system

Abstract

We provide the system. [Solution] A means of receiving user input information and aggregating necessary information related to plants, A means of generating guidelines for plant cultivation based on aggregated information, A means of communication for providing the generated guidelines to the user, A means of analyzing voice input from the user using speech recognition technology, A means for outputting guidelines generated using speech synthesis technology as audio, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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] For users lacking the knowledge and experience necessary for plant cultivation and management, there are problems in home gardens where plants do not grow sufficiently or management becomes complicated. It is necessary to solve this problem and provide support for a wide range of users to succeed in home gardening.

Means for Solving the Problems

[0005] The present invention provides a system that receives input data from a user, collects appropriate plant-related information from a database based on the input data, and further generates advanced advice on cultivation using a generative artificial intelligence. The generated advice is provided to the user through communication means, enabling the user to know the optimal method for plant cultivation.

[0006] A "user" is someone who uses the system to seek advice on plant cultivation.

[0007] "Input data" refers to information that the user provides to the system, including the type of plant they want to grow and any problems they are currently facing.

[0008] "Information aggregation" is the process by which a system collects relevant data based on input data received from users.

[0009] "Advice generation" is the process of creating optimal guidelines and suggestions for plant cultivation and management based on aggregated information.

[0010] "Communication methods" refer to the technical means used to transmit generated advice to the user, and in most cases, the internet or various communication protocols are used.

[0011] A "database" is a data structure or system that stores data on plant characteristics, cultivation methods, and problem-solving methods, and allows for retrieval of this data as needed.

[0012] "Generative artificial intelligence" refers to technologies that include algorithms and models for analyzing data according to user needs and deriving appropriate advice. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4]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.

Mode for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the 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.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a system for providing advice on home gardening and horticulture to a wide range of users, from beginners to advanced gardeners. This system is designed to solve the challenges users face in growing and managing plants. The following describes embodiments of the system.

[0035] The user inputs information about the plant they want to grow, its current cultivation status, and any particular concerns they have, through the terminal. The terminal then formats this input data and sends it to the server.

[0036] The server searches the database based on the input data received from the user. This database contains information such as how to grow plants, required conditions, common problems, and pest control methods.

[0037] After retrieving relevant information from the database, the server utilizes generative artificial intelligence to generate optimal plant care advice. This AI can provide personalized advice tailored to the user's needs, offering specific guidance such as "how to adjust watering and fertilization when tomato leaves turn yellow," "effective countermeasures against specific pests," and "the optimal harvest time."

[0038] The generated advice is sent from the server to the terminal. The user can review the advice through the terminal and follow the instructions to cultivate the plants.

[0039] For example, if a user enters "I want to grow tomatoes," the server retrieves tomato cultivation information from its database and provides advice such as "Place them in a sunny spot" and "Water them in the morning with an appropriate amount." This advice allows the user to efficiently carry out tasks in their home garden.

[0040] Thus, the system of the present invention supports home gardening and horticultural endeavors by providing users with specific and practical information to help them grow plants more effectively and successfully.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user uses a device to input information about the type of plant they want to grow and the problems they are currently facing. This includes text input and selecting options in response to a series of questions.

[0044] Step 2:

[0045] The terminal formats the information entered by the user into a standard format and generates a request to send it to the server.

[0046] Step 3:

[0047] The server analyzes the request received from the terminal and extracts information about the plant entered by the user. Based on this information, it queries the database for relevant data.

[0048] Step 4:

[0049] The database searches for the most appropriate plant information and related solutions based on the received query and returns the results to the server.

[0050] Step 5:

[0051] The server provides information retrieved from the database to a generative artificial intelligence system, which then generates user-focused training advice. This advice includes optimized instructions and suggestions based on an analysis of the retrieved data.

[0052] Step 6:

[0053] The server sends the generated advice back to the terminal. At this time, the output is formatted in a way that is easy for the user to understand.

[0054] Step 7:

[0055] The terminal displays advice received from the server on its screen. Users can review the provided instructions and advice and carry out tasks in their home garden.

[0056] (Example 1)

[0057] 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."

[0058] In home gardening and horticulture, there is a need to effectively solve the challenges related to plant cultivation and management faced by a wide range of users, from beginners to advanced gardeners. Specifically, many users are troubled by the inability to obtain timely and accurate advice and information tailored to the condition of their plants.

[0059] 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.

[0060] In this invention, the server includes means for receiving user input information and formatting necessary data about plants, means for retrieving information related to plant cultivation methods from a data storage medium using the formatted data, and means for creating cultivation advice using generative artificial intelligence based on the information obtained from the data storage medium. This makes it possible to quickly provide personalized and appropriate advice to each user.

[0061] A "user" refers to a person who uses this system to obtain information about plant cultivation and management.

[0062] "Input information" refers to the data and concerns about plants that users provide to the system.

[0063] "Data formatting" refers to the process of converting received input information into a format that is easy for the server to process.

[0064] "Data storage media" refers to any information storage device, including databases containing plant cultivation methods and related information.

[0065] "Generative artificial intelligence" refers to an artificial intelligence system that can generate new information based on given data.

[0066] "Cultivation advice" refers to specific guidelines and suggestions for users to effectively grow plants.

[0067] "Communication methods" refer to network-related technologies used to transmit information from a server to a user's terminal.

[0068] The system of this invention is designed to provide advice on home gardening and horticulture. Users first input information about the plants they wish to grow, their current cultivation status, and any concerns they may have via a terminal. This terminal can be a standard computer or smartphone.

[0069] The terminal formats the information entered by the user and sends it to the server using a data communication protocol. The formatted data may be represented in formats such as JSON or XML. The server should ideally consist of a high-performance computer or server system with high data processing capabilities.

[0070] The server searches a data storage medium based on the formatted data sent from the terminal. This data storage medium consists of SQL databases, NoSQL databases, etc., and stores a variety of data related to plant cultivation. The server extracts relevant information from this database and uses generative artificial intelligence to generate specific cultivation advice.

[0071] Generative artificial intelligence, often implemented using machine learning models, provides optimal solutions to user input. For example, in response to the problem "tomato leaves are turning yellow," it can advise on the appropriate watering timing and nutrient supply. The generated advice is then sent back from the server to the terminal via a communication protocol, allowing the user to view it through the application interface.

[0072] For example, if a user enters the prompt "I want to know what to do if my tomato leaves are turning yellow," the server can receive this prompt, search for the necessary data, and use a generative AI model to generate and provide advice such as "Add nitrogen-containing fertilizer and water moderately."

[0073] In this way, this system provides users with accurate and personalized plant cultivation advice, supporting their success in home gardening and horticulture.

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

[0075] Step 1:

[0076] Users input information via the terminal, including the plants they want to grow, their cultivation status, and any particular problems they are concerned about. This input is provided in text format, such as "The tomato leaves have turned yellow." The terminal receives this input and performs data formatting. Specifically, this involves converting uneven text into a standardized format (e.g., JSON or XML). The output is the formatted data.

[0077] Step 2:

[0078] The terminal sends formatted data to the server. Standardized data is used as input, and the output is the completion of the transmission to the server. Specifically, the process involves securely and efficiently sending data to the server using the HTTPS protocol.

[0079] Step 3:

[0080] The server searches the data storage medium based on the formatted data it receives. The input is the data sent to the server, and the output is related plant information. Specifically, it uses SQL queries to search the database and extract information on cultivation methods and solutions related to "yellow tomato leaves."

[0081] Step 4:

[0082] The server generates cultivation advice using a generative AI model based on information obtained from data storage media. The input is information extracted as search results, and the output is personalized cultivation advice. The specific operation includes an advice generation process that applies a machine learning model. This process provides specific guidance, such as "It is necessary to add nitrogen-containing fertilizer."

[0083] Step 5:

[0084] The server sends the generated advice to the terminal. The input is the generated advice, and the output is the completion of its transmission to the terminal. Specifically, this involves the delivery of the advice to the terminal via a transmission protocol.

[0085] Step 6:

[0086] The user reviews the advice sent via their device. The input is the advice displayed on the device, and the output is the user's understanding and actions based on that understanding. Specifically, information is displayed in a pop-up format from the device's application, and the user takes care of the plants accordingly.

[0087] In this way, the process from user input to advice provision is smooth, allowing users to effectively cultivate plants.

[0088] (Application Example 1)

[0089] 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."

[0090] In home gardening and plant cultivation, there is a need for easy implementation of specific plant cultivation methods and problem-solving solutions, regardless of whether the user is a beginner or experienced. However, conventional methods make it difficult to quickly obtain specific and customized advice for plants, hindering efficient cultivation. Furthermore, the lack of sufficient development of technologies that enable intuitive interaction using voice means that user convenience is not improved.

[0091] 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.

[0092] In this invention, the server includes means for receiving user input information and aggregating necessary information related to plants, means for generating guidelines for plant cultivation based on the aggregated information, communication means for providing the generated guidelines to the user, means for analyzing voice input from the user using voice recognition technology, and means for outputting the generated guidelines as voice using voice synthesis technology. This makes it possible to provide intuitive and efficient support for plant cultivation in a home environment.

[0093] "A means of receiving user input information and aggregating necessary information related to plants" refers to a system that receives requests and questions from users and collects and organizes information about plants that correspond to those requests.

[0094] "Means for generating guidelines for plant cultivation" refers to a system that uses collected plant-related information to create specific methods and solutions for promoting plant growth.

[0095] "Communication means for providing generated guidelines to users" refers to digital or analog communication technologies used to convey generated training methods and advice to users.

[0096] "Means of analyzing voice input from a user using speech recognition technology" refers to acoustic analysis technology that can understand the user's utterances and interpret their content as instructions or information.

[0097] "A means of outputting guidelines generated using speech synthesis technology as audio" refers to a technology that converts computer-generated text information into speech and provides it to the user in an easily understandable format.

[0098] The system that implements this invention is in the form of a home assistant robot. The system is configured as follows:

[0099] The system uses speech recognition technology to receive voice input from the user. This technology utilizes "Google® Speech-to-Text" as the speech recognition library. This library converts the user's voice into digital data and performs analysis.

[0100] Next, the server aggregates information about plants from its information storage unit based on keywords extracted from the audio data. This information includes plant cultivation methods, requirements, common problems, and pest control methods.

[0101] Based on aggregated information, a generative artificial intelligence model is used to generate customized training guidelines for the user. An example of this AI model is the "GPT-based" technology, which generates advice tailored to the user's specific needs.

[0102] The generated training guidelines are provided to the user via voice using speech synthesis technology. This technology utilizes "Google Text-to-Speech," which converts the generated text information into natural-sounding speech and delivers it to the user via a robot.

[0103] For example, if a user asks, "What should I do if my cucumber leaves turn yellow?", the system will generate a prompt message such as "cucumber leaves yellow countermeasures" based on this question and send it to the AI ​​model. Possible advice generated might be, "Cucumbers often suffer from a lack of water, so you should water them thoroughly in the morning." This advice is conveyed to the user via voice, promoting effective plant growth.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The user asks questions about the plant they want to grow using voice commands via their device. This voice input is converted into text data by speech recognition technology. The input is the user's voice, and the output is query data in text format. The user's questions, now converted into text data, are then passed on to the next step.

[0107] Step 2:

[0108] The server extracts keywords from the text data. For example, important keywords such as "cucumber," "leaf," and "yellow" are extracted. The input is text data obtained from a speech recognition process, and the output is the keyword set obtained from this analysis. Natural language processing techniques are used for keyword extraction.

[0109] Step 3:

[0110] The server searches its information storage based on a set of keywords and aggregates relevant plant information. The input consists of keywords extracted from text data, and the output is cultivation information for the corresponding plants. Database query techniques are used for information retrieval.

[0111] Step 4:

[0112] Based on aggregated plant information, the server uses a generative AI model to generate individual cultivation guidelines. The input is plant information obtained from the database, and the output is specific cultivation advice delivered to the user. Generative AI is used for the data calculations performed here.

[0113] Step 5:

[0114] The generated training advice is provided to the user as voice output using speech synthesis technology. The input is advice in text format, and the output is advice in voice format. The system uses a speech synthesis engine to generate natural and easy-to-understand speech.

[0115] Step 6:

[0116] The user receives advice via voice through their device and then performs specific plant care. User feedback is also considered at this stage, potentially improving the system in preparation for the next interaction. The user then cultivates the plants based on the information received via voice.

[0117] 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.

[0118] This invention incorporates an emotion engine into a system that provides users with advice on home gardening and horticulture, in order to achieve more personalized support that takes user emotions into account. The aim of this invention is to provide users with a better experience in growing and managing plants.

[0119] Users input information about plant care and any problems they are facing through their devices. This input also includes data that reflects the user's emotional state (for example, the emotions conveyed in text messages).

[0120] The terminal formats the user's input data for transmission to the server. This request includes information about the plant, as well as information about the user's emotional state, as measured by the emotion engine.

[0121] The server analyzes the received input data and searches the database to collect relevant plant information. Simultaneously, it utilizes an emotion engine to analyze the user's emotions, thereby understanding their psychological state.

[0122] The server uses generative artificial intelligence to generate cultivation advice based on acquired plant information and the user's emotional state. This advice includes encouraging messages and specific cultivation methods tailored to the user's psychological state. For example, if the user is feeling stressed, it will suggest simple and quick cultivation and management methods.

[0123] The generated advice is sent from the server to the terminal. The terminal displays the advice in a format that is easy for the user to understand, and the user can refer to it while growing the plants.

[0124] For example, if a user inputs "I'm feeling stressed because my tomatoes haven't been growing well lately," the emotion engine recognizes this emotion and transmits it to the server. Based on this information, the server generates advice that includes simple steps to improve the situation, along with encouraging words such as, "Start with a simple improvement like watering in the morning, and believe in good results."

[0125] Thus, the embodiment of the present invention aims to make gardening and horticultural work more positive by providing effective and emotional support while taking into account the user's emotional state.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] Users use their devices to input information about the type of plant they want to grow and any problems they are currently experiencing. They also input emotional statements, such as, "I'm worried because my plants have been growing slowly lately."

[0129] Step 2:

[0130] The device analyzes the input data from the user and generates a request to send it to the server. This request includes data such as the user's emotions, along with plant information.

[0131] Step 3:

[0132] The server analyzes the received request and searches the database for data about the plant specified by the user. At the same time, it uses an emotion engine to analyze the user's emotional state and identify emotions such as "anxiety" or "impatience."

[0133] Step 4:

[0134] The server combines training information retrieved from the database with analysis results from the emotion engine, and uses generative artificial intelligence to generate advice for the user. This advice includes specific training methods and encouraging messages that take the user's emotions into consideration.

[0135] Step 5:

[0136] The server formats the generated advice and sends it to the terminal. This advice is formatted to be easy to understand and emotionally responsive.

[0137] Step 6:

[0138] The terminal receives advice from the server and displays it on the screen for the user. The user can then review the displayed advice and follow it to cultivate and manage their plants.

[0139] Step 7:

[0140] Users can follow the advice as needed and enter the results back into their device to receive further support. Based on this feedback, the server can continue to adjust the advice.

[0141] (Example 2)

[0142] 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 will be referred to as the "terminal."

[0143] Conventional plant cultivation support systems provide uniform advice without considering the user's emotions or psychological state, resulting in a lack of support tailored to individual user needs. Furthermore, while empathetic advice is particularly needed for users experiencing stress or anxiety, current systems struggle to provide this.

[0144] 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.

[0145] In this invention, the server includes means for receiving user input data and analyzing the user's emotional state using an emotion analysis engine, means for aggregating the analyzed emotional state and necessary information about the plant, and means for generating personalized advice on plant cultivation based on the aggregated information and emotional state. This makes it possible to provide more individualized and appropriate cultivation advice that takes the user's emotions into consideration.

[0146] "User input data" refers to information that users provide to the system via their devices regarding plant cultivation and management.

[0147] An "emotion analysis engine" refers to a software component that analyzes a user's emotions and psychological state based on their text data to identify their feelings.

[0148] "Necessary information about plants" refers to data required for the cultivation and management of specific plants, and includes vegetation information obtained from databases.

[0149] "Personalized advice" refers to customized support messages and instructions tailored to the user's specific situation or emotional state.

[0150] "Communication methods" refer to the infrastructure and protocols used to send advice generated by a server to a user's terminal and provide information to the user.

[0151] "Generative artificial intelligence" refers to machine learning models that automatically generate information based on pre-set prompts.

[0152] This invention provides a system to solve problems and anxieties that users face in home gardening and horticultural activities. Specific embodiments are described below.

[0153] Users input information about plant care and any problems they are experiencing using the device. The device has an input interface that accepts text and reads the user's emotional state. For example, users can input specific details such as, "I'm worried because my tomato leaves have recently turned yellow."

[0154] The terminal formats the input data and sends it to the server. The server analyzes the received data, using an emotion analysis engine to analyze the user's emotions. This emotion analysis engine has the ability to extract psychological states such as anxiety, stress, and joy from the user's text messages.

[0155] The server collects necessary information about the plants from the database based on the analysis results. This lays the groundwork for providing appropriate advice tailored to the plant's condition. Next, generative artificial intelligence is used to generate personalized advice based on the emotional state and plant information. This AI model uses pre-set prompts to derive the best advice. An example of a prompt might be, "If the user is worried about growing tomatoes, please suggest specific improvements along with a message of encouragement."

[0156] The generated advice is sent from the server to the terminal. The terminal displays it in a format that is easy for the user to understand. The user can then use this advice to implement specific cultivation methods and improvement measures. This system provides users with more user-friendly and effective gardening support.

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1:

[0159] The user uses the terminal's input interface to enter information about the plant and current problems in text format. This input data may include information such as, "I'm worried because the tomato leaves are turning yellow." The terminal then prepares this input data for direct transmission to the server.

[0160] Step 2:

[0161] The terminal formats the received text data and generates a request to send it to the server. This data formatting process transforms the user's input into a format suitable for database queries and sentiment analysis. The formatted data, which includes elements of plant information and sentiment information, is added to the transmission list.

[0162] Step 3:

[0163] The server receives requests sent from the terminal. Based on the received data, the server uses an emotion analysis engine to analyze the user's psychological state. As part of the data processing, natural language processing technology is used to extract emotional information from the text. The output of this analysis is a tag or score that indicates the user's feelings.

[0164] Step 4:

[0165] The server searches and extracts relevant plant cultivation information from the database, along with the analyzed sentiment information. The database search uses a database query language to retrieve information that matches the plant type and condition. The output of this step is a list of relevant cultivation information.

[0166] Step 5:

[0167] The server uses a generative AI model to create personalized growing advice based on acquired plant information and emotion analysis results. During this process, prompts are used to instruct the AI ​​to consider the user's emotions and the plant's growing status, generating appropriate advice. The output is a customized message provided to the user.

[0168] Step 6:

[0169] The server sends the generated advice to the terminal. The terminal displays the received advice on its screen in a format that is easy for the user to understand. Specifically, this involves updating the UI to display a text message on the terminal's digital screen. The output of this step is visually appealing support information for the user.

[0170] (Application Example 2)

[0171] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0172] Users who engage in home gardening or horticulture face challenges in receiving appropriate advice tailored to their emotional state, in addition to information and methods for growing plants. Furthermore, there is a lack of support that addresses the stress and anxiety users experience, creating a need for systems that make the plant-growing process more enjoyable and stress-free.

[0173] 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.

[0174] In this invention, the server includes means for receiving user input data and aggregating necessary information about the plant, means for processing information to analyze the user's emotional state, and means for generating personalized cultivation advice based on the emotional state using generative artificial intelligence. This enables individualized cultivation support that takes the user's emotional needs into consideration.

[0175] A "user" refers to someone who inputs information about plant cultivation in their home garden or horticulture and receives advice from the system.

[0176] "Input data" includes information about plants and their emotional state provided by the user.

[0177] "Plants" refers to living organisms cultivated in home gardens or horticulture, and are the subjects for which the system provides information.

[0178] "Information processing means" refers to a device or software that analyzes user input data, aggregates necessary information, and executes a process for analyzing the emotional state.

[0179] "Emotional state" refers to the emotional state expressed by the user through the input data.

[0180] "Generative artificial intelligence" refers to artificial intelligence technology that automatically generates advice on plant cultivation based on user input data.

[0181] "Personalized advice" refers to information that includes cultivation methods and support messages optimized according to the user's individual emotional state and the condition of their plants.

[0182] "Communication means" refers to the medium or technology used to present the generated advice to the user, either audibly or visually.

[0183] "Data storage" refers to a database or similar system that stores existing information about plants and allows for searching and retrieval as needed.

[0184] A system implementing this invention consists of three main components: a user, a terminal, and a server.

[0185] First, the user inputs information about their home garden or horticulture, as well as their emotional state, into a device. This device can be a smartphone, tablet, or a robot equipped with voice recognition. In the case of voice input, voice recognition software (e.g., Google Speech-to-Text API) converts the voice data into text.

[0186] Next, the terminal sends the converted text data to the server. The server first analyzes the received data using information processing tools to identify the user's emotional state. This process utilizes sentiment analysis libraries (e.g., TextBlob and Transformers). Along with the analysis results, the server retrieves and aggregates information about plants from its data storage.

[0187] Subsequently, the server uses generative artificial intelligence (e.g., OpenAI® GPT API) to generate personalized parenting advice based on the acquired information and analyzed emotional state. This advice is presented in a user-friendly format and includes encouragement and specific suggestions for improvement.

[0188] The generated advice is sent to the user's device via a communication method. The device then presents this advice to the user either audibly (e.g., Google Text-to-Speech) or visually. After the user reviews this advice, they can then cultivate their plants accordingly.

[0189] For example, if a user enters "I'm worried because my tomatoes are wilting due to the recent weather," the server will analyze the results and generate specific advice, including emotional support. For instance, it might advise, "Pay attention to temperature fluctuations. This week, water them thoroughly in the morning and check the temperature. You'll have wonderful tomatoes."

[0190] Examples of prompts for a generative AI model:

[0191] "The user's emotion is worry, and the plant is a tomato. Please provide the user with the best growing method and words of encouragement."

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

[0193] Step 1:

[0194] The user inputs information and emotional states about the plant into the device. In the case of voice input, the voice data is converted into text data by the Google Speech-to-Text API. The input is converted from the user's words into digital format, becoming text data ready to be sent to the server.

[0195] Step 2:

[0196] The terminal sends the converted text data to the server. The transmitted data is text data containing the user's plant cultivation information and emotional state.

[0197] Step 3:

[0198] The server analyzes the received data using information processing tools. This analysis uses sentiment analysis libraries (such as TextBlob and Transformers) to quantify and identify the user's emotional state. The input data is broken down into the user's sentiment score and information about plants.

[0199] Step 4:

[0200] The server searches and extracts relevant plant data from its data storage. Using the plant type entered by the user as a key, it retrieves cultivation information for that plant. This output is the specific cultivation data for the plant stored in the data storage.

[0201] Step 5:

[0202] The server uses a generative artificial intelligence model (such as OpenAI GPT) to generate personalized advice based on analysis results and cultivation data. Prompt sentences are input to the generative AI model, which generates advice tailored to the user's emotions and the plant's condition. This output is text data containing specific cultivation methods and encouraging words.

[0203] Step 6:

[0204] The generated advice is sent from the server to the device, which then presents this advice to the user via voice output (such as Google Text-to-Speech) or visual display. The user receives the advice through the device and cultivates the plants based on its content.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] [Second Embodiment]

[0209] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0210] 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.

[0211] 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).

[0212] 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.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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".

[0221] This invention is a system for providing advice on home gardening and horticulture to a wide range of users, from beginners to advanced gardeners. This system is designed to solve the challenges users face in growing and managing plants. The following describes embodiments of the system.

[0222] The user inputs information about the plant they want to grow, its current cultivation status, and any particular concerns they have, through the terminal. The terminal then formats this input data and sends it to the server.

[0223] The server searches the database based on the input data received from the user. This database contains information such as how to grow plants, required conditions, common problems, and pest control methods.

[0224] After retrieving relevant information from the database, the server utilizes generative artificial intelligence to generate optimal plant care advice. This AI can provide personalized advice tailored to the user's needs, offering specific guidance such as "how to adjust watering and fertilization when tomato leaves turn yellow," "effective countermeasures against specific pests," and "the optimal harvest time."

[0225] The generated advice is sent from the server to the terminal. The user can review the advice through the terminal and follow the instructions to cultivate the plants.

[0226] For example, if a user enters "I want to grow tomatoes," the server retrieves tomato cultivation information from its database and provides advice such as "Place them in a sunny spot" and "Water them in the morning with an appropriate amount." This advice allows the user to efficiently carry out tasks in their home garden.

[0227] Thus, the system of the present invention supports home gardening and horticulture efforts by providing users with specific and practical information to help them grow plants more effectively and successfully.

[0228] The following describes the processing flow.

[0229] Step 1:

[0230] The user uses a device to input information about the type of plant they want to grow and the problems they are currently facing. This includes text input and selecting options in response to a series of questions.

[0231] Step 2:

[0232] The terminal formats the information entered by the user into a standard format and generates a request to send it to the server.

[0233] Step 3:

[0234] The server analyzes the request received from the terminal and extracts information about the plant entered by the user. Based on this information, it queries the database for relevant data.

[0235] Step 4:

[0236] The database searches for the most appropriate plant information and related solutions based on the received query and returns the results to the server.

[0237] Step 5:

[0238] The server provides information retrieved from the database to a generative artificial intelligence system, which then generates user-focused training advice. This advice includes optimized instructions and suggestions based on an analysis of the retrieved data.

[0239] Step 6:

[0240] The server sends the generated advice back to the terminal. At this time, the output is formatted in a way that is easy for the user to understand.

[0241] Step 7:

[0242] The terminal displays advice received from the server on its screen. Users can review the provided instructions and advice and carry out tasks in their home garden.

[0243] (Example 1)

[0244] 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."

[0245] In home gardening and horticulture, there is a need to effectively solve the challenges related to plant cultivation and management faced by a wide range of users, from beginners to advanced gardeners. Specifically, many users are troubled by the inability to obtain timely and accurate advice and information tailored to the condition of their plants.

[0246] 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.

[0247] In this invention, the server includes means for receiving user input information and formatting necessary data about plants, means for retrieving information related to plant cultivation methods from a data storage medium using the formatted data, and means for creating cultivation advice using generative artificial intelligence based on the information obtained from the data storage medium. This makes it possible to quickly provide personalized and appropriate advice to each user.

[0248] A "user" refers to a person who uses this system to obtain information about plant cultivation and management.

[0249] "Input information" refers to the data and concerns about plants that users provide to the system.

[0250] "Data formatting" refers to the process of converting received input information into a format that is easy for the server to process.

[0251] "Data storage media" refers to any information storage device, including databases containing plant cultivation methods and related information.

[0252] "Generative artificial intelligence" refers to an artificial intelligence system that can generate new information based on given data.

[0253] "Cultivation advice" refers to specific guidelines and suggestions for users to effectively grow plants.

[0254] "Communication methods" refer to network-related technologies used to transmit information from a server to a user's terminal.

[0255] The system of this invention is designed to provide advice on home gardening and horticulture. Users first input information about the plants they wish to grow, their current cultivation status, and any concerns they may have via a terminal. This terminal can be a standard computer or smartphone.

[0256] The terminal formats the information entered by the user and sends it to the server using a data communication protocol. The formatted data may be represented in formats such as JSON or XML. The server should ideally consist of a high-performance computer or server system with high data processing capabilities.

[0257] The server searches a data storage medium based on the formatted data sent from the terminal. This data storage medium consists of SQL databases, NoSQL databases, etc., and stores a variety of data related to plant cultivation. The server extracts relevant information from this database and uses generative artificial intelligence to generate specific cultivation advice.

[0258] Generative artificial intelligence, often implemented using machine learning models, provides optimal solutions to user input. For example, in response to the problem "tomato leaves are turning yellow," it can advise on the appropriate watering timing and nutrient supply. The generated advice is then sent back from the server to the terminal via a communication protocol, allowing the user to view it through the application interface.

[0259] For example, if a user enters the prompt "I want to know what to do if my tomato leaves are turning yellow," the server can receive this prompt, search for the necessary data, and use a generative AI model to generate and provide advice such as "Add nitrogen-containing fertilizer and water moderately."

[0260] In this way, this system provides users with accurate and personalized plant cultivation advice, supporting their success in home gardening and horticulture.

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

[0262] Step 1:

[0263] Users input information via the terminal, including the plants they want to grow, their cultivation status, and any particular problems they are concerned about. This input is provided in text format, such as "The tomato leaves have turned yellow." The terminal receives this input and performs data formatting. Specifically, this involves converting uneven text into a standardized format (e.g., JSON or XML). The output is the formatted data.

[0264] Step 2:

[0265] The terminal sends formatted data to the server. Standardized data is used as input, and the output is the completion of the transmission to the server. Specifically, the process involves securely and efficiently sending data to the server using the HTTPS protocol.

[0266] Step 3:

[0267] The server searches the data storage medium based on the formatted data it receives. The input is the data sent to the server, and the output is related plant information. Specifically, it uses SQL queries to search the database and extract information on cultivation methods and solutions related to "yellow tomato leaves."

[0268] Step 4:

[0269] The server generates cultivation advice using a generative AI model based on information obtained from data storage media. The input is information extracted as search results, and the output is personalized cultivation advice. The specific operation includes an advice generation process that applies a machine learning model. This process provides specific guidance, such as "It is necessary to add nitrogen-containing fertilizer."

[0270] Step 5:

[0271] The server sends the generated advice to the terminal. The input is the generated advice, and the output is the completion of its transmission to the terminal. Specifically, this involves the delivery of the advice to the terminal via a transmission protocol.

[0272] Step 6:

[0273] The user reviews the advice sent via their device. The input is the advice displayed on the device, and the output is the user's understanding and actions based on that understanding. Specifically, information is displayed in a pop-up format from the device's application, and the user takes care of the plants accordingly.

[0274] In this way, the process from user input to advice provision is smooth, allowing users to effectively cultivate plants.

[0275] (Application Example 1)

[0276] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0277] In home gardening and plant cultivation, there is a need for easy implementation of specific plant cultivation methods and problem-solving solutions, regardless of whether the user is a beginner or experienced. However, conventional methods make it difficult to quickly obtain specific and customized advice for plants, hindering efficient cultivation. Furthermore, the lack of sufficient development of technologies that enable intuitive interaction using voice means that user convenience is not improved.

[0278] 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.

[0279] In this invention, the server includes means for receiving user input information and aggregating necessary information related to plants, means for generating guidelines for plant cultivation based on the aggregated information, communication means for providing the generated guidelines to the user, means for analyzing voice input from the user using voice recognition technology, and means for outputting the generated guidelines as voice using voice synthesis technology. This makes it possible to provide intuitive and efficient support for plant cultivation in a home environment.

[0280] "A means of receiving user input information and aggregating necessary information related to plants" refers to a system that receives requests and questions from users and collects and organizes information about plants that correspond to those requests.

[0281] "Means for generating guidelines for plant cultivation" refers to a system that uses collected plant-related information to create specific methods and solutions for promoting plant growth.

[0282] "Communication means for providing generated guidelines to users" refers to digital or analog communication technologies used to convey generated training methods and advice to users.

[0283] The means of "analyzing voice input from the user using voice recognition technology" is an acoustic analysis technology that can understand the user's speech and interpret its content as instructions or information.

[0284] The means of "outputting the guidance generated using voice synthesis technology as voice" is a technology that converts the text information generated by a computer into voice and provides it to the user in an easy-to-hear form.

[0285] The system for implementing this invention is in a form mounted on a home assistant robot. The system is configured as follows.

[0286] The system uses voice recognition technology to receive voice input from the user. For this technology, "Google Speech-to-Text" is used as a voice recognition library. This library converts the user's voice into digital data and performs analysis.

[0287] Next, the server aggregates information about plants from the information storage section based on the keywords extracted from the voice data. These information includes the cultivation methods of plants, necessary conditions, frequently occurring problems, pest control methods, etc.

[0288] Based on the aggregated information, a customized cultivation guidance is generated for the user by utilizing a generation-based artificial intelligence model. As an example of this AI model, "GPT-based" technology is used, and advice corresponding to the specific requirements of the user is generated.

[0289] The generated cultivation guidance is provided to the user as voice using voice synthesis technology. "Google Text-to-Speech" is used for this technology, which converts the generated text information into natural voice and delivers it to the user via the robot.

[0290] For example, if a user asks, "What should I do if my cucumber leaves turn yellow?", the system will generate a prompt message such as "cucumber leaves yellow countermeasures" based on this question and send it to the AI ​​model. Possible advice generated might be, "Cucumbers often suffer from a lack of water, so you should water them thoroughly in the morning." This advice is conveyed to the user via voice, promoting effective plant growth.

[0291] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0292] Step 1:

[0293] The user asks questions about the plant they want to grow using voice commands via their device. This voice input is converted into text data by speech recognition technology. The input is the user's voice, and the output is query data in text format. The user's questions, now converted into text data, are then passed on to the next step.

[0294] Step 2:

[0295] The server extracts keywords from the text data. For example, important keywords such as "cucumber," "leaf," and "yellow" are extracted. The input is text data obtained from a speech recognition process, and the output is the keyword set obtained from this analysis. Natural language processing techniques are used for keyword extraction.

[0296] Step 3:

[0297] The server searches its information storage based on a set of keywords and aggregates relevant plant information. The input consists of keywords extracted from text data, and the output is cultivation information for the corresponding plants. Database query techniques are used for information retrieval.

[0298] Step 4:

[0299] Based on aggregated plant information, the server uses a generative AI model to generate individual cultivation guidelines. The input is plant information obtained from the database, and the output is specific cultivation advice delivered to the user. Generative AI is used for the data calculations performed here.

[0300] Step 5:

[0301] The generated training advice is provided to the user as voice output using speech synthesis technology. The input is advice in text format, and the output is advice in voice format. The system uses a speech synthesis engine to generate natural and easy-to-understand speech.

[0302] Step 6:

[0303] The user receives advice via voice through their device and then performs specific plant care. User feedback is also considered at this stage, potentially improving the system in preparation for the next interaction. The user then cultivates the plants based on the information received via voice.

[0304] 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.

[0305] This invention incorporates an emotion engine into a system that provides users with advice on home gardening and horticulture, in order to provide more personalized support that takes user emotions into consideration. The aim of this invention is to give users a better experience in growing and managing plants.

[0306] Users input information about plant care and any problems they are facing through their devices. This input also includes data that reflects the user's emotional state (for example, the emotions conveyed in text messages).

[0307] The terminal formats the user's input data for transmission to the server. This request includes information about the user's emotional state using the emotion engine, along with information about the plant.

[0308] The server analyzes the received input data, searches the database to collect relevant plant information, and at the same time utilizes the emotion engine to analyze the user's emotions, thereby understanding the user's psychological state.

[0309] Based on the obtained plant information and the user's emotional state, the server uses a generative artificial intelligence to generate cultivation advice. This advice includes encouraging messages and specific cultivation methods according to the user's psychological state. For example, when the user is feeling stressed, it proposes easy and quick cultivation and management methods.

[0310] The generated advice is sent from the server to the terminal. The terminal displays the advice in a form that is easy for the user to understand, and the user can refer to it to grow the plant.

[0311] As a specific example, when the user inputs "Recently, the crops have not grown much and I'm feeling stressed" regarding tomato cultivation, the emotion engine recognizes this emotion and conveys it to the server. Based on this information, the server generates advice including easily improvable procedures and provides it with encouraging words such as "Start with the simple improvement of watering in the morning and believe in good results."

[0312] [[ID=2l]]Thus, the form of the present invention aims to make the work in home gardening and horticulture more positive by providing effective and emotional support considering the user's emotional state.

[0313] The following explains the processing flow.

[0314] Step 1:

[0315] Users use their devices to input information about the type of plant they want to grow and any problems they are currently experiencing. They also input emotional statements, such as, "I'm worried because my plants have been growing slowly lately."

[0316] Step 2:

[0317] The device analyzes the input data from the user and generates a request to send it to the server. This request includes data such as the user's emotions, along with plant information.

[0318] Step 3:

[0319] The server analyzes the received request and searches the database for data about the plant specified by the user. At the same time, it uses an emotion engine to analyze the user's emotional state and identify emotions such as "anxiety" or "impatience."

[0320] Step 4:

[0321] The server combines training information retrieved from the database with analysis results from the emotion engine, and uses generative artificial intelligence to generate advice for the user. This advice includes specific training methods and encouraging messages that take the user's emotions into consideration.

[0322] Step 5:

[0323] The server formats the generated advice and sends it to the terminal. This advice is formatted to be easy to understand and emotionally responsive.

[0324] Step 6:

[0325] The terminal receives advice from the server and displays it on the screen for the user. The user can then review the displayed advice and follow it to cultivate and manage their plants.

[0326] Step 7:

[0327] Users can follow the advice as needed and enter the results back into their device to receive further support. Based on this feedback, the server can continue to adjust the advice.

[0328] (Example 2)

[0329] 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".

[0330] Conventional plant cultivation support systems provide uniform advice without considering the user's emotions or psychological state, resulting in a lack of support tailored to individual user needs. Furthermore, while empathetic advice is particularly needed for users experiencing stress or anxiety, current systems struggle to provide this.

[0331] 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.

[0332] In this invention, the server includes means for receiving user input data and analyzing the user's emotional state using an emotion analysis engine, means for aggregating the analyzed emotional state and necessary information about the plant, and means for generating personalized advice on plant cultivation based on the aggregated information and emotional state. This makes it possible to provide more individualized and appropriate cultivation advice that takes the user's emotions into consideration.

[0333] "User input data" refers to information that users provide to the system via their devices regarding plant cultivation and management.

[0334] An "emotion analysis engine" refers to a software component that analyzes a user's emotions and psychological state based on their text data to identify their feelings.

[0335] "Necessary information about plants" refers to data required for the cultivation and management of specific plants, and includes vegetation information obtained from databases.

[0336] "Personalized advice" refers to customized support messages and instructions tailored to the user's specific situation or emotional state.

[0337] "Communication methods" refer to the infrastructure and protocols used to send advice generated by a server to a user's terminal and provide information to the user.

[0338] "Generative artificial intelligence" refers to machine learning models that automatically generate information based on pre-set prompts.

[0339] This invention provides a system to solve problems and anxieties that users face in home gardening and horticultural activities. Specific embodiments are described below.

[0340] Users input information about plant care and any problems they are experiencing using the device. The device has an input interface that accepts text and reads the user's emotional state. For example, users can input specific details such as, "I'm worried because my tomato leaves have recently turned yellow."

[0341] The terminal formats the input data and sends it to the server. The server analyzes the received data, using an emotion analysis engine to analyze the user's emotions. This emotion analysis engine has the ability to extract psychological states such as anxiety, stress, and joy from the user's text messages.

[0342] The server collects necessary information about the plants from the database based on the analysis results. This lays the groundwork for providing appropriate advice tailored to the plant's condition. Next, generative artificial intelligence is used to generate personalized advice based on the emotional state and plant information. This AI model uses pre-set prompts to derive the best advice. An example of a prompt might be, "If the user is worried about growing tomatoes, please suggest specific improvements along with a message of encouragement."

[0343] The generated advice is sent from the server to the terminal. The terminal displays it in a format that is easy for the user to understand. The user can then use this advice to implement specific cultivation methods and improvement measures. This system provides users with more user-friendly and effective gardening support.

[0344] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0345] Step 1:

[0346] The user uses the terminal's input interface to enter information about the plant and current problems in text format. This input data may include information such as, "I'm worried because the tomato leaves are turning yellow." The terminal then prepares this input data for direct transmission to the server.

[0347] Step 2:

[0348] The terminal formats the received text data and generates a request to send it to the server. This data formatting process transforms the user's input into a format suitable for database queries and sentiment analysis. The formatted data, which includes elements of plant information and sentiment information, is added to the transmission list.

[0349] Step 3:

[0350] The server receives requests sent from the terminal. Based on the received data, the server uses an emotion analysis engine to analyze the user's psychological state. As part of the data processing, natural language processing technology is used to extract emotional information from the text. The output of this analysis is a tag or score that indicates the user's feelings.

[0351] Step 4:

[0352] The server searches and extracts relevant plant cultivation information from the database, along with the analyzed sentiment information. The database search uses a database query language to retrieve information that matches the plant type and condition. The output of this step is a list of relevant cultivation information.

[0353] Step 5:

[0354] The server uses a generative AI model to create personalized growing advice based on acquired plant information and emotion analysis results. During this process, prompts are used to instruct the AI ​​to consider the user's emotions and the plant's growing status, generating appropriate advice. The output is a customized message provided to the user.

[0355] Step 6:

[0356] The server sends the generated advice to the terminal. The terminal displays the received advice on its screen in a format that is easy for the user to understand. Specifically, this involves updating the UI to display a text message on the terminal's digital screen. The output of this step is visually appealing support information for the user.

[0357] (Application Example 2)

[0358] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0359] Users who engage in home gardening or horticulture face challenges in receiving appropriate advice tailored to their emotional state, in addition to information and methods for growing plants. Furthermore, there is a lack of support that addresses the stress and anxiety users experience, creating a need for systems that make the plant-growing process more enjoyable and stress-free.

[0360] 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.

[0361] In this invention, the server includes means for receiving user input data and aggregating necessary information about the plant, means for processing information to analyze the user's emotional state, and means for generating personalized cultivation advice based on the emotional state using generative artificial intelligence. This enables individualized cultivation support that takes the user's emotional needs into consideration.

[0362] A "user" refers to someone who inputs information about plant cultivation in their home garden or horticulture and receives advice from the system.

[0363] "Input data" includes information about plants and their emotional state provided by the user.

[0364] "Plants" refers to living organisms cultivated in home gardens or horticulture, and are the subjects for which the system provides information.

[0365] "Information processing means" refers to a device or software that analyzes user input data, aggregates necessary information, and executes a process for analyzing the emotional state.

[0366] "Emotional state" refers to the emotional state expressed by the user through the input data.

[0367] "Generative artificial intelligence" refers to artificial intelligence technology that automatically generates advice on plant cultivation based on user input data.

[0368] "Personalized advice" refers to information that includes cultivation methods and support messages optimized according to the user's individual emotional state and the condition of their plants.

[0369] "Communication means" refers to the medium or technology used to present the generated advice to the user, either audibly or visually.

[0370] "Data storage" refers to a database or similar system that stores existing information about plants and allows for searching and retrieval as needed.

[0371] A system implementing this invention consists of three main components: a user, a terminal, and a server.

[0372] First, the user inputs information about their home garden or horticulture, as well as their emotional state, into a device. This device can be a smartphone, tablet, or a robot equipped with voice recognition. In the case of voice input, voice recognition software (e.g., Google Speech-to-Text API) converts the voice data into text.

[0373] Next, the terminal sends the converted text data to the server. The server first analyzes the received data using information processing tools to identify the user's emotional state. This process utilizes sentiment analysis libraries (e.g., TextBlob and Transformers). Along with the analysis results, the server retrieves and aggregates information about plants from its data storage.

[0374] Subsequently, the server uses generative artificial intelligence (e.g., OpenAI GPT API) to generate personalized parenting advice based on the acquired information and analyzed emotional state. This advice is presented in a user-friendly format and includes encouragement and specific suggestions for improvement.

[0375] The generated advice is sent to the user's device via a communication method. The device then presents this advice to the user either audibly (e.g., Google Text-to-Speech) or visually. After the user reviews this advice, they can then cultivate their plants accordingly.

[0376] For example, if a user enters "I'm worried because my tomatoes are wilting due to the recent weather," the server will analyze the results and generate specific advice, including emotional support. For instance, it might advise, "Pay attention to temperature fluctuations. This week, water them thoroughly in the morning and check the temperature. You'll have wonderful tomatoes."

[0377] Examples of prompts for a generative AI model:

[0378] "The user's emotion is worry, and the plant is a tomato. Please provide the user with the best growing method and words of encouragement."

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

[0380] Step 1:

[0381] The user inputs information and emotional states about the plant into the device. In the case of voice input, the voice data is converted into text data by the Google Speech-to-Text API. The input is converted from the user's words into digital format, becoming text data ready to be sent to the server.

[0382] Step 2:

[0383] The terminal sends the converted text data to the server. The transmitted data is text data containing the user's plant cultivation information and emotional state.

[0384] Step 3:

[0385] The server analyzes the received data using information processing tools. This analysis uses sentiment analysis libraries (such as TextBlob and Transformers) to quantify and identify the user's emotional state. The input data is broken down into the user's sentiment score and information about plants.

[0386] Step 4:

[0387] The server searches and extracts relevant plant data from its data storage. Using the plant type entered by the user as a key, it retrieves cultivation information for that plant. This output is the specific cultivation data for the plant stored in the data storage.

[0388] Step 5:

[0389] The server uses a generative artificial intelligence model (such as OpenAI GPT) to generate personalized advice based on analysis results and cultivation data. Prompt sentences are input to the generative AI model, which generates advice tailored to the user's emotions and the plant's condition. This output is text data containing specific cultivation methods and encouraging words.

[0390] Step 6:

[0391] The generated advice is sent from the server to the device, which then presents this advice to the user via voice output (such as Google Text-to-Speech) or visual display. The user receives the advice through the device and cultivates the plants based on its content.

[0392] 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.

[0393] 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.

[0394] 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.

[0395] [Third Embodiment]

[0396] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0397] 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.

[0398] 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).

[0399] 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.

[0400] 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.

[0401] 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).

[0402] 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.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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".

[0408] This invention is a system for providing advice on home gardening and horticulture to a wide range of users, from beginners to advanced gardeners. This system is designed to solve the challenges users face in growing and managing plants. The following describes embodiments of the system.

[0409] The user inputs information about the plant they want to grow, its current cultivation status, and any particular concerns they have, through the terminal. The terminal then formats this input data and sends it to the server.

[0410] The server searches the database based on the input data received from the user. This database contains information such as how to grow plants, required conditions, common problems, and pest control methods.

[0411] After retrieving relevant information from the database, the server utilizes generative artificial intelligence to generate optimal plant care advice. This AI can provide personalized advice tailored to the user's needs, offering specific guidance such as "how to adjust watering and fertilization when tomato leaves turn yellow," "effective countermeasures against specific pests," and "the optimal harvest time."

[0412] The generated advice is sent from the server to the terminal. The user can review the advice through the terminal and follow the instructions to cultivate the plants.

[0413] For example, if a user enters "I want to grow tomatoes," the server retrieves tomato cultivation information from its database and provides advice such as "Place them in a sunny spot" and "Water them in the morning with an appropriate amount." This advice allows the user to efficiently carry out tasks in their home garden.

[0414] Thus, the system of the present invention supports home gardening and horticulture efforts by providing users with specific and practical information to help them grow plants more effectively and successfully.

[0415] The following describes the processing flow.

[0416] Step 1:

[0417] The user uses a device to input information about the type of plant they want to grow and the problems they are currently facing. This includes text input and selecting options in response to a series of questions.

[0418] Step 2:

[0419] The terminal formats the information entered by the user into a standard format and generates a request to send it to the server.

[0420] Step 3:

[0421] The server analyzes the request received from the terminal and extracts information about the plant entered by the user. Based on this information, it queries the database for relevant data.

[0422] Step 4:

[0423] The database searches for the most appropriate plant information and related solutions based on the received query and returns the results to the server.

[0424] Step 5:

[0425] The server provides information retrieved from the database to a generative artificial intelligence system, which then generates user-focused training advice. This advice includes optimized instructions and suggestions based on an analysis of the retrieved data.

[0426] Step 6:

[0427] The server sends the generated advice back to the terminal. At this time, the output is formatted in a way that is easy for the user to understand.

[0428] Step 7:

[0429] The terminal displays advice received from the server on its screen. Users can review the provided instructions and advice and carry out tasks in their home garden.

[0430] (Example 1)

[0431] 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."

[0432] In home gardening and horticulture, there is a need to effectively solve the challenges related to plant cultivation and management faced by a wide range of users, from beginners to advanced gardeners. Specifically, many users are troubled by the inability to obtain timely and accurate advice and information tailored to the condition of their plants.

[0433] 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.

[0434] In this invention, the server includes means for receiving user input information and formatting necessary data about plants, means for retrieving information related to plant cultivation methods from a data storage medium using the formatted data, and means for creating cultivation advice using generative artificial intelligence based on the information obtained from the data storage medium. This makes it possible to quickly provide personalized and appropriate advice to each user.

[0435] A "user" refers to a person who uses this system to obtain information about plant cultivation and management.

[0436] "Input information" refers to the data and concerns about plants that users provide to the system.

[0437] "Data formatting" refers to the process of converting received input information into a format that is easy for the server to process.

[0438] "Data storage media" refers to any information storage device, including databases containing plant cultivation methods and related information.

[0439] "Generative artificial intelligence" refers to an artificial intelligence system that can generate new information based on given data.

[0440] "Cultivation advice" refers to specific guidelines and suggestions for users to effectively grow plants.

[0441] "Communication methods" refer to network-related technologies used to transmit information from a server to a user's terminal.

[0442] The system of this invention is designed to provide advice on home gardening and horticulture. Users first input information about the plants they wish to grow, their current cultivation status, and any concerns they may have via a terminal. This terminal can be a standard computer or smartphone.

[0443] The terminal formats the information entered by the user and sends it to the server using a data communication protocol. The formatted data may be represented in formats such as JSON or XML. The server should ideally consist of a high-performance computer or server system with high data processing capabilities.

[0444] The server searches a data storage medium based on the formatted data sent from the terminal. This data storage medium consists of SQL databases, NoSQL databases, etc., and stores a variety of data related to plant cultivation. The server extracts relevant information from this database and uses generative artificial intelligence to generate specific cultivation advice.

[0445] Generative artificial intelligence, often implemented using machine learning models, provides optimal solutions to user input. For example, in response to the problem "tomato leaves are turning yellow," it can advise on the appropriate watering timing and nutrient supply. The generated advice is then sent back from the server to the terminal via a communication protocol, allowing the user to view it through the application interface.

[0446] For example, if a user enters the prompt "I want to know what to do if my tomato leaves are turning yellow," the server can receive this prompt, search for the necessary data, and use a generative AI model to generate and provide advice such as "Add nitrogen-containing fertilizer and water moderately."

[0447] In this way, this system provides users with accurate and personalized plant cultivation advice, supporting their success in home gardening and horticulture.

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

[0449] Step 1:

[0450] Users input information via the terminal, including the plants they want to grow, their cultivation status, and any particular problems they are concerned about. This input is provided in text format, such as "The tomato leaves have turned yellow." The terminal receives this input and performs data formatting. Specifically, this involves converting uneven text into a standardized format (e.g., JSON or XML). The output is the formatted data.

[0451] Step 2:

[0452] The terminal sends formatted data to the server. Standardized data is used as input, and the output is the completion of the transmission to the server. Specifically, the process involves securely and efficiently sending data to the server using the HTTPS protocol.

[0453] Step 3:

[0454] The server searches the data storage medium based on the formatted data it receives. The input is the data sent to the server, and the output is related plant information. Specifically, it uses SQL queries to search the database and extract information on cultivation methods and solutions related to "yellow tomato leaves."

[0455] Step 4:

[0456] The server generates cultivation advice using a generative AI model based on information obtained from data storage media. The input is information extracted as search results, and the output is personalized cultivation advice. The specific operation includes an advice generation process that applies a machine learning model. This process provides specific guidance, such as "It is necessary to add nitrogen-containing fertilizer."

[0457] Step 5:

[0458] The server sends the generated advice to the terminal. The input is the generated advice, and the output is the completion of its transmission to the terminal. Specifically, this involves the delivery of the advice to the terminal via a transmission protocol.

[0459] Step 6:

[0460] The user reviews the advice sent via their device. The input is the advice displayed on the device, and the output is the user's understanding and actions based on that understanding. Specifically, information is displayed in a pop-up format from the device's application, and the user takes care of the plants accordingly.

[0461] In this way, the process from user input to advice provision is smooth, allowing users to effectively cultivate plants.

[0462] (Application Example 1)

[0463] 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."

[0464] In home gardening and plant cultivation, there is a need for easy implementation of specific plant cultivation methods and problem-solving solutions, regardless of whether the user is a beginner or experienced. However, conventional methods make it difficult to quickly obtain specific and customized advice for plants, hindering efficient cultivation. Furthermore, the lack of sufficient development of technologies that enable intuitive interaction using voice means that user convenience is not improved.

[0465] 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.

[0466] In this invention, the server includes means for receiving user input information and aggregating necessary information related to plants, means for generating guidelines for plant cultivation based on the aggregated information, communication means for providing the generated guidelines to the user, means for analyzing voice input from the user using voice recognition technology, and means for outputting the generated guidelines as voice using voice synthesis technology. This makes it possible to provide intuitive and efficient support for plant cultivation in a home environment.

[0467] "A means of receiving user input information and aggregating necessary information related to plants" refers to a system that receives requests and questions from users and collects and organizes information about plants that correspond to those requests.

[0468] "Means for generating guidelines for plant cultivation" refers to a system that uses collected plant-related information to create specific methods and solutions for promoting plant growth.

[0469] "Communication means for providing generated guidelines to users" refers to digital or analog communication technologies used to convey generated training methods and advice to users.

[0470] "Means of analyzing voice input from a user using speech recognition technology" refers to acoustic analysis technology that can understand the user's utterances and interpret their content as instructions or information.

[0471] "A means of outputting guidelines generated using speech synthesis technology as audio" refers to a technology that converts computer-generated text information into speech and provides it to the user in an easily understandable format.

[0472] The system that implements this invention is in the form of a home assistant robot. The system is configured as follows:

[0473] The system uses speech recognition technology to receive voice input from the user. This technology utilizes the "Google Speech-to-Text" library. This library converts the user's voice into digital data and then performs analysis.

[0474] Next, the server aggregates information about plants from its information storage unit based on keywords extracted from the audio data. This information includes plant cultivation methods, requirements, common problems, and pest control methods.

[0475] Based on aggregated information, a generative artificial intelligence model is used to generate customized training guidelines for the user. An example of this AI model is the "GPT-based" technology, which generates advice tailored to the user's specific needs.

[0476] The generated training guidelines are provided to the user via voice using speech synthesis technology. This technology utilizes "Google Text-to-Speech," which converts the generated text information into natural-sounding speech and delivers it to the user via a robot.

[0477] For example, if a user asks, "What should I do if my cucumber leaves turn yellow?", the system will generate a prompt message such as "cucumber leaves yellow countermeasures" based on this question and send it to the AI ​​model. Possible advice generated might be, "Cucumbers often suffer from a lack of water, so you should water them thoroughly in the morning." This advice is conveyed to the user via voice, promoting effective plant growth.

[0478] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0479] Step 1:

[0480] The user asks questions about the plant they want to grow using voice commands via their device. This voice input is converted into text data by speech recognition technology. The input is the user's voice, and the output is query data in text format. The user's questions, now converted into text data, are then passed on to the next step.

[0481] Step 2:

[0482] The server extracts keywords from the text data. For example, important keywords such as "cucumber," "leaf," and "yellow" are extracted. The input is text data obtained from a speech recognition process, and the output is the keyword set obtained from this analysis. Natural language processing techniques are used for keyword extraction.

[0483] Step 3:

[0484] The server searches its information storage based on a set of keywords and aggregates relevant plant information. The input consists of keywords extracted from text data, and the output is cultivation information for the corresponding plants. Database query techniques are used for information retrieval.

[0485] Step 4:

[0486] Based on aggregated plant information, the server uses a generative AI model to generate individual cultivation guidelines. The input is plant information obtained from the database, and the output is specific cultivation advice delivered to the user. Generative AI is used for the data calculations performed here.

[0487] Step 5:

[0488] The generated training advice is provided to the user as voice output using speech synthesis technology. The input is advice in text format, and the output is advice in voice format. The system uses a speech synthesis engine to generate natural and easy-to-understand speech.

[0489] Step 6:

[0490] The user receives advice via voice through their device and then performs specific plant care. User feedback is also considered at this stage, potentially improving the system in preparation for the next interaction. The user then cultivates the plants based on the information received via voice.

[0491] 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.

[0492] This invention incorporates an emotion engine into a system that provides users with advice on home gardening and horticulture, in order to provide more personalized support that takes user emotions into consideration. The aim of this invention is to give users a better experience in growing and managing plants.

[0493] Users input information about plant care and any problems they are facing through their devices. This input also includes data that reflects the user's emotional state (for example, the emotions conveyed in text messages).

[0494] The terminal formats the user's input data for transmission to the server. This request includes information about the plant, as well as information about the user's emotional state, as measured by the emotion engine.

[0495] The server analyzes the received input data and searches the database to collect relevant plant information. Simultaneously, it utilizes an emotion engine to analyze the user's emotions, thereby understanding their psychological state.

[0496] The server uses generative artificial intelligence to generate cultivation advice based on acquired plant information and the user's emotional state. This advice includes encouraging messages and specific cultivation methods tailored to the user's psychological state. For example, if the user is feeling stressed, it will suggest simple and quick cultivation and management methods.

[0497] The generated advice is sent from the server to the terminal. The terminal displays the advice in a format that is easy for the user to understand, and the user can refer to it while growing the plants.

[0498] For example, if a user inputs "I'm feeling stressed because my tomatoes haven't been growing well lately," the emotion engine recognizes this emotion and transmits it to the server. Based on this information, the server generates advice that includes simple steps to improve the situation, along with encouraging words such as, "Start with a simple improvement like watering in the morning, and believe in good results."

[0499] Thus, the embodiment of the present invention aims to make gardening and horticultural work more positive by providing effective and emotional support while taking into account the user's emotional state.

[0500] The following describes the processing flow.

[0501] Step 1:

[0502] Users use their devices to input information about the type of plant they want to grow and any problems they are currently experiencing. They also input emotional statements, such as "I'm worried because my plants have been growing slowly lately."

[0503] Step 2:

[0504] The device analyzes the input data from the user and generates a request to send it to the server. This request includes data such as the user's emotions, along with plant information.

[0505] Step 3:

[0506] The server analyzes the received request and searches the database for data about the plant specified by the user. At the same time, it uses an emotion engine to analyze the user's emotional state and identify emotions such as "anxiety" or "impatience."

[0507] Step 4:

[0508] The server combines training information retrieved from the database with analysis results from the emotion engine, and uses generative artificial intelligence to generate advice for the user. This advice includes specific training methods and encouraging messages that take the user's emotions into consideration.

[0509] Step 5:

[0510] The server formats the generated advice and sends it to the terminal. This advice is formatted to be easy to understand and emotionally responsive.

[0511] Step 6:

[0512] The terminal receives advice from the server and displays it on the screen for the user. The user can then review the displayed advice and follow it to cultivate and manage their plants.

[0513] Step 7:

[0514] Users can follow the advice as needed and enter the results back into their device to receive further support. Based on this feedback, the server can continue to adjust the advice.

[0515] (Example 2)

[0516] 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."

[0517] Conventional plant cultivation support systems provide uniform advice without considering the user's emotions or psychological state, resulting in a lack of support tailored to individual user needs. Furthermore, while empathetic advice is particularly needed for users experiencing stress or anxiety, current systems struggle to provide this.

[0518] 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.

[0519] In this invention, the server includes means for receiving user input data and analyzing the user's emotional state using an emotion analysis engine, means for aggregating the analyzed emotional state and necessary information about the plant, and means for generating personalized advice on plant cultivation based on the aggregated information and emotional state. This makes it possible to provide more individualized and appropriate cultivation advice that takes the user's emotions into consideration.

[0520] "User input data" refers to information that users provide to the system via their devices regarding plant cultivation and management.

[0521] An "emotion analysis engine" refers to a software component that analyzes a user's emotions and psychological state based on their text data to identify their feelings.

[0522] "Necessary information about plants" refers to data required for the cultivation and management of specific plants, and includes vegetation information obtained from databases.

[0523] "Personalized advice" refers to customized support messages and instructions tailored to the user's specific situation or emotional state.

[0524] "Communication methods" refer to the infrastructure and protocols used to send advice generated by a server to a user's terminal and provide information to the user.

[0525] "Generative artificial intelligence" refers to machine learning models that automatically generate information based on pre-set prompts.

[0526] This invention provides a system to solve problems and anxieties that users face in home gardening and horticultural activities. Specific embodiments are described below.

[0527] Users input information about plant care and any problems they are experiencing using the device. The device has an input interface that accepts text and reads the user's emotional state. For example, users can input specific details such as, "I'm worried because my tomato leaves have recently turned yellow."

[0528] The terminal formats the input data and sends it to the server. The server analyzes the received data, using an emotion analysis engine to analyze the user's emotions. This emotion analysis engine has the ability to extract psychological states such as anxiety, stress, and joy from the user's text messages.

[0529] The server collects necessary information about the plants from the database based on the analysis results. This lays the groundwork for providing appropriate advice tailored to the plant's condition. Next, generative artificial intelligence is used to generate personalized advice based on the emotional state and plant information. This AI model uses pre-set prompts to derive the best advice. An example of a prompt might be, "If the user is worried about growing tomatoes, please suggest specific improvements along with a message of encouragement."

[0530] The generated advice is sent from the server to the terminal. The terminal displays it in a format that is easy for the user to understand. The user can then use this advice to implement specific cultivation methods and improvement measures. This system provides users with more user-friendly and effective gardening support.

[0531] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0532] Step 1:

[0533] The user uses the terminal's input interface to enter information about the plant and current problems in text format. This input data may include information such as, "I'm worried because the tomato leaves are turning yellow." The terminal then prepares this input data for direct transmission to the server.

[0534] Step 2:

[0535] The terminal formats the received text data and generates a request to send it to the server. This data formatting process transforms the user's input into a format suitable for database queries and sentiment analysis. The formatted data, which includes elements of plant and sentiment information, is added to the transmission list.

[0536] Step 3:

[0537] The server receives requests sent from the terminal. Based on the received data, the server uses an emotion analysis engine to analyze the user's psychological state. As part of the data processing, natural language processing technology is used to extract emotional information from the text. The output of this analysis is a tag or score that indicates the user's feelings.

[0538] Step 4:

[0539] The server searches and extracts relevant plant cultivation information from the database, along with the analyzed sentiment information. The database search uses a database query language to retrieve information that matches the plant type and condition. The output of this step is a list of relevant cultivation information.

[0540] Step 5:

[0541] The server uses a generative AI model to create personalized growing advice based on acquired plant information and emotion analysis results. During this process, prompts are used to instruct the AI ​​to consider the user's emotions and the plant's growing status, generating appropriate advice. The output is a customized message provided to the user.

[0542] Step 6:

[0543] The server sends the generated advice to the terminal. The terminal displays the received advice on its screen in a format that is easy for the user to understand. Specifically, this involves updating the UI to display a text message on the terminal's digital screen. The output of this step is visually appealing support information for the user.

[0544] (Application Example 2)

[0545] 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."

[0546] Users who engage in home gardening or horticulture face challenges in receiving appropriate advice tailored to their emotional state, in addition to information and methods for growing plants. Furthermore, there is a lack of support that addresses the stress and anxiety users experience, creating a need for systems that make the plant-growing process more enjoyable and stress-free.

[0547] 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.

[0548] In this invention, the server includes means for receiving user input data and aggregating necessary information about the plant, means for processing information to analyze the user's emotional state, and means for generating personalized cultivation advice based on the emotional state using generative artificial intelligence. This enables individualized cultivation support that takes the user's emotional needs into consideration.

[0549] A "user" refers to someone who inputs information about plant cultivation in their home garden or horticulture and receives advice from the system.

[0550] "Input data" includes information about plants and their emotional state provided by the user.

[0551] "Plants" refers to living organisms cultivated in home gardens or horticulture, and are the subjects for which the system provides information.

[0552] "Information processing means" refers to a device or software that analyzes user input data, aggregates necessary information, and executes a process for analyzing the emotional state.

[0553] "Emotional state" refers to the emotional state expressed by the user through the input data.

[0554] "Generative artificial intelligence" refers to artificial intelligence technology that automatically generates advice on plant cultivation based on user input data.

[0555] "Personalized advice" refers to information that includes cultivation methods and support messages optimized according to the user's individual emotional state and the condition of their plants.

[0556] "Communication means" refers to the medium or technology used to present the generated advice to the user, either audibly or visually.

[0557] "Data storage" refers to a database or similar system that stores existing information about plants and allows for searching and retrieval as needed.

[0558] A system implementing this invention consists of three main components: a user, a terminal, and a server.

[0559] First, the user inputs information about their home garden or horticulture, as well as their emotional state, into a device. This device can be a smartphone, tablet, or a robot equipped with voice recognition. In the case of voice input, voice recognition software (e.g., Google Speech-to-Text API) converts the voice data into text.

[0560] Next, the terminal sends the converted text data to the server. The server first analyzes the received data using information processing tools to identify the user's emotional state. This process utilizes sentiment analysis libraries (e.g., TextBlob and Transformers). Along with the analysis results, the server retrieves and aggregates information about plants from its data storage.

[0561] Subsequently, the server uses generative artificial intelligence (e.g., OpenAI GPT API) to generate personalized parenting advice based on the acquired information and analyzed emotional state. This advice is presented in a user-friendly format and includes encouragement and specific suggestions for improvement.

[0562] The generated advice is sent to the user's device via a communication method. The device then presents this advice to the user either audibly (e.g., Google Text-to-Speech) or visually. After the user reviews this advice, they can then cultivate their plants accordingly.

[0563] For example, if a user enters "I'm worried because my tomatoes are wilting due to the recent weather," the server will analyze the results and generate specific advice, including emotional support. For instance, it might advise, "Pay attention to temperature fluctuations. This week, water them thoroughly in the morning and check the temperature. You'll have wonderful tomatoes."

[0564] Examples of prompts for a generative AI model:

[0565] "The user's emotion is worry, and the plant is a tomato. Please provide the user with the best growing method and words of encouragement."

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

[0567] Step 1:

[0568] The user inputs information and emotional states about the plant into the device. In the case of voice input, the voice data is converted into text data by the Google Speech-to-Text API. The input is converted from the user's words into digital format, becoming text data ready to be sent to the server.

[0569] Step 2:

[0570] The terminal sends the converted text data to the server. The transmitted data is text data containing the user's plant cultivation information and emotional state.

[0571] Step 3:

[0572] The server analyzes the received data using information processing tools. This analysis uses sentiment analysis libraries (such as TextBlob and Transformers) to quantify and identify the user's emotional state. The input data is broken down into the user's sentiment score and information about plants.

[0573] Step 4:

[0574] The server searches and extracts relevant plant data from its data storage. Using the plant type entered by the user as a key, it retrieves cultivation information for that plant. This output is the specific cultivation data for the plant stored in the data storage.

[0575] Step 5:

[0576] The server uses a generative artificial intelligence model (such as OpenAI GPT) to generate personalized advice based on analysis results and cultivation data. Prompt sentences are input to the generative AI model, which generates advice tailored to the user's emotions and the plant's condition. This output is text data containing specific cultivation methods and encouraging words.

[0577] Step 6:

[0578] The generated advice is sent from the server to the device, which then presents this advice to the user via voice output (such as Google Text-to-Speech) or visual display. The user receives the advice through the device and cultivates the plants based on its content.

[0579] 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.

[0580] 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.

[0581] 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.

[0582] [Fourth Embodiment]

[0583] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0584] 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.

[0585] 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).

[0586] 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.

[0587] 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.

[0588] 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).

[0589] 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.

[0590] 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.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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".

[0596] This invention is a system for providing advice on home gardening and horticulture to a wide range of users, from beginners to advanced gardeners. This system is designed to solve the challenges users face in growing and managing plants. The following describes embodiments of the system.

[0597] The user inputs information about the plant they want to grow, its current cultivation status, and any particular concerns they have, through the terminal. The terminal then formats this input data and sends it to the server.

[0598] The server searches the database based on the input data received from the user. This database contains information such as how to grow plants, required conditions, common problems, and pest control methods.

[0599] After retrieving relevant information from the database, the server utilizes generative artificial intelligence to generate optimal plant care advice. This AI can provide personalized advice tailored to the user's needs, offering specific guidance such as "how to adjust watering and fertilization when tomato leaves turn yellow," "effective countermeasures against specific pests," and "the optimal harvest time."

[0600] The generated advice is sent from the server to the terminal. The user can review the advice through the terminal and follow the instructions to cultivate the plants.

[0601] For example, if a user enters "I want to grow tomatoes," the server retrieves tomato cultivation information from its database and provides advice such as "Place them in a sunny spot" and "Water them in the morning with an appropriate amount." This advice allows the user to efficiently carry out tasks in their home garden.

[0602] Thus, the system of the present invention supports home gardening and horticulture efforts by providing users with specific and practical information to help them grow plants more effectively and successfully.

[0603] The following describes the processing flow.

[0604] Step 1:

[0605] The user uses a device to input information about the type of plant they want to grow and the problems they are currently facing. This includes text input and selecting options in response to a series of questions.

[0606] Step 2:

[0607] The terminal formats the information entered by the user into a standard format and generates a request to send it to the server.

[0608] Step 3:

[0609] The server analyzes the request received from the terminal and extracts information about the plant entered by the user. Based on this information, it queries the database for relevant data.

[0610] Step 4:

[0611] The database searches for the most appropriate plant information and related solutions based on the received query and returns the results to the server.

[0612] Step 5:

[0613] The server provides information retrieved from the database to a generative artificial intelligence system, which then generates user-focused training advice. This advice includes optimized instructions and suggestions based on an analysis of the retrieved data.

[0614] Step 6:

[0615] The server sends the generated advice back to the terminal. At this time, the output is formatted in a way that is easy for the user to understand.

[0616] Step 7:

[0617] The terminal displays advice received from the server on its screen. Users can review the provided instructions and advice and carry out tasks in their home garden.

[0618] (Example 1)

[0619] 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".

[0620] In home gardening and horticulture, there is a need to effectively solve the challenges related to plant cultivation and management faced by a wide range of users, from beginners to advanced gardeners. Specifically, many users are troubled by the inability to obtain timely and accurate advice and information tailored to the condition of their plants.

[0621] 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.

[0622] In this invention, the server includes means for receiving user input information and formatting necessary data about plants, means for retrieving information related to plant cultivation methods from a data storage medium using the formatted data, and means for creating cultivation advice using generative artificial intelligence based on the information obtained from the data storage medium. This makes it possible to quickly provide personalized and appropriate advice to each user.

[0623] A "user" refers to a person who uses this system to obtain information about plant cultivation and management.

[0624] "Input information" refers to the data and concerns about plants that users provide to the system.

[0625] "Data formatting" refers to the process of converting received input information into a format that is easy for the server to process.

[0626] "Data storage media" refers to any information storage device, including databases containing plant cultivation methods and related information.

[0627] "Generative artificial intelligence" refers to an artificial intelligence system that can generate new information based on given data.

[0628] "Cultivation advice" refers to specific guidelines and suggestions for users to effectively grow plants.

[0629] "Communication methods" refer to network-related technologies used to transmit information from a server to a user's terminal.

[0630] The system of this invention is designed to provide advice on home gardening and horticulture. Users first input information about the plants they wish to grow, their current cultivation status, and any concerns they may have via a terminal. This terminal can be a standard computer or smartphone.

[0631] The terminal formats the information entered by the user and sends it to the server using a data communication protocol. The formatted data may be represented in formats such as JSON or XML. The server should ideally consist of a high-performance computer or server system with high data processing capabilities.

[0632] The server searches a data storage medium based on the formatted data sent from the terminal. This data storage medium consists of SQL databases, NoSQL databases, etc., and stores a variety of data related to plant cultivation. The server extracts relevant information from this database and uses generative artificial intelligence to generate specific cultivation advice.

[0633] Generative artificial intelligence, often implemented using machine learning models, provides optimal solutions to user input. For example, in response to the problem "tomato leaves are turning yellow," it can advise on the appropriate watering timing and nutrient supply. The generated advice is then sent back from the server to the terminal via a communication protocol, allowing the user to view it through the application interface.

[0634] For example, if a user enters the prompt "I want to know what to do if my tomato leaves are turning yellow," the server can receive this prompt, search for the necessary data, and use a generative AI model to generate and provide advice such as "Add nitrogen-containing fertilizer and water moderately."

[0635] In this way, this system provides users with accurate and personalized plant cultivation advice, supporting their success in home gardening and horticulture.

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

[0637] Step 1:

[0638] Users input information via the terminal, including the plants they want to grow, their cultivation status, and any particular problems they are concerned about. This input is provided in text format, such as "The tomato leaves have turned yellow." The terminal receives this input and performs data formatting. Specifically, this involves converting uneven text into a standardized format (e.g., JSON or XML). The output is the formatted data.

[0639] Step 2:

[0640] The terminal sends formatted data to the server. Standardized data is used as input, and the output is the completion of the transmission to the server. Specifically, the process involves securely and efficiently sending data to the server using the HTTPS protocol.

[0641] Step 3:

[0642] The server searches the data storage medium based on the formatted data it receives. The input is the data sent to the server, and the output is related plant information. Specifically, it uses SQL queries to search the database and extract information on cultivation methods and solutions related to "yellow tomato leaves."

[0643] Step 4:

[0644] The server generates cultivation advice using a generative AI model based on information obtained from data storage media. The input is information extracted as search results, and the output is personalized cultivation advice. The specific operation includes an advice generation process that applies a machine learning model. This process provides specific guidance, such as "It is necessary to add nitrogen-containing fertilizer."

[0645] Step 5:

[0646] The server sends the generated advice to the terminal. The input is the generated advice, and the output is the completion of its transmission to the terminal. Specifically, this involves the delivery of the advice to the terminal via a transmission protocol.

[0647] Step 6:

[0648] The user reviews the advice sent via their device. The input is the advice displayed on the device, and the output is the user's understanding and actions based on that understanding. Specifically, information is displayed in a pop-up format from the device's application, and the user takes care of the plants accordingly.

[0649] In this way, the process from user input to advice provision is smooth, allowing users to effectively cultivate plants.

[0650] (Application Example 1)

[0651] 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".

[0652] In home gardening and plant cultivation, there is a need for easy implementation of specific plant cultivation methods and problem-solving solutions, regardless of whether the user is a beginner or experienced. However, conventional methods make it difficult to quickly obtain specific and customized advice for plants, hindering efficient cultivation. Furthermore, the lack of sufficient development of technologies that enable intuitive interaction using voice means that user convenience is not improved.

[0653] 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.

[0654] In this invention, the server includes means for receiving user input information and aggregating necessary information related to plants, means for generating guidelines for plant cultivation based on the aggregated information, communication means for providing the generated guidelines to the user, means for analyzing voice input from the user using voice recognition technology, and means for outputting the generated guidelines as voice using voice synthesis technology. This makes it possible to provide intuitive and efficient support for plant cultivation in a home environment.

[0655] "A means of receiving user input information and aggregating necessary information related to plants" refers to a system that receives requests and questions from users and collects and organizes information about plants that correspond to those requests.

[0656] "Means for generating guidelines for plant cultivation" refers to a system that uses collected plant-related information to create specific methods and solutions for promoting plant growth.

[0657] "Communication means for providing generated guidelines to users" refers to digital or analog communication technologies used to convey generated training methods and advice to users.

[0658] "Means of analyzing voice input from a user using speech recognition technology" refers to acoustic analysis technology that can understand the user's utterances and interpret their content as instructions or information.

[0659] "A means of outputting guidelines generated using speech synthesis technology as audio" refers to a technology that converts computer-generated text information into speech and provides it to the user in an easily understandable format.

[0660] The system that implements this invention is in the form of a home assistant robot. The system is configured as follows:

[0661] The system uses speech recognition technology to receive voice input from the user. This technology utilizes the "Google Speech-to-Text" library. This library converts the user's voice into digital data and then performs analysis.

[0662] Next, the server aggregates information about plants from its information storage unit based on keywords extracted from the audio data. This information includes plant cultivation methods, requirements, common problems, and pest control methods.

[0663] Based on aggregated information, a generative artificial intelligence model is used to generate customized training guidelines for the user. An example of this AI model is the "GPT-based" technology, which generates advice tailored to the user's specific needs.

[0664] The generated training guidelines are provided to the user via voice using speech synthesis technology. This technology utilizes "Google Text-to-Speech," which converts the generated text information into natural-sounding speech and delivers it to the user via a robot.

[0665] For example, if a user asks, "What should I do if my cucumber leaves turn yellow?", the system will generate a prompt message such as "cucumber leaves yellow countermeasures" based on this question and send it to the AI ​​model. Possible advice generated might be, "Cucumbers often suffer from a lack of water, so you should water them thoroughly in the morning." This advice is conveyed to the user via voice, promoting effective plant growth.

[0666] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0667] Step 1:

[0668] The user asks questions about the plant they want to grow using voice commands via their device. This voice input is converted into text data by speech recognition technology. The input is the user's voice, and the output is query data in text format. The user's questions, now converted into text data, are then passed on to the next step.

[0669] Step 2:

[0670] The server extracts keywords from the text data. For example, important keywords such as "cucumber," "leaf," and "yellow" are extracted. The input is text data obtained from a speech recognition process, and the output is the keyword set obtained from this analysis. Natural language processing techniques are used for keyword extraction.

[0671] Step 3:

[0672] The server searches its information storage based on a set of keywords and aggregates relevant plant information. The input consists of keywords extracted from text data, and the output is cultivation information for the corresponding plants. Database query techniques are used for information retrieval.

[0673] Step 4:

[0674] Based on aggregated plant information, the server uses a generative AI model to generate individual cultivation guidelines. The input is plant information obtained from the database, and the output is specific cultivation advice delivered to the user. Generative AI is used for the data calculations performed here.

[0675] Step 5:

[0676] The generated training advice is provided to the user as voice output using speech synthesis technology. The input is advice in text format, and the output is advice in voice format. The system uses a speech synthesis engine to generate natural and easy-to-understand speech.

[0677] Step 6:

[0678] The user receives advice via voice through their device and then performs specific plant care. User feedback is also considered at this stage, potentially improving the system in preparation for the next interaction. The user then cultivates the plants based on the information received via voice.

[0679] 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.

[0680] This invention incorporates an emotion engine into a system that provides users with advice on home gardening and horticulture, in order to provide more personalized support that takes user emotions into consideration. The aim of this invention is to give users a better experience in growing and managing plants.

[0681] Users input information about plant care and any problems they are facing through their devices. This input also includes data that reflects the user's emotional state (for example, the emotions conveyed in text messages).

[0682] The terminal formats the user's input data for transmission to the server. This request includes information about the plant, as well as information about the user's emotional state, as measured by the emotion engine.

[0683] The server analyzes the received input data and searches the database to collect relevant plant information. Simultaneously, it utilizes an emotion engine to analyze the user's emotions, thereby understanding their psychological state.

[0684] The server uses generative artificial intelligence to generate cultivation advice based on acquired plant information and the user's emotional state. This advice includes encouraging messages and specific cultivation methods tailored to the user's psychological state. For example, if the user is feeling stressed, it will suggest simple and quick cultivation and management methods.

[0685] The generated advice is sent from the server to the terminal. The terminal displays the advice in a format that is easy for the user to understand, and the user can refer to it while growing the plants.

[0686] For example, if a user inputs "I'm feeling stressed because my tomatoes haven't been growing well lately," the emotion engine recognizes this emotion and transmits it to the server. Based on this information, the server generates advice that includes simple steps to improve the situation, along with encouraging words such as, "Start with a simple improvement like watering in the morning, and believe in good results."

[0687] Thus, the embodiment of the present invention aims to make gardening and horticultural work more positive by providing effective and emotional support while taking into account the user's emotional state.

[0688] The following describes the processing flow.

[0689] Step 1:

[0690] Users use their devices to input information about the type of plant they want to grow and any problems they are currently experiencing. They also input emotional statements, such as "I'm worried because my plants have been growing slowly lately."

[0691] Step 2:

[0692] The device analyzes the input data from the user and generates a request to send it to the server. This request includes data such as the user's emotions, along with plant information.

[0693] Step 3:

[0694] The server analyzes the received request and searches the database for data about the plant specified by the user. At the same time, it uses an emotion engine to analyze the user's emotional state and identify emotions such as "anxiety" or "impatience."

[0695] Step 4:

[0696] The server combines training information retrieved from the database with analysis results from the emotion engine, and uses generative artificial intelligence to generate advice for the user. This advice includes specific training methods and encouraging messages that take the user's emotions into consideration.

[0697] Step 5:

[0698] The server formats the generated advice and sends it to the terminal. This advice is formatted to be easy to understand and emotionally responsive.

[0699] Step 6:

[0700] The terminal receives advice from the server and displays it on the screen for the user. The user can then review the displayed advice and follow it to cultivate and manage their plants.

[0701] Step 7:

[0702] Users can follow the advice as needed and enter the results back into their device to receive further support. Based on this feedback, the server can continue to adjust the advice.

[0703] (Example 2)

[0704] 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".

[0705] Conventional plant cultivation support systems provide uniform advice without considering the user's emotions or psychological state, resulting in a lack of support tailored to individual user needs. Furthermore, while empathetic advice is particularly needed for users experiencing stress or anxiety, current systems struggle to provide this.

[0706] 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.

[0707] In this invention, the server includes means for receiving user input data and analyzing the user's emotional state using an emotion analysis engine, means for aggregating the analyzed emotional state and necessary information about the plant, and means for generating personalized advice on plant cultivation based on the aggregated information and emotional state. This makes it possible to provide more individualized and appropriate cultivation advice that takes the user's emotions into consideration.

[0708] "User input data" refers to information that users provide to the system via their devices regarding plant cultivation and management.

[0709] An "emotion analysis engine" refers to a software component that analyzes a user's emotions and psychological state based on their text data to identify their feelings.

[0710] "Necessary information about plants" refers to data required for the cultivation and management of specific plants, and includes vegetation information obtained from databases.

[0711] "Personalized advice" refers to customized support messages and instructions tailored to the user's specific situation or emotional state.

[0712] "Communication methods" refer to the infrastructure and protocols used to send advice generated by a server to a user's terminal and provide information to the user.

[0713] "Generative artificial intelligence" refers to machine learning models that automatically generate information based on pre-set prompts.

[0714] This invention provides a system to solve problems and anxieties that users face in home gardening and horticultural activities. Specific embodiments are described below.

[0715] Users input information about plant care and any problems they are experiencing using the device. The device has an input interface that accepts text and reads the user's emotional state. For example, users can input specific details such as, "I'm worried because my tomato leaves have recently turned yellow."

[0716] The terminal formats the input data and sends it to the server. The server analyzes the received data, using an emotion analysis engine to analyze the user's emotions. This emotion analysis engine has the ability to extract psychological states such as anxiety, stress, and joy from the user's text messages.

[0717] The server collects necessary information about the plants from the database based on the analysis results. This lays the groundwork for providing appropriate advice tailored to the plant's condition. Next, generative artificial intelligence is used to generate personalized advice based on the emotional state and plant information. This AI model uses pre-set prompts to derive the best advice. An example of a prompt might be, "If the user is worried about growing tomatoes, please suggest specific improvements along with a message of encouragement."

[0718] The generated advice is sent from the server to the terminal. The terminal displays it in a format that is easy for the user to understand. The user can then use this advice to implement specific cultivation methods and improvement measures. This system provides users with more user-friendly and effective gardening support.

[0719] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0720] Step 1:

[0721] The user uses the terminal's input interface to enter information about the plant and current problems in text format. This input data may include information such as, "I'm worried because the tomato leaves are turning yellow." The terminal then prepares this input data for direct transmission to the server.

[0722] Step 2:

[0723] The terminal formats the received text data and generates a request to send it to the server. This data formatting process transforms the user's input into a format suitable for database queries and sentiment analysis. The formatted data, which includes elements of plant and sentiment information, is added to the transmission list.

[0724] Step 3:

[0725] The server receives requests sent from the terminal. Based on the received data, the server uses an emotion analysis engine to analyze the user's psychological state. As part of the data processing, natural language processing technology is used to extract emotional information from the text. The output of this analysis is a tag or score that indicates the user's feelings.

[0726] Step 4:

[0727] The server searches and extracts relevant plant cultivation information from the database, along with the analyzed sentiment information. The database search uses a database query language to retrieve information that matches the plant type and condition. The output of this step is a list of relevant cultivation information.

[0728] Step 5:

[0729] The server uses a generative AI model to create personalized growing advice based on acquired plant information and emotion analysis results. During this process, prompts are used to instruct the AI ​​to consider the user's emotions and the plant's growing status, generating appropriate advice. The output is a customized message provided to the user.

[0730] Step 6:

[0731] The server sends the generated advice to the terminal. The terminal displays the received advice on its screen in a format that is easy for the user to understand. Specifically, this involves updating the UI to display a text message on the terminal's digital screen. The output of this step is visually appealing support information for the user.

[0732] (Application Example 2)

[0733] 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".

[0734] Users who engage in home gardening or horticulture face challenges in receiving appropriate advice tailored to their emotional state, in addition to information and methods for growing plants. Furthermore, there is a lack of support that addresses the stress and anxiety users experience, creating a need for systems that make the plant-growing process more enjoyable and stress-free.

[0735] 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.

[0736] In this invention, the server includes means for receiving user input data and aggregating necessary information about the plant, means for processing information to analyze the user's emotional state, and means for generating personalized cultivation advice based on the emotional state using generative artificial intelligence. This enables individualized cultivation support that takes the user's emotional needs into consideration.

[0737] A "user" refers to someone who inputs information about plant cultivation in their home garden or horticulture and receives advice from the system.

[0738] "Input data" includes information about plants and their emotional state provided by the user.

[0739] "Plants" refers to living organisms cultivated in home gardens or horticulture, and are the subjects for which the system provides information.

[0740] "Information processing means" refers to a device or software that analyzes user input data, aggregates necessary information, and executes a process for analyzing the emotional state.

[0741] "Emotional state" refers to the emotional state expressed by the user through the input data.

[0742] "Generative artificial intelligence" refers to artificial intelligence technology that automatically generates advice on plant cultivation based on user input data.

[0743] "Personalized advice" refers to information that includes cultivation methods and support messages optimized according to the user's individual emotional state and the condition of their plants.

[0744] "Communication means" refers to the medium or technology used to present the generated advice to the user, either audibly or visually.

[0745] "Data storage" refers to a database or similar system that stores existing information about plants and allows for searching and retrieval as needed.

[0746] A system implementing this invention consists of three main components: a user, a terminal, and a server.

[0747] First, the user inputs information about their home garden or horticulture, as well as their emotional state, into a device. This device can be a smartphone, tablet, or a robot equipped with voice recognition. In the case of voice input, voice recognition software (e.g., Google Speech-to-Text API) converts the voice data into text.

[0748] Next, the terminal sends the converted text data to the server. The server first analyzes the received data using information processing tools to identify the user's emotional state. This process utilizes sentiment analysis libraries (e.g., TextBlob and Transformers). Along with the analysis results, the server retrieves and aggregates information about plants from its data storage.

[0749] Subsequently, the server uses generative artificial intelligence (e.g., OpenAI GPT API) to generate personalized parenting advice based on the acquired information and analyzed emotional state. This advice is presented in a user-friendly format and includes encouragement and specific suggestions for improvement.

[0750] The generated advice is sent to the user's device via a communication method. The device then presents this advice to the user either audibly (e.g., Google Text-to-Speech) or visually. After the user reviews this advice, they can then cultivate their plants accordingly.

[0751] For example, if a user enters "I'm worried because my tomatoes are wilting due to the recent weather," the server will analyze the results and generate specific advice, including emotional support. For instance, it might advise, "Pay attention to temperature fluctuations. This week, water them thoroughly in the morning and check the temperature. You'll have wonderful tomatoes."

[0752] Examples of prompts for a generative AI model:

[0753] "The user's emotion is worry, and the plant is a tomato. Please provide the user with the best growing method and words of encouragement."

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

[0755] Step 1:

[0756] The user inputs information and emotional states about the plant into the device. In the case of voice input, the voice data is converted into text data by the Google Speech-to-Text API. The input is converted from the user's words into digital format, becoming text data ready to be sent to the server.

[0757] Step 2:

[0758] The terminal sends the converted text data to the server. The transmitted data is text data containing the user's plant cultivation information and emotional state.

[0759] Step 3:

[0760] The server analyzes the received data using information processing tools. This analysis uses sentiment analysis libraries (such as TextBlob and Transformers) to quantify and identify the user's emotional state. The input data is broken down into the user's sentiment score and information about plants.

[0761] Step 4:

[0762] The server searches and extracts relevant plant data from its data storage. Using the plant type entered by the user as a key, it retrieves cultivation information for that plant. This output is the specific cultivation data for the plant stored in the data storage.

[0763] Step 5:

[0764] The server uses a generative artificial intelligence model (such as OpenAI GPT) to generate personalized advice based on analysis results and cultivation data. Prompt sentences are input to the generative AI model, which generates advice tailored to the user's emotions and the plant's condition. This output is text data containing specific cultivation methods and encouraging words.

[0765] Step 6:

[0766] The generated advice is sent from the server to the device, which then presents this advice to the user via voice output (such as Google Text-to-Speech) or visual display. The user receives the advice through the device and cultivates the plants based on its content.

[0767] 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.

[0768] 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.

[0769] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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."

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0788] The following is further disclosed regarding the embodiments described above.

[0789] (Claim 1)

[0790] A means of receiving user input data and aggregating necessary information about plants,

[0791] A means of generating advice on plant cultivation based on aggregated information,

[0792] A means of communication for providing the generated advice to the user,

[0793] A system that includes this.

[0794] (Claim 2)

[0795] The system according to claim 1, further comprising means for searching and extracting relevant plant information from a database in response to user input.

[0796] (Claim 3)

[0797] The system according to claim 1, comprising means for optimizing training advice using generative artificial intelligence based on information obtained from a database.

[0798] "Example 1"

[0799] (Claim 1)

[0800] A means of receiving user input information and formatting the necessary data about plants,

[0801] A means for retrieving information related to plant cultivation methods from a data storage medium using formatted data,

[0802] A method for creating training advice using generative artificial intelligence based on information obtained from a data storage medium,

[0803] A means of communication for providing the created advice to the user,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, further comprising means for extracting relevant plant information from a data storage medium in response to user input.

[0807] (Claim 3)

[0808] The system according to claim 1, comprising means for personalizing training advice using generative artificial intelligence based on information obtained from a data storage medium.

[0809] "Application Example 1"

[0810] (Claim 1)

[0811] A means of receiving user input information and aggregating necessary information related to plants,

[0812] A means of generating guidelines for plant cultivation based on aggregated information,

[0813] A means of communication for providing the generated guidelines to the user,

[0814] A means of analyzing voice input from the user using speech recognition technology,

[0815] A means for outputting guidelines generated using speech synthesis technology as audio,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, further comprising means for searching and extracting relevant plant information from an information storage unit in response to user input.

[0819] (Claim 3)

[0820] The system according to claim 1, comprising means for optimizing training guidelines using generative artificial intelligence based on information acquired from an information storage unit.

[0821] "Example 2 of combining an emotion engine"

[0822] (Claim 1)

[0823] A means of receiving user input data and analyzing the user's emotional state using an emotion analysis engine,

[0824] A means of aggregating the analyzed emotional state and necessary information about the plant,

[0825] A means of generating personalized advice on plant cultivation based on aggregated information and emotional states,

[0826] A means of communication for providing generated advice in a format that responds to the user's emotions,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, further comprising means for searching and extracting relevant plant information from a database in response to user input and integrating it together with emotion analysis results.

[0830] (Claim 3)

[0831] The system according to claim 1, comprising means for optimizing training advice using generative artificial intelligence based on information obtained from a database and emotion analysis results, and for creating advice that includes content tailored to the user's psychological state.

[0832] "Application example 2 when combining with an emotional engine"

[0833] (Claim 1)

[0834] A means of receiving user input data and aggregating necessary information about plants,

[0835] An information processing means for analyzing the user's emotional state based on aggregated information,

[0836] A means for generating personalized advice on plant cultivation using generative artificial intelligence based on an analyzed emotional state,

[0837] A communication means that uses a medium to provide the generated advice to the user through audio output or visual display,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, further comprising means for searching and extracting relevant plant information from data storage in response to user input.

[0841] (Claim 3)

[0842] The system according to claim 1, comprising means for optimizing training advice using generative artificial intelligence based on information obtained from data storage and the emotional state of the user. [Explanation of Symbols]

[0843] 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 aggregating necessary information related to plants, A means of generating guidelines for plant cultivation based on aggregated information, A means of communication for providing the generated guidelines to the user, A means of analyzing voice input from the user using speech recognition technology, A means for outputting guidelines generated using speech synthesis technology as audio, A system that includes this.

2. The system according to claim 1, further comprising means for searching and extracting relevant plant information from an information storage unit in response to user input.

3. The system according to claim 1, comprising means for optimizing training guidelines using generative artificial intelligence based on information acquired from an information storage unit.

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