System

Abstract

A system is provided.SOLUTION: A system for converting underutilized roof space into a sustainable green-rich ecosystem, comprising: means for receiving information from a user regarding roof location, area, budget, and preferences; means for storing the received information in a database; means comprising a generative AI model for generating optimal green roof designs based on the stored information; and means for providing the generated designs to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Due to a lack of green spaces in urban areas, residents have limited opportunities to interact with nature. This problem is causing environmental problems such as urban heat islanding and a decline in biodiversity. It is also a cause of a decline in residents' social and psychological well-being. Furthermore, the rooftop space of many buildings remains unused, leading to resource waste. Therefore, there is a need to find ways to make effective use of unused rooftop space and balance environmental protection with social well-being. [Means for solving the problem]

[0005] A system for converting unused rooftop space in urban areas into sustainable green ecosystems is provided. The system includes means for receiving information from a user regarding the location, area, budget, and preferences of the rooftop and storing this information in a database. The system also includes means for using a generative AI model to generate optimal rooftop greening designs based on the stored information. The generative AI model receives the location information, rooftop area, budget, and preferences as input, and simulates optimal layouts, plant species, and eco-friendly materials. The system also includes means for providing the generated designs to users and providing installation and maintenance services. This can promote the improvement of urban environments and the social and psychological well-being of residents.

[0006] "User" refers to an individual or organization that uses the system to provide information for greening unused rooftop space.

[0007] "Roof space" refers to the rooftop area of ​​a building that is generally unused or has limited use.

[0008] An "ecosystem" is a system in which plants, other living organisms, and environmental factors interact in a particular place.

[0009] "Means for receiving information" refers to the interface or tools for collecting information from the user regarding rooftop location, area, budget, and preferences.

[0010] A "database" is a system for systematically storing collected information and making it searchable and accessible as needed.

[0011] A "generative AI model" is an artificial intelligence model designed to generate optimal rooftop greenery designs based on given input data.

[0012] A "layout" is a plan that shows the placement and configuration of elements within a space (e.g., plants, equipment, energy solutions, etc.).

[0013] "Eco-friendly materials" are sustainable materials used to minimize the impact on the environment.

[0014] "Simulation" refers to the process by which the generative AI model creates hypothetical scenarios based on given conditions and predicts the optimal rooftop greening design.

[0015] "Services" refers to a set of support activities provided to Users, including the installation and ongoing maintenance of the Green Roof. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention relates to a system for greening unused rooftop spaces in urban areas and transforming them into sustainable ecosystems. The invention builds a system that uses generative AI models to propose optimal rooftop greening designs based on information collected from users. The services provided also include installation and maintenance.

[0038] Overall system picture

[0039] 1. An interface where users enter information

[0040] The user inputs information about the rooftop's location, area, budget, and preferences (e.g., type of plants, desired equipment, etc.) through a terminal. This information becomes the basis for the system to generate the optimal design.

[0041] 2. A server that receives the information and stores it in a database

[0042] The server receives the user's input and stores it in a database, which is then fed into a generative AI model.

[0043] 3. Generating designs using generative AI models

[0044] The server sends the stored information to a generative AI model, which generates a rooftop green design that best suits the user's requirements. The AI ​​model takes location information, rooftop area, budget, and preferences as inputs and simulates the optimal layout, plant species to use, and eco-friendly materials.

[0045] 4. Providing design proposals

[0046] The generated design is provided to the user through the server, and the proposal is displayed in a visual form that allows the user to easily understand and implement it.

[0047] 5. Installation and maintenance services

[0048] The system also provides specific installation and ongoing maintenance services, allowing users to select the services they need and receive assistance with post-installation maintenance work.

[0049] Program processing overview

[0050] User Input

[0051] The user enters information into the form on the terminal and presses the "Submit" button. For example, the user enters information such as "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels."

[0052] Data storage

[0053] The server receives the information sent by the user and stores it in a database.

[0054] Design generation using AI models

[0055] The server inputs the stored data into a generative AI model to generate an optimal design. For example, the AI ​​model suggests optimal layouts for rooftop spaces in Tokyo, including native plants and solar panels, based on spaces of 100.5 square meters or more.

[0056] Design provision

[0057] The server processes the generated design results and provides them to the user in a visually easy-to-understand format, resulting in a proposed green design for a 100.5 square meter rooftop in Tokyo, including native plants and solar panels, within a budget of $50,000.

[0058] Installation and Maintenance

[0059] When a user requests installation services through the system, the server forwards the information to the relevant service provider, and the installation and maintenance are scheduled. For ongoing maintenance, the server also provides regular reminders and specific advice to the user.

[0060] In this way, the system of the present invention optimally utilizes underutilized rooftop space in urban areas based on user input, providing a sustainable, green ecosystem.

[0061] The processing flow will be explained below.

[0062] Step 1:

[0063] The user accesses the input form on the device and enters the necessary information (rooftop location, area, budget, and preferences). For example, the user enters "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels."

[0064] Step 2:

[0065] When the user clicks the "Submit" button on the input form, the device sends the entered information to the server. The sent data is sent to the endpoint in JSON format.

[0066] Step 3:

[0067] The server receives the information sent from the device, checks the integrity of the data, and stores the received information in a database.

[0068] Step 4:

[0069] The server retrieves stored user information from the database and passes it as input to the generative AI model, including location, rooftop area, budget, and preferences.

[0070] Step 5:

[0071] The server generates the optimal rooftop greenery design using a generative AI model, which simulates the optimal layout, plant species, and eco-friendly materials based on the input data.

[0072] Step 6:

[0073] The server organizes and processes the generated design data and converts it into a format that is easy for users to understand. Specifically, it generates design proposals in graphic and text formats.

[0074] Step 7:

[0075] The server generates design proposals and sends them to the device. For example, it might suggest a greening design for a 100.5 square meter rooftop in Tokyo, including native plants and solar panels, within a budget of $50,000.

[0076] Step 8:

[0077] The user reviews the proposed design and provides feedback or requests for revisions as needed, in which case the data is sent back to the server for re-evaluation by the generative AI model.

[0078] Step 9:

[0079] Based on the generated design, the user requests installation and maintenance services, and the server receives this request and forwards it to the relevant service provider.

[0080] Step 10:

[0081] The terminal receives notifications from the server and displays the installation and maintenance schedule and progress to the user, allowing the user to check the status of the service through the terminal.

[0082] Example 1

[0083] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0084] There are many unused rooftop spaces in urban areas, and there is a need to convert them into sustainable, lush green ecosystems. However, rooftop greening projects require specialized knowledge for design, installation, and maintenance, and there is a lack of a system that is easily accessible to many users. This invention solves this problem by providing a system that allows users to easily obtain optimal rooftop greening designs and smoothly implement and maintain them.

[0085] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0086] In this invention, the server includes: means for receiving information on the location, area, budget, and preferences of the rooftop from a user; means for storing the received information in a database; means including a generative AI model for generating an optimal green roof design based on the stored information; means for visually presenting the generated design to the user; means for forwarding a request for installation service from the user to a related service provider; and means for providing maintenance reminders and advice to the user. This enables users, even without specialized knowledge, to obtain an optimal design for converting underutilized rooftop space into a sustainable ecosystem and to easily install and maintain it.

[0087] "User" refers to an individual or corporation that uses the system to receive proposals for rooftop greening designs.

[0088] "Rooftop space" refers to the unused space located at the top of a building.

[0089] "Location information" is data that indicates the geographic location of a rooftop space, including the city and specific address.

[0090] "Area" is a numerical value that represents the size of the rooftop space, and is expressed in units such as square meters.

[0091] "Budget" refers to the amount of money a user can spend on a green roof project.

[0092] "Preferences" refer to individual requests and wishes of the user, such as the type of plants they want or the equipment they want to install.

[0093] A "database" is a digital system for structuring and storing received user information.

[0094] A "generative AI model" is an artificial intelligence model that proposes optimal rooftop greening designs based on user input data.

[0095] A "prompt sentence" is an input sentence that is used by a generative AI model to generate an optimal design based on specific information.

[0096] "Means for visual presentation" refers to a method for visually displaying the generated design to the user in an easy-to-understand manner.

[0097] "Service Provider" means a company or organization that provides green roof installation and maintenance services.

[0098] "Maintenance" refers to the periodic management work required to ensure that a green roof project is properly maintained.

[0099] "Reminders" are a means of notifying users of important maintenance dates and tasks.

[0100] "Advice" refers to specific instructions or recommendations provided by the system to the user.

[0101] This invention is a system for converting unused rooftop spaces in urban areas into sustainable ecosystems. Specifically, it uses a generative AI model based on information collected from users to provide optimal rooftop greening designs. The main components of the hardware and software used are a server, terminal, generative AI model, and database.

[0102] System Overview

[0103] The system has the following main functions:

[0104] 1. An interface that receives user input

[0105] 2. Data storage

[0106] 3. Generate the design

[0107] 4. Providing designs in visual form

[0108] 5. Installation and Maintenance

[0109] User Input

[0110] Users use a terminal to input information about the location, area, budget, and preferences of their rooftop. This information is collected through a form on the terminal and sent to the system by pressing a "Submit" button. For example, a user might enter information such as "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels."

[0111] Data storage

[0112] The server receives the information sent by the user and stores it in a database, which is later used to provide input data to the generative AI model. Specifically, the server analyzes the HTTP request and extracts the data sent. The extracted data is then stored in a database table.

[0113] Generate the design

[0114] The server uses a generative AI model to generate the optimal design based on the stored data. The generative AI model used is OpenAI's GPT-4, which creates prompts for design generation. For example, the following prompts are generated:

[0115] "Please suggest the best greening design, including native plants and solar panels, that can be applied to a 100.5 square meter rooftop space in Tokyo with a budget of $50,000."

[0116] The generative AI model simulates the optimal design based on this prompt and returns the results in text format to the server, which then stores the results in a database.

[0117] Providing visual design

[0118] The server uses templates to process the design into HTML or PDF format to provide a visual presentation of the resulting design. The results are then sent to the user via email or a dedicated dashboard. The result is a proposed green design for a 100.5 square meter rooftop in Tokyo, including native plants and solar panels, within a budget of $50,000.

[0119] Installation and Maintenance

[0120] When a user requests installation services through the system, the server forwards the information to the relevant service provider. Specifically, the server analyzes the request and sends a notification to the relevant service provider via API or email. The service provider then checks the schedule and contacts the user to confirm and arrange the date. The server also periodically provides the user with maintenance reminders and specific advice.

[0121] This allows users, even without specialized knowledge, to obtain the optimal design for transforming underutilized rooftop space into a sustainable ecosystem, and makes installation and ongoing management easy.

[0122] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0123] Step 1:

[0124] The user accesses the terminal interface and inputs information regarding the rooftop location, area, budget and preferences.

[0125] Input: A user enters information into a form, such as "Tokyo," "100.5 square meters," "$50,000," "native plants, solar panels."

[0126] Output: The entered information is sent to the server as form data.

[0127] Specific behavior: The user enters data into all fields and clicks the "Submit" button.

[0128] Step 2:

[0129] The server receives the information sent by the user and stores it in a database.

[0130] Input: The server receives the HTTP request and retrieves a payload containing the user's input data.

[0131] Output: The extracted data is saved in the corresponding tables in the database.

[0132] Specific operation: The server analyzes the received data, maps it to location information, area, budget, and preference fields, and stores it in a database.

[0133] Step 3:

[0134] The server retrieves information from the database and creates prompts to input into the generative AI model.

[0135] Input: User information retrieved from the database (e.g., "Tokyo", "100.5 sq m", "50,000 dollars", "native plants, solar panels")

[0136] Output: The prompt to send to the generative AI model (e.g., "Please suggest the optimal greening design, including native plants and solar panels, for a 100.5 square meter rooftop space in Tokyo with a budget of $50,000.")

[0137] Specific operation: The server automatically generates a prompt sentence based on the data it obtains and sends it to the generative AI model.

[0138] Step 4:

[0139] The generative AI model receives a prompt and generates an optimal green roof design.

[0140] Input: Prompt statement (e.g., "Please suggest the best greening design, including native plants and solar panels, for a 100.5 square meter rooftop space in Tokyo with a budget of $50,000.")

[0141] Output: Generated design data (e.g. layout diagram, plant list, list of materials used, etc.)

[0142] Specific operation: The generative AI model analyzes the prompt text and outputs the optimal design that meets the conditions in text format.

[0143] Step 5:

[0144] The server receives the generated design data and provides it visually to the user.

[0145] Input: Design data received from the generative AI model

[0146] Output: The design results provided to the user in a visually understandable format (e.g. HTML, PDF files)

[0147] Specific operation: The server processes the design data using the template and provides the design results to the user via a dedicated dashboard or email.

[0148] Step 6:

[0149] The user requests installation and maintenance services on the terminal.

[0150] Input: The user selects the installation service option and clicks the "Request" button.

[0151] Output: Installation service request information sent to the server

[0152] Specific operation: The user selects the installation service through the terminal interface and confirms the request.

[0153] Step 7:

[0154] The server forwards the installation service request information to the relevant service provider.

[0155] Input: Installation service request information sent by the user

[0156] Output: Request information sent to the service provider

[0157] Specific operation: The server analyzes the request and sends a notification to the relevant service provider via API or email.

[0158] Step 8:

[0159] The server provides users with maintenance reminders and advice.

[0160] Input: Internal data regarding the schedule and progress of scheduled maintenance

[0161] Output: Reminders and maintenance advice sent to users

[0162] What it does: The server automatically sends scheduled reminders to users and provides instructions for necessary maintenance tasks.

[0163] (Application example 1)

[0164] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0165] In modern urban areas, there is a large amount of unused rooftop space, and there is a need to make effective use of it. However, conventional rooftop greening projects are expensive and require specialized knowledge for design and maintenance, making them difficult for ordinary users to use. Furthermore, even when a design proposal for a rooftop greening system is received, it is difficult for users to understand or accept it because it is not visually specific. The present invention aims to solve these problems and provide a system that effectively converts unused rooftop space into a sustainable, lush ecosystem.

[0166] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0167] In this invention, the server includes means for receiving information on the location, area, budget, and preferences of the roof from the user, means for storing the received information in a database, means including a generative AI model for generating an optimal green roof design based on the stored information, means for providing the generated design to the user, and means for providing a visual proposal of the design to the user via a smartphone, thereby enabling the user to visually understand the optimal green roof design that suits their preferences and budget even without specialized knowledge.

[0168] The "means for receiving information about the location, area, budget, and preferences of the rooftop from the user" is a mechanism that provides an interface for the user to input basic information about the rooftop and individual preferences.

[0169] "Means for storing said received information in a database" refers to a system that stores the information entered by a user in digital form so that it can be used for subsequent processing and analysis.

[0170] "Means including a generative AI model that generates optimal rooftop greenery designs based on stored information" refers to a mechanism that utilizes information stored in a database and uses artificial intelligence technology to create optimal designs that meet user requirements.

[0171] A "means for providing the generated design to a user" is a system or method for presenting the design generated by the AI ​​model to a user in an effective and understandable manner.

[0172] The "means for providing a visual proposal of the design to the user via a smartphone" is a mechanism for visually displaying and proposing the generated design to the user using a smartphone.

[0173] This invention relates to a system for greening unused rooftop spaces in urban areas and transforming them into sustainable ecosystems. The invention builds a system that uses generative AI models to propose optimal rooftop greening designs based on information collected from users. The services provided also include installation and maintenance.

[0174] Overall system picture

[0175] The system consists of several major components, which are:

[0176] 1. An interface where users enter information

[0177] Users enter information about the rooftop's location, area, budget, and preferences via their smartphone. Specifically, they fill out a form in the application and press the "Submit" button. This information becomes the basis for the system to generate the optimal design.

[0178] 2. A server that receives the information and stores it in a database

[0179] The server receives user input and stores it in a database, which is then fed into the generative AI model. The database used is SQLite, which ensures accurate storage and access of information.

[0180] 3. Generating designs using generative AI models

[0181] The server sends the stored information to a generative AI model, which generates a rooftop green design that best suits the user's requirements. Specifically, it takes location information, rooftop area, budget, and preferences as input and simulates the optimal layout, plant species to use, and eco-friendly materials. The generative AI model uses OpenAI's API to generate prompts and create the design.

[0182] 4. Providing design proposals

[0183] The generated design is provided to the user via the server. The proposal is displayed to the user in a visually easy-to-understand format on their smartphone. For example, a proposed greening design for a 100.5 square meter rooftop in Tokyo, including native plants and solar panels, within a budget of $50,000, might be proposed.

[0184] 5. Installation and maintenance services

[0185] When a user requests installation services through the system, the server forwards the information to the relevant service provider, and the installation and maintenance are scheduled. For ongoing maintenance, the server also provides regular reminders and specific advice to the user.

[0186] Examples of concrete examples and prompts

[0187] An example of a user inputting specific information is "Tokyo," "150 square meters," "70,000 dollars," "medicinal plants, relaxation area." The following is an example of a prompt sent to the generative AI model based on this input information:

[0188] Create a design for your green roof project:

[0189] Location: Tokyo

[0190] Area: 150 square meters

[0191] Budget: $70,000

[0192] Favorites: Medicinal plants, relaxation areas

[0193] This allows users to visually understand the optimal rooftop green design that suits their tastes and budget, even without specialized knowledge. It also makes it easy to connect with installation and maintenance services. The system provides sustainable green ecosystems in urban areas and promotes the effective use of underutilized rooftop space.

[0194] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0195] Step 1:

[0196] The user uses a smartphone to enter information about the rooftop's location, area, budget, and preferences into the application's form and presses the "Submit" button.

[0197] Input: Information entered by the user (e.g., "Tokyo", "150 square meters", "70,000 dollars", "medicinal plants, relaxation area")

[0198] Output: User information sent to the server

[0199] What happens: A user enters information into an application, clicks a button, and the information is sent to a server.

[0200] Step 2:

[0201] The server receives the information sent by the user and stores it in a database.

[0202] Input: Information received from the user

[0203] Output: User information stored in the database

[0204] Specific operation: The server stores user information (location, area, budget, preferences) in an SQLite database.

[0205] Step 3:

[0206] The server retrieves the stored data and sends it to a generative AI model to generate the optimal green roof design.

[0207] Input: Saved user information

[0208] Output: Design proposals generated by the generative AI model

[0209] How it works: The server retrieves user information from the database and sends it as a prompt to the generative AI model. OpenAI's API then generates the optimal design based on the prompt.

[0210] Step 4:

[0211] The server receives the design output by the generative AI model and presents it visually to the user.

[0212] Input: Design suggestions from a generative AI model

[0213] Output: Visually displayed design proposal

[0214] Specific operation: The server receives the generated design and provides it visually to the user through a smartphone application. The user can view the design details on the smartphone screen.

[0215] Step 5:

[0216] If the user is satisfied with the design proposal, they request installation and maintenance services.

[0217] Input: User request

[0218] Output: Forwarding of appropriate information to the service provider

[0219] Specific operation: When a user requests installation services through a smartphone application, the server forwards the information to the relevant service provider, and the installation and maintenance is scheduled.

[0220] This allows users, even those without specialist knowledge, to visually understand the optimal rooftop greening design that suits their tastes and budget, and easily request installation and maintenance services.

[0221] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0222] This invention relates to a system for transforming underutilized rooftop spaces into sustainable green ecosystems, and in particular to a system that enhances the user experience by incorporating an emotion engine that recognizes user emotions. The system receives user input, stores it in a database, and generates optimal rooftop greening designs using a generative AI model. It also has the ability to analyze the user's emotional state using the emotion engine and reflect this in the customized design.

[0223] Overall system picture

[0224] 1. An interface where users enter information

[0225] When a user accesses the input form on the device and enters the required information (rooftop location, area, budget, and preferences), the emotion engine analyzes the user's facial expressions, typing speed, keystrokes, etc. to collect emotional data, which is then sent to the system.

[0226] 2. A server that receives the information and stores it in a database

[0227] The server receives the user's input information and emotional data and stores it in a database. For example, emotional data such as "relaxed" and "stressed" are stored along with user information such as "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels."

[0228] 3. Generating designs using generative AI models

[0229] The server uses a generative AI model to generate the optimal rooftop greening design based on the stored information and emotion data. The generative AI model receives location information, rooftop area, budget, and preferences as input, and simulates the optimal layout, plant species, and eco-friendly materials. The analysis results of the emotion engine are also reflected, and a design proposal suited to the user's emotions is made.

[0230] 4. Providing design proposals

[0231] The generated design is provided to the user via the server. For example, if the user wants to relax, the server will recommend plant selection and placement to create a tranquil environment. The server sends the generated design to the device in a visually easy-to-understand format.

[0232] 5. Installation and maintenance services

[0233] The system also provides specific installation and ongoing maintenance services. Users can select the services they need through the system and receive assistance with maintenance work after installation. The server can customize the timing and method of maintenance based on the user's emotional data.

[0234] Program processing overview

[0235] User Input

[0236] As users enter information into a form on their device, the emotion engine recognizes their facial expressions and typing speed to analyze their emotional state. For example, if a user enters "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels," and their facial expression indicates that they are relaxed.

[0237] Data storage

[0238] The server receives the information and emotional data sent by the user and stores them in a database. The stored data includes the emotional state as well as the information entered by the user.

[0239] Design generation using AI models

[0240] The server inputs the stored data into a generative AI model to generate an optimal design that reflects the user's emotional state. For example, if a user wants to relax, a design that creates a comfortable space will be suggested.

[0241] Design provision

[0242] The server processes the generated design results and provides them to the user in an easy-to-understand format. For example, it provides a green design for a 100.5 square meter rooftop in Tokyo that creates a relaxing environment, including native plants and solar panels, within a budget of $50,000.

[0243] Installation and Maintenance

[0244] When a user requests a service, the server transfers it to the relevant service provider, who then installs it. For ongoing maintenance, the server periodically checks the user's emotional data and provides the optimal maintenance plan.

[0245] As described above, the system of the present invention generates optimal rooftop greening designs based on the user's input information and emotional state, and provides installation and maintenance services, thereby improving the urban environment and increasing resident satisfaction.

[0246] The processing flow will be explained below.

[0247] Step 1:

[0248] The user accesses an input form on the device and enters information about the location, area, budget, and preferences of the rooftop. For example, the user might enter "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels." The device's built-in camera, microphone, and keystroke analysis tool record the user's facial expressions, voice, and typing speed, and the emotion engine analyzes the emotional data.

[0249] Step 2:

[0250] When the user has finished entering information, they press the "Send" button. The device sends the user's input information and emotion data in JSON format to the server. For example, the following data is sent:

[0251] json

[0252] {

[0253] "location": "Tokyo",

[0254] "rooftop_area": ​​100.5,

[0255] "budget": 50000,

[0256] "preferences": "Native plants, Solar panels",

[0257] "emotional_state": "Relaxed"

[0258] }

[0259] Step 3:

[0260] The server receives the information sent from the device and checks the integrity of the data. The server then stores the received information in a database. For example, information such as "User ID," "Tokyo," "100.5 square meters," "50,000 dollars," "Native plants, solar panels," and "Relax" is stored.

[0261] Step 4:

[0262] The server retrieves stored user information and emotional data from the database and passes it as input to the generative AI model. The input data includes location, rooftop area, budget, and preferences. Emotional data is also passed to the model, which then influences the design.

[0263] Step 5:

[0264] The server uses a generative AI model to generate the optimal rooftop greenery design. Based on the input data, the generative AI model simulates the optimal layout, plant species, and eco-friendly materials. In doing so, it takes into account the emotion engine's relaxed state and selects plants and placements that have a high relaxing effect.

[0265] Step 6:

[0266] The server organizes and processes the generated design data and converts it into a format that is easy for users to understand. For example, it generates a visual layout diagram or a concrete plantation proposal. The generated design proposal is sent to the terminal in JSON format.

[0267] Step 7:

[0268] The device will then display design proposals received from the server to the user, such as "A greening design for a 100.5 square meter rooftop in Tokyo that will create a relaxing environment, including native plants and solar panels, within a budget of $50,000."

[0269] Step 8:

[0270] The user can review the proposed design and provide feedback or request revisions as necessary. If revisions are required, the device resends the new information to the server, and the design is regenerated using the generative AI model.

[0271] Step 9:

[0272] Based on the generated design, the user requests installation services. The server receives this request and forwards it to the relevant service provider, who then schedules and initiates the actual installation.

[0273] Step 10:

[0274] The device receives notifications from the server and displays installation and maintenance schedules and progress to the user. For ongoing maintenance, the server periodically checks the user's emotional data and provides an optimal maintenance plan. For example, if the user is feeling stressed, it will suggest adding or rearranging plants that have a relaxing effect.

[0275] Example 2

[0276] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0277] In modern urban environments, there is a need to make effective use of unused rooftop space, but proposing and implementing effective designs is difficult. Another issue is the lack of personalized design proposals that take into account the user's emotional state, which tends to decrease user satisfaction. Furthermore, there is a lack of standardization in the provision of installation and maintenance services, making ongoing maintenance difficult.

[0278] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0279] In this invention, the server includes: means for receiving information from a user regarding the location, area, budget, and preferences of the rooftop; means for storing the received information in a database; means including a generative AI model for generating an optimal rooftop greening design based on the stored information and the user's emotional state; and means for providing the generated design to the user. This allows for the proposal of an optimal rooftop greening design that reflects the user's emotional state, enabling the effective use of unused rooftop space as a sustainable ecosystem. It also improves user satisfaction and streamlines installation and maintenance.

[0280] "User" refers to an individual or organization that uses the system to request a rooftop greenery design.

[0281] "Rooftop location" refers to the geographic location of the rooftop of the building where the user wishes to green.

[0282] "Area" refers to a numerical value indicating the size of the rooftop to be greened.

[0283] "Budget" refers to the maximum amount of funds set by a User to be used for a Green Roof Project.

[0284] "Preferences" refers to information that indicates personal preferences such as the type of plants or installations (e.g., solar panels) desired by the user.

[0285] An "emotion engine" refers to a technology that analyzes a user's facial expressions, input speed, keystrokes, etc. in real time to infer the user's emotional state.

[0286] "Generative AI model" refers to an artificial intelligence model that generates optimal rooftop greenery designs based on input information.

[0287] "Database" refers to a storage device within the system for storing information and emotional data received from users.

[0288] "Design" refers to a rooftop greenery design proposal generated based on the user's input information and emotional data.

[0289] "Installation and maintenance" refers to a series of services that involve the implementation and subsequent maintenance of a green roof based on the generated design.

[0290] This invention relates to a system for converting underutilized rooftop space into a sustainable, green ecosystem. In particular, it combines an emotion engine that recognizes user emotions to improve the user experience. The system receives user input, stores it in a database, and uses a generative AI model to generate optimal rooftop greening designs. Furthermore, it has the ability to analyze the user's emotional state using the emotion engine and reflect this in the customization of the design.

[0291] Hardware and software configuration

[0292] This system uses the following hardware and software:

[0293] Terminal: The device through which a user enters information (e.g., a computer, tablet, smartphone)

[0294] Server: A central processing unit for storing information in a database and running generative AI models.

[0295] Database: A data storage system for storing user input information and emotion data.

[0296] Generative AI model: an algorithm for generating optimal green roof designs based on stored data

[0297] Emotion engine: Software that analyzes a user's facial expressions, typing speed, and keystrokes to recognize their emotional state.

[0298] Program processing and specific examples

[0299] User Input

[0300] When a user enters information into a form on their device, they provide the necessary information (e.g., rooftop location, area, budget, and preferences). The emotion engine then analyzes the user's facial expressions, typing speed, and keystrokes in real time to collect emotional data. For example, if a user enters "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels," the emotion engine determines that the user is relaxed.

[0301] Data storage

[0302] The server receives the information and emotional data sent by the user and stores it in a database. This stored data includes the emotional state along with the information entered by the user. Specifically, data such as "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels," and "relaxed" are stored.

[0303] Design generation using AI models

[0304] The server inputs the stored data into a generative AI model to generate an optimal design that reflects the user's emotional state. The generative AI model takes location information, rooftop area, budget, and preferences as input and simulates the optimal layout, plant species, and eco-friendly materials. For example, if a user wants to relax, a design that creates a comfortable space will be suggested.

[0305] Design provision

[0306] The server processes the generated design results and presents them to the user in an easy-to-understand format. For example, a green design for a 100.5 square meter rooftop in Tokyo, including native plants and solar panels, that creates a relaxing environment within a budget of $50,000, is displayed on the device.

[0307] Installation and Maintenance

[0308] When a user requests a service, the server transfers it to the relevant service provider, who then begins the actual installation process. Furthermore, ongoing maintenance services are also provided. The server periodically checks the user's emotional data and provides an optimal maintenance plan based on that data. For example, if the user is feeling stressed, the server will suggest additional plans to enhance relaxation during maintenance.

[0309] Prompt Sentence Examples

[0310] "I would like to create a relaxing environment on a 100.5 square meter rooftop in Tokyo, including native plants and solar panels, within a budget of $50,000. Please suggest the best design."

[0311] In this way, the system generates optimal rooftop greening designs and provides installation and maintenance services based on the user's input information and emotional state, resulting in improved urban environments and increased resident satisfaction.

[0312] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0313] Step 1: User Enters Information

[0314] Input: The user enters information about the rooftop location, area, budget, and preferences into a form on the terminal.

[0315] Action: User enters "Tokyo", "100.5 sq m", "50,000 dollars", "native plants, solar panels".

[0316] Data processing: The emotion engine analyzes the user's facial expressions, typing speed, and keystrokes in real time to generate emotion data.

[0317] Output: Emotional data such as "relaxed" is added to the user's input information.

[0318] Step 2: Receiving and storing data

[0319] Input: User information and emotional data sent from the device.

[0320] How it works: The server receives the information sent by the user and stores it in a database.

[0321] Data processing: User input information and emotion data are converted into a unified format and saved.

[0322] Output: The database contains information such as "Tokyo", "100.5 sq m", "50,000 dollars", "native plants, solar panels", and "relaxation".

[0323] Step 3: Generate a design using a generative AI model

[0324] Input: User information and emotion data stored in a database.

[0325] How it works: The server inputs data into a generative AI model to generate an optimal green roof design.

[0326] Data Processing: Generative AI models simulate optimal layouts, plant species, and eco-friendly materials based on location, area, budget, preferences, and sentiment data.

[0327] Output: Generated design ideas (e.g., a relaxing arrangement of native plants and solar panels).

[0328] Step 4: Submit your design

[0329] Input: Design proposals output by the generative AI model.

[0330] Operation: The server processes the generated design into a format that is easy for the user to understand and sends it to the terminal.

[0331] Data processing: Visualize the design proposal and format it with explanatory text.

[0332] Output: "A green design for a 100.5 square meter rooftop in Tokyo that creates a relaxing environment, including native plants and solar panels, within a budget of $50,000," is displayed on the device.

[0333] Step 5: Installation and Maintenance

[0334] Input: A request from a user for installation and maintenance services.

[0335] How it works: A user requests a service on their device and the information is sent to the server, which then forwards it to the service provider, who then begins preparing the specific installation and maintenance plan.

[0336] Data processing: Regularly check user sentiment data and customize maintenance plans.

[0337] Output: An optimal maintenance plan is provided based on the user's emotional state. For example, if the user is feeling stressed, additional maintenance tasks that will enhance relaxation are suggested.

[0338] In this way, by performing specific operations at each step, an optimal rooftop greening design that takes into account the user's emotions can be generated, and subsequent installation and maintenance can be achieved.

[0339] (Application example 2)

[0340] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0341] Converting unused rooftop space or factory space into a sustainable, lush green ecosystem will improve the environment and enhance people's quality of life, but it requires specialized knowledge and effort. It is also difficult to customize the design based on the individual feelings and desires of users, and efficient management and maintenance are also challenges. This is particularly difficult to achieve in specialized environments such as factories, so there is a need for a greening system that achieves both sustainability and user satisfaction.

[0342] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0343] In this invention, the server includes means for receiving information from a user regarding the location, size, cost, and preferences of the building, means for storing the received information in a data storage unit, means including a generative AI model for generating an optimal rooftop greening design based on the stored information, means for providing the generated design to the user, emotion recognition means for analyzing the user's emotional state and reflecting the analysis results in the design, means for sending instructions to a work robot for realizing the greening design, and means for continuously monitoring and maintaining the designed greening area. This makes it possible to efficiently realize a sustainable greening design that corresponds to the individual emotions and preferences of the user and to easily perform maintenance after installation.

[0344] "Users" are those who use the system to customize and maintain rooftop and factory greenery designs.

[0345] "Building location" refers to the specific location information of the rooftop or factory space where the green design will be applied.

[0346] "Size" refers to the area of ​​the building rooftop or factory interior that will be greened.

[0347] "Expenses" refers to the budget that a user can spend on greenery design and maintenance.

[0348] "Desires" are specific conditions and preferences that users desire in green design.

[0349] The "data storage unit" is an area for storing information and emotion data received from the user.

[0350] A "generative AI model" is an artificial intelligence system that generates optimal greening designs based on information provided by the user.

[0351] "Emotion recognition means" is a technology that analyzes the user's emotional state and reflects the results of that analysis in the design.

[0352] A "working robot" is an autonomous device that performs physical tasks to realize a green design.

[0353] "Maintenance measures" are techniques and devices that continuously monitor green areas and carry out the necessary management and maintenance work.

[0354] MODE FOR CARRYING OUT THE INVENTION

[0355] This invention is a system for converting unused space in a factory or on a roof into a sustainable green ecosystem. The following describes in detail how this system can be implemented.

[0356] First, a user uses a device such as a smartphone or tablet to input information about the location, size, cost, and preferences of the building to be greened. For example, a user might input the following information:

[0357] Location: Factory A

[0358] Area: 500.0 square meters

[0359] Expenses: $100,000

[0360] Preferred: Native plants, eco-friendly materials

[0361] Once the user has completed their input, the device sends this information to the server. The emotion engine, which is an emotion recognition means, then analyzes the user's facial expressions and input speed to determine their emotional state (e.g., "relaxed" or "stressed"). The user's input information, along with this emotion data, is stored in the data storage unit.

[0362] The server then uses a generative AI model based on the stored information to generate an optimal green space design. This generative AI model simulates the optimal layout, plant species, and sustainable materials based on the user's location, size, budget, and desired information. It also takes the user's emotional state into account to suggest designs that suit their emotions. For example, if the user is looking to relax, the server will recommend specific plants and placements to create a comfortable space.

[0363] The generated green design is then sent back to the user's device from the server and displayed in a visually easy-to-understand format. As a concrete example, the following green design proposals are provided:

[0364] Location: Factory A

[0365] Area: 500.0 square meters

[0366] Expenses: $100,000

[0367] Proposal: Use native plants and eco-friendly materials, with specific layout and plant species selected to create a relaxing environment.

[0368] Furthermore, once the greening design is approved, the server sends instructions to the work robot to realize the greening design. The work robot then carries out the greening work based on the specified design, automatically placing the plants and installing the necessary infrastructure.

[0369] Once installed, the robot will continuously monitor and maintain the designed green area. The work robot will periodically check the condition of the area and automatically carry out any necessary maintenance work. It is also possible to customize maintenance plans based on the analysis results of the emotion engine. For example, if it determines that "maintenance is required," it will perform additional watering or pruning of plants.

[0370] An example prompt would send the following information to the server:

[0371] Location: Factory rooftop

[0372] Area: 700.0 square meters

[0373] Expenses: $150,000

[0374] Hope: Sustainable materials, automated irrigation systems

[0375] Emotional state: Relaxed

[0376] In this way, this system efficiently realizes sustainable greening designs that respond to the individual feelings and desires of users, and also makes maintenance after installation easy.

[0377] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0378] Step 1:

[0379] The user inputs information for the greenery design using a smartphone or tablet. The user fills in the building's location, size, cost, and preferences into an input form on the device. This input data might include, for example, "Factory A, 500.0 square meters, $100,000, native plants, eco-friendly materials." Once the input is complete, the device sends this information to the server.

[0380] Step 2:

[0381] The device acquires the user's emotional data. Specifically, it uses the device's built-in camera and emotion recognition software (emotion engine) to analyze the user's facial expressions and input speed. This determines the user's emotional state, such as whether they are "relaxed" or "stressed." This emotional data is also sent to the server.

[0382] Step 3:

[0383] The server stores the user's input information and emotion data in the data repository. The input data is "Factory A, 500.0 square meters, $100,000, native plants, eco-friendly materials," and the emotion data is "Relaxed." This prepares the data for use in subsequent processing.

[0384] Step 4:

[0385] The server uses a generative AI model based on the stored data to generate an optimal greening design. The input data includes location, size, cost, desired information, and emotional data, and outputs a specific example of a greening design: a 500.0 square meter relaxing environment using native plants and eco-friendly materials. This process involves a series of data processing and calculations.

[0386] Step 5:

[0387] The server provides the generated greening design to the user's device. The design created by the generative AI model is displayed on the device in a visually easy-to-understand format. The user can then review the specific design and provide feedback to the server, indicating approval or corrections.

[0388] Step 6:

[0389] After receiving the user's approval, the server sends instructions to the robot to realize the greening design. The robot then carries out the specific layout and use of materials based on the design data received from the server, and also prepares the necessary infrastructure and arranges the plants.

[0390] Step 7:

[0391] The robot carries out the greening work based on the design. The robot places plants in the designated locations and installs sustainable materials. After completing the installation work, it reports its progress to the server.

[0392] Step 8:

[0393] The server continuously monitors the completed greening area and sends maintenance instructions to the work robot. It automatically performs regular observations and necessary maintenance tasks (e.g., watering and pruning). It also takes into account data from emotion recognition and performs maintenance to increase user satisfaction.

[0394] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0395] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0396] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0397] [Second embodiment]

[0398] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0399] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0400] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0401] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0402] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0403] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0404] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0405] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0406] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0407] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0408] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0409] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0410] This invention relates to a system for greening unused rooftop spaces in urban areas and transforming them into sustainable ecosystems. The invention builds a system that uses generative AI models to propose optimal rooftop greening designs based on information collected from users. The services provided also include installation and maintenance.

[0411] Overall system picture

[0412] 1. An interface where users enter information

[0413] The user inputs information about the rooftop's location, area, budget, and preferences (e.g., type of plants, desired equipment, etc.) through a terminal. This information becomes the basis for the system to generate the optimal design.

[0414] 2. A server that receives the information and stores it in a database

[0415] The server receives the user's input and stores it in a database, which is then fed into a generative AI model.

[0416] 3. Generating designs using generative AI models

[0417] The server sends the stored information to a generative AI model, which generates a rooftop green design that best suits the user's requirements. The AI ​​model takes location information, rooftop area, budget, and preferences as inputs and simulates the optimal layout, plant species to use, and eco-friendly materials.

[0418] 4. Providing design proposals

[0419] The generated design is provided to the user through the server, and the proposal is displayed in a visual form that allows the user to easily understand and implement it.

[0420] 5. Installation and maintenance services

[0421] The system also provides specific installation and ongoing maintenance services, allowing users to select the services they need and receive assistance with post-installation maintenance work.

[0422] Program processing overview

[0423] User Input

[0424] The user enters information into the form on the terminal and presses the "Submit" button. For example, the user enters information such as "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels."

[0425] Data storage

[0426] The server receives the information sent by the user and stores it in a database.

[0427] Design generation using AI models

[0428] The server inputs the stored data into a generative AI model to generate an optimal design. For example, the AI ​​model suggests optimal layouts for rooftop spaces in Tokyo, including native plants and solar panels, based on spaces of 100.5 square meters or more.

[0429] Design provision

[0430] The server processes the generated design results and provides them to the user in a visually easy-to-understand format, resulting in a proposed green design for a 100.5 square meter rooftop in Tokyo, including native plants and solar panels, within a budget of $50,000.

[0431] Installation and Maintenance

[0432] When a user requests installation services through the system, the server forwards the information to the relevant service provider, and the installation and maintenance are scheduled. For ongoing maintenance, the server also provides regular reminders and specific advice to the user.

[0433] In this way, the system of the present invention optimally utilizes underutilized rooftop space in urban areas based on user input, providing a sustainable, green ecosystem.

[0434] The processing flow will be explained below.

[0435] Step 1:

[0436] The user accesses the input form on the device and enters the necessary information (rooftop location, area, budget, and preferences). For example, the user enters "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels."

[0437] Step 2:

[0438] When the user clicks the "Submit" button on the input form, the device sends the entered information to the server. The sent data is sent to the endpoint in JSON format.

[0439] Step 3:

[0440] The server receives the information sent from the device, checks the integrity of the data, and stores the received information in a database.

[0441] Step 4:

[0442] The server retrieves stored user information from the database and passes it as input to the generative AI model, including location, rooftop area, budget, and preferences.

[0443] Step 5:

[0444] The server generates the optimal rooftop greenery design using a generative AI model, which simulates the optimal layout, plant species, and eco-friendly materials based on the input data.

[0445] Step 6:

[0446] The server organizes and processes the generated design data and converts it into a format that is easy for users to understand. Specifically, it generates design proposals in graphic and text formats.

[0447] Step 7:

[0448] The server generates design proposals and sends them to the device. For example, it might suggest a greening design for a 100.5 square meter rooftop in Tokyo, including native plants and solar panels, within a budget of $50,000.

[0449] Step 8:

[0450] The user reviews the proposed design and provides feedback or requests for revisions as needed, in which case the data is sent back to the server for re-evaluation by the generative AI model.

[0451] Step 9:

[0452] Based on the generated design, the user requests installation and maintenance services, and the server receives this request and forwards it to the relevant service provider.

[0453] Step 10:

[0454] The terminal receives notifications from the server and displays the installation and maintenance schedule and progress to the user, allowing the user to check the status of the service through the terminal.

[0455] Example 1

[0456] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0457] There are many unused rooftop spaces in urban areas, and there is a need to convert them into sustainable, lush green ecosystems. However, rooftop greening projects require specialized knowledge for design, installation, and maintenance, and there is a lack of a system that is easily accessible to many users. This invention solves this problem by providing a system that allows users to easily obtain optimal rooftop greening designs and smoothly implement and maintain them.

[0458] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0459] In this invention, the server includes: means for receiving information on the location, area, budget, and preferences of the rooftop from a user; means for storing the received information in a database; means including a generative AI model for generating an optimal green roof design based on the stored information; means for visually presenting the generated design to the user; means for forwarding a request for installation service from the user to a related service provider; and means for providing maintenance reminders and advice to the user. This enables users, even without specialized knowledge, to obtain an optimal design for converting underutilized rooftop space into a sustainable ecosystem and to easily install and maintain it.

[0460] "User" refers to an individual or corporation that uses the system to receive proposals for rooftop greening designs.

[0461] "Rooftop space" refers to the unused space located at the top of a building.

[0462] "Location information" is data that indicates the geographic location of a rooftop space, including the city and specific address.

[0463] "Area" is a numerical value that represents the size of the rooftop space, and is expressed in units such as square meters.

[0464] "Budget" refers to the amount of money a user can spend on a green roof project.

[0465] "Preferences" refer to individual requests and wishes of the user, such as the type of plants they want or the equipment they want to install.

[0466] A "database" is a digital system for structuring and storing received user information.

[0467] A "generative AI model" is an artificial intelligence model that proposes optimal rooftop greening designs based on user input data.

[0468] A "prompt sentence" is an input sentence that is used by a generative AI model to generate an optimal design based on specific information.

[0469] "Means for visual presentation" refers to a method for visually displaying the generated design to the user in an easy-to-understand manner.

[0470] "Service Provider" means a company or organization that provides green roof installation and maintenance services.

[0471] "Maintenance" refers to the periodic management work required to ensure that a green roof project is properly maintained.

[0472] "Reminders" are a means of notifying users of important maintenance dates and tasks.

[0473] "Advice" refers to specific instructions or recommendations provided by the system to the user.

[0474] This invention is a system for converting unused rooftop spaces in urban areas into sustainable ecosystems. Specifically, it uses a generative AI model based on information collected from users to provide optimal rooftop greening designs. The main components of the hardware and software used are a server, terminal, generative AI model, and database.

[0475] System Overview

[0476] The system has the following main functions:

[0477] 1. An interface that receives user input

[0478] 2. Data storage

[0479] 3. Generate the design

[0480] 4. Providing designs in visual form

[0481] 5. Installation and Maintenance

[0482] User Input

[0483] Users use a terminal to input information about the location, area, budget, and preferences of their rooftop. This information is collected through a form on the terminal and sent to the system by pressing a "Submit" button. For example, a user might enter information such as "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels."

[0484] Data storage

[0485] The server receives the information sent by the user and stores it in a database, which is later used to provide input data to the generative AI model. Specifically, the server analyzes the HTTP request and extracts the data sent. The extracted data is then stored in a database table.

[0486] Generate the design

[0487] The server uses a generative AI model to generate the optimal design based on the stored data. The generative AI model used is OpenAI's GPT-4, which creates prompts for design generation. For example, the following prompts are generated:

[0488] "Please suggest the best greening design, including native plants and solar panels, that can be applied to a 100.5 square meter rooftop space in Tokyo with a budget of $50,000."

[0489] The generative AI model simulates the optimal design based on this prompt and returns the results in text format to the server, which then stores the results in a database.

[0490] Providing visual design

[0491] The server uses templates to process the design into HTML or PDF format to provide a visual presentation of the resulting design. The results are then sent to the user via email or a dedicated dashboard. The result is a proposed green design for a 100.5 square meter rooftop in Tokyo, including native plants and solar panels, within a budget of $50,000.

[0492] Installation and Maintenance

[0493] When a user requests installation services through the system, the server forwards the information to the relevant service provider. Specifically, the server analyzes the request and sends a notification to the relevant service provider via API or email. The service provider then checks the schedule and contacts the user to confirm and arrange the date. The server also periodically provides the user with maintenance reminders and specific advice.

[0494] This allows users, even without specialized knowledge, to obtain the optimal design for transforming underutilized rooftop space into a sustainable ecosystem, and makes installation and ongoing management easy.

[0495] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0496] Step 1:

[0497] The user accesses the terminal interface and inputs information regarding the rooftop location, area, budget and preferences.

[0498] Input: A user enters information into a form, such as "Tokyo," "100.5 square meters," "$50,000," "native plants, solar panels."

[0499] Output: The entered information is sent to the server as form data.

[0500] Specific behavior: The user enters data into all fields and clicks the "Submit" button.

[0501] Step 2:

[0502] The server receives the information sent by the user and stores it in a database.

[0503] Input: The server receives the HTTP request and retrieves a payload containing the user's input data.

[0504] Output: The extracted data is saved in the corresponding tables in the database.

[0505] Specific operation: The server analyzes the received data, maps it to location information, area, budget, and preference fields, and stores it in a database.

[0506] Step 3:

[0507] The server retrieves information from the database and creates prompts to input into the generative AI model.

[0508] Input: User information retrieved from the database (e.g., "Tokyo", "100.5 sq m", "50,000 dollars", "native plants, solar panels")

[0509] Output: The prompt to send to the generative AI model (e.g., "Please suggest the optimal greening design, including native plants and solar panels, for a 100.5 square meter rooftop space in Tokyo with a budget of $50,000.")

[0510] Specific operation: The server automatically generates a prompt sentence based on the data it obtains and sends it to the generative AI model.

[0511] Step 4:

[0512] The generative AI model receives a prompt and generates an optimal green roof design.

[0513] Input: Prompt statement (e.g., "Please suggest the best greening design, including native plants and solar panels, for a 100.5 square meter rooftop space in Tokyo with a budget of $50,000.")

[0514] Output: Generated design data (e.g. layout diagram, plant list, list of materials used, etc.)

[0515] Specific operation: The generative AI model analyzes the prompt text and outputs the optimal design that meets the conditions in text format.

[0516] Step 5:

[0517] The server receives the generated design data and provides it visually to the user.

[0518] Input: Design data received from the generative AI model

[0519] Output: The design results provided to the user in a visually understandable format (e.g. HTML, PDF files)

[0520] Specific operation: The server processes the design data using the template and provides the design results to the user via a dedicated dashboard or email.

[0521] Step 6:

[0522] The user requests installation and maintenance services on the terminal.

[0523] Input: The user selects the installation service option and clicks the "Request" button.

[0524] Output: Installation service request information sent to the server

[0525] Specific operation: The user selects the installation service through the terminal interface and confirms the request.

[0526] Step 7:

[0527] The server forwards the installation service request information to the relevant service provider.

[0528] Input: Installation service request information sent by the user

[0529] Output: Request information sent to the service provider

[0530] Specific operation: The server analyzes the request and sends a notification to the relevant service provider via API or email.

[0531] Step 8:

[0532] The server provides users with maintenance reminders and advice.

[0533] Input: Internal data regarding the schedule and progress of scheduled maintenance

[0534] Output: Reminders and maintenance advice sent to users

[0535] What it does: The server automatically sends scheduled reminders to users and provides instructions for necessary maintenance tasks.

[0536] (Application example 1)

[0537] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0538] In modern urban areas, there is a large amount of unused rooftop space, and there is a need to make effective use of it. However, conventional rooftop greening projects are expensive and require specialized knowledge for design and maintenance, making them difficult for ordinary users to use. Furthermore, even when a design proposal for a rooftop greening system is received, it is difficult for users to understand or accept it because it is not visually specific. The present invention aims to solve these problems and provide a system that effectively converts unused rooftop space into a sustainable, lush ecosystem.

[0539] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0540] In this invention, the server includes means for receiving information on the location, area, budget, and preferences of the roof from the user, means for storing the received information in a database, means including a generative AI model for generating an optimal green roof design based on the stored information, means for providing the generated design to the user, and means for providing a visual proposal of the design to the user via a smartphone, thereby enabling the user to visually understand the optimal green roof design that suits their preferences and budget even without specialized knowledge.

[0541] The "means for receiving information about the location, area, budget, and preferences of the rooftop from the user" is a mechanism that provides an interface for the user to input basic information about the rooftop and individual preferences.

[0542] "Means for storing said received information in a database" refers to a system that stores the information entered by a user in digital form so that it can be used for subsequent processing and analysis.

[0543] "Means including a generative AI model that generates optimal rooftop greenery designs based on stored information" refers to a mechanism that utilizes information stored in a database and uses artificial intelligence technology to create optimal designs that meet user requirements.

[0544] A "means for providing the generated design to a user" is a system or method for presenting the design generated by the AI ​​model to a user in an effective and understandable manner.

[0545] The "means for providing a visual proposal of the design to the user via a smartphone" is a mechanism for visually displaying and proposing the generated design to the user using a smartphone.

[0546] This invention relates to a system for greening unused rooftop spaces in urban areas and transforming them into sustainable ecosystems. The invention builds a system that uses generative AI models to propose optimal rooftop greening designs based on information collected from users. The services provided also include installation and maintenance.

[0547] Overall system picture

[0548] The system consists of several major components, which are:

[0549] 1. An interface where users enter information

[0550] Users enter information about the rooftop's location, area, budget, and preferences via their smartphone. Specifically, they fill out a form in the application and press the "Submit" button. This information becomes the basis for the system to generate the optimal design.

[0551] 2. A server that receives the information and stores it in a database

[0552] The server receives user input and stores it in a database, which is then fed into the generative AI model. The database used is SQLite, which ensures accurate storage and access of information.

[0553] 3. Generating designs using generative AI models

[0554] The server sends the stored information to a generative AI model, which generates a rooftop green design that best suits the user's requirements. Specifically, it takes location information, rooftop area, budget, and preferences as input and simulates the optimal layout, plant species to use, and eco-friendly materials. The generative AI model uses OpenAI's API to generate prompts and create the design.

[0555] 4. Providing design proposals

[0556] The generated design is provided to the user via the server. The proposal is displayed to the user in a visually easy-to-understand format on their smartphone. For example, a proposed greening design for a 100.5 square meter rooftop in Tokyo, including native plants and solar panels, within a budget of $50,000, might be proposed.

[0557] 5. Installation and maintenance services

[0558] When a user requests installation services through the system, the server forwards the information to the relevant service provider, and the installation and maintenance are scheduled. For ongoing maintenance, the server also provides regular reminders and specific advice to the user.

[0559] Examples of concrete examples and prompts

[0560] An example of a user inputting specific information is "Tokyo," "150 square meters," "70,000 dollars," "medicinal plants, relaxation area." The following is an example of a prompt sent to the generative AI model based on this input information:

[0561] Create a design for your green roof project:

[0562] Location: Tokyo

[0563] Area: 150 square meters

[0564] Budget: $70,000

[0565] Favorites: Medicinal plants, relaxation areas

[0566] This allows users to visually understand the optimal rooftop green design that suits their tastes and budget, even without specialized knowledge. It also makes it easy to connect with installation and maintenance services. The system provides sustainable green ecosystems in urban areas and promotes the effective use of underutilized rooftop space.

[0567] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0568] Step 1:

[0569] The user uses a smartphone to enter information about the rooftop's location, area, budget, and preferences into the application's form and presses the "Submit" button.

[0570] Input: Information entered by the user (e.g., "Tokyo", "150 square meters", "70,000 dollars", "medicinal plants, relaxation area")

[0571] Output: User information sent to the server

[0572] What happens: A user enters information into an application, clicks a button, and the information is sent to a server.

[0573] Step 2:

[0574] The server receives the information sent by the user and stores it in a database.

[0575] Input: Information received from the user

[0576] Output: User information stored in the database

[0577] Specific operation: The server stores user information (location, area, budget, preferences) in an SQLite database.

[0578] Step 3:

[0579] The server retrieves the stored data and sends it to a generative AI model to generate the optimal green roof design.

[0580] Input: Saved user information

[0581] Output: Design proposals generated by the generative AI model

[0582] How it works: The server retrieves user information from the database and sends it as a prompt to the generative AI model. OpenAI's API then generates the optimal design based on the prompt.

[0583] Step 4:

[0584] The server receives the design output by the generative AI model and presents it visually to the user.

[0585] Input: Design suggestions from a generative AI model

[0586] Output: Visually displayed design proposal

[0587] Specific operation: The server receives the generated design and provides it visually to the user through a smartphone application. The user can view the design details on the smartphone screen.

[0588] Step 5:

[0589] If the user is satisfied with the design proposal, they request installation and maintenance services.

[0590] Input: User request

[0591] Output: Forwarding of appropriate information to the service provider

[0592] Specific operation: When a user requests installation services through a smartphone application, the server forwards the information to the relevant service provider, and the installation and maintenance is scheduled.

[0593] This allows users, even those without specialist knowledge, to visually understand the optimal rooftop greening design that suits their tastes and budget, and easily request installation and maintenance services.

[0594] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0595] This invention relates to a system for transforming underutilized rooftop spaces into sustainable green ecosystems, and in particular to a system that enhances the user experience by incorporating an emotion engine that recognizes user emotions. The system receives user input, stores it in a database, and generates optimal rooftop greening designs using a generative AI model. It also has the ability to analyze the user's emotional state using the emotion engine and reflect this in the customized design.

[0596] Overall system picture

[0597] 1. An interface where users enter information

[0598] When a user accesses the input form on the device and enters the required information (rooftop location, area, budget, and preferences), the emotion engine analyzes the user's facial expressions, typing speed, keystrokes, etc. to collect emotional data, which is then sent to the system.

[0599] 2. A server that receives the information and stores it in a database

[0600] The server receives the user's input information and emotional data and stores it in a database. For example, emotional data such as "relaxed" and "stressed" are stored along with user information such as "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels."

[0601] 3. Generating designs using generative AI models

[0602] The server uses a generative AI model to generate the optimal rooftop greening design based on the stored information and emotion data. The generative AI model receives location information, rooftop area, budget, and preferences as input, and simulates the optimal layout, plant species, and eco-friendly materials. The analysis results of the emotion engine are also reflected, and a design proposal suited to the user's emotions is made.

[0603] 4. Providing design proposals

[0604] The generated design is provided to the user via the server. For example, if the user wants to relax, the server will recommend plant selection and placement to create a tranquil environment. The server sends the generated design to the device in a visually easy-to-understand format.

[0605] 5. Installation and maintenance services

[0606] The system also provides specific installation and ongoing maintenance services. Users can select the services they need through the system and receive assistance with maintenance work after installation. The server can customize the timing and method of maintenance based on the user's emotional data.

[0607] Program processing overview

[0608] User Input

[0609] As users enter information into a form on their device, the emotion engine recognizes their facial expressions and typing speed to analyze their emotional state. For example, if a user enters "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels," and their facial expression indicates that they are relaxed.

[0610] Data storage

[0611] The server receives the information and emotional data sent by the user and stores them in a database. The stored data includes the emotional state as well as the information entered by the user.

[0612] Design generation using AI models

[0613] The server inputs the stored data into a generative AI model to generate an optimal design that reflects the user's emotional state. For example, if a user wants to relax, a design that creates a comfortable space will be suggested.

[0614] Design provision

[0615] The server processes the generated design results and provides them to the user in an easy-to-understand format. For example, it provides a green design for a 100.5 square meter rooftop in Tokyo that creates a relaxing environment, including native plants and solar panels, within a budget of $50,000.

[0616] Installation and Maintenance

[0617] When a user requests a service, the server transfers it to the relevant service provider, who then installs it. For ongoing maintenance, the server periodically checks the user's emotional data and provides the optimal maintenance plan.

[0618] As described above, the system of the present invention generates optimal rooftop greening designs based on the user's input information and emotional state, and provides installation and maintenance services, thereby improving the urban environment and increasing resident satisfaction.

[0619] The processing flow will be explained below.

[0620] Step 1:

[0621] The user accesses an input form on the device and enters information about the location, area, budget, and preferences of the rooftop. For example, the user might enter "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels." The device's built-in camera, microphone, and keystroke analysis tool record the user's facial expressions, voice, and typing speed, and the emotion engine analyzes the emotional data.

[0622] Step 2:

[0623] When the user has finished entering information, they press the "Send" button. The device sends the user's input information and emotion data in JSON format to the server. For example, the following data is sent:

[0624] json

[0625] {

[0626] "location": "Tokyo",

[0627] "rooftop_area": ​​100.5,

[0628] "budget": 50000,

[0629] "preferences": "Native plants, Solar panels",

[0630] "emotional_state": "Relaxed"

[0631] }

[0632] Step 3:

[0633] The server receives the information sent from the device and checks the integrity of the data. The server then stores the received information in a database. For example, information such as "User ID," "Tokyo," "100.5 square meters," "50,000 dollars," "Native plants, solar panels," and "Relax" is stored.

[0634] Step 4:

[0635] The server retrieves stored user information and emotional data from the database and passes it as input to the generative AI model. The input data includes location, rooftop area, budget, and preferences. Emotional data is also passed to the model, which then influences the design.

[0636] Step 5:

[0637] The server uses a generative AI model to generate the optimal rooftop greenery design. Based on the input data, the generative AI model simulates the optimal layout, plant species, and eco-friendly materials. In doing so, it takes into account the emotion engine's relaxed state and selects plants and placements that have a high relaxing effect.

[0638] Step 6:

[0639] The server organizes and processes the generated design data and converts it into a format that is easy for users to understand. For example, it generates a visual layout diagram or a concrete plantation proposal. The generated design proposal is sent to the terminal in JSON format.

[0640] Step 7:

[0641] The device will then display design proposals received from the server to the user, such as "A greening design for a 100.5 square meter rooftop in Tokyo that will create a relaxing environment, including native plants and solar panels, within a budget of $50,000."

[0642] Step 8:

[0643] The user can review the proposed design and provide feedback or request revisions as necessary. If revisions are required, the device resends the new information to the server, and the design is regenerated using the generative AI model.

[0644] Step 9:

[0645] Based on the generated design, the user requests installation services. The server receives this request and forwards it to the relevant service provider, who then schedules and initiates the actual installation.

[0646] Step 10:

[0647] The device receives notifications from the server and displays installation and maintenance schedules and progress to the user. For ongoing maintenance, the server periodically checks the user's emotional data and provides an optimal maintenance plan. For example, if the user is feeling stressed, it will suggest adding or rearranging plants that have a relaxing effect.

[0648] Example 2

[0649] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0650] In modern urban environments, there is a need to make effective use of unused rooftop space, but proposing and implementing effective designs is difficult. Another issue is the lack of personalized design proposals that take into account the user's emotional state, which tends to decrease user satisfaction. Furthermore, there is a lack of standardization in the provision of installation and maintenance services, making ongoing maintenance difficult.

[0651] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0652] In this invention, the server includes: means for receiving information from a user regarding the location, area, budget, and preferences of the rooftop; means for storing the received information in a database; means including a generative AI model for generating an optimal rooftop greening design based on the stored information and the user's emotional state; and means for providing the generated design to the user. This allows for the proposal of an optimal rooftop greening design that reflects the user's emotional state, enabling the effective use of unused rooftop space as a sustainable ecosystem. It also improves user satisfaction and streamlines installation and maintenance.

[0653] "User" refers to an individual or organization that uses the system to request a rooftop greenery design.

[0654] "Rooftop location" refers to the geographic location of the rooftop of the building where the user wishes to green.

[0655] "Area" refers to a numerical value indicating the size of the rooftop to be greened.

[0656] "Budget" refers to the maximum amount of funds set by a User to be used for a Green Roof Project.

[0657] "Preferences" refers to information that indicates personal preferences such as the type of plants or installations (e.g., solar panels) desired by the user.

[0658] An "emotion engine" refers to a technology that analyzes a user's facial expressions, input speed, keystrokes, etc. in real time to infer the user's emotional state.

[0659] "Generative AI model" refers to an artificial intelligence model that generates optimal rooftop greenery designs based on input information.

[0660] "Database" refers to a storage device within the system for storing information and emotional data received from users.

[0661] "Design" refers to a rooftop greenery design proposal generated based on the user's input information and emotional data.

[0662] "Installation and maintenance" refers to a series of services that involve the implementation and subsequent maintenance of a green roof based on the generated design.

[0663] This invention relates to a system for converting underutilized rooftop space into a sustainable, green ecosystem. In particular, it combines an emotion engine that recognizes user emotions to improve the user experience. The system receives user input, stores it in a database, and uses a generative AI model to generate optimal rooftop greening designs. Furthermore, it has the ability to analyze the user's emotional state using the emotion engine and reflect this in the customization of the design.

[0664] Hardware and software configuration

[0665] This system uses the following hardware and software:

[0666] Terminal: The device through which a user enters information (e.g., a computer, tablet, smartphone)

[0667] Server: A central processing unit for storing information in a database and running generative AI models.

[0668] Database: A data storage system for storing user input information and emotion data.

[0669] Generative AI model: an algorithm for generating optimal green roof designs based on stored data

[0670] Emotion engine: Software that analyzes a user's facial expressions, typing speed, and keystrokes to recognize their emotional state.

[0671] Program processing and specific examples

[0672] User Input

[0673] When a user enters information into a form on their device, they provide the necessary information (e.g., rooftop location, area, budget, and preferences). The emotion engine then analyzes the user's facial expressions, typing speed, and keystrokes in real time to collect emotional data. For example, if a user enters "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels," the emotion engine determines that the user is relaxed.

[0674] Data storage

[0675] The server receives the information and emotional data sent by the user and stores it in a database. This stored data includes the emotional state along with the information entered by the user. Specifically, data such as "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels," and "relaxed" are stored.

[0676] Design generation using AI models

[0677] The server inputs the stored data into a generative AI model to generate an optimal design that reflects the user's emotional state. The generative AI model takes location information, rooftop area, budget, and preferences as input and simulates the optimal layout, plant species, and eco-friendly materials. For example, if a user wants to relax, a design that creates a comfortable space will be suggested.

[0678] Design provision

[0679] The server processes the generated design results and presents them to the user in an easy-to-understand format. For example, a green design for a 100.5 square meter rooftop in Tokyo, including native plants and solar panels, that creates a relaxing environment within a budget of $50,000, is displayed on the device.

[0680] Installation and Maintenance

[0681] When a user requests a service, the server transfers it to the relevant service provider, who then begins the actual installation process. Furthermore, ongoing maintenance services are also provided. The server periodically checks the user's emotional data and provides an optimal maintenance plan based on that data. For example, if the user is feeling stressed, the server will suggest additional plans to enhance relaxation during maintenance.

[0682] Prompt Sentence Examples

[0683] "I would like to create a relaxing environment on a 100.5 square meter rooftop in Tokyo, including native plants and solar panels, within a budget of $50,000. Please suggest the best design."

[0684] In this way, the system generates optimal rooftop greening designs and provides installation and maintenance services based on the user's input information and emotional state, resulting in improved urban environments and increased resident satisfaction.

[0685] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0686] Step 1: User Enters Information

[0687] Input: The user enters information about the rooftop location, area, budget, and preferences into a form on the terminal.

[0688] Action: User enters "Tokyo", "100.5 sq m", "50,000 dollars", "native plants, solar panels".

[0689] Data processing: The emotion engine analyzes the user's facial expressions, typing speed, and keystrokes in real time to generate emotion data.

[0690] Output: Emotional data such as "relaxed" is added to the user's input information.

[0691] Step 2: Receiving and storing data

[0692] Input: User information and emotional data sent from the device.

[0693] How it works: The server receives the information sent by the user and stores it in a database.

[0694] Data processing: User input information and emotion data are converted into a unified format and saved.

[0695] Output: The database contains information such as "Tokyo", "100.5 sq m", "50,000 dollars", "native plants, solar panels", and "relaxation".

[0696] Step 3: Generate a design using a generative AI model

[0697] Input: User information and emotion data stored in a database.

[0698] How it works: The server inputs data into a generative AI model to generate an optimal green roof design.

[0699] Data Processing: Generative AI models simulate optimal layouts, plant species, and eco-friendly materials based on location, area, budget, preferences, and sentiment data.

[0700] Output: Generated design ideas (e.g., a relaxing arrangement of native plants and solar panels).

[0701] Step 4: Submit your design

[0702] Input: Design proposals output by the generative AI model.

[0703] Operation: The server processes the generated design into a format that is easy for the user to understand and sends it to the terminal.

[0704] Data processing: Visualize the design proposal and format it with explanatory text.

[0705] Output: "A green design for a 100.5 square meter rooftop in Tokyo that creates a relaxing environment, including native plants and solar panels, within a budget of $50,000," is displayed on the device.

[0706] Step 5: Installation and Maintenance

[0707] Input: A request from a user for installation and maintenance services.

[0708] How it works: A user requests a service on their device and the information is sent to the server, which then forwards it to the service provider, who then begins preparing the specific installation and maintenance plan.

[0709] Data processing: Regularly check user sentiment data and customize maintenance plans.

[0710] Output: An optimal maintenance plan is provided based on the user's emotional state. For example, if the user is feeling stressed, additional maintenance tasks that will enhance relaxation are suggested.

[0711] In this way, by performing specific operations at each step, an optimal rooftop greening design that takes into account the user's emotions can be generated, and subsequent installation and maintenance can be achieved.

[0712] (Application example 2)

[0713] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0714] Converting unused rooftop space or factory space into a sustainable, lush green ecosystem will improve the environment and enhance people's quality of life, but it requires specialized knowledge and effort. It is also difficult to customize the design based on the individual feelings and desires of users, and efficient management and maintenance are also challenges. This is particularly difficult to achieve in specialized environments such as factories, so there is a need for a greening system that achieves both sustainability and user satisfaction.

[0715] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0716] In this invention, the server includes means for receiving information from a user regarding the location, size, cost, and preferences of the building, means for storing the received information in a data storage unit, means including a generative AI model for generating an optimal rooftop greening design based on the stored information, means for providing the generated design to the user, emotion recognition means for analyzing the user's emotional state and reflecting the analysis results in the design, means for sending instructions to a work robot for realizing the greening design, and means for continuously monitoring and maintaining the designed greening area. This makes it possible to efficiently realize a sustainable greening design that corresponds to the individual emotions and preferences of the user and to easily perform maintenance after installation.

[0717] "Users" are those who use the system to customize and maintain rooftop and factory greenery designs.

[0718] "Building location" refers to the specific location information of the rooftop or factory space where the green design will be applied.

[0719] "Size" refers to the area of ​​the building rooftop or factory interior that will be greened.

[0720] "Expenses" refers to the budget that a user can spend on greenery design and maintenance.

[0721] "Desires" are specific conditions and preferences that users desire in green design.

[0722] The "data storage unit" is an area for storing information and emotion data received from the user.

[0723] A "generative AI model" is an artificial intelligence system that generates optimal greening designs based on information provided by the user.

[0724] "Emotion recognition means" is a technology that analyzes the user's emotional state and reflects the results of that analysis in the design.

[0725] A "working robot" is an autonomous device that performs physical tasks to realize a green design.

[0726] "Maintenance measures" are techniques and devices that continuously monitor green areas and carry out the necessary management and maintenance work.

[0727] MODE FOR CARRYING OUT THE INVENTION

[0728] This invention is a system for converting unused space in a factory or on a roof into a sustainable green ecosystem. The following describes in detail how this system can be implemented.

[0729] First, a user uses a device such as a smartphone or tablet to input information about the location, size, cost, and preferences of the building to be greened. For example, a user might input the following information:

[0730] Location: Factory A

[0731] Area: 500.0 square meters

[0732] Expenses: $100,000

[0733] Preferred: Native plants, eco-friendly materials

[0734] Once the user has completed their input, the device sends this information to the server. The emotion engine, which is an emotion recognition means, then analyzes the user's facial expressions and input speed to determine their emotional state (e.g., "relaxed" or "stressed"). The user's input information, along with this emotion data, is stored in the data storage unit.

[0735] The server then uses a generative AI model based on the stored information to generate an optimal green space design. This generative AI model simulates the optimal layout, plant species, and sustainable materials based on the user's location, size, budget, and desired information. It also takes the user's emotional state into account to suggest designs that suit their emotions. For example, if the user is looking to relax, the server will recommend specific plants and placements to create a comfortable space.

[0736] The generated green design is then sent back to the user's device from the server and displayed in a visually easy-to-understand format. As a concrete example, the following green design proposals are provided:

[0737] Location: Factory A

[0738] Area: 500.0 square meters

[0739] Expenses: $100,000

[0740] Proposal: Use native plants and eco-friendly materials, with specific layout and plant species selected to create a relaxing environment.

[0741] Furthermore, once the greening design is approved, the server sends instructions to the work robot to realize the greening design. The work robot then carries out the greening work based on the specified design, automatically placing the plants and installing the necessary infrastructure.

[0742] Once installed, the robot will continuously monitor and maintain the designed green area. The work robot will periodically check the condition of the area and automatically carry out any necessary maintenance work. It is also possible to customize maintenance plans based on the analysis results of the emotion engine. For example, if it determines that "maintenance is required," it will perform additional watering or pruning of plants.

[0743] An example prompt would send the following information to the server:

[0744] Location: Factory rooftop

[0745] Area: 700.0 square meters

[0746] Expenses: $150,000

[0747] Hope: Sustainable materials, automated irrigation systems

[0748] Emotional state: Relaxed

[0749] In this way, this system efficiently realizes sustainable greening designs that respond to the individual feelings and desires of users, and also makes maintenance after installation easy.

[0750] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0751] Step 1:

[0752] The user inputs information for the greenery design using a smartphone or tablet. The user fills in the building's location, size, cost, and preferences into an input form on the device. This input data might include, for example, "Factory A, 500.0 square meters, $100,000, native plants, eco-friendly materials." Once the input is complete, the device sends this information to the server.

[0753] Step 2:

[0754] The device acquires the user's emotional data. Specifically, it uses the device's built-in camera and emotion recognition software (emotion engine) to analyze the user's facial expressions and input speed. This determines the user's emotional state, such as whether they are "relaxed" or "stressed." This emotional data is also sent to the server.

[0755] Step 3:

[0756] The server stores the user's input information and emotion data in the data repository. The input data is "Factory A, 500.0 square meters, $100,000, native plants, eco-friendly materials," and the emotion data is "Relaxed." This prepares the data for use in subsequent processing.

[0757] Step 4:

[0758] The server uses a generative AI model based on the stored data to generate an optimal greening design. The input data includes location, size, cost, desired information, and emotional data, and outputs a specific example of a greening design: a 500.0 square meter relaxing environment using native plants and eco-friendly materials. This process involves a series of data processing and calculations.

[0759] Step 5:

[0760] The server provides the generated greening design to the user's device. The design created by the generative AI model is displayed on the device in a visually easy-to-understand format. The user can then review the specific design and provide feedback to the server, indicating approval or corrections.

[0761] Step 6:

[0762] After receiving the user's approval, the server sends instructions to the robot to realize the greening design. The robot then carries out the specific layout and use of materials based on the design data received from the server, and also prepares the necessary infrastructure and arranges the plants.

[0763] Step 7:

[0764] The robot carries out the greening work based on the design. The robot places plants in the designated locations and installs sustainable materials. After completing the installation work, it reports its progress to the server.

[0765] Step 8:

[0766] The server continuously monitors the completed greening area and sends maintenance instructions to the work robot. It automatically performs regular observations and necessary maintenance tasks (e.g., watering and pruning). It also takes into account data from emotion recognition and performs maintenance to increase user satisfaction.

[0767] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0768] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0769] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0770] [Third embodiment]

[0771] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0772] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0773] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0774] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0775] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0776] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0777] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0778] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0779] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0780] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0781] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0782] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0783] This invention relates to a system for greening unused rooftop spaces in urban areas and transforming them into sustainable ecosystems. The invention builds a system that uses generative AI models to propose optimal rooftop greening designs based on information collected from users. The services provided also include installation and maintenance.

[0784] Overall system picture

[0785] 1. An interface where users enter information

[0786] The user inputs information about the rooftop's location, area, budget, and preferences (e.g., type of plants, desired equipment, etc.) through a terminal. This information becomes the basis for the system to generate the optimal design.

[0787] 2. A server that receives the information and stores it in a database

[0788] The server receives the user's input and stores it in a database, which is then fed into a generative AI model.

[0789] 3. Generating designs using generative AI models

[0790] The server sends the stored information to a generative AI model, which generates a rooftop green design that best suits the user's requirements. The AI ​​model takes location information, rooftop area, budget, and preferences as inputs and simulates the optimal layout, plant species to use, and eco-friendly materials.

[0791] 4. Providing design proposals

[0792] The generated design is provided to the user through the server, and the proposal is displayed in a visual form that allows the user to easily understand and implement it.

[0793] 5. Installation and maintenance services

[0794] The system also provides specific installation and ongoing maintenance services, allowing users to select the services they need and receive assistance with post-installation maintenance work.

[0795] Program processing overview

[0796] User Input

[0797] The user enters information into the form on the terminal and presses the "Submit" button. For example, the user enters information such as "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels."

[0798] Data storage

[0799] The server receives the information sent by the user and stores it in a database.

[0800] Design generation using AI models

[0801] The server inputs the stored data into a generative AI model to generate an optimal design. For example, the AI ​​model suggests optimal layouts for rooftop spaces in Tokyo, including native plants and solar panels, based on spaces of 100.5 square meters or more.

[0802] Design provision

[0803] The server processes the generated design results and provides them to the user in a visually easy-to-understand format, resulting in a proposed green design for a 100.5 square meter rooftop in Tokyo, including native plants and solar panels, within a budget of $50,000.

[0804] Installation and Maintenance

[0805] When a user requests installation services through the system, the server forwards the information to the relevant service provider, and the installation and maintenance are scheduled. For ongoing maintenance, the server also provides regular reminders and specific advice to the user.

[0806] In this way, the system of the present invention optimally utilizes underutilized rooftop space in urban areas based on user input, providing a sustainable, green ecosystem.

[0807] The processing flow will be explained below.

[0808] Step 1:

[0809] The user accesses the input form on the device and enters the necessary information (rooftop location, area, budget, and preferences). For example, the user enters "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels."

[0810] Step 2:

[0811] When the user clicks the "Submit" button on the input form, the device sends the entered information to the server. The sent data is sent to the endpoint in JSON format.

[0812] Step 3:

[0813] The server receives the information sent from the device, checks the integrity of the data, and stores the received information in a database.

[0814] Step 4:

[0815] The server retrieves stored user information from the database and passes it as input to the generative AI model, including location, rooftop area, budget, and preferences.

[0816] Step 5:

[0817] The server generates the optimal rooftop greenery design using a generative AI model, which simulates the optimal layout, plant species, and eco-friendly materials based on the input data.

[0818] Step 6:

[0819] The server organizes and processes the generated design data and converts it into a format that is easy for users to understand. Specifically, it generates design proposals in graphic and text formats.

[0820] Step 7:

[0821] The server generates design proposals and sends them to the device. For example, it might suggest a greening design for a 100.5 square meter rooftop in Tokyo, including native plants and solar panels, within a budget of $50,000.

[0822] Step 8:

[0823] The user reviews the proposed design and provides feedback or requests for revisions as needed, in which case the data is sent back to the server for re-evaluation by the generative AI model.

[0824] Step 9:

[0825] Based on the generated design, the user requests installation and maintenance services, and the server receives this request and forwards it to the relevant service provider.

[0826] Step 10:

[0827] The terminal receives notifications from the server and displays the installation and maintenance schedule and progress to the user, allowing the user to check the status of the service through the terminal.

[0828] Example 1

[0829] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0830] There are many unused rooftop spaces in urban areas, and there is a need to convert them into sustainable, lush green ecosystems. However, rooftop greening projects require specialized knowledge for design, installation, and maintenance, and there is a lack of a system that is easily accessible to many users. This invention solves this problem by providing a system that allows users to easily obtain optimal rooftop greening designs and smoothly implement and maintain them.

[0831] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0832] In this invention, the server includes: means for receiving information on the location, area, budget, and preferences of the rooftop from a user; means for storing the received information in a database; means including a generative AI model for generating an optimal green roof design based on the stored information; means for visually presenting the generated design to the user; means for forwarding a request for installation service from the user to a related service provider; and means for providing maintenance reminders and advice to the user. This enables users, even without specialized knowledge, to obtain an optimal design for converting underutilized rooftop space into a sustainable ecosystem and to easily install and maintain it.

[0833] "User" refers to an individual or corporation that uses the system to receive proposals for rooftop greening designs.

[0834] "Rooftop space" refers to the unused space located at the top of a building.

[0835] "Location information" is data that indicates the geographic location of a rooftop space, including the city and specific address.

[0836] "Area" is a numerical value that represents the size of the rooftop space, and is expressed in units such as square meters.

[0837] "Budget" refers to the amount of money a user can spend on a green roof project.

[0838] "Preferences" refer to individual requests and wishes of the user, such as the type of plants they want or the equipment they want to install.

[0839] A "database" is a digital system for structuring and storing received user information.

[0840] A "generative AI model" is an artificial intelligence model that proposes optimal rooftop greening designs based on user input data.

[0841] A "prompt sentence" is an input sentence that is used by a generative AI model to generate an optimal design based on specific information.

[0842] "Means for visual presentation" refers to a method for visually displaying the generated design to the user in an easy-to-understand manner.

[0843] "Service Provider" means a company or organization that provides green roof installation and maintenance services.

[0844] "Maintenance" refers to the periodic management work required to ensure that a green roof project is properly maintained.

[0845] "Reminders" are a means of notifying users of important maintenance dates and tasks.

[0846] "Advice" refers to specific instructions or recommendations provided by the system to the user.

[0847] This invention is a system for converting unused rooftop spaces in urban areas into sustainable ecosystems. Specifically, it uses a generative AI model based on information collected from users to provide optimal rooftop greening designs. The main components of the hardware and software used are a server, terminal, generative AI model, and database.

[0848] System Overview

[0849] The system has the following main functions:

[0850] 1. An interface that receives user input

[0851] 2. Data storage

[0852] 3. Generate the design

[0853] 4. Providing designs in visual form

[0854] 5. Installation and Maintenance

[0855] User Input

[0856] Users use a terminal to input information about the location, area, budget, and preferences of their rooftop. This information is collected through a form on the terminal and sent to the system by pressing a "Submit" button. For example, a user might enter information such as "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels."

[0857] Data storage

[0858] The server receives the information sent by the user and stores it in a database, which is later used to provide input data to the generative AI model. Specifically, the server analyzes the HTTP request and extracts the data sent. The extracted data is then stored in a database table.

[0859] Generate the design

[0860] The server uses a generative AI model to generate the optimal design based on the stored data. The generative AI model used is OpenAI's GPT-4, which creates prompts for design generation. For example, the following prompts are generated:

[0861] "Please suggest the best greening design, including native plants and solar panels, that can be applied to a 100.5 square meter rooftop space in Tokyo with a budget of $50,000."

[0862] The generative AI model simulates the optimal design based on this prompt and returns the results in text format to the server, which then stores the results in a database.

[0863] Providing visual design

[0864] The server uses templates to process the design into HTML or PDF format to provide a visual presentation of the resulting design. The results are then sent to the user via email or a dedicated dashboard. The result is a proposed green design for a 100.5 square meter rooftop in Tokyo, including native plants and solar panels, within a budget of $50,000.

[0865] Installation and Maintenance

[0866] When a user requests installation services through the system, the server forwards the information to the relevant service provider. Specifically, the server analyzes the request and sends a notification to the relevant service provider via API or email. The service provider then checks the schedule and contacts the user to confirm and arrange the date. The server also periodically provides the user with maintenance reminders and specific advice.

[0867] This allows users, even without specialized knowledge, to obtain the optimal design for transforming underutilized rooftop space into a sustainable ecosystem, and makes installation and ongoing management easy.

[0868] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0869] Step 1:

[0870] The user accesses the terminal interface and inputs information regarding the rooftop location, area, budget and preferences.

[0871] Input: A user enters information into a form, such as "Tokyo," "100.5 square meters," "$50,000," "native plants, solar panels."

[0872] Output: The entered information is sent to the server as form data.

[0873] Specific behavior: The user enters data into all fields and clicks the "Submit" button.

[0874] Step 2:

[0875] The server receives the information sent by the user and stores it in a database.

[0876] Input: The server receives the HTTP request and retrieves a payload containing the user's input data.

[0877] Output: The extracted data is saved in the corresponding tables in the database.

[0878] Specific operation: The server analyzes the received data, maps it to location information, area, budget, and preference fields, and stores it in a database.

[0879] Step 3:

[0880] The server retrieves information from the database and creates prompts to input into the generative AI model.

[0881] Input: User information retrieved from the database (e.g., "Tokyo", "100.5 sq m", "50,000 dollars", "native plants, solar panels")

[0882] Output: The prompt to send to the generative AI model (e.g., "Please suggest the optimal greening design, including native plants and solar panels, for a 100.5 square meter rooftop space in Tokyo with a budget of $50,000.")

[0883] Specific operation: The server automatically generates a prompt sentence based on the data it obtains and sends it to the generative AI model.

[0884] Step 4:

[0885] The generative AI model receives a prompt and generates an optimal green roof design.

[0886] Input: Prompt statement (e.g., "Please suggest the best greening design, including native plants and solar panels, for a 100.5 square meter rooftop space in Tokyo with a budget of $50,000.")

[0887] Output: Generated design data (e.g. layout diagram, plant list, list of materials used, etc.)

[0888] Specific operation: The generative AI model analyzes the prompt text and outputs the optimal design that meets the conditions in text format.

[0889] Step 5:

[0890] The server receives the generated design data and provides it visually to the user.

[0891] Input: Design data received from the generative AI model

[0892] Output: The design results provided to the user in a visually understandable format (e.g. HTML, PDF files)

[0893] Specific operation: The server processes the design data using the template and provides the design results to the user via a dedicated dashboard or email.

[0894] Step 6:

[0895] The user requests installation and maintenance services on the terminal.

[0896] Input: The user selects the installation service option and clicks the "Request" button.

[0897] Output: Installation service request information sent to the server

[0898] Specific operation: The user selects the installation service through the terminal interface and confirms the request.

[0899] Step 7:

[0900] The server forwards the installation service request information to the relevant service provider.

[0901] Input: Installation service request information sent by the user

[0902] Output: Request information sent to the service provider

[0903] Specific operation: The server analyzes the request and sends a notification to the relevant service provider via API or email.

[0904] Step 8:

[0905] The server provides users with maintenance reminders and advice.

[0906] Input: Internal data regarding the schedule and progress of scheduled maintenance

[0907] Output: Reminders and maintenance advice sent to users

[0908] What it does: The server automatically sends scheduled reminders to users and provides instructions for necessary maintenance tasks.

[0909] (Application example 1)

[0910] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0911] In modern urban areas, there is a large amount of unused rooftop space, and there is a need to make effective use of it. However, conventional rooftop greening projects are expensive and require specialized knowledge for design and maintenance, making them difficult for ordinary users to use. Furthermore, even when a design proposal for a rooftop greening system is received, it is difficult for users to understand or accept it because it is not visually specific. The present invention aims to solve these problems and provide a system that effectively converts unused rooftop space into a sustainable, lush ecosystem.

[0912] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0913] In this invention, the server includes means for receiving information on the location, area, budget, and preferences of the roof from the user, means for storing the received information in a database, means including a generative AI model for generating an optimal green roof design based on the stored information, means for providing the generated design to the user, and means for providing a visual proposal of the design to the user via a smartphone, thereby enabling the user to visually understand the optimal green roof design that suits their preferences and budget even without specialized knowledge.

[0914] The "means for receiving information about the location, area, budget, and preferences of the rooftop from the user" is a mechanism that provides an interface for the user to input basic information about the rooftop and individual preferences.

[0915] "Means for storing said received information in a database" refers to a system that stores the information entered by a user in digital form so that it can be used for subsequent processing and analysis.

[0916] "Means including a generative AI model that generates optimal rooftop greenery designs based on stored information" refers to a mechanism that utilizes information stored in a database and uses artificial intelligence technology to create optimal designs that meet user requirements.

[0917] A "means for providing the generated design to a user" is a system or method for presenting the design generated by the AI ​​model to a user in an effective and understandable manner.

[0918] The "means for providing a visual proposal of the design to the user via a smartphone" is a mechanism for visually displaying and proposing the generated design to the user using a smartphone.

[0919] This invention relates to a system for greening unused rooftop spaces in urban areas and transforming them into sustainable ecosystems. The invention builds a system that uses generative AI models to propose optimal rooftop greening designs based on information collected from users. The services provided also include installation and maintenance.

[0920] Overall system picture

[0921] The system consists of several major components, which are:

[0922] 1. An interface where users enter information

[0923] Users enter information about the rooftop's location, area, budget, and preferences via their smartphone. Specifically, they fill out a form in the application and press the "Submit" button. This information becomes the basis for the system to generate the optimal design.

[0924] 2. A server that receives the information and stores it in a database

[0925] The server receives user input and stores it in a database, which is then fed into the generative AI model. The database used is SQLite, which ensures accurate storage and access of information.

[0926] 3. Generating designs using generative AI models

[0927] The server sends the stored information to a generative AI model, which generates a rooftop green design that best suits the user's requirements. Specifically, it takes location information, rooftop area, budget, and preferences as input and simulates the optimal layout, plant species to use, and eco-friendly materials. The generative AI model uses OpenAI's API to generate prompts and create the design.

[0928] 4. Providing design proposals

[0929] The generated design is provided to the user via the server. The proposal is displayed to the user in a visually easy-to-understand format on their smartphone. For example, a proposed greening design for a 100.5 square meter rooftop in Tokyo, including native plants and solar panels, within a budget of $50,000, might be proposed.

[0930] 5. Installation and maintenance services

[0931] When a user requests installation services through the system, the server forwards the information to the relevant service provider, and the installation and maintenance are scheduled. For ongoing maintenance, the server also provides regular reminders and specific advice to the user.

[0932] Examples of concrete examples and prompts

[0933] An example of a user inputting specific information is "Tokyo," "150 square meters," "70,000 dollars," "medicinal plants, relaxation area." The following is an example of a prompt sent to the generative AI model based on this input information:

[0934] Create a design for your green roof project:

[0935] Location: Tokyo

[0936] Area: 150 square meters

[0937] Budget: $70,000

[0938] Favorites: Medicinal plants, relaxation areas

[0939] This allows users to visually understand the optimal rooftop green design that suits their tastes and budget, even without specialized knowledge. It also makes it easy to connect with installation and maintenance services. The system provides sustainable green ecosystems in urban areas and promotes the effective use of underutilized rooftop space.

[0940] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0941] Step 1:

[0942] The user uses a smartphone to enter information about the rooftop's location, area, budget, and preferences into the application's form and presses the "Submit" button.

[0943] Input: Information entered by the user (e.g., "Tokyo", "150 square meters", "70,000 dollars", "medicinal plants, relaxation area")

[0944] Output: User information sent to the server

[0945] What happens: A user enters information into an application, clicks a button, and the information is sent to a server.

[0946] Step 2:

[0947] The server receives the information sent by the user and stores it in a database.

[0948] Input: Information received from the user

[0949] Output: User information stored in the database

[0950] Specific operation: The server stores user information (location, area, budget, preferences) in an SQLite database.

[0951] Step 3:

[0952] The server retrieves the stored data and sends it to a generative AI model to generate the optimal green roof design.

[0953] Input: Saved user information

[0954] Output: Design proposals generated by the generative AI model

[0955] How it works: The server retrieves user information from the database and sends it as a prompt to the generative AI model. OpenAI's API then generates the optimal design based on the prompt.

[0956] Step 4:

[0957] The server receives the design output by the generative AI model and presents it visually to the user.

[0958] Input: Design suggestions from a generative AI model

[0959] Output: Visually displayed design proposal

[0960] Specific operation: The server receives the generated design and provides it visually to the user through a smartphone application. The user can view the design details on the smartphone screen.

[0961] Step 5:

[0962] If the user is satisfied with the design proposal, they request installation and maintenance services.

[0963] Input: User request

[0964] Output: Forwarding of appropriate information to the service provider

[0965] Specific operation: When a user requests installation services through a smartphone application, the server forwards the information to the relevant service provider, and the installation and maintenance is scheduled.

[0966] This allows users, even those without specialist knowledge, to visually understand the optimal rooftop greening design that suits their tastes and budget, and easily request installation and maintenance services.

[0967] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0968] This invention relates to a system for transforming underutilized rooftop spaces into sustainable green ecosystems, and in particular to a system that enhances the user experience by incorporating an emotion engine that recognizes user emotions. The system receives user input, stores it in a database, and generates optimal rooftop greening designs using a generative AI model. It also has the ability to analyze the user's emotional state using the emotion engine and reflect this in the customized design.

[0969] Overall system picture

[0970] 1. An interface where users enter information

[0971] When a user accesses the input form on the device and enters the required information (rooftop location, area, budget, and preferences), the emotion engine analyzes the user's facial expressions, typing speed, keystrokes, etc. to collect emotional data, which is then sent to the system.

[0972] 2. A server that receives the information and stores it in a database

[0973] The server receives the user's input information and emotional data and stores it in a database. For example, emotional data such as "relaxed" and "stressed" are stored along with user information such as "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels."

[0974] 3. Generating designs using generative AI models

[0975] The server uses a generative AI model to generate the optimal rooftop greening design based on the stored information and emotion data. The generative AI model receives location information, rooftop area, budget, and preferences as input, and simulates the optimal layout, plant species, and eco-friendly materials. The analysis results of the emotion engine are also reflected, and a design proposal suited to the user's emotions is made.

[0976] 4. Providing design proposals

[0977] The generated design is provided to the user via the server. For example, if the user wants to relax, the server will recommend plant selection and placement to create a tranquil environment. The server sends the generated design to the device in a visually easy-to-understand format.

[0978] 5. Installation and maintenance services

[0979] The system also provides specific installation and ongoing maintenance services. Users can select the services they need through the system and receive assistance with maintenance work after installation. The server can customize the timing and method of maintenance based on the user's emotional data.

[0980] Program processing overview

[0981] User Input

[0982] As users enter information into a form on their device, the emotion engine recognizes their facial expressions and typing speed to analyze their emotional state. For example, if a user enters "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels," and their facial expression indicates that they are relaxed.

[0983] Data storage

[0984] The server receives the information and emotional data sent by the user and stores them in a database. The stored data includes the emotional state as well as the information entered by the user.

[0985] Design generation using AI models

[0986] The server inputs the stored data into a generative AI model to generate an optimal design that reflects the user's emotional state. For example, if a user wants to relax, a design that creates a comfortable space will be suggested.

[0987] Design provision

[0988] The server processes the generated design results and provides them to the user in an easy-to-understand format. For example, it provides a green design for a 100.5 square meter rooftop in Tokyo that creates a relaxing environment, including native plants and solar panels, within a budget of $50,000.

[0989] Installation and Maintenance

[0990] When a user requests a service, the server transfers it to the relevant service provider, who then installs it. For ongoing maintenance, the server periodically checks the user's emotional data and provides the optimal maintenance plan.

[0991] As described above, the system of the present invention generates optimal rooftop greening designs based on the user's input information and emotional state, and provides installation and maintenance services, thereby improving the urban environment and increasing resident satisfaction.

[0992] The processing flow will be explained below.

[0993] Step 1:

[0994] The user accesses an input form on the device and enters information about the location, area, budget, and preferences of the rooftop. For example, the user might enter "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels." The device's built-in camera, microphone, and keystroke analysis tool record the user's facial expressions, voice, and typing speed, and the emotion engine analyzes the emotional data.

[0995] Step 2:

[0996] When the user has finished entering information, they press the "Send" button. The device sends the user's input information and emotion data in JSON format to the server. For example, the following data is sent:

[0997] json

[0998] {

[0999] "location": "Tokyo",

[1000] "rooftop_area": ​​100.5,

[1001] "budget": 50000,

[1002] "preferences": "Native plants, Solar panels",

[1003] "emotional_state": "Relaxed"

[1004] }

[1005] Step 3:

[1006] The server receives the information sent from the device and checks the integrity of the data. The server then stores the received information in a database. For example, information such as "User ID," "Tokyo," "100.5 square meters," "50,000 dollars," "Native plants, solar panels," and "Relax" is stored.

[1007] Step 4:

[1008] The server retrieves stored user information and emotional data from the database and passes it as input to the generative AI model. The input data includes location, rooftop area, budget, and preferences. Emotional data is also passed to the model, which then influences the design.

[1009] Step 5:

[1010] The server uses a generative AI model to generate the optimal rooftop greenery design. Based on the input data, the generative AI model simulates the optimal layout, plant species, and eco-friendly materials. In doing so, it takes into account the emotion engine's relaxed state and selects plants and placements that have a high relaxing effect.

[1011] Step 6:

[1012] The server organizes and processes the generated design data and converts it into a format that is easy for users to understand. For example, it generates a visual layout diagram or a concrete plantation proposal. The generated design proposal is sent to the terminal in JSON format.

[1013] Step 7:

[1014] The device will then display design proposals received from the server to the user, such as "A greening design for a 100.5 square meter rooftop in Tokyo that will create a relaxing environment, including native plants and solar panels, within a budget of $50,000."

[1015] Step 8:

[1016] The user can review the proposed design and provide feedback or request revisions as necessary. If revisions are required, the device resends the new information to the server, and the design is regenerated using the generative AI model.

[1017] Step 9:

[1018] Based on the generated design, the user requests installation services. The server receives this request and forwards it to the relevant service provider, who then schedules and initiates the actual installation.

[1019] Step 10:

[1020] The device receives notifications from the server and displays installation and maintenance schedules and progress to the user. For ongoing maintenance, the server periodically checks the user's emotional data and provides an optimal maintenance plan. For example, if the user is feeling stressed, it will suggest adding or rearranging plants that have a relaxing effect.

[1021] Example 2

[1022] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1023] In modern urban environments, there is a need to make effective use of unused rooftop space, but proposing and implementing effective designs is difficult. Another issue is the lack of personalized design proposals that take into account the user's emotional state, which tends to decrease user satisfaction. Furthermore, there is a lack of standardization in the provision of installation and maintenance services, making ongoing maintenance difficult.

[1024] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1025] In this invention, the server includes: means for receiving information from a user regarding the location, area, budget, and preferences of the rooftop; means for storing the received information in a database; means including a generative AI model for generating an optimal rooftop greening design based on the stored information and the user's emotional state; and means for providing the generated design to the user. This allows for the proposal of an optimal rooftop greening design that reflects the user's emotional state, enabling the effective use of unused rooftop space as a sustainable ecosystem. It also improves user satisfaction and streamlines installation and maintenance.

[1026] "User" refers to an individual or organization that uses the system to request a rooftop greenery design.

[1027] "Rooftop location" refers to the geographic location of the rooftop of the building where the user wishes to green.

[1028] "Area" refers to a numerical value indicating the size of the rooftop to be greened.

[1029] "Budget" refers to the maximum amount of funds set by a User to be used for a Green Roof Project.

[1030] "Preferences" refers to information that indicates personal preferences such as the type of plants or installations (e.g., solar panels) desired by the user.

[1031] An "emotion engine" refers to a technology that analyzes a user's facial expressions, input speed, keystrokes, etc. in real time to infer the user's emotional state.

[1032] "Generative AI model" refers to an artificial intelligence model that generates optimal rooftop greenery designs based on input information.

[1033] "Database" refers to a storage device within the system for storing information and emotional data received from users.

[1034] "Design" refers to a rooftop greenery design proposal generated based on the user's input information and emotional data.

[1035] "Installation and maintenance" refers to a series of services that involve the implementation and subsequent maintenance of a green roof based on the generated design.

[1036] This invention relates to a system for converting underutilized rooftop space into a sustainable, green ecosystem. In particular, it combines an emotion engine that recognizes user emotions to improve the user experience. The system receives user input, stores it in a database, and uses a generative AI model to generate optimal rooftop greening designs. Furthermore, it has the ability to analyze the user's emotional state using the emotion engine and reflect this in the customization of the design.

[1037] Hardware and software configuration

[1038] This system uses the following hardware and software:

[1039] Terminal: The device through which a user enters information (e.g., a computer, tablet, smartphone)

[1040] Server: A central processing unit for storing information in a database and running generative AI models.

[1041] Database: A data storage system for storing user input information and emotion data.

[1042] Generative AI model: an algorithm for generating optimal green roof designs based on stored data

[1043] Emotion engine: Software that analyzes a user's facial expressions, typing speed, and keystrokes to recognize their emotional state.

[1044] Program processing and specific examples

[1045] User Input

[1046] When a user enters information into a form on their device, they provide the necessary information (e.g., rooftop location, area, budget, and preferences). The emotion engine then analyzes the user's facial expressions, typing speed, and keystrokes in real time to collect emotional data. For example, if a user enters "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels," the emotion engine determines that the user is relaxed.

[1047] Data storage

[1048] The server receives the information and emotional data sent by the user and stores it in a database. This stored data includes the emotional state along with the information entered by the user. Specifically, data such as "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels," and "relaxed" are stored.

[1049] Design generation using AI models

[1050] The server inputs the stored data into a generative AI model to generate an optimal design that reflects the user's emotional state. The generative AI model takes location information, rooftop area, budget, and preferences as input and simulates the optimal layout, plant species, and eco-friendly materials. For example, if a user wants to relax, a design that creates a comfortable space will be suggested.

[1051] Design provision

[1052] The server processes the generated design results and presents them to the user in an easy-to-understand format. For example, a green design for a 100.5 square meter rooftop in Tokyo, including native plants and solar panels, that creates a relaxing environment within a budget of $50,000, is displayed on the device.

[1053] Installation and Maintenance

[1054] When a user requests a service, the server transfers it to the relevant service provider, who then begins the actual installation process. Furthermore, ongoing maintenance services are also provided. The server periodically checks the user's emotional data and provides an optimal maintenance plan based on that data. For example, if the user is feeling stressed, the server will suggest additional plans to enhance relaxation during maintenance.

[1055] Prompt Sentence Examples

[1056] "I would like to create a relaxing environment on a 100.5 square meter rooftop in Tokyo, including native plants and solar panels, within a budget of $50,000. Please suggest the best design."

[1057] In this way, the system generates optimal rooftop greening designs and provides installation and maintenance services based on the user's input information and emotional state, resulting in improved urban environments and increased resident satisfaction.

[1058] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1059] Step 1: User Enters Information

[1060] Input: The user enters information about the rooftop location, area, budget, and preferences into a form on the terminal.

[1061] Action: User enters "Tokyo", "100.5 sq m", "50,000 dollars", "native plants, solar panels".

[1062] Data processing: The emotion engine analyzes the user's facial expressions, typing speed, and keystrokes in real time to generate emotion data.

[1063] Output: Emotional data such as "relaxed" is added to the user's input information.

[1064] Step 2: Receiving and storing data

[1065] Input: User information and emotional data sent from the device.

[1066] How it works: The server receives the information sent by the user and stores it in a database.

[1067] Data processing: User input information and emotion data are converted into a unified format and saved.

[1068] Output: The database contains information such as "Tokyo", "100.5 sq m", "50,000 dollars", "native plants, solar panels", and "relaxation".

[1069] Step 3: Generate a design using a generative AI model

[1070] Input: User information and emotion data stored in a database.

[1071] How it works: The server inputs data into a generative AI model to generate an optimal green roof design.

[1072] Data Processing: Generative AI models simulate optimal layouts, plant species, and eco-friendly materials based on location, area, budget, preferences, and sentiment data.

[1073] Output: Generated design ideas (e.g., a relaxing arrangement of native plants and solar panels).

[1074] Step 4: Submit your design

[1075] Input: Design proposals output by the generative AI model.

[1076] Operation: The server processes the generated design into a format that is easy for the user to understand and sends it to the terminal.

[1077] Data processing: Visualize the design proposal and format it with explanatory text.

[1078] Output: "A green design for a 100.5 square meter rooftop in Tokyo that creates a relaxing environment, including native plants and solar panels, within a budget of $50,000," is displayed on the device.

[1079] Step 5: Installation and Maintenance

[1080] Input: A request from a user for installation and maintenance services.

[1081] How it works: A user requests a service on their device and the information is sent to the server, which then forwards it to the service provider, who then begins preparing the specific installation and maintenance plan.

[1082] Data processing: Regularly check user sentiment data and customize maintenance plans.

[1083] Output: An optimal maintenance plan is provided based on the user's emotional state. For example, if the user is feeling stressed, additional maintenance tasks that will enhance relaxation are suggested.

[1084] In this way, by performing specific operations at each step, an optimal rooftop greening design that takes into account the user's emotions can be generated, and subsequent installation and maintenance can be achieved.

[1085] (Application example 2)

[1086] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1087] Converting unused rooftop space or factory space into a sustainable, lush green ecosystem will improve the environment and enhance people's quality of life, but it requires specialized knowledge and effort. It is also difficult to customize the design based on the individual feelings and desires of users, and efficient management and maintenance are also challenges. This is particularly difficult to achieve in specialized environments such as factories, so there is a need for a greening system that achieves both sustainability and user satisfaction.

[1088] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1089] In this invention, the server includes means for receiving information from a user regarding the location, size, cost, and preferences of the building, means for storing the received information in a data storage unit, means including a generative AI model for generating an optimal rooftop greening design based on the stored information, means for providing the generated design to the user, emotion recognition means for analyzing the user's emotional state and reflecting the analysis results in the design, means for sending instructions to a work robot for realizing the greening design, and means for continuously monitoring and maintaining the designed greening area. This makes it possible to efficiently realize a sustainable greening design that corresponds to the individual emotions and preferences of the user and to easily perform maintenance after installation.

[1090] "Users" are those who use the system to customize and maintain rooftop and factory greenery designs.

[1091] "Building location" refers to the specific location information of the rooftop or factory space where the green design will be applied.

[1092] "Size" refers to the area of ​​the building rooftop or factory interior that will be greened.

[1093] "Expenses" refers to the budget that a user can spend on greenery design and maintenance.

[1094] "Desires" are specific conditions and preferences that users desire in green design.

[1095] The "data storage unit" is an area for storing information and emotion data received from the user.

[1096] A "generative AI model" is an artificial intelligence system that generates optimal greening designs based on information provided by the user.

[1097] "Emotion recognition means" is a technology that analyzes the user's emotional state and reflects the results of that analysis in the design.

[1098] A "working robot" is an autonomous device that performs physical tasks to realize a green design.

[1099] "Maintenance measures" are techniques and devices that continuously monitor green areas and carry out the necessary management and maintenance work.

[1100] MODE FOR CARRYING OUT THE INVENTION

[1101] This invention is a system for converting unused space in a factory or on a roof into a sustainable green ecosystem. The following describes in detail how this system can be implemented.

[1102] First, a user uses a device such as a smartphone or tablet to input information about the location, size, cost, and preferences of the building to be greened. For example, a user might input the following information:

[1103] Location: Factory A

[1104] Area: 500.0 square meters

[1105] Expenses: $100,000

[1106] Preferred: Native plants, eco-friendly materials

[1107] Once the user has completed their input, the device sends this information to the server. The emotion engine, which is an emotion recognition means, then analyzes the user's facial expressions and input speed to determine their emotional state (e.g., "relaxed" or "stressed"). The user's input information, along with this emotion data, is stored in the data storage unit.

[1108] The server then uses a generative AI model based on the stored information to generate an optimal green space design. This generative AI model simulates the optimal layout, plant species, and sustainable materials based on the user's location, size, budget, and desired information. It also takes the user's emotional state into account to suggest designs that suit their emotions. For example, if the user is looking to relax, the server will recommend specific plants and placements to create a comfortable space.

[1109] The generated green design is then sent back to the user's device from the server and displayed in a visually easy-to-understand format. As a concrete example, the following green design proposals are provided:

[1110] Location: Factory A

[1111] Area: 500.0 square meters

[1112] Expenses: $100,000

[1113] Proposal: Use native plants and eco-friendly materials, with specific layout and plant species selected to create a relaxing environment.

[1114] Furthermore, once the greening design is approved, the server sends instructions to the work robot to realize the greening design. The work robot then carries out the greening work based on the specified design, automatically placing the plants and installing the necessary infrastructure.

[1115] Once installed, the robot will continuously monitor and maintain the designed green area. The work robot will periodically check the condition of the area and automatically carry out any necessary maintenance work. It is also possible to customize maintenance plans based on the analysis results of the emotion engine. For example, if it determines that "maintenance is required," it will perform additional watering or pruning of plants.

[1116] An example prompt would send the following information to the server:

[1117] Location: Factory rooftop

[1118] Area: 700.0 square meters

[1119] Expenses: $150,000

[1120] Hope: Sustainable materials, automated irrigation systems

[1121] Emotional state: Relaxed

[1122] In this way, this system efficiently realizes sustainable greening designs that respond to the individual feelings and desires of users, and also makes maintenance after installation easy.

[1123] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1124] Step 1:

[1125] The user inputs information for the greenery design using a smartphone or tablet. The user fills in the building's location, size, cost, and preferences into an input form on the device. This input data might include, for example, "Factory A, 500.0 square meters, $100,000, native plants, eco-friendly materials." Once the input is complete, the device sends this information to the server.

[1126] Step 2:

[1127] The device acquires the user's emotional data. Specifically, it uses the device's built-in camera and emotion recognition software (emotion engine) to analyze the user's facial expressions and input speed. This determines the user's emotional state, such as whether they are "relaxed" or "stressed." This emotional data is also sent to the server.

[1128] Step 3:

[1129] The server stores the user's input information and emotion data in the data repository. The input data is "Factory A, 500.0 square meters, $100,000, native plants, eco-friendly materials," and the emotion data is "Relaxed." This prepares the data for use in subsequent processing.

[1130] Step 4:

[1131] The server uses a generative AI model based on the stored data to generate an optimal greening design. The input data includes location, size, cost, desired information, and emotional data, and outputs a specific example of a greening design: a 500.0 square meter relaxing environment using native plants and eco-friendly materials. This process involves a series of data processing and calculations.

[1132] Step 5:

[1133] The server provides the generated greening design to the user's device. The design created by the generative AI model is displayed on the device in a visually easy-to-understand format. The user can then review the specific design and provide feedback to the server, indicating approval or corrections.

[1134] Step 6:

[1135] After receiving the user's approval, the server sends instructions to the robot to realize the greening design. The robot then carries out the specific layout and use of materials based on the design data received from the server, and also prepares the necessary infrastructure and arranges the plants.

[1136] Step 7:

[1137] The robot carries out the greening work based on the design. The robot places plants in the designated locations and installs sustainable materials. After completing the installation work, it reports its progress to the server.

[1138] Step 8:

[1139] The server continuously monitors the completed greening area and sends maintenance instructions to the work robot. It automatically performs regular observations and necessary maintenance tasks (e.g., watering and pruning). It also takes into account data from emotion recognition and performs maintenance to increase user satisfaction.

[1140] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1141] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1142] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1143] [Fourth embodiment]

[1144] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1145] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1147] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1148] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1151] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1152] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1153] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1155] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1156] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1157] This invention relates to a system for greening unused rooftop spaces in urban areas and transforming them into sustainable ecosystems. The invention builds a system that uses generative AI models to propose optimal rooftop greening designs based on information collected from users. The services provided also include installation and maintenance.

[1158] Overall system picture

[1159] 1. An interface where users enter information

[1160] The user inputs information about the rooftop's location, area, budget, and preferences (e.g., type of plants, desired equipment, etc.) through a terminal. This information becomes the basis for the system to generate the optimal design.

[1161] 2. A server that receives the information and stores it in a database

[1162] The server receives the user's input and stores it in a database, which is then fed into a generative AI model.

[1163] 3. Generating designs using generative AI models

[1164] The server sends the stored information to a generative AI model, which generates a rooftop green design that best suits the user's requirements. The AI ​​model takes location information, rooftop area, budget, and preferences as inputs and simulates the optimal layout, plant species to use, and eco-friendly materials.

[1165] 4. Providing design proposals

[1166] The generated design is provided to the user through the server, and the proposal is displayed in a visual form that allows the user to easily understand and implement it.

[1167] 5. Installation and maintenance services

[1168] The system also provides specific installation and ongoing maintenance services, allowing users to select the services they need and receive assistance with post-installation maintenance work.

[1169] Program processing overview

[1170] User Input

[1171] The user enters information into the form on the terminal and presses the "Submit" button. For example, the user enters information such as "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels."

[1172] Data storage

[1173] The server receives the information sent by the user and stores it in a database.

[1174] Design generation using AI models

[1175] The server inputs the stored data into a generative AI model to generate an optimal design. For example, the AI ​​model suggests optimal layouts for rooftop spaces in Tokyo, including native plants and solar panels, based on spaces of 100.5 square meters or more.

[1176] Design provision

[1177] The server processes the generated design results and provides them to the user in a visually easy-to-understand format, resulting in a proposed green design for a 100.5 square meter rooftop in Tokyo, including native plants and solar panels, within a budget of $50,000.

[1178] Installation and Maintenance

[1179] When a user requests installation services through the system, the server forwards the information to the relevant service provider, and the installation and maintenance are scheduled. For ongoing maintenance, the server also provides regular reminders and specific advice to the user.

[1180] In this way, the system of the present invention optimally utilizes underutilized rooftop space in urban areas based on user input, providing a sustainable, green ecosystem.

[1181] The processing flow will be explained below.

[1182] Step 1:

[1183] The user accesses the input form on the device and enters the necessary information (rooftop location, area, budget, and preferences). For example, the user enters "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels."

[1184] Step 2:

[1185] When the user clicks the "Submit" button on the input form, the device sends the entered information to the server. The sent data is sent to the endpoint in JSON format.

[1186] Step 3:

[1187] The server receives the information sent from the device, checks the integrity of the data, and stores the received information in a database.

[1188] Step 4:

[1189] The server retrieves stored user information from the database and passes it as input to the generative AI model, including location, rooftop area, budget, and preferences.

[1190] Step 5:

[1191] The server generates the optimal rooftop greenery design using a generative AI model, which simulates the optimal layout, plant species, and eco-friendly materials based on the input data.

[1192] Step 6:

[1193] The server organizes and processes the generated design data and converts it into a format that is easy for users to understand. Specifically, it generates design proposals in graphic and text formats.

[1194] Step 7:

[1195] The server generates design proposals and sends them to the device. For example, it might suggest a greening design for a 100.5 square meter rooftop in Tokyo, including native plants and solar panels, within a budget of $50,000.

[1196] Step 8:

[1197] The user reviews the proposed design and provides feedback or requests for revisions as needed, in which case the data is sent back to the server for re-evaluation by the generative AI model.

[1198] Step 9:

[1199] Based on the generated design, the user requests installation and maintenance services, and the server receives this request and forwards it to the relevant service provider.

[1200] Step 10:

[1201] The terminal receives notifications from the server and displays the installation and maintenance schedule and progress to the user, allowing the user to check the status of the service through the terminal.

[1202] Example 1

[1203] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1204] There are many unused rooftop spaces in urban areas, and there is a need to convert them into sustainable, lush green ecosystems. However, rooftop greening projects require specialized knowledge for design, installation, and maintenance, and there is a lack of a system that is easily accessible to many users. This invention solves this problem by providing a system that allows users to easily obtain optimal rooftop greening designs and smoothly implement and maintain them.

[1205] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1206] In this invention, the server includes: means for receiving information on the location, area, budget, and preferences of the rooftop from a user; means for storing the received information in a database; means including a generative AI model for generating an optimal green roof design based on the stored information; means for visually presenting the generated design to the user; means for forwarding a request for installation service from the user to a related service provider; and means for providing maintenance reminders and advice to the user. This enables users, even without specialized knowledge, to obtain an optimal design for converting underutilized rooftop space into a sustainable ecosystem and to easily install and maintain it.

[1207] "User" refers to an individual or corporation that uses the system to receive proposals for rooftop greening designs.

[1208] "Rooftop space" refers to the unused space located at the top of a building.

[1209] "Location information" is data that indicates the geographic location of a rooftop space, including the city and specific address.

[1210] "Area" is a numerical value that represents the size of the rooftop space, and is expressed in units such as square meters.

[1211] "Budget" refers to the amount of money a user can spend on a green roof project.

[1212] "Preferences" refer to individual requests and wishes of the user, such as the type of plants they want or the equipment they want to install.

[1213] A "database" is a digital system for structuring and storing received user information.

[1214] A "generative AI model" is an artificial intelligence model that proposes optimal rooftop greening designs based on user input data.

[1215] A "prompt sentence" is an input sentence that is used by a generative AI model to generate an optimal design based on specific information.

[1216] "Means for visual presentation" refers to a method for visually displaying the generated design to the user in an easy-to-understand manner.

[1217] "Service Provider" means a company or organization that provides green roof installation and maintenance services.

[1218] "Maintenance" refers to the periodic management work required to ensure that a green roof project is properly maintained.

[1219] "Reminders" are a means of notifying users of important maintenance dates and tasks.

[1220] "Advice" refers to specific instructions or recommendations provided by the system to the user.

[1221] This invention is a system for converting unused rooftop spaces in urban areas into sustainable ecosystems. Specifically, it uses a generative AI model based on information collected from users to provide optimal rooftop greening designs. The main components of the hardware and software used are a server, terminal, generative AI model, and database.

[1222] System Overview

[1223] The system has the following main functions:

[1224] 1. An interface that receives user input

[1225] 2. Data storage

[1226] 3. Generate the design

[1227] 4. Providing designs in visual form

[1228] 5. Installation and Maintenance

[1229] User Input

[1230] Users use a terminal to input information about the location, area, budget, and preferences of their rooftop. This information is collected through a form on the terminal and sent to the system by pressing a "Submit" button. For example, a user might enter information such as "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels."

[1231] Data storage

[1232] The server receives the information sent by the user and stores it in a database, which is later used to provide input data to the generative AI model. Specifically, the server analyzes the HTTP request and extracts the data sent. The extracted data is then stored in a database table.

[1233] Generate the design

[1234] The server uses a generative AI model to generate the optimal design based on the stored data. The generative AI model used is OpenAI's GPT-4, which creates prompts for design generation. For example, the following prompts are generated:

[1235] "Please suggest the best greening design, including native plants and solar panels, that can be applied to a 100.5 square meter rooftop space in Tokyo with a budget of $50,000."

[1236] The generative AI model simulates the optimal design based on this prompt and returns the results in text format to the server, which then stores the results in a database.

[1237] Providing visual design

[1238] The server uses templates to process the design into HTML or PDF format to provide a visual presentation of the resulting design. The results are then sent to the user via email or a dedicated dashboard. The result is a proposed green design for a 100.5 square meter rooftop in Tokyo, including native plants and solar panels, within a budget of $50,000.

[1239] Installation and Maintenance

[1240] When a user requests installation services through the system, the server forwards the information to the relevant service provider. Specifically, the server analyzes the request and sends a notification to the relevant service provider via API or email. The service provider then checks the schedule and contacts the user to confirm and arrange the date. The server also periodically provides the user with maintenance reminders and specific advice.

[1241] This allows users, even without specialized knowledge, to obtain the optimal design for transforming underutilized rooftop space into a sustainable ecosystem, and makes installation and ongoing management easy.

[1242] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1243] Step 1:

[1244] The user accesses the terminal interface and inputs information regarding the rooftop location, area, budget and preferences.

[1245] Input: A user enters information into a form, such as "Tokyo," "100.5 square meters," "$50,000," "native plants, solar panels."

[1246] Output: The entered information is sent to the server as form data.

[1247] Specific behavior: The user enters data into all fields and clicks the "Submit" button.

[1248] Step 2:

[1249] The server receives the information sent by the user and stores it in a database.

[1250] Input: The server receives the HTTP request and retrieves a payload containing the user's input data.

[1251] Output: The extracted data is saved in the corresponding tables in the database.

[1252] Specific operation: The server analyzes the received data, maps it to location information, area, budget, and preference fields, and stores it in a database.

[1253] Step 3:

[1254] The server retrieves information from the database and creates prompts to input into the generative AI model.

[1255] Input: User information retrieved from the database (e.g., "Tokyo", "100.5 sq m", "50,000 dollars", "native plants, solar panels")

[1256] Output: The prompt to send to the generative AI model (e.g., "Please suggest the optimal greening design, including native plants and solar panels, for a 100.5 square meter rooftop space in Tokyo with a budget of $50,000.")

[1257] Specific operation: The server automatically generates a prompt sentence based on the data it obtains and sends it to the generative AI model.

[1258] Step 4:

[1259] The generative AI model receives a prompt and generates an optimal green roof design.

[1260] Input: Prompt statement (e.g., "Please suggest the best greening design, including native plants and solar panels, for a 100.5 square meter rooftop space in Tokyo with a budget of $50,000.")

[1261] Output: Generated design data (e.g. layout diagram, plant list, list of materials used, etc.)

[1262] Specific operation: The generative AI model analyzes the prompt text and outputs the optimal design that meets the conditions in text format.

[1263] Step 5:

[1264] The server receives the generated design data and provides it visually to the user.

[1265] Input: Design data received from the generative AI model

[1266] Output: The design results provided to the user in a visually understandable format (e.g. HTML, PDF files)

[1267] Specific operation: The server processes the design data using the template and provides the design results to the user via a dedicated dashboard or email.

[1268] Step 6:

[1269] The user requests installation and maintenance services on the terminal.

[1270] Input: The user selects the installation service option and clicks the "Request" button.

[1271] Output: Installation service request information sent to the server

[1272] Specific operation: The user selects the installation service through the terminal interface and confirms the request.

[1273] Step 7:

[1274] The server forwards the installation service request information to the relevant service provider.

[1275] Input: Installation service request information sent by the user

[1276] Output: Request information sent to the service provider

[1277] Specific operation: The server analyzes the request and sends a notification to the relevant service provider via API or email.

[1278] Step 8:

[1279] The server provides users with maintenance reminders and advice.

[1280] Input: Internal data regarding the schedule and progress of scheduled maintenance

[1281] Output: Reminders and maintenance advice sent to users

[1282] What it does: The server automatically sends scheduled reminders to users and provides instructions for necessary maintenance tasks.

[1283] (Application example 1)

[1284] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1285] In modern urban areas, there is a large amount of unused rooftop space, and there is a need to make effective use of it. However, conventional rooftop greening projects are expensive and require specialized knowledge for design and maintenance, making them difficult for ordinary users to use. Furthermore, even when a design proposal for a rooftop greening system is received, it is difficult for users to understand or accept it because it is not visually specific. The present invention aims to solve these problems and provide a system that effectively converts unused rooftop space into a sustainable, lush ecosystem.

[1286] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1287] In this invention, the server includes means for receiving information on the location, area, budget, and preferences of the roof from the user, means for storing the received information in a database, means including a generative AI model for generating an optimal green roof design based on the stored information, means for providing the generated design to the user, and means for providing a visual proposal of the design to the user via a smartphone, thereby enabling the user to visually understand the optimal green roof design that suits their preferences and budget even without specialized knowledge.

[1288] The "means for receiving information about the location, area, budget, and preferences of the rooftop from the user" is a mechanism that provides an interface for the user to input basic information about the rooftop and individual preferences.

[1289] "Means for storing said received information in a database" refers to a system that stores the information entered by a user in digital form so that it can be used for subsequent processing and analysis.

[1290] "Means including a generative AI model that generates optimal rooftop greenery designs based on stored information" refers to a mechanism that utilizes information stored in a database and uses artificial intelligence technology to create optimal designs that meet user requirements.

[1291] A "means for providing the generated design to a user" is a system or method for presenting the design generated by the AI ​​model to a user in an effective and understandable manner.

[1292] The "means for providing a visual proposal of the design to the user via a smartphone" is a mechanism for visually displaying and proposing the generated design to the user using a smartphone.

[1293] This invention relates to a system for greening unused rooftop spaces in urban areas and transforming them into sustainable ecosystems. The invention builds a system that uses generative AI models to propose optimal rooftop greening designs based on information collected from users. The services provided also include installation and maintenance.

[1294] Overall system picture

[1295] The system consists of several major components, which are:

[1296] 1. An interface where users enter information

[1297] Users enter information about the rooftop's location, area, budget, and preferences via their smartphone. Specifically, they fill out a form in the application and press the "Submit" button. This information becomes the basis for the system to generate the optimal design.

[1298] 2. A server that receives the information and stores it in a database

[1299] The server receives user input and stores it in a database, which is then fed into the generative AI model. The database used is SQLite, which ensures accurate storage and access of information.

[1300] 3. Generating designs using generative AI models

[1301] The server sends the stored information to a generative AI model, which generates a rooftop green design that best suits the user's requirements. Specifically, it takes location information, rooftop area, budget, and preferences as input and simulates the optimal layout, plant species to use, and eco-friendly materials. The generative AI model uses OpenAI's API to generate prompts and create the design.

[1302] 4. Providing design proposals

[1303] The generated design is provided to the user via the server. The proposal is displayed to the user in a visually easy-to-understand format on their smartphone. For example, a proposed greening design for a 100.5 square meter rooftop in Tokyo, including native plants and solar panels, within a budget of $50,000, might be proposed.

[1304] 5. Installation and maintenance services

[1305] When a user requests installation services through the system, the server forwards the information to the relevant service provider, and the installation and maintenance are scheduled. For ongoing maintenance, the server also provides regular reminders and specific advice to the user.

[1306] Examples of concrete examples and prompts

[1307] An example of a user inputting specific information is "Tokyo," "150 square meters," "70,000 dollars," "medicinal plants, relaxation area." The following is an example of a prompt sent to the generative AI model based on this input information:

[1308] Create a design for your green roof project:

[1309] Location: Tokyo

[1310] Area: 150 square meters

[1311] Budget: $70,000

[1312] Favorites: Medicinal plants, relaxation areas

[1313] This allows users to visually understand the optimal rooftop green design that suits their tastes and budget, even without specialized knowledge. It also makes it easy to connect with installation and maintenance services. The system provides sustainable green ecosystems in urban areas and promotes the effective use of underutilized rooftop space.

[1314] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1315] Step 1:

[1316] The user uses a smartphone to enter information about the rooftop's location, area, budget, and preferences into the application's form and presses the "Submit" button.

[1317] Input: Information entered by the user (e.g., "Tokyo", "150 square meters", "70,000 dollars", "medicinal plants, relaxation area")

[1318] Output: User information sent to the server

[1319] What happens: A user enters information into an application, clicks a button, and the information is sent to a server.

[1320] Step 2:

[1321] The server receives the information sent by the user and stores it in a database.

[1322] Input: Information received from the user

[1323] Output: User information stored in the database

[1324] Specific operation: The server stores user information (location, area, budget, preferences) in an SQLite database.

[1325] Step 3:

[1326] The server retrieves the stored data and sends it to a generative AI model to generate the optimal green roof design.

[1327] Input: Saved user information

[1328] Output: Design proposals generated by the generative AI model

[1329] How it works: The server retrieves user information from the database and sends it as a prompt to the generative AI model. OpenAI's API then generates the optimal design based on the prompt.

[1330] Step 4:

[1331] The server receives the design output by the generative AI model and presents it visually to the user.

[1332] Input: Design suggestions from a generative AI model

[1333] Output: Visually displayed design proposal

[1334] Specific operation: The server receives the generated design and provides it visually to the user through a smartphone application. The user can view the design details on the smartphone screen.

[1335] Step 5:

[1336] If the user is satisfied with the design proposal, they request installation and maintenance services.

[1337] Input: User request

[1338] Output: Forwarding of appropriate information to the service provider

[1339] Specific operation: When a user requests installation services through a smartphone application, the server forwards the information to the relevant service provider, and the installation and maintenance is scheduled.

[1340] This allows users, even those without specialist knowledge, to visually understand the optimal rooftop greening design that suits their tastes and budget, and easily request installation and maintenance services.

[1341] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1342] This invention relates to a system for transforming underutilized rooftop spaces into sustainable green ecosystems, and in particular to a system that enhances the user experience by incorporating an emotion engine that recognizes user emotions. The system receives user input, stores it in a database, and generates optimal rooftop greening designs using a generative AI model. It also has the ability to analyze the user's emotional state using the emotion engine and reflect this in the customized design.

[1343] Overall system picture

[1344] 1. An interface where users enter information

[1345] When a user accesses the input form on the device and enters the required information (rooftop location, area, budget, and preferences), the emotion engine analyzes the user's facial expressions, typing speed, keystrokes, etc. to collect emotional data, which is then sent to the system.

[1346] 2. A server that receives the information and stores it in a database

[1347] The server receives the user's input information and emotional data and stores it in a database. For example, emotional data such as "relaxed" and "stressed" are stored along with user information such as "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels."

[1348] 3. Generating designs using generative AI models

[1349] The server uses a generative AI model to generate the optimal rooftop greening design based on the stored information and emotion data. The generative AI model receives location information, rooftop area, budget, and preferences as input, and simulates the optimal layout, plant species, and eco-friendly materials. The analysis results of the emotion engine are also reflected, and a design proposal suited to the user's emotions is made.

[1350] 4. Providing design proposals

[1351] The generated design is provided to the user via the server. For example, if the user wants to relax, the server will recommend plant selection and placement to create a tranquil environment. The server sends the generated design to the device in a visually easy-to-understand format.

[1352] 5. Installation and maintenance services

[1353] The system also provides specific installation and ongoing maintenance services. Users can select the services they need through the system and receive assistance with maintenance work after installation. The server can customize the timing and method of maintenance based on the user's emotional data.

[1354] Program processing overview

[1355] User Input

[1356] As users enter information into a form on their device, the emotion engine recognizes their facial expressions and typing speed to analyze their emotional state. For example, if a user enters "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels," and their facial expression indicates that they are relaxed.

[1357] Data storage

[1358] The server receives the information and emotional data sent by the user and stores them in a database. The stored data includes the emotional state as well as the information entered by the user.

[1359] Design generation using AI models

[1360] The server inputs the stored data into a generative AI model to generate an optimal design that reflects the user's emotional state. For example, if a user wants to relax, a design that creates a comfortable space will be suggested.

[1361] Design provision

[1362] The server processes the generated design results and provides them to the user in an easy-to-understand format. For example, it provides a green design for a 100.5 square meter rooftop in Tokyo that creates a relaxing environment, including native plants and solar panels, within a budget of $50,000.

[1363] Installation and Maintenance

[1364] When a user requests a service, the server transfers it to the relevant service provider, who then installs it. For ongoing maintenance, the server periodically checks the user's emotional data and provides the optimal maintenance plan.

[1365] As described above, the system of the present invention generates optimal rooftop greening designs based on the user's input information and emotional state, and provides installation and maintenance services, thereby improving the urban environment and increasing resident satisfaction.

[1366] The processing flow will be explained below.

[1367] Step 1:

[1368] The user accesses an input form on the device and enters information about the location, area, budget, and preferences of the rooftop. For example, the user might enter "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels." The device's built-in camera, microphone, and keystroke analysis tool record the user's facial expressions, voice, and typing speed, and the emotion engine analyzes the emotional data.

[1369] Step 2:

[1370] When the user has finished entering information, they press the "Send" button. The device sends the user's input information and emotion data in JSON format to the server. For example, the following data is sent:

[1371] json

[1372] {

[1373] "location": "Tokyo",

[1374] "rooftop_area": ​​100.5,

[1375] "budget": 50000,

[1376] "preferences": "Native plants, Solar panels",

[1377] "emotional_state": "Relaxed"

[1378] }

[1379] Step 3:

[1380] The server receives the information sent from the device and checks the integrity of the data. The server then stores the received information in a database. For example, information such as "User ID," "Tokyo," "100.5 square meters," "50,000 dollars," "Native plants, solar panels," and "Relax" is stored.

[1381] Step 4:

[1382] The server retrieves stored user information and emotional data from the database and passes it as input to the generative AI model. The input data includes location, rooftop area, budget, and preferences. Emotional data is also passed to the model, which then influences the design.

[1383] Step 5:

[1384] The server uses a generative AI model to generate the optimal rooftop greenery design. Based on the input data, the generative AI model simulates the optimal layout, plant species, and eco-friendly materials. In doing so, it takes into account the emotion engine's relaxed state and selects plants and placements that have a high relaxing effect.

[1385] Step 6:

[1386] The server organizes and processes the generated design data and converts it into a format that is easy for users to understand. For example, it generates a visual layout diagram or a concrete plantation proposal. The generated design proposal is sent to the terminal in JSON format.

[1387] Step 7:

[1388] The device will then display design proposals received from the server to the user, such as "A greening design for a 100.5 square meter rooftop in Tokyo that will create a relaxing environment, including native plants and solar panels, within a budget of $50,000."

[1389] Step 8:

[1390] The user can review the proposed design and provide feedback or request revisions as necessary. If revisions are required, the device resends the new information to the server, and the design is regenerated using the generative AI model.

[1391] Step 9:

[1392] Based on the generated design, the user requests installation services. The server receives this request and forwards it to the relevant service provider, who then schedules and initiates the actual installation.

[1393] Step 10:

[1394] The device receives notifications from the server and displays installation and maintenance schedules and progress to the user. For ongoing maintenance, the server periodically checks the user's emotional data and provides an optimal maintenance plan. For example, if the user is feeling stressed, it will suggest adding or rearranging plants that have a relaxing effect.

[1395] Example 2

[1396] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1397] In modern urban environments, there is a need to make effective use of unused rooftop space, but proposing and implementing effective designs is difficult. Another issue is the lack of personalized design proposals that take into account the user's emotional state, which tends to decrease user satisfaction. Furthermore, there is a lack of standardization in the provision of installation and maintenance services, making ongoing maintenance difficult.

[1398] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1399] In this invention, the server includes: means for receiving information from a user regarding the location, area, budget, and preferences of the rooftop; means for storing the received information in a database; means including a generative AI model for generating an optimal rooftop greening design based on the stored information and the user's emotional state; and means for providing the generated design to the user. This allows for the proposal of an optimal rooftop greening design that reflects the user's emotional state, enabling the effective use of unused rooftop space as a sustainable ecosystem. It also improves user satisfaction and streamlines installation and maintenance.

[1400] "User" refers to an individual or organization that uses the system to request a rooftop greenery design.

[1401] "Rooftop location" refers to the geographic location of the rooftop of the building where the user wishes to green.

[1402] "Area" refers to a numerical value indicating the size of the rooftop to be greened.

[1403] "Budget" refers to the maximum amount of funds set by a User to be used for a Green Roof Project.

[1404] "Preferences" refers to information that indicates personal preferences such as the type of plants or installations (e.g., solar panels) desired by the user.

[1405] An "emotion engine" refers to a technology that analyzes a user's facial expressions, input speed, keystrokes, etc. in real time to infer the user's emotional state.

[1406] "Generative AI model" refers to an artificial intelligence model that generates optimal rooftop greenery designs based on input information.

[1407] "Database" refers to a storage device within the system for storing information and emotional data received from users.

[1408] "Design" refers to a rooftop greenery design proposal generated based on the user's input information and emotional data.

[1409] "Installation and maintenance" refers to a series of services that involve the implementation and subsequent maintenance of a green roof based on the generated design.

[1410] This invention relates to a system for converting underutilized rooftop space into a sustainable, green ecosystem. In particular, it combines an emotion engine that recognizes user emotions to improve the user experience. The system receives user input, stores it in a database, and uses a generative AI model to generate optimal rooftop greening designs. Furthermore, it has the ability to analyze the user's emotional state using the emotion engine and reflect this in the customization of the design.

[1411] Hardware and software configuration

[1412] This system uses the following hardware and software:

[1413] Terminal: The device through which a user enters information (e.g., a computer, tablet, smartphone)

[1414] Server: A central processing unit for storing information in a database and running generative AI models.

[1415] Database: A data storage system for storing user input information and emotion data.

[1416] Generative AI model: an algorithm for generating optimal green roof designs based on stored data

[1417] Emotion engine: Software that analyzes a user's facial expressions, typing speed, and keystrokes to recognize their emotional state.

[1418] Program processing and specific examples

[1419] User Input

[1420] When a user enters information into a form on their device, they provide the necessary information (e.g., rooftop location, area, budget, and preferences). The emotion engine then analyzes the user's facial expressions, typing speed, and keystrokes in real time to collect emotional data. For example, if a user enters "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels," the emotion engine determines that the user is relaxed.

[1421] Data storage

[1422] The server receives the information and emotional data sent by the user and stores it in a database. This stored data includes the emotional state along with the information entered by the user. Specifically, data such as "Tokyo," "100.5 square meters," "50,000 dollars," "native plants, solar panels," and "relaxed" are stored.

[1423] Design generation using AI models

[1424] The server inputs the stored data into a generative AI model to generate an optimal design that reflects the user's emotional state. The generative AI model takes location information, rooftop area, budget, and preferences as input and simulates the optimal layout, plant species, and eco-friendly materials. For example, if a user wants to relax, a design that creates a comfortable space will be suggested.

[1425] Design provision

[1426] The server processes the generated design results and presents them to the user in an easy-to-understand format. For example, a green design for a 100.5 square meter rooftop in Tokyo, including native plants and solar panels, that creates a relaxing environment within a budget of $50,000, is displayed on the device.

[1427] Installation and Maintenance

[1428] When a user requests a service, the server transfers it to the relevant service provider, who then begins the actual installation process. Furthermore, ongoing maintenance services are also provided. The server periodically checks the user's emotional data and provides an optimal maintenance plan based on that data. For example, if the user is feeling stressed, the server will suggest additional plans to enhance relaxation during maintenance.

[1429] Prompt Sentence Examples

[1430] "I would like to create a relaxing environment on a 100.5 square meter rooftop in Tokyo, including native plants and solar panels, within a budget of $50,000. Please suggest the best design."

[1431] In this way, the system generates optimal rooftop greening designs and provides installation and maintenance services based on the user's input information and emotional state, resulting in improved urban environments and increased resident satisfaction.

[1432] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1433] Step 1: User Enters Information

[1434] Input: The user enters information about the rooftop location, area, budget, and preferences into a form on the terminal.

[1435] Action: User enters "Tokyo", "100.5 sq m", "50,000 dollars", "native plants, solar panels".

[1436] Data processing: The emotion engine analyzes the user's facial expressions, typing speed, and keystrokes in real time to generate emotion data.

[1437] Output: Emotional data such as "relaxed" is added to the user's input information.

[1438] Step 2: Receiving and storing data

[1439] Input: User information and emotional data sent from the device.

[1440] How it works: The server receives the information sent by the user and stores it in a database.

[1441] Data processing: User input information and emotion data are converted into a unified format and saved.

[1442] Output: The database contains information such as "Tokyo", "100.5 sq m", "50,000 dollars", "native plants, solar panels", and "relaxation".

[1443] Step 3: Generate a design using a generative AI model

[1444] Input: User information and emotion data stored in a database.

[1445] How it works: The server inputs data into a generative AI model to generate an optimal green roof design.

[1446] Data Processing: Generative AI models simulate optimal layouts, plant species, and eco-friendly materials based on location, area, budget, preferences, and sentiment data.

[1447] Output: Generated design ideas (e.g., a relaxing arrangement of native plants and solar panels).

[1448] Step 4: Submit your design

[1449] Input: Design proposals output by the generative AI model.

[1450] Operation: The server processes the generated design into a format that is easy for the user to understand and sends it to the terminal.

[1451] Data processing: Visualize the design proposal and format it with explanatory text.

[1452] Output: "A green design for a 100.5 square meter rooftop in Tokyo that creates a relaxing environment, including native plants and solar panels, within a budget of $50,000," is displayed on the device.

[1453] Step 5: Installation and Maintenance

[1454] Input: A request from a user for installation and maintenance services.

[1455] How it works: A user requests a service on their device and the information is sent to the server, which then forwards it to the service provider, who then begins preparing the specific installation and maintenance plan.

[1456] Data processing: Regularly check user sentiment data and customize maintenance plans.

[1457] Output: An optimal maintenance plan is provided based on the user's emotional state. For example, if the user is feeling stressed, additional maintenance tasks that will enhance relaxation are suggested.

[1458] In this way, by performing specific operations at each step, an optimal rooftop greening design that takes into account the user's emotions can be generated, and subsequent installation and maintenance can be achieved.

[1459] (Application example 2)

[1460] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1461] Converting unused rooftop space or factory space into a sustainable, lush green ecosystem will improve the environment and enhance people's quality of life, but it requires specialized knowledge and effort. It is also difficult to customize the design based on the individual feelings and desires of users, and efficient management and maintenance are also challenges. This is particularly difficult to achieve in specialized environments such as factories, so there is a need for a greening system that achieves both sustainability and user satisfaction.

[1462] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1463] In this invention, the server includes means for receiving information from a user regarding the location, size, cost, and preferences of the building, means for storing the received information in a data storage unit, means including a generative AI model for generating an optimal rooftop greening design based on the stored information, means for providing the generated design to the user, emotion recognition means for analyzing the user's emotional state and reflecting the analysis results in the design, means for sending instructions to a work robot for realizing the greening design, and means for continuously monitoring and maintaining the designed greening area. This makes it possible to efficiently realize a sustainable greening design that corresponds to the individual emotions and preferences of the user and to easily perform maintenance after installation.

[1464] "Users" are those who use the system to customize and maintain rooftop and factory greenery designs.

[1465] "Building location" refers to the specific location information of the rooftop or factory space where the green design will be applied.

[1466] "Size" refers to the area of ​​the building rooftop or factory interior that will be greened.

[1467] "Expenses" refers to the budget that a user can spend on greenery design and maintenance.

[1468] "Desires" are specific conditions and preferences that users desire in green design.

[1469] The "data storage unit" is an area for storing information and emotion data received from the user.

[1470] A "generative AI model" is an artificial intelligence system that generates optimal greening designs based on information provided by the user.

[1471] "Emotion recognition means" is a technology that analyzes the user's emotional state and reflects the results of that analysis in the design.

[1472] A "working robot" is an autonomous device that performs physical tasks to realize a green design.

[1473] "Maintenance measures" are techniques and devices that continuously monitor green areas and carry out the necessary management and maintenance work.

[1474] MODE FOR CARRYING OUT THE INVENTION

[1475] This invention is a system for converting unused space in a factory or on a roof into a sustainable green ecosystem. The following describes in detail how this system can be implemented.

[1476] First, a user uses a device such as a smartphone or tablet to input information about the location, size, cost, and preferences of the building to be greened. For example, a user might input the following information:

[1477] Location: Factory A

[1478] Area: 500.0 square meters

[1479] Expenses: $100,000

[1480] Preferred: Native plants, eco-friendly materials

[1481] Once the user has completed their input, the device sends this information to the server. The emotion engine, which is an emotion recognition means, then analyzes the user's facial expressions and input speed to determine their emotional state (e.g., "relaxed" or "stressed"). The user's input information, along with this emotion data, is stored in the data storage unit.

[1482] The server then uses a generative AI model based on the stored information to generate an optimal green space design. This generative AI model simulates the optimal layout, plant species, and sustainable materials based on the user's location, size, budget, and desired information. It also takes the user's emotional state into account to suggest designs that suit their emotions. For example, if the user is looking to relax, the server will recommend specific plants and placements to create a comfortable space.

[1483] The generated green design is then sent back to the user's device from the server and displayed in a visually easy-to-understand format. As a concrete example, the following green design proposals are provided:

[1484] Location: Factory A

[1485] Area: 500.0 square meters

[1486] Expenses: $100,000

[1487] Proposal: Use native plants and eco-friendly materials, with specific layout and plant species selected to create a relaxing environment.

[1488] Furthermore, once the greening design is approved, the server sends instructions to the work robot to realize the greening design. The work robot then carries out the greening work based on the specified design, automatically placing the plants and installing the necessary infrastructure.

[1489] Once installed, the robot will continuously monitor and maintain the designed green area. The work robot will periodically check the condition of the area and automatically carry out any necessary maintenance work. It is also possible to customize maintenance plans based on the analysis results of the emotion engine. For example, if it determines that "maintenance is required," it will perform additional watering or pruning of plants.

[1490] An example prompt would send the following information to the server:

[1491] Location: Factory rooftop

[1492] Area: 700.0 square meters

[1493] Expenses: $150,000

[1494] Hope: Sustainable materials, automated irrigation systems

[1495] Emotional state: Relaxed

[1496] In this way, this system efficiently realizes sustainable greening designs that respond to the individual feelings and desires of users, and also makes maintenance after installation easy.

[1497] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1498] Step 1:

[1499] The user inputs information for the greenery design using a smartphone or tablet. The user fills in the building's location, size, cost, and preferences into an input form on the device. This input data might include, for example, "Factory A, 500.0 square meters, $100,000, native plants, eco-friendly materials." Once the input is complete, the device sends this information to the server.

[1500] Step 2:

[1501] The device acquires the user's emotional data. Specifically, it uses the device's built-in camera and emotion recognition software (emotion engine) to analyze the user's facial expressions and input speed. This determines the user's emotional state, such as whether they are "relaxed" or "stressed." This emotional data is also sent to the server.

[1502] Step 3:

[1503] The server stores the user's input information and emotion data in the data repository. The input data is "Factory A, 500.0 square meters, $100,000, native plants, eco-friendly materials," and the emotion data is "Relaxed." This prepares the data for use in subsequent processing.

[1504] Step 4:

[1505] The server uses a generative AI model based on the stored data to generate an optimal greening design. The input data includes location, size, cost, desired information, and emotional data, and outputs a specific example of a greening design: a 500.0 square meter relaxing environment using native plants and eco-friendly materials. This process involves a series of data processing and calculations.

[1506] Step 5:

[1507] The server provides the generated greening design to the user's device. The design created by the generative AI model is displayed on the device in a visually easy-to-understand format. The user can then review the specific design and provide feedback to the server, indicating approval or corrections.

[1508] Step 6:

[1509] After receiving the user's approval, the server sends instructions to the robot to realize the greening design. The robot then carries out the specific layout and use of materials based on the design data received from the server, and also prepares the necessary infrastructure and arranges the plants.

[1510] Step 7:

[1511] The robot carries out the greening work based on the design. The robot places plants in the designated locations and installs sustainable materials. After completing the installation work, it reports its progress to the server.

[1512] Step 8:

[1513] The server continuously monitors the completed greening area and sends maintenance instructions to the work robot. It automatically performs regular observations and necessary maintenance tasks (e.g., watering and pruning). It also takes into account data from emotion recognition and performs maintenance to increase user satisfaction.

[1514] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1515] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1516] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1517] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1518] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1519] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1520] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1521] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1522] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1523] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1524] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1525] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1526] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1527] 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.

[1528] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1529] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1530] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1531] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1532] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1533] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1534] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1535] The following is further disclosed regarding the above embodiment.

[1536] (Claim 1)

[1537] A system for converting underutilized rooftop space into a sustainable green ecosystem,

[1538] means for receiving information from a user regarding rooftop location, area, budget, and preferences;

[1539] means for storing the received information in a database;

[1540] a means including a generative AI model that generates an optimal green roof design based on the stored information;

[1541] means for providing the generated design to a user;

[1542] A system including:

[1543] (Claim 2)

[1544] 2. The system of claim 1, wherein the generative AI model includes means for receiving location information, rooftop area, budget, and preference information as inputs and simulating optimal layouts, plant species, and eco-friendly materials.

[1545] (Claim 3)

[1546] 10. The system of claim 1, further comprising means for providing installation and maintenance services to the user after receiving information from the user.

[1547] "Example 1"

[1548] (Claim 1)

[1549] means for receiving information from a user regarding rooftop location, area, budget, and preferences;

[1550] means for storing the received information in a database;

[1551] a means including a generative AI model that generates an optimal green roof design based on the stored information;

[1552] a means for visually presenting the generated design to a user;

[1553] means for forwarding a request for installation services from a user to a relevant service provider;

[1554] means for providing maintenance reminders and advice to the user;

[1555] A system including:

[1556] (Claim 2)

[1557] 2. The system of claim 1, wherein the generative AI model includes means for receiving location information, rooftop area, budget, and preference information as inputs and simulating optimal layouts, plant species, and eco-friendly materials.

[1558] (Claim 3)

[1559] 10. The system of claim 1, further comprising means for providing installation and maintenance services to the user after receiving information from the user.

[1560] "Application Example 1"

[1561] (Claim 1)

[1562] A system for converting underutilized rooftop space into a sustainable green ecosystem,

[1563] means for receiving information from a user regarding rooftop location, area, budget, and preferences;

[1564] means for storing the received information in a database;

[1565] a means including a generative AI model that generates an optimal green roof design based on the stored information;

[1566] means for providing the generated design to a user;

[1567] A means to provide users with a visual proposal of the design via their smartphone, and

[1568] A system including:

[1569] (Claim 2)

[1570] 2. The system of claim 1, wherein the generative AI model includes means for receiving location information, rooftop area, budget, and preference information as inputs and simulating optimal layouts, plant species, and eco-friendly materials.

[1571] (Claim 3)

[1572] 10. The system of claim 1, further comprising means for providing installation and maintenance services to the user after receiving information from the user.

[1573] "Example 2: Combining Emotion Engines"

[1574] (Claim 1)

[1575] means for receiving information from a user regarding rooftop location, area, budget, and preferences;

[1576] means for storing the received information in a database;

[1577] a means including a generative AI model for generating an optimal green roof design based on the stored information and the user's emotional state;

[1578] means for providing the generated design to a user;

[1579] A system including:

[1580] (Claim 2)

[1581] 2. The system of claim 1, wherein the generative AI model includes means for receiving location information, rooftop area, budget and preference information, and the user's emotional state as inputs and simulating optimal layouts, plant species, and eco-friendly materials.

[1582] (Claim 3)

[1583] 10. The system of claim 1, further comprising means for providing installation and maintenance services to the user after receiving information from the user.

[1584] "Application example 2 when combining emotion engines"

[1585] (Claim 1)

[1586] means for receiving information from a user regarding the location, size, cost, and preferences of the building;

[1587] means for storing the received information in a data storage unit;

[1588] a means including a generative AI model that generates an optimal green roof design based on the stored information;

[1589] means for providing the generated design to a user;

[1590] an emotion recognition means for analyzing the user's emotional state and reflecting the analysis results in the design;

[1591] A means for sending instructions to a work robot to realize a greening design;

[1592] A means of continually monitoring and maintaining the designed green areas;

[1593] A system including:

[1594] (Claim 2)

[1595] 10. The system of claim 1, wherein the generative AI model includes means for receiving location information, rooftop area, cost, and desired information as inputs and simulating optimal layout, plant species, and sustainable materials.

[1596] (Claim 3)

[1597] 10. The system of claim 1, further comprising means for providing installation and maintenance services to the user after receiving information from the user. [Explanation of symbols]

[1598] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A system for converting underutilized rooftop space into a sustainable green ecosystem, means for receiving information from a user regarding rooftop location, area, budget, and preferences; means for storing the received information in a database; a means including a generative AI model that generates an optimal green roof design based on the stored information; means for providing the generated design to a user; A system including:

2. The system of claim 1, wherein the generative AI model includes means for receiving location information, rooftop area, budget, and preference information as inputs and simulating optimal layouts, plant species, and eco-friendly materials.

3. 10. The system of claim 1, further comprising means for providing installation and maintenance services to the user after receiving information from the user.

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