System

Abstract

A system is provided.SOLUTION: A system comprising: means for acquiring an image of a plant using a camera; means for analyzing the acquired image to determine a type of the plant and make a growth prediction; sensor means for measuring humidity of soil and concentration in air; means for analyzing measurement data and calculating an appropriate irrigation timing; means for detecting a disease or a pest of the plant and generating an improvement method; means for notifying a user of the generated growth prediction, irrigation timing, and improvement method; and means for generating a list of related products and suggesting the list to the user.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] Growing houseplants requires proper care and management, but many people lack the necessary knowledge and skills, making it difficult to maintain optimal plant growth and health. For example, they often fail to properly manage the timing and amount of watering, or fail to detect early signs of plant disease or pests, resulting in plant death. Furthermore, plant management is particularly difficult when people are in remote locations. Another issue is the lack of suggestions for purchasing appropriate related products in a timely manner. The present invention aims to comprehensively solve these problems and support the cultivation of houseplants. [Means for solving the problem]

[0005] The present invention provides a system that uses a camera to capture images of plants, analyzes those images to determine the plant's species, and predicts its growth. It also includes sensors that measure soil humidity and airborne concentrations, and calculates the appropriate watering timing based on the measured data. It also has a function that detects plant diseases and pests, generates remedial measures, and notifies the user. By integrating these functions, the system can capture real-time images of plants and provide them to users in remote locations, and automatically water them based on humidity sensor data. Furthermore, the system can recommend optimal related products based on the plant's species, growth status, soil condition, and environmental data, and provide users with a link to purchase them. This allows users to easily manage their houseplants appropriately and cultivate them in the best possible condition.

[0006] Below are definitions of important terms that may be found in the claims.

[0007] A "camera" is a device that takes images and stores or transmits them as digital data.

[0008] An "image" is digital data that contains visual information about a plant or its surroundings.

[0009] An "AI model" is a collection of algorithms that use artificial intelligence to analyze image data and determine the type and condition of plants.

[0010] A "humidity sensor" is a device that measures the humidity of the soil and outputs it as a digital signal.

[0011] An "air concentration meter" is a device that measures the concentration of a specific component (such as carbon dioxide) in the air.

[0012] An "irrigation means" is a device or system for providing water to plants.

[0013] "Growth prediction" is the process of predicting future growth and changes based on the current state of a plant and past data.

[0014] "Disease detection" refers to analyzing the condition of plants and discovering abnormalities such as diseases and pests.

[0015] "Remedial measures" refer to specific treatments or measures to solve problems with plant diseases or pests.

[0016] "Remote location" refers to a location where the user is not physically present, including locations accessed via the internet or other communications means.

[0017] "Related products" are products such as pots, fertilizers, and pest control products that are necessary to help plants grow.

[0018] "User" refers to an individual or organization that uses this system to manage and cultivate plants. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] This invention relates to a system that comprehensively supports optimal houseplant care. This system combines multiple elements, including cameras, humidity sensors, air concentration meters, AI models, and smartphone apps, to monitor houseplant growth, health, watering timing, disease and pest detection, and recommend related products.

[0041] System Configuration

[0042] The system includes the following components:

[0043] Camera-equipped device: Take regular pictures of your houseplants.

[0044] Terminal equipped with humidity sensor and air concentration meter: Measures the humidity of the soil and the concentration of components in the air.

[0045] Server: Analyzes collected data and generates plant type determinations, growth forecasts, irrigation timing calculations, disease and pest detection, and remediation methods.

[0046] Smartphone app: Provides an interface for users to receive notifications and suggestions from the server and manage their houseplants.

[0047] Program processing

[0048] The specific program processing of the system is shown below.

[0049] 1. Plant image acquisition and analysis

[0050] The device (camera-equipped device) periodically takes photos of the houseplant and sends them to the server. The server analyzes the received images using an AI model to identify the plant's type. It then references a database of plant characteristics to predict its growth. This prediction generates advice on the optimal placement, temperature, light conditions, and pot size. The server then notifies the user of this advice via a smartphone app.

[0051] 2. Environmental data acquisition and notification

[0052] The terminal (a device equipped with a humidity sensor and air concentration meter) periodically measures the soil humidity and the concentration of elements in the air and sends the data to a server. The server analyzes this data and calculates the appropriate watering timing based on the plant type and growth stage. The calculation results are notified to the user via a smartphone app.

[0053] 3. Disease and pest detection and improvement method suggestions

[0054] The device (a device with a camera) takes detailed images of plant leaves and stems and sends them to a server. The server then analyzes these images using an AI model to detect signs of disease or pests. Based on the detection results, it generates remedial measures (e.g., the use of specific pesticides or changing the type of fertilizer) and notifies the user via a smartphone app.

[0055] 4. Automatic irrigation and real-time monitoring

[0056] The terminal (automatic irrigation system) automatically irrigates plants based on soil moisture data. The terminal also periodically sends real-time images to a server, which then provides the images to users via a smartphone app. This allows users to check the status of their plants in real time, even from remote locations.

[0057] 5. Related product suggestions and purchasing support

[0058] The server suggests related products (pots, fertilizer, pest control products, etc.) that users need based on the plant type, growth status, soil condition, and environmental data. These suggestions are notified to users through a smartphone app, and users can easily purchase the products through links within the app.

[0059] Specific examples

[0060] Specific examples are shown below.

[0061] Example 1: Growth predictions and advice

[0062] The device (with a camera) takes a photo of the plant and sends it to the server. The server analyzes the image and determines that the plant is a cactus. The user is then notified via a smartphone app of the results of the growth prediction, the appropriate placement location, lighting conditions, and other information.

[0063] Example 2: Watering notification

[0064] The device (humidity sensor) measures the soil humidity to be 30% and sends this to the server. The server, taking into account that the plant is a succulent, determines that watering is necessary. The smartphone app notifies the user when it is time to water.

[0065] Example 3: Disease detection and improvement suggestions

[0066] The device (a device with a camera) takes detailed images of plant leaves and sends them to a server. The server analyzes the images and detects signs of powdery mildew on the leaves. The server then suggests the use of a specific fungicide as a countermeasure and notifies the user via a smartphone app.

[0067] In this way, the system of the present invention allows users to manage their houseplants efficiently and effectively.

[0068] The processing flow will be explained below.

[0069] 1. Plant image acquisition and analysis

[0070] Step 1:

[0071] The terminal (device with a camera) takes images of the houseplant at set time intervals.

[0072] Step 2:

[0073] The terminal transmits the captured image to the server.

[0074] Step 3:

[0075] The server inputs the received images into an AI model to identify the type of plant.

[0076] Step 4:

[0077] The server makes a growth prediction based on the identified type by referring to a characteristics database.

[0078] Step 5:

[0079] The server generates advice based on growth prediction results, such as the optimal location, temperature, light conditions, and pot size.

[0080] Step 6:

[0081] The server sends the generated advice to the smartphone app.

[0082] Step 7:

[0083] Users can check the advice through a smartphone app and manage their belongings appropriately.

[0084] 2. Environmental data acquisition and notification

[0085] Step 1:

[0086] The terminal (a device equipped with a humidity sensor and air concentration meter) periodically measures the humidity of the soil and the concentration of components in the air.

[0087] Step 2:

[0088] The terminal transmits the measured environmental data to the server.

[0089] Step 3:

[0090] The server analyzes the received data and calculates the optimal watering timing based on the plant type and growth stage.

[0091] Step 4:

[0092] The server notifies the smartphone app of the calculation results.

[0093] Step 5:

[0094] Users can check the watering timing through a smartphone app and either water appropriately or set the watering to be automated.

[0095] 3. Disease and pest detection and improvement method suggestions

[0096] Step 1:

[0097] The device (camera-equipped device) periodically takes detailed images of the plant's leaves and stems.

[0098] Step 2:

[0099] The terminal transmits the captured detailed image to the server.

[0100] Step 3:

[0101] The server uses AI models to analyze the images and detect signs of disease or pests.

[0102] Step 4:

[0103] The server generates remedial measures (e.g., use of specific chemicals or changes in fertilizer) based on the detection results.

[0104] Step 5:

[0105] The server notifies the smartphone app how to improve the situation.

[0106] Step 6:

[0107] Users can check the notification via their smartphone app and take appropriate measures.

[0108] 4. Automatic irrigation and real-time monitoring

[0109] Step 1:

[0110] The terminal (humidity sensor and automatic irrigation system) continuously monitors the soil moisture.

[0111] Step 2:

[0112] The server determines the need for watering based on the humidity data.

[0113] Step 3:

[0114] If the server determines that watering is necessary, it sends a watering instruction to the terminal (automatic watering system).

[0115] Step 4:

[0116] The terminal supplies the set amount of water to the plants according to the watering instructions.

[0117] Step 5:

[0118] The terminal periodically takes real-time images and transmits them to the server.

[0119] Step 6:

[0120] The server sends real-time images to a smartphone app.

[0121] Step 7:

[0122] Users can check the status of their plants in real time from a remote location via a smartphone app.

[0123] 5. Related product suggestions and purchasing support

[0124] Step 1:

[0125] The server collects plant type, growth status, soil conditions, and environmental data.

[0126] Step 2:

[0127] The server generates a list of the most suitable related products (pots, fertilizer, pest control products, etc.) based on the collected data.

[0128] Step 3:

[0129] The server notifies the smartphone app of the generated related product list.

[0130] Step 4:

[0131] Users can check the suggested products through the smartphone app and, if necessary, purchase the products via a link to an e-commerce site.

[0132] Thus, the system of the present invention provides a comprehensive solution for efficient and effective houseplant care.

[0133] Example 1

[0134] 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."

[0135] Properly caring for houseplants is a difficult task for modern urban dwellers. There is a lack of effective solutions for consistently managing a wide range of factors, including plant health management, growth prediction, appropriate watering timing, early detection of diseases and pests, and even the selection of appropriate related products. This task is particularly difficult for users in remote locations or who lead busy lives. The present invention aims to provide a system that comprehensively solves these challenges.

[0136] 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.

[0137] In this invention, the server includes: means for acquiring plant images using a camera; means for inputting the acquired images into a generative AI model to determine the plant's type and for generating a growth forecast and advice on optimal placement, temperature, light conditions, and pot size by referencing a characteristics database; means for measuring soil humidity and airborne constituent concentrations; means for inputting and analyzing the measured data into the generative AI model to calculate appropriate watering timing based on the plant's type and growth stage; means for acquiring detailed images of the plant's leaves and stems and inputting them into the generative AI model to detect signs of disease or pests and generate remediation methods; means for providing a smartphone app that notifies the user of the generated growth forecast, watering timing, and remediation methods; and means for suggesting related products based on the plant's type, growth status, soil condition, and environmental data and providing the user with links to purchase the suggested related products, enabling users to efficiently and effectively manage their houseplants.

[0138] A "camera" is a device that uses light to capture images or videos and record or transmit the data.

[0139] A "generative AI model" is an artificial intelligence model that uses machine learning and deep learning to analyze data and make predictions and classifications.

[0140] The "characteristics database" is a database that systematically accumulates information on plant species and growing conditions.

[0141] A "sensor" is a device that detects physical or chemical conditions or changes and outputs that information as an electrical signal.

[0142] A "humidity sensor" is a device that measures the humidity of an environment or object and outputs that data.

[0143] An "air concentration meter" is a device that measures the concentration of a specific component in the air and outputs the value.

[0144] "Watering timing" refers to the optimal time and frequency for watering plants.

[0145] A "smartphone app" is software that runs on a smartphone and functions as an interface with the user.

[0146] "Signs of disease or pests" refers to early symptoms or signs of disease or pest abnormalities that appear on plants.

[0147] "Remedial measures" are specific measures or countermeasures to address plant disease or pest problems.

[0148] "Related products" refers to products such as fertilizers, pots, and chemicals used to care for and grow plants.

[0149] "Link" means a hypertext reference that directs a user to a specified web page or online store.

[0150] This invention relates to a system that comprehensively supports optimal houseplant care. This system combines multiple elements, including cameras, humidity sensors, air concentration meters, generative AI models, and smartphone apps, to monitor houseplant growth, health, watering timing, disease and pest detection, and recommend related products.

[0151] System Configuration

[0152] The system includes the following components:

[0153] Camera-equipped device: A device that periodically takes images of houseplants. The user sets up the device in an appropriate position near the plant, and it automatically takes images periodically.

[0154] A device equipped with a humidity sensor and an air concentration meter: This device measures the humidity of the soil and the concentration of components in the air. This data is periodically sent to the server.

[0155] Server: The central control unit that inputs the received data into the generative AI model and performs analysis, judgment, calculation, and generation. The server refers to the characteristic database and provides optimal growth conditions and improvement methods.

[0156] Smartphone app: Software that provides an interface for users to receive notifications and suggestions from the server and manage their houseplants. Through this app, users can check the status of their plants and implement the suggestions.

[0157] Plant image acquisition and analysis

[0158] The terminal (a device with a camera) periodically takes pictures of the houseplant and sends them to a server. The server inputs the received images into a generative AI model to identify the plant's type. It then references a characteristics database to generate advice such as growth predictions, optimal placement, temperature, light conditions, and pot size. The generated advice is then sent to the user via a smartphone app.

[0159] Examples:

[0160] A camera-equipped device takes a photo of a houseplant and sends it to a server, which analyzes the image with a generative AI model and determines that the plant is a cactus. The server then sends a growth forecast and advice on optimal placement and lighting conditions to the user via a smartphone app.

[0161] Example prompt for a generative AI model:

[0162] "Analyze this plant's type and growth predictions with a generative AI model and generate advice."

[0163] Environmental data acquisition and notification

[0164] The terminal (a device equipped with a humidity sensor and air concentration meter) periodically measures soil humidity and the concentration of air components, and sends the data to a server. The server inputs this data into a generative AI model for analysis, and calculates the appropriate watering timing based on the plant type and growth stage. The calculation results are notified to the user via a smartphone app.

[0165] Examples:

[0166] The humidity sensor measures the soil humidity at 30% and sends this to the server. The server, taking into account that the plant is a succulent, determines that it actually needs watering. The smartphone app notifies the user when it's time to water.

[0167] Example prompt for a generative AI model:

[0168] "Based on this humidity data, calculate the optimal watering timing for your plants."

[0169] Detecting diseases and pests and proposing remedial measures

[0170] The device (a device with a camera) takes detailed images of plant leaves and stems and sends them to a server. The server then inputs these images into a generative AI model to detect signs of disease or pests. Based on the detection results, it generates remedial measures (e.g., the use of specific pesticides or changing the type of fertilizer) and notifies the user via a smartphone app.

[0171] Examples:

[0172] The camera-equipped device captures detailed images of plant leaves and sends them to a server, where they are analyzed by a generative AI model to detect signs of powdery mildew, suggesting the use of specific fungicides as a treatment, and notifying the user via a smartphone app.

[0173] Example prompt for a generative AI model:

[0174] "Analyze images of this plant to detect signs of disease and pests and suggest ways to improve it."

[0175] Automatic irrigation and real-time monitoring

[0176] The terminal (automatic irrigation system) automatically irrigates plants based on soil moisture data. It periodically sends real-time images to a server, which then provides the images to users via a smartphone app. This allows users to check the status of their plants in real time, even from remote locations.

[0177] Examples:

[0178] The automatic irrigation system receives humidity data and automatically waters plants when the humidity falls below a specified level, while simultaneously sending the latest real-time plant images to a server for users to view via a smartphone app.

[0179] Example prompt for a generative AI model:

[0180] "Automate irrigation based on this humidity data and provide real-time images to the user."

[0181] Related product suggestions and purchasing support

[0182] The server then suggests relevant products based on the plant's type, growth status, soil condition, and environmental data. The suggestions are sent to the user via a smartphone app, and the user can easily purchase the products via the provided link.

[0183] Examples:

[0184] The server analyzes the plant's growth and determines if it needs specific fertilizer, and then sends a fertilizer recommendation via a smartphone app along with a link to purchase it.

[0185] Example prompt for a generative AI model:

[0186] "Analyze the type of plant and its growth status to suggest relevant products suitable for the user."

[0187] In this way, the system of the present invention allows users to manage their houseplants efficiently and effectively.

[0188] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0189] Step 1: Image capture

[0190] The device (device with camera) periodically takes images of the houseplant. The user first sets up the device's camera in an appropriate position around the plant. The camera then automatically takes images at the set interval.

[0191] Input: Visual information of houseplants

[0192] Output: Image data of houseplants

[0193] Step 2: Send image

[0194] The device sends the captured image to the server, at high resolution and without data compression.

[0195] Input: Image data of houseplants

[0196] Output: Image data sent to the server

[0197] Step 3: Image analysis

[0198] The server inputs the received images into a generative AI model to identify the plant type, and then refers to a database of plant characteristics to generate growth predictions and advice on the best location, temperature, light conditions, pot size, and more.

[0199] Input: Image data of houseplants

[0200] Data processing: Plant species identification and growth prediction using generative AI models

[0201] Output: Advice information (optimal location, temperature, light conditions, pot size)

[0202] Step 4: Advice Notification

[0203] The server sends the generated advice to the smartphone app and notifies the user.

[0204] Input: Advice information

[0205] Output: Advice information sent to the smartphone app

[0206] Step 5: Environmental data measurement

[0207] The terminal (a device equipped with a humidity sensor and air concentration meter) periodically measures the humidity of the soil and the concentration of components in the air.

[0208] Input: Environmental data in soil and air

[0209] Output: Measured humidity data and air composition data

[0210] Step 6: Send data

[0211] The terminal transmits the measured data to the server in real time.

[0212] Input: Humidity data and air composition data

[0213] Output: Environment data sent to the server

[0214] Step 7: Data analysis

[0215] The server inputs the received environmental data into a generative AI model for analysis, and calculates the appropriate watering timing based on the plant type and growth stage.

[0216] Input: Environmental data

[0217] Data processing: Data analysis and irrigation timing calculations using generative AI models

[0218] Output: Watering timing information

[0219] Step 8: Watering Notification

[0220] The server sends the calculation results to a smartphone app and notifies the user.

[0221] Input: Watering timing information

[0222] Output: Watering timing information notified to the smartphone app

[0223] Step 9: Take detailed photos

[0224] The device (a device with a camera) takes detailed images of the plant's leaves and stems.

[0225] Input: Visual information of leaves and stems

[0226] Output: Detailed image data

[0227] Step 10: Send detailed images

[0228] The terminal transmits the captured detailed image to the server.

[0229] Input: Detailed image data

[0230] Output: Detailed image data sent to the server

[0231] Step 11: Image analysis

[0232] The server inputs the received images into a generative AI model to detect signs of disease or pests.

[0233] Input: Detailed image data

[0234] Data processing: disease and pest detection with generative AI models

[0235] Output: Information on detection results and remediation methods

[0236] Step 12: Notification of improvement method

[0237] The server sends the generated improvement method to the smartphone app and notifies the user.

[0238] Input: Information on how to improve

[0239] Output: Information on how to improve the app

[0240] Step 13: Humidity Data Analysis

[0241] The terminal (automatic irrigation system) periodically analyzes soil moisture data.

[0242] Input: Soil moisture data

[0243] Data processing: Analysis of humidity data

[0244] Output: Determine the need for irrigation

[0245] Step 14: Automatic Watering

[0246] The device automatically irrigates based on humidity data, starting when humidity falls below a set threshold.

[0247] Input: Determining the need for irrigation

[0248] Output: Watering execution actions

[0249] Step 15: Real-time image transmission

[0250] The terminal periodically transmits real-time images to the server.

[0251] Input: Real-time image of plants

[0252] Output: Real-time image data sent to the server

[0253] Step 16: Real-time monitoring

[0254] The server receives real-time images and provides them to users via a smartphone app, allowing them to check the status of the plants.

[0255] Input: Real-time image data

[0256] Output: Real-time images provided to a smartphone app

[0257] Step 17: Generate related product suggestions

[0258] The server uses a generative AI model to suggest relevant products needed by the user based on the plant type, growth status, soil condition, and environmental data.

[0259] Input: Plant management data

[0260] Data processing: Generative AI models suggest related products

[0261] Output: A list of related products optimized for the user

[0262] Step 18: Notification of related product suggestions

[0263] The server sends the generated related product suggestions to the smartphone app and notifies the user, who can then easily purchase the products via a link in the app.

[0264] Input: List of related products

[0265] Output: List of related products and purchase links sent to the smartphone app

[0266] (Application example 1)

[0267] 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."

[0268] Traditional houseplant management often involves manually checking the health and growth of plants, which is laborious, time-consuming, and requires specialized knowledge. It can also be difficult to detect diseases and pests early or understand appropriate management methods, which can result in difficult plant development. Furthermore, while physical stores are required to provide accurate information to customers and efficiently recommend related products, it can be difficult for staff to always have the latest information and respond appropriately.

[0269] 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.

[0270] In this invention, the server includes: means for acquiring images of plants using a camera; means for analyzing the acquired images to determine the type of plant and making a growth prediction; sensor means for measuring soil humidity and airborne concentrations; means for analyzing the measurement data to calculate appropriate watering timing; means for detecting plant diseases and pests and generating remediation methods; means for notifying the user of the generated growth prediction, watering timing, and remediation methods; means for allowing store staff to wear smart glasses to check and manage the status of houseplants in real time; means for analyzing images and sensor data and displaying the results on the smart glasses display; and means for providing links to purchase suggested related products. This allows the status of houseplants to be managed efficiently, and allows store staff to provide appropriate advice and suggest related products to customers.

[0271] The "means for acquiring images of plants using a camera" is a combination of hardware and software that takes images of plants and transmits the information to a server.

[0272] "Means of analyzing acquired images to determine the type of plant and predict its growth" refers to technology that uses AI models and machine learning algorithms to analyze acquired images, identify the type of plant, and predict its future growth.

[0273] The "sensor means for measuring the humidity of the soil and the concentration of components in the air" refers to a sensor that measures the humidity of the soil and the concentration of components in the air, thereby collecting environmental data in which the plant is placed.

[0274] The "means for analyzing the measurement data and calculating the appropriate timing for irrigation" refers to an algorithm and program that analyzes the data obtained from the sensor and calculates the optimal timing for irrigation for the plants.

[0275] "Means for detecting plant diseases and pests and generating remedial measures" refers to technology that analyzes plant image data, detects signs of disease or pests, and suggests appropriate remedial measures based on that data.

[0276] The "means for notifying the user of the generated growth forecast, irrigation timing, and improvement methods" refers to an application and communication means for notifying the user of the analysis results and suggestions via a smart device, etc.

[0277] "A means for store staff to wear smart glasses to check and manage the status of houseplants in real time" refers to a technology that uses smart glasses to check and manage the current status of houseplants in real time.

[0278] "Means for analyzing images and sensor data and displaying the results on the smart glasses display" refers to a system that analyzes acquired images and sensor data using an AI model and displays the results on the smart glasses display.

[0279] The "means for providing links to purchase suggested related products" refers to a technology that provides users with links to related products based on the analysis results and suggestions, allowing them to easily purchase the products.

[0280] This invention is a comprehensive system for effectively managing houseplants, consisting of multiple elements including a camera, humidity sensor, air concentration meter, AI model, smart glasses, server, and smartphone app.

[0281] System configuration and program processing overview

[0282] 1. Plant image acquisition and analysis

[0283] The server uses a camera to capture images of the plant and analyzes the transmitted images to determine the plant's type. Specifically, this process uses an image recognition algorithm (using TensorFlow or PyTorch). Based on the analysis results, the server predicts the plant's growth and suggests the environmental conditions and care methods necessary for growth. This information is displayed on the smart glasses' display and provided to store staff.

[0284] 2. Environmental data acquisition and notification

[0285] The server periodically measures soil humidity and the concentration of airborne elements using humidity sensors and air concentration meters, and collects and analyzes the data. This provides environmental data on the plant's location and allows it to calculate the optimal watering timing. The results are then communicated to the user via the smart glasses' display or a smartphone app.

[0286] 3. Disease and pest detection and improvement method suggestions

[0287] The server uses a camera to capture detailed images of the plant's leaves and stems, which are then analyzed using an AI model to detect signs of disease or pests and generate remedial measures (such as the use of specific pesticides).These remedial measures are then displayed on the smart glasses' display and communicated to store staff.

[0288] 4. Automatic irrigation and real-time monitoring

[0289] The server includes a means for automatically watering plants based on data from the humidity sensor, and also captures real-time images of the plants and transmits them to the server, which analyzes the images and provides real-time information to the user, enabling plant monitoring even in remote locations.

[0290] 5. Related product suggestions and purchasing support

[0291] The server will suggest relevant products (pots, fertilizer, pest control products, etc.) that users need based on the plant type, growth status, soil condition, and environmental data. These suggestions will be displayed on the smart glasses display or smartphone app, and users can easily purchase the products via the provided link.

[0292] Examples and prompts

[0293] Below are some examples and prompts:

[0294] Example 1: Growth forecasts and advice

[0295] The server analyzes images of plants taken with the camera-equipped smart glasses, determines that the plant is a cactus, and displays advice on the smart glasses' display regarding appropriate placement and lighting conditions based on the results of growth predictions.

[0296] Example 2: Watering notification

[0297] The server checks that the soil humidity measured by the humidity sensor is 30% and calculates the optimum watering time for the succulents. When it is time to water, a notification will appear on the smart glasses display.

[0298] Example 3: Disease detection and improvement suggestions

[0299] The server detects signs of powdery mildew from detailed images of plant leaves taken by camera-equipped smart glasses, and notifies the user on the glasses' display to recommend the use of specific fungicides as a countermeasure.

[0300] Prompt Sentence Examples

[0301] "Upload an image of your houseplant and we'll identify the plant's type, its health, and how to properly care for it."

[0302] Houseplant image: [Upload form]

[0303] Humidity data: [Acquired in real time]

[0304] Air concentration data: [Acquired in real time]

[0305] result:

[0306] Plant Type: Cactus

[0307] Health: Good

[0308] Watering timing: Next time in 3 days

[0309] Related Products: [Link]

[0310] This will clarify the specific treatment and usage methods, allowing for efficient management and sales support of houseplants in physical stores.

[0311] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0312] Step 1:

[0313] A camera-equipped device takes pictures of plants and sends them to a server. Specifically, the device's camera periodically takes photos of plants and uploads the data to the server via wireless communication (Wi-Fi or Bluetooth). The input is the plant image, and the output is the image data sent to the server.

[0314] Step 2:

[0315] The server analyzes the image data it receives using an AI model to determine the type of plant. Specifically, the AI ​​model (using TensorFlow and PyTorch) analyzes the image and performs feature extraction and classification. The input is the image data of the plant, and the output is the type of plant.

[0316] Step 3:

[0317] The server predicts growth based on the type of plant and calculates the necessary environmental conditions and management methods. Specifically, a growth prediction algorithm is used to calculate the optimal growth conditions (light intensity, temperature, humidity, etc.) based on the type of plant and past data. The input is the type of plant, and the output is growth prediction data.

[0318] Step 4:

[0319] Humidity sensors and air concentration meters periodically measure the humidity of the soil and the concentrations of components in the air, and send this data to a server. Specifically, the sensors acquire data in real time and transmit it to the server via wireless communication. The input is the sensor measurement data, and the output is the environmental data sent to the server.

[0320] Step 5:

[0321] The server analyzes the received environmental data and calculates the appropriate watering timing. Specifically, the watering algorithm determines the watering timing by taking into account the sensor data, plant type, and growth stage. The inputs are environmental data, plant type, and growth prediction data, and the output is the watering timing.

[0322] Step 6:

[0323] A camera-equipped device takes detailed images of plants and sends them to a server. Specifically, the device's camera takes close-up images of plant leaves and stems and sends the image data to the server. The input is the detailed plant image, and the output is the image data sent to the server.

[0324] Step 7:

[0325] The server analyzes detailed image data using an AI model to detect signs of disease or pests. Specifically, the AI ​​model uses image analysis technology to identify the characteristics of disease or pests and generates remediation methods based on the findings. The input is detailed image data, and the output is the disease or pest detection results and remediation methods.

[0326] Step 8:

[0327] The server displays the growth prediction, irrigation timing, and improvement methods on the smart glasses display. Specifically, the server sends the analysis results to the smart glasses application and notifies the user in real time. The input is the growth prediction data, irrigation timing, and improvement methods, and the output is the information displayed on the smart glasses display.

[0328] Step 9:

[0329] The server generates and suggests a list of related products based on the plant's type and growth status. Specifically, it references a database to create a list of related products suitable for the plant (pots, fertilizer, pest control products, etc.), which are then displayed on the smart glasses or smartphone app. The input is data on the plant's type and growth status, and the output is a list of related products.

[0330] Step 10:

[0331] The server provides users with purchase links for related products, allowing them to easily make purchases. Specifically, it generates online shop links and provides them to users via smart glasses or a smartphone app. The input is a list of related products, and the output is the purchase links.

[0332] This will create a system that efficiently manages the condition of houseplants and allows staff to provide customers with appropriate advice and suggest related products.

[0333] 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.

[0334] This invention combines a comprehensive support system for optimal houseplant care with an emotion engine. By combining multiple elements, including a camera, humidity sensor, air concentration meter, AI model, emotion engine, and smartphone app, the system monitors houseplant growth, health, watering timing, disease and pest detection, suggests related products, and even recognizes the user's emotional state to provide customized advice.

[0335] System Configuration

[0336] The system includes the following components:

[0337] Camera-equipped device: Take regular pictures of your houseplants.

[0338] Terminal equipped with humidity sensor and air concentration meter: Measures the humidity of the soil and the concentration of components in the air.

[0339] Server: Analyzes collected data to determine plant species, predict growth, calculate irrigation timing, detect diseases and pests, generate improvement methods, analyze sentiment data, generate customized advice, and suggest related products.

[0340] Emotion Engine: Recognize and analyze user emotions.

[0341] Smartphone app: Provides an interface for users to receive notifications and suggestions from the server and manage their houseplants.

[0342] Program processing

[0343] The specific program processing of the system is shown below.

[0344] 1. Plant image acquisition and analysis

[0345] The device (camera-equipped device) periodically takes photos of the houseplant and sends them to the server. The server analyzes the received images using an AI model to identify the plant's type. It also references a database of plant characteristics to predict its growth. This prediction generates advice on the optimal location, temperature, light conditions, and pot size. The server then notifies the user of this advice via a smartphone app.

[0346] 2. Environmental data acquisition and notification

[0347] The terminal (a device equipped with a humidity sensor and air concentration meter) periodically measures the humidity of the soil and the concentration of elements in the air, and sends the data to a server. The server analyzes this data and calculates the appropriate watering timing based on the type of plant and its growth stage. The calculation results are notified to the user via a smartphone app.

[0348] 3. Disease and pest detection and improvement method suggestions

[0349] The device (a device with a camera) takes detailed images of plant leaves and stems and sends them to a server. The server then analyzes these images using an AI model to detect signs of disease or pests. Based on the detection results, it generates remedial measures (e.g., the use of specific pesticides or changing the type of fertilizer) and notifies the user via a smartphone app.

[0350] 4. Automatic irrigation and real-time monitoring

[0351] The terminal (automatic irrigation system) automatically irrigates based on soil moisture data. The terminal also periodically sends real-time images to a server, which then provides the images to the user via a smartphone app. This allows users to check the condition of their plants in real time, even from remote locations.

[0352] 5. Related product suggestions and purchasing support

[0353] The server collects information on the plant's type, growth status, soil condition, and environmental data. Based on the collected data, it generates a list of the most suitable related products (pots, fertilizer, pest control products, etc.) and notifies the user of the list via a smartphone app. Users can easily purchase the products via links within the app.

[0354] 6. Customized advice using emotion engine

[0355] The device (smartphone app) collects emotional data from the user's social media posts and daily usage logs and sends it to a server. The server then uses an emotion engine to analyze the user's emotional state and generate optimal advice based on the user's mood and stress level. For example, if the user is feeling stressed, the server can suggest the characteristics and care methods of houseplants that have a soothing effect. The server can also accumulate the user's emotional data and reflect it in future advice.

[0356] Specific examples

[0357] Specific examples are shown below.

[0358] Example 1: Emotion-based growth advice

[0359] The device (camera-equipped device) takes a photo of the plant and sends it to the server. The server analyzes the image and determines that the plant is a ficus. It predicts its growth and generates advice on the appropriate placement and lighting conditions. It also collects emotional data from the user's smartphone app and detects whether the user is in a relaxed mood. Based on this, advice emphasizing the relaxing effects and characteristics of ficus is sent via the smartphone app.

[0360] Example 2: Watering notification and sentiment analysis

[0361] The device (humidity sensor) measures the soil humidity to be 25% and sends this to the server. The server, considering that the plant is a sansevieria, determines that it needs watering. It notifies the user via a smartphone app when it is time to water it. At the same time, the server, through its emotion engine, detects that the user is feeling busy and provides advice on simple watering methods or the use of an automatic watering system.

[0362] Example 3: Disease detection and customised improvement suggestions

[0363] The device (with a camera) takes detailed images of plant leaves and sends them to the server. The server analyzes the images and detects signs of black spot disease on the leaves. The server then suggests the use of a specific fungicide as a countermeasure. Furthermore, the server detects the user's anxiety through an emotion engine and sends reassuring information, including detailed instructions for disease prevention and success stories, via a smartphone app.

[0364] In this way, the system of the present invention, which is combined with an emotion engine, can provide individual advice according to the user's emotional state, making houseplant management more effective and user-friendly.

[0365] The processing flow will be explained below.

[0366] 1. Plant image acquisition and analysis

[0367] Step 1:

[0368] The terminal (device with a camera) takes images of the houseplant at set time intervals.

[0369] Step 2:

[0370] The terminal transmits the captured image to the server.

[0371] Step 3:

[0372] The server inputs the received images into an AI model to identify the type of plant.

[0373] Step 4:

[0374] The server makes a growth prediction based on the identified type by referring to a characteristics database.

[0375] Step 5:

[0376] The server generates advice based on growth prediction results, such as the optimal location, temperature, light conditions, and pot size.

[0377] Step 6:

[0378] The server sends the generated advice to the smartphone app.

[0379] Step 7:

[0380] Users can check the advice through a smartphone app and manage their belongings appropriately.

[0381] 2. Environmental data acquisition and notification

[0382] Step 1:

[0383] The terminal (a device equipped with a humidity sensor and air concentration meter) periodically measures the humidity of the soil and the concentration of components in the air.

[0384] Step 2:

[0385] The terminal transmits the measured environmental data to the server.

[0386] Step 3:

[0387] The server analyzes the received data and calculates the optimal watering timing based on the plant type and growth stage.

[0388] Step 4:

[0389] The server notifies the smartphone app of the calculation results.

[0390] Step 5:

[0391] Users can check the watering timing through a smartphone app and either water appropriately or set the watering to be automated.

[0392] 3. Disease and pest detection and improvement method suggestions

[0393] Step 1:

[0394] The device (camera-equipped device) periodically takes detailed images of the plant's leaves and stems.

[0395] Step 2:

[0396] The terminal transmits the captured detailed image to the server.

[0397] Step 3:

[0398] The server uses AI models to analyze the images and detect signs of disease or pests.

[0399] Step 4:

[0400] The server generates remedial measures (e.g., use of specific chemicals or changes in fertilizer) based on the detection results.

[0401] Step 5:

[0402] The server notifies the smartphone app how to improve the situation.

[0403] Step 6:

[0404] Users can check the notification via their smartphone app and take appropriate measures.

[0405] 4. Automatic irrigation and real-time monitoring

[0406] Step 1:

[0407] The terminal (humidity sensor and automatic irrigation system) continuously monitors the soil moisture.

[0408] Step 2:

[0409] The server determines the need for watering based on the humidity data.

[0410] Step 3:

[0411] If the server determines that watering is necessary, it sends a watering instruction to the terminal (automatic watering system).

[0412] Step 4:

[0413] The terminal supplies the set amount of water to the plants according to the watering instructions.

[0414] Step 5:

[0415] The terminal periodically takes real-time images and transmits them to the server.

[0416] Step 6:

[0417] The server sends real-time images to a smartphone app.

[0418] Step 7:

[0419] Users can check the status of their plants in real time from a remote location via a smartphone app.

[0420] 5. Related product suggestions and purchasing support

[0421] Step 1:

[0422] The server collects plant type, growth status, soil conditions, and environmental data.

[0423] Step 2:

[0424] The server generates a list of the most suitable related products (pots, fertilizer, pest control products, etc.) based on the collected data.

[0425] Step 3:

[0426] The server notifies the smartphone app of the generated related product list.

[0427] Step 4:

[0428] Users can check the suggested products through the smartphone app and, if necessary, purchase the products via a link to an e-commerce site.

[0429] 6. Customized advice using emotion engine

[0430] Step 1:

[0431] The device (smartphone app) collects emotional data from users' social media posts and daily usage logs.

[0432] Step 2:

[0433] The terminal transmits the emotion data to the server.

[0434] Step 3:

[0435] The server uses an emotion engine to analyze the user's emotional state.

[0436] Step 4:

[0437] The server generates customized advice based on the emotional state.

[0438] Step 5:

[0439] The server then sends customized advice to the smartphone app.

[0440] Step 6:

[0441] Users can check the advice through a smartphone app and manage their belongings appropriately.

[0442] Specific examples

[0443] Example 1: Emotion-based growth advice

[0444] Step 1:

[0445] The device (camera-equipped device) takes a photo of the plant and sends it to the server.

[0446] Step 2:

[0447] The server analyzes the image and determines that the plant is a ficus.

[0448] Step 3:

[0449] The server makes growth predictions and generates advice on suitable placement and lighting conditions.

[0450] Step 4:

[0451] The device (smartphone app) collects the user's emotional data and sends it to the server.

[0452] Step 5:

[0453] The server detects that the user is in a relaxed mood.

[0454] Step 6:

[0455] The server generates advice that emphasizes the relaxing effects and notifies the smartphone app.

[0456] Step 7:

[0457] Users can check advice through a smartphone app and carry out plant management.

[0458] Example 2: Watering notification and sentiment analysis

[0459] Step 1:

[0460] The terminal (humidity sensor) measures the soil humidity to be 25% and sends it to the server.

[0461] Step 2:

[0462] The server considers the plant to be a sansevieria and determines that it needs watering.

[0463] Step 3:

[0464] The server notifies the smartphone app when it is time to water the plants.

[0465] Step 4:

[0466] The device (smartphone app) collects the user's emotional data and sends it to the server.

[0467] Step 5:

[0468] The server detects that the user is in a busy mood.

[0469] Step 6:

[0470] The server generates advice recommending simple watering methods and the use of automatic watering systems, and notifies the smartphone app.

[0471] Step 7:

[0472] Users can check the advice via a smartphone app and carry out or set up irrigation.

[0473] Example 3: Disease detection and customization of remediation suggestions

[0474] Step 1:

[0475] The terminal (device with a camera) takes detailed images of plant leaves and sends them to the server.

[0476] Step 2:

[0477] The server analyzes the images and detects signs of black spot disease on the leaves.

[0478] Step 3:

[0479] The server suggests using certain disinfectants as a countermeasure.

[0480] Step 4:

[0481] The device (smartphone app) collects the user's emotional data and sends it to the server.

[0482] Step 5:

[0483] The server detects that the user is feeling anxious.

[0484] Step 6:

[0485] The server generates reassuring information, including detailed procedures for disease prevention and success stories, and sends it to the smartphone app.

[0486] Step 7:

[0487] Users can check the advice through a smartphone app and implement measures.

[0488] Example 2

[0489] 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."

[0490] While existing houseplant management systems provide basic management functions such as plant health and growth prediction, watering timing, and disease and pest detection, they lack the ability to provide customized advice based on the user's emotional state. Furthermore, they lack the functionality to suggest related products, provide real-time remote monitoring, and provide watering advice that takes emotional state into account. This makes it difficult to optimize management for each individual user, and there is a need for an improved user experience.

[0491] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring plant images using a camera, means for analyzing the acquired images to determine the plant's type and perform growth prediction, sensor means for measuring soil humidity and airborne concentrations, means for analyzing the measurement data to calculate appropriate watering timing, means for detecting plant diseases and pests and generating remedial measures, means for notifying the user of the generated growth prediction, watering timing, and remedial measures, means for generating a list of related products and suggesting them to the user, and means for analyzing the user's emotional state and generating customized advice. This allows users to receive optimal plant care advice tailored to their individual emotional state, enabling effective and user-friendly houseplant care.

[0492] The "means for acquiring images of plants using a camera" refers to a camera device for taking images of plants and a control system for automating its operation.

[0493] "Means for analyzing acquired images to determine the type of plant and predict growth" refers to a system that uses AI models and algorithms to identify the type of plant from image data and predict growth patterns.

[0494] The "sensor means for measuring soil moisture and airborne concentrations" refers to a soil moisture sensor and a sensor device for measuring airborne components (e.g., CO2 concentration).

[0495] The "means for analyzing measurement data and calculating appropriate irrigation timing" is a system that calculates the timing for irrigation based on data obtained from sensors, taking into account the type of plant and its growth stage.

[0496] The "means for detecting plant diseases and pests and generating remedial measures" is a system that uses AI models and image analysis technology to detect signs of plant diseases and pests and then suggests appropriate remedial measures based on that information.

[0497] "Means for notifying the user of the generated growth forecast, irrigation timing, and improvement methods" refers to a communication and interface system for notifying the user of the generated information via their smartphone or other device.

[0498] The "means for generating a list of related products and suggesting them to the user" is a system for generating a list of related products based on the plant's condition and environmental data and suggesting the list to the user.

[0499] The "means for analyzing the user's emotional state and generating customized advice" refers to an emotion analysis engine and generation system for analyzing the user's emotional data and providing individually customized advice based on the results.

[0500] "Means for obtaining real-time images of plants using a camera and providing them to a remote user" is a system for distributing real-time plant images taken by a camera to a remote user.

[0501] The "means for automatically performing irrigation based on data from a humidity sensor" is an automatic irrigation system for automatically performing irrigation based on data from a humidity sensor.

[0502] The "means for proposing a watering method based on the user's emotional state" is a system that takes into account the user's emotional data and proposes the optimal watering method depending on the situation.

[0503] The "means for providing links to purchase suggested related products" is a system that provides users with links to purchase suggested related products online.

[0504] The "means for customizing the content of related product suggestions according to the user's emotional state" is a system that individually customizes the content of related product suggestions based on the user's emotional data.

[0505] This invention combines a comprehensive support system for optimal houseplant care with an emotion engine that understands the user's emotional state and provides customized advice accordingly. The system's main components are a camera, a humidity sensor, an air concentration meter, an AI model, an emotion engine, and a smartphone app.

[0506] System configuration

[0507] 1. Camera-equipped device: Takes pictures of the houseplant periodically and sends them to the server.

[0508] 2. Terminal with humidity sensor and air concentration meter: Measures the humidity of the soil and the concentration of components in the air and sends the data to the server.

[0509] 3. Server: Receives and analyzes images and sensor data to determine plant species, predict growth, calculate irrigation timing, detect diseases and pests, generate improvement methods, analyze emotional data, generate customized advice, and suggest related products.

[0510] 4. Emotion engine: Recognizes and analyzes user emotions from social media posts and usage logs.

[0511] 5. Smartphone app: Provides an interface for users to receive notifications and suggestions from the server and manage their houseplants.

[0512] System Operation

[0513] Plant image acquisition and analysis

[0514] The device periodically takes pictures of the houseplant using a camera and sends them to a server. The server then analyzes the received images using an AI model to determine the plant's type. The growth management system also references a characteristics database to predict the plant's growth. The generated advice includes detailed information such as lighting conditions, optimal placement, and pot size. This advice is then sent to the user via a smartphone app.

[0515] Example: The device takes a picture of a ficus, and the server predicts its growth and advises that the best place to place it is near a window in the living room.

[0516] Example prompt: "What growth predictions can you give me for the ficus and what are the best locations and temperature conditions?"

[0517] Environmental data acquisition and notification

[0518] The terminal (a device equipped with a humidity sensor and air concentration meter) periodically measures the humidity of the soil and the concentration of components in the air, and sends the data to a server. The server receives the data and calculates the appropriate timing for watering. The results are then notified to the user via a smartphone app.

[0519] Example: The soil moisture is measured at 25%, the server determines that watering is required, and sends a notification saying "Water now."

[0520] Example prompt: "When soil moisture is 25%, should I irrigate?"

[0521] Detecting diseases and pests and proposing remedial measures

[0522] The device (a device with a camera) takes detailed images of plant leaves and stems and sends them to a server. The server then analyzes the images using AI models to detect signs of disease or pests. Based on the results, it generates improvement measures, such as the use of specific pesticides or changes to fertilizer, and notifies the user via a smartphone app.

[0523] Example: Detecting black spot on leaves and suggesting the use of a fungicide.

[0524] Example prompt: "If the plant has black spot on its leaves, what fungicide should I use?"

[0525] Automatic irrigation and real-time monitoring

[0526] The terminal (automatic irrigation system) automatically irrigates plants based on soil moisture data. The terminal also periodically sends real-time images to a server, which then provides the images to users via a smartphone app. This allows users to check the status of their plants even from remote locations.

[0527] Example: An automatic irrigation system irrigates when soil moisture falls below 20%.

[0528] Example prompt: "Write a scenario in which an automated irrigation system would operate if soil moisture dropped."

[0529] Related product suggestions and purchasing support

[0530] The server analyzes the plant's type, growth status, soil condition, and environmental data to generate a list of related products, which are then sent to the user via a smartphone app, where they can conveniently purchase the products via a link in the app.

[0531] Example: The server recommends the best fertilizer, and the user purchases it through the app.

[0532] Example prompt: "Please suggest the best fertilizer for houseplants and generate a link to purchase it."

[0533] Customized advice with an emotional engine

[0534] The device (smartphone app) collects emotional data from users' social media posts and daily usage logs, and sends it to a server. The server then uses an emotion engine to analyze the user's emotional state and generate optimal advice based on their mood and stress level.

[0535] Example: If a user is feeling stressed, the emotion engine suggests soothing plants and care methods.

[0536] Example prompt: "What are the properties of houseplants that are effective in relieving stress and how should you care for them?"

[0537] In this way, the system of the present invention can effectively and user-friendly manage houseplants, and can also improve the user experience by analyzing the user's emotions and providing customized advice based on those emotions.

[0538] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0539] Step 1:

[0540] Acquiring and sending plant images

[0541] The device periodically acquires images of the houseplant. The device's camera takes a photo of the plant at a specified time (e.g., 10:00 AM every day) and sends the image data to the server. The input is the image data, and the output is transmission to the server.

[0542] Step 2:

[0543] Receiving and analyzing images

[0544] The server receives the images sent from the device. The received image data is input into the AI ​​model to determine the plant's type. It then references a characteristics database to predict growth. This allows it to predict the plant's growth pattern and calculate the appropriate placement and environmental conditions. The input is image data, and the output is the plant's type determination result and growth prediction data.

[0545] Step 3:

[0546] Advice generation and notification

[0547] The server generates advice for the user based on the growth prediction results. For example, specific advice such as "The best place to place a ficus is by a window in the living room" is generated for the growth prediction of the ficus. The generated advice is notified to the user via a smartphone app. The input is the growth prediction data, and the output is the generated advice.

[0548] Step 4:

[0549] Acquiring and sending environmental data

[0550] The terminal (a device with a humidity sensor and air concentration meter) periodically measures the humidity of the soil and the concentration of components in the air and sends the data to the server. For example, it measures and sends the humidity (e.g., 25%) and carbon dioxide concentration in the air (e.g., 300 ppm) every hour. The input is environmental sensor data, and the output is sent to the server.

[0551] Step 5:

[0552] Analyzing environmental data and calculating irrigation timing

[0553] The server analyzes the environmental data sent from the device. Taking into account the type of plant and its growth stage, it calculates when watering is necessary. For example, if the humidity is 25%, it will determine that a sansevieria needs watering. The results of this calculation are notified to the user via a smartphone app. The input is environmental sensor data, and the output is a notification of when to water.

[0554] Step 6:

[0555] Shooting and transmitting diseases and pests

[0556] The terminal (device with a camera) takes detailed images of plant leaves and stems and sends them to a server. For example, high-resolution images are taken to detect signs of black spot disease on leaves. The input is the detailed image data, and the output is transmission to the server.

[0557] Step 7:

[0558] Disease and pest detection and remediation methods

[0559] The server uses an AI model to analyze detailed images sent from the device and detect signs of disease or pests. Based on the detection results, it generates improvement measures, such as the use of specific pesticides or changes to fertilizer. For example, if black spot disease is detected, it will suggest the use of an appropriate fungicide. The input is detailed image data, and the output is improvement measure suggestions.

[0560] Step 8:

[0561] Automatic irrigation

[0562] The terminal (automatic irrigation system) automatically irrigates based on soil moisture data. For example, irrigation is automatically performed when the humidity falls below 20%. The input is humidity data, and the output is irrigation execution.

[0563] Step 9:

[0564] Real-time image acquisition and transmission

[0565] The device periodically takes real-time images of the plants and sends them to the server. For example, it acquires and sends the latest plant images every hour. The input is real-time image data, and the output is sent to the server.

[0566] Step 10:

[0567] Related product suggestions and notifications

[0568] The server generates a list of related products based on the plant type, growth status, soil condition, and environmental data. This list is then notified to the user. For example, "organic fertilizer B" and "insect repellent spray C" are recommended for a ficus. The input is various sensor data and a plant database, and the output is a list of related products.

[0569] Step 11:

[0570] Collecting and transmitting emotional data

[0571] The device (smartphone app) collects emotional data from users' social media posts and daily usage logs, and sends it to a server. For example, the keyword "stress" is collected from users' social media posts. The input is emotional data, and the output is transmission to the server.

[0572] Step 12:

[0573] Emotional state analysis and customized advice generation

[0574] The server analyzes the emotional data using an emotion engine and generates optimal advice based on the user's emotional state. For example, if the user is feeling stressed, it will suggest plants that have a relaxing effect and how to care for them. The input is emotional data, and the output is customized advice.

[0575] Thus, through each processing step, a specific embodiment of the invention is realized.

[0576] (Application example 2)

[0577] 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."

[0578] When caring for houseplants, maintaining optimal plant growth and health requires consideration of various factors, such as appropriate watering timing and disease and pest control. Furthermore, providing effective advice based on the user's emotional state would provide a more personalized experience. However, until now, there has been no comprehensive management system that can effectively and easily solve these challenges.

[0579] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0580] In this invention, the server includes means for capturing images of plants using a camera, means for analyzing the captured images to determine the type of plant and making a growth prediction, sensor means for measuring soil humidity and its concentration in the air, means for analyzing the measurement data to calculate appropriate watering timing, means for detecting plant diseases and pests and generating remedial measures, means for notifying the user of the generated growth prediction, watering timing, and remedial measures, means for generating a list of related products and suggesting them to the user, and means for recognizing the user's emotional state and generating customized advice, thereby enabling the user to receive comprehensive support for optimal care of their houseplants.

[0581] "Camera means" refers to a device for capturing images of plants.

[0582] The "plant type determination means" is a device or method that analyzes the acquired image to determine the type of plant.

[0583] A "growth prediction means" is a device or method that predicts future growth based on plant type.

[0584] "Sensor means" refers to a device for measuring the moisture content of soil and the concentration in the air.

[0585] The "watering timing calculation means" is a device or method that analyzes measurement data and calculates the appropriate watering timing.

[0586] "Disease and pest detection means" means a device or method for detecting plant diseases or pests.

[0587] The "remediation method generating means" is a device or method that generates a remediation method for a detected disease or pest.

[0588] "Notification means" refers to a device or method that notifies the user of the generated growth forecast, irrigation timing, and improvement methods.

[0589] The "related product suggestion means" is a device or method that generates and suggests a list of related products that are most suitable for the user.

[0590] An "emotion recognizer" is a device or method that recognizes the emotional state of a user.

[0591] A "customized advice generator" is a device or method that generates customized advice based on a recognized emotional state of a user.

[0592] The system embodying this invention provides comprehensive support for users to optimally manage their houseplants, and specifically comprises a camera means, a sensor means, a server, a notification means, and an application.

[0593] First, each component of the system will be described.

[0594] Camera Means

[0595] The system acquires images of the plants by means of a camera, which periodically takes high-resolution images to monitor the plant's growth status and detect diseases and pests.

[0596] Sensor means

[0597] The sensor means measures the humidity of the soil, the carbon dioxide concentration in the air, the temperature, and the amount of light. Examples of such sensors include humidity sensors, air concentration meters, and light sensors.

[0598] server

[0599] The server receives and analyzes data acquired from the camera and sensor means. The server is equipped with an AI model and emotion engine, and performs the following processes:

[0600] Image analysis:

[0601] The AI ​​model analyzes the images of plants captured by the camera and determines the plant's type. After determining the type, it refers to a characteristics database and predicts its growth.

[0602] Environmental Data Analysis:

[0603] Data from the sensor means is analyzed to calculate the timing of irrigation based on soil moisture and carbon dioxide levels in the air.

[0604] Disease and pest detection:

[0605] Based on image data, an AI model is used to identify signs of plant disease and pests and generate improvement methods.

[0606] Sentiment Data Analysis:

[0607] It uses an emotion engine to recognize users' emotions, analyzes their social media posts and smartphone app usage logs, and generates optimal advice based on their emotional state.

[0608] Notification means

[0609] The system provides users with growth forecasts, watering timing, improvement methods, and customized advice based on the user's emotional state via a smartphone app or smart glasses.

[0610] Application Overview

[0611] The system's application provides information to users through smartphones or smart glasses.

[0612] 1. Houseplant status notification:

[0613] Displays real-time information on plant health, growth status, irrigation timing, and pest and disease information.

[0614] 2. Emotion-based personalized advice:

[0615] The emotion engine analyzes the user's emotions and provides special advice and related product suggestions based on that.

[0616] 3. Interactive storefront:

[0617] Augmented reality (AR) technology is used to visually display plant characteristics and health status.

[0618] Specific examples

[0619] Example 1:

[0620] When users scan their houseplant with the smart glasses, they are given real-time information about the plant's type, health, and growth forecast, and if they're in the mood to relax, they're given suggestions on how to care for the plant to help them relax.

[0621] Example prompt sentence:

[0622] "Scan your houseplants with smart glasses and display optimal recommendations based on plant type, health, watering timing, and user sentiment."

[0623] In this way, the invention allows users to provide more effective and personalized houseplant care.

[0624] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0625] Step 1:

[0626] The server periodically acquires images of the houseplants from the camera means.

[0627] Input: An image of a plant taken by camera means.

[0628] Data processing and calculation: The server uses an AI model to analyze the image and identify the plant species. It then references a database of plant characteristics to predict growth.

[0629] Output: Plant type and growth prediction data based on it.

[0630] Specific operation: The server runs an image processing algorithm to analyze the shape and color of the plant leaves.

[0631] Step 2:

[0632] The terminal acquires soil humidity data and air component concentration data from a device equipped with a humidity sensor and air concentration meter.

[0633] Input: Soil moisture data and air element concentration data.

[0634] Data processing and calculation: The server analyzes this data and calculates the appropriate watering timing based on the type of plant and its growth stage.

[0635] Output: Watering notification data.

[0636] Specific operation: The server compares the humidity data with the plant's humidity requirements and calculates the amount of water needed.

[0637] Step 3:

[0638] The device captures detailed images of plant leaves and stems and sends them to the server.

[0639] Input: Images of plant leaves and stems.

[0640] Data processing and calculation: The server analyzes the images and uses AI models to detect signs of disease or pests.

[0641] Output: Disease and pest detection results and how to remedy them.

[0642] Specific operation: The server extracts features from the image and identifies parts that match characteristic data of diseases and pests.

[0643] Step 4:

[0644] The server obtains the user's emotional data from the smartphone app.

[0645] Input: User emotion data (e.g., social media posting data, smartphone app usage logs).

[0646] Data processing and calculation: The server uses an emotion engine to analyze the user's emotional state.

[0647] Output: User's emotional state data.

[0648] Specific operation: The server uses natural language processing technology to analyze emotions from social media post data.

[0649] Step 5:

[0650] The server generates customized advice based on the emotional state.

[0651] Input: User emotional state data.

[0652] Data processing and calculation: The server refers to a pre-configured advice database and selects advice that matches the user's emotions.

[0653] Output: Customized advice.

[0654] Specific operation: If the user is feeling stressed, the server selects the characteristics of plants that have a relaxing effect and how to care for them.

[0655] Step 6:

[0656] The server then sends the generated growth forecast, irrigation timing, improvement methods, and customized advice to the user via a smartphone app or smart glasses.

[0657] Input: growth forecasts, irrigation timing, remediation methods, customized advice.

[0658] Data calculation and processing: The server converts this information into a user-friendly format.

[0659] Output: Notification data to the user.

[0660] Specific behavior: The server provides real-time notifications through the user interface.

[0661] 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.

[0662] 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.

[0663] 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.

[0664] [Second embodiment]

[0665] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0666] 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.

[0667] 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).

[0668] 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.

[0669] 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.

[0670] 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).

[0671] 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. 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.

[0672] 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.

[0673] 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.

[0674] 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.

[0675] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0676] 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."

[0677] This invention relates to a system that comprehensively supports optimal houseplant care. This system combines multiple elements, including cameras, humidity sensors, air concentration meters, AI models, and smartphone apps, to monitor houseplant growth, health, watering timing, disease and pest detection, and recommend related products.

[0678] System Configuration

[0679] The system includes the following components:

[0680] Camera-equipped device: Take regular pictures of your houseplants.

[0681] Terminal equipped with humidity sensor and air concentration meter: Measures the humidity of the soil and the concentration of components in the air.

[0682] Server: Analyzes collected data and generates plant type determinations, growth forecasts, irrigation timing calculations, disease and pest detection, and remediation methods.

[0683] Smartphone app: Provides an interface for users to receive notifications and suggestions from the server and manage their houseplants.

[0684] Program processing

[0685] The specific program processing of the system is shown below.

[0686] 1. Plant image acquisition and analysis

[0687] The device (camera-equipped device) periodically takes photos of the houseplant and sends them to the server. The server analyzes the received images using an AI model to identify the plant's type. It then references a database of plant characteristics to predict its growth. This prediction generates advice on the optimal placement, temperature, light conditions, and pot size. The server then notifies the user of this advice via a smartphone app.

[0688] 2. Environmental data acquisition and notification

[0689] The terminal (a device equipped with a humidity sensor and air concentration meter) periodically measures the soil humidity and the concentration of elements in the air and sends the data to a server. The server analyzes this data and calculates the appropriate watering timing based on the plant type and growth stage. The calculation results are notified to the user via a smartphone app.

[0690] 3. Disease and pest detection and improvement method suggestions

[0691] The device (a device with a camera) takes detailed images of plant leaves and stems and sends them to a server. The server then analyzes these images using an AI model to detect signs of disease or pests. Based on the detection results, it generates remedial measures (e.g., the use of specific pesticides or changing the type of fertilizer) and notifies the user via a smartphone app.

[0692] 4. Automatic irrigation and real-time monitoring

[0693] The terminal (automatic irrigation system) automatically irrigates plants based on soil moisture data. The terminal also periodically sends real-time images to a server, which then provides the images to users via a smartphone app. This allows users to check the status of their plants in real time, even from remote locations.

[0694] 5. Related product suggestions and purchasing support

[0695] The server suggests related products (pots, fertilizer, pest control products, etc.) that users need based on the plant type, growth status, soil condition, and environmental data. These suggestions are notified to users through a smartphone app, and users can easily purchase the products through links within the app.

[0696] Specific examples

[0697] Specific examples are shown below.

[0698] Example 1: Growth predictions and advice

[0699] The device (with a camera) takes a photo of the plant and sends it to the server. The server analyzes the image and determines that the plant is a cactus. The user is then notified via a smartphone app of the results of the growth prediction, the appropriate placement location, lighting conditions, and other information.

[0700] Example 2: Watering notification

[0701] The device (humidity sensor) measures the soil humidity to be 30% and sends this to the server. The server, taking into account that the plant is a succulent, determines that watering is necessary. The smartphone app notifies the user when it is time to water.

[0702] Example 3: Disease detection and improvement suggestions

[0703] The device (a device with a camera) takes detailed images of plant leaves and sends them to a server. The server analyzes the images and detects signs of powdery mildew on the leaves. The server then suggests the use of a specific fungicide as a countermeasure and notifies the user via a smartphone app.

[0704] In this way, the system of the present invention allows users to manage their houseplants efficiently and effectively.

[0705] The processing flow will be explained below.

[0706] 1. Plant image acquisition and analysis

[0707] Step 1:

[0708] The terminal (device with a camera) takes images of the houseplant at set time intervals.

[0709] Step 2:

[0710] The terminal transmits the captured image to the server.

[0711] Step 3:

[0712] The server inputs the received images into an AI model to identify the type of plant.

[0713] Step 4:

[0714] The server makes a growth prediction based on the identified type by referring to a characteristics database.

[0715] Step 5:

[0716] The server generates advice based on growth prediction results, such as the optimal location, temperature, light conditions, and pot size.

[0717] Step 6:

[0718] The server sends the generated advice to the smartphone app.

[0719] Step 7:

[0720] Users can check the advice through a smartphone app and manage their belongings appropriately.

[0721] 2. Environmental data acquisition and notification

[0722] Step 1:

[0723] The terminal (a device equipped with a humidity sensor and air concentration meter) periodically measures the humidity of the soil and the concentration of components in the air.

[0724] Step 2:

[0725] The terminal transmits the measured environmental data to the server.

[0726] Step 3:

[0727] The server analyzes the received data and calculates the optimal watering timing based on the plant type and growth stage.

[0728] Step 4:

[0729] The server notifies the smartphone app of the calculation results.

[0730] Step 5:

[0731] Users can check the watering timing through a smartphone app and either water appropriately or set the watering to be automated.

[0732] 3. Disease and pest detection and improvement method suggestions

[0733] Step 1:

[0734] The device (camera-equipped device) periodically takes detailed images of the plant's leaves and stems.

[0735] Step 2:

[0736] The terminal transmits the captured detailed image to the server.

[0737] Step 3:

[0738] The server uses AI models to analyze the images and detect signs of disease or pests.

[0739] Step 4:

[0740] The server generates remedial measures (e.g., use of specific chemicals or changes in fertilizer) based on the detection results.

[0741] Step 5:

[0742] The server notifies the smartphone app how to improve the situation.

[0743] Step 6:

[0744] Users can check the notification via their smartphone app and take appropriate measures.

[0745] 4. Automatic irrigation and real-time monitoring

[0746] Step 1:

[0747] The terminal (humidity sensor and automatic irrigation system) continuously monitors the soil moisture.

[0748] Step 2:

[0749] The server determines the need for watering based on the humidity data.

[0750] Step 3:

[0751] If the server determines that watering is necessary, it sends a watering instruction to the terminal (automatic watering system).

[0752] Step 4:

[0753] The terminal supplies the set amount of water to the plants according to the watering instructions.

[0754] Step 5:

[0755] The terminal periodically takes real-time images and transmits them to the server.

[0756] Step 6:

[0757] The server sends real-time images to a smartphone app.

[0758] Step 7:

[0759] Users can check the status of their plants in real time from a remote location via a smartphone app.

[0760] 5. Related product suggestions and purchasing support

[0761] Step 1:

[0762] The server collects plant type, growth status, soil conditions, and environmental data.

[0763] Step 2:

[0764] The server generates a list of the most suitable related products (pots, fertilizer, pest control products, etc.) based on the collected data.

[0765] Step 3:

[0766] The server notifies the smartphone app of the generated related product list.

[0767] Step 4:

[0768] Users can check the suggested products through the smartphone app and, if necessary, purchase the products via a link to an e-commerce site.

[0769] Thus, the system of the present invention provides a comprehensive solution for efficient and effective houseplant care.

[0770] Example 1

[0771] 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."

[0772] Properly caring for houseplants is a difficult task for modern urban dwellers. There is a lack of effective solutions for consistently managing a wide range of factors, including plant health management, growth prediction, appropriate watering timing, early detection of diseases and pests, and even the selection of appropriate related products. This task is particularly difficult for users in remote locations or who lead busy lives. The present invention aims to provide a system that comprehensively solves these challenges.

[0773] 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.

[0774] In this invention, the server includes: means for acquiring plant images using a camera; means for inputting the acquired images into a generative AI model to determine the plant's type and for generating a growth forecast and advice on optimal placement, temperature, light conditions, and pot size by referencing a characteristics database; means for measuring soil humidity and airborne constituent concentrations; means for inputting and analyzing the measured data into the generative AI model to calculate appropriate watering timing based on the plant's type and growth stage; means for acquiring detailed images of the plant's leaves and stems and inputting them into the generative AI model to detect signs of disease or pests and generate remediation methods; means for providing a smartphone app that notifies the user of the generated growth forecast, watering timing, and remediation methods; and means for suggesting related products based on the plant's type, growth status, soil condition, and environmental data and providing the user with links to purchase the suggested related products, enabling users to efficiently and effectively manage their houseplants.

[0775] A "camera" is a device that uses light to capture images or videos and record or transmit the data.

[0776] A "generative AI model" is an artificial intelligence model that uses machine learning and deep learning to analyze data and make predictions and classifications.

[0777] The "characteristics database" is a database that systematically accumulates information on plant species and growing conditions.

[0778] A "sensor" is a device that detects physical or chemical conditions or changes and outputs that information as an electrical signal.

[0779] A "humidity sensor" is a device that measures the humidity of an environment or object and outputs that data.

[0780] An "air concentration meter" is a device that measures the concentration of a specific component in the air and outputs the value.

[0781] "Watering timing" refers to the optimal time and frequency for watering plants.

[0782] A "smartphone app" is software that runs on a smartphone and functions as an interface with the user.

[0783] "Signs of disease or pests" refers to early symptoms or signs of disease or pest abnormalities that appear on plants.

[0784] "Remedial measures" are specific measures or countermeasures to address plant disease or pest problems.

[0785] "Related products" refers to products such as fertilizers, pots, and chemicals used to care for and grow plants.

[0786] "Link" means a hypertext reference that directs a user to a specified web page or online store.

[0787] This invention relates to a system that comprehensively supports optimal houseplant care. This system combines multiple elements, including cameras, humidity sensors, air concentration meters, generative AI models, and smartphone apps, to monitor houseplant growth, health, watering timing, disease and pest detection, and recommend related products.

[0788] System Configuration

[0789] The system includes the following components:

[0790] Camera-equipped device: A device that periodically takes images of houseplants. The user sets up the device in an appropriate position near the plant, and it automatically takes images periodically.

[0791] A device equipped with a humidity sensor and an air concentration meter: This device measures the humidity of the soil and the concentration of components in the air. This data is periodically sent to the server.

[0792] Server: The central control unit that inputs the received data into the generative AI model and performs analysis, judgment, calculation, and generation. The server refers to the characteristic database and provides optimal growth conditions and improvement methods.

[0793] Smartphone app: Software that provides an interface for users to receive notifications and suggestions from the server and manage their houseplants. Through this app, users can check the status of their plants and implement the suggestions.

[0794] Plant image acquisition and analysis

[0795] The terminal (a device with a camera) periodically takes pictures of the houseplant and sends them to a server. The server inputs the received images into a generative AI model to identify the plant's type. It then references a characteristics database to generate advice such as growth predictions, optimal placement, temperature, light conditions, and pot size. The generated advice is then sent to the user via a smartphone app.

[0796] Examples:

[0797] A camera-equipped device takes a photo of a houseplant and sends it to a server, which analyzes the image with a generative AI model and determines that the plant is a cactus. The server then sends a growth forecast and advice on optimal placement and lighting conditions to the user via a smartphone app.

[0798] Example prompt for a generative AI model:

[0799] "Analyze this plant's type and growth predictions with a generative AI model and generate advice."

[0800] Environmental data acquisition and notification

[0801] The terminal (a device equipped with a humidity sensor and air concentration meter) periodically measures soil humidity and the concentration of air components, and sends the data to a server. The server inputs this data into a generative AI model for analysis, and calculates the appropriate watering timing based on the plant type and growth stage. The calculation results are notified to the user via a smartphone app.

[0802] Examples:

[0803] The humidity sensor measures the soil humidity at 30% and sends this to the server. The server, taking into account that the plant is a succulent, determines that it actually needs watering. The smartphone app notifies the user when it's time to water.

[0804] Example prompt for a generative AI model:

[0805] "Based on this humidity data, calculate the optimal watering timing for your plants."

[0806] Detecting diseases and pests and proposing remedial measures

[0807] The device (a device with a camera) takes detailed images of plant leaves and stems and sends them to a server. The server then inputs these images into a generative AI model to detect signs of disease or pests. Based on the detection results, it generates remedial measures (e.g., the use of specific pesticides or changing the type of fertilizer) and notifies the user via a smartphone app.

[0808] Examples:

[0809] The camera-equipped device captures detailed images of plant leaves and sends them to a server, where they are analyzed by a generative AI model to detect signs of powdery mildew, suggesting the use of specific fungicides as a treatment, and notifying the user via a smartphone app.

[0810] Example prompt for a generative AI model:

[0811] "Analyze images of this plant to detect signs of disease and pests and suggest ways to improve it."

[0812] Automatic irrigation and real-time monitoring

[0813] The terminal (automatic irrigation system) automatically irrigates plants based on soil moisture data. It periodically sends real-time images to a server, which then provides the images to users via a smartphone app. This allows users to check the status of their plants in real time, even from remote locations.

[0814] Examples:

[0815] The automatic irrigation system receives humidity data and automatically waters plants when the humidity falls below a specified level, while simultaneously sending the latest real-time plant images to a server for users to view via a smartphone app.

[0816] Example prompt for a generative AI model:

[0817] "Automate irrigation based on this humidity data and provide real-time images to the user."

[0818] Related product suggestions and purchasing support

[0819] The server then suggests relevant products based on the plant's type, growth status, soil condition, and environmental data. The suggestions are sent to the user via a smartphone app, and the user can easily purchase the products via the provided link.

[0820] Examples:

[0821] The server analyzes the plant's growth and determines if it needs specific fertilizer, and then sends a fertilizer recommendation via a smartphone app along with a link to purchase it.

[0822] Example prompt for a generative AI model:

[0823] "Analyze the type of plant and its growth status to suggest relevant products suitable for the user."

[0824] In this way, the system of the present invention allows users to manage their houseplants efficiently and effectively.

[0825] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0826] Step 1: Image capture

[0827] The device (device with camera) periodically takes images of the houseplant. The user first sets up the device's camera in an appropriate position around the plant. The camera then automatically takes images at the set interval.

[0828] Input: Visual information of houseplants

[0829] Output: Image data of houseplants

[0830] Step 2: Send image

[0831] The device sends the captured image to the server, at high resolution and without data compression.

[0832] Input: Image data of houseplants

[0833] Output: Image data sent to the server

[0834] Step 3: Image analysis

[0835] The server inputs the received images into a generative AI model to identify the plant type, and then refers to a database of plant characteristics to generate growth predictions and advice on the best location, temperature, light conditions, pot size, and more.

[0836] Input: Image data of houseplants

[0837] Data processing: Plant species identification and growth prediction using generative AI models

[0838] Output: Advice information (optimal location, temperature, light conditions, pot size)

[0839] Step 4: Advice Notification

[0840] The server sends the generated advice to the smartphone app and notifies the user.

[0841] Input: Advice information

[0842] Output: Advice information sent to the smartphone app

[0843] Step 5: Environmental data measurement

[0844] The terminal (a device equipped with a humidity sensor and air concentration meter) periodically measures the humidity of the soil and the concentration of components in the air.

[0845] Input: Environmental data in soil and air

[0846] Output: Measured humidity data and air composition data

[0847] Step 6: Send data

[0848] The terminal transmits the measured data to the server in real time.

[0849] Input: Humidity data and air composition data

[0850] Output: Environment data sent to the server

[0851] Step 7: Data analysis

[0852] The server inputs the received environmental data into a generative AI model for analysis, and calculates the appropriate watering timing based on the plant type and growth stage.

[0853] Input: Environmental data

[0854] Data processing: Data analysis and irrigation timing calculations using generative AI models

[0855] Output: Watering timing information

[0856] Step 8: Watering Notification

[0857] The server sends the calculation results to a smartphone app and notifies the user.

[0858] Input: Watering timing information

[0859] Output: Watering timing information notified to the smartphone app

[0860] Step 9: Take detailed photos

[0861] The device (a device with a camera) takes detailed images of the plant's leaves and stems.

[0862] Input: Visual information of leaves and stems

[0863] Output: Detailed image data

[0864] Step 10: Send detailed images

[0865] The terminal transmits the captured detailed image to the server.

[0866] Input: Detailed image data

[0867] Output: Detailed image data sent to the server

[0868] Step 11: Image analysis

[0869] The server inputs the received images into a generative AI model to detect signs of disease or pests.

[0870] Input: Detailed image data

[0871] Data processing: disease and pest detection with generative AI models

[0872] Output: Information on detection results and remediation methods

[0873] Step 12: Notification of improvement method

[0874] The server sends the generated improvement method to the smartphone app and notifies the user.

[0875] Input: Information on how to improve

[0876] Output: Information on how to improve the app

[0877] Step 13: Humidity Data Analysis

[0878] The terminal (automatic irrigation system) periodically analyzes soil moisture data.

[0879] Input: Soil moisture data

[0880] Data processing: Analysis of humidity data

[0881] Output: Determine the need for irrigation

[0882] Step 14: Automatic Watering

[0883] The device automatically irrigates based on humidity data, starting when humidity falls below a set threshold.

[0884] Input: Determining the need for irrigation

[0885] Output: Watering execution actions

[0886] Step 15: Real-time image transmission

[0887] The terminal periodically transmits real-time images to the server.

[0888] Input: Real-time image of plants

[0889] Output: Real-time image data sent to the server

[0890] Step 16: Real-time monitoring

[0891] The server receives real-time images and provides them to users via a smartphone app, allowing them to check the status of the plants.

[0892] Input: Real-time image data

[0893] Output: Real-time images provided to a smartphone app

[0894] Step 17: Generate related product suggestions

[0895] The server uses a generative AI model to suggest relevant products needed by the user based on the plant type, growth status, soil condition, and environmental data.

[0896] Input: Plant management data

[0897] Data processing: Generative AI models suggest related products

[0898] Output: A list of related products optimized for the user

[0899] Step 18: Notification of related product suggestions

[0900] The server sends the generated related product suggestions to the smartphone app and notifies the user, who can then easily purchase the products via a link in the app.

[0901] Input: List of related products

[0902] Output: List of related products and purchase links sent to the smartphone app

[0903] (Application example 1)

[0904] 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."

[0905] Traditional houseplant management often involves manually checking the health and growth of plants, which is laborious, time-consuming, and requires specialized knowledge. It can also be difficult to detect diseases and pests early or understand appropriate management methods, which can result in difficult plant development. Furthermore, while physical stores are required to provide accurate information to customers and efficiently recommend related products, it can be difficult for staff to always have the latest information and respond appropriately.

[0906] 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.

[0907] In this invention, the server includes: means for acquiring images of plants using a camera; means for analyzing the acquired images to determine the type of plant and making a growth prediction; sensor means for measuring soil humidity and airborne concentrations; means for analyzing the measurement data to calculate appropriate watering timing; means for detecting plant diseases and pests and generating remediation methods; means for notifying the user of the generated growth prediction, watering timing, and remediation methods; means for allowing store staff to wear smart glasses to check and manage the status of houseplants in real time; means for analyzing images and sensor data and displaying the results on the smart glasses display; and means for providing links to purchase suggested related products. This allows the status of houseplants to be managed efficiently, and allows store staff to provide appropriate advice and suggest related products to customers.

[0908] The "means for acquiring images of plants using a camera" is a combination of hardware and software that takes images of plants and transmits the information to a server.

[0909] "Means of analyzing acquired images to determine the type of plant and predict its growth" refers to technology that uses AI models and machine learning algorithms to analyze acquired images, identify the type of plant, and predict its future growth.

[0910] The "sensor means for measuring the humidity of the soil and the concentration of components in the air" refers to a sensor that measures the humidity of the soil and the concentration of components in the air, thereby collecting environmental data in which the plant is placed.

[0911] The "means for analyzing the measurement data and calculating the appropriate timing for irrigation" refers to an algorithm and program that analyzes the data obtained from the sensor and calculates the optimal timing for irrigation for the plants.

[0912] "Means for detecting plant diseases and pests and generating remedial measures" refers to technology that analyzes plant image data, detects signs of disease or pests, and suggests appropriate remedial measures based on that data.

[0913] The "means for notifying the user of the generated growth forecast, irrigation timing, and improvement methods" refers to an application and communication means for notifying the user of the analysis results and suggestions via a smart device, etc.

[0914] "A means for store staff to wear smart glasses to check and manage the status of houseplants in real time" refers to a technology that uses smart glasses to check and manage the current status of houseplants in real time.

[0915] "Means for analyzing images and sensor data and displaying the results on the smart glasses display" refers to a system that analyzes acquired images and sensor data using an AI model and displays the results on the smart glasses display.

[0916] The "means for providing links to purchase suggested related products" refers to a technology that provides users with links to related products based on the analysis results and suggestions, allowing them to easily purchase the products.

[0917] This invention is a comprehensive system for effectively managing houseplants, consisting of multiple elements including a camera, humidity sensor, air concentration meter, AI model, smart glasses, server, and smartphone app.

[0918] System configuration and program processing overview

[0919] 1. Plant image acquisition and analysis

[0920] The server uses a camera to capture images of the plant and analyzes the transmitted images to determine the plant's type. Specifically, this process uses an image recognition algorithm (using TensorFlow or PyTorch). Based on the analysis results, the server predicts the plant's growth and suggests the environmental conditions and care methods necessary for growth. This information is displayed on the smart glasses' display and provided to store staff.

[0921] 2. Environmental data acquisition and notification

[0922] The server periodically measures soil humidity and the concentration of airborne elements using humidity sensors and air concentration meters, and collects and analyzes the data. This provides environmental data on the plant's location and allows it to calculate the optimal watering timing. The results are then communicated to the user via the smart glasses' display or a smartphone app.

[0923] 3. Disease and pest detection and improvement method suggestions

[0924] The server uses a camera to capture detailed images of the plant's leaves and stems, which are then analyzed using an AI model to detect signs of disease or pests and generate remedial measures (such as the use of specific pesticides).These remedial measures are then displayed on the smart glasses' display and communicated to store staff.

[0925] 4. Automatic irrigation and real-time monitoring

[0926] The server includes a means for automatically watering plants based on data from the humidity sensor, and also captures real-time images of the plants and transmits them to the server, which analyzes the images and provides real-time information to the user, enabling plant monitoring even in remote locations.

[0927] 5. Related product suggestions and purchasing support

[0928] The server will suggest relevant products (pots, fertilizer, pest control products, etc.) that users need based on the plant type, growth status, soil condition, and environmental data. These suggestions will be displayed on the smart glasses display or smartphone app, and users can easily purchase the products via the provided link.

[0929] Examples and prompts

[0930] Below are some examples and prompts:

[0931] Example 1: Growth forecasts and advice

[0932] The server analyzes images of plants taken with the camera-equipped smart glasses, determines that the plant is a cactus, and displays advice on the smart glasses' display regarding appropriate placement and lighting conditions based on the results of growth predictions.

[0933] Example 2: Watering notification

[0934] The server checks that the soil humidity measured by the humidity sensor is 30% and calculates the optimum watering time for the succulents. When it is time to water, a notification will appear on the smart glasses display.

[0935] Example 3: Disease detection and improvement suggestions

[0936] The server detects signs of powdery mildew from detailed images of plant leaves taken by camera-equipped smart glasses, and notifies the user on the glasses' display to recommend the use of specific fungicides as a countermeasure.

[0937] Prompt Sentence Examples

[0938] "Upload an image of your houseplant and we'll identify the plant's type, its health, and how to properly care for it."

[0939] Houseplant image: [Upload form]

[0940] Humidity data: [Acquired in real time]

[0941] Air concentration data: [Acquired in real time]

[0942] result:

[0943] Plant Type: Cactus

[0944] Health: Good

[0945] Watering timing: Next time in 3 days

[0946] Related Products: [Link]

[0947] This will clarify the specific treatment and usage methods, allowing for efficient management and sales support of houseplants in physical stores.

[0948] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0949] Step 1:

[0950] A camera-equipped device takes pictures of plants and sends them to a server. Specifically, the device's camera periodically takes photos of plants and uploads the data to the server via wireless communication (Wi-Fi or Bluetooth). The input is the plant image, and the output is the image data sent to the server.

[0951] Step 2:

[0952] The server analyzes the image data it receives using an AI model to determine the type of plant. Specifically, the AI ​​model (using TensorFlow and PyTorch) analyzes the image and performs feature extraction and classification. The input is the image data of the plant, and the output is the type of plant.

[0953] Step 3:

[0954] The server predicts growth based on the type of plant and calculates the necessary environmental conditions and management methods. Specifically, a growth prediction algorithm is used to calculate the optimal growth conditions (light intensity, temperature, humidity, etc.) based on the type of plant and past data. The input is the type of plant, and the output is growth prediction data.

[0955] Step 4:

[0956] Humidity sensors and air concentration meters periodically measure the humidity of the soil and the concentrations of components in the air, and send this data to a server. Specifically, the sensors acquire data in real time and transmit it to the server via wireless communication. The input is the sensor measurement data, and the output is the environmental data sent to the server.

[0957] Step 5:

[0958] The server analyzes the received environmental data and calculates the appropriate watering timing. Specifically, the watering algorithm determines the watering timing by taking into account the sensor data, plant type, and growth stage. The inputs are environmental data, plant type, and growth prediction data, and the output is the watering timing.

[0959] Step 6:

[0960] A camera-equipped device takes detailed images of plants and sends them to a server. Specifically, the device's camera takes close-up images of plant leaves and stems and sends the image data to the server. The input is the detailed plant image, and the output is the image data sent to the server.

[0961] Step 7:

[0962] The server analyzes detailed image data using an AI model to detect signs of disease or pests. Specifically, the AI ​​model uses image analysis technology to identify the characteristics of disease or pests and generates remediation methods based on the findings. The input is detailed image data, and the output is the disease or pest detection results and remediation methods.

[0963] Step 8:

[0964] The server displays the growth prediction, irrigation timing, and improvement methods on the smart glasses display. Specifically, the server sends the analysis results to the smart glasses application and notifies the user in real time. The input is the growth prediction data, irrigation timing, and improvement methods, and the output is the information displayed on the smart glasses display.

[0965] Step 9:

[0966] The server generates and suggests a list of related products based on the plant's type and growth status. Specifically, it references a database to create a list of related products suitable for the plant (pots, fertilizer, pest control products, etc.), which are then displayed on the smart glasses or smartphone app. The input is data on the plant's type and growth status, and the output is a list of related products.

[0967] Step 10:

[0968] The server provides users with purchase links for related products, allowing them to easily make purchases. Specifically, it generates online shop links and provides them to users via smart glasses or a smartphone app. The input is a list of related products, and the output is the purchase links.

[0969] This will create a system that efficiently manages the condition of houseplants and allows staff to provide customers with appropriate advice and suggest related products.

[0970] 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.

[0971] This invention combines a comprehensive support system for optimal houseplant care with an emotion engine. By combining multiple elements, including a camera, humidity sensor, air concentration meter, AI model, emotion engine, and smartphone app, the system monitors houseplant growth, health, watering timing, disease and pest detection, suggests related products, and even recognizes the user's emotional state to provide customized advice.

[0972] System Configuration

[0973] The system includes the following components:

[0974] Camera-equipped device: Take regular pictures of your houseplants.

[0975] Terminal equipped with humidity sensor and air concentration meter: Measures the humidity of the soil and the concentration of components in the air.

[0976] Server: Analyzes collected data to determine plant species, predict growth, calculate irrigation timing, detect diseases and pests, generate improvement methods, analyze sentiment data, generate customized advice, and suggest related products.

[0977] Emotion Engine: Recognize and analyze user emotions.

[0978] Smartphone app: Provides an interface for users to receive notifications and suggestions from the server and manage their houseplants.

[0979] Program processing

[0980] The specific program processing of the system is shown below.

[0981] 1. Plant image acquisition and analysis

[0982] The device (camera-equipped device) periodically takes photos of the houseplant and sends them to the server. The server analyzes the received images using an AI model to identify the plant's type. It also references a database of plant characteristics to predict its growth. This prediction generates advice on the optimal location, temperature, light conditions, and pot size. The server then notifies the user of this advice via a smartphone app.

[0983] 2. Environmental data acquisition and notification

[0984] The terminal (a device equipped with a humidity sensor and air concentration meter) periodically measures the humidity of the soil and the concentration of elements in the air, and sends the data to a server. The server analyzes this data and calculates the appropriate watering timing based on the type of plant and its growth stage. The calculation results are notified to the user via a smartphone app.

[0985] 3. Disease and pest detection and improvement method suggestions

[0986] The device (a device with a camera) takes detailed images of plant leaves and stems and sends them to a server. The server then analyzes these images using an AI model to detect signs of disease or pests. Based on the detection results, it generates remedial measures (e.g., the use of specific pesticides or changing the type of fertilizer) and notifies the user via a smartphone app.

[0987] 4. Automatic irrigation and real-time monitoring

[0988] The terminal (automatic irrigation system) automatically irrigates based on soil moisture data. The terminal also periodically sends real-time images to a server, which then provides the images to the user via a smartphone app. This allows users to check the condition of their plants in real time, even from remote locations.

[0989] 5. Related product suggestions and purchasing support

[0990] The server collects information on the plant's type, growth status, soil condition, and environmental data. Based on the collected data, it generates a list of the most suitable related products (pots, fertilizer, pest control products, etc.) and notifies the user of the list via a smartphone app. Users can easily purchase the products via links within the app.

[0991] 6. Customized advice using emotion engine

[0992] The device (smartphone app) collects emotional data from the user's social media posts and daily usage logs and sends it to a server. The server then uses an emotion engine to analyze the user's emotional state and generate optimal advice based on the user's mood and stress level. For example, if the user is feeling stressed, the server can suggest the characteristics and care methods of houseplants that have a soothing effect. The server can also accumulate the user's emotional data and reflect it in future advice.

[0993] Specific examples

[0994] Specific examples are shown below.

[0995] Example 1: Emotion-based growth advice

[0996] The device (camera-equipped device) takes a photo of the plant and sends it to the server. The server analyzes the image and determines that the plant is a ficus. It predicts its growth and generates advice on the appropriate placement and lighting conditions. It also collects emotional data from the user's smartphone app and detects whether the user is in a relaxed mood. Based on this, advice emphasizing the relaxing effects and characteristics of ficus is sent via the smartphone app.

[0997] Example 2: Watering notification and sentiment analysis

[0998] The device (humidity sensor) measures the soil humidity to be 25% and sends this to the server. The server, considering that the plant is a sansevieria, determines that it needs watering. It notifies the user via a smartphone app when it is time to water it. At the same time, the server, through its emotion engine, detects that the user is feeling busy and provides advice on simple watering methods or the use of an automatic watering system.

[0999] Example 3: Disease detection and customised improvement suggestions

[1000] The device (with a camera) takes detailed images of plant leaves and sends them to the server. The server analyzes the images and detects signs of black spot disease on the leaves. The server then suggests the use of a specific fungicide as a countermeasure. Furthermore, the server detects the user's anxiety through an emotion engine and sends reassuring information, including detailed instructions for disease prevention and success stories, via a smartphone app.

[1001] In this way, the system of the present invention, which is combined with an emotion engine, can provide individual advice according to the user's emotional state, making houseplant management more effective and user-friendly.

[1002] The processing flow will be explained below.

[1003] 1. Plant image acquisition and analysis

[1004] Step 1:

[1005] The terminal (device with a camera) takes images of the houseplant at set time intervals.

[1006] Step 2:

[1007] The terminal transmits the captured image to the server.

[1008] Step 3:

[1009] The server inputs the received images into an AI model to identify the type of plant.

[1010] Step 4:

[1011] The server makes a growth prediction based on the identified type by referring to a characteristics database.

[1012] Step 5:

[1013] The server generates advice based on growth prediction results, such as the optimal location, temperature, light conditions, and pot size.

[1014] Step 6:

[1015] The server sends the generated advice to the smartphone app.

[1016] Step 7:

[1017] Users can check the advice through a smartphone app and manage their belongings appropriately.

[1018] 2. Environmental data acquisition and notification

[1019] Step 1:

[1020] The terminal (a device equipped with a humidity sensor and air concentration meter) periodically measures the humidity of the soil and the concentration of components in the air.

[1021] Step 2:

[1022] The terminal transmits the measured environmental data to the server.

[1023] Step 3:

[1024] The server analyzes the received data and calculates the optimal watering timing based on the plant type and growth stage.

[1025] Step 4:

[1026] The server notifies the smartphone app of the calculation results.

[1027] Step 5:

[1028] Users can check the watering timing through a smartphone app and either water appropriately or set the watering to be automated.

[1029] 3. Disease and pest detection and improvement method suggestions

[1030] Step 1:

[1031] The device (camera-equipped device) periodically takes detailed images of the plant's leaves and stems.

[1032] Step 2:

[1033] The terminal transmits the captured detailed image to the server.

[1034] Step 3:

[1035] The server uses AI models to analyze the images and detect signs of disease or pests.

[1036] Step 4:

[1037] The server generates remedial measures (e.g., use of specific chemicals or changes in fertilizer) based on the detection results.

[1038] Step 5:

[1039] The server notifies the smartphone app how to improve the situation.

[1040] Step 6:

[1041] Users can check the notification via their smartphone app and take appropriate measures.

[1042] 4. Automatic irrigation and real-time monitoring

[1043] Step 1:

[1044] The terminal (humidity sensor and automatic irrigation system) continuously monitors the soil moisture.

[1045] Step 2:

[1046] The server determines the need for watering based on the humidity data.

[1047] Step 3:

[1048] If the server determines that watering is necessary, it sends a watering instruction to the terminal (automatic watering system).

[1049] Step 4:

[1050] The terminal supplies the set amount of water to the plants according to the watering instructions.

[1051] Step 5:

[1052] The terminal periodically takes real-time images and transmits them to the server.

[1053] Step 6:

[1054] The server sends real-time images to a smartphone app.

[1055] Step 7:

[1056] Users can check the status of their plants in real time from a remote location via a smartphone app.

[1057] 5. Related product suggestions and purchasing support

[1058] Step 1:

[1059] The server collects plant type, growth status, soil conditions, and environmental data.

[1060] Step 2:

[1061] The server generates a list of the most suitable related products (pots, fertilizer, pest control products, etc.) based on the collected data.

[1062] Step 3:

[1063] The server notifies the smartphone app of the generated related product list.

[1064] Step 4:

[1065] Users can check the suggested products through the smartphone app and, if necessary, purchase the products via a link to an e-commerce site.

[1066] 6. Customized advice using emotion engine

[1067] Step 1:

[1068] The device (smartphone app) collects emotional data from users' social media posts and daily usage logs.

[1069] Step 2:

[1070] The terminal transmits the emotion data to the server.

[1071] Step 3:

[1072] The server uses an emotion engine to analyze the user's emotional state.

[1073] Step 4:

[1074] The server generates customized advice based on the emotional state.

[1075] Step 5:

[1076] The server then sends customized advice to the smartphone app.

[1077] Step 6:

[1078] Users can check the advice through a smartphone app and manage their belongings appropriately.

[1079] Specific examples

[1080] Example 1: Emotion-based growth advice

[1081] Step 1:

[1082] The device (camera-equipped device) takes a photo of the plant and sends it to the server.

[1083] Step 2:

[1084] The server analyzes the image and determines that the plant is a ficus.

[1085] Step 3:

[1086] The server makes growth predictions and generates advice on suitable placement and lighting conditions.

[1087] Step 4:

[1088] The device (smartphone app) collects the user's emotional data and sends it to the server.

[1089] Step 5:

[1090] The server detects that the user is in a relaxed mood.

[1091] Step 6:

[1092] The server generates advice that emphasizes the relaxing effects and notifies the smartphone app.

[1093] Step 7:

[1094] Users can check advice through a smartphone app and carry out plant management.

[1095] Example 2: Watering notification and sentiment analysis

[1096] Step 1:

[1097] The terminal (humidity sensor) measures the soil humidity to be 25% and sends it to the server.

[1098] Step 2:

[1099] The server considers the plant to be a sansevieria and determines that it needs watering.

[1100] Step 3:

[1101] The server notifies the smartphone app when it is time to water the plants.

[1102] Step 4:

[1103] The device (smartphone app) collects the user's emotional data and sends it to the server.

[1104] Step 5:

[1105] The server detects that the user is in a busy mood.

[1106] Step 6:

[1107] The server generates advice recommending simple watering methods and the use of automatic watering systems, and notifies the smartphone app.

[1108] Step 7:

[1109] Users can check the advice via a smartphone app and carry out or set up irrigation.

[1110] Example 3: Disease detection and customization of remediation suggestions

[1111] Step 1:

[1112] The terminal (device with a camera) takes detailed images of plant leaves and sends them to the server.

[1113] Step 2:

[1114] The server analyzes the images and detects signs of black spot disease on the leaves.

[1115] Step 3:

[1116] The server suggests using certain disinfectants as a countermeasure.

[1117] Step 4:

[1118] The device (smartphone app) collects the user's emotional data and sends it to the server.

[1119] Step 5:

[1120] The server detects that the user is feeling anxious.

[1121] Step 6:

[1122] The server generates reassuring information, including detailed procedures for disease prevention and success stories, and sends it to the smartphone app.

[1123] Step 7:

[1124] Users can check the advice through a smartphone app and implement measures.

[1125] Example 2

[1126] 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."

[1127] While existing houseplant management systems provide basic management functions such as plant health and growth prediction, watering timing, and disease and pest detection, they lack the ability to provide customized advice based on the user's emotional state. Furthermore, they lack the functionality to suggest related products, provide real-time remote monitoring, and provide watering advice that takes emotional state into account. This makes it difficult to optimize management for each individual user, and there is a need for an improved user experience.

[1128] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring plant images using a camera, means for analyzing the acquired images to determine the plant's type and perform growth prediction, sensor means for measuring soil humidity and airborne concentrations, means for analyzing the measurement data to calculate appropriate watering timing, means for detecting plant diseases and pests and generating remedial measures, means for notifying the user of the generated growth prediction, watering timing, and remedial measures, means for generating a list of related products and suggesting them to the user, and means for analyzing the user's emotional state and generating customized advice. This allows users to receive optimal plant care advice tailored to their individual emotional state, enabling effective and user-friendly houseplant care.

[1129] The "means for acquiring images of plants using a camera" refers to a camera device for taking images of plants and a control system for automating its operation.

[1130] "Means for analyzing acquired images to determine the type of plant and predict growth" refers to a system that uses AI models and algorithms to identify the type of plant from image data and predict growth patterns.

[1131] The "sensor means for measuring soil moisture and airborne concentrations" refers to a soil moisture sensor and a sensor device for measuring airborne components (e.g., CO2 concentration).

[1132] The "means for analyzing measurement data and calculating appropriate irrigation timing" is a system that calculates the timing for irrigation based on data obtained from sensors, taking into account the type of plant and its growth stage.

[1133] The "means for detecting plant diseases and pests and generating remedial measures" is a system that uses AI models and image analysis technology to detect signs of plant diseases and pests and then suggests appropriate remedial measures based on that information.

[1134] "Means for notifying the user of the generated growth forecast, irrigation timing, and improvement methods" refers to a communication and interface system for notifying the user of the generated information via their smartphone or other device.

[1135] The "means for generating a list of related products and suggesting them to the user" is a system for generating a list of related products based on the plant's condition and environmental data and suggesting the list to the user.

[1136] The "means for analyzing the user's emotional state and generating customized advice" refers to an emotion analysis engine and generation system for analyzing the user's emotional data and providing individually customized advice based on the results.

[1137] "Means for obtaining real-time images of plants using a camera and providing them to a remote user" is a system for distributing real-time plant images taken by a camera to a remote user.

[1138] The "means for automatically performing irrigation based on data from a humidity sensor" is an automatic irrigation system for automatically performing irrigation based on data from a humidity sensor.

[1139] The "means for proposing a watering method based on the user's emotional state" is a system that takes into account the user's emotional data and proposes the optimal watering method depending on the situation.

[1140] The "means for providing links to purchase suggested related products" is a system that provides users with links to purchase suggested related products online.

[1141] The "means for customizing the content of related product suggestions according to the user's emotional state" is a system that individually customizes the content of related product suggestions based on the user's emotional data.

[1142] This invention combines a comprehensive support system for optimal houseplant care with an emotion engine that understands the user's emotional state and provides customized advice accordingly. The system's main components are a camera, a humidity sensor, an air concentration meter, an AI model, an emotion engine, and a smartphone app.

[1143] System configuration

[1144] 1. Camera-equipped device: Takes pictures of the houseplant periodically and sends them to the server.

[1145] 2. Terminal with humidity sensor and air concentration meter: Measures the humidity of the soil and the concentration of components in the air and sends the data to the server.

[1146] 3. Server: Receives and analyzes images and sensor data to determine plant species, predict growth, calculate irrigation timing, detect diseases and pests, generate improvement methods, analyze emotional data, generate customized advice, and suggest related products.

[1147] 4. Emotion engine: Recognizes and analyzes user emotions from social media posts and usage logs.

[1148] 5. Smartphone app: Provides an interface for users to receive notifications and suggestions from the server and manage their houseplants.

[1149] System Operation

[1150] Plant image acquisition and analysis

[1151] The device periodically takes pictures of the houseplant using a camera and sends them to a server. The server then analyzes the received images using an AI model to determine the plant's type. The growth management system also references a characteristics database to predict the plant's growth. The generated advice includes detailed information such as lighting conditions, optimal placement, and pot size. This advice is then sent to the user via a smartphone app.

[1152] Example: The device takes a picture of a ficus, and the server predicts its growth and advises that the best place to place it is near a window in the living room.

[1153] Example prompt: "What growth predictions can you give me for the ficus and what are the best locations and temperature conditions?"

[1154] Environmental data acquisition and notification

[1155] The terminal (a device equipped with a humidity sensor and air concentration meter) periodically measures the humidity of the soil and the concentration of components in the air, and sends the data to a server. The server receives the data and calculates the appropriate timing for watering. The results are then notified to the user via a smartphone app.

[1156] Example: The soil moisture is measured at 25%, the server determines that watering is required, and sends a notification saying "Water now."

[1157] Example prompt: "When soil moisture is 25%, should I irrigate?"

[1158] Detecting diseases and pests and proposing remedial measures

[1159] The device (a device with a camera) takes detailed images of plant leaves and stems and sends them to a server. The server then analyzes the images using AI models to detect signs of disease or pests. Based on the results, it generates improvement measures, such as the use of specific pesticides or changes to fertilizer, and notifies the user via a smartphone app.

[1160] Example: Detecting black spot on leaves and suggesting the use of a fungicide.

[1161] Example prompt: "If the plant has black spot on its leaves, what fungicide should I use?"

[1162] Automatic irrigation and real-time monitoring

[1163] The terminal (automatic irrigation system) automatically irrigates plants based on soil moisture data. The terminal also periodically sends real-time images to a server, which then provides the images to users via a smartphone app. This allows users to check the status of their plants even from remote locations.

[1164] Example: An automatic irrigation system irrigates when soil moisture falls below 20%.

[1165] Example prompt: "Write a scenario in which an automated irrigation system would operate if soil moisture dropped."

[1166] Related product suggestions and purchasing support

[1167] The server analyzes the plant's type, growth status, soil condition, and environmental data to generate a list of related products, which are then sent to the user via a smartphone app, where they can conveniently purchase the products via a link in the app.

[1168] Example: The server recommends the best fertilizer, and the user purchases it through the app.

[1169] Example prompt: "Please suggest the best fertilizer for houseplants and generate a link to purchase it."

[1170] Customized advice with an emotional engine

[1171] The device (smartphone app) collects emotional data from users' social media posts and daily usage logs, and sends it to a server. The server then uses an emotion engine to analyze the user's emotional state and generate optimal advice based on their mood and stress level.

[1172] Example: If a user is feeling stressed, the emotion engine suggests soothing plants and care methods.

[1173] Example prompt: "What are the properties of houseplants that are effective in relieving stress and how should you care for them?"

[1174] In this way, the system of the present invention can effectively and user-friendly manage houseplants, and can also improve the user experience by analyzing the user's emotions and providing customized advice based on those emotions.

[1175] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1176] Step 1:

[1177] Acquiring and sending plant images

[1178] The device periodically acquires images of the houseplant. The device's camera takes a photo of the plant at a specified time (e.g., 10:00 AM every day) and sends the image data to the server. The input is the image data, and the output is transmission to the server.

[1179] Step 2:

[1180] Receiving and analyzing images

[1181] The server receives the images sent from the device. The received image data is input into the AI ​​model to determine the plant's type. It then references a characteristics database to predict growth. This allows it to predict the plant's growth pattern and calculate the appropriate placement and environmental conditions. The input is image data, and the output is the plant's type determination result and growth prediction data.

[1182] Step 3:

[1183] Advice generation and notification

[1184] The server generates advice for the user based on the growth prediction results. For example, specific advice such as "The best place to place a ficus is by a window in the living room" is generated for the growth prediction of the ficus. The generated advice is notified to the user via a smartphone app. The input is the growth prediction data, and the output is the generated advice.

[1185] Step 4:

[1186] Acquiring and sending environmental data

[1187] The terminal (a device with a humidity sensor and air concentration meter) periodically measures the humidity of the soil and the concentration of components in the air and sends the data to the server. For example, it measures and sends the humidity (e.g., 25%) and carbon dioxide concentration in the air (e.g., 300 ppm) every hour. The input is environmental sensor data, and the output is sent to the server.

[1188] Step 5:

[1189] Analyzing environmental data and calculating irrigation timing

[1190] The server analyzes the environmental data sent from the device. Taking into account the type of plant and its growth stage, it calculates when watering is necessary. For example, if the humidity is 25%, it will determine that a sansevieria needs watering. The results of this calculation are notified to the user via a smartphone app. The input is environmental sensor data, and the output is a notification of when to water.

[1191] Step 6:

[1192] Shooting and transmitting diseases and pests

[1193] The terminal (device with a camera) takes detailed images of plant leaves and stems and sends them to a server. For example, high-resolution images are taken to detect signs of black spot disease on leaves. The input is the detailed image data, and the output is transmission to the server.

[1194] Step 7:

[1195] Disease and pest detection and remediation methods

[1196] The server uses an AI model to analyze detailed images sent from the device and detect signs of disease or pests. Based on the detection results, it generates improvement measures, such as the use of specific pesticides or changes to fertilizer. For example, if black spot disease is detected, it will suggest the use of an appropriate fungicide. The input is detailed image data, and the output is improvement measure suggestions.

[1197] Step 8:

[1198] Automatic irrigation

[1199] The terminal (automatic irrigation system) automatically irrigates based on soil moisture data. For example, irrigation is automatically performed when the humidity falls below 20%. The input is humidity data, and the output is irrigation execution.

[1200] Step 9:

[1201] Real-time image acquisition and transmission

[1202] The device periodically takes real-time images of the plants and sends them to the server. For example, it acquires and sends the latest plant images every hour. The input is real-time image data, and the output is sent to the server.

[1203] Step 10:

[1204] Related product suggestions and notifications

[1205] The server generates a list of related products based on the plant type, growth status, soil condition, and environmental data. This list is then notified to the user. For example, "organic fertilizer B" and "insect repellent spray C" are recommended for a ficus. The input is various sensor data and a plant database, and the output is a list of related products.

[1206] Step 11:

[1207] Collecting and transmitting emotional data

[1208] The device (smartphone app) collects emotional data from users' social media posts and daily usage logs, and sends it to a server. For example, the keyword "stress" is collected from users' social media posts. The input is emotional data, and the output is transmission to the server.

[1209] Step 12:

[1210] Emotional state analysis and customized advice generation

[1211] The server analyzes the emotional data using an emotion engine and generates optimal advice based on the user's emotional state. For example, if the user is feeling stressed, it will suggest plants that have a relaxing effect and how to care for them. The input is emotional data, and the output is customized advice.

[1212] Thus, through each processing step, a specific embodiment of the invention is realized.

[1213] (Application example 2)

[1214] 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."

[1215] When caring for houseplants, maintaining optimal plant growth and health requires consideration of various factors, such as appropriate watering timing and disease and pest control. Furthermore, providing effective advice based on the user's emotional state would provide a more personalized experience. However, until now, there has been no comprehensive management system that can effectively and easily solve these challenges.

[1216] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1217] In this invention, the server includes means for capturing images of plants using a camera, means for analyzing the captured images to determine the type of plant and making a growth prediction, sensor means for measuring soil humidity and its concentration in the air, means for analyzing the measurement data to calculate appropriate watering timing, means for detecting plant diseases and pests and generating remedial measures, means for notifying the user of the generated growth prediction, watering timing, and remedial measures, means for generating a list of related products and suggesting them to the user, and means for recognizing the user's emotional state and generating customized advice, thereby enabling the user to receive comprehensive support for optimal care of their houseplants.

[1218] "Camera means" refers to a device for capturing images of plants.

[1219] The "plant type determination means" is a device or method that analyzes the acquired image to determine the type of plant.

[1220] A "growth prediction means" is a device or method that predicts future growth based on plant type.

[1221] "Sensor means" refers to a device for measuring the moisture content of soil and the concentration in the air.

[1222] The "watering timing calculation means" is a device or method that analyzes measurement data and calculates the appropriate watering timing.

[1223] "Disease and pest detection means" means a device or method for detecting plant diseases or pests.

[1224] The "remediation method generating means" is a device or method that generates a remediation method for a detected disease or pest.

[1225] "Notification means" refers to a device or method that notifies the user of the generated growth forecast, irrigation timing, and improvement methods.

[1226] The "related product suggestion means" is a device or method that generates and suggests a list of related products that are most suitable for the user.

[1227] An "emotion recognizer" is a device or method that recognizes the emotional state of a user.

[1228] A "customized advice generator" is a device or method that generates customized advice based on a recognized emotional state of a user.

[1229] The system embodying this invention provides comprehensive support for users to optimally manage their houseplants, and specifically comprises a camera means, a sensor means, a server, a notification means, and an application.

[1230] First, each component of the system will be described.

[1231] Camera Means

[1232] The system acquires images of the plants by means of a camera, which periodically takes high-resolution images to monitor the plant's growth status and detect diseases and pests.

[1233] Sensor means

[1234] The sensor means measures the humidity of the soil, the carbon dioxide concentration in the air, the temperature, and the amount of light. Examples of such sensors include humidity sensors, air concentration meters, and light sensors.

[1235] server

[1236] The server receives and analyzes data acquired from the camera and sensor means. The server is equipped with an AI model and emotion engine, and performs the following processes:

[1237] Image analysis:

[1238] The AI ​​model analyzes the images of plants captured by the camera and determines the plant's type. After determining the type, it refers to a characteristics database and predicts its growth.

[1239] Environmental Data Analysis:

[1240] Data from the sensor means is analyzed to calculate the timing of irrigation based on soil moisture and carbon dioxide levels in the air.

[1241] Disease and pest detection:

[1242] Based on image data, an AI model is used to identify signs of plant disease and pests and generate improvement methods.

[1243] Sentiment Data Analysis:

[1244] It uses an emotion engine to recognize users' emotions, analyzes their social media posts and smartphone app usage logs, and generates optimal advice based on their emotional state.

[1245] Notification means

[1246] The system provides users with growth forecasts, watering timing, improvement methods, and customized advice based on the user's emotional state via a smartphone app or smart glasses.

[1247] Application Overview

[1248] The system's application provides information to users through smartphones or smart glasses.

[1249] 1. Houseplant status notification:

[1250] Displays real-time information on plant health, growth status, irrigation timing, and pest and disease information.

[1251] 2. Emotion-based personalized advice:

[1252] The emotion engine analyzes the user's emotions and provides special advice and related product suggestions based on that.

[1253] 3. Interactive storefront:

[1254] Augmented reality (AR) technology is used to visually display plant characteristics and health status.

[1255] Specific examples

[1256] Example 1:

[1257] When users scan their houseplant with the smart glasses, they are given real-time information about the plant's type, health, and growth forecast, and if they're in the mood to relax, they're given suggestions on how to care for the plant to help them relax.

[1258] Example prompt sentence:

[1259] "Scan your houseplants with smart glasses and display optimal recommendations based on plant type, health, watering timing, and user sentiment."

[1260] In this way, the invention allows users to provide more effective and personalized houseplant care.

[1261] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1262] Step 1:

[1263] The server periodically acquires images of the houseplants from the camera means.

[1264] Input: An image of a plant taken by camera means.

[1265] Data processing and calculation: The server uses an AI model to analyze the image and identify the plant species. It then references a database of plant characteristics to predict growth.

[1266] Output: Plant type and growth prediction data based on it.

[1267] Specific operation: The server runs an image processing algorithm to analyze the shape and color of the plant leaves.

[1268] Step 2:

[1269] The terminal acquires soil humidity data and air component concentration data from a device equipped with a humidity sensor and air concentration meter.

[1270] Input: Soil moisture data and air element concentration data.

[1271] Data processing and calculation: The server analyzes this data and calculates the appropriate watering timing based on the type of plant and its growth stage.

[1272] Output: Watering notification data.

[1273] Specific operation: The server compares the humidity data with the plant's humidity requirements and calculates the amount of water needed.

[1274] Step 3:

[1275] The device captures detailed images of plant leaves and stems and sends them to the server.

[1276] Input: Images of plant leaves and stems.

[1277] Data processing and calculation: The server analyzes the images and uses AI models to detect signs of disease or pests.

[1278] Output: Disease and pest detection results and how to remedy them.

[1279] Specific operation: The server extracts features from the image and identifies parts that match characteristic data of diseases and pests.

[1280] Step 4:

[1281] The server obtains the user's emotional data from the smartphone app.

[1282] Input: User emotion data (e.g., social media posting data, smartphone app usage logs).

[1283] Data processing and calculation: The server uses an emotion engine to analyze the user's emotional state.

[1284] Output: User's emotional state data.

[1285] Specific operation: The server uses natural language processing technology to analyze emotions from social media post data.

[1286] Step 5:

[1287] The server generates customized advice based on the emotional state.

[1288] Input: User emotional state data.

[1289] Data processing and calculation: The server refers to a pre-configured advice database and selects advice that matches the user's emotions.

[1290] Output: Customized advice.

[1291] Specific operation: If the user is feeling stressed, the server selects the characteristics of plants that have a relaxing effect and how to care for them.

[1292] Step 6:

[1293] The server then sends the generated growth forecast, irrigation timing, improvement methods, and customized advice to the user via a smartphone app or smart glasses.

[1294] Input: growth forecasts, irrigation timing, remediation methods, customized advice.

[1295] Data calculation and processing: The server converts this information into a user-friendly format.

[1296] Output: Notification data to the user.

[1297] Specific behavior: The server provides real-time notifications through the user interface.

[1298] 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.

[1299] 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.

[1300] 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.

[1301] [Third embodiment]

[1302] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1303] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1304] 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).

[1305] 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.

[1306] 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.

[1307] 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).

[1308] 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. 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.

[1309] 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.

[1310] 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.

[1311] 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.

[1312] 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.

[1313] 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."

[1314] This invention relates to a system that comprehensively supports optimal houseplant care. This system combines multiple elements, including cameras, humidity sensors, air concentration meters, AI models, and smartphone apps, to monitor houseplant growth, health, watering timing, disease and pest detection, and recommend related products.

[1315] System Configuration

[1316] The system includes the following components:

[1317] Camera-equipped device: Take regular pictures of your houseplants.

[1318] Terminal equipped with humidity sensor and air concentration meter: Measures the humidity of the soil and the concentration of components in the air.

[1319] Server: Analyzes collected data and generates plant type determinations, growth forecasts, irrigation timing calculations, disease and pest detection, and remediation methods.

[1320] Smartphone app: Provides an interface for users to receive notifications and suggestions from the server and manage their houseplants.

[1321] Program processing

[1322] The specific program processing of the system is shown below.

[1323] 1. Plant image acquisition and analysis

[1324] The device (camera-equipped device) periodically takes photos of the houseplant and sends them to the server. The server analyzes the received images using an AI model to identify the plant's type. It then references a database of plant characteristics to predict its growth. This prediction generates advice on the optimal placement, temperature, light conditions, and pot size. The server then notifies the user of this advice via a smartphone app.

[1325] 2. Environmental data acquisition and notification

[1326] The terminal (a device equipped with a humidity sensor and air concentration meter) periodically measures the soil humidity and the concentration of elements in the air and sends the data to a server. The server analyzes this data and calculates the appropriate watering timing based on the plant type and growth stage. The calculation results are notified to the user via a smartphone app.

[1327] 3. Disease and pest detection and improvement method suggestions

[1328] The device (a device with a camera) takes detailed images of plant leaves and stems and sends them to a server. The server then analyzes these images using an AI model to detect signs of disease or pests. Based on the detection results, it generates remedial measures (e.g., the use of specific pesticides or changing the type of fertilizer) and notifies the user via a smartphone app.

[1329] 4. Automatic irrigation and real-time monitoring

[1330] The terminal (automatic irrigation system) automatically irrigates plants based on soil moisture data. The terminal also periodically sends real-time images to a server, which then provides the images to users via a smartphone app. This allows users to check the status of their plants in real time, even from remote locations.

[1331] 5. Related product suggestions and purchasing support

[1332] The server suggests related products (pots, fertilizer, pest control products, etc.) that users need based on the plant type, growth status, soil condition, and environmental data. These suggestions are notified to users through a smartphone app, and users can easily purchase the products through links within the app.

[1333] Specific examples

[1334] Specific examples are shown below.

[1335] Example 1: Growth predictions and advice

[1336] The device (with a camera) takes a photo of the plant and sends it to the server. The server analyzes the image and determines that the plant is a cactus. The user is then notified via a smartphone app of the results of the growth prediction, the appropriate placement location, lighting conditions, and other information.

[1337] Example 2: Watering notification

[1338] The device (humidity sensor) measures the soil humidity to be 30% and sends this to the server. The server, taking into account that the plant is a succulent, determines that watering is necessary. The smartphone app notifies the user when it is time to water.

[1339] Example 3: Disease detection and improvement suggestions

[1340] The device (a device with a camera) takes detailed images of plant leaves and sends them to a server. The server analyzes the images and detects signs of powdery mildew on the leaves. The server then suggests the use of a specific fungicide as a countermeasure and notifies the user via a smartphone app.

[1341] In this way, the system of the present invention allows users to manage their houseplants efficiently and effectively.

[1342] The processing flow will be explained below.

[1343] 1. Plant image acquisition and analysis

[1344] Step 1:

[1345] The terminal (device with a camera) takes images of the houseplant at set time intervals.

[1346] Step 2:

[1347] The terminal transmits the captured image to the server.

[1348] Step 3:

[1349] The server inputs the received images into an AI model to identify the type of plant.

[1350] Step 4:

[1351] The server makes a growth prediction based on the identified type by referring to a characteristics database.

[1352] Step 5:

[1353] The server generates advice based on growth prediction results, such as the optimal location, temperature, light conditions, and pot size.

[1354] Step 6:

[1355] The server sends the generated advice to the smartphone app.

[1356] Step 7:

[1357] Users can check the advice through a smartphone app and manage their belongings appropriately.

[1358] 2. Environmental data acquisition and notification

[1359] Step 1:

[1360] The terminal (a device equipped with a humidity sensor and air concentration meter) periodically measures the humidity of the soil and the concentration of components in the air.

[1361] Step 2:

[1362] The terminal transmits the measured environmental data to the server.

[1363] Step 3:

[1364] The server analyzes the received data and calculates the optimal watering timing based on the plant type and growth stage.

[1365] Step 4:

[1366] The server notifies the smartphone app of the calculation results.

[1367] Step 5:

[1368] Users can check the watering timing through a smartphone app and either water appropriately or set the watering to be automated.

[1369] 3. Disease and pest detection and improvement method suggestions

[1370] Step 1:

[1371] The device (camera-equipped device) periodically takes detailed images of the plant's leaves and stems.

[1372] Step 2:

[1373] The terminal transmits the captured detailed image to the server.

[1374] Step 3:

[1375] The server uses AI models to analyze the images and detect signs of disease or pests.

[1376] Step 4:

[1377] The server generates remedial measures (e.g., use of specific chemicals or changes in fertilizer) based on the detection results.

[1378] Step 5:

[1379] The server notifies the smartphone app how to improve the situation.

[1380] Step 6:

[1381] Users can check the notification via their smartphone app and take appropriate measures.

[1382] 4. Automatic irrigation and real-time monitoring

[1383] Step 1:

[1384] The terminal (humidity sensor and automatic irrigation system) continuously monitors the soil moisture.

[1385] Step 2:

[1386] The server determines the need for watering based on the humidity data.

[1387] Step 3:

[1388] If the server determines that watering is necessary, it sends a watering instruction to the terminal (automatic watering system).

[1389] Step 4:

[1390] The terminal supplies the set amount of water to the plants according to the watering instructions.

[1391] Step 5:

[1392] The terminal periodically takes real-time images and transmits them to the server.

[1393] Step 6:

[1394] The server sends real-time images to a smartphone app.

[1395] Step 7:

[1396] Users can check the status of their plants in real time from a remote location via a smartphone app.

[1397] 5. Related product suggestions and purchasing support

[1398] Step 1:

[1399] The server collects plant type, growth status, soil conditions, and environmental data.

[1400] Step 2:

[1401] The server generates a list of the most suitable related products (pots, fertilizer, pest control products, etc.) based on the collected data.

[1402] Step 3:

[1403] The server notifies the smartphone app of the generated related product list.

[1404] Step 4:

[1405] Users can check the suggested products through the smartphone app and, if necessary, purchase the products via a link to an e-commerce site.

[1406] Thus, the system of the present invention provides a comprehensive solution for efficient and effective houseplant care.

[1407] Example 1

[1408] 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."

[1409] Properly caring for houseplants is a difficult task for modern urban dwellers. There is a lack of effective solutions for consistently managing a wide range of factors, including plant health management, growth prediction, appropriate watering timing, early detection of diseases and pests, and even the selection of appropriate related products. This task is particularly difficult for users in remote locations or who lead busy lives. The present invention aims to provide a system that comprehensively solves these challenges.

[1410] 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.

[1411] In this invention, the server includes: means for acquiring plant images using a camera; means for inputting the acquired images into a generative AI model to determine the plant's type and for generating a growth forecast and advice on optimal placement, temperature, light conditions, and pot size by referencing a characteristics database; means for measuring soil humidity and airborne constituent concentrations; means for inputting and analyzing the measured data into the generative AI model to calculate appropriate watering timing based on the plant's type and growth stage; means for acquiring detailed images of the plant's leaves and stems and inputting them into the generative AI model to detect signs of disease or pests and generate remediation methods; means for providing a smartphone app that notifies the user of the generated growth forecast, watering timing, and remediation methods; and means for suggesting related products based on the plant's type, growth status, soil condition, and environmental data and providing the user with links to purchase the suggested related products, enabling users to efficiently and effectively manage their houseplants.

[1412] A "camera" is a device that uses light to capture images or videos and record or transmit the data.

[1413] A "generative AI model" is an artificial intelligence model that uses machine learning and deep learning to analyze data and make predictions and classifications.

[1414] The "characteristics database" is a database that systematically accumulates information on plant species and growing conditions.

[1415] A "sensor" is a device that detects physical or chemical conditions or changes and outputs that information as an electrical signal.

[1416] A "humidity sensor" is a device that measures the humidity of an environment or object and outputs that data.

[1417] An "air concentration meter" is a device that measures the concentration of a specific component in the air and outputs the value.

[1418] "Watering timing" refers to the optimal time and frequency for watering plants.

[1419] A "smartphone app" is software that runs on a smartphone and functions as an interface with the user.

[1420] "Signs of disease or pests" refers to early symptoms or signs of disease or pest abnormalities that appear on plants.

[1421] "Remedial measures" are specific measures or countermeasures to address plant disease or pest problems.

[1422] "Related products" refers to products such as fertilizers, pots, and chemicals used to care for and grow plants.

[1423] "Link" means a hypertext reference that directs a user to a specified web page or online store.

[1424] This invention relates to a system that comprehensively supports optimal houseplant care. This system combines multiple elements, including cameras, humidity sensors, air concentration meters, generative AI models, and smartphone apps, to monitor houseplant growth, health, watering timing, disease and pest detection, and recommend related products.

[1425] System Configuration

[1426] The system includes the following components:

[1427] Camera-equipped device: A device that periodically takes images of houseplants. The user sets up the device in an appropriate position near the plant, and it automatically takes images periodically.

[1428] A device equipped with a humidity sensor and an air concentration meter: This device measures the humidity of the soil and the concentration of components in the air. This data is periodically sent to the server.

[1429] Server: The central control unit that inputs the received data into the generative AI model and performs analysis, judgment, calculation, and generation. The server refers to the characteristic database and provides optimal growth conditions and improvement methods.

[1430] Smartphone app: Software that provides an interface for users to receive notifications and suggestions from the server and manage their houseplants. Through this app, users can check the status of their plants and implement the suggestions.

[1431] Plant image acquisition and analysis

[1432] The terminal (a device with a camera) periodically takes pictures of the houseplant and sends them to a server. The server inputs the received images into a generative AI model to identify the plant's type. It then references a characteristics database to generate advice such as growth predictions, optimal placement, temperature, light conditions, and pot size. The generated advice is then sent to the user via a smartphone app.

[1433] Examples:

[1434] A camera-equipped device takes a photo of a houseplant and sends it to a server, which analyzes the image with a generative AI model and determines that the plant is a cactus. The server then sends a growth forecast and advice on optimal placement and lighting conditions to the user via a smartphone app.

[1435] Example prompt for a generative AI model:

[1436] "Analyze this plant's type and growth predictions with a generative AI model and generate advice."

[1437] Environmental data acquisition and notification

[1438] The terminal (a device equipped with a humidity sensor and air concentration meter) periodically measures soil humidity and the concentration of air components, and sends the data to a server. The server inputs this data into a generative AI model for analysis, and calculates the appropriate watering timing based on the plant type and growth stage. The calculation results are notified to the user via a smartphone app.

[1439] Examples:

[1440] The humidity sensor measures the soil humidity at 30% and sends this to the server. The server, taking into account that the plant is a succulent, determines that it actually needs watering. The smartphone app notifies the user when it's time to water.

[1441] Example prompt for a generative AI model:

[1442] "Based on this humidity data, calculate the optimal watering timing for your plants."

[1443] Detecting diseases and pests and proposing remedial measures

[1444] The device (a device with a camera) takes detailed images of plant leaves and stems and sends them to a server. The server then inputs these images into a generative AI model to detect signs of disease or pests. Based on the detection results, it generates remedial measures (e.g., the use of specific pesticides or changing the type of fertilizer) and notifies the user via a smartphone app.

[1445] Examples:

[1446] The camera-equipped device captures detailed images of plant leaves and sends them to a server, where they are analyzed by a generative AI model to detect signs of powdery mildew, suggesting the use of specific fungicides as a treatment, and notifying the user via a smartphone app.

[1447] Example prompt for a generative AI model:

[1448] "Analyze images of this plant to detect signs of disease and pests and suggest ways to improve it."

[1449] Automatic irrigation and real-time monitoring

[1450] The terminal (automatic irrigation system) automatically irrigates plants based on soil moisture data. It periodically sends real-time images to a server, which then provides the images to users via a smartphone app. This allows users to check the status of their plants in real time, even from remote locations.

[1451] Examples:

[1452] The automatic irrigation system receives humidity data and automatically waters plants when the humidity falls below a specified level, while simultaneously sending the latest real-time plant images to a server for users to view via a smartphone app.

[1453] Example prompt for a generative AI model:

[1454] "Automate irrigation based on this humidity data and provide real-time images to the user."

[1455] Related product suggestions and purchasing support

[1456] The server then suggests relevant products based on the plant's type, growth status, soil condition, and environmental data. The suggestions are sent to the user via a smartphone app, and the user can easily purchase the products via the provided link.

[1457] Examples:

[1458] The server analyzes the plant's growth and determines if it needs specific fertilizer, and then sends a fertilizer recommendation via a smartphone app along with a link to purchase it.

[1459] Example prompt for a generative AI model:

[1460] "Analyze the type of plant and its growth status to suggest relevant products suitable for the user."

[1461] In this way, the system of the present invention allows users to manage their houseplants efficiently and effectively.

[1462] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1463] Step 1: Image capture

[1464] The device (device with camera) periodically takes images of the houseplant. The user first sets up the device's camera in an appropriate position around the plant. The camera then automatically takes images at the set interval.

[1465] Input: Visual information of houseplants

[1466] Output: Image data of houseplants

[1467] Step 2: Send image

[1468] The device sends the captured image to the server, at high resolution and without data compression.

[1469] Input: Image data of houseplants

[1470] Output: Image data sent to the server

[1471] Step 3: Image analysis

[1472] The server inputs the received images into a generative AI model to identify the plant type, and then refers to a database of plant characteristics to generate growth predictions and advice on the best location, temperature, light conditions, pot size, and more.

[1473] Input: Image data of houseplants

[1474] Data processing: Plant species identification and growth prediction using generative AI models

[1475] Output: Advice information (optimal location, temperature, light conditions, pot size)

[1476] Step 4: Advice Notification

[1477] The server sends the generated advice to the smartphone app and notifies the user.

[1478] Input: Advice information

[1479] Output: Advice information sent to the smartphone app

[1480] Step 5: Environmental data measurement

[1481] The terminal (a device equipped with a humidity sensor and air concentration meter) periodically measures the humidity of the soil and the concentration of components in the air.

[1482] Input: Environmental data in soil and air

[1483] Output: Measured humidity data and air composition data

[1484] Step 6: Send data

[1485] The terminal transmits the measured data to the server in real time.

[1486] Input: Humidity data and air composition data

[1487] Output: Environment data sent to the server

[1488] Step 7: Data analysis

[1489] The server inputs the received environmental data into a generative AI model for analysis, and calculates the appropriate watering timing based on the plant type and growth stage.

[1490] Input: Environmental data

[1491] Data processing: Data analysis and irrigation timing calculations using generative AI models

[1492] Output: Watering timing information

[1493] Step 8: Watering Notification

[1494] The server sends the calculation results to a smartphone app and notifies the user.

[1495] Input: Watering timing information

[1496] Output: Watering timing information notified to the smartphone app

[1497] Step 9: Take detailed photos

[1498] The device (a device with a camera) takes detailed images of the plant's leaves and stems.

[1499] Input: Visual information of leaves and stems

[1500] Output: Detailed image data

[1501] Step 10: Send detailed images

[1502] The terminal transmits the captured detailed image to the server.

[1503] Input: Detailed image data

[1504] Output: Detailed image data sent to the server

[1505] Step 11: Image analysis

[1506] The server inputs the received images into a generative AI model to detect signs of disease or pests.

[1507] Input: Detailed image data

[1508] Data processing: disease and pest detection with generative AI models

[1509] Output: Information on detection results and remediation methods

[1510] Step 12: Notification of improvement method

[1511] The server sends the generated improvement method to the smartphone app and notifies the user.

[1512] Input: Information on how to improve

[1513] Output: Information on how to improve the app

[1514] Step 13: Humidity Data Analysis

[1515] The terminal (automatic irrigation system) periodically analyzes soil moisture data.

[1516] Input: Soil moisture data

[1517] Data processing: Analysis of humidity data

[1518] Output: Determine the need for irrigation

[1519] Step 14: Automatic Watering

[1520] The device automatically irrigates based on humidity data, starting when humidity falls below a set threshold.

[1521] Input: Determining the need for irrigation

[1522] Output: Watering execution actions

[1523] Step 15: Real-time image transmission

[1524] The terminal periodically transmits real-time images to the server.

[1525] Input: Real-time image of plants

[1526] Output: Real-time image data sent to the server

[1527] Step 16: Real-time monitoring

[1528] The server receives real-time images and provides them to users via a smartphone app, allowing them to check the status of the plants.

[1529] Input: Real-time image data

[1530] Output: Real-time images provided to a smartphone app

[1531] Step 17: Generate related product suggestions

[1532] The server uses a generative AI model to suggest relevant products needed by the user based on the plant type, growth status, soil condition, and environmental data.

[1533] Input: Plant management data

[1534] Data processing: Generative AI models suggest related products

[1535] Output: A list of related products optimized for the user

[1536] Step 18: Notification of related product suggestions

[1537] The server sends the generated related product suggestions to the smartphone app and notifies the user, who can then easily purchase the products via a link in the app.

[1538] Input: List of related products

[1539] Output: List of related products and purchase links sent to the smartphone app

[1540] (Application example 1)

[1541] 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."

[1542] Traditional houseplant management often involves manually checking the health and growth of plants, which is laborious, time-consuming, and requires specialized knowledge. It can also be difficult to detect diseases and pests early or understand appropriate management methods, which can result in difficult plant development. Furthermore, while physical stores are required to provide accurate information to customers and efficiently recommend related products, it can be difficult for staff to always have the latest information and respond appropriately.

[1543] 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.

[1544] In this invention, the server includes: means for acquiring images of plants using a camera; means for analyzing the acquired images to determine the type of plant and making a growth prediction; sensor means for measuring soil humidity and airborne concentrations; means for analyzing the measurement data to calculate appropriate watering timing; means for detecting plant diseases and pests and generating remediation methods; means for notifying the user of the generated growth prediction, watering timing, and remediation methods; means for allowing store staff to wear smart glasses to check and manage the status of houseplants in real time; means for analyzing images and sensor data and displaying the results on the smart glasses display; and means for providing links to purchase suggested related products. This allows the status of houseplants to be managed efficiently, and allows store staff to provide appropriate advice and suggest related products to customers.

[1545] The "means for acquiring images of plants using a camera" is a combination of hardware and software that takes images of plants and transmits the information to a server.

[1546] "Means of analyzing acquired images to determine the type of plant and predict its growth" refers to technology that uses AI models and machine learning algorithms to analyze acquired images, identify the type of plant, and predict its future growth.

[1547] The "sensor means for measuring the humidity of the soil and the concentration of components in the air" refers to a sensor that measures the humidity of the soil and the concentration of components in the air, thereby collecting environmental data in which the plant is placed.

[1548] The "means for analyzing the measurement data and calculating the appropriate timing for irrigation" refers to an algorithm and program that analyzes the data obtained from the sensor and calculates the optimal timing for irrigation for the plants.

[1549] "Means for detecting plant diseases and pests and generating remedial measures" refers to technology that analyzes plant image data, detects signs of disease or pests, and suggests appropriate remedial measures based on that data.

[1550] The "means for notifying the user of the generated growth forecast, irrigation timing, and improvement methods" refers to an application and communication means for notifying the user of the analysis results and suggestions via a smart device, etc.

[1551] "A means for store staff to wear smart glasses to check and manage the status of houseplants in real time" refers to a technology that uses smart glasses to check and manage the current status of houseplants in real time.

[1552] "Means for analyzing images and sensor data and displaying the results on the smart glasses display" refers to a system that analyzes acquired images and sensor data using an AI model and displays the results on the smart glasses display.

[1553] The "means for providing links to purchase suggested related products" refers to a technology that provides users with links to related products based on the analysis results and suggestions, allowing them to easily purchase the products.

[1554] This invention is a comprehensive system for effectively managing houseplants, consisting of multiple elements including a camera, humidity sensor, air concentration meter, AI model, smart glasses, server, and smartphone app.

[1555] System configuration and program processing overview

[1556] 1. Plant image acquisition and analysis

[1557] The server uses a camera to capture images of the plant and analyzes the transmitted images to determine the plant's type. Specifically, this process uses an image recognition algorithm (using TensorFlow or PyTorch). Based on the analysis results, the server predicts the plant's growth and suggests the environmental conditions and care methods necessary for growth. This information is displayed on the smart glasses' display and provided to store staff.

[1558] 2. Environmental data acquisition and notification

[1559] The server periodically measures soil humidity and the concentration of airborne elements using humidity sensors and air concentration meters, and collects and analyzes the data. This provides environmental data on the plant's location and allows it to calculate the optimal watering timing. The results are then communicated to the user via the smart glasses' display or a smartphone app.

[1560] 3. Disease and pest detection and improvement method suggestions

[1561] The server uses a camera to capture detailed images of the plant's leaves and stems, which are then analyzed using an AI model to detect signs of disease or pests and generate remedial measures (such as the use of specific pesticides).These remedial measures are then displayed on the smart glasses' display and communicated to store staff.

[1562] 4. Automatic irrigation and real-time monitoring

[1563] The server includes a means for automatically watering plants based on data from the humidity sensor, and also captures real-time images of the plants and transmits them to the server, which analyzes the images and provides real-time information to the user, enabling plant monitoring even in remote locations.

[1564] 5. Related product suggestions and purchasing support

[1565] The server will suggest relevant products (pots, fertilizer, pest control products, etc.) that users need based on the plant type, growth status, soil condition, and environmental data. These suggestions will be displayed on the smart glasses display or smartphone app, and users can easily purchase the products via the provided link.

[1566] Examples and prompts

[1567] Below are some examples and prompts:

[1568] Example 1: Growth forecasts and advice

[1569] The server analyzes images of plants taken with the camera-equipped smart glasses, determines that the plant is a cactus, and displays advice on the smart glasses' display regarding appropriate placement and lighting conditions based on the results of growth predictions.

[1570] Example 2: Watering notification

[1571] The server checks that the soil humidity measured by the humidity sensor is 30% and calculates the optimum watering time for the succulents. When it is time to water, a notification will appear on the smart glasses display.

[1572] Example 3: Disease detection and improvement suggestions

[1573] The server detects signs of powdery mildew from detailed images of plant leaves taken by camera-equipped smart glasses, and notifies the user on the glasses' display to recommend the use of specific fungicides as a countermeasure.

[1574] Prompt Sentence Examples

[1575] "Upload an image of your houseplant and we'll identify the plant's type, its health, and how to properly care for it."

[1576] Houseplant image: [Upload form]

[1577] Humidity data: [Acquired in real time]

[1578] Air concentration data: [Acquired in real time]

[1579] result:

[1580] Plant Type: Cactus

[1581] Health: Good

[1582] Watering timing: Next time in 3 days

[1583] Related Products: [Link]

[1584] This will clarify the specific treatment and usage methods, allowing for efficient management and sales support of houseplants in physical stores.

[1585] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1586] Step 1:

[1587] A camera-equipped device takes pictures of plants and sends them to a server. Specifically, the device's camera periodically takes photos of plants and uploads the data to the server via wireless communication (Wi-Fi or Bluetooth). The input is the plant image, and the output is the image data sent to the server.

[1588] Step 2:

[1589] The server analyzes the image data it receives using an AI model to determine the type of plant. Specifically, the AI ​​model (using TensorFlow and PyTorch) analyzes the image and performs feature extraction and classification. The input is the image data of the plant, and the output is the type of plant.

[1590] Step 3:

[1591] The server predicts growth based on the type of plant and calculates the necessary environmental conditions and management methods. Specifically, a growth prediction algorithm is used to calculate the optimal growth conditions (light intensity, temperature, humidity, etc.) based on the type of plant and past data. The input is the type of plant, and the output is growth prediction data.

[1592] Step 4:

[1593] Humidity sensors and air concentration meters periodically measure the humidity of the soil and the concentrations of components in the air, and send this data to a server. Specifically, the sensors acquire data in real time and transmit it to the server via wireless communication. The input is the sensor measurement data, and the output is the environmental data sent to the server.

[1594] Step 5:

[1595] The server analyzes the received environmental data and calculates the appropriate watering timing. Specifically, the watering algorithm determines the watering timing by taking into account the sensor data, plant type, and growth stage. The inputs are environmental data, plant type, and growth prediction data, and the output is the watering timing.

[1596] Step 6:

[1597] A camera-equipped device takes detailed images of plants and sends them to a server. Specifically, the device's camera takes close-up images of plant leaves and stems and sends the image data to the server. The input is the detailed plant image, and the output is the image data sent to the server.

[1598] Step 7:

[1599] The server analyzes detailed image data using an AI model to detect signs of disease or pests. Specifically, the AI ​​model uses image analysis technology to identify the characteristics of disease or pests and generates remediation methods based on the findings. The input is detailed image data, and the output is the disease or pest detection results and remediation methods.

[1600] Step 8:

[1601] The server displays the growth prediction, irrigation timing, and improvement methods on the smart glasses display. Specifically, the server sends the analysis results to the smart glasses application and notifies the user in real time. The input is the growth prediction data, irrigation timing, and improvement methods, and the output is the information displayed on the smart glasses display.

[1602] Step 9:

[1603] The server generates and suggests a list of related products based on the plant's type and growth status. Specifically, it references a database to create a list of related products suitable for the plant (pots, fertilizer, pest control products, etc.), which are then displayed on the smart glasses or smartphone app. The input is data on the plant's type and growth status, and the output is a list of related products.

[1604] Step 10:

[1605] The server provides users with purchase links for related products, allowing them to easily make purchases. Specifically, it generates online shop links and provides them to users via smart glasses or a smartphone app. The input is a list of related products, and the output is the purchase links.

[1606] This will create a system that efficiently manages the condition of houseplants and allows staff to provide customers with appropriate advice and suggest related products.

[1607] 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.

[1608] This invention combines a comprehensive support system for optimal houseplant care with an emotion engine. By combining multiple elements, including a camera, humidity sensor, air concentration meter, AI model, emotion engine, and smartphone app, the system monitors houseplant growth, health, watering timing, disease and pest detection, suggests related products, and even recognizes the user's emotional state to provide customized advice.

[1609] System Configuration

[1610] The system includes the following components:

[1611] Camera-equipped device: Take regular pictures of your houseplants.

[1612] Terminal equipped with humidity sensor and air concentration meter: Measures the humidity of the soil and the concentration of components in the air.

[1613] Server: Analyzes collected data to determine plant species, predict growth, calculate irrigation timing, detect diseases and pests, generate improvement methods, analyze sentiment data, generate customized advice, and suggest related products.

[1614] Emotion Engine: Recognize and analyze user emotions.

[1615] Smartphone app: Provides an interface for users to receive notifications and suggestions from the server and manage their houseplants.

[1616] Program processing

[1617] The specific program processing of the system is shown below.

[1618] 1. Plant image acquisition and analysis

[1619] The device (camera-equipped device) periodically takes photos of the houseplant and sends them to the server. The server analyzes the received images using an AI model to identify the plant's type. It also references a database of plant characteristics to predict its growth. This prediction generates advice on the optimal location, temperature, light conditions, and pot size. The server then notifies the user of this advice via a smartphone app.

[1620] 2. Environmental data acquisition and notification

[1621] The terminal (a device equipped with a humidity sensor and air concentration meter) periodically measures the humidity of the soil and the concentration of elements in the air, and sends the data to a server. The server analyzes this data and calculates the appropriate watering timing based on the type of plant and its growth stage. The calculation results are notified to the user via a smartphone app.

[1622] 3. Disease and pest detection and improvement method suggestions

[1623] The device (a device with a camera) takes detailed images of plant leaves and stems and sends them to a server. The server then analyzes these images using an AI model to detect signs of disease or pests. Based on the detection results, it generates remedial measures (e.g., the use of specific pesticides or changing the type of fertilizer) and notifies the user via a smartphone app.

[1624] 4. Automatic irrigation and real-time monitoring

[1625] The terminal (automatic irrigation system) automatically irrigates based on soil moisture data. The terminal also periodically sends real-time images to a server, which then provides the images to the user via a smartphone app. This allows users to check the condition of their plants in real time, even from remote locations.

[1626] 5. Related product suggestions and purchasing support

[1627] The server collects information on the plant's type, growth status, soil condition, and environmental data. Based on the collected data, it generates a list of the most suitable related products (pots, fertilizer, pest control products, etc.) and notifies the user of the list via a smartphone app. Users can easily purchase the products via links within the app.

[1628] 6. Customized advice using emotion engine

[1629] The device (smartphone app) collects emotional data from the user's social media posts and daily usage logs and sends it to a server. The server then uses an emotion engine to analyze the user's emotional state and generate optimal advice based on the user's mood and stress level. For example, if the user is feeling stressed, the server can suggest the characteristics and care methods of houseplants that have a soothing effect. The server can also accumulate the user's emotional data and reflect it in future advice.

[1630] Specific examples

[1631] Specific examples are shown below.

[1632] Example 1: Emotion-based growth advice

[1633] The device (camera-equipped device) takes a photo of the plant and sends it to the server. The server analyzes the image and determines that the plant is a ficus. It predicts its growth and generates advice on the appropriate placement and lighting conditions. It also collects emotional data from the user's smartphone app and detects whether the user is in a relaxed mood. Based on this, advice emphasizing the relaxing effects and characteristics of ficus is sent via the smartphone app.

[1634] Example 2: Watering notification and sentiment analysis

[1635] The device (humidity sensor) measures the soil humidity to be 25% and sends this to the server. The server, considering that the plant is a sansevieria, determines that it needs watering. It notifies the user via a smartphone app when it is time to water it. At the same time, the server, through its emotion engine, detects that the user is feeling busy and provides advice on simple watering methods or the use of an automatic watering system.

[1636] Example 3: Disease detection and customised improvement suggestions

[1637] The device (with a camera) takes detailed images of plant leaves and sends them to the server. The server analyzes the images and detects signs of black spot disease on the leaves. The server then suggests the use of a specific fungicide as a countermeasure. Furthermore, the server detects the user's anxiety through an emotion engine and sends reassuring information, including detailed instructions for disease prevention and success stories, via a smartphone app.

[1638] In this way, the system of the present invention, which is combined with an emotion engine, can provide individual advice according to the user's emotional state, making houseplant management more effective and user-friendly.

[1639] The processing flow will be explained below.

[1640] 1. Plant image acquisition and analysis

[1641] Step 1:

[1642] The terminal (device with a camera) takes images of the houseplant at set time intervals.

[1643] Step 2:

[1644] The terminal transmits the captured image to the server.

[1645] Step 3:

[1646] The server inputs the received images into an AI model to identify the type of plant.

[1647] Step 4:

[1648] The server makes a growth prediction based on the identified type by referring to a characteristics database.

[1649] Step 5:

[1650] The server generates advice based on growth prediction results, such as the optimal location, temperature, light conditions, and pot size.

[1651] Step 6:

[1652] The server sends the generated advice to the smartphone app.

[1653] Step 7:

[1654] Users can check the advice through a smartphone app and manage their belongings appropriately.

[1655] 2. Environmental data acquisition and notification

[1656] Step 1:

[1657] The terminal (a device equipped with a humidity sensor and air concentration meter) periodically measures the humidity of the soil and the concentration of components in the air.

[1658] Step 2:

[1659] The terminal transmits the measured environmental data to the server.

[1660] Step 3:

[1661] The server analyzes the received data and calculates the optimal watering timing based on the plant type and growth stage.

[1662] Step 4:

[1663] The server notifies the smartphone app of the calculation results.

[1664] Step 5:

[1665] Users can check the watering timing through a smartphone app and either water appropriately or set the watering to be automated.

[1666] 3. Disease and pest detection and improvement method suggestions

[1667] Step 1:

[1668] The device (camera-equipped device) periodically takes detailed images of the plant's leaves and stems.

[1669] Step 2:

[1670] The terminal transmits the captured detailed image to the server.

[1671] Step 3:

[1672] The server uses AI models to analyze the images and detect signs of disease or pests.

[1673] Step 4:

[1674] The server generates remedial measures (e.g., use of specific chemicals or changes in fertilizer) based on the detection results.

[1675] Step 5:

[1676] The server notifies the smartphone app how to improve the situation.

[1677] Step 6:

[1678] Users can check the notification via their smartphone app and take appropriate measures.

[1679] 4. Automatic irrigation and real-time monitoring

[1680] Step 1:

[1681] The terminal (humidity sensor and automatic irrigation system) continuously monitors the soil moisture.

[1682] Step 2:

[1683] The server determines the need for watering based on the humidity data.

[1684] Step 3:

[1685] If the server determines that watering is necessary, it sends a watering instruction to the terminal (automatic watering system).

[1686] Step 4:

[1687] The terminal supplies the set amount of water to the plants according to the watering instructions.

[1688] Step 5:

[1689] The terminal periodically takes real-time images and transmits them to the server.

[1690] Step 6:

[1691] The server sends real-time images to a smartphone app.

[1692] Step 7:

[1693] Users can check the status of their plants in real time from a remote location via a smartphone app.

[1694] 5. Related product suggestions and purchasing support

[1695] Step 1:

[1696] The server collects plant type, growth status, soil conditions, and environmental data.

[1697] Step 2:

[1698] The server generates a list of the most suitable related products (pots, fertilizer, pest control products, etc.) based on the collected data.

[1699] Step 3:

[1700] The server notifies the smartphone app of the generated related product list.

[1701] Step 4:

[1702] Users can check the suggested products through the smartphone app and, if necessary, purchase the products via a link to an e-commerce site.

[1703] 6. Customized advice using emotion engine

[1704] Step 1:

[1705] The device (smartphone app) collects emotional data from users' social media posts and daily usage logs.

[1706] Step 2:

[1707] The terminal transmits the emotion data to the server.

[1708] Step 3:

[1709] The server uses an emotion engine to analyze the user's emotional state.

[1710] Step 4:

[1711] The server generates customized advice based on the emotional state.

[1712] Step 5:

[1713] The server then sends customized advice to the smartphone app.

[1714] Step 6:

[1715] Users can check the advice through a smartphone app and manage their belongings appropriately.

[1716] Specific examples

[1717] Example 1: Emotion-based growth advice

[1718] Step 1:

[1719] The device (camera-equipped device) takes a photo of the plant and sends it to the server.

[1720] Step 2:

[1721] The server analyzes the image and determines that the plant is a ficus.

[1722] Step 3:

[1723] The server makes growth predictions and generates advice on suitable placement and lighting conditions.

[1724] Step 4:

[1725] The device (smartphone app) collects the user's emotional data and sends it to the server.

[1726] Step 5:

[1727] The server detects that the user is in a relaxed mood.

[1728] Step 6:

[1729] The server generates advice that emphasizes the relaxing effects and notifies the smartphone app.

[1730] Step 7:

[1731] Users can check advice through a smartphone app and carry out plant management.

[1732] Example 2: Watering notification and sentiment analysis

[1733] Step 1:

[1734] The terminal (humidity sensor) measures the soil humidity to be 25% and sends it to the server.

[1735] Step 2:

[1736] The server considers the plant to be a sansevieria and determines that it needs watering.

[1737] Step 3:

[1738] The server notifies the smartphone app when it is time to water the plants.

[1739] Step 4:

[1740] The device (smartphone app) collects the user's emotional data and sends it to the server.

[1741] Step 5:

[1742] The server detects that the user is in a busy mood.

[1743] Step 6:

[1744] The server generates advice recommending simple watering methods and the use of automatic watering systems, and notifies the smartphone app.

[1745] Step 7:

[1746] Users can check the advice via a smartphone app and carry out or set up irrigation.

[1747] Example 3: Disease detection and customization of remediation suggestions

[1748] Step 1:

[1749] The terminal (device with a camera) takes detailed images of plant leaves and sends them to the server.

[1750] Step 2:

[1751] The server analyzes the images and detects signs of black spot disease on the leaves.

[1752] Step 3:

[1753] The server suggests using certain disinfectants as a countermeasure.

[1754] Step 4:

[1755] The device (smartphone app) collects the user's emotional data and sends it to the server.

[1756] Step 5:

[1757] The server detects that the user is feeling anxious.

[1758] Step 6:

[1759] The server generates reassuring information, including detailed procedures for disease prevention and success stories, and sends it to the smartphone app.

[1760] Step 7:

[1761] Users can check the advice through a smartphone app and implement measures.

[1762] Example 2

[1763] 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."

[1764] While existing houseplant management systems provide basic management functions such as plant health and growth prediction, watering timing, and disease and pest detection, they lack the ability to provide customized advice based on the user's emotional state. Furthermore, they lack the functionality to suggest related products, provide real-time remote monitoring, and provide watering advice that takes emotional state into account. This makes it difficult to optimize management for each individual user, and there is a need for an improved user experience.

[1765] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring plant images using a camera, means for analyzing the acquired images to determine the plant's type and perform growth prediction, sensor means for measuring soil humidity and airborne concentrations, means for analyzing the measurement data to calculate appropriate watering timing, means for detecting plant diseases and pests and generating remedial measures, means for notifying the user of the generated growth prediction, watering timing, and remedial measures, means for generating a list of related products and suggesting them to the user, and means for analyzing the user's emotional state and generating customized advice. This allows users to receive optimal plant care advice tailored to their individual emotional state, enabling effective and user-friendly houseplant care.

[1766] The "means for acquiring images of plants using a camera" refers to a camera device for taking images of plants and a control system for automating its operation.

[1767] "Means for analyzing acquired images to determine the type of plant and predict growth" refers to a system that uses AI models and algorithms to identify the type of plant from image data and predict growth patterns.

[1768] The "sensor means for measuring soil moisture and airborne concentrations" refers to a soil moisture sensor and a sensor device for measuring airborne components (e.g., CO2 concentration).

[1769] The "means for analyzing measurement data and calculating appropriate irrigation timing" is a system that calculates the timing for irrigation based on data obtained from sensors, taking into account the type of plant and its growth stage.

[1770] The "means for detecting plant diseases and pests and generating remedial measures" is a system that uses AI models and image analysis technology to detect signs of plant diseases and pests and then suggests appropriate remedial measures based on that information.

[1771] "Means for notifying the user of the generated growth forecast, irrigation timing, and improvement methods" refers to a communication and interface system for notifying the user of the generated information via their smartphone or other device.

[1772] The "means for generating a list of related products and suggesting them to the user" is a system for generating a list of related products based on the plant's condition and environmental data and suggesting the list to the user.

[1773] The "means for analyzing the user's emotional state and generating customized advice" refers to an emotion analysis engine and generation system for analyzing the user's emotional data and providing individually customized advice based on the results.

[1774] "Means for obtaining real-time images of plants using a camera and providing them to a remote user" is a system for distributing real-time plant images taken by a camera to a remote user.

[1775] The "means for automatically performing irrigation based on data from a humidity sensor" is an automatic irrigation system for automatically performing irrigation based on data from a humidity sensor.

[1776] The "means for proposing a watering method based on the user's emotional state" is a system that takes into account the user's emotional data and proposes the optimal watering method depending on the situation.

[1777] The "means for providing links to purchase suggested related products" is a system that provides users with links to purchase suggested related products online.

[1778] The "means for customizing the content of related product suggestions according to the user's emotional state" is a system that individually customizes the content of related product suggestions based on the user's emotional data.

[1779] This invention combines a comprehensive support system for optimal houseplant care with an emotion engine that understands the user's emotional state and provides customized advice accordingly. The system's main components are a camera, a humidity sensor, an air concentration meter, an AI model, an emotion engine, and a smartphone app.

[1780] System configuration

[1781] 1. Camera-equipped device: Takes pictures of the houseplant periodically and sends them to the server.

[1782] 2. Terminal with humidity sensor and air concentration meter: Measures the humidity of the soil and the concentration of components in the air and sends the data to the server.

[1783] 3. Server: Receives and analyzes images and sensor data to determine plant species, predict growth, calculate irrigation timing, detect diseases and pests, generate improvement methods, analyze emotional data, generate customized advice, and suggest related products.

[1784] 4. Emotion engine: Recognizes and analyzes user emotions from social media posts and usage logs.

[1785] 5. Smartphone app: Provides an interface for users to receive notifications and suggestions from the server and manage their houseplants.

[1786] System Operation

[1787] Plant image acquisition and analysis

[1788] The device periodically takes pictures of the houseplant using a camera and sends them to a server. The server then analyzes the received images using an AI model to determine the plant's type. The growth management system also references a characteristics database to predict the plant's growth. The generated advice includes detailed information such as lighting conditions, optimal placement, and pot size. This advice is then sent to the user via a smartphone app.

[1789] Example: The device takes a picture of a ficus, and the server predicts its growth and advises that the best place to place it is near a window in the living room.

[1790] Example prompt: "What growth predictions can you give me for the ficus and what are the best locations and temperature conditions?"

[1791] Environmental data acquisition and notification

[1792] The terminal (a device equipped with a humidity sensor and air concentration meter) periodically measures the humidity of the soil and the concentration of components in the air, and sends the data to a server. The server receives the data and calculates the appropriate timing for watering. The results are then notified to the user via a smartphone app.

[1793] Example: The soil moisture is measured at 25%, the server determines that watering is required, and sends a notification saying "Water now."

[1794] Example prompt: "When soil moisture is 25%, should I irrigate?"

[1795] Detecting diseases and pests and proposing remedial measures

[1796] The device (a device with a camera) takes detailed images of plant leaves and stems and sends them to a server. The server then analyzes the images using AI models to detect signs of disease or pests. Based on the results, it generates improvement measures, such as the use of specific pesticides or changes to fertilizer, and notifies the user via a smartphone app.

[1797] Example: Detecting black spot on leaves and suggesting the use of a fungicide.

[1798] Example prompt: "If the plant has black spot on its leaves, what fungicide should I use?"

[1799] Automatic irrigation and real-time monitoring

[1800] The terminal (automatic irrigation system) automatically irrigates plants based on soil moisture data. The terminal also periodically sends real-time images to a server, which then provides the images to users via a smartphone app. This allows users to check the status of their plants even from remote locations.

[1801] Example: An automatic irrigation system irrigates when soil moisture falls below 20%.

[1802] Example prompt: "Write a scenario in which an automated irrigation system would operate if soil moisture dropped."

[1803] Related product suggestions and purchasing support

[1804] The server analyzes the plant's type, growth status, soil condition, and environmental data to generate a list of related products, which are then sent to the user via a smartphone app, where they can conveniently purchase the products via a link in the app.

[1805] Example: The server recommends the best fertilizer, and the user purchases it through the app.

[1806] Example prompt: "Please suggest the best fertilizer for houseplants and generate a link to purchase it."

[1807] Customized advice with an emotional engine

[1808] The device (smartphone app) collects emotional data from users' social media posts and daily usage logs, and sends it to a server. The server then uses an emotion engine to analyze the user's emotional state and generate optimal advice based on their mood and stress level.

[1809] Example: If a user is feeling stressed, the emotion engine suggests soothing plants and care methods.

[1810] Example prompt: "What are the properties of houseplants that are effective in relieving stress and how should you care for them?"

[1811] In this way, the system of the present invention can effectively and user-friendly manage houseplants, and can also improve the user experience by analyzing the user's emotions and providing customized advice based on those emotions.

[1812] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1813] Step 1:

[1814] Acquiring and sending plant images

[1815] The device periodically acquires images of the houseplant. The device's camera takes a photo of the plant at a specified time (e.g., 10:00 AM every day) and sends the image data to the server. The input is the image data, and the output is transmission to the server.

[1816] Step 2:

[1817] Receiving and analyzing images

[1818] The server receives the images sent from the device. The received image data is input into the AI ​​model to determine the plant's type. It then references a characteristics database to predict growth. This allows it to predict the plant's growth pattern and calculate the appropriate placement and environmental conditions. The input is image data, and the output is the plant's type determination result and growth prediction data.

[1819] Step 3:

[1820] Advice generation and notification

[1821] The server generates advice for the user based on the growth prediction results. For example, specific advice such as "The best place to place a ficus is by a window in the living room" is generated for the growth prediction of the ficus. The generated advice is notified to the user via a smartphone app. The input is the growth prediction data, and the output is the generated advice.

[1822] Step 4:

[1823] Acquiring and sending environmental data

[1824] The terminal (a device with a humidity sensor and air concentration meter) periodically measures the humidity of the soil and the concentration of components in the air and sends the data to the server. For example, it measures and sends the humidity (e.g., 25%) and carbon dioxide concentration in the air (e.g., 300 ppm) every hour. The input is environmental sensor data, and the output is sent to the server.

[1825] Step 5:

[1826] Analyzing environmental data and calculating irrigation timing

[1827] The server analyzes the environmental data sent from the device. Taking into account the type of plant and its growth stage, it calculates when watering is necessary. For example, if the humidity is 25%, it will determine that a sansevieria needs watering. The results of this calculation are notified to the user via a smartphone app. The input is environmental sensor data, and the output is a notification of when to water.

[1828] Step 6:

[1829] Shooting and transmitting diseases and pests

[1830] The terminal (device with a camera) takes detailed images of plant leaves and stems and sends them to a server. For example, high-resolution images are taken to detect signs of black spot disease on leaves. The input is the detailed image data, and the output is transmission to the server.

[1831] Step 7:

[1832] Disease and pest detection and remediation methods

[1833] The server uses an AI model to analyze detailed images sent from the device and detect signs of disease or pests. Based on the detection results, it generates improvement measures, such as the use of specific pesticides or changes to fertilizer. For example, if black spot disease is detected, it will suggest the use of an appropriate fungicide. The input is detailed image data, and the output is improvement measure suggestions.

[1834] Step 8:

[1835] Automatic irrigation

[1836] The terminal (automatic irrigation system) automatically irrigates based on soil moisture data. For example, irrigation is automatically performed when the humidity falls below 20%. The input is humidity data, and the output is irrigation execution.

[1837] Step 9:

[1838] Real-time image acquisition and transmission

[1839] The device periodically takes real-time images of the plants and sends them to the server. For example, it acquires and sends the latest plant images every hour. The input is real-time image data, and the output is sent to the server.

[1840] Step 10:

[1841] Related product suggestions and notifications

[1842] The server generates a list of related products based on the plant type, growth status, soil condition, and environmental data. This list is then notified to the user. For example, "organic fertilizer B" and "insect repellent spray C" are recommended for a ficus. The input is various sensor data and a plant database, and the output is a list of related products.

[1843] Step 11:

[1844] Collecting and transmitting emotional data

[1845] The device (smartphone app) collects emotional data from users' social media posts and daily usage logs, and sends it to a server. For example, the keyword "stress" is collected from users' social media posts. The input is emotional data, and the output is transmission to the server.

[1846] Step 12:

[1847] Emotional state analysis and customized advice generation

[1848] The server analyzes the emotional data using an emotion engine and generates optimal advice based on the user's emotional state. For example, if the user is feeling stressed, it will suggest plants that have a relaxing effect and how to care for them. The input is emotional data, and the output is customized advice.

[1849] Thus, through each processing step, a specific embodiment of the invention is realized.

[1850] (Application example 2)

[1851] 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."

[1852] When caring for houseplants, maintaining optimal plant growth and health requires consideration of various factors, such as appropriate watering timing and disease and pest control. Furthermore, providing effective advice based on the user's emotional state would provide a more personalized experience. However, until now, there has been no comprehensive management system that can effectively and easily solve these challenges.

[1853] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1854] In this invention, the server includes means for capturing images of plants using a camera, means for analyzing the captured images to determine the type of plant and making a growth prediction, sensor means for measuring soil humidity and its concentration in the air, means for analyzing the measurement data to calculate appropriate watering timing, means for detecting plant diseases and pests and generating remedial measures, means for notifying the user of the generated growth prediction, watering timing, and remedial measures, means for generating a list of related products and suggesting them to the user, and means for recognizing the user's emotional state and generating customized advice, thereby enabling the user to receive comprehensive support for optimal care of their houseplants.

[1855] "Camera means" refers to a device for capturing images of plants.

[1856] The "plant type determination means" is a device or method that analyzes the acquired image to determine the type of plant.

[1857] A "growth prediction means" is a device or method that predicts future growth based on plant type.

[1858] "Sensor means" refers to a device for measuring the moisture content of soil and the concentration in the air.

[1859] The "watering timing calculation means" is a device or method that analyzes measurement data and calculates the appropriate watering timing.

[1860] "Disease and pest detection means" means a device or method for detecting plant diseases or pests.

[1861] The "remediation method generating means" is a device or method that generates a remediation method for a detected disease or pest.

[1862] "Notification means" refers to a device or method that notifies the user of the generated growth forecast, irrigation timing, and improvement methods.

[1863] The "related product suggestion means" is a device or method that generates and suggests a list of related products that are most suitable for the user.

[1864] An "emotion recognizer" is a device or method that recognizes the emotional state of a user.

[1865] A "customized advice generator" is a device or method that generates customized advice based on a recognized emotional state of a user.

[1866] The system embodying this invention provides comprehensive support for users to optimally manage their houseplants, and specifically comprises a camera means, a sensor means, a server, a notification means, and an application.

[1867] First, each component of the system will be described.

[1868] Camera Means

[1869] The system acquires images of the plants by means of a camera, which periodically takes high-resolution images to monitor the plant's growth status and detect diseases and pests.

[1870] Sensor means

[1871] The sensor means measures the humidity of the soil, the carbon dioxide concentration in the air, the temperature, and the amount of light. Examples of such sensors include humidity sensors, air concentration meters, and light sensors.

[1872] server

[1873] The server receives and analyzes data acquired from the camera and sensor means. The server is equipped with an AI model and emotion engine, and performs the following processes:

[1874] Image analysis:

[1875] The AI ​​model analyzes the images of plants captured by the camera and determines the plant's type. After determining the type, it refers to a characteristics database and predicts its growth.

[1876] Environmental Data Analysis:

[1877] Data from the sensor means is analyzed to calculate the timing of irrigation based on soil moisture and carbon dioxide levels in the air.

[1878] Disease and pest detection:

[1879] Based on image data, an AI model is used to identify signs of plant disease and pests and generate improvement methods.

[1880] Sentiment Data Analysis:

[1881] It uses an emotion engine to recognize users' emotions, analyzes their social media posts and smartphone app usage logs, and generates optimal advice based on their emotional state.

[1882] Notification means

[1883] The system provides users with growth forecasts, watering timing, improvement methods, and customized advice based on the user's emotional state via a smartphone app or smart glasses.

[1884] Application Overview

[1885] The system's application provides information to users through smartphones or smart glasses.

[1886] 1. Houseplant status notification:

[1887] Displays real-time information on plant health, growth status, irrigation timing, and pest and disease information.

[1888] 2. Emotion-based personalized advice:

[1889] The emotion engine analyzes the user's emotions and provides special advice and related product suggestions based on that.

[1890] 3. Interactive storefront:

[1891] Augmented reality (AR) technology is used to visually display plant characteristics and health status.

[1892] Specific examples

[1893] Example 1:

[1894] When users scan their houseplant with the smart glasses, they are given real-time information about the plant's type, health, and growth forecast, and if they're in the mood to relax, they're given suggestions on how to care for the plant to help them relax.

[1895] Example prompt sentence:

[1896] "Scan your houseplants with smart glasses and display optimal recommendations based on plant type, health, watering timing, and user sentiment."

[1897] In this way, the invention allows users to provide more effective and personalized houseplant care.

[1898] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1899] Step 1:

[1900] The server periodically acquires images of the houseplants from the camera means.

[1901] Input: An image of a plant taken by camera means.

[1902] Data processing and calculation: The server uses an AI model to analyze the image and identify the plant species. It then references a database of plant characteristics to predict growth.

[1903] Output: Plant type and growth prediction data based on it.

[1904] Specific operation: The server runs an image processing algorithm to analyze the shape and color of the plant leaves.

[1905] Step 2:

[1906] The terminal acquires soil humidity data and air component concentration data from a device equipped with a humidity sensor and air concentration meter.

[1907] Input: Soil moisture data and air element concentration data.

[1908] Data processing and calculation: The server analyzes this data and calculates the appropriate watering timing based on the type of plant and its growth stage.

[1909] Output: Watering notification data.

[1910] Specific operation: The server compares the humidity data with the plant's humidity requirements and calculates the amount of water needed.

[1911] Step 3:

[1912] The device captures detailed images of plant leaves and stems and sends them to the server.

[1913] Input: Images of plant leaves and stems.

[1914] Data processing and calculation: The server analyzes the images and uses AI models to detect signs of disease or pests.

[1915] Output: Disease and pest detection results and how to remedy them.

[1916] Specific operation: The server extracts features from the image and identifies parts that match characteristic data of diseases and pests.

[1917] Step 4:

[1918] The server obtains the user's emotional data from the smartphone app.

[1919] Input: User emotion data (e.g., social media posting data, smartphone app usage logs).

[1920] Data processing and calculation: The server uses an emotion engine to analyze the user's emotional state.

[1921] Output: User's emotional state data.

[1922] Specific operation: The server uses natural language processing technology to analyze emotions from social media post data.

[1923] Step 5:

[1924] The server generates customized advice based on the emotional state.

[1925] Input: User emotional state data.

[1926] Data processing and calculation: The server refers to a pre-configured advice database and selects advice that matches the user's emotions.

[1927] Output: Customized advice.

[1928] Specific operation: If the user is feeling stressed, the server selects the characteristics of plants that have a relaxing effect and how to care for them.

[1929] Step 6:

[1930] The server then sends the generated growth forecast, irrigation timing, improvement methods, and customized advice to the user via a smartphone app or smart glasses.

[1931] Input: growth forecasts, irrigation timing, remediation methods, customized advice.

[1932] Data calculation and processing: The server converts this information into a user-friendly format.

[1933] Output: Notification data to the user.

[1934] Specific behavior: The server provides real-time notifications through the user interface.

[1935] 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.

[1936] 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.

[1937] 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.

[1938] [Fourth embodiment]

[1939] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1940] 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.

[1941] 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).

[1942] 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.

[1943] 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.

[1944] 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).

[1945] 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. 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.

[1946] 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.

[1947] 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.

[1948] 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.

[1949] 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.

[1950] 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.

[1951] 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."

[1952] This invention relates to a system that comprehensively supports optimal houseplant care. This system combines multiple elements, including cameras, humidity sensors, air concentration meters, AI models, and smartphone apps, to monitor houseplant growth, health, watering timing, disease and pest detection, and recommend related products.

[1953] System Configuration

[1954] The system includes the following components:

[1955] Camera-equipped device: Take regular pictures of your houseplants.

[1956] Terminal equipped with humidity sensor and air concentration meter: Measures the humidity of the soil and the concentration of components in the air.

[1957] Server: Analyzes collected data and generates plant type determinations, growth forecasts, irrigation timing calculations, disease and pest detection, and remediation methods.

[1958] Smartphone app: Provides an interface for users to receive notifications and suggestions from the server and manage their houseplants.

[1959] Program processing

[1960] The specific program processing of the system is shown below.

[1961] 1. Plant image acquisition and analysis

[1962] The device (camera-equipped device) periodically takes photos of the houseplant and sends them to the server. The server analyzes the received images using an AI model to identify the plant's type. It then references a database of plant characteristics to predict its growth. This prediction generates advice on the optimal placement, temperature, light conditions, and pot size. The server then notifies the user of this advice via a smartphone app.

[1963] 2. Environmental data acquisition and notification

[1964] The terminal (a device equipped with a humidity sensor and air concentration meter) periodically measures the soil humidity and the concentration of elements in the air and sends the data to a server. The server analyzes this data and calculates the appropriate watering timing based on the plant type and growth stage. The calculation results are notified to the user via a smartphone app.

[1965] 3. Disease and pest detection and improvement method suggestions

[1966] The device (a device with a camera) takes detailed images of plant leaves and stems and sends them to a server. The server then analyzes these images using an AI model to detect signs of disease or pests. Based on the detection results, it generates remedial measures (e.g., the use of specific pesticides or changing the type of fertilizer) and notifies the user via a smartphone app.

[1967] 4. Automatic irrigation and real-time monitoring

[1968] The terminal (automatic irrigation system) automatically irrigates plants based on soil moisture data. The terminal also periodically sends real-time images to a server, which then provides the images to users via a smartphone app. This allows users to check the status of their plants in real time, even from remote locations.

[1969] 5. Related product suggestions and purchasing support

[1970] The server suggests related products (pots, fertilizer, pest control products, etc.) that users need based on the plant type, growth status, soil condition, and environmental data. These suggestions are notified to users through a smartphone app, and users can easily purchase the products through links within the app.

[1971] Specific examples

[1972] Specific examples are shown below.

[1973] Example 1: Growth predictions and advice

[1974] The device (with a camera) takes a photo of the plant and sends it to the server. The server analyzes the image and determines that the plant is a cactus. The user is then notified via a smartphone app of the results of the growth prediction, the appropriate placement location, lighting conditions, and other information.

[1975] Example 2: Watering notification

[1976] The device (humidity sensor) measures the soil humidity to be 30% and sends this to the server. The server, taking into account that the plant is a succulent, determines that watering is necessary. The smartphone app notifies the user when it is time to water.

[1977] Example 3: Disease detection and improvement suggestions

[1978] The device (a device with a camera) takes detailed images of plant leaves and sends them to a server. The server analyzes the images and detects signs of powdery mildew on the leaves. The server then suggests the use of a specific fungicide as a countermeasure and notifies the user via a smartphone app.

[1979] In this way, the system of the present invention allows users to manage their houseplants efficiently and effectively.

[1980] The processing flow will be explained below.

[1981] 1. Plant image acquisition and analysis

[1982] Step 1:

[1983] The terminal (device with a camera) takes images of the houseplant at set time intervals.

[1984] Step 2:

[1985] The terminal transmits the captured image to the server.

[1986] Step 3:

[1987] The server inputs the received images into an AI model to identify the type of plant.

[1988] Step 4:

[1989] The server makes a growth prediction based on the identified type by referring to a characteristics database.

[1990] Step 5:

[1991] The server generates advice based on growth prediction results, such as the optimal location, temperature, light conditions, and pot size.

[1992] Step 6:

[1993] The server sends the generated advice to the smartphone app.

[1994] Step 7:

[1995] Users can check the advice through a smartphone app and manage their belongings appropriately.

[1996] 2. Environmental data acquisition and notification

[1997] Step 1:

[1998] The terminal (a device equipped with a humidity sensor and air concentration meter) periodically measures the humidity of the soil and the concentration of components in the air.

[1999] Step 2:

[2000] The terminal transmits the measured environmental data to the server.

[2001] Step 3:

[2002] The server analyzes the received data and calculates the optimal watering timing based on the plant type and growth stage.

[2003] Step 4:

[2004] The server notifies the smartphone app of the calculation results.

[2005] Step 5:

[2006] Users can check the watering timing through a smartphone app and either water appropriately or set the watering to be automated.

[2007] 3. Disease and pest detection and improvement method suggestions

[2008] Step 1:

[2009] The device (camera-equipped device) periodically takes detailed images of the plant's leaves and stems.

[2010] Step 2:

[2011] The terminal transmits the captured detailed image to the server.

[2012] Step 3:

[2013] The server uses AI models to analyze the images and detect signs of disease or pests.

[2014] Step 4:

[2015] The server generates remedial measures (e.g., use of specific chemicals or changes in fertilizer) based on the detection results.

[2016] Step 5:

[2017] The server notifies the smartphone app how to improve the situation.

[2018] Step 6:

[2019] Users can check the notification via their smartphone app and take appropriate measures.

[2020] 4. Automatic irrigation and real-time monitoring

[2021] Step 1:

[2022] The terminal (humidity sensor and automatic irrigation system) continuously monitors the soil moisture.

[2023] Step 2:

[2024] The server determines the need for watering based on the humidity data.

[2025] Step 3:

[2026] If the server determines that watering is necessary, it sends a watering instruction to the terminal (automatic watering system).

[2027] Step 4:

[2028] The terminal supplies the set amount of water to the plants according to the watering instructions.

[2029] Step 5:

[2030] The terminal periodically takes real-time images and transmits them to the server.

[2031] Step 6:

[2032] The server sends real-time images to a smartphone app.

[2033] Step 7:

[2034] Users can check the status of their plants in real time from a remote location via a smartphone app.

[2035] 5. Related product suggestions and purchasing support

[2036] Step 1:

[2037] The server collects plant type, growth status, soil conditions, and environmental data.

[2038] Step 2:

[2039] The server generates a list of the most suitable related products (pots, fertilizer, pest control products, etc.) based on the collected data.

[2040] Step 3:

[2041] The server notifies the smartphone app of the generated related product list.

[2042] Step 4:

[2043] Users can check the suggested products through the smartphone app and, if necessary, purchase the products via a link to an e-commerce site.

[2044] Thus, the system of the present invention provides a comprehensive solution for efficient and effective houseplant care.

[2045] Example 1

[2046] 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."

[2047] Properly caring for houseplants is a difficult task for modern urban dwellers. There is a lack of effective solutions for consistently managing a wide range of factors, including plant health management, growth prediction, appropriate watering timing, early detection of diseases and pests, and even the selection of appropriate related products. This task is particularly difficult for users in remote locations or who lead busy lives. The present invention aims to provide a system that comprehensively solves these challenges.

[2048] 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.

[2049] In this invention, the server includes: means for acquiring plant images using a camera; means for inputting the acquired images into a generative AI model to determine the plant's type and for generating a growth forecast and advice on optimal placement, temperature, light conditions, and pot size by referencing a characteristics database; means for measuring soil humidity and airborne constituent concentrations; means for inputting and analyzing the measured data into the generative AI model to calculate appropriate watering timing based on the plant's type and growth stage; means for acquiring detailed images of the plant's leaves and stems and inputting them into the generative AI model to detect signs of disease or pests and generate remediation methods; means for providing a smartphone app that notifies the user of the generated growth forecast, watering timing, and remediation methods; and means for suggesting related products based on the plant's type, growth status, soil condition, and environmental data and providing the user with links to purchase the suggested related products, enabling users to efficiently and effectively manage their houseplants.

[2050] A "camera" is a device that uses light to capture images or videos and record or transmit the data.

[2051] A "generative AI model" is an artificial intelligence model that uses machine learning and deep learning to analyze data and make predictions and classifications.

[2052] The "characteristics database" is a database that systematically accumulates information on plant species and growing conditions.

[2053] A "sensor" is a device that detects physical or chemical conditions or changes and outputs that information as an electrical signal.

[2054] A "humidity sensor" is a device that measures the humidity of an environment or object and outputs that data.

[2055] An "air concentration meter" is a device that measures the concentration of a specific component in the air and outputs the value.

[2056] "Watering timing" refers to the optimal time and frequency for watering plants.

[2057] A "smartphone app" is software that runs on a smartphone and functions as an interface with the user.

[2058] "Signs of disease or pests" refers to early symptoms or signs of disease or pest abnormalities that appear on plants.

[2059] "Remedial measures" are specific measures or countermeasures to address plant disease or pest problems.

[2060] "Related products" refers to products such as fertilizers, pots, and chemicals used to care for and grow plants.

[2061] "Link" means a hypertext reference that directs a user to a specified web page or online store.

[2062] This invention relates to a system that comprehensively supports optimal houseplant care. This system combines multiple elements, including cameras, humidity sensors, air concentration meters, generative AI models, and smartphone apps, to monitor houseplant growth, health, watering timing, disease and pest detection, and recommend related products.

[2063] System Configuration

[2064] The system includes the following components:

[2065] Camera-equipped device: A device that periodically takes images of houseplants. The user sets up the device in an appropriate position near the plant, and it automatically takes images periodically.

[2066] A device equipped with a humidity sensor and an air concentration meter: This device measures the humidity of the soil and the concentration of components in the air. This data is periodically sent to the server.

[2067] Server: The central control unit that inputs the received data into the generative AI model and performs analysis, judgment, calculation, and generation. The server refers to the characteristic database and provides optimal growth conditions and improvement methods.

[2068] Smartphone app: Software that provides an interface for users to receive notifications and suggestions from the server and manage their houseplants. Through this app, users can check the status of their plants and implement the suggestions.

[2069] Plant image acquisition and analysis

[2070] The terminal (a device with a camera) periodically takes pictures of the houseplant and sends them to a server. The server inputs the received images into a generative AI model to identify the plant's type. It then references a characteristics database to generate advice such as growth predictions, optimal placement, temperature, light conditions, and pot size. The generated advice is then sent to the user via a smartphone app.

[2071] Examples:

[2072] A camera-equipped device takes a photo of a houseplant and sends it to a server, which analyzes the image with a generative AI model and determines that the plant is a cactus. The server then sends a growth forecast and advice on optimal placement and lighting conditions to the user via a smartphone app.

[2073] Example prompt for a generative AI model:

[2074] "Analyze this plant's type and growth predictions with a generative AI model and generate advice."

[2075] Environmental data acquisition and notification

[2076] The terminal (a device equipped with a humidity sensor and air concentration meter) periodically measures soil humidity and the concentration of air components, and sends the data to a server. The server inputs this data into a generative AI model for analysis, and calculates the appropriate watering timing based on the plant type and growth stage. The calculation results are notified to the user via a smartphone app.

[2077] Examples:

[2078] The humidity sensor measures the soil humidity at 30% and sends this to the server. The server, taking into account that the plant is a succulent, determines that it actually needs watering. The smartphone app notifies the user when it's time to water.

[2079] Example prompt for a generative AI model:

[2080] "Based on this humidity data, calculate the optimal watering timing for your plants."

[2081] Detecting diseases and pests and proposing remedial measures

[2082] The device (a device with a camera) takes detailed images of plant leaves and stems and sends them to a server. The server then inputs these images into a generative AI model to detect signs of disease or pests. Based on the detection results, it generates remedial measures (e.g., the use of specific pesticides or changing the type of fertilizer) and notifies the user via a smartphone app.

[2083] Examples:

[2084] The camera-equipped device captures detailed images of plant leaves and sends them to a server, where they are analyzed by a generative AI model to detect signs of powdery mildew, suggesting the use of specific fungicides as a treatment, and notifying the user via a smartphone app.

[2085] Example prompt for a generative AI model:

[2086] "Analyze images of this plant to detect signs of disease and pests and suggest ways to improve it."

[2087] Automatic irrigation and real-time monitoring

[2088] The terminal (automatic irrigation system) automatically irrigates plants based on soil moisture data. It periodically sends real-time images to a server, which then provides the images to users via a smartphone app. This allows users to check the status of their plants in real time, even from remote locations.

[2089] Examples:

[2090] The automatic irrigation system receives humidity data and automatically waters plants when the humidity falls below a specified level, while simultaneously sending the latest real-time plant images to a server for users to view via a smartphone app.

[2091] Example prompt for a generative AI model:

[2092] "Automate irrigation based on this humidity data and provide real-time images to the user."

[2093] Related product suggestions and purchasing support

[2094] The server then suggests relevant products based on the plant's type, growth status, soil condition, and environmental data. The suggestions are sent to the user via a smartphone app, and the user can easily purchase the products via the provided link.

[2095] Examples:

[2096] The server analyzes the plant's growth and determines if it needs specific fertilizer, and then sends a fertilizer recommendation via a smartphone app along with a link to purchase it.

[2097] Example prompt for a generative AI model:

[2098] "Analyze the type of plant and its growth status to suggest relevant products suitable for the user."

[2099] In this way, the system of the present invention allows users to manage their houseplants efficiently and effectively.

[2100] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2101] Step 1: Image capture

[2102] The device (device with camera) periodically takes images of the houseplant. The user first sets up the device's camera in an appropriate position around the plant. The camera then automatically takes images at the set interval.

[2103] Input: Visual information of houseplants

[2104] Output: Image data of houseplants

[2105] Step 2: Send image

[2106] The device sends the captured image to the server, at high resolution and without data compression.

[2107] Input: Image data of houseplants

[2108] Output: Image data sent to the server

[2109] Step 3: Image analysis

[2110] The server inputs the received images into a generative AI model to identify the plant type, and then refers to a database of plant characteristics to generate growth predictions and advice on the best location, temperature, light conditions, pot size, and more.

[2111] Input: Image data of houseplants

[2112] Data processing: Plant species identification and growth prediction using generative AI models

[2113] Output: Advice information (optimal location, temperature, light conditions, pot size)

[2114] Step 4: Advice Notification

[2115] The server sends the generated advice to the smartphone app and notifies the user.

[2116] Input: Advice information

[2117] Output: Advice information sent to the smartphone app

[2118] Step 5: Environmental data measurement

[2119] The terminal (a device equipped with a humidity sensor and air concentration meter) periodically measures the humidity of the soil and the concentration of components in the air.

[2120] Input: Environmental data in soil and air

[2121] Output: Measured humidity data and air composition data

[2122] Step 6: Send data

[2123] The terminal transmits the measured data to the server in real time.

[2124] Input: Humidity data and air composition data

[2125] Output: Environment data sent to the server

[2126] Step 7: Data analysis

[2127] The server inputs the received environmental data into a generative AI model for analysis, and calculates the appropriate watering timing based on the plant type and growth stage.

[2128] Input: Environmental data

[2129] Data processing: Data analysis and irrigation timing calculations using generative AI models

[2130] Output: Watering timing information

[2131] Step 8: Watering Notification

[2132] The server sends the calculation results to a smartphone app and notifies the user.

[2133] Input: Watering timing information

[2134] Output: Watering timing information notified to the smartphone app

[2135] Step 9: Take detailed photos

[2136] The device (a device with a camera) takes detailed images of the plant's leaves and stems.

[2137] Input: Visual information of leaves and stems

[2138] Output: Detailed image data

[2139] Step 10: Send detailed images

[2140] The terminal transmits the captured detailed image to the server.

[2141] Input: Detailed image data

[2142] Output: Detailed image data sent to the server

[2143] Step 11: Image analysis

[2144] The server inputs the received images into a generative AI model to detect signs of disease or pests.

[2145] Input: Detailed image data

[2146] Data processing: disease and pest detection with generative AI models

[2147] Output: Information on detection results and remediation methods

[2148] Step 12: Notification of improvement method

[2149] The server sends the generated improvement method to the smartphone app and notifies the user.

[2150] Input: Information on how to improve

[2151] Output: Information on how to improve the app

[2152] Step 13: Humidity Data Analysis

[2153] The terminal (automatic irrigation system) periodically analyzes soil moisture data.

[2154] Input: Soil moisture data

[2155] Data processing: Analysis of humidity data

[2156] Output: Determine the need for irrigation

[2157] Step 14: Automatic Watering

[2158] The device automatically irrigates based on humidity data, starting when humidity falls below a set threshold.

[2159] Input: Determining the need for irrigation

[2160] Output: Watering execution actions

[2161] Step 15: Real-time image transmission

[2162] The terminal periodically transmits real-time images to the server.

[2163] Input: Real-time image of plants

[2164] Output: Real-time image data sent to the server

[2165] Step 16: Real-time monitoring

[2166] The server receives real-time images and provides them to users via a smartphone app, allowing them to check the status of the plants.

[2167] Input: Real-time image data

[2168] Output: Real-time images provided to a smartphone app

[2169] Step 17: Generate related product suggestions

[2170] The server uses a generative AI model to suggest relevant products needed by the user based on the plant type, growth status, soil condition, and environmental data.

[2171] Input: Plant management data

[2172] Data processing: Generative AI models suggest related products

[2173] Output: A list of related products optimized for the user

[2174] Step 18: Notification of related product suggestions

[2175] The server sends the generated related product suggestions to the smartphone app and notifies the user, who can then easily purchase the products via a link in the app.

[2176] Input: List of related products

[2177] Output: List of related products and purchase links sent to the smartphone app

[2178] (Application example 1)

[2179] 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."

[2180] Traditional houseplant management often involves manually checking the health and growth of plants, which is laborious, time-consuming, and requires specialized knowledge. It can also be difficult to detect diseases and pests early or understand appropriate management methods, which can result in difficult plant development. Furthermore, while physical stores are required to provide accurate information to customers and efficiently recommend related products, it can be difficult for staff to always have the latest information and respond appropriately.

[2181] 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.

[2182] In this invention, the server includes: means for acquiring images of plants using a camera; means for analyzing the acquired images to determine the type of plant and making a growth prediction; sensor means for measuring soil humidity and airborne concentrations; means for analyzing the measurement data to calculate appropriate watering timing; means for detecting plant diseases and pests and generating remediation methods; means for notifying the user of the generated growth prediction, watering timing, and remediation methods; means for allowing store staff to wear smart glasses to check and manage the status of houseplants in real time; means for analyzing images and sensor data and displaying the results on the smart glasses display; and means for providing links to purchase suggested related products. This allows the status of houseplants to be managed efficiently, and allows store staff to provide appropriate advice and suggest related products to customers.

[2183] The "means for acquiring images of plants using a camera" is a combination of hardware and software that takes images of plants and transmits the information to a server.

[2184] "Means of analyzing acquired images to determine the type of plant and predict its growth" refers to technology that uses AI models and machine learning algorithms to analyze acquired images, identify the type of plant, and predict its future growth.

[2185] The "sensor means for measuring the humidity of the soil and the concentration of components in the air" refers to a sensor that measures the humidity of the soil and the concentration of components in the air, thereby collecting environmental data in which the plant is placed.

[2186] The "means for analyzing the measurement data and calculating the appropriate timing for irrigation" refers to an algorithm and program that analyzes the data obtained from the sensor and calculates the optimal timing for irrigation for the plants.

[2187] "Means for detecting plant diseases and pests and generating remedial measures" refers to technology that analyzes plant image data, detects signs of disease or pests, and suggests appropriate remedial measures based on that data.

[2188] The "means for notifying the user of the generated growth forecast, irrigation timing, and improvement methods" refers to an application and communication means for notifying the user of the analysis results and suggestions via a smart device, etc.

[2189] "A means for store staff to wear smart glasses to check and manage the status of houseplants in real time" refers to a technology that uses smart glasses to check and manage the current status of houseplants in real time.

[2190] "Means for analyzing images and sensor data and displaying the results on the smart glasses display" refers to a system that analyzes acquired images and sensor data using an AI model and displays the results on the smart glasses display.

[2191] The "means for providing links to purchase suggested related products" refers to a technology that provides users with links to related products based on the analysis results and suggestions, allowing them to easily purchase the products.

[2192] This invention is a comprehensive system for effectively managing houseplants, consisting of multiple elements including a camera, humidity sensor, air concentration meter, AI model, smart glasses, server, and smartphone app.

[2193] System configuration and program processing overview

[2194] 1. Plant image acquisition and analysis

[2195] The server uses a camera to capture images of the plant and analyzes the transmitted images to determine the plant's type. Specifically, this process uses an image recognition algorithm (using TensorFlow or PyTorch). Based on the analysis results, the server predicts the plant's growth and suggests the environmental conditions and care methods necessary for growth. This information is displayed on the smart glasses' display and provided to store staff.

[2196] 2. Environmental data acquisition and notification

[2197] The server periodically measures soil humidity and the concentration of airborne elements using humidity sensors and air concentration meters, and collects and analyzes the data. This provides environmental data on the plant's location and allows it to calculate the optimal watering timing. The results are then communicated to the user via the smart glasses' display or a smartphone app.

[2198] 3. Disease and pest detection and improvement method suggestions

[2199] The server uses a camera to capture detailed images of the plant's leaves and stems, which are then analyzed using an AI model to detect signs of disease or pests and generate remedial measures (such as the use of specific pesticides).These remedial measures are then displayed on the smart glasses' display and communicated to store staff.

[2200] 4. Automatic irrigation and real-time monitoring

[2201] The server includes a means for automatically watering plants based on data from the humidity sensor, and also captures real-time images of the plants and transmits them to the server, which analyzes the images and provides real-time information to the user, enabling plant monitoring even in remote locations.

[2202] 5. Related product suggestions and purchasing support

[2203] The server will suggest relevant products (pots, fertilizer, pest control products, etc.) that users need based on the plant type, growth status, soil condition, and environmental data. These suggestions will be displayed on the smart glasses display or smartphone app, and users can easily purchase the products via the provided link.

[2204] Examples and prompts

[2205] Below are some examples and prompts:

[2206] Example 1: Growth forecasts and advice

[2207] The server analyzes images of plants taken with the camera-equipped smart glasses, determines that the plant is a cactus, and displays advice on the smart glasses' display regarding appropriate placement and lighting conditions based on the results of growth predictions.

[2208] Example 2: Watering notification

[2209] The server checks that the soil humidity measured by the humidity sensor is 30% and calculates the optimum watering time for the succulents. When it is time to water, a notification will appear on the smart glasses display.

[2210] Example 3: Disease detection and improvement suggestions

[2211] The server detects signs of powdery mildew from detailed images of plant leaves taken by camera-equipped smart glasses, and notifies the user on the glasses' display to recommend the use of specific fungicides as a countermeasure.

[2212] Prompt Sentence Examples

[2213] "Upload an image of your houseplant and we'll identify the plant's type, its health, and how to properly care for it."

[2214] Houseplant image: [Upload form]

[2215] Humidity data: [Acquired in real time]

[2216] Air concentration data: [Acquired in real time]

[2217] result:

[2218] Plant Type: Cactus

[2219] Health: Good

[2220] Watering timing: Next time in 3 days

[2221] Related Products: [Link]

[2222] This will clarify the specific treatment and usage methods, allowing for efficient management and sales support of houseplants in physical stores.

[2223] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2224] Step 1:

[2225] A camera-equipped device takes pictures of plants and sends them to a server. Specifically, the device's camera periodically takes photos of plants and uploads the data to the server via wireless communication (Wi-Fi or Bluetooth). The input is the plant image, and the output is the image data sent to the server.

[2226] Step 2:

[2227] The server analyzes the image data it receives using an AI model to determine the type of plant. Specifically, the AI ​​model (using TensorFlow and PyTorch) analyzes the image and performs feature extraction and classification. The input is the image data of the plant, and the output is the type of plant.

[2228] Step 3:

[2229] The server predicts growth based on the type of plant and calculates the necessary environmental conditions and management methods. Specifically, a growth prediction algorithm is used to calculate the optimal growth conditions (light intensity, temperature, humidity, etc.) based on the type of plant and past data. The input is the type of plant, and the output is growth prediction data.

[2230] Step 4:

[2231] Humidity sensors and air concentration meters periodically measure the humidity of the soil and the concentrations of components in the air, and send this data to a server. Specifically, the sensors acquire data in real time and transmit it to the server via wireless communication. The input is the sensor measurement data, and the output is the environmental data sent to the server.

[2232] Step 5:

[2233] The server analyzes the received environmental data and calculates the appropriate watering timing. Specifically, the watering algorithm determines the watering timing by taking...

Claims

1. means for acquiring an image of the plant using a camera; A means for analyzing the acquired image to determine the type of plant and predict its growth; a sensor means for measuring the moisture content of the soil and the concentration of the moisture in the air; A means for analyzing the measurement data and calculating the appropriate timing for irrigation; means for detecting plant diseases and pests and generating remedial measures; a means for informing a user of the generated growth forecast, irrigation timing, and improvement methods; a means for generating and suggesting to the user a list of related products; A system including:

2. a means for acquiring real-time images of plants using a camera and providing the images to a remote user; A means for automatically performing irrigation based on data from the humidity sensor; The system of claim 1 , comprising:

3. A means to suggest the most suitable related products based on the type of plant, its growth status, soil condition and environmental data; a means for providing links to purchase suggested related products; The system of claim 1 , comprising:

Images