System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting a plurality of pieces of environmental data related to a growth state of a plant using a sensor; means for analyzing the environmental data using generative artificial intelligence and proposing an optimal growth condition; means for automatically adjusting an environment based on the proposed growth condition; and means for collecting the adjusted environmental data again and performing feedback.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern agriculture, achieving optimal crop growth requires a great deal of experience and knowledge. However, for many agricultural workers and individual farmers who lack this knowledge and experience, maintaining crop growth is difficult. Furthermore, real-time data collection and analysis is essential to respond quickly to subtle changes in the environment, but doing this manually is not practical. For this reason, there is a demand for a system that can automatically and efficiently optimize plant growth conditions. [Means for solving the problem]

[0005] The present invention provides a system including a means for collecting multiple environmental data related to the growth status of plants using sensors, a means for analyzing the environmental data using generative artificial intelligence and proposing optimal growth conditions, a means for automatically adjusting the environment based on the proposed growth conditions, and a means for re-collecting the adjusted environmental data and providing feedback. This system allows users to maintain an optimal growth environment for plants without specialized knowledge, and enables real-time data analysis and environmental adjustment. Furthermore, by notifying the user of the proposed growth conditions and adjusting the environment only after receiving the user's approval, optimization that reflects the user's intentions is possible. Furthermore, by analyzing the collected data using generative artificial intelligence and applying an optimization algorithm, highly accurate environmental adjustment is achieved.

[0006] A "sensor" is a device for collecting environmental data such as temperature, humidity, illuminance, and soil moisture content related to the growth status of plants.

[0007] "Generative AI" is an AI system with data analysis and prediction capabilities that analyzes collected environmental data and proposes optimal growing conditions.

[0008] "Growth status" is a collection of data that describes the current health of a plant and the environmental factors that affect its growth.

[0009] "Optimal growing conditions" are the ideal environmental parameters, such as temperature, humidity, light intensity, and soil moisture, that plants need to grow healthily.

[0010] A "proposal" is the optimal growing conditions for a plant determined by the generative artificial intelligence based on the analysis results, along with specific operational instructions for achieving those conditions.

[0011] "Automatically" means that the system operates under automatic control without user intervention.

[0012] "Environmental adjustment" refers to changing environmental parameters such as temperature, humidity, light intensity, and soil moisture content to optimize plant growth conditions.

[0013] "Feedback" refers to collecting adjusted environmental data again and reporting the results of the system's operation to the user.

[0014] "System" is a general term for a complex set of devices and programs that include sensors, generative artificial intelligence, environmental control devices, feedback devices, etc. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] The present invention relates to a system that monitors the growth status of plants in real time and automatically provides optimal environmental conditions. This system operates by combining sensors, generative artificial intelligence, control devices, and feedback devices.

[0037] First, the server collects multiple environmental data related to the plant's growth status using sensors. These sensors measure temperature, humidity, illuminance, and soil moisture. For example, the temperature sensor collects data such as 30°C, humidity sensor collects data such as 40%, illuminance sensor collects data such as 300 lux, and soil moisture sensor collects data such as 15%.

[0038] The server then sends the collected data to a generative AI, which analyzes the data and evaluates the plant's current growth status. For example, it compares the data with a learning model and determines that "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable for illumination," and "30% soil moisture is appropriate."

[0039] Based on the analysis results, the server identifies optimal growing conditions and generates specific recommendations for environmental adjustments, such as adjusting the air conditioning to lower the temperature, automatically irrigating to increase humidity, or adjusting the lighting. These recommendations are returned to the server in JSON format.

[0040] To notify the user of the proposed changes, the server sends a push notification to the device (e.g., the user's smartphone). The notification includes information such as "Lower the current temperature from 30°C to 25°C," "Increase the humidity from 40% to 60%, "Increase the illuminance from 300 lux to 500 lux," and "Increase the soil moisture from 15% to 30%."

[0041] The user checks the notification via their device and approves the automatic control. After approval is obtained, the server sends instructions to various control devices. For example, it sends specific instructions such as "adjust the temperature to 25°C" to the air conditioning unit, "sprinkle water to adjust the humidity to 60%" to the irrigation system, and "adjust the illuminance to 500 lux" to the lights.

[0042] After the environmental adjustments are complete, the server again collects data from the sensors and checks the results. This information is again analyzed by the generative AI, and feedback is provided on whether the growing conditions have improved.

[0043] Finally, the server notifies the terminal that the adjustment is complete and that the plant's growth condition has improved, and the terminal displays this to the user. For example, information such as "Adjustment complete. Plant condition has improved" is displayed.

[0044] In this way, the system automatically provides the optimal growing environment for plants, enabling real-time data analysis and environmental adjustment, even without the user's specialized knowledge. Furthermore, since optimization reflects the user's wishes, highly accurate environmental adjustment is achieved.

[0045] The processing flow will be explained below.

[0046] Step 1:

[0047] The server collects multiple environmental data related to the plant's growth status through sensors: a temperature sensor measures the current temperature (e.g., 30°C), a humidity sensor measures humidity (e.g., 40%), an illuminance sensor measures light intensity (e.g., 300 lux), and a soil moisture sensor measures soil humidity (e.g., 15%).

[0048] Step 2:

[0049] The server sends the collected data to the generative AI via an API, requesting analysis and optimization. For example, it sends a POST request to the " / ai / analyze" endpoint.

[0050] Step 3:

[0051] The generative AI analyzes the received data. Specifically, it compares it with the learning model within the generative AI and calculates the temperature, humidity, illuminance, and soil moisture content that are optimal for plant growth. This results in judgments such as "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable illuminance," and "30% soil moisture is appropriate."

[0052] Step 4:

[0053] The generative AI returns the analysis results to the server in JSON format, for example, { "temperature": "reduce to 25℃", "humidity": "increase to 60%", "light": "increase to 500 lux", "soilMoisture": "increase to 30%"}.

[0054] Step 5:

[0055] The server receives the analysis results and prepares to notify the user. Specifically, the analysis results are sent to the user's device as a push notification. The push notification contains information such as "Lower the current temperature from 30°C to 25°C," "Increase the humidity from 40% to 60%, "Increase the illuminance from 300 lux to 500 lux," and "Increase the soil moisture from 15% to 30%."

[0056] Step 6:

[0057] The device displays the received notification to the user, and provides an interface that clearly displays the suggestion on the screen so that the user can check the notification.

[0058] Step 7:

[0059] The user checks the notification and approves the automatic control by pressing a button such as "Start automatic control" on the device screen.

[0060] Step 8:

[0061] The server receives user approval and sends instructions to various control devices, such as "adjust the temperature to 25°C" to the air conditioning control device, "sprinkle water to adjust the humidity to 60%" to the irrigation system, and "adjust the illuminance to 500 lux" to the lights.

[0062] Step 9:

[0063] The server monitors the data obtained during the adjustment process and checks whether the adjustment is complete. Specifically, it collects feedback data from the sensors again and evaluates whether the set value has been reached.

[0064] Step 10:

[0065] The server notifies the terminal of the adjustment results, which are then displayed to the user. The terminal displays a message to the user saying, "Adjustment complete. Plant condition has improved," informing the user of the improvement in growth conditions.

[0066] This series of steps automatically provides the optimal growing environment for plants, allowing users to effectively grow plants without having specialized knowledge.

[0067] Example 1

[0068] 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."

[0069] Conventional plant growth management systems require manual collection and analysis of environmental data to adjust the environment. This makes it difficult for users without specialized knowledge to manage plants efficiently. In addition, they are unable to respond immediately to environmental changes, which can have a negative impact on plant growth.

[0070] 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.

[0071] In this invention, the server includes means for collecting multiple pieces of environmental data related to the growth status of plants using sensors, means for analyzing the environmental data using generative artificial intelligence and proposing optimal growth conditions, means for sending a push notification to a user's device based on the proposed growth conditions and receiving user approval, means for sending instructions to a control device for environmental adjustment after the user's approval and automatically adjusting the environment based on the instructions, and means for re-collecting the adjusted environmental data, analyzing it using generative artificial intelligence, and providing feedback. This makes it possible to automatically and efficiently provide an optimal growth environment for plants without the user having specialized knowledge.

[0072] A "sensor" is a device used to collect data on the growth status of plants and the surrounding environment, specifically measuring temperature, humidity, illuminance, soil moisture, etc.

[0073] "Generative AI" is an AI model that analyzes collected environmental data and compares it with past learning data to propose optimal growth conditions.

[0074] "Environmental data" refers to various data related to the growth state of plants, such as temperature, humidity, illuminance, and soil moisture content.

[0075] "Growth conditions" are specific environmental settings such as temperature, humidity, light intensity, and soil moisture required for optimal plant growth.

[0076] A "terminal" is an information terminal such as a smartphone or tablet used by a user, which receives push notifications and performs operations such as approving environmental adjustments.

[0077] "Push notifications" are a communication method that sends information from a server to a device in real time, and include messages suggesting growing conditions and encouraging approval of environmental adjustments.

[0078] A "control device" is hardware for adjusting the plant growth environment, and includes air conditioning devices, irrigation systems, lighting devices, etc.

[0079] "Feedback" is the process of analyzing data collected again after environmental adjustments have been made to check whether the adjustments have been made appropriately and to evaluate any improvements in growth conditions.

[0080] This invention relates to a system that monitors the growth status of plants in real time and automatically provides optimal environmental conditions. This system operates through the collaboration of a server, terminals, and users. The following describes the specific hardware and software usage methods, data processing, and data calculation.

[0081] Hardware and software used

[0082] The system consists of the following hardware and software:

[0083] Sensors (temperature sensor, humidity sensor, light sensor, soil moisture sensor)

[0084] Generative Artificial Intelligence (AI Model)

[0085] Server (server for data collection and analysis)

[0086] Device (user's smartphone, tablet, etc.)

[0087] Control equipment (air conditioning equipment, irrigation systems, lighting equipment)

[0088] Data collection and analysis

[0089] The server periodically collects environmental data about the plants through sensors. Specifically, it obtains data on temperature, humidity, illuminance, and soil moisture. For example, the temperature sensor collects data on 30°C, the humidity sensor collects data on 40%, the illuminance sensor collects data on 300 lux, and the soil moisture sensor collects data on 15%.

[0090] Data analysis

[0091] The server sends the collected data to the generative AI, which analyzes it. The generative AI evaluates the current data by comparing it with past learning data and proposes optimal growing conditions. For example, by comparing it with the learning model, it determines that "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable for illumination," and "30% soil moisture is appropriate."

[0092] Generate environmental adjustment proposals

[0093] Based on the analysis results, the server generates specific suggestions for adjusting the environment, such as operating the air conditioning to lower the temperature, automatically irrigating the area to increase humidity, adjusting the lighting, etc. These suggestions are returned to the server in JSON format.

[0094] User Notifications

[0095] The server sends suggested growing conditions to the user's device as a push notification, including specific examples such as "Lower the current temperature from 30°C to 25°C," "Increase the humidity from 40% to 60%, "Increase the illumination from 300 lux to 500 lux," and "Increase the soil moisture from 15% to 30%."

[0096] Authorization and execution of automated controls

[0097] The user checks the notification on their device and approves the automatic control. After receiving approval, the server sends instructions to various control devices. For example, instructions such as "adjust the temperature to 25°C" to the air conditioning unit, "sprinkle water to adjust the humidity to 60%" to the irrigation system, and "adjust the illuminance to 500 lux" to the lights are sent.

[0098] Get feedback

[0099] After the environmental adjustments are complete, the server collects data from the sensors again and analyzes the results using generative artificial intelligence. The analysis provides feedback on whether the growth conditions have improved. For example, it checks whether the adjusted temperature is 25°C, humidity is 60%, light intensity is 500 lux, and soil moisture is 30%.

[0100] Notification of adjustment results

[0101] The server notifies the terminal that the adjustment is complete and that the plant's growth condition has improved, and the terminal displays this to the user. For example, information such as "Adjustment complete. The plant's condition has improved" is displayed.

[0102] Prompt Sentence Examples

[0103] An example of a prompt for a generative AI model is as follows:

[0104] Analyze the following environmental data and suggest the optimum growing conditions for the plant: temperature 30°C, humidity 40%, light intensity 300 lux, soil moisture 15%.

[0105] The above is an embodiment of the present invention. This system provides an optimal growing environment for plants, and allows real-time data analysis and environmental adjustment, even if the user does not have specialized knowledge. Furthermore, optimization that reflects the user's intentions is realized, allowing for efficient and highly accurate environmental adjustment.

[0106] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0107] Step 1: Data collection

[0108] The server uses sensors to collect a plurality of environmental data related to the growth state of the plants.

[0109] Input: Data from sensors measuring temperature, humidity, light intensity, and soil moisture.

[0110] Output: A set of collected environmental data (e.g., temperature 30°C, humidity 40%, illuminance 300 lux, soil moisture 15%).

[0111] Specific operation: The server sends a polling request to each sensor and obtains data on temperature, humidity, illuminance, and soil moisture content from the sensors in response.

[0112] Step 2: Data analysis

[0113] The server sends the collected environmental data to the generative artificial intelligence for analysis.

[0114] Input: Collected environmental data (temperature 30°C, humidity 40%, illuminance 300 lux, soil moisture 15%).

[0115] Output: Analysis of optimal growing conditions (e.g. temperature 25°C, humidity 60%, light intensity 500 lux, soil moisture 30%).

[0116] Specific operation: The server sends environmental data to the generative AI's API and receives the results. The generative AI analyzes the results by comparing them with past learning models and determines the optimal growth conditions.

[0117] Step 3: Generate environmental adjustment proposals

[0118] The server generates optimal environmental adjustment proposals based on the analysis results of the generative artificial intelligence.

[0119] Input: Analysis results (temperature 25°C is appropriate, humidity 60% is ideal, illuminance 500 lux is desirable, soil moisture 30% is appropriate).

[0120] Output: Specific environmental adjustment suggestions (e.g., temperature adjustment, humidity adjustment, lighting adjustment).

[0121] Specific operation: Based on the analysis results, the server generates suggestions in JSON format, such as air conditioning to lower the temperature, irrigation to increase humidity, and adjusting lights to increase brightness.

[0122] Step 4: User Notification

[0123] The server sends the generated environmental adjustment proposal to the user's device as a push notification.

[0124] Input: Proposed environmental adjustments (reduce temperature from 30°C to 25°C, increase humidity from 40% to 60%, increase light intensity from 300 lux to 500 lux, increase soil moisture from 15% to 30%).

[0125] Output: A push notification sent to the user's device containing the specific adjustments.

[0126] Specific operation: The server uses the push notification service to send a notification to the user's smartphone.

[0127] Step 5: Accepting automation

[0128] The user checks the notification through the terminal and approves the automatic control.

[0129] Input: Push notification (environmental adjustment suggestion) received by the user.

[0130] Output: User approval (sent from the terminal).

[0131] Specific operation: The user taps the notification on their smartphone and presses the "Approve" button to approve the environmental adjustment.

[0132] Step 6: Execute control instructions

[0133] After receiving the user's approval, the server sends specific instructions to the various control devices.

[0134] Input: User approval and generated environmental adjustment suggestions.

[0135] Output: Specific instructions sent to a control device (adjust temperature, humidity, light).

[0136] Specific actions: The server sends specific instructions to the air conditioner, such as "adjust the temperature to 25°C," to the irrigation system, "adjust the humidity to 60%, and" to the lights, "adjust the illuminance to 500 lux."

[0137] Step 7: Check the results of the environment adjustment

[0138] After the environmental adjustment is complete, the server again collects and analyzes data from the sensors.

[0139] Input: Calibrated environmental data (latest data from sensors).

[0140] Output: Feedback result after adjustment (whether the environment improved or not).

[0141] What it does: The server sends another request to the sensor to collect the latest environmental data, which it then sends to the generative AI for reanalysis.

[0142] Step 8: Notification of adjustment results

[0143] The server notifies the user's terminal that the adjustment has been completed and that the plant's growth condition has improved, and the terminal displays this to the user.

[0144] Input: Adjusted feedback results.

[0145] Output: Notification to user device (adjustment completed, improvement notification).

[0146] Specific operation: The server sends a notification to the user's smartphone via a push notification service, such as "Adjustment completed. Plant condition has improved."

[0147] This concludes the explanation of the specific processing steps of the program. Each step works closely together to provide the optimal growing environment for plants in real time.

[0148] (Application example 1)

[0149] 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."

[0150] While current plant growth systems automate the adjustment of environmental conditions to maintain optimal plant growth, they have the problem of difficulty in responding in real time to security risks such as intruders and suspicious activity. This means that even if the plant growth environment is secured, overall safety cannot be ensured if physical security is threatened.

[0151] 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.

[0152] In this invention, the server includes means for collecting multiple pieces of environmental data and security data related to the growth status of plants using sensors, means for analyzing the environmental data and security data using generative artificial intelligence and proposing optimal growth conditions and vigilance situations, means for automatically adjusting the environment and security measures based on the proposed growth conditions and vigilance situations, and means for recollecting the adjusted environmental data and security data and providing feedback. This not only provides an optimal growth environment for plants, but also makes it possible to respond to security risks in real time.

[0153] A "sensor" is a device for collecting environmental data related to plant growth and security.

[0154] "Generative AI" is an AI technology that analyzes collected environmental and security data to suggest optimal growth conditions and vigilance situations.

[0155] "Environmental data" refers to data necessary for evaluating the growth status of plants, such as temperature, humidity, illuminance, and soil moisture.

[0156] "Crime prevention data" refers to data necessary for evaluating crime prevention measures, such as motion detection and vibration detection.

[0157] "Growth conditions" are the environmental conditions necessary to promote healthy plant growth.

[0158] An "alert situation" is a state of surveillance and vigilance that is considered optimal from a crime prevention perspective.

[0159] "Adjustment means" refers to means for automatically adjusting the environment and security posture based on proposed growth and alert conditions.

[0160] "Feedback" is the process of collecting environmental and security data again after adjustments have been made and reevaluating the results.

[0161] This invention relates to a system that monitors the growth status and security of plants in real time to provide optimal environmental and security conditions. The system operates by combining sensors, generative artificial intelligence, control devices, and feedback devices. Each major processing step of the system and the hardware and software used for each step are described below.

[0162] First, the server uses sensors to collect multiple environmental data related to plant growth and security. These sensors measure temperature, humidity, illuminance, soil moisture, and security data (motion detection, vibration detection, etc.). For example, the server collects data that the temperature sensor is 30°C, the humidity sensor is 40%, the motion detection sensor detects motion, and the vibration sensor does not detect vibration.

[0163] The server then sends the collected environmental and security data to the generative AI, which analyzes this data and evaluates the plant's current growth and alert status. For example, by comparing it with the learning model, it can determine that "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable for illumination," "30% soil moisture is appropriate," "turn on the security light to detect motion," and "do not sound the alarm because there is no vibration."

[0164] Based on the analysis, the server identifies optimal growing conditions and vigilance situations and generates specific recommendations for adjusting the environment and security posture, such as adjusting the air conditioning to lower the temperature or turning on security lights in response to motion detection. These recommendations are returned to the server in JSON format.

[0165] To notify the user of the proposed content, the server sends a push notification to the device (e.g., the user's smartphone). The notification content includes information such as "Lower the current temperature from 30°C to 25°C," "Increase the humidity from 40% to 60%," and "Turn on the security lights to detect motion."

[0166] The user checks the notification via their device and approves the automatic control. After approval is obtained, the server sends instructions to the various control devices. For example, it sends specific instructions such as "adjust the temperature to 25°C" to the air conditioner and "turn on the security light" to the security light.

[0167] After the environmental adjustments and security measures are complete, the server again collects data from the sensors and checks the results of the adjustments. This information is again analyzed by the generative AI to provide feedback on whether the growth and security conditions have improved.

[0168] Finally, the server notifies the terminal that the adjustment is complete and that the plant's growth and security status have improved, and the terminal displays this to the user. For example, the terminal displays information such as "Adjustment complete. Plant status and security status have improved."

[0169] As a concrete example, to maintain public order in a certain town, patrol staff wear smart glasses with this application installed. When the sensor detects an abnormality, the AI ​​automatically analyzes and evaluates it, and proposes and implements the necessary security measures. The sensor detects motion and suggests the optimal alarm to sound and security lights to turn on. After execution, the user receives a notification on the smart glasses display saying "Motion detected, alarm sounding."

[0170] Example prompt sentence:

[0171] plaintext

[0172] I would like you to suggest the best security measures for a situation where the temperature sensor indicates an outside temperature of 25°C, the humidity sensor indicates 50%, the motion detection sensor detects motion, and the vibration sensor does not detect vibration.

[0173] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0174] Step 1:

[0175] Input - The server collects data from sensors (temperature, humidity, light intensity, soil moisture, motion detection, vibration detection).

[0176] Data processing and calculation: The collected data is temporarily stored on the server, where it is checked for consistency and format converted.

[0177] Output - The consistent environmental and security data is sent to the generative AI.

[0178] The server collects environmental and security data from various installed sensors. For example, the temperature sensor may indicate a temperature of 30°C, and the motion sensor may indicate that a motion has been detected.

[0179] Step 2:

[0180] Input - Environmental data and security data sent from the server to the generative artificial intelligence.

[0181] Data processing, calculation, and generation AI uses trained models to analyze data and propose optimal growth conditions and vigilance situations.

[0182] The output-generative AI's suggestions (e.g., adjust the temperature to 25°C, turn on the security lights) are returned to the server in JSON format.

[0183] The server requests a generative artificial intelligence to analyze the transmitted data and propose optimal growth conditions and vigilance situations.

[0184] Step 3:

[0185] Suggestions returned from the input-generative AI.

[0186] Data processing and calculation - The server analyzes the proposal content and generates push notification data to notify the user.

[0187] Output - Notifications about suggested growing conditions and alert status are sent to the user's terminal.

[0188] Based on the suggestions received from the generative artificial intelligence, the server prepares notification content for the user's device and sends a push notification.

[0189] Step 4:

[0190] Input - Push notifications from the server (e.g. adjust the temperature to 25°C, turn on the security lights).

[0191] Data processing and calculation - The user checks the notification and decides whether to approve or reject it.

[0192] Output - The user's approval or denial is returned to the server.

[0193] The user checks the proposal through the terminal and accepts or rejects it. If the proposal is accepted, the result is sent to the server.

[0194] Step 5:

[0195] Input - Approval result from user.

[0196] Data processing and calculation - The server generates commands to operate various control devices (air conditioners, security lights, etc.) based on the approved proposal.

[0197] Output - The command is sent to the control device.

[0198] The server receives the approval result from the user, generates specific instructions, and sends them to the air conditioner, security lights, etc.

[0199] Step 6:

[0200] Inputs - Adjusted environmental and crime prevention data.

[0201] Data processing and calculation - The server collects data from the sensors again and requests the generative AI to reanalyze it.

[0202] Output - Reanalysis results are obtained and feedback on adjusted growth and vigilance status is generated.

[0203] After the environmental adjustments and security measures have been completed, the server will collect data from the sensors again and have the generative artificial intelligence reanalyze the growth and alert status.

[0204] Step 7:

[0205] Input - Reparse result.

[0206] Data processing and calculation - Based on the reanalysis results, the server generates data to provide final feedback to the user.

[0207] Output - A notification is sent to the user's device indicating the adjustment completion and the improvement results.

[0208] The server uses the results of the reanalysis to prepare final feedback and sends a notification to the user's device, for example, "Adjustments completed. Plant condition and security have improved."

[0209] As described above, the system is designed to function smoothly as a whole, with necessary data processing and calculations being performed based on input data at each step.

[0210] 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.

[0211] The present invention relates to a system that monitors the growth status of plants in real time and provides optimal environmental conditions taking into account the user's emotions. This system operates by combining sensors, generative artificial intelligence, an emotion engine, a control device, and a feedback device.

[0212] First, the server collects multiple environmental data related to the plant's growth status using sensors. These sensors measure temperature, humidity, illuminance, and soil moisture. For example, the temperature sensor collects data such as 30°C, humidity sensor collects data such as 40%, illuminance sensor collects data such as 300 lux, and soil moisture sensor collects data such as 15%.

[0213] The server then sends the collected data to a generative AI, which analyzes the data and evaluates the plant's current growth status. For example, it compares the data with a learning model and determines that "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable for illumination," and "30% soil moisture is appropriate."

[0214] Based on the analysis results, the server identifies optimal growing conditions and generates specific recommendations for environmental adjustments, such as adjusting the air conditioning to lower the temperature, automatically irrigating to increase humidity, or adjusting the lighting. These recommendations are returned to the server in JSON format.

[0215] Here, an emotion engine that recognizes the user's emotions analyzes the user's facial expressions and voice. The emotion engine uses image analysis and voice recognition technology to evaluate the user's emotional state, such as whether the user is feeling stressed or comfortable.

[0216] To notify the user of the proposed content, the server sends a push notification to the device (e.g., the user's smartphone). At this time, the notification is sent in a way that takes into consideration the user's emotions based on the analysis results of the emotion engine. For example, if the user is relaxed, the notification may be soft-spoken, such as "Lowering the current temperature from 30°C to 25°C will help your plants thrive," whereas if the user is in a hurry, the notification may be simple, such as "The temperature needs to be adjusted."

[0217] The user checks the notification via their device and approves the automatic control. At this time, the emotion engine analyzes the user's reaction again to ensure that approval is given without stress. When the user presses the approval button, the server sends instructions to various control devices. Specific instructions are sent to the air conditioning control device, such as "adjust the temperature to 25°C," to the irrigation system, "sprinkle water and adjust the humidity to 60%, and to the lights, "adjust the illuminance to 500 lux."

[0218] After the environmental adjustments are complete, the server collects data from the sensors again to confirm the results. This information is again analyzed by the generative AI to obtain feedback on whether the growth conditions have improved, and the server sends this information to the device.

[0219] The device notifies the user that the adjustment is complete and that the plant's growth has improved, for example by displaying a message such as "Adjustment complete. Plant condition has improved." Furthermore, the emotion engine re-evaluates the user's reaction to confirm whether the user is satisfied.

[0220] Through this series of steps, the system can automatically provide the optimal growing environment for plants while taking into consideration emotions, even if the user does not have specialized knowledge. Furthermore, optimization that reflects the user's intentions and emotions is carried out, achieving highly accurate environmental adjustment.

[0221] The processing flow will be explained below.

[0222] Step 1:

[0223] The server collects multiple environmental data related to the plant's growth status through sensors: a temperature sensor measures the current temperature (e.g., 30°C), a humidity sensor measures humidity (e.g., 40%), an illuminance sensor measures light intensity (e.g., 300 lux), and a soil moisture sensor measures soil humidity (e.g., 15%).

[0224] Step 2:

[0225] The server sends the collected data to the generative AI via an API, requesting analysis and optimization. For example, it sends a POST request to the " / ai / analyze" endpoint.

[0226] Step 3:

[0227] The generative AI analyzes the received data. Specifically, it compares it with the learning model within the generative AI and calculates the temperature, humidity, illuminance, and soil moisture content that are optimal for plant growth. This results in judgments such as "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable illuminance," and "30% soil moisture is appropriate."

[0228] Step 4:

[0229] The generative AI returns the analysis results to the server in JSON format, for example, { "temperature": "reduce to 25℃", "humidity": "increase to 60%", "light": "increase to 500 lux", "soilMoisture": "increase to 30%"}.

[0230] Step 5:

[0231] The server receives the analysis results from the generative AI and activates the emotion engine to ascertain the user's emotional state. The emotion engine uses image and audio analysis to assess the user's current emotions and determine whether the user is stressed or relaxed.

[0232] Step 6:

[0233] The server adjusts the generative AI's suggestions based on the analysis results of the emotion engine. If the user is relaxed, the server will notify them in a gentle way, such as "Lowering the current temperature from 30°C to 25°C will help your plants thrive," but if the user is in a hurry, the server will notify them in a simpler way, such as "The temperature needs to be adjusted."

[0234] Step 7:

[0235] The server then sends the adjusted proposal to the device as a push notification. Specifically, the proposal is sent to the user's smartphone and displayed in a format that the user can check.

[0236] Step 8:

[0237] The device displays the received notification to the user. As the user checks the content of the notification, the emotion engine analyzes the user's reaction again to confirm whether the user is in a state where they can accept the notification without feeling stressed.

[0238] Step 9:

[0239] The user checks the notification and approves the automatic control via the device by pressing a button such as "Start automatic control."

[0240] Step 10:

[0241] With the user's approval, the server sends instructions to various control devices, such as to the air conditioning control device to "adjust the temperature to 25°C," to the irrigation system to "sprinkle water and adjust the humidity to 60%, and to the lights to "adjust the illuminance to 500 lux."

[0242] Step 11:

[0243] The server monitors the adjustment process and, once the adjustment is complete, collects data from the sensors again to confirm whether the temperature, humidity, light intensity, and soil moisture content have reached their target values.

[0244] Step 12:

[0245] The server checks the adjustment results and the improvement in the plant's condition, and sends the information to the terminal. The server notifies the user by saying, "Adjustment complete. The plant's condition has improved."

[0246] Step 13:

[0247] The device will display the completion of the adjustment and the improvement of the growth status to the user, while the emotion engine will analyze the user's reaction to check whether the user is satisfied or stressed.

[0248] This series of steps not only automatically provides the optimal growing environment for plants, but also creates a system that takes into account the user's emotional state.

[0249] Example 2

[0250] 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."

[0251] Conventional plant growth management systems were able to suggest growth conditions by collecting and analyzing environmental data, but it was difficult to adjust the environment optimally while taking the user's emotions into consideration. Furthermore, because the notification of growth condition suggestions was made without regard for the user's situation or emotions, it was difficult for the user to accept the suggestions, making it difficult to maintain an optimal growth environment for plants.

[0252] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting multiple pieces of environmental data related to the growth state of plants using sensors, means for analyzing the environmental data using generative artificial intelligence and proposing optimal growth conditions, means for automatically adjusting the environment based on the proposed growth conditions, means for re-collecting the adjusted environmental data and providing feedback, means for evaluating the user's emotions using an emotion engine that analyzes the user's emotional state, and means for determining an appropriate notification method based on the evaluated user emotions. This makes it possible to provide an optimal plant growth environment that takes the user's emotions into consideration.

[0253] A "sensor" is a measuring device for collecting environmental data.

[0254] "Plant growth status" refers to various parameters that indicate the growth and health status of a plant.

[0255] "Environmental data" refers to data related to plant growth conditions such as temperature, humidity, illuminance, and soil moisture content.

[0256] "Generative artificial intelligence" refers to machine learning models and algorithms that analyze collected environmental data and suggest optimal growing conditions.

[0257] "Optimal growing conditions" are ideal environmental parameters for promoting plant growth and health.

[0258] The "means for automatically adjusting the environment" is a system that automatically controls air conditioning, irrigation systems, lighting, etc. based on collected data and the analysis results of generative artificial intelligence.

[0259] The "means of providing feedback" is a function that collects data again after the environmental adjustment, analyzes the results, and evaluates the effectiveness of the adjustment.

[0260] An "emotion engine" is a technology for analyzing and evaluating a user's emotional state based on facial expressions, voice, etc.

[0261] "User's emotions" refer to the psychological state, such as stress or relaxation, that the user feels.

[0262] An "appropriate notification method" is a method of notifying the user in an appropriate manner and at an appropriate timing based on the user's emotional state.

[0263] The present invention relates to a system that monitors the growth status of plants in real time and provides optimal environmental conditions taking into account the user's emotions. This system operates by combining sensors, generative artificial intelligence, an emotion engine, a control device, and a feedback device.

[0264] First, the server collects multiple environmental data related to the plant's growth status using sensors. The sensors include a temperature sensor, humidity sensor, illuminance sensor, and soil moisture sensor. For example, the server collects data such as 30°C from the temperature sensor, 40% from the humidity sensor, 300 lux from the illuminance sensor, and 15% from the soil moisture sensor.

[0265] The server then sends the collected data to a generative AI, which analyzes the data and evaluates the plant's current growth status. For example, it compares the data with a learning model and determines that "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable for illumination," and "30% soil moisture is appropriate."

[0266] Based on the analysis results, the server identifies optimal growing conditions and generates specific recommendations for environmental adjustments, such as adjusting the air conditioning to lower the temperature, automatically irrigating to increase humidity, or adjusting the lighting. These recommendations are returned to the server in JSON format.

[0267] Here, an emotion engine that recognizes the user's emotions analyzes the user's facial expressions and voice. The emotion engine uses image analysis and voice recognition technology to evaluate the user's emotional state, such as whether the user is feeling stressed or comfortable.

[0268] To notify the user of the proposed content, the server sends a push notification to the device (e.g., the user's smartphone). At this time, the notification is sent in a way that takes into consideration the user's emotions based on the analysis results of the emotion engine. For example, if the user is relaxed, the notification may be soft-spoken, such as "Lowering the current temperature from 30°C to 25°C will help your plants thrive," whereas if the user is in a hurry, the notification may be simple, such as "The temperature needs to be adjusted."

[0269] The user checks the notification via their device and approves the automatic control. At this time, the emotion engine analyzes the user's reaction again to ensure that approval is given without stress. When the user presses the approval button, the server sends instructions to various control devices. Specific instructions are sent to the air conditioning control device, such as "adjust the temperature to 25°C," to the irrigation system, "sprinkle water and adjust the humidity to 60%, and to the lights, "adjust the illuminance to 500 lux."

[0270] After the environmental adjustments are complete, the server collects data from the sensors again to confirm the results. This information is again analyzed by the generative AI to obtain feedback on whether the growth conditions have improved, and the server sends this information to the device.

[0271] The device notifies the user that the adjustment is complete and that the plant's growth condition has improved, displaying a message such as "Adjustment complete. Plant condition has improved." The emotion engine then reevaluates the user's response to confirm whether the user is satisfied. This series of steps enables the system to automatically and emotionally provide the optimal growth environment for plants, even without the user's specialized knowledge.

[0272] Specific examples

[0273] 1. Collect data from the temperature sensor at 30°C, the humidity sensor at 40%, the illuminance sensor at 300 lux, and the soil moisture sensor at 15%.

[0274] 2. The generative artificial intelligence analyzes this data and determines that "25°C is the appropriate temperature," "60% is ideal humidity," "500 lux is desirable illumination," and "30% is the appropriate soil moisture."

[0275] 3. The server generates suggestions such as lowering the temperature to 25°C, increasing the humidity to 60%, and adjusting the light intensity to 500 lux.

[0276] 4. The emotion engine analyzes that the user is relaxed.

[0277] 5. The server notifies the device in gentle terms that "Lowering the current temperature from 30°C to 25°C will help the plants thrive."

[0278] 6. The user checks the notification and presses the approval button.

[0279] 7. The server sends instructions to the air conditioning control unit to "adjust the temperature to 25°C," the irrigation system to "adjust the humidity to 60%," and the lights to "adjust the illuminance to 500 lux."

[0280] Prompt Sentence Examples

[0281] "You have collected data: a temperature sensor reading 30°C, a humidity sensor reading 40%, a light sensor reading 300 lux, and a soil moisture sensor reading 15%. Describe the steps you would take to have a generative AI analyze this data and identify the optimal conditions for plant growth."

[0282] The above is a specific embodiment of this system.

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

[0284] Step 1:

[0285] The server uses sensors to collect multiple environmental data related to the plant's growth status. Specifically, it acquires data from a temperature sensor, humidity sensor, illuminance sensor, and soil moisture sensor. For example, the temperature sensor collects data such as 30°C, humidity sensor collects data such as 40%, illuminance sensor collects data such as 300 lux, and soil moisture sensor collects data such as 15%. This data becomes input data for subsequent analysis.

[0286] Step 2:

[0287] The server sends the collected environmental data to the generative AI, which then takes the data as input into the analysis system. The generative AI then evaluates the current growth status of the plant based on this input data. For example, it outputs optimal growth conditions such as "the appropriate temperature is 25°C," "the ideal humidity is 60%," "the desired illumination level is 500 lux," and "the appropriate soil moisture level is 30%."

[0288] Step 3:

[0289] The server generates specific proposals for environmental adjustments based on the optimal growing conditions output by the generative AI. These proposals include operating the air conditioning to lower the temperature, automatic irrigation to increase humidity, and adjusting the lighting. For example, suggestions such as "adjust the temperature to 25°C," "adjust the humidity to 60%," and "adjust the lighting to 500 lux" are output in JSON format.

[0290] Step 4:

[0291] The emotion engine analyzes emotions from the user's facial expressions and voice. Camera footage and microphone audio are used as input data. The emotion engine analyzes this data and evaluates the user's emotional state, such as whether they are feeling stressed or comfortable. The results of this evaluation become input data for determining how to notify the user of the proposed content.

[0292] Step 5:

[0293] The server notifies the user of specific suggestions based on the user's emotional data obtained from the emotion engine. The content of the notification and the way it is expressed are determined taking into consideration the user's emotional state. For example, if the user is relaxed, the server may notify them in a gentle manner, such as "Lowering the current temperature from 30°C to 25°C will help your plants thrive," while if the user is in a hurry, the server may notify them in a simpler manner, such as "The temperature needs to be adjusted."

[0294] Step 6:

[0295] The user checks the notification via their device and approves the automatic control. When the user presses the approval button on the device where the notification is displayed, this approval information is sent to the server. The emotion engine then analyzes the user's reaction again to confirm whether approval was given without stress.

[0296] Step 7:

[0297] With the user's approval, the server sends specific instructions to various control devices. For example, it sends instructions such as "adjust the temperature to 25°C" to the air conditioning control device, "adjust the humidity to 60%" to the irrigation system, and "adjust the illuminance to 500 lux" to the light. This automatically adjusts the physical environment.

[0298] Step 8:

[0299] After the environmental adjustment is complete, the server collects environmental data from the sensors again. The data collected as the adjustment results is input into the generative AI and re-evaluated. This confirms, for example, that the adjusted temperature is 25°C, humidity is 60%, illuminance is 500 lux, and soil moisture is 30%.

[0300] Step 9:

[0301] The server sends the adjustment results to the device and notifies the user that the adjustment is complete and that the plant's growth has improved. The device displays a message such as "Adjustment complete. The plant's condition has improved." The emotion engine again evaluates the user's reaction and checks whether the user is satisfied.

[0302] (Application example 2)

[0303] 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."

[0304] The present invention aims to provide a system that not only optimizes the growing environment of plants but also notifies users and adjusts the environment taking into account their emotions. In particular, the objective is to improve product quality and work efficiency by optimizing the working environment and taking into account the emotional state of workers in quality control on production lines.

[0305] 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.

[0306] In this invention, the server includes means for collecting multiple pieces of environmental data related to the growth status of plants using sensors, means for analyzing the environmental data using generative artificial intelligence and proposing optimal growth conditions, means for automatically adjusting the environment based on the proposed growth conditions, means for recollecting the adjusted environmental data and providing feedback, means for recognizing the user's emotions and generating a notification message according to the emotions, and means for reevaluating the user's emotional state and confirming that the user is not stressed if the user approves. This makes it possible to optimize the plant growth environment and the work environment of the production line, and to adjust the environment taking into account the emotional states of users and workers.

[0307] A "sensor" is a device that measures and collects environmental data such as temperature, humidity, light intensity, and soil moisture content in real time.

[0308] "Generative AI" is an AI technology that analyzes collected data and proposes optimal conditions for plant growth and working environments.

[0309] "Environmental adjustment" refers to automatically operating various devices such as air conditioning, irrigation systems, and lighting based on the optimal conditions proposed by generative AI, to adjust the growing conditions for plants and the working environment of the production line.

[0310] "Feedback" is the process of collecting data from sensors again after environmental adjustments have been made, and using the results to evaluate growth and environmental conditions.

[0311] The "emotion engine" is a system that analyzes the user's facial expressions and voice to evaluate the user's emotional state and reflects this in the content of notifications and suggestions for environmental adjustments.

[0312] A "notification message" is a message that notifies the user of suggested growing conditions or the need for environmental adjustments, and its expression is adjusted according to the user's emotional state.

[0313] "Acceptance" refers to the user's explicit acceptance of the proposed growing conditions and environmental adjustments.

[0314] "Stress assessment" is the process by which the emotion engine re-analyzes the user's emotional state to see if the user is feeling stressed.

[0315] This invention is a system that monitors the growing environment of plants in real time and provides optimal environmental conditions taking into account the user's emotions. This system can also be applied to quality control on factory production lines.

[0316] This system mainly uses the following hardware and software:

[0317] Sensors: Used to measure temperature, humidity, light intensity, and soil moisture. For example, temperature sensors, humidity sensors, and light intensity sensors are used on production lines in factories.

[0318] Generative AI: Refers to artificial intelligence technology that analyzes collected data and suggests optimal conditions for plants or products.

[0319] Emotion engine: Evaluates the user's emotions using image analysis and voice recognition technology.

[0320] Server: Collects data from the aforementioned sensors and analyzes it using generative artificial intelligence and an emotion engine to identify optimal environmental conditions.

[0321] Terminal: A device such as a smartphone or smart glasses that is used to send notifications to the user and receive user approval.

[0322] To explain how the system works in detail, the server first uses sensors to collect environmental data. For example, assume that the temperature on the production line is 28°C, humidity is 35%, and illuminance is 400 lux. This data is sent to the server, where generative AI analyzes the data and identifies the ideal environmental conditions. For example, it determines that the ideal temperature is 25°C, humidity is 40%, and illuminance is 500 lux.

[0323] The emotion engine then analyzes the user's facial expressions and voice to recognize their emotions. For example, if the server determines that the user is relaxed, it will send a soft message to the device, such as, "Lowering the current temperature from 28°C to 25°C will improve product quality." Conversely, if the user is in a hurry, it will send a simple message, such as, "The temperature needs to be adjusted."

[0324] The user checks the notification via their device and approves the proposal. After approval, the server again uses the emotion engine to evaluate the user's emotional state and confirm that they are not stressed. The server then sends specific instructions to various control devices to adjust the temperature, humidity, and light level.

[0325] After the environmental adjustment is complete, data is collected again from the sensors and analyzed by the generative artificial intelligence. Feedback is obtained on whether the adjustment results have improved, and the server sends this information to the device. For example, a message such as "Adjustment complete. The environment has been optimized and product quality has improved" is displayed.

[0326] Through this series of operations, the system can automatically provide the optimal growing and manufacturing environment for plants and products, even without the user's specialized knowledge. It also performs optimization that reflects the user's intentions and emotions, achieving highly accurate environmental adjustment.

[0327] Here are some illustrative prompts:

[0328] "Temperature sensor: 28°C, humidity sensor: 35%, illuminance sensor: 400 lux. Evaluate the difference from ideal environmental conditions (temperature 25°C, humidity 40%, illuminance 500 lux) and create adjustment suggestions. Additionally, generate a notification message when the user's emotions are relaxed."

[0329] Using this example, the detailed operation of the system can be clearly understood.

[0330] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0331] Step 1:

[0332] The server uses sensors to collect environmental data. Specifically, it obtains data in real time from temperature, humidity, and illuminance sensors. For example, the sensors can provide data such as "temperature 28°C, humidity 35%, illuminance 400 lux." The input is the raw data from each sensor, and the output is this environmental data.

[0333] Step 2:

[0334] The server sends the collected environmental data to the generative AI, which analyzes this data and identifies the optimal environmental conditions for plants and production lines. For example, it suggests conditions such as "a temperature of 25°C, humidity of 40%, and illuminance of 500 lux are desirable." The input is the collected environmental data, and the output is a proposal for the optimal environmental conditions.

[0335] Step 3:

[0336] The server uses an emotion engine to generate a notification message that takes the user's emotions into account based on the optimal environmental conditions proposed by the generative artificial intelligence. The emotion engine analyzes the user's facial expressions and voice to evaluate the user's emotional state. For example, if the user is relaxed, it generates a soft notification message saying, "Lowering the current temperature from 28°C to 25°C will improve product quality." The input is the proposed environmental conditions and the user's emotional data, and the output is the notification message.

[0337] Step 4:

[0338] The server sends a notification message to the user's device. The device displays the notification message to the user, and the user approves the environmental adjustment based on the suggestion. For example, a notification saying "Lowering the temperature to 25°C will improve product quality" appears on a smartphone, and the user presses the "Approve" button. The input is the notification message, and the output is the user's approval.

[0339] Step 5:

[0340] After receiving the user's approval, the server uses the emotion engine to evaluate the user's emotional state again and confirm that they are not stressed. For example, it checks whether the user remains relaxed after approval. The input is the user's approval and emotion data, and the output is the result of the stress assessment.

[0341] Step 6:

[0342] After the server confirms that there is no stress, it sends specific instructions to the various control devices to adjust the environment. For example, it sends instructions such as "adjust the temperature to 25°C" to the temperature control device and "adjust the illuminance to 500 lux" to the illuminance control device. The input is the optimal environmental conditions, and the output is the specific control instructions.

[0343] Step 7:

[0344] After the environmental adjustments are made, the server collects environmental data from the sensors again. For example, it checks whether the temperature is 25°C, the humidity is 40%, and the illuminance is 500 lux. The input is the sensor data after the environmental adjustments, and the output is the adjusted environmental data.

[0345] Step 8:

[0346] The server then sends the collected environmental data to the generative AI, which analyzes whether the growth conditions and quality have improved. For example, it receives feedback such as, "Optimal conditions are being maintained at a temperature of 25°C." The input is the adjusted environmental data, and the output is the feedback information.

[0347] Step 9:

[0348] The server sends feedback information to the user's device and notifies them of the adjustment results. For example, a message such as "Adjustment complete. Product quality has improved" is displayed on a smartphone. The input is feedback information, and the output is a notification to the user.

[0349] Through these steps, the invention can automatically provide the optimal growing and manufacturing environment for plants and products, even without the user's specialized knowledge. It also performs optimization that reflects the user's intentions and emotions, achieving highly accurate environmental adjustment.

[0350] 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.

[0351] 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.

[0352] 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.

[0353] [Second embodiment]

[0354] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0355] 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.

[0356] 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).

[0357] 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.

[0358] 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.

[0359] 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).

[0360] 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.

[0361] 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.

[0362] 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.

[0363] 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.

[0364] 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.

[0365] 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."

[0366] The present invention relates to a system that monitors the growth status of plants in real time and automatically provides optimal environmental conditions. This system operates by combining sensors, generative artificial intelligence, control devices, and feedback devices.

[0367] First, the server collects multiple environmental data related to the plant's growth status using sensors. These sensors measure temperature, humidity, illuminance, and soil moisture. For example, the temperature sensor collects data such as 30°C, humidity sensor collects data such as 40%, illuminance sensor collects data such as 300 lux, and soil moisture sensor collects data such as 15%.

[0368] The server then sends the collected data to a generative AI, which analyzes the data and evaluates the plant's current growth status. For example, it compares the data with a learning model and determines that "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable for illumination," and "30% soil moisture is appropriate."

[0369] Based on the analysis results, the server identifies optimal growing conditions and generates specific recommendations for environmental adjustments, such as adjusting the air conditioning to lower the temperature, automatically irrigating to increase humidity, or adjusting the lighting. These recommendations are returned to the server in JSON format.

[0370] To notify the user of the proposed changes, the server sends a push notification to the device (e.g., the user's smartphone). The notification includes information such as "Lower the current temperature from 30°C to 25°C," "Increase the humidity from 40% to 60%, "Increase the illuminance from 300 lux to 500 lux," and "Increase the soil moisture from 15% to 30%."

[0371] The user checks the notification via their device and approves the automatic control. After approval is obtained, the server sends instructions to various control devices. For example, it sends specific instructions such as "adjust the temperature to 25°C" to the air conditioning unit, "sprinkle water to adjust the humidity to 60%" to the irrigation system, and "adjust the illuminance to 500 lux" to the lights.

[0372] After the environmental adjustments are complete, the server again collects data from the sensors and checks the results. This information is again analyzed by the generative AI, and feedback is provided on whether the growing conditions have improved.

[0373] Finally, the server notifies the terminal that the adjustment is complete and that the plant's growth condition has improved, and the terminal displays this to the user. For example, information such as "Adjustment complete. Plant condition has improved" is displayed.

[0374] In this way, the system automatically provides the optimal growing environment for plants, enabling real-time data analysis and environmental adjustment, even without the user's specialized knowledge. Furthermore, since optimization reflects the user's wishes, highly accurate environmental adjustment is achieved.

[0375] The processing flow will be explained below.

[0376] Step 1:

[0377] The server collects multiple environmental data related to the plant's growth status through sensors: a temperature sensor measures the current temperature (e.g., 30°C), a humidity sensor measures humidity (e.g., 40%), an illuminance sensor measures light intensity (e.g., 300 lux), and a soil moisture sensor measures soil humidity (e.g., 15%).

[0378] Step 2:

[0379] The server sends the collected data to the generative AI via an API, requesting analysis and optimization. For example, it sends a POST request to the " / ai / analyze" endpoint.

[0380] Step 3:

[0381] The generative AI analyzes the received data. Specifically, it compares it with the learning model within the generative AI and calculates the temperature, humidity, illuminance, and soil moisture content that are optimal for plant growth. This results in judgments such as "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable illuminance," and "30% soil moisture is appropriate."

[0382] Step 4:

[0383] The generative AI returns the analysis results to the server in JSON format, for example, { "temperature": "reduce to 25℃", "humidity": "increase to 60%", "light": "increase to 500 lux", "soilMoisture": "increase to 30%"}.

[0384] Step 5:

[0385] The server receives the analysis results and prepares to notify the user. Specifically, the analysis results are sent to the user's device as a push notification. The push notification contains information such as "Lower the current temperature from 30°C to 25°C," "Increase the humidity from 40% to 60%, "Increase the illuminance from 300 lux to 500 lux," and "Increase the soil moisture from 15% to 30%."

[0386] Step 6:

[0387] The device displays the received notification to the user, and provides an interface that clearly displays the suggestion on the screen so that the user can check the notification.

[0388] Step 7:

[0389] The user checks the notification and approves the automatic control by pressing a button such as "Start automatic control" on the device screen.

[0390] Step 8:

[0391] The server receives user approval and sends instructions to various control devices, such as "adjust the temperature to 25°C" to the air conditioning control device, "sprinkle water to adjust the humidity to 60%" to the irrigation system, and "adjust the illuminance to 500 lux" to the lights.

[0392] Step 9:

[0393] The server monitors the data obtained during the adjustment process and checks whether the adjustment is complete. Specifically, it collects feedback data from the sensors again and evaluates whether the set value has been reached.

[0394] Step 10:

[0395] The server notifies the terminal of the adjustment results, which are then displayed to the user. The terminal displays a message to the user saying, "Adjustment complete. Plant condition has improved," informing the user of the improvement in growth conditions.

[0396] This series of steps automatically provides the optimal growing environment for plants, allowing users to effectively grow plants without having specialized knowledge.

[0397] Example 1

[0398] 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."

[0399] Conventional plant growth management systems require manual collection and analysis of environmental data to adjust the environment. This makes it difficult for users without specialized knowledge to manage plants efficiently. In addition, they are unable to respond immediately to environmental changes, which can have a negative impact on plant growth.

[0400] 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.

[0401] In this invention, the server includes means for collecting multiple pieces of environmental data related to the growth status of plants using sensors, means for analyzing the environmental data using generative artificial intelligence and proposing optimal growth conditions, means for sending a push notification to a user's device based on the proposed growth conditions and receiving user approval, means for sending instructions to a control device for environmental adjustment after the user's approval and automatically adjusting the environment based on the instructions, and means for re-collecting the adjusted environmental data, analyzing it using generative artificial intelligence, and providing feedback. This makes it possible to automatically and efficiently provide an optimal growth environment for plants without the user having specialized knowledge.

[0402] A "sensor" is a device used to collect data on the growth status of plants and the surrounding environment, specifically measuring temperature, humidity, illuminance, soil moisture, etc.

[0403] "Generative AI" is an AI model that analyzes collected environmental data and compares it with past learning data to propose optimal growth conditions.

[0404] "Environmental data" refers to various data related to the growth state of plants, such as temperature, humidity, illuminance, and soil moisture content.

[0405] "Growth conditions" are specific environmental settings such as temperature, humidity, light intensity, and soil moisture required for optimal plant growth.

[0406] A "terminal" is an information terminal such as a smartphone or tablet used by a user, which receives push notifications and performs operations such as approving environmental adjustments.

[0407] "Push notifications" are a communication method that sends information from a server to a device in real time, and include messages suggesting growing conditions and encouraging approval of environmental adjustments.

[0408] A "control device" is hardware for adjusting the plant growth environment, and includes air conditioning devices, irrigation systems, lighting devices, etc.

[0409] "Feedback" is the process of analyzing data collected again after environmental adjustments have been made to check whether the adjustments have been made appropriately and to evaluate any improvements in growth conditions.

[0410] This invention relates to a system that monitors the growth status of plants in real time and automatically provides optimal environmental conditions. This system operates through the collaboration of a server, terminals, and users. The following describes the specific hardware and software usage methods, data processing, and data calculation.

[0411] Hardware and software used

[0412] The system consists of the following hardware and software:

[0413] Sensors (temperature sensor, humidity sensor, light sensor, soil moisture sensor)

[0414] Generative Artificial Intelligence (AI Model)

[0415] Server (server for data collection and analysis)

[0416] Device (user's smartphone, tablet, etc.)

[0417] Control equipment (air conditioning equipment, irrigation systems, lighting equipment)

[0418] Data collection and analysis

[0419] The server periodically collects environmental data about the plants through sensors. Specifically, it obtains data on temperature, humidity, illuminance, and soil moisture. For example, the temperature sensor collects data on 30°C, the humidity sensor collects data on 40%, the illuminance sensor collects data on 300 lux, and the soil moisture sensor collects data on 15%.

[0420] Data analysis

[0421] The server sends the collected data to the generative AI, which analyzes it. The generative AI evaluates the current data by comparing it with past learning data and proposes optimal growing conditions. For example, by comparing it with the learning model, it determines that "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable for illumination," and "30% soil moisture is appropriate."

[0422] Generate environmental adjustment proposals

[0423] Based on the analysis results, the server generates specific suggestions for adjusting the environment, such as operating the air conditioning to lower the temperature, automatically irrigating the area to increase humidity, adjusting the lighting, etc. These suggestions are returned to the server in JSON format.

[0424] User Notifications

[0425] The server sends suggested growing conditions to the user's device as a push notification, including specific examples such as "Lower the current temperature from 30°C to 25°C," "Increase the humidity from 40% to 60%, "Increase the illumination from 300 lux to 500 lux," and "Increase the soil moisture from 15% to 30%."

[0426] Authorization and execution of automated controls

[0427] The user checks the notification on their device and approves the automatic control. After receiving approval, the server sends instructions to various control devices. For example, instructions such as "adjust the temperature to 25°C" to the air conditioning unit, "sprinkle water to adjust the humidity to 60%" to the irrigation system, and "adjust the illuminance to 500 lux" to the lights are sent.

[0428] Get feedback

[0429] After the environmental adjustments are complete, the server collects data from the sensors again and analyzes the results using generative artificial intelligence. The analysis provides feedback on whether the growth conditions have improved. For example, it checks whether the adjusted temperature is 25°C, humidity is 60%, light intensity is 500 lux, and soil moisture is 30%.

[0430] Notification of adjustment results

[0431] The server notifies the terminal that the adjustment is complete and that the plant's growth condition has improved, and the terminal displays this to the user. For example, information such as "Adjustment complete. The plant's condition has improved" is displayed.

[0432] Prompt Sentence Examples

[0433] An example of a prompt for a generative AI model is as follows:

[0434] Analyze the following environmental data and suggest the optimum growing conditions for the plant: temperature 30°C, humidity 40%, light intensity 300 lux, soil moisture 15%.

[0435] The above is an embodiment of the present invention. This system provides an optimal growing environment for plants, and allows real-time data analysis and environmental adjustment, even if the user does not have specialized knowledge. Furthermore, optimization that reflects the user's intentions is realized, allowing for efficient and highly accurate environmental adjustment.

[0436] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0437] Step 1: Data collection

[0438] The server uses sensors to collect a plurality of environmental data related to the growth state of the plants.

[0439] Input: Data from sensors measuring temperature, humidity, light intensity, and soil moisture.

[0440] Output: A set of collected environmental data (e.g., temperature 30°C, humidity 40%, illuminance 300 lux, soil moisture 15%).

[0441] Specific operation: The server sends a polling request to each sensor and obtains data on temperature, humidity, illuminance, and soil moisture content from the sensors in response.

[0442] Step 2: Data analysis

[0443] The server sends the collected environmental data to the generative artificial intelligence for analysis.

[0444] Input: Collected environmental data (temperature 30°C, humidity 40%, illuminance 300 lux, soil moisture 15%).

[0445] Output: Analysis of optimal growing conditions (e.g. temperature 25°C, humidity 60%, light intensity 500 lux, soil moisture 30%).

[0446] Specific operation: The server sends environmental data to the generative AI's API and receives the results. The generative AI analyzes the results by comparing them with past learning models and determines the optimal growth conditions.

[0447] Step 3: Generate environmental adjustment proposals

[0448] The server generates optimal environmental adjustment proposals based on the analysis results of the generative artificial intelligence.

[0449] Input: Analysis results (temperature 25°C is appropriate, humidity 60% is ideal, illuminance 500 lux is desirable, soil moisture 30% is appropriate).

[0450] Output: Specific environmental adjustment suggestions (e.g., temperature adjustment, humidity adjustment, lighting adjustment).

[0451] Specific operation: Based on the analysis results, the server generates suggestions in JSON format, such as air conditioning to lower the temperature, irrigation to increase humidity, and adjusting lights to increase brightness.

[0452] Step 4: User Notification

[0453] The server sends the generated environmental adjustment proposal to the user's device as a push notification.

[0454] Input: Proposed environmental adjustments (reduce temperature from 30°C to 25°C, increase humidity from 40% to 60%, increase light intensity from 300 lux to 500 lux, increase soil moisture from 15% to 30%).

[0455] Output: A push notification sent to the user's device containing the specific adjustments.

[0456] Specific operation: The server uses the push notification service to send a notification to the user's smartphone.

[0457] Step 5: Accepting automation

[0458] The user checks the notification through the terminal and approves the automatic control.

[0459] Input: Push notification (environmental adjustment suggestion) received by the user.

[0460] Output: User approval (sent from the terminal).

[0461] Specific operation: The user taps the notification on their smartphone and presses the "Approve" button to approve the environmental adjustment.

[0462] Step 6: Execute control instructions

[0463] After receiving the user's approval, the server sends specific instructions to the various control devices.

[0464] Input: User approval and generated environmental adjustment suggestions.

[0465] Output: Specific instructions sent to a control device (adjust temperature, humidity, light).

[0466] Specific actions: The server sends specific instructions to the air conditioner, such as "adjust the temperature to 25°C," to the irrigation system, "adjust the humidity to 60%, and" to the lights, "adjust the illuminance to 500 lux."

[0467] Step 7: Check the results of the environment adjustment

[0468] After the environmental adjustment is complete, the server again collects and analyzes data from the sensors.

[0469] Input: Calibrated environmental data (latest data from sensors).

[0470] Output: Feedback result after adjustment (whether the environment improved or not).

[0471] What it does: The server sends another request to the sensor to collect the latest environmental data, which it then sends to the generative AI for reanalysis.

[0472] Step 8: Notification of adjustment results

[0473] The server notifies the user's terminal that the adjustment has been completed and that the plant's growth condition has improved, and the terminal displays this to the user.

[0474] Input: Adjusted feedback results.

[0475] Output: Notification to user device (adjustment completed, improvement notification).

[0476] Specific operation: The server sends a notification to the user's smartphone via a push notification service, such as "Adjustment completed. Plant condition has improved."

[0477] This concludes the explanation of the specific processing steps of the program. Each step works closely together to provide the optimal growing environment for plants in real time.

[0478] (Application example 1)

[0479] 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."

[0480] While current plant growth systems automate the adjustment of environmental conditions to maintain optimal plant growth, they have the problem of difficulty in responding in real time to security risks such as intruders and suspicious activity. This means that even if the plant growth environment is secured, overall safety cannot be ensured if physical security is threatened.

[0481] 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.

[0482] In this invention, the server includes means for collecting multiple pieces of environmental data and security data related to the growth status of plants using sensors, means for analyzing the environmental data and security data using generative artificial intelligence and proposing optimal growth conditions and vigilance situations, means for automatically adjusting the environment and security measures based on the proposed growth conditions and vigilance situations, and means for recollecting the adjusted environmental data and security data and providing feedback. This not only provides an optimal growth environment for plants, but also makes it possible to respond to security risks in real time.

[0483] A "sensor" is a device for collecting environmental data related to plant growth and security.

[0484] "Generative AI" is an AI technology that analyzes collected environmental and security data to suggest optimal growth conditions and vigilance situations.

[0485] "Environmental data" refers to data necessary for evaluating the growth status of plants, such as temperature, humidity, illuminance, and soil moisture.

[0486] "Crime prevention data" refers to data necessary for evaluating crime prevention measures, such as motion detection and vibration detection.

[0487] "Growth conditions" are the environmental conditions necessary to promote healthy plant growth.

[0488] An "alert situation" is a state of surveillance and vigilance that is considered optimal from a crime prevention perspective.

[0489] "Adjustment means" refers to means for automatically adjusting the environment and security posture based on proposed growth and alert conditions.

[0490] "Feedback" is the process of collecting environmental and security data again after adjustments have been made and reevaluating the results.

[0491] This invention relates to a system that monitors the growth status and security of plants in real time to provide optimal environmental and security conditions. The system operates by combining sensors, generative artificial intelligence, control devices, and feedback devices. Each major processing step of the system and the hardware and software used for each step are described below.

[0492] First, the server uses sensors to collect multiple environmental data related to plant growth and security. These sensors measure temperature, humidity, illuminance, soil moisture, and security data (motion detection, vibration detection, etc.). For example, the server collects data that the temperature sensor is 30°C, the humidity sensor is 40%, the motion detection sensor detects motion, and the vibration sensor does not detect vibration.

[0493] The server then sends the collected environmental and security data to the generative AI, which analyzes this data and evaluates the plant's current growth and alert status. For example, by comparing it with the learning model, it can determine that "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable for illumination," "30% soil moisture is appropriate," "turn on the security light to detect motion," and "do not sound the alarm because there is no vibration."

[0494] Based on the analysis, the server identifies optimal growing conditions and vigilance situations and generates specific recommendations for adjusting the environment and security posture, such as adjusting the air conditioning to lower the temperature or turning on security lights in response to motion detection. These recommendations are returned to the server in JSON format.

[0495] To notify the user of the proposed content, the server sends a push notification to the device (e.g., the user's smartphone). The notification content includes information such as "Lower the current temperature from 30°C to 25°C," "Increase the humidity from 40% to 60%," and "Turn on the security lights to detect motion."

[0496] The user checks the notification via their device and approves the automatic control. After approval is obtained, the server sends instructions to the various control devices. For example, it sends specific instructions such as "adjust the temperature to 25°C" to the air conditioner and "turn on the security light" to the security light.

[0497] After the environmental adjustments and security measures are complete, the server again collects data from the sensors and checks the results of the adjustments. This information is again analyzed by the generative AI to provide feedback on whether the growth and security conditions have improved.

[0498] Finally, the server notifies the terminal that the adjustment is complete and that the plant's growth and security status have improved, and the terminal displays this to the user. For example, the terminal displays information such as "Adjustment complete. Plant status and security status have improved."

[0499] As a concrete example, to maintain public order in a certain town, patrol staff wear smart glasses with this application installed. When the sensor detects an abnormality, the AI ​​automatically analyzes and evaluates it, and proposes and implements the necessary security measures. The sensor detects motion and suggests the optimal alarm to sound and security lights to turn on. After execution, the user receives a notification on the smart glasses display saying "Motion detected, alarm sounding."

[0500] Example prompt sentence:

[0501] plaintext

[0502] I would like you to suggest the best security measures for a situation where the temperature sensor indicates an outside temperature of 25°C, the humidity sensor indicates 50%, the motion detection sensor detects motion, and the vibration sensor does not detect vibration.

[0503] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0504] Step 1:

[0505] Input - The server collects data from sensors (temperature, humidity, light intensity, soil moisture, motion detection, vibration detection).

[0506] Data processing and calculation: The collected data is temporarily stored on the server, where it is checked for consistency and format converted.

[0507] Output - The consistent environmental and security data is sent to the generative AI.

[0508] The server collects environmental and security data from various installed sensors. For example, the temperature sensor may indicate a temperature of 30°C, and the motion sensor may indicate that a motion has been detected.

[0509] Step 2:

[0510] Input - Environmental data and security data sent from the server to the generative artificial intelligence.

[0511] Data processing, calculation, and generation AI uses trained models to analyze data and propose optimal growth conditions and vigilance situations.

[0512] The output-generative AI's suggestions (e.g., adjust the temperature to 25°C, turn on the security lights) are returned to the server in JSON format.

[0513] The server requests a generative artificial intelligence to analyze the transmitted data and propose optimal growth conditions and vigilance situations.

[0514] Step 3:

[0515] Suggestions returned from the input-generative AI.

[0516] Data processing and calculation - The server analyzes the proposal content and generates push notification data to notify the user.

[0517] Output - Notifications about suggested growing conditions and alert status are sent to the user's terminal.

[0518] Based on the suggestions received from the generative artificial intelligence, the server prepares notification content for the user's device and sends a push notification.

[0519] Step 4:

[0520] Input - Push notifications from the server (e.g. adjust the temperature to 25°C, turn on the security lights).

[0521] Data processing and calculation - The user checks the notification and decides whether to approve or reject it.

[0522] Output - The user's approval or denial is returned to the server.

[0523] The user checks the proposal through the terminal and accepts or rejects it. If the proposal is accepted, the result is sent to the server.

[0524] Step 5:

[0525] Input - Approval result from user.

[0526] Data processing and calculation - The server generates commands to operate various control devices (air conditioners, security lights, etc.) based on the approved proposal.

[0527] Output - The command is sent to the control device.

[0528] The server receives the approval result from the user, generates specific instructions, and sends them to the air conditioner, security lights, etc.

[0529] Step 6:

[0530] Inputs - Adjusted environmental and crime prevention data.

[0531] Data processing and calculation - The server collects data from the sensors again and requests the generative AI to reanalyze it.

[0532] Output - Reanalysis results are obtained and feedback on adjusted growth and vigilance status is generated.

[0533] After the environmental adjustments and security measures have been completed, the server will collect data from the sensors again and have the generative artificial intelligence reanalyze the growth and alert status.

[0534] Step 7:

[0535] Input - Reparse result.

[0536] Data processing and calculation - Based on the reanalysis results, the server generates data to provide final feedback to the user.

[0537] Output - A notification is sent to the user's device indicating the adjustment completion and the improvement results.

[0538] The server uses the results of the reanalysis to prepare final feedback and sends a notification to the user's device, for example, "Adjustments completed. Plant condition and security have improved."

[0539] As described above, the system is designed to function smoothly as a whole, with necessary data processing and calculations being performed based on input data at each step.

[0540] 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.

[0541] The present invention relates to a system that monitors the growth status of plants in real time and provides optimal environmental conditions taking into account the user's emotions. This system operates by combining sensors, generative artificial intelligence, an emotion engine, a control device, and a feedback device.

[0542] First, the server collects multiple environmental data related to the plant's growth status using sensors. These sensors measure temperature, humidity, illuminance, and soil moisture. For example, the temperature sensor collects data such as 30°C, humidity sensor collects data such as 40%, illuminance sensor collects data such as 300 lux, and soil moisture sensor collects data such as 15%.

[0543] The server then sends the collected data to a generative AI, which analyzes the data and evaluates the plant's current growth status. For example, it compares the data with a learning model and determines that "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable for illumination," and "30% soil moisture is appropriate."

[0544] Based on the analysis results, the server identifies optimal growing conditions and generates specific recommendations for environmental adjustments, such as adjusting the air conditioning to lower the temperature, automatically irrigating to increase humidity, or adjusting the lighting. These recommendations are returned to the server in JSON format.

[0545] Here, an emotion engine that recognizes the user's emotions analyzes the user's facial expressions and voice. The emotion engine uses image analysis and voice recognition technology to evaluate the user's emotional state, such as whether the user is feeling stressed or comfortable.

[0546] To notify the user of the proposed content, the server sends a push notification to the device (e.g., the user's smartphone). At this time, the notification is sent in a way that takes into consideration the user's emotions based on the analysis results of the emotion engine. For example, if the user is relaxed, the notification may be soft-spoken, such as "Lowering the current temperature from 30°C to 25°C will help your plants thrive," whereas if the user is in a hurry, the notification may be simple, such as "The temperature needs to be adjusted."

[0547] The user checks the notification via their device and approves the automatic control. At this time, the emotion engine analyzes the user's reaction again to ensure that approval is given without stress. When the user presses the approval button, the server sends instructions to various control devices. Specific instructions are sent to the air conditioning control device, such as "adjust the temperature to 25°C," to the irrigation system, "sprinkle water and adjust the humidity to 60%, and to the lights, "adjust the illuminance to 500 lux."

[0548] After the environmental adjustments are complete, the server collects data from the sensors again to confirm the results. This information is again analyzed by the generative AI to obtain feedback on whether the growth conditions have improved, and the server sends this information to the device.

[0549] The device notifies the user that the adjustment is complete and that the plant's growth has improved, for example by displaying a message such as "Adjustment complete. Plant condition has improved." Furthermore, the emotion engine re-evaluates the user's reaction to confirm whether the user is satisfied.

[0550] Through this series of steps, the system can automatically provide the optimal growing environment for plants while taking into consideration emotions, even if the user does not have specialized knowledge. Furthermore, optimization that reflects the user's intentions and emotions is carried out, achieving highly accurate environmental adjustment.

[0551] The processing flow will be explained below.

[0552] Step 1:

[0553] The server collects multiple environmental data related to the plant's growth status through sensors: a temperature sensor measures the current temperature (e.g., 30°C), a humidity sensor measures humidity (e.g., 40%), an illuminance sensor measures light intensity (e.g., 300 lux), and a soil moisture sensor measures soil humidity (e.g., 15%).

[0554] Step 2:

[0555] The server sends the collected data to the generative AI via an API, requesting analysis and optimization. For example, it sends a POST request to the " / ai / analyze" endpoint.

[0556] Step 3:

[0557] The generative AI analyzes the received data. Specifically, it compares it with the learning model within the generative AI and calculates the temperature, humidity, illuminance, and soil moisture content that are optimal for plant growth. This results in judgments such as "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable illuminance," and "30% soil moisture is appropriate."

[0558] Step 4:

[0559] The generative AI returns the analysis results to the server in JSON format, for example, { "temperature": "reduce to 25℃", "humidity": "increase to 60%", "light": "increase to 500 lux", "soilMoisture": "increase to 30%"}.

[0560] Step 5:

[0561] The server receives the analysis results from the generative AI and activates the emotion engine to ascertain the user's emotional state. The emotion engine uses image and audio analysis to assess the user's current emotions and determine whether the user is stressed or relaxed.

[0562] Step 6:

[0563] The server adjusts the generative AI's suggestions based on the analysis results of the emotion engine. If the user is relaxed, the server will notify them in a gentle way, such as "Lowering the current temperature from 30°C to 25°C will help your plants thrive," but if the user is in a hurry, the server will notify them in a simpler way, such as "The temperature needs to be adjusted."

[0564] Step 7:

[0565] The server then sends the adjusted proposal to the device as a push notification. Specifically, the proposal is sent to the user's smartphone and displayed in a format that the user can check.

[0566] Step 8:

[0567] The device displays the received notification to the user. As the user checks the content of the notification, the emotion engine analyzes the user's reaction again to confirm whether the user is in a state where they can accept the notification without feeling stressed.

[0568] Step 9:

[0569] The user checks the notification and approves the automatic control via the device by pressing a button such as "Start automatic control."

[0570] Step 10:

[0571] With the user's approval, the server sends instructions to various control devices, such as to the air conditioning control device to "adjust the temperature to 25°C," to the irrigation system to "sprinkle water and adjust the humidity to 60%, and to the lights to "adjust the illuminance to 500 lux."

[0572] Step 11:

[0573] The server monitors the adjustment process and, once the adjustment is complete, collects data from the sensors again to confirm whether the temperature, humidity, light intensity, and soil moisture content have reached their target values.

[0574] Step 12:

[0575] The server checks the adjustment results and the improvement in the plant's condition, and sends the information to the terminal. The server notifies the user by saying, "Adjustment complete. The plant's condition has improved."

[0576] Step 13:

[0577] The device will display the completion of the adjustment and the improvement of the growth status to the user, while the emotion engine will analyze the user's reaction to check whether the user is satisfied or stressed.

[0578] This series of steps not only automatically provides the optimal growing environment for plants, but also creates a system that takes into account the user's emotional state.

[0579] Example 2

[0580] 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."

[0581] Conventional plant growth management systems were able to suggest growth conditions by collecting and analyzing environmental data, but it was difficult to adjust the environment optimally while taking the user's emotions into consideration. Furthermore, because the notification of growth condition suggestions was made without regard for the user's situation or emotions, it was difficult for the user to accept the suggestions, making it difficult to maintain an optimal growth environment for plants.

[0582] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting multiple pieces of environmental data related to the growth state of plants using sensors, means for analyzing the environmental data using generative artificial intelligence and proposing optimal growth conditions, means for automatically adjusting the environment based on the proposed growth conditions, means for re-collecting the adjusted environmental data and providing feedback, means for evaluating the user's emotions using an emotion engine that analyzes the user's emotional state, and means for determining an appropriate notification method based on the evaluated user emotions. This makes it possible to provide an optimal plant growth environment that takes the user's emotions into consideration.

[0583] A "sensor" is a measuring device for collecting environmental data.

[0584] "Plant growth status" refers to various parameters that indicate the growth and health status of a plant.

[0585] "Environmental data" refers to data related to plant growth conditions such as temperature, humidity, illuminance, and soil moisture content.

[0586] "Generative artificial intelligence" refers to machine learning models and algorithms that analyze collected environmental data and suggest optimal growing conditions.

[0587] "Optimal growing conditions" are ideal environmental parameters for promoting plant growth and health.

[0588] The "means for automatically adjusting the environment" is a system that automatically controls air conditioning, irrigation systems, lighting, etc. based on collected data and the analysis results of generative artificial intelligence.

[0589] The "means of providing feedback" is a function that collects data again after the environmental adjustment, analyzes the results, and evaluates the effectiveness of the adjustment.

[0590] An "emotion engine" is a technology for analyzing and evaluating a user's emotional state based on facial expressions, voice, etc.

[0591] "User's emotions" refer to the psychological state, such as stress or relaxation, that the user feels.

[0592] An "appropriate notification method" is a method of notifying the user in an appropriate manner and at an appropriate timing based on the user's emotional state.

[0593] The present invention relates to a system that monitors the growth status of plants in real time and provides optimal environmental conditions taking into account the user's emotions. This system operates by combining sensors, generative artificial intelligence, an emotion engine, a control device, and a feedback device.

[0594] First, the server collects multiple environmental data related to the plant's growth status using sensors. The sensors include a temperature sensor, humidity sensor, illuminance sensor, and soil moisture sensor. For example, the server collects data such as 30°C from the temperature sensor, 40% from the humidity sensor, 300 lux from the illuminance sensor, and 15% from the soil moisture sensor.

[0595] The server then sends the collected data to a generative AI, which analyzes the data and evaluates the plant's current growth status. For example, it compares the data with a learning model and determines that "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable for illumination," and "30% soil moisture is appropriate."

[0596] Based on the analysis results, the server identifies optimal growing conditions and generates specific recommendations for environmental adjustments, such as adjusting the air conditioning to lower the temperature, automatically irrigating to increase humidity, or adjusting the lighting. These recommendations are returned to the server in JSON format.

[0597] Here, an emotion engine that recognizes the user's emotions analyzes the user's facial expressions and voice. The emotion engine uses image analysis and voice recognition technology to evaluate the user's emotional state, such as whether the user is feeling stressed or comfortable.

[0598] To notify the user of the proposed content, the server sends a push notification to the device (e.g., the user's smartphone). At this time, the notification is sent in a way that takes into consideration the user's emotions based on the analysis results of the emotion engine. For example, if the user is relaxed, the notification may be soft-spoken, such as "Lowering the current temperature from 30°C to 25°C will help your plants thrive," whereas if the user is in a hurry, the notification may be simple, such as "The temperature needs to be adjusted."

[0599] The user checks the notification via their device and approves the automatic control. At this time, the emotion engine analyzes the user's reaction again to ensure that approval is given without stress. When the user presses the approval button, the server sends instructions to various control devices. Specific instructions are sent to the air conditioning control device, such as "adjust the temperature to 25°C," to the irrigation system, "sprinkle water and adjust the humidity to 60%, and to the lights, "adjust the illuminance to 500 lux."

[0600] After the environmental adjustments are complete, the server collects data from the sensors again to confirm the results. This information is again analyzed by the generative AI to obtain feedback on whether the growth conditions have improved, and the server sends this information to the device.

[0601] The device notifies the user that the adjustment is complete and that the plant's growth condition has improved, displaying a message such as "Adjustment complete. Plant condition has improved." The emotion engine then reevaluates the user's response to confirm whether the user is satisfied. This series of steps enables the system to automatically and emotionally provide the optimal growth environment for plants, even without the user's specialized knowledge.

[0602] Specific examples

[0603] 1. Collect data from the temperature sensor at 30°C, the humidity sensor at 40%, the illuminance sensor at 300 lux, and the soil moisture sensor at 15%.

[0604] 2. The generative artificial intelligence analyzes this data and determines that "25°C is the appropriate temperature," "60% is ideal humidity," "500 lux is desirable illumination," and "30% is the appropriate soil moisture."

[0605] 3. The server generates suggestions such as lowering the temperature to 25°C, increasing the humidity to 60%, and adjusting the light intensity to 500 lux.

[0606] 4. The emotion engine analyzes that the user is relaxed.

[0607] 5. The server notifies the device in gentle terms that "Lowering the current temperature from 30°C to 25°C will help the plants thrive."

[0608] 6. The user checks the notification and presses the approval button.

[0609] 7. The server sends instructions to the air conditioning control unit to "adjust the temperature to 25°C," the irrigation system to "adjust the humidity to 60%," and the lights to "adjust the illuminance to 500 lux."

[0610] Prompt Sentence Examples

[0611] "You have collected data: a temperature sensor reading 30°C, a humidity sensor reading 40%, a light sensor reading 300 lux, and a soil moisture sensor reading 15%. Describe the steps you would take to have a generative AI analyze this data and identify the optimal conditions for plant growth."

[0612] The above is a specific embodiment of this system.

[0613] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0614] Step 1:

[0615] The server uses sensors to collect multiple environmental data related to the plant's growth status. Specifically, it acquires data from a temperature sensor, humidity sensor, illuminance sensor, and soil moisture sensor. For example, the temperature sensor collects data such as 30°C, humidity sensor collects data such as 40%, illuminance sensor collects data such as 300 lux, and soil moisture sensor collects data such as 15%. This data becomes input data for subsequent analysis.

[0616] Step 2:

[0617] The server sends the collected environmental data to the generative AI, which then takes the data as input into the analysis system. The generative AI then evaluates the current growth status of the plant based on this input data. For example, it outputs optimal growth conditions such as "the appropriate temperature is 25°C," "the ideal humidity is 60%," "the desired illumination level is 500 lux," and "the appropriate soil moisture level is 30%."

[0618] Step 3:

[0619] The server generates specific proposals for environmental adjustments based on the optimal growing conditions output by the generative AI. These proposals include operating the air conditioning to lower the temperature, automatic irrigation to increase humidity, and adjusting the lighting. For example, suggestions such as "adjust the temperature to 25°C," "adjust the humidity to 60%," and "adjust the lighting to 500 lux" are output in JSON format.

[0620] Step 4:

[0621] The emotion engine analyzes emotions from the user's facial expressions and voice. Camera footage and microphone audio are used as input data. The emotion engine analyzes this data and evaluates the user's emotional state, such as whether they are feeling stressed or comfortable. The results of this evaluation become input data for determining how to notify the user of the proposed content.

[0622] Step 5:

[0623] The server notifies the user of specific suggestions based on the user's emotional data obtained from the emotion engine. The content of the notification and the way it is expressed are determined taking into consideration the user's emotional state. For example, if the user is relaxed, the server may notify them in a gentle manner, such as "Lowering the current temperature from 30°C to 25°C will help your plants thrive," while if the user is in a hurry, the server may notify them in a simpler manner, such as "The temperature needs to be adjusted."

[0624] Step 6:

[0625] The user checks the notification via their device and approves the automatic control. When the user presses the approval button on the device where the notification is displayed, this approval information is sent to the server. The emotion engine then analyzes the user's reaction again to confirm whether approval was given without stress.

[0626] Step 7:

[0627] With the user's approval, the server sends specific instructions to various control devices. For example, it sends instructions such as "adjust the temperature to 25°C" to the air conditioning control device, "adjust the humidity to 60%" to the irrigation system, and "adjust the illuminance to 500 lux" to the light. This automatically adjusts the physical environment.

[0628] Step 8:

[0629] After the environmental adjustment is complete, the server collects environmental data from the sensors again. The data collected as the adjustment results is input into the generative AI and re-evaluated. This confirms, for example, that the adjusted temperature is 25°C, humidity is 60%, illuminance is 500 lux, and soil moisture is 30%.

[0630] Step 9:

[0631] The server sends the adjustment results to the device and notifies the user that the adjustment is complete and that the plant's growth has improved. The device displays a message such as "Adjustment complete. The plant's condition has improved." The emotion engine again evaluates the user's reaction and checks whether the user is satisfied.

[0632] (Application example 2)

[0633] 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."

[0634] The present invention aims to provide a system that not only optimizes the growing environment of plants but also notifies users and adjusts the environment taking into account their emotions. In particular, the objective is to improve product quality and work efficiency by optimizing the working environment and taking into account the emotional state of workers in quality control on production lines.

[0635] 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.

[0636] In this invention, the server includes means for collecting multiple pieces of environmental data related to the growth status of plants using sensors, means for analyzing the environmental data using generative artificial intelligence and proposing optimal growth conditions, means for automatically adjusting the environment based on the proposed growth conditions, means for recollecting the adjusted environmental data and providing feedback, means for recognizing the user's emotions and generating a notification message according to the emotions, and means for reevaluating the user's emotional state and confirming that the user is not stressed if the user approves. This makes it possible to optimize the plant growth environment and the work environment of the production line, and to adjust the environment taking into account the emotional states of users and workers.

[0637] A "sensor" is a device that measures and collects environmental data such as temperature, humidity, light intensity, and soil moisture content in real time.

[0638] "Generative AI" is an AI technology that analyzes collected data and proposes optimal conditions for plant growth and working environments.

[0639] "Environmental adjustment" refers to automatically operating various devices such as air conditioning, irrigation systems, and lighting based on the optimal conditions proposed by generative AI, to adjust the growing conditions for plants and the working environment of the production line.

[0640] "Feedback" is the process of collecting data from sensors again after environmental adjustments have been made, and using the results to evaluate growth and environmental conditions.

[0641] The "emotion engine" is a system that analyzes the user's facial expressions and voice to evaluate the user's emotional state and reflects this in the content of notifications and suggestions for environmental adjustments.

[0642] A "notification message" is a message that notifies the user of suggested growing conditions or the need for environmental adjustments, and its expression is adjusted according to the user's emotional state.

[0643] "Acceptance" refers to the user's explicit acceptance of the proposed growing conditions and environmental adjustments.

[0644] "Stress assessment" is the process by which the emotion engine re-analyzes the user's emotional state to see if the user is feeling stressed.

[0645] This invention is a system that monitors the growing environment of plants in real time and provides optimal environmental conditions taking into account the user's emotions. This system can also be applied to quality control on factory production lines.

[0646] This system mainly uses the following hardware and software:

[0647] Sensors: Used to measure temperature, humidity, light intensity, and soil moisture. For example, temperature sensors, humidity sensors, and light intensity sensors are used on production lines in factories.

[0648] Generative AI: Refers to artificial intelligence technology that analyzes collected data and suggests optimal conditions for plants or products.

[0649] Emotion engine: Evaluates the user's emotions using image analysis and voice recognition technology.

[0650] Server: Collects data from the aforementioned sensors and analyzes it using generative artificial intelligence and an emotion engine to identify optimal environmental conditions.

[0651] Terminal: A device such as a smartphone or smart glasses that is used to send notifications to the user and receive user approval.

[0652] To explain how the system works in detail, the server first uses sensors to collect environmental data. For example, assume that the temperature on the production line is 28°C, humidity is 35%, and illuminance is 400 lux. This data is sent to the server, where generative AI analyzes the data and identifies the ideal environmental conditions. For example, it determines that the ideal temperature is 25°C, humidity is 40%, and illuminance is 500 lux.

[0653] The emotion engine then analyzes the user's facial expressions and voice to recognize their emotions. For example, if the server determines that the user is relaxed, it will send a soft message to the device, such as, "Lowering the current temperature from 28°C to 25°C will improve product quality." Conversely, if the user is in a hurry, it will send a simple message, such as, "The temperature needs to be adjusted."

[0654] The user checks the notification via their device and approves the proposal. After approval, the server again uses the emotion engine to evaluate the user's emotional state and confirm that they are not stressed. The server then sends specific instructions to various control devices to adjust the temperature, humidity, and light level.

[0655] After the environmental adjustment is complete, data is collected again from the sensors and analyzed by the generative artificial intelligence. Feedback is obtained on whether the adjustment results have improved, and the server sends this information to the device. For example, a message such as "Adjustment complete. The environment has been optimized and product quality has improved" is displayed.

[0656] Through this series of operations, the system can automatically provide the optimal growing and manufacturing environment for plants and products, even without the user's specialized knowledge. It also performs optimization that reflects the user's intentions and emotions, achieving highly accurate environmental adjustment.

[0657] Here are some illustrative prompts:

[0658] "Temperature sensor: 28°C, humidity sensor: 35%, illuminance sensor: 400 lux. Evaluate the difference from ideal environmental conditions (temperature 25°C, humidity 40%, illuminance 500 lux) and create adjustment suggestions. Additionally, generate a notification message when the user's emotions are relaxed."

[0659] Using this example, the detailed operation of the system can be clearly understood.

[0660] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0661] Step 1:

[0662] The server uses sensors to collect environmental data. Specifically, it obtains data in real time from temperature, humidity, and illuminance sensors. For example, the sensors can provide data such as "temperature 28°C, humidity 35%, illuminance 400 lux." The input is the raw data from each sensor, and the output is this environmental data.

[0663] Step 2:

[0664] The server sends the collected environmental data to the generative AI, which analyzes this data and identifies the optimal environmental conditions for plants and production lines. For example, it suggests conditions such as "a temperature of 25°C, humidity of 40%, and illuminance of 500 lux are desirable." The input is the collected environmental data, and the output is a proposal for the optimal environmental conditions.

[0665] Step 3:

[0666] The server uses an emotion engine to generate a notification message that takes the user's emotions into account based on the optimal environmental conditions proposed by the generative artificial intelligence. The emotion engine analyzes the user's facial expressions and voice to evaluate the user's emotional state. For example, if the user is relaxed, it generates a soft notification message saying, "Lowering the current temperature from 28°C to 25°C will improve product quality." The input is the proposed environmental conditions and the user's emotional data, and the output is the notification message.

[0667] Step 4:

[0668] The server sends a notification message to the user's device. The device displays the notification message to the user, and the user approves the environmental adjustment based on the suggestion. For example, a notification saying "Lowering the temperature to 25°C will improve product quality" appears on a smartphone, and the user presses the "Approve" button. The input is the notification message, and the output is the user's approval.

[0669] Step 5:

[0670] After receiving the user's approval, the server uses the emotion engine to evaluate the user's emotional state again and confirm that they are not stressed. For example, it checks whether the user remains relaxed after approval. The input is the user's approval and emotion data, and the output is the result of the stress assessment.

[0671] Step 6:

[0672] After the server confirms that there is no stress, it sends specific instructions to the various control devices to adjust the environment. For example, it sends instructions such as "adjust the temperature to 25°C" to the temperature control device and "adjust the illuminance to 500 lux" to the illuminance control device. The input is the optimal environmental conditions, and the output is the specific control instructions.

[0673] Step 7:

[0674] After the environmental adjustments are made, the server collects environmental data from the sensors again. For example, it checks whether the temperature is 25°C, the humidity is 40%, and the illuminance is 500 lux. The input is the sensor data after the environmental adjustments, and the output is the adjusted environmental data.

[0675] Step 8:

[0676] The server then sends the collected environmental data to the generative AI, which analyzes whether the growth conditions and quality have improved. For example, it receives feedback such as, "Optimal conditions are being maintained at a temperature of 25°C." The input is the adjusted environmental data, and the output is the feedback information.

[0677] Step 9:

[0678] The server sends feedback information to the user's device and notifies them of the adjustment results. For example, a message such as "Adjustment complete. Product quality has improved" is displayed on a smartphone. The input is feedback information, and the output is a notification to the user.

[0679] Through these steps, the invention can automatically provide the optimal growing and manufacturing environment for plants and products, even without the user's specialized knowledge. It also performs optimization that reflects the user's intentions and emotions, achieving highly accurate environmental adjustment.

[0680] 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.

[0681] 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.

[0682] 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.

[0683] [Third embodiment]

[0684] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0685] 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.

[0686] 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).

[0687] 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.

[0688] 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.

[0689] 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).

[0690] 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.

[0691] 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.

[0692] 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.

[0693] 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.

[0694] 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.

[0695] 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."

[0696] The present invention relates to a system that monitors the growth status of plants in real time and automatically provides optimal environmental conditions. This system operates by combining sensors, generative artificial intelligence, control devices, and feedback devices.

[0697] First, the server collects multiple environmental data related to the plant's growth status using sensors. These sensors measure temperature, humidity, illuminance, and soil moisture. For example, the temperature sensor collects data such as 30°C, humidity sensor collects data such as 40%, illuminance sensor collects data such as 300 lux, and soil moisture sensor collects data such as 15%.

[0698] The server then sends the collected data to a generative AI, which analyzes the data and evaluates the plant's current growth status. For example, it compares the data with a learning model and determines that "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable for illumination," and "30% soil moisture is appropriate."

[0699] Based on the analysis results, the server identifies optimal growing conditions and generates specific recommendations for environmental adjustments, such as adjusting the air conditioning to lower the temperature, automatically irrigating to increase humidity, or adjusting the lighting. These recommendations are returned to the server in JSON format.

[0700] To notify the user of the proposed changes, the server sends a push notification to the device (e.g., the user's smartphone). The notification includes information such as "Lower the current temperature from 30°C to 25°C," "Increase the humidity from 40% to 60%, "Increase the illuminance from 300 lux to 500 lux," and "Increase the soil moisture from 15% to 30%."

[0701] The user checks the notification via their device and approves the automatic control. After approval is obtained, the server sends instructions to various control devices. For example, it sends specific instructions such as "adjust the temperature to 25°C" to the air conditioning unit, "sprinkle water to adjust the humidity to 60%" to the irrigation system, and "adjust the illuminance to 500 lux" to the lights.

[0702] After the environmental adjustments are complete, the server again collects data from the sensors and checks the results. This information is again analyzed by the generative AI, and feedback is provided on whether the growing conditions have improved.

[0703] Finally, the server notifies the terminal that the adjustment is complete and that the plant's growth condition has improved, and the terminal displays this to the user. For example, information such as "Adjustment complete. Plant condition has improved" is displayed.

[0704] In this way, the system automatically provides the optimal growing environment for plants, enabling real-time data analysis and environmental adjustment, even without the user's specialized knowledge. Furthermore, since optimization reflects the user's wishes, highly accurate environmental adjustment is achieved.

[0705] The processing flow will be explained below.

[0706] Step 1:

[0707] The server collects multiple environmental data related to the plant's growth status through sensors: a temperature sensor measures the current temperature (e.g., 30°C), a humidity sensor measures humidity (e.g., 40%), an illuminance sensor measures light intensity (e.g., 300 lux), and a soil moisture sensor measures soil humidity (e.g., 15%).

[0708] Step 2:

[0709] The server sends the collected data to the generative AI via an API, requesting analysis and optimization. For example, it sends a POST request to the " / ai / analyze" endpoint.

[0710] Step 3:

[0711] The generative AI analyzes the received data. Specifically, it compares it with the learning model within the generative AI and calculates the temperature, humidity, illuminance, and soil moisture content that are optimal for plant growth. This results in judgments such as "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable illuminance," and "30% soil moisture is appropriate."

[0712] Step 4:

[0713] The generative AI returns the analysis results to the server in JSON format, for example, { "temperature": "reduce to 25℃", "humidity": "increase to 60%", "light": "increase to 500 lux", "soilMoisture": "increase to 30%"}.

[0714] Step 5:

[0715] The server receives the analysis results and prepares to notify the user. Specifically, the analysis results are sent to the user's device as a push notification. The push notification contains information such as "Lower the current temperature from 30°C to 25°C," "Increase the humidity from 40% to 60%, "Increase the illuminance from 300 lux to 500 lux," and "Increase the soil moisture from 15% to 30%."

[0716] Step 6:

[0717] The device displays the received notification to the user, and provides an interface that clearly displays the suggestion on the screen so that the user can check the notification.

[0718] Step 7:

[0719] The user checks the notification and approves the automatic control by pressing a button such as "Start automatic control" on the device screen.

[0720] Step 8:

[0721] The server receives user approval and sends instructions to various control devices, such as "adjust the temperature to 25°C" to the air conditioning control device, "sprinkle water to adjust the humidity to 60%" to the irrigation system, and "adjust the illuminance to 500 lux" to the lights.

[0722] Step 9:

[0723] The server monitors the data obtained during the adjustment process and checks whether the adjustment is complete. Specifically, it collects feedback data from the sensors again and evaluates whether the set value has been reached.

[0724] Step 10:

[0725] The server notifies the terminal of the adjustment results, which are then displayed to the user. The terminal displays a message to the user saying, "Adjustment complete. Plant condition has improved," informing the user of the improvement in growth conditions.

[0726] This series of steps automatically provides the optimal growing environment for plants, allowing users to effectively grow plants without having specialized knowledge.

[0727] Example 1

[0728] 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."

[0729] Conventional plant growth management systems require manual collection and analysis of environmental data to adjust the environment. This makes it difficult for users without specialized knowledge to manage plants efficiently. In addition, they are unable to respond immediately to environmental changes, which can have a negative impact on plant growth.

[0730] 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.

[0731] In this invention, the server includes means for collecting multiple pieces of environmental data related to the growth status of plants using sensors, means for analyzing the environmental data using generative artificial intelligence and proposing optimal growth conditions, means for sending a push notification to a user's device based on the proposed growth conditions and receiving user approval, means for sending instructions to a control device for environmental adjustment after the user's approval and automatically adjusting the environment based on the instructions, and means for re-collecting the adjusted environmental data, analyzing it using generative artificial intelligence, and providing feedback. This makes it possible to automatically and efficiently provide an optimal growth environment for plants without the user having specialized knowledge.

[0732] A "sensor" is a device used to collect data on the growth status of plants and the surrounding environment, specifically measuring temperature, humidity, illuminance, soil moisture, etc.

[0733] "Generative AI" is an AI model that analyzes collected environmental data and compares it with past learning data to propose optimal growth conditions.

[0734] "Environmental data" refers to various data related to the growth state of plants, such as temperature, humidity, illuminance, and soil moisture content.

[0735] "Growth conditions" are specific environmental settings such as temperature, humidity, light intensity, and soil moisture required for optimal plant growth.

[0736] A "terminal" is an information terminal such as a smartphone or tablet used by a user, which receives push notifications and performs operations such as approving environmental adjustments.

[0737] "Push notifications" are a communication method that sends information from a server to a device in real time, and include messages suggesting growing conditions and encouraging approval of environmental adjustments.

[0738] A "control device" is hardware for adjusting the plant growth environment, and includes air conditioning devices, irrigation systems, lighting devices, etc.

[0739] "Feedback" is the process of analyzing data collected again after environmental adjustments have been made to check whether the adjustments have been made appropriately and to evaluate any improvements in growth conditions.

[0740] This invention relates to a system that monitors the growth status of plants in real time and automatically provides optimal environmental conditions. This system operates through the collaboration of a server, terminals, and users. The following describes the specific hardware and software usage methods, data processing, and data calculation.

[0741] Hardware and software used

[0742] The system consists of the following hardware and software:

[0743] Sensors (temperature sensor, humidity sensor, light sensor, soil moisture sensor)

[0744] Generative Artificial Intelligence (AI Model)

[0745] Server (server for data collection and analysis)

[0746] Device (user's smartphone, tablet, etc.)

[0747] Control equipment (air conditioning equipment, irrigation systems, lighting equipment)

[0748] Data collection and analysis

[0749] The server periodically collects environmental data about the plants through sensors. Specifically, it obtains data on temperature, humidity, illuminance, and soil moisture. For example, the temperature sensor collects data on 30°C, the humidity sensor collects data on 40%, the illuminance sensor collects data on 300 lux, and the soil moisture sensor collects data on 15%.

[0750] Data analysis

[0751] The server sends the collected data to the generative AI, which analyzes it. The generative AI evaluates the current data by comparing it with past learning data and proposes optimal growing conditions. For example, by comparing it with the learning model, it determines that "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable for illumination," and "30% soil moisture is appropriate."

[0752] Generate environmental adjustment proposals

[0753] Based on the analysis results, the server generates specific suggestions for adjusting the environment, such as operating the air conditioning to lower the temperature, automatically irrigating the area to increase humidity, adjusting the lighting, etc. These suggestions are returned to the server in JSON format.

[0754] User Notifications

[0755] The server sends suggested growing conditions to the user's device as a push notification, including specific examples such as "Lower the current temperature from 30°C to 25°C," "Increase the humidity from 40% to 60%, "Increase the illumination from 300 lux to 500 lux," and "Increase the soil moisture from 15% to 30%."

[0756] Authorization and execution of automated controls

[0757] The user checks the notification on their device and approves the automatic control. After receiving approval, the server sends instructions to various control devices. For example, instructions such as "adjust the temperature to 25°C" to the air conditioning unit, "sprinkle water to adjust the humidity to 60%" to the irrigation system, and "adjust the illuminance to 500 lux" to the lights are sent.

[0758] Get feedback

[0759] After the environmental adjustments are complete, the server collects data from the sensors again and analyzes the results using generative artificial intelligence. The analysis provides feedback on whether the growth conditions have improved. For example, it checks whether the adjusted temperature is 25°C, humidity is 60%, light intensity is 500 lux, and soil moisture is 30%.

[0760] Notification of adjustment results

[0761] The server notifies the terminal that the adjustment is complete and that the plant's growth condition has improved, and the terminal displays this to the user. For example, information such as "Adjustment complete. The plant's condition has improved" is displayed.

[0762] Prompt Sentence Examples

[0763] An example of a prompt for a generative AI model is as follows:

[0764] Analyze the following environmental data and suggest the optimum growing conditions for the plant: temperature 30°C, humidity 40%, light intensity 300 lux, soil moisture 15%.

[0765] The above is an embodiment of the present invention. This system provides an optimal growing environment for plants, and allows real-time data analysis and environmental adjustment, even if the user does not have specialized knowledge. Furthermore, optimization that reflects the user's intentions is realized, allowing for efficient and highly accurate environmental adjustment.

[0766] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0767] Step 1: Data collection

[0768] The server uses sensors to collect a plurality of environmental data related to the growth state of the plants.

[0769] Input: Data from sensors measuring temperature, humidity, light intensity, and soil moisture.

[0770] Output: A set of collected environmental data (e.g., temperature 30°C, humidity 40%, illuminance 300 lux, soil moisture 15%).

[0771] Specific operation: The server sends a polling request to each sensor and obtains data on temperature, humidity, illuminance, and soil moisture content from the sensors in response.

[0772] Step 2: Data analysis

[0773] The server sends the collected environmental data to the generative artificial intelligence for analysis.

[0774] Input: Collected environmental data (temperature 30°C, humidity 40%, illuminance 300 lux, soil moisture 15%).

[0775] Output: Analysis of optimal growing conditions (e.g. temperature 25°C, humidity 60%, light intensity 500 lux, soil moisture 30%).

[0776] Specific operation: The server sends environmental data to the generative AI's API and receives the results. The generative AI analyzes the results by comparing them with past learning models and determines the optimal growth conditions.

[0777] Step 3: Generate environmental adjustment proposals

[0778] The server generates optimal environmental adjustment proposals based on the analysis results of the generative artificial intelligence.

[0779] Input: Analysis results (temperature 25°C is appropriate, humidity 60% is ideal, illuminance 500 lux is desirable, soil moisture 30% is appropriate).

[0780] Output: Specific environmental adjustment suggestions (e.g., temperature adjustment, humidity adjustment, lighting adjustment).

[0781] Specific operation: Based on the analysis results, the server generates suggestions in JSON format, such as air conditioning to lower the temperature, irrigation to increase humidity, and adjusting lights to increase brightness.

[0782] Step 4: User Notification

[0783] The server sends the generated environmental adjustment proposal to the user's device as a push notification.

[0784] Input: Proposed environmental adjustments (reduce temperature from 30°C to 25°C, increase humidity from 40% to 60%, increase light intensity from 300 lux to 500 lux, increase soil moisture from 15% to 30%).

[0785] Output: A push notification sent to the user's device containing the specific adjustments.

[0786] Specific operation: The server uses the push notification service to send a notification to the user's smartphone.

[0787] Step 5: Accepting automation

[0788] The user checks the notification through the terminal and approves the automatic control.

[0789] Input: Push notification (environmental adjustment suggestion) received by the user.

[0790] Output: User approval (sent from the terminal).

[0791] Specific operation: The user taps the notification on their smartphone and presses the "Approve" button to approve the environmental adjustment.

[0792] Step 6: Execute control instructions

[0793] After receiving the user's approval, the server sends specific instructions to the various control devices.

[0794] Input: User approval and generated environmental adjustment suggestions.

[0795] Output: Specific instructions sent to a control device (adjust temperature, humidity, light).

[0796] Specific actions: The server sends specific instructions to the air conditioner, such as "adjust the temperature to 25°C," to the irrigation system, "adjust the humidity to 60%, and" to the lights, "adjust the illuminance to 500 lux."

[0797] Step 7: Check the results of the environment adjustment

[0798] After the environmental adjustment is complete, the server again collects and analyzes data from the sensors.

[0799] Input: Calibrated environmental data (latest data from sensors).

[0800] Output: Feedback result after adjustment (whether the environment improved or not).

[0801] What it does: The server sends another request to the sensor to collect the latest environmental data, which it then sends to the generative AI for reanalysis.

[0802] Step 8: Notification of adjustment results

[0803] The server notifies the user's terminal that the adjustment has been completed and that the plant's growth condition has improved, and the terminal displays this to the user.

[0804] Input: Adjusted feedback results.

[0805] Output: Notification to user device (adjustment completed, improvement notification).

[0806] Specific operation: The server sends a notification to the user's smartphone via a push notification service, such as "Adjustment completed. Plant condition has improved."

[0807] This concludes the explanation of the specific processing steps of the program. Each step works closely together to provide the optimal growing environment for plants in real time.

[0808] (Application example 1)

[0809] 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."

[0810] While current plant growth systems automate the adjustment of environmental conditions to maintain optimal plant growth, they have the problem of difficulty in responding in real time to security risks such as intruders and suspicious activity. This means that even if the plant growth environment is secured, overall safety cannot be ensured if physical security is threatened.

[0811] 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.

[0812] In this invention, the server includes means for collecting multiple pieces of environmental data and security data related to the growth status of plants using sensors, means for analyzing the environmental data and security data using generative artificial intelligence and proposing optimal growth conditions and vigilance situations, means for automatically adjusting the environment and security measures based on the proposed growth conditions and vigilance situations, and means for recollecting the adjusted environmental data and security data and providing feedback. This not only provides an optimal growth environment for plants, but also makes it possible to respond to security risks in real time.

[0813] A "sensor" is a device for collecting environmental data related to plant growth and security.

[0814] "Generative AI" is an AI technology that analyzes collected environmental and security data to suggest optimal growth conditions and vigilance situations.

[0815] "Environmental data" refers to data necessary for evaluating the growth status of plants, such as temperature, humidity, illuminance, and soil moisture.

[0816] "Crime prevention data" refers to data necessary for evaluating crime prevention measures, such as motion detection and vibration detection.

[0817] "Growth conditions" are the environmental conditions necessary to promote healthy plant growth.

[0818] An "alert situation" is a state of surveillance and vigilance that is considered optimal from a crime prevention perspective.

[0819] "Adjustment means" refers to means for automatically adjusting the environment and security posture based on proposed growth and alert conditions.

[0820] "Feedback" is the process of collecting environmental and security data again after adjustments have been made and reevaluating the results.

[0821] This invention relates to a system that monitors the growth status and security of plants in real time to provide optimal environmental and security conditions. The system operates by combining sensors, generative artificial intelligence, control devices, and feedback devices. Each major processing step of the system and the hardware and software used for each step are described below.

[0822] First, the server uses sensors to collect multiple environmental data related to plant growth and security. These sensors measure temperature, humidity, illuminance, soil moisture, and security data (motion detection, vibration detection, etc.). For example, the server collects data that the temperature sensor is 30°C, the humidity sensor is 40%, the motion detection sensor detects motion, and the vibration sensor does not detect vibration.

[0823] The server then sends the collected environmental and security data to the generative AI, which analyzes this data and evaluates the plant's current growth and alert status. For example, by comparing it with the learning model, it can determine that "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable for illumination," "30% soil moisture is appropriate," "turn on the security light to detect motion," and "do not sound the alarm because there is no vibration."

[0824] Based on the analysis, the server identifies optimal growing conditions and vigilance situations and generates specific recommendations for adjusting the environment and security posture, such as adjusting the air conditioning to lower the temperature or turning on security lights in response to motion detection. These recommendations are returned to the server in JSON format.

[0825] To notify the user of the proposed content, the server sends a push notification to the device (e.g., the user's smartphone). The notification content includes information such as "Lower the current temperature from 30°C to 25°C," "Increase the humidity from 40% to 60%," and "Turn on the security lights to detect motion."

[0826] The user checks the notification via their device and approves the automatic control. After approval is obtained, the server sends instructions to the various control devices. For example, it sends specific instructions such as "adjust the temperature to 25°C" to the air conditioner and "turn on the security light" to the security light.

[0827] After the environmental adjustments and security measures are complete, the server again collects data from the sensors and checks the results of the adjustments. This information is again analyzed by the generative AI to provide feedback on whether the growth and security conditions have improved.

[0828] Finally, the server notifies the terminal that the adjustment is complete and that the plant's growth and security status have improved, and the terminal displays this to the user. For example, the terminal displays information such as "Adjustment complete. Plant status and security status have improved."

[0829] As a concrete example, to maintain public order in a certain town, patrol staff wear smart glasses with this application installed. When the sensor detects an abnormality, the AI ​​automatically analyzes and evaluates it, and proposes and implements the necessary security measures. The sensor detects motion and suggests the optimal alarm to sound and security lights to turn on. After execution, the user receives a notification on the smart glasses display saying "Motion detected, alarm sounding."

[0830] Example prompt sentence:

[0831] plaintext

[0832] I would like you to suggest the best security measures for a situation where the temperature sensor indicates an outside temperature of 25°C, the humidity sensor indicates 50%, the motion detection sensor detects motion, and the vibration sensor does not detect vibration.

[0833] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0834] Step 1:

[0835] Input - The server collects data from sensors (temperature, humidity, light intensity, soil moisture, motion detection, vibration detection).

[0836] Data processing and calculation: The collected data is temporarily stored on the server, where it is checked for consistency and format converted.

[0837] Output - The consistent environmental and security data is sent to the generative AI.

[0838] The server collects environmental and security data from various installed sensors. For example, the temperature sensor may indicate a temperature of 30°C, and the motion sensor may indicate that a motion has been detected.

[0839] Step 2:

[0840] Input - Environmental data and security data sent from the server to the generative artificial intelligence.

[0841] Data processing, calculation, and generation AI uses trained models to analyze data and propose optimal growth conditions and vigilance situations.

[0842] The output-generative AI's suggestions (e.g., adjust the temperature to 25°C, turn on the security lights) are returned to the server in JSON format.

[0843] The server requests a generative artificial intelligence to analyze the transmitted data and propose optimal growth conditions and vigilance situations.

[0844] Step 3:

[0845] Suggestions returned from the input-generative AI.

[0846] Data processing and calculation - The server analyzes the proposal content and generates push notification data to notify the user.

[0847] Output - Notifications about suggested growing conditions and alert status are sent to the user's terminal.

[0848] Based on the suggestions received from the generative artificial intelligence, the server prepares notification content for the user's device and sends a push notification.

[0849] Step 4:

[0850] Input - Push notifications from the server (e.g. adjust the temperature to 25°C, turn on the security lights).

[0851] Data processing and calculation - The user checks the notification and decides whether to approve or reject it.

[0852] Output - The user's approval or denial is returned to the server.

[0853] The user checks the proposal through the terminal and accepts or rejects it. If the proposal is accepted, the result is sent to the server.

[0854] Step 5:

[0855] Input - Approval result from user.

[0856] Data processing and calculation - The server generates commands to operate various control devices (air conditioners, security lights, etc.) based on the approved proposal.

[0857] Output - The command is sent to the control device.

[0858] The server receives the approval result from the user, generates specific instructions, and sends them to the air conditioner, security lights, etc.

[0859] Step 6:

[0860] Inputs - Adjusted environmental and crime prevention data.

[0861] Data processing and calculation - The server collects data from the sensors again and requests the generative AI to reanalyze it.

[0862] Output - Reanalysis results are obtained and feedback on adjusted growth and vigilance status is generated.

[0863] After the environmental adjustments and security measures have been completed, the server will collect data from the sensors again and have the generative artificial intelligence reanalyze the growth and alert status.

[0864] Step 7:

[0865] Input - Reparse result.

[0866] Data processing and calculation - Based on the reanalysis results, the server generates data to provide final feedback to the user.

[0867] Output - A notification is sent to the user's device indicating the adjustment completion and the improvement results.

[0868] The server uses the results of the reanalysis to prepare final feedback and sends a notification to the user's device, for example, "Adjustments completed. Plant condition and security have improved."

[0869] As described above, the system is designed to function smoothly as a whole, with necessary data processing and calculations being performed based on input data at each step.

[0870] 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.

[0871] The present invention relates to a system that monitors the growth status of plants in real time and provides optimal environmental conditions taking into account the user's emotions. This system operates by combining sensors, generative artificial intelligence, an emotion engine, a control device, and a feedback device.

[0872] First, the server collects multiple environmental data related to the plant's growth status using sensors. These sensors measure temperature, humidity, illuminance, and soil moisture. For example, the temperature sensor collects data such as 30°C, humidity sensor collects data such as 40%, illuminance sensor collects data such as 300 lux, and soil moisture sensor collects data such as 15%.

[0873] The server then sends the collected data to a generative AI, which analyzes the data and evaluates the plant's current growth status. For example, it compares the data with a learning model and determines that "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable for illumination," and "30% soil moisture is appropriate."

[0874] Based on the analysis results, the server identifies optimal growing conditions and generates specific recommendations for environmental adjustments, such as adjusting the air conditioning to lower the temperature, automatically irrigating to increase humidity, or adjusting the lighting. These recommendations are returned to the server in JSON format.

[0875] Here, an emotion engine that recognizes the user's emotions analyzes the user's facial expressions and voice. The emotion engine uses image analysis and voice recognition technology to evaluate the user's emotional state, such as whether the user is feeling stressed or comfortable.

[0876] To notify the user of the proposed content, the server sends a push notification to the device (e.g., the user's smartphone). At this time, the notification is sent in a way that takes into consideration the user's emotions based on the analysis results of the emotion engine. For example, if the user is relaxed, the notification may be soft-spoken, such as "Lowering the current temperature from 30°C to 25°C will help your plants thrive," whereas if the user is in a hurry, the notification may be simple, such as "The temperature needs to be adjusted."

[0877] The user checks the notification via their device and approves the automatic control. At this time, the emotion engine analyzes the user's reaction again to ensure that approval is given without stress. When the user presses the approval button, the server sends instructions to various control devices. Specific instructions are sent to the air conditioning control device, such as "adjust the temperature to 25°C," to the irrigation system, "sprinkle water and adjust the humidity to 60%, and to the lights, "adjust the illuminance to 500 lux."

[0878] After the environmental adjustments are complete, the server collects data from the sensors again to confirm the results. This information is again analyzed by the generative AI to obtain feedback on whether the growth conditions have improved, and the server sends this information to the device.

[0879] The device notifies the user that the adjustment is complete and that the plant's growth has improved, for example by displaying a message such as "Adjustment complete. Plant condition has improved." Furthermore, the emotion engine re-evaluates the user's reaction to confirm whether the user is satisfied.

[0880] Through this series of steps, the system can automatically provide the optimal growing environment for plants while taking into consideration emotions, even if the user does not have specialized knowledge. Furthermore, optimization that reflects the user's intentions and emotions is carried out, achieving highly accurate environmental adjustment.

[0881] The processing flow will be explained below.

[0882] Step 1:

[0883] The server collects multiple environmental data related to the plant's growth status through sensors: a temperature sensor measures the current temperature (e.g., 30°C), a humidity sensor measures humidity (e.g., 40%), an illuminance sensor measures light intensity (e.g., 300 lux), and a soil moisture sensor measures soil humidity (e.g., 15%).

[0884] Step 2:

[0885] The server sends the collected data to the generative AI via an API, requesting analysis and optimization. For example, it sends a POST request to the " / ai / analyze" endpoint.

[0886] Step 3:

[0887] The generative AI analyzes the received data. Specifically, it compares it with the learning model within the generative AI and calculates the temperature, humidity, illuminance, and soil moisture content that are optimal for plant growth. This results in judgments such as "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable illuminance," and "30% soil moisture is appropriate."

[0888] Step 4:

[0889] The generative AI returns the analysis results to the server in JSON format, for example, { "temperature": "reduce to 25℃", "humidity": "increase to 60%", "light": "increase to 500 lux", "soilMoisture": "increase to 30%"}.

[0890] Step 5:

[0891] The server receives the analysis results from the generative AI and activates the emotion engine to ascertain the user's emotional state. The emotion engine uses image and audio analysis to assess the user's current emotions and determine whether the user is stressed or relaxed.

[0892] Step 6:

[0893] The server adjusts the generative AI's suggestions based on the analysis results of the emotion engine. If the user is relaxed, the server will notify them in a gentle way, such as "Lowering the current temperature from 30°C to 25°C will help your plants thrive," but if the user is in a hurry, the server will notify them in a simpler way, such as "The temperature needs to be adjusted."

[0894] Step 7:

[0895] The server then sends the adjusted proposal to the device as a push notification. Specifically, the proposal is sent to the user's smartphone and displayed in a format that the user can check.

[0896] Step 8:

[0897] The device displays the received notification to the user. As the user checks the content of the notification, the emotion engine analyzes the user's reaction again to confirm whether the user is in a state where they can accept the notification without feeling stressed.

[0898] Step 9:

[0899] The user checks the notification and approves the automatic control via the device by pressing a button such as "Start automatic control."

[0900] Step 10:

[0901] With the user's approval, the server sends instructions to various control devices, such as to the air conditioning control device to "adjust the temperature to 25°C," to the irrigation system to "sprinkle water and adjust the humidity to 60%, and to the lights to "adjust the illuminance to 500 lux."

[0902] Step 11:

[0903] The server monitors the adjustment process and, once the adjustment is complete, collects data from the sensors again to confirm whether the temperature, humidity, light intensity, and soil moisture content have reached their target values.

[0904] Step 12:

[0905] The server checks the adjustment results and the improvement in the plant's condition, and sends the information to the terminal. The server notifies the user by saying, "Adjustment complete. The plant's condition has improved."

[0906] Step 13:

[0907] The device will display the completion of the adjustment and the improvement of the growth status to the user, while the emotion engine will analyze the user's reaction to check whether the user is satisfied or stressed.

[0908] This series of steps not only automatically provides the optimal growing environment for plants, but also creates a system that takes into account the user's emotional state.

[0909] Example 2

[0910] 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."

[0911] Conventional plant growth management systems were able to suggest growth conditions by collecting and analyzing environmental data, but it was difficult to adjust the environment optimally while taking the user's emotions into consideration. Furthermore, because the notification of growth condition suggestions was made without regard for the user's situation or emotions, it was difficult for the user to accept the suggestions, making it difficult to maintain an optimal growth environment for plants.

[0912] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting multiple pieces of environmental data related to the growth state of plants using sensors, means for analyzing the environmental data using generative artificial intelligence and proposing optimal growth conditions, means for automatically adjusting the environment based on the proposed growth conditions, means for re-collecting the adjusted environmental data and providing feedback, means for evaluating the user's emotions using an emotion engine that analyzes the user's emotional state, and means for determining an appropriate notification method based on the evaluated user emotions. This makes it possible to provide an optimal plant growth environment that takes the user's emotions into consideration.

[0913] A "sensor" is a measuring device for collecting environmental data.

[0914] "Plant growth status" refers to various parameters that indicate the growth and health status of a plant.

[0915] "Environmental data" refers to data related to plant growth conditions such as temperature, humidity, illuminance, and soil moisture content.

[0916] "Generative artificial intelligence" refers to machine learning models and algorithms that analyze collected environmental data and suggest optimal growing conditions.

[0917] "Optimal growing conditions" are ideal environmental parameters for promoting plant growth and health.

[0918] The "means for automatically adjusting the environment" is a system that automatically controls air conditioning, irrigation systems, lighting, etc. based on collected data and the analysis results of generative artificial intelligence.

[0919] The "means of providing feedback" is a function that collects data again after the environmental adjustment, analyzes the results, and evaluates the effectiveness of the adjustment.

[0920] An "emotion engine" is a technology for analyzing and evaluating a user's emotional state based on facial expressions, voice, etc.

[0921] "User's emotions" refer to the psychological state, such as stress or relaxation, that the user feels.

[0922] An "appropriate notification method" is a method of notifying the user in an appropriate manner and at an appropriate timing based on the user's emotional state.

[0923] The present invention relates to a system that monitors the growth status of plants in real time and provides optimal environmental conditions taking into account the user's emotions. This system operates by combining sensors, generative artificial intelligence, an emotion engine, a control device, and a feedback device.

[0924] First, the server collects multiple environmental data related to the plant's growth status using sensors. The sensors include a temperature sensor, humidity sensor, illuminance sensor, and soil moisture sensor. For example, the server collects data such as 30°C from the temperature sensor, 40% from the humidity sensor, 300 lux from the illuminance sensor, and 15% from the soil moisture sensor.

[0925] The server then sends the collected data to a generative AI, which analyzes the data and evaluates the plant's current growth status. For example, it compares the data with a learning model and determines that "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable for illumination," and "30% soil moisture is appropriate."

[0926] Based on the analysis results, the server identifies optimal growing conditions and generates specific recommendations for environmental adjustments, such as adjusting the air conditioning to lower the temperature, automatically irrigating to increase humidity, or adjusting the lighting. These recommendations are returned to the server in JSON format.

[0927] Here, an emotion engine that recognizes the user's emotions analyzes the user's facial expressions and voice. The emotion engine uses image analysis and voice recognition technology to evaluate the user's emotional state, such as whether the user is feeling stressed or comfortable.

[0928] To notify the user of the proposed content, the server sends a push notification to the device (e.g., the user's smartphone). At this time, the notification is sent in a way that takes into consideration the user's emotions based on the analysis results of the emotion engine. For example, if the user is relaxed, the notification may be soft-spoken, such as "Lowering the current temperature from 30°C to 25°C will help your plants thrive," whereas if the user is in a hurry, the notification may be simple, such as "The temperature needs to be adjusted."

[0929] The user checks the notification via their device and approves the automatic control. At this time, the emotion engine analyzes the user's reaction again to ensure that approval is given without stress. When the user presses the approval button, the server sends instructions to various control devices. Specific instructions are sent to the air conditioning control device, such as "adjust the temperature to 25°C," to the irrigation system, "sprinkle water and adjust the humidity to 60%, and to the lights, "adjust the illuminance to 500 lux."

[0930] After the environmental adjustments are complete, the server collects data from the sensors again to confirm the results. This information is again analyzed by the generative AI to obtain feedback on whether the growth conditions have improved, and the server sends this information to the device.

[0931] The device notifies the user that the adjustment is complete and that the plant's growth condition has improved, displaying a message such as "Adjustment complete. Plant condition has improved." The emotion engine then reevaluates the user's response to confirm whether the user is satisfied. This series of steps enables the system to automatically and emotionally provide the optimal growth environment for plants, even without the user's specialized knowledge.

[0932] Specific examples

[0933] 1. Collect data from the temperature sensor at 30°C, the humidity sensor at 40%, the illuminance sensor at 300 lux, and the soil moisture sensor at 15%.

[0934] 2. The generative artificial intelligence analyzes this data and determines that "25°C is the appropriate temperature," "60% is ideal humidity," "500 lux is desirable illumination," and "30% is the appropriate soil moisture."

[0935] 3. The server generates suggestions such as lowering the temperature to 25°C, increasing the humidity to 60%, and adjusting the light intensity to 500 lux.

[0936] 4. The emotion engine analyzes that the user is relaxed.

[0937] 5. The server notifies the device in gentle terms that "Lowering the current temperature from 30°C to 25°C will help the plants thrive."

[0938] 6. The user checks the notification and presses the approval button.

[0939] 7. The server sends instructions to the air conditioning control unit to "adjust the temperature to 25°C," the irrigation system to "adjust the humidity to 60%," and the lights to "adjust the illuminance to 500 lux."

[0940] Prompt Sentence Examples

[0941] "You have collected data: a temperature sensor reading 30°C, a humidity sensor reading 40%, a light sensor reading 300 lux, and a soil moisture sensor reading 15%. Describe the steps you would take to have a generative AI analyze this data and identify the optimal conditions for plant growth."

[0942] The above is a specific embodiment of this system.

[0943] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0944] Step 1:

[0945] The server uses sensors to collect multiple environmental data related to the plant's growth status. Specifically, it acquires data from a temperature sensor, humidity sensor, illuminance sensor, and soil moisture sensor. For example, the temperature sensor collects data such as 30°C, humidity sensor collects data such as 40%, illuminance sensor collects data such as 300 lux, and soil moisture sensor collects data such as 15%. This data becomes input data for subsequent analysis.

[0946] Step 2:

[0947] The server sends the collected environmental data to the generative AI, which then takes the data as input into the analysis system. The generative AI then evaluates the current growth status of the plant based on this input data. For example, it outputs optimal growth conditions such as "the appropriate temperature is 25°C," "the ideal humidity is 60%," "the desired illumination level is 500 lux," and "the appropriate soil moisture level is 30%."

[0948] Step 3:

[0949] The server generates specific proposals for environmental adjustments based on the optimal growing conditions output by the generative AI. These proposals include operating the air conditioning to lower the temperature, automatic irrigation to increase humidity, and adjusting the lighting. For example, suggestions such as "adjust the temperature to 25°C," "adjust the humidity to 60%," and "adjust the lighting to 500 lux" are output in JSON format.

[0950] Step 4:

[0951] The emotion engine analyzes emotions from the user's facial expressions and voice. Camera footage and microphone audio are used as input data. The emotion engine analyzes this data and evaluates the user's emotional state, such as whether they are feeling stressed or comfortable. The results of this evaluation become input data for determining how to notify the user of the proposed content.

[0952] Step 5:

[0953] The server notifies the user of specific suggestions based on the user's emotional data obtained from the emotion engine. The content of the notification and the way it is expressed are determined taking into consideration the user's emotional state. For example, if the user is relaxed, the server may notify them in a gentle manner, such as "Lowering the current temperature from 30°C to 25°C will help your plants thrive," while if the user is in a hurry, the server may notify them in a simpler manner, such as "The temperature needs to be adjusted."

[0954] Step 6:

[0955] The user checks the notification via their device and approves the automatic control. When the user presses the approval button on the device where the notification is displayed, this approval information is sent to the server. The emotion engine then analyzes the user's reaction again to confirm whether approval was given without stress.

[0956] Step 7:

[0957] With the user's approval, the server sends specific instructions to various control devices. For example, it sends instructions such as "adjust the temperature to 25°C" to the air conditioning control device, "adjust the humidity to 60%" to the irrigation system, and "adjust the illuminance to 500 lux" to the light. This automatically adjusts the physical environment.

[0958] Step 8:

[0959] After the environmental adjustment is complete, the server collects environmental data from the sensors again. The data collected as the adjustment results is input into the generative AI and re-evaluated. This confirms, for example, that the adjusted temperature is 25°C, humidity is 60%, illuminance is 500 lux, and soil moisture is 30%.

[0960] Step 9:

[0961] The server sends the adjustment results to the device and notifies the user that the adjustment is complete and that the plant's growth has improved. The device displays a message such as "Adjustment complete. The plant's condition has improved." The emotion engine again evaluates the user's reaction and checks whether the user is satisfied.

[0962] (Application example 2)

[0963] 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."

[0964] The present invention aims to provide a system that not only optimizes the growing environment of plants but also notifies users and adjusts the environment taking into account their emotions. In particular, the objective is to improve product quality and work efficiency by optimizing the working environment and taking into account the emotional state of workers in quality control on production lines.

[0965] 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.

[0966] In this invention, the server includes means for collecting multiple pieces of environmental data related to the growth status of plants using sensors, means for analyzing the environmental data using generative artificial intelligence and proposing optimal growth conditions, means for automatically adjusting the environment based on the proposed growth conditions, means for recollecting the adjusted environmental data and providing feedback, means for recognizing the user's emotions and generating a notification message according to the emotions, and means for reevaluating the user's emotional state and confirming that the user is not stressed if the user approves. This makes it possible to optimize the plant growth environment and the work environment of the production line, and to adjust the environment taking into account the emotional states of users and workers.

[0967] A "sensor" is a device that measures and collects environmental data such as temperature, humidity, light intensity, and soil moisture content in real time.

[0968] "Generative AI" is an AI technology that analyzes collected data and proposes optimal conditions for plant growth and working environments.

[0969] "Environmental adjustment" refers to automatically operating various devices such as air conditioning, irrigation systems, and lighting based on the optimal conditions proposed by generative AI, to adjust the growing conditions for plants and the working environment of the production line.

[0970] "Feedback" is the process of collecting data from sensors again after environmental adjustments have been made, and using the results to evaluate growth and environmental conditions.

[0971] The "emotion engine" is a system that analyzes the user's facial expressions and voice to evaluate the user's emotional state and reflects this in the content of notifications and suggestions for environmental adjustments.

[0972] A "notification message" is a message that notifies the user of suggested growing conditions or the need for environmental adjustments, and its expression is adjusted according to the user's emotional state.

[0973] "Acceptance" refers to the user's explicit acceptance of the proposed growing conditions and environmental adjustments.

[0974] "Stress assessment" is the process by which the emotion engine re-analyzes the user's emotional state to see if the user is feeling stressed.

[0975] This invention is a system that monitors the growing environment of plants in real time and provides optimal environmental conditions taking into account the user's emotions. This system can also be applied to quality control on factory production lines.

[0976] This system mainly uses the following hardware and software:

[0977] Sensors: Used to measure temperature, humidity, light intensity, and soil moisture. For example, temperature sensors, humidity sensors, and light intensity sensors are used on production lines in factories.

[0978] Generative AI: Refers to artificial intelligence technology that analyzes collected data and suggests optimal conditions for plants or products.

[0979] Emotion engine: Evaluates the user's emotions using image analysis and voice recognition technology.

[0980] Server: Collects data from the aforementioned sensors and analyzes it using generative artificial intelligence and an emotion engine to identify optimal environmental conditions.

[0981] Terminal: A device such as a smartphone or smart glasses that is used to send notifications to the user and receive user approval.

[0982] To explain how the system works in detail, the server first uses sensors to collect environmental data. For example, assume that the temperature on the production line is 28°C, humidity is 35%, and illuminance is 400 lux. This data is sent to the server, where generative AI analyzes the data and identifies the ideal environmental conditions. For example, it determines that the ideal temperature is 25°C, humidity is 40%, and illuminance is 500 lux.

[0983] The emotion engine then analyzes the user's facial expressions and voice to recognize their emotions. For example, if the server determines that the user is relaxed, it will send a soft message to the device, such as, "Lowering the current temperature from 28°C to 25°C will improve product quality." Conversely, if the user is in a hurry, it will send a simple message, such as, "The temperature needs to be adjusted."

[0984] The user checks the notification via their device and approves the proposal. After approval, the server again uses the emotion engine to evaluate the user's emotional state and confirm that they are not stressed. The server then sends specific instructions to various control devices to adjust the temperature, humidity, and light level.

[0985] After the environmental adjustment is complete, data is collected again from the sensors and analyzed by the generative artificial intelligence. Feedback is obtained on whether the adjustment results have improved, and the server sends this information to the device. For example, a message such as "Adjustment complete. The environment has been optimized and product quality has improved" is displayed.

[0986] Through this series of operations, the system can automatically provide the optimal growing and manufacturing environment for plants and products, even without the user's specialized knowledge. It also performs optimization that reflects the user's intentions and emotions, achieving highly accurate environmental adjustment.

[0987] Here are some illustrative prompts:

[0988] "Temperature sensor: 28°C, humidity sensor: 35%, illuminance sensor: 400 lux. Evaluate the difference from ideal environmental conditions (temperature 25°C, humidity 40%, illuminance 500 lux) and create adjustment suggestions. Additionally, generate a notification message when the user's emotions are relaxed."

[0989] Using this example, the detailed operation of the system can be clearly understood.

[0990] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0991] Step 1:

[0992] The server uses sensors to collect environmental data. Specifically, it obtains data in real time from temperature, humidity, and illuminance sensors. For example, the sensors can provide data such as "temperature 28°C, humidity 35%, illuminance 400 lux." The input is the raw data from each sensor, and the output is this environmental data.

[0993] Step 2:

[0994] The server sends the collected environmental data to the generative AI, which analyzes this data and identifies the optimal environmental conditions for plants and production lines. For example, it suggests conditions such as "a temperature of 25°C, humidity of 40%, and illuminance of 500 lux are desirable." The input is the collected environmental data, and the output is a proposal for the optimal environmental conditions.

[0995] Step 3:

[0996] The server uses an emotion engine to generate a notification message that takes the user's emotions into account based on the optimal environmental conditions proposed by the generative artificial intelligence. The emotion engine analyzes the user's facial expressions and voice to evaluate the user's emotional state. For example, if the user is relaxed, it generates a soft notification message saying, "Lowering the current temperature from 28°C to 25°C will improve product quality." The input is the proposed environmental conditions and the user's emotional data, and the output is the notification message.

[0997] Step 4:

[0998] The server sends a notification message to the user's device. The device displays the notification message to the user, and the user approves the environmental adjustment based on the suggestion. For example, a notification saying "Lowering the temperature to 25°C will improve product quality" appears on a smartphone, and the user presses the "Approve" button. The input is the notification message, and the output is the user's approval.

[0999] Step 5:

[1000] After receiving the user's approval, the server uses the emotion engine to evaluate the user's emotional state again and confirm that they are not stressed. For example, it checks whether the user remains relaxed after approval. The input is the user's approval and emotion data, and the output is the result of the stress assessment.

[1001] Step 6:

[1002] After the server confirms that there is no stress, it sends specific instructions to the various control devices to adjust the environment. For example, it sends instructions such as "adjust the temperature to 25°C" to the temperature control device and "adjust the illuminance to 500 lux" to the illuminance control device. The input is the optimal environmental conditions, and the output is the specific control instructions.

[1003] Step 7:

[1004] After the environmental adjustments are made, the server collects environmental data from the sensors again. For example, it checks whether the temperature is 25°C, the humidity is 40%, and the illuminance is 500 lux. The input is the sensor data after the environmental adjustments, and the output is the adjusted environmental data.

[1005] Step 8:

[1006] The server then sends the collected environmental data to the generative AI, which analyzes whether the growth conditions and quality have improved. For example, it receives feedback such as, "Optimal conditions are being maintained at a temperature of 25°C." The input is the adjusted environmental data, and the output is the feedback information.

[1007] Step 9:

[1008] The server sends feedback information to the user's device and notifies them of the adjustment results. For example, a message such as "Adjustment complete. Product quality has improved" is displayed on a smartphone. The input is feedback information, and the output is a notification to the user.

[1009] Through these steps, the invention can automatically provide the optimal growing and manufacturing environment for plants and products, even without the user's specialized knowledge. It also performs optimization that reflects the user's intentions and emotions, achieving highly accurate environmental adjustment.

[1010] 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.

[1011] 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.

[1012] 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.

[1013] [Fourth embodiment]

[1014] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1015] 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.

[1016] 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).

[1017] 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.

[1018] 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.

[1019] 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).

[1020] 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.

[1021] 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.

[1022] 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.

[1023] 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.

[1024] 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.

[1025] 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.

[1026] 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."

[1027] The present invention relates to a system that monitors the growth status of plants in real time and automatically provides optimal environmental conditions. This system operates by combining sensors, generative artificial intelligence, control devices, and feedback devices.

[1028] First, the server collects multiple environmental data related to the plant's growth status using sensors. These sensors measure temperature, humidity, illuminance, and soil moisture. For example, the temperature sensor collects data such as 30°C, humidity sensor collects data such as 40%, illuminance sensor collects data such as 300 lux, and soil moisture sensor collects data such as 15%.

[1029] The server then sends the collected data to a generative AI, which analyzes the data and evaluates the plant's current growth status. For example, it compares the data with a learning model and determines that "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable for illumination," and "30% soil moisture is appropriate."

[1030] Based on the analysis results, the server identifies optimal growing conditions and generates specific recommendations for environmental adjustments, such as adjusting the air conditioning to lower the temperature, automatically irrigating to increase humidity, or adjusting the lighting. These recommendations are returned to the server in JSON format.

[1031] To notify the user of the proposed changes, the server sends a push notification to the device (e.g., the user's smartphone). The notification includes information such as "Lower the current temperature from 30°C to 25°C," "Increase the humidity from 40% to 60%, "Increase the illuminance from 300 lux to 500 lux," and "Increase the soil moisture from 15% to 30%."

[1032] The user checks the notification via their device and approves the automatic control. After approval is obtained, the server sends instructions to various control devices. For example, it sends specific instructions such as "adjust the temperature to 25°C" to the air conditioning unit, "sprinkle water to adjust the humidity to 60%" to the irrigation system, and "adjust the illuminance to 500 lux" to the lights.

[1033] After the environmental adjustments are complete, the server again collects data from the sensors and checks the results. This information is again analyzed by the generative AI, and feedback is provided on whether the growing conditions have improved.

[1034] Finally, the server notifies the terminal that the adjustment is complete and that the plant's growth condition has improved, and the terminal displays this to the user. For example, information such as "Adjustment complete. Plant condition has improved" is displayed.

[1035] In this way, the system automatically provides the optimal growing environment for plants, enabling real-time data analysis and environmental adjustment, even without the user's specialized knowledge. Furthermore, since optimization reflects the user's wishes, highly accurate environmental adjustment is achieved.

[1036] The processing flow will be explained below.

[1037] Step 1:

[1038] The server collects multiple environmental data related to the plant's growth status through sensors: a temperature sensor measures the current temperature (e.g., 30°C), a humidity sensor measures humidity (e.g., 40%), an illuminance sensor measures light intensity (e.g., 300 lux), and a soil moisture sensor measures soil humidity (e.g., 15%).

[1039] Step 2:

[1040] The server sends the collected data to the generative AI via an API, requesting analysis and optimization. For example, it sends a POST request to the " / ai / analyze" endpoint.

[1041] Step 3:

[1042] The generative AI analyzes the received data. Specifically, it compares it with the learning model within the generative AI and calculates the temperature, humidity, illuminance, and soil moisture content that are optimal for plant growth. This results in judgments such as "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable illuminance," and "30% soil moisture is appropriate."

[1043] Step 4:

[1044] The generative AI returns the analysis results to the server in JSON format, for example, { "temperature": "reduce to 25℃", "humidity": "increase to 60%", "light": "increase to 500 lux", "soilMoisture": "increase to 30%"}.

[1045] Step 5:

[1046] The server receives the analysis results and prepares to notify the user. Specifically, the analysis results are sent to the user's device as a push notification. The push notification contains information such as "Lower the current temperature from 30°C to 25°C," "Increase the humidity from 40% to 60%, "Increase the illuminance from 300 lux to 500 lux," and "Increase the soil moisture from 15% to 30%."

[1047] Step 6:

[1048] The device displays the received notification to the user, and provides an interface that clearly displays the suggestion on the screen so that the user can check the notification.

[1049] Step 7:

[1050] The user checks the notification and approves the automatic control by pressing a button such as "Start automatic control" on the device screen.

[1051] Step 8:

[1052] The server receives user approval and sends instructions to various control devices, such as "adjust the temperature to 25°C" to the air conditioning control device, "sprinkle water to adjust the humidity to 60%" to the irrigation system, and "adjust the illuminance to 500 lux" to the lights.

[1053] Step 9:

[1054] The server monitors the data obtained during the adjustment process and checks whether the adjustment is complete. Specifically, it collects feedback data from the sensors again and evaluates whether the set value has been reached.

[1055] Step 10:

[1056] The server notifies the terminal of the adjustment results, which are then displayed to the user. The terminal displays a message to the user saying, "Adjustment complete. Plant condition has improved," informing the user of the improvement in growth conditions.

[1057] This series of steps automatically provides the optimal growing environment for plants, allowing users to effectively grow plants without having specialized knowledge.

[1058] Example 1

[1059] 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."

[1060] Conventional plant growth management systems require manual collection and analysis of environmental data to adjust the environment. This makes it difficult for users without specialized knowledge to manage plants efficiently. In addition, they are unable to respond immediately to environmental changes, which can have a negative impact on plant growth.

[1061] 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.

[1062] In this invention, the server includes means for collecting multiple pieces of environmental data related to the growth status of plants using sensors, means for analyzing the environmental data using generative artificial intelligence and proposing optimal growth conditions, means for sending a push notification to a user's device based on the proposed growth conditions and receiving user approval, means for sending instructions to a control device for environmental adjustment after the user's approval and automatically adjusting the environment based on the instructions, and means for re-collecting the adjusted environmental data, analyzing it using generative artificial intelligence, and providing feedback. This makes it possible to automatically and efficiently provide an optimal growth environment for plants without the user having specialized knowledge.

[1063] A "sensor" is a device used to collect data on the growth status of plants and the surrounding environment, specifically measuring temperature, humidity, illuminance, soil moisture, etc.

[1064] "Generative AI" is an AI model that analyzes collected environmental data and compares it with past learning data to propose optimal growth conditions.

[1065] "Environmental data" refers to various data related to the growth state of plants, such as temperature, humidity, illuminance, and soil moisture content.

[1066] "Growth conditions" are specific environmental settings such as temperature, humidity, light intensity, and soil moisture required for optimal plant growth.

[1067] A "terminal" is an information terminal such as a smartphone or tablet used by a user, which receives push notifications and performs operations such as approving environmental adjustments.

[1068] "Push notifications" are a communication method that sends information from a server to a device in real time, and include messages suggesting growing conditions and encouraging approval of environmental adjustments.

[1069] A "control device" is hardware for adjusting the plant growth environment, and includes air conditioning devices, irrigation systems, lighting devices, etc.

[1070] "Feedback" is the process of analyzing data collected again after environmental adjustments have been made to check whether the adjustments have been made appropriately and to evaluate any improvements in growth conditions.

[1071] This invention relates to a system that monitors the growth status of plants in real time and automatically provides optimal environmental conditions. This system operates through the collaboration of a server, terminals, and users. The following describes the specific hardware and software usage methods, data processing, and data calculation.

[1072] Hardware and software used

[1073] The system consists of the following hardware and software:

[1074] Sensors (temperature sensor, humidity sensor, light sensor, soil moisture sensor)

[1075] Generative Artificial Intelligence (AI Model)

[1076] Server (server for data collection and analysis)

[1077] Device (user's smartphone, tablet, etc.)

[1078] Control equipment (air conditioning equipment, irrigation systems, lighting equipment)

[1079] Data collection and analysis

[1080] The server periodically collects environmental data about the plants through sensors. Specifically, it obtains data on temperature, humidity, illuminance, and soil moisture. For example, the temperature sensor collects data on 30°C, the humidity sensor collects data on 40%, the illuminance sensor collects data on 300 lux, and the soil moisture sensor collects data on 15%.

[1081] Data analysis

[1082] The server sends the collected data to the generative AI, which analyzes it. The generative AI evaluates the current data by comparing it with past learning data and proposes optimal growing conditions. For example, by comparing it with the learning model, it determines that "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable for illumination," and "30% soil moisture is appropriate."

[1083] Generate environmental adjustment proposals

[1084] Based on the analysis results, the server generates specific suggestions for adjusting the environment, such as operating the air conditioning to lower the temperature, automatically irrigating the area to increase humidity, adjusting the lighting, etc. These suggestions are returned to the server in JSON format.

[1085] User Notifications

[1086] The server sends suggested growing conditions to the user's device as a push notification, including specific examples such as "Lower the current temperature from 30°C to 25°C," "Increase the humidity from 40% to 60%, "Increase the illumination from 300 lux to 500 lux," and "Increase the soil moisture from 15% to 30%."

[1087] Authorization and execution of automated controls

[1088] The user checks the notification on their device and approves the automatic control. After receiving approval, the server sends instructions to various control devices. For example, instructions such as "adjust the temperature to 25°C" to the air conditioning unit, "sprinkle water to adjust the humidity to 60%" to the irrigation system, and "adjust the illuminance to 500 lux" to the lights are sent.

[1089] Get feedback

[1090] After the environmental adjustments are complete, the server collects data from the sensors again and analyzes the results using generative artificial intelligence. The analysis provides feedback on whether the growth conditions have improved. For example, it checks whether the adjusted temperature is 25°C, humidity is 60%, light intensity is 500 lux, and soil moisture is 30%.

[1091] Notification of adjustment results

[1092] The server notifies the terminal that the adjustment is complete and that the plant's growth condition has improved, and the terminal displays this to the user. For example, information such as "Adjustment complete. The plant's condition has improved" is displayed.

[1093] Prompt Sentence Examples

[1094] An example of a prompt for a generative AI model is as follows:

[1095] Analyze the following environmental data and suggest the optimum growing conditions for the plant: temperature 30°C, humidity 40%, light intensity 300 lux, soil moisture 15%.

[1096] The above is an embodiment of the present invention. This system provides an optimal growing environment for plants, and allows real-time data analysis and environmental adjustment, even if the user does not have specialized knowledge. Furthermore, optimization that reflects the user's intentions is realized, allowing for efficient and highly accurate environmental adjustment.

[1097] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1098] Step 1: Data collection

[1099] The server uses sensors to collect a plurality of environmental data related to the growth state of the plants.

[1100] Input: Data from sensors measuring temperature, humidity, light intensity, and soil moisture.

[1101] Output: A set of collected environmental data (e.g., temperature 30°C, humidity 40%, illuminance 300 lux, soil moisture 15%).

[1102] Specific operation: The server sends a polling request to each sensor and obtains data on temperature, humidity, illuminance, and soil moisture content from the sensors in response.

[1103] Step 2: Data analysis

[1104] The server sends the collected environmental data to the generative artificial intelligence for analysis.

[1105] Input: Collected environmental data (temperature 30°C, humidity 40%, illuminance 300 lux, soil moisture 15%).

[1106] Output: Analysis of optimal growing conditions (e.g. temperature 25°C, humidity 60%, light intensity 500 lux, soil moisture 30%).

[1107] Specific operation: The server sends environmental data to the generative AI's API and receives the results. The generative AI analyzes the results by comparing them with past learning models and determines the optimal growth conditions.

[1108] Step 3: Generate environmental adjustment proposals

[1109] The server generates optimal environmental adjustment proposals based on the analysis results of the generative artificial intelligence.

[1110] Input: Analysis results (temperature 25°C is appropriate, humidity 60% is ideal, illuminance 500 lux is desirable, soil moisture 30% is appropriate).

[1111] Output: Specific environmental adjustment suggestions (e.g., temperature adjustment, humidity adjustment, lighting adjustment).

[1112] Specific operation: Based on the analysis results, the server generates suggestions in JSON format, such as air conditioning to lower the temperature, irrigation to increase humidity, and adjusting lights to increase brightness.

[1113] Step 4: User Notification

[1114] The server sends the generated environmental adjustment proposal to the user's device as a push notification.

[1115] Input: Proposed environmental adjustments (reduce temperature from 30°C to 25°C, increase humidity from 40% to 60%, increase light intensity from 300 lux to 500 lux, increase soil moisture from 15% to 30%).

[1116] Output: A push notification sent to the user's device containing the specific adjustments.

[1117] Specific operation: The server uses the push notification service to send a notification to the user's smartphone.

[1118] Step 5: Accepting automation

[1119] The user checks the notification through the terminal and approves the automatic control.

[1120] Input: Push notification (environmental adjustment suggestion) received by the user.

[1121] Output: User approval (sent from the terminal).

[1122] Specific operation: The user taps the notification on their smartphone and presses the "Approve" button to approve the environmental adjustment.

[1123] Step 6: Execute control instructions

[1124] After receiving the user's approval, the server sends specific instructions to the various control devices.

[1125] Input: User approval and generated environmental adjustment suggestions.

[1126] Output: Specific instructions sent to a control device (adjust temperature, humidity, light).

[1127] Specific actions: The server sends specific instructions to the air conditioner, such as "adjust the temperature to 25°C," to the irrigation system, "adjust the humidity to 60%, and" to the lights, "adjust the illuminance to 500 lux."

[1128] Step 7: Check the results of the environment adjustment

[1129] After the environmental adjustment is complete, the server again collects and analyzes data from the sensors.

[1130] Input: Calibrated environmental data (latest data from sensors).

[1131] Output: Feedback result after adjustment (whether the environment improved or not).

[1132] What it does: The server sends another request to the sensor to collect the latest environmental data, which it then sends to the generative AI for reanalysis.

[1133] Step 8: Notification of adjustment results

[1134] The server notifies the user's terminal that the adjustment has been completed and that the plant's growth condition has improved, and the terminal displays this to the user.

[1135] Input: Adjusted feedback results.

[1136] Output: Notification to user device (adjustment completed, improvement notification).

[1137] Specific operation: The server sends a notification to the user's smartphone via a push notification service, such as "Adjustment completed. Plant condition has improved."

[1138] This concludes the explanation of the specific processing steps of the program. Each step works closely together to provide the optimal growing environment for plants in real time.

[1139] (Application example 1)

[1140] 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."

[1141] While current plant growth systems automate the adjustment of environmental conditions to maintain optimal plant growth, they have the problem of difficulty in responding in real time to security risks such as intruders and suspicious activity. This means that even if the plant growth environment is secured, overall safety cannot be ensured if physical security is threatened.

[1142] 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.

[1143] In this invention, the server includes means for collecting multiple pieces of environmental data and security data related to the growth status of plants using sensors, means for analyzing the environmental data and security data using generative artificial intelligence and proposing optimal growth conditions and vigilance situations, means for automatically adjusting the environment and security measures based on the proposed growth conditions and vigilance situations, and means for recollecting the adjusted environmental data and security data and providing feedback. This not only provides an optimal growth environment for plants, but also makes it possible to respond to security risks in real time.

[1144] A "sensor" is a device for collecting environmental data related to plant growth and security.

[1145] "Generative AI" is an AI technology that analyzes collected environmental and security data to suggest optimal growth conditions and vigilance situations.

[1146] "Environmental data" refers to data necessary for evaluating the growth status of plants, such as temperature, humidity, illuminance, and soil moisture.

[1147] "Crime prevention data" refers to data necessary for evaluating crime prevention measures, such as motion detection and vibration detection.

[1148] "Growth conditions" are the environmental conditions necessary to promote healthy plant growth.

[1149] An "alert situation" is a state of surveillance and vigilance that is considered optimal from a crime prevention perspective.

[1150] "Adjustment means" refers to means for automatically adjusting the environment and security posture based on proposed growth and alert conditions.

[1151] "Feedback" is the process of collecting environmental and security data again after adjustments have been made and reevaluating the results.

[1152] This invention relates to a system that monitors the growth status and security of plants in real time to provide optimal environmental and security conditions. The system operates by combining sensors, generative artificial intelligence, control devices, and feedback devices. Each major processing step of the system and the hardware and software used for each step are described below.

[1153] First, the server uses sensors to collect multiple environmental data related to plant growth and security. These sensors measure temperature, humidity, illuminance, soil moisture, and security data (motion detection, vibration detection, etc.). For example, the server collects data that the temperature sensor is 30°C, the humidity sensor is 40%, the motion detection sensor detects motion, and the vibration sensor does not detect vibration.

[1154] The server then sends the collected environmental and security data to the generative AI, which analyzes this data and evaluates the plant's current growth and alert status. For example, by comparing it with the learning model, it can determine that "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable for illumination," "30% soil moisture is appropriate," "turn on the security light to detect motion," and "do not sound the alarm because there is no vibration."

[1155] Based on the analysis, the server identifies optimal growing conditions and vigilance situations and generates specific recommendations for adjusting the environment and security posture, such as adjusting the air conditioning to lower the temperature or turning on security lights in response to motion detection. These recommendations are returned to the server in JSON format.

[1156] To notify the user of the proposed content, the server sends a push notification to the device (e.g., the user's smartphone). The notification content includes information such as "Lower the current temperature from 30°C to 25°C," "Increase the humidity from 40% to 60%," and "Turn on the security lights to detect motion."

[1157] The user checks the notification via their device and approves the automatic control. After approval is obtained, the server sends instructions to the various control devices. For example, it sends specific instructions such as "adjust the temperature to 25°C" to the air conditioner and "turn on the security light" to the security light.

[1158] After the environmental adjustments and security measures are complete, the server again collects data from the sensors and checks the results of the adjustments. This information is again analyzed by the generative AI to provide feedback on whether the growth and security conditions have improved.

[1159] Finally, the server notifies the terminal that the adjustment is complete and that the plant's growth and security status have improved, and the terminal displays this to the user. For example, the terminal displays information such as "Adjustment complete. Plant status and security status have improved."

[1160] As a concrete example, to maintain public order in a certain town, patrol staff wear smart glasses with this application installed. When the sensor detects an abnormality, the AI ​​automatically analyzes and evaluates it, and proposes and implements the necessary security measures. The sensor detects motion and suggests the optimal alarm to sound and security lights to turn on. After execution, the user receives a notification on the smart glasses display saying "Motion detected, alarm sounding."

[1161] Example prompt sentence:

[1162] plaintext

[1163] I would like you to suggest the best security measures for a situation where the temperature sensor indicates an outside temperature of 25°C, the humidity sensor indicates 50%, the motion detection sensor detects motion, and the vibration sensor does not detect vibration.

[1164] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1165] Step 1:

[1166] Input - The server collects data from sensors (temperature, humidity, light intensity, soil moisture, motion detection, vibration detection).

[1167] Data processing and calculation: The collected data is temporarily stored on the server, where it is checked for consistency and format converted.

[1168] Output - The consistent environmental and security data is sent to the generative AI.

[1169] The server collects environmental and security data from various installed sensors. For example, the temperature sensor may indicate a temperature of 30°C, and the motion sensor may indicate that a motion has been detected.

[1170] Step 2:

[1171] Input - Environmental data and security data sent from the server to the generative artificial intelligence.

[1172] Data processing, calculation, and generation AI uses trained models to analyze data and propose optimal growth conditions and vigilance situations.

[1173] The output-generative AI's suggestions (e.g., adjust the temperature to 25°C, turn on the security lights) are returned to the server in JSON format.

[1174] The server requests a generative artificial intelligence to analyze the transmitted data and propose optimal growth conditions and vigilance situations.

[1175] Step 3:

[1176] Suggestions returned from the input-generative AI.

[1177] Data processing and calculation - The server analyzes the proposal content and generates push notification data to notify the user.

[1178] Output - Notifications about suggested growing conditions and alert status are sent to the user's terminal.

[1179] Based on the suggestions received from the generative artificial intelligence, the server prepares notification content for the user's device and sends a push notification.

[1180] Step 4:

[1181] Input - Push notifications from the server (e.g. adjust the temperature to 25°C, turn on the security lights).

[1182] Data processing and calculation - The user checks the notification and decides whether to approve or reject it.

[1183] Output - The user's approval or denial is returned to the server.

[1184] The user checks the proposal through the terminal and accepts or rejects it. If the proposal is accepted, the result is sent to the server.

[1185] Step 5:

[1186] Input - Approval result from user.

[1187] Data processing and calculation - The server generates commands to operate various control devices (air conditioners, security lights, etc.) based on the approved proposal.

[1188] Output - The command is sent to the control device.

[1189] The server receives the approval result from the user, generates specific instructions, and sends them to the air conditioner, security lights, etc.

[1190] Step 6:

[1191] Inputs - Adjusted environmental and crime prevention data.

[1192] Data processing and calculation - The server collects data from the sensors again and requests the generative AI to reanalyze it.

[1193] Output - Reanalysis results are obtained and feedback on adjusted growth and vigilance status is generated.

[1194] After the environmental adjustments and security measures have been completed, the server will collect data from the sensors again and have the generative artificial intelligence reanalyze the growth and alert status.

[1195] Step 7:

[1196] Input - Reparse result.

[1197] Data processing and calculation - Based on the reanalysis results, the server generates data to provide final feedback to the user.

[1198] Output - A notification is sent to the user's device indicating the adjustment completion and the improvement results.

[1199] The server uses the results of the reanalysis to prepare final feedback and sends a notification to the user's device, for example, "Adjustments completed. Plant condition and security have improved."

[1200] As described above, the system is designed to function smoothly as a whole, with necessary data processing and calculations being performed based on input data at each step.

[1201] 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.

[1202] The present invention relates to a system that monitors the growth status of plants in real time and provides optimal environmental conditions taking into account the user's emotions. This system operates by combining sensors, generative artificial intelligence, an emotion engine, a control device, and a feedback device.

[1203] First, the server collects multiple environmental data related to the plant's growth status using sensors. These sensors measure temperature, humidity, illuminance, and soil moisture. For example, the temperature sensor collects data such as 30°C, humidity sensor collects data such as 40%, illuminance sensor collects data such as 300 lux, and soil moisture sensor collects data such as 15%.

[1204] The server then sends the collected data to a generative AI, which analyzes the data and evaluates the plant's current growth status. For example, it compares the data with a learning model and determines that "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable for illumination," and "30% soil moisture is appropriate."

[1205] Based on the analysis results, the server identifies optimal growing conditions and generates specific recommendations for environmental adjustments, such as adjusting the air conditioning to lower the temperature, automatically irrigating to increase humidity, or adjusting the lighting. These recommendations are returned to the server in JSON format.

[1206] Here, an emotion engine that recognizes the user's emotions analyzes the user's facial expressions and voice. The emotion engine uses image analysis and voice recognition technology to evaluate the user's emotional state, such as whether the user is feeling stressed or comfortable.

[1207] To notify the user of the proposed content, the server sends a push notification to the device (e.g., the user's smartphone). At this time, the notification is sent in a way that takes into consideration the user's emotions based on the analysis results of the emotion engine. For example, if the user is relaxed, the notification may be soft-spoken, such as "Lowering the current temperature from 30°C to 25°C will help your plants thrive," whereas if the user is in a hurry, the notification may be simple, such as "The temperature needs to be adjusted."

[1208] The user checks the notification via their device and approves the automatic control. At this time, the emotion engine analyzes the user's reaction again to ensure that approval is given without stress. When the user presses the approval button, the server sends instructions to various control devices. Specific instructions are sent to the air conditioning control device, such as "adjust the temperature to 25°C," to the irrigation system, "sprinkle water and adjust the humidity to 60%, and to the lights, "adjust the illuminance to 500 lux."

[1209] After the environmental adjustments are complete, the server collects data from the sensors again to confirm the results. This information is again analyzed by the generative AI to obtain feedback on whether the growth conditions have improved, and the server sends this information to the device.

[1210] The device notifies the user that the adjustment is complete and that the plant's growth has improved, for example by displaying a message such as "Adjustment complete. Plant condition has improved." Furthermore, the emotion engine re-evaluates the user's reaction to confirm whether the user is satisfied.

[1211] Through this series of steps, the system can automatically provide the optimal growing environment for plants while taking into consideration emotions, even if the user does not have specialized knowledge. Furthermore, optimization that reflects the user's intentions and emotions is carried out, achieving highly accurate environmental adjustment.

[1212] The processing flow will be explained below.

[1213] Step 1:

[1214] The server collects multiple environmental data related to the plant's growth status through sensors: a temperature sensor measures the current temperature (e.g., 30°C), a humidity sensor measures humidity (e.g., 40%), an illuminance sensor measures light intensity (e.g., 300 lux), and a soil moisture sensor measures soil humidity (e.g., 15%).

[1215] Step 2:

[1216] The server sends the collected data to the generative AI via an API, requesting analysis and optimization. For example, it sends a POST request to the " / ai / analyze" endpoint.

[1217] Step 3:

[1218] The generative AI analyzes the received data. Specifically, it compares it with the learning model within the generative AI and calculates the temperature, humidity, illuminance, and soil moisture content that are optimal for plant growth. This results in judgments such as "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable illuminance," and "30% soil moisture is appropriate."

[1219] Step 4:

[1220] The generative AI returns the analysis results to the server in JSON format, for example, { "temperature": "reduce to 25℃", "humidity": "increase to 60%", "light": "increase to 500 lux", "soilMoisture": "increase to 30%"}.

[1221] Step 5:

[1222] The server receives the analysis results from the generative AI and activates the emotion engine to ascertain the user's emotional state. The emotion engine uses image and audio analysis to assess the user's current emotions and determine whether the user is stressed or relaxed.

[1223] Step 6:

[1224] The server adjusts the generative AI's suggestions based on the analysis results of the emotion engine. If the user is relaxed, the server will notify them in a gentle way, such as "Lowering the current temperature from 30°C to 25°C will help your plants thrive," but if the user is in a hurry, the server will notify them in a simpler way, such as "The temperature needs to be adjusted."

[1225] Step 7:

[1226] The server then sends the adjusted proposal to the device as a push notification. Specifically, the proposal is sent to the user's smartphone and displayed in a format that the user can check.

[1227] Step 8:

[1228] The device displays the received notification to the user. As the user checks the content of the notification, the emotion engine analyzes the user's reaction again to confirm whether the user is in a state where they can accept the notification without feeling stressed.

[1229] Step 9:

[1230] The user checks the notification and approves the automatic control via the device by pressing a button such as "Start automatic control."

[1231] Step 10:

[1232] With the user's approval, the server sends instructions to various control devices, such as to the air conditioning control device to "adjust the temperature to 25°C," to the irrigation system to "sprinkle water and adjust the humidity to 60%, and to the lights to "adjust the illuminance to 500 lux."

[1233] Step 11:

[1234] The server monitors the adjustment process and, once the adjustment is complete, collects data from the sensors again to confirm whether the temperature, humidity, light intensity, and soil moisture content have reached their target values.

[1235] Step 12:

[1236] The server checks the adjustment results and the improvement in the plant's condition, and sends the information to the terminal. The server notifies the user by saying, "Adjustment complete. The plant's condition has improved."

[1237] Step 13:

[1238] The device will display the completion of the adjustment and the improvement of the growth status to the user, while the emotion engine will analyze the user's reaction to check whether the user is satisfied or stressed.

[1239] This series of steps not only automatically provides the optimal growing environment for plants, but also creates a system that takes into account the user's emotional state.

[1240] Example 2

[1241] 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."

[1242] Conventional plant growth management systems were able to suggest growth conditions by collecting and analyzing environmental data, but it was difficult to adjust the environment optimally while taking the user's emotions into consideration. Furthermore, because the notification of growth condition suggestions was made without regard for the user's situation or emotions, it was difficult for the user to accept the suggestions, making it difficult to maintain an optimal growth environment for plants.

[1243] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting multiple pieces of environmental data related to the growth state of plants using sensors, means for analyzing the environmental data using generative artificial intelligence and proposing optimal growth conditions, means for automatically adjusting the environment based on the proposed growth conditions, means for re-collecting the adjusted environmental data and providing feedback, means for evaluating the user's emotions using an emotion engine that analyzes the user's emotional state, and means for determining an appropriate notification method based on the evaluated user emotions. This makes it possible to provide an optimal plant growth environment that takes the user's emotions into consideration.

[1244] A "sensor" is a measuring device for collecting environmental data.

[1245] "Plant growth status" refers to various parameters that indicate the growth and health status of a plant.

[1246] "Environmental data" refers to data related to plant growth conditions such as temperature, humidity, illuminance, and soil moisture content.

[1247] "Generative artificial intelligence" refers to machine learning models and algorithms that analyze collected environmental data and suggest optimal growing conditions.

[1248] "Optimal growing conditions" are ideal environmental parameters for promoting plant growth and health.

[1249] The "means for automatically adjusting the environment" is a system that automatically controls air conditioning, irrigation systems, lighting, etc. based on collected data and the analysis results of generative artificial intelligence.

[1250] The "means of providing feedback" is a function that collects data again after the environmental adjustment, analyzes the results, and evaluates the effectiveness of the adjustment.

[1251] An "emotion engine" is a technology for analyzing and evaluating a user's emotional state based on facial expressions, voice, etc.

[1252] "User's emotions" refer to the psychological state, such as stress or relaxation, that the user feels.

[1253] An "appropriate notification method" is a method of notifying the user in an appropriate manner and at an appropriate timing based on the user's emotional state.

[1254] The present invention relates to a system that monitors the growth status of plants in real time and provides optimal environmental conditions taking into account the user's emotions. This system operates by combining sensors, generative artificial intelligence, an emotion engine, a control device, and a feedback device.

[1255] First, the server collects multiple environmental data related to the plant's growth status using sensors. The sensors include a temperature sensor, humidity sensor, illuminance sensor, and soil moisture sensor. For example, the server collects data such as 30°C from the temperature sensor, 40% from the humidity sensor, 300 lux from the illuminance sensor, and 15% from the soil moisture sensor.

[1256] The server then sends the collected data to a generative AI, which analyzes the data and evaluates the plant's current growth status. For example, it compares the data with a learning model and determines that "25°C is the appropriate temperature," "60% humidity is ideal," "500 lux is desirable for illumination," and "30% soil moisture is appropriate."

[1257] Based on the analysis results, the server identifies optimal growing conditions and generates specific recommendations for environmental adjustments, such as adjusting the air conditioning to lower the temperature, automatically irrigating to increase humidity, or adjusting the lighting. These recommendations are returned to the server in JSON format.

[1258] Here, an emotion engine that recognizes the user's emotions analyzes the user's facial expressions and voice. The emotion engine uses image analysis and voice recognition technology to evaluate the user's emotional state, such as whether the user is feeling stressed or comfortable.

[1259] To notify the user of the proposed content, the server sends a push notification to the device (e.g., the user's smartphone). At this time, the notification is sent in a way that takes into consideration the user's emotions based on the analysis results of the emotion engine. For example, if the user is relaxed, the notification may be soft-spoken, such as "Lowering the current temperature from 30°C to 25°C will help your plants thrive," whereas if the user is in a hurry, the notification may be simple, such as "The temperature needs to be adjusted."

[1260] The user checks the notification via their device and approves the automatic control. At this time, the emotion engine analyzes the user's reaction again to ensure that approval is given without stress. When the user presses the approval button, the server sends instructions to various control devices. Specific instructions are sent to the air conditioning control device, such as "adjust the temperature to 25°C," to the irrigation system, "sprinkle water and adjust the humidity to 60%, and to the lights, "adjust the illuminance to 500 lux."

[1261] After the environmental adjustments are complete, the server collects data from the sensors again to confirm the results. This information is again analyzed by the generative AI to obtain feedback on whether the growth conditions have improved, and the server sends this information to the device.

[1262] The device notifies the user that the adjustment is complete and that the plant's growth condition has improved, displaying a message such as "Adjustment complete. Plant condition has improved." The emotion engine then reevaluates the user's response to confirm whether the user is satisfied. This series of steps enables the system to automatically and emotionally provide the optimal growth environment for plants, even without the user's specialized knowledge.

[1263] Specific examples

[1264] 1. Collect data from the temperature sensor at 30°C, the humidity sensor at 40%, the illuminance sensor at 300 lux, and the soil moisture sensor at 15%.

[1265] 2. The generative artificial intelligence analyzes this data and determines that "25°C is the appropriate temperature," "60% is ideal humidity," "500 lux is desirable illumination," and "30% is the appropriate soil moisture."

[1266] 3. The server generates suggestions such as lowering the temperature to 25°C, increasing the humidity to 60%, and adjusting the light intensity to 500 lux.

[1267] 4. The emotion engine analyzes that the user is relaxed.

[1268] 5. The server notifies the device in gentle terms that "Lowering the current temperature from 30°C to 25°C will help the plants thrive."

[1269] 6. The user checks the notification and presses the approval button.

[1270] 7. The server sends instructions to the air conditioning control unit to "adjust the temperature to 25°C," the irrigation system to "adjust the humidity to 60%," and the lights to "adjust the illuminance to 500 lux."

[1271] Prompt Sentence Examples

[1272] "You have collected data: a temperature sensor reading 30°C, a humidity sensor reading 40%, a light sensor reading 300 lux, and a soil moisture sensor reading 15%. Describe the steps you would take to have a generative AI analyze this data and identify the optimal conditions for plant growth."

[1273] The above is a specific embodiment of this system.

[1274] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1275] Step 1:

[1276] The server uses sensors to collect multiple environmental data related to the plant's growth status. Specifically, it acquires data from a temperature sensor, humidity sensor, illuminance sensor, and soil moisture sensor. For example, the temperature sensor collects data such as 30°C, humidity sensor collects data such as 40%, illuminance sensor collects data such as 300 lux, and soil moisture sensor collects data such as 15%. This data becomes input data for subsequent analysis.

[1277] Step 2:

[1278] The server sends the collected environmental data to the generative AI, which then takes the data as input into the analysis system. The generative AI then evaluates the current growth status of the plant based on this input data. For example, it outputs optimal growth conditions such as "the appropriate temperature is 25°C," "the ideal humidity is 60%," "the desired illumination level is 500 lux," and "the appropriate soil moisture level is 30%."

[1279] Step 3:

[1280] The server generates specific proposals for environmental adjustments based on the optimal growing conditions output by the generative AI. These proposals include operating the air conditioning to lower the temperature, automatic irrigation to increase humidity, and adjusting the lighting. For example, suggestions such as "adjust the temperature to 25°C," "adjust the humidity to 60%," and "adjust the lighting to 500 lux" are output in JSON format.

[1281] Step 4:

[1282] The emotion engine analyzes emotions from the user's facial expressions and voice. Camera footage and microphone audio are used as input data. The emotion engine analyzes this data and evaluates the user's emotional state, such as whether they are feeling stressed or comfortable. The results of this evaluation become input data for determining how to notify the user of the proposed content.

[1283] Step 5:

[1284] The server notifies the user of specific suggestions based on the user's emotional data obtained from the emotion engine. The content of the notification and the way it is expressed are determined taking into consideration the user's emotional state. For example, if the user is relaxed, the server may notify them in a gentle manner, such as "Lowering the current temperature from 30°C to 25°C will help your plants thrive," while if the user is in a hurry, the server may notify them in a simpler manner, such as "The temperature needs to be adjusted."

[1285] Step 6:

[1286] The user checks the notification via their device and approves the automatic control. When the user presses the approval button on the device where the notification is displayed, this approval information is sent to the server. The emotion engine then analyzes the user's reaction again to confirm whether approval was given without stress.

[1287] Step 7:

[1288] With the user's approval, the server sends specific instructions to various control devices. For example, it sends instructions such as "adjust the temperature to 25°C" to the air conditioning control device, "adjust the humidity to 60%" to the irrigation system, and "adjust the illuminance to 500 lux" to the light. This automatically adjusts the physical environment.

[1289] Step 8:

[1290] After the environmental adjustment is complete, the server collects environmental data from the sensors again. The data collected as the adjustment results is input into the generative AI and re-evaluated. This confirms, for example, that the adjusted temperature is 25°C, humidity is 60%, illuminance is 500 lux, and soil moisture is 30%.

[1291] Step 9:

[1292] The server sends the adjustment results to the device and notifies the user that the adjustment is complete and that the plant's growth has improved. The device displays a message such as "Adjustment complete. The plant's condition has improved." The emotion engine again evaluates the user's reaction and checks whether the user is satisfied.

[1293] (Application example 2)

[1294] 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."

[1295] The present invention aims to provide a system that not only optimizes the growing environment of plants but also notifies users and adjusts the environment taking into account their emotions. In particular, the objective is to improve product quality and work efficiency by optimizing the working environment and taking into account the emotional state of workers in quality control on production lines.

[1296] 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.

[1297] In this invention, the server includes means for collecting multiple pieces of environmental data related to the growth status of plants using sensors, means for analyzing the environmental data using generative artificial intelligence and proposing optimal growth conditions, means for automatically adjusting the environment based on the proposed growth conditions, means for recollecting the adjusted environmental data and providing feedback, means for recognizing the user's emotions and generating a notification message according to the emotions, and means for reevaluating the user's emotional state and confirming that the user is not stressed if the user approves. This makes it possible to optimize the plant growth environment and the work environment of the production line, and to adjust the environment taking into account the emotional states of users and workers.

[1298] A "sensor" is a device that measures and collects environmental data such as temperature, humidity, light intensity, and soil moisture content in real time.

[1299] "Generative AI" is an AI technology that analyzes collected data and proposes optimal conditions for plant growth and working environments.

[1300] "Environmental adjustment" refers to automatically operating various devices such as air conditioning, irrigation systems, and lighting based on the optimal conditions proposed by generative AI, to adjust the growing conditions for plants and the working environment of the production line.

[1301] "Feedback" is the process of collecting data from sensors again after environmental adjustments have been made, and using the results to evaluate growth and environmental conditions.

[1302] The "emotion engine" is a system that analyzes the user's facial expressions and voice to evaluate the user's emotional state and reflects this in the content of notifications and suggestions for environmental adjustments.

[1303] A "notification message" is a message that notifies the user of suggested growing conditions or the need for environmental adjustments, and its expression is adjusted according to the user's emotional state.

[1304] "Acceptance" refers to the user's explicit acceptance of the proposed growing conditions and environmental adjustments.

[1305] "Stress assessment" is the process by which the emotion engine re-analyzes the user's emotional state to see if the user is feeling stressed.

[1306] This invention is a system that monitors the growing environment of plants in real time and provides optimal environmental conditions taking into account the user's emotions. This system can also be applied to quality control on factory production lines.

[1307] This system mainly uses the following hardware and software:

[1308] Sensors: Used to measure temperature, humidity, light intensity, and soil moisture. For example, temperature sensors, humidity sensors, and light intensity sensors are used on production lines in factories.

[1309] Generative AI: Refers to artificial intelligence technology that analyzes collected data and suggests optimal conditions for plants or products.

[1310] Emotion engine: Evaluates the user's emotions using image analysis and voice recognition technology.

[1311] Server: Collects data from the aforementioned sensors and analyzes it using generative artificial intelligence and an emotion engine to identify optimal environmental conditions.

[1312] Terminal: A device such as a smartphone or smart glasses that is used to send notifications to the user and receive user approval.

[1313] To explain how the system works in detail, the server first uses sensors to collect environmental data. For example, assume that the temperature on the production line is 28°C, humidity is 35%, and illuminance is 400 lux. This data is sent to the server, where generative AI analyzes the data and identifies the ideal environmental conditions. For example, it determines that the ideal temperature is 25°C, humidity is 40%, and illuminance is 500 lux.

[1314] The emotion engine then analyzes the user's facial expressions and voice to recognize their emotions. For example, if the server determines that the user is relaxed, it will send a soft message to the device, such as, "Lowering the current temperature from 28°C to 25°C will improve product quality." Conversely, if the user is in a hurry, it will send a simple message, such as, "The temperature needs to be adjusted."

[1315] The user checks the notification via their device and approves the proposal. After approval, the server again uses the emotion engine to evaluate the user's emotional state and confirm that they are not stressed. The server then sends specific instructions to various control devices to adjust the temperature, humidity, and light level.

[1316] After the environmental adjustment is complete, data is collected again from the sensors and analyzed by the generative artificial intelligence. Feedback is obtained on whether the adjustment results have improved, and the server sends this information to the device. For example, a message such as "Adjustment complete. The environment has been optimized and product quality has improved" is displayed.

[1317] Through this series of operations, the system can automatically provide the optimal growing and manufacturing environment for plants and products, even without the user's specialized knowledge. It also performs optimization that reflects the user's intentions and emotions, achieving highly accurate environmental adjustment.

[1318] Here are some illustrative prompts:

[1319] "Temperature sensor: 28°C, humidity sensor: 35%, illuminance sensor: 400 lux. Evaluate the difference from ideal environmental conditions (temperature 25°C, humidity 40%, illuminance 500 lux) and create adjustment suggestions. Additionally, generate a notification message when the user's emotions are relaxed."

[1320] Using this example, the detailed operation of the system can be clearly understood.

[1321] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1322] Step 1:

[1323] The server uses sensors to collect environmental data. Specifically, it obtains data in real time from temperature, humidity, and illuminance sensors. For example, the sensors can provide data such as "temperature 28°C, humidity 35%, illuminance 400 lux." The input is the raw data from each sensor, and the output is this environmental data.

[1324] Step 2:

[1325] The server sends the collected environmental data to the generative AI, which analyzes this data and identifies the optimal environmental conditions for plants and production lines. For example, it suggests conditions such as "a temperature of 25°C, humidity of 40%, and illuminance of 500 lux are desirable." The input is the collected environmental data, and the output is a proposal for the optimal environmental conditions.

[1326] Step 3:

[1327] The server uses an emotion engine to generate a notification message that takes the user's emotions into account based on the optimal environmental conditions proposed by the generative artificial intelligence. The emotion engine analyzes the user's facial expressions and voice to evaluate the user's emotional state. For example, if the user is relaxed, it generates a soft notification message saying, "Lowering the current temperature from 28°C to 25°C will improve product quality." The input is the proposed environmental conditions and the user's emotional data, and the output is the notification message.

[1328] Step 4:

[1329] The server sends a notification message to the user's device. The device displays the notification message to the user, and the user approves the environmental adjustment based on the suggestion. For example, a notification saying "Lowering the temperature to 25°C will improve product quality" appears on a smartphone, and the user presses the "Approve" button. The input is the notification message, and the output is the user's approval.

[1330] Step 5:

[1331] After receiving the user's approval, the server uses the emotion engine to evaluate the user's emotional state again and confirm that they are not stressed. For example, it checks whether the user remains relaxed after approval. The input is the user's approval and emotion data, and the output is the result of the stress assessment.

[1332] Step 6:

[1333] After the server confirms that there is no stress, it sends specific instructions to the various control devices to adjust the environment. For example, it sends instructions such as "adjust the temperature to 25°C" to the temperature control device and "adjust the illuminance to 500 lux" to the illuminance control device. The input is the optimal environmental conditions, and the output is the specific control instructions.

[1334] Step 7:

[1335] After the environmental adjustments are made, the server collects environmental data from the sensors again. For example, it checks whether the temperature is 25°C, the humidity is 40%, and the illuminance is 500 lux. The input is the sensor data after the environmental adjustments, and the output is the adjusted environmental data.

[1336] Step 8:

[1337] The server then sends the collected environmental data to the generative AI, which analyzes whether the growth conditions and quality have improved. For example, it receives feedback such as, "Optimal conditions are being maintained at a temperature of 25°C." The input is the adjusted environmental data, and the output is the feedback information.

[1338] Step 9:

[1339] The server sends feedback information to the user's device and notifies them of the adjustment results. For example, a message such as "Adjustment complete. Product quality has improved" is displayed on a smartphone. The input is feedback information, and the output is a notification to the user.

[1340] Through these steps, the invention can automatically provide the optimal growing and manufacturing environment for plants and products, even without the user's specialized knowledge. It also performs optimization that reflects the user's intentions and emotions, achieving highly accurate environmental adjustment.

[1341] 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.

[1342] 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.

[1343] 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.

[1344] 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.

[1345] FIG. 9 illustrates 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 behaviors 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.

[1346] 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.

[1347] 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).

[1348] 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.

[1349] 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."

[1350] 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.

[1351] 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).

[1352] 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.

[1353] 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.

[1354] 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.

[1355] 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.

[1356] 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.

[1357] 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.

[1358] 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.

[1359] 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.

[1360] 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.

[1361] 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.

[1362] The following is further disclosed regarding the above embodiment.

[1363] (Claim 1)

[1364] A means for collecting a plurality of environmental data related to the growth state of the plant using a sensor;

[1365] means for analyzing the environmental data using generative artificial intelligence and proposing optimal growing conditions;

[1366] means for automatically adjusting the environment based on the proposed growing conditions;

[1367] a means for recollecting and providing feedback on the adjusted environmental data;

[1368] A system including:

[1369] (Claim 2)

[1370] 10. The system of claim 1, wherein the system notifies the user of the proposed growing conditions and adjusts the environment after receiving the user's approval.

[1371] (Claim 3)

[1372] The system according to claim 1, wherein the collected environmental data is transmitted to a generative artificial intelligence, and an optimization algorithm is applied based on the analysis results to adjust the growth conditions.

[1373] "Example 1"

[1374] (Claim 1)

[1375] A means for collecting a plurality of environmental data related to the growth state of the plant using a sensor;

[1376] means for analyzing the environmental data using generative artificial intelligence and proposing optimal growing conditions;

[1377] means for sending a push notification to a user's device based on the proposed growing conditions and receiving approval from the user;

[1378] means for sending an instruction to a control device for adjusting the environment after the user's approval, and automatically adjusting the environment based on the instruction;

[1379] a means for collecting the adjusted environmental data again, analyzing it using generative artificial intelligence, and providing feedback;

[1380] A system including:

[1381] (Claim 2)

[1382] 2. The system of claim 1, wherein the system notifies the user of the proposed growing conditions and adjusts the environment after receiving approval via the user's terminal.

[1383] (Claim 3)

[1384] The system according to claim 1, wherein the collected environmental data is transmitted to a generative artificial intelligence, and an optimization algorithm is applied based on the analysis results to adjust the growth conditions.

[1385] "Application Example 1"

[1386] (Claim 1)

[1387] A means for collecting a plurality of environmental data related to the growth state of the plant using a sensor;

[1388] means for analyzing the environmental data using generative artificial intelligence and proposing optimal growing conditions;

[1389] A means of analyzing data collected from security sensors and proposing optimal security situations,

[1390] means for automatically adjusting the environment and security posture based on the suggested growing conditions and alert conditions;

[1391] A means for collecting and feeding back the adjusted environmental data and crime prevention data again;

[1392] A system including:

[1393] (Claim 2)

[1394] 10. The system of claim 1, wherein the system notifies the user of suggested growing conditions and alert status and adjusts the environment and security posture after receiving user approval.

[1395] (Claim 3)

[1396] The system of claim 1 transmits the collected environmental data and security data to a generative artificial intelligence, and applies an optimization algorithm based on the analysis results to adjust the growth conditions and alert status.

[1397] "Example 2: Combining Emotion Engines"

[1398] (Claim 1)

[1399] A means for collecting a plurality of environmental data related to the growth state of the plant using a sensor;

[1400] means for analyzing the environmental data using generative artificial intelligence and proposing optimal growing conditions;

[1401] means for automatically adjusting the environment based on the proposed growing conditions;

[1402] a means for recollecting and providing feedback on the adjusted environmental data;

[1403] means for assessing the user's emotions using an emotion engine that analyzes the user's emotional state;

[1404] A means for determining an appropriate notification method based on the evaluated user sentiment;

[1405] A system including:

[1406] (Claim 2)

[1407] 10. The system of claim 1, wherein the system notifies the user of the proposed growing conditions and adjusts the environment after receiving the user's approval.

[1408] (Claim 3)

[1409] The system according to claim 1, wherein the collected environmental data is transmitted to a generative artificial intelligence, and an optimization algorithm is applied based on the analysis results to adjust the growth conditions.

[1410] "Application example 2 when combining emotion engines"

[1411] (Claim 1)

[1412] A means for collecting a plurality of environmental data related to the growth state of the plant using a sensor;

[1413] means for analyzing the environmental data using generative artificial intelligence and proposing optimal growing conditions;

[1414] means for automatically adjusting the environment based on the proposed growing conditions;

[1415] a means for recollecting and providing feedback on the adjusted environmental data;

[1416] means for recognizing a user's emotion and generating a notification message according to the emotion;

[1417] If the user approves, means for reassessing the user's emotional...

Claims

1. A means for collecting a plurality of environmental data related to the growth state of the plant using a sensor; means for analyzing the environmental data using generative artificial intelligence and proposing optimal growing conditions; means for automatically adjusting the environment based on the proposed growing conditions; a means for recollecting and providing feedback on the adjusted environmental data; A system including:

2. 10. The system of claim 1, wherein the system notifies the user of the proposed growing conditions and adjusts the environment after receiving the user's approval.

3. 2. The system according to claim 1, wherein the collected environmental data is transmitted to a generative artificial intelligence, and an optimization algorithm is applied based on the analysis results to adjust the growth conditions.

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