System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting a type of plant and a particular planting date; means for using an artificial intelligence model to diagnose a health condition from a captured image of the plant; means for creating a personalized care plan based on the health condition diagnosed by the artificial intelligence model; means for scheduling a care reminder for the plant; and means for notifying a user of the care reminder.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] Many plant lovers face the problem of not knowing the appropriate timing for watering and fertilizing, making it difficult to maintain the health of their plants. They also tend to forget to care for their plants, making it difficult to efficiently manage plant health in their busy daily lives. Furthermore, learning the different care methods for each plant requires considerable time and effort. Given these circumstances, there is a need for a system that can provide appropriate care plans for users, from beginners to veterans, without relying on their knowledge or experience. [Means for solving the problem]

[0005] To address this issue, the present invention has the following features. Specifically, the system includes a means for inputting a plant type and a specific planting date, a means for using an artificial intelligence model to diagnose the health condition of a plant from a photographed image, a means for creating an individualized care plan based on the health condition diagnosed by the artificial intelligence model, a means for scheduling plant care reminders, and a means for notifying a user of the care reminders. This supports the user in providing appropriate plant care and maintains the health of the plant. Furthermore, the system automatically diagnoses the plant's health condition using the artificial intelligence model and proposes an optimal care plan to the user, thereby reducing the user's burden. Furthermore, the system periodically notifies the user of plant care reminders, helping the user remember to water and fertilize the plant.

[0006] A "plant type" is the taxonomic group to which a particular plant belongs, and is generally classified based on the characteristics of the plant.

[0007] The "planting date" refers to the date on which a particular plant is planted in soil or the like, and indicates the date on which the plant begins to grow.

[0008] An "artificial intelligence model" is an algorithm that uses machine learning or deep learning techniques to learn specific patterns or characteristics from input data and make predictions or classifications.

[0009] A "care plan" is a plan that sets out the specific schedule and methods of care that should be performed to maintain the health of a particular plant.

[0010] "Care reminder" is a reminder function that notifies the user about plant care (for example, watering and fertilizing).

[0011] A "system" is a comprehensive mechanism consisting of multiple elements (hardware, software, networks, etc.) configured to achieve a specific function.

[0012] "Health status" refers to the current state of a particular plant, such as its growth status and whether it is diseased or not.

[0013] A "user" is a person who takes care of a plant, that is, a person who uses this system.

[0014] "Diagnosis" refers to the act of determining whether a plant is healthy or has a specific problem based on data such as plant images. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The present invention is a system for efficient and effective plant care that allows users to input the type of plant and planting date, and then uses an artificial intelligence model to diagnose the plant's health, create a personalized care plan, and schedule care reminders.

[0037] 1. System initialization

[0038] When the server initializes the system, it first loads the plant diagnosis model, a pre-trained artificial intelligence model for automatically diagnosing plant health from images.

[0039] 2. Adding plants

[0040] To add a new plant to the system, a user inputs the plant's type, planting date, and provides the plant's ID (unique identifier), the name of the plant's type (e.g., "tomato"), and the date the plant was planted.

[0041] The server receives this information, stores it in a database, and sets care reminders for the plant.

[0042] 3. Plant health check

[0043] To diagnose the health of a plant, users take a photo of the plant with their smartphone and upload it to the system.

[0044] The server receives the uploaded images and preprocesses them, for example by resizing them to a standard size and converting them into the format required by the AI ​​model.

[0045] The server then inputs the pre-processed images into an artificial intelligence model to diagnose the plant's health, which is then expressed as a "good" or "needs attention" status and stored in a database.

[0046] 4. Creating a care plan

[0047] The server automatically creates an appropriate care plan based on the plant type and the diagnostic results, including specific watering frequency and fertilization timing.

[0048] The server sets a schedule for care reminders based on the created care plan.

[0049] 5. Send reminders

[0050] The server sends care reminders to the user according to a schedule, informing the user of specific care tasks (e.g., "water," "fertilize," etc.).

[0051] Users can follow the reminders to care for their plants and provide feedback to the system, which will further optimize the next care plan.

[0052] For example, if a user is growing tomatoes, they enter "tomato" and "planting date" into the system. When the user takes a photo of the tomato and uploads it, the server performs a health check based on the image and the diagnosis is "good." In this case, the server creates a care plan based on the characteristics of the "tomato" by setting the watering frequency to every two days and fertilizing every 14 days. It also sends the user a watering reminder every two days and a fertilizing reminder every 14 days. This allows users to take care of their plants at the appropriate time without forgetting, even in their busy daily lives.

[0053] As described above, the present invention enables users to efficiently care for plants and maintain their health.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] To add a plant to the system, the user inputs the plant's ID, type, and planting date. This information is entered through the user interface and sent to the server.

[0057] Step 2:

[0058] The server stores the plant data in an internal database based on the input information received from the user, including the plant ID, type, planting date, and initial health status, which is set to "good."

[0059] Step 3:

[0060] The user takes a photo of the plant using the smartphone camera, and then uploads the image file from the smartphone to the server.

[0061] Step 4:

[0062] The server reads the received image file and performs preprocessing using an image processing library. Specifically, it resizes the image to the specified size (e.g., 224x224 pixels) and converts it into the format required by the AI ​​model.

[0063] Step 5:

[0064] The server then inputs the preprocessed image data into an artificial intelligence model for diagnosing the plant's health. The AI ​​model analyzes the images and determines the plant's health status ("good" or "needs attention").

[0065] Step 6:

[0066] The server retrieves the diagnostic results determined by the AI ​​model and updates the existing plant database, keeping the plant's health up to date.

[0067] Step 7:

[0068] The server then creates an appropriate care plan based on the type of plant and the diagnostic results, including how often to water the plant, when to fertilize it, and so on.

[0069] Step 8:

[0070] The server schedules periodic care reminders based on the created care plan: watering reminders every two days, fertilizing reminders every 14 days.

[0071] Step 9:

[0072] The server will send a notification to the user at the scheduled time according to the reminder schedule, via the user's smartphone or email.

[0073] Step 10:

[0074] The user receives notifications from the server and takes care of the plants, specifically by watering and fertilizing them according to the notifications.

[0075] Step 11:

[0076] After performing care, the user provides feedback on the results to the server, such as "watering completed" or "fertilization completed."

[0077] Step 12:

[0078] The server receives user feedback and updates the plant database, further optimizing the next care plan.

[0079] This allows users to efficiently care for their plants and maintain their health.

[0080] Example 1

[0081] 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."

[0082] Conventional plant care systems lack the functionality to automatically generate a specific care plan for a plant after inputting information such as the plant type and planting date, and to notify the user of appropriate reminders. They also have limited functionality for efficiently diagnosing the plant's health and optimizing the care plan based on the diagnosis results. Furthermore, they lack a mechanism for utilizing user feedback on care reminders to improve future care plans. A new system that can solve these problems and enable users to effectively maintain the health of their plants is needed.

[0083] 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.

[0084] In this invention, the server includes means for inputting the type of plant and a specific planting date, means for using a machine learning model to diagnose the health condition of the plant from photographed images, and means for creating an individual care plan based on the health condition diagnosed by the machine learning model. This enables plant health diagnosis and optimization of the care plan based on the information input by the user. Furthermore, by adding means for scheduling plant care reminders, means for notifying the user of the care reminders, means for receiving feedback from the user related to the notifications, and means for optimizing future care plans based on the feedback, the server enables the user to continuously manage the health of their plants.

[0085] A "plant type" is a category or name that identifies a particular plant, and examples include "tomato," "basil," and "rose."

[0086] "Specific planting date" refers to the specific date on which the subject plant was planted.

[0087] A "machine learning model" is an algorithm that is trained using data and has the ability to make predictions or classifications based on a given input.

[0088] A "care plan" refers to a specific plan of action or response required to maintain the health of a plant, including, for example, how often to water it and when to use fertilizer.

[0089] "Care reminders" refer to messages and alerts that notify users about plant care tasks.

[0090] "User" means an individual or organization that uses the system to care for plants.

[0091] "Feedback" refers to information provided by the user to the system, and in particular refers to the results and status of care work performed in accordance with care reminders.

[0092] "Server" refers to a computer device that processes the entire system and manages data.

[0093] "Database" means a system for systematically storing, managing, and retrieving information.

[0094] "Notification" refers to a messaging method used to convey instructions or information to the user.

[0095] The present invention relates to a system for efficient and effective plant care that allows a user to input the type of plant and a specific planting date, and then uses machine learning models to diagnose the plant's health, create a personalized care plan, and schedule care reminders.

[0096] System configuration

[0097] The main components of the system are as follows:

[0098] Server: A computer device that processes the entire system and manages data. It uses machine learning frameworks such as TensorFlow and PyTorch.

[0099] User terminal: A smartphone or PC that allows users to take photos of plants and upload them to the system.

[0100] Database: A system for storing plant information, health check results, and care plans.

[0101] System Operation

[0102] During system initialization, the server loads a pre-trained plant diagnostic model, which is built using machine learning frameworks such as TensorFlow or PyTorch.

[0103] To register a new plant in the system, a user inputs the plant type and planting date through a dedicated application or a web interface. The user terminal then sends the input information to the server.

[0104] The server stores the received information in a database and initializes care reminders for the plant.

[0105] To diagnose the health of a plant, users take a photo of the plant with their smartphone and upload it to the system, where the device sends the image to the server.

[0106] The server preprocesses the images (e.g., resizes them to a standard size) and converts them into a format suitable for input into an artificial intelligence model. The preprocessed images are then input into the model, and a diagnosis (e.g., "good" or "needs attention") is obtained and stored in a database.

[0107] The server automatically generates an appropriate care plan based on the plant type and the diagnosis results, including information such as watering frequency and fertilization timing, and schedules care reminders based on the generated care plan and saves it in a database.

[0108] The server sends reminders to users according to a schedule, either via push notification or email. Users then take care of their plants according to the reminders and provide feedback to the system.

[0109] This feedback information is sent from the user's device to the server, which stores it in a database and optimizes future care plans based on this feedback.

[0110] Specific examples

[0111] For example, if a user is growing tomatoes, they enter "tomato" and "planting date" into the system. When the user takes a photo of the tomato and uploads it, the server performs a health check based on the image and obtains a diagnosis of "good condition." In this case, the server creates a care plan based on the characteristics of the "tomato" by setting the watering frequency to every two days and fertilizing every 14 days. The server then sends the user a watering reminder every two days and a fertilizing reminder every 14 days.

[0112] Prompt Sentence Examples

[0113] "Enter the tomato planting date as April 1st and upload a photo of the tomato for a health check. The AI ​​model will analyze the image and create an appropriate care plan and reminders."

[0114] This system allows users to efficiently and effectively care for their plants and maintain their health.

[0115] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0116] Step 1: Initialize the system

[0117] The server loads the plant diagnosis model when initializing the system. The input data is a machine learning model file, and the output is the model loaded in memory. Specifically, it uses the TensorFlow or PyTorch library to read a pre-trained model file and load it into memory.

[0118] Step 2: Add plants

[0119] The user inputs the plant type and planting date through the application or web interface. The input data is the plant ID, type, and planting date, and the output is request data that stores this information.

[0120] The terminal sends the input data to the server. The input is the user's input data, and the output is the request data sent to the server.

[0121] The server stores the received data in a database and sets up an early care reminder. The input is the request data received from the terminal, and the output is the plant information and reminder settings stored in the database.

[0122] Step 3: Plant Health Check

[0123] Users take photos of plants with their smartphones and upload them to the system through the application. The input data is the captured image, and the output is the request data sent to the terminal.

[0124] The device sends the captured image to the server. The input is the image uploaded by the user, and the output is the image data sent to the server.

[0125] The server preprocesses the received images, specifically resizing them to a standard size and converting their format. The input data is the image sent from the device, and the output is the preprocessed image. Image processing libraries (OpenCV or Pillow) are used to resize and convert the format.

[0126] The server uses the preprocessed images to perform a health diagnosis using a machine learning model. The input is the preprocessed image, and the output is the diagnosis result ('good' or 'needs attention'). The diagnosis result is stored in a database.

[0127] Step 4: Create a care plan

[0128] The server creates an appropriate care plan based on the plant type and the diagnosis results. The input data are the plant type and the diagnosis results, and the output is the generated care plan. It uses predefined rules and data to set watering frequency, fertilizer timing, and other settings.

[0129] The server schedules care reminders based on the care plan. The input is the created care plan, and the output is a reminder schedule. This schedule is stored in a database.

[0130] Step 5: Send a reminder

[0131] The server sends care reminders to the user according to the schedule. The input data is the reminder schedule, and the output is a reminder notification to the user's device. Notifications are sent to the user's smartphone or PC using a push notification service (such as Firebase Cloud Messaging).

[0132] Step 6: Care feedback

[0133] The user cares for the plants according to the reminders and provides feedback on the results to the system. The input is the information after care, and the output is the feedback data. The terminal sends the user's feedback to the server.

[0134] The server stores the feedback information in a database and optimizes the care plan for the next time onward. The input is the feedback data from the device, and the output is the improvement details for the next care plan.

[0135] (Application example 1)

[0136] 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."

[0137] Traditionally, caring for plants has been time-consuming and laborious, making it difficult for modern people with busy lifestyles to care for them at the right time. It is also difficult to accurately grasp the health status of plants, which often results in the plants becoming unhealthy. Even in virtual stores, there is a lack of support for plant care after purchase, forcing users to care for their plants at their own discretion, and the appropriateness of such care cannot be guaranteed, which is an issue.

[0138] 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.

[0139] In this invention, the server includes a means for inputting the type of plant and a specific planting date, a means for using an artificial intelligence model to diagnose the health condition of the plant from photographed images, a means for creating an individual care plan based on the health condition diagnosed by the artificial intelligence model, a means for scheduling care reminders for the plant, a means for notifying the user of the care reminders, a means for automatically synchronizing purchase information from the virtual store, and a means for the user to provide feedback on the health condition of the plant using a smartphone. This allows users to care for their plants at appropriate times without forgetting, even during their busy daily lives. In addition, collaboration with the virtual store makes post-purchase care easier, allowing plants to always be kept in optimal health.

[0140] "Plant types" are classifications of plants with various forms and characteristics.

[0141] The "specific planting date" is information that indicates the specific date on which the user planted the plant.

[0142] An "artificial intelligence model" is an algorithm that mimics human knowledge and behavior to automatically perform specific tasks.

[0143] A "smartphone" is a portable electronic device that has advanced computing capabilities in addition to the functionality of a mobile phone.

[0144] "Health" is an indicator of a plant's vitality and growth status.

[0145] A "care plan" refers to specific steps and schedules for maintaining and improving the health of a plant.

[0146] "Care reminders" are messages or alerts that inform users about proper care for their plants.

[0147] A "virtual store" is a commercial facility that operates on the Internet, an online shop that does not have a physical store.

[0148] "Feedback" refers to the act or information that a user reports to the system about the results of the care they have provided.

[0149] A "server" is a high-performance computer that provides services to computers and other devices over a network.

[0150] The present invention is a system for efficiently and effectively caring for plants, which includes the following various means:

[0151] 1. System initialization

[0152] When the system is initialized, the server first loads a plant diagnosis model. This is a pre-trained artificial intelligence model that automatically diagnoses the health of plants from images. The server is built on a cloud platform (e.g., AWS, Google Cloud) and runs the AI ​​model using TensorFlow or PyTorch.

[0153] 2. Plant registration and purchase information synchronization

[0154] When a user purchases a plant from the virtual store, the purchase information (plant type, planting date) is automatically synchronized with the system. The server stores this information in a database (e.g., MySQL, Firebase) and generates a unique identifier for each plant, saving the user the trouble of manually entering it.

[0155] 3. Plant health check

[0156] Users use their smartphones to take photos of plants and upload them to the system. The server preprocesses the received images, resizes them to a standard size, and converts them into the format required by the AI ​​model. The preprocessed images are then input into a TensorFlow or PyTorch model, which generates a diagnosis. This diagnosis is then stored in a database and notified to the user.

[0157] 4. Creating a care plan

[0158] The server automatically creates an individualized care plan based on the plant type and diagnostic results. The care plan includes specific tasks such as watering frequency and fertilization timing. The created care plan is saved in a database and a care reminder schedule is set.

[0159] 5. Send reminders

[0160] The server sends care reminders to the user's smartphone via push notifications according to the schedule, informing them of specific actions such as when to water or fertilize plants.

[0161] 6. Feedback function

[0162] Users can provide feedback to the system via their smartphone about the results of their care work, which is then saved in a database and used to optimize the next care plan.

[0163] Examples of concrete examples and prompts

[0164] As a concrete example, consider the case where a user purchases tomatoes from a virtual store. The purchase information is automatically synchronized and registered in the system. When the user uploads a photo of the tomato taken with their smartphone, the server diagnoses the image and determines that the tomato is in good condition. The server then creates a care plan appropriate for the tomato, scheduling reminders to water it every two days and fertilize it every 14 days.

[0165] Prompt Sentence Examples

[0166] Please enter the plant type and planting date.

[0167] "Upload the images you've taken and diagnose the health of your plants."

[0168] "Please follow the care plan below to care for your plants."

[0169] This system allows users to take care of their plants at the right time without forgetting, even in their busy daily lives. In addition, by linking with the virtual store, post-purchase care becomes easier, allowing plants to be kept in optimal health at all times.

[0170] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0171] Step 1:

[0172] The server loads a plant diagnostic model when initializing the system. This diagnostic model is a pre-trained generative AI model that automatically diagnoses the health of plants from images. During this initialization, the server runs the AI ​​model using TensorFlow or PyTorch on a cloud platform (e.g., AWS or Google Cloud). This gives the system the ability to diagnose plant images.

[0173] Step 2:

[0174] When a user purchases a plant from a virtual store, the purchase information (type of plant, planting date) is automatically synchronized with the system. In this synchronization process, the server receives the data sent from the virtual store and stores it in a database (e.g., MySQL, Firebase). This allows the user to complete the plant registration without having to enter any information.

[0175] Step 3:

[0176] Users take photos of plants using their smartphones and upload them to the system. The uploaded images are sent to a server, which then receives them. At this time, preprocessing such as resizing and format conversion is performed on the images, and the standardized images are then input into the generative AI model. This process prepares the images to be used to diagnose the plant's health.

[0177] Step 4:

[0178] The server inputs the preprocessed images into a generative AI model to diagnose the health of the plant. The diagnosis results are expressed as a status such as "good" or "needs attention." The diagnosis results are stored in a database and notified to the user. This notification is important for allowing the user to understand the current state of the plant.

[0179] Step 5:

[0180] The server automatically creates an individual care plan based on the diagnosis results and the type of plant. The care plan includes specific care instructions such as how often to water and when to fertilize. This care plan is saved in a database and a care reminder schedule is set, helping users to provide the necessary care at the appropriate time.

[0181] Step 6:

[0182] The server sends care reminders to the user's smartphone via push notifications according to the schedule. The reminders include specific care tasks (e.g., watering and fertilizing), so that the user does not miss the appropriate care timing.

[0183] Step 7:

[0184] Users provide feedback to the system via their smartphone on the results of their care work. This feedback is sent to the server and stored in a database. The server uses this feedback information to further optimize the next care plan and notify the user again. This makes the care plan more personalized and optimized, making it easier to maintain plant health.

[0185] 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.

[0186] The present invention provides a system for efficient and effective plant care, and further enhances the user experience by combining it with an emotion engine that recognizes the user's emotions. This system allows the user to input the type of plant and the planting date, and then uses an artificial intelligence model to diagnose the plant's health and create an individualized care plan, schedule care reminders, and notify the user. The emotion engine also adjusts the content and timing of care reminders based on the user's emotions.

[0187] 1. System initialization

[0188] When the server initializes the system, it loads a plant diagnosis model and initializes an emotion engine. The plant diagnosis model is for diagnosing the health of plants from images, and the emotion engine is for recognizing the user's emotions.

[0189] 2. Adding plants

[0190] To add a new plant to the system, a user inputs the plant's ID, type, and planting date, which is then sent to the server.

[0191] The server stores the input plant information in a database and sets care reminders for the plant.

[0192] 3. Plant health check

[0193] To diagnose the health of a plant, users take a photo of the plant with their smartphone and upload it to the server.

[0194] The server receives the images, preprocesses them, and then inputs them into an artificial intelligence model to diagnose the plant's health. The results are stored in a database.

[0195] 4. Creating a care plan

[0196] The server creates an appropriate care plan based on the type of plant and the diagnosis results, and sets care reminders.

[0197] 5. Functions of the Emotion Engine

[0198] The server periodically recognizes the user's emotions using an emotion engine, which analyzes the user's text, voice, or image data to assess their emotional state.

[0199] The server decreases the frequency of care reminders when the user is stressed and increases the frequency when the user is relaxed.

[0200] 6. Send Reminders

[0201] The server notifies the user of care reminders according to the schedule via smartphone or email.

[0202] The user cares for the plants based on the reminder and then sends the results back to the server.

[0203] Specific examples

[0204] For example, if a user is growing tomatoes, they enter "tomato" and "planting date" into the system. When the user takes a photo of the tomato and uploads it, the server performs a health check based on the image and determines whether the plant is in good condition or in need of attention. Based on the results, a care plan is created, including how often to water the plant and when to apply fertilizer.

[0205] The emotion engine then recognizes the user's emotions: for example, if the user is stressed, the server will reduce the frequency of watering reminders to ease the user's burden, while if the user is relaxed, more frequent reminders will be sent to optimize plant care.

[0206] This system allows users to efficiently care for their plants, maintaining their health while also reducing the mental burden on the user.

[0207] The processing flow will be explained below.

[0208] Step 1:

[0209] To add a plant to the system, the user inputs the plant's ID, type, and planting date. This information is entered through the user interface and sent to the server.

[0210] Step 2:

[0211] The server stores the plant data in an internal database based on the input information received from the user, including the plant ID, type, planting date, and initial health status, which is set to "good."

[0212] Step 3:

[0213] The user takes a photo of the plant using the smartphone camera, and then uploads the image file from the smartphone to the server.

[0214] Step 4:

[0215] The server reads the received image file and performs preprocessing using an image processing library. Specifically, it resizes the image to the specified size (e.g., 224x224 pixels) and converts it into the format required by the AI ​​model.

[0216] Step 5:

[0217] The server then inputs the preprocessed image data into an artificial intelligence model for diagnosing the plant's health. The AI ​​model analyzes the images and determines the plant's health status ("good" or "needs attention").

[0218] Step 6:

[0219] The server retrieves the diagnostic results determined by the AI ​​model and updates the existing plant database, keeping the plant's health up to date.

[0220] Step 7:

[0221] The server then creates an appropriate care plan based on the type of plant and the diagnostic results, including how often to water the plant and when to fertilize it.

[0222] Step 8:

[0223] The server schedules periodic care reminders based on the created care plan: watering reminders every two days, fertilizing reminders every 14 days.

[0224] Step 9:

[0225] The server periodically recognizes the user's emotions using an emotion engine, which analyzes the user's text, voice, or image data to assess their emotional state.

[0226] Step 10:

[0227] The server reduces the frequency of care reminders when the user is feeling stressed, and increases the frequency of reminders when the user is feeling relaxed, thereby reducing the burden on the user and ensuring that appropriate care is provided.

[0228] Step 11:

[0229] The server notifies the user of care reminders according to the schedule via smartphone or email.

[0230] Step 12:

[0231] The user can take care of the plants based on the reminders, specifically by watering and fertilizing them according to the notifications.

[0232] Step 13:

[0233] After performing care, the user provides feedback to the server about the results, such as "watering completed" or "fertilization completed."

[0234] Step 14:

[0235] The server receives user feedback and updates the plant database, further optimizing the next care plan.

[0236] This allows users to efficiently care for their plants and maintain their health. The emotion engine also reduces the user's mental burden.

[0237] Example 2

[0238] 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."

[0239] Conventional plant care systems have limitations in their ability to diagnose plant health and provide appropriate care plans, and they are unable to consider the user's emotional state. This can cause users to feel stressed, reducing the effectiveness of plant care. Furthermore, the frequency and content of reminder notifications are fixed, making it difficult to flexibly adjust them to suit the user's situation.

[0240] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0241] In this invention, the server includes means for inputting the type of plant and a specific planting date, means for using an artificial intelligence model to diagnose the health condition of the plant from a photographed image, means for creating an individual care plan based on the health condition diagnosed by the artificial intelligence model, means for scheduling care reminders for the plant, means for notifying the user of the care reminders, means for using an emotion recognition engine to recognize the user's emotions, and means for adjusting the frequency and content of the care reminders based on the emotion recognition engine. This enables flexible adjustment of reminders according to the user's emotional state, enabling effective plant care while reducing user stress.

[0242] "Plant type" is classification information for distinguishing a particular plant from other plants.

[0243] The "specific planting date" is information about the specific date on which the user planted the plant.

[0244] An "artificial intelligence model" is an algorithm that uses technologies such as machine learning and deep learning to diagnose the health of plants.

[0245] A "care plan" is a plan that provides appropriate care methods and schedules for plants based on diagnostic results.

[0246] "Care reminder" refers to a notification that informs the user when it is time to care for their plants.

[0247] An "emotion recognition engine" is a technology that analyzes data such as a user's text and voice to determine the user's emotional state.

[0248] "Notification" is a means of sending a message to communicate care reminders to the user.

[0249] The system of the present invention is designed to provide efficient and effective plant care and further enhances the user experience by incorporating an emotion recognition engine that recognizes the user's emotions. The system allows the user to input the plant's type and planting date, and then uses an artificial intelligence model to diagnose the plant's health, creating an individualized care plan and scheduling care reminders to notify the user. The emotion recognition engine also adjusts the content and timing of care reminders based on the user's emotions.

[0250] Specific examples of hardware and software used

[0251] This system includes a server, terminals (smartphones and PCs), and various software. Specifically, it consists of the following:

[0252] The server is the heart of the system and is responsible for various processes. It uses TensorFlow to implement an artificial intelligence model for plant diagnosis, and OpenAI GPT-4 or IBM Watson to build an emotion recognition engine.

[0253] The terminal (smartphone or PC) is a device where the user can input information, take pictures of the plants, receive care reminders, etc. A dedicated application is installed on the terminal, and communication with the server is carried out through this application.

[0254] The database system uses MySQL or PostgreSQL to store data on plant information, diagnosis results, and the user's emotional state.

[0255] Specific examples

[0256] For example, if a user wants to grow tomatoes, they enter "tomato" and the "planting date" into the system. When the user takes a photo of the tomato with their smartphone and uploads it, the server performs a health check based on the image and determines whether the plant is in good condition or in need of attention. Based on the results, the server creates a care plan that includes watering frequency and fertilizer application timing.

[0257] The emotion engine then recognizes the user's emotions: for example, if the user is stressed, the server will reduce the frequency of watering reminders to ease the burden, while if the user is relaxed, it will send more frequent reminders to optimize plant care.

[0258] Prompt Sentence Examples

[0259] The following prompt sentence is used as input to the generative AI model:

[0260] Prompt: Create a care plan for a specific plant. Use the information below.

[0261] Plant Type: Tomato

[0262] Health check result: Good

[0263] User's emotional state: Relaxed

[0264] Sample answer: The care plan for your tomato plants is as follows: Water them every day at 8:00 AM and fertilize them once a week with liquid fertilizer. Maintain this frequency as your emotional state is relaxed.

[0265] This system allows users to efficiently care for their plants, maintaining their health while also reducing the mental burden on the user.

[0266] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0267] Step 1:

[0268] During system initialization, the server loads the plant diagnosis model and emotion recognition engine. This includes loading a TensorFlow model and configuring it to diagnose plant health from images. It also uses emotion recognition technologies such as OpenAI GPT-4 to analyze the user's text and voice data and prepare for emotion recognition. The input is the initial setup data, and the output is the prepared system environment.

[0269] Step 2:

[0270] A user adds a new plant to the system using a terminal. The user inputs the plant's ID, type, and planting date, and sends the information to the server through a dedicated application. For example, the user might input "ID: 12345, type: tomato, planting date: October 1, 2023." The input is plant information, and the output is the plant information stored in the database.

[0271] Step 3:

[0272] The server stores the received plant information in a database. The input plant ID, type, and planting date are stored in a database such as MySQL or PostgreSQL, and care reminders are set for the plant. The input is plant information, and the output is the updated database and the set reminders.

[0273] Step 4:

[0274] To diagnose the health of a plant, a user takes a photo of the plant with their smartphone and uploads the photo to the server using a dedicated application. The input is the image of the plant, and the output is the transmission of the image to the server.

[0275] Step 5:

[0276] The server preprocesses the received images, adjusting the image resolution and removing noise using the OpenCV library. The preprocessed images are then input into an artificial intelligence model to diagnose the plant's health. Based on the diagnosis results, the health status is determined as "good" or "needs attention." The input is the image before preprocessing, and the output is the diagnosis result.

[0277] Step 6:

[0278] The server stores the diagnostic results in a database. The input is the diagnostic results and the output is the updated database.

[0279] Step 7:

[0280] The server creates a care plan based on the plant type and the diagnosis results. For example, if the diagnosis result is "good," it generates a care plan including watering frequency and fertilizer timing. The input is plant information and diagnosis results, and the output is the generated care plan.

[0281] Step 8:

[0282] The server schedules care reminders. It uses a cron job or similar to set the timing for notifying users. The input is the care plan, and the output is the configured reminder schedule.

[0283] Step 9:

[0284] The server recognizes the user's emotions using an emotion recognition engine. It analyzes the user's text messages and voice data to determine their current emotional state (e.g., stressed, relaxed). The input is the user's text or voice data, and the output is the recognized emotional state.

[0285] Step 10:

[0286] The server adjusts the frequency and content of reminders based on the user's emotional state. For example, if the user is feeling stressed, the server reduces the frequency of reminders. The inputs are the perceived emotional state and the care plan, and the output is the adjusted reminders.

[0287] Step 11:

[0288] The server notifies the user of the care reminder at the scheduled time. It sends the reminder to the user's device using Firebase Cloud Messaging or an email service. The input is the adjusted reminder schedule, and the output is the notification to the user.

[0289] Step 12:

[0290] The user cares for the plants based on the reminders. After the care, the user sends feedback to the server using a dedicated application. The input is the results of the care, and the output is a database update as feedback.

[0291] (Application example 2)

[0292] 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."

[0293] Conventional plant care systems can diagnose the health of plants and create care plans, but they do not adjust reminders based on the user's emotional state, making it difficult to improve the user experience. In particular, they were unable to provide appropriate care timing when the user was stressed or relaxed, making it difficult to provide efficient and effective plant care.

[0294] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting the type of plant and a specific planting date, means for using an AI model to diagnose the health condition of the plant from a photographed image, means for creating an individual care plan based on the health condition diagnosed by the AI ​​model, means for scheduling care reminders for the plant, means for recognizing the user's emotional state and adjusting the content and timing of the care reminders based on the emotional state, and means for notifying the user of the care reminders. This enables optimal adjustment of care reminders according to the user's emotional state, resulting in efficient and effective plant care and an improved user experience.

[0295] "Plant type" refers to a specific classification of the plant to be cared for, and appropriate care methods vary depending on the classification.

[0296] The "specific planting date" refers to the date the plant was planted, which is important information that influences care plans and reminder schedules.

[0297] "Photographed image" refers to an image showing the current state of a plant that has been photographed by a user using a device such as a smartphone.

[0298] "Health" is an indicator used to evaluate the growth and condition of plants, and is diagnosed by an artificial intelligence model.

[0299] The "artificial intelligence model" is a technology that uses image recognition and machine learning to automatically diagnose the health of plants and create care plans.

[0300] A "care plan" refers to specific care methods and schedules created based on the health of a plant.

[0301] "Care reminders" refer to messages and alerts that inform users how and when to care for their plants.

[0302] "Emotional state" refers to a user's current mental and psychological state and is detected by emotion recognition technology.

[0303] "Emotion recognition" refers to technology that analyzes voice, facial expressions, and text data to assess a user's emotions.

[0304] "Means of notification" refers to the methods and functions for transmitting information to users using smartphones, email, etc.

[0305] This invention is a system for efficiently and effectively caring for plants, which recognizes the user's emotional state and adjusts the content and timing of care reminders based on that state. To realize this system, a server and a smartphone are used.

[0306] First, the user inputs the plant type and specific planting date on their smartphone. The input information is sent to the server and stored in a database. Next, the user takes a photo of the plant with their smartphone and uploads it to the server to diagnose the plant's health. The server preprocesses the received image and inputs it into an artificial intelligence model to diagnose the plant's health. The diagnosis results are stored in a database, and the server creates an individual care plan based on the results.

[0307] The server analyzes voice, facial expressions, text data, etc. to periodically recognize the user's emotional state. Specifically, it uses emotion recognition technology to evaluate the user's stress level and relaxation level. This process uses emotion recognition engines such as Amazon Rekognition and IBM Watson.

[0308] Based on the user's emotional state, the server adjusts the timing and content of care reminders. For example, if the user is stressed, the frequency of reminders is reduced, and if the user is relaxed, the frequency is increased. The adjusted reminders are then sent to the user via Firebase Cloud Messaging or Apple Push Notification Service (APNS).

[0309] Specific examples

[0310] For example, if a user is tired and stressed from work, the smartphone camera will recognize this and the server will suggest a care plan to help them relax. By inputting "tomato" and "planting date" into the system and uploading a photo of the tomato, the server will perform a health check based on the image and determine whether the plant is in good condition or in need of attention. Based on the results, a care plan including watering frequency and fertilizer application timing is created. Furthermore, if the user is feeling stressed, the server will reduce the frequency of reminders.

[0311] Prompt Sentence Examples

[0312] "Siri, how do you feel about me?"

[0313] "Stuck on dinner suggestions? Let me know what you're feeling today."

[0314] "I've been busy at work lately and I'm feeling a bit restless. Can you suggest some dishes that will help me relax?"

[0315] "You seem a little tired today. Can we adjust your delivery time a little later?"

[0316] This allows optimal care reminders to be adjusted according to the user's emotional state, resulting in efficient and effective plant care and an improved user experience.

[0317] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0318] Step 1: Enter plant information

[0319] Users use their smartphones to input the type of plant they want to grow and the specific planting date, and this information is sent to a server and stored in a database.

[0320] Input: Plant type, specific planting date

[0321] Data processing: The server saves the input information in a database

[0322] Output: Plant information stored in a database

[0323] Step 2: Plant Health Check

[0324] Users take images of plants with their smartphones and upload them to a server, which pre-processes the images and feeds them into an artificial intelligence model to diagnose the plant's health.

[0325] Input: Captured image

[0326] Data processing: Image preprocessing (noise removal, resizing, etc.)

[0327] Data Computing: Health Diagnosis Using Artificial Intelligence Models

[0328] Output: Health check result ("Good" or "Needs attention")

[0329] Step 3: Create a care plan

[0330] The server creates a personalized care plan based on the plant's diagnosis and type, including how often to water it and when to fertilize it.

[0331] Input: diagnosis result, plant type

[0332] Data processing: Care plan information generation

[0333] Output: Individual care plan

[0334] Step 4: Recognizing your emotional state

[0335] To recognize the user's emotional state, the smartphone's camera and microphone are used to collect voice and facial expression data. The server then uses an emotion recognition engine to analyze this data and evaluate the user's emotional state.

[0336] Input: Audio data, image data

[0337] Data processing: voice recognition, face recognition

[0338] Data Calculation: Analysis by Emotion Recognition Engine

[0339] Output: User's emotional state

[0340] Step 5: Adjust your reminders

[0341] The server adjusts the content and timing of care reminders based on the user's emotional state, for example, decreasing the frequency of reminders for a stressed user and increasing the frequency for a relaxed user.

[0342] Input: User's emotional state, care plan

[0343] Data processing: Reminder schedule adjustment

[0344] Output: Adjusted reminders

[0345] Step 6: Reminder Notifications

[0346] The server then notifies the user of the adjusted care reminders using a push notification system, via smartphone or email.

[0347] Input: Adjusted reminders

[0348] Data processing: Creating push notification messages

[0349] Output: Reminder notification to the user

[0350] Step 7: Plant care implementation and feedback

[0351] The user cares for the plants based on the reminders and provides feedback to the server, which uses the feedback as learning data for the system.

[0352] Input: Care outcome

[0353] Data processing: collection and storage of feedback data

[0354] Output: Updated database

[0355] In this way, optimal care reminders are provided according to the user's emotional state, realizing efficient and effective plant care and improving the user experience.

[0356] 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.

[0357] 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.

[0358] 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.

[0359] [Second embodiment]

[0360] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0361] 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.

[0362] 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).

[0363] 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.

[0364] 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.

[0365] 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).

[0366] 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.

[0367] 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.

[0368] 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.

[0369] 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.

[0370] 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.

[0371] 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."

[0372] The present invention is a system for efficient and effective plant care that allows users to input the type of plant and planting date, and then uses an artificial intelligence model to diagnose the plant's health, create a personalized care plan, and schedule care reminders.

[0373] 1. System initialization

[0374] When the server initializes the system, it first loads the plant diagnosis model, a pre-trained artificial intelligence model for automatically diagnosing plant health from images.

[0375] 2. Adding plants

[0376] To add a new plant to the system, a user inputs the plant's type, planting date, and provides the plant's ID (unique identifier), the name of the plant's type (e.g., "tomato"), and the date the plant was planted.

[0377] The server receives this information, stores it in a database, and sets care reminders for the plant.

[0378] 3. Plant health check

[0379] To diagnose the health of a plant, users take a photo of the plant with their smartphone and upload it to the system.

[0380] The server receives the uploaded images and preprocesses them, for example by resizing them to a standard size and converting them into the format required by the AI ​​model.

[0381] The server then inputs the pre-processed images into an artificial intelligence model to diagnose the plant's health, which is then expressed as a "good" or "needs attention" status and stored in a database.

[0382] 4. Creating a care plan

[0383] The server automatically creates an appropriate care plan based on the plant type and the diagnostic results, including specific watering frequency and fertilization timing.

[0384] The server sets a schedule for care reminders based on the created care plan.

[0385] 5. Send reminders

[0386] The server sends care reminders to the user according to a schedule, informing the user of specific care tasks (e.g., "water," "fertilize," etc.).

[0387] Users can follow the reminders to care for their plants and provide feedback to the system, which will further optimize the next care plan.

[0388] For example, if a user is growing tomatoes, they enter "tomato" and "planting date" into the system. When the user takes a photo of the tomato and uploads it, the server performs a health check based on the image and the diagnosis is "good." In this case, the server creates a care plan based on the characteristics of the "tomato" by setting the watering frequency to every two days and fertilizing every 14 days. It also sends the user a watering reminder every two days and a fertilizing reminder every 14 days. This allows users to take care of their plants at the appropriate time without forgetting, even in their busy daily lives.

[0389] As described above, the present invention enables users to efficiently care for plants and maintain their health.

[0390] The processing flow will be explained below.

[0391] Step 1:

[0392] To add a plant to the system, the user inputs the plant's ID, type, and planting date. This information is entered through the user interface and sent to the server.

[0393] Step 2:

[0394] The server stores the plant data in an internal database based on the input information received from the user, including the plant ID, type, planting date, and initial health status, which is set to "good."

[0395] Step 3:

[0396] The user takes a photo of the plant using the smartphone camera, and then uploads the image file from the smartphone to the server.

[0397] Step 4:

[0398] The server reads the received image file and performs preprocessing using an image processing library. Specifically, it resizes the image to the specified size (e.g., 224x224 pixels) and converts it into the format required by the AI ​​model.

[0399] Step 5:

[0400] The server then inputs the preprocessed image data into an artificial intelligence model for diagnosing the plant's health. The AI ​​model analyzes the images and determines the plant's health status ("good" or "needs attention").

[0401] Step 6:

[0402] The server retrieves the diagnostic results determined by the AI ​​model and updates the existing plant database, keeping the plant's health up to date.

[0403] Step 7:

[0404] The server then creates an appropriate care plan based on the type of plant and the diagnostic results, including how often to water the plant, when to fertilize it, and so on.

[0405] Step 8:

[0406] The server schedules periodic care reminders based on the created care plan: watering reminders every two days, fertilizing reminders every 14 days.

[0407] Step 9:

[0408] The server will send a notification to the user at the scheduled time according to the reminder schedule, via the user's smartphone or email.

[0409] Step 10:

[0410] The user receives notifications from the server and takes care of the plants, specifically by watering and fertilizing them according to the notifications.

[0411] Step 11:

[0412] After performing care, the user provides feedback on the results to the server, such as "watering completed" or "fertilization completed."

[0413] Step 12:

[0414] The server receives user feedback and updates the plant database, further optimizing the next care plan.

[0415] This allows users to efficiently care for their plants and maintain their health.

[0416] Example 1

[0417] 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."

[0418] Conventional plant care systems lack the functionality to automatically generate a specific care plan for a plant after inputting information such as the plant type and planting date, and to notify the user of appropriate reminders. They also have limited functionality for efficiently diagnosing the plant's health and optimizing the care plan based on the diagnosis results. Furthermore, they lack a mechanism for utilizing user feedback on care reminders to improve future care plans. A new system that can solve these problems and enable users to effectively maintain the health of their plants is needed.

[0419] 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.

[0420] In this invention, the server includes means for inputting the type of plant and a specific planting date, means for using a machine learning model to diagnose the health condition of the plant from photographed images, and means for creating an individual care plan based on the health condition diagnosed by the machine learning model. This enables plant health diagnosis and optimization of the care plan based on the information input by the user. Furthermore, by adding means for scheduling plant care reminders, means for notifying the user of the care reminders, means for receiving feedback from the user related to the notifications, and means for optimizing future care plans based on the feedback, the server enables the user to continuously manage the health of their plants.

[0421] A "plant type" is a category or name that identifies a particular plant, and examples include "tomato," "basil," and "rose."

[0422] "Specific planting date" refers to the specific date on which the subject plant was planted.

[0423] A "machine learning model" is an algorithm that is trained using data and has the ability to make predictions or classifications based on a given input.

[0424] A "care plan" refers to a specific plan of action or response required to maintain the health of a plant, including, for example, how often to water it and when to use fertilizer.

[0425] "Care reminders" refer to messages and alerts that notify users about plant care tasks.

[0426] "User" means an individual or organization that uses the system to care for plants.

[0427] "Feedback" refers to information provided by the user to the system, and in particular refers to the results and status of care work performed in accordance with care reminders.

[0428] "Server" refers to a computer device that processes the entire system and manages data.

[0429] "Database" means a system for systematically storing, managing, and retrieving information.

[0430] "Notification" refers to a messaging method used to convey instructions or information to the user.

[0431] The present invention relates to a system for efficient and effective plant care that allows a user to input the type of plant and a specific planting date, and then uses machine learning models to diagnose the plant's health, create a personalized care plan, and schedule care reminders.

[0432] System configuration

[0433] The main components of the system are as follows:

[0434] Server: A computer device that processes the entire system and manages data. It uses machine learning frameworks such as TensorFlow and PyTorch.

[0435] User terminal: A smartphone or PC that allows users to take photos of plants and upload them to the system.

[0436] Database: A system for storing plant information, health check results, and care plans.

[0437] System Operation

[0438] During system initialization, the server loads a pre-trained plant diagnostic model, which is built using machine learning frameworks such as TensorFlow or PyTorch.

[0439] To register a new plant in the system, the user inputs the plant type and planting date through a dedicated application or a web interface. The user terminal then sends the input information to the server.

[0440] The server stores the received information in a database and initializes care reminders for the plant.

[0441] To diagnose the health of a plant, users take a photo of the plant with their smartphone and upload it to the system, where the device sends the image to the server.

[0442] The server preprocesses the images (e.g., resizes them to a standard size) and converts them into a format suitable for input into an artificial intelligence model. The preprocessed images are then input into the model, and a diagnosis (e.g., "good" or "needs attention") is obtained and stored in a database.

[0443] The server automatically generates an appropriate care plan based on the type of plant and the diagnosis results, including information such as watering frequency and fertilization timing, and schedules care reminders based on the generated care plan and saves it in a database.

[0444] The server sends reminders to users according to a schedule, either via push notification or email. Users then take care of their plants according to the reminders and provide feedback to the system.

[0445] This feedback information is sent from the user's device to the server, which stores it in a database and optimizes future care plans based on this feedback.

[0446] Specific examples

[0447] For example, if a user is growing tomatoes, they enter "tomato" and "planting date" into the system. When the user takes a photo of the tomato and uploads it, the server performs a health check based on the image and obtains a diagnosis of "good condition." In this case, the server creates a care plan based on the characteristics of the "tomato" by setting the watering frequency to every two days and fertilizing every 14 days. The server then sends the user a watering reminder every two days and a fertilizing reminder every 14 days.

[0448] Prompt Sentence Examples

[0449] "Enter the tomato planting date as April 1st and upload a photo of the tomato for a health check. The AI ​​model will analyze the image and create an appropriate care plan and reminders."

[0450] This system allows users to efficiently and effectively care for their plants and maintain their health.

[0451] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0452] Step 1: Initialize the system

[0453] The server loads the plant diagnosis model when initializing the system. The input data is a machine learning model file, and the output is the model loaded in memory. Specifically, it uses the TensorFlow or PyTorch library to read a pre-trained model file and load it into memory.

[0454] Step 2: Add plants

[0455] The user inputs the plant type and planting date through the application or web interface. The input data is the plant ID, type, and planting date, and the output is request data that stores this information.

[0456] The terminal sends the input data to the server. The input is the user's input data, and the output is the request data sent to the server.

[0457] The server stores the received data in a database and sets up an early care reminder. The input is the request data received from the terminal, and the output is the plant information and reminder settings stored in the database.

[0458] Step 3: Plant Health Check

[0459] Users take photos of plants with their smartphones and upload them to the system through the application. The input data is the captured image, and the output is the request data sent to the terminal.

[0460] The device sends the captured image to the server. The input is the image uploaded by the user, and the output is the image data sent to the server.

[0461] The server preprocesses the received images, specifically resizing them to a standard size and converting their format. The input data is the image sent from the device, and the output is the preprocessed image. Image processing libraries (OpenCV or Pillow) are used to resize and convert the format.

[0462] The server uses the preprocessed images to perform a health diagnosis using a machine learning model. The input is the preprocessed image, and the output is the diagnosis result ("good" or "needs attention"). The diagnosis result is stored in a database.

[0463] Step 4: Create a care plan

[0464] The server creates an appropriate care plan based on the plant type and the diagnosis results. The input data are the plant type and the diagnosis results, and the output is the generated care plan. It uses predefined rules and data to set watering frequency, fertilizer timing, and other settings.

[0465] The server schedules care reminders based on the care plan. The input is the created care plan, and the output is a reminder schedule. This schedule is stored in a database.

[0466] Step 5: Send a reminder

[0467] The server sends care reminders to the user according to the schedule. The input data is the reminder schedule, and the output is a reminder notification to the user's device. Notifications are sent to the user's smartphone or PC using a push notification service (such as Firebase Cloud Messaging).

[0468] Step 6: Care feedback

[0469] The user follows the reminders to care for the plants and provides feedback to the system. The input is the information after care, and the output is the feedback data. The terminal sends the user's feedback to the server.

[0470] The server stores the feedback information in a database and optimizes the care plan for the next time onward. The input is the feedback data from the device, and the output is the improvement details for the next care plan.

[0471] (Application example 1)

[0472] 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."

[0473] Traditionally, caring for plants has been time-consuming and laborious, making it difficult for modern people with busy lifestyles to care for them at the right time. It is also difficult to accurately grasp the health status of plants, which often results in the plants becoming unhealthy. Even in virtual stores, there is a lack of support for plant care after purchase, forcing users to care for their plants at their own discretion, and the appropriateness of such care cannot be guaranteed, which is an issue.

[0474] 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.

[0475] In this invention, the server includes a means for inputting the type of plant and a specific planting date, a means for using an artificial intelligence model to diagnose the health condition of the plant from photographed images, a means for creating an individual care plan based on the health condition diagnosed by the artificial intelligence model, a means for scheduling care reminders for the plant, a means for notifying the user of the care reminders, a means for automatically synchronizing purchase information from the virtual store, and a means for the user to provide feedback on the health condition of the plant using a smartphone. This allows users to care for their plants at appropriate times without forgetting, even during their busy daily lives. In addition, collaboration with the virtual store makes post-purchase care easier, allowing plants to always be kept in optimal health.

[0476] "Plant types" are classifications of plants with various forms and characteristics.

[0477] The "specific planting date" is information that indicates the specific date on which the user planted the plant.

[0478] An "artificial intelligence model" is an algorithm that mimics human knowledge and behavior to automatically perform specific tasks.

[0479] A "smartphone" is a portable electronic device that has advanced computing capabilities in addition to the functionality of a mobile phone.

[0480] "Health" is an indicator of a plant's vitality and growth status.

[0481] A "care plan" refers to specific steps and schedules for maintaining and improving the health of a plant.

[0482] "Care reminders" are messages or alerts that inform users about proper care for their plants.

[0483] A "virtual store" is a commercial facility that operates on the Internet, an online shop that does not have a physical store.

[0484] "Feedback" refers to the act or information that a user reports to the system about the results of the care they have provided.

[0485] A "server" is a high-performance computer that provides services to computers and other devices over a network.

[0486] The present invention is a system for efficiently and effectively caring for plants, which includes the following various means:

[0487] 1. System initialization

[0488] When the system is initialized, the server first loads a plant diagnosis model. This is a pre-trained artificial intelligence model that automatically diagnoses the health of plants from images. The server is built on a cloud platform (e.g., AWS, Google Cloud) and runs the AI ​​model using TensorFlow or PyTorch.

[0489] 2. Plant registration and purchase information synchronization

[0490] When a user purchases a plant from the virtual store, the purchase information (plant type, planting date) is automatically synchronized with the system. The server stores this information in a database (e.g., MySQL, Firebase) and generates a unique identifier for each plant, saving the user the trouble of manually entering it.

[0491] 3. Plant health check

[0492] Users use their smartphones to take photos of plants and upload them to the system. The server preprocesses the received images, resizes them to a standard size, and converts them into the format required by the AI ​​model. The preprocessed images are then input into a TensorFlow or PyTorch model, which generates a diagnosis. This diagnosis is then stored in a database and notified to the user.

[0493] 4. Creating a care plan

[0494] The server automatically creates an individualized care plan based on the plant type and diagnostic results. The care plan includes specific tasks such as watering frequency and fertilization timing. The created care plan is saved in a database and a care reminder schedule is set.

[0495] 5. Send reminders

[0496] The server sends care reminders to the user's smartphone via push notifications according to the schedule, informing them of specific actions such as when to water or fertilize plants.

[0497] 6. Feedback function

[0498] Users can provide feedback to the system via their smartphone about the results of their care work, which is then saved in a database and used to optimize the next care plan.

[0499] Examples of specific examples and prompts

[0500] As a concrete example, consider the case where a user purchases tomatoes from a virtual store. The purchase information is automatically synchronized and registered in the system. When the user uploads a photo of the tomato taken with their smartphone, the server diagnoses the image and determines that the tomato is in good condition. The server then creates a care plan appropriate for the tomato, scheduling reminders to water it every two days and fertilize it every 14 days.

[0501] Prompt Sentence Examples

[0502] Please enter the plant type and planting date.

[0503] "Upload the images you've taken and diagnose the health of your plants."

[0504] "Please follow the care plan below to care for your plants."

[0505] This system allows users to take care of their plants at the right time without forgetting, even in their busy daily lives. In addition, by linking with the virtual store, post-purchase care becomes easier, allowing plants to be kept in optimal health at all times.

[0506] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0507] Step 1:

[0508] The server loads a plant diagnostic model when initializing the system. This diagnostic model is a pre-trained generative AI model that automatically diagnoses the health of plants from images. During this initialization, the server runs the AI ​​model using TensorFlow or PyTorch on a cloud platform (e.g., AWS or Google Cloud). This gives the system the ability to diagnose plant images.

[0509] Step 2:

[0510] When a user purchases a plant from a virtual store, the purchase information (type of plant, planting date) is automatically synchronized with the system. In this synchronization process, the server receives the data sent from the virtual store and stores it in a database (e.g., MySQL, Firebase). This allows the user to complete the plant registration without having to enter any information.

[0511] Step 3:

[0512] Users take photos of plants using their smartphones and upload them to the system. The uploaded images are sent to a server, which then receives them. At this time, preprocessing such as resizing and format conversion is performed on the images, and the standardized images are then input into the generative AI model. This process prepares the images to be used to diagnose the plant's health.

[0513] Step 4:

[0514] The server inputs the preprocessed images into a generative AI model to diagnose the health of the plant. The diagnosis results are expressed as a status such as "good" or "needs attention." The diagnosis results are stored in a database and notified to the user. This notification is important for allowing the user to understand the current state of the plant.

[0515] Step 5:

[0516] The server automatically creates an individual care plan based on the diagnosis results and the type of plant. The care plan includes specific care instructions such as how often to water and when to fertilize. This care plan is saved in a database and a care reminder schedule is set, helping users to provide the necessary care at the appropriate time.

[0517] Step 6:

[0518] The server sends care reminders to the user's smartphone via push notifications according to the schedule. The reminders include specific care tasks (e.g., watering and fertilizing), so that the user does not miss the appropriate care timing.

[0519] Step 7:

[0520] Users provide feedback to the system via their smartphone on the results of their care work. This feedback is sent to the server and stored in a database. The server uses this feedback information to further optimize the next care plan and notify the user again. This makes the care plan more personalized and optimized, making it easier to maintain plant health.

[0521] 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.

[0522] The present invention provides a system for efficient and effective plant care, and further enhances the user experience by combining it with an emotion engine that recognizes the user's emotions. This system allows the user to input the type of plant and the planting date, and then uses an artificial intelligence model to diagnose the plant's health and create an individualized care plan, schedule care reminders, and notify the user. The emotion engine also adjusts the content and timing of care reminders based on the user's emotions.

[0523] 1. System initialization

[0524] When the server initializes the system, it loads a plant diagnosis model and initializes an emotion engine. The plant diagnosis model is for diagnosing the health of plants from images, and the emotion engine is for recognizing the user's emotions.

[0525] 2. Adding plants

[0526] To add a new plant to the system, a user inputs the plant's ID, type, and planting date, which is then sent to the server.

[0527] The server stores the input plant information in a database and sets care reminders for the plant.

[0528] 3. Plant health check

[0529] To diagnose the health of a plant, users take a photo of the plant with their smartphone and upload it to the server.

[0530] The server receives the images, preprocesses them, and then inputs them into an artificial intelligence model to diagnose the plant's health. The results are stored in a database.

[0531] 4. Creating a care plan

[0532] The server creates an appropriate care plan based on the type of plant and the diagnosis results, and sets care reminders.

[0533] 5. Functions of the Emotion Engine

[0534] The server periodically recognizes the user's emotions using an emotion engine, which analyzes the user's text, voice, or image data to assess their emotional state.

[0535] The server decreases the frequency of care reminders when the user is stressed and increases the frequency when the user is relaxed.

[0536] 6. Send Reminders

[0537] The server notifies the user of care reminders according to the schedule via smartphone or email.

[0538] The user cares for the plants based on the reminder and then sends the results back to the server.

[0539] Specific examples

[0540] For example, if a user is growing tomatoes, they enter "tomato" and "planting date" into the system. When the user takes a photo of the tomato and uploads it, the server performs a health check based on the image and determines whether the plant is in good condition or in need of attention. Based on the results, a care plan is created, including how often to water the plant and when to apply fertilizer.

[0541] The emotion engine then recognizes the user's emotions: for example, if the user is stressed, the server will reduce the frequency of watering reminders to ease the user's burden, while if the user is relaxed, more frequent reminders will be sent to optimize plant care.

[0542] This system allows users to efficiently care for their plants, maintaining their health while also reducing the mental burden on the user.

[0543] The processing flow will be explained below.

[0544] Step 1:

[0545] To add a plant to the system, the user inputs the plant's ID, type, and planting date. This information is entered through the user interface and sent to the server.

[0546] Step 2:

[0547] The server stores the plant data in an internal database based on the input information received from the user, including the plant ID, type, planting date, and initial health status, which is set to "good."

[0548] Step 3:

[0549] The user takes a photo of the plant using the smartphone camera, and then uploads the image file from the smartphone to the server.

[0550] Step 4:

[0551] The server reads the received image file and performs preprocessing using an image processing library. Specifically, it resizes the image to the specified size (e.g., 224x224 pixels) and converts it into the format required by the AI ​​model.

[0552] Step 5:

[0553] The server then inputs the preprocessed image data into an artificial intelligence model for diagnosing the plant's health. The AI ​​model analyzes the images and determines the plant's health status ("good" or "needs attention").

[0554] Step 6:

[0555] The server retrieves the diagnostic results determined by the AI ​​model and updates the existing plant database, keeping the plant's health up to date.

[0556] Step 7:

[0557] The server then creates an appropriate care plan based on the type of plant and the diagnostic results, including how often to water the plant, when to fertilize it, and so on.

[0558] Step 8:

[0559] The server schedules periodic care reminders based on the created care plan: watering reminders every two days, fertilizing reminders every 14 days.

[0560] Step 9:

[0561] The server periodically recognizes the user's emotions using an emotion engine, which analyzes the user's text, voice, or image data to assess their emotional state.

[0562] Step 10:

[0563] The server reduces the frequency of care reminders when the user is feeling stressed, and increases the frequency of reminders when the user is feeling relaxed, thereby reducing the burden on the user and ensuring that appropriate care is provided.

[0564] Step 11:

[0565] The server notifies the user of care reminders according to the schedule via smartphone or email.

[0566] Step 12:

[0567] The user can take care of the plants based on the reminders, specifically by watering and fertilizing them according to the notifications.

[0568] Step 13:

[0569] After performing care, the user provides feedback to the server about the results, such as "watering completed" or "fertilization completed."

[0570] Step 14:

[0571] The server receives user feedback and updates the plant database, further optimizing the next care plan.

[0572] This allows users to efficiently care for their plants and maintain their health. The emotion engine also reduces the user's mental burden.

[0573] Example 2

[0574] 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."

[0575] Conventional plant care systems have limitations in their ability to diagnose plant health and provide appropriate care plans, and they are unable to consider the user's emotional state. This can cause users to feel stressed, reducing the effectiveness of plant care. Furthermore, the frequency and content of reminder notifications are fixed, making it difficult to flexibly adjust them to suit the user's situation.

[0576] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0577] In this invention, the server includes means for inputting the type of plant and a specific planting date, means for using an artificial intelligence model to diagnose the health condition of the plant from a photographed image, means for creating an individual care plan based on the health condition diagnosed by the artificial intelligence model, means for scheduling care reminders for the plant, means for notifying the user of the care reminders, means for using an emotion recognition engine to recognize the user's emotions, and means for adjusting the frequency and content of the care reminders based on the emotion recognition engine. This enables flexible adjustment of reminders according to the user's emotional state, enabling effective plant care while reducing user stress.

[0578] "Plant type" is classification information for distinguishing a particular plant from other plants.

[0579] The "specific planting date" is information about the specific date on which the user planted the plant.

[0580] An "artificial intelligence model" is an algorithm that uses technologies such as machine learning and deep learning to diagnose the health of plants.

[0581] A "care plan" is a plan that provides appropriate care methods and schedules for plants based on diagnostic results.

[0582] "Care reminder" refers to a notification that informs the user when it is time to care for their plants.

[0583] An "emotion recognition engine" is a technology that analyzes data such as a user's text and voice to determine the user's emotional state.

[0584] "Notification" is a means of sending a message to communicate care reminders to the user.

[0585] The system of the present invention is designed to provide efficient and effective plant care and further enhances the user experience by incorporating an emotion recognition engine that recognizes the user's emotions. The system allows the user to input the plant's type and planting date, and then uses an artificial intelligence model to diagnose the plant's health, creating an individualized care plan and scheduling care reminders to notify the user. The emotion recognition engine also adjusts the content and timing of care reminders based on the user's emotions.

[0586] Specific examples of hardware and software used

[0587] This system includes a server, terminals (smartphones and PCs), and various software. Specifically, it consists of the following:

[0588] The server is the heart of the system and is responsible for various processes. It uses TensorFlow to implement an artificial intelligence model for plant diagnosis, and OpenAI GPT-4 or IBM Watson to build an emotion recognition engine.

[0589] The terminal (smartphone or PC) is a device where the user can input information, take pictures of the plants, receive care reminders, etc. A dedicated application is installed on the terminal, and communication with the server is carried out through this application.

[0590] The database system uses MySQL or PostgreSQL to store data on plant information, diagnosis results, and the user's emotional state.

[0591] Specific examples

[0592] For example, if a user wants to grow tomatoes, they enter "tomato" and the "planting date" into the system. When the user takes a photo of the tomato with their smartphone and uploads it, the server performs a health check based on the image and determines whether the plant is in good condition or in need of attention. Based on the results, the server creates a care plan that includes watering frequency and fertilizer application timing.

[0593] The emotion engine then recognizes the user's emotions: for example, if the user is stressed, the server will reduce the frequency of watering reminders to ease the burden, while if the user is relaxed, it will send more frequent reminders to optimize plant care.

[0594] Prompt Sentence Examples

[0595] The following prompt sentence is used as input to the generative AI model:

[0596] Prompt: Create a care plan for a specific plant. Use the information below.

[0597] Plant Type: Tomato

[0598] Health check result: Good

[0599] User's emotional state: Relaxed

[0600] Sample answer: The care plan for your tomato plants is as follows: Water them every day at 8:00 AM and fertilize them once a week with liquid fertilizer. Maintain this frequency as your emotional state is relaxed.

[0601] This system allows users to efficiently care for their plants, maintaining their health while also reducing the mental burden on the user.

[0602] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0603] Step 1:

[0604] During system initialization, the server loads the plant diagnosis model and emotion recognition engine. This includes loading a TensorFlow model and configuring it to diagnose plant health from images. It also uses emotion recognition technologies such as OpenAI GPT-4 to analyze the user's text and voice data and prepare for emotion recognition. The input is the initial setup data, and the output is the prepared system environment.

[0605] Step 2:

[0606] A user adds a new plant to the system using a terminal. The user inputs the plant's ID, type, and planting date, and sends the information to the server through a dedicated application. For example, the user might input "ID: 12345, type: tomato, planting date: October 1, 2023." The input is plant information, and the output is the plant information stored in the database.

[0607] Step 3:

[0608] The server saves the received plant information in a database. The input plant ID, type, and planting date are stored in a database such as MySQL or PostgreSQL, and care reminders are set for the plant. The input is plant information, and the output is the updated database and the set reminders.

[0609] Step 4:

[0610] To diagnose the health of a plant, a user takes a photo of the plant with their smartphone and uploads the photo to the server using a dedicated application. The input is the image of the plant, and the output is the transmission of the image to the server.

[0611] Step 5:

[0612] The server preprocesses the received images, adjusting the image resolution and removing noise using the OpenCV library. The preprocessed images are then input into an artificial intelligence model to diagnose the plant's health. Based on the diagnosis results, the health status is determined as "good" or "needs attention." The input is the image before preprocessing, and the output is the diagnosis result.

[0613] Step 6:

[0614] The server stores the diagnostic results in a database. The input is the diagnostic results and the output is the updated database.

[0615] Step 7:

[0616] The server creates a care plan based on the plant type and the diagnosis results. For example, if the diagnosis result is "good," it generates a care plan including watering frequency and fertilizer timing. The input is plant information and the diagnosis results, and the output is the generated care plan.

[0617] Step 8:

[0618] The server schedules care reminders. It uses a cron job or similar to set the timing for notifying users. The input is the care plan, and the output is the configured reminder schedule.

[0619] Step 9:

[0620] The server recognizes the user's emotions using an emotion recognition engine. It analyzes the user's text messages and voice data to determine their current emotional state (e.g., stressed, relaxed). The input is the user's text or voice data, and the output is the recognized emotional state.

[0621] Step 10:

[0622] The server adjusts the frequency and content of reminders based on the user's emotional state. For example, if the user is feeling stressed, it reduces the frequency of reminders. The inputs are the perceived emotional state and the care plan, and the output is the adjusted reminders.

[0623] Step 11:

[0624] The server notifies the user of the care reminder at the scheduled time. It sends the reminder to the user's device using Firebase Cloud Messaging or an email service. The input is the adjusted reminder schedule, and the output is the notification to the user.

[0625] Step 12:

[0626] The user cares for the plants based on the reminders. After the care, the user sends feedback to the server using a dedicated application. The input is the results of the care, and the output is a database update as feedback.

[0627] (Application example 2)

[0628] 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."

[0629] Conventional plant care systems can diagnose the health of plants and create care plans, but they do not adjust reminders based on the user's emotional state, making it difficult to improve the user experience. In particular, they were unable to provide appropriate care timing when the user was stressed or relaxed, making it difficult to provide efficient and effective plant care.

[0630] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting the type of plant and a specific planting date, means for using an artificial intelligence model to diagnose the health condition of the plant from a photographed image, means for creating an individual care plan based on the health condition diagnosed by the artificial intelligence model, means for scheduling care reminders for the plant, means for recognizing the user's emotional state and adjusting the content and timing of the care reminders based on the emotional state, and means for notifying the user of the care reminders. This enables optimal adjustment of care reminders according to the user's emotional state, resulting in efficient and effective plant care and an improved user experience.

[0631] "Plant type" refers to a specific classification of the plant to be cared for, and appropriate care methods vary depending on the classification.

[0632] The "specific planting date" refers to the date the plant was planted, which is important information that influences care plans and reminder schedules.

[0633] "Photographed image" refers to an image showing the current state of a plant that has been photographed by a user using a device such as a smartphone.

[0634] "Health" is an indicator used to evaluate the growth and condition of plants, and is diagnosed by an artificial intelligence model.

[0635] The "artificial intelligence model" is a technology that uses image recognition and machine learning to automatically diagnose the health of plants and create care plans.

[0636] A "care plan" refers to specific care methods and schedules created based on the health of a plant.

[0637] "Care reminders" refer to messages and alerts that inform users how and when to care for their plants.

[0638] "Emotional state" refers to a user's current mental and psychological state and is detected by emotion recognition technology.

[0639] "Emotion recognition" refers to technology that analyzes voice, facial expressions, and text data to assess a user's emotions.

[0640] "Means of notification" refers to the methods and functions for transmitting information to users using smartphones, email, etc.

[0641] This invention is a system for efficiently and effectively caring for plants, which recognizes the user's emotional state and adjusts the content and timing of care reminders based on that state. To realize this system, a server and a smartphone are used.

[0642] First, the user inputs the plant type and specific planting date on their smartphone. The input information is sent to the server and stored in a database. Next, the user takes a photo of the plant with their smartphone and uploads it to the server to diagnose the plant's health. The server preprocesses the received image and inputs it into an artificial intelligence model to diagnose the plant's health. The diagnosis results are stored in a database, and the server creates an individual care plan based on the results.

[0643] The server analyzes voice, facial expressions, text data, etc. to periodically recognize the user's emotional state. Specifically, it uses emotion recognition technology to evaluate the user's stress level and relaxation level. This process uses emotion recognition engines such as Amazon Rekognition and IBM Watson.

[0644] Based on the user's emotional state, the server adjusts the timing and content of care reminders. For example, if the user is stressed, the frequency of reminders is reduced, and if the user is relaxed, the frequency is increased. The adjusted reminders are then sent to the user via Firebase Cloud Messaging or Apple Push Notification Service (APNS).

[0645] Specific examples

[0646] For example, if a user is tired and stressed from work, the smartphone camera will recognize this and the server will suggest a care plan to help them relax. By inputting "tomato" and "planting date" into the system and uploading a photo of the tomato, the server will perform a health check based on the image and determine whether the plant is in good condition or in need of attention. Based on the results, a care plan including watering frequency and fertilizer application timing is created. Furthermore, if the user is feeling stressed, the server will reduce the frequency of reminders.

[0647] Prompt Sentence Examples

[0648] "Siri, how do you feel about me?"

[0649] "Stuck on dinner suggestions? Let me know what you're feeling today."

[0650] "I've been busy at work lately and I'm feeling a bit restless. Can you suggest some dishes that will help me relax?"

[0651] "You seem a little tired today. Can we adjust your delivery time a little later?"

[0652] This allows optimal care reminders to be adjusted according to the user's emotional state, resulting in efficient and effective plant care and an improved user experience.

[0653] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0654] Step 1: Enter plant information

[0655] Users use their smartphones to input the type of plant they want to grow and the specific planting date, and this information is sent to a server and stored in a database.

[0656] Input: Plant type, specific planting date

[0657] Data processing: The server saves the input information in a database

[0658] Output: Plant information stored in a database

[0659] Step 2: Plant Health Check

[0660] Users take images of plants with their smartphones and upload them to a server, which pre-processes the images and feeds them into an artificial intelligence model to diagnose the plant's health.

[0661] Input: Captured image

[0662] Data processing: Image preprocessing (noise removal, resizing, etc.)

[0663] Data Computing: Health Diagnosis Using Artificial Intelligence Models

[0664] Output: Health check result ("Good" or "Needs attention")

[0665] Step 3: Create a care plan

[0666] The server creates a personalized care plan based on the plant's diagnosis and type, including how often to water it and when to fertilize it.

[0667] Input: diagnosis result, plant type

[0668] Data processing: generating care plan information

[0669] Output: Individual care plan

[0670] Step 4: Recognizing your emotional state

[0671] To recognize the user's emotional state, the smartphone's camera and microphone are used to collect voice and facial expression data. The server then uses an emotion recognition engine to analyze this data and evaluate the user's emotional state.

[0672] Input: Audio data, image data

[0673] Data processing: voice recognition, face recognition

[0674] Data Calculation: Analysis by Emotion Recognition Engine

[0675] Output: User's emotional state

[0676] Step 5: Adjust reminders

[0677] The server adjusts the content and timing of care reminders based on the user's emotional state, for example, decreasing the frequency of reminders for a stressed user and increasing the frequency for a relaxed user.

[0678] Input: User's emotional state, care plan

[0679] Data processing: Reminder schedule adjustment

[0680] Output: Adjusted reminders

[0681] Step 6: Reminder Notifications

[0682] The server then notifies the user of the adjusted care reminders using a push notification system, via smartphone or email.

[0683] Input: Adjusted reminders

[0684] Data processing: Creating push notification messages

[0685] Output: Reminder notification to the user

[0686] Step 7: Plant care implementation and feedback

[0687] The user cares for the plants based on the reminders and provides feedback to the server, which uses the feedback as learning data for the system.

[0688] Input: Care outcome

[0689] Data processing: collection and storage of feedback data

[0690] Output: Updated database

[0691] In this way, optimal care reminders are provided according to the user's emotional state, realizing efficient and effective plant care and improving the user experience.

[0692] 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.

[0693] 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.

[0694] 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.

[0695] [Third embodiment]

[0696] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0697] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0698] 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).

[0699] 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.

[0700] 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.

[0701] 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).

[0702] 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.

[0703] 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.

[0704] 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.

[0705] 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.

[0706] 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.

[0707] 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."

[0708] The present invention is a system for efficient and effective plant care that allows users to input the type of plant and planting date, and then uses an artificial intelligence model to diagnose the plant's health, create a personalized care plan, and schedule care reminders.

[0709] 1. System initialization

[0710] When the server initializes the system, it first loads the plant diagnosis model, a pre-trained artificial intelligence model for automatically diagnosing plant health from images.

[0711] 2. Adding plants

[0712] To add a new plant to the system, a user inputs the plant's type, planting date, and provides the plant's ID (unique identifier), the name of the plant's type (e.g., "tomato"), and the date the plant was planted.

[0713] The server receives this information, stores it in a database, and sets care reminders for the plant.

[0714] 3. Plant health check

[0715] To diagnose the health of a plant, users take a photo of the plant with their smartphone and upload it to the system.

[0716] The server receives the uploaded images and preprocesses them, for example by resizing them to a standard size and converting them into the format required by the AI ​​model.

[0717] The server then inputs the pre-processed images into an artificial intelligence model to diagnose the plant's health, which is then expressed as a "good" or "needs attention" status and stored in a database.

[0718] 4. Creating a care plan

[0719] The server automatically creates an appropriate care plan based on the plant type and the diagnostic results, including specific watering frequency and fertilization timing.

[0720] The server sets a schedule for care reminders based on the created care plan.

[0721] 5. Send reminders

[0722] The server sends care reminders to the user according to a schedule, informing the user of specific care tasks (e.g., "water," "fertilize," etc.).

[0723] Users can follow the reminders to care for their plants and provide feedback to the system, which will further optimize the next care plan.

[0724] For example, if a user is growing tomatoes, they enter "tomato" and "planting date" into the system. When the user takes a photo of the tomato and uploads it, the server performs a health check based on the image and the diagnosis is "good." In this case, the server creates a care plan based on the characteristics of the "tomato" by setting the watering frequency to every two days and fertilizing every 14 days. It also sends the user a watering reminder every two days and a fertilizing reminder every 14 days. This allows users to take care of their plants at the appropriate time without forgetting, even in their busy daily lives.

[0725] As described above, the present invention enables users to efficiently care for plants and maintain their health.

[0726] The processing flow will be explained below.

[0727] Step 1:

[0728] To add a plant to the system, the user inputs the plant's ID, type, and planting date. This information is entered through the user interface and sent to the server.

[0729] Step 2:

[0730] The server stores the plant data in an internal database based on the input information received from the user, including the plant ID, type, planting date, and initial health status, which is set to "good."

[0731] Step 3:

[0732] The user takes a photo of the plant using the smartphone camera, and then uploads the image file from the smartphone to the server.

[0733] Step 4:

[0734] The server reads the received image file and performs preprocessing using an image processing library. Specifically, it resizes the image to the specified size (e.g., 224x224 pixels) and converts it into the format required by the AI ​​model.

[0735] Step 5:

[0736] The server then inputs the preprocessed image data into an artificial intelligence model for diagnosing the plant's health. The AI ​​model analyzes the images and determines the plant's health status ("good" or "needs attention").

[0737] Step 6:

[0738] The server retrieves the diagnostic results determined by the AI ​​model and updates the existing plant database, keeping the plant's health up to date.

[0739] Step 7:

[0740] The server then creates an appropriate care plan based on the type of plant and the diagnostic results, including how often to water the plant, when to fertilize it, and so on.

[0741] Step 8:

[0742] The server schedules periodic care reminders based on the created care plan: watering reminders every two days, fertilizing reminders every 14 days.

[0743] Step 9:

[0744] The server will send a notification to the user at the scheduled time according to the reminder schedule, via the user's smartphone or email.

[0745] Step 10:

[0746] The user receives notifications from the server and takes care of the plants, specifically by watering and fertilizing them according to the notifications.

[0747] Step 11:

[0748] After performing care, the user provides feedback on the results to the server, such as "watering completed" or "fertilization completed."

[0749] Step 12:

[0750] The server receives user feedback and updates the plant database, further optimizing the next care plan.

[0751] This allows users to efficiently care for their plants and maintain their health.

[0752] Example 1

[0753] 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."

[0754] Conventional plant care systems lack the functionality to automatically generate a specific care plan for a plant after inputting information such as the plant type and planting date, and to notify the user of appropriate reminders. They also have limited functionality for efficiently diagnosing the plant's health and optimizing the care plan based on the diagnosis results. Furthermore, they lack a mechanism for utilizing user feedback on care reminders to improve future care plans. A new system that can solve these problems and enable users to effectively maintain the health of their plants is needed.

[0755] 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.

[0756] In this invention, the server includes means for inputting the type of plant and a specific planting date, means for using a machine learning model to diagnose the health condition of the plant from photographed images, and means for creating an individual care plan based on the health condition diagnosed by the machine learning model. This enables plant health diagnosis and optimization of the care plan based on the information input by the user. Furthermore, by adding means for scheduling plant care reminders, means for notifying the user of the care reminders, means for receiving feedback from the user related to the notifications, and means for optimizing future care plans based on the feedback, the server enables the user to continuously manage the health of their plants.

[0757] A "plant type" is a category or name that identifies a particular plant, and examples include "tomato," "basil," and "rose."

[0758] "Specific planting date" refers to the specific date on which the subject plant was planted.

[0759] A "machine learning model" is an algorithm that is trained using data and has the ability to make predictions or classifications based on a given input.

[0760] A "care plan" refers to a specific plan of action or response required to maintain the health of a plant, including, for example, how often to water it and when to use fertilizer.

[0761] "Care reminders" refer to messages and alerts that notify users about plant care tasks.

[0762] "User" means an individual or organization that uses the system to care for plants.

[0763] "Feedback" refers to information provided by the user to the system, and in particular refers to the results and status of care work performed in accordance with care reminders.

[0764] "Server" refers to a computer device that processes the entire system and manages data.

[0765] "Database" means a system for systematically storing, managing, and retrieving information.

[0766] "Notification" refers to a messaging method used to convey instructions or information to the user.

[0767] The present invention relates to a system for efficient and effective plant care that allows a user to input the type of plant and a specific planting date, and then uses machine learning models to diagnose the plant's health, create a personalized care plan, and schedule care reminders.

[0768] System configuration

[0769] The main components of the system are as follows:

[0770] Server: A computer device that processes the entire system and manages data. It uses machine learning frameworks such as TensorFlow and PyTorch.

[0771] User terminal: A smartphone or PC that allows users to take photos of plants and upload them to the system.

[0772] Database: A system for storing plant information, health check results, and care plans.

[0773] System Operation

[0774] During system initialization, the server loads a pre-trained plant diagnostic model, which is built using machine learning frameworks such as TensorFlow or PyTorch.

[0775] To register a new plant in the system, the user inputs the plant type and planting date through a dedicated application or a web interface. The user terminal then sends the input information to the server.

[0776] The server stores the received information in a database and initializes care reminders for the plant.

[0777] To diagnose the health of a plant, users take a photo of the plant with their smartphone and upload it to the system, where the device sends the image to the server.

[0778] The server preprocesses the images (e.g., resizes them to a standard size) and converts them into a format suitable for input into an artificial intelligence model. The preprocessed images are then input into the model, and a diagnosis (e.g., "good" or "needs attention") is obtained and stored in a database.

[0779] The server automatically generates an appropriate care plan based on the type of plant and the diagnosis results, including information such as watering frequency and fertilization timing, and schedules care reminders based on the generated care plan and saves it in a database.

[0780] The server sends reminders to users according to a schedule, either via push notification or email. Users then take care of their plants according to the reminders and provide feedback to the system.

[0781] This feedback information is sent from the user's device to the server, which stores it in a database and optimizes future care plans based on this feedback.

[0782] Specific examples

[0783] For example, if a user is growing tomatoes, they enter "tomato" and "planting date" into the system. When the user takes a photo of the tomato and uploads it, the server performs a health check based on the image and obtains a diagnosis of "good condition." In this case, the server creates a care plan based on the characteristics of the "tomato" by setting the watering frequency to every two days and fertilizing every 14 days. The server then sends the user a watering reminder every two days and a fertilizing reminder every 14 days.

[0784] Prompt Sentence Examples

[0785] "Enter the tomato planting date as April 1st and upload a photo of the tomato for a health check. The AI ​​model will analyze the image and create an appropriate care plan and reminders."

[0786] This system allows users to efficiently and effectively care for their plants and maintain their health.

[0787] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0788] Step 1: Initialize the system

[0789] The server loads the plant diagnosis model when initializing the system. The input data is a machine learning model file, and the output is the model loaded in memory. Specifically, it uses the TensorFlow or PyTorch library to read a pre-trained model file and load it into memory.

[0790] Step 2: Add plants

[0791] The user inputs the plant type and planting date through the application or web interface. The input data is the plant ID, type, and planting date, and the output is request data that stores this information.

[0792] The terminal sends the input data to the server. The input is the user's input data, and the output is the request data sent to the server.

[0793] The server stores the received data in a database and sets up an early care reminder. The input is the request data received from the terminal, and the output is the plant information and reminder settings stored in the database.

[0794] Step 3: Plant Health Check

[0795] Users take photos of plants with their smartphones and upload them to the system through the application. The input data is the captured image, and the output is the request data sent to the terminal.

[0796] The device sends the captured image to the server. The input is the image uploaded by the user, and the output is the image data sent to the server.

[0797] The server preprocesses the received images, specifically resizing them to a standard size and converting their format. The input data is the image sent from the device, and the output is the preprocessed image. Image processing libraries (OpenCV or Pillow) are used to resize and convert the format.

[0798] The server uses the preprocessed images to perform a health diagnosis using a machine learning model. The input is the preprocessed image, and the output is the diagnosis result ("good" or "needs attention"). The diagnosis result is stored in a database.

[0799] Step 4: Create a care plan

[0800] The server creates an appropriate care plan based on the plant type and the diagnosis results. The input data are the plant type and the diagnosis results, and the output is the generated care plan. It uses predefined rules and data to set watering frequency, fertilizer timing, and other settings.

[0801] The server schedules care reminders based on the care plan. The input is the created care plan, and the output is a reminder schedule. This schedule is stored in a database.

[0802] Step 5: Send a reminder

[0803] The server sends care reminders to the user according to the schedule. The input data is the reminder schedule, and the output is a reminder notification to the user's device. Notifications are sent to the user's smartphone or PC using a push notification service (such as Firebase Cloud Messaging).

[0804] Step 6: Care feedback

[0805] The user follows the reminders to care for the plants and provides feedback to the system. The input is the information after care, and the output is the feedback data. The terminal sends the user's feedback to the server.

[0806] The server stores the feedback information in a database and optimizes the care plan for the next time onward. The input is the feedback data from the device, and the output is the improvement details for the next care plan.

[0807] (Application example 1)

[0808] 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."

[0809] Traditionally, caring for plants has been time-consuming and laborious, making it difficult for modern people with busy lifestyles to care for them at the right time. It is also difficult to accurately grasp the health status of plants, which often results in the plants becoming unhealthy. Even in virtual stores, there is a lack of support for plant care after purchase, forcing users to care for their plants at their own discretion, and the appropriateness of such care cannot be guaranteed, which is an issue.

[0810] 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.

[0811] In this invention, the server includes a means for inputting the type of plant and a specific planting date, a means for using an artificial intelligence model to diagnose the health condition of the plant from photographed images, a means for creating an individual care plan based on the health condition diagnosed by the artificial intelligence model, a means for scheduling care reminders for the plant, a means for notifying the user of the care reminders, a means for automatically synchronizing purchase information from the virtual store, and a means for the user to provide feedback on the health condition of the plant using a smartphone. This allows users to care for their plants at appropriate times without forgetting, even during their busy daily lives. In addition, collaboration with the virtual store makes post-purchase care easier, allowing plants to always be kept in optimal health.

[0812] "Plant types" are classifications of plants with various forms and characteristics.

[0813] The "specific planting date" is information that indicates the specific date on which the user planted the plant.

[0814] An "artificial intelligence model" is an algorithm that mimics human knowledge and behavior to automatically perform specific tasks.

[0815] A "smartphone" is a portable electronic device that has advanced computing capabilities in addition to the functionality of a mobile phone.

[0816] "Health" is an indicator of a plant's vitality and growth status.

[0817] A "care plan" refers to specific steps and schedules for maintaining and improving the health of a plant.

[0818] "Care reminders" are messages or alerts that inform users about proper care for their plants.

[0819] A "virtual store" is a commercial facility that operates on the Internet, an online shop that does not have a physical store.

[0820] "Feedback" refers to the act or information that a user reports to the system about the results of the care they have provided.

[0821] A "server" is a high-performance computer that provides services to computers and other devices over a network.

[0822] The present invention is a system for efficiently and effectively caring for plants, which includes the following various means:

[0823] 1. System initialization

[0824] When the system is initialized, the server first loads a plant diagnosis model. This is a pre-trained artificial intelligence model that automatically diagnoses the health of plants from images. The server is built on a cloud platform (e.g., AWS, Google Cloud) and runs the AI ​​model using TensorFlow or PyTorch.

[0825] 2. Plant registration and purchase information synchronization

[0826] When a user purchases a plant from the virtual store, the purchase information (plant type, planting date) is automatically synchronized with the system. The server stores this information in a database (e.g., MySQL, Firebase) and generates a unique identifier for each plant, saving the user the trouble of manually entering it.

[0827] 3. Plant health check

[0828] Users use their smartphones to take photos of plants and upload them to the system. The server preprocesses the received images, resizes them to a standard size, and converts them into the format required by the AI ​​model. The preprocessed images are then input into a TensorFlow or PyTorch model, which generates a diagnosis. This diagnosis is then stored in a database and notified to the user.

[0829] 4. Creating a care plan

[0830] The server automatically creates an individualized care plan based on the plant type and diagnostic results. The care plan includes specific tasks such as watering frequency and fertilization timing. The created care plan is saved in a database and a care reminder schedule is set.

[0831] 5. Send reminders

[0832] The server sends care reminders to the user's smartphone via push notifications according to the schedule, informing them of specific actions such as when to water or fertilize plants.

[0833] 6. Feedback function

[0834] Users can provide feedback to the system via their smartphone about the results of their care work, which is then saved in a database and used to optimize the next care plan.

[0835] Examples of specific examples and prompts

[0836] As a concrete example, consider the case where a user purchases tomatoes from a virtual store. The purchase information is automatically synchronized and registered in the system. When the user uploads a photo of the tomato taken with their smartphone, the server diagnoses the image and determines that the tomato is in good condition. The server then creates a care plan appropriate for the tomato, scheduling reminders to water it every two days and fertilize it every 14 days.

[0837] Prompt Sentence Examples

[0838] Please enter the plant type and planting date.

[0839] "Upload the images you've taken and diagnose the health of your plants."

[0840] "Please follow the care plan below to care for your plants."

[0841] This system allows users to take care of their plants at the right time without forgetting, even in their busy daily lives. In addition, by linking with the virtual store, post-purchase care becomes easier, allowing plants to be kept in optimal health at all times.

[0842] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0843] Step 1:

[0844] The server loads a plant diagnostic model when initializing the system. This diagnostic model is a pre-trained generative AI model that automatically diagnoses the health of plants from images. During this initialization, the server runs the AI ​​model using TensorFlow or PyTorch on a cloud platform (e.g., AWS or Google Cloud). This gives the system the ability to diagnose plant images.

[0845] Step 2:

[0846] When a user purchases a plant from a virtual store, the purchase information (type of plant, planting date) is automatically synchronized with the system. In this synchronization process, the server receives the data sent from the virtual store and stores it in a database (e.g., MySQL, Firebase). This allows the user to complete the plant registration without having to enter any information.

[0847] Step 3:

[0848] Users take photos of plants using their smartphones and upload them to the system. The uploaded images are sent to a server, which then receives them. At this time, preprocessing such as resizing and format conversion is performed on the images, and the standardized images are then input into the generative AI model. This process prepares the images to be used to diagnose the plant's health.

[0849] Step 4:

[0850] The server inputs the preprocessed images into a generative AI model to diagnose the health of the plant. The diagnosis results are expressed as a status such as "good" or "needs attention." The diagnosis results are stored in a database and notified to the user. This notification is important for allowing the user to understand the current state of the plant.

[0851] Step 5:

[0852] The server automatically creates an individual care plan based on the diagnosis results and the type of plant. The care plan includes specific care instructions such as how often to water and when to fertilize. This care plan is saved in a database and a care reminder schedule is set, helping users to provide the necessary care at the appropriate time.

[0853] Step 6:

[0854] The server sends care reminders to the user's smartphone via push notifications according to the schedule. The reminders include specific care tasks (e.g., watering and fertilizing), so that the user does not miss the appropriate care timing.

[0855] Step 7:

[0856] Users provide feedback to the system via their smartphone on the results of their care work. This feedback is sent to the server and stored in a database. The server uses this feedback information to further optimize the next care plan and notify the user again. This makes the care plan more personalized and optimized, making it easier to maintain plant health.

[0857] 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.

[0858] The present invention provides a system for efficient and effective plant care, and further enhances the user experience by combining it with an emotion engine that recognizes the user's emotions. This system allows the user to input the type of plant and the planting date, and then uses an artificial intelligence model to diagnose the plant's health and create an individualized care plan, schedule care reminders, and notify the user. The emotion engine also adjusts the content and timing of care reminders based on the user's emotions.

[0859] 1. System initialization

[0860] When the server initializes the system, it loads a plant diagnosis model and initializes an emotion engine. The plant diagnosis model is for diagnosing the health of plants from images, and the emotion engine is for recognizing the user's emotions.

[0861] 2. Adding plants

[0862] To add a new plant to the system, a user inputs the plant's ID, type, and planting date, which is then sent to the server.

[0863] The server stores the input plant information in a database and sets care reminders for the plant.

[0864] 3. Plant health check

[0865] To diagnose the health of a plant, users take a photo of the plant with their smartphone and upload it to the server.

[0866] The server receives the images, preprocesses them, and then inputs them into an artificial intelligence model to diagnose the plant's health. The results are stored in a database.

[0867] 4. Creating a care plan

[0868] The server creates an appropriate care plan based on the type of plant and the diagnosis results, and sets care reminders.

[0869] 5. Functions of the Emotion Engine

[0870] The server periodically recognizes the user's emotions using an emotion engine, which analyzes the user's text, voice, or image data to assess their emotional state.

[0871] The server decreases the frequency of care reminders when the user is stressed and increases the frequency when the user is relaxed.

[0872] 6. Send Reminders

[0873] The server notifies the user of care reminders according to the schedule via smartphone or email.

[0874] The user cares for the plants based on the reminder and then sends the results back to the server.

[0875] Specific examples

[0876] For example, if a user is growing tomatoes, they enter "tomato" and "planting date" into the system. When the user takes a photo of the tomato and uploads it, the server performs a health check based on the image and determines whether the plant is in good condition or in need of attention. Based on the results, a care plan is created, including how often to water the plant and when to apply fertilizer.

[0877] The emotion engine then recognizes the user's emotions: for example, if the user is stressed, the server will reduce the frequency of watering reminders to ease the user's burden, while if the user is relaxed, more frequent reminders will be sent to optimize plant care.

[0878] This system allows users to efficiently care for their plants, maintaining their health while also reducing the mental burden on the user.

[0879] The processing flow will be explained below.

[0880] Step 1:

[0881] To add a plant to the system, the user inputs the plant's ID, type, and planting date. This information is entered through the user interface and sent to the server.

[0882] Step 2:

[0883] The server stores the plant data in an internal database based on the input information received from the user, including the plant ID, type, planting date, and initial health status, which is set to "good."

[0884] Step 3:

[0885] The user takes a photo of the plant using the smartphone camera, and then uploads the image file from the smartphone to the server.

[0886] Step 4:

[0887] The server reads the received image file and performs preprocessing using an image processing library. Specifically, it resizes the image to the specified size (e.g., 224x224 pixels) and converts it into the format required by the AI ​​model.

[0888] Step 5:

[0889] The server then inputs the preprocessed image data into an artificial intelligence model for diagnosing the plant's health. The AI ​​model analyzes the images and determines the plant's health status ("good" or "needs attention").

[0890] Step 6:

[0891] The server retrieves the diagnostic results determined by the AI ​​model and updates the existing plant database, keeping the plant's health up to date.

[0892] Step 7:

[0893] The server then creates an appropriate care plan based on the type of plant and the diagnostic results, including how often to water the plant, when to fertilize it, and so on.

[0894] Step 8:

[0895] The server schedules periodic care reminders based on the created care plan: watering reminders every two days, fertilizing reminders every 14 days.

[0896] Step 9:

[0897] The server periodically recognizes the user's emotions using an emotion engine, which analyzes the user's text, voice, or image data to assess their emotional state.

[0898] Step 10:

[0899] The server reduces the frequency of care reminders when the user is feeling stressed, and increases the frequency of reminders when the user is feeling relaxed, thereby reducing the burden on the user and ensuring that appropriate care is provided.

[0900] Step 11:

[0901] The server notifies the user of care reminders according to the schedule via smartphone or email.

[0902] Step 12:

[0903] The user can take care of the plants based on the reminders, specifically by watering and fertilizing them according to the notifications.

[0904] Step 13:

[0905] After performing care, the user provides feedback to the server about the results, such as "watering completed" or "fertilization completed."

[0906] Step 14:

[0907] The server receives user feedback and updates the plant database, further optimizing the next care plan.

[0908] This allows users to efficiently care for their plants and maintain their health. The emotion engine also reduces the user's mental burden.

[0909] Example 2

[0910] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0911] Conventional plant care systems have limitations in their ability to diagnose plant health and provide appropriate care plans, and they are unable to consider the user's emotional state. This can cause users to feel stressed, reducing the effectiveness of plant care. Furthermore, the frequency and content of reminder notifications are fixed, making it difficult to flexibly adjust them to suit the user's situation.

[0912] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0913] In this invention, the server includes means for inputting the type of plant and a specific planting date, means for using an artificial intelligence model to diagnose the health condition of the plant from a photographed image, means for creating an individual care plan based on the health condition diagnosed by the artificial intelligence model, means for scheduling care reminders for the plant, means for notifying the user of the care reminders, means for using an emotion recognition engine to recognize the user's emotions, and means for adjusting the frequency and content of the care reminders based on the emotion recognition engine. This enables flexible adjustment of reminders according to the user's emotional state, enabling effective plant care while reducing user stress.

[0914] "Plant type" is classification information for distinguishing a particular plant from other plants.

[0915] The "specific planting date" is information about the specific date on which the user planted the plant.

[0916] An "artificial intelligence model" is an algorithm that uses technologies such as machine learning and deep learning to diagnose the health of plants.

[0917] A "care plan" is a plan that provides appropriate care methods and schedules for plants based on diagnostic results.

[0918] "Care reminder" refers to a notification that informs the user when it is time to care for their plants.

[0919] An "emotion recognition engine" is a technology that analyzes data such as a user's text and voice to determine the user's emotional state.

[0920] "Notification" is a means of sending a message to communicate care reminders to the user.

[0921] The system of the present invention is designed to provide efficient and effective plant care and further enhances the user experience by incorporating an emotion recognition engine that recognizes the user's emotions. The system allows the user to input the plant's type and planting date, and then uses an artificial intelligence model to diagnose the plant's health, creating an individualized care plan and scheduling care reminders to notify the user. The emotion recognition engine also adjusts the content and timing of care reminders based on the user's emotions.

[0922] Specific examples of hardware and software used

[0923] This system includes a server, terminals (smartphones and PCs), and various software. Specifically, it consists of the following:

[0924] The server is the heart of the system and is responsible for various processes. It uses TensorFlow to implement an artificial intelligence model for plant diagnosis, and OpenAI GPT-4 or IBM Watson to build an emotion recognition engine.

[0925] The terminal (smartphone or PC) is a device where the user can input information, take pictures of the plants, receive care reminders, etc. A dedicated application is installed on the terminal, and communication with the server is carried out through this application.

[0926] The database system uses MySQL or PostgreSQL to store data on plant information, diagnosis results, and the user's emotional state.

[0927] Specific examples

[0928] For example, if a user wants to grow tomatoes, they enter "tomato" and the "planting date" into the system. When the user takes a photo of the tomato with their smartphone and uploads it, the server performs a health check based on the image and determines whether the plant is in good condition or in need of attention. Based on the results, the server creates a care plan that includes watering frequency and fertilizer application timing.

[0929] The emotion engine then recognizes the user's emotions: for example, if the user is stressed, the server will reduce the frequency of watering reminders to ease the burden, while if the user is relaxed, it will send more frequent reminders to optimize plant care.

[0930] Prompt Sentence Examples

[0931] The following prompt sentence is used as input to the generative AI model:

[0932] Prompt: Create a care plan for a specific plant. Use the information below.

[0933] Plant Type: Tomato

[0934] Health check result: Good

[0935] User's emotional state: Relaxed

[0936] Sample answer: The care plan for your tomato plants is as follows: Water them every day at 8:00 AM and fertilize them once a week with liquid fertilizer. Maintain this frequency as your emotional state is relaxed.

[0937] This system allows users to efficiently care for their plants, maintaining their health while also reducing the mental burden on the user.

[0938] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0939] Step 1:

[0940] During system initialization, the server loads the plant diagnosis model and emotion recognition engine. This includes loading a TensorFlow model and configuring it to diagnose plant health from images. It also uses emotion recognition technologies such as OpenAI GPT-4 to analyze the user's text and voice data and prepare for emotion recognition. The input is the initial setup data, and the output is the prepared system environment.

[0941] Step 2:

[0942] A user adds a new plant to the system using a terminal. The user inputs the plant's ID, type, and planting date, and sends the information to the server through a dedicated application. For example, the user might input "ID: 12345, type: tomato, planting date: October 1, 2023." The input is plant information, and the output is the plant information stored in the database.

[0943] Step 3:

[0944] The server saves the received plant information in a database. The input plant ID, type, and planting date are stored in a database such as MySQL or PostgreSQL, and care reminders are set for the plant. The input is plant information, and the output is the updated database and the set reminders.

[0945] Step 4:

[0946] To diagnose the health of a plant, a user takes a photo of the plant with their smartphone and uploads the photo to the server using a dedicated application. The input is the image of the plant, and the output is the transmission of the image to the server.

[0947] Step 5:

[0948] The server preprocesses the received images, adjusting the image resolution and removing noise using the OpenCV library. The preprocessed images are then input into an artificial intelligence model to diagnose the plant's health. Based on the diagnosis results, the health status is determined as "good" or "needs attention." The input is the image before preprocessing, and the output is the diagnosis result.

[0949] Step 6:

[0950] The server stores the diagnostic results in a database. The input is the diagnostic results and the output is the updated database.

[0951] Step 7:

[0952] The server creates a care plan based on the plant type and the diagnosis results. For example, if the diagnosis result is "good," it generates a care plan including watering frequency and fertilizer timing. The input is plant information and the diagnosis results, and the output is the generated care plan.

[0953] Step 8:

[0954] The server schedules care reminders. It uses a cron job or similar to set the timing for notifying users. The input is the care plan, and the output is the configured reminder schedule.

[0955] Step 9:

[0956] The server recognizes the user's emotions using an emotion recognition engine. It analyzes the user's text messages and voice data to determine their current emotional state (e.g., stressed, relaxed). The input is the user's text or voice data, and the output is the recognized emotional state.

[0957] Step 10:

[0958] The server adjusts the frequency and content of reminders based on the user's emotional state. For example, if the user is feeling stressed, it reduces the frequency of reminders. The inputs are the perceived emotional state and the care plan, and the output is the adjusted reminders.

[0959] Step 11:

[0960] The server notifies the user of the care reminder at the scheduled time. It sends the reminder to the user's device using Firebase Cloud Messaging or an email service. The input is the adjusted reminder schedule, and the output is the notification to the user.

[0961] Step 12:

[0962] The user cares for the plants based on the reminders. After the care, the user sends feedback to the server using a dedicated application. The input is the results of the care, and the output is a database update as feedback.

[0963] (Application example 2)

[0964] 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."

[0965] Conventional plant care systems can diagnose the health of plants and create care plans, but they do not adjust reminders based on the user's emotional state, making it difficult to improve the user experience. In particular, they were unable to provide appropriate care timing when the user was stressed or relaxed, making it difficult to provide efficient and effective plant care.

[0966] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting the type of plant and a specific planting date, means for using an artificial intelligence model to diagnose the health condition of the plant from a photographed image, means for creating an individual care plan based on the health condition diagnosed by the artificial intelligence model, means for scheduling care reminders for the plant, means for recognizing the user's emotional state and adjusting the content and timing of the care reminders based on the emotional state, and means for notifying the user of the care reminders. This enables optimal adjustment of care reminders according to the user's emotional state, resulting in efficient and effective plant care and an improved user experience.

[0967] "Plant type" refers to a specific classification of the plant to be cared for, and appropriate care methods vary depending on the classification.

[0968] The "specific planting date" refers to the date the plant was planted, which is important information that influences care plans and reminder schedules.

[0969] "Photographed image" refers to an image showing the current state of a plant that has been photographed by a user using a device such as a smartphone.

[0970] "Health" is an indicator used to evaluate the growth and condition of plants, and is diagnosed by an artificial intelligence model.

[0971] The "artificial intelligence model" is a technology that uses image recognition and machine learning to automatically diagnose the health of plants and create care plans.

[0972] A "care plan" refers to specific care methods and schedules created based on the health of a plant.

[0973] "Care reminders" refer to messages and alerts that inform users how and when to care for their plants.

[0974] "Emotional state" refers to a user's current mental and psychological state and is detected by emotion recognition technology.

[0975] "Emotion recognition" refers to technology that analyzes voice, facial expressions, and text data to assess a user's emotions.

[0976] "Means of notification" refers to the methods and functions for transmitting information to users using smartphones, email, etc.

[0977] This invention is a system for efficiently and effectively caring for plants, which recognizes the user's emotional state and adjusts the content and timing of care reminders based on that state. To realize this system, a server and a smartphone are used.

[0978] First, the user inputs the plant type and specific planting date on their smartphone. The input information is sent to the server and stored in a database. Next, the user takes a photo of the plant with their smartphone and uploads it to the server to diagnose the plant's health. The server preprocesses the received image and inputs it into an artificial intelligence model to diagnose the plant's health. The diagnosis results are stored in a database, and the server creates an individual care plan based on the results.

[0979] The server analyzes voice, facial expressions, text data, etc. to periodically recognize the user's emotional state. Specifically, it uses emotion recognition technology to evaluate the user's stress level and relaxation level. This process uses emotion recognition engines such as Amazon Rekognition and IBM Watson.

[0980] Based on the user's emotional state, the server adjusts the timing and content of care reminders. For example, if the user is stressed, the frequency of reminders is reduced, and if the user is relaxed, the frequency is increased. The adjusted reminders are then sent to the user via Firebase Cloud Messaging or Apple Push Notification Service (APNS).

[0981] Specific examples

[0982] For example, if a user is tired and stressed from work, the smartphone camera will recognize this and the server will suggest a care plan to help them relax. By inputting "tomato" and "planting date" into the system and uploading a photo of the tomato, the server will perform a health check based on the image and determine whether the plant is in good condition or in need of attention. Based on the results, a care plan including watering frequency and fertilizer application timing is created. Furthermore, if the user is feeling stressed, the server will reduce the frequency of reminders.

[0983] Prompt Sentence Examples

[0984] "Siri, how do you feel about me?"

[0985] "Stuck on dinner suggestions? Let me know what you're feeling today."

[0986] "I've been busy at work lately and I'm feeling a bit restless. Can you suggest some dishes that will help me relax?"

[0987] "You seem a little tired today. Can we adjust your delivery time a little later?"

[0988] This allows optimal care reminders to be adjusted according to the user's emotional state, resulting in efficient and effective plant care and an improved user experience.

[0989] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0990] Step 1: Enter plant information

[0991] Users use their smartphones to input the type of plant they want to grow and the specific planting date, and this information is sent to a server and stored in a database.

[0992] Input: Plant type, specific planting date

[0993] Data processing: The server saves the input information in a database

[0994] Output: Plant information stored in a database

[0995] Step 2: Plant Health Check

[0996] Users take images of plants with their smartphones and upload them to a server, which pre-processes the images and feeds them into an artificial intelligence model to diagnose the plant's health.

[0997] Input: Captured image

[0998] Data processing: Image preprocessing (noise removal, resizing, etc.)

[0999] Data Computing: Health Diagnosis Using Artificial Intelligence Models

[1000] Output: Health check result ("Good" or "Needs attention")

[1001] Step 3: Create a care plan

[1002] The server creates a personalized care plan based on the plant's diagnosis and type, including how often to water it and when to fertilize it.

[1003] Input: diagnosis result, plant type

[1004] Data processing: generating care plan information

[1005] Output: Individual care plan

[1006] Step 4: Recognizing your emotional state

[1007] To recognize the user's emotional state, the smartphone's camera and microphone are used to collect voice and facial expression data. The server then uses an emotion recognition engine to analyze this data and evaluate the user's emotional state.

[1008] Input: Audio data, image data

[1009] Data processing: voice recognition, face recognition

[1010] Data Calculation: Analysis by Emotion Recognition Engine

[1011] Output: User's emotional state

[1012] Step 5: Adjust reminders

[1013] The server adjusts the content and timing of care reminders based on the user's emotional state, for example, decreasing the frequency of reminders for a stressed user and increasing the frequency for a relaxed user.

[1014] Input: User's emotional state, care plan

[1015] Data processing: Reminder schedule adjustment

[1016] Output: Adjusted reminders

[1017] Step 6: Reminder Notifications

[1018] The server then notifies the user of the adjusted care reminders using a push notification system, via smartphone or email.

[1019] Input: Adjusted reminders

[1020] Data processing: Creating push notification messages

[1021] Output: Reminder notification to the user

[1022] Step 7: Plant care implementation and feedback

[1023] The user cares for the plants based on the reminders and provides feedback to the server, which uses the feedback as learning data for the system.

[1024] Input: Care outcome

[1025] Data processing: collection and storage of feedback data

[1026] Output: Updated database

[1027] In this way, optimal care reminders are provided according to the user's emotional state, realizing efficient and effective plant care and improving the user experience.

[1028] 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.

[1029] 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.

[1030] 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.

[1031] [Fourth embodiment]

[1032] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1033] 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.

[1034] 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).

[1035] 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.

[1036] 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.

[1037] 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).

[1038] 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.

[1039] 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.

[1040] 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.

[1041] 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.

[1042] 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.

[1043] 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.

[1044] 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."

[1045] The present invention is a system for efficient and effective plant care that allows users to input the type of plant and planting date, and then uses an artificial intelligence model to diagnose the plant's health, create a personalized care plan, and schedule care reminders.

[1046] 1. System initialization

[1047] When the server initializes the system, it first loads the plant diagnosis model, a pre-trained artificial intelligence model for automatically diagnosing plant health from images.

[1048] 2. Adding plants

[1049] To add a new plant to the system, a user inputs the plant's type, planting date, and provides the plant's ID (unique identifier), the name of the plant's type (e.g., "tomato"), and the date the plant was planted.

[1050] The server receives this information, stores it in a database, and sets care reminders for the plant.

[1051] 3. Plant health check

[1052] To diagnose the health of a plant, users take a photo of the plant with their smartphone and upload it to the system.

[1053] The server receives the uploaded images and preprocesses them, for example by resizing them to a standard size and converting them into the format required by the AI ​​model.

[1054] The server then inputs the pre-processed images into an artificial intelligence model to diagnose the plant's health, which is then expressed as a "good" or "needs attention" status and stored in a database.

[1055] 4. Creating a care plan

[1056] The server automatically creates an appropriate care plan based on the plant type and the diagnostic results, including specific watering frequency and fertilization timing.

[1057] The server sets a schedule for care reminders based on the created care plan.

[1058] 5. Send reminders

[1059] The server sends care reminders to the user according to a schedule, informing the user of specific care tasks (e.g., "water," "fertilize," etc.).

[1060] Users can follow the reminders to care for their plants and provide feedback to the system, which will further optimize the next care plan.

[1061] For example, if a user is growing tomatoes, they enter "tomato" and "planting date" into the system. When the user takes a photo of the tomato and uploads it, the server performs a health check based on the image and the diagnosis is "good." In this case, the server creates a care plan based on the characteristics of the "tomato" by setting the watering frequency to every two days and fertilizing every 14 days. It also sends the user a watering reminder every two days and a fertilizing reminder every 14 days. This allows users to take care of their plants at the appropriate time without forgetting, even in their busy daily lives.

[1062] As described above, the present invention enables users to efficiently care for plants and maintain their health.

[1063] The processing flow will be explained below.

[1064] Step 1:

[1065] To add a plant to the system, the user inputs the plant's ID, type, and planting date. This information is entered through the user interface and sent to the server.

[1066] Step 2:

[1067] The server stores the plant data in an internal database based on the input information received from the user, including the plant ID, type, planting date, and initial health status, which is set to "good."

[1068] Step 3:

[1069] The user takes a photo of the plant using the smartphone camera, and then uploads the image file from the smartphone to the server.

[1070] Step 4:

[1071] The server reads the received image file and performs preprocessing using an image processing library. Specifically, it resizes the image to the specified size (e.g., 224x224 pixels) and converts it into the format required by the AI ​​model.

[1072] Step 5:

[1073] The server then inputs the preprocessed image data into an artificial intelligence model for diagnosing the plant's health. The AI ​​model analyzes the images and determines the plant's health status ("good" or "needs attention").

[1074] Step 6:

[1075] The server retrieves the diagnostic results determined by the AI ​​model and updates the existing plant database, keeping the plant's health up to date.

[1076] Step 7:

[1077] The server then creates an appropriate care plan based on the type of plant and the diagnostic results, including how often to water the plant and when to fertilize it.

[1078] Step 8:

[1079] The server schedules periodic care reminders based on the created care plan: watering reminders every two days, fertilizing reminders every 14 days.

[1080] Step 9:

[1081] The server will send a notification to the user at the scheduled time according to the reminder schedule, via the user's smartphone or email.

[1082] Step 10:

[1083] The user receives notifications from the server and takes care of the plants, specifically by watering and fertilizing them according to the notifications.

[1084] Step 11:

[1085] After performing care, the user provides feedback on the results to the server, such as "watering completed" or "fertilization completed."

[1086] Step 12:

[1087] The server receives user feedback and updates the plant database, further optimizing the next care plan.

[1088] This allows users to efficiently care for their plants and maintain their health.

[1089] Example 1

[1090] 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."

[1091] Conventional plant care systems lack the functionality to automatically generate a specific care plan for a plant after inputting information such as the plant type and planting date, and to notify the user of appropriate reminders. They also have limited functionality for efficiently diagnosing the plant's health and optimizing the care plan based on the diagnosis results. Furthermore, they lack a mechanism for utilizing user feedback on care reminders to improve future care plans. A new system that can solve these problems and enable users to effectively maintain the health of their plants is needed.

[1092] 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.

[1093] In this invention, the server includes means for inputting the type of plant and a specific planting date, means for using a machine learning model to diagnose the health condition of the plant from photographed images, and means for creating an individual care plan based on the health condition diagnosed by the machine learning model. This enables plant health diagnosis and optimization of the care plan based on the information input by the user. Furthermore, by adding means for scheduling plant care reminders, means for notifying the user of the care reminders, means for receiving feedback from the user related to the notifications, and means for optimizing future care plans based on the feedback, the server enables the user to continuously manage the health of their plants.

[1094] A "plant type" is a category or name that identifies a particular plant, and examples include "tomato," "basil," and "rose."

[1095] "Specific planting date" refers to the specific date on which the subject plant was planted.

[1096] A "machine learning model" is an algorithm that is trained using data and has the ability to make predictions or classifications based on a given input.

[1097] A "care plan" refers to a specific plan of action or response required to maintain the health of a plant, including, for example, how often to water it and when to use fertilizer.

[1098] "Care reminders" refer to messages and alerts that notify users about plant care tasks.

[1099] "User" means an individual or organization that uses the system to care for plants.

[1100] "Feedback" refers to information provided by the user to the system, and in particular refers to the results and status of care work performed in accordance with care reminders.

[1101] "Server" refers to a computer device that processes the entire system and manages data.

[1102] "Database" means a system for systematically storing, managing, and retrieving information.

[1103] "Notification" refers to a messaging method used to convey instructions or information to the user.

[1104] The present invention relates to a system for efficient and effective plant care that allows a user to input the type of plant and a specific planting date, and then uses machine learning models to diagnose the plant's health, create a personalized care plan, and schedule care reminders.

[1105] System configuration

[1106] The main components of the system are as follows:

[1107] Server: A computer device that processes the entire system and manages data. It uses machine learning frameworks such as TensorFlow and PyTorch.

[1108] User terminal: A smartphone or PC that allows users to take photos of plants and upload them to the system.

[1109] Database: A system for storing plant information, health check results, and care plans.

[1110] System Operation

[1111] During system initialization, the server loads a pre-trained plant diagnostic model, which is built using machine learning frameworks such as TensorFlow or PyTorch.

[1112] To register a new plant in the system, the user inputs the plant type and planting date through a dedicated application or a web interface. The user terminal then sends the input information to the server.

[1113] The server stores the received information in a database and initializes care reminders for the plant.

[1114] To diagnose the health of a plant, users take a photo of the plant with their smartphone and upload it to the system, where the device sends the image to the server.

[1115] The server preprocesses the images (e.g., resizes them to a standard size) and converts them into a format suitable for input into an artificial intelligence model. The preprocessed images are then input into the model, and a diagnosis (e.g., "good" or "needs attention") is obtained and stored in a database.

[1116] The server automatically generates an appropriate care plan based on the type of plant and the diagnosis results, including information such as watering frequency and fertilization timing, and schedules care reminders based on the generated care plan and saves it in a database.

[1117] The server sends reminders to users according to a schedule, either via push notification or email. Users then take care of their plants according to the reminders and provide feedback to the system.

[1118] This feedback information is sent from the user's device to the server, which stores it in a database and optimizes future care plans based on this feedback.

[1119] Specific examples

[1120] For example, if a user is growing tomatoes, they enter "tomato" and "planting date" into the system. When the user takes a photo of the tomato and uploads it, the server performs a health check based on the image and obtains a diagnosis of "good condition." In this case, the server creates a care plan based on the characteristics of the "tomato" by setting the watering frequency to every two days and fertilizing every 14 days. The server then sends the user a watering reminder every two days and a fertilizing reminder every 14 days.

[1121] Prompt Sentence Examples

[1122] "Enter the tomato planting date as April 1st and upload a photo of the tomato for a health check. The AI ​​model will analyze the image and create an appropriate care plan and reminders."

[1123] This system allows users to efficiently and effectively care for their plants and maintain their health.

[1124] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1125] Step 1: Initialize the system

[1126] The server loads the plant diagnosis model when initializing the system. The input data is a machine learning model file, and the output is the model loaded in memory. Specifically, it uses the TensorFlow or PyTorch library to read a pre-trained model file and load it into memory.

[1127] Step 2: Add plants

[1128] The user inputs the plant type and planting date through the application or web interface. The input data is the plant ID, type, and planting date, and the output is request data that stores this information.

[1129] The terminal sends the input data to the server. The input is the user's input data, and the output is the request data sent to the server.

[1130] The server stores the received data in a database and sets up an early care reminder. The input is the request data received from the terminal, and the output is the plant information and reminder settings stored in the database.

[1131] Step 3: Plant Health Check

[1132] Users take photos of plants with their smartphones and upload them to the system through the application. The input data is the captured image, and the output is the request data sent to the terminal.

[1133] The device sends the captured image to the server. The input is the image uploaded by the user, and the output is the image data sent to the server.

[1134] The server preprocesses the received images, specifically resizing them to a standard size and converting their format. The input data is the image sent from the device, and the output is the preprocessed image. Image processing libraries (OpenCV or Pillow) are used to resize and convert the format.

[1135] The server uses the preprocessed images to perform a health diagnosis using a machine learning model. The input is the preprocessed image, and the output is the diagnosis result ("good" or "needs attention"). The diagnosis result is stored in a database.

[1136] Step 4: Create a care plan

[1137] The server creates an appropriate care plan based on the plant type and the diagnosis results. The input data are the plant type and the diagnosis results, and the output is the generated care plan. It uses predefined rules and data to set watering frequency, fertilizer timing, and other settings.

[1138] The server schedules care reminders based on the care plan. The input is the created care plan, and the output is a reminder schedule. This schedule is stored in a database.

[1139] Step 5: Send a reminder

[1140] The server sends care reminders to the user according to the schedule. The input data is the reminder schedule, and the output is a reminder notification to the user's device. Notifications are sent to the user's smartphone or PC using a push notification service (such as Firebase Cloud Messaging).

[1141] Step 6: Care feedback

[1142] The user follows the reminders to care for the plants and provides feedback to the system. The input is the information after care, and the output is the feedback data. The terminal sends the user's feedback to the server.

[1143] The server stores the feedback information in a database and optimizes the care plan for the next time onward. The input is the feedback data from the device, and the output is the improvement details for the next care plan.

[1144] (Application example 1)

[1145] 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."

[1146] Traditionally, caring for plants has been time-consuming and laborious, making it difficult for modern people with busy lifestyles to care for them at the right time. It is also difficult to accurately grasp the health status of plants, which often results in the plants becoming unhealthy. Even in virtual stores, there is a lack of support for plant care after purchase, forcing users to care for their plants at their own discretion, and the appropriateness of such care cannot be guaranteed, which is an issue.

[1147] 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.

[1148] In this invention, the server includes a means for inputting the type of plant and a specific planting date, a means for using an artificial intelligence model to diagnose the health condition of the plant from photographed images, a means for creating an individual care plan based on the health condition diagnosed by the artificial intelligence model, a means for scheduling care reminders for the plant, a means for notifying the user of the care reminders, a means for automatically synchronizing purchase information from the virtual store, and a means for the user to provide feedback on the health condition of the plant using a smartphone. This allows users to care for their plants at appropriate times without forgetting, even during their busy daily lives. In addition, collaboration with the virtual store makes post-purchase care easier, allowing plants to always be kept in optimal health.

[1149] "Plant types" are classifications of plants with various forms and characteristics.

[1150] The "specific planting date" is information that indicates the specific date on which the user planted the plant.

[1151] An "artificial intelligence model" is an algorithm that mimics human knowledge and behavior to automatically perform specific tasks.

[1152] A "smartphone" is a portable electronic device that has advanced computing capabilities in addition to the functionality of a mobile phone.

[1153] "Health" is an indicator of a plant's vitality and growth status.

[1154] A "care plan" refers to specific steps and schedules for maintaining and improving the health of a plant.

[1155] "Care reminders" are messages or alerts that inform users about proper care for their plants.

[1156] A "virtual store" is a commercial facility that operates on the Internet, an online shop that does not have a physical store.

[1157] "Feedback" refers to the act or information that a user reports to the system about the results of the care they have provided.

[1158] A "server" is a high-performance computer that provides services to computers and other devices over a network.

[1159] The present invention is a system for efficiently and effectively caring for plants, which includes the following various means:

[1160] 1. System initialization

[1161] When the system is initialized, the server first loads a plant diagnosis model. This is a pre-trained artificial intelligence model that automatically diagnoses the health of plants from images. The server is built on a cloud platform (e.g., AWS, Google Cloud) and runs the AI ​​model using TensorFlow or PyTorch.

[1162] 2. Plant registration and purchase information synchronization

[1163] When a user purchases a plant from the virtual store, the purchase information (plant type, planting date) is automatically synchronized with the system. The server stores this information in a database (e.g., MySQL, Firebase) and generates a unique identifier for each plant, saving the user the trouble of manually entering it.

[1164] 3. Plant health check

[1165] Users use their smartphones to take photos of plants and upload them to the system. The server preprocesses the received images, resizes them to a standard size, and converts them into the format required by the AI ​​model. The preprocessed images are then input into a TensorFlow or PyTorch model, which generates a diagnosis. This diagnosis is then stored in a database and notified to the user.

[1166] 4. Creating a care plan

[1167] The server automatically creates an individualized care plan based on the plant type and diagnostic results. The care plan includes specific tasks such as watering frequency and fertilization timing. The created care plan is saved in a database and a care reminder schedule is set.

[1168] 5. Send reminders

[1169] The server sends care reminders to the user's smartphone via push notifications according to the schedule, informing them of specific actions such as when to water or fertilize plants.

[1170] 6. Feedback function

[1171] Users can provide feedback to the system via their smartphone about the results of their care work, which is then saved in a database and used to optimize the next care plan.

[1172] Examples of concrete examples and prompts

[1173] As a concrete example, consider the case where a user purchases tomatoes from a virtual store. The purchase information is automatically synchronized and registered in the system. When the user uploads a photo of the tomato taken with their smartphone, the server diagnoses the image and determines that the tomato is in good condition. The server then creates a care plan appropriate for the tomato, scheduling reminders to water it every two days and fertilize it every 14 days.

[1174] Prompt Sentence Examples

[1175] Please enter the plant type and planting date.

[1176] "Upload the images you've taken and diagnose the health of your plants."

[1177] "Please follow the care plan below to care for your plants."

[1178] This system allows users to take care of their plants at the right time without forgetting, even in their busy daily lives. In addition, by linking with the virtual store, post-purchase care becomes easier, allowing plants to be kept in optimal health at all times.

[1179] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1180] Step 1:

[1181] The server loads a plant diagnostic model when initializing the system. This diagnostic model is a pre-trained generative AI model that automatically diagnoses the health of plants from images. During this initialization, the server runs the AI ​​model using TensorFlow or PyTorch on a cloud platform (e.g., AWS or Google Cloud). This gives the system the ability to diagnose plant images.

[1182] Step 2:

[1183] When a user purchases a plant from a virtual store, the purchase information (type of plant, planting date) is automatically synchronized with the system. In this synchronization process, the server receives the data sent from the virtual store and stores it in a database (e.g., MySQL, Firebase). This allows the user to complete the plant registration without having to enter any information.

[1184] Step 3:

[1185] Users take photos of plants using their smartphones and upload them to the system. The uploaded images are sent to a server, which then receives them. At this time, preprocessing such as resizing and format conversion is performed on the images, and the standardized images are then input into the generative AI model. This process prepares the images to be used to diagnose the plant's health.

[1186] Step 4:

[1187] The server inputs the preprocessed images into a generative AI model to diagnose the health of the plant. The diagnosis results are expressed as a status such as "good" or "needs attention." The diagnosis results are stored in a database and notified to the user. This notification is important for allowing the user to understand the current state of the plant.

[1188] Step 5:

[1189] The server automatically creates an individual care plan based on the diagnosis results and the type of plant. The care plan includes specific care instructions such as how often to water and when to fertilize. This care plan is saved in a database and a care reminder schedule is set, helping users to provide the necessary care at the appropriate time.

[1190] Step 6:

[1191] The server sends care reminders to the user's smartphone via push notifications according to the schedule. The reminders include specific care tasks (e.g., watering and fertilizing), so that the user does not miss the appropriate care timing.

[1192] Step 7:

[1193] Users provide feedback to the system via their smartphone on the results of their care work. This feedback is sent to the server and stored in a database. The server uses this feedback information to further optimize the next care plan and notify the user again. This makes the care plan more personalized and optimized, making it easier to maintain plant health.

[1194] 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.

[1195] The present invention provides a system for efficient and effective plant care, and further enhances the user experience by combining it with an emotion engine that recognizes the user's emotions. This system allows the user to input the type of plant and the planting date, and then uses an artificial intelligence model to diagnose the plant's health and create an individualized care plan, schedule care reminders, and notify the user. The emotion engine also adjusts the content and timing of care reminders based on the user's emotions.

[1196] 1. System initialization

[1197] When the server initializes the system, it loads a plant diagnosis model and initializes an emotion engine. The plant diagnosis model is for diagnosing the health of plants from images, and the emotion engine is for recognizing the user's emotions.

[1198] 2. Adding plants

[1199] To add a new plant to the system, a user inputs the plant's ID, type, and planting date, which is then sent to the server.

[1200] The server stores the input plant information in a database and sets care reminders for the plant.

[1201] 3. Plant health check

[1202] To diagnose the health of a plant, users take a photo of the plant with their smartphone and upload it to the server.

[1203] The server receives the images, preprocesses them, and then inputs them into an artificial intelligence model to diagnose the plant's health. The results are stored in a database.

[1204] 4. Creating a care plan

[1205] The server creates an appropriate care plan based on the type of plant and the diagnosis results, and sets care reminders.

[1206] 5. Functions of the Emotion Engine

[1207] The server periodically recognizes the user's emotions using an emotion engine, which analyzes the user's text, voice, or image data to assess their emotional state.

[1208] The server decreases the frequency of care reminders when the user is stressed and increases the frequency when the user is relaxed.

[1209] 6. Send Reminders

[1210] The server notifies the user of care reminders according to the schedule via smartphone or email.

[1211] The user cares for the plants based on the reminder and then sends the results back to the server.

[1212] Specific examples

[1213] For example, if a user is growing tomatoes, they enter "tomato" and "planting date" into the system. When the user takes a photo of the tomato and uploads it, the server performs a health check based on the image and determines whether the plant is in good condition or in need of attention. Based on the results, a care plan is created, including how often to water the plant and when to apply fertilizer.

[1214] The emotion engine then recognizes the user's emotions: for example, if the user is stressed, the server will reduce the frequency of watering reminders to ease the user's burden, while if the user is relaxed, more frequent reminders will be sent to optimize plant care.

[1215] This system allows users to efficiently care for their plants, maintaining their health while also reducing the mental burden on the user.

[1216] The processing flow will be explained below.

[1217] Step 1:

[1218] To add a plant to the system, the user inputs the plant's ID, type, and planting date. This information is entered through the user interface and sent to the server.

[1219] Step 2:

[1220] The server stores the plant data in an internal database based on the input information received from the user, including the plant ID, type, planting date, and initial health status, which is set to "good."

[1221] Step 3:

[1222] The user takes a photo of the plant using the smartphone camera, and then uploads the image file from the smartphone to the server.

[1223] Step 4:

[1224] The server reads the received image file and performs preprocessing using an image processing library. Specifically, it resizes the image to the specified size (e.g., 224x224 pixels) and converts it into the format required by the AI ​​model.

[1225] Step 5:

[1226] The server then inputs the preprocessed image data into an artificial intelligence model for diagnosing the plant's health. The AI ​​model analyzes the images and determines the plant's health status ("good" or "needs attention").

[1227] Step 6:

[1228] The server retrieves the diagnostic results determined by the AI ​​model and updates the existing plant database, keeping the plant's health up to date.

[1229] Step 7:

[1230] The server then creates an appropriate care plan based on the type of plant and the diagnostic results, including how often to water the plant and when to fertilize it.

[1231] Step 8:

[1232] The server schedules periodic care reminders based on the created care plan: watering reminders every two days, fertilizing reminders every 14 days.

[1233] Step 9:

[1234] The server periodically recognizes the user's emotions using an emotion engine, which analyzes the user's text, voice, or image data to assess their emotional state.

[1235] Step 10:

[1236] The server reduces the frequency of care reminders when the user is feeling stressed, and increases the frequency of reminders when the user is feeling relaxed, thereby reducing the burden on the user and ensuring that appropriate care is provided.

[1237] Step 11:

[1238] The server notifies the user of care reminders according to the schedule via smartphone or email.

[1239] Step 12:

[1240] The user can take care of the plants based on the reminders, specifically by watering and fertilizing them according to the notifications.

[1241] Step 13:

[1242] After performing care, the user provides feedback to the server about the results, such as "watering completed" or "fertilization completed."

[1243] Step 14:

[1244] The server receives user feedback and updates the plant database, further optimizing the next care plan.

[1245] This allows users to efficiently care for their plants and maintain their health. The emotion engine also reduces the user's mental burden.

[1246] Example 2

[1247] 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."

[1248] Conventional plant care systems have limitations in their ability to diagnose plant health and provide appropriate care plans, and they are unable to consider the user's emotional state. This can cause users to feel stressed, reducing the effectiveness of plant care. Furthermore, the frequency and content of reminder notifications are fixed, making it difficult to flexibly adjust them to suit the user's situation.

[1249] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1250] In this invention, the server includes means for inputting the type of plant and a specific planting date, means for using an artificial intelligence model to diagnose the health condition of the plant from a photographed image, means for creating an individual care plan based on the health condition diagnosed by the artificial intelligence model, means for scheduling care reminders for the plant, means for notifying the user of the care reminders, means for using an emotion recognition engine to recognize the user's emotions, and means for adjusting the frequency and content of the care reminders based on the emotion recognition engine. This enables flexible adjustment of reminders according to the user's emotional state, enabling effective plant care while reducing user stress.

[1251] "Plant type" is classification information for distinguishing a particular plant from other plants.

[1252] The "specific planting date" is information about the specific date on which the user planted the plant.

[1253] An "artificial intelligence model" is an algorithm that uses technologies such as machine learning and deep learning to diagnose the health of plants.

[1254] A "care plan" is a plan that provides appropriate care methods and schedules for plants based on diagnostic results.

[1255] "Care reminder" refers to a notification that informs the user when it is time to care for their plants.

[1256] An "emotion recognition engine" is a technology that analyzes data such as a user's text and voice to determine the user's emotional state.

[1257] "Notification" is a means of sending a message to communicate care reminders to the user.

[1258] The system of the present invention is designed to provide efficient and effective plant care and further enhances the user experience by incorporating an emotion recognition engine that recognizes the user's emotions. The system allows the user to input the plant's type and planting date, and then uses an artificial intelligence model to diagnose the plant's health, creating an individualized care plan and scheduling care reminders to notify the user. The emotion recognition engine also adjusts the content and timing of care reminders based on the user's emotions.

[1259] Specific examples of hardware and software used

[1260] This system includes a server, terminals (smartphones and PCs), and various software. Specifically, it consists of the following:

[1261] The server is the heart of the system and is responsible for various processes. It uses TensorFlow to implement an artificial intelligence model for plant diagnosis, and OpenAI GPT-4 or IBM Watson to build an emotion recognition engine.

[1262] The terminal (smartphone or PC) is a device where the user can input information, take pictures of the plants, receive care reminders, etc. A dedicated application is installed on the terminal, and communication with the server is carried out through this application.

[1263] The database system uses MySQL or PostgreSQL to store data on plant information, diagnosis results, and the user's emotional state.

[1264] Specific examples

[1265] For example, if a user wants to grow tomatoes, they enter "tomato" and the "planting date" into the system. When the user takes a photo of the tomato with their smartphone and uploads it, the server performs a health check based on the image and determines whether the plant is in good condition or in need of attention. Based on the results, the server creates a care plan that includes watering frequency and fertilizer application timing.

[1266] The emotion engine then recognizes the user's emotions: for example, if the user is stressed, the server will reduce the frequency of watering reminders to ease the burden, while if the user is relaxed, it will send more frequent reminders to optimize plant care.

[1267] Prompt Sentence Examples

[1268] The following prompt sentence is used as input to the generative AI model:

[1269] Prompt: Create a care plan for a specific plant. Use the information below.

[1270] Plant Type: Tomato

[1271] Health check result: Good

[1272] User's emotional state: Relaxed

[1273] Sample answer: The care plan for your tomato plants is as follows: Water them every day at 8:00 AM and fertilize them once a week with liquid fertilizer. Maintain this frequency as your emotional state is relaxed.

[1274] This system allows users to efficiently care for their plants, maintaining their health while also reducing the mental burden on the user.

[1275] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1276] Step 1:

[1277] During system initialization, the server loads the plant diagnosis model and emotion recognition engine. This includes loading a TensorFlow model and configuring it to diagnose plant health from images. It also uses emotion recognition technologies such as OpenAI GPT-4 to analyze the user's text and voice data and prepare for emotion recognition. The input is the initial setup data, and the output is the prepared system environment.

[1278] Step 2:

[1279] A user adds a new plant to the system using a terminal. The user inputs the plant's ID, type, and planting date, and sends the information to the server through a dedicated application. For example, the user might input "ID: 12345, type: tomato, planting date: October 1, 2023." The input is plant information, and the output is the plant information stored in the database.

[1280] Step 3:

[1281] The server saves the received plant information in a database. The input plant ID, type, and planting date are stored in a database such as MySQL or PostgreSQL, and care reminders are set for the plant. The input is plant information, and the output is the updated database and the set reminders.

[1282] Step 4:

[1283] To diagnose the health of a plant, a user takes a photo of the plant with their smartphone and uploads the photo to the server using a dedicated application. The input is the image of the plant, and the output is the transmission of the image to the server.

[1284] Step 5:

[1285] The server preprocesses the received images, adjusting the image resolution and removing noise using the OpenCV library. The preprocessed images are then input into an artificial intelligence model to diagnose the plant's health. Based on the diagnosis results, the health status is determined as "good" or "needs attention." The input is the image before preprocessing, and the output is the diagnosis result.

[1286] Step 6:

[1287] The server stores the diagnostic results in a database. The input is the diagnostic results and the output is the updated database.

[1288] Step 7:

[1289] The server creates a care plan based on the plant type and the diagnosis results. For example, if the diagnosis result is "good," it generates a care plan including watering frequency and fertilizer timing. The input is plant information and the diagnosis results, and the output is the generated care plan.

[1290] Step 8:

[1291] The server schedules care reminders. It uses a cron job or similar to set the timing for notifying users. The input is the care plan, and the output is the configured reminder schedule.

[1292] Step 9:

[1293] The server recognizes the user's emotions using an emotion recognition engine. It analyzes the user's text messages and voice data to determine their current emotional state (e.g., stressed, relaxed). The input is the user's text or voice data, and the output is the recognized emotional state.

[1294] Step 10:

[1295] The server adjusts the frequency and content of reminders based on the user's emotional state. For example, if the user is feeling stressed, it reduces the frequency of reminders. The inputs are the perceived emotional state and the care plan, and the output is the adjusted reminders.

[1296] Step 11:

[1297] The server notifies the user of the care reminder at the scheduled time. It sends the reminder to the user's device using Firebase Cloud Messaging or an email service. The input is the adjusted reminder schedule, and the output is the notification to the user.

[1298] Step 12:

[1299] The user cares for the plants based on the reminders. After the care, the user sends feedback to the server using a dedicated application. The input is the results of the care, and the output is a database update as feedback.

[1300] (Application example 2)

[1301] 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."

[1302] Conventional plant care systems can diagnose the health of plants and create care plans, but they do not adjust reminders based on the user's emotional state, making it difficult to improve the user experience. In particular, they were unable to provide appropriate care timing when the user was stressed or relaxed, making it difficult to provide efficient and effective plant care.

[1303] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting the type of plant and a specific planting date, means for using an artificial intelligence model to diagnose the health condition of the plant from a photographed image, means for creating an individual care plan based on the health condition diagnosed by the artificial intelligence model, means for scheduling care reminders for the plant, means for recognizing the user's emotional state and adjusting the content and timing of the care reminders based on the emotional state, and means for notifying the user of the care reminders. This enables optimal adjustment of care reminders according to the user's emotional state, resulting in efficient and effective plant care and an improved user experience.

[1304] "Plant type" refers to a specific classification of the plant to be cared for, and appropriate care methods vary depending on the classification.

[1305] The "specific planting date" refers to the date the plant was planted, which is important information that influences care plans and reminder schedules.

[1306] "Photographed image" refers to an image showing the current state of a plant that has been photographed by a user using a device such as a smartphone.

[1307] "Health" is an indicator used to evaluate the growth and condition of plants, and is diagnosed by an artificial intelligence model.

[1308] The "artificial intelligence model" is a technology that uses image recognition and machine learning to automatically diagnose the health of plants and create care plans.

[1309] A "care plan" refers to specific care methods and schedules created based on the health of a plant.

[1310] "Care reminders" refer to messages and alerts that inform users how and when to care for their plants.

[1311] "Emotional state" refers to a user's current mental and psychological state and is detected by emotion recognition technology.

[1312] "Emotion recognition" refers to technology that analyzes voice, facial expressions, and text data to assess a user's emotions.

[1313] "Means of notification" refers to the methods and functions for transmitting information to users using smartphones, email, etc.

[1314] This invention is a system for efficiently and effectively caring for plants, which recognizes the user's emotional state and adjusts the content and timing of care reminders based on that state. To realize this system, a server and a smartphone are used.

[1315] First, the user inputs the plant type and specific planting date on their smartphone. The input information is sent to the server and stored in a database. Next, the user takes a photo of the plant with their smartphone and uploads it to the server to diagnose the plant's health. The server preprocesses the received image and inputs it into an artificial intelligence model to diagnose the plant's health. The diagnosis results are stored in a database, and the server creates an individual care plan based on the results.

[1316] The server analyzes voice, facial expressions, text data, etc. to periodically recognize the user's emotional state. Specifically, it uses emotion recognition technology to evaluate the user's stress level and relaxation level. This process uses emotion recognition engines such as Amazon Rekognition and IBM Watson.

[1317] Based on the user's emotional state, the server adjusts the timing and content of care reminders. For example, if the user is stressed, the frequency of reminders is reduced, and if the user is relaxed, the frequency is increased. The adjusted reminders are then sent to the user via Firebase Cloud Messaging or Apple Push Notification Service (APNS).

[1318] Specific examples

[1319] For example, if a user is tired and stressed from work, the smartphone camera will recognize this and the server will suggest a care plan to help them relax. By inputting "tomato" and "planting date" into the system and uploading a photo of the tomato, the server will perform a health check based on the image and determine whether the plant is in good condition or in need of attention. Based on the results, a care plan including watering frequency and fertilizer application timing is created. Furthermore, if the user is feeling stressed, the server will reduce the frequency of reminders.

[1320] Prompt Sentence Examples

[1321] "Siri, how do you feel about me?"

[1322] "Stuck on dinner suggestions? Let me know what you're feeling today."

[1323] "I've been busy at work lately and I'm feeling a bit restless. Can you suggest some dishes that will help me relax?"

[1324] "You seem a little tired today. Can we adjust your delivery time a little later?"

[1325] This allows optimal care reminders to be adjusted according to the user's emotional state, resulting in efficient and effective plant care and an improved user experience.

[1326] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1327] Step 1: Enter plant information

[1328] Users use their smartphones to input the type of plant they want to grow and the specific planting date, and this information is sent to a server and stored in a database.

[1329] Input: Plant type, specific planting date

[1330] Data processing: The server saves the input information in a database

[1331] Output: Plant information stored in a database

[1332] Step 2: Plant Health Check

[1333] Users take images of plants with their smartphones and upload them to a server, which pre-processes the images and feeds them into an artificial intelligence model to diagnose the plant's health.

[1334] Input: Captured image

[1335] Data processing: Image preprocessing (noise removal, resizing, etc.)

[1336] Data Computing: Health Diagnosis Using Artificial Intelligence Models

[1337] Output: Health check result ("Good" or "Needs attention")

[1338] Step 3: Create a care plan

[1339] The server creates a personalized care plan based on the plant's diagnosis and type, including how often to water it and when to fertilize it.

[1340] Input: diagnosis result, plant type

[1341] Data processing: generating care plan information

[1342] Output: Individual care plan

[1343] Step 4: Recognizing your emotional state

[1344] To recognize the user's emotional state, the smartphone's camera and microphone are used to collect voice and facial expression data. The server then uses an emotion recognition engine to analyze this data and evaluate the user's emotional state.

[1345] Input: Audio data, image data

[1346] Data processing: voice recognition, face recognition

[1347] Data Calculation: Analysis by Emotion Recognition Engine

[1348] Output: User's emotional state

[1349] Step 5: Adjust reminders

[1350] The server adjusts the content and timing of care reminders based on the user's emotional state, for example, decreasing the frequency of reminders for a stressed user and increasing the frequency for a relaxed user.

[1351] Input: User's emotional state, care plan

[1352] Data processing: Reminder schedule adjustment

[1353] Output: Adjusted reminders

[1354] Step 6: Reminder Notifications

[1355] The server then notifies the user of the adjusted care reminders using a push notification system, via smartphone or email.

[1356] Input: Adjusted reminders

[1357] Data processing: Creating push notification messages

[1358] Output: Reminder notification to the user

[1359] Step 7: Plant care implementation and feedback

[1360] The user cares for the plants based on the reminders and provides feedback to the server, which uses the feedback as learning data for the system.

[1361] Input: Care outcome

[1362] Data processing: collection and storage of feedback data

[1363] Output: Updated database

[1364] In this way, optimal care reminders are provided according to the user's emotional state, realizing efficient and effective plant care and improving the user experience.

[1365] 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.

[1366] 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.

[1367] 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.

[1368] 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.

[1369] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1370] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1371] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1372] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1373] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1374] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1375] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1376] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1377] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1378] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1379] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1380] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1381] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1382] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1383] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1384] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1385] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1386] The following is further disclosed regarding the above embodiment.

[1387] (Claim 1)

[1388] means for inputting the plant type and specific planting date;

[1389] A means for using an artificial intelligence model to diagnose the health status of a plant from photographed images;

[1390] means for generating an individualized care plan based on the health condition diagnosed by the artificial intelligence model;

[1391] means for scheduling plant care reminders;

[1392] means for notifying a user of the care reminder;

[1393] A system including:

[1394] (Claim 2)

[1395] 2. The system of claim 1, wherein the artificial intelligence model uses a method for classifying plant health into "good" and "needs attention."

[1396] (Claim 3)

[1397] 10. The system of claim 1, wherein the care reminders are configured to notify a user when to water and fertilize plants.

[1398] "Example 1"

[1399] (Claim 1)

[1400] means for inputting the plant type and specific planting date;

[1401] A means for using a machine learning model to diagnose the health status of a plant from photographed images;

[1402] means for generating an individualized care plan based on the health condition diagnosed by the machine learning model; and

[1403] means for scheduling plant care reminders;

[1404] means for notifying a user of the care reminder;

[1405] means for receiving feedback from a user in connection with said notification;

[1406] means for optimizing subsequent care plans based on said feedback;

[1407] A system including:

[1408] (Claim 2)

[1409] 2. The system of claim 1, wherein the machine learning model uses a method to classify plant health into "good" and "needs attention."

[1410] (Claim 3)

[1411] 10. The system of claim 1, wherein the care reminders are configured to notify a user when to water and fertilize plants.

[1412] "Application Example 1"

[1413] (Claim 1)

[1414] means for inputting the plant type and specific planting date;

[1415] A means for using an artificial intelligence model to diagnose the health status of a plant from photographed images;

[1416] means for generating an individualized care plan based on the health condition diagnosed by the artificial intelligence model;

[1417] means for scheduling plant care reminders;

[1418] means for notifying a user of the care reminder;

[1419] means for automatically synchronizing purchase information from the virtual store;

[1420] A means for a user to provide feedback on the health status of the plant using a smartphone;

[1421] A system including:

[1422] (Claim 2)

[1423] 2. The system of claim 1, wherein the artificial intelligence model uses a method for classifying plant health into "good" and "needs attention."

[1424] (Claim 3)

[1425] 10. The system of claim 1, wherein the care reminders are configured to notify a user when to water and fertilize plants.

[1426] "Example 2: Combining Emotion Engines"

[1427] (Claim 1)

[1428] means for inputting the plant type and specific planting date;

[1429] A means for using an artificial intelligence model to diagnose the health status of a plant from photographed images;

[1430] means for generating an individualized care plan based on the health condition diagnosed by the artificial intelligence model;

[1431] means for scheduling plant care reminders;

[1432] means for notifying a user of the care reminder;

[1433] means for using an emotion recognition engine to recognize an emotion of a user;

[1434] means for adjusting the frequency and content of care reminders based on the emotion recognition engine;

[1435] A system including:

[1436] (Claim 2)

[1437] 2. The system of claim 1, wherein the artificial intelligence model uses a method for classifying plant health into "good" and "needs attention."

[1438] (Claim 3)

[1439] 10. The system of claim 1, wherein the care reminders are configured to notify a user when to water and fertilize plants.

[1440] "Application example 2 when combining emotion engines"

[1441] (Claim 1)

[1442] means for inputting the plant type and specific planting date;

[1443] A means for using an artificial intelligence model to diagnose the health status of a plant from photographed images;

[1444] means for generating an individualized care plan based on the health condition diagnosed by the artificial intelligence model;

[1445] means for scheduling plant care reminders;

[1446] means for recognizing an emotional state of a user and adjusting content and timing of care reminders based on the emotional state;

[1447] means for notifying a user of the care reminder;

[1448] A system including:

[1449] (Claim 2)

[1450] 2. The system of claim 1, wherein the artificial intelligence model uses a method for classifying plant health into "good" and "needs attention."

[1451] (Claim 3)

[1452] 10. The system of claim 1, wherein the care reminders are configured to notify a user when to water and fertilize plants. [Explanation of symbols]

[1453] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for inputting the plant type and specific planting date; A means for using an artificial intelligence model to diagnose the health status of a plant from photographed images; means for generating an individualized care plan based on the health condition diagnosed by the artificial intelligence model; means for scheduling plant care reminders; means for notifying a user of the care reminder; A system including:

2. 2. The system of claim 1, wherein the artificial intelligence model uses a method for classifying plant health into "good" and "needs attention."

3. 10. The system of claim 1, wherein the care reminders notify the user when to water and fertilize plants.

Images