system

Abstract

We provide the system. [Solution] A means of receiving user input information and identifying preferences, A means of using a generative model that generates plant selections and cultivation method suggestions based on those preferences, A means of acquiring environmental data in real time using a sensor device, A means of evaluating the growth status of plants using acquired environmental data and notifying appropriate work, A means of proposing appropriate countermeasures when an anomaly is detected, 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 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 character of the chatbot, 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] When growing a home garden, there are problems that it is difficult for beginners to select suitable vegetables and know how to grow them, and it is also difficult to manage the growth status of plants and detect abnormalities at an early stage. Furthermore, depending on climate and environmental conditions, effective vegetable cultivation becomes difficult, and the labor required to maintain a home garden increases. Therefore, there is a demand for providing a method that allows all people who grow a home garden to cultivate vegetables more simply and efficiently.

Means for Solving the Problems

[0005] This invention provides a system that acquires user preferences and cultivation conditions as input information and proposes the optimal vegetable selection and cultivation method based on a generated AI model. Furthermore, this system acquires environmental data in real time using sensor devices and analyzes this data to evaluate the growth status of plants and notify the user of necessary care or any abnormalities. In addition, the generated AI model can continuously optimize the cultivation plan in response to the user's cultivation status and changes in the external environment. In this way, even beginners can easily and efficiently manage their home gardens.

[0006] A "user" is an individual or group that uses this system to conduct home gardening.

[0007] "Input information" refers to data that users provide to the system, including their plant preferences, cultivation conditions, and purpose.

[0008] A "sensor device" is a device used to acquire environmental data such as soil moisture, temperature, and sunlight intensity.

[0009] A "generative AI model" is an artificial intelligence algorithm that generates the optimal plant selection and cultivation method based on user input.

[0010] "Environmental data" refers to data that includes physical information about soil and air obtained from sensor devices.

[0011] "Growth status" refers to an indicator of a plant's growth and health, and is evaluated through data analysis from sensor devices.

[0012] "Abnormality" refers to the occurrence of unexpected malfunctions or problems in the growth or health of a plant.

[0013] A "cultivation plan" refers to the procedures and schedule for efficiently growing plants in a user's home garden. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0015] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a 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.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] To implement this invention, a system is provided that allows users to efficiently manage their home gardens. The system has a terminal with an interface for users to input their preferred vegetables and cultivation conditions, thereby allowing users to send necessary data to the system. The terminal is equipped with a communication module for sending this data to a server.

[0036] The server records the received user data and uses that information to run a generative AI model. The generative AI model has the function of suggesting plants suitable for cultivation and creating detailed cultivation guidelines. This model accesses historical databases, seasonal climate patterns, and region-specific agricultural information to provide the user with the most appropriate opinions.

[0037] In home gardens, sensor devices monitor soil and air to analyze environmental data in real time and identify factors that affect plant growth. These sensor devices measure temperature, soil moisture, and sunlight at regular intervals and transmit this information to a server.

[0038] The server receives sensor data and analyzes the plant's growth status. Based on this, it notifies the user of necessary maintenance tasks and any abnormalities. For example, a user who wants to grow tomatoes will see advice on their device regarding the optimal planting time, soil pH adjustment, and seasonal fertilization timing.

[0039] Users receive notifications issued by the server through their devices and care for their plants as needed. The server also continuously optimizes the cultivation plan based on environmental factors and user feedback. This allows even beginners to enjoy home gardening safely and efficiently.

[0040] For example, if a user inputs a request to "grow cucumbers" via a terminal, the server analyzes the input and recommends the most suitable cucumber variety and cultivation method. Subsequently, if a sensor detects a decrease in soil moisture, the server analyzes the need for watering and notifies the user. Through this process, the user can maintain an efficient and highly successful home garden from start to finish.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user operates the device to input information such as the vegetables they want to grow and the current growing conditions (e.g., garden size, amount of sunlight).

[0044] Step 2:

[0045] The terminal receives information entered by the user and sends it to the server as structured data.

[0046] Step 3:

[0047] The server analyzes the received user data, calls upon a generation AI model, and generates suggestions for the optimal plant selection and cultivation methods for the user.

[0048] Step 4:

[0049] Based on the generated advice, the server sends recommendations, including that advice, to the terminal.

[0050] Step 5:

[0051] Users can review the suggestions through their devices and proceed with their home gardening plans.

[0052] Step 6:

[0053] The sensor continuously acquires environmental data (e.g., humidity, temperature, sunlight) at a designated location (e.g., soil).

[0054] Step 7:

[0055] The sensor periodically sends the collected environmental data to the server.

[0056] Step 8:

[0057] The server analyzes the acquired sensor data to evaluate the current growth status and necessary care.

[0058] Step 9:

[0059] Based on the analysis results, the server generates notifications and suggestions for the user, including specific care instructions and information about any abnormalities, and sends them to the device.

[0060] Step 10:

[0061] Users receive notifications on their devices and take care of their plants as needed.

[0062] (Example 1)

[0063] 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."

[0064] Traditional home gardening presents challenges for beginners, including difficulty in selecting appropriate plants, learning cultivation methods, and providing adequate care in response to changing environmental conditions. This often results in low success rates and unhealthy plant growth. There is a need to address these issues and provide a system that allows anyone to easily manage a home garden and cultivate healthy plants.

[0065] 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.

[0066] In this invention, the server includes means for receiving information that identifies the user's preferences, means for performing generation calculations to create plant species and cultivation instructions based on those preferences, and means for collecting environmental information in chronological order using a detection device. This enables the user to select and manage appropriate plants and receive precise instructions to optimize plant growth.

[0067] "Information that identifies user preferences" refers to data entered to understand the types of plants and cultivation conditions desired by the user.

[0068] "Means for performing generation calculations" refers to a device or software that performs a calculation process to determine the appropriate plant species and generate detailed cultivation instructions based on information obtained from the user.

[0069] A "detection device" refers to hardware used to measure various environmental information, such as temperature, humidity, and sunlight, in real time and to collect that data.

[0070] "Means for collecting environmental information in a time series" refers to a function that uses detection devices to periodically acquire and record environmental data that changes over time.

[0071] This invention is a system for streamlining the operation of a home garden and is implemented as a configuration including a user, terminal, server, and sensor device.

[0072] The user selects and inputs their preferred plants and cultivation conditions through the terminal's interface. This interface provides a user-friendly interface and accepts information such as plant type, soil conditions, and sunlight conditions. The terminal processes the input information and transmits it to the server via a communication module.

[0073] The server receives and stores the data sent from the terminal. Next, the server starts a generative AI model. This AI model incorporates machine learning techniques using TENSORFLOW® and PyTorch, and generates appropriate plant selections and cultivation instructions based on the user's preferences. This process allows even inexperienced users to properly manage their home gardens.

[0074] Sensor devices are installed in the home garden to monitor environmental conditions in real time. The sensors periodically measure data such as temperature, humidity, and sunlight, and transmit it to a server. This allows the server to analyze the environmental data and evaluate the growth status of the plants.

[0075] Based on the analysis results, the server notifies the user's terminal of necessary care and reports of anomalies. This allows the user to resolve problems before it's too late and maintain the health of their plants.

[0076] For example, if a user enters "I want to grow cucumbers" into their device, the server analyzes the user data and suggests appropriate cucumber varieties and cultivation methods. Furthermore, if a sensor detects a decrease in soil moisture, it notifies the user that watering is necessary.

[0077] As an example of a prompt, input would be in the format, "I want to grow cucumbers, so please tell me the best variety and how to grow them." This specific process allows users to efficiently manage their home garden.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] Users input their preferred plants and cultivation conditions using a terminal. Specifically, they select and input "vegetable type," "sunlight conditions," "soil quality," etc., using the on-screen interface. The input data is formatted within the terminal and then sent to the server.

[0081] Step 2:

[0082] The server receives user data sent from the terminal. By analyzing the received data, it makes an initial determination of which plants match the user's criteria. It records this data in a database and performs preprocessing to prepare the AI ​​model for generation.

[0083] Step 3:

[0084] The server activates a generative AI model to generate appropriate plant species and cultivation methods based on user data. Specifically, it performs calculations using historical databases and climate data, and compiles the generated results into a proposal. These proposals are then converted into a format for transmission to the terminal.

[0085] Step 4:

[0086] The sensor device collects environmental data from the home garden. Specifically, it periodically measures "temperature," "humidity," and "sunshine duration," and transmits the data to a server. The transmitted data is recorded in real time and used for subsequent analysis.

[0087] Step 5:

[0088] The server analyzes the plant's growth status based on environmental data obtained from sensors. For example, it analyzes soil moisture data, runs an algorithm to determine whether watering is necessary, and generates the result as output data.

[0089] Step 6:

[0090] The server notifies the user of necessary maintenance information and any anomalies based on the analysis results. Specific instructions are sent to the terminal, and the user performs plant care according to those instructions. The notification clearly states exactly what needs to be done.

[0091] Step 7:

[0092] After performing maintenance, the user provides feedback to the server via their device. Based on this feedback, the server further optimizes the generated AI model and provides more refined suggestions for subsequent uses.

[0093] (Application Example 1)

[0094] 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."

[0095] Traditional home gardening systems have struggled to provide accurate cultivation guidance tailored to individual users' environments and skill levels. Therefore, it's essential to enable beginners, in particular, to successfully manage their home gardens without excessive effort. Furthermore, even in large-scale facilities such as factories, real-time environmental data collection and work instructions based on that data are necessary to efficiently manage plant cultivation and improve productivity.

[0096] 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.

[0097] In this invention, the server includes means for receiving user input information and identifying preferences; means for using a generative model that generates plant selection and cultivation method suggestions based on those preferences; means for acquiring environmental data in real time using sensor devices; means for evaluating the plant growth status using the acquired environmental data and notifying appropriate work; means for suggesting appropriate countermeasures when an abnormality is detected; and means for providing optimal cultivation guidance based on real-time data and transmitting instructions to machines that perform the work in order to support automated plant cultivation in factories. This enables efficient environmental management and cultivation assistance in home gardens and factory plant cultivation.

[0098] "User input information" refers to the information that users enter into the system to communicate their preferred plants and cultivation conditions.

[0099] A "generative model" is an AI algorithm that suggests the optimal plant selection and cultivation method based on the user's preferences.

[0100] A "sensor device" is a measuring instrument used to acquire environmental data in real time and evaluate the growth status of plants.

[0101] "Environmental data" refers to data on factors that affect plant growth, such as temperature, humidity, and sunlight.

[0102] A "cultivation guide" is a set of guidelines created based on acquired environmental data to optimize plant growth.

[0103] "Machines that perform tasks" are factory equipment that automatically carries out plant care tasks based on cultivation guidelines.

[0104] "Real-time data" refers to data that instantly measures and analyzes the current environment and state.

[0105] The system implementing this invention is designed to support efficient management of plants in home gardens and factory settings. The server receives input information from the user and, based on their preferences, uses a generative model to suggest the optimal plant selection and cultivation method. The generative AI model creates an optimal cultivation guide based on a historical database and current environmental conditions. Sensor devices (e.g., Arduino sensors) acquire environmental data (temperature, humidity, sunlight, etc.) in real time and transmit this data to the server. The server analyzes this environmental data to determine necessary maintenance tasks and whether there are any abnormalities. For example, if soil moisture decreases, the server analyzes whether automatic watering is necessary and issues instructions to a machine (e.g., a robot using a Raspberry Pi) to perform the task.

[0106] The program is implemented using the following hardware and software: Arduino sensors are used to collect environmental data, and TensorFlow is used on the server side for data processing and analysis. The user's device (smartphone, etc.) sends input information and receives notifications. The server communicates with sensors and robots using the MQTT protocol.

[0107] For example, if a user inputs "I want to grow tomatoes," the server will suggest suitable tomato varieties and cultivation methods. Later, if the sensor detects insufficient sunlight, it will instruct a robot to turn on lights to increase the amount of light necessary for growth.

[0108] Examples of prompts for the generated AI model include "Please tell me a tomato variety suitable for spring" and "Please suggest the optimal tomato cultivation procedure at humidity levels below 50%." This allows users to grow plants efficiently and in the best possible way.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The user uses a terminal to input the type of plant they want to grow and the conditions under which it will be grown. This input includes the plant's name, preferred growing conditions, and expected yield. This information is sent from the terminal to the server, which stores it in a database.

[0112] Step 2:

[0113] The server activates an AI model based on the user's input information to suggest plant selections and cultivation methods. The AI ​​model analyzes historical databases, climate patterns, and regional information to generate optimal guidance. The suggested results are then sent to the user's device.

[0114] Step 3:

[0115] Sensor devices are activated to acquire environmental data. These devices measure data such as temperature, humidity, and sunlight in real time and transmit it to a server. This allows the server to always be aware of the latest environmental conditions.

[0116] Step 4:

[0117] The server uses the acquired environmental data to evaluate the plant's growth status. For example, if the humidity falls below a specified range, the AI ​​model determines that watering is necessary and notifies the user. Alternatively, it transmits instructions to a machine that automatically performs the appropriate task.

[0118] Step 5:

[0119] If an anomaly is detected, the server will use a generated AI model to propose appropriate countermeasures. These countermeasures will be notified to the user's device, providing specific instructions on maintenance and adjustments. Furthermore, if the user grants permission, the countermeasures will be executed automatically.

[0120] Step 6:

[0121] The server continuously optimizes the cultivation plan based on user feedback and environmental changes. This feedback acts as new input to the AI ​​model, contributing to the generation of better guidelines. In conclusion, this allows users to manage their home gardens efficiently, and optimizes plant production in factories.

[0122] 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.

[0123] To implement this invention, an emotion engine is incorporated into the home gardening support system to provide cultivation support that takes into account the user's emotional state. In this system, the user selects the plant they want to grow via a terminal and also inputs or automatically acquires their own emotional data through the terminal's sensors. This emotional data includes tendencies such as moments when the user feels bored or when they feel enjoyment.

[0124] The device sends user selections and emotional data to the server. The server uses a generative AI model and emotion engine to generate emotionally-based plant selections, cultivation guidelines, and care suggestions. The program makes suggestions that take the user's psychological burden into consideration, such as recommending easy-to-care-for, high-success-rate plants for users who are feeling down.

[0125] Furthermore, the sensor device monitors environmental conditions and reports the plant's growth status to the server in real time. The server analyzes this data and notifies the user of work suggestions or anomaly alerts as needed. In addition, the emotion engine analyzes the user's emotional patterns and personalizes the experience, for example, suggesting more challenging cultivation methods if the user is feeling amused.

[0126] For example, if a user enters "I want to grow sunflowers," and the emotion engine detects from the data that the user is experiencing stress on a daily basis, the device will receive recommendations from the server and simultaneously suggest plants with aromatherapy effects that can contribute to stress reduction and plants with high ornamental value. Furthermore, if the user's mood improves and there are signs that they want a new challenge, the emotion engine will adjust the growing schedule and encourage them to try different plants or advanced cultivation methods. This allows users to enjoy managing their home garden in a flexible and appropriate way according to their psychological state.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] Users can input the plant they want to grow and their current emotional state through their device, or their emotions can be automatically recorded using the device's sensors.

[0130] Step 2:

[0131] The device sends the plant selection and emotion data received from the user to the server.

[0132] Step 3:

[0133] The server uses a generative AI model to identify the user's preferences and emotional state from the received data, and then generates suggestions for the optimal plant selection and cultivation method.

[0134] Step 4:

[0135] The server uses an emotion engine to customize suggestions based on the user's emotional state and sends them to the device.

[0136] Step 5:

[0137] Users can view suggested plants and cultivation methods through their devices and incorporate them into their home garden plans.

[0138] Step 6:

[0139] The sensors are installed on-site to acquire environmental data such as soil moisture, temperature, and sunlight in real time.

[0140] Step 7:

[0141] The sensors collect environmental data, which is then sent to a server and updated periodically.

[0142] Step 8:

[0143] The server analyzes sensor data to evaluate the plant's growth status and environmental conditions. Based on this information, it generates notifications for necessary care and any anomalies.

[0144] Step 9:

[0145] The server optimizes notifications based on the user's emotional patterns, which are analyzed by the emotion engine, and sends them to the device.

[0146] Step 10:

[0147] Users perform plant care based on notifications and suggestions received via their devices. This provides users with ways to enjoy home gardening that take their emotional state at any given time into consideration.

[0148] (Example 2)

[0149] 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".

[0150] In modern society, home gardening is popular as a means of relaxation and self-expression for many people. However, many systems do not take into account the user's psychological state when selecting plants and providing cultivation advice, resulting in a lack of support that allows users to achieve emotional satisfaction. Furthermore, many existing systems have limited ability to properly monitor the plant growing environment and respond quickly as needed. Therefore, there is a need for a system that integrates appropriate cultivation support tailored to the user's psychological state with real-time environmental monitoring.

[0151] 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.

[0152] This invention includes a server that receives user selection information and emotional information and proposes plant selection and cultivation methods that take the user's psychological state into consideration; a means of using a generative AI model that generates cultivation guidelines based on the user's emotional state using an emotional engine; and a means of acquiring environmental information in real time using a sensor device. This makes it possible to operate a home garden optimally according to the user's psychological state and environmental conditions.

[0153] "User selection information" refers to information about specific plants and their cultivation that the user has selected using the system.

[0154] "Emotional information" refers to data that indicates the user's psychological state, and is acquired through input or sensors.

[0155] "Means for proposing plant selection and cultivation methods that take psychological state into consideration" refers to a process or device for analyzing the user's psychological state and determining the appropriate plant type and cultivation method based on that analysis.

[0156] An "emotion engine" is a system or module for analyzing a user's emotional information and has the function of generating advice and suggestions based on the user's emotional patterns.

[0157] A "generative AI model" is an algorithmic model that uses artificial intelligence technology to generate new proposals and guidelines.

[0158] A "sensor device" is a device used to measure environmental conditions and the growth status of plants, and it has the function of acquiring data in real time.

[0159] "Environmental information" refers to data that indicates the conditions of the environment in which plants grow, and includes temperature, humidity, light intensity, and other factors.

[0160] "A means of evaluating the growth status of plants and notifying them of necessary tasks" refers to a system that analyzes the current growth status of plants and, based on that, informs users of specific tasks such as watering or adding fertilizer.

[0161] "Means of providing appropriate countermeasures when an abnormality is identified" refers to a function that, when an abnormality is determined to exist in the plant's growth environment or condition, presents the user with specific methods for resolving that situation.

[0162] The system of this invention provides support for plant cultivation based on the user's psychological state through the cooperation of the user, terminal, server, and sensor device. Specifically, it is implemented as follows.

[0163] Users select the plant they want to grow and input emotional data using a home computing device (such as a smartphone or tablet). This emotional data is either manually entered in the form of a questionnaire or automatically collected using the device's built-in camera or vital sign sensors.

[0164] The device transmits selected plant information and user emotion data to the server via the network. The server then analyzes this data using a generative AI model and emotion engine built in Python. The generative AI model has the capability to calculate optimal plant selection and cultivation guidelines based on the user's emotions.

[0165] Furthermore, sensor devices are used to monitor various environmental factors in which the plants are placed, such as soil humidity, temperature, and light intensity, in real time. This environmental information is transmitted to a server, and the plant's growth status is evaluated. If necessary, the server sends notifications to the terminal suggesting how to care for the plants or how to deal with any abnormalities.

[0166] As a concrete example, suppose a user selects "I want to grow sunflowers" on their device, and the emotion engine analyzes that the user is experiencing stress. In this case, the server would suggest additional plants that are relatively easy to grow, fragrant, and have a relaxing effect. An example of a prompt might be, "Please tell me about plants that can reduce stress and how to easily grow them." In this way, flexible management of a home garden becomes possible, tailored to the user's psychological state and the plant's growing environment.

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

[0168] Step 1:

[0169] The user selects the plant they want to grow using a device and inputs emotional information. This input is done through the device's interface. The device uses a camera and biosensors to infer and supplement the user's emotions from their facial expressions and heart rate. This process collects both plant selection information and emotional data.

[0170] Step 2:

[0171] The terminal transmits the collected plant selection information and emotional data to the server. Specifically, the terminal sends this data to the server in packet format via an internet connection. This is used as the basis for data analysis in the next step.

[0172] Step 3:

[0173] The server analyzes the received plant selection information and emotional data using a generating AI model and emotion engine. A Python program is executed to generate plant cultivation guidelines and recommendations that take the user's emotional state into account. Data processing is performed by comparing the characteristics of plants that match the emotional state with the user's preferences. The output is an optimal cultivation guideline.

[0174] Step 4:

[0175] The sensor device monitors the plant's growing environment and transmits the data to a server. The sensor measures soil moisture, ambient temperature, and light intensity at regular intervals and provides this information to the server using wireless communication technology. This allows for the collection of environmental condition data.

[0176] Step 5:

[0177] The server analyzes environmental data and evaluates the plant's growth status based on the information obtained. The evaluation process compares the results to pre-set growth conditions to check for any abnormalities. The output is provided to the user as a growth status report and suggestions for necessary maintenance.

[0178] Step 6:

[0179] Based on the analysis results, the server sends a notification to the user's device, suggesting necessary actions and countermeasures for any anomalies. The notification includes specific work procedures and alerts, and is displayed as a pop-up on the device screen. This allows the user to take appropriate action in real time.

[0180] (Application Example 2)

[0181] 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 device 14 will be referred to as the "terminal."

[0182] In modern times, incorporating plant cultivation into daily life is gaining attention as a means of mental refreshment and improving quality of life. However, plant cultivation requires a certain level of knowledge and effort, and for users who are unfamiliar with plants in particular, there are problems such as psychological burden and failure to continue due to cultivation failures. Furthermore, there is a challenge in that there are no cultivation plans that reflect the emotional state of the user, so plant cultivation does not necessarily lead to stress relief or mental stability.

[0183] 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.

[0184] In this invention, the server includes means for receiving user input information and emotional data to identify preferences and emotional states, means for using a generative model to generate suggestions for plant selection and cultivation methods based on those preferences and emotional states, and means for acquiring environmental data in real time using a sensor device. This makes it possible to cultivate plants in accordance with the user's emotional state.

[0185] "User input information" refers to data that the system receives from the user, reflecting their choices, settings, specific needs, and preferences.

[0186] "Emotional data" refers to information that indicates a user's emotional state, and is obtained through sensors or user self-reporting.

[0187] A "generative model" is an algorithm or program used to generate plant selections and cultivation methods based on user input and sentiment data.

[0188] A "sensor device" is a device used to acquire environmental data in real time and is used to detect information such as temperature, humidity, and illuminance.

[0189] "Environmental data" refers to information that indicates the surrounding physical and chemical conditions related to plant growth, and is acquired from sensor devices.

[0190] A "smart device" is a digital terminal designed to present information in a way that responds to the user's emotions, and is an electronic device that provides information through one of the following methods: visual, auditory, or otherwise.

[0191] A "personalized growing experience" refers to a plant growing experience optimized based on each user's emotional state and preferences, and a customized growing method that differs for each user.

[0192] To implement this invention, the user, terminal, and server must work together. The user selects the type of plant they want to grow through the terminal and simultaneously inputs emotional data, or has the terminal's emotional sensor automatically acquire it. The terminal then transmits this data to the server.

[0193] The server receives user input information and emotional data, and uses a generative AI model to generate plant selection and cultivation suggestions based on the user's individual emotional state. This generative AI model is implemented on a cloud-based API platform. For example, if the server determines that the user is in a "stressed state," it will recommend plants that are easy to care for and have a high success rate. This proposal is sent to the terminal and displayed on the user's screen in real time.

[0194] Meanwhile, sensor devices are installed in locations where the user operates and are used to acquire environmental data in real time. The server uses this environmental data to evaluate the plant's growth status and, if necessary, performs appropriate tasks or notifies the user of any abnormalities. For example, if the humidity around the plants is too low, the server sends a message to the user such as, "Please add water."

[0195] As a concrete example, when a user is looking at ornamental plants in a flower shop, a smart device can detect the emotion of "seeking happiness" and generate a prompt such as "You are looking for plants that are effective in reducing stress." The server can then suggest plants like "lavender with aromatic effects" and display them on the user's screen. In this way, by presenting information according to the user's emotional state, users can enjoy the psychological benefits while cultivating plants.

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The user selects the plant they want to cultivate via a smart device and obtains emotional data either by inputting it or automatically through an emotional sensor. The input data includes the plant type and emotional data. The device collects this data and sends it to the server.

[0199] Step 2:

[0200] The server invokes a generative AI model based on user input information and emotional data received from the terminal. The generative AI model processes the input data and generates suggestions for plant selection and cultivation methods that take the user's emotional state into account. The output data consists of a list of suggested plants and cultivation guidelines.

[0201] Step 3:

[0202] The server sends suggestions derived from the generated AI model to the terminal. The terminal receives these suggestions and displays them on the user's device screen. The screen displays suggested plants tailored to the user's emotional state, along with specific cultivation methods.

[0203] Step 4:

[0204] The system acquires environmental data in real time in an environment where a sensor device is installed, and transmits that data to a server. Input data includes environmental temperature, humidity, and light intensity.

[0205] Step 5:

[0206] The server evaluates the acquired environmental data and determines the plant's growth status. In particular, if an abnormal value is detected, such as insufficient water, a warning message is generated. The output data includes a message to notify the user.

[0207] Step 6:

[0208] Users receive notifications from the server on their devices and take appropriate action based on the displayed message. For example, if the message indicates that the plants need hydration, the user can follow the instructions and water them.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] [Second Embodiment]

[0213] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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".

[0225] To implement this invention, a system is provided that allows users to efficiently manage their home gardens. The system has a terminal with an interface for users to input their preferred vegetables and cultivation conditions, thereby allowing users to send necessary data to the system. The terminal is equipped with a communication module for sending this data to a server.

[0226] The server records the received user data and uses that information to run a generative AI model. The generative AI model has the function of suggesting plants suitable for cultivation and creating detailed cultivation guidelines. This model accesses historical databases, seasonal climate patterns, and region-specific agricultural information to provide the user with the most appropriate opinions.

[0227] In home gardens, sensor devices monitor soil and air to analyze environmental data in real time and identify factors that affect plant growth. These sensor devices measure temperature, soil moisture, and sunlight at regular intervals and transmit this information to a server.

[0228] The server receives sensor data and analyzes the plant's growth status. Based on this, it notifies the user of necessary maintenance tasks and any abnormalities. For example, a user who wants to grow tomatoes will see advice on their device regarding the optimal planting time, soil pH adjustment, and seasonal fertilization timing.

[0229] Users receive notifications issued by the server through their devices and care for their plants as needed. The server also continuously optimizes the cultivation plan based on environmental factors and user feedback. This allows even beginners to enjoy home gardening safely and efficiently.

[0230] For example, if a user inputs a request to "grow cucumbers" via a terminal, the server analyzes the input and recommends the most suitable cucumber variety and cultivation method. Subsequently, if a sensor detects a decrease in soil moisture, the server analyzes the need for watering and notifies the user. Through this process, the user can maintain an efficient and highly successful home garden from start to finish.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] The user operates the device to input information such as the vegetables they want to grow and the current growing conditions (e.g., garden size, amount of sunlight).

[0234] Step 2:

[0235] The terminal receives information entered by the user and sends it to the server as structured data.

[0236] Step 3:

[0237] The server analyzes the received user data, calls upon a generation AI model, and generates suggestions for the optimal plant selection and cultivation methods for the user.

[0238] Step 4:

[0239] Based on the generated advice, the server sends recommendations, including that advice, to the terminal.

[0240] Step 5:

[0241] Users can review the suggestions through their devices and proceed with their home gardening plans.

[0242] Step 6:

[0243] The sensor continuously acquires environmental data (e.g., humidity, temperature, sunlight) at a designated location (e.g., soil).

[0244] Step 7:

[0245] The sensor periodically sends the collected environmental data to the server.

[0246] Step 8:

[0247] The server analyzes the acquired sensor data to evaluate the current growth status and necessary care.

[0248] Step 9:

[0249] Based on the analysis results, the server generates notifications and suggestions for the user, including specific care instructions and information about any abnormalities, and sends them to the device.

[0250] Step 10:

[0251] Users receive notifications on their devices and take care of their plants as needed.

[0252] (Example 1)

[0253] 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."

[0254] Traditional home gardening presents challenges for beginners, including difficulty in selecting appropriate plants, learning cultivation methods, and providing adequate care in response to changing environmental conditions. This often results in low success rates and unhealthy plant growth. There is a need to address these issues and provide a system that allows anyone to easily manage a home garden and cultivate healthy plants.

[0255] 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.

[0256] In this invention, the server includes means for receiving information that identifies the user's preferences, means for performing generation calculations to create plant species and cultivation instructions based on those preferences, and means for collecting environmental information in chronological order using a detection device. This enables the user to select and manage appropriate plants and receive precise instructions to optimize plant growth.

[0257] "Information that identifies user preferences" refers to data entered to understand the types of plants and cultivation conditions desired by the user.

[0258] "Means for performing generation calculations" refers to a device or software that performs a calculation process to determine the appropriate plant species and generate detailed cultivation instructions based on information obtained from the user.

[0259] A "detection device" refers to hardware used to measure various environmental information, such as temperature, humidity, and sunlight, in real time and to collect that data.

[0260] "Means for collecting environmental information in a time series" refers to a function that uses detection devices to periodically acquire and record environmental data that changes over time.

[0261] This invention is a system for streamlining the operation of a home garden and is implemented as a configuration including a user, terminal, server, and sensor device.

[0262] The user selects and inputs their preferred plants and cultivation conditions through the terminal's interface. This interface provides a user-friendly interface and accepts information such as plant type, soil conditions, and sunlight conditions. The terminal processes the input information and transmits it to the server via a communication module.

[0263] The server receives and stores the data sent from the terminal. Next, the server starts a generative AI model. This AI model incorporates machine learning techniques using TensorFlow and PyTorch, and generates appropriate plant selections and cultivation instructions based on the user's preferences. This process allows even inexperienced users to properly manage their home gardens.

[0264] Sensor devices are installed in the home garden to monitor environmental conditions in real time. The sensors periodically measure data such as temperature, humidity, and sunlight, and transmit it to a server. This allows the server to analyze the environmental data and evaluate the growth status of the plants.

[0265] Based on the analysis results, the server notifies the user's terminal of necessary care and reports of anomalies. This allows the user to resolve problems before it's too late and maintain the health of their plants.

[0266] For example, if a user enters "I want to grow cucumbers" into their device, the server analyzes the user data and suggests appropriate cucumber varieties and cultivation methods. Furthermore, if a sensor detects a decrease in soil moisture, it notifies the user that watering is necessary.

[0267] As an example of a prompt, input would be in the format, "I want to grow cucumbers, so please tell me the best variety and how to grow them." This specific process allows users to efficiently manage their home garden.

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] Users input their preferred plants and cultivation conditions using a terminal. Specifically, they select and input "vegetable type," "sunlight conditions," "soil quality," etc., using the on-screen interface. The input data is formatted within the terminal and then sent to the server.

[0271] Step 2:

[0272] The server receives user data sent from the terminal. By analyzing the received data, it makes an initial determination of which plants match the user's criteria. It records this data in a database and performs preprocessing to prepare the AI ​​model for generation.

[0273] Step 3:

[0274] The server activates a generative AI model to generate appropriate plant species and cultivation methods based on user data. Specifically, it performs calculations using historical databases and climate data, and compiles the generated results into a proposal. These proposals are then converted into a format for transmission to the terminal.

[0275] Step 4:

[0276] The sensor device collects environmental data from the home garden. Specifically, it periodically measures "temperature," "humidity," and "sunshine duration," and transmits the data to a server. The transmitted data is recorded in real time and used for subsequent analysis.

[0277] Step 5:

[0278] The server analyzes the plant's growth status based on environmental data obtained from sensors. For example, it analyzes soil moisture data, runs an algorithm to determine whether watering is necessary, and generates the result as output data.

[0279] Step 6:

[0280] The server notifies the user of necessary maintenance information and any anomalies based on the analysis results. Specific instructions are sent to the terminal, and the user performs plant care according to those instructions. The notification clearly states exactly what needs to be done.

[0281] Step 7:

[0282] After performing maintenance, the user provides feedback to the server via their device. Based on this feedback, the server further optimizes the generated AI model and provides more refined suggestions for subsequent uses.

[0283] (Application Example 1)

[0284] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0285] In a conventional home garden system, it has been difficult to provide accurate cultivation guidance according to the environment and skill level of individual users. Therefore, it is necessary to enable beginners in particular to succeed in home gardening without much effort. Furthermore, in large-scale facilities such as factories, in order to efficiently manage plant cultivation and improve productivity, real-time environmental data collection and work instructions based on it are necessary.

[0286] 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.

[0287] In this invention, the server includes means for receiving user input information and specifying preferences, means for using a generation model that generates proposals for plant selection and cultivation methods based on those preferences, means for acquiring environmental data in real time using a sensor device, means for evaluating the growth state of plants using the acquired environmental data and notifying appropriate operations, means for proposing appropriate countermeasures when an abnormality is detected, and means for providing an optimal growth guide based on real-time data and transmitting instructions to the machines that execute the operations in order to support automatic plant cultivation in the factory. Thereby, efficient environmental management and growth assistance in home gardening and plant cultivation in factories become possible.

[0288] The "user input information" is information that the user inputs to convey their preferred plants and cultivation conditions to the system.

[0289] The "generation model" is an AI algorithm for proposing optimal plant selection and cultivation methods based on user preferences.

[0290] The "sensor device" is a measuring instrument used to acquire environmental data in real time and evaluate the growth state of plants.

[0291] "Environmental data" refers to data on factors that affect plant growth, such as temperature, humidity, and sunlight.

[0292] A "cultivation guide" is a set of guidelines created based on acquired environmental data to optimize plant growth.

[0293] "Machines that perform tasks" are factory equipment that automatically carries out plant care tasks based on cultivation guidelines.

[0294] "Real-time data" refers to data that instantly measures and analyzes the current environment and state.

[0295] The system implementing this invention is designed to support efficient management of plants in home gardens and factory settings. The server receives input information from the user and, based on their preferences, uses a generative model to suggest the optimal plant selection and cultivation method. The generative AI model creates an optimal cultivation guide based on a historical database and current environmental conditions. Sensor devices (e.g., Arduino sensors) acquire environmental data (temperature, humidity, sunlight, etc.) in real time and transmit this data to the server. The server analyzes this environmental data to determine necessary maintenance tasks and whether there are any abnormalities. For example, if soil moisture decreases, the server analyzes whether automatic watering is necessary and issues instructions to a machine (e.g., a robot using a Raspberry Pi) to perform the task.

[0296] The program is implemented using the following hardware and software: Arduino sensors are used to collect environmental data, and TensorFlow is used on the server side for data processing and analysis. The user's device (smartphone, etc.) sends input information and receives notifications. The server communicates with sensors and robots using the MQTT protocol.

[0297] For example, if a user inputs "I want to grow tomatoes," the server will suggest suitable tomato varieties and cultivation methods. Later, if the sensor detects insufficient sunlight, it will instruct a robot to turn on lights to increase the amount of light necessary for growth.

[0298] Examples of prompts for the generated AI model include "Please tell me a tomato variety suitable for spring" and "Please suggest the optimal tomato cultivation procedure at humidity levels below 50%." This allows users to grow plants efficiently and in the best possible way.

[0299] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0300] Step 1:

[0301] The user uses a terminal to input the type of plant they want to grow and the conditions under which it will be grown. This input includes the plant's name, preferred growing conditions, and expected yield. This information is sent from the terminal to the server, which stores it in a database.

[0302] Step 2:

[0303] The server activates an AI model based on the user's input information to suggest plant selections and cultivation methods. The AI ​​model analyzes historical databases, climate patterns, and regional information to generate optimal guidance. The suggested results are then sent to the user's device.

[0304] Step 3:

[0305] Sensor devices are activated to acquire environmental data. These devices measure data such as temperature, humidity, and sunlight in real time and transmit it to a server. This allows the server to always be aware of the latest environmental conditions.

[0306] Step 4:

[0307] The server evaluates the growth state of plants using the acquired environmental data. For example, if the humidity falls below the specified range, the AI model determines that watering is necessary and notifies the user. Or it transmits instructions to a machine that automatically performs appropriate operations.

[0308] Step 5:

[0309] If an abnormality is detected, the server proposes appropriate countermeasures by the generated AI model. This countermeasure is notified to the user's terminal, and specific maintenance methods and adjustment methods are guided. Also, if the user permits, the countermeasure is automatically executed.

[0310] Step 6:

[0311] The server continuously optimizes the growth plan based on feedback from the user and changes in the environment. This feedback functions as new input to the AI model and contributes to the generation of better guidelines. As a conclusion, this enables the user to efficiently operate a home garden, and in a factory, the optimization of plant production is achieved.

[0312] Furthermore, an emotion engine that estimates the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion identification model 59 and perform specific processing using the user's emotions.

[0313] To implement this invention, by incorporating an emotion engine into the home garden support system, growth support considering the user's emotional state is provided. In this system, the user selects the plant they want to grow via a terminal, and further inputs their own emotion data or automatically acquires it with the sensors of the terminal. The emotion data includes moments when they feel bothered and tendencies when they feel enjoyment.

[0314] The device sends user selections and emotional data to the server. The server uses a generative AI model and emotion engine to generate emotionally-based plant selections, cultivation guidelines, and care suggestions. The program makes suggestions that take the user's psychological burden into consideration, such as recommending easy-to-care-for, high-success-rate plants for users who are feeling down.

[0315] Furthermore, the sensor device monitors environmental conditions and reports the plant's growth status to the server in real time. The server analyzes this data and notifies the user of work suggestions or anomaly alerts as needed. In addition, the emotion engine analyzes the user's emotional patterns and personalizes the experience, for example, suggesting more challenging cultivation methods if the user is feeling amused.

[0316] For example, if a user enters "I want to grow sunflowers," and the emotion engine detects from the data that the user is experiencing stress on a daily basis, the device will receive recommendations from the server and simultaneously suggest plants with aromatherapy effects that can contribute to stress reduction and plants with high ornamental value. Furthermore, if the user's mood improves and there are signs that they want a new challenge, the emotion engine will adjust the growing schedule and encourage them to try different plants or advanced cultivation methods. This allows users to enjoy managing their home garden in a flexible and appropriate way according to their psychological state.

[0317] The following describes the processing flow.

[0318] Step 1:

[0319] Users can input the plant they want to grow and their current emotional state through their device, or their emotions can be automatically recorded using the device's sensors.

[0320] Step 2:

[0321] The device sends the plant selection and emotion data received from the user to the server.

[0322] Step 3:

[0323] The server uses a generative AI model to identify the user's preferences and emotional state from the received data, and then generates suggestions for the optimal plant selection and cultivation method.

[0324] Step 4:

[0325] The server uses an emotion engine to customize suggestions based on the user's emotional state and sends them to the device.

[0326] Step 5:

[0327] Users can view suggested plants and cultivation methods through their devices and incorporate them into their home garden plans.

[0328] Step 6:

[0329] The sensors are installed on-site to acquire environmental data such as soil moisture, temperature, and sunlight in real time.

[0330] Step 7:

[0331] The sensors collect environmental data, which is then sent to a server and updated periodically.

[0332] Step 8:

[0333] The server analyzes sensor data to evaluate the plant's growth status and environmental conditions. Based on this information, it generates notifications for necessary care and any anomalies.

[0334] Step 9:

[0335] The server optimizes notifications based on the user's emotional patterns, which are analyzed by the emotion engine, and sends them to the device.

[0336] Step 10:

[0337] Users perform plant care based on notifications and suggestions received via their devices. This provides users with ways to enjoy home gardening that take their emotional state at any given time into consideration.

[0338] (Example 2)

[0339] 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".

[0340] In modern society, home gardening is popular as a means of relaxation and self-expression for many people. However, many systems do not take into account the user's psychological state when selecting plants and providing cultivation advice, resulting in a lack of support that allows users to achieve emotional satisfaction. Furthermore, many existing systems have limited ability to properly monitor the plant growing environment and respond quickly as needed. Therefore, there is a need for a system that integrates appropriate cultivation support tailored to the user's psychological state with real-time environmental monitoring.

[0341] 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.

[0342] This invention includes a server that receives user selection information and emotional information and proposes plant selection and cultivation methods that take the user's psychological state into consideration; a means of using a generative AI model that generates cultivation guidelines based on the user's emotional state using an emotional engine; and a means of acquiring environmental information in real time using a sensor device. This makes it possible to operate a home garden optimally according to the user's psychological state and environmental conditions.

[0343] "User selection information" refers to information about specific plants and their cultivation that the user has selected using the system.

[0344] "Emotional information" refers to data that indicates the user's psychological state, and is acquired through input or sensors.

[0345] "Means for proposing plant selection and cultivation methods that take psychological state into consideration" refers to a process or device for analyzing the user's psychological state and determining the appropriate plant type and cultivation method based on that analysis.

[0346] An "emotion engine" is a system or module for analyzing a user's emotional information and has the function of generating advice and suggestions based on the user's emotional patterns.

[0347] A "generative AI model" is an algorithmic model that uses artificial intelligence technology to generate new proposals and guidelines.

[0348] A "sensor device" is a device used to measure environmental conditions and the growth status of plants, and it has the function of acquiring data in real time.

[0349] "Environmental information" refers to data that indicates the conditions of the environment in which plants grow, and includes temperature, humidity, light intensity, and other factors.

[0350] "A means of evaluating the growth status of plants and notifying them of necessary tasks" refers to a system that analyzes the current growth status of plants and, based on that, informs users of specific tasks such as watering or adding fertilizer.

[0351] "Means of providing appropriate countermeasures when an abnormality is identified" refers to a function that, when an abnormality is determined to exist in the plant's growth environment or condition, presents the user with specific methods for resolving that situation.

[0352] The system of this invention provides support for plant cultivation based on the user's psychological state through the cooperation of the user, terminal, server, and sensor device. Specifically, it is implemented as follows.

[0353] Users select the plant they want to grow and input emotional data using a home computing device (such as a smartphone or tablet). This emotional data is either manually entered in the form of a questionnaire or automatically collected using the device's built-in camera or vital sign sensors.

[0354] The device transmits selected plant information and user emotion data to the server via the network. The server then analyzes this data using a generative AI model and emotion engine built in Python. The generative AI model has the capability to calculate optimal plant selection and cultivation guidelines based on the user's emotions.

[0355] Furthermore, sensor devices are used to monitor various environmental factors in which the plants are placed, such as soil humidity, temperature, and light intensity, in real time. This environmental information is transmitted to a server, and the plant's growth status is evaluated. If necessary, the server sends notifications to the terminal suggesting how to care for the plants or how to deal with any abnormalities.

[0356] As a concrete example, suppose a user selects "I want to grow sunflowers" on their device, and the emotion engine analyzes that the user is experiencing stress. In this case, the server would suggest additional plants that are relatively easy to grow, fragrant, and have a relaxing effect. An example of a prompt might be, "Please tell me about plants that can reduce stress and how to easily grow them." In this way, flexible management of a home garden becomes possible, tailored to the user's psychological state and the plant's growing environment.

[0357] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0358] Step 1:

[0359] The user selects the plant they want to grow using a device and inputs emotional information. This input is done through the device's interface. The device uses a camera and biosensors to infer and supplement the user's emotions from their facial expressions and heart rate. This process collects both plant selection information and emotional data.

[0360] Step 2:

[0361] The terminal transmits the collected plant selection information and emotional data to the server. Specifically, the terminal sends this data to the server in packet format via an internet connection. This is used as the basis for data analysis in the next step.

[0362] Step 3:

[0363] The server analyzes the received plant selection information and emotional data using a generating AI model and emotion engine. A Python program is executed to generate plant cultivation guidelines and recommendations that take the user's emotional state into account. Data processing is performed by comparing the characteristics of plants that match the emotional state with the user's preferences. The output is an optimal cultivation guideline.

[0364] Step 4:

[0365] The sensor device monitors the plant's growing environment and transmits the data to a server. The sensor measures soil moisture, ambient temperature, and light intensity at regular intervals and provides this information to the server using wireless communication technology. This allows for the collection of environmental condition data.

[0366] Step 5:

[0367] The server analyzes environmental data and evaluates the plant's growth status based on the information obtained. The evaluation process compares the results to pre-set growth conditions to check for any abnormalities. The output is provided to the user as a growth status report and suggestions for necessary maintenance.

[0368] Step 6:

[0369] Based on the analysis results, the server sends a notification to the user's device, suggesting necessary actions and countermeasures for any anomalies. The notification includes specific work procedures and alerts, and is displayed as a pop-up on the device screen. This allows the user to take appropriate action in real time.

[0370] (Application Example 2)

[0371] 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 as the "terminal".

[0372] In modern times, incorporating plant cultivation into daily life is gaining attention as a means of mental refreshment and improving quality of life. However, plant cultivation requires a certain level of knowledge and effort, and for users who are unfamiliar with plants in particular, there are problems such as psychological burden and failure to continue due to cultivation failures. Furthermore, there is a challenge in that there are no cultivation plans that reflect the emotional state of the user, so plant cultivation does not necessarily lead to stress relief or mental stability.

[0373] 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.

[0374] In this invention, the server includes means for receiving user input information and emotional data to identify preferences and emotional states, means for using a generative model to generate suggestions for plant selection and cultivation methods based on those preferences and emotional states, and means for acquiring environmental data in real time using a sensor device. This makes it possible to cultivate plants in accordance with the user's emotional state.

[0375] "User input information" refers to data that the system receives from the user, reflecting their choices, settings, specific needs, and preferences.

[0376] "Emotional data" refers to information that indicates a user's emotional state, and is obtained through sensors or user self-reporting.

[0377] A "generative model" is an algorithm or program used to generate plant selections and cultivation methods based on user input and sentiment data.

[0378] A "sensor device" is a device used to acquire environmental data in real time and is used to detect information such as temperature, humidity, and illuminance.

[0379] "Environmental data" refers to information that indicates the surrounding physical and chemical conditions related to plant growth, and is acquired from sensor devices.

[0380] A "smart device" is a digital terminal designed to present information in a way that responds to the user's emotions, and is an electronic device that provides information through one of the following methods: visual, auditory, or otherwise.

[0381] A "personalized growing experience" refers to a plant growing experience optimized based on each user's emotional state and preferences, and a customized growing method that differs for each user.

[0382] To implement this invention, the user, terminal, and server must work together. The user selects the type of plant they want to grow through the terminal and simultaneously inputs emotional data, or has the terminal's emotional sensor automatically acquire it. The terminal then transmits this data to the server.

[0383] The server receives user input information and emotional data, and uses a generative AI model to generate plant selection and cultivation suggestions based on the user's individual emotional state. This generative AI model is implemented on a cloud-based API platform. For example, if the server determines that the user is in a "stressed state," it will recommend plants that are easy to care for and have a high success rate. This proposal is sent to the terminal and displayed on the user's screen in real time.

[0384] Meanwhile, sensor devices are installed in locations where the user operates and are used to acquire environmental data in real time. The server uses this environmental data to evaluate the plant's growth status and, if necessary, performs appropriate tasks or notifies the user of any abnormalities. For example, if the humidity around the plants is too low, the server sends a message to the user such as, "Please add water."

[0385] As a concrete example, when a user is looking at ornamental plants in a flower shop, a smart device can detect the emotion of "seeking happiness" and generate a prompt such as "You are looking for plants that are effective in reducing stress." The server can then suggest plants like "lavender with aromatic effects" and display them on the user's screen. In this way, by presenting information according to the user's emotional state, users can enjoy the psychological benefits while cultivating plants.

[0386] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0387] Step 1:

[0388] The user selects the plant they want to cultivate via a smart device and obtains emotional data either by inputting it or automatically through an emotional sensor. The input data includes the plant type and emotional data. The device collects this data and sends it to the server.

[0389] Step 2:

[0390] The server invokes a generative AI model based on user input information and emotional data received from the terminal. The generative AI model processes the input data and generates suggestions for plant selection and cultivation methods that take the user's emotional state into account. The output data consists of a list of suggested plants and cultivation guidelines.

[0391] Step 3:

[0392] The server sends suggestions derived from the generated AI model to the terminal. The terminal receives these suggestions and displays them on the user's device screen. The screen displays suggested plants tailored to the user's emotional state, along with specific cultivation methods.

[0393] Step 4:

[0394] The system acquires environmental data in real time in an environment where a sensor device is installed, and transmits that data to a server. Input data includes environmental temperature, humidity, and light intensity.

[0395] Step 5:

[0396] The server evaluates the acquired environmental data and determines the plant's growth status. In particular, if an abnormal value is detected, such as insufficient water, a warning message is generated. The output data includes a message to notify the user.

[0397] Step 6:

[0398] Users receive notifications from the server on their devices and take appropriate action based on the displayed message. For example, if the message indicates that the plants need hydration, the user can follow the instructions and water them.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] [Third Embodiment]

[0403] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0404] 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.

[0405] 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).

[0406] 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.

[0407] 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.

[0408] 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).

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] 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".

[0415] To implement this invention, a system is provided that allows users to efficiently manage their home gardens. The system has a terminal with an interface for users to input their preferred vegetables and cultivation conditions, thereby allowing users to send necessary data to the system. The terminal is equipped with a communication module for sending this data to a server.

[0416] The server records the received user data and uses that information to run a generative AI model. The generative AI model has the function of suggesting plants suitable for cultivation and creating detailed cultivation guidelines. This model accesses historical databases, seasonal climate patterns, and region-specific agricultural information to provide the user with the most appropriate opinions.

[0417] In home gardens, sensor devices monitor soil and air to analyze environmental data in real time and identify factors that affect plant growth. These sensor devices measure temperature, soil moisture, and sunlight at regular intervals and transmit this information to a server.

[0418] The server receives sensor data and analyzes the plant's growth status. Based on this, it notifies the user of necessary maintenance tasks and any abnormalities. For example, a user who wants to grow tomatoes will see advice on their device regarding the optimal planting time, soil pH adjustment, and seasonal fertilization timing.

[0419] Users receive notifications issued by the server through their devices and care for their plants as needed. The server also continuously optimizes the cultivation plan based on environmental factors and user feedback. This allows even beginners to enjoy home gardening safely and efficiently.

[0420] For example, if a user inputs a request to "grow cucumbers" via a terminal, the server analyzes the input and recommends the most suitable cucumber variety and cultivation method. Subsequently, if a sensor detects a decrease in soil moisture, the server analyzes the need for watering and notifies the user. Through this process, the user can maintain an efficient and highly successful home garden from start to finish.

[0421] The following describes the processing flow.

[0422] Step 1:

[0423] The user operates the device to input information such as the vegetables they want to grow and the current growing conditions (e.g., garden size, amount of sunlight).

[0424] Step 2:

[0425] The terminal receives information entered by the user and sends it to the server as structured data.

[0426] Step 3:

[0427] The server analyzes the received user data, calls upon a generation AI model, and generates suggestions for the optimal plant selection and cultivation methods for the user.

[0428] Step 4:

[0429] Based on the generated advice, the server sends recommendations, including that advice, to the terminal.

[0430] Step 5:

[0431] Users can review the suggestions through their devices and proceed with their home gardening plans.

[0432] Step 6:

[0433] The sensor continuously acquires environmental data (e.g., humidity, temperature, sunlight) at a designated location (e.g., soil).

[0434] Step 7:

[0435] The sensor periodically sends the collected environmental data to the server.

[0436] Step 8:

[0437] The server analyzes the acquired sensor data to evaluate the current growth status and necessary care.

[0438] Step 9:

[0439] Based on the analysis results, the server generates notifications and suggestions for the user, including specific care instructions and information about any abnormalities, and sends them to the device.

[0440] Step 10:

[0441] Users receive notifications on their devices and take care of their plants as needed.

[0442] (Example 1)

[0443] 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."

[0444] Traditional home gardening presents challenges for beginners, including difficulty in selecting appropriate plants, learning cultivation methods, and providing adequate care in response to changing environmental conditions. This often results in low success rates and unhealthy plant growth. There is a need to address these issues and provide a system that allows anyone to easily manage a home garden and cultivate healthy plants.

[0445] 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.

[0446] In this invention, the server includes means for receiving information that identifies the user's preferences, means for performing generation calculations to create plant species and cultivation instructions based on those preferences, and means for collecting environmental information in chronological order using a detection device. This enables the user to select and manage appropriate plants and receive precise instructions to optimize plant growth.

[0447] "Information that identifies user preferences" refers to data entered to understand the types of plants and cultivation conditions desired by the user.

[0448] "Means for performing generation calculations" refers to a device or software that performs a calculation process to determine the appropriate plant species and generate detailed cultivation instructions based on information obtained from the user.

[0449] A "detection device" refers to hardware used to measure various environmental information, such as temperature, humidity, and sunlight, in real time and to collect that data.

[0450] "Means for collecting environmental information in a time series" refers to a function that uses detection devices to periodically acquire and record environmental data that changes over time.

[0451] This invention is a system for streamlining the operation of a home garden and is implemented as a configuration including a user, terminal, server, and sensor device.

[0452] The user selects and inputs their preferred plants and cultivation conditions through the terminal's interface. This interface provides a user-friendly interface and accepts information such as plant type, soil conditions, and sunlight conditions. The terminal processes the input information and transmits it to the server via a communication module.

[0453] The server receives and stores the data sent from the terminal. Next, the server starts a generative AI model. This AI model incorporates machine learning techniques using TensorFlow and PyTorch, and generates appropriate plant selections and cultivation instructions based on the user's preferences. This process allows even inexperienced users to properly manage their home gardens.

[0454] Sensor devices are installed in the home garden to monitor environmental conditions in real time. The sensors periodically measure data such as temperature, humidity, and sunlight, and transmit it to a server. This allows the server to analyze the environmental data and evaluate the growth status of the plants.

[0455] Based on the analysis results, the server notifies the user's terminal of necessary care and reports of anomalies. This allows the user to resolve problems before it's too late and maintain the health of their plants.

[0456] For example, if a user enters "I want to grow cucumbers" into their device, the server analyzes the user data and suggests appropriate cucumber varieties and cultivation methods. Furthermore, if a sensor detects a decrease in soil moisture, it notifies the user that watering is necessary.

[0457] As an example of a prompt, input would be in the format, "I want to grow cucumbers, so please tell me the best variety and how to grow them." This specific process allows users to efficiently manage their home garden.

[0458] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0459] Step 1:

[0460] Users input their preferred plants and cultivation conditions using a terminal. Specifically, they select and input "vegetable type," "sunlight conditions," "soil quality," etc., using the on-screen interface. The input data is formatted within the terminal and then sent to the server.

[0461] Step 2:

[0462] The server receives user data sent from the terminal. By analyzing the received data, it makes an initial determination of which plants match the user's criteria. It records this data in a database and performs preprocessing to prepare the AI ​​model for generation.

[0463] Step 3:

[0464] The server activates a generative AI model to generate appropriate plant species and cultivation methods based on user data. Specifically, it performs calculations using historical databases and climate data, and compiles the generated results into a proposal. These proposals are then converted into a format for transmission to the terminal.

[0465] Step 4:

[0466] The sensor device collects environmental data from the home garden. Specifically, it periodically measures "temperature," "humidity," and "sunshine duration," and transmits the data to a server. The transmitted data is recorded in real time and used for subsequent analysis.

[0467] Step 5:

[0468] The server analyzes the plant's growth status based on environmental data obtained from sensors. For example, it analyzes soil moisture data, runs an algorithm to determine whether watering is necessary, and generates the result as output data.

[0469] Step 6:

[0470] The server notifies the user of necessary maintenance information and any anomalies based on the analysis results. Specific instructions are sent to the terminal, and the user performs plant care according to those instructions. The notification clearly states exactly what needs to be done.

[0471] Step 7:

[0472] After performing maintenance, the user provides feedback to the server via their device. Based on this feedback, the server further optimizes the generated AI model and provides more refined suggestions for subsequent uses.

[0473] (Application Example 1)

[0474] 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."

[0475] Traditional home gardening systems have struggled to provide accurate cultivation guidance tailored to individual users' environments and skill levels. Therefore, it's essential to enable beginners, in particular, to successfully manage their home gardens without excessive effort. Furthermore, even in large-scale facilities such as factories, real-time environmental data collection and work instructions based on that data are necessary to efficiently manage plant cultivation and improve productivity.

[0476] 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.

[0477] In this invention, the server includes means for receiving user input information and identifying preferences; means for using a generative model that generates plant selection and cultivation method suggestions based on those preferences; means for acquiring environmental data in real time using sensor devices; means for evaluating the plant growth status using the acquired environmental data and notifying appropriate work; means for suggesting appropriate countermeasures when an abnormality is detected; and means for providing optimal cultivation guidance based on real-time data and transmitting instructions to machines that perform the work in order to support automated plant cultivation in factories. This enables efficient environmental management and cultivation assistance in home gardens and factory plant cultivation.

[0478] "User input information" refers to the information that users enter into the system to communicate their preferred plants and cultivation conditions.

[0479] A "generative model" is an AI algorithm that suggests the optimal plant selection and cultivation method based on the user's preferences.

[0480] A "sensor device" is a measuring instrument used to acquire environmental data in real time and evaluate the growth status of plants.

[0481] "Environmental data" refers to data on factors that affect plant growth, such as temperature, humidity, and sunlight.

[0482] A "cultivation guide" is a set of guidelines created based on acquired environmental data to optimize plant growth.

[0483] "Machines that perform tasks" are factory equipment that automatically carries out plant care tasks based on cultivation guidelines.

[0484] "Real-time data" refers to data that instantly measures and analyzes the current environment and state.

[0485] The system implementing this invention is designed to support efficient management of plants in home gardens and factory settings. The server receives input information from the user and, based on their preferences, uses a generative model to suggest the optimal plant selection and cultivation method. The generative AI model creates an optimal cultivation guide based on a historical database and current environmental conditions. Sensor devices (e.g., Arduino sensors) acquire environmental data (temperature, humidity, sunlight, etc.) in real time and transmit this data to the server. The server analyzes this environmental data to determine necessary maintenance tasks and whether there are any abnormalities. For example, if soil moisture decreases, the server analyzes whether automatic watering is necessary and issues instructions to a machine (e.g., a robot using a Raspberry Pi) to perform the task.

[0486] The program is implemented using the following hardware and software: Arduino sensors are used to collect environmental data, and TensorFlow is used on the server side for data processing and analysis. The user's device (smartphone, etc.) sends input information and receives notifications. The server communicates with sensors and robots using the MQTT protocol.

[0487] For example, if a user inputs "I want to grow tomatoes," the server will suggest suitable tomato varieties and cultivation methods. Later, if the sensor detects insufficient sunlight, it will instruct a robot to turn on lights to increase the amount of light necessary for growth.

[0488] Examples of prompts for the generated AI model include "Please tell me a tomato variety suitable for spring" and "Please suggest the optimal tomato cultivation procedure at humidity levels below 50%." This allows users to grow plants efficiently and in the best possible way.

[0489] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0490] Step 1:

[0491] The user uses a terminal to input the type of plant they want to grow and the conditions under which it will be grown. This input includes the plant's name, preferred growing conditions, and expected yield. This information is sent from the terminal to the server, which stores it in a database.

[0492] Step 2:

[0493] The server activates an AI model based on the user's input information to suggest plant selections and cultivation methods. The AI ​​model analyzes historical databases, climate patterns, and regional information to generate optimal guidance. The suggested results are then sent to the user's device.

[0494] Step 3:

[0495] Sensor devices are activated to acquire environmental data. These devices measure data such as temperature, humidity, and sunlight in real time and transmit it to a server. This allows the server to always be aware of the latest environmental conditions.

[0496] Step 4:

[0497] The server uses the acquired environmental data to evaluate the plant's growth status. For example, if the humidity falls below a specified range, the AI ​​model determines that watering is necessary and notifies the user. Alternatively, it transmits instructions to a machine that automatically performs the appropriate task.

[0498] Step 5:

[0499] If an anomaly is detected, the server will use a generated AI model to propose appropriate countermeasures. These countermeasures will be notified to the user's device, providing specific instructions on maintenance and adjustments. Furthermore, if the user grants permission, the countermeasures will be executed automatically.

[0500] Step 6:

[0501] The server continuously optimizes the cultivation plan based on user feedback and environmental changes. This feedback acts as new input to the AI ​​model, contributing to the generation of better guidelines. In conclusion, this allows users to manage their home gardens efficiently, and optimizes plant production in factories.

[0502] 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.

[0503] To implement this invention, an emotion engine is incorporated into the home gardening support system to provide cultivation support that takes into account the user's emotional state. In this system, the user selects the plant they want to grow via a terminal and also inputs or automatically acquires their own emotional data through the terminal's sensors. This emotional data includes tendencies such as moments when the user feels bored or when they feel enjoyment.

[0504] The device sends user selections and emotional data to the server. The server uses a generative AI model and emotion engine to generate emotionally-based plant selections, cultivation guidelines, and care suggestions. The program makes suggestions that take the user's psychological burden into consideration, such as recommending easy-to-care-for, high-success-rate plants for users who are feeling down.

[0505] Furthermore, the sensor device monitors environmental conditions and reports the plant's growth status to the server in real time. The server analyzes this data and notifies the user of work suggestions or anomaly alerts as needed. In addition, the emotion engine analyzes the user's emotional patterns and personalizes the experience, for example, suggesting more challenging cultivation methods if the user is feeling amused.

[0506] For example, if a user enters "I want to grow sunflowers," and the emotion engine detects from the data that the user is experiencing stress on a daily basis, the device will receive recommendations from the server and simultaneously suggest plants with aromatherapy effects that can contribute to stress reduction and plants with high ornamental value. Furthermore, if the user's mood improves and there are signs that they want a new challenge, the emotion engine will adjust the growing schedule and encourage them to try different plants or advanced cultivation methods. This allows users to enjoy managing their home garden in a flexible and appropriate way according to their psychological state.

[0507] The following describes the processing flow.

[0508] Step 1:

[0509] Users can input the plant they want to grow and their current emotional state through their device, or their emotions can be automatically recorded using the device's sensors.

[0510] Step 2:

[0511] The device sends the plant selection and emotion data received from the user to the server.

[0512] Step 3:

[0513] The server uses a generative AI model to identify the user's preferences and emotional state from the received data, and then generates suggestions for the optimal plant selection and cultivation method.

[0514] Step 4:

[0515] The server uses an emotion engine to customize suggestions based on the user's emotional state and sends them to the device.

[0516] Step 5:

[0517] Users can view suggested plants and cultivation methods through their devices and incorporate them into their home garden plans.

[0518] Step 6:

[0519] The sensors are installed on-site to acquire environmental data such as soil moisture, temperature, and sunlight in real time.

[0520] Step 7:

[0521] The sensors collect environmental data, which is then sent to a server and updated periodically.

[0522] Step 8:

[0523] The server analyzes sensor data to evaluate the plant's growth status and environmental conditions. Based on this information, it generates notifications for necessary care and any anomalies.

[0524] Step 9:

[0525] The server optimizes notifications based on the user's emotional patterns, which are analyzed by the emotion engine, and sends them to the device.

[0526] Step 10:

[0527] Users perform plant care based on notifications and suggestions received via their devices. This provides users with ways to enjoy home gardening that take their emotional state at any given time into consideration.

[0528] (Example 2)

[0529] 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."

[0530] In modern society, home gardening is popular as a means of relaxation and self-expression for many people. However, many systems do not take into account the user's psychological state when selecting plants and providing cultivation advice, resulting in a lack of support that allows users to achieve emotional satisfaction. Furthermore, many existing systems have limited ability to properly monitor the plant growing environment and respond quickly as needed. Therefore, there is a need for a system that integrates appropriate cultivation support tailored to the user's psychological state with real-time environmental monitoring.

[0531] 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.

[0532] This invention includes a server that receives user selection information and emotional information and proposes plant selection and cultivation methods that take the user's psychological state into consideration; a means of using a generative AI model that generates cultivation guidelines based on the user's emotional state using an emotional engine; and a means of acquiring environmental information in real time using a sensor device. This makes it possible to operate a home garden optimally according to the user's psychological state and environmental conditions.

[0533] "User selection information" refers to information about specific plants and their cultivation that the user has selected using the system.

[0534] "Emotional information" refers to data that indicates the user's psychological state, and is acquired through input or sensors.

[0535] "Means for proposing plant selection and cultivation methods that take psychological state into consideration" refers to a process or device for analyzing the user's psychological state and determining the appropriate plant type and cultivation method based on that analysis.

[0536] An "emotion engine" is a system or module for analyzing a user's emotional information and has the function of generating advice and suggestions based on the user's emotional patterns.

[0537] A "generative AI model" is an algorithmic model that uses artificial intelligence technology to generate new proposals and guidelines.

[0538] A "sensor device" is a device used to measure environmental conditions and the growth status of plants, and it has the function of acquiring data in real time.

[0539] "Environmental information" refers to data that indicates the conditions of the environment in which plants grow, and includes temperature, humidity, light intensity, and other factors.

[0540] "A means of evaluating the growth status of plants and notifying them of necessary tasks" refers to a system that analyzes the current growth status of plants and, based on that, informs users of specific tasks such as watering or adding fertilizer.

[0541] "Means of providing appropriate countermeasures when an abnormality is identified" refers to a function that, when an abnormality is determined to exist in the plant's growth environment or condition, presents the user with specific methods for resolving that situation.

[0542] The system of this invention provides support for plant cultivation based on the user's psychological state through the cooperation of the user, terminal, server, and sensor device. Specifically, it is implemented as follows.

[0543] Users select the plant they want to grow and input emotional data using a home computing device (such as a smartphone or tablet). This emotional data is either manually entered in the form of a questionnaire or automatically collected using the device's built-in camera or vital sign sensors.

[0544] The device transmits selected plant information and user emotion data to the server via the network. The server then analyzes this data using a generative AI model and emotion engine built in Python. The generative AI model has the capability to calculate optimal plant selection and cultivation guidelines based on the user's emotions.

[0545] Furthermore, sensor devices are used to monitor various environmental factors in which the plants are placed, such as soil humidity, temperature, and light intensity, in real time. This environmental information is transmitted to a server, and the plant's growth status is evaluated. If necessary, the server sends notifications to the terminal suggesting how to care for the plants or how to deal with any abnormalities.

[0546] As a concrete example, suppose a user selects "I want to grow sunflowers" on their device, and the emotion engine analyzes that the user is experiencing stress. In this case, the server would suggest additional plants that are relatively easy to grow, fragrant, and have a relaxing effect. An example of a prompt might be, "Please tell me about plants that can reduce stress and how to easily grow them." In this way, flexible management of a home garden becomes possible, tailored to the user's psychological state and the plant's growing environment.

[0547] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0548] Step 1:

[0549] The user selects the plant they want to grow using a device and inputs emotional information. This input is done through the device's interface. The device uses a camera and biosensors to infer and supplement the user's emotions from their facial expressions and heart rate. This process collects both plant selection information and emotional data.

[0550] Step 2:

[0551] The terminal transmits the collected plant selection information and emotional data to the server. Specifically, the terminal sends this data to the server in packet format via an internet connection. This is used as the basis for data analysis in the next step.

[0552] Step 3:

[0553] The server analyzes the received plant selection information and emotional data using a generating AI model and emotion engine. A Python program is executed to generate plant cultivation guidelines and recommendations that take the user's emotional state into account. Data processing is performed by comparing the characteristics of plants that match the emotional state with the user's preferences. The output is an optimal cultivation guideline.

[0554] Step 4:

[0555] The sensor device monitors the plant's growing environment and transmits the data to a server. The sensor measures soil moisture, ambient temperature, and light intensity at regular intervals and provides this information to the server using wireless communication technology. This allows for the collection of environmental condition data.

[0556] Step 5:

[0557] The server analyzes environmental data and evaluates the plant's growth status based on the information obtained. The evaluation process compares the results to pre-set growth conditions to check for any abnormalities. The output is provided to the user as a growth status report and suggestions for necessary maintenance.

[0558] Step 6:

[0559] Based on the analysis results, the server sends a notification to the user's device, suggesting necessary actions and countermeasures for any anomalies. The notification includes specific work procedures and alerts, and is displayed as a pop-up on the device screen. This allows the user to take appropriate action in real time.

[0560] (Application Example 2)

[0561] 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."

[0562] In modern times, incorporating plant cultivation into daily life is gaining attention as a means of mental refreshment and improving quality of life. However, plant cultivation requires a certain level of knowledge and effort, and for users who are unfamiliar with plants in particular, there are problems such as psychological burden and failure to continue due to cultivation failures. Furthermore, there is a challenge in that there are no cultivation plans that reflect the emotional state of the user, so plant cultivation does not necessarily lead to stress relief or mental stability.

[0563] 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.

[0564] In this invention, the server includes means for receiving user input information and emotional data to identify preferences and emotional states, means for using a generative model to generate suggestions for plant selection and cultivation methods based on those preferences and emotional states, and means for acquiring environmental data in real time using a sensor device. This makes it possible to cultivate plants in accordance with the user's emotional state.

[0565] "User input information" refers to data that the system receives from the user, reflecting their choices, settings, specific needs, and preferences.

[0566] "Emotional data" refers to information that indicates a user's emotional state, and is obtained through sensors or user self-reporting.

[0567] A "generative model" is an algorithm or program used to generate plant selections and cultivation methods based on user input and sentiment data.

[0568] A "sensor device" is a device used to acquire environmental data in real time and is used to detect information such as temperature, humidity, and illuminance.

[0569] "Environmental data" refers to information that indicates the surrounding physical and chemical conditions related to plant growth, and is acquired from sensor devices.

[0570] A "smart device" is a digital terminal designed to present information in a way that responds to the user's emotions, and is an electronic device that provides information through one of the following methods: visual, auditory, or otherwise.

[0571] A "personalized growing experience" refers to a plant growing experience optimized based on each user's emotional state and preferences, and a customized growing method that differs for each user.

[0572] To implement this invention, the user, terminal, and server must work together. The user selects the type of plant they want to grow through the terminal and simultaneously inputs emotional data, or has the terminal's emotional sensor automatically acquire it. The terminal then transmits this data to the server.

[0573] The server receives user input information and emotional data, and uses a generative AI model to generate plant selection and cultivation suggestions based on the user's individual emotional state. This generative AI model is implemented on a cloud-based API platform. For example, if the server determines that the user is in a "stressed state," it will recommend plants that are easy to care for and have a high success rate. This proposal is sent to the terminal and displayed on the user's screen in real time.

[0574] Meanwhile, sensor devices are installed in locations where the user operates and are used to acquire environmental data in real time. The server uses this environmental data to evaluate the plant's growth status and, if necessary, performs appropriate tasks or notifies the user of any abnormalities. For example, if the humidity around the plants is too low, the server sends a message to the user such as, "Please add water."

[0575] As a concrete example, when a user is looking at ornamental plants in a flower shop, a smart device can detect the emotion of "seeking happiness" and generate a prompt such as "You are looking for plants that are effective in reducing stress." The server can then suggest plants like "lavender with aromatic effects" and display them on the user's screen. In this way, by presenting information according to the user's emotional state, users can enjoy the psychological benefits while cultivating plants.

[0576] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0577] Step 1:

[0578] The user selects the plant they want to cultivate via a smart device and obtains emotional data either by inputting it or automatically through an emotional sensor. The input data includes the plant type and emotional data. The device collects this data and sends it to the server.

[0579] Step 2:

[0580] The server invokes a generative AI model based on user input information and emotional data received from the terminal. The generative AI model processes the input data and generates suggestions for plant selection and cultivation methods that take the user's emotional state into account. The output data consists of a list of suggested plants and cultivation guidelines.

[0581] Step 3:

[0582] The server sends suggestions derived from the generated AI model to the terminal. The terminal receives these suggestions and displays them on the user's device screen. The screen displays suggested plants tailored to the user's emotional state, along with specific cultivation methods.

[0583] Step 4:

[0584] The system acquires environmental data in real time in an environment where a sensor device is installed, and transmits that data to a server. Input data includes environmental temperature, humidity, and light intensity.

[0585] Step 5:

[0586] The server evaluates the acquired environmental data and determines the plant's growth status. In particular, if an abnormal value is detected, such as insufficient water, a warning message is generated. The output data includes a message to notify the user.

[0587] Step 6:

[0588] Users receive notifications from the server on their devices and take appropriate action based on the displayed message. For example, if the message indicates that the plants need hydration, the user can follow the instructions and water them.

[0589] 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.

[0590] 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.

[0591] 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.

[0592] [Fourth Embodiment]

[0593] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0594] 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.

[0595] 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).

[0596] 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.

[0597] 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.

[0598] 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).

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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".

[0606] To implement this invention, a system is provided that allows users to efficiently manage their home gardens. The system has a terminal with an interface for users to input their preferred vegetables and cultivation conditions, thereby allowing users to send necessary data to the system. The terminal is equipped with a communication module for sending this data to a server.

[0607] The server records the received user data and uses that information to run a generative AI model. The generative AI model has the function of suggesting plants suitable for cultivation and creating detailed cultivation guidelines. This model accesses historical databases, seasonal climate patterns, and region-specific agricultural information to provide the user with the most appropriate opinions.

[0608] In home gardens, sensor devices monitor soil and air to analyze environmental data in real time and identify factors that affect plant growth. These sensor devices measure temperature, soil moisture, and sunlight at regular intervals and transmit this information to a server.

[0609] The server receives sensor data and analyzes the plant's growth status. Based on this, it notifies the user of necessary maintenance tasks and any abnormalities. For example, a user who wants to grow tomatoes will see advice on their device regarding the optimal planting time, soil pH adjustment, and seasonal fertilization timing.

[0610] Users receive notifications issued by the server through their devices and care for their plants as needed. The server also continuously optimizes the cultivation plan based on environmental factors and user feedback. This allows even beginners to enjoy home gardening safely and efficiently.

[0611] For example, if a user inputs a request to "grow cucumbers" via a terminal, the server analyzes the input and recommends the most suitable cucumber variety and cultivation method. Subsequently, if a sensor detects a decrease in soil moisture, the server analyzes the need for watering and notifies the user. Through this process, the user can maintain an efficient and highly successful home garden from start to finish.

[0612] The following describes the processing flow.

[0613] Step 1:

[0614] The user operates the device to input information such as the vegetables they want to grow and the current growing conditions (e.g., garden size, amount of sunlight).

[0615] Step 2:

[0616] The terminal receives information entered by the user and sends it to the server as structured data.

[0617] Step 3:

[0618] The server analyzes the received user data, calls upon a generation AI model, and generates suggestions for the optimal plant selection and cultivation methods for the user.

[0619] Step 4:

[0620] Based on the generated advice, the server sends recommendations, including that advice, to the terminal.

[0621] Step 5:

[0622] Users can review the suggestions through their devices and proceed with their home gardening plans.

[0623] Step 6:

[0624] The sensor continuously acquires environmental data (e.g., humidity, temperature, sunlight) at a designated location (e.g., soil).

[0625] Step 7:

[0626] The sensor periodically sends the collected environmental data to the server.

[0627] Step 8:

[0628] The server analyzes the acquired sensor data to evaluate the current growth status and necessary care.

[0629] Step 9:

[0630] Based on the analysis results, the server generates notifications and suggestions for the user, including specific care instructions and information about any abnormalities, and sends them to the device.

[0631] Step 10:

[0632] Users receive notifications on their devices and take care of their plants as needed.

[0633] (Example 1)

[0634] 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".

[0635] Traditional home gardening presents challenges for beginners, including difficulty in selecting appropriate plants, learning cultivation methods, and providing adequate care in response to changing environmental conditions. This often results in low success rates and unhealthy plant growth. There is a need to address these issues and provide a system that allows anyone to easily manage a home garden and cultivate healthy plants.

[0636] 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.

[0637] In this invention, the server includes means for receiving information that identifies the user's preferences, means for performing generation calculations to create plant species and cultivation instructions based on those preferences, and means for collecting environmental information in chronological order using a detection device. This enables the user to select and manage appropriate plants and receive precise instructions to optimize plant growth.

[0638] "Information that identifies user preferences" refers to data entered to understand the types of plants and cultivation conditions desired by the user.

[0639] "Means for performing generation calculations" refers to a device or software that performs a calculation process to determine the appropriate plant species and generate detailed cultivation instructions based on information obtained from the user.

[0640] A "detection device" refers to hardware used to measure various environmental information, such as temperature, humidity, and sunlight, in real time and to collect that data.

[0641] "Means for collecting environmental information in a time series" refers to a function that uses detection devices to periodically acquire and record environmental data that changes over time.

[0642] This invention is a system for streamlining the operation of a home garden and is implemented as a configuration including a user, terminal, server, and sensor device.

[0643] The user selects and inputs their preferred plants and cultivation conditions through the terminal's interface. This interface provides a user-friendly interface and accepts information such as plant type, soil conditions, and sunlight conditions. The terminal processes the input information and transmits it to the server via a communication module.

[0644] The server receives and stores the data sent from the terminal. Next, the server starts a generative AI model. This AI model incorporates machine learning techniques using TensorFlow and PyTorch, and generates appropriate plant selections and cultivation instructions based on the user's preferences. This process allows even inexperienced users to properly manage their home gardens.

[0645] Sensor devices are installed in the home garden to monitor environmental conditions in real time. The sensors periodically measure data such as temperature, humidity, and sunlight, and transmit it to a server. This allows the server to analyze the environmental data and evaluate the growth status of the plants.

[0646] Based on the analysis results, the server notifies the user's terminal of necessary care and reports of anomalies. This allows the user to resolve problems before it's too late and maintain the health of their plants.

[0647] For example, if a user enters "I want to grow cucumbers" into their device, the server analyzes the user data and suggests appropriate cucumber varieties and cultivation methods. Furthermore, if a sensor detects a decrease in soil moisture, it notifies the user that watering is necessary.

[0648] As an example of a prompt, input would be in the format, "I want to grow cucumbers, so please tell me the best variety and how to grow them." This specific process allows users to efficiently manage their home garden.

[0649] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0650] Step 1:

[0651] Users input their preferred plants and cultivation conditions using a terminal. Specifically, they select and input "vegetable type," "sunlight conditions," "soil quality," etc., using the on-screen interface. The input data is formatted within the terminal and then sent to the server.

[0652] Step 2:

[0653] The server receives user data sent from the terminal. By analyzing the received data, it makes an initial determination of which plants match the user's criteria. It records this data in a database and performs preprocessing to prepare the AI ​​model for generation.

[0654] Step 3:

[0655] The server activates a generative AI model to generate appropriate plant species and cultivation methods based on user data. Specifically, it performs calculations using historical databases and climate data, and compiles the generated results into a proposal. These proposals are then converted into a format for transmission to the terminal.

[0656] Step 4:

[0657] The sensor device collects environmental data from the home garden. Specifically, it periodically measures "temperature," "humidity," and "sunshine duration," and transmits the data to a server. The transmitted data is recorded in real time and used for subsequent analysis.

[0658] Step 5:

[0659] The server analyzes the plant's growth status based on environmental data obtained from sensors. For example, it analyzes soil moisture data, runs an algorithm to determine whether watering is necessary, and generates the result as output data.

[0660] Step 6:

[0661] The server notifies the user of necessary maintenance information and any anomalies based on the analysis results. Specific instructions are sent to the terminal, and the user performs plant care according to those instructions. The notification clearly states exactly what needs to be done.

[0662] Step 7:

[0663] After performing maintenance, the user provides feedback to the server via their device. Based on this feedback, the server further optimizes the generated AI model and provides more refined suggestions for subsequent uses.

[0664] (Application Example 1)

[0665] 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".

[0666] Traditional home gardening systems have struggled to provide accurate cultivation guidance tailored to individual users' environments and skill levels. Therefore, it's essential to enable beginners, in particular, to successfully manage their home gardens without excessive effort. Furthermore, even in large-scale facilities such as factories, real-time environmental data collection and work instructions based on that data are necessary to efficiently manage plant cultivation and improve productivity.

[0667] 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.

[0668] In this invention, the server includes means for receiving user input information and identifying preferences; means for using a generative model that generates plant selection and cultivation method suggestions based on those preferences; means for acquiring environmental data in real time using sensor devices; means for evaluating the plant growth status using the acquired environmental data and notifying appropriate work; means for suggesting appropriate countermeasures when an abnormality is detected; and means for providing optimal cultivation guidance based on real-time data and transmitting instructions to machines that perform the work in order to support automated plant cultivation in factories. This enables efficient environmental management and cultivation assistance in home gardens and factory plant cultivation.

[0669] "User input information" refers to the information that users enter into the system to communicate their preferred plants and cultivation conditions.

[0670] A "generative model" is an AI algorithm that suggests the optimal plant selection and cultivation method based on the user's preferences.

[0671] A "sensor device" is a measuring instrument used to acquire environmental data in real time and evaluate the growth status of plants.

[0672] "Environmental data" refers to data on factors that affect plant growth, such as temperature, humidity, and sunlight.

[0673] A "cultivation guide" is a set of guidelines created based on acquired environmental data to optimize plant growth.

[0674] "Machines that perform tasks" are factory equipment that automatically carries out plant care tasks based on cultivation guidelines.

[0675] "Real-time data" refers to data that instantly measures and analyzes the current environment and state.

[0676] The system implementing this invention is designed to support efficient management of plants in home gardens and factory settings. The server receives input information from the user and, based on their preferences, uses a generative model to suggest the optimal plant selection and cultivation method. The generative AI model creates an optimal cultivation guide based on a historical database and current environmental conditions. Sensor devices (e.g., Arduino sensors) acquire environmental data (temperature, humidity, sunlight, etc.) in real time and transmit this data to the server. The server analyzes this environmental data to determine necessary maintenance tasks and whether there are any abnormalities. For example, if soil moisture decreases, the server analyzes whether automatic watering is necessary and issues instructions to a machine (e.g., a robot using a Raspberry Pi) to perform the task.

[0677] The program is implemented using the following hardware and software: Arduino sensors are used to collect environmental data, and TensorFlow is used on the server side for data processing and analysis. The user's device (smartphone, etc.) sends input information and receives notifications. The server communicates with sensors and robots using the MQTT protocol.

[0678] For example, if a user inputs "I want to grow tomatoes," the server will suggest suitable tomato varieties and cultivation methods. Later, if the sensor detects insufficient sunlight, it will instruct a robot to turn on lights to increase the amount of light necessary for growth.

[0679] Examples of prompts for the generated AI model include "Please tell me a tomato variety suitable for spring" and "Please suggest the optimal tomato cultivation procedure at humidity levels below 50%." This allows users to grow plants efficiently and in the best possible way.

[0680] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0681] Step 1:

[0682] The user uses a terminal to input the type of plant they want to grow and the conditions under which it will be grown. This input includes the plant's name, preferred growing conditions, and expected yield. This information is sent from the terminal to the server, which stores it in a database.

[0683] Step 2:

[0684] The server activates an AI model based on the user's input information to suggest plant selections and cultivation methods. The AI ​​model analyzes historical databases, climate patterns, and regional information to generate optimal guidance. The suggested results are then sent to the user's device.

[0685] Step 3:

[0686] Sensor devices are activated to acquire environmental data. These devices measure data such as temperature, humidity, and sunlight in real time and transmit it to a server. This allows the server to always be aware of the latest environmental conditions.

[0687] Step 4:

[0688] The server uses the acquired environmental data to evaluate the plant's growth status. For example, if the humidity falls below a specified range, the AI ​​model determines that watering is necessary and notifies the user. Alternatively, it transmits instructions to a machine that automatically performs the appropriate task.

[0689] Step 5:

[0690] If an anomaly is detected, the server will use a generated AI model to propose appropriate countermeasures. These countermeasures will be notified to the user's device, providing specific instructions on maintenance and adjustments. Furthermore, if the user grants permission, the countermeasures will be executed automatically.

[0691] Step 6:

[0692] The server continuously optimizes the cultivation plan based on user feedback and environmental changes. This feedback acts as new input to the AI ​​model, contributing to the generation of better guidelines. In conclusion, this allows users to manage their home gardens efficiently, and optimizes plant production in factories.

[0693] 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.

[0694] To implement this invention, an emotion engine is incorporated into the home gardening support system to provide cultivation support that takes into account the user's emotional state. In this system, the user selects the plant they want to grow via a terminal and also inputs or automatically acquires their own emotional data through the terminal's sensors. This emotional data includes tendencies such as moments when the user feels bored or when they feel enjoyment.

[0695] The device sends user selections and emotional data to the server. The server uses a generative AI model and emotion engine to generate emotionally-based plant selections, cultivation guidelines, and care suggestions. The program makes suggestions that take the user's psychological burden into consideration, such as recommending easy-to-care-for, high-success-rate plants for users who are feeling down.

[0696] Furthermore, the sensor device monitors environmental conditions and reports the plant's growth status to the server in real time. The server analyzes this data and notifies the user of work suggestions or anomaly alerts as needed. In addition, the emotion engine analyzes the user's emotional patterns and personalizes the experience, for example, suggesting more challenging cultivation methods if the user is feeling amused.

[0697] For example, if a user enters "I want to grow sunflowers," and the emotion engine detects from the data that the user is experiencing stress on a daily basis, the device will receive recommendations from the server and simultaneously suggest plants with aromatherapy effects that can contribute to stress reduction and plants with high ornamental value. Furthermore, if the user's mood improves and there are signs that they want a new challenge, the emotion engine will adjust the growing schedule and encourage them to try different plants or advanced cultivation methods. This allows users to enjoy managing their home garden in a flexible and appropriate way according to their psychological state.

[0698] The following describes the processing flow.

[0699] Step 1:

[0700] Users can input the plant they want to grow and their current emotional state through their device, or their emotions can be automatically recorded using the device's sensors.

[0701] Step 2:

[0702] The device sends the plant selection and emotion data received from the user to the server.

[0703] Step 3:

[0704] The server uses a generative AI model to identify the user's preferences and emotional state from the received data, and then generates suggestions for the optimal plant selection and cultivation method.

[0705] Step 4:

[0706] The server uses an emotion engine to customize suggestions based on the user's emotional state and sends them to the device.

[0707] Step 5:

[0708] Users can view suggested plants and cultivation methods through their devices and incorporate them into their home garden plans.

[0709] Step 6:

[0710] The sensors are installed on-site to acquire environmental data such as soil moisture, temperature, and sunlight in real time.

[0711] Step 7:

[0712] The sensors collect environmental data, which is then sent to a server and updated periodically.

[0713] Step 8:

[0714] The server analyzes sensor data to evaluate the plant's growth status and environmental conditions. Based on this information, it generates notifications for necessary care and any anomalies.

[0715] Step 9:

[0716] The server optimizes notifications based on the user's emotional patterns, which are analyzed by the emotion engine, and sends them to the device.

[0717] Step 10:

[0718] Users perform plant care based on notifications and suggestions received via their devices. This provides users with ways to enjoy home gardening that take their emotional state at any given time into consideration.

[0719] (Example 2)

[0720] 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".

[0721] In modern society, home gardening is popular as a means of relaxation and self-expression for many people. However, many systems do not take into account the user's psychological state when selecting plants and providing cultivation advice, resulting in a lack of support that allows users to achieve emotional satisfaction. Furthermore, many existing systems have limited ability to properly monitor the plant growing environment and respond quickly as needed. Therefore, there is a need for a system that integrates appropriate cultivation support tailored to the user's psychological state with real-time environmental monitoring.

[0722] 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.

[0723] This invention includes a server that receives user selection information and emotional information and proposes plant selection and cultivation methods that take the user's psychological state into consideration; a means of using a generative AI model that generates cultivation guidelines based on the user's emotional state using an emotional engine; and a means of acquiring environmental information in real time using a sensor device. This makes it possible to operate a home garden optimally according to the user's psychological state and environmental conditions.

[0724] "User selection information" refers to information about specific plants and their cultivation that the user has selected using the system.

[0725] "Emotional information" refers to data that indicates the user's psychological state, and is acquired through input or sensors.

[0726] "Means for proposing plant selection and cultivation methods that take psychological state into consideration" refers to a process or device for analyzing the user's psychological state and determining the appropriate plant type and cultivation method based on that analysis.

[0727] An "emotion engine" is a system or module for analyzing a user's emotional information and has the function of generating advice and suggestions based on the user's emotional patterns.

[0728] A "generative AI model" is an algorithmic model that uses artificial intelligence technology to generate new proposals and guidelines.

[0729] A "sensor device" is a device used to measure environmental conditions and the growth status of plants, and it has the function of acquiring data in real time.

[0730] "Environmental information" refers to data that indicates the conditions of the environment in which plants grow, and includes temperature, humidity, light intensity, and other factors.

[0731] "A means of evaluating the growth status of plants and notifying them of necessary tasks" refers to a system that analyzes the current growth status of plants and, based on that, informs users of specific tasks such as watering or adding fertilizer.

[0732] "Means of providing appropriate countermeasures when an abnormality is identified" refers to a function that, when an abnormality is determined to exist in the plant's growth environment or condition, presents the user with specific methods for resolving that situation.

[0733] The system of this invention provides support for plant cultivation based on the user's psychological state through the cooperation of the user, terminal, server, and sensor device. Specifically, it is implemented as follows.

[0734] Users select the plant they want to grow and input emotional data using a home computing device (such as a smartphone or tablet). This emotional data is either manually entered in the form of a questionnaire or automatically collected using the device's built-in camera or vital sign sensors.

[0735] The device transmits selected plant information and user emotion data to the server via the network. The server then analyzes this data using a generative AI model and emotion engine built in Python. The generative AI model has the capability to calculate optimal plant selection and cultivation guidelines based on the user's emotions.

[0736] Furthermore, sensor devices are used to monitor various environmental factors in which the plants are placed, such as soil humidity, temperature, and light intensity, in real time. This environmental information is transmitted to a server, and the plant's growth status is evaluated. If necessary, the server sends notifications to the terminal suggesting how to care for the plants or how to deal with any abnormalities.

[0737] As a concrete example, suppose a user selects "I want to grow sunflowers" on their device, and the emotion engine analyzes that the user is experiencing stress. In this case, the server would suggest additional plants that are relatively easy to grow, fragrant, and have a relaxing effect. An example of a prompt might be, "Please tell me about plants that can reduce stress and how to easily grow them." In this way, flexible management of a home garden becomes possible, tailored to the user's psychological state and the plant's growing environment.

[0738] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0739] Step 1:

[0740] The user selects the plant they want to grow using a device and inputs emotional information. This input is done through the device's interface. The device uses a camera and biosensors to infer and supplement the user's emotions from their facial expressions and heart rate. This process collects both plant selection information and emotional data.

[0741] Step 2:

[0742] The terminal transmits the collected plant selection information and emotional data to the server. Specifically, the terminal sends this data to the server in packet format via an internet connection. This is used as the basis for data analysis in the next step.

[0743] Step 3:

[0744] The server analyzes the received plant selection information and emotional data using a generating AI model and emotion engine. A Python program is executed to generate plant cultivation guidelines and recommendations that take the user's emotional state into account. Data processing is performed by comparing the characteristics of plants that match the emotional state with the user's preferences. The output is an optimal cultivation guideline.

[0745] Step 4:

[0746] The sensor device monitors the plant's growing environment and transmits the data to a server. The sensor measures soil moisture, ambient temperature, and light intensity at regular intervals and provides this information to the server using wireless communication technology. This allows for the collection of environmental condition data.

[0747] Step 5:

[0748] The server analyzes environmental data and evaluates the plant's growth status based on the information obtained. The evaluation process compares the results to pre-set growth conditions to check for any abnormalities. The output is provided to the user as a growth status report and suggestions for necessary maintenance.

[0749] Step 6:

[0750] Based on the analysis results, the server sends a notification to the user's device, suggesting necessary actions and countermeasures for any anomalies. The notification includes specific work procedures and alerts, and is displayed as a pop-up on the device screen. This allows the user to take appropriate action in real time.

[0751] (Application Example 2)

[0752] 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".

[0753] In modern times, incorporating plant cultivation into daily life is gaining attention as a means of mental refreshment and improving quality of life. However, plant cultivation requires a certain level of knowledge and effort, and for users who are unfamiliar with plants in particular, there are problems such as psychological burden and failure to continue due to cultivation failures. Furthermore, there is a challenge in that there are no cultivation plans that reflect the emotional state of the user, so plant cultivation does not necessarily lead to stress relief or mental stability.

[0754] 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.

[0755] In this invention, the server includes means for receiving user input information and emotional data to identify preferences and emotional states, means for using a generative model to generate suggestions for plant selection and cultivation methods based on those preferences and emotional states, and means for acquiring environmental data in real time using a sensor device. This makes it possible to cultivate plants in accordance with the user's emotional state.

[0756] "User input information" refers to data that the system receives from the user, reflecting their choices, settings, specific needs, and preferences.

[0757] "Emotional data" refers to information that indicates a user's emotional state, and is obtained through sensors or user self-reporting.

[0758] A "generative model" is an algorithm or program used to generate plant selections and cultivation methods based on user input and sentiment data.

[0759] A "sensor device" is a device used to acquire environmental data in real time and is used to detect information such as temperature, humidity, and illuminance.

[0760] "Environmental data" refers to information that indicates the surrounding physical and chemical conditions related to plant growth, and is acquired from sensor devices.

[0761] A "smart device" is a digital terminal designed to present information in a way that responds to the user's emotions, and is an electronic device that provides information through one of the following methods: visual, auditory, or otherwise.

[0762] A "personalized growing experience" refers to a plant growing experience optimized based on each user's emotional state and preferences, and a customized growing method that differs for each user.

[0763] To implement this invention, the user, terminal, and server must work together. The user selects the type of plant they want to grow through the terminal and simultaneously inputs emotional data, or has the terminal's emotional sensor automatically acquire it. The terminal then transmits this data to the server.

[0764] The server receives user input information and emotional data, and uses a generative AI model to generate plant selection and cultivation suggestions based on the user's individual emotional state. This generative AI model is implemented on a cloud-based API platform. For example, if the server determines that the user is in a "stressed state," it will recommend plants that are easy to care for and have a high success rate. This proposal is sent to the terminal and displayed on the user's screen in real time.

[0765] Meanwhile, sensor devices are installed in locations where the user operates and are used to acquire environmental data in real time. The server uses this environmental data to evaluate the plant's growth status and, if necessary, performs appropriate tasks or notifies the user of any abnormalities. For example, if the humidity around the plants is too low, the server sends a message to the user such as, "Please add water."

[0766] As a concrete example, when a user is looking at ornamental plants in a flower shop, a smart device can detect the emotion of "seeking happiness" and generate a prompt such as "You are looking for plants that are effective in reducing stress." The server can then suggest plants like "lavender with aromatic effects" and display them on the user's screen. In this way, by presenting information according to the user's emotional state, users can enjoy the psychological benefits while cultivating plants.

[0767] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0768] Step 1:

[0769] The user selects the plant they want to cultivate via a smart device and obtains emotional data either by inputting it or automatically through an emotional sensor. The input data includes the plant type and emotional data. The device collects this data and sends it to the server.

[0770] Step 2:

[0771] The server invokes a generative AI model based on user input information and emotional data received from the terminal. The generative AI model processes the input data and generates suggestions for plant selection and cultivation methods that take the user's emotional state into account. The output data consists of a list of suggested plants and cultivation guidelines.

[0772] Step 3:

[0773] The server sends suggestions derived from the generated AI model to the terminal. The terminal receives these suggestions and displays them on the user's device screen. The screen displays suggested plants tailored to the user's emotional state, along with specific cultivation methods.

[0774] Step 4:

[0775] The system acquires environmental data in real time in an environment where a sensor device is installed, and transmits that data to a server. Input data includes environmental temperature, humidity, and light intensity.

[0776] Step 5:

[0777] The server evaluates the acquired environmental data and determines the plant's growth status. In particular, if an abnormal value is detected, such as insufficient water, a warning message is generated. The output data includes a message to notify the user.

[0778] Step 6:

[0779] Users receive notifications from the server on their devices and take appropriate action based on the displayed message. For example, if the message indicates that the plants need hydration, the user can follow the instructions and water them.

[0780] 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.

[0781] 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.

[0782] 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 robot 414.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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."

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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 as being incorporated by reference.

[0801] The following is further disclosed regarding the embodiments described above.

[0802] (Claim 1)

[0803] A means of receiving user input information and identifying preferences,

[0804] A means of using a generative model that generates plant selections and cultivation method suggestions based on those preferences,

[0805] A means of acquiring environmental data in real time using a sensor device,

[0806] A means of evaluating the growth status of plants using acquired environmental data and notifying appropriate work,

[0807] A means of proposing appropriate countermeasures when an anomaly is detected,

[0808] A system that includes this.

[0809] (Claim 2)

[0810] The system according to claim 1, further comprising means for monitoring the plant growth environment using data from a sensor device installed by the user.

[0811] (Claim 3)

[0812] The system according to claim 1, further comprising means for continuously optimizing the plant cultivation plan based on the user's cultivation status and environmental changes.

[0813] "Example 1"

[0814] (Claim 1)

[0815] A means of receiving information that identifies user preferences,

[0816] A means for performing generation calculations to create plant species and cultivation instructions based on those preferences,

[0817] A means of collecting environmental information in a time series using a detection device,

[0818] A means of analyzing the growth status of plants using collected environmental information and notifying appropriate actions,

[0819] A means of providing an appropriate course of action when a malfunction is detected,

[0820] A system that includes this.

[0821] (Claim 2)

[0822] The system according to claim 1, further comprising means for monitoring plant growth conditions using information from a detection device placed by the user.

[0823] (Claim 3)

[0824] The system according to claim 1, further comprising means for continuously improving the plant growth plan based on the user's cultivation status and environmental changes.

[0825] "Application Example 1"

[0826] (Claim 1)

[0827] A means of receiving user input information and identifying preferences,

[0828] A means of using a generative model that generates plant selections and cultivation method suggestions based on those preferences,

[0829] A means of acquiring environmental data in real time using a sensor device,

[0830] A means of evaluating the growth status of plants using acquired environmental data and notifying appropriate work,

[0831] A means of proposing appropriate countermeasures when an anomaly is detected,

[0832] To support automated plant cultivation within factories, we provide optimal cultivation guidance based on real-time data and a means of transmitting instructions to the machines that perform the work.

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, further comprising means for monitoring the plant's growth environment using data from a sensor device installed by the user and issuing instructions to a machine to implement countermeasures.

[0836] (Claim 3)

[0837] The system according to claim 1, further comprising means for continuously optimizing a plant cultivation plan based on the user's cultivation status and environmental changes, and outputting work instructions to a machine based on the optimized plan.

[0838] "Example 2 of combining an emotion engine"

[0839] (Claim 1)

[0840] A means of receiving user selection and emotional information, and proposing plant selection and cultivation methods that take into account the user's psychological state.

[0841] A means of using a generative AI model that generates training guidelines based on the user's emotional state using an emotion engine,

[0842] A means of acquiring environmental information in real time using a sensor device,

[0843] A means of evaluating the growth status of plants using acquired environmental information and notifying necessary tasks,

[0844] A means of providing appropriate countermeasures when an anomaly is identified,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, further comprising means for monitoring the plant growing environment using information from sensor devices installed by the user.

[0848] (Claim 3)

[0849] The system according to claim 1, further comprising means for analyzing emotional patterns based on the user's growing conditions and environmental changes to continuously optimize the plant growing plan.

[0850] "Application example 2 of combining emotional engines"

[0851] (Claim 1)

[0852] A means of receiving user input information and emotional data to identify preferences and emotional states,

[0853] A means of using a generative model that generates suggestions for plant selection and cultivation methods based on their preferences and emotional state,

[0854] A means of acquiring environmental data in real time using a sensor device,

[0855] A means of evaluating the growth status of plants using acquired environmental and emotional data, and notifying appropriate work,

[0856] In addition to proposing appropriate countermeasures when an anomaly is detected, the system also proposes countermeasures that take into account the user's emotional state.

[0857] A means of presenting information in response to the user's emotions via a smart device,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, further comprising means for monitoring the growth environment of plants, taking into account their emotional state, using data from sensor devices and emotion sensors installed by the user.

[0861] (Claim 3)

[0862] The system according to claim 1, further comprising means for continuously optimizing the plant cultivation plan based on the user's cultivation status, environmental changes, and emotional data, and for providing a personalized cultivation experience that corresponds to the user's emotional state. [Explanation of Symbols]

[0863] 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 receiving user input information and identifying preferences, A means of using a generative model that generates plant selections and cultivation method suggestions based on those preferences, A means of acquiring environmental data in real time using a sensor device, A means of evaluating the growth status of plants using acquired environmental data and notifying appropriate work, A means of proposing appropriate countermeasures when an anomaly is detected, A system that includes this.

2. The system according to claim 1, further comprising means for monitoring the plant growth environment using data from a sensor device installed by the user.

3. The system according to claim 1, further comprising means for continuously optimizing the plant cultivation plan based on the user's cultivation status and environmental changes.

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