system

Abstract

We provide the system. [Solution] A means of analyzing plant images to determine the type and health status of the plant, A means for generating an optimal care plan based on the type and health condition of the aforementioned plants, A means for automatically controlling watering and fertilizer supply based on the aforementioned care plan, Means for acquiring weather and environmental data and dynamically adjusting the care plan, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, growing plants is an important hobby and a form of relaxation for many people. However, there is a problem that it is difficult to maintain the health of plants due to busyness and lack of experience. Especially when managing a large number of plants in beginners or commercial facilities, it is essential to supply water, fertilizer, and adjust sunlight at appropriate times, but these require time and knowledge. Against this background, there is a demand for a system that allows people to grow plants healthily without much effort and efficiently.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a system that analyzes images of plants to determine their species and health status and generates an optimal care plan. This system acquires plant information using image analysis means and determines a care plan tailored to each individual plant using care plan generation means. Furthermore, an automatic control means automatically performs watering and fertilizer supply based on the care plan, and dynamically adjusts the plan using weather and environmental data, thereby significantly reducing the effort required from the user. As a result, a system has been realized that makes it easy for anyone to manage and grow plants healthily.

[0006] "Methods for analyzing plant images" refer to technologies that use computer vision technology and image recognition algorithms to analyze photographed plant images and identify the type and condition of the plant.

[0007] "Means for determining plant species and health status" refers to a function that analyzes plant species, leaf condition, and growth rate based on data obtained through image analysis, and evaluates the current health status of the plant.

[0008] "A means of generating an optimal care plan" refers to a system that automatically creates care instructions, including the timing of necessary watering and fertilizing, and the amount of sunlight, according to the type of plant and its health condition.

[0009] "Means of automatic control" refers to a mechanism that allows the platform to autonomously perform necessary tasks (e.g., watering and fertilizing) based on the generated care plan.

[0010] "Means for acquiring and adjusting weather and environmental data" refers to a function that collects weather and environmental information from external sensors and internet services, and dynamically changes the timing and content of care plans accordingly. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, the terms used in the following description will be explained.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), etc.

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the 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.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0025] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] This invention relates to a system that automates plant care, allowing users to manage plant health without effort. This system takes images of plants, analyzes those images to determine the plant's species and health condition, and generates an optimal care plan.

[0033] The user first takes a picture of the plant through an application on their device. This image is sent from the device to a server. The server uses image processing technology to analyze the transmitted image and identify the type of plant and its health condition. This information is then cross-referenced with a plant care database to generate the most suitable care plan for each individual plant.

[0034] The generated care plan is communicated to the user via a terminal. The notification includes specific watering amounts and timings, fertilizer types, and their supply schedules. Furthermore, the system monitors weather and environmental data and adjusts the care plan in real time. This enables flexible plant care that adapts to seasonal and weather changes.

[0035] The device can connect with smart pots and automatic watering systems based on instructions from the server. This functionality allows the system to automatically water and fertilize plants even when the user is absent, maintaining an optimal environment for the plants.

[0036] For example, a specific care plan is provided for a particular houseplant, such as "water with 200ml every Monday and Thursday" or "add liquid fertilizer in two weeks." If it's raining, the notification will be changed to skip watering the following Monday. These features allow users to manage their plants' health with peace of mind.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] The user launches the application on their device and takes a picture of a plant. The captured image is set to be automatically sent to the server.

[0040] Step 2:

[0041] The server receives images of plants sent from the terminal. The received images are first analyzed using an image recognition algorithm to identify the type of plant.

[0042] Step 3:

[0043] The server refers to a database corresponding to the analyzed plant species and performs a general health check. This allows it to assess the plant's health based on characteristics such as leaf color and shape.

[0044] Step 4:

[0045] The server generates an optimal care plan based on the plant type and health status, using a standard care database. This includes the amount and frequency of watering, the type of fertilizer needed, and more.

[0046] Step 5:

[0047] The generated care plan is sent from the server to the terminal. The user can then view the details of the care plan (e.g., the next watering date, time, and amount) via the terminal.

[0048] Step 6:

[0049] Users can accept or fine-tune care plans through their device. If automatic mode is selected as needed, the system will automatically prepare to execute instructions for watering and fertilizing.

[0050] Step 7:

[0051] The terminal sends instructions to smart pots and automatic watering systems, initiating watering and fertilization according to the care plan. Simultaneously, the server acquires data from environmental sensors and periodically adjusts the care plan.

[0052] Step 8:

[0053] The server updates weather information as needed, adjusts watering timing based on forecast weather data, and prepares information for the next care cycle. This information is then sent back to the terminal and notified to the user.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] Traditional plant management methods struggled to provide optimal care tailored to the individual characteristics of each plant and specific environmental conditions, and required significant manual effort from the user. Furthermore, they lacked the flexibility to adapt to weather and environmental fluctuations. In addition, automated management was limited to certain functions, resulting in inadequate management during user absences.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes means for analyzing images of organisms to determine the type and health status of the organisms, means for generating an optimal maintenance plan based on the type and health status of the organisms, and means for automatically controlling water supply and nutrient supply based on the maintenance plan. This enables personalized care for individual plants, flexible responses to weather and environmental changes, and effective management even when the user is absent.

[0059] The term "living organism" refers to all living things in the natural world, including plants and animals, but in this context, it often specifically refers to plants.

[0060] "Analysis" refers to the process of thoroughly investigating information and revealing its structure and elements.

[0061] "Health status" refers to the physical and physiological state of an organism, and specifically to the evaluation of conditions that require maintenance or management.

[0062] A "maintenance plan" refers to a systematically organized series of activities and measures necessary to maintain the health of an organism.

[0063] "Watering" refers to the act of supplying necessary water to living organisms such as plants.

[0064] "Nutrient supply" refers to the act of providing organisms with the nutrients they need to grow or stay alive.

[0065] "Automatically controlled" refers to a system operating without human intervention, based on pre-set conditions.

[0066] "Meteorology" refers to the state of the atmosphere and encompasses natural phenomena including elements such as temperature, humidity, wind speed, and precipitation.

[0067] "Environmental information" refers to data about the surrounding conditions, such as weather conditions, topography, and vegetation, for a specific location and time.

[0068] This invention is a system that automates the health management of living organisms, reducing the burden on the user. The process begins with the user taking pictures of plants using an application. The captured images are transmitted to a server via Wi-Fi or mobile data communication.

[0069] The server processes the received images using image analysis techniques. Specifically, it uses image recognition libraries such as OpenCV and TENSORFLOW®. This identifies the species and health status of the organism, and then compares it with a database.

[0070] Based on the matching data, the server generates an optimal maintenance plan. This process utilizes a generative AI model and leverages predictive capabilities based on historical data. The generated maintenance plan is then notified to the terminal. The notification includes specific instructions regarding the timing and amount of watering and nutrient supply.

[0071] Furthermore, the terminal can follow instructions from the server and connect with smart pots and automatic watering systems. This allows for automatic management of living organisms even when the user is absent.

[0072] For example, a certain houseplant might be instructed to "water with 200ml of water every Monday and Thursday." Furthermore, a predictive model analyzes weather information and makes adjustments, such as skipping watering instructions on days when rain is expected.

[0073] An example of a prompt message is, "Analyze the plant image, determine its health status, and provide the optimal care plan." Based on this prompt message, the generating AI model creates an appropriate care plan, enabling efficient biological management throughout the entire system.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The user takes an image of a living organism using a device application. The input is image data acquired via the device's camera. The user presses a capture button within the application, the device activates its camera function, and captures the image. This image data is then sent directly to the server.

[0077] Step 2:

[0078] The server receives image data sent from the terminal. The input is image data from the terminal, and processing begins upon receiving this image data. Specifically, the server decodes the image data and converts it into a format that can be appropriately analyzed.

[0079] Step 3:

[0080] The server uses image analysis techniques to identify the species and health status of organisms. The input is image data formatted in the previous step, and the output is information about the identified species and health status of the organisms. The server uses OpenCV and TensorFlow tools to extract features such as color, shape, and texture, and then compares them with a database to perform identification.

[0081] Step 4:

[0082] The server generates an optimal maintenance plan based on the analysis results. The input is the identified species and health status information of the organism, and the output is a detailed maintenance plan. This generation utilizes a generative AI model, and a predictive algorithm formulates the optimal care plan based on historical management data.

[0083] Step 5:

[0084] The terminal receives maintenance plans sent from the server and notifies the user. The input is maintenance plan data from the server, and the output is the content of the notified care plan. In operation, the terminal displays specific instructions for watering and fertilizing to the user via push notifications.

[0085] Step 6:

[0086] The terminal interacts with smart pots and automatic watering systems based on instructions from the server, performing the necessary actions. The input is a control command based on the care plan, and the output is the result of that execution. Specifically, the terminal sends commands to each device, for example, to accurately perform an action such as "supply 200ml of water."

[0087] (Application Example 1)

[0088] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0089] Managing plants in public areas requires considerable effort and time, and maintaining their health is particularly difficult in urban areas. Traditional methods require manually checking each plant species and its health condition, and generating instructions for appropriate care; automation is not yet widespread. Furthermore, the lack of a system to respond promptly to changes in weather and environment makes effective management difficult. There is a need for efficient solutions to these challenges.

[0090] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0091] In this invention, the server includes means for analyzing images of plants to determine the type and health status of the plants, means for generating an optimal care plan based on the type and health status of the plants, and a terminal for photographing plants in public areas. This makes it possible to automatically evaluate the health status of plants in public areas and quickly provide appropriate care instructions.

[0092] "Plant images" are visual data taken to determine the type and health status of a plant.

[0093] "Analysis" is the process of extracting plant characteristics from captured image data and evaluating their species and health status.

[0094] "Health" is an indicator of the degree to which a plant is able to maintain its original shape and color and continue normal growth.

[0095] A "care plan" is a document that outlines specific instructions, such as watering and fertilizing, to maintain or improve the health of plants, based on the analysis results.

[0096] "Automatic control" refers to a process in which a system adjusts its actions according to conditions without human intervention.

[0097] "Weather information" refers to data that indicates environmental conditions such as temperature, precipitation, and sunshine duration.

[0098] "Dynamic adjustment" means modifying plans and actions in real time in response to changing circumstances.

[0099] A "terminal" is a device used to take pictures of plants and transmit them to the system.

[0100] "Health assessment" is the process of indicating the health of plants using numerical values ​​and indicators based on analyzed data.

[0101] "Management instructions" are guidelines that show specific actions to be taken to maintain the health of plants.

[0102] The system implementing this invention provides a comprehensive method for efficiently managing plant health. The main components of the system include a user terminal, a server, and a database for plant management.

[0103] The server receives images of plants and uses the image processing library OpenCV to analyze them. TensorFlow is used as a deep learning model during the analysis to identify the plant species and health status. The identified data is then compared with a plant care database to generate an optimal care plan. The care plan is updated in real time in response to changes in weather and environmental data. Weather information APIs such as the OpenWeatherMap API are used to acquire environmental data.

[0104] The user terminal functions as a camera, taking pictures of plants in public areas and sending them to the server. It also receives notifications from the server and provides the user with specific management instructions. For example, if the health of a photographed cherry tree is assessed as "good," a notification is sent stating that the next pruning should be done in two weeks.

[0105] An example of a prompt for a generative AI model that supports part of this process would be: "Identify the types and health status of plants in the image and generate an appropriate care plan."

[0106] This system automates the management of plants in public areas, making it possible to maintain their health efficiently and effectively.

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The terminal is operated by the user to take images of plants in a public area. The input is visual data obtained from the camera sensor, and the output is the captured image file. The terminal then prepares to send this image to the server.

[0110] Step 2:

[0111] The terminal sends the captured image to the server. The input is the image file acquired in step 1, and the output is the image on the server ready for analysis. Here, the operation of sending image data is performed using network communication.

[0112] Step 3:

[0113] The server uses OpenCV to preprocess the received images for analysis. This preprocessing takes image data as input and outputs data in a shape suitable for analysis. Specifically, data processing such as image resizing and noise reduction is performed.

[0114] Step 4:

[0115] The server uses a TensorFlow model to determine the plant species and health status from preprocessed image data. The input is the output data from step 3, and the output is information indicating the specific plant species and its health status. The operation here is a prediction by a machine learning model.

[0116] Step 5:

[0117] The server uses the identified plant information to compare it with a plant care database and generate the optimal care plan. Input is information about the plant type and health status, and output is a specific plan for watering and fertilizing. Database queries are used to extract care plans that match the specified criteria.

[0118] Step 6:

[0119] The server retrieves the latest weather data via a weather information API. The input is an API request, and the output is weather data. This involves retrieving necessary data from online services.

[0120] Step 7:

[0121] The server uses acquired weather data to dynamically adjust care plans as needed. The inputs are the care plan and weather data, and the output is the adjusted care plan. Comparison operations are performed to modify the care content based on the conditions.

[0122] Step 8:

[0123] The server notifies the terminal of the adjusted care plan and information regarding the plant's health. The input is the adjusted care plan from step 7, and the output is the notification to the user. The notification data is then sent over the network.

[0124] Step 9:

[0125] The user takes the necessary actions based on the notification received. The input is the information displayed on the device, and the output is the physical action of plant care. At this stage, the user performs actions to properly manage the plants according to the instructions.

[0126] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0127] This invention combines an emotion engine with an automated plant care system to propose and adjust care plans based on the user's emotions. Through this emotion recognition function, the system aims not only to manage plant health but also to improve the user's psychological satisfaction.

[0128] The user first takes a picture of the plant using an application installed on their device. The device then sends the specified image to the server. The server has the functionality to analyze the transmitted image using image processing technology and evaluate the plant's type and health condition. Based on this information, an optimal plant care plan is generated.

[0129] Simultaneously, the device recognizes the user's emotional state. The emotion engine analyzes the user's facial expressions and tone of voice through the video camera and voice input to determine their current emotional state (e.g., stressed, relaxed). This emotional data is sent to the server and incorporated into the plant care plan.

[0130] The server takes in emotional data and adapts the plant care plan to the user's emotional state. For example, if a user is feeling stressed, the system sends a message to the device recommending observing plants in nature as a comforting remedy to alleviate the pressure, in addition to the care plan. In this way, the content of the care plan and the notifications sent to the user are customized according to their emotions.

[0131] This invention enhances the accuracy of plant health management while providing further value to users' lives, representing an innovative approach to reducing stress and improving quality of life. Specifically, if the user senses pleasure from sunbathing, it sends a notification guiding them to observe afternoon sunlight and plants, aiming to make the overall environment more comfortable.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user takes a picture of a plant using an application on their device, and the device sends that image to the server.

[0135] Step 2:

[0136] The server uses image recognition algorithms to analyze the received plant images and identify the plant species and health status.

[0137] Step 3:

[0138] The server automatically generates the most suitable care plan for the plant by referencing the appropriate care database based on the analysis results.

[0139] Step 4:

[0140] The device uses an emotion engine to recognize the user's emotional state through their facial expressions and voice, and sends that data to the server.

[0141] Step 5:

[0142] The server receives data from the emotion engine and adjusts notifications and care plans according to the user's emotional state. For example, if the user needs relaxation, it might recommend a simple plant care task.

[0143] Step 6:

[0144] Users view and receive notifications for customized care plans sent from the server via their devices. These plans include messages and care instructions that are sensitive to the user's feelings.

[0145] Step 7:

[0146] The terminal issues instructions to smart pots and automatic watering systems as needed, ensuring watering and fertilization are carried out according to the care plan. It also adjusts schedules and plans based on predictive data regarding plant care.

[0147] Step 8:

[0148] The server continuously analyzes weather and environmental data, dynamically adjusts care plans as needed, and prepares subsequent notifications. Based on user feedback, it collects new data to further improve the plans.

[0149] (Example 2)

[0150] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0151] Traditional plant care systems focused primarily on managing plant health, but rarely considered user psychological satisfaction. This resulted in users not receiving appropriate care tailored to their individual emotional states. Furthermore, notifications to users tended to be fixed and lacked a personalized approach.

[0152] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0153] In this invention, the server includes means for analyzing images of plants to determine the type and health status of the plants, means for recognizing the user's emotional state and reflecting and adjusting the emotional state in the care plan, and means for generating advice messages corresponding to the emotional state and notifying the user's device. This makes it possible not only to manage the health of plants but also to provide and notify users of care plans tailored to their individual emotional state.

[0154] "Analyzing plant images" involves processing plant image data to identify plant characteristics within the image and evaluate their species and health status.

[0155] "Determining the type and health status of a plant" means determining, based on the characteristics identified through analysis, which type of plant it belongs to and whether or not its physiological state is good.

[0156] "Generating a care plan" means formulating maintenance procedures appropriate to the type of plant and its health condition, and then shaping them into a concrete plan.

[0157] "Recognizing the user's emotional state" means estimating the user's psychological state from their facial expressions, voice, and other behaviors, and recognizing that as data.

[0158] "Reflecting and adjusting emotional states in care plans" means taking the user's emotional state into consideration, modifying existing care plans, and providing more individualized care suggestions.

[0159] "Generating and notifying advice messages" means compiling useful information into text based on the user's emotional state and plant care plan, and sending it to the user's device.

[0160] This invention is a system for efficiently caring for plants, and includes plant image analysis, user emotion recognition, and the generation and notification of care plans based on these. Specifically, it is implemented in the following form:

[0161] The user takes pictures of plants using an application installed on the device. The device is equipped with a camera and microphone, allowing it to collect not only plant images but also the user's facial expressions and voice. The captured images are then sent from the device to a server.

[0162] The server analyzes the received plant images using an image processing library (e.g., OpenCV). This analysis identifies the plant species and health status, and then generates a corresponding care plan using an AI model.

[0163] The device also utilizes a video camera and voice input to recognize the user's emotional state. This emotion recognition is performed using the Emotion API and Google Cloud's Natural Language API, among others. The resulting emotional data is also sent to a server and incorporated into the plant's care plan.

[0164] The server integrates plant and user emotional information to generate tailored care plans and advice messages. These messages are sent to the device and notified to the user. Because the notification content is customized according to the user's individual emotional state, it is more likely to improve the user's psychological satisfaction.

[0165] For example, if a user takes a picture of a plant and the device detects that the user is in a relaxed state, a notification such as "How about observing your plant in the afternoon sunlight?" will be sent, providing a more comfortable plan. Another example of a prompt message is: "Please come up with a prompt message example for this system. Please suggest a plant care plan for a cactus that is in a relaxed emotional state and is healthy."

[0166] In this way, this system provides comprehensive care that not only manages the health of plants but also takes into account the user's emotions.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The user takes a picture of a plant using an application on their device. The plant image is taken into the device as input, and the image data is sent to the server. The image data is saved in JPEG or PNG format and sent to the server via the HTTP protocol over the internet.

[0170] Step 2:

[0171] The server uses plant image data received as input. Using an image processing library (e.g., OpenCV), it extracts plant features and analyzes the plant species and health status. The analysis results are obtained as output, and these results include the plant species name, leaf condition, color, and other health indicators.

[0172] Step 3:

[0173] The device acquires data for emotion recognition. It uses a video camera and microphone to collect the user's facial expressions and voice data. This facial and voice data is passed to the emotion engine as input. The engine uses, for example, the Emotion API to estimate emotions and outputs the user's emotional state (relaxed, stressed, etc.).

[0174] Step 4:

[0175] The server integrates the analysis results obtained in steps 2 and 3 with the emotional data. It receives this data as input and uses a generative AI model to generate a care plan that takes the user's emotional state into account. The output is a customized care plan and advice message. This plan might include, for example, "The plants are healthy and the user is relaxed, so sunbathing is recommended."

[0176] Step 5:

[0177] The server sends the generated care plan and advice messages to the device. Using the generated messages as input, it notifies the user's device as output. This notification is presented to the user as a pop-up or in-app message on the device, allowing the user to care for the plants accordingly.

[0178] (Application Example 2)

[0179] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0180] Conventional plant care systems have the drawback of focusing solely on plant health management without considering the user's psychological satisfaction. Furthermore, because plant care plans are not tailored to the user's individual emotional state, the process of growing plants does not fully realize the user's relaxation and stress-relief benefits.

[0181] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0182] In this invention, the server includes means for analyzing visual data of plants to determine their classification and health status, means for analyzing the user's emotional state, and means for dynamically adjusting the plant care plan to suit the user's emotions according to the analysis results. This makes it possible to manage the health of plants while improving the user's psychological satisfaction and providing a plant cultivation experience tailored to the user's individual emotional state.

[0183] "Visual plant data" refers to visual information such as images and videos of plants, and is used to classify plants and determine their health status.

[0184] A "user information terminal" refers to a portable or stationary electronic device that a user can operate, and is used to transmit visual data and emotional state data of plants to a server.

[0185] A "care plan" refers to a set of instructions, including schedules and methods for watering and fertilizing, designed to promote the healthy growth of plants.

[0186] "Emotional state" refers to the user's psychological condition, which is analyzed through facial expressions, voice, and other biosensors.

[0187] "Dynamic adjustment" refers to continuously changing and modifying care plans in real time according to the situation.

[0188] To implement this invention, the following hardware and software are used in the plant care system. First, the user information terminal is a mobile device such as a smartphone or tablet, which captures and transmits visual data of plants through an application installed on the terminal. The captured data is sent to a server, which uses image processing technology such as "Google Cloud Vision API" to classify the plants and evaluate their health status.

[0189] Simultaneously, the user's emotional state is collected through the camera and microphone on the device. Emotion analysis uses "Microsoft® Face API" and voice tone analysis technology to recognize the user's emotional state from their facial expressions and voice.

[0190] The server generates a plant care plan based on this data, and this plan is dynamically adjusted according to the user's emotional state. If the server determines that the user is stressed, the care plan may include suggestions for plant observation to promote relaxation. Finally, this information is sent as a notification to the user's information terminal, and plant care is performed in real time.

[0191] For example, if the user expresses feelings of joy, a plan to expose the plant to sunlight will be recommended, and a message will appear on the device stating, "The plant has been moved to the optimal location for afternoon sunlight." An example of a prompt using the generative AI model is, "Please suggest the best plant care plan if the user's emotional state is stress. For example, please suggest ways to care for the plant in a relaxing location or manner."

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] The user takes a visual image of a plant using their device. The device then sends the captured image to a server via an application. The input is the image of the plant taken by the device's camera, and the output is the digital image data sent to the server.

[0195] Step 2:

[0196] The server analyzes the received plant image data using the Google Cloud Vision API. Image processing is performed to classify the plants and determine their health status. The input is digital image data, and the output is the plant species and its health status assessment. The analyzed information is then used to generate subsequent care plans.

[0197] Step 3:

[0198] The device captures the user's emotional state using its camera and microphone. The captured video and audio data is analyzed within the device using the "Microsoft Face API" and voice analysis technology. The input is data of the user's facial expressions and voice, and the output is an evaluation of the user's emotional state.

[0199] Step 4:

[0200] The server generates an optimal plant care plan based on the plant's health and the user's emotional state. This process includes customization that takes the user's psychological state into account. The input is plant health data and user emotional data, and the output is a customized care plan.

[0201] Step 5:

[0202] The server notifies the user's terminal of the generated care plan. The user receives this notification and performs plant care as needed. The input is a customized care plan, and the output is a notification message to the terminal. The user can take specific actions based on this notification.

[0203] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0204] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0205] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0209] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0210] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0211] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0212] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0213] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0214] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0215] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0216] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0217] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0218] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0219] This invention relates to a system that automates plant care, allowing users to manage plant health without effort. This system takes images of plants, analyzes those images to determine the plant's species and health condition, and generates an optimal care plan.

[0220] The user first takes a picture of the plant through an application on their device. This image is sent from the device to a server. The server uses image processing technology to analyze the transmitted image and identify the type of plant and its health condition. This information is then cross-referenced with a plant care database to generate the most suitable care plan for each individual plant.

[0221] The generated care plan is communicated to the user via a terminal. The notification includes specific watering amounts and timings, fertilizer types, and their supply schedules. Furthermore, the system monitors weather and environmental data and adjusts the care plan in real time. This enables flexible plant care that adapts to seasonal and weather changes.

[0222] The device can connect with smart pots and automatic watering systems based on instructions from the server. This functionality allows the system to automatically water and fertilize plants even when the user is absent, maintaining an optimal environment for the plants.

[0223] For example, a specific care plan is provided for a particular houseplant, such as "water with 200ml every Monday and Thursday" or "add liquid fertilizer in two weeks." If it's raining, the notification will be changed to skip watering the following Monday. These features allow users to manage their plants' health with peace of mind.

[0224] The following describes the processing flow.

[0225] Step 1:

[0226] The user launches the application on their device and takes a picture of a plant. The captured image is set to be automatically sent to the server.

[0227] Step 2:

[0228] The server receives images of plants sent from the terminal. The received images are first analyzed using an image recognition algorithm to identify the type of plant.

[0229] Step 3:

[0230] The server refers to a database corresponding to the analyzed plant species and performs a general health check. This allows it to assess the plant's health based on characteristics such as leaf color and shape.

[0231] Step 4:

[0232] The server generates an optimal care plan based on the plant type and health status, using a standard care database. This includes the amount and frequency of watering, the type of fertilizer needed, and more.

[0233] Step 5:

[0234] The generated care plan is sent from the server to the terminal. The user can then view the details of the care plan (e.g., the next watering date, time, and amount) via the terminal.

[0235] Step 6:

[0236] Users can accept or fine-tune care plans through their device. If automatic mode is selected as needed, the system will automatically prepare to execute instructions for watering and fertilizing.

[0237] Step 7:

[0238] The terminal sends instructions to smart pots and automatic watering systems, initiating watering and fertilization according to the care plan. Simultaneously, the server acquires data from environmental sensors and periodically adjusts the care plan.

[0239] Step 8:

[0240] The server updates weather information as needed, adjusts watering timing based on forecast weather data, and prepares information for the next care cycle. This information is then sent back to the terminal and notified to the user.

[0241] (Example 1)

[0242] Next, we will describe Example 1. 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."

[0243] Traditional plant management methods struggled to provide optimal care tailored to the individual characteristics of each plant and specific environmental conditions, and required significant manual effort from the user. Furthermore, they lacked the flexibility to adapt to weather and environmental fluctuations. In addition, automated management was limited to certain functions, resulting in inadequate management during user absences.

[0244] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0245] In this invention, the server includes means for analyzing images of organisms to determine the type and health status of the organisms, means for generating an optimal maintenance plan based on the type and health status of the organisms, and means for automatically controlling water supply and nutrient supply based on the maintenance plan. This enables personalized care for individual plants, flexible responses to weather and environmental changes, and effective management even when the user is absent.

[0246] The term "living organism" refers to all living things in the natural world, including plants and animals, and in this context, it often specifically refers to plants.

[0247] "Analysis" refers to the process of thoroughly investigating information and revealing its structure and elements.

[0248] "Health status" refers to the physical and physiological state of an organism, and specifically to the evaluation of conditions that require maintenance or management.

[0249] A "maintenance plan" refers to a systematically organized series of activities and measures necessary to maintain the health of an organism.

[0250] "Watering" refers to the act of supplying necessary water to living organisms such as plants.

[0251] "Nutrient supply" refers to the act of providing organisms with the nutrients they need to grow or stay alive.

[0252] "Automatically controlled" refers to a system operating without human intervention, based on pre-set conditions.

[0253] "Meteorology" refers to the state of the atmosphere and encompasses natural phenomena including elements such as temperature, humidity, wind speed, and precipitation.

[0254] "Environmental information" refers to data about the surrounding conditions, such as weather conditions, topography, and vegetation, for a specific location and time.

[0255] This invention is a system that automates the health management of living organisms, reducing the burden on the user. The process begins with the user taking pictures of plants using an application. The captured images are transmitted to a server via Wi-Fi or mobile data communication.

[0256] The server processes the received images using image analysis techniques. Specifically, it uses image recognition libraries such as OpenCV and TensorFlow. This identifies the species and health status of the organism, and then compares it with a database.

[0257] Based on the matching data, the server generates an optimal maintenance plan. This process utilizes a generative AI model and leverages predictive capabilities based on historical data. The generated maintenance plan is then notified to the terminal. The notification includes specific instructions regarding the timing and amount of watering and nutrient supply.

[0258] Furthermore, the terminal can follow instructions from the server and connect with smart pots and automatic watering systems. This allows for automatic management of living organisms even when the user is absent.

[0259] For example, a certain houseplant might be instructed to "water with 200ml of water every Monday and Thursday." Furthermore, a predictive model analyzes weather information and makes adjustments, such as skipping watering instructions on days when rain is expected.

[0260] An example of a prompt message is, "Analyze the plant image, determine its health status, and provide the optimal care plan." Based on this prompt message, the generating AI model creates an appropriate care plan, enabling efficient biological management throughout the entire system.

[0261] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0262] Step 1:

[0263] The user takes an image of a living organism using a device application. The input is image data acquired via the device's camera. The user presses a capture button within the application, the device activates its camera function, and captures the image. This image data is then sent directly to the server.

[0264] Step 2:

[0265] The server receives image data sent from the terminal. The input is image data from the terminal, and processing begins upon receiving this image data. Specifically, the server decodes the image data and converts it into a format that can be appropriately analyzed.

[0266] Step 3:

[0267] The server uses image analysis techniques to identify the species and health status of organisms. The input is image data formatted in the previous step, and the output is information about the identified species and health status of the organisms. The server uses OpenCV and TensorFlow tools to extract features such as color, shape, and texture, and then compares them with a database to perform identification.

[0268] Step 4:

[0269] The server generates an optimal maintenance plan based on the analysis results. The input is the identified species and health status information of the organism, and the output is a detailed maintenance plan. This generation utilizes a generative AI model, and a predictive algorithm formulates the optimal care plan based on historical management data.

[0270] Step 5:

[0271] The terminal receives maintenance plans sent from the server and notifies the user. The input is maintenance plan data from the server, and the output is the content of the notified care plan. In operation, the terminal displays specific instructions for watering and fertilizing to the user via push notifications.

[0272] Step 6:

[0273] The terminal interacts with smart pots and automatic watering systems based on instructions from the server, performing the necessary actions. The input is a control command based on the care plan, and the output is the result of that execution. Specifically, the terminal sends commands to each device, for example, to accurately perform an action such as "supply 200ml of water."

[0274] (Application Example 1)

[0275] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0276] Managing plants in public areas requires considerable effort and time, and maintaining their health is particularly difficult in urban areas. Traditional methods require manually checking each plant species and its health condition, and generating instructions for appropriate care; automation is not yet widespread. Furthermore, the lack of a system to respond promptly to changes in weather and environment makes effective management difficult. There is a need for efficient solutions to these challenges.

[0277] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0278] In this invention, the server includes means for analyzing an image of a plant to determine the type and health status of the plant, means for generating an optimal care plan based on the type and health status of the plant, and a terminal for taking pictures of plants in a public area. Thereby, it becomes possible to automatically evaluate the health status of plants in a public area and quickly provide appropriate care instructions.

[0279] The "image of a plant" is visual data taken to determine the type and health status of the plant.

[0280] "Analysis" is a process of extracting the characteristics of a plant from the captured image data and evaluating the type and health status.

[0281] The "health status" is an index indicating the degree of the ability of a plant to maintain its original shape and color and continue normal growth.

[0282] The "care plan" is a summary of specific instructions such as watering and fertilizer supply for maintaining or improving the health of a plant based on the analysis results.

[0283] "Automatically control" is a process in which the system adjusts its actions according to conditions without human intervention.

[0284] The "weather information" is data indicating environmental conditions such as temperature, precipitation, and sunshine hours.

[0285] "Dynamically adjust" means modifying plans and actions in real time according to changes in the situation.

[0286] The "terminal" is a device for taking pictures of a plant image and transmitting it to the system. 1]

[0287] "Health assessment" is the process of indicating the health of plants using numerical values ​​and indicators based on analyzed data.

[0288] "Management instructions" are guidelines that show specific actions to be taken to maintain the health of plants.

[0289] The system implementing this invention provides a comprehensive method for efficiently managing plant health. The main components of the system include a user terminal, a server, and a database for plant management.

[0290] The server receives images of plants and uses the image processing library OpenCV to analyze them. TensorFlow is used as a deep learning model during the analysis to identify the plant species and health status. The identified data is then compared with a plant care database to generate an optimal care plan. The care plan is updated in real time in response to changes in weather and environmental data. Weather information APIs such as the OpenWeatherMap API are used to acquire environmental data.

[0291] The user terminal functions as a camera, taking pictures of plants in public areas and sending them to the server. It also receives notifications from the server and provides the user with specific management instructions. For example, if the health of a photographed cherry tree is assessed as "good," a notification is sent stating that the next pruning should be done in two weeks.

[0292] An example of a prompt for a generative AI model that supports part of this process would be: "Identify the types and health status of plants in the image and generate an appropriate care plan."

[0293] This system automates the management of plants in public areas, making it possible to maintain their health efficiently and effectively.

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] The terminal is operated by the user to take images of plants in a public area. The input is visual data obtained from the camera sensor, and the output is the captured image file. The terminal then prepares to send this image to the server.

[0297] Step 2:

[0298] The terminal sends the captured image to the server. The input is the image file acquired in step 1, and the output is the image on the server ready for analysis. Here, the operation of sending image data is performed using network communication.

[0299] Step 3:

[0300] The server uses OpenCV to preprocess the received images for analysis. This preprocessing takes image data as input and outputs data in a shape suitable for analysis. Specifically, data processing such as image resizing and noise reduction is performed.

[0301] Step 4:

[0302] The server uses a TensorFlow model to determine the plant species and health status from preprocessed image data. The input is the output data from step 3, and the output is information indicating the specific plant species and its health status. The operation here is a prediction by a machine learning model.

[0303] Step 5:

[0304] The server uses the identified plant information to compare it with a plant care database and generate the optimal care plan. Input is information about the plant type and health status, and output is a specific plan for watering and fertilizing. Database queries are used to extract care plans that match the specified criteria.

[0305] Step 6:

[0306] The server obtains the latest weather data via the weather information API. The input is the API request, and the output is the weather data. This operation includes obtaining the necessary data from an online service.

[0307] Step 7:

[0308] The server dynamically adjusts the care plan as needed using the obtained weather data. The input is the care plan and the weather data, and the output is the adjusted care plan. A comparison operation is performed to modify the content of the care according to the conditions.

[0309] Step 8:

[0310] The server notifies the terminal of the adjusted care plan and information regarding the health status of the plant. The input is the adjusted care plan in Step 7, and the output is the notification to the user. An operation of transmitting notification data via the network is performed.

[0311] Step 9:

[0312] The user takes the necessary actions based on the received notification. The input is the information displayed on the terminal, and the output is the implementation of physical plant care. At this stage, the user performs an operation of appropriately managing the plant according to the instructions.

[0313] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0314] This invention combines an emotion engine with an automated plant care system to propose and adjust care plans based on the user's emotions. Through this emotion recognition function, the system aims not only to manage plant health but also to improve the user's psychological satisfaction.

[0315] The user first takes a picture of the plant using an application installed on their device. The device then sends the specified image to the server. The server has the functionality to analyze the transmitted image using image processing technology and evaluate the plant's type and health condition. Based on this information, an optimal plant care plan is generated.

[0316] Simultaneously, the device recognizes the user's emotional state. The emotion engine analyzes the user's facial expressions and tone of voice through the video camera and voice input to determine their current emotional state (e.g., stressed, relaxed). This emotional data is sent to the server and incorporated into the plant care plan.

[0317] The server takes in emotional data and adapts the plant care plan to the user's emotional state. For example, if a user is feeling stressed, the system sends a message to the device recommending observing plants in nature as a comforting remedy to alleviate the pressure, in addition to the care plan. In this way, the content of the care plan and the notifications sent to the user are customized according to their emotions.

[0318] This invention enhances the accuracy of plant health management while providing further value to users' lives, representing an innovative approach to reducing stress and improving quality of life. Specifically, if the user senses pleasure from sunbathing, it sends a notification guiding them to observe afternoon sunlight and plants, aiming to make the overall environment more comfortable.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] The user takes a picture of a plant using an application on their device, and the device sends that image to the server.

[0322] Step 2:

[0323] The server uses image recognition algorithms to analyze the received plant images and identify the plant species and health status.

[0324] Step 3:

[0325] The server automatically generates the most suitable care plan for the plant by referencing the appropriate care database based on the analysis results.

[0326] Step 4:

[0327] The device uses an emotion engine to recognize the user's emotional state through their facial expressions and voice, and sends that data to the server.

[0328] Step 5:

[0329] The server receives data from the emotion engine and adjusts notifications and care plans according to the user's emotional state. For example, if the user needs relaxation, it might recommend a simple plant care task.

[0330] Step 6:

[0331] Users view and receive notifications for customized care plans sent from the server via their devices. These plans include messages and care instructions that are sensitive to the user's feelings.

[0332] Step 7:

[0333] The terminal issues instructions to smart pots and automatic watering systems as needed, ensuring watering and fertilization are carried out according to the care plan. It also adjusts schedules and plans based on predictive data regarding plant care.

[0334] Step 8:

[0335] The server continuously analyzes weather and environmental data, dynamically adjusts care plans as needed, and prepares subsequent notifications. Based on user feedback, it collects new data to further improve the plans.

[0336] (Example 2)

[0337] Next, we will describe Example 2. 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".

[0338] Traditional plant care systems focused primarily on managing plant health, but rarely considered user psychological satisfaction. This resulted in users not receiving appropriate care tailored to their individual emotional states. Furthermore, notifications to users tended to be fixed and lacked a personalized approach.

[0339] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0340] In this invention, the server includes means for analyzing images of plants to determine the type and health status of the plants, means for recognizing the user's emotional state and reflecting and adjusting the emotional state in the care plan, and means for generating advice messages corresponding to the emotional state and notifying the user's device. This makes it possible not only to manage the health of plants but also to provide and notify users of care plans tailored to their individual emotional state.

[0341] "Analyzing plant images" involves processing plant image data to identify plant characteristics within the image and evaluate their species and health status.

[0342] "Determining the type and health status of a plant" means determining, based on the characteristics identified through analysis, which type of plant it belongs to and whether or not its physiological state is good.

[0343] "Generating a care plan" means formulating maintenance procedures appropriate to the type of plant and its health condition, and then shaping them into a concrete plan.

[0344] "Recognizing the user's emotional state" means estimating the user's psychological state from their facial expressions, voice, and other behaviors, and recognizing that as data.

[0345] "Reflecting and adjusting emotional states in care plans" means taking the user's emotional state into consideration, modifying existing care plans, and providing more individualized care suggestions.

[0346] "Generating and notifying advice messages" means compiling useful information into text based on the user's emotional state and plant care plan, and sending it to the user's device.

[0347] This invention is a system for efficiently caring for plants, and includes image analysis of plants, recognition of user emotions, and generation and notification of care plans based on these. Specifically, it is implemented in the following form:

[0348] The user takes pictures of plants using an application installed on the device. The device is equipped with a camera and microphone, allowing it to collect not only plant images but also the user's facial expressions and voice. The captured images are then sent from the device to a server.

[0349] The server analyzes the received plant images using an image processing library (e.g., OpenCV). This analysis identifies the plant species and health status, and then generates a corresponding care plan using an AI model.

[0350] The device also utilizes a video camera and voice input to recognize the user's emotional state. This emotion recognition is performed using the Emotion API and Google Cloud's Natural Language API, among others. The resulting emotional data is also sent to a server and incorporated into the plant's care plan.

[0351] The server integrates plant and user emotional information to generate tailored care plans and advice messages. These messages are sent to the device and notified to the user. Because the notification content is customized according to the user's individual emotional state, it is more likely to improve the user's psychological satisfaction.

[0352] For example, if a user takes a picture of a plant and the device detects that the user is in a relaxed state, a notification such as "How about observing your plant in the afternoon sunlight?" will be sent, providing a more comfortable plan. Another example of a prompt message is: "Please come up with a prompt message example for this system. Please suggest a plant care plan for a cactus that is in a relaxed emotional state and is healthy."

[0353] In this way, this system provides comprehensive care that not only manages the health of plants but also takes into account the user's emotions.

[0354] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0355] Step 1:

[0356] The user takes a picture of a plant using an application on their device. The plant image is taken into the device as input, and the image data is sent to the server. The image data is saved in JPEG or PNG format and sent to the server via the HTTP protocol over the internet.

[0357] Step 2:

[0358] The server uses plant image data received as input. Using an image processing library (e.g., OpenCV), it extracts plant features and analyzes the plant species and health status. The analysis results are obtained as output, and these results include the plant species name, leaf condition, color, and other health indicators.

[0359] Step 3:

[0360] The device acquires data for emotion recognition. It uses a video camera and microphone to collect the user's facial expressions and voice data. This facial and voice data is passed to the emotion engine as input. The engine uses, for example, the Emotion API to estimate emotions and outputs the user's emotional state (relaxed, stressed, etc.).

[0361] Step 4:

[0362] The server integrates the analysis results obtained in steps 2 and 3 with the emotional data. It receives this data as input and uses a generative AI model to generate a care plan that takes the user's emotional state into account. The output is a customized care plan and advice message. This plan might include, for example, "The plants are healthy and the user is relaxed, so sunbathing is recommended."

[0363] Step 5:

[0364] The server sends the generated care plan and advice messages to the device. Using the generated messages as input, it notifies the user's device as output. This notification is presented to the user as a pop-up or in-app message on the device, allowing the user to care for the plants accordingly.

[0365] (Application Example 2)

[0366] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0367] Conventional plant care systems have the drawback of focusing solely on plant health management without considering the user's psychological satisfaction. Furthermore, because plant care plans are not tailored to the user's individual emotional state, the process of growing plants does not fully realize the user's relaxation and stress-relief benefits.

[0368] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0369] In this invention, the server includes means for analyzing visual data of plants to determine their classification and health status, means for analyzing the user's emotional state, and means for dynamically adjusting the plant care plan to suit the user's emotions according to the analysis results. This makes it possible to manage the health of plants while improving the user's psychological satisfaction and providing a plant cultivation experience tailored to the user's individual emotional state.

[0370] "Visual plant data" refers to visual information such as images and videos of plants, and is used to classify plants and determine their health status.

[0371] A "user information terminal" refers to a portable or stationary electronic device that a user can operate, and is used to transmit visual data and emotional state data of plants to a server.

[0372] A "care plan" refers to a set of instructions, including schedules and methods for watering and fertilizing, designed to promote the healthy growth of plants.

[0373] "Emotional state" refers to the user's psychological condition, which is analyzed through facial expressions, voice, and other biosensors.

[0374] "Dynamic adjustment" refers to continuously changing and modifying care plans in real time according to the situation.

[0375] To implement this invention, the following hardware and software are used in the plant care system. First, the user information terminal is a mobile device such as a smartphone or tablet, which captures and transmits visual data of plants through an application installed on the terminal. The captured data is sent to a server, which uses image processing technology such as "Google Cloud Vision API" to classify the plants and evaluate their health status.

[0376] Simultaneously, the user's emotional state is collected through the camera and microphone on the device. Emotional analysis uses "Microsoft Face API" and voice tone analysis technology to recognize the user's emotional state from their facial expressions and voice.

[0377] The server generates a plant care plan based on this data, and this plan is dynamically adjusted according to the user's emotional state. If the server determines that the user is stressed, the care plan may include suggestions for plant observation to promote relaxation. Finally, this information is sent as a notification to the user's information terminal, and plant care is performed in real time.

[0378] For example, if the user expresses feelings of joy, a plan to expose the plant to sunlight will be recommended, and a message will appear on the device stating, "The plant has been moved to the optimal location for afternoon sunlight." An example of a prompt using the generative AI model is, "Please suggest the best plant care plan if the user's emotional state is stress. For example, please suggest ways to care for the plant in a relaxing location or manner."

[0379] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0380] Step 1:

[0381] The user takes a visual image of a plant using their device. The device then sends the captured image to a server via an application. The input is the image of the plant taken by the device's camera, and the output is the digital image data sent to the server.

[0382] Step 2:

[0383] The server analyzes the received plant image data using the Google Cloud Vision API. Image processing is performed to classify the plants and determine their health status. The input is digital image data, and the output is the plant species and its health status assessment. The analyzed information is then used to generate subsequent care plans.

[0384] Step 3:

[0385] The device captures the user's emotional state using its camera and microphone. The captured video and audio data is analyzed within the device using the "Microsoft Face API" and voice analysis technology. The input is data of the user's facial expressions and voice, and the output is an evaluation of the user's emotional state.

[0386] Step 4:

[0387] The server generates an optimal plant care plan based on the plant's health and the user's emotional state. This process includes customization that takes the user's psychological state into account. The input is plant health data and user emotional data, and the output is a customized care plan.

[0388] Step 5:

[0389] The server notifies the user's terminal of the generated care plan. The user receives this notification and performs plant care as needed. The input is a customized care plan, and the output is a notification message to the terminal. The user can take specific actions based on this notification.

[0390] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0391] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0392] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0393] [Third Embodiment]

[0394] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0395] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0396] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0397] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0398] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0399] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0400] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0401] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0402] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0403] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0404] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0405] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0406] This invention relates to a system that automates plant care, allowing users to manage plant health without effort. This system takes images of plants, analyzes those images to determine the plant's species and health condition, and generates an optimal care plan.

[0407] The user first takes a picture of the plant through an application on their device. This image is sent from the device to a server. The server uses image processing technology to analyze the transmitted image and identify the type of plant and its health condition. This information is then cross-referenced with a plant care database to generate the most suitable care plan for each individual plant.

[0408] The generated care plan is communicated to the user via a terminal. The notification includes specific watering amounts and timings, fertilizer types, and their supply schedules. Furthermore, the system monitors weather and environmental data and adjusts the care plan in real time. This enables flexible plant care that adapts to seasonal and weather changes.

[0409] The device can connect with smart pots and automatic watering systems based on instructions from the server. This functionality allows the system to automatically water and fertilize plants even when the user is absent, maintaining an optimal environment for the plants.

[0410] For example, a specific care plan is provided for a particular houseplant, such as "water with 200ml every Monday and Thursday" or "add liquid fertilizer in two weeks." If it's raining, the notification will be changed to skip watering the following Monday. These features allow users to manage their plants' health with peace of mind.

[0411] The following describes the processing flow.

[0412] Step 1:

[0413] The user launches the application on their device and takes a picture of a plant. The captured image is set to be automatically sent to the server.

[0414] Step 2:

[0415] The server receives images of plants sent from the terminal. The received images are first analyzed using an image recognition algorithm to identify the type of plant.

[0416] Step 3:

[0417] The server refers to a database corresponding to the analyzed plant species and performs a general health check. This allows it to assess the plant's health based on characteristics such as leaf color and shape.

[0418] Step 4:

[0419] The server generates an optimal care plan based on the plant type and health status, using a standard care database. This includes the amount and frequency of watering, the type of fertilizer needed, and more.

[0420] Step 5:

[0421] The generated care plan is sent from the server to the terminal. The user can then view the details of the care plan (e.g., the next watering date, time, and amount) via the terminal.

[0422] Step 6:

[0423] Users can accept or fine-tune care plans through their device. If automatic mode is selected as needed, the system will automatically prepare to execute instructions for watering and fertilizing.

[0424] Step 7:

[0425] The terminal sends instructions to smart pots and automatic watering systems, initiating watering and fertilization according to the care plan. Simultaneously, the server acquires data from environmental sensors and periodically adjusts the care plan.

[0426] Step 8:

[0427] The server updates weather information as needed, adjusts watering timing based on forecast weather data, and prepares information for the next care cycle. This information is then sent back to the terminal and notified to the user.

[0428] (Example 1)

[0429] Next, we will describe Example 1. 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."

[0430] Traditional plant management methods struggled to provide optimal care tailored to the individual characteristics of each plant and specific environmental conditions, and required significant manual effort from the user. Furthermore, they lacked the flexibility to adapt to weather and environmental fluctuations. In addition, automated management was limited to certain functions, resulting in inadequate management during user absences.

[0431] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0432] In this invention, the server includes means for analyzing images of organisms to determine the type and health status of the organisms, means for generating an optimal maintenance plan based on the type and health status of the organisms, and means for automatically controlling water supply and nutrient supply based on the maintenance plan. This enables personalized care for individual plants, flexible responses to weather and environmental changes, and effective management even when the user is absent.

[0433] The term "living organism" refers to all living things in the natural world, including plants and animals, but in this context, it often specifically refers to plants.

[0434] "Analysis" refers to the process of thoroughly investigating information and revealing its structure and elements.

[0435] "Health status" refers to the physical and physiological state of an organism, and specifically to the evaluation of conditions that require maintenance or management.

[0436] A "maintenance plan" refers to a systematically organized series of activities and measures necessary to maintain the health of an organism.

[0437] "Watering" refers to the act of supplying necessary water to living organisms such as plants.

[0438] "Nutrient supply" refers to the act of providing organisms with the nutrients they need to grow or stay alive.

[0439] "Automatically controlled" refers to a system operating without human intervention, based on pre-set conditions.

[0440] "Meteorology" refers to the state of the atmosphere and encompasses natural phenomena including elements such as temperature, humidity, wind speed, and precipitation.

[0441] "Environmental information" refers to data about the surrounding conditions, such as weather conditions, topography, and vegetation, for a specific location and time.

[0442] This invention is a system that automates the health management of living organisms, reducing the burden on the user. The process begins with the user taking pictures of plants using an application. The captured images are transmitted to a server via Wi-Fi or mobile data communication.

[0443] The server processes the received images using image analysis techniques. Specifically, it uses image recognition libraries such as OpenCV and TensorFlow. This identifies the species and health status of the organism, and then compares it with a database.

[0444] Based on the matching data, the server generates an optimal maintenance plan. This process utilizes a generative AI model and leverages predictive capabilities based on historical data. The generated maintenance plan is then notified to the terminal. The notification includes specific instructions regarding the timing and amount of watering and nutrient supply.

[0445] Furthermore, the terminal can follow instructions from the server and connect with smart pots and automatic watering systems. This allows for automatic management of living organisms even when the user is absent.

[0446] For example, a certain houseplant might be instructed to "water with 200ml of water every Monday and Thursday." Furthermore, a predictive model analyzes weather information and makes adjustments, such as skipping watering instructions on days when rain is expected.

[0447] An example of a prompt message is, "Analyze the plant image, determine its health status, and provide the optimal care plan." Based on this prompt message, the generating AI model creates an appropriate care plan, enabling efficient biological management throughout the entire system.

[0448] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0449] Step 1:

[0450] The user takes an image of a living organism using a device application. The input is image data acquired via the device's camera. The user presses a capture button within the application, the device activates its camera function, and captures the image. This image data is then sent directly to the server.

[0451] Step 2:

[0452] The server receives image data sent from the terminal. The input is image data from the terminal, and processing begins upon receiving this image data. Specifically, the server decodes the image data and converts it into a format that can be appropriately analyzed.

[0453] Step 3:

[0454] The server uses image analysis techniques to identify the species and health status of organisms. The input is image data formatted in the previous step, and the output is information about the identified species and health status of the organisms. The server uses OpenCV and TensorFlow tools to extract features such as color, shape, and texture, and then compares them with a database to perform identification.

[0455] Step 4:

[0456] The server generates an optimal maintenance plan based on the analysis results. The input is the identified species and health status information of the organism, and the output is a detailed maintenance plan. This generation utilizes a generative AI model, and a predictive algorithm formulates the optimal care plan based on historical management data.

[0457] Step 5:

[0458] The terminal receives maintenance plans sent from the server and notifies the user. The input is maintenance plan data from the server, and the output is the content of the notified care plan. In operation, the terminal displays specific instructions for watering and fertilizing to the user via push notifications.

[0459] Step 6:

[0460] The terminal interacts with smart pots and automatic watering systems based on instructions from the server, performing the necessary actions. The input is a control command based on the care plan, and the output is the result of that execution. Specifically, the terminal sends commands to each device, for example, to accurately perform an action such as "supply 200ml of water."

[0461] (Application Example 1)

[0462] Next, we will explain Application Example 1. In the following explanation, 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."

[0463] Managing plants in public areas requires considerable effort and time, and maintaining their health is particularly difficult in urban areas. Traditional methods require manually checking each plant species and its health condition, and generating instructions for appropriate care; automation is not yet widespread. Furthermore, the lack of a system to respond promptly to changes in weather and environment makes effective management difficult. There is a need for efficient solutions to these challenges.

[0464] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0465] In this invention, the server includes means for analyzing images of plants to determine the type and health status of the plants, means for generating an optimal care plan based on the type and health status of the plants, and a terminal for photographing plants in public areas. This makes it possible to automatically evaluate the health status of plants in public areas and quickly provide appropriate care instructions.

[0466] "Plant images" are visual data taken to determine the type and health status of a plant.

[0467] "Analysis" is the process of extracting plant characteristics from captured image data and evaluating their species and health status.

[0468] "Health" is an indicator of the degree to which a plant is able to maintain its original shape and color and continue normal growth.

[0469] A "care plan" is a document that outlines specific instructions, such as watering and fertilizing, to maintain or improve the health of plants, based on the analysis results.

[0470] "Automatic control" refers to a process in which a system adjusts its actions according to conditions without human intervention.

[0471] "Weather information" refers to data that indicates environmental conditions such as temperature, precipitation, and sunshine duration.

[0472] "Dynamic adjustment" means modifying plans and actions in real time in response to changing circumstances.

[0473] A "terminal" is a device used to take pictures of plants and transmit them to the system.

[0474] "Health assessment" is the process of indicating the health of plants using numerical values ​​and indicators based on analyzed data.

[0475] "Management instructions" are guidelines that show specific actions to be taken to maintain the health of plants.

[0476] The system implementing this invention provides a comprehensive method for efficiently managing plant health. The main components of the system include a user terminal, a server, and a database for plant management.

[0477] The server receives images of plants and uses the image processing library OpenCV to analyze them. TensorFlow is used as a deep learning model during the analysis to identify the plant species and health status. The identified data is then compared with a plant care database to generate an optimal care plan. The care plan is updated in real time in response to changes in weather and environmental data. Weather information APIs such as the OpenWeatherMap API are used to acquire environmental data.

[0478] The user terminal functions as a camera, taking pictures of plants in public areas and sending them to the server. It also receives notifications from the server and provides the user with specific management instructions. For example, if the health of a photographed cherry tree is assessed as "good," a notification is sent stating that the next pruning should be done in two weeks.

[0479] An example of a prompt for a generative AI model that supports part of this process would be: "Identify the types and health status of plants in the image and generate an appropriate care plan."

[0480] This system automates the management of plants in public areas, making it possible to maintain their health efficiently and effectively.

[0481] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0482] Step 1:

[0483] The terminal is operated by the user to take images of plants in a public area. The input is visual data obtained from the camera sensor, and the output is the captured image file. The terminal then prepares to send this image to the server.

[0484] Step 2:

[0485] The terminal sends the captured image to the server. The input is the image file acquired in step 1, and the output is the image on the server ready for analysis. Here, the operation of sending image data is performed using network communication.

[0486] Step 3:

[0487] The server uses OpenCV to preprocess the received images for analysis. This preprocessing takes image data as input and outputs data in a shape suitable for analysis. Specifically, data processing such as image resizing and noise reduction is performed.

[0488] Step 4:

[0489] The server uses a TensorFlow model to determine the plant species and health status from preprocessed image data. The input is the output data from step 3, and the output is information indicating the specific plant species and its health status. The operation here is a prediction by a machine learning model.

[0490] Step 5:

[0491] The server uses the identified plant information to compare it with a plant care database and generate the optimal care plan. Input is information about the plant type and health status, and output is a specific plan for watering and fertilizing. Database queries are used to extract care plans that match the specified criteria.

[0492] Step 6:

[0493] The server retrieves the latest weather data via a weather information API. The input is an API request, and the output is weather data. This involves retrieving necessary data from online services.

[0494] Step 7:

[0495] The server uses acquired weather data to dynamically adjust care plans as needed. The inputs are the care plan and weather data, and the output is the adjusted care plan. Comparison operations are performed to modify the care content based on the conditions.

[0496] Step 8:

[0497] The server notifies the terminal of the adjusted care plan and information regarding the plant's health. The input is the adjusted care plan from step 7, and the output is the notification to the user. The notification data is then sent over the network.

[0498] Step 9:

[0499] The user takes the necessary actions based on the notification received. The input is the information displayed on the device, and the output is the physical action of plant care. At this stage, the user performs actions to properly manage the plants according to the instructions.

[0500] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0501] This invention combines an emotion engine with an automated plant care system to propose and adjust care plans based on the user's emotions. Through this emotion recognition function, the system aims not only to manage plant health but also to improve the user's psychological satisfaction.

[0502] The user first takes a picture of the plant using an application installed on their device. The device then sends the specified image to the server. The server has the functionality to analyze the transmitted image using image processing technology and evaluate the plant's type and health condition. Based on this information, an optimal plant care plan is generated.

[0503] Simultaneously, the device recognizes the user's emotional state. The emotion engine analyzes the user's facial expressions and tone of voice through the video camera and voice input to determine their current emotional state (e.g., stressed, relaxed). This emotional data is sent to the server and incorporated into the plant care plan.

[0504] The server takes in emotional data and adapts the plant care plan to the user's emotional state. For example, if a user is feeling stressed, the system sends a message to the device recommending observing plants in nature as a comforting remedy to alleviate the pressure, in addition to the care plan. In this way, the content of the care plan and the notifications sent to the user are customized according to their emotions.

[0505] This invention enhances the accuracy of plant health management while providing further value to users' lives, representing an innovative approach to reducing stress and improving quality of life. Specifically, if the user senses pleasure from sunbathing, it sends a notification guiding them to observe afternoon sunlight and plants, aiming to make the overall environment more comfortable.

[0506] The following describes the processing flow.

[0507] Step 1:

[0508] The user takes a picture of a plant using an application on their device, and the device sends that image to the server.

[0509] Step 2:

[0510] The server uses image recognition algorithms to analyze the received plant images and identify the plant species and health status.

[0511] Step 3:

[0512] The server automatically generates the most suitable care plan for the plant by referencing the appropriate care database based on the analysis results.

[0513] Step 4:

[0514] The device uses an emotion engine to recognize the user's emotional state through their facial expressions and voice, and sends that data to the server.

[0515] Step 5:

[0516] The server receives data from the emotion engine and adjusts notifications and care plans according to the user's emotional state. For example, if the user needs relaxation, it might recommend a simple plant care task.

[0517] Step 6:

[0518] Users view and receive notifications for customized care plans sent from the server via their devices. These plans include messages and care instructions that are sensitive to the user's feelings.

[0519] Step 7:

[0520] The terminal issues instructions to smart pots and automatic watering systems as needed, ensuring watering and fertilization are carried out according to the care plan. It also adjusts schedules and plans based on predictive data regarding plant care.

[0521] Step 8:

[0522] The server continuously analyzes weather and environmental data, dynamically adjusts care plans as needed, and prepares subsequent notifications. Based on user feedback, it collects new data to further improve the plans.

[0523] (Example 2)

[0524] Next, we will describe Example 2. 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."

[0525] Traditional plant care systems focused primarily on managing plant health, but rarely considered user psychological satisfaction. This resulted in users not receiving appropriate care tailored to their individual emotional states. Furthermore, notifications to users tended to be fixed and lacked a personalized approach.

[0526] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0527] In this invention, the server includes means for analyzing images of plants to determine the type and health status of the plants, means for recognizing the user's emotional state and reflecting and adjusting the emotional state in the care plan, and means for generating advice messages corresponding to the emotional state and notifying the user's device. This makes it possible not only to manage the health of plants but also to provide and notify users of care plans tailored to their individual emotional state.

[0528] "Analyzing plant images" involves processing plant image data to identify plant characteristics within the image and evaluate their species and health status.

[0529] "Determining the type and health status of a plant" means determining, based on the characteristics identified through analysis, which type of plant it belongs to and whether or not its physiological state is good.

[0530] "Generating a care plan" means formulating maintenance procedures appropriate to the type of plant and its health condition, and then shaping them into a concrete plan.

[0531] "Recognizing the user's emotional state" means estimating the user's psychological state from their facial expressions, voice, and other behaviors, and recognizing that as data.

[0532] "Reflecting and adjusting emotional states in care plans" means taking the user's emotional state into consideration, modifying existing care plans, and providing more individualized care suggestions.

[0533] "Generating and notifying advice messages" means compiling useful information into text based on the user's emotional state and plant care plan, and sending it to the user's device.

[0534] This invention is a system for efficiently caring for plants, and includes image analysis of plants, recognition of user emotions, and generation and notification of care plans based on these. Specifically, it is implemented in the following form:

[0535] The user takes pictures of plants using an application installed on the device. The device is equipped with a camera and microphone, allowing it to collect not only plant images but also the user's facial expressions and voice. The captured images are then sent from the device to a server.

[0536] The server analyzes the received plant images using an image processing library (e.g., OpenCV). This analysis identifies the plant species and health status, and then generates a corresponding care plan using an AI model.

[0537] The device also utilizes a video camera and voice input to recognize the user's emotional state. This emotion recognition is performed using the Emotion API and Google Cloud's Natural Language API, among others. The resulting emotional data is also sent to a server and incorporated into the plant's care plan.

[0538] The server integrates plant and user emotional information to generate tailored care plans and advice messages. These messages are sent to the device and notified to the user. Because the notification content is customized according to the user's individual emotional state, it is more likely to improve the user's psychological satisfaction.

[0539] For example, if a user takes a picture of a plant and the device detects that the user is in a relaxed state, a notification such as "How about observing your plant in the afternoon sunlight?" will be sent, providing a more comfortable plan. Another example of a prompt message is: "Please come up with a prompt message example for this system. Please suggest a plant care plan for a cactus that is in a relaxed emotional state and is healthy."

[0540] In this way, this system provides comprehensive care that not only manages the health of plants but also takes into account the user's emotions.

[0541] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0542] Step 1:

[0543] The user takes a picture of a plant using an application on their device. The plant image is taken into the device as input, and the image data is sent to the server. The image data is saved in JPEG or PNG format and sent to the server via the HTTP protocol over the internet.

[0544] Step 2:

[0545] The server uses plant image data received as input. Using an image processing library (e.g., OpenCV), it extracts plant features and analyzes the plant species and health status. The analysis results are obtained as output, and these results include the plant species name, leaf condition, color, and other health indicators.

[0546] Step 3:

[0547] The device acquires data for emotion recognition. It uses a video camera and microphone to collect the user's facial expressions and voice data. This facial and voice data is passed to the emotion engine as input. The engine uses, for example, the Emotion API to estimate emotions and outputs the user's emotional state (relaxed, stressed, etc.).

[0548] Step 4:

[0549] The server integrates the analysis results obtained in steps 2 and 3 with the emotional data. It receives this data as input and uses a generative AI model to generate a care plan that takes the user's emotional state into account. The output is a customized care plan and advice message. This plan might include, for example, "The plants are healthy and the user is relaxed, so sunbathing is recommended."

[0550] Step 5:

[0551] The server sends the generated care plan and advice messages to the device. Using the generated messages as input, it notifies the user's device as output. This notification is presented to the user as a pop-up or in-app message on the device, allowing the user to care for the plants accordingly.

[0552] (Application Example 2)

[0553] Next, we will explain application example 2. In the following explanation, 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."

[0554] Conventional plant care systems have the drawback of focusing solely on plant health management without considering the user's psychological satisfaction. Furthermore, because plant care plans are not tailored to the user's individual emotional state, the process of growing plants does not fully realize the user's relaxation and stress-relief benefits.

[0555] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0556] In this invention, the server includes means for analyzing visual data of plants to determine their classification and health status, means for analyzing the user's emotional state, and means for dynamically adjusting the plant care plan to suit the user's emotions according to the analysis results. This makes it possible to manage the health of plants while improving the user's psychological satisfaction and providing a plant cultivation experience tailored to the user's individual emotional state.

[0557] "Visual plant data" refers to visual information such as images and videos of plants, and is used to classify plants and determine their health status.

[0558] A "user information terminal" refers to a portable or stationary electronic device that a user can operate, and is used to transmit visual data and emotional state data of plants to a server.

[0559] A "care plan" refers to a set of instructions, including schedules and methods for watering and fertilizing, designed to promote the healthy growth of plants.

[0560] "Emotional state" refers to the user's psychological condition, which is analyzed through facial expressions, voice, and other biosensors.

[0561] "Dynamic adjustment" refers to continuously changing and modifying care plans in real time according to the situation.

[0562] To implement this invention, the following hardware and software are used in the plant care system. First, the user information terminal is a mobile device such as a smartphone or tablet, which captures and transmits visual data of plants through an application installed on the terminal. The captured data is sent to a server, which uses image processing technology such as "Google Cloud Vision API" to classify the plants and evaluate their health status.

[0563] Simultaneously, the user's emotional state is collected through the camera and microphone on the device. Emotional analysis uses "Microsoft Face API" and voice tone analysis technology to recognize the user's emotional state from their facial expressions and voice.

[0564] The server generates a plant care plan based on this data, and this plan is dynamically adjusted according to the user's emotional state. If the server determines that the user is stressed, the care plan may include suggestions for plant observation to promote relaxation. Finally, this information is sent as a notification to the user's information terminal, and plant care is performed in real time.

[0565] For example, if the user expresses feelings of joy, a plan to expose the plant to sunlight will be recommended, and a message will appear on the device stating, "The plant has been moved to the optimal location for afternoon sunlight." An example of a prompt using the generative AI model is, "Please suggest the best plant care plan if the user's emotional state is stress. For example, please suggest ways to care for the plant in a relaxing location or manner."

[0566] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0567] Step 1:

[0568] The user takes a visual image of a plant using their device. The device then sends the captured image to a server via an application. The input is the image of the plant taken by the device's camera, and the output is the digital image data sent to the server.

[0569] Step 2:

[0570] The server analyzes the received plant image data using the Google Cloud Vision API. Image processing is performed to classify the plants and determine their health status. The input is digital image data, and the output is the plant species and its health status assessment. The analyzed information is then used to generate subsequent care plans.

[0571] Step 3:

[0572] The device captures the user's emotional state using its camera and microphone. The captured video and audio data is analyzed within the device using the "Microsoft Face API" and voice analysis technology. The input is data of the user's facial expressions and voice, and the output is an evaluation of the user's emotional state.

[0573] Step 4:

[0574] The server generates an optimal plant care plan based on the plant's health and the user's emotional state. This process includes customization that takes the user's psychological state into account. The input is plant health data and user emotional data, and the output is a customized care plan.

[0575] Step 5:

[0576] The server notifies the user's terminal of the generated care plan. The user receives this notification and performs plant care as needed. The input is a customized care plan, and the output is a notification message to the terminal. The user can take specific actions based on this notification.

[0577] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0578] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0579] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0580] [Fourth Embodiment]

[0581] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0582] As shown in Figure 7, the 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.

[0583] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0584] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0585] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0586] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0587] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0588] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0589] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0590] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0591] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0592] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0593] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0594] This invention relates to a system that automates plant care, allowing users to manage plant health without effort. This system takes images of plants, analyzes those images to determine the plant's species and health condition, and generates an optimal care plan.

[0595] The user first takes a picture of the plant through an application on their device. This image is sent from the device to a server. The server uses image processing technology to analyze the transmitted image and identify the type of plant and its health condition. This information is then cross-referenced with a plant care database to generate the most suitable care plan for each individual plant.

[0596] The generated care plan is communicated to the user via a terminal. The notification includes specific watering amounts and timings, fertilizer types, and their supply schedules. Furthermore, the system monitors weather and environmental data and adjusts the care plan in real time. This enables flexible plant care that adapts to seasonal and weather changes.

[0597] The device can connect with smart pots and automatic watering systems based on instructions from the server. This functionality allows the system to automatically water and fertilize plants even when the user is absent, maintaining an optimal environment for the plants.

[0598] For example, a specific care plan is provided for a particular houseplant, such as "water with 200ml every Monday and Thursday" or "add liquid fertilizer in two weeks." If it's raining, the notification will be changed to skip watering the following Monday. These features allow users to manage their plants' health with peace of mind.

[0599] The following describes the processing flow.

[0600] Step 1:

[0601] The user launches the application on their device and takes a picture of a plant. The captured image is set to be automatically sent to the server.

[0602] Step 2:

[0603] The server receives images of plants sent from the terminal. The received images are first analyzed using an image recognition algorithm to identify the type of plant.

[0604] Step 3:

[0605] The server refers to a database corresponding to the analyzed plant species and performs a general health check. This allows it to assess the plant's health based on characteristics such as leaf color and shape.

[0606] Step 4:

[0607] The server generates an optimal care plan based on the plant type and health status, using a standard care database. This includes the amount and frequency of watering, the type of fertilizer needed, and more.

[0608] Step 5:

[0609] The generated care plan is sent from the server to the terminal. The user can then view the details of the care plan (e.g., the next watering date, time, and amount) via the terminal.

[0610] Step 6:

[0611] Users can accept or fine-tune care plans through their device. If automatic mode is selected as needed, the system will automatically prepare to execute instructions for watering and fertilizing.

[0612] Step 7:

[0613] The terminal sends instructions to smart pots and automatic watering systems, initiating watering and fertilization according to the care plan. Simultaneously, the server acquires data from environmental sensors and periodically adjusts the care plan.

[0614] Step 8:

[0615] The server updates weather information as needed, adjusts watering timing based on forecast weather data, and prepares information for the next care cycle. This information is then sent back to the terminal and notified to the user.

[0616] (Example 1)

[0617] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0618] Traditional plant management methods struggled to provide optimal care tailored to the individual characteristics of each plant and specific environmental conditions, and required significant manual effort from the user. Furthermore, they lacked the flexibility to adapt to weather and environmental fluctuations. In addition, automated management was limited to certain functions, resulting in inadequate management during user absences.

[0619] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0620] In this invention, the server includes means for analyzing images of organisms to determine the type and health status of the organisms, means for generating an optimal maintenance plan based on the type and health status of the organisms, and means for automatically controlling water supply and nutrient supply based on the maintenance plan. This enables personalized care for individual plants, flexible responses to weather and environmental changes, and effective management even when the user is absent.

[0621] The term "living organism" refers to all living things in the natural world, including plants and animals, but in this context, it often specifically refers to plants.

[0622] "Analysis" refers to the process of thoroughly investigating information and revealing its structure and elements.

[0623] "Health status" refers to the physical and physiological state of an organism, and specifically to the evaluation of conditions that require maintenance or management.

[0624] A "maintenance plan" refers to a systematically organized series of activities and measures necessary to maintain the health of an organism.

[0625] "Watering" refers to the act of supplying necessary water to living organisms such as plants.

[0626] "Nutrient supply" refers to the act of providing organisms with the nutrients they need to grow or stay alive.

[0627] "Automatically controlled" refers to a system operating without human intervention, based on pre-set conditions.

[0628] "Meteorology" refers to the state of the atmosphere and encompasses natural phenomena including elements such as temperature, humidity, wind speed, and precipitation.

[0629] "Environmental information" refers to data about the surrounding conditions, such as weather conditions, topography, and vegetation, for a specific location and time.

[0630] This invention is a system that automates the health management of living organisms, reducing the burden on the user. The process begins with the user taking pictures of plants using an application. The captured images are transmitted to a server via Wi-Fi or mobile data communication.

[0631] The server processes the received images using image analysis techniques. Specifically, it uses image recognition libraries such as OpenCV and TensorFlow. This identifies the species and health status of the organism, and then compares it with a database.

[0632] Based on the matching data, the server generates an optimal maintenance plan. This process utilizes a generative AI model and leverages predictive capabilities based on historical data. The generated maintenance plan is then notified to the terminal. The notification includes specific instructions regarding the timing and amount of watering and nutrient supply.

[0633] Furthermore, the terminal can follow instructions from the server and connect with smart pots and automatic watering systems. This allows for automatic management of living organisms even when the user is absent.

[0634] For example, a certain houseplant might be instructed to "water with 200ml of water every Monday and Thursday." Furthermore, a predictive model analyzes weather information and makes adjustments, such as skipping watering instructions on days when rain is expected.

[0635] An example of a prompt message is, "Analyze the plant image, determine its health status, and provide the optimal care plan." Based on this prompt message, the generating AI model creates an appropriate care plan, enabling efficient biological management throughout the entire system.

[0636] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0637] Step 1:

[0638] The user takes an image of a living organism using a device application. The input is image data acquired via the device's camera. The user presses a capture button within the application, the device activates its camera function, and captures the image. This image data is then sent directly to the server.

[0639] Step 2:

[0640] The server receives image data sent from the terminal. The input is image data from the terminal, and processing begins upon receiving this image data. Specifically, the server decodes the image data and converts it into a format that can be appropriately analyzed.

[0641] Step 3:

[0642] The server uses image analysis techniques to identify the species and health status of organisms. The input is image data formatted in the previous step, and the output is information about the identified species and health status of the organisms. The server uses OpenCV and TensorFlow tools to extract features such as color, shape, and texture, and then compares them with a database to perform identification.

[0643] Step 4:

[0644] The server generates an optimal maintenance plan based on the analysis results. The input is the identified species and health status information of the organism, and the output is a detailed maintenance plan. This generation utilizes a generative AI model, and a predictive algorithm formulates the optimal care plan based on historical management data.

[0645] Step 5:

[0646] The terminal receives maintenance plans sent from the server and notifies the user. The input is maintenance plan data from the server, and the output is the content of the notified care plan. In operation, the terminal displays specific instructions for watering and fertilizing to the user via push notifications.

[0647] Step 6:

[0648] The terminal interacts with smart pots and automatic watering systems based on instructions from the server, performing the necessary actions. The input is a control command based on the care plan, and the output is the result of that execution. Specifically, the terminal sends commands to each device, for example, to accurately perform an action such as "supply 200ml of water."

[0649] (Application Example 1)

[0650] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0651] Managing plants in public areas requires considerable effort and time, and maintaining their health is particularly difficult in urban areas. Traditional methods require manually checking each plant species and its health condition, and generating instructions for appropriate care; automation is not yet widespread. Furthermore, the lack of a system to respond promptly to changes in weather and environment makes effective management difficult. There is a need for efficient solutions to these challenges.

[0652] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0653] In this invention, the server includes means for analyzing images of plants to determine the type and health status of the plants, means for generating an optimal care plan based on the type and health status of the plants, and a terminal for photographing plants in public areas. This makes it possible to automatically evaluate the health status of plants in public areas and quickly provide appropriate care instructions.

[0654] "Plant images" are visual data taken to determine the type and health status of a plant.

[0655] "Analysis" is the process of extracting plant characteristics from captured image data and evaluating their species and health status.

[0656] "Health" is an indicator of the degree to which a plant is able to maintain its original shape and color and continue normal growth.

[0657] A "care plan" is a document that outlines specific instructions, such as watering and fertilizing, to maintain or improve the health of plants, based on the analysis results.

[0658] "Automatic control" refers to a process in which a system adjusts its actions according to conditions without human intervention.

[0659] "Weather information" refers to data that indicates environmental conditions such as temperature, precipitation, and sunshine duration.

[0660] "Dynamic adjustment" means modifying plans and actions in real time in response to changing circumstances.

[0661] A "terminal" is a device used to take pictures of plants and transmit them to the system.

[0662] "Health assessment" is the process of indicating the health of plants using numerical values ​​and indicators based on analyzed data.

[0663] "Management instructions" are guidelines that show specific actions to be taken to maintain the health of plants.

[0664] The system implementing this invention provides a comprehensive method for efficiently managing plant health. The main components of the system include a user terminal, a server, and a database for plant management.

[0665] The server receives images of plants and uses the image processing library OpenCV to analyze them. TensorFlow is used as a deep learning model during the analysis to identify the plant species and health status. The identified data is then compared with a plant care database to generate an optimal care plan. The care plan is updated in real time in response to changes in weather and environmental data. Weather information APIs such as the OpenWeatherMap API are used to acquire environmental data.

[0666] The user terminal functions as a camera, taking pictures of plants in public areas and sending them to the server. It also receives notifications from the server and provides the user with specific management instructions. For example, if the health of a photographed cherry tree is assessed as "good," a notification is sent stating that the next pruning should be done in two weeks.

[0667] An example of a prompt for a generative AI model that supports part of this process would be: "Identify the types and health status of plants in the image and generate an appropriate care plan."

[0668] This system automates the management of plants in public areas, making it possible to maintain their health efficiently and effectively.

[0669] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0670] Step 1:

[0671] The terminal is operated by the user to take images of plants in a public area. The input is visual data obtained from the camera sensor, and the output is the captured image file. The terminal then prepares to send this image to the server.

[0672] Step 2:

[0673] The terminal sends the captured image to the server. The input is the image file acquired in step 1, and the output is the image on the server ready for analysis. Here, the operation of sending image data is performed using network communication.

[0674] Step 3:

[0675] The server uses OpenCV to preprocess the received images for analysis. This preprocessing takes image data as input and outputs data in a shape suitable for analysis. Specifically, data processing such as image resizing and noise reduction is performed.

[0676] Step 4:

[0677] The server uses a TensorFlow model to determine the plant species and health status from preprocessed image data. The input is the output data from step 3, and the output is information indicating the specific plant species and its health status. The operation here is a prediction by a machine learning model.

[0678] Step 5:

[0679] The server uses the identified plant information to compare it with a plant care database and generate the optimal care plan. Input is information about the plant type and health status, and output is a specific plan for watering and fertilizing. Database queries are used to extract care plans that match the specified criteria.

[0680] Step 6:

[0681] The server retrieves the latest weather data via a weather information API. The input is an API request, and the output is weather data. This involves retrieving necessary data from online services.

[0682] Step 7:

[0683] The server uses acquired weather data to dynamically adjust care plans as needed. Inputs are the care plan and weather data, and output is the adjusted care plan. Comparison operations are performed to modify the care content based on the conditions.

[0684] Step 8:

[0685] The server notifies the terminal of the adjusted care plan and information regarding the plant's health. The input is the adjusted care plan from step 7, and the output is the notification to the user. The notification data is then sent over the network.

[0686] Step 9:

[0687] The user takes the necessary actions based on the notification received. The input is the information displayed on the device, and the output is the physical action of plant care. At this stage, the user performs actions to properly manage the plants according to the instructions.

[0688] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0689] This invention combines an emotion engine with an automated plant care system to propose and adjust care plans based on the user's emotions. Through this emotion recognition function, the system aims not only to manage plant health but also to improve the user's psychological satisfaction.

[0690] The user first takes a picture of the plant using an application installed on their device. The device then sends the specified image to the server. The server has the functionality to analyze the transmitted image using image processing technology and evaluate the plant's type and health condition. Based on this information, an optimal plant care plan is generated.

[0691] Simultaneously, the device recognizes the user's emotional state. The emotion engine analyzes the user's facial expressions and tone of voice through the video camera and voice input to determine their current emotional state (e.g., stressed, relaxed). This emotional data is sent to the server and incorporated into the plant care plan.

[0692] The server takes in emotional data and adapts the plant care plan to the user's emotional state. For example, if a user is feeling stressed, the system sends a message to the device recommending observing plants in nature as a comforting remedy to alleviate the pressure, in addition to the care plan. In this way, the content of the care plan and the notifications sent to the user are customized according to their emotions.

[0693] This invention enhances the accuracy of plant health management while providing further value to users' lives, representing an innovative approach to reducing stress and improving quality of life. Specifically, if the user senses pleasure from sunbathing, it sends a notification guiding them to observe afternoon sunlight and plants, aiming to make the overall environment more comfortable.

[0694] The following describes the processing flow.

[0695] Step 1:

[0696] The user takes a picture of a plant using an application on their device, and the device sends that image to the server.

[0697] Step 2:

[0698] The server uses image recognition algorithms to analyze the received plant images and identify the plant species and health status.

[0699] Step 3:

[0700] The server automatically generates the most suitable care plan for the plant by referencing the appropriate care database based on the analysis results.

[0701] Step 4:

[0702] The device uses an emotion engine to recognize the user's emotional state through their facial expressions and voice, and sends that data to the server.

[0703] Step 5:

[0704] The server receives data from the emotion engine and adjusts notifications and care plans according to the user's emotional state. For example, if the user needs relaxation, it might recommend a simple plant care task.

[0705] Step 6:

[0706] Users view and receive notifications for customized care plans sent from the server via their devices. These plans include messages and care instructions that are sensitive to the user's feelings.

[0707] Step 7:

[0708] The terminal issues instructions to smart pots and automatic watering systems as needed, ensuring watering and fertilization are carried out according to the care plan. It also adjusts schedules and plans based on predictive data regarding plant care.

[0709] Step 8:

[0710] The server continuously analyzes weather and environmental data, dynamically adjusts care plans as needed, and prepares subsequent notifications. Based on user feedback, it collects new data to further improve the plans.

[0711] (Example 2)

[0712] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0713] Traditional plant care systems focused primarily on managing plant health, but rarely considered user psychological satisfaction. This resulted in users not receiving appropriate care tailored to their individual emotional states. Furthermore, notifications to users tended to be fixed and lacked a personalized approach.

[0714] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0715] In this invention, the server includes means for analyzing images of plants to determine the type and health status of the plants, means for recognizing the user's emotional state and reflecting and adjusting the emotional state in the care plan, and means for generating advice messages corresponding to the emotional state and notifying the user's device. This makes it possible not only to manage the health of plants but also to provide and notify users of care plans tailored to their individual emotional state.

[0716] "Analyzing plant images" involves processing plant image data to identify plant characteristics within the image and evaluate their species and health status.

[0717] "Determining the type and health status of a plant" means determining, based on the characteristics identified through analysis, which type of plant it belongs to and whether or not its physiological state is good.

[0718] "Generating a care plan" means formulating maintenance procedures appropriate to the type of plant and its health condition, and then shaping them into a concrete plan.

[0719] "Recognizing the user's emotional state" means estimating the user's psychological state from their facial expressions, voice, and other behaviors, and recognizing that as data.

[0720] "Reflecting and adjusting emotional states in care plans" means taking the user's emotional state into consideration, modifying existing care plans, and providing more individualized care suggestions.

[0721] "Generating and notifying advice messages" means compiling useful information into text based on the user's emotional state and plant care plan, and sending it to the user's device.

[0722] This invention is a system for efficiently caring for plants, and includes image analysis of plants, recognition of user emotions, and generation and notification of care plans based on these. Specifically, it is implemented in the following form:

[0723] The user takes pictures of plants using an application installed on the device. The device is equipped with a camera and microphone, allowing it to collect not only plant images but also the user's facial expressions and voice. The captured images are then sent from the device to a server.

[0724] The server analyzes the received plant images using an image processing library (e.g., OpenCV). This analysis identifies the plant species and health status, and then generates a corresponding care plan using an AI model.

[0725] The device also utilizes a video camera and voice input to recognize the user's emotional state. This emotion recognition is performed using the Emotion API and Google Cloud's Natural Language API, among others. The resulting emotional data is also sent to a server and incorporated into the plant's care plan.

[0726] The server integrates plant and user emotional information to generate tailored care plans and advice messages. These messages are sent to the device and notified to the user. Because the notification content is customized according to the user's individual emotional state, it is more likely to improve the user's psychological satisfaction.

[0727] For example, if a user takes a picture of a plant and the device detects that the user is in a relaxed state, a notification such as "How about observing your plant in the afternoon sunlight?" will be sent, providing a more comfortable plan. Another example of a prompt message is: "Please come up with a prompt message example for this system. Please suggest a plant care plan for a cactus that is in a relaxed emotional state and is healthy."

[0728] In this way, this system provides comprehensive care that not only manages the health of plants but also takes into account the user's emotions.

[0729] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0730] Step 1:

[0731] The user takes a picture of a plant using an application on their device. The plant image is taken into the device as input, and the image data is sent to the server. The image data is saved in JPEG or PNG format and sent to the server via the HTTP protocol over the internet.

[0732] Step 2:

[0733] The server uses plant image data received as input. Using an image processing library (e.g., OpenCV), it extracts plant features and analyzes the plant species and health status. The analysis results are obtained as output, and these results include the plant species name, leaf condition, color, and other health indicators.

[0734] Step 3:

[0735] The device acquires data for emotion recognition. It uses a video camera and microphone to collect the user's facial expressions and voice data. This facial and voice data is passed to the emotion engine as input. The engine uses, for example, the Emotion API to estimate emotions and outputs the user's emotional state (relaxed, stressed, etc.).

[0736] Step 4:

[0737] The server integrates the analysis results obtained in steps 2 and 3 with the emotional data. It receives this data as input and uses a generative AI model to generate a care plan that takes the user's emotional state into account. The output is a customized care plan and advice message. This plan might include, for example, "The plants are healthy and the user is relaxed, so sunbathing is recommended."

[0738] Step 5:

[0739] The server sends the generated care plan and advice messages to the device. Using the generated messages as input, it notifies the user's device as output. This notification is presented to the user as a pop-up or in-app message on the device, allowing the user to care for the plants accordingly.

[0740] (Application Example 2)

[0741] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0742] Conventional plant care systems have the drawback of focusing solely on plant health management without considering the user's psychological satisfaction. Furthermore, because plant care plans are not tailored to the user's individual emotional state, the process of growing plants does not fully realize the user's relaxation and stress-relief benefits.

[0743] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0744] In this invention, the server includes means for analyzing visual data of plants to determine their classification and health status, means for analyzing the user's emotional state, and means for dynamically adjusting the plant care plan to suit the user's emotions according to the analysis results. This makes it possible to manage the health of plants while improving the user's psychological satisfaction and providing a plant cultivation experience tailored to the user's individual emotional state.

[0745] "Visual plant data" refers to visual information such as images and videos of plants, and is used to classify plants and determine their health status.

[0746] A "user information terminal" refers to a portable or stationary electronic device that a user can operate, and is used to transmit visual data and emotional state data of plants to a server.

[0747] A "care plan" refers to a set of instructions, including schedules and methods for watering and fertilizing, designed to promote the healthy growth of plants.

[0748] "Emotional state" refers to the user's psychological condition, which is analyzed through facial expressions, voice, and other biosensors.

[0749] "Dynamic adjustment" refers to continuously changing and modifying care plans in real time according to the situation.

[0750] To implement this invention, the following hardware and software are used in the plant care system. First, the user information terminal is a mobile device such as a smartphone or tablet, which captures and transmits visual data of plants through an application installed on the terminal. The captured data is sent to a server, which uses image processing technology such as "Google Cloud Vision API" to classify the plants and evaluate their health status.

[0751] Simultaneously, the user's emotional state is collected through the camera and microphone on the device. Emotional analysis uses "Microsoft Face API" and voice tone analysis technology to recognize the user's emotional state from their facial expressions and voice.

[0752] The server generates a plant care plan based on this data, and this plan is dynamically adjusted according to the user's emotional state. If the server determines that the user is stressed, the care plan may include suggestions for plant observation to promote relaxation. Finally, this information is sent as a notification to the user's information terminal, and plant care is performed in real time.

[0753] For example, if the user expresses feelings of joy, a plan to expose the plant to sunlight will be recommended, and a message will appear on the device stating, "The plant has been moved to the optimal location for afternoon sunlight." An example of a prompt using the generative AI model is, "Please suggest the best plant care plan if the user's emotional state is stress. For example, please suggest ways to care for the plant in a relaxing location or manner."

[0754] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0755] Step 1:

[0756] The user takes a visual image of a plant using their device. The device then sends the captured image to a server via an application. The input is the image of the plant taken by the device's camera, and the output is the digital image data sent to the server.

[0757] Step 2:

[0758] The server analyzes the received plant image data using the Google Cloud Vision API. Image processing is performed to classify the plants and determine their health status. The input is digital image data, and the output is the plant species and its health status assessment. The analyzed information is then used to generate subsequent care plans.

[0759] Step 3:

[0760] The device captures the user's emotional state using its camera and microphone. The captured video and audio data is analyzed within the device using the "Microsoft Face API" and voice analysis technology. The input is data of the user's facial expressions and voice, and the output is an evaluation of the user's emotional state.

[0761] Step 4:

[0762] The server generates an optimal plant care plan based on the plant's health and the user's emotional state. This process includes customization that takes the user's psychological state into account. The input is plant health data and user emotional data, and the output is a customized care plan.

[0763] Step 5:

[0764] The server notifies the user's terminal of the generated care plan. The user receives this notification and performs plant care as needed. The input is a customized care plan, and the output is a notification message to the terminal. The user can take specific actions based on this notification.

[0765] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0766] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0767] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0768] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0769] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0770] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0771] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0772] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0773] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0774] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0775] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0776] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0777] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0778] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0779] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0780] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0781] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0782] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0783] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0784] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0785] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0786] The following is further disclosed regarding the embodiments described above.

[0787] (Claim 1)

[0788] A means of analyzing plant images to determine the type and health status of the plant,

[0789] A means for generating an optimal care plan based on the type and health condition of the aforementioned plants,

[0790] A means for automatically controlling watering and fertilizer supply based on the aforementioned care plan,

[0791] Means for acquiring weather and environmental data and dynamically adjusting the care plan,

[0792] A system that includes this.

[0793] (Claim 2)

[0794] The system according to claim 1, which receives images of the aforementioned plants from a user terminal.

[0795] (Claim 3)

[0796] The system according to claim 1, which sends a notification to the user's terminal and adapts the care plan to reflect feedback based on the user's environment.

[0797] "Example 1"

[0798] (Claim 1)

[0799] A means of analyzing images of organisms to determine the species and health status of those organisms,

[0800] Means for generating an optimal maintenance plan based on the type and health status of the organism,

[0801] Means for automatically controlling water supply and nutrient supply based on the aforementioned maintenance plan,

[0802] Means for acquiring weather and environmental information and dynamically adjusting the maintenance plan,

[0803] A means of conducting health checkups using image recognition and prediction technologies and generating specific care instructions,

[0804] A means for transmitting control commands from the user's terminal,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, which receives an image of the organism from an information device.

[0808] (Claim 3)

[0809] The system according to claim 1, which sends notifications to information devices and adapts the maintenance plan to reflect feedback based on the user's environment.

[0810] "Application Example 1"

[0811] (Claim 1)

[0812] A means of analyzing plant images to determine the type and health status of the plant,

[0813] A means for generating an optimal care plan based on the type and health condition of the aforementioned plants,

[0814] A means for automatically controlling watering and fertilizer supply based on the aforementioned care plan,

[0815] A means for dynamically adjusting the care plan based on acquired weather and environmental data,

[0816] A terminal for photographing plants in public areas,

[0817] A means for generating and transmitting management instructions based on the health status assessment of photographed plants,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, which receives images of the aforementioned plants from a user terminal.

[0821] (Claim 3)

[0822] The system according to claim 1, which sends a notification to the user's terminal and adapts the care plan to reflect feedback based on the user's environment.

[0823] "Example 2 of combining an emotion engine"

[0824] (Claim 1)

[0825] A means of analyzing plant images to determine the type and health status of the plant,

[0826] A means for generating an optimal care plan based on the type and health condition of the aforementioned plants,

[0827] A means for recognizing the user's emotional state and reflecting and adjusting the said emotional state in the care plan,

[0828] Means for generating an advice message corresponding to the aforementioned emotional state and notifying the user's device,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, which receives images of the plant and user emotion data from the user's device.

[0832] (Claim 3)

[0833] The system according to claim 1, which sends customized notifications to the user's device and adapts the care plan to reflect feedback based on the user's environment and emotions.

[0834] "Application example 2 when combining with an emotional engine"

[0835] (Claim 1)

[0836] A means of analyzing visual data of plants to determine their classification and health status,

[0837] A means for generating an optimal care plan based on the classification and health status of the aforementioned plants,

[0838] A means for automatically controlling hydration and fertilizer supply based on the aforementioned care plan,

[0839] Means for acquiring weather and environmental data and dynamically adjusting the care plan,

[0840] A means of analyzing the user's emotional state and adapting the plant care plan to the user's emotions,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, which receives visual data of the plant from a user information terminal.

[0844] (Claim 3)

[0845] The system according to claim 1, which sends a notification to a user information terminal and adapts the care plan to reflect the user's emotional response. [Explanation of symbols]

[0846] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of analyzing plant images to determine the type and health status of the plant, A means for generating an optimal care plan based on the type and health condition of the aforementioned plants, A means for automatically controlling watering and fertilizer supply based on the aforementioned care plan, Means for acquiring weather and environmental data and dynamically adjusting the care plan, A system that includes this.

2. The system according to claim 1, which receives images of the aforementioned plants from a user terminal.

3. The system according to claim 1, which sends a notification to the user's terminal and adapts the care plan to reflect feedback based on the user's environment.

Images