System

Abstract

A system is provided.SOLUTION: A system, comprising: means for receiving plant type, current temperature, current humidity, and growth stage information input from a user; means for obtaining an optimal temperature range and an optimal humidity range from a database based on the plant type; means for evaluating whether the current temperature is within the optimal temperature range and the current humidity is within the optimal humidity range; means for predicting whether current conditions are optimal using an artificial intelligence model; and means for determining and presenting a recommended action to the user based on the evaluation and prediction.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 traditional plant cultivation, many farmers and gardening enthusiasts spend a lot of time and effort maintaining optimal environmental conditions for plants. It is also difficult to understand the impact of changes in environmental conditions on plant growth and health in real time and take appropriate measures. As a result, problems arise, such as reduced plant productivity and an increased risk of disease outbreaks. [Means for solving the problem]

[0005] The present invention provides a system that receives information on the type of plant, current temperature, current humidity, and growth stage input by a user, and retrieves optimal temperature and humidity ranges from a database based on the information. It also includes a system that evaluates whether the current temperature and humidity are within the optimal temperature and humidity ranges and predicts whether the current conditions are optimal using an artificial intelligence model. The system also includes a system that determines and presents recommended actions to the user based on the evaluation and prediction, thereby improving the efficiency and accuracy of plant cultivation. This system allows users to receive optimal plant cultivation guidance in real time, allowing them to manage environmental conditions more efficiently.

[0006] The "means for receiving information on the type of plant, current temperature, current humidity and growth stage input by the user" refers to an interface that allows the user to input information on the type of plant, current environmental conditions and growth status into the system.

[0007] The "means for obtaining the optimum temperature range and optimum humidity range from the database based on the type of plant" is a mechanism for searching and obtaining the optimum environmental conditions for the plant from the database according to the input type of plant.

[0008] The "means for evaluating whether the current temperature is within the optimal temperature range and the current humidity is within the optimal humidity range" is a function for determining whether the current temperature and humidity provided by the user are within the ideal range for the plant.

[0009] "Means for predicting whether current conditions are optimal using an artificial intelligence model" refers to a system that uses an AI algorithm to analyze and predict whether current environmental conditions are optimal for plant growth.

[0010] "Means for determining recommended actions based on evaluation and prediction and presenting them to the user" refers to a method for determining optimal countermeasures and actions based on the evaluation of environmental conditions and the prediction results of AI, and notifying the user of them.

[0011] The "system" refers to the overall technological infrastructure that encompasses all of the above means and operates in coordination to provide the information and action guidance necessary for plant growth. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0013] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0015] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0016] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0017] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0018] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0020] [First embodiment]

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

[0022] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0025] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0029] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0032] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0033] This invention relates to a system that allows users to check the optimal growing conditions for a plant in real time by inputting information on the plant's type, current temperature, current humidity, and growth stage. In this system, a server uses a database and an artificial intelligence model to evaluate the current environmental conditions and provide optimal growing guidance.

[0034] Basic system configuration

[0035] User Input

[0036] The user uses the input form on the terminal screen to input the type of plant, the current temperature, the current humidity, and the growth stage of the plant. For example, the user inputs into the terminal that the type of plant is "tomato," the current temperature is 22°C, the current humidity is 60%, and the growth stage is "flowering stage."

[0037] Data transmission

[0038] The terminal transmits the data entered by the user to the server, properly formatting the data so that it can be uniquely identified by the server.

[0039] Database Reference

[0040] Based on the received data, the server retrieves the optimal environmental conditions (temperature range and humidity range) for the corresponding plant from the database. For example, it may retrieve from the database that the optimal temperature for tomatoes is 20 to 25°C and the optimal humidity is 45 to 70%.

[0041] Environmental Condition Assessment

[0042] The server evaluates whether the current temperature and humidity are within the optimum range, and the evaluation results are organized in a format such as "optimum temperature" or "optimum humidity."

[0043] Prediction by AI model

[0044] The server uses an artificial intelligence model (e.g., random forest) to predict whether the current environmental conditions are optimal, given the current temperature and humidity inputs from the user.

[0045] Determining the recommended action

[0046] The server determines the recommended action to present to the user based on the results of the environmental condition evaluation and the prediction results of the AI ​​model. For example, specific action guidance such as "The current conditions are optimal" or "Please lower the temperature" is determined.

[0047] Guidance display

[0048] The device displays the recommended actions sent from the server to the user, who can then check the appropriate training guidance on the device screen.

[0049] Specific examples

[0050] For example, consider the case where a user inputs information into the terminal about "tomato," the current temperature of 22°C, the current humidity of 60%, and the growth stage of "flowering."

[0051] 1. Sending input data

[0052] The user enters information into the terminal and clicks the "Submit" button. The terminal sends the input data to the server.

[0053] 2. Database lookup and environmental condition evaluation

[0054] The server receives the transmitted data and retrieves the optimal temperature range (20-25°C) and humidity range (45-70%) for tomatoes from the database. The server then evaluates whether the current temperature of 22°C and humidity of 60% are within the range. As both are within the range, the server evaluates them as "optimal temperature" and "optimal humidity."

[0055] 3. AI models make predictions and recommend actions

[0056] The server inputs the current temperature and humidity into the AI ​​model to make a prediction. The model predicts that the current conditions are optimal and determines the recommended action: "Current conditions are optimal."

[0057] 4. Guidance display

[0058] The terminal receives the guidance sent from the server, saying "Current conditions are optimal," and displays it to the user. The user can check the guidance on the terminal screen and take appropriate measures to grow the plant.

[0059] In this way, the system of the present invention provides users with an effective means of solving plant growth challenges in real time and optimizing plant productivity and health.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] The user inputs information into the terminal. Using the terminal's input form, the user inputs the plant type (e.g., tomato), the current temperature (e.g., 22°C), the current humidity (e.g., 60%), and the growth stage (e.g., flowering stage).

[0063] Step 2:

[0064] The terminal transmits the input information to the server, which then formats the information appropriately and transmits it to the server. The transmitted data includes the plant type, current temperature, current humidity, and growth stage information.

[0065] Step 3:

[0066] The server receives the information sent by the user and verifies that the entered data is in the correct format.

[0067] Step 4:

[0068] The server retrieves the optimal environmental conditions for a plant from the database. Based on the type of plant, the server retrieves the optimal temperature and humidity ranges from the database. For example, the server retrieves that the optimal temperature range for tomatoes is 20-25°C and the optimal humidity range is 45-70%.

[0069] Step 5:

[0070] The server evaluates the current temperature and humidity. The server determines whether the current temperature (22°C) and humidity (60%) received from the user are within the optimal temperature and humidity ranges. As a result, the server evaluates the temperature as "suitable" and the humidity as "suitable."

[0071] Step 6:

[0072] The server uses an artificial intelligence model to predict the optimal action. The server inputs the current temperature and humidity data into an artificial intelligence model (e.g., a random forest classifier) ​​to predict whether the current environmental conditions are optimal.

[0073] Step 7:

[0074] The server determines the recommended action. The server determines the recommended action based on the prediction results of the artificial intelligence model and the evaluation results of the environmental conditions. For example, the server determines the recommended action as "The current conditions are optimal."

[0075] Step 8:

[0076] The server sends the recommended action to the terminal. The server sends the determined recommended action to the terminal. The sent data includes the temperature status, humidity status, and recommended action.

[0077] Step 9:

[0078] The terminal displays the recommended action to the user. The terminal displays the recommended action received from the server to the user. For example, a message saying "Current conditions are optimal" is displayed on the user's terminal screen. As a result, the user can take appropriate measures to grow the plant.

[0079] Example 1

[0080] 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."

[0081] In conventional plant cultivation systems, it was difficult for users to manually evaluate the plant's type, growth stage, and current environmental conditions to determine the optimal cultivation conditions. Furthermore, there was a high possibility of errors occurring during the manual data evaluation process, and optimal cultivation conditions were not always guaranteed. Furthermore, it was difficult to quickly respond to changes in environmental conditions in real time. Given these issues, there was a need for a system that would allow users to easily check the optimal cultivation conditions for plants and quickly take appropriate cultivation actions.

[0082] 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.

[0083] In this invention, the server includes means for receiving information on the type of plant, current temperature, current humidity, and growth stage input by a user, means for retrieving an optimal temperature range and an optimal humidity range from a database based on the type of plant, means for evaluating whether the current temperature and current humidity are within the optimal temperature and humidity ranges, means for predicting whether the current conditions are optimal using an artificial intelligence model, means for determining a recommended action based on the evaluation and prediction and presenting it to the user, means for appropriately formatting the input from the user and transmitting it to the server, and means for displaying the recommended action transmitted from the server to the user. This enables a user to check the optimal growing conditions for a plant in real time and quickly take appropriate growing action.

[0084] A "user" is an individual or entity that provides plant-growing information and receives recommended actions from the system.

[0085] An "input form" is a screen element provided on a terminal for a user to input information about the type of plant, the current temperature, the current humidity, and the growth stage.

[0086] A "terminal" is an electronic device that a user uses to enter information and communicate with a server.

[0087] "Type of plant" is information indicating the classification of a particular plant that the user is cultivating.

[0088] "Growth stage" is information indicating the developmental phase in which the plant is currently located.

[0089] A "database" is a system for storing and managing information such as optimal environmental conditions for plants.

[0090] The "optimum temperature range" is information indicating the temperature range that is most suitable for a particular plant.

[0091] The "optimum humidity range" is information indicating the humidity range that is most suitable for a particular plant.

[0092] "Evaluation" is the process of determining whether the current temperature is within the optimum temperature range and whether the current humidity is within the optimum humidity range.

[0093] An "artificial intelligence model" is a model that uses machine learning algorithms to analyze data and make predictions and judgments.

[0094] "Recommended actions" are specific measures for development that are presented to the user based on the evaluation and prediction.

[0095] "Formatting" is the process of properly shaping data so that it can be properly understood and processed by the server.

[0096] "Send" is the process of transferring data entered by the user from the terminal to the server.

[0097] "Display" is the process of showing the recommended actions received from the server to the user.

[0098] This invention relates to a system that allows users to check the optimal growing conditions for a plant in real time by inputting information on the plant's type, current temperature, current humidity, and growth stage. In this system, a server uses a database and an artificial intelligence model to evaluate the current environmental conditions and provide optimal growing guidance.

[0099] User Input

[0100] The user uses an input form on the terminal screen to input the type of plant, the current temperature, the current humidity, and the growth stage of the plant. For example, the user inputs into the terminal that the plant type is "tomato," the current temperature is 22°C, the current humidity is 60%, and the growth stage is "flowering stage." This information is entered manually by the user and is in a format that can be modified as needed.

[0101] Data transmission

[0102] The terminal sends the data entered by the user to the server, properly formatting it so that it can be uniquely identified by the server. Data formats such as JSON and XML are commonly used, but other formats can also be applied depending on the system requirements.

[0103] Database Reference

[0104] Based on the received data, the server retrieves the optimal environmental conditions (temperature and humidity ranges) for the corresponding plant from the database. For example, it may learn from the database that the optimal temperature for tomatoes is 20-25°C and the optimal humidity is 45-70%. This database holds detailed growing information for each type of plant and is designed to allow the server to access it quickly.

[0105] Environmental Condition Assessment

[0106] The server evaluates whether the current temperature and humidity are within the optimal range. The evaluation results are organized as, for example, "optimal temperature" or "optimal humidity." The evaluation process is carried out by an algorithm that compares the current environmental data with the optimal conditions retrieved from a database.

[0107] Prediction by AI model

[0108] The server uses an artificial intelligence model (e.g., random forest) to predict whether the current environmental conditions are optimal. The current temperature and humidity are input into the model, which incorporates learning data based on past data and environmental conditions, allowing it to make highly accurate predictions.

[0109] Determining the recommended action

[0110] The server determines the recommended action to present to the user based on the results of the environmental condition evaluation and the prediction results of the AI ​​model. For example, specific action guidance such as "Current conditions are optimal" or "Please lower the temperature" is determined. This allows the user to quickly take appropriate cultivation measures.

[0111] Guidance display

[0112] The device displays the recommended actions sent from the server to the user, who can then check the appropriate training guidance on the device screen. While various display formats are possible, such as text messages or graphical interfaces, a format that is intuitively easy for users to understand is desirable.

[0113] Specific examples

[0114] For example, consider the case where a user inputs information into the terminal about "tomato," the current temperature of 22°C, the current humidity of 60%, and the growth stage of "flowering."

[0115] 1. Sending input data

[0116] The user enters information into the terminal and clicks the "Submit" button. The terminal sends the input data to the server.

[0117] 2. Database lookup and environmental condition evaluation

[0118] The server receives the transmitted data and retrieves the optimal temperature range (20-25°C) and humidity range (45-70%) for tomatoes from the database. The server then evaluates whether the current temperature of 22°C and humidity of 60% are within the range. As both are within the range, the server evaluates them as "optimal temperature" and "optimal humidity."

[0119] 3. AI models make predictions and recommend actions

[0120] The server inputs the current temperature and humidity into the AI ​​model to make a prediction. The model predicts that the current conditions are optimal and determines the recommended action: "Current conditions are optimal."

[0121] 4. Guidance display

[0122] The terminal receives the guidance sent from the server, saying "Current conditions are optimal," and displays it to the user. The user can check the guidance on the terminal screen and take appropriate measures to grow the plant.

[0123] Prompt Sentence Examples

[0124] The following prompt would confirm the optimal growing conditions for a "tomato" in the "flowering" growth stage when the current temperature is 22°C and humidity is 60%:

[0125] What are the optimal growing conditions for tomatoes during the flowering stage? The current temperature is 22°C and humidity is 60%.

[0126] In this way, the system of the present invention provides users with an effective means of solving plant growth challenges in real time and optimizing plant productivity and health.

[0127] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0128] Step 1:

[0129] User Input

[0130] The user uses the input form on the terminal screen to input the plant type, current temperature, current humidity, and plant growth stage. Specifically, the user inputs the information corresponding to each field via a keyboard or touch screen. The input information is converted into the appropriate format by the system and prepared for the next step of processing.

[0131] Input: Plant type, current temperature, current humidity, plant growth stage

[0132] Output: The formatted input data

[0133] Step 2:

[0134] Data transmission

[0135] The terminal properly formats the data entered by the user and sends it to the server using an HTTP request, typically in JSON format, where it is parsed once it reaches the server.

[0136] Input: Formatted input data

[0137] Output: Data sent to the server

[0138] Step 3:

[0139] Database Reference

[0140] The server analyzes the received data and retrieves the optimal temperature and humidity ranges for the corresponding plant from a database, which contains pre-registered optimal conditions for each plant, using an algorithm that performs a database query.

[0141] Input: Data sent to the server (type of plant)

[0142] Output: Optimal temperature and humidity range

[0143] Step 4:

[0144] Environmental Condition Assessment

[0145] The server evaluates whether the current temperature and humidity are within the optimal range retrieved from the database. This evaluation process is handled by an algorithm, and the evaluation result is formatted as "optimum temperature" or "optimum humidity."

[0146] Input: Current temperature, Current humidity, Optimal temperature range, Optimal humidity range

[0147] Output: Environmental condition evaluation results (optimal temperature, optimal humidity)

[0148] Step 5:

[0149] Prediction by AI model

[0150] The server uses an artificial intelligence model (e.g., random forest) to predict whether the current environmental conditions are optimal or not. The model is fed with current environmental data entered by the user and provides predictions based on past learning data.

[0151] Input: Current temperature, current humidity, past learning data

[0152] Output: Prediction results from the AI ​​model

[0153] Step 6:

[0154] Determining the recommended action

[0155] The server determines the recommended actions to present to the user based on the evaluation of environmental conditions and the predictions of the AI ​​model, and this decision process may involve the use of a condition-based rules engine.

[0156] Input: Environmental condition evaluation results, AI model prediction results

[0157] Output: Recommended Action

[0158] Step 7:

[0159] Guidance display

[0160] The device displays the recommended actions sent from the server to the user, who can then take appropriate training actions. The display format is easy for the user to understand, such as a text message or graph.

[0161] Input: Recommended Action

[0162] Output: Guidance displayed on the terminal

[0163] This series of processes allows users to check the optimal growing conditions for their plants in real time and take appropriate action immediately.

[0164] (Application example 1)

[0165] 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."

[0166] Maintaining optimal weather conditions is essential for plant growth. However, conventional methods have made it difficult to grasp the optimal growth conditions, which differ for each plant, in real time and manage them appropriately. Maintaining optimal environmental conditions and providing appropriate growth guidance is particularly challenging when managing a large number of plants at once in a brick-and-mortar store. Furthermore, users must manually determine the appropriate growth conditions each time, requiring efficient and accurate management. The objective of the present invention is to solve these problems and optimize plant growth and efficiency.

[0167] 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.

[0168] In this invention, the server includes means for receiving information on the plant type, current temperature, current humidity, and growth stage input by a user; means for retrieving optimal temperature and humidity ranges from a database based on the plant type; and means for evaluating whether the current temperature and humidity are within the optimal temperature and humidity ranges. This makes it possible to provide a user interface for managing plant inventory and display in a physical store. Furthermore, the optimal growing conditions for a plant can be presented as real-time guidance based on the information input by the user, thereby achieving effective plant growth. Furthermore, the server uses an artificial intelligence model to predict whether the current conditions are optimal, and determines and presents recommended actions to the user, thereby improving the efficiency and optimization of plant management.

[0169] "Plant type" refers to the taxonomic attributes and name of a particular plant, and is the basic data for identifying optimal growing conditions based on this information.

[0170] "Current temperature" refers to the temperature of the air surrounding the plant, and is an important variable for assessing the suitability of growing conditions.

[0171] "Current humidity" refers to the water vapor content in the environment in which the plant is located, and is an important factor in maintaining an environment suitable for growth.

[0172] "Growth stage" refers to the current developmental state of a plant in its life cycle and is a reference index for providing appropriate growing conditions.

[0173] "Database" refers to a system that stores information on the optimal temperature and humidity ranges for each type of plant and searches and retrieves it as needed.

[0174] "Artificial intelligence model" refers to a statistical model that uses machine learning algorithms to predict optimal development conditions and recommended actions from input data.

[0175] "User interface" refers to the screen and operating means through which the user inputs plant information and receives feedback from the system.

[0176] "Recommended actions" refer to specific courses of action the system suggests to the user based on current growing conditions and are necessary to optimize plant health.

[0177] The above definitions clarify each component of the invention and make it easier to understand its technical scope.

[0178] The present invention relates to a system that can check the optimal growing conditions for plants in real time. This system mainly consists of three components: a server, a terminal, and a user. The details are explained below.

[0179] 1. Basic system configuration

[0180] User Input

[0181] The user uses an input form on the device to input information about the plant type, current temperature, current humidity, and growth stage. Based on this information, the system determines the optimal growing conditions for the plant. For example, consider the case where a user inputs the plant type "rose," the current temperature is 25°C, the current humidity is 70%, and the growth stage is "pre-flowering."

[0182] Data transmission

[0183] The terminal transmits the data entered by the user to the server, properly formatting the data so that it can be uniquely identified by the server.

[0184] Database Reference

[0185] Based on the received data, the server retrieves the optimal temperature and humidity ranges for the corresponding plant from the database. For example, the optimal temperature for roses is 20-25°C, and the optimal humidity is 45-70%.

[0186] Environmental Condition Assessment

[0187] The server evaluates whether the current temperature and humidity are within the optimal range, and the results are summarized as "optimal temperature" and "optimal humidity."

[0188] Prediction by AI model

[0189] The server uses an artificial intelligence model (e.g., random forest) to predict whether the current environmental conditions are optimal, given the current temperature and humidity inputs from the user.

[0190] Determining the recommended action

[0191] The server determines the recommended actions to present to the user based on the results of the environmental condition evaluation and the prediction results of the AI ​​model.

[0192] Guidance display

[0193] The device displays the recommended actions sent from the server to the user. The user can check appropriate training guidance on the device screen. For example, "Current conditions are optimal" or "Conditions are not optimal. Please make adjustments."

[0194] 2. Hardware and Software Used

[0195] Hardware

[0196] Device (smartphone, tablet, etc.)

[0197] server

[0198] software

[0199] Front-end: HTML, CSS, and JavaScript to build the user interface

[0200] Backend: Python for data processing and running AI models

[0201] Database: SQL database for storing plant growth conditions

[0202] AI models: machine learning algorithms such as random forests

[0203] 3. Specific Examples

[0204] For example, suppose a clerk at a gardening shop inputs the following information into a terminal: "rose," current temperature 25°C, current humidity 70%, and growth stage "before flowering."

[0205] 1. User Input

[0206] The clerk enters the plant information and clicks the "Submit" button.

[0207] 2. Data Transmission

[0208] The terminal sends the input data to the server.

[0209] 3. Database Reference

[0210] The server receives the transmitted data and obtains the optimum temperature and humidity ranges for the roses.

[0211] 4. Assessment of Environmental Conditions

[0212] The server evaluates whether the current temperature and humidity are within the optimum range.

[0213] 5. Predictions using AI models

[0214] The server inputs the current temperature and humidity into the AI ​​model to predict whether it is optimal.

[0215] 6. Determining the recommended action

[0216] The server determines the recommended action based on the evaluation and prediction results.

[0217] 7. Guidance display

[0218] The terminal displays the guidance "Current conditions are optimal" received from the server to the store clerk.

[0219] Prompt Sentence Examples

[0220] Example of information the user enters into the terminal:

[0221] Plant type: Rose

[0222] Current temperature: 25℃

[0223] Current humidity: 70%

[0224] Growth stage: Pre-flowering

[0225] The above is an embodiment of the present invention.

[0226] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0227] Step 1:

[0228] The user uses the input form on the device to input information about the plant type, current temperature, current humidity, and growth stage. This information is used as the initial input data for the program. The input data is temporarily stored in the device's memory, and the user can proceed to the next step by clicking the "Submit" button.

[0229] Input: Plant type, current temperature, current humidity, growth stage

[0230] Output: Initial input data stored in the terminal memory

[0231] Step 2:

[0232] The terminal sends the data entered by the user to the server, properly formatting it so that it can be uniquely identified by the server, for example, in JSON format.

[0233] Input: Initial input data stored in the terminal memory

[0234] Output: The formatted data sent to the server

[0235] Step 3:

[0236] Based on the received data, the server retrieves the optimal temperature and humidity ranges for the corresponding plant from the database. The server then queries the database based on the type of plant to retrieve the appropriate temperature and humidity ranges. This data is stored in the server's memory.

[0237] Input: Formatted data sent to the server

[0238] Output: Optimal temperature and humidity ranges retrieved from the database

[0239] Step 4:

[0240] The server evaluates whether the current temperature and humidity are within the optimal range. It compares the acquired optimal temperature and humidity range with the current temperature and humidity sent by the user and generates an evaluation result. This evaluation result is organized as "optimum temperature" or "optimum humidity."

[0241] Input: Current temperature, current humidity, optimal temperature range, optimal humidity range

[0242] Output: Evaluation results (suitable / unsuitable temperature, suitable / unsuitable humidity)

[0243] Step 5:

[0244] The server uses an artificial intelligence model (e.g., random forest) to predict whether the current environmental conditions are optimal. The AI ​​model receives the current temperature and humidity pair entered by the user and outputs a predicted value, such as "optimal" or "not optimal."

[0245] Input: Current temperature, Current humidity

[0246] Output: Prediction results from the AI ​​model (optimal / non-optimal)

[0247] Step 6:

[0248] The server determines the recommended action to present to the user based on the results of the environmental condition evaluation and the prediction results of the AI ​​model. Recommended actions are expressed in the form of, for example, "The current conditions are optimal" or "The conditions are not optimal. Please make adjustments."

[0249] Input: Evaluation results, prediction results by AI model

[0250] Output: Recommended Action

[0251] Step 7:

[0252] The device receives the recommended actions sent from the server and displays them to the user. The recommended actions are displayed on the device screen, allowing the user to check appropriate plant cultivation guidance in real time.

[0253] Input: Recommended action sent by the server

[0254] Output: Recommended actions displayed on the terminal screen

[0255] 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.

[0256] This invention relates to a system that allows users to check the optimal conditions for growing plants in real time by inputting information on the type of plant, current temperature, current humidity, and growth stage. By further combining this invention with an emotion engine, it is possible to recognize the user's emotions and provide cultivation guidance in a more appropriate and user-friendly format.

[0257] Basic system configuration

[0258] User Input

[0259] The user uses an input form on the device screen to input the type of plant, the current temperature, the current humidity, and the plant's growth stage. The user's facial expression, voice, or text data is also collected at the same time. For example, the user inputs into the device that the plant is a "tomato," that the current temperature is 22°C, the current humidity is 60%, and that the growth stage is "flowering."

[0260] Data transmission

[0261] The device transmits the data and emotion information input by the user to the server, where the data is appropriately formatted and transmitted in a form that can be uniquely identified by the server.

[0262] Database Reference

[0263] Based on the received user data, the server retrieves the optimal environmental conditions (temperature range and humidity range) for the corresponding plant from the database. For example, it retrieves from the database that the optimal temperature for tomatoes is 20 to 25°C and the optimal humidity is 45 to 70%.

[0264] Environmental Condition Assessment

[0265] The server evaluates whether the current temperature and humidity are within the optimum range, and the evaluation results are organized in a format such as "optimum temperature" or "optimum humidity."

[0266] Prediction by AI model

[0267] The server uses an artificial intelligence model (e.g., random forest) to predict whether the current environmental conditions are optimal, given the current temperature and humidity inputs from the user.

[0268] Emotion engine for user emotion evaluation

[0269] The server uses an emotion engine to analyze the user's facial expressions, voice, and text data during input and recognize the user's emotions, for example, determining whether the user is expressing emotions such as "happy" or "troubled."

[0270] Determining the recommended action

[0271] The server determines the recommended action based on the results of the environmental condition evaluation, the AI ​​model's predictions, and the user's emotion recognition. If the emotion engine recognizes that the user is confused, it adjusts the guidance to provide more detailed explanations and additional advice. For example, if the user is confused, it provides specific guidance such as, "The temperature is a little high. Try lowering the temperature by 1-2 degrees. This will improve plant growth."

[0272] Guidance display

[0273] The device displays the recommended actions sent from the server to the user, who can then check the appropriate training guidance on the device screen. For example, a message such as "Current conditions are optimal" may be displayed, along with additional advice based on the user's emotions.

[0274] Specific examples

[0275] For example, consider a case where a user inputs information into a terminal about "tomato," the current temperature of 22°C, the current humidity of 60%, and the growth stage of "flowering," and shows a confused expression.

[0276] 1. Sending input data

[0277] The user enters information into the device and clicks the "Send" button. The device then sends the input data and emotion data to the server.

[0278] 2. Database lookup and environmental condition evaluation

[0279] The server receives the transmitted data and retrieves the optimal temperature range (20-25°C) and humidity range (45-70%) for tomatoes from the database. The server then evaluates whether the current temperature of 22°C and humidity of 60% are within the range. As a result, both are evaluated as "optimal temperature" and "optimal humidity."

[0280] 3. AI models make predictions and recommend actions

[0281] The server inputs the current temperature and humidity into the AI ​​model to make a prediction. The model predicts that the current conditions are optimal and determines the recommended action: "Current conditions are optimal."

[0282] 4. Emotion evaluation using an emotion engine

[0283] The server analyzes the user's facial expressions, voice, and text data sent from the terminal and recognizes that the user is confused.

[0284] 5. Adjusting recommended actions

[0285] Based on the user's sentiment, the server provides guidance such as "The current conditions are optimal," as well as additional advice such as "The temperature is a little high. You would get better results if you lowered the temperature by 1-2 degrees."

[0286] 6. Guidance display

[0287] The device receives the guidance sent from the server and displays it to the user. The user can see messages on the device screen such as "Current conditions are optimal" and "The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results."

[0288] In this way, the system of the present invention not only solves plant cultivation challenges faced by users in real time and provides effective means for optimizing plant productivity and health, but also takes user emotions into account, making the system even easier to use.

[0289] The processing flow will be explained below.

[0290] Step 1:

[0291] The user inputs information into the device. Using the device's input form, the user inputs the type of plant (e.g., tomato), the current temperature (e.g., 22°C), the current humidity (e.g., 60%), and the growth stage (e.g., flowering stage). The user's facial expressions, voice, or text messages are also collected.

[0292] Step 2:

[0293] The device transmits the input information and emotion data to the server. The device then formats the information appropriately and transmits it to the server. The transmitted data includes the plant type, current temperature, current humidity, growth stage information, and the user's emotion data.

[0294] Step 3:

[0295] The server receives the information sent by the user, verifies that the input data is in the correct format, and prepares to analyze the user's emotion data.

[0296] Step 4:

[0297] The server retrieves the optimal environmental conditions for a plant from the database. Based on the type of plant, the server retrieves the optimal temperature and humidity ranges from the database. For example, the server retrieves that the optimal temperature range for tomatoes is 20-25°C and the optimal humidity range is 45-70%.

[0298] Step 5:

[0299] The server evaluates the current temperature and humidity. The server determines whether the current temperature (22°C) and humidity (60%) received from the user are within the optimum temperature and humidity ranges. As a result, the server evaluates the temperature as "suitable" and the humidity as "suitable."

[0300] Step 6:

[0301] The server uses an artificial intelligence model to predict the optimal action. The server inputs the current temperature and humidity data into an artificial intelligence model (e.g., a random forest classifier) ​​to predict whether the current environmental conditions are optimal.

[0302] Step 7:

[0303] The server uses an emotion engine to recognize the user's emotions. The server analyzes the user's facial expressions, voice, and text data to determine what emotions the user is expressing. For example, it recognizes whether the user is confused.

[0304] Step 8:

[0305] The server determines the recommended action based on the results of the environmental condition evaluation, the AI ​​model predictions, and the user's emotion recognition. For example, if the user is confused, in addition to the basic action guidance of "The current conditions are optimal," the server provides additional advice such as "The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results."

[0306] Step 9:

[0307] The server transmits the recommended action to the terminal. The server transmits the determined recommended action to the terminal. The transmitted data includes the temperature status, the humidity status, the recommended action, and additional advice based on the user's emotion.

[0308] Step 10:

[0309] The device displays the recommended action to the user. The device displays the recommended action received from the server to the user. For example, messages such as "Current conditions are optimal" and "The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results" are displayed on the user's device screen, allowing the user to see appropriate cultivation guidance.

[0310] Example 2

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

[0312] Conventional plant cultivation systems can present optimal cultivation conditions based on the plant type and environmental conditions input by the user, but do not provide individualized responses that take the user's emotions into consideration. As a result, user satisfaction and system utilization efficiency may decrease. The purpose of this invention is to realize a user-friendly plant cultivation system by recognizing the user's emotions and providing more appropriate guidance based on them.

[0313] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving information on the type of plant, current temperature, current humidity, and growth stage input by the user; means for acquiring an optimal temperature range and an optimal humidity range from a database based on the type of plant; means for evaluating whether the current temperature and current humidity are within the optimal temperature and humidity ranges; means for predicting whether the current conditions are optimal using an artificial intelligence model; means including an emotion engine for analyzing the user's facial expression data, voice data, and text data and recognizing the user's emotions; and means for determining recommended actions based on the evaluation, prediction, and emotion recognition and presenting them to the user. This enables individual responses according to the user's emotions and makes it possible to provide plant cultivation guidance that will provide a higher level of satisfaction.

[0314] A "user" is an entity that operates the system and inputs information necessary for growing plants.

[0315] A "terminal" is a device through which a user inputs information and transmits that information to a server.

[0316] A "server" is a central computer system that receives information sent by users and performs processing such as database lookups, running artificial intelligence models, and sentiment analysis.

[0317] "Type of plant" is information indicating the name and type of plant designated as the target for cultivation.

[0318] "Current temperature" is information indicating the temperature value at the current time in the plant growing environment.

[0319] "Current humidity" is information indicating the current humidity value in the plant growing environment.

[0320] The "growth stage" is information that indicates the current stage of the growth of the plant, including, for example, the germination stage, growth stage, flowering stage, and harvest stage.

[0321] A "database" is an information system that stores information about optimal growing conditions for plants.

[0322] "Optimal temperature range" refers to the temperature range established to promote healthy plant growth.

[0323] The "optimal humidity range" refers to the humidity range established to promote healthy plant growth.

[0324] An "artificial intelligence model" is a model that uses machine learning algorithms to predict whether the current environmental conditions input by the user are optimal.

[0325] The "emotion engine" is a system that analyzes the user's facial expressions, voice, and text data to recognize the user's emotions.

[0326] "Recommended actions" refer to specific actions or advice suggested to the user based on the results of the assessment of environmental conditions, the prediction results of the artificial intelligence model, and the results of the user's emotion recognition.

[0327] This invention relates to a system that allows users to check the optimal conditions for growing plants in real time by inputting information on the type of plant, current temperature, current humidity, and growth stage. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotions and provide cultivation guidance in a more appropriate and user-friendly manner.

[0328] Basic system configuration

[0329] User Input

[0330] The user uses an input form on the device screen to input the type of plant, current temperature, current humidity, and growth stage. In addition, the user's facial expression data, voice data, and text data are also collected at the same time. For example, a user inputs into the device the type of plant "tomato," the current temperature is 22°C, the current humidity is 60%, and the growth stage is "flowering stage." At the same time, the device's camera and microphone record the user's facial expression and voice.

[0331] Data transmission

[0332] The device sends the data and emotion information entered by the user to the server. The data is properly formatted and sent in a way that allows the server to uniquely identify it. For example, when the device presses the "send" button, a JSON-formatted data packet is sent to the server.

[0333] Database Reference

[0334] Based on the received user data, the server retrieves the optimal environmental conditions for the corresponding plant from the database. For example, the server retrieves from the database that the optimal temperature for tomatoes is 20-25°C and the optimal humidity is 45-70%. To do this, the server sends a query to the database to retrieve the necessary information.

[0335] Environmental Condition Assessment

[0336] The server evaluates whether the current temperature and humidity are within the optimal range. The evaluation results are organized in the form of "suitable temperature" and "suitable humidity." For example, the server checks whether the current temperature of 22°C and humidity of 60% are within the range, and evaluates them as "suitable temperature" and "suitable humidity."

[0337] Prediction by AI model

[0338] The server uses an artificial intelligence model, such as a random forest, to predict whether the current environmental conditions are optimal. The current temperature and humidity, entered by the user, are input into the model. For example, the server passes this data through a random forest model and obtains a result predicting that the current conditions are optimal.

[0339] Emotion engine for user emotion evaluation

[0340] The server uses an emotion engine to analyze the user's facial expressions, voice, and text data to recognize the user's emotions. For example, the server executes an emotion analysis algorithm to determine whether the user is expressing an emotion such as "distress."

[0341] Determining the recommended action

[0342] The server determines the recommended action based on the results of the environmental condition evaluation, the AI ​​model's predictions, and the user's emotion recognition. For example, if the emotion engine recognizes that the user is confused, it will provide a more detailed explanation or additional advice. Specifically, in addition to the message "The current conditions are optimal," it will determine additional advice such as "The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results."

[0343] Guidance display

[0344] The device displays the recommended actions sent from the server to the user. The user can then view appropriate cultivation guidance and additional advice on the device screen. For example, the device display might say, "The current conditions are optimal. The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results."

[0345] Specific examples

[0346] For example, consider a case where a user inputs information into a terminal about "tomato," the current temperature of 22°C, the current humidity of 60%, and the growth stage of "flowering," and shows a troubled expression.

[0347] 1. User Input

[0348] The user enters "tomato," the current temperature of 22°C, the current humidity of 60%, and the growth stage of "flowering" into the input form on the device, and clicks the "send" button. The device's camera also records the user's facial expressions.

[0349] 2. Data Transmission

[0350] The device sends this information to the server in JSON format.

[0351] 3. Database Reference

[0352] Based on the data received by the server, the optimal temperature range (20-25°C) and humidity range (45-70%) for tomatoes are retrieved from the database.

[0353] 4. Assessment of Environmental Conditions

[0354] The server evaluates the current temperature of 22°C and humidity of 60% and determines that the temperature and humidity are "appropriate."

[0355] 5. Predictions using AI models

[0356] The server inputs this data into a random forest model and predicts which current conditions are optimal.

[0357] 6. User Emotion Evaluation Using an Emotion Engine

[0358] The server analyzes the user's facial expression data and recognizes that the user is confused.

[0359] 7. Determining the recommended action

[0360] The server determines the message "Current conditions are optimal" plus additional advice such as "The temperature is a little high. Lowering the temperature by 1-2 degrees would give better results."

[0361] 8. Guidance display

[0362] The terminal displays these messages on the display and the user confirms them.

[0363] Prompt Sentence Examples

[0364] For example, you can simulate the behavior of this system by feeding the following prompts to the generative AI model:

[0365] If a user inputs information about "Tomato," current temperature 22°C, current humidity 60%, and growth stage "Flowering" and shows a confused expression, please explain in detail the growing guidance and additional advice that your server provides.

[0366] In this way, the system of the present invention not only solves plant cultivation challenges faced by users in real time and provides effective means for optimizing plant productivity and health, but also takes user emotions into account, making the system even easier to use.

[0367] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0368] Step 1: User Input

[0369] The user inputs the plant's type, current temperature, current humidity, and growth stage using an input form on the device screen, while facial expression, voice, and text data are also collected.

[0370] Input: Plant type (e.g., "Tomato"), current temperature (e.g., 22°C), current humidity (e.g., 60%), growth stage (e.g., "flowering stage"), user's facial expression data, voice data, and text data.

[0371] Output: This data is temporarily stored on the device.

[0372] Specific operation: The user enters "tomato," temperature 22°C, humidity 60%, and growth stage "flowering stage" into the input form on the device, and clicks the "Send" button. The device's camera and microphone record the user's facial expressions and voice data.

[0373] Step 2: Send data

[0374] The terminal transmits the data and emotion information input by the user to the server, where the data is appropriately formatted and transmitted in a form that can be uniquely identified by the server.

[0375] Input: The data entered in step 1.

[0376] Output: Data formatted in JSON is sent to the server.

[0377] Specific operation: When the "Send" button is pressed, the device sends the information entered by the user to the server in JSON format.

[0378] Step 3: Database Reference

[0379] The server retrieves the optimum environmental conditions for the relevant plant from the database based on the received user data.

[0380] Input: User data formatted in JSON.

[0381] Output: Optimal temperature range (e.g. 20-25°C) and optimal humidity range (e.g. 45-70%) retrieved from the database.

[0382] What it does: The server queries the database to get the optimal environmental conditions for tomatoes.

[0383] Step 4: Evaluate environmental conditions

[0384] The server evaluates whether the current temperature and humidity are within the optimal range, and the results are summarized as "optimal temperature" and "optimal humidity."

[0385] Input: Optimal temperature and humidity ranges retrieved from the database, current temperature and humidity.

[0386] Output: Evaluation result (e.g., "suitable temperature" or "suitable humidity").

[0387] Specific operation: The server compares the current temperature of 22°C and humidity of 60% with the optimal range and evaluates the result as "optimal temperature and humidity."

[0388] Step 5: Prediction by AI model

[0389] The server uses artificial intelligence models such as random forests to predict whether the current environmental conditions are optimal.

[0390] Input: Current temperature and humidity.

[0391] Output: The predicted result of the AI ​​model (e.g., "Conditions are optimal").

[0392] Specific operation: The server inputs the current environmental data into the random forest model and obtains the prediction results.

[0393] Step 6: Evaluating user emotions with the emotion engine

[0394] The server uses an emotion engine to analyze the user's facial expressions, voice, and text data to recognize the user's emotions.

[0395] Input: User's facial expression data, voice data, and text data.

[0396] Output: User emotion recognition result (e.g., "confused").

[0397] Specific operation: The server runs an emotion analysis algorithm and detects the emotion "confused" from the user's facial expression data.

[0398] Step 7: Determine the recommended action

[0399] The server determines the recommended action based on the results of the environmental condition evaluation, the prediction results of the AI ​​model, and the user's emotion recognition results.

[0400] Input: Environmental condition assessment results, AI model prediction results, and user emotion recognition results.

[0401] Output: Recommended action (e.g., "Current conditions are optimal. The temperature is a little high. Lowering the temperature by 1-2 degrees would produce better results.").

[0402] Specific operation: The server integrates the evaluation results, prediction results, and emotion recognition results to determine the recommended action.

[0403] Step 8: View Guidance

[0404] The terminal displays the recommended actions sent from the server to the user.

[0405] Input: The recommended action sent by the server.

[0406] Output: Guidance shown on the device display (e.g., "Current conditions are optimal" and "The temperature is a little high. Lowering the temperature by 1-2 degrees would produce better results.").

[0407] Specific operation: The device receives a message from the server and displays it on the screen, allowing the user to check it on the screen.

[0408] (Application example 2)

[0409] 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."

[0410] While conventional plant cultivation support systems can evaluate and optimize plant cultivation conditions, they do not provide support that takes into account the user's emotions, which leads to issues such as a lack of effective support and improved user satisfaction. Another problem is that they are unable to provide appropriate feedback to address problems and anxieties specific to plant cultivation.

[0411] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving information on the type of plant, current temperature, current humidity, and growth stage input by the user, means for retrieving an optimal temperature range and an optimal humidity range from a database based on the type of plant, means for evaluating whether the current temperature and current humidity are within the optimal temperature and humidity ranges, means for predicting whether the current conditions are optimal using an artificial intelligence model, means for determining a recommended action based on the evaluation and prediction and presenting it to the user, means for inputting the user's emotions, and means for adjusting the presented recommended action based on the user's emotional information. This makes it possible to not only optimize plant growth but also provide flexible support that corresponds to the user's emotions.

[0412] "User" refers to a person who inputs information about plant growing conditions and receives recommended actions from the system.

[0413] "Plant type" refers to the name of the particular plant that the user wants to grow.

[0414] "Current temperature" refers to the current temperature of the environment where the plant is located, as entered by the user.

[0415] "Current humidity" refers to the current humidity of the environment in which the plant is placed, as input by the user.

[0416] "Growth stage" is information that indicates the current growth phase of the plant.

[0417] The "database" is an information management system that stores information on optimal growing conditions for plants.

[0418] "Optimal temperature range" refers to the range of temperatures in which a particular plant can grow optimally.

[0419] The "optimal humidity range" refers to the humidity range in which a particular plant can grow optimally.

[0420] An "artificial intelligence model" is a machine learning algorithm used to optimize plant growth conditions.

[0421] "Recommended Actions" refers to specific instructions for action to optimize plant growth based on current growing conditions.

[0422] "Emotion information" refers to data related to emotions input by the user, such as information extracted from facial expressions, voice, and text.

[0423] "Adjustment means" refers to the ability to change the recommended actions and guidance presented based on the user's emotional information.

[0424] This invention relates to a system that allows users to input information for plant cultivation and presents optimal cultivation conditions based on that information, and also provides support taking into account the user's feelings. The system is mainly composed of a server, terminals, and users.

[0425] 1. System Program Overview

[0426] User Input:

[0427] The device is equipped with a camera and voice input, and collects information about the plant's type, current temperature, current humidity, and growth stage from the device, as well as emotional information from the user's facial expressions and voice.

[0428] Data transmission:

[0429] The information and emotion information entered by the user are properly formatted and sent to the server using the device's communication function.

[0430] Database Reference:

[0431] Based on the received data, the server retrieves the optimal growing conditions (temperature and humidity ranges) for the corresponding plant from the database. For example, it retrieves information such as the optimal temperature for tomatoes being 20-25°C and the optimal humidity being 45-70%.

[0432] Environmental condition assessment:

[0433] The server evaluates whether the current temperature and humidity input are within the optimal range, and the evaluation results are presented in the form of "suitable temperature" and "suitable humidity."

[0434] Artificial intelligence model predictions:

[0435] The server uses a generative AI model, specifically an algorithm such as random forest, to predict whether the current environmental conditions are optimal for the plant.

[0436] Emotional evaluation by emotion engine:

[0437] The server analyzes the user's facial expressions and voice to recognize their emotional state, such as "confusion" or "fun," using OpenCV and emotion engine software built on top of it.

[0438] Determine and adjust the recommended actions:

[0439] The server determines the optimal recommended action based on the results of the environmental condition assessment, the AI ​​model predictions, and the emotion recognition results. For example, if the user is confused, more detailed explanations or additional advice will be provided.

[0440] Display guidance:

[0441] Finally, the device displays the recommended actions and guidance sent from the server to the user in a format that is easy for the user to view.

[0442] 2. System Operation

[0443] The hardware used includes smart glasses and smartphones, which input and display information.

[0444] The software uses Python, OpenCV, emotion engines, and generative AI models such as random forests.

[0445] Examples:

[0446] In a plant specialty store, a staff member puts on smart glasses and inputs "tomato," along with the current temperature of 22°C, humidity of 60%, and the growth stage of "flowering." If the staff member's expression looks confused, this information is sent to the server. The server evaluates the optimal growing conditions based on the input information and provides specific advice, such as "The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results."

[0447] Example prompt sentence:

[0448] "Scan the type of plant in the store, the current temperature, the current humidity, and the growth stage, and generate a sentence that provides optimal growing guidance if the user has a confused look on their face."

[0449] In this way, the present invention can solve the plant cultivation problems faced by users and further provide flexible and effective support that responds to the user's feelings.

[0450] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0451] Step 1: User Input

[0452] The user uses the device's input form to input the plant's type, current temperature, current humidity, and growth stage. The device also uses a camera and voice input to collect emotional information from the user's facial expressions and voice. If the user inputs information such as tomato, 22°C, 60%, and flowering stage, and shows a confused expression, this data is treated as input.

[0453] Step 2: Send data

[0454] The device formats the plant type, current temperature, current humidity, growth stage, and emotion information entered by the user and sends it to the server in JSON format as a POST request.

[0455] Step 3: Database Reference

[0456] The server receives the transmitted data and retrieves the optimal growing conditions (temperature and humidity ranges) for the corresponding plant from the database. For example, the server recognizes that the plant is a tomato and retrieves from the database the optimal temperature of 20 to 25°C and the optimal humidity of 45 to 70%.

[0457] Step 4: Evaluate environmental conditions

[0458] The server evaluates whether the current temperature (22°C) and humidity (60%) are within the optimal temperature and humidity ranges. Based on this evaluation, it determines which environmental conditions are suitable and outputs the results as "optimal temperature" and "optimal humidity."

[0459] Step 5: Prediction by artificial intelligence model

[0460] The server uses a generative AI model (e.g., random forest) to predict whether the current environmental conditions are optimal for the plant. The model takes the current temperature and humidity as input and outputs a prediction of whether the conditions are optimal.

[0461] Step 6: Emotion evaluation by the emotion engine

[0462] The server analyzes the user's facial expression and voice data sent from the device and uses an emotion engine to determine whether the user is confused. Based on this analysis, the server outputs the user's emotional state as "confused."

[0463] Step 7: Determine and adjust recommended actions

[0464] The server determines the optimal recommended action based on the results of the environmental condition evaluation, the AI ​​model predictions, and the emotion recognition results. For example, the server may use the message "The current conditions are optimal" as a base message, and if the user is confused, include additional advice such as "The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results."

[0465] Step 8: View Guidance

[0466] The device receives the recommended actions and guidance sent from the server and displays them to the user. The user can see messages on the device screen such as "Current conditions are optimal" and "The temperature is a little high. Lowering the temperature by 1-2 degrees will give better results."

[0467] Through the above processing steps, the system of the present invention can support the user's plant cultivation in real time and also provide flexible support according to the user's emotions.

[0468] 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.

[0469] 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.

[0470] 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.

[0471] [Second embodiment]

[0472] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0473] 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.

[0474] 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).

[0475] 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.

[0476] 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.

[0477] 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).

[0478] 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.

[0479] 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.

[0480] 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.

[0481] 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.

[0482] 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.

[0483] 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."

[0484] This invention relates to a system that allows users to check the optimal growing conditions for a plant in real time by inputting information on the plant's type, current temperature, current humidity, and growth stage. In this system, a server uses a database and an artificial intelligence model to evaluate the current environmental conditions and provide optimal growing guidance.

[0485] Basic system configuration

[0486] User Input

[0487] The user uses the input form on the terminal screen to input the type of plant, the current temperature, the current humidity, and the growth stage of the plant. For example, the user inputs into the terminal that the type of plant is "tomato," the current temperature is 22°C, the current humidity is 60%, and the growth stage is "flowering stage."

[0488] Data transmission

[0489] The terminal transmits the data entered by the user to the server, properly formatting the data so that it can be uniquely identified by the server.

[0490] Database Reference

[0491] Based on the received data, the server retrieves the optimal environmental conditions (temperature range and humidity range) for the corresponding plant from the database. For example, it may retrieve from the database that the optimal temperature for tomatoes is 20 to 25°C and the optimal humidity is 45 to 70%.

[0492] Environmental Condition Assessment

[0493] The server evaluates whether the current temperature and humidity are within the optimum range, and the evaluation results are organized in a format such as "optimum temperature" or "optimum humidity."

[0494] Prediction by AI model

[0495] The server uses an artificial intelligence model (e.g., random forest) to predict whether the current environmental conditions are optimal, given the current temperature and humidity inputs from the user.

[0496] Determining the recommended action

[0497] The server determines the recommended action to present to the user based on the results of the environmental condition evaluation and the prediction results of the AI ​​model. For example, specific action guidance such as "The current conditions are optimal" or "Please lower the temperature" is determined.

[0498] Guidance display

[0499] The device displays the recommended actions sent from the server to the user, who can then check the appropriate training guidance on the device screen.

[0500] Specific examples

[0501] For example, consider the case where a user inputs information into the terminal about "tomato," the current temperature of 22°C, the current humidity of 60%, and the growth stage of "flowering."

[0502] 1. Sending input data

[0503] The user enters information into the terminal and clicks the "Submit" button. The terminal sends the input data to the server.

[0504] 2. Database lookup and environmental condition evaluation

[0505] The server receives the transmitted data and retrieves the optimal temperature range (20-25°C) and humidity range (45-70%) for tomatoes from the database. The server then evaluates whether the current temperature of 22°C and humidity of 60% are within the range. As both are within the range, the server evaluates them as "optimal temperature" and "optimal humidity."

[0506] 3. AI models make predictions and recommend actions

[0507] The server inputs the current temperature and humidity into the AI ​​model to make a prediction. The model predicts that the current conditions are optimal and determines the recommended action: "Current conditions are optimal."

[0508] 4. Guidance display

[0509] The terminal receives the guidance sent from the server, saying "Current conditions are optimal," and displays it to the user. The user can check the guidance on the terminal screen and take appropriate measures to grow the plant.

[0510] In this way, the system of the present invention provides users with an effective means of solving plant growth challenges in real time and optimizing plant productivity and health.

[0511] The processing flow will be explained below.

[0512] Step 1:

[0513] The user inputs information into the terminal. Using the terminal's input form, the user inputs the plant type (e.g., tomato), the current temperature (e.g., 22°C), the current humidity (e.g., 60%), and the growth stage (e.g., flowering stage).

[0514] Step 2:

[0515] The terminal transmits the input information to the server, which then formats the information appropriately and transmits it to the server. The transmitted data includes the plant type, current temperature, current humidity, and growth stage information.

[0516] Step 3:

[0517] The server receives the information sent by the user and verifies that the entered data is in the correct format.

[0518] Step 4:

[0519] The server retrieves the optimal environmental conditions for a plant from the database. Based on the type of plant, the server retrieves the optimal temperature and humidity ranges from the database. For example, the server retrieves that the optimal temperature range for tomatoes is 20-25°C and the optimal humidity range is 45-70%.

[0520] Step 5:

[0521] The server evaluates the current temperature and humidity. The server determines whether the current temperature (22°C) and humidity (60%) received from the user are within the optimal temperature and humidity ranges. As a result, the server evaluates the temperature as "suitable" and the humidity as "suitable."

[0522] Step 6:

[0523] The server uses an artificial intelligence model to predict the optimal action. The server inputs the current temperature and humidity data into an artificial intelligence model (e.g., a random forest classifier) ​​to predict whether the current environmental conditions are optimal.

[0524] Step 7:

[0525] The server determines the recommended action. The server determines the recommended action based on the prediction results of the artificial intelligence model and the evaluation results of the environmental conditions. For example, the server determines the recommended action as "The current conditions are optimal."

[0526] Step 8:

[0527] The server sends the recommended action to the terminal. The server sends the determined recommended action to the terminal. The sent data includes the temperature status, humidity status, and recommended action.

[0528] Step 9:

[0529] The terminal displays the recommended action to the user. The terminal displays the recommended action received from the server to the user. For example, a message saying "Current conditions are optimal" is displayed on the user's terminal screen. As a result, the user can take appropriate measures to grow the plant.

[0530] Example 1

[0531] 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."

[0532] In conventional plant cultivation systems, it was difficult for users to manually evaluate the plant's type, growth stage, and current environmental conditions to determine the optimal cultivation conditions. Furthermore, there was a high possibility of errors occurring during the manual data evaluation process, and optimal cultivation conditions were not always guaranteed. Furthermore, it was difficult to quickly respond to changes in environmental conditions in real time. Given these issues, there was a need for a system that would allow users to easily check the optimal cultivation conditions for plants and quickly take appropriate cultivation actions.

[0533] 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.

[0534] In this invention, the server includes means for receiving information on the type of plant, current temperature, current humidity, and growth stage input by a user, means for retrieving an optimal temperature range and an optimal humidity range from a database based on the type of plant, means for evaluating whether the current temperature and current humidity are within the optimal temperature and humidity ranges, means for predicting whether the current conditions are optimal using an artificial intelligence model, means for determining a recommended action based on the evaluation and prediction and presenting it to the user, means for appropriately formatting the input from the user and transmitting it to the server, and means for displaying the recommended action transmitted from the server to the user. This enables a user to check the optimal growing conditions for a plant in real time and quickly take appropriate growing action.

[0535] A "user" is an individual or entity that provides plant-growing information and receives recommended actions from the system.

[0536] An "input form" is a screen element provided on a terminal for a user to input information about the type of plant, the current temperature, the current humidity, and the growth stage.

[0537] A "terminal" is an electronic device that a user uses to enter information and communicate with a server.

[0538] "Type of plant" is information indicating the classification of a particular plant that the user is cultivating.

[0539] "Growth stage" is information indicating the developmental phase in which the plant is currently located.

[0540] A "database" is a system for storing and managing information such as optimal environmental conditions for plants.

[0541] The "optimum temperature range" is information indicating the temperature range that is most suitable for a particular plant.

[0542] The "optimum humidity range" is information indicating the humidity range that is most suitable for a particular plant.

[0543] "Evaluation" is the process of determining whether the current temperature is within the optimum temperature range and whether the current humidity is within the optimum humidity range.

[0544] An "artificial intelligence model" is a model that uses machine learning algorithms to analyze data and make predictions and judgments.

[0545] "Recommended actions" are specific measures for development that are presented to the user based on the evaluation and prediction.

[0546] "Formatting" is the process of properly shaping data so that it can be properly understood and processed by the server.

[0547] "Send" is the process of transferring data entered by the user from the terminal to the server.

[0548] "Display" is the process of showing the recommended actions received from the server to the user.

[0549] This invention relates to a system that allows users to check the optimal growing conditions for a plant in real time by inputting information on the plant's type, current temperature, current humidity, and growth stage. In this system, a server uses a database and an artificial intelligence model to evaluate the current environmental conditions and provide optimal growing guidance.

[0550] User Input

[0551] The user uses an input form on the terminal screen to input the type of plant, the current temperature, the current humidity, and the growth stage of the plant. For example, the user inputs into the terminal that the plant type is "tomato," the current temperature is 22°C, the current humidity is 60%, and the growth stage is "flowering stage." This information is entered manually by the user and is in a format that can be modified as needed.

[0552] Data transmission

[0553] The terminal sends the data entered by the user to the server, properly formatting it so that it can be uniquely identified by the server. Data formats such as JSON and XML are commonly used, but other formats can also be applied depending on the system requirements.

[0554] Database Reference

[0555] Based on the received data, the server retrieves the optimal environmental conditions (temperature and humidity ranges) for the corresponding plant from the database. For example, it may learn from the database that the optimal temperature for tomatoes is 20-25°C and the optimal humidity is 45-70%. This database holds detailed growing information for each type of plant and is designed to allow the server to access it quickly.

[0556] Environmental Condition Assessment

[0557] The server evaluates whether the current temperature and humidity are within the optimal range. The evaluation results are organized as, for example, "optimal temperature" or "optimal humidity." The evaluation process is carried out by an algorithm that compares the current environmental data with the optimal conditions retrieved from a database.

[0558] Prediction by AI model

[0559] The server uses an artificial intelligence model (e.g., random forest) to predict whether the current environmental conditions are optimal. The current temperature and humidity are input into the model, which incorporates learning data based on past data and environmental conditions, allowing it to make highly accurate predictions.

[0560] Determining the recommended action

[0561] The server determines the recommended action to present to the user based on the results of the environmental condition evaluation and the prediction results of the AI ​​model. For example, specific action guidance such as "Current conditions are optimal" or "Please lower the temperature" is determined. This allows the user to quickly take appropriate cultivation measures.

[0562] Guidance display

[0563] The device displays the recommended actions sent from the server to the user, who can then check the appropriate training guidance on the device screen. While various display formats are possible, such as text messages or graphical interfaces, a format that is intuitively easy for users to understand is desirable.

[0564] Specific examples

[0565] For example, consider the case where a user inputs information into the terminal about "tomato," the current temperature of 22°C, the current humidity of 60%, and the growth stage of "flowering."

[0566] 1. Sending input data

[0567] The user enters information into the terminal and clicks the "Submit" button. The terminal sends the input data to the server.

[0568] 2. Database lookup and environmental condition evaluation

[0569] The server receives the transmitted data and retrieves the optimal temperature range (20-25°C) and humidity range (45-70%) for tomatoes from the database. The server then evaluates whether the current temperature of 22°C and humidity of 60% are within the range. As both are within the range, the server evaluates them as "optimal temperature" and "optimal humidity."

[0570] 3. AI models make predictions and recommend actions

[0571] The server inputs the current temperature and humidity into the AI ​​model to make a prediction. The model predicts that the current conditions are optimal and determines the recommended action: "Current conditions are optimal."

[0572] 4. Guidance display

[0573] The terminal receives the guidance sent from the server, saying "Current conditions are optimal," and displays it to the user. The user can check the guidance on the terminal screen and take appropriate measures to grow the plant.

[0574] Prompt Sentence Examples

[0575] The following prompt would confirm the optimal growing conditions for a "tomato" in the "flowering" growth stage when the current temperature is 22°C and humidity is 60%:

[0576] What are the optimal growing conditions for tomatoes during the flowering stage? The current temperature is 22°C and humidity is 60%.

[0577] In this way, the system of the present invention provides users with an effective means of solving plant growth challenges in real time and optimizing plant productivity and health.

[0578] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0579] Step 1:

[0580] User Input

[0581] The user uses the input form on the terminal screen to input the plant type, current temperature, current humidity, and plant growth stage. Specifically, the user inputs the information corresponding to each field via a keyboard or touch screen. The input information is converted into the appropriate format by the system and prepared for the next step of processing.

[0582] Input: Plant type, current temperature, current humidity, plant growth stage

[0583] Output: The formatted input data

[0584] Step 2:

[0585] Data transmission

[0586] The terminal properly formats the data entered by the user and sends it to the server using an HTTP request, typically in JSON format, where it is parsed once it reaches the server.

[0587] Input: Formatted input data

[0588] Output: Data sent to the server

[0589] Step 3:

[0590] Database Reference

[0591] The server analyzes the received data and retrieves the optimal temperature and humidity ranges for the corresponding plant from a database, which contains pre-registered optimal conditions for each plant, using an algorithm that performs a database query.

[0592] Input: Data sent to the server (type of plant)

[0593] Output: Optimal temperature and humidity range

[0594] Step 4:

[0595] Environmental Condition Assessment

[0596] The server evaluates whether the current temperature and humidity are within the optimal range retrieved from the database. This evaluation process is handled by an algorithm, and the evaluation result is formatted as "optimum temperature" or "optimum humidity."

[0597] Input: Current temperature, Current humidity, Optimal temperature range, Optimal humidity range

[0598] Output: Environmental condition evaluation results (optimal temperature, optimal humidity)

[0599] Step 5:

[0600] Prediction by AI model

[0601] The server uses an artificial intelligence model (e.g., random forest) to predict whether the current environmental conditions are optimal or not. The model is fed with current environmental data entered by the user and provides predictions based on past learning data.

[0602] Input: Current temperature, current humidity, past learning data

[0603] Output: Prediction results from the AI ​​model

[0604] Step 6:

[0605] Determining the recommended action

[0606] The server determines the recommended actions to present to the user based on the evaluation of environmental conditions and the predictions of the AI ​​model, and this decision process may involve the use of a condition-based rules engine.

[0607] Input: Environmental condition evaluation results, AI model prediction results

[0608] Output: Recommended Action

[0609] Step 7:

[0610] Guidance display

[0611] The device displays the recommended actions sent from the server to the user, who can then take appropriate training actions. The display format is easy for the user to understand, such as a text message or graph.

[0612] Input: Recommended Action

[0613] Output: Guidance displayed on the terminal

[0614] This series of processes allows users to check the optimal growing conditions for their plants in real time and take appropriate action immediately.

[0615] (Application example 1)

[0616] 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."

[0617] Maintaining optimal weather conditions is essential for plant growth. However, conventional methods have made it difficult to grasp the optimal growth conditions, which differ for each plant, in real time and manage them appropriately. Maintaining optimal environmental conditions and providing appropriate growth guidance is particularly challenging when managing a large number of plants at once in a brick-and-mortar store. Furthermore, users must manually determine the appropriate growth conditions each time, requiring efficient and accurate management. The objective of the present invention is to solve these problems and optimize plant growth and efficiency.

[0618] 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.

[0619] In this invention, the server includes means for receiving information on the plant type, current temperature, current humidity, and growth stage input by a user; means for retrieving optimal temperature and humidity ranges from a database based on the plant type; and means for evaluating whether the current temperature and humidity are within the optimal temperature and humidity ranges. This makes it possible to provide a user interface for managing plant inventory and display in a physical store. Furthermore, the optimal growing conditions for a plant can be presented as real-time guidance based on the information input by the user, thereby achieving effective plant growth. Furthermore, the server uses an artificial intelligence model to predict whether the current conditions are optimal, and determines and presents recommended actions to the user, thereby improving the efficiency and optimization of plant management.

[0620] "Plant type" refers to the taxonomic attributes and name of a particular plant, and is the basic data for identifying optimal growing conditions based on this information.

[0621] "Current temperature" refers to the temperature of the air surrounding the plant, and is an important variable for assessing the suitability of growing conditions.

[0622] "Current humidity" refers to the water vapor content in the environment in which the plant is located, and is an important factor in maintaining an environment suitable for growth.

[0623] "Growth stage" refers to the current developmental state of a plant in its life cycle and is a reference index for providing appropriate growing conditions.

[0624] "Database" refers to a system that stores information on the optimal temperature and humidity ranges for each type of plant and searches and retrieves it as needed.

[0625] "Artificial intelligence model" refers to a statistical model that uses machine learning algorithms to predict optimal development conditions and recommended actions from input data.

[0626] "User interface" refers to the screen and operating means through which the user inputs plant information and receives feedback from the system.

[0627] "Recommended actions" refer to specific courses of action the system suggests to the user based on current growing conditions and are necessary to optimize plant health.

[0628] The above definitions clarify each component of the invention and make it easier to understand its technical scope.

[0629] The present invention relates to a system that can check the optimal growing conditions for plants in real time. This system mainly consists of three components: a server, a terminal, and a user. The details are explained below.

[0630] 1. Basic system configuration

[0631] User Input

[0632] The user uses an input form on the device to input information about the plant type, current temperature, current humidity, and growth stage. Based on this information, the system determines the optimal growing conditions for the plant. For example, consider the case where a user inputs the plant type "rose," the current temperature is 25°C, the current humidity is 70%, and the growth stage is "pre-flowering."

[0633] Data transmission

[0634] The terminal transmits the data entered by the user to the server, properly formatting the data so that it can be uniquely identified by the server.

[0635] Database Reference

[0636] Based on the received data, the server retrieves the optimal temperature and humidity ranges for the corresponding plant from the database. For example, the optimal temperature for roses is 20-25°C, and the optimal humidity is 45-70%.

[0637] Environmental Condition Assessment

[0638] The server evaluates whether the current temperature and humidity are within the optimal range, and the results are summarized as "optimal temperature" and "optimal humidity."

[0639] Prediction by AI model

[0640] The server uses an artificial intelligence model (e.g., random forest) to predict whether the current environmental conditions are optimal, given the current temperature and humidity inputs from the user.

[0641] Determining the recommended action

[0642] The server determines the recommended actions to present to the user based on the results of the environmental condition evaluation and the prediction results of the AI ​​model.

[0643] Guidance display

[0644] The device displays the recommended actions sent from the server to the user. The user can check appropriate training guidance on the device screen. For example, "Current conditions are optimal" or "Conditions are not optimal. Please make adjustments."

[0645] 2. Hardware and Software Used

[0646] Hardware

[0647] Device (smartphone, tablet, etc.)

[0648] server

[0649] software

[0650] Front-end: HTML, CSS, and JavaScript to build the user interface

[0651] Backend: Python for data processing and running AI models

[0652] Database: SQL database for storing plant growth conditions

[0653] AI models: machine learning algorithms such as random forests

[0654] 3. Specific Examples

[0655] For example, suppose a clerk at a gardening shop inputs the following information into a terminal: "rose," current temperature 25°C, current humidity 70%, and growth stage "before flowering."

[0656] 1. User Input

[0657] The clerk enters the plant information and clicks the "Submit" button.

[0658] 2. Data Transmission

[0659] The terminal sends the input data to the server.

[0660] 3. Database Reference

[0661] The server receives the transmitted data and obtains the optimum temperature and humidity ranges for the roses.

[0662] 4. Assessment of Environmental Conditions

[0663] The server evaluates whether the current temperature and humidity are within the optimum range.

[0664] 5. Predictions using AI models

[0665] The server inputs the current temperature and humidity into the AI ​​model to predict whether it is optimal.

[0666] 6. Determining the recommended action

[0667] The server determines the recommended action based on the evaluation and prediction results.

[0668] 7. Guidance display

[0669] The terminal displays the guidance "Current conditions are optimal" received from the server to the store clerk.

[0670] Prompt Sentence Examples

[0671] Example of information the user enters into the terminal:

[0672] Plant type: Rose

[0673] Current temperature: 25℃

[0674] Current humidity: 70%

[0675] Growth stage: Pre-flowering

[0676] The above is an embodiment of the present invention.

[0677] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0678] Step 1:

[0679] The user uses the input form on the device to input information about the plant type, current temperature, current humidity, and growth stage. This information is used as the initial input data for the program. The input data is temporarily stored in the device's memory, and the user can proceed to the next step by clicking the "Submit" button.

[0680] Input: Plant type, current temperature, current humidity, growth stage

[0681] Output: Initial input data stored in the terminal memory

[0682] Step 2:

[0683] The terminal sends the data entered by the user to the server, properly formatting it so that it can be uniquely identified by the server, for example, in JSON format.

[0684] Input: Initial input data stored in the terminal memory

[0685] Output: The formatted data sent to the server

[0686] Step 3:

[0687] Based on the received data, the server retrieves the optimal temperature and humidity ranges for the corresponding plant from the database. The server then queries the database based on the type of plant to retrieve the appropriate temperature and humidity ranges. This data is stored in the server's memory.

[0688] Input: Formatted data sent to the server

[0689] Output: Optimal temperature and humidity ranges retrieved from the database

[0690] Step 4:

[0691] The server evaluates whether the current temperature and humidity are within the optimal range. It compares the acquired optimal temperature and humidity range with the current temperature and humidity sent by the user and generates an evaluation result. This evaluation result is organized as "optimum temperature" or "optimum humidity."

[0692] Input: Current temperature, current humidity, optimal temperature range, optimal humidity range

[0693] Output: Evaluation results (suitable / unsuitable temperature, suitable / unsuitable humidity)

[0694] Step 5:

[0695] The server uses an artificial intelligence model (e.g., random forest) to predict whether the current environmental conditions are optimal. The AI ​​model receives the current temperature and humidity pair entered by the user and outputs a predicted value, such as "optimal" or "not optimal."

[0696] Input: Current temperature, Current humidity

[0697] Output: Prediction results from the AI ​​model (optimal / non-optimal)

[0698] Step 6:

[0699] The server determines the recommended action to present to the user based on the results of the environmental condition evaluation and the prediction results of the AI ​​model. Recommended actions are expressed in the form of, for example, "The current conditions are optimal" or "The conditions are not optimal. Please make adjustments."

[0700] Input: Evaluation results, prediction results by AI model

[0701] Output: Recommended Action

[0702] Step 7:

[0703] The device receives the recommended actions sent from the server and displays them to the user. The recommended actions are displayed on the device screen, allowing the user to check appropriate plant cultivation guidance in real time.

[0704] Input: Recommended action sent by the server

[0705] Output: Recommended actions displayed on the terminal screen

[0706] 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.

[0707] This invention relates to a system that allows users to check the optimal conditions for growing plants in real time by inputting information on the type of plant, current temperature, current humidity, and growth stage. By further combining this invention with an emotion engine, it is possible to recognize the user's emotions and provide cultivation guidance in a more appropriate and user-friendly format.

[0708] Basic system configuration

[0709] User Input

[0710] The user uses an input form on the device screen to input the type of plant, the current temperature, the current humidity, and the plant's growth stage. The user's facial expression, voice, or text data is also collected at the same time. For example, the user inputs into the device that the plant is a "tomato," that the current temperature is 22°C, the current humidity is 60%, and that the growth stage is "flowering."

[0711] Data transmission

[0712] The device transmits the data and emotion information input by the user to the server, where the data is appropriately formatted and transmitted in a form that can be uniquely identified by the server.

[0713] Database Reference

[0714] Based on the received user data, the server retrieves the optimal environmental conditions (temperature range and humidity range) for the corresponding plant from the database. For example, it retrieves from the database that the optimal temperature for tomatoes is 20 to 25°C and the optimal humidity is 45 to 70%.

[0715] Environmental Condition Assessment

[0716] The server evaluates whether the current temperature and humidity are within the optimum range, and the evaluation results are organized in a format such as "optimum temperature" or "optimum humidity."

[0717] Prediction by AI model

[0718] The server uses an artificial intelligence model (e.g., random forest) to predict whether the current environmental conditions are optimal, given the current temperature and humidity inputs from the user.

[0719] Emotion engine for user emotion evaluation

[0720] The server uses an emotion engine to analyze the user's facial expressions, voice, and text data during input and recognize the user's emotions, for example, determining whether the user is expressing emotions such as "happy" or "troubled."

[0721] Determining the recommended action

[0722] The server determines the recommended action based on the results of the environmental condition evaluation, the AI ​​model's predictions, and the user's emotion recognition. If the emotion engine recognizes that the user is confused, it adjusts the guidance to provide more detailed explanations and additional advice. For example, if the user is confused, it provides specific guidance such as, "The temperature is a little high. Try lowering the temperature by 1-2 degrees. This will improve plant growth."

[0723] Guidance display

[0724] The device displays the recommended actions sent from the server to the user, who can then check the appropriate training guidance on the device screen. For example, a message such as "Current conditions are optimal" may be displayed, along with additional advice based on the user's emotions.

[0725] Specific examples

[0726] For example, consider a case where a user inputs information into a terminal about "tomato," the current temperature of 22°C, the current humidity of 60%, and the growth stage of "flowering," and shows a confused expression.

[0727] 1. Sending input data

[0728] The user enters information into the device and clicks the "Send" button. The device then sends the input data and emotion data to the server.

[0729] 2. Database lookup and environmental condition evaluation

[0730] The server receives the transmitted data and retrieves the optimal temperature range (20-25°C) and humidity range (45-70%) for tomatoes from the database. The server then evaluates whether the current temperature of 22°C and humidity of 60% are within the range. As a result, both are evaluated as "optimal temperature" and "optimal humidity."

[0731] 3. AI models make predictions and recommend actions

[0732] The server inputs the current temperature and humidity into the AI ​​model to make a prediction. The model predicts that the current conditions are optimal and determines the recommended action: "Current conditions are optimal."

[0733] 4. Emotion evaluation using an emotion engine

[0734] The server analyzes the user's facial expressions, voice, and text data sent from the terminal and recognizes that the user is confused.

[0735] 5. Adjusting recommended actions

[0736] Based on the user's sentiment, the server provides guidance such as "The current conditions are optimal," as well as additional advice such as "The temperature is a little high. You would get better results if you lowered the temperature by 1-2 degrees."

[0737] 6. Guidance display

[0738] The device receives the guidance sent from the server and displays it to the user. The user can see messages on the device screen such as "Current conditions are optimal" and "The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results."

[0739] In this way, the system of the present invention not only solves plant cultivation challenges faced by users in real time and provides effective means for optimizing plant productivity and health, but also takes user emotions into account, making the system even easier to use.

[0740] The processing flow will be explained below.

[0741] Step 1:

[0742] The user inputs information into the device. Using the device's input form, the user inputs the type of plant (e.g., tomato), the current temperature (e.g., 22°C), the current humidity (e.g., 60%), and the growth stage (e.g., flowering stage). The user's facial expressions, voice, or text messages are also collected.

[0743] Step 2:

[0744] The device transmits the input information and emotion data to the server. The device then formats the information appropriately and transmits it to the server. The transmitted data includes the plant type, current temperature, current humidity, growth stage information, and the user's emotion data.

[0745] Step 3:

[0746] The server receives the information sent by the user, verifies that the input data is in the correct format, and prepares to analyze the user's emotion data.

[0747] Step 4:

[0748] The server retrieves the optimal environmental conditions for a plant from the database. Based on the type of plant, the server retrieves the optimal temperature and humidity ranges from the database. For example, the server retrieves that the optimal temperature range for tomatoes is 20-25°C and the optimal humidity range is 45-70%.

[0749] Step 5:

[0750] The server evaluates the current temperature and humidity. The server determines whether the current temperature (22°C) and humidity (60%) received from the user are within the optimum temperature and humidity ranges. As a result, the server evaluates the temperature as "suitable" and the humidity as "suitable."

[0751] Step 6:

[0752] The server uses an artificial intelligence model to predict the optimal action. The server inputs the current temperature and humidity data into an artificial intelligence model (e.g., a random forest classifier) ​​to predict whether the current environmental conditions are optimal.

[0753] Step 7:

[0754] The server uses an emotion engine to recognize the user's emotions. The server analyzes the user's facial expressions, voice, and text data to determine what emotions the user is expressing. For example, it recognizes whether the user is confused.

[0755] Step 8:

[0756] The server determines the recommended action based on the results of the environmental condition evaluation, the AI ​​model predictions, and the user's emotion recognition. For example, if the user is confused, in addition to the basic action guidance of "The current conditions are optimal," the server provides additional advice such as "The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results."

[0757] Step 9:

[0758] The server transmits the recommended action to the terminal. The server transmits the determined recommended action to the terminal. The transmitted data includes the temperature status, the humidity status, the recommended action, and additional advice based on the user's emotion.

[0759] Step 10:

[0760] The device displays the recommended action to the user. The device displays the recommended action received from the server to the user. For example, messages such as "Current conditions are optimal" and "The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results" are displayed on the user's device screen, allowing the user to see appropriate cultivation guidance.

[0761] Example 2

[0762] 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."

[0763] Conventional plant cultivation systems can present optimal cultivation conditions based on the plant type and environmental conditions input by the user, but do not provide individualized responses that take the user's emotions into consideration. As a result, user satisfaction and system utilization efficiency may decrease. The purpose of this invention is to realize a user-friendly plant cultivation system by recognizing the user's emotions and providing more appropriate guidance based on them.

[0764] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving information on the type of plant, current temperature, current humidity, and growth stage input by the user; means for acquiring an optimal temperature range and an optimal humidity range from a database based on the type of plant; means for evaluating whether the current temperature and current humidity are within the optimal temperature and humidity ranges; means for predicting whether the current conditions are optimal using an artificial intelligence model; means including an emotion engine for analyzing the user's facial expression data, voice data, and text data and recognizing the user's emotions; and means for determining recommended actions based on the evaluation, prediction, and emotion recognition and presenting them to the user. This enables individual responses according to the user's emotions and makes it possible to provide plant cultivation guidance that will provide a higher level of satisfaction.

[0765] A "user" is an entity that operates the system and inputs information necessary for growing plants.

[0766] A "terminal" is a device through which a user inputs information and transmits that information to a server.

[0767] A "server" is a central computer system that receives information sent by users and performs processing such as database lookups, running artificial intelligence models, and sentiment analysis.

[0768] "Type of plant" is information indicating the name and type of plant designated as the target for cultivation.

[0769] "Current temperature" is information indicating the temperature value at the current time in the plant growing environment.

[0770] "Current humidity" is information indicating the current humidity value in the plant growing environment.

[0771] The "growth stage" is information that indicates the current stage of the growth of the plant, including, for example, the germination stage, growth stage, flowering stage, and harvest stage.

[0772] A "database" is an information system that stores information about optimal growing conditions for plants.

[0773] "Optimal temperature range" refers to the temperature range established to promote healthy plant growth.

[0774] The "optimal humidity range" refers to the humidity range established to promote healthy plant growth.

[0775] An "artificial intelligence model" is a model that uses machine learning algorithms to predict whether the current environmental conditions input by the user are optimal.

[0776] The "emotion engine" is a system that analyzes the user's facial expressions, voice, and text data to recognize the user's emotions.

[0777] "Recommended actions" refer to specific actions or advice suggested to the user based on the results of the assessment of environmental conditions, the prediction results of the artificial intelligence model, and the results of user emotion recognition.

[0778] This invention relates to a system that allows users to check the optimal conditions for growing plants in real time by inputting information on the type of plant, current temperature, current humidity, and growth stage. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotions and provide cultivation guidance in a more appropriate and user-friendly manner.

[0779] Basic system configuration

[0780] User Input

[0781] The user uses an input form on the device screen to input the type of plant, current temperature, current humidity, and growth stage. In addition, the user's facial expression data, voice data, and text data are also collected at the same time. For example, a user inputs into the device the type of plant "tomato," the current temperature is 22°C, the current humidity is 60%, and the growth stage is "flowering stage." At the same time, the device's camera and microphone record the user's facial expression and voice.

[0782] Data transmission

[0783] The device sends the data and emotion information entered by the user to the server. The data is properly formatted and sent in a way that allows the server to uniquely identify it. For example, when the device presses the "Send" button, a JSON-formatted data packet is sent to the server.

[0784] Database Reference

[0785] Based on the received user data, the server retrieves the optimal environmental conditions for the corresponding plant from the database. For example, the server retrieves from the database that the optimal temperature for tomatoes is 20-25°C and the optimal humidity is 45-70%. To do this, the server sends a query to the database to retrieve the necessary information.

[0786] Environmental Condition Assessment

[0787] The server evaluates whether the current temperature and humidity are within the optimal range. The evaluation results are organized in the form of "suitable temperature" and "suitable humidity." For example, the server checks whether the current temperature of 22°C and humidity of 60% are within the range, and evaluates them as "suitable temperature" and "suitable humidity."

[0788] Prediction by AI model

[0789] The server uses an artificial intelligence model, such as a random forest, to predict whether the current environmental conditions are optimal. The current temperature and humidity, entered by the user, are input into the model. For example, the server passes this data through a random forest model and obtains a result predicting that the current conditions are optimal.

[0790] Emotion engine for user emotion evaluation

[0791] The server uses an emotion engine to analyze the user's facial expressions, voice, and text data to recognize the user's emotions. For example, the server executes an emotion analysis algorithm to determine whether the user is expressing an emotion such as "distress."

[0792] Determining the recommended action

[0793] The server determines the recommended action based on the results of the environmental condition evaluation, the AI ​​model's predictions, and the user's emotion recognition. For example, if the emotion engine recognizes that the user is confused, it will provide a more detailed explanation or additional advice. Specifically, in addition to the message "The current conditions are optimal," it will determine additional advice such as "The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results."

[0794] Guidance display

[0795] The device displays the recommended actions sent from the server to the user. The user can then view appropriate cultivation guidance and additional advice on the device screen. For example, the device display might say, "The current conditions are optimal. The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results."

[0796] Specific examples

[0797] For example, consider a case where a user inputs information into a terminal about "tomato," the current temperature of 22°C, the current humidity of 60%, and the growth stage of "flowering," and shows a troubled expression.

[0798] 1. User Input

[0799] The user enters "tomato," the current temperature of 22°C, the current humidity of 60%, and the growth stage of "flowering" into the input form on the device, and clicks the "send" button. The device's camera also records the user's facial expressions.

[0800] 2. Data Transmission

[0801] The device sends this information to the server in JSON format.

[0802] 3. Database Reference

[0803] Based on the data received by the server, the optimal temperature range (20-25°C) and humidity range (45-70%) for tomatoes are retrieved from the database.

[0804] 4. Assessment of Environmental Conditions

[0805] The server evaluates the current temperature of 22°C and humidity of 60% and determines that the temperature and humidity are "appropriate."

[0806] 5. Predictions using AI models

[0807] The server inputs this data into a random forest model and predicts which current conditions are optimal.

[0808] 6. User Emotion Evaluation Using an Emotion Engine

[0809] The server analyzes the user's facial expression data and recognizes that the user is confused.

[0810] 7. Determining the recommended action

[0811] The server determines the message "Current conditions are optimal" plus additional advice such as "The temperature is a little high. Lowering the temperature by 1-2 degrees would give better results."

[0812] 8. Guidance display

[0813] The terminal displays these messages on the display and the user confirms them.

[0814] Prompt Sentence Examples

[0815] For example, you can simulate the behavior of this system by feeding the following prompts to the generative AI model:

[0816] If a user inputs information about "Tomato," current temperature 22°C, current humidity 60%, and growth stage "Flowering" and shows a confused expression, please explain in detail the growing guidance and additional advice that your server provides.

[0817] In this way, the system of the present invention not only solves plant cultivation challenges faced by users in real time and provides effective means for optimizing plant productivity and health, but also takes user emotions into account, making the system even easier to use.

[0818] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0819] Step 1: User Input

[0820] The user inputs the plant's type, current temperature, current humidity, and growth stage using an input form on the device screen, while facial expression, voice, and text data are also collected.

[0821] Input: Plant type (e.g., "Tomato"), current temperature (e.g., 22°C), current humidity (e.g., 60%), growth stage (e.g., "flowering stage"), user's facial expression data, voice data, and text data.

[0822] Output: This data is temporarily stored on the device.

[0823] Specific operation: The user enters "tomato," temperature 22°C, humidity 60%, and growth stage "flowering stage" into the input form on the device, and clicks the "Send" button. The device's camera and microphone record the user's facial expressions and voice data.

[0824] Step 2: Send data

[0825] The terminal transmits the data and emotion information input by the user to the server, where the data is appropriately formatted and transmitted in a form that can be uniquely identified by the server.

[0826] Input: The data entered in step 1.

[0827] Output: Data formatted in JSON is sent to the server.

[0828] Specific operation: When the "Send" button is pressed, the device sends the information entered by the user to the server in JSON format.

[0829] Step 3: Database Reference

[0830] The server retrieves the optimum environmental conditions for the plant from the database based on the received user data.

[0831] Input: User data formatted in JSON.

[0832] Output: Optimal temperature range (e.g. 20-25°C) and optimal humidity range (e.g. 45-70%) retrieved from the database.

[0833] What it does: The server queries the database to get the optimal environmental conditions for tomatoes.

[0834] Step 4: Evaluate environmental conditions

[0835] The server evaluates whether the current temperature and humidity are within the optimal range, and the results are summarized as "optimal temperature" and "optimal humidity."

[0836] Input: Optimal temperature and humidity ranges retrieved from the database, current temperature and humidity.

[0837] Output: Evaluation result (e.g., "suitable temperature" or "suitable humidity").

[0838] Specific operation: The server compares the current temperature of 22°C and humidity of 60% with the optimal range and evaluates the result as "optimal temperature and humidity."

[0839] Step 5: Prediction by AI model

[0840] The server uses artificial intelligence models such as random forests to predict whether the current environmental conditions are optimal.

[0841] Input: Current temperature and humidity.

[0842] Output: The predicted result of the AI ​​model (e.g., "Conditions are optimal").

[0843] Specific operation: The server inputs the current environmental data into the random forest model and obtains the prediction results.

[0844] Step 6: Evaluating user emotions with the emotion engine

[0845] The server uses an emotion engine to analyze the user's facial expressions, voice, and text data to recognize the user's emotions.

[0846] Input: User's facial expression data, voice data, and text data.

[0847] Output: User emotion recognition result (e.g., "confused").

[0848] Specific operation: The server runs an emotion analysis algorithm and detects the emotion "confused" from the user's facial expression data.

[0849] Step 7: Determine the recommended action

[0850] The server determines the recommended action based on the results of the environmental condition evaluation, the prediction results of the AI ​​model, and the user's emotion recognition results.

[0851] Input: Environmental condition assessment results, AI model prediction results, and user emotion recognition results.

[0852] Output: Recommended action (e.g., "Current conditions are optimal. The temperature is a little high. Lowering the temperature by 1-2 degrees would produce better results.").

[0853] Specific operation: The server integrates the evaluation results, prediction results, and emotion recognition results to determine the recommended action.

[0854] Step 8: View Guidance

[0855] The terminal displays the recommended actions sent from the server to the user.

[0856] Input: The recommended action sent by the server.

[0857] Output: Guidance shown on the device display (e.g., "Current conditions are optimal" and "The temperature is a little high. Lowering the temperature by 1-2 degrees would produce better results.").

[0858] Specific operation: The device receives a message from the server and displays it on the screen, allowing the user to check it on the screen.

[0859] (Application example 2)

[0860] 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."

[0861] While conventional plant cultivation support systems can evaluate and optimize plant cultivation conditions, they do not provide support that takes into account the user's emotions, which leads to issues such as a lack of effective support and improved user satisfaction. Another problem is that they are unable to provide appropriate feedback to address problems and anxieties specific to plant cultivation.

[0862] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving information on the type of plant, current temperature, current humidity, and growth stage input by the user, means for retrieving an optimal temperature range and an optimal humidity range from a database based on the type of plant, means for evaluating whether the current temperature and current humidity are within the optimal temperature and humidity ranges, means for predicting whether the current conditions are optimal using an artificial intelligence model, means for determining a recommended action based on the evaluation and prediction and presenting it to the user, means for inputting the user's emotions, and means for adjusting the presented recommended action based on the user's emotional information. This makes it possible to not only optimize plant growth but also provide flexible support that corresponds to the user's emotions.

[0863] "User" refers to a person who inputs information about plant growing conditions and receives recommended actions from the system.

[0864] "Plant type" refers to the name of the particular plant that the user wants to grow.

[0865] "Current temperature" refers to the current temperature of the environment where the plant is located, as entered by the user.

[0866] "Current humidity" refers to the current humidity of the environment in which the plant is placed, as input by the user.

[0867] "Growth stage" is information that indicates the current growth phase of the plant.

[0868] The "database" is an information management system that stores information on optimal growing conditions for plants.

[0869] "Optimal temperature range" refers to the range of temperatures in which a particular plant can grow optimally.

[0870] The "optimal humidity range" refers to the humidity range in which a particular plant can grow optimally.

[0871] An "artificial intelligence model" is a machine learning algorithm used to optimize plant growth conditions.

[0872] "Recommended Actions" refers to specific instructions for action to optimize plant growth based on current growing conditions.

[0873] "Emotion information" refers to data related to emotions input by the user, such as information extracted from facial expressions, voice, and text.

[0874] "Adjustment means" refers to the ability to change the recommended actions and guidance presented based on the user's emotional information.

[0875] This invention relates to a system that allows users to input information for plant cultivation and presents optimal cultivation conditions based on that information, and also provides support taking into account the user's feelings. The system is mainly composed of a server, terminals, and users.

[0876] 1. System Program Overview

[0877] User Input:

[0878] The device is equipped with a camera and voice input, and collects information about the plant's type, current temperature, current humidity, and growth stage from the device, as well as emotional information from the user's facial expressions and voice.

[0879] Data transmission:

[0880] The information and emotion information entered by the user are properly formatted and sent to the server using the device's communication function.

[0881] Database Reference:

[0882] Based on the received data, the server retrieves the optimal growing conditions (temperature and humidity ranges) for the corresponding plant from the database. For example, it retrieves information such as the optimal temperature for tomatoes being 20-25°C and the optimal humidity being 45-70%.

[0883] Environmental condition assessment:

[0884] The server evaluates whether the current temperature and humidity input are within the optimal range, and the evaluation results are presented in the form of "suitable temperature" and "suitable humidity."

[0885] Artificial intelligence model predictions:

[0886] The server uses a generative AI model, specifically an algorithm such as random forest, to predict whether the current environmental conditions are optimal for the plant.

[0887] Emotional evaluation by emotion engine:

[0888] The server analyzes the user's facial expressions and voice to recognize their emotional state, such as "confusion" or "fun," using OpenCV and emotion engine software built on top of it.

[0889] Determine and adjust the recommended actions:

[0890] The server determines the optimal recommended action based on the results of the environmental condition assessment, the AI ​​model predictions, and the emotion recognition results. For example, if the user is confused, more detailed explanations or additional advice will be provided.

[0891] Display guidance:

[0892] Finally, the device displays the recommended actions and guidance sent from the server to the user in a format that is easy for the user to view.

[0893] 2. System Operation

[0894] The hardware used includes smart glasses and smartphones, which input and display information.

[0895] The software uses Python, OpenCV, emotion engines, and generative AI models such as random forests.

[0896] Examples:

[0897] In a plant specialty store, a staff member puts on smart glasses and inputs "tomato," along with the current temperature of 22°C, humidity of 60%, and the growth stage of "flowering." If the staff member's expression looks confused, this information is sent to the server. The server evaluates the optimal growing conditions based on the input information and provides specific advice, such as "The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results."

[0898] Example prompt sentence:

[0899] "Scan the type of plant in the store, the current temperature, the current humidity, and the growth stage, and generate a sentence that provides optimal growing guidance if the user has a confused look on their face."

[0900] In this way, the present invention can solve the plant cultivation problems faced by users and further provide flexible and effective support that responds to the user's feelings.

[0901] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0902] Step 1: User Input

[0903] The user uses the device's input form to input the plant's type, current temperature, current humidity, and growth stage. The device also uses a camera and voice input to collect emotional information from the user's facial expressions and voice. If the user inputs information such as tomato, 22°C, 60%, and flowering stage, and shows a confused expression, this data is treated as input.

[0904] Step 2: Send data

[0905] The device formats the plant type, current temperature, current humidity, growth stage, and emotion information entered by the user and sends it to the server in JSON format as a POST request.

[0906] Step 3: Database Reference

[0907] The server receives the transmitted data and retrieves the optimal growing conditions (temperature and humidity ranges) for the corresponding plant from the database. For example, the server recognizes that the plant is a tomato and retrieves from the database the optimal temperature of 20 to 25°C and the optimal humidity of 45 to 70%.

[0908] Step 4: Evaluate environmental conditions

[0909] The server evaluates whether the current temperature (22°C) and humidity (60%) are within the optimal temperature and humidity ranges. Based on this evaluation, it determines which environmental conditions are suitable and outputs the results as "optimal temperature" and "optimal humidity."

[0910] Step 5: Prediction by artificial intelligence model

[0911] The server uses a generative AI model (e.g., random forest) to predict whether the current environmental conditions are optimal for the plant. The model takes the current temperature and humidity as input and outputs a prediction of whether the conditions are optimal.

[0912] Step 6: Emotion evaluation by the emotion engine

[0913] The server analyzes the user's facial expression and voice data sent from the device and uses an emotion engine to determine whether the user is confused. Based on this analysis, the server outputs the user's emotional state as "confused."

[0914] Step 7: Determine and adjust recommended actions

[0915] The server determines the optimal recommended action based on the results of the environmental condition evaluation, the AI ​​model predictions, and the emotion recognition results. For example, the server may use the message "The current conditions are optimal" as a base message, and if the user is confused, include additional advice such as "The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results."

[0916] Step 8: View Guidance

[0917] The device receives the recommended actions and guidance sent from the server and displays them to the user. The user can see messages on the device screen such as "Current conditions are optimal" and "The temperature is a little high. Lowering the temperature by 1-2 degrees will give better results."

[0918] Through the above processing steps, the system of the present invention can support the user's plant cultivation in real time and also provide flexible support according to the user's emotions.

[0919] 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.

[0920] 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.

[0921] 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.

[0922] [Third embodiment]

[0923] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0924] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0925] 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).

[0926] 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.

[0927] 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.

[0928] 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).

[0929] 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.

[0930] 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.

[0931] 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.

[0932] 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.

[0933] 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.

[0934] 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."

[0935] This invention relates to a system that allows users to check the optimal growing conditions for a plant in real time by inputting information on the plant's type, current temperature, current humidity, and growth stage. In this system, a server uses a database and an artificial intelligence model to evaluate the current environmental conditions and provide optimal growing guidance.

[0936] Basic system configuration

[0937] User Input

[0938] The user uses the input form on the terminal screen to input the type of plant, the current temperature, the current humidity, and the growth stage of the plant. For example, the user inputs into the terminal that the type of plant is "tomato," the current temperature is 22°C, the current humidity is 60%, and the growth stage is "flowering stage."

[0939] Data transmission

[0940] The terminal transmits the data entered by the user to the server, properly formatting the data so that it can be uniquely identified by the server.

[0941] Database Reference

[0942] Based on the received data, the server retrieves the optimal environmental conditions (temperature range and humidity range) for the corresponding plant from the database. For example, it may retrieve from the database that the optimal temperature for tomatoes is 20 to 25°C and the optimal humidity is 45 to 70%.

[0943] Environmental Condition Assessment

[0944] The server evaluates whether the current temperature and humidity are within the optimum range, and the evaluation results are organized in a format such as "optimum temperature" or "optimum humidity."

[0945] Prediction by AI model

[0946] The server uses an artificial intelligence model (e.g., random forest) to predict whether the current environmental conditions are optimal, given the current temperature and humidity inputs from the user.

[0947] Determining the recommended action

[0948] The server determines the recommended action to present to the user based on the results of the environmental condition evaluation and the prediction results of the AI ​​model. For example, specific action guidance such as "The current conditions are optimal" or "Please lower the temperature" is determined.

[0949] Guidance display

[0950] The device displays the recommended actions sent from the server to the user, who can then check the appropriate training guidance on the device screen.

[0951] Specific examples

[0952] For example, consider the case where a user inputs information into the terminal about "tomato," the current temperature of 22°C, the current humidity of 60%, and the growth stage of "flowering."

[0953] 1. Sending input data

[0954] The user enters information into the terminal and clicks the "Submit" button. The terminal sends the input data to the server.

[0955] 2. Database lookup and environmental condition evaluation

[0956] The server receives the transmitted data and retrieves the optimal temperature range (20-25°C) and humidity range (45-70%) for tomatoes from the database. The server then evaluates whether the current temperature of 22°C and humidity of 60% are within the range. As both are within the range, the server evaluates them as "optimal temperature" and "optimal humidity."

[0957] 3. AI models make predictions and recommend actions

[0958] The server inputs the current temperature and humidity into the AI ​​model to make a prediction. The model predicts that the current conditions are optimal and determines the recommended action: "Current conditions are optimal."

[0959] 4. Guidance display

[0960] The terminal receives the guidance sent from the server, saying "Current conditions are optimal," and displays it to the user. The user can check the guidance on the terminal screen and take appropriate measures to grow the plant.

[0961] In this way, the system of the present invention provides users with an effective means of solving plant growth challenges in real time and optimizing plant productivity and health.

[0962] The processing flow will be explained below.

[0963] Step 1:

[0964] The user inputs information into the terminal. Using the terminal's input form, the user inputs the plant type (e.g., tomato), the current temperature (e.g., 22°C), the current humidity (e.g., 60%), and the growth stage (e.g., flowering stage).

[0965] Step 2:

[0966] The terminal transmits the input information to the server, which then formats the information appropriately and transmits it to the server. The transmitted data includes the plant type, current temperature, current humidity, and growth stage information.

[0967] Step 3:

[0968] The server receives the information sent by the user and verifies that the entered data is in the correct format.

[0969] Step 4:

[0970] The server retrieves the optimal environmental conditions for a plant from the database. Based on the type of plant, the server retrieves the optimal temperature and humidity ranges from the database. For example, the server retrieves that the optimal temperature range for tomatoes is 20-25°C and the optimal humidity range is 45-70%.

[0971] Step 5:

[0972] The server evaluates the current temperature and humidity. The server determines whether the current temperature (22°C) and humidity (60%) received from the user are within the optimal temperature and humidity ranges. As a result, the server evaluates the temperature as "suitable" and the humidity as "suitable."

[0973] Step 6:

[0974] The server uses an artificial intelligence model to predict the optimal action. The server inputs the current temperature and humidity data into an artificial intelligence model (e.g., a random forest classifier) ​​to predict whether the current environmental conditions are optimal.

[0975] Step 7:

[0976] The server determines the recommended action. The server determines the recommended action based on the prediction results of the artificial intelligence model and the evaluation results of the environmental conditions. For example, the server determines the recommended action as "The current conditions are optimal."

[0977] Step 8:

[0978] The server sends the recommended action to the terminal. The server sends the determined recommended action to the terminal. The sent data includes the temperature status, humidity status, and recommended action.

[0979] Step 9:

[0980] The terminal displays the recommended action to the user. The terminal displays the recommended action received from the server to the user. For example, a message saying "Current conditions are optimal" is displayed on the user's terminal screen. As a result, the user can take appropriate measures to grow the plant.

[0981] Example 1

[0982] 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."

[0983] In conventional plant cultivation systems, it was difficult for users to manually evaluate the plant's type, growth stage, and current environmental conditions to determine the optimal cultivation conditions. Furthermore, there was a high possibility of errors occurring during the manual data evaluation process, and optimal cultivation conditions were not always guaranteed. Furthermore, it was difficult to quickly respond to changes in environmental conditions in real time. Given these issues, there was a need for a system that would allow users to easily check the optimal cultivation conditions for plants and quickly take appropriate cultivation actions.

[0984] 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.

[0985] In this invention, the server includes means for receiving information on the type of plant, current temperature, current humidity, and growth stage input by a user, means for retrieving an optimal temperature range and an optimal humidity range from a database based on the type of plant, means for evaluating whether the current temperature and current humidity are within the optimal temperature and humidity ranges, means for predicting whether the current conditions are optimal using an artificial intelligence model, means for determining a recommended action based on the evaluation and prediction and presenting it to the user, means for appropriately formatting the input from the user and transmitting it to the server, and means for displaying the recommended action transmitted from the server to the user. This enables a user to check the optimal growing conditions for a plant in real time and quickly take appropriate growing action.

[0986] A "user" is an individual or entity that provides plant-growing information and receives recommended actions from the system.

[0987] An "input form" is a screen element provided on a terminal for a user to input information about the type of plant, the current temperature, the current humidity, and the growth stage.

[0988] A "terminal" is an electronic device that a user uses to enter information and communicate with a server.

[0989] "Type of plant" is information indicating the classification of a particular plant that the user is cultivating.

[0990] "Growth stage" is information indicating the developmental phase in which the plant is currently located.

[0991] A "database" is a system for storing and managing information such as optimal environmental conditions for plants.

[0992] The "optimum temperature range" is information indicating the temperature range that is most suitable for a particular plant.

[0993] The "optimum humidity range" is information indicating the humidity range that is most suitable for a particular plant.

[0994] "Evaluation" is the process of determining whether the current temperature is within the optimum temperature range and whether the current humidity is within the optimum humidity range.

[0995] An "artificial intelligence model" is a model that uses machine learning algorithms to analyze data and make predictions and judgments.

[0996] "Recommended actions" are specific measures for development that are presented to the user based on the evaluation and prediction.

[0997] "Formatting" is the process of properly shaping data so that it can be properly understood and processed by the server.

[0998] "Send" is the process of transferring data entered by the user from the terminal to the server.

[0999] "Display" is the process of showing the recommended actions received from the server to the user.

[1000] This invention relates to a system that allows users to check the optimal growing conditions for a plant in real time by inputting information on the plant's type, current temperature, current humidity, and growth stage. In this system, a server uses a database and an artificial intelligence model to evaluate the current environmental conditions and provide optimal growing guidance.

[1001] User Input

[1002] The user uses an input form on the terminal screen to input the type of plant, the current temperature, the current humidity, and the growth stage of the plant. For example, the user inputs into the terminal that the plant type is "tomato," the current temperature is 22°C, the current humidity is 60%, and the growth stage is "flowering stage." This information is entered manually by the user and is in a format that can be modified as needed.

[1003] Data transmission

[1004] The terminal sends the data entered by the user to the server, properly formatting it so that it can be uniquely identified by the server. Data formats such as JSON and XML are commonly used, but other formats can also be applied depending on the system requirements.

[1005] Database Reference

[1006] Based on the received data, the server retrieves the optimal environmental conditions (temperature and humidity ranges) for the corresponding plant from the database. For example, it may learn from the database that the optimal temperature for tomatoes is 20-25°C and the optimal humidity is 45-70%. This database holds detailed growing information for each type of plant and is designed to allow the server to access it quickly.

[1007] Environmental Condition Assessment

[1008] The server evaluates whether the current temperature and humidity are within the optimal range. The evaluation results are organized as, for example, "optimal temperature" or "optimal humidity." The evaluation process is carried out by an algorithm that compares the current environmental data with the optimal conditions retrieved from a database.

[1009] Prediction by AI model

[1010] The server uses an artificial intelligence model (e.g., random forest) to predict whether the current environmental conditions are optimal. The current temperature and humidity are input into the model, which incorporates learning data based on past data and environmental conditions, allowing it to make highly accurate predictions.

[1011] Determining the recommended action

[1012] The server determines the recommended action to present to the user based on the results of the environmental condition evaluation and the prediction results of the AI ​​model. For example, specific action guidance such as "Current conditions are optimal" or "Please lower the temperature" is determined. This allows the user to quickly take appropriate cultivation measures.

[1013] Guidance display

[1014] The device displays the recommended actions sent from the server to the user, who can then check the appropriate training guidance on the device screen. While various display formats are possible, such as text messages or graphical interfaces, a format that is intuitively easy for users to understand is desirable.

[1015] Specific examples

[1016] For example, consider the case where a user inputs information into the terminal about "tomato," the current temperature of 22°C, the current humidity of 60%, and the growth stage of "flowering."

[1017] 1. Sending input data

[1018] The user enters information into the terminal and clicks the "Submit" button. The terminal sends the input data to the server.

[1019] 2. Database lookup and environmental condition evaluation

[1020] The server receives the transmitted data and retrieves the optimal temperature range (20-25°C) and humidity range (45-70%) for tomatoes from the database. The server then evaluates whether the current temperature of 22°C and humidity of 60% are within the range. As both are within the range, the server evaluates them as "optimal temperature" and "optimal humidity."

[1021] 3. AI models make predictions and recommend actions

[1022] The server inputs the current temperature and humidity into the AI ​​model to make a prediction. The model predicts that the current conditions are optimal and determines the recommended action: "Current conditions are optimal."

[1023] 4. Guidance display

[1024] The terminal receives the guidance sent from the server, saying "Current conditions are optimal," and displays it to the user. The user can check the guidance on the terminal screen and take appropriate measures to grow the plant.

[1025] Prompt Sentence Examples

[1026] The following prompt would confirm the optimal growing conditions for a "tomato" in the "flowering" growth stage when the current temperature is 22°C and humidity is 60%:

[1027] What are the optimal growing conditions for tomatoes during the flowering stage? The current temperature is 22°C and humidity is 60%.

[1028] In this way, the system of the present invention provides users with an effective means of solving plant growth challenges in real time and optimizing plant productivity and health.

[1029] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1030] Step 1:

[1031] User Input

[1032] The user uses the input form on the terminal screen to input the plant type, current temperature, current humidity, and plant growth stage. Specifically, the user inputs the information corresponding to each field via a keyboard or touch screen. The input information is converted into the appropriate format by the system and prepared for the next step of processing.

[1033] Input: Plant type, current temperature, current humidity, plant growth stage

[1034] Output: The formatted input data

[1035] Step 2:

[1036] Data transmission

[1037] The terminal properly formats the data entered by the user and sends it to the server using an HTTP request, typically in JSON format, where it is parsed once it reaches the server.

[1038] Input: Formatted input data

[1039] Output: Data sent to the server

[1040] Step 3:

[1041] Database Reference

[1042] The server analyzes the received data and retrieves the optimal temperature and humidity ranges for the corresponding plant from a database, which contains pre-registered optimal conditions for each plant, using an algorithm that performs a database query.

[1043] Input: Data sent to the server (type of plant)

[1044] Output: Optimal temperature and humidity range

[1045] Step 4:

[1046] Environmental Condition Assessment

[1047] The server evaluates whether the current temperature and humidity are within the optimal range retrieved from the database. This evaluation process is handled by an algorithm, and the evaluation result is formatted as "optimum temperature" or "optimum humidity."

[1048] Input: Current temperature, Current humidity, Optimal temperature range, Optimal humidity range

[1049] Output: Environmental condition evaluation results (optimal temperature, optimal humidity)

[1050] Step 5:

[1051] Prediction by AI model

[1052] The server uses an artificial intelligence model (e.g., random forest) to predict whether the current environmental conditions are optimal or not. The model is fed with current environmental data entered by the user and provides predictions based on past learning data.

[1053] Input: Current temperature, current humidity, past learning data

[1054] Output: Prediction results from the AI ​​model

[1055] Step 6:

[1056] Determining the recommended action

[1057] The server determines the recommended actions to present to the user based on the evaluation of environmental conditions and the predictions of the AI ​​model, and this decision process may involve the use of a condition-based rules engine.

[1058] Input: Environmental condition evaluation results, AI model prediction results

[1059] Output: Recommended Action

[1060] Step 7:

[1061] Guidance display

[1062] The device displays the recommended actions sent from the server to the user, who can then take appropriate training actions. The display format is easy for the user to understand, such as a text message or graph.

[1063] Input: Recommended Action

[1064] Output: Guidance displayed on the terminal

[1065] This series of processes allows users to check the optimal growing conditions for their plants in real time and take appropriate action immediately.

[1066] (Application example 1)

[1067] 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."

[1068] Maintaining optimal weather conditions is essential for plant growth. However, conventional methods have made it difficult to grasp the optimal growth conditions, which differ for each plant, in real time and manage them appropriately. Maintaining optimal environmental conditions and providing appropriate growth guidance is particularly challenging when managing a large number of plants at once in a brick-and-mortar store. Furthermore, users must manually determine the appropriate growth conditions each time, requiring efficient and accurate management. The objective of the present invention is to solve these problems and optimize plant growth and efficiency.

[1069] 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.

[1070] In this invention, the server includes means for receiving information on the plant type, current temperature, current humidity, and growth stage input by a user; means for retrieving optimal temperature and humidity ranges from a database based on the plant type; and means for evaluating whether the current temperature and humidity are within the optimal temperature and humidity ranges. This makes it possible to provide a user interface for managing plant inventory and display in a physical store. Furthermore, the optimal growing conditions for a plant can be presented as real-time guidance based on the information input by the user, thereby achieving effective plant growth. Furthermore, the server uses an artificial intelligence model to predict whether the current conditions are optimal, and determines and presents recommended actions to the user, thereby improving the efficiency and optimization of plant management.

[1071] "Plant type" refers to the taxonomic attributes and name of a particular plant, and is the basic data for identifying optimal growing conditions based on this information.

[1072] "Current temperature" refers to the temperature of the air surrounding the plant, and is an important variable for assessing the suitability of growing conditions.

[1073] "Current humidity" refers to the water vapor content in the environment in which the plant is located, and is an important factor in maintaining an environment suitable for growth.

[1074] "Growth stage" refers to the current developmental state of a plant in its life cycle and is a reference index for providing appropriate growing conditions.

[1075] "Database" refers to a system that stores information on the optimal temperature and humidity ranges for each type of plant and searches and retrieves it as needed.

[1076] "Artificial intelligence model" refers to a statistical model that uses machine learning algorithms to predict optimal development conditions and recommended actions from input data.

[1077] "User interface" refers to the screen and operating means through which the user inputs plant information and receives feedback from the system.

[1078] "Recommended actions" refer to specific courses of action the system suggests to the user based on current growing conditions and are necessary to optimize plant health.

[1079] The above definitions clarify each component of the invention and make it easier to understand its technical scope.

[1080] The present invention relates to a system that can check the optimal growing conditions for plants in real time. This system mainly consists of three components: a server, a terminal, and a user. The details are explained below.

[1081] 1. Basic system configuration

[1082] User Input

[1083] The user uses an input form on the device to input information about the plant type, current temperature, current humidity, and growth stage. Based on this information, the system determines the optimal growing conditions for the plant. For example, consider the case where a user inputs the plant type "rose," the current temperature is 25°C, the current humidity is 70%, and the growth stage is "pre-flowering."

[1084] Data transmission

[1085] The terminal transmits the data entered by the user to the server, properly formatting the data so that it can be uniquely identified by the server.

[1086] Database Reference

[1087] Based on the received data, the server retrieves the optimal temperature and humidity ranges for the corresponding plant from the database. For example, the optimal temperature for roses is 20-25°C, and the optimal humidity is 45-70%.

[1088] Environmental Condition Assessment

[1089] The server evaluates whether the current temperature and humidity are within the optimal range, and the results are summarized as "optimal temperature" and "optimal humidity."

[1090] Prediction by AI model

[1091] The server uses an artificial intelligence model (e.g., random forest) to predict whether the current environmental conditions are optimal, given the current temperature and humidity inputs from the user.

[1092] Determining the recommended action

[1093] The server determines the recommended actions to present to the user based on the results of the environmental condition evaluation and the prediction results of the AI ​​model.

[1094] Guidance display

[1095] The device displays the recommended actions sent from the server to the user. The user can check appropriate training guidance on the device screen. For example, "Current conditions are optimal" or "Conditions are not optimal. Please make adjustments."

[1096] 2. Hardware and Software Used

[1097] Hardware

[1098] Device (smartphone, tablet, etc.)

[1099] server

[1100] software

[1101] Front-end: HTML, CSS, and JavaScript to build the user interface

[1102] Backend: Python for data processing and running AI models

[1103] Database: SQL database for storing plant growth conditions

[1104] AI models: machine learning algorithms such as random forests

[1105] 3. Specific Examples

[1106] For example, suppose a clerk at a gardening shop inputs the following information into a terminal: "rose," current temperature 25°C, current humidity 70%, and growth stage "before flowering."

[1107] 1. User Input

[1108] The clerk enters the plant information and clicks the "Submit" button.

[1109] 2. Data Transmission

[1110] The terminal sends the input data to the server.

[1111] 3. Database Reference

[1112] The server receives the transmitted data and obtains the optimum temperature and humidity ranges for the roses.

[1113] 4. Assessment of Environmental Conditions

[1114] The server evaluates whether the current temperature and humidity are within the optimum range.

[1115] 5. Predictions using AI models

[1116] The server inputs the current temperature and humidity into the AI ​​model to predict whether it is optimal.

[1117] 6. Determining the recommended action

[1118] The server determines the recommended action based on the evaluation and prediction results.

[1119] 7. Guidance display

[1120] The terminal displays the guidance "Current conditions are optimal" received from the server to the store clerk.

[1121] Prompt Sentence Examples

[1122] Example of information the user enters into the terminal:

[1123] Plant type: Rose

[1124] Current temperature: 25℃

[1125] Current humidity: 70%

[1126] Growth stage: Pre-flowering

[1127] The above is an embodiment of the present invention.

[1128] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1129] Step 1:

[1130] The user uses the input form on the device to input information about the plant type, current temperature, current humidity, and growth stage. This information is used as the initial input data for the program. The input data is temporarily stored in the device's memory, and the user can proceed to the next step by clicking the "Submit" button.

[1131] Input: Plant type, current temperature, current humidity, growth stage

[1132] Output: Initial input data stored in the terminal memory

[1133] Step 2:

[1134] The terminal sends the data entered by the user to the server, properly formatting it so that it can be uniquely identified by the server, for example, in JSON format.

[1135] Input: Initial input data stored in the terminal memory

[1136] Output: The formatted data sent to the server

[1137] Step 3:

[1138] Based on the received data, the server retrieves the optimal temperature and humidity ranges for the corresponding plant from the database. The server then queries the database based on the type of plant to retrieve the appropriate temperature and humidity ranges. This data is stored in the server's memory.

[1139] Input: Formatted data sent to the server

[1140] Output: Optimal temperature and humidity ranges retrieved from the database

[1141] Step 4:

[1142] The server evaluates whether the current temperature and humidity are within the optimal range. It compares the acquired optimal temperature and humidity range with the current temperature and humidity sent by the user and generates an evaluation result. This evaluation result is organized as "optimum temperature" or "optimum humidity."

[1143] Input: Current temperature, current humidity, optimal temperature range, optimal humidity range

[1144] Output: Evaluation results (suitable / unsuitable temperature, suitable / unsuitable humidity)

[1145] Step 5:

[1146] The server uses an artificial intelligence model (e.g., random forest) to predict whether the current environmental conditions are optimal. The AI ​​model receives the current temperature and humidity pair entered by the user and outputs a predicted value, such as "optimal" or "not optimal."

[1147] Input: Current temperature, Current humidity

[1148] Output: Prediction results from the AI ​​model (optimal / non-optimal)

[1149] Step 6:

[1150] The server determines the recommended action to present to the user based on the results of the environmental condition evaluation and the prediction results of the AI ​​model. Recommended actions are expressed in the form of, for example, "The current conditions are optimal" or "The conditions are not optimal. Please make adjustments."

[1151] Input: Evaluation results, prediction results by AI model

[1152] Output: Recommended Action

[1153] Step 7:

[1154] The device receives the recommended actions sent from the server and displays them to the user. The recommended actions are displayed on the device screen, allowing the user to check appropriate plant cultivation guidance in real time.

[1155] Input: Recommended action sent by the server

[1156] Output: Recommended actions displayed on the terminal screen

[1157] 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.

[1158] This invention relates to a system that allows users to check the optimal conditions for growing plants in real time by inputting information on the type of plant, current temperature, current humidity, and growth stage. By further combining this invention with an emotion engine, it is possible to recognize the user's emotions and provide cultivation guidance in a more appropriate and user-friendly format.

[1159] Basic system configuration

[1160] User Input

[1161] The user uses an input form on the device screen to input the type of plant, the current temperature, the current humidity, and the plant's growth stage. The user's facial expression, voice, or text data is also collected at the same time. For example, the user inputs into the device that the plant is a "tomato," that the current temperature is 22°C, the current humidity is 60%, and that the growth stage is "flowering."

[1162] Data transmission

[1163] The device transmits the data and emotion information input by the user to the server, where the data is appropriately formatted and transmitted in a form that can be uniquely identified by the server.

[1164] Database Reference

[1165] Based on the received user data, the server retrieves the optimal environmental conditions (temperature range and humidity range) for the corresponding plant from the database. For example, it retrieves from the database that the optimal temperature for tomatoes is 20 to 25°C and the optimal humidity is 45 to 70%.

[1166] Environmental Condition Assessment

[1167] The server evaluates whether the current temperature and humidity are within the optimum range, and the evaluation results are organized in a format such as "optimum temperature" or "optimum humidity."

[1168] Prediction by AI model

[1169] The server uses an artificial intelligence model (e.g., random forest) to predict whether the current environmental conditions are optimal, given the current temperature and humidity inputs from the user.

[1170] Emotion engine for user emotion evaluation

[1171] The server uses an emotion engine to analyze the user's facial expressions, voice, and text data during input and recognize the user's emotions, for example, determining whether the user is expressing emotions such as "happy" or "troubled."

[1172] Determining the recommended action

[1173] The server determines the recommended action based on the results of the environmental condition evaluation, the AI ​​model's predictions, and the user's emotion recognition. If the emotion engine recognizes that the user is confused, it adjusts the guidance to provide more detailed explanations and additional advice. For example, if the user is confused, it provides specific guidance such as, "The temperature is a little high. Try lowering the temperature by 1-2 degrees. This will improve plant growth."

[1174] Guidance display

[1175] The device displays the recommended actions sent from the server to the user, who can then check the appropriate training guidance on the device screen. For example, a message such as "Current conditions are optimal" may be displayed, along with additional advice based on the user's emotions.

[1176] Specific examples

[1177] For example, consider a case where a user inputs information into a terminal about "tomato," the current temperature of 22°C, the current humidity of 60%, and the growth stage of "flowering," and shows a confused expression.

[1178] 1. Sending input data

[1179] The user enters information into the device and clicks the "Send" button. The device then sends the input data and emotion data to the server.

[1180] 2. Database lookup and environmental condition evaluation

[1181] The server receives the transmitted data and retrieves the optimal temperature range (20-25°C) and humidity range (45-70%) for tomatoes from the database. The server then evaluates whether the current temperature of 22°C and humidity of 60% are within the range. As a result, both are evaluated as "optimal temperature" and "optimal humidity."

[1182] 3. AI models make predictions and recommend actions

[1183] The server inputs the current temperature and humidity into the AI ​​model to make a prediction. The model predicts that the current conditions are optimal and determines the recommended action: "Current conditions are optimal."

[1184] 4. Emotion evaluation using an emotion engine

[1185] The server analyzes the user's facial expressions, voice, and text data sent from the terminal and recognizes that the user is confused.

[1186] 5. Adjusting recommended actions

[1187] Based on the user's sentiment, the server provides guidance such as "The current conditions are optimal," as well as additional advice such as "The temperature is a little high. You would get better results if you lowered the temperature by 1-2 degrees."

[1188] 6. Guidance display

[1189] The device receives the guidance sent from the server and displays it to the user. The user can see messages on the device screen such as "Current conditions are optimal" and "The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results."

[1190] In this way, the system of the present invention not only solves plant cultivation challenges faced by users in real time and provides effective means for optimizing plant productivity and health, but also takes user emotions into account, making the system even easier to use.

[1191] The processing flow will be explained below.

[1192] Step 1:

[1193] The user inputs information into the device. Using the device's input form, the user inputs the type of plant (e.g., tomato), the current temperature (e.g., 22°C), the current humidity (e.g., 60%), and the growth stage (e.g., flowering stage). The user's facial expressions, voice, or text messages are also collected.

[1194] Step 2:

[1195] The device transmits the input information and emotion data to the server. The device then formats the information appropriately and transmits it to the server. The transmitted data includes the plant type, current temperature, current humidity, growth stage information, and the user's emotion data.

[1196] Step 3:

[1197] The server receives the information sent by the user, verifies that the input data is in the correct format, and prepares to analyze the user's emotion data.

[1198] Step 4:

[1199] The server retrieves the optimal environmental conditions for a plant from the database. Based on the type of plant, the server retrieves the optimal temperature and humidity ranges from the database. For example, the server retrieves that the optimal temperature range for tomatoes is 20-25°C and the optimal humidity range is 45-70%.

[1200] Step 5:

[1201] The server evaluates the current temperature and humidity. The server determines whether the current temperature (22°C) and humidity (60%) received from the user are within the optimum temperature and humidity ranges. As a result, the server evaluates the temperature as "suitable" and the humidity as "suitable."

[1202] Step 6:

[1203] The server uses an artificial intelligence model to predict the optimal action. The server inputs the current temperature and humidity data into an artificial intelligence model (e.g., a random forest classifier) ​​to predict whether the current environmental conditions are optimal.

[1204] Step 7:

[1205] The server uses an emotion engine to recognize the user's emotions. The server analyzes the user's facial expressions, voice, and text data to determine what emotions the user is expressing. For example, it recognizes whether the user is confused.

[1206] Step 8:

[1207] The server determines the recommended action based on the results of the environmental condition evaluation, the AI ​​model predictions, and the user's emotion recognition. For example, if the user is confused, in addition to the basic action guidance of "The current conditions are optimal," the server provides additional advice such as "The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results."

[1208] Step 9:

[1209] The server transmits the recommended action to the terminal. The server transmits the determined recommended action to the terminal. The transmitted data includes the temperature status, the humidity status, the recommended action, and additional advice based on the user's emotion.

[1210] Step 10:

[1211] The device displays the recommended action to the user. The device displays the recommended action received from the server to the user. For example, messages such as "Current conditions are optimal" and "The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results" are displayed on the user's device screen, allowing the user to see appropriate cultivation guidance.

[1212] Example 2

[1213] 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."

[1214] Conventional plant cultivation systems can present optimal cultivation conditions based on the plant type and environmental conditions input by the user, but do not provide individualized responses that take the user's emotions into consideration. As a result, user satisfaction and system utilization efficiency may decrease. The purpose of this invention is to realize a user-friendly plant cultivation system by recognizing the user's emotions and providing more appropriate guidance based on them.

[1215] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving information on the type of plant, current temperature, current humidity, and growth stage input by the user; means for acquiring an optimal temperature range and an optimal humidity range from a database based on the type of plant; means for evaluating whether the current temperature and current humidity are within the optimal temperature and humidity ranges; means for predicting whether the current conditions are optimal using an artificial intelligence model; means including an emotion engine for analyzing the user's facial expression data, voice data, and text data and recognizing the user's emotions; and means for determining recommended actions based on the evaluation, prediction, and emotion recognition and presenting them to the user. This enables individual responses according to the user's emotions and makes it possible to provide plant cultivation guidance that will provide a higher level of satisfaction.

[1216] A "user" is an entity that operates the system and inputs information necessary for growing plants.

[1217] A "terminal" is a device through which a user inputs information and transmits that information to a server.

[1218] A "server" is a central computer system that receives information sent by users and performs processing such as database lookups, running artificial intelligence models, and sentiment analysis.

[1219] "Type of plant" is information indicating the name and type of plant designated as the target for cultivation.

[1220] "Current temperature" is information indicating the temperature value at the current time in the plant growing environment.

[1221] "Current humidity" is information indicating the current humidity value in the plant growing environment.

[1222] The "growth stage" is information that indicates the current stage of the growth of the plant, including, for example, the germination stage, growth stage, flowering stage, and harvest stage.

[1223] A "database" is an information system that stores information about optimal growing conditions for plants.

[1224] "Optimal temperature range" refers to the temperature range established to promote healthy plant growth.

[1225] The "optimal humidity range" refers to the humidity range established to promote healthy plant growth.

[1226] An "artificial intelligence model" is a model that uses machine learning algorithms to predict whether the current environmental conditions input by the user are optimal.

[1227] The "emotion engine" is a system that analyzes the user's facial expressions, voice, and text data to recognize the user's emotions.

[1228] "Recommended actions" refer to specific actions or advice suggested to the user based on the results of the assessment of environmental conditions, the prediction results of the artificial intelligence model, and the results of user emotion recognition.

[1229] This invention relates to a system that allows users to check the optimal conditions for growing plants in real time by inputting information on the type of plant, current temperature, current humidity, and growth stage. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotions and provide cultivation guidance in a more appropriate and user-friendly manner.

[1230] Basic system configuration

[1231] User Input

[1232] The user uses an input form on the device screen to input the type of plant, current temperature, current humidity, and growth stage. In addition, the user's facial expression data, voice data, and text data are also collected at the same time. For example, a user inputs into the device the type of plant "tomato," the current temperature is 22°C, the current humidity is 60%, and the growth stage is "flowering stage." At the same time, the device's camera and microphone record the user's facial expression and voice.

[1233] Data transmission

[1234] The device sends the data and emotion information entered by the user to the server. The data is properly formatted and sent in a way that allows the server to uniquely identify it. For example, when the device presses the "Send" button, a JSON-formatted data packet is sent to the server.

[1235] Database Reference

[1236] Based on the received user data, the server retrieves the optimal environmental conditions for the corresponding plant from the database. For example, the server retrieves from the database that the optimal temperature for tomatoes is 20-25°C and the optimal humidity is 45-70%. To do this, the server sends a query to the database to retrieve the necessary information.

[1237] Environmental Condition Assessment

[1238] The server evaluates whether the current temperature and humidity are within the optimal range. The evaluation results are organized in the form of "suitable temperature" and "suitable humidity." For example, the server checks whether the current temperature of 22°C and humidity of 60% are within the range, and evaluates them as "suitable temperature" and "suitable humidity."

[1239] Prediction by AI model

[1240] The server uses an artificial intelligence model, such as a random forest, to predict whether the current environmental conditions are optimal. The current temperature and humidity, entered by the user, are input into the model. For example, the server passes this data through a random forest model and obtains a result predicting that the current conditions are optimal.

[1241] Emotion engine for user emotion evaluation

[1242] The server uses an emotion engine to analyze the user's facial expressions, voice, and text data to recognize the user's emotions. For example, the server executes an emotion analysis algorithm to determine whether the user is expressing an emotion such as "distress."

[1243] Determining the recommended action

[1244] The server determines the recommended action based on the results of the environmental condition evaluation, the AI ​​model's predictions, and the user's emotion recognition. For example, if the emotion engine recognizes that the user is confused, it will provide a more detailed explanation or additional advice. Specifically, in addition to the message "The current conditions are optimal," it will determine additional advice such as "The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results."

[1245] Guidance display

[1246] The device displays the recommended actions sent from the server to the user. The user can then view appropriate cultivation guidance and additional advice on the device screen. For example, the device display might say, "The current conditions are optimal. The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results."

[1247] Specific examples

[1248] For example, consider a case where a user inputs information into a terminal about "tomato," the current temperature of 22°C, the current humidity of 60%, and the growth stage of "flowering," and shows a troubled expression.

[1249] 1. User Input

[1250] The user enters "tomato," the current temperature of 22°C, the current humidity of 60%, and the growth stage of "flowering" into the input form on the device, and clicks the "send" button. The device's camera also records the user's facial expressions.

[1251] 2. Data Transmission

[1252] The device sends this information to the server in JSON format.

[1253] 3. Database Reference

[1254] Based on the data received by the server, the optimal temperature range (20-25°C) and humidity range (45-70%) for tomatoes are retrieved from the database.

[1255] 4. Assessment of Environmental Conditions

[1256] The server evaluates the current temperature of 22°C and humidity of 60% and determines that the temperature and humidity are "appropriate."

[1257] 5. Predictions using AI models

[1258] The server inputs this data into a random forest model and predicts which current conditions are optimal.

[1259] 6. User Emotion Evaluation Using an Emotion Engine

[1260] The server analyzes the user's facial expression data and recognizes that the user is confused.

[1261] 7. Determining the recommended action

[1262] The server determines the message "Current conditions are optimal" plus additional advice such as "The temperature is a little high. Lowering the temperature by 1-2 degrees would give better results."

[1263] 8. Guidance display

[1264] The terminal displays these messages on the display and the user confirms them.

[1265] Prompt Sentence Examples

[1266] For example, you can simulate the behavior of this system by feeding the following prompts to the generative AI model:

[1267] If a user inputs information about "Tomato," current temperature 22°C, current humidity 60%, and growth stage "Flowering" and shows a confused expression, please explain in detail the growing guidance and additional advice that your server provides.

[1268] In this way, the system of the present invention not only solves plant cultivation challenges faced by users in real time and provides effective means for optimizing plant productivity and health, but also takes user emotions into account, making the system even easier to use.

[1269] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1270] Step 1: User Input

[1271] The user inputs the plant's type, current temperature, current humidity, and growth stage using an input form on the device screen, while facial expression, voice, and text data are also collected.

[1272] Input: Plant type (e.g., "Tomato"), current temperature (e.g., 22°C), current humidity (e.g., 60%), growth stage (e.g., "flowering stage"), user's facial expression data, voice data, and text data.

[1273] Output: This data is temporarily stored on the device.

[1274] Specific operation: The user enters "tomato," temperature 22°C, humidity 60%, and growth stage "flowering stage" into the input form on the device, and clicks the "Send" button. The device's camera and microphone record the user's facial expressions and voice data.

[1275] Step 2: Send data

[1276] The terminal transmits the data and emotion information input by the user to the server, where the data is appropriately formatted and transmitted in a form that can be uniquely identified by the server.

[1277] Input: The data entered in step 1.

[1278] Output: Data formatted in JSON is sent to the server.

[1279] Specific operation: When the "Send" button is pressed, the device sends the information entered by the user to the server in JSON format.

[1280] Step 3: Database Reference

[1281] The server retrieves the optimum environmental conditions for the plant from the database based on the received user data.

[1282] Input: User data formatted in JSON.

[1283] Output: Optimal temperature range (e.g. 20-25°C) and optimal humidity range (e.g. 45-70%) retrieved from the database.

[1284] What it does: The server queries the database to get the optimal environmental conditions for tomatoes.

[1285] Step 4: Evaluate environmental conditions

[1286] The server evaluates whether the current temperature and humidity are within the optimal range, and the results are summarized as "optimal temperature" and "optimal humidity."

[1287] Input: Optimal temperature and humidity ranges retrieved from the database, current temperature and humidity.

[1288] Output: Evaluation result (e.g., "suitable temperature" or "suitable humidity").

[1289] Specific operation: The server compares the current temperature of 22°C and humidity of 60% with the optimal range and evaluates the result as "optimal temperature and humidity."

[1290] Step 5: Prediction by AI model

[1291] The server uses artificial intelligence models such as random forests to predict whether the current environmental conditions are optimal.

[1292] Input: Current temperature and humidity.

[1293] Output: The predicted result of the AI ​​model (e.g., "Conditions are optimal").

[1294] Specific operation: The server inputs the current environmental data into the random forest model and obtains the prediction results.

[1295] Step 6: Evaluating user emotions with the emotion engine

[1296] The server uses an emotion engine to analyze the user's facial expressions, voice, and text data to recognize the user's emotions.

[1297] Input: User's facial expression data, voice data, and text data.

[1298] Output: User emotion recognition result (e.g., "confused").

[1299] Specific operation: The server runs an emotion analysis algorithm and detects the emotion "confused" from the user's facial expression data.

[1300] Step 7: Determine the recommended action

[1301] The server determines the recommended action based on the results of the environmental condition evaluation, the prediction results of the AI ​​model, and the user's emotion recognition results.

[1302] Input: Environmental condition assessment results, AI model prediction results, and user emotion recognition results.

[1303] Output: Recommended action (e.g., "Current conditions are optimal. The temperature is a little high. Lowering the temperature by 1-2 degrees would produce better results.").

[1304] Specific operation: The server integrates the evaluation results, prediction results, and emotion recognition results to determine the recommended action.

[1305] Step 8: View Guidance

[1306] The terminal displays the recommended actions sent from the server to the user.

[1307] Input: The recommended action sent by the server.

[1308] Output: Guidance shown on the device display (e.g., "Current conditions are optimal" and "The temperature is a little high. Lowering the temperature by 1-2 degrees would produce better results.").

[1309] Specific operation: The device receives a message from the server and displays it on the screen, allowing the user to check it on the screen.

[1310] (Application example 2)

[1311] 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."

[1312] While conventional plant cultivation support systems can evaluate and optimize plant cultivation conditions, they do not provide support that takes into account the user's emotions, which leads to issues such as a lack of effective support and improved user satisfaction. Another problem is that they are unable to provide appropriate feedback to address problems and anxieties specific to plant cultivation.

[1313] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving information on the type of plant, current temperature, current humidity, and growth stage input by the user, means for retrieving an optimal temperature range and an optimal humidity range from a database based on the type of plant, means for evaluating whether the current temperature and current humidity are within the optimal temperature and humidity ranges, means for predicting whether the current conditions are optimal using an artificial intelligence model, means for determining a recommended action based on the evaluation and prediction and presenting it to the user, means for inputting the user's emotions, and means for adjusting the presented recommended action based on the user's emotional information. This makes it possible to not only optimize plant growth but also provide flexible support that corresponds to the user's emotions.

[1314] "User" refers to a person who inputs information about plant growing conditions and receives recommended actions from the system.

[1315] "Plant type" refers to the name of the particular plant that the user wants to grow.

[1316] "Current temperature" refers to the current temperature of the environment where the plant is located, as entered by the user.

[1317] "Current humidity" refers to the current humidity of the environment in which the plant is placed, as input by the user.

[1318] "Growth stage" is information that indicates the current growth phase of the plant.

[1319] The "database" is an information management system that stores information on optimal growing conditions for plants.

[1320] "Optimal temperature range" refers to the range of temperatures in which a particular plant can grow optimally.

[1321] The "optimal humidity range" refers to the humidity range in which a particular plant can grow optimally.

[1322] An "artificial intelligence model" is a machine learning algorithm used to optimize plant growth conditions.

[1323] "Recommended Actions" refers to specific instructions for action to optimize plant growth based on current growing conditions.

[1324] "Emotion information" refers to data related to emotions input by the user, such as information extracted from facial expressions, voice, and text.

[1325] "Adjustment means" refers to the ability to change the recommended actions and guidance presented based on the user's emotional information.

[1326] This invention relates to a system that allows users to input information for plant cultivation and presents optimal cultivation conditions based on that information, and also provides support taking into account the user's feelings. The system is mainly composed of a server, terminals, and users.

[1327] 1. System Program Overview

[1328] User Input:

[1329] The device is equipped with a camera and voice input, and collects information about the plant's type, current temperature, current humidity, and growth stage from the device, as well as emotional information from the user's facial expressions and voice.

[1330] Data transmission:

[1331] The information and emotion information entered by the user are properly formatted and sent to the server using the device's communication function.

[1332] Database Reference:

[1333] Based on the received data, the server retrieves the optimal growing conditions (temperature and humidity ranges) for the corresponding plant from the database. For example, it retrieves information such as the optimal temperature for tomatoes being 20-25°C and the optimal humidity being 45-70%.

[1334] Environmental condition assessment:

[1335] The server evaluates whether the current temperature and humidity input are within the optimal range, and the evaluation results are presented in the form of "suitable temperature" and "suitable humidity."

[1336] Artificial intelligence model predictions:

[1337] The server uses a generative AI model, specifically an algorithm such as random forest, to predict whether the current environmental conditions are optimal for the plant.

[1338] Emotional evaluation by emotion engine:

[1339] The server analyzes the user's facial expressions and voice to recognize their emotional state, such as "confusion" or "fun," using OpenCV and emotion engine software built on top of it.

[1340] Determine and adjust the recommended actions:

[1341] The server determines the optimal recommended action based on the results of the environmental condition assessment, the AI ​​model predictions, and the emotion recognition results. For example, if the user is confused, more detailed explanations or additional advice will be provided.

[1342] Display guidance:

[1343] Finally, the device displays the recommended actions and guidance sent from the server to the user in a format that is easy for the user to view.

[1344] 2. System Operation

[1345] The hardware used includes smart glasses and smartphones, which input and display information.

[1346] The software uses Python, OpenCV, emotion engines, and generative AI models such as random forests.

[1347] Examples:

[1348] In a plant specialty store, a staff member puts on smart glasses and inputs "tomato," along with the current temperature of 22°C, humidity of 60%, and the growth stage of "flowering." If the staff member's expression looks confused, this information is sent to the server. The server evaluates the optimal growing conditions based on the input information and provides specific advice, such as "The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results."

[1349] Example prompt sentence:

[1350] "Scan the type of plant in the store, the current temperature, the current humidity, and the growth stage, and generate a sentence that provides optimal growing guidance if the user has a confused look on their face."

[1351] In this way, the present invention can solve the plant cultivation problems faced by users and further provide flexible and effective support that responds to the user's feelings.

[1352] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1353] Step 1: User Input

[1354] The user uses the device's input form to input the plant's type, current temperature, current humidity, and growth stage. The device also uses a camera and voice input to collect emotional information from the user's facial expressions and voice. If the user inputs information such as tomato, 22°C, 60%, and flowering stage, and shows a confused expression, this data is treated as input.

[1355] Step 2: Send data

[1356] The device formats the plant type, current temperature, current humidity, growth stage, and emotion information entered by the user and sends it to the server in JSON format as a POST request.

[1357] Step 3: Database Reference

[1358] The server receives the transmitted data and retrieves the optimal growing conditions (temperature and humidity ranges) for the corresponding plant from the database. For example, the server recognizes that the plant is a tomato and retrieves from the database the optimal temperature of 20 to 25°C and the optimal humidity of 45 to 70%.

[1359] Step 4: Evaluate environmental conditions

[1360] The server evaluates whether the current temperature (22°C) and humidity (60%) are within the optimal temperature and humidity ranges. Based on this evaluation, it determines which environmental conditions are suitable and outputs the results as "optimal temperature" and "optimal humidity."

[1361] Step 5: Prediction by artificial intelligence model

[1362] The server uses a generative AI model (e.g., random forest) to predict whether the current environmental conditions are optimal for the plant. The model takes the current temperature and humidity as input and outputs a prediction of whether the conditions are optimal.

[1363] Step 6: Emotion evaluation by the emotion engine

[1364] The server analyzes the user's facial expression and voice data sent from the device and uses an emotion engine to determine whether the user is confused. Based on this analysis, the server outputs the user's emotional state as "confused."

[1365] Step 7: Determine and adjust recommended actions

[1366] The server determines the optimal recommended action based on the results of the environmental condition evaluation, the AI ​​model predictions, and the emotion recognition results. For example, the server may use the message "The current conditions are optimal" as a base message, and if the user is confused, include additional advice such as "The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results."

[1367] Step 8: View Guidance

[1368] The device receives the recommended actions and guidance sent from the server and displays them to the user. The user can see messages on the device screen such as "Current conditions are optimal" and "The temperature is a little high. Lowering the temperature by 1-2 degrees will give better results."

[1369] Through the above processing steps, the system of the present invention can support the user's plant cultivation in real time and also provide flexible support according to the user's emotions.

[1370] 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.

[1371] 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.

[1372] 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.

[1373] [Fourth embodiment]

[1374] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1375] 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.

[1376] 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).

[1377] 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.

[1378] 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.

[1379] 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).

[1380] 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.

[1381] 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.

[1382] 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.

[1383] 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.

[1384] 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.

[1385] 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.

[1386] 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."

[1387] This invention relates to a system that allows users to check the optimal growing conditions for a plant in real time by inputting information on the plant's type, current temperature, current humidity, and growth stage. In this system, a server uses a database and an artificial intelligence model to evaluate the current environmental conditions and provide optimal growing guidance.

[1388] Basic system configuration

[1389] User Input

[1390] The user uses the input form on the terminal screen to input the type of plant, the current temperature, the current humidity, and the growth stage of the plant. For example, the user inputs into the terminal that the type of plant is "tomato," the current temperature is 22°C, the current humidity is 60%, and the growth stage is "flowering stage."

[1391] Data transmission

[1392] The terminal transmits the data entered by the user to the server, properly formatting the data so that it can be uniquely identified by the server.

[1393] Database Reference

[1394] Based on the received data, the server retrieves the optimal environmental conditions (temperature range and humidity range) for the corresponding plant from the database. For example, it may retrieve from the database that the optimal temperature for tomatoes is 20 to 25°C and the optimal humidity is 45 to 70%.

[1395] Environmental Condition Assessment

[1396] The server evaluates whether the current temperature and humidity are within the optimum range, and the evaluation results are organized in a format such as "optimum temperature" or "optimum humidity."

[1397] Prediction by AI model

[1398] The server uses an artificial intelligence model (e.g., random forest) to predict whether the current environmental conditions are optimal, given the current temperature and humidity inputs from the user.

[1399] Determining the recommended action

[1400] The server determines the recommended action to present to the user based on the results of the environmental condition evaluation and the prediction results of the AI ​​model. For example, specific action guidance such as "The current conditions are optimal" or "Please lower the temperature" is determined.

[1401] Guidance display

[1402] The device displays the recommended actions sent from the server to the user, who can then check the appropriate training guidance on the device screen.

[1403] Specific examples

[1404] For example, consider the case where a user inputs information into the terminal about "tomato," the current temperature of 22°C, the current humidity of 60%, and the growth stage of "flowering."

[1405] 1. Sending input data

[1406] The user enters information into the terminal and clicks the "Submit" button. The terminal sends the input data to the server.

[1407] 2. Database lookup and environmental condition evaluation

[1408] The server receives the transmitted data and retrieves the optimal temperature range (20-25°C) and humidity range (45-70%) for tomatoes from the database. The server then evaluates whether the current temperature of 22°C and humidity of 60% are within the range. As both are within the range, the server evaluates them as "optimal temperature" and "optimal humidity."

[1409] 3. AI models make predictions and recommend actions

[1410] The server inputs the current temperature and humidity into the AI ​​model to make a prediction. The model predicts that the current conditions are optimal and determines the recommended action: "Current conditions are optimal."

[1411] 4. Guidance display

[1412] The terminal receives the guidance sent from the server, saying "Current conditions are optimal," and displays it to the user. The user can check the guidance on the terminal screen and take appropriate measures to grow the plant.

[1413] In this way, the system of the present invention provides users with an effective means of solving plant growth challenges in real time and optimizing plant productivity and health.

[1414] The processing flow will be explained below.

[1415] Step 1:

[1416] The user inputs information into the terminal. Using the terminal's input form, the user inputs the plant type (e.g., tomato), the current temperature (e.g., 22°C), the current humidity (e.g., 60%), and the growth stage (e.g., flowering stage).

[1417] Step 2:

[1418] The terminal transmits the input information to the server, which then formats the information appropriately and transmits it to the server. The transmitted data includes the plant type, current temperature, current humidity, and growth stage information.

[1419] Step 3:

[1420] The server receives the information sent by the user and verifies that the entered data is in the correct format.

[1421] Step 4:

[1422] The server retrieves the optimal environmental conditions for a plant from the database. Based on the type of plant, the server retrieves the optimal temperature and humidity ranges from the database. For example, the server retrieves that the optimal temperature range for tomatoes is 20-25°C and the optimal humidity range is 45-70%.

[1423] Step 5:

[1424] The server evaluates the current temperature and humidity. The server determines whether the current temperature (22°C) and humidity (60%) received from the user are within the optimal temperature and humidity ranges. As a result, the server evaluates the temperature as "suitable" and the humidity as "suitable."

[1425] Step 6:

[1426] The server uses an artificial intelligence model to predict the optimal action. The server inputs the current temperature and humidity data into an artificial intelligence model (e.g., a random forest classifier) ​​to predict whether the current environmental conditions are optimal.

[1427] Step 7:

[1428] The server determines the recommended action. The server determines the recommended action based on the prediction results of the artificial intelligence model and the evaluation results of the environmental conditions. For example, the server determines the recommended action as "The current conditions are optimal."

[1429] Step 8:

[1430] The server sends the recommended action to the terminal. The server sends the determined recommended action to the terminal. The sent data includes the temperature status, humidity status, and recommended action.

[1431] Step 9:

[1432] The terminal displays the recommended action to the user. The terminal displays the recommended action received from the server to the user. For example, a message saying "Current conditions are optimal" is displayed on the user's terminal screen. As a result, the user can take appropriate measures to grow the plant.

[1433] Example 1

[1434] 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."

[1435] In conventional plant cultivation systems, it was difficult for users to manually evaluate the plant's type, growth stage, and current environmental conditions to determine the optimal cultivation conditions. Furthermore, there was a high possibility of errors occurring during the manual data evaluation process, and optimal cultivation conditions were not always guaranteed. Furthermore, it was difficult to quickly respond to changes in environmental conditions in real time. Given these issues, there was a need for a system that would allow users to easily check the optimal cultivation conditions for plants and quickly take appropriate cultivation actions.

[1436] 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.

[1437] In this invention, the server includes means for receiving information on the type of plant, current temperature, current humidity, and growth stage input by a user, means for retrieving an optimal temperature range and an optimal humidity range from a database based on the type of plant, means for evaluating whether the current temperature and current humidity are within the optimal temperature and humidity ranges, means for predicting whether the current conditions are optimal using an artificial intelligence model, means for determining a recommended action based on the evaluation and prediction and presenting it to the user, means for appropriately formatting the input from the user and transmitting it to the server, and means for displaying the recommended action transmitted from the server to the user. This enables a user to check the optimal growing conditions for a plant in real time and quickly take appropriate growing action.

[1438] A "user" is an individual or entity that provides plant-growing information and receives recommended actions from the system.

[1439] An "input form" is a screen element provided on a terminal for a user to input information about the type of plant, the current temperature, the current humidity, and the growth stage.

[1440] A "terminal" is an electronic device that a user uses to enter information and communicate with a server.

[1441] "Type of plant" is information indicating the classification of a particular plant that the user is cultivating.

[1442] "Growth stage" is information indicating the developmental phase in which the plant is currently located.

[1443] A "database" is a system for storing and managing information such as optimal environmental conditions for plants.

[1444] The "optimum temperature range" is information indicating the temperature range that is most suitable for a particular plant.

[1445] The "optimum humidity range" is information indicating the humidity range that is most suitable for a particular plant.

[1446] "Evaluation" is the process of determining whether the current temperature is within the optimum temperature range and whether the current humidity is within the optimum humidity range.

[1447] An "artificial intelligence model" is a model that uses machine learning algorithms to analyze data and make predictions and judgments.

[1448] "Recommended actions" are specific measures for development that are presented to the user based on the evaluation and prediction.

[1449] "Formatting" is the process of properly shaping data so that it can be properly understood and processed by the server.

[1450] "Send" is the process of transferring data entered by the user from the terminal to the server.

[1451] "Display" is the process of showing the recommended actions received from the server to the user.

[1452] This invention relates to a system that allows users to check the optimal growing conditions for a plant in real time by inputting information on the plant's type, current temperature, current humidity, and growth stage. In this system, a server uses a database and an artificial intelligence model to evaluate the current environmental conditions and provide optimal growing guidance.

[1453] User Input

[1454] The user uses an input form on the terminal screen to input the type of plant, the current temperature, the current humidity, and the growth stage of the plant. For example, the user inputs into the terminal that the plant type is "tomato," the current temperature is 22°C, the current humidity is 60%, and the growth stage is "flowering stage." This information is entered manually by the user and is in a format that can be modified as needed.

[1455] Data transmission

[1456] The terminal sends the data entered by the user to the server, properly formatting it so that it can be uniquely identified by the server. Data formats such as JSON and XML are commonly used, but other formats can also be applied depending on the system requirements.

[1457] Database Reference

[1458] Based on the received data, the server retrieves the optimal environmental conditions (temperature and humidity ranges) for the corresponding plant from the database. For example, it may learn from the database that the optimal temperature for tomatoes is 20-25°C and the optimal humidity is 45-70%. This database holds detailed growing information for each type of plant and is designed to allow the server to access it quickly.

[1459] Environmental Condition Assessment

[1460] The server evaluates whether the current temperature and humidity are within the optimal range. The evaluation results are organized as, for example, "optimal temperature" or "optimal humidity." The evaluation process is carried out by an algorithm that compares the current environmental data with the optimal conditions retrieved from a database.

[1461] Prediction by AI model

[1462] The server uses an artificial intelligence model (e.g., random forest) to predict whether the current environmental conditions are optimal. The current temperature and humidity are input into the model, which incorporates learning data based on past data and environmental conditions, allowing it to make highly accurate predictions.

[1463] Determining the recommended action

[1464] The server determines the recommended action to present to the user based on the results of the environmental condition evaluation and the prediction results of the AI ​​model. For example, specific action guidance such as "Current conditions are optimal" or "Please lower the temperature" is determined. This allows the user to quickly take appropriate cultivation measures.

[1465] Guidance display

[1466] The device displays the recommended actions sent from the server to the user, who can then check the appropriate training guidance on the device screen. While various display formats are possible, such as text messages or graphical interfaces, a format that is intuitively easy for users to understand is desirable.

[1467] Specific examples

[1468] For example, consider the case where a user inputs information into the terminal about "tomato," the current temperature of 22°C, the current humidity of 60%, and the growth stage of "flowering."

[1469] 1. Sending input data

[1470] The user enters information into the terminal and clicks the "Submit" button. The terminal sends the input data to the server.

[1471] 2. Database lookup and environmental condition evaluation

[1472] The server receives the transmitted data and retrieves the optimal temperature range (20-25°C) and humidity range (45-70%) for tomatoes from the database. The server then evaluates whether the current temperature of 22°C and humidity of 60% are within the range. As both are within the range, the server evaluates them as "optimal temperature" and "optimal humidity."

[1473] 3. AI models make predictions and recommend actions

[1474] The server inputs the current temperature and humidity into the AI ​​model to make a prediction. The model predicts that the current conditions are optimal and determines the recommended action: "Current conditions are optimal."

[1475] 4. Guidance display

[1476] The terminal receives the guidance sent from the server, saying "Current conditions are optimal," and displays it to the user. The user can check the guidance on the terminal screen and take appropriate measures to grow the plant.

[1477] Prompt Sentence Examples

[1478] The following prompt would confirm the optimal growing conditions for a "tomato" in the "flowering" growth stage when the current temperature is 22°C and humidity is 60%:

[1479] What are the optimal growing conditions for tomatoes during the flowering stage? The current temperature is 22°C and humidity is 60%.

[1480] In this way, the system of the present invention provides users with an effective means of solving plant growth challenges in real time and optimizing plant productivity and health.

[1481] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1482] Step 1:

[1483] User Input

[1484] The user uses the input form on the terminal screen to input the plant type, current temperature, current humidity, and plant growth stage. Specifically, the user inputs the information corresponding to each field via a keyboard or touch screen. The input information is converted into the appropriate format by the system and prepared for the next step of processing.

[1485] Input: Plant type, current temperature, current humidity, plant growth stage

[1486] Output: The formatted input data

[1487] Step 2:

[1488] Data transmission

[1489] The terminal properly formats the data entered by the user and sends it to the server using an HTTP request, typically in JSON format, where it is parsed once it reaches the server.

[1490] Input: Formatted input data

[1491] Output: Data sent to the server

[1492] Step 3:

[1493] Database Reference

[1494] The server analyzes the received data and retrieves the optimal temperature and humidity ranges for the corresponding plant from a database, which contains pre-registered optimal conditions for each plant, using an algorithm that performs a database query.

[1495] Input: Data sent to the server (type of plant)

[1496] Output: Optimal temperature and humidity range

[1497] Step 4:

[1498] Environmental Condition Assessment

[1499] The server evaluates whether the current temperature and humidity are within the optimal range retrieved from the database. This evaluation process is handled by an algorithm, and the evaluation result is formatted as "optimum temperature" or "optimum humidity."

[1500] Input: Current temperature, Current humidity, Optimal temperature range, Optimal humidity range

[1501] Output: Environmental condition evaluation results (optimal temperature, optimal humidity)

[1502] Step 5:

[1503] Prediction by AI model

[1504] The server uses an artificial intelligence model (e.g., random forest) to predict whether the current environmental conditions are optimal or not. The model is fed with current environmental data entered by the user and provides predictions based on past learning data.

[1505] Input: Current temperature, current humidity, past learning data

[1506] Output: Prediction results from the AI ​​model

[1507] Step 6:

[1508] Determining the recommended action

[1509] The server determines the recommended actions to present to the user based on the evaluation of environmental conditions and the predictions of the AI ​​model, and this decision process may involve the use of a condition-based rules engine.

[1510] Input: Environmental condition evaluation results, AI model prediction results

[1511] Output: Recommended Action

[1512] Step 7:

[1513] Guidance display

[1514] The device displays the recommended actions sent from the server to the user, who can then take appropriate training actions. The display format is easy for the user to understand, such as a text message or graph.

[1515] Input: Recommended Action

[1516] Output: Guidance displayed on the terminal

[1517] This series of processes allows users to check the optimal growing conditions for their plants in real time and take appropriate action immediately.

[1518] (Application example 1)

[1519] 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."

[1520] Maintaining optimal weather conditions is essential for plant growth. However, conventional methods have made it difficult to grasp the optimal growth conditions, which differ for each plant, in real time and manage them appropriately. Maintaining optimal environmental conditions and providing appropriate growth guidance is particularly challenging when managing a large number of plants at once in a brick-and-mortar store. Furthermore, users must manually determine the appropriate growth conditions each time, requiring efficient and accurate management. The objective of the present invention is to solve these problems and optimize plant growth and efficiency.

[1521] 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.

[1522] In this invention, the server includes means for receiving information on the plant type, current temperature, current humidity, and growth stage input by a user; means for retrieving optimal temperature and humidity ranges from a database based on the plant type; and means for evaluating whether the current temperature and humidity are within the optimal temperature and humidity ranges. This makes it possible to provide a user interface for managing plant inventory and display in a physical store. Furthermore, the optimal growing conditions for a plant can be presented as real-time guidance based on the information input by the user, thereby achieving effective plant growth. Furthermore, the server uses an artificial intelligence model to predict whether the current conditions are optimal, and determines and presents recommended actions to the user, thereby improving the efficiency and optimization of plant management.

[1523] "Plant type" refers to the taxonomic attributes and name of a particular plant, and is the basic data for identifying optimal growing conditions based on this information.

[1524] "Current temperature" refers to the temperature of the air surrounding the plant, and is an important variable for assessing the suitability of growing conditions.

[1525] "Current humidity" refers to the water vapor content in the environment in which the plant is located, and is an important factor in maintaining an environment suitable for growth.

[1526] "Growth stage" refers to the current developmental state of a plant in its life cycle and is a reference index for providing appropriate growing conditions.

[1527] "Database" refers to a system that stores information on the optimal temperature and humidity ranges for each type of plant and searches and retrieves it as needed.

[1528] "Artificial intelligence model" refers to a statistical model that uses machine learning algorithms to predict optimal development conditions and recommended actions from input data.

[1529] "User interface" refers to the screen and operating means through which the user inputs plant information and receives feedback from the system.

[1530] "Recommended actions" refer to specific courses of action the system suggests to the user based on current growing conditions and are necessary to optimize plant health.

[1531] The above definitions clarify each component of the invention and make it easier to understand its technical scope.

[1532] The present invention relates to a system that can check the optimal growing conditions for plants in real time. This system mainly consists of three components: a server, a terminal, and a user. The details are explained below.

[1533] 1. Basic system configuration

[1534] User Input

[1535] The user uses an input form on the device to input information about the plant type, current temperature, current humidity, and growth stage. Based on this information, the system determines the optimal growing conditions for the plant. For example, consider the case where a user inputs the plant type "rose," the current temperature is 25°C, the current humidity is 70%, and the growth stage is "pre-flowering."

[1536] Data transmission

[1537] The terminal transmits the data entered by the user to the server, properly formatting the data so that it can be uniquely identified by the server.

[1538] Database Reference

[1539] Based on the received data, the server retrieves the optimal temperature and humidity ranges for the corresponding plant from the database. For example, the optimal temperature for roses is 20-25°C, and the optimal humidity is 45-70%.

[1540] Environmental Condition Assessment

[1541] The server evaluates whether the current temperature and humidity are within the optimal range, and the results are summarized as "optimal temperature" and "optimal humidity."

[1542] Prediction by AI model

[1543] The server uses an artificial intelligence model (e.g., random forest) to predict whether the current environmental conditions are optimal, given the current temperature and humidity inputs from the user.

[1544] Determining the recommended action

[1545] The server determines the recommended actions to present to the user based on the results of the environmental condition evaluation and the prediction results of the AI ​​model.

[1546] Guidance display

[1547] The device displays the recommended actions sent from the server to the user. The user can check appropriate training guidance on the device screen. For example, "Current conditions are optimal" or "Conditions are not optimal. Please make adjustments."

[1548] 2. Hardware and Software Used

[1549] Hardware

[1550] Device (smartphone, tablet, etc.)

[1551] server

[1552] software

[1553] Front-end: HTML, CSS, and JavaScript to build the user interface

[1554] Backend: Python for data processing and running AI models

[1555] Database: SQL database for storing plant growth conditions

[1556] AI models: machine learning algorithms such as random forests

[1557] 3. Specific Examples

[1558] For example, suppose a clerk at a gardening shop inputs the following information into a terminal: "rose," current temperature 25°C, current humidity 70%, and growth stage "before flowering."

[1559] 1. User Input

[1560] The clerk enters the plant information and clicks the "Submit" button.

[1561] 2. Data Transmission

[1562] The terminal sends the input data to the server.

[1563] 3. Database Reference

[1564] The server receives the transmitted data and obtains the optimum temperature and humidity ranges for the roses.

[1565] 4. Assessment of Environmental Conditions

[1566] The server evaluates whether the current temperature and humidity are within the optimum range.

[1567] 5. Predictions using AI models

[1568] The server inputs the current temperature and humidity into the AI ​​model to predict whether it is optimal.

[1569] 6. Determining the recommended action

[1570] The server determines the recommended action based on the evaluation and prediction results.

[1571] 7. Guidance display

[1572] The terminal displays the guidance "Current conditions are optimal" received from the server to the store clerk.

[1573] Prompt Sentence Examples

[1574] Example of information the user enters into the terminal:

[1575] Plant type: Rose

[1576] Current temperature: 25℃

[1577] Current humidity: 70%

[1578] Growth stage: Pre-flowering

[1579] The above is an embodiment of the present invention.

[1580] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1581] Step 1:

[1582] The user uses the input form on the device to input information about the plant type, current temperature, current humidity, and growth stage. This information is used as the initial input data for the program. The input data is temporarily stored in the device's memory, and the user can proceed to the next step by clicking the "Submit" button.

[1583] Input: Plant type, current temperature, current humidity, growth stage

[1584] Output: Initial input data stored in the terminal memory

[1585] Step 2:

[1586] The terminal sends the data entered by the user to the server, properly formatting it so that it can be uniquely identified by the server, for example, in JSON format.

[1587] Input: Initial input data stored in the terminal memory

[1588] Output: The formatted data sent to the server

[1589] Step 3:

[1590] Based on the received data, the server retrieves the optimal temperature and humidity ranges for the corresponding plant from the database. The server then queries the database based on the type of plant to retrieve the appropriate temperature and humidity ranges. This data is stored in the server's memory.

[1591] Input: Formatted data sent to the server

[1592] Output: Optimal temperature and humidity ranges retrieved from the database

[1593] Step 4:

[1594] The server evaluates whether the current temperature and humidity are within the optimal range. It compares the acquired optimal temperature and humidity range with the current temperature and humidity sent by the user and generates an evaluation result. This evaluation result is organized as "optimum temperature" or "optimum humidity."

[1595] Input: Current temperature, current humidity, optimal temperature range, optimal humidity range

[1596] Output: Evaluation results (suitable / unsuitable temperature, suitable / unsuitable humidity)

[1597] Step 5:

[1598] The server uses an artificial intelligence model (e.g., random forest) to predict whether the current environmental conditions are optimal. The AI ​​model receives the current temperature and humidity pair entered by the user and outputs a predicted value, such as "optimal" or "not optimal."

[1599] Input: Current temperature, Current humidity

[1600] Output: Prediction results from the AI ​​model (optimal / non-optimal)

[1601] Step 6:

[1602] The server determines the recommended action to present to the user based on the results of the environmental condition evaluation and the prediction results of the AI ​​model. Recommended actions are expressed in the form of, for example, "The current conditions are optimal" or "The conditions are not optimal. Please make adjustments."

[1603] Input: Evaluation results, prediction results by AI model

[1604] Output: Recommended Action

[1605] Step 7:

[1606] The device receives the recommended actions sent from the server and displays them to the user. The recommended actions are displayed on the device screen, allowing the user to check appropriate plant cultivation guidance in real time.

[1607] Input: Recommended action sent by the server

[1608] Output: Recommended actions displayed on the terminal screen

[1609] 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.

[1610] This invention relates to a system that allows users to check the optimal conditions for growing plants in real time by inputting information on the type of plant, current temperature, current humidity, and growth stage. By further combining this invention with an emotion engine, it is possible to recognize the user's emotions and provide cultivation guidance in a more appropriate and user-friendly format.

[1611] Basic system configuration

[1612] User Input

[1613] The user uses an input form on the device screen to input the type of plant, the current temperature, the current humidity, and the plant's growth stage. The user's facial expression, voice, or text data is also collected at the same time. For example, the user inputs into the device that the plant is a "tomato," that the current temperature is 22°C, the current humidity is 60%, and that the growth stage is "flowering."

[1614] Data transmission

[1615] The device transmits the data and emotion information input by the user to the server, where the data is appropriately formatted and transmitted in a form that can be uniquely identified by the server.

[1616] Database Reference

[1617] Based on the received user data, the server retrieves the optimal environmental conditions (temperature range and humidity range) for the corresponding plant from the database. For example, it retrieves from the database that the optimal temperature for tomatoes is 20 to 25°C and the optimal humidity is 45 to 70%.

[1618] Environmental Condition Assessment

[1619] The server evaluates whether the current temperature and humidity are within the optimum range, and the evaluation results are organized in a format such as "optimum temperature" or "optimum humidity."

[1620] Prediction by AI model

[1621] The server uses an artificial intelligence model (e.g., random forest) to predict whether the current environmental conditions are optimal, given the current temperature and humidity inputs from the user.

[1622] Emotion engine for user emotion evaluation

[1623] The server uses an emotion engine to analyze the user's facial expressions, voice, and text data during input and recognize the user's emotions, for example, determining whether the user is expressing emotions such as "happy" or "troubled."

[1624] Determining the recommended action

[1625] The server determines the recommended action based on the results of the environmental condition evaluation, the AI ​​model's predictions, and the user's emotion recognition. If the emotion engine recognizes that the user is confused, it adjusts the guidance to provide more detailed explanations and additional advice. For example, if the user is confused, it provides specific guidance such as, "The temperature is a little high. Try lowering the temperature by 1-2 degrees. This will improve plant growth."

[1626] Guidance display

[1627] The device displays the recommended actions sent from the server to the user, who can then check the appropriate training guidance on the device screen. For example, a message such as "Current conditions are optimal" may be displayed, along with additional advice based on the user's emotions.

[1628] Specific examples

[1629] For example, consider a case where a user inputs information into a terminal about "tomato," the current temperature of 22°C, the current humidity of 60%, and the growth stage of "flowering," and shows a confused expression.

[1630] 1. Sending input data

[1631] The user enters information into the device and clicks the "Send" button. The device then sends the input data and emotion data to the server.

[1632] 2. Database lookup and environmental condition evaluation

[1633] The server receives the transmitted data and retrieves the optimal temperature range (20-25°C) and humidity range (45-70%) for tomatoes from the database. The server then evaluates whether the current temperature of 22°C and humidity of 60% are within the range. As a result, both are evaluated as "optimal temperature" and "optimal humidity."

[1634] 3. AI models make predictions and recommend actions

[1635] The server inputs the current temperature and humidity into the AI ​​model to make a prediction. The model predicts that the current conditions are optimal and determines the recommended action: "Current conditions are optimal."

[1636] 4. Emotion evaluation using an emotion engine

[1637] The server analyzes the user's facial expressions, voice, and text data sent from the terminal and recognizes that the user is confused.

[1638] 5. Adjusting recommended actions

[1639] Based on the user's sentiment, the server provides guidance such as "The current conditions are optimal," as well as additional advice such as "The temperature is a little high. You would get better results if you lowered the temperature by 1-2 degrees."

[1640] 6. Guidance display

[1641] The device receives the guidance sent from the server and displays it to the user. The user can see messages on the device screen such as "Current conditions are optimal" and "The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results."

[1642] In this way, the system of the present invention not only solves plant cultivation challenges faced by users in real time and provides effective means for optimizing plant productivity and health, but also takes user emotions into account, making the system even easier to use.

[1643] The processing flow will be explained below.

[1644] Step 1:

[1645] The user inputs information into the device. Using the device's input form, the user inputs the type of plant (e.g., tomato), the current temperature (e.g., 22°C), the current humidity (e.g., 60%), and the growth stage (e.g., flowering stage). The user's facial expressions, voice, or text messages are also collected.

[1646] Step 2:

[1647] The device transmits the input information and emotion data to the server. The device then formats the information appropriately and transmits it to the server. The transmitted data includes the plant type, current temperature, current humidity, growth stage information, and the user's emotion data.

[1648] Step 3:

[1649] The server receives the information sent by the user, verifies that the input data is in the correct format, and prepares to analyze the user's emotion data.

[1650] Step 4:

[1651] The server retrieves the optimal environmental conditions for a plant from the database. Based on the type of plant, the server retrieves the optimal temperature and humidity ranges from the database. For example, the server retrieves that the optimal temperature range for tomatoes is 20-25°C and the optimal humidity range is 45-70%.

[1652] Step 5:

[1653] The server evaluates the current temperature and humidity. The server determines whether the current temperature (22°C) and humidity (60%) received from the user are within the optimum temperature and humidity ranges. As a result, the server evaluates the temperature as "suitable" and the humidity as "suitable."

[1654] Step 6:

[1655] The server uses an artificial intelligence model to predict the optimal action. The server inputs the current temperature and humidity data into an artificial intelligence model (e.g., a random forest classifier) ​​to predict whether the current environmental conditions are optimal.

[1656] Step 7:

[1657] The server uses an emotion engine to recognize the user's emotions. The server analyzes the user's facial expressions, voice, and text data to determine what emotions the user is expressing. For example, it recognizes whether the user is confused.

[1658] Step 8:

[1659] The server determines the recommended action based on the results of the environmental condition evaluation, the AI ​​model predictions, and the user's emotion recognition. For example, if the user is confused, in addition to the basic action guidance of "The current conditions are optimal," the server provides additional advice such as "The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results."

[1660] Step 9:

[1661] The server transmits the recommended action to the terminal. The server transmits the determined recommended action to the terminal. The transmitted data includes the temperature status, the humidity status, the recommended action, and additional advice based on the user's emotion.

[1662] Step 10:

[1663] The device displays the recommended action to the user. The device displays the recommended action received from the server to the user. For example, messages such as "Current conditions are optimal" and "The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results" are displayed on the user's device screen, allowing the user to see appropriate cultivation guidance.

[1664] Example 2

[1665] 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."

[1666] Conventional plant cultivation systems can present optimal cultivation conditions based on the plant type and environmental conditions input by the user, but do not provide individualized responses that take the user's emotions into consideration. As a result, user satisfaction and system utilization efficiency may decrease. The purpose of this invention is to realize a user-friendly plant cultivation system by recognizing the user's emotions and providing more appropriate guidance based on them.

[1667] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving information on the type of plant, current temperature, current humidity, and growth stage input by the user; means for acquiring an optimal temperature range and an optimal humidity range from a database based on the type of plant; means for evaluating whether the current temperature and current humidity are within the optimal temperature and humidity ranges; means for predicting whether the current conditions are optimal using an artificial intelligence model; means including an emotion engine for analyzing the user's facial expression data, voice data, and text data and recognizing the user's emotions; and means for determining recommended actions based on the evaluation, prediction, and emotion recognition and presenting them to the user. This enables individual responses according to the user's emotions and makes it possible to provide plant cultivation guidance that will provide a higher level of satisfaction.

[1668] A "user" is an entity that operates the system and inputs information necessary for growing plants.

[1669] A "terminal" is a device through which a user inputs information and transmits that information to a server.

[1670] A "server" is a central computer system that receives information sent by users and performs processing such as database lookups, running artificial intelligence models, and sentiment analysis.

[1671] "Type of plant" is information indicating the name and type of plant designated as the target for cultivation.

[1672] "Current temperature" is information indicating the temperature value at the current time in the plant growing environment.

[1673] "Current humidity" is information indicating the current humidity value in the plant growing environment.

[1674] The "growth stage" is information that indicates the current stage of the growth of the plant, including, for example, the germination stage, growth stage, flowering stage, and harvest stage.

[1675] A "database" is an information system that stores information about optimal growing conditions for plants.

[1676] "Optimal temperature range" refers to the temperature range established to promote healthy plant growth.

[1677] The "optimal humidity range" refers to the humidity range established to promote healthy plant growth.

[1678] An "artificial intelligence model" is a model that uses machine learning algorithms to predict whether the current environmental conditions input by the user are optimal.

[1679] The "emotion engine" is a system that analyzes the user's facial expressions, voice, and text data to recognize the user's emotions.

[1680] "Recommended actions" refer to specific actions or advice suggested to the user based on the results of the assessment of environmental conditions, the prediction results of the artificial intelligence model, and the results of the user's emotion recognition.

[1681] This invention relates to a system that allows users to check the optimal conditions for growing plants in real time by inputting information on the type of plant, current temperature, current humidity, and growth stage. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotions and provide cultivation guidance in a more appropriate and user-friendly manner.

[1682] Basic system configuration

[1683] User Input

[1684] The user uses an input form on the device screen to input the type of plant, current temperature, current humidity, and growth stage. In addition, the user's facial expression data, voice data, and text data are also collected at the same time. For example, a user inputs into the device the type of plant "tomato," the current temperature is 22°C, the current humidity is 60%, and the growth stage is "flowering stage." At the same time, the device's camera and microphone record the user's facial expression and voice.

[1685] Data transmission

[1686] The device sends the data and emotion information entered by the user to the server. The data is properly formatted and sent in a way that allows the server to uniquely identify it. For example, when the device presses the "Send" button, a JSON-formatted data packet is sent to the server.

[1687] Database Reference

[1688] Based on the received user data, the server retrieves the optimal environmental conditions for the corresponding plant from the database. For example, the server retrieves from the database that the optimal temperature for tomatoes is 20-25°C and the optimal humidity is 45-70%. To do this, the server sends a query to the database to retrieve the necessary information.

[1689] Environmental Condition Assessment

[1690] The server evaluates whether the current temperature and humidity are within the optimal range. The evaluation results are organized in the form of "suitable temperature" and "suitable humidity." For example, the server checks whether the current temperature of 22°C and humidity of 60% are within the range, and evaluates them as "suitable temperature" and "suitable humidity."

[1691] Prediction by AI model

[1692] The server uses an artificial intelligence model, such as a random forest, to predict whether the current environmental conditions are optimal. The current temperature and humidity, entered by the user, are input into the model. For example, the server passes this data through a random forest model and obtains a result predicting that the current conditions are optimal.

[1693] Emotion engine for user emotion evaluation

[1694] The server uses an emotion engine to analyze the user's facial expressions, voice, and text data to recognize the user's emotions. For example, the server executes an emotion analysis algorithm to determine whether the user is expressing an emotion such as "distress."

[1695] Determining the recommended action

[1696] The server determines the recommended action based on the results of the environmental condition evaluation, the AI ​​model's predictions, and the user's emotion recognition. For example, if the emotion engine recognizes that the user is confused, it will provide a more detailed explanation or additional advice. Specifically, in addition to the message "The current conditions are optimal," it will determine additional advice such as "The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results."

[1697] Guidance display

[1698] The device displays the recommended actions sent from the server to the user. The user can then view appropriate cultivation guidance and additional advice on the device screen. For example, the device display might say, "The current conditions are optimal. The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results."

[1699] Specific examples

[1700] For example, consider a case where a user inputs information into a terminal about "tomato," the current temperature of 22°C, the current humidity of 60%, and the growth stage of "flowering," and shows a troubled expression.

[1701] 1. User Input

[1702] The user enters "tomato," the current temperature of 22°C, the current humidity of 60%, and the growth stage of "flowering" into the input form on the device, and clicks the "send" button. The device's camera also records the user's facial expressions.

[1703] 2. Data Transmission

[1704] The device sends this information to the server in JSON format.

[1705] 3. Database Reference

[1706] Based on the data received by the server, the optimal temperature range (20-25°C) and humidity range (45-70%) for tomatoes are retrieved from the database.

[1707] 4. Assessment of Environmental Conditions

[1708] The server evaluates the current temperature of 22°C and humidity of 60% and determines that the temperature and humidity are "appropriate."

[1709] 5. Predictions using AI models

[1710] The server inputs this data into a random forest model and predicts which current conditions are optimal.

[1711] 6. User Emotion Evaluation Using an Emotion Engine

[1712] The server analyzes the user's facial expression data and recognizes that the user is confused.

[1713] 7. Determining the recommended action

[1714] The server determines the message "Current conditions are optimal" plus additional advice such as "The temperature is a little high. Lowering the temperature by 1-2 degrees would give better results."

[1715] 8. Guidance display

[1716] The terminal displays these messages on the display and the user confirms them.

[1717] Prompt Sentence Examples

[1718] For example, you can simulate the behavior of this system by feeding the following prompts to the generative AI model:

[1719] If a user inputs information about "Tomato," current temperature 22°C, current humidity 60%, and growth stage "Flowering" and shows a confused expression, please explain in detail the growing guidance and additional advice that your server provides.

[1720] In this way, the system of the present invention not only solves plant cultivation challenges faced by users in real time and provides effective means for optimizing plant productivity and health, but also takes user emotions into account, making the system even easier to use.

[1721] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1722] Step 1: User Input

[1723] The user inputs the plant's type, current temperature, current humidity, and growth stage using an input form on the device screen, while facial expression, voice, and text data are also collected.

[1724] Input: Plant type (e.g., "Tomato"), current temperature (e.g., 22°C), current humidity (e.g., 60%), growth stage (e.g., "flowering stage"), user's facial expression data, voice data, and text data.

[1725] Output: This data is temporarily stored on the device.

[1726] Specific operation: The user enters "tomato," temperature 22°C, humidity 60%, and growth stage "flowering stage" into the input form on the device, and clicks the "Send" button. The device's camera and microphone record the user's facial expressions and voice data.

[1727] Step 2: Send data

[1728] The terminal transmits the data and emotion information input by the user to the server, where the data is appropriately formatted and transmitted in a form that can be uniquely identified by the server.

[1729] Input: The data entered in step 1.

[1730] Output: Data formatted in JSON is sent to the server.

[1731] Specific operation: When the "Send" button is pressed, the device sends the information entered by the user to the server in JSON format.

[1732] Step 3: Database Reference

[1733] The server retrieves the optimum environmental conditions for the plant from the database based on the received user data.

[1734] Input: User data formatted in JSON.

[1735] Output: Optimal temperature range (e.g. 20-25°C) and optimal humidity range (e.g. 45-70%) retrieved from the database.

[1736] What it does: The server queries the database to get the optimal environmental conditions for tomatoes.

[1737] Step 4: Evaluate environmental conditions

[1738] The server evaluates whether the current temperature and humidity are within the optimal range, and the results are summarized as "optimal temperature" and "optimal humidity."

[1739] Input: Optimal temperature and humidity ranges retrieved from the database, current temperature and humidity.

[1740] Output: Evaluation result (e.g., "suitable temperature" or "suitable humidity").

[1741] Specific operation: The server compares the current temperature of 22°C and humidity of 60% with the optimal range and evaluates the result as "optimal temperature and humidity."

[1742] Step 5: Prediction by AI model

[1743] The server uses artificial intelligence models such as random forests to predict whether the current environmental conditions are optimal.

[1744] Input: Current temperature and humidity.

[1745] Output: The predicted result of the AI ​​model (e.g., "Conditions are optimal").

[1746] Specific operation: The server inputs the current environmental data into the random forest model and obtains the prediction results.

[1747] Step 6: Evaluating user emotions with the emotion engine

[1748] The server uses an emotion engine to analyze the user's facial expressions, voice, and text data to recognize the user's emotions.

[1749] Input: User's facial expression data, voice data, and text data.

[1750] Output: User emotion recognition result (e.g., "confused").

[1751] Specific operation: The server runs an emotion analysis algorithm and detects the emotion "confused" from the user's facial expression data.

[1752] Step 7: Determine the recommended action

[1753] The server determines the recommended action based on the results of the environmental condition evaluation, the prediction results of the AI ​​model, and the user's emotion recognition results.

[1754] Input: Environmental condition assessment results, AI model prediction results, and user emotion recognition results.

[1755] Output: Recommended action (e.g., "Current conditions are optimal. The temperature is a little high. Lowering the temperature by 1-2 degrees would produce better results.").

[1756] Specific operation: The server integrates the evaluation results, prediction results, and emotion recognition results to determine the recommended action.

[1757] Step 8: View Guidance

[1758] The terminal displays the recommended actions sent from the server to the user.

[1759] Input: The recommended action sent by the server.

[1760] Output: Guidance shown on the device display (e.g., "Current conditions are optimal" and "The temperature is a little high. Lowering the temperature by 1-2 degrees would produce better results.").

[1761] Specific operation: The device receives a message from the server and displays it on the screen, allowing the user to check it on the screen.

[1762] (Application example 2)

[1763] 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."

[1764] While conventional plant cultivation support systems can evaluate and optimize plant cultivation conditions, they do not provide support that takes into account the user's emotions, which leads to issues such as a lack of effective support and improved user satisfaction. Another problem is that they are unable to provide appropriate feedback to address problems and anxieties specific to plant cultivation.

[1765] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving information on the type of plant, current temperature, current humidity, and growth stage input by the user, means for retrieving an optimal temperature range and an optimal humidity range from a database based on the type of plant, means for evaluating whether the current temperature and current humidity are within the optimal temperature and humidity ranges, means for predicting whether the current conditions are optimal using an artificial intelligence model, means for determining a recommended action based on the evaluation and prediction and presenting it to the user, means for inputting the user's emotions, and means for adjusting the presented recommended action based on the user's emotional information. This makes it possible to not only optimize plant growth but also provide flexible support that corresponds to the user's emotions.

[1766] "User" refers to a person who inputs information about plant growing conditions and receives recommended actions from the system.

[1767] "Plant type" refers to the name of the particular plant that the user wants to grow.

[1768] "Current temperature" refers to the current temperature of the environment where the plant is located, as entered by the user.

[1769] "Current humidity" refers to the current humidity of the environment in which the plant is placed, as input by the user.

[1770] "Growth stage" is information that indicates the current growth phase of the plant.

[1771] The "database" is an information management system that stores information on optimal growing conditions for plants.

[1772] "Optimal temperature range" refers to the range of temperatures in which a particular plant can grow optimally.

[1773] The "optimal humidity range" refers to the humidity range in which a particular plant can grow optimally.

[1774] An "artificial intelligence model" is a machine learning algorithm used to optimize plant growth conditions.

[1775] "Recommended Actions" refers to specific instructions for action to optimize plant growth based on current growing conditions.

[1776] "Emotion information" refers to data related to emotions input by the user, such as information extracted from facial expressions, voice, and text.

[1777] "Adjustment means" refers to the ability to change the recommended actions and guidance presented based on the user's emotional information.

[1778] This invention relates to a system that allows users to input information for plant cultivation and presents optimal cultivation conditions based on that information, and also provides support taking into account the user's feelings. The system is mainly composed of a server, terminals, and users.

[1779] 1. System Program Overview

[1780] User Input:

[1781] The device is equipped with a camera and voice input, and collects information about the plant's type, current temperature, current humidity, and growth stage from the device, as well as emotional information from the user's facial expressions and voice.

[1782] Data transmission:

[1783] The information and emotion information entered by the user are properly formatted and sent to the server using the device's communication function.

[1784] Database Reference:

[1785] Based on the received data, the server retrieves the optimal growing conditions (temperature and humidity ranges) for the corresponding plant from the database. For example, it retrieves information such as the optimal temperature for tomatoes being 20-25°C and the optimal humidity being 45-70%.

[1786] Environmental condition assessment:

[1787] The server evaluates whether the current temperature and humidity input are within the optimal range, and the evaluation results are presented in the form of "suitable temperature" and "suitable humidity."

[1788] Artificial intelligence model predictions:

[1789] The server uses a generative AI model, specifically an algorithm such as random forest, to predict whether the current environmental conditions are optimal for the plant.

[1790] Emotional evaluation by emotion engine:

[1791] The server analyzes the user's facial expressions and voice to recognize their emotional state, such as "confusion" or "fun," using OpenCV and emotion engine software built on top of it.

[1792] Determine and adjust the recommended actions:

[1793] The server determines the optimal recommended action based on the results of the environmental condition assessment, the AI ​​model predictions, and the emotion recognition results. For example, if the user is confused, more detailed explanations or additional advice will be provided.

[1794] Display guidance:

[1795] Finally, the device displays the recommended actions and guidance sent from the server to the user in a format that is easy for the user to view.

[1796] 2. System Operation

[1797] The hardware used includes smart glasses and smartphones, which input and display information.

[1798] The software uses Python, OpenCV, emotion engines, and generative AI models such as random forests.

[1799] Examples:

[1800] In a plant specialty store, a staff member puts on smart glasses and inputs "tomato," along with the current temperature of 22°C, humidity of 60%, and the growth stage of "flowering." If the staff member's expression looks confused, this information is sent to the server. The server evaluates the optimal growing conditions based on the input information and provides specific advice, such as "The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results."

[1801] Example prompt sentence:

[1802] "Scan the type of plant in the store, the current temperature, the current humidity, and the growth stage, and generate a sentence that provides optimal growing guidance if the user has a confused look on their face."

[1803] In this way, the present invention can solve the plant cultivation problems faced by users and further provide flexible and effective support that responds to the user's feelings.

[1804] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1805] Step 1: User Input

[1806] The user uses the device's input form to input the plant's type, current temperature, current humidity, and growth stage. The device also uses a camera and voice input to collect emotional information from the user's facial expressions and voice. If the user inputs information such as tomato, 22°C, 60%, and flowering stage, and shows a confused expression, this data is treated as input.

[1807] Step 2: Send data

[1808] The device formats the plant type, current temperature, current humidity, growth stage, and emotion information entered by the user and sends it to the server in JSON format as a POST request.

[1809] Step 3: Database Reference

[1810] The server receives the transmitted data and retrieves the optimal growing conditions (temperature and humidity ranges) for the corresponding plant from the database. For example, the server recognizes that the plant is a tomato and retrieves from the database the optimal temperature of 20 to 25°C and the optimal humidity of 45 to 70%.

[1811] Step 4: Evaluate environmental conditions

[1812] The server evaluates whether the current temperature (22°C) and humidity (60%) are within the optimal temperature and humidity ranges. Based on this evaluation, it determines which environmental conditions are suitable and outputs the results as "optimal temperature" and "optimal humidity."

[1813] Step 5: Prediction by artificial intelligence model

[1814] The server uses a generative AI model (e.g., random forest) to predict whether the current environmental conditions are optimal for the plant. The model takes the current temperature and humidity as input and outputs a prediction of whether the conditions are optimal.

[1815] Step 6: Emotion evaluation by the emotion engine

[1816] The server analyzes the user's facial expression and voice data sent from the device and uses an emotion engine to determine whether the user is confused. Based on this analysis, the server outputs the user's emotional state as "confused."

[1817] Step 7: Determine and adjust recommended actions

[1818] The server determines the optimal recommended action based on the results of the environmental condition evaluation, the AI ​​model predictions, and the emotion recognition results. For example, the server may use the message "The current conditions are optimal" as a base message, and if the user is confused, include additional advice such as "The temperature is a little high. Lowering the temperature by 1-2 degrees will produce better results."

[1819] Step 8: View Guidance

[1820] The device receives the recommended actions and guidance sent from the server and displays them to the user. The user can see messages on the device screen such as "Current conditions are optimal" and "The temperature is a little high. Lowering the temperature by 1-2 degrees will give better results."

[1821] Through the above processing steps, the system of the present invention can support the user's plant cultivation in real time and also provide flexible support according to the user's emotions.

[1822] 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.

[1823] 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.

[1824] 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.

[1825] 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.

[1826] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally gene...

Claims

1. means for receiving information on the type of plant, the current temperature, the current humidity, and the growth stage inputted by a user; means for obtaining an optimum temperature range and an optimum humidity range from a database based on the type of plant; means for assessing whether the current temperature is within an optimal temperature range and whether the current humidity is within an optimal humidity range; a means of predicting whether current conditions are optimal using an artificial intelligence model; means for determining and presenting to a user a recommended action based on said evaluation and prediction; A system including:

2. 2. The system according to claim 1, wherein the information input by the user is transmitted to a server via a terminal.

3. The system of claim 1 , wherein the recommended actions are displayed to a user through a terminal.

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