device, and a system for causing the environmental control device to suggest or execute environmental adjustments based on data from the emotion recognition device. This makes it possible for anyone to easily maintain an optimal cultivation environment and grow coffee beans without stress.

[1499] 1. "Sensor means for measuring soil moisture" means a device for measuring and collecting data on the moisture content of soil in real time.

[1500] 2. "Sensor means for measuring temperature" means a device for accurately measuring the temperature within a growing environment and collecting that data.

[1501] 3. "Sensor means for measuring daylight hours" means a device for measuring light intensity and daylight hours in a growing environment and collecting such data.

[1502] 4. "Data Processing Device" means a computer system or device for collecting and analyzing data obtained from each sensor means.

[1503] 5. An "environmental control device" is a device that performs environmental adjustments such as watering, temperature control, and lighting control based on the analysis results of the server.

[1504] 6. "User interface device" refers to a display device or application that allows the user to check the system status, the growth status of coffee beans, harvest timing, etc.

[1505] 7. An "emotion recognition device" is a device that analyzes a user's facial expressions and voice to recognize their emotional state.

[1506] 8. "System for proposing or executing environmental adjustment" means a system that makes suggestions or automatic environmental adjustments to optimize the cultivation environment based on data from an emotion recognition device.

[1507] The automated coffee garden system for individuals of the present invention includes multiple sensors, a data processing device, an environmental control device, a user interface device, and an emotion recognition device, which optimize the coffee bean cultivation environment and allow users to enjoy stress-free cultivation.

[1508] Sensor means

[1509] The device is equipped with a soil moisture sensor, a temperature sensor, and a light sensor. The soil moisture sensor measures the moisture content of the soil, the temperature sensor measures the temperature in the cultivation environment, and the light sensor measures the ambient light intensity and sunlight duration. The data collected by these sensors is sent to the device.

[1510] Data Processing Unit

[1511] The server periodically collects and automatically analyzes sensor data sent from the device. The server is equipped with AI algorithms that analyze data such as humidity, temperature, and sunlight duration to generate specific instructions for maintaining an optimal growing environment. For example, if the server determines that the soil humidity is below the optimal range, it will send instructions to the environmental control device to water the plants.

[1512] Environmental Control Device

[1513] The terminal receives instructions from the server and adjusts the cultivation environment. This includes watering devices, temperature control devices, lighting devices, etc., and automatically controls these to optimize the environment. For example, if the server determines that the humidity is low and sends the command to the terminal to "add 100ml of water to the soil," the terminal will open the nozzle and supply 100ml of water to the soil.

[1514] User Interface Device

[1515] Users can use a smartphone app or web application to check the growth status of coffee beans and the condition of resources in real time. The application displays data on soil humidity, temperature, and sunlight hours, and receives notifications from the server with information such as growth status and optimal harvest timing. For example, when a user opens the app, the current soil humidity is 35%, the temperature is 22°C, and the sunlight hours are 8 hours, and the user receives a notification that "the optimal time for harvest is in 3 days."

[1516] emotion recognition device

[1517] An emotion recognition device is installed on the device or the user's smartphone. This device uses a camera and microphone to analyze the user's facial expressions and voice to recognize their emotional state. The recognized emotion data is sent to a server, which then provides information and adjusts the environment based on the analysis results, adapting to the user's emotions. For example, if the emotion recognition device determines that the user is tired, the server will reduce detailed notifications and provide only concise information.

[1518] Environmental adjustment based on emotion analysis

[1519] The server receives data from the emotion recognition device and suggests or automatically adjusts the cultivation environment based on the user's emotional state, allowing the user to relax and enjoy cultivation. For example, if the emotion engine recognizes that the user is relaxed, it will notify the user in detail of the optimal harvesting time to enjoy the aroma of coffee.

[1520] Prompt Sentence Examples

[1521] Please explain in detail each step of the automated personal coffee garden system. In each step, which of the server, device, or user is primarily responsible? What specific actions are performed?

[1522] This system allows users to easily grow high-quality coffee beans and enjoy stress-free cultivation with the support of the emotion engine.

[1523] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1524] Step 1:

[1525] Data collection by sensors

[1526] The device collects environmental data through soil humidity, temperature, and light sensors, which measure soil humidity, temperature, and sunlight hours, respectively, and transmits the collected data to the device.

[1527] Input: Sensor data for soil moisture, temperature, and sunshine hours

[1528] Output: Collected environmental data (e.g. soil humidity 35%, temperature 22°C, sunshine duration 8 hours)

[1529] Specific operation: The soil humidity sensor measures humidity at 35%, the temperature sensor measures 22°C, and the light sensor records 8 hours of sunlight.

[1530] Step 2:

[1531] Sending data to the server

[1532] The device transmits the collected environmental data to the server at regular intervals, using a network protocol.

[1533] Input: Collected environmental data

[1534] Output: Environment data sent to the server

[1535] Specific operation: The device uploads current soil humidity, temperature, and sunshine hours data to the server every 5 minutes using the HTTP protocol.

[1536] Step 3:

[1537] Data analysis

[1538] The server analyzes the received environmental data using AI algorithms and generates specific instructions to maintain an optimal cultivation environment.

[1539] Input: Environment data sent to the server

[1540] Output: Specific instructions for environmental adjustments as a result of data analysis (e.g., amount of watering, temperature adjustment, sunlight adjustment, etc.)

[1541] Specific operation: The server receives data that the humidity is 35%, and the AI ​​algorithm determines that this is below the optimal humidity of 40-60%, and generates the instruction to "add 100ml of water to the soil."

[1542] Step 4:

[1543] Sending environmental control instructions

[1544] The server sends an instruction to adjust the environment to the terminal based on the analysis result.

[1545] Input: Environmental adjustment instructions

[1546] Output: Environmental adjustment instructions sent to the device

[1547] Specific operation: The server sends the instruction "Add 100ml of water to the soil" to the terminal.

[1548] Step 5:

[1549] Automatic environment adjustment

[1550] The terminal adjusts the environment based on the received instructions, automatically controlling watering devices, temperature control devices, lighting devices, etc.

[1551] Input: Environment adjustment instructions from the server

[1552] Output: Environmental adjustment made (e.g., 100ml of water added)

[1553] Specific operation: The device opens the nozzle and automatically dispenses 100ml of water into the soil.

[1554] Step 6:

[1555] User interface display of information

[1556] Users can check environmental data and growth status through a smartphone app or web application.

[1557] Input: Latest data and notifications from the server

[1558] Output: Environmental data and notifications displayed in the application

[1559] Specific behavior: When the user opens the app, the current soil humidity is displayed as 35%, the temperature is 22°C, and there are 8 hours of sunshine, and a notification is displayed saying, "The best time to harvest is in 3 days."

[1560] Step 7:

[1561] Emotion Recognition and Data Transmission

[1562] An emotion recognition device installed on the terminal or the user's smartphone analyzes the user's facial expressions and voice to obtain emotion data, which is then sent to a server.

[1563] Input: User's facial expression and voice data

[1564] Output: Emotion data sent to the server

[1565] Specific operation: While the user is using the app, the smartphone camera reads their facial expressions, the emotion recognition device determines that they are "tired," and sends that data to the server.

[1566] Step 8:

[1567] Environmental adjustment based on emotion analysis

[1568] The server receives the emotional data and makes environmental adjustment suggestions or implementations based on the user's emotional state.

[1569] Input: Emotion data, environmental data

[1570] Output: Suggest or implement environmental adjustments based on emotions (e.g., simplified notifications, detailed notifications on optimal harvest timing)

[1571] Specific behavior: The server receives data that the user is tired and reduces detailed notifications, providing only concise information. If the server determines that the user is relaxed, it notifies the user in detail about the best time to harvest the coffee so that they can enjoy the aroma.

[1572] This series of processing steps allows users to enjoy growing coffee beans stress-free while automatically maintaining the optimal cultivation environment.

[1573] (Application example 2)

[1574] 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."

[1575] Currently, there are many tools and technologies on the market for personal coffee bean cultivation, but no system exists that can maintain an optimal cultivation environment while providing support tailored to the user's emotions. By incorporating emotion recognition, a more personalized experience can be provided, reducing the burden and stress on users while providing an environment for cultivating high-quality coffee beans. However, achieving this requires a system that integrates advanced data collection, analysis, and emotion recognition technologies.

[1576] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1577] In this invention, the server includes sensor means for measuring soil humidity, sensor means for measuring environmental temperature, sensor means for measuring sunshine hours, means for collecting and analyzing data obtained from each sensor means, means for controlling watering and other environmental adjustments based on the results of the data analysis, interface means for the user to check the growth status and harvest timing, means for recognizing the user's emotional state using an emotion engine and transmitting that data to the server, and means for adjusting the cultivation environment and robot operation based on the user's emotional state. This not only enables the user to easily cultivate high-quality coffee beans while maintaining an optimal cultivation environment, but also enables stress-free cultivation work because environmental adjustments and notifications are made based on the user's emotions.

[1578] "Sensor means" refers to devices used to measure soil moisture, environmental temperature, and hours of sunlight.

[1579] The "server means" is a computer system for collecting and analyzing data acquired from each sensor means.

[1580] The "control means" is a device that performs watering and other environmental adjustments based on the data analyzed by the server means.

[1581] "Interface means" refers to the connection means and screen that the user uses to check the growth status of the coffee beans and the timing of harvest.

[1582] An "emotion engine" is a software or hardware component that recognizes a user's emotional state through a camera or microphone and analyzes the data.

[1583] The "recognition means" is a function that uses an emotion engine to recognize the user's emotional state and transmits that data to the server.

[1584] The "cultivation environment adjusting means" is a means for automatically adjusting the cultivation environment and the operation of the robot based on the recognized emotional data of the user.

[1585] "Data collection" is the process of aggregating data acquired by sensor means and transmitting it to server means.

[1586] "High-quality coffee beans" are coffee seeds that meet the quality standards required by users when cultivating them.

[1587] "Reducing stress" refers to reducing the mental burden felt by the user when performing cultivation work.

[1588] The personal automated coffee garden system of the present invention provides an optimal coffee bean cultivation environment by integrating multiple sensors, a server that collects and analyzes data, a user interface, and an emotion engine that recognizes the user's emotions. The system of the present invention is composed of the following components:

[1589] Sensor means

[1590] The system is equipped with sensors to measure soil moisture, ambient temperature, and sunlight duration. These sensors work as follows:

[1591] Soil moisture sensor: Inserted into the soil, it measures the humidity in real time and transmits the data to the device.

[1592] Temperature sensor: Measures the temperature of the cultivation environment and sends the data to the terminal.

[1593] Light sensor: Measures the intensity of ambient light, records the hours of daylight, and sends this data to your device.

[1594] Data collection and analysis server

[1595] The server periodically collects and analyzes the data sent from the sensors. The analysis process includes the following steps:

[1596] Data collection: Collect and store data from each sensor.

[1597] Data analysis: The collected data is analyzed using an AI algorithm to calculate the optimal cultivation conditions.

[1598] Control measures for environmental regulation

[1599] Based on the results of the server's analysis, the following environmental adjustments are made:

[1600] Watering device: Provides the specified amount of water if the soil moisture level is below the specified level.

[1601] Temperature control device: Adjusts the temperature of the growing environment to the optimum range.

[1602] Lighting system: Adjust lighting to provide the required hours of sunlight.

[1603] User Interface

[1604] Users can check the following information in real time through a smartphone app or web application:

[1605] Growing conditions: Current soil moisture, temperature and sunshine data.

[1606] Harvest timing: Notification of optimal harvest time.

[1607] Emotion Engine

[1608] The emotion engine recognizes the user's emotional state using a camera and microphone and sends that data to the server, which then adjusts the cultivation environment and notifications accordingly.

[1609] Specific examples

[1610] Consider the following specific use case:

[1611] 1. Data collection: The soil moisture sensor detects 40% humidity, the temperature sensor records 22°C, and the light sensor records 8 hours of sunlight.

[1612] 2. Data analysis: The server analyzes this data and determines that the humidity is below the optimum range, indicating that watering is necessary.

[1613] 3. Environmental adjustment: The server sends an instruction to the watering device to add 100 ml of water, which the device executes.

[1614] 4. Emotion recognition: When the user checks the growth status on their smartphone, the camera analyzes the user's facial expression and recognizes that they are relaxed.

[1615] 5. Notification adjustment: The emotion engine provides detailed harvest timing notifications according to the user's relaxation state.

[1616] Prompt Sentence Examples

[1617] "Generates prompts based on the user's emotional state to determine if watering is necessary within the next 30 minutes."

[1618] "If the current soil moisture is not within the optimal range, generate a prompt with instructions on how much water to water."

[1619] The system of the present invention not only allows users to easily cultivate high-quality coffee beans, but also allows them to enjoy the cultivation process without feeling stressed thanks to the emotion-based environmental adjustment function.

[1620] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1621] Step 1: Collecting data with sensors

[1622] The device receives soil humidity data from the soil humidity sensor, ambient temperature data from the temperature sensor, and sunlight duration data from the light sensor. These sensors perform measurements periodically and generate data in real time.

[1623] Input: soil humidity, ambient temperature, sunshine hours

[1624] Output: Measured data for each sensor (e.g. soil humidity 40%, ambient temperature 22°C, sunshine duration 8 hours)

[1625] Step 2: Send data to the server

[1626] The device sends the sensor data acquired in step 1 to the server. The data is sent automatically at regular intervals.

[1627] Input: Data from each sensor

[1628] Output: The data sent.

[1629] Step 3: Data collection by the server

[1630] The server receives the data sent from the terminal and stores it in a database.

[1631] Input: Data sent from the terminal

[1632] Output: Saved data

[1633] Step 4: Data analysis

[1634] The server analyzes the collected data using AI algorithms to assess whether each environmental parameter is within an optimal range, for example, checking whether soil moisture is within the appropriate range (40-60%).

[1635] Input: Saved data

[1636] Output: Optimal conditions determined (e.g. soil moisture is low, watering is required)

[1637] Step 5: Generate environmental adjustment instructions

[1638] The server generates instructions for necessary environmental adjustments based on the results of data analysis, such as watering instructions if humidity is low.

[1639] Input: Data analysis results

[1640] Output: Environmental adjustment instructions (e.g., water 100ml)

[1641] Step 6: Send instructions to the device

[1642] The server transmits the generated environment adjustment instruction to the terminal.

[1643] Input: Environmental adjustment instructions

[1644] Output: Instructions sent

[1645] Step 7: Perform environmental adjustments

[1646] The terminal controls watering devices, temperature control devices, lighting devices, etc. based on instructions from the server, and performs the necessary environmental adjustments.

[1647] Input: Environment adjustment instructions from the server

[1648] Output: Environmental adjustment performed (e.g., 100ml watering)

[1649] Step 8: Collect emotion data

[1650] The device uses a camera and microphone to recognize the user's emotional state and collect that data.

[1651] Input: User's facial expression, voice

[1652] Output: Recognized emotion data (e.g., user is relaxed)

[1653] Step 9: Send emotion data to the server

[1654] The terminal transmits the collected emotion data to the server.

[1655] Input: Recognized emotion data

[1656] Output: Sent emotion data

[1657] Step 10: Sentiment Data Analysis

[1658] The server analyzes the received emotional data and assesses the user's current emotional state.

[1659] Input: Emotion data sent

[1660] Output: Evaluation of the emotional state (e.g., whether the user is relaxed)

[1661] Step 11: Adjust notification content

[1662] The server tailors the notifications to the user based on the evaluation of the user's emotional state, for example providing more detailed notifications if the user is relaxed.

[1663] Input: Emotional state assessment result

[1664] Output: Adjusted notification content (e.g. detailed notification of harvest time)

[1665] Step 12: Notify the user interface

[1666] The server sends the adjusted notification content to the user interface for the user to review.

[1667] Input: Adjusted notification content

[1668] Output: Information displayed on a user interface (e.g., detailed harvest time display on a smartphone app)

[1669] This processing step not only enables the user to maintain an optimal coffee bean cultivation environment at all times, but also provides appropriate information according to the user's emotional state, allowing for a comfortable cultivation experience.

[1670] 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.

[1671] 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.

[1672] 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.

[1673] 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.

[1674] 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.

[1675] 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.

[1676] 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).

[1677] 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.

[1678] 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."

[1679] 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.

[1680] 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).

[1681] 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.

[1682] 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.

[1683] 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.

[1684] 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.

[1685] 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.

[1686] 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.

[1687] 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.

[1688] 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.

[1689] 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.

[1690] 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.

[1691] The following is further disclosed regarding the above embodiment.

[1692] (Claim 1)

[1693] sensor means for measuring soil moisture;

[1694] sensor means for measuring temperature;

[1695] sensor means for measuring the hours of sunshine;

[1696] a server means for collecting and analyzing data acquired from each sensor means;

[1697] control measures for controlling watering and other environmental adjustments based on the results of the data analysis;

[1698] an interface means for a user to check the growth status and harvest timing;

[1699] A system including:

[1700] (Claim 2)

[1701] 10. The system of claim 1, wherein data collection from each sensor means occurs periodically.

[1702] (Claim 3)

[1703] 2. The system according to claim 1, wherein the server means notifies the user of the growth status and harvest timing based on the analysis results.

[1704] "Example 1"

[1705] (Claim 1)

[1706] sensor means for measuring soil moisture;

[1707] sensor means for measuring temperature;

[1708] sensor means for measuring the hours of sunshine;

[1709] data processing means for collecting and analyzing data obtained from each sensor means;

[1710] control measures for controlling watering and other environmental adjustments based on the results of the data analysis;

[1711] a display means for allowing a user to check the growth status and harvest timing;

[1712] A system including:

[1713] (Claim 2)

[1714] 10. The system of claim 1, wherein data collection from each sensor means occurs periodically.

[1715] (Claim 3)

[1716] 2. The system according to claim 1, wherein the data processing means notifies the user of the growth status and harvest timing based on the analysis results.

[1717] (Claim 4)

[1718] 2. The system according to claim 1, wherein the display means is configured as a smartphone application or a web application.

[1719] (Claim 5)

[1720] The system according to claim 1, wherein the data processing means analyzes the collected data using a machine learning model to provide an optimal growth environment.

[1721] (Claim 6)

[1722] 2. The system according to claim 1, wherein the control means controls environmental control devices such as watering devices, temperature control devices, and lighting devices.

[1723] "Application Example 1"

[1724] (Claim 1)

[1725] detection means for measuring soil moisture;

[1726] detection means for measuring temperature;

[1727] detection means for measuring the hours of sunshine;

[1728] a computing means for collecting and analyzing data obtained from each of the detecting means;

[1729] operational means for controlling watering and other environmental adjustments based on the results of the data analysis;

[1730] A communication means for users to check the growth status and harvest timing;

[1731] A system comprising:

[1732] A system that uses smart glasses to acquire environmental data, analyzes the data on a cloud server, and displays instructions for optimal environmental adjustments based on the analysis results.

[1733] (Claim 2)

[1734] 2. The system of claim 1, wherein data collection from each detection means is performed periodically, and wherein the smart glasses display environmental data in real time.

[1735] (Claim 3)

[1736] 2. The system according to claim 1, wherein the calculation means notifies the user of the growth status and harvest timing based on the analysis results, and the system further includes a function for displaying notifications on the smart glasses.

[1737] "Example 2: Combining Emotion Engines"

[1738] (Claim 1)

[1739] sensor means for measuring soil moisture;

[1740] sensor means for measuring temperature;

[1741] sensor means for measuring the hours of sunshine;

[1742] a data processing unit for collecting and analyzing data obtained from each sensor means;

[1743] an environmental control device for controlling watering and other environmental adjustments based on the data analysis results;

[1744] a user interface device for allowing a user to check the growth status and harvest timing;

[1745] an emotion recognition device for recognizing an emotion of a user and transmitting the data to the data processing device;

[1746] A system for the environmental control device to suggest or implement environmental adjustments based on data from an emotion recognition device.

[1747] (Claim 2)

[1748] 10. The system of claim 1, wherein data collection from each sensor means occurs periodically.

[1749] (Claim 3)

[1750] 2. The system according to claim 1, wherein the data processing device notifies the user of the growth status and harvest timing based on the analysis results.

[1751] "Application example 2 when combining emotion engines"

[1752] (Claim 1)

[1753] sensor means for measuring soil moisture;

[1754] sensor means for measuring the ambient temperature;

[1755] sensor means for measuring the hours of sunshine;

[1756] a server means for collecting and analyzing data acquired from each sensor means;

[1757] control measures for controlling watering and other environmental adjustments based on the results of the data analysis;

[1758] an interface means for a user to check the growth status and harvest timing;

[1759] means for recognizing the emotional state of the user using an emotion engine and transmitting that data to a server;

[1760] means for adjusting the cultivation environment and the behavior of the robot based on the emotional state of the user;

[1761] A system including:

[1762] (Claim 2)

[1763] 10. The system of claim 1, wherein data collection from each sensor means occurs periodically.

[1764] (Claim 3)

[1765] 2. The system according to claim 1, wherein the server means notifies the user of the growth status and harvest timing based on the analysis results and emotion data. [Explanation of symbols]

[1766] 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. sensor means for measuring soil moisture; sensor means for measuring temperature; sensor means for measuring the hours of sunshine; a server means for collecting and analyzing data acquired from each sensor means; control measures for controlling watering and other environmental adjustments based on the results of the data analysis; an interface means for a user to check the growth status and harvest timing; A system including:

2. 2. The system of claim 1, wherein data collection from each sensor means occurs periodically.

3. 2. The system according to claim 1, wherein the server means notifies the user of the growth status and harvest timing based on the analysis results.

Images