JP7830583B2 - system - Google Patents

Description

The technology disclosed herein relates to a system.

Patent Document 1 discloses a persona chatbot control method performed by at least one processor, comprising the steps of: receiving a user utterance; adding the user utterance to a prompt containing instructions related to a description of the chatbot's character; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Japanese Patent Publication No. 2022-180282

When users design a home that suits their preferences and desires, they need to consider a lot of information, making the process complex. Furthermore, effectively utilizing real estate information to design a home is also difficult.

To solve this problem, the present invention includes a system that automatically generates a design drawing of a house based on user-related housing information, including the user's preferred housing design, style, and conditions, and real estate information, by inputting a specific prompt sentence into a generating AI model; a system that proposes the house based on the generated design drawing; an emotion engine that recognizes the user's emotions; a system that adjusts the interior and layout included in the design drawing of the house based on the emotions; and a system that displays the adjusted design drawing using a virtual reality display device.

This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. This shows an emotion map where multiple emotions are mapped. This shows an emotion map where multiple emotions are mapped. This is a sequence diagram showing the processing flow of the data processing system in Embodiment 1 of Example 1. This is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1. This is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 of Example 2. This is a sequence diagram showing the processing flow of the data processing system in Application Example 2 of Form Example 2. This is a sequence diagram showing the processing flow of the data processing system in Embodiment 3 of Example 3. This is a sequence diagram showing the processing flow of the data processing system in Application Example 3 of Form Example 3. This is a sequence diagram showing the processing flow of the data processing system in Example 1 of Embodiment 1, which is an example of a configuration in which an emotion engine is combined. This is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. This is a sequence diagram showing the processing flow of the data processing system in Example 2 of the Form 2 when an emotion engine is combined. This is a sequence diagram showing the processing flow of the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. This is a sequence diagram showing the processing flow of the data processing system in Embodiment 3 of Example 3, which combines an emotion engine. This is a sequence diagram showing the processing flow of the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined.

The following describes an example of an embodiment of the system relating to the technology of this disclosure, with reference to the attached drawings.

to do.

First, let's explain the terminology used in the following explanation.

In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (TENSOR PROCESSING UNIT®).

In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

In the following embodiments, the coded communication interface (I/F) is an interface including a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi®, or Bluetooth®.

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

[First Embodiment]

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

As shown in Figure 1, the data processing system 10 comprises a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting it in a form perceptible to the user 20 (e.g., audio and/or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

As shown in Figure 2, in the data processing device 12, specific processing is performed by the processor 28. The storage 32 stores a specific processing program 56. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be explained.

"Example of Morphology 1"

In one embodiment of the system, a user saves images of their preferred homes from social media such as Instagram and inputs the image information into this system. The user also inputs their family structure, desired address, price range, whether or not they want renovations, and whether they prefer a house or apartment. Based on this preference and desired information, the system automatically generates a blueprint for the house.

"Example of Morphology 2"

Furthermore, this system acquires land and building information from real estate information websites and automatically generates house blueprints based on this information. Specifically, it proposes window orientations, sizes, and floor plans, taking into account the shape of the land and building, location conditions, etc.

"Example of Morphology 3"

As a concrete example, if a user inputs an image of a modern exterior saved from Instagram, along with information such as a family of four, a desire for a detached house in Tokyo, and a budget of 50 million yen, this system will automatically generate a blueprint for a detached house in Tokyo with a modern exterior based on this information. Furthermore, based on land and building information in Tokyo obtained from real estate information websites, it will suggest window orientations and sizes, floor plans, and other details.

The following describes the processing flow for each example form.

"Example of Morphology 1"

Step 1: The user saves images of their preferred homes from social media such as Instagram.

Step 2: The user inputs the saved image information into this system.

Step 3: The user enters their family structure, desired address, price range, whether or not they want renovations, and other desired information such as a house or apartment into the system.

Step 4: This system automatically generates house blueprints based on these preference and desired information.

"Example of Morphology 2"

Step 1: This system retrieves land and building information from real estate information websites.

Step 2: This system automatically generates house plans based on the acquired land and building information. Specifically, it proposes window orientations, sizes, and floor plans, taking into account the shape of the land and building, location conditions, etc.

"Example of Morphology 3"

Step 1: The user enters a modern exterior image saved from Instagram, along with information such as being a family of four, wanting a detached house in Tokyo, and having a budget of 50 million yen.

Step 2: Based on this information, the system automatically generates a blueprint for a modern-looking detached house in Tokyo.

Step 3: This system uses land and building information in Tokyo obtained from real estate information websites to suggest window orientations, sizes, floor plans, etc.

(Example 1)

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

Conventional residential design systems struggled to automatically generate blueprints that reflected users' preferences and desires. Furthermore, a lack of effective means to utilize image information obtained from social media meant that specific user preferences could not be reflected in the design. Additionally, when proposing housing designs based on the generated blueprints, it was difficult to create proposals that truly met the user's needs.

The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes means for receiving user preference information and desired information as input and automatically generating a house design based on this information; means for receiving image information saved by the user as input and analyzing this information to identify the user's preferences; means for generating and inputting prompt statements to the generation AI model; and means for making house proposals based on the generated design. This enables the automatic generation of house design plans that reflect the user's specific preferences and desires, and allows for the proposal of houses that match the user's wishes.

"User preference information" refers to information about the housing designs and styles that users prefer, and includes images and text data obtained from social media.

"Desired information" refers to the specific conditions and requirements of the user's desired housing, including family structure, desired address, price range, whether renovations are needed, and whether it's a detached house or an apartment.

"Image information" refers to image data of houses that users obtain from social media or other sources and upload to the system.

A "generative AI model" is an artificial intelligence model that automatically generates house blueprints based on user preferences and desired information, utilizing natural language processing and image analysis technologies.

A "prompt message" is an instruction given to the AI generation model, and it is generated based on the user's preferences and desired information.

A "house design plan" refers to a design drawing of a house generated based on the user's preferences and desired information, including specific layouts and designs.

A "housing proposal" refers to a housing design proposal made to the user based on the generated housing blueprints, and includes content that matches the user's wishes.

This invention is based on the premise that users save images of their preferred homes from social media such as Instagram and input that image information into the system. Users also input their family structure, desired address, price range, whether or not they want renovations, and whether they want a house or an apartment. Based on this information, the system automatically generates a blueprint for the house.

Hardware and software to be used

The server receives image information and request information entered by the user and stores it in a database. Specifically, it uses the following hardware and software:

Server: High-performance cloud server (e.g., Amazon Web Services, Google Cloud Platform)

Database: Relational database (e.g., MySQL®, PostgreSQL)

Generative AI models: AI models using natural language processing and image analysis technologies (e.g., OpenAI®'s GPT-4®, DALL-E)

Image analysis software: Computer vision technology (e.g., TENSORFLOW®, PyTorch)

Data processing and data calculation

The server receives image files uploaded by the user and requested information, and stores this data in a database. Based on the stored data, it generates prompt messages for a generative AI model and inputs them. The generative AI model then generates a house design based on these prompt messages and provides it to the user via the server.

Specific examples

As a concrete example, suppose a user saves an image of a modern-designed house from Instagram and enters the following desired information:

Family composition: 4 people (2 adults, 2 children)

Desired address: Tokyo

Price range: Under 50 million yen

Renovation status: Yes

House or apartment: House

Based on this information, the server inputs the following prompt message into the AI model:

Based on images of modern-designed homes saved by the user from Instagram, please generate a blueprint for a home that matches the following requirements.

Family composition: 4 people (2 adults, 2 children)

Desired address: Tokyo

Price range: Under 50 million yen

Renovation status: Yes

House or apartment: House

The AI model generates a house design based on this prompt, matching the user's preferences, and provides it to the user via the server. The user can review the received design on their device and, if necessary, modify their preferences to generate a new design.

In this way, it becomes possible to automatically generate house designs that reflect the user's specific preferences and desires, and to propose houses that match the user's wishes.

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

Step 1:

Users save images of their favorite houses from social media such as Instagram. Users use their smartphones or computers to find and save images of their favorite houses from social media such as Instagram. The input is image data obtained from social media, and the output is an image file saved on the user's device.

Step 2:

The user uploads saved images to the system. The user accesses the system's website or application and uploads saved images of their home. Specifically, they click the upload button and select the saved image file. The input is the image file saved on the user's device, and the output is the image data sent to the server.

Step 3:

The user enters their desired information, such as family structure, preferred address, price range, whether renovations are needed, and whether they prefer a house or apartment. The user enters their desired information into the system's input form, including family structure (e.g., 2 adults, 2 children), preferred address (e.g., Tokyo), price range (e.g., under 50 million yen), whether renovations are needed (e.g., yes), and whether they prefer a house or apartment (e.g., house). The input is the desired information entered by the user, and the output is the desired information sent to the server.

Step 4:

The server receives image and request information from the user and stores it in the database. The server receives uploaded image files and input request information from the user and stores this data in the database. When saving, the data is managed in association with the user ID. The input is the image and request information sent by the user, and the output is the data stored in the database.

Step 5:

The server generates prompt messages for the AI model based on the stored data and inputs them. The server generates prompt messages for the AI model based on image information and desired information stored in the database. For example, it generates prompt messages like the following:

Based on images of modern-designed homes saved by the user from Instagram, please generate a blueprint for a home that matches the following requirements.

Family composition: 4 people (2 adults, 2 children)

Desired address: Tokyo

Price range: Under 50 million yen

Renovation status: Yes

House or apartment: House

The input consists of image information and desired information stored in a database, while the output is the prompt text input to the generating AI model.

Step 6:

The generative AI model generates a house design based on the prompt text. The generative AI model (e.g., OpenAI's GPT-4 or DALL-E) generates the house design based on the prompt text sent from the server. The generative AI model utilizes image analysis and natural language processing techniques to create a design that meets the user's preferences. The input is the prompt text, and the output is the generated house design.

Step 7:

The server sends the generated blueprint to the user's terminal. The server sends the blueprint received from the generated AI model to the user's terminal. The user can download or view the blueprint through the system's website or application. The input is the generated house blueprint, and the output is the blueprint sent to the user's terminal.

Step 8:

The user reviews the blueprint. The user reviews the blueprint received on their device. They can check if the blueprint meets their requirements and, if necessary, modify the required information to generate a new blueprint. The input is the blueprint sent to the user's device, and the output is the user's review result.

(Application Example 1)

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

Traditional home design systems struggled to automatically generate blueprints that reflected user preferences and desires. In particular, if a user had a specific image in mind, specialized knowledge was required to translate that image into a blueprint. Furthermore, real-time blueprint generation and display were difficult when users viewed home designs in physical showrooms. This led to decreased user satisfaction and delays in home purchase decisions.

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

This invention includes a server that receives user preference and desired information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that proposes houses based on the generated design; a server that analyzes house images taken by the user and extracts their features; a server that generates prompt text based on the extracted features and the user's desired information and generates a house design using a generation AI model; and a server that presents the generated design to the user via a display device. This enables the real-time generation of house design plans that reflect the user's specific image, allowing for on-site verification at a physical location.

"User preference information" refers to information that indicates a user's personal preferences and design tastes regarding housing.

"Desired information" refers to information that indicates the user's specific requests and conditions regarding housing (family structure, desired address, price range, whether renovations are needed, whether it's a detached house or an apartment, etc.).

"Real estate information" refers to information that provides detailed data about a real estate property (location, price, floor plan, year built, etc.).

A "house design drawing" is a diagram that specifically shows the structure, layout, and design of a house.

"Automatic generation methods" refer to devices or programs that have the function of automatically creating design drawings using algorithms or AI models based on input information.

"Means of making proposals" refers to devices or programs that have the function of making housing proposals to users based on the generated blueprints.

"Residential images" refer to photographs or image data of houses taken by users.

"Means of analysis" refers to devices or programs that have the function of analyzing input image data and extracting its features.

A "means for extracting features" refers to a device or program that has the function of extracting important features from data obtained through image analysis.

A "prompt" is a text-based input message used to give instructions to a generative AI model.

A "generative AI model" is an artificial intelligence model that generates blueprints and other data based on input prompts.

A "display device" is a device (such as a display or VR headset) used to visually present generated design drawings to the user.

The system for carrying out this invention includes means for receiving user preference information and desired information as input and automatically generating a house design based on this information; means for receiving real estate information as input and automatically generating a house design based on this information; means for making house proposals based on the generated design; means for analyzing house images taken by the user and extracting their features; means for generating prompt sentences based on the extracted features and the user's desired information, and generating a house design using a generation AI model; and means for presenting the generated design to the user through a display device.

Hardware and software to be used

Hardware

Smartphone or tablet (iOS or Android®): Used by the user to take pictures of the house and input information.

Smart glasses (e.g., Google Glass®): Used to analyze images taken by the user in real time.

Large-screen display or VR headset (e.g., Oculus Rift): Used to present the generated blueprints to the user.

Software

Image recognition libraries (e.g., OpenCV): Used to analyze residential images taken by the user and extract their features.

Generative AI models (e.g., GPT-4, DALL-E): Used to generate house blueprints based on prompt messages.

Database (e.g., Firebase): Used to store and manage user preference information, desired information, and real estate information.

Frontend frameworks (e.g., React Native): Used to build user interfaces.

Processing Flow

1. User Input

Users use their smartphones or tablets to take photos of their preferred homes and upload them to the application. They also input information such as family composition, desired address, price range, whether renovations are needed, and whether they prefer a house or apartment.

2. Image Analysis

The server uses an image recognition library (OpenCV) to analyze uploaded residential images and extract their features.

3. Generating the prompt message

The server generates prompt messages for the generative AI model (GPT-4) based on the extracted features and user preferences.

Example of a prompt message

Based on the images and desired information provided by the user, please generate a house design that meets the following conditions:

Family composition: 4 people

Desired address: Tokyo

Price range: Under 50 million yen

Renovation: Yes

Detached house

Modern design

4. Generating Blueprints

The server uses a generative AI model (DALL-E) to generate house blueprints based on the prompt text.

5. Displaying Results

The generated blueprints are presented to the user via a large screen display or VR headset. Users can then view their ideal home design in real time at a physical store.

Specific examples

For example, a user uploads an image of a modern living room taken with their smartphone and enters their desired information as follows: "Family size: 4 people, Desired address: Tokyo, Price range: under 50 million yen, Renovation: Yes, Detached house." Based on this information, the server generates a blueprint for a modern detached house in Tokyo, suitable for a family of four, within a 50 million yen budget, and presents it to the user.

In this way, users can view their ideal home design in real time at a physical store.

The flow of the specific processing in Application Example 1 will be explained using Figure 12.

Step 1:

Users use their smartphones or tablets to take photos of their preferred homes and upload them to the application. They also input information such as family composition, desired address, price range, whether renovations are needed, and whether they prefer a house or apartment.

Input: Housing images, family composition, desired address, price range, whether renovation is needed, desired type of property (house or apartment), etc.

Output: Uploaded house images and desired information

Step 2:

The server uses an image recognition library (OpenCV) to analyze uploaded residential images and extract their features.

Input: Uploaded house image

Output: Extracted image features

Specific operation: Using an image recognition library, perform edge detection and color analysis on residential images, and extract features.

Step 3:

The server generates prompt messages for the generative AI model (GPT-4) based on the extracted features and user preferences.

Input: Extracted image features, desired information

Output: Generated prompt message

Specific operation: Combine the extracted features and desired information to create prompt sentences for input into the generative AI model.

Step 4:

The server uses a generative AI model (DALL-E) to generate house blueprints based on the prompt text.

Input: Generated prompt text

Output: Generated house blueprints

Specific operation: The system takes prompt text as input to a generation AI model and generates a house design.

Step 5:

The server presents the generated blueprints to the user via a large screen display or VR headset.

Input: Generated house blueprints

Output: Design drawings presented to the user

Specific operation: The generated design drawings are sent to a display or VR headset, allowing the user to visually confirm them.

In this way, users can view their ideal home design in real time at a physical store.

(Example 2)

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

Traditional residential design systems had the problem of requiring a great deal of time and effort to reflect user preferences and desired information. Furthermore, efficiently acquiring real estate information and automatically generating optimal residential designs based on that information was difficult. As a result, it was difficult to quickly provide residential designs that satisfied users.

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

In this invention, the server includes means for acquiring land and building information from a real estate information provision site, means for storing the acquired information in a data frame and processing the data, means for generating prompt sentences to be input into a generation AI model based on the processed data, means for automatically generating house blueprints using the generation AI model, and means for making house proposals based on the generated blueprints. This makes it possible to quickly and efficiently provide house blueprints that reflect the user's preferences and desired information.

A "real estate information website" is a website that provides information about land and buildings.

"Land and building information" refers to detailed data such as the shape of the land, the location of the building, the orientation and size of the windows, and the floor plan.

A "data frame" is a two-dimensional data structure consisting of rows and columns, used for storing and processing data.

"Data processing" refers to the process of transforming acquired data into a format that is easy to analyze and use, and includes tasks such as imputing missing values and normalizing data.

A "generative AI model" is a model that uses artificial intelligence to perform specific tasks; in this case, it is used to automatically generate blueprints for houses.

A "prompt message" is an instruction message used to input data into a generation AI model, and it is generated based on information about the land and buildings.

A "house design drawing" is a diagram showing the layout and structure of a house, and forms the basis of the building plan.

A "housing proposal" refers to suggestions regarding the design and layout of a house, made to the user based on the generated blueprints.

Modes for carrying out the invention

This invention relates to a system that acquires land and building information from real estate information websites and automatically generates house blueprints based on this information. A specific embodiment of this system is described below.

1. Program Generation

The server generates a program to retrieve land and building information from real estate information websites. This program is developed using Python and performs web scraping using libraries such as BeautifulSoup and Selenium.

2. Data Acquisition and Processing

The server executes the generated program and retrieves land and building information from real estate information websites. Specifically, it collects data such as land shape, building location, window orientation and size, and floor plan. The collected data is stored in a dataframe using the Pandas library, and necessary data processing is performed. For example, missing value imputation and data normalization are performed.

3. Generating prompt sentences for the generative AI model

The server generates prompt statements to input into the AI model based on the processed data. These prompt statements include information such as the shape of the land and building, location conditions, window orientation, size, and floor plan.

Example of a prompt message:

Based on land information in Shibuya Ward, Tokyo, please generate a house design plan considering the following conditions.

Land shape: Rectangle

Building location: South-facing

Window orientation: South-facing

Window size: Large

Floor plan: 3LDK

4. Generating House Design Plans

The server inputs the generated prompt text into a generation AI model (for example, a generation AI model) and generates a house design. The generation AI model then proposes the optimal design based on the prompt text.

5. Displaying Results

The server displays the generated house design plans on the user's terminal. The user can review the proposed design plans and request revisions as needed.

Specific examples

If a user requests to generate a house design based on land information in Shibuya Ward, Tokyo, the server will input the following prompt into the AI model:

Example of a prompt message:

Based on land information in Shibuya Ward, Tokyo, please generate a house design plan considering the following conditions.

Land shape: Rectangle

Building location: South-facing

Window orientation: South-facing

Window size: Large

Floor plan: 3LDK

The AI model generates a house design based on this prompt. The proposed design is displayed on the user's device for review.

In this way, the present invention makes it possible to quickly and efficiently provide residential design plans that reflect the user's preferences and desired information.

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

Program execution flow

Step 1: Obtaining real estate information

The server retrieves land and building information from real estate information websites. Specifically, it uses Python and libraries such as BeautifulSoup and Selenium to perform web scraping.

Input: URL of the real estate information website

Output: Land and building information (e.g., land shape, building location, window orientation, size, floor plan)

Specific actions:

The server uses Selenium to automatically control the browser and access the specified URL. Next, it uses BeautifulSoup to parse the HTML and extract the necessary information.

Step 2: Data Processing

The server stores the retrieved real estate information in a dataframe using the Pandas library and performs necessary data processing. For example, it might impute missing values or normalize the data.

Input: Land and building information

Output: Processed data frame

Specific actions:

The server converts the retrieved data into a DataFrame using the Pandas `read_html` function. Next, it imputes missing values using the `fillna` function and normalizes the data using the `normalize` function.

Step 3: Generating prompts for the generative AI model

The server generates prompt statements to input into the AI model based on the processed data. These prompt statements include information such as the shape of the land and building, location conditions, window orientation, size, and floor plan.

Input: Processed data frame

Output: Prompt message

Specific actions:

The server extracts the necessary information from the data frame and generates a prompt message like the following:

Based on land information in Shibuya Ward, Tokyo, please generate a house design plan considering the following conditions.

Land shape: Rectangle

Building location: South-facing

Window orientation: South-facing

Window size: Large

Floor plan: 3LDK

Step 4: Generating the House Design Plan

The server inputs the generated prompt text into a generation AI model (for example, a generation AI model) and generates a blueprint for a house. The generation AI model then proposes the optimal blueprint based on the prompt text.

Input: Prompt text

Output: House design plans

Specific actions:

The server sends prompts to the API for generating AI models and receives proposed blueprints in return. For example, it might use the `openai.Completion.create` function to send prompts.

Step 5: Displaying Results

The server displays the generated house design plans on the user's terminal. The user can review the proposed design plans and request revisions as needed.

Input: House blueprints

Output: Blueprint displayed on the user's terminal

Specific actions:

The server formats the generated blueprint in HTML format and sends it to the user's terminal. The user then views the blueprint through their browser.

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

Conventional residential design systems have difficulty reflecting user preferences and desires, and their automatic generation of design plans based on real estate information has been limited. Furthermore, interior design for physical stores requires specialized knowledge and is not easily accessible to the average user. Therefore, there is a need for a system that allows users to easily design homes and stores that meet their specific needs.

In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving user preference information and desired information as input and automatically generating house blueprints based on this information; means for receiving real estate information as input and automatically generating house blueprints based on this information; means for making house proposals based on the generated blueprints; means for automatically generating interior designs for physical stores based on information obtained from real estate information provision sites; means for creating prompt statements for generating blueprints using a generation AI model; and means for generating actual blueprints using the generation AI model. This makes it easy for users to design houses and stores that meet their preferences.

"User preference information" refers to information about the style, functions, and design that users desire in the design of their homes or shops.

"Desired information" refers to information about the specific conditions and requirements that users desire in the design of their homes or shops.

"Real estate information" refers to information about the shape, location, and area of land and buildings.

A "design drawing" is a diagram showing the layout of a house or shop, including the direction and size of windows, and the arrangement of interior furnishings.

A "real estate information website" is a website that provides information about land and buildings on the internet.

"Interior design" refers to planning the layout and design of the interior spaces of shops and residences.

A "generative AI model" is an algorithm or program that uses artificial intelligence to automatically generate blueprints and designs.

A "prompt statement" is an instruction statement used to input data into a generative AI model, containing conditions and requirements for generating blueprints or designs.

The system for carrying out this invention includes a server, a user terminal, and a generative AI model. Specific embodiments of this system are described below.

First, the user terminal receives user preference and desired information as input. This information concerns the style, functions, and design the user desires for the design of their home or store. The user terminal then transmits this information to the server.

Next, the server retrieves real estate information from a real estate information website. This real estate information includes details such as the shape of the land and building, location conditions, and area. Based on the retrieved real estate information, the server automatically generates blueprints for houses and shops.

The server uses a generative AI model to create prompt statements for generating blueprints. These prompt statements include user preference information, desired information, and real estate information. For example, prompt statements like the following are generated:

"Land shape: Rectangle, Location: Urban area, Window orientation: South-facing, Lighting position: Center of ceiling"

Based on the generated prompt text, the generative AI model creates the actual design drawing. For example, OpenAI's GPT-3 (registered trademark) is used as the generative AI model. This model receives the prompt text as input and outputs the design drawing.

The generated blueprints are sent from the server to the user's terminal and presented to the user. The user can then review the design of the house or shop based on the presented blueprints and make modifications as needed.

This system allows users to easily design houses and shops that meet their specific needs. Specifically, users can easily generate and review design plans using their smartphones or computers. This enables them to realize their desired designs even without specialized knowledge.

The above describes a specific embodiment for carrying out this invention.

The flow of the specific processing in Application Example 2 will be explained using Figure 14.

Step 1:

The user terminal receives user preference and desired information as input. Users use smartphones or computers to input information regarding their desired style, function, and design for residential or commercial buildings. The entered information is stored on the user terminal.

Step 2:

The user terminal sends the entered preference and desired information to the server. The transmitted information is stored in the server's database.

Step 3:

The server retrieves real estate information from real estate information websites. The server uses an API to send requests to these websites, obtaining information such as the shape, location, and area of the land and buildings. The retrieved real estate information is stored on the server.

Step 4:

The server generates prompt statements to create a blueprint based on the acquired real estate information and the user's preferences and requests. These prompt statements include information such as the land shape, location, window orientation, and lighting position. For example, a prompt statement like "Land shape: Rectangle, Location: Urban area, Window orientation: South-facing, Lighting position: Center of ceiling" might be generated.

Step 5:

The server uses a generative AI model to generate a blueprint using prompt text as input. For example, OpenAI's GPT-3 is used as the generative AI model. The generative AI model, upon receiving prompt text as input, outputs a blueprint. The generated blueprint is saved on the server.

Step 6:

The server sends the generated blueprint to the user's terminal. The user's terminal displays the received blueprint to the user. The user can then view the blueprint on their smartphone or computer screen.

Step 7:

Users can review the provided blueprints for houses and shops, and make modifications as needed. The modified information is then sent back to the server, and the blueprints are regenerated. This allows users to realize a design that meets their specific needs.

(Example 3)

Next, we will describe Embodiment 3 of Example 3. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

Conventional residential design systems have difficulty automatically generating blueprints that reflect user preferences and desires, and have been unable to provide optimal design proposals based on real estate information. Therefore, there is a need to efficiently provide residential designs that meet user needs.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

This invention includes a server that receives user preference and desire information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that makes house design proposals based on the generated design; a server that sends prompt text and images to a generation AI model to generate a design; a server that acquires land and building information from a real estate information website; a server that makes optimal design proposals based on the acquired real estate information; and a server that displays the generated design and proposal content to the user. This makes it possible to automatically generate house design plans that reflect the user's preferences and desires, and to make optimal design proposals based on real estate information.

"User preference information" refers to information indicating a user's personal preferences regarding design, style, and functionality in residential design.

"Desired information" refers to specific conditions and requirements that users have in mind when designing a house, such as family structure, budget, and location.

"Real estate information" refers to detailed data about land and buildings, such as price, area, and surrounding environment.

A "generative AI model" is an algorithm or system that uses artificial intelligence to generate design plans based on user input.

A "prompt" is a text-based input message used to give instructions to a generative AI model.

A "design drawing" is a diagram showing the structure, layout, and design of a house.

A "real estate information website" is an online platform that provides information about land and buildings.

"Optimal design proposal" refers to proposing the most suitable residential design based on the user's preferences, desires, and real estate information.

"Means of display" refers to methods or devices for visually providing users with generated design drawings or proposed content.

This invention is a system that automatically generates house blueprints based on user preference and desired information, and provides optimal design suggestions based on real estate information. A specific embodiment of this system is described below.

System Configuration

This system consists of three main elements: a server, a terminal, and a user. The server receives input information from the user, generates design plans using a generative AI model, and provides optimal design suggestions based on real estate information. The terminal provides an interface for the user to input information and check the results. The user accesses the system through the terminal and inputs the necessary information.

Hardware and software to be used

Server: A server with high-performance computing capabilities is required. Specifically, a cloud-based server (e.g., Amazon Web Services or Google Cloud Platform) should be used.

Terminal: A device used by a user to input information. This includes internet-connected devices such as personal computers, tablets, and smartphones.

Generative AI Model: We use advanced artificial intelligence models such as OpenAI's GPT-4 and DALL-E as generative AI models.

Real estate information websites: Use online platforms (e.g., SUUMO or At Home) to obtain real estate information.

Data processing and calculations

1. User Information Input: The user accesses a form on a web browser using their device and uploads a modern-looking image saved from Instagram. Next, they enter their family composition (four people), desired location (Tokyo), and budget (50 million yen).

2. Prompt Generation: The server receives the information entered by the user and generates prompts for input into the AI model. Specifically, it generates prompts like the following:

"Based on images of modern exteriors saved by users on Instagram, please automatically generate blueprints for a detached house in Tokyo suitable for a family of four, with a budget of under 50 million yen."

3. Sending to the Generating AI Model: The server sends the generated prompt text and the user-uploaded image to the Generating AI model. This transmission is done via API.

4. Blueprint Generation: The generation AI model generates a blueprint for a modern-looking detached house in Tokyo based on the received prompt text and image. The blueprint includes details such as window orientation and size, and floor plan. The generated blueprint is returned to the server.

5. Acquisition of Real Estate Information: The server uses the API of a real estate information provider website to acquire information on land and buildings in Tokyo. The acquired information includes land prices, building area, and surrounding environment.

6. Optimal Design Proposal: Based on the acquired real estate information, the server proposes optimal window orientations, sizes, and layouts for the generated design drawings. For example, by proposing a design drawing with many south-facing windows, it provides a living environment with plenty of sunlight.

7. Displaying Results: The server displays the final design and proposal to the user. The user can review this information in a web browser and make modifications or regenerations as needed. An interactive UI is used for display to ensure user-friendliness.

Specific examples

If the user enters the following information:

Images of modern exteriors saved from Instagram

family of 4

I'm looking for a detached house in Tokyo.

The budget is 50 million yen.

The server generates the following prompt:

"Based on images of modern exteriors saved by users on Instagram, please automatically generate blueprints for a detached house in Tokyo suitable for a family of four, with a budget of under 50 million yen."

The AI model generates a design drawing based on this prompt text and image, and the server proposes the optimal window orientation, size, and floor plan based on data obtained from a real estate information website. The specific processing flow in Example 3 is explained using Figure 15.

Step 1:

The user enters information.

The user accesses a form on a web browser using their device and uploads a modern-looking image saved from Instagram. Next, they enter their family size (four people), desired location (Tokyo), and budget (50 million yen). This information is then sent to the server.

Input: Images saved from Instagram, family composition, desired region, budget

Output: User preference information and desired information

Step 2:

The server generates the prompt message.

The server receives the information entered by the user and generates prompt messages for input into the generated AI model. Specifically, it generates prompt messages like the following:

"Based on images of modern exteriors saved by users on Instagram, please automatically generate blueprints for a detached house in Tokyo suitable for a family of four, with a budget of under 50 million yen."

Input: User preference information and desired information

Output: Prompt text to send to the generated AI model

Step 3:

The server sends prompt text and images to the generated AI model.

The server sends the generated prompt text and the user-uploaded image to the AI model. This transmission is done via API.

Input: Prompt text, user-uploaded image

Output: Input data for the generated AI model

Step 4:

The generative AI model generates the blueprint.

The AI model generates a blueprint of a modern-looking detached house in Tokyo based on the received prompt text and image. The blueprint includes details such as window orientation and size, and floor plan. The generated blueprint is then returned to the server.

Input: Prompt text, user-uploaded image

Output: Generated blueprint

Step 5:

The server retrieves real estate information.

The server uses the API of a real estate information website to retrieve information on land and buildings in Tokyo. The retrieved information includes land prices, building area, and surrounding environment.

Input: API of a real estate information website

Output: Acquired real estate information

Step 6:

The server will propose the optimal design.

The server uses the acquired real estate information to suggest optimal window orientations, sizes, and layouts for the generated blueprints. For example, by suggesting blueprints with many south-facing windows, it provides a living environment with plenty of sunlight.

Input: Generated blueprints, acquired property information

Output: Optimal design proposal

Step 7:

The server displays the results to the user.

The server displays the final design blueprint and proposal to the user. Users can view this information in a web browser and modify or regenerate it as needed. An interactive UI is used for display to ensure user-friendliness.

Input: Final design drawings, proposed content

Output: Results displayed to the user

(Application Example 3)

Next, we will explain Application Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

Conventional residential design systems struggled to automatically generate blueprints that reflected user preferences and desires, and were unable to effectively utilize real estate information to propose blueprints. Furthermore, they could not leverage images saved by users or information from social media, making it difficult to provide blueprints that met specific user needs. This resulted in a challenge in increasing user satisfaction.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

This invention includes a server that receives user preference and desired information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that proposes a house design based on the generated design; a server that generates prompt text based on images saved by the user and generates a design using a generation AI model; and a server that proposes window orientation, size, floor plan, etc., based on land and building information obtained from a real estate information website. This enables the automatic generation of design plans tailored to the user's specific needs and the effective utilization of real estate information.

"User preference information" refers to information about the design and style that users desire, and is obtained from sources such as social media and saved images.

"Desired information" refers to information about the conditions and requirements of a home that the user desires, including specific preferences such as family structure, budget, and location.

A "house design plan" is a drawing that shows the exterior, floor plan, window orientation, size, etc., of a house, and is automatically generated based on the user's preferences and requests.

"Real estate information" refers to information about land and buildings, and is obtained from real estate information websites and other sources.

A "generative AI model" is a model that uses artificial intelligence to generate design plans or proposals from specific input information.

A "prompt message" is an instruction message used to input into a generative AI model, and it is generated based on the user's preferences and desired information.

A "real estate information website" is a website that provides information about land and buildings, and users can access and obtain this information.

"Window orientation, size, and floor plan" are important elements in residential design and are proposed based on the user's wishes and real estate information.

The system for implementing this invention includes means for receiving user preference information and desired information as input and automatically generating house blueprints based on this information; means for receiving real estate information as input and automatically generating house blueprints based on this information; means for making house proposals based on the generated blueprints; means for generating prompt text based on images saved by the user and generating blueprints using a generation AI model; and means for proposing window orientation, size, floor plan, etc., based on land and building information obtained from real estate information provision sites.

System Program

The server receives preference and request information entered by the user via a device such as a smartphone or computer. This includes images obtained from social media such as Instagram, as well as information such as family structure, budget, and desired location. Next, the server generates prompt statements based on this information. These prompt statements are input into a generation AI model, which serves as instructions for generating house blueprints.

Based on the generated prompt text, the AI model automatically generates a blueprint for a house. This blueprint reflects the user's preferences and desires, including specific exterior details and floor plans. Furthermore, the server suggests window orientations and sizes, floor plans, etc., based on land and building information obtained from real estate information websites.

Hardware and software to be used

This system uses the following hardware and software:

Hardware: Servers, user terminals (smartphones, PCs, etc.)

Software: Generative AI models (e.g., OpenAI's GPT-3), prompt generation programs, image acquisition programs (e.g., requests, PIL)

Specific examples

For example, if the user enters the following conditions:

Instagram image URL: https://example.com/instagram_image.jpg

Family structure: 4 people family

Desired area: Tokyo

Budget: 50 million yen

The generated prompt will look like this:

Based on images of modern exteriors saved by the user on Instagram, we need a design for a detached house in Tokyo for a family of four with a budget of 50 million yen. Please generate a blueprint for a detached house with a modern exterior based on this information.

When this prompt is entered into the AI model, the model automatically generates a house design that meets the user's preferences. The generated design reflects the user's desired modern exterior and a floor plan suitable for their family structure. Furthermore, based on land and building information obtained from real estate information websites, the model also provides specific suggestions regarding window orientation and size, floor plan, and other details.

The flow of the specific processing in Application Example 3 will be explained using Figure 16.

Step 1:

Users input their preferences and desired information from devices such as smartphones and computers. This includes image URLs obtained from social media such as Instagram, as well as information such as family structure, budget, and desired region. The entered information is sent to the server.

Input: Instagram image URL, family composition, budget, desired region

Output: User preference and desired information sent to the server.

Step 2:

The server receives preference and request information from the user. Based on this information, it performs data processing to generate prompt messages. Specifically, it retrieves image URLs and converts information such as family structure, budget, and desired region into text format.

Input: User preference information and desired information

Output: Data for prompt message generation

Step 3:

The server downloads the image based on the retrieved image URL and loads it as an image object. This process uses the requests library and the PIL library.

Input: Image URL

Output: Image object

Step 4:

The server generates prompt text based on image objects and user information in text format. The generated prompt text serves as instructions for inputting into the AI model.

Input: Image object, user information in text format

Output: Prompt message

Step 5:

The server inputs the generated prompt text into a generative AI model to automatically generate a house design. This process uses generative AI models such as OpenAI's GPT-3.

Input: Prompt text

Output: Automatically generated house blueprints

Step 6:

The server retrieves land and building information from real estate information websites. This information includes window orientation and size, floor plan, etc.

Input: URL of the real estate information website

Output: Land and building information

Step 7:

Based on the acquired real estate information, the server provides specific suggestions regarding window orientation, size, and floor plan for the generated house blueprints.

Input: Land and building information, automatically generated house blueprints

Output: Residential blueprints including specific proposals

Step 8:

The server sends the final design to the user's terminal and provides a proposal to the user. The user can review the proposed design and make modifications or regenerate it as needed.

Input: Residential blueprints including specific proposals

Output: Final design drawings sent to the user's terminal.

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

"Example of Morphology 1"

One embodiment of the present invention incorporates an emotion engine that recognizes the user's emotions. This emotion engine recognizes the user's emotions and adjusts the house design based on those emotions. Specifically, when the user is feeling happy, it suggests bright-colored interiors and an open floor plan. Conversely, when the user is feeling depressed, it suggests calm-colored interiors and a floor plan that prioritizes privacy.

"Example of Morphology 2"

Furthermore, the emotion engine adjusts housing suggestions based on the user's emotions. Specifically, when the user is feeling happy, it suggests luxurious homes. Conversely, when the user is feeling down, it suggests simple and functional homes.

"Example of Morphology 3"

As a concrete example, when a user is happy about getting a new job, the emotion engine recognizes that happiness and automatically generates blueprints for a luxurious house. These blueprints feature large windows, a spacious living room, and high-quality interior materials. Conversely, when a user is depressed after losing their job, the emotion engine recognizes that depression and automatically generates blueprints for a simple, functional house. These blueprints have only the bare minimum number of rooms and functions, keeping costs down.

The following describes the processing flow for each example form.

"Example of Morphology 1"

Step 1: The emotion engine recognizes the user's emotions.

Step 2: The emotion engine adjusts the house design based on the user's emotions. Specifically, when the user is feeling happy, it suggests bright interior colors and an open floor plan.

Step 3: On the other hand, when the user is feeling down, suggest interiors with calming color schemes and floor plans that prioritize privacy.

"Example of Morphology 2"

Step 1: The emotion engine recognizes the user's emotions.

Step 2: The emotion engine adjusts housing suggestions based on the user's emotions. Specifically, when the user is feeling happy, it suggests luxurious homes.

Step 3: On the other hand, when the user is feeling down, propose a simple and functional home.

"Example of Morphology 3"

Step 1: When a user feels joy after acquiring a new job, the emotion engine recognizes that joy.

Step 2: The emotion engine automatically generates blueprints for a luxurious house. These blueprints feature large windows, a spacious living room, and high-quality interior materials.

Step 3: On the other hand, when a user is depressed after losing their job, the emotion engine recognizes that depression.

Step 4: The emotion engine automatically generates a simple and functional house design. This design includes the minimum necessary number of rooms and functions, keeping costs down.

(Example 1)

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

Conventional residential design systems struggled to automatically generate blueprints that reflected user preferences and desires. Furthermore, they failed to adjust blueprints to accommodate user emotions, resulting in lower user satisfaction. Additionally, they lacked effective means of utilizing information from social media and real estate information websites. This led to challenges in creating residential designs that met diverse user needs.

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

This invention includes a server that receives user preference and desire information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that proposes a house design based on the generated design; a server that recognizes the user's emotions and adjusts the house design based on those emotions; and a server that proposes interior design and floor plans based on the user's emotions. This enables the automatic generation of house design plans that reflect the user's preferences and desires, and further allows for adjustments to the design plans that take the user's emotions into consideration, thereby increasing user satisfaction. Furthermore, by effectively utilizing information from social media and real estate information websites, it is possible to realize house designs that meet the diverse needs of users.

"User preference information" refers to images and text information about housing that users have obtained from social media and other sources on the internet.

"Desired information" refers to specific requests entered by the user, such as family structure, desired address, price range, whether renovations are needed, and whether they prefer a house or apartment.

"Real estate information" refers to detailed property information obtained from real estate information websites and other databases.

"House design plans" refer to floor plans, interior and exterior drawings of a house that are automatically generated based on the user's preferences, desired features, and real estate information.

"Means of recognizing emotions" refers to algorithms and software that analyze a user's facial expressions and entered text to determine their emotional state.

"Means for adjusting the blueprint" refers to algorithms and software used to modify or correct the content of a house's blueprint based on recognized user emotions.

"Methods for proposing interior design and floor plans" refers to algorithms and software that propose optimal interior designs and floor plans based on the user's emotions and preferences.

"Social media" refers to a platform on the internet for users to share information.

A "real estate information website" refers to a website or online service that provides detailed information about properties.

Modes for carrying out the invention

This invention is a system that automatically generates house blueprints based on user preference and desired information, and further adjusts the blueprints to take user emotions into consideration. This system consists of a server, a terminal, and the user.

System Configuration

1. User actions

Users save their favorite house images from social media on the internet and upload them to the system.

Users input their preferences into the system, including family structure, desired address, price range, whether renovations are needed, and whether they prefer a house or apartment.

2. Server Processing

The server receives residential images uploaded by users and extracts image features using image recognition algorithms (e.g., TensorFlow, OpenCV).

The server analyzes the user's input using a natural language processing engine (e.g., GPT-3) and organizes it as structured data.

The server integrates data obtained from image recognition algorithms and natural language processing engines to generate blueprints for a house.

The server uses an emotion engine to recognize the user's emotions and analyzes them from the user's facial expressions and entered text.

The server adjusts the design based on the user's perceived emotions. For example, if the user is feeling happy, it suggests bright color schemes and an open layout.

The server generates the final, adjusted blueprint and provides it to the user.

Hardware and software to be used

Hardware: High-performance servers (e.g., servers with NVIDIA GPUs)

Software: Image recognition algorithms (e.g., TensorFlow, OpenCV), natural language processing engines (e.g., GPT-3), emotion recognition engines

Specific examples

For example, suppose a user uploads a modern house design image saved from Instagram to the system and inputs their desired information, such as family size (four people), desired address (Tokyo), price range (under 50 million yen), whether renovation is desired (yes), and whether they prefer a house or apartment (house).

The server first uses an image recognition algorithm to extract modern design features from residential images. Next, it analyzes the user's preferences using a natural language processing engine to generate floor plans suitable for a family of four, designs appropriate for residential areas in Tokyo, specifications feasible within a budget of 50 million yen, designs that consider renovations, and plans for detached houses.

Furthermore, if the emotion engine recognizes the user's emotion as "joy," the server will suggest interior design with bright colors and an open living room. Finally, the server integrates this information to provide the user with a blueprint for their ideal home.

Example of a prompt message

The user provided images of modern-designed houses saved from social media, along with their family size (4 people), desired address (Tokyo), price range (under 50 million yen), whether renovations were desired (yes), and whether they preferred a house or apartment (house). Based on this information, please generate a house design. Additionally, if the user's emotion is "joy," please suggest a bright interior color scheme and an open floor plan.

In this way, the system can automatically generate house designs based on the user's preferences and desires, and further adjust the designs to take the user's emotions into consideration.

The flow of the specific processing in Example 1 will be explained using Figure 17.

Step 1:

Users save their preferred house images and input them into the system.

Specifically, the user downloads images of houses they like from social media on the internet and uploads those images through the system's interface.

Input: Residential images obtained from social media

Output: Residential images uploaded to the system

Step 2:

The user enters their desired information into the system.

In terms of specific operations, the user inputs desired information such as family structure, preferred address, price range, whether renovations are needed, and whether they prefer a house or apartment, through the system interface.

Input: Family composition, desired address, price range, whether renovation is required, preferred type of property (house or apartment), etc.

Output: Desired information entered into the system

Step 3:

The server analyzes residential images using an image recognition algorithm.

In terms of specific operation, the server receives uploaded images of houses and extracts image features using image recognition algorithms (e.g., TensorFlow, OpenCV). For example, it analyzes information such as the building's exterior, interior style, and color scheme.

Input: Residential images uploaded to the system

Output: Image feature data (building exterior, interior style, color tone, etc.)

Step 4:

The server uses a natural language processing engine to analyze the requested information.

In terms of specific operations, the server analyzes the user's input using a natural language processing engine (e.g., GPT-3) and organizes it as structured data. For example, it understands information such as family structure, desired address, and price range, and stores it in a database.

Input: Desired information entered into the system

Output: Structured desired information data

Step 5:

The server integrates the analysis results and generates the house blueprints.

In terms of specific operations, the server integrates data obtained from image recognition algorithms and natural language processing engines to generate blueprints for a house. For example, it designs the floor plan, interior, and exterior based on the user's preferences and desires.

Input: Image feature data, structured desired information data

Output: House design plans

Step 6:

The server uses an emotion engine to recognize the user's emotions.

Specifically, the server uses an emotion engine to recognize the user's emotions. It analyzes the text and facial expression data entered by the user into the system to determine the user's current emotional state.

Input: User text input, facial expression data

Output: User emotional state data

Step 7:

The server adjusts the design based on the user's emotions.

Specifically, the server adjusts the design based on the user's perceived emotions. For example, if the user is feeling happy, it suggests bright color schemes and an open layout. Conversely, if the user is feeling depressed, it suggests calm color schemes and a layout that prioritizes privacy.

Input: User emotional state data, house blueprints

Output: Adjusted house blueprints

Step 8:

The server provides the user with the final blueprint.

Specifically, the server generates a final, adjusted blueprint and provides it to the user. The user can review the blueprint through the system interface and provide feedback as needed.

Input: Adjusted house blueprints

Output: Final design drawings provided to the user

(Application Example 1)

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

Traditional residential design systems could generate blueprints based on user preferences and requests, but they lacked the ability to adjust the blueprints to reflect the user's emotions. Furthermore, it was difficult to provide real-time, emotionally responsive suggestions when users consulted with design and renovation companies in person. This resulted in a challenge in increasing user satisfaction.

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

This invention includes a server that receives user preference and desired information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that proposes a house design based on the generated design; a server that recognizes the user's emotions and adjusts the house design based on those emotions; and a server that analyzes the user's facial expressions using a smart device and recognizes their emotions. This enables the generation and proposal of house design plans that reflect the user's emotions in real time.

"User preference information" refers to images related to housing obtained by users from social media such as Instagram, as well as information indicating the user's personal preferences.

"Desired information" refers to information that indicates the user's specific requests, such as desired family structure, address, price range, whether or not renovations are needed, and whether they prefer a detached house or an apartment.

"Real estate information" refers to detailed information about a property, such as its location, price, floor plan, and year of construction, obtained from real estate information websites.

An "automatic blueprint generation method" refers to a system or algorithm for automatically generating blueprints for a house based on user preference information, desired information, and real estate information.

A "proposal method" refers to a system or algorithm for proposing housing designs to users based on generated blueprints.

An "emotion recognition system" is a system or algorithm that recognizes a user's emotions and adjusts the design plans of a house based on those emotions.

A "smart device" is a device, such as a smartphone or smart glasses, that analyzes a user's facial expressions and recognizes their emotions.

"Facial expression analysis means" refers to a system or algorithm that uses a smart device to analyze a user's facial expressions and recognize their emotions.

The system for implementing this invention automatically generates a house design based on the user's preferences, desired information, and real estate information, and adjusts the design based on the user's emotions. Specific embodiments are shown below.

System Configuration

1. Hardware:

Smart devices: Devices such as smartphones and smart glasses that analyze a user's facial expressions and recognize their emotions.

Server: A server used for data processing and blueprint generation.

2. Software:

Emotion Recognition Model: A machine learning model that analyzes a user's facial expressions and recognizes their emotions (e.g., an emotion recognition model using Keras).

Blueprint Generation Algorithm: An algorithm that automatically generates house blueprints based on user preferences, desired features, and real estate information.

Data Acquisition Module: A module for acquiring necessary information from social media and real estate information websites.

Processing Flow

1. Input of user preference and desired information:

Users upload images of houses they've saved from social media like Instagram to the app and enter information such as family composition, desired address, price range, whether renovations are planned, and whether it's a house or apartment.

2. Obtaining real estate information:

The server retrieves detailed information such as property location, price, floor plan, and year of construction from real estate information websites.

3. Emotion recognition:

Using smart devices, we analyze the user's facial expressions and recognize the user's emotions using an emotion recognition model.

4. Automatic generation of blueprints:

The server automatically generates house blueprints using a blueprint generation algorithm, based on user preference information, desired property information, and recognized emotional information.

5. Proposal:

Based on the generated blueprints, we propose housing designs to the user. This includes suggesting interior designs and floor plans that align with the user's preferences.

Specific examples

For example, a user uploads an image saved from Instagram to the app and enters their desired address and price range. The app analyzes the user's facial expressions through smart glasses and recognizes their emotions. Based on this information, the server automatically generates a design plan and proposes it to the user.

Example of a prompt message

Users upload images of houses they've saved from Instagram and enter information such as family composition, desired address, price range, whether renovations are needed, and whether it's a house or apartment. The system analyzes facial expressions through smart glasses to recognize emotions. Based on this information, it automatically generates an optimal house design.

In this way, a housing design assistant system based on the user's emotions and preferences can be realized.

The flow of the specific processing in Application Example 1 will be explained using Figure 18.

Step 1:

Users upload images of houses they have saved from social media such as Instagram to their devices.

Input: Image file of a house

Output: Uploaded image data

Specific operation: The user uploads saved images of their home to the application using their smartphone or smart glasses. The application then sends the image data to the server.

Step 2:

The user enters their desired information into the terminal, including family structure, preferred address, price range, whether renovations are needed, and whether they prefer a house or apartment.

Input: Family composition, address, price range, whether renovations were done, housing type

Output: User's requested information data

Specific operation: The user enters the desired information into the application's input form and presses the submit button. The application sends the entered data to the server.

Step 3:

The server retrieves detailed information such as property location, price, floor plan, and year of construction from real estate information websites.

Input: API of a real estate information website

Output: Real estate information data

Specific operation: The server calls the API of a real estate information website to retrieve the necessary real estate information. The retrieved data is stored in an internal database.

Step 4:

The device uses a smart device to analyze the user's facial expressions and recognizes the user's emotions using an emotion recognition model.

Input: User's facial expression image

Output: User sentiment data

Specific operation: The smart device's camera captures the user's facial expressions and sends the image data to the server. The server analyzes the facial image using an emotion recognition model to recognize the user's emotions.

Step 5:

The server automatically generates house blueprints using a blueprint generation algorithm based on user preference information, desired properties, real estate information, and emotional information.

Input: User preference information, desired information, real estate information, emotional information

Output: Automatically generated house blueprints

Specific operation: The server integrates all input data and executes the blueprint generation algorithm. The algorithm generates blueprints that take into account the user's emotions regarding interior design and floor plans.

Step 6:

The server generates blueprints and then proposes housing designs to the user.

Input: Automatically generated house blueprints

Output: Suggestions for the user

Specific operation: The server presents the generated blueprints to the user and offers suggestions for interior design and floor plans that align with their emotions. The user can review the suggestions through the application.

(Example 2)

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

Conventional residential design systems could generate blueprints based on user preferences and requests, but they lacked the ability to consider user emotions when making proposals. Furthermore, they struggled to effectively utilize real estate information for residential design, making it difficult to provide optimal proposals to users. There was a growing need to improve user satisfaction by offering housing proposals that resonated with users' emotions.

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

This invention includes a server that receives user preference and desire information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that proposes a house based on the generated design; and a server that analyzes the user's emotions and adjusts the house proposal based on the analysis results. This makes it possible to propose an optimal house design that not only reflects the user's preferences and desires but also their emotions.

"User preference information" refers to information that indicates a user's personal preferences and desires regarding their home.

"Desired information" refers to information that indicates the specific conditions and requirements that the user desires for a home.

"Real estate information" refers to information that includes detailed data on land and buildings, such as their shape, location, and price.

A "design drawing" is a diagram showing the layout, structure, window orientation, and size of a house.

A "proposal" refers to a specific plan regarding the design and layout of a house, provided to the user.

"Analyzing emotions" means identifying and evaluating a user's emotional state based on their facial expressions, behavior, and input data.

"Adjusting" means modifying and optimizing the proposed content based on the analysis results.

This invention is a system that automatically generates house designs and provides optimal housing proposals, taking into account the user's preferences, desires, real estate information, and emotions. A specific embodiment of this system is described below.

System Configuration

1. Obtaining real estate information

The server retrieves land and building information from real estate information websites. In this process, the server uses specific APIs to collect data. For example, it might utilize the API of a real estate information website.

2. Data Analysis and Processing

The server analyzes the acquired land and building information, extracting data such as shape and location conditions. This is done using a data analysis library.

3. Automatic generation of residential building plans

The server automatically generates house blueprints based on the analyzed data. Specifically, it considers the shape and location of the land and proposes window orientations, sizes, and floor plans. This process utilizes the API of design software.

4. Adjustment by the Emotional Engine

The device analyzes the user's emotions using an emotion engine. This emotion engine utilizes an emotion analysis API. If the user is feeling happy, the device suggests a luxurious home. Conversely, if the user is feeling depressed, it suggests a simple and functional home.

Specific examples

Example 1: Obtaining real estate information

The server uses the API of a real estate information website to retrieve land information for a specific area. For example, it sends an API request like the following:

"Retrieve land information for San Francisco and generate blueprints that suggest luxurious homes if the user is happy, and simple homes if they are depressed."

Example 2: Data Analysis and Processing

The server analyzes the acquired data using a data analysis library. For example, it extracts information about the land's shape and location, preparing it for use in the next step.

Example 3: Automatic generation of residential building plans

The server generates design drawings using the design software's API. For example, it considers the shape and location of the land and proposes window orientations, sizes, and floor plans.

Example 4: Adjustment by an emotional engine

The device uses an emotion analysis API to analyze the user's emotions. For example, if the user is happy, it suggests a luxurious house; if they are depressed, it suggests a simple house.

In this way, it becomes possible to propose optimal house designs that not only reflect the user's preferences and desires, but also their emotions. This system can improve user satisfaction.

The flow of the specific processing in Example 2 will be explained using Figure 19.

Step 1:

The server retrieves land and building information from real estate information websites. Specifically, the server sends an API request and receives JSON data containing land information for a specified area. The input is the parameters of the API request (e.g., area, property type), and the output is the retrieved real estate information in JSON data.

Step 2:

The server converts the retrieved JSON data into a data frame using a data analysis library. Specifically, the server uses the pandas library to read the JSON data and convert it into a data frame. The input is the retrieved JSON data, and the output is real estate information in data frame format.

Step 3:

The server extracts the necessary data from the data frame. Specifically, it extracts information such as land shape and location conditions and prepares it for use in the next step. The input is real estate information in data frame format, and the output is the extracted data on land shape and location conditions.

Step 4:

The server generates house blueprints using the design software's API. Specifically, it considers the land's shape and location to suggest window orientations and sizes, floor plans, and other details. The input is extracted data on land shape and location, and the output is the generated house blueprints.

Step 5:

The device analyzes the user's emotions using an emotion analysis API. Specifically, it identifies and evaluates the emotional state from the user's facial expressions, actions, and input data. Input consists of the user's images and text data, and output is the analyzed emotion data.

Step 6:

The device adjusts housing suggestions based on the analysis results. Specifically, it suggests a luxurious house if the user is happy, and a simple house if they are depressed. The input is the analyzed emotion data and the generated house blueprints, and the output is the adjusted housing suggestion.

In this way, by clearly defining the specific actions, inputs, and outputs performed at each processing step, the program's processing flow was explained in detail.

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

Conventional housing design systems could automatically generate blueprints based on user preferences, desires, and real estate information, but they lacked the ability to adjust proposals to reflect user emotions or display blueprints using virtual reality. Therefore, it was difficult to provide housing proposals that resonated with users' emotions or offer a more realistic experience. This resulted in a challenge in increasing user satisfaction.

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

This invention includes a server that receives user preference and desire information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that makes house proposals based on the generated design; a server that receives user emotion information as input and adjusts the house proposals based on this information; and a server that displays the generated design using a virtual reality display device. This enables the adjustment of house proposals according to the user's emotions and provides a realistic experience using virtual reality.

"User preference information" refers to information that indicates a user's personal preferences and desires regarding their home.

"Desired information" refers to information that indicates the specific conditions and requirements that the user desires for a home.

"Real estate information" refers to information that includes detailed data about land and buildings.

A "house design drawing" is a detailed drawing showing the layout, structure, and facilities of a house.

"Emotional information" refers to information that indicates the user's current emotional state.

A "virtual reality display device" is a device used to visually display a virtual space.

"Automatic generation methods" refer to devices or programs that have the function of automatically creating design drawings based on input information.

A "means for adjusting suggestions" refers to a device or program that has the function of modifying and optimizing suggestion content based on user sentiment information.

"Means of display" refers to devices or programs that have the function of visually presenting the generated design drawings to the user.

The system for implementing this invention automatically generates a house design based on the user's preference information, desired information, real estate information, and emotional information, and presents it to the user using a virtual reality display device. Specific embodiments are described below.

System Configuration

The system consists of the following main components:

1. User Terminal: A device such as a smartphone or head-mounted display collects user input information and displays it in virtual reality.

2. Server: Receives user preference information, desired information, real estate information, and emotional information, and automatically generates house blueprints.

3. Emotion Engine: Analyzes user emotion information and adjusts housing recommendations accordingly.

4. Design Generation Engine: Generates house blueprints based on real estate information, user preferences, and desired information.

5. Virtual Reality Display Device: Displays the generated blueprint in a virtual reality space.

Program processing

The server processes the data using the following steps:

1. Data Acquisition: Acquire preference information, desired information, and real estate information from the user's device. Acquire user sentiment information using the sentiment engine.

2. Design Drawing Generation: Using a design generation engine, the design drawings for the house are automatically generated based on the acquired information.

3. Proposal Adjustment: Using an emotion engine, housing proposals are adjusted based on the user's emotional information.

4. Virtual Reality Display: The generated design drawings are presented to the user using a virtual reality display device.

Hardware and software to be used

Hardware: Smartphones, head-mounted displays, servers

Software: EmotionEngine, DesignGenerator, VRDisplay (Virtual Reality Display Engine)

Specific examples

For example, when a user experiences designing a house using a smartphone, a luxurious house is suggested when the user is happy, and a simple, functional house is suggested when the user is depressed. The user wears a head-mounted display and can realistically experience the design blueprints generated in a virtual reality space.

Example of a prompt message

"Please generate blueprints for luxurious homes that will be presented to users when they are happy."

"Please generate blueprints for a simple and functional home that will be suitable for users who are feeling down."

In this way, we can provide housing proposals tailored to the user's emotions and realistic experiences using virtual reality.

The flow of the specific processing in Application Example 2 will be explained using Figure 20.

Step 1:

The user terminal receives user preference and desired information as input. Users input their housing preferences and specific requests using smartphones or head-mounted displays. This information is then transmitted to the server.

Input: User preference information, desired information

Output: User preference information and desired information sent to the server.

Step 2:

The server retrieves real estate information from real estate information websites. The server accesses the sites via APIs and collects detailed data about land and buildings.

Input: URL of the real estate information website

Output: Acquired real estate information

Step 3:

The user terminal uses an emotion engine to acquire the user's emotional information. It analyzes the user's facial expressions and voice to detect their current emotional state. This emotional information is then transmitted to the server.

Input: User's facial expressions, voice

Output: Emotional information sent to the server

Step 4:

The server uses a design generation engine to automatically generate house blueprints based on acquired preference information, desired information, and real estate information. The design generation engine analyzes this information to design the optimal floor plan and structure.

Input: User preference information, desired information, real estate information

Output: Generated house blueprints

Step 5:

The server uses an emotion engine to adjust housing suggestions based on the user's emotional information. For example, if the user is happy, it suggests a luxurious house; if they are depressed, it suggests a simple and functional house.

Input: Generated house blueprints, emotional information

Output: Proposed adjusted housing design

Step 6:

The server uses a virtual reality display to present the generated blueprints to the user. The user wears a head-mounted display and can realistically experience the blueprints within the virtual reality space.

Input: Adapted housing proposal

Output: Residential blueprints displayed in a virtual reality space.

In this way, we can provide housing proposals tailored to the user's emotions and realistic experiences using virtual reality.

(Example 3)

Next, we will describe Embodiment 3 of Example 3. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

Conventional residential design systems have difficulty automatically generating blueprints that reflect user preferences and desires, and generating blueprints that take real estate information into account is also time-consuming. Furthermore, they cannot generate blueprints that take into account the user's emotional state, thus failing to enhance user psychological satisfaction. There is a need for a system that solves these problems and automatically generates residential blueprints that meet the diverse needs of users.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

This invention includes a server that receives user preference and desire information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; and a server that recognizes the user's emotional state and adjusts the house design according to that emotional state. This enables the automatic generation of a house design that takes into account the user's preferences, desires, real estate information, and even their emotional state.

"User preference information" refers to information about the designs and styles that users prefer, and is obtained from sources such as social media.

"Desired information" refers to information about the user's specific requirements, such as the desired location, budget, and family structure of their home.

"Real estate information" refers to information about land and buildings obtained from real estate information websites.

"Emotional state" refers to information that indicates the user's current psychological state and is recognized by the emotion engine.

A "design drawing" is a diagram showing the structure, layout, window orientation, and size of a house.

"Automatic generation" refers to the process where a system automatically creates a blueprint based on user input, property information, and emotional state.

A "proposal" is the process of presenting a specific house design and layout to the user based on the generated blueprints.

Modes for carrying out the invention

This invention is a system that automatically generates and proposes house designs to users based on their preferences, desires, real estate information, and emotional state. A specific embodiment of this system is described below.

Hardware and software to be used

Hardware: Servers, user terminals (PCs, smartphones)

Software: Generative AI models (e.g., GPT-4), emotion recognition engine, database management system (e.g., MySQL), real estate information site API

System Operation Overview

1. Entering User Information

Users use their devices to input information such as images saved from Instagram, family composition, desired housing location (within Tokyo), and budget (50 million yen).

Specific example input: "We are a family of four, looking for a modern-looking detached house in Tokyo, with a budget of 50 million yen."

2. Data Transmission and Analysis

The terminal sends the entered information to the server.

The server analyzes the received information and inputs it as a prompt message into the generating AI model.

Specific prompt example: "The user has saved an image of a modern exterior from Instagram. They are a family of four looking for a detached house in Tokyo with a budget of 50 million yen. Please generate a design plan based on this information."

3. Generating Blueprints

The server inputs prompt messages into the generated AI model and generates the blueprint.

The server uses land and building information in Tokyo obtained from real estate information websites to suggest window orientations, sizes, floor plans, and other details.

4. Emotion Recognition and Adjustment of the Blueprint

The server uses an emotion engine to recognize the user's emotional state.

The server adjusts the blueprint based on the results of the emotion engine.

Specific prompt example: "The user is delighted to have a new job. Please generate blueprints for a luxurious house with large windows, a spacious living room, and high-quality interior materials."

5. Provision of design drawings

The server sends the generated blueprint to the user's terminal.

The terminal displays the received blueprints to the user.

Specific examples

User input: "We are a family of four, looking for a modern-looking detached house in Tokyo, with a budget of 50 million yen."

Prompt: "The user has saved an image of a modern exterior from Instagram. They are a family of four looking for a detached house in Tokyo with a budget of 50 million yen. Please generate a design plan based on this information."

Emotion recognition prompt message: "The user is happy to have a new job. Please generate blueprints for a luxurious house with large windows, a spacious living room, and high-quality interior materials."

This system enables the automatic generation of residential blueprints that take into account the user's preferences, desired information, real estate information, and even their emotional state. This allows for residential designs that meet the diverse needs of users. The specific processing flow in Example 3 will be explained using Figure 21.

Step 1:

Entering user information

The user uses their device to input information such as images saved from Instagram, family composition, desired housing location (within Tokyo), and budget (50 million yen).

Input: User preference information, desired information

Output: The entered information is saved to the device.

Specific action: The user enters information into the input form on the device and clicks the "Submit" button.

Step 2:

Data transmission and analysis

The terminal sends the entered information to the server.

Input: Information entered by the user

Output: User information sent to the server

Specific operation: The terminal uses an HTTP request to send input data to the server.

The server analyzes the received information and inputs it as a prompt message into the generating AI model.

Input: User information sent from the device

Output: Prompt text to input to the generated AI model

Specific operation: The server analyzes the data and generates a prompt message like the following: "The user has saved an image of a modern exterior from Instagram. They are a family of four looking for a detached house in Tokyo with a budget of 50 million yen. Please generate a design plan based on this information."

Step 3:

Blueprint generation

The server inputs prompt messages into the generated AI model and generates the blueprint.

Input: Prompt text

Output: Generated design drawing data

Specific operation: The server sends prompt messages to the generated AI model (e.g., GPT-4) and receives the blueprint data returned from the model.

The server uses land and building information in Tokyo obtained from real estate information websites to suggest window orientations, sizes, floor plans, and other details.

Input: Information obtained from a real estate information website.

Output: Proposed window orientation and size, floor plan, etc.

Specific operation: The server uses an API to retrieve real estate information and reflects it in the design plans.

Step 4:

Emotion recognition and blueprint adjustment

The server uses an emotion engine to recognize the user's emotional state.

Input: User sentiment data

Output: Recognized emotional state

Specific operation: The server sends user emotion data to the emotion engine and analyzes the emotional state.

The server adjusts the blueprint based on the results of the emotion engine.

Input: Recognized emotional state

Output: Adjusted blueprint

Specific operation: The server receives the result from the emotion engine and generates a prompt message like the following: "The user is happy to have a new job. Please generate blueprints for a luxurious house with large windows, a spacious living room, and high-quality interior materials."

Step 5:

Provision of design drawings

The server sends the generated blueprint to the user's terminal.

Input: Generated design data

Output: Blueprint sent to the user's terminal

Specific operation: The server sends the design data as an HTTP response to the user's terminal.

The terminal displays the received blueprints to the user.

Input: Design drawing data sent from the server

Output: Blueprint displayed to the user

Specific operation: The terminal analyzes the received design data and displays it on the user interface.

(Application Example 3)

Next, we will explain Application Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

Conventional residential design systems struggled to automatically generate blueprints that reflected user preferences and desires, and their proposals based on real estate information were limited. Furthermore, they lacked the means to generate blueprints that considered the user's emotional state, and to visually confirm the generated blueprints as 3D models. This made it difficult to increase user satisfaction.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

This invention includes a server that receives user preference and desire information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that makes house proposals based on the generated design; a server that receives the user's emotional state as input and automatically generates a house design based on this; and a server that displays the generated design as a 3D model. This enables the automatic generation of house design plans that reflect the user's preferences, desires, and emotional state, as well as visual confirmation.

"User preference information" refers to information about the designs and styles that users prefer, and is obtained from social media and image storage services.

"Desired information" refers to information about the conditions and specifications of a home that the user desires, including family structure, budget, and location.

"Real estate information" refers to information about land and buildings obtained from real estate information websites, including details such as price, area, and location.

"Emotional state" refers to information that indicates the user's current emotions and mood, including feelings such as joy and sadness.

A "design plan" is a drawing that shows the structure, layout, and design of a house, and is automatically generated based on the user's preferences, desires, real estate information, and emotional state.

A "3D model" is a three-dimensional visual model created based on design drawings, allowing users to view the exterior and interior of a house in three dimensions.

"Automatic generation" refers to the process by which a system automatically creates design drawings and 3D models based on user input and acquired data.

A "proposal" is the act of showing the user specific features of a house, such as window orientation and size, and floor plan, based on the generated design drawings.

The system for implementing this invention automatically generates a house design based on the user's preferences, desires, real estate information, and emotional state, and displays it as a 3D model. A specific embodiment is shown below.

System Configuration

The system consists of user terminals, a server, and a display device. User terminals include smartphones and head-mounted displays (HMDs). The server performs data processing and automatically generates design drawings using a generated AI model. The display device is used to display the generated design drawings as 3D models.

Hardware and software to be used

Hardware: Smartphones, head-mounted displays (HMDs), servers

Software: Python, OpenAI API, 3D modeling libraries

Data processing and data calculation

1. Receiving user input:

The user terminal receives user preference information (e.g., images obtained from social media), desired information (e.g., family structure, budget, location), and emotional state (e.g., joy or sadness) as input.

2. Obtaining real estate information:

The server retrieves information about land and buildings in Tokyo from real estate information websites. This includes details such as price, area, and location.

3. Automatic generation of blueprints:

The server uses a generative AI model (e.g., OpenAI's GPT-3) to automatically generate house blueprints based on user input and acquired real estate information. An example of a prompt message for the generative AI model is as follows:

User input: {'image': 'modern_house_image.jpg', 'family_size': 4, 'location': 'Tokyo', 'budget': 5000, 'emotion': 'happy'}

Real estate data: {'Land information': '...', 'Building information': '...'}

Please generate the blueprint.

4. Creating and displaying 3D models:

The server creates a 3D model using a 3D modeling library based on the generated blueprint. The created 3D model is then displayed on the user's device (smartphone or HMD).

Specific examples

If a user provides an image of a modern exterior saved from Instagram, enters that they are a family of four looking for a detached house in Tokyo with a budget of 50 million yen, the server will automatically generate a blueprint for a modern-looking detached house in Tokyo based on this information. Furthermore, when a user is feeling happy about getting a new job, the emotion engine recognizes this happiness and automatically generates a blueprint for a luxurious home. This blueprint will feature large windows, a spacious living room, and high-quality interior materials.

In this way, it becomes possible to automatically generate and visually confirm house designs that reflect the user's preferences, desires, and emotional state.

The flow of the specific processing in Application Example 3 will be explained using Figure 22.

Step 1:

Users input preference information (e.g., images from social media), desired information (e.g., family structure, budget, location), and emotional state (e.g., joy, sadness) using a smartphone or head-mounted display (HMD).

Input: User preference information, desired information, emotional state

Output: User input data

Specific action: The user enters information into the application's input form and presses the submit button.

Step 2:

The server retrieves information about land and buildings in Tokyo from real estate information websites.

Input: User's desired information (e.g., location)

Output: Real estate information data

Specific operation: The server uses an API to send requests to real estate information websites and retrieve the necessary data.

Step 3:

The server uses an AI model (e.g., OpenAI's GPT-3) to automatically generate house blueprints based on user input and acquired real estate information.

Input: User input data, real estate information data

Output: House design drawing data

Specific operation: The server sends prompt messages to the generated AI model to generate the blueprint. An example of a prompt message is as follows:

User input: {'image': 'modern_house_image.jpg', 'family_size': 4, 'location': 'Tokyo', 'budget': 5000, 'emotion': 'happy'}

Real estate data: {'Land information': '...', 'Building information': '...'}

Please generate the blueprints.

Step 4:

The server creates a 3D model using a 3D modeling library based on the generated blueprint.

Input: House design drawing data

Output: 3D model data

Specific operation: The server inputs design data into the 3D modeling library and generates a 3D model.

Step 5:

The server sends the generated 3D model to the user's terminal, and the user's terminal displays it.

Input: 3D model data

Output: 3D model displayed on the user's terminal

Specific operation: The server sends 3D model data to the user's terminal, and the user's terminal displays it on a display device (smartphone or HMD).

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

The data generation model 58 is a form of so-called generative AI (Artificial Intelligence).

An example of a data generation model 58 is the generation AI of ChatGPT (registered trademark) (Internet search <URL: https://openai.com/blog/chatgpt>).

The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives prompts containing instructions, as well as inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 performs inference on the input inference data according to the instructions provided by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization.

Another example of generative AI is Gemini® (registered trademark) (Internet search <URL: https://gemini.google.com/?hl=ja>).

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

[Second Embodiment]

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

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

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

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

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

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

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

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

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

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

In the smart glasses 214, the processor 46 performs the reception and output processing. The storage 50 stores the reception and output program 60. The processor 46 reads the reception and output program 60 from the storage 50 and executes the read program 60 on the RAM 48. The reception and output processing is realized by the processor 46 acting as a control unit 46A according to the reception and output program 60 executed on the RAM 48.

Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be explained.

"Example of Morphology 1"

In one embodiment of the system, a user saves images of their preferred homes from social media such as Instagram and inputs the image information into this system. The user also inputs their family structure, desired address, price range, whether or not they want renovations, and whether they prefer a house or apartment. Based on this preference and desired information, the system automatically generates a blueprint for the house.

"Example of Morphology 2"

Furthermore, this system acquires land and building information from real estate information websites and automatically generates house blueprints based on this information. Specifically, it proposes window orientations, sizes, and floor plans, taking into account the shape of the land and building, location conditions, etc.

"Example of Morphology 3"

As a concrete example, if a user inputs an image of a modern exterior saved from Instagram, along with information such as a family of four, a desire for a detached house in Tokyo, and a budget of 50 million yen, this system will automatically generate a blueprint for a detached house in Tokyo with a modern exterior based on this information. Furthermore, based on land and building information in Tokyo obtained from real estate information websites, it will suggest window orientations and sizes, floor plans, and other details.

The following describes the processing flow for each example form.

"Example of Morphology 1"

Step 1: The user saves images of their preferred homes from social media such as Instagram.

Step 2: The user inputs the saved image information into this system.

Step 3: The user enters their family structure, desired address, price range, whether or not they want renovations, and other desired information such as a house or apartment into the system.

Step 4: This system automatically generates house blueprints based on these preference and desired information.

"Example of Morphology 2"

Step 1: This system retrieves land and building information from real estate information websites.

Step 2: This system automatically generates house plans based on the acquired land and building information. Specifically, it proposes window orientations, sizes, and floor plans, taking into account the shape of the land and building, location conditions, etc.

"Example of Morphology 3"

Step 1: The user enters a modern exterior image saved from Instagram, along with information such as being a family of four, wanting a detached house in Tokyo, and having a budget of 50 million yen.

Step 2: Based on this information, the system automatically generates a blueprint for a modern-looking detached house in Tokyo.

Step 3: This system uses land and building information in Tokyo obtained from real estate information websites to suggest window orientations, sizes, floor plans, etc.

(Example 1)

Next, we will describe Example 1 of Embodiment 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

Conventional residential design systems struggled to automatically generate blueprints that reflected users' preferences and desires. Furthermore, a lack of effective means to utilize image information obtained from social media meant that specific user preferences could not be reflected in the design. Additionally, when proposing housing designs based on the generated blueprints, it was difficult to create proposals that truly met the user's needs.

The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes means for receiving user preference information and desired information as input and automatically generating a house design based on this information; means for receiving image information saved by the user as input and analyzing this information to identify the user's preferences; means for generating and inputting prompt statements to the generation AI model; and means for making house proposals based on the generated design. This enables the automatic generation of house design plans that reflect the user's specific preferences and desires, and allows for the proposal of houses that match the user's wishes.

"User preference information" refers to information about the housing designs and styles that users prefer, and includes images and text data obtained from social media.

"Desired information" refers to the specific conditions and requirements of the user's desired housing, including family structure, desired address, price range, whether renovations are needed, and whether it's a detached house or an apartment.

"Image information" refers to image data of houses that users obtain from social media or other sources and upload to the system.

A "generative AI model" is an artificial intelligence model that automatically generates house blueprints based on user preferences and desired information, utilizing natural language processing and image analysis technologies.

A "prompt message" is an instruction given to the AI generation model, and it is generated based on the user's preferences and desired information.

A "house design plan" refers to a design drawing of a house generated based on the user's preferences and desired information, including specific layouts and designs.

A "housing proposal" refers to a housing design proposal made to the user based on the generated housing blueprints, and includes content that matches the user's wishes.

This invention is based on the premise that users save images of their preferred homes from social media such as Instagram and input that image information into the system. Users also input their family structure, desired address, price range, whether or not they want renovations, and whether they want a house or an apartment. Based on this information, the system automatically generates a blueprint for the house.

Hardware and software to be used

The server receives image information and request information entered by the user and stores it in a database. Specifically, it uses the following hardware and software:

Server: High-performance cloud server (e.g., Amazon Web Services, Google Cloud Platform)

Database: Relational database (e.g., MySQL, PostgreSQL)

Generative AI models: AI models using natural language processing and image analysis techniques (e.g., OpenAI's GPT-4, DALL-E)

Image analysis software: Computer vision technology (e.g., TensorFlow, PyTorch)

Data processing and data calculation

The server receives image files uploaded by the user and requested information, and stores this data in a database. Based on the stored data, it generates prompt messages for a generative AI model and inputs them. The generative AI model then generates a house design based on these prompt messages and provides it to the user via the server.

Specific examples

As a concrete example, suppose a user saves an image of a modern-designed house from Instagram and enters the following desired information:

Family composition: 4 people (2 adults, 2 children)

Desired address: Tokyo

Price range: Under 50 million yen

Renovation status: Yes

House or apartment: House

Based on this information, the server inputs the following prompt message into the AI model:

Based on images of modern-designed homes saved by the user from Instagram, please generate a blueprint for a home that matches the following requirements.

Family composition: 4 people (2 adults, 2 children)

Desired address: Tokyo

Price range: Under 50 million yen

Renovation status: Yes

House or apartment: House

The AI model generates a house design based on this prompt, matching the user's preferences, and provides it to the user via the server. The user can review the received design on their device and, if necessary, modify their preferences to generate a new design.

In this way, it becomes possible to automatically generate house designs that reflect the user's specific preferences and desires, and to propose houses that match the user's wishes.

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

Step 1:

Users save images of their favorite houses from social media such as Instagram. Users use their smartphones or computers to find and save images of their favorite houses from social media such as Instagram. The input is image data obtained from social media, and the output is an image file saved on the user's device.

Step 2:

The user uploads saved images to the system. The user accesses the system's website or application and uploads saved images of their home. Specifically, they click the upload button and select the saved image file. The input is the image file saved on the user's device, and the output is the image data sent to the server.

Step 3:

The user enters their desired information, such as family structure, preferred address, price range, whether renovations are needed, and whether they prefer a house or apartment. The user enters their desired information into the system's input form, including family structure (e.g., 2 adults, 2 children), preferred address (e.g., Tokyo), price range (e.g., under 50 million yen), whether renovations are needed (e.g., yes), and whether they prefer a house or apartment (e.g., house). The input is the desired information entered by the user, and the output is the desired information sent to the server.

Step 4:

The server receives image and request information from the user and stores it in the database. The server receives uploaded image files and input request information from the user and stores this data in the database. When saving, the data is managed in association with the user ID. The input is the image and request information sent by the user, and the output is the data stored in the database.

Step 5:

The server generates prompt messages for the AI model based on the stored data and inputs them. The server generates prompt messages for the AI model based on image information and desired information stored in the database. For example, it generates prompt messages like the following:

Based on images of modern-designed homes saved by the user from Instagram, please generate a blueprint for a home that matches the following requirements.

Family composition: 4 people (2 adults, 2 children)

Desired address: Tokyo

Price range: Under 50 million yen

Renovation status: Yes

House or apartment: House

The input consists of image information and desired information stored in a database, while the output is the prompt text input to the generating AI model.

Step 6:

The generative AI model generates a house design based on the prompt text. The generative AI model (e.g., OpenAI's GPT-4 or DALL-E) generates the house design based on the prompt text sent from the server. The generative AI model utilizes image analysis and natural language processing techniques to create a design that meets the user's preferences. The input is the prompt text, and the output is the generated house design.

Step 7:

The server sends the generated blueprint to the user's terminal. The server sends the blueprint received from the generated AI model to the user's terminal. The user can download or view the blueprint through the system's website or application. The input is the generated house blueprint, and the output is the blueprint sent to the user's terminal.

Step 8:

The user reviews the blueprint. The user reviews the blueprint received on their device. They can check if the blueprint meets their requirements and, if necessary, modify the required information to generate a new blueprint. The input is the blueprint sent to the user's device, and the output is the user's review result.

(Application Example 1)

Next, we will describe Application Example 1 of Form 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."

Traditional home design systems struggled to automatically generate blueprints that reflected user preferences and desires. In particular, if a user had a specific image in mind, specialized knowledge was required to translate that image into a blueprint. Furthermore, real-time blueprint generation and display were difficult when users viewed home designs in physical showrooms. This led to decreased user satisfaction and delays in home purchase decisions.

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

This invention includes a server that receives user preference and desired information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that proposes houses based on the generated design; a server that analyzes house images taken by the user and extracts their features; a server that generates prompt text based on the extracted features and the user's desired information and generates a house design using a generation AI model; and a server that presents the generated design to the user via a display device. This enables the real-time generation of house design plans that reflect the user's specific image, allowing for on-site verification at a physical location.

"User preference information" refers to information that indicates a user's personal preferences and design tastes regarding housing.

"Desired information" refers to information that indicates the user's specific requests and conditions regarding housing (family structure, desired address, price range, whether renovations are needed, whether it's a detached house or an apartment, etc.).

"Real estate information" refers to information that provides detailed data about a real estate property (location, price, floor plan, year built, etc.).

A "house design drawing" is a diagram that specifically shows the structure, layout, and design of a house.

"Automatic generation methods" refer to devices or programs that have the function of automatically creating design drawings using algorithms or AI models based on input information.

"Means of making proposals" refers to devices or programs that have the function of making housing proposals to users based on the generated blueprints.

"Residential images" refer to photographs or image data of houses taken by users.

"Means of analysis" refers to devices or programs that have the function of analyzing input image data and extracting its features.

A "means for extracting features" refers to a device or program that has the function of extracting important features from data obtained through image analysis.

A "prompt" is a text-based input message used to give instructions to a generative AI model.

A "generative AI model" is an artificial intelligence model that generates blueprints and other data based on input prompts.

A "display device" is a device (such as a display or VR headset) used to visually present generated design drawings to the user.

The system for carrying out this invention includes means for receiving user preference information and desired information as input and automatically generating a house design based on this information; means for receiving real estate information as input and automatically generating a house design based on this information; means for making house proposals based on the generated design; means for analyzing house images taken by the user and extracting their features; means for generating prompt sentences based on the extracted features and the user's desired information, and generating a house design using a generation AI model; and means for presenting the generated design to the user through a display device.

Hardware and software to be used

Hardware

Smartphone or tablet (iOS or Android): Used by the user to take pictures of the house and enter information.

Smart glasses (e.g., Google Glass): Used to analyze images taken by the user in real time.

Large-screen display or VR headset (e.g., Oculus Rift): Used to present the generated blueprints to the user.

Software

Image recognition libraries (e.g., OpenCV): Used to analyze residential images taken by the user and extract their features.

Generative AI models (e.g., GPT-4, DALL-E): Used to generate house blueprints based on prompt messages.

Database (e.g., Firebase): Used to store and manage user preference information, desired information, and real estate information.

Frontend frameworks (e.g., React Native): Used to build user interfaces.

Processing Flow

1. User Input

Users use their smartphones or tablets to take photos of their preferred homes and upload them to the application. They also input information such as family composition, desired address, price range, whether renovations are needed, and whether they prefer a house or apartment.

2. Image Analysis

The server uses an image recognition library (OpenCV) to analyze uploaded residential images and extract their features.

3. Generating the prompt message

The server generates prompt messages for the generative AI model (GPT-4) based on the extracted features and user preferences.

Example of a prompt message

Based on the images and desired information provided by the user, please generate a house design that meets the following conditions:

Family composition: 4 people

Desired address: Tokyo

Price range: Under 50 million yen

Renovation: Yes

Detached house

Modern design

4. Generating Blueprints

The server uses a generative AI model (DALL-E) to generate house blueprints based on the prompt text.

5. Displaying Results

The generated blueprints are presented to the user via a large screen display or VR headset. Users can then view their ideal home design in real time at a physical store.

Specific examples

For example, a user uploads an image of a modern living room taken with their smartphone and enters their desired information as follows: "Family size: 4 people, Desired address: Tokyo, Price range: under 50 million yen, Renovation: Yes, Detached house." Based on this information, the server generates a blueprint for a modern detached house in Tokyo, suitable for a family of four, within a 50 million yen budget, and presents it to the user.

In this way, users can view their ideal home design in real time at a physical store.

The flow of the specific processing in Application Example 1 will be explained using Figure 12.

Step 1:

Users use their smartphones or tablets to take photos of their preferred homes and upload them to the application. They also input information such as family composition, desired address, price range, whether renovations are needed, and whether they prefer a house or apartment.

Input: Housing images, family composition, desired address, price range, whether renovation is needed, desired type of property (house or apartment), etc.

Output: Uploaded house images and desired information

Step 2:

The server uses an image recognition library (OpenCV) to analyze uploaded residential images and extract their features.

Input: Uploaded house image

Output: Extracted image features

Specific operation: Using an image recognition library, perform edge detection and color analysis on residential images, and extract features.

Step 3:

The server generates prompt messages for the generative AI model (GPT-4) based on the extracted features and user preferences.

Input: Extracted image features, desired information

Output: Generated prompt message

Specific operation: Combine the extracted features and desired information to create prompt sentences for input into the generating AI model.

Step 4:

The server uses a generative AI model (DALL-E) to generate house blueprints based on the prompt text.

Input: Generated prompt text

Output: Generated house blueprints

Specific operation: The system takes prompt text as input to a generation AI model and generates a house design.

Step 5:

The server presents the generated blueprints to the user via a large screen display or VR headset.

Input: Generated house blueprints

Output: Design drawings presented to the user

Specific operation: The generated design drawings are sent to a display or VR headset, allowing the user to visually confirm them.

In this way, users can view their ideal home design in real time at a physical store.

(Example 2)

Next, we will describe Example 2 of the 2nd embodiment. 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."

Traditional residential design systems had the problem of requiring a great deal of time and effort to reflect user preferences and desired information. Furthermore, efficiently acquiring real estate information and automatically generating optimal residential designs based on that information was difficult. As a result, it was difficult to quickly provide residential designs that satisfied users.

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

In this invention, the server includes means for acquiring land and building information from a real estate information provision site, means for storing the acquired information in a data frame and processing the data, means for generating prompt sentences to be input into a generation AI model based on the processed data, means for automatically generating house blueprints using the generation AI model, and means for making house proposals based on the generated blueprints. This makes it possible to quickly and efficiently provide house blueprints that reflect the user's preferences and desired information.

A "real estate information website" is a website that provides information about land and buildings.

"Land and building information" refers to detailed data such as the shape of the land, the location of the building, the orientation and size of the windows, and the floor plan.

A "data frame" is a two-dimensional data structure consisting of rows and columns, used for storing and processing data.

"Data processing" refers to the process of transforming acquired data into a format that is easy to analyze and use, and includes processes such as imputing missing values and normalizing data.

A "generative AI model" is a model that uses artificial intelligence to perform a specific task; in this case, it is used to automatically generate blueprints for a house.

A "prompt message" is an instruction message used to input data into a generation AI model, and it is generated based on information about the land and buildings.

A "house design drawing" is a diagram showing the layout and structure of a house, and forms the basis of the building plan.

A "housing proposal" refers to suggestions regarding the design and layout of a house, made to the user based on the generated blueprints.

Modes for carrying out the invention

This invention relates to a system that acquires land and building information from real estate information websites and automatically generates house blueprints based on this information. A specific embodiment of this system is described below.

1. Program Generation

The server generates a program to retrieve land and building information from real estate information websites. This program is developed using Python and performs web scraping using libraries such as BeautifulSoup and Selenium.

2. Data Acquisition and Processing

The server executes the generated program and retrieves land and building information from real estate information websites. Specifically, it collects data such as land shape, building location, window orientation and size, and floor plan. The collected data is stored in a dataframe using the Pandas library, and necessary data processing is performed. For example, missing value imputation and data normalization are performed.

3. Generating prompt sentences for the generative AI model

The server generates prompt statements to input into the AI model based on the processed data. These prompt statements include information such as the shape of the land and building, location conditions, window orientation, size, and floor plan.

Example of a prompt message:

Based on land information in Shibuya Ward, Tokyo, please generate a house design plan considering the following conditions.

Land shape: Rectangle

Building location: South-facing

Window orientation: South-facing

Window size: Large

Floor plan: 3LDK

4. Generating House Design Plans

The server inputs the generated prompt text into a generation AI model (for example, a generation AI model) and generates a blueprint for a house. The generation AI model then proposes the optimal blueprint based on the prompt text.

5. Displaying Results

The server displays the generated house design plans on the user's terminal. The user can review the proposed design plans and request revisions as needed.

Specific examples

If a user requests to generate a house design based on land information in Shibuya Ward, Tokyo, the server will input the following prompt into the AI model:

Example of a prompt message:

Based on land information in Shibuya Ward, Tokyo, please generate a house design plan considering the following conditions.

Land shape: Rectangle

Building location: South-facing

Window orientation: South-facing

Window size: Large

Floor plan: 3LDK

The AI model generates a house design based on this prompt. The proposed design is displayed on the user's device for review.

In this way, the present invention makes it possible to quickly and efficiently provide residential design plans that reflect the user's preferences and desired information.

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

Program execution flow

Step 1: Obtaining real estate information

The server retrieves land and building information from real estate information websites. Specifically, it uses Python and libraries such as BeautifulSoup and Selenium to perform web scraping.

Input: URL of the real estate information website

Output: Land and building information (e.g., land shape, building location, window orientation, size, floor plan)

Specific actions:

The server uses Selenium to automatically control the browser and access the specified URL. Next, it uses BeautifulSoup to parse the HTML and extract the necessary information.

Step 2: Data Processing

The server stores the retrieved real estate information in a dataframe using the Pandas library and performs necessary data processing. For example, it might impute missing values or normalize the data.

Input: Land and building information

Output: Processed data frame

Specific actions:

The server converts the retrieved data into a DataFrame using the Pandas `read_html` function. Next, it imputes missing values using the `fillna` function and normalizes the data using the `normalize` function.

Step 3: Generating prompts for the generative AI model

The server generates prompt statements to input into the AI model based on the processed data. These prompt statements include information such as the shape of the land and building, location conditions, window orientation, size, and floor plan.

Input: Processed data frame

Output: Prompt message

Specific actions:

The server extracts the necessary information from the data frame and generates a prompt message like the following:

Based on land information in Shibuya Ward, Tokyo, please generate a house design plan considering the following conditions.

Land shape: Rectangle

Building location: South-facing

Window orientation: South-facing

Window size: Large

Floor plan: 3LDK

Step 4: Generating the House Design Plan

The server inputs the generated prompt text into a generation AI model (for example, a generation AI model) and generates a house design. The generation AI model then proposes the optimal design based on the prompt text.

Input: Prompt text

Output: House design plans

Specific actions:

The server sends prompts to the API for generating AI models and receives proposed blueprints in return. For example, it might use the `openai.Completion.create` function to send prompts.

Step 5: Displaying Results

The server displays the generated house design plans on the user's terminal. The user can review the proposed design plans and request revisions as needed.

Input: House blueprints

Output: Blueprint displayed on the user's terminal

Specific actions:

The server formats the generated blueprint in HTML format and sends it to the user's terminal. The user then views the blueprint through their browser.

(Application Example 2)

Next, we will describe Application Example 2 of Form 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."

Conventional residential design systems have difficulty reflecting user preferences and desires, and their automatic generation of design plans based on real estate information has been limited. Furthermore, interior design for physical stores requires specialized knowledge and is not easily accessible to the average user. Therefore, there is a need for a system that allows users to easily design homes and stores that meet their specific needs.

In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving user preference information and desired information as input and automatically generating house blueprints based on this information; means for receiving real estate information as input and automatically generating house blueprints based on this information; means for making house proposals based on the generated blueprints; means for automatically generating interior designs for physical stores based on information obtained from real estate information provision sites; means for creating prompt statements for generating blueprints using a generation AI model; and means for generating actual blueprints using the generation AI model. This makes it easy for users to design houses and stores that meet their preferences.

"User preference information" refers to information about the style, functions, and design that users desire in the design of their homes or shops.

"Desired information" refers to information about the specific conditions and requirements that users desire in the design of their homes or shops.

"Real estate information" refers to information about the shape, location, and area of land and buildings.

A "design drawing" is a diagram showing the layout of a house or shop, including the direction and size of windows, and the arrangement of interior furnishings.

A "real estate information website" is a website that provides information about land and buildings on the internet.

"Interior design" refers to planning the layout and design of the interior spaces of shops and residences.

A "generative AI model" is an algorithm or program that uses artificial intelligence to automatically generate blueprints and designs.

A "prompt statement" is an instruction statement used to input data into a generative AI model, containing conditions and requirements for generating blueprints or designs.

The system for carrying out this invention includes a server, a user terminal, and a generative AI model. Specific embodiments of this system are described below.

First, the user terminal receives user preference and desired information as input. This information concerns the style, functions, and design the user desires for the design of their home or store. The user terminal then transmits this information to the server.

Next, the server retrieves real estate information from a real estate information website. This real estate information includes details such as the shape of the land and building, location conditions, and area. Based on the retrieved real estate information, the server automatically generates blueprints for houses and shops.

The server uses a generative AI model to create prompt statements for generating blueprints. These prompt statements include user preference information, desired information, and real estate information. For example, prompt statements like the following are generated:

"Land shape: Rectangle, Location: Urban area, Window orientation: South-facing, Lighting position: Center of ceiling"

Based on the generated prompt text, the generative AI model creates the actual design drawing. For example, OpenAI's GPT-3 is used as the generative AI model. This model receives the prompt text as input and outputs the design drawing.

The generated blueprints are sent from the server to the user's terminal and presented to the user. The user can then review the design of the house or shop based on the presented blueprints and make modifications as needed.

This system allows users to easily design houses and shops that meet their specific needs. Specifically, users can easily generate and review design plans using their smartphones or computers. This enables them to realize their desired designs even without specialized knowledge.

The above describes a specific embodiment for carrying out this invention.

The flow of the specific processing in Application Example 2 will be explained using Figure 14.

Step 1:

The user terminal receives user preference and desired information as input. Users use smartphones or computers to input information regarding their desired style, function, and design for residential or commercial buildings. The entered information is stored on the user terminal.

Step 2:

The user terminal sends the entered preference and desired information to the server. The transmitted information is stored in the server's database.

Step 3:

The server retrieves real estate information from real estate information websites. The server uses an API to send requests to these websites, obtaining information such as the shape, location, and area of the land and buildings. The retrieved real estate information is stored on the server.

Step 4:

The server generates prompt statements to create a blueprint based on the acquired real estate information and the user's preferences and requests. These prompt statements include information such as the land shape, location, window orientation, and lighting position. For example, a prompt statement like "Land shape: Rectangle, Location: Urban area, Window orientation: South-facing, Lighting position: Center of ceiling" might be generated.

Step 5:

The server uses a generative AI model to generate a blueprint using prompt text as input. For example, OpenAI's GPT-3 is used as the generative AI model. The generative AI model, upon receiving prompt text as input, outputs a blueprint. The generated blueprint is saved on the server.

Step 6:

The server sends the generated blueprint to the user's terminal. The user's terminal displays the received blueprint to the user. The user can then view the blueprint on their smartphone or computer screen.

Step 7:

Users can review the provided blueprints for houses and shops, and make modifications as needed. The modified information is then sent back to the server, and the blueprints are regenerated. This allows users to realize a design that meets their specific needs.

(Example 3)

Next, we will describe Embodiment 3 of Example 3. 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."

Conventional residential design systems have difficulty automatically generating blueprints that reflect user preferences and desires, and have been unable to provide optimal design proposals based on real estate information. Therefore, there is a need to efficiently provide residential designs that meet user needs.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

This invention includes a server that receives user preference and desire information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that makes house design proposals based on the generated design; a server that sends prompt text and images to a generation AI model to generate a design; a server that acquires land and building information from a real estate information website; a server that makes optimal design proposals based on the acquired real estate information; and a server that displays the generated design and proposal content to the user. This makes it possible to automatically generate house design plans that reflect the user's preferences and desires, and to make optimal design proposals based on real estate information.

"User preference information" refers to information indicating a user's personal preferences regarding design, style, and functionality in residential design.

"Desired information" refers to specific conditions and requirements that users have in mind when designing a house, such as family structure, budget, and location.

"Real estate information" refers to detailed data about land and buildings, such as price, area, and surrounding environment.

A "generative AI model" is an algorithm or system that uses artificial intelligence to generate design plans based on user input.

A "prompt" is a text-based input message used to give instructions to a generative AI model.

A "design drawing" is a diagram showing the structure, layout, and design of a house.

A "real estate information website" is an online platform that provides information about land and buildings.

"Optimal design proposal" refers to proposing the most suitable residential design based on the user's preferences, desires, and real estate information.

"Means of display" refers to methods or devices for visually providing users with generated design drawings or proposed content.

This invention is a system that automatically generates house blueprints based on user preference and desired information, and provides optimal design suggestions based on real estate information. A specific embodiment of this system is described below.

System Configuration

This system consists of three main elements: a server, a terminal, and a user. The server receives input information from the user, generates design plans using a generative AI model, and provides optimal design suggestions based on real estate information. The terminal provides an interface for the user to input information and check the results. The user accesses the system through the terminal and inputs the necessary information.

Hardware and software to be used

Server: A server with high-performance computing capabilities is required. Specifically, a cloud-based server (e.g., Amazon Web Services or Google Cloud Platform) should be used.

Terminal: A device used by a user to input information. This includes internet-connected devices such as personal computers, tablets, and smartphones.

Generative AI Model: We use advanced artificial intelligence models such as OpenAI's GPT-4 and DALL-E as generative AI models.

Real estate information websites: Use online platforms (e.g., SUUMO or At Home) to obtain real estate information.

Data processing and calculations

1. User Information Input: The user accesses a form on a web browser using their device and uploads a modern-looking image saved from Instagram. Next, they enter their family composition (four people), desired location (Tokyo), and budget (50 million yen).

2. Prompt Generation: The server receives the information entered by the user and generates prompts for input into the generated AI model. Specifically, it generates prompts like the following:

"Based on images of modern exteriors saved by users on Instagram, please automatically generate blueprints for a detached house in Tokyo suitable for a family of four, with a budget of under 50 million yen."

3. Sending to the Generating AI Model: The server sends the generated prompt text and the user-uploaded image to the Generating AI model. This transmission is done via API.

4. Blueprint Generation: The generation AI model generates a blueprint for a modern-looking detached house in Tokyo based on the received prompt text and image. The blueprint includes details such as window orientation and size, and floor plan. The generated blueprint is returned to the server.

5. Acquisition of Real Estate Information: The server uses the API of a real estate information provider website to acquire information on land and buildings in Tokyo. The acquired information includes land prices, building area, and surrounding environment.

6. Optimal Design Proposal: Based on the acquired real estate information, the server proposes optimal window orientations, sizes, and layouts for the generated design drawings. For example, by proposing a design drawing with many south-facing windows, it provides a living environment with plenty of sunlight.

7. Displaying Results: The server displays the final design and proposal to the user. The user can review this information in a web browser and make modifications or regenerations as needed. An interactive UI is used for display to ensure user-friendliness.

Specific examples

If the user enters the following information:

Images of modern exteriors saved from Instagram

family of 4

I'm looking for a detached house in Tokyo.

The budget is 50 million yen.

The server generates the following prompt:

"Based on images of modern exteriors saved by users on Instagram, please automatically generate blueprints for a detached house in Tokyo suitable for a family of four, with a budget of under 50 million yen."

The AI model generates a design drawing based on this prompt text and image, and the server proposes the optimal window orientation, size, and floor plan based on data obtained from a real estate information website. The specific processing flow in Example 3 is explained using Figure 15.

Step 1:

The user enters information.

The user accesses a form on a web browser using their device and uploads a modern-looking image saved from Instagram. Next, they enter their family size (four people), desired location (Tokyo), and budget (50 million yen). This information is then sent to the server.

Input: Images saved from Instagram, family composition, desired region, budget

Output: User preference information and desired information

Step 2:

The server generates the prompt message.

The server receives the information entered by the user and generates prompt messages for input into the generated AI model. Specifically, it generates prompt messages like the following:

"Based on images of modern exteriors saved by users on Instagram, please automatically generate blueprints for a detached house in Tokyo suitable for a family of four, with a budget of under 50 million yen."

Input: User preference information and desired information

Output: Prompt text to send to the generated AI model

Step 3:

The server sends prompt text and images to the generated AI model.

The server sends the generated prompt text and the user-uploaded image to the AI model. This transmission is done via API.

Input: Prompt text, user-uploaded image

Output: Input data for the generated AI model

Step 4:

The generative AI model generates the blueprint.

The AI model generates a blueprint for a modern-looking detached house in Tokyo based on the received prompt text and image. The blueprint includes details such as window orientation and size, and floor plan. The generated blueprint is then returned to the server.

Input: Prompt text, user-uploaded image

Output: Generated blueprint

Step 5:

The server retrieves real estate information.

The server uses the API of a real estate information website to retrieve information on land and buildings in Tokyo. The retrieved information includes land prices, building area, and surrounding environment.

Input: API of a real estate information website

Output: Acquired real estate information

Step 6:

The server will propose the optimal design.

The server uses the acquired real estate information to suggest optimal window orientations, sizes, and layouts for the generated blueprints. For example, by suggesting blueprints with many south-facing windows, it provides a living environment with plenty of sunlight.

Input: Generated blueprints, acquired property information

Output: Optimal design proposal

Step 7:

The server displays the results to the user.

The server displays the final design blueprint and proposal to the user. Users can view this information in a web browser and modify or regenerate it as needed. An interactive UI is used for display to ensure user-friendliness.

Input: Final design drawings, proposed content

Output: Results displayed to the user

(Application Example 3)

Next, we will describe Application Example 3 of Form Example 3. 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."

Conventional residential design systems struggled to automatically generate blueprints that reflected user preferences and desires, and were unable to effectively utilize real estate information to propose blueprints. Furthermore, they could not leverage images saved by users or information from social media, making it difficult to provide blueprints that met specific user needs. This resulted in a challenge in increasing user satisfaction.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

This invention includes a server that receives user preference and desired information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that proposes a house design based on the generated design; a server that generates prompt text based on images saved by the user and generates a design using a generation AI model; and a server that proposes window orientation, size, floor plan, etc., based on land and building information obtained from a real estate information website. This enables the automatic generation of design plans tailored to the user's specific needs and the effective utilization of real estate information.

"User preference information" refers to information about the design and style that users desire, and is obtained from sources such as social media and saved images.

"Desired information" refers to information about the conditions and requirements of a home that the user desires, including specific preferences such as family structure, budget, and location.

A "house design plan" is a drawing that shows the exterior, floor plan, window orientation, size, etc., of a house, and is automatically generated based on the user's preferences and requests.

"Real estate information" refers to information about land and buildings, and is obtained from real estate information websites and other sources.

A "generative AI model" is a model that uses artificial intelligence to generate design plans or proposals from specific input information.

A "prompt message" is an instruction message used to input into a generative AI model, and it is generated based on the user's preferences and desired information.

A "real estate information website" is a website that provides information about land and buildings, and users can access and obtain this information.

"Window orientation, size, and floor plan" are important elements in residential design and are proposed based on the user's wishes and real estate information.

The system for implementing this invention includes means for receiving user preference information and desired information as input and automatically generating house blueprints based on this information; means for receiving real estate information as input and automatically generating house blueprints based on this information; means for making house proposals based on the generated blueprints; means for generating prompt text based on images saved by the user and generating blueprints using a generation AI model; and means for proposing window orientation, size, floor plan, etc., based on land and building information obtained from real estate information provision sites.

System Program

The server receives preference and request information entered by the user via a device such as a smartphone or computer. This includes images obtained from social media such as Instagram, as well as information such as family structure, budget, and desired location. Next, the server generates prompt statements based on this information. These prompt statements are input into a generation AI model, which serves as instructions for generating house blueprints.

Based on the generated prompt text, the AI model automatically generates a blueprint for a house. This blueprint reflects the user's preferences and desires, including specific exterior details and floor plans. Furthermore, the server suggests window orientations and sizes, floor plans, etc., based on land and building information obtained from real estate information websites.

Hardware and software to be used

This system uses the following hardware and software:

Hardware: Servers, user terminals (smartphones, PCs, etc.)

Software: Generative AI models (e.g., OpenAI's GPT-3), prompt generation programs, image acquisition programs (e.g., requests, PIL)

Specific examples

For example, if the user enters the following conditions:

Instagram image URL: https://example.com/instagram_image.jpg

Family structure: 4 people family

Desired area: Tokyo

Budget: 50 million yen

The generated prompt will look like this:

Based on images of modern exteriors saved by the user on Instagram, we need a design for a detached house in Tokyo for a family of four with a budget of 50 million yen. Please generate a blueprint for a detached house with a modern exterior based on this information.

When this prompt is entered into the AI model, the model automatically generates a house design that meets the user's preferences. The generated design reflects the user's desired modern exterior and a floor plan suitable for their family structure. Furthermore, based on land and building information obtained from real estate information websites, the model also provides specific suggestions regarding window orientation and size, floor plan, and other details.

The flow of the specific processing in Application Example 3 will be explained using Figure 16.

Step 1:

Users input their preferences and desired information from devices such as smartphones and computers. This includes image URLs obtained from social media such as Instagram, as well as information such as family structure, budget, and desired region. The entered information is sent to the server.

Input: Instagram image URL, family composition, budget, desired region

Output: User preference and desired information sent to the server.

Step 2:

The server receives preference and request information from the user. Based on this information, it performs data processing to generate prompt messages. Specifically, it retrieves image URLs and converts information such as family structure, budget, and desired region into text format.

Input: User preference information and desired information

Output: Data for prompt message generation

Step 3:

The server downloads the image based on the retrieved image URL and loads it as an image object. This process uses the requests library and the PIL library.

Input: Image URL

Output: Image object

Step 4:

The server generates prompt text based on image objects and user information in text format. The generated prompt text serves as instructions for inputting into the AI model.

Input: Image object, user information in text format

Output: Prompt message

Step 5:

The server inputs the generated prompt text into a generative AI model to automatically generate a house design. This process uses generative AI models such as OpenAI's GPT-3.

Input: Prompt text

Output: Automatically generated house blueprints

Step 6:

The server retrieves land and building information from real estate information websites. This information includes window orientation and size, floor plan, etc.

Input: URL of the real estate information website

Output: Land and building information

Step 7:

Based on the acquired real estate information, the server provides specific suggestions regarding window orientation, size, and floor plan for the generated house blueprints.

Input: Land and building information, automatically generated house blueprints

Output: Residential blueprints including specific proposals

Step 8:

The server sends the final design to the user's terminal and provides a proposal to the user. The user can review the proposed design and make modifications or regenerate it as needed.

Input: Residential blueprints including specific proposals

Output: Final design drawings sent to the user's terminal.

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

"Example of Morphology 1"

One embodiment of the present invention incorporates an emotion engine that recognizes the user's emotions. This emotion engine recognizes the user's emotions and adjusts the house design based on those emotions. Specifically, when the user is feeling happy, it suggests bright-colored interiors and an open floor plan. Conversely, when the user is feeling depressed, it suggests calm-colored interiors and a floor plan that prioritizes privacy.

"Example of Morphology 2"

Furthermore, the emotion engine adjusts housing suggestions based on the user's emotions. Specifically, when the user is feeling happy, it suggests luxurious homes. Conversely, when the user is feeling down, it suggests simple and functional homes.

"Example of Morphology 3"

As a concrete example, when a user is happy about getting a new job, the emotion engine recognizes that happiness and automatically generates blueprints for a luxurious house. These blueprints feature large windows, a spacious living room, and high-quality interior materials. Conversely, when a user is depressed after losing their job, the emotion engine recognizes that depression and automatically generates blueprints for a simple, functional house. These blueprints have only the bare minimum number of rooms and functions, keeping costs down.

The following describes the processing flow for each example form.

"Example of Morphology 1"

Step 1: The emotion engine recognizes the user's emotions.

Step 2: The emotion engine adjusts the house design based on the user's emotions. Specifically, when the user is feeling happy, it suggests bright interior colors and an open floor plan.

Step 3: On the other hand, when the user is feeling down, suggest interiors with calming color schemes and floor plans that prioritize privacy.

"Example of Morphology 2"

Step 1: The emotion engine recognizes the user's emotions.

Step 2: The emotion engine adjusts housing suggestions based on the user's emotions. Specifically, when the user is feeling happy, it suggests luxurious homes.

Step 3: On the other hand, when the user is feeling down, propose a simple and functional home.

"Example of Morphology 3"

Step 1: When a user feels joy after acquiring a new job, the emotion engine recognizes that joy.

Step 2: The emotion engine automatically generates blueprints for a luxurious house. These blueprints feature large windows, a spacious living room, and high-quality interior materials.

Step 3: On the other hand, when a user is depressed after losing their job, the emotion engine recognizes that depression.

Step 4: The emotion engine automatically generates a simple and functional house design. This design includes the minimum necessary number of rooms and functions, keeping costs down.

(Example 1)

Next, we will describe Example 1 of Embodiment 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

Conventional residential design systems struggled to automatically generate blueprints that reflected user preferences and desires. Furthermore, they failed to adjust blueprints to accommodate user emotions, resulting in lower user satisfaction. Additionally, they lacked effective means of utilizing information from social media and real estate information websites. This led to challenges in creating residential designs that met diverse user needs.

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

This invention includes a server that receives user preference and desire information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that proposes a house design based on the generated design; a server that recognizes the user's emotions and adjusts the house design based on those emotions; and a server that proposes interior design and floor plans based on the user's emotions. This enables the automatic generation of house design plans that reflect the user's preferences and desires, and further allows for adjustments to the design plans that take the user's emotions into consideration, thereby increasing user satisfaction. Furthermore, by effectively utilizing information from social media and real estate information websites, it is possible to realize house designs that meet the diverse needs of users.

"User preference information" refers to images and text information about housing that users have obtained from social media and other sources on the internet.

"Desired information" refers to specific requests entered by the user, such as family structure, desired address, price range, whether renovations are needed, and whether they prefer a house or apartment.

"Real estate information" refers to detailed property information obtained from real estate information websites and other databases.

"House design plans" refer to floor plans, interior and exterior drawings of a house that are automatically generated based on the user's preferences, desired features, and real estate information.

"Means of recognizing emotions" refers to algorithms and software that analyze a user's facial expressions and entered text to determine their emotional state.

"Means for adjusting the blueprint" refers to algorithms and software used to modify or correct the content of a house's blueprint based on recognized user emotions.

"Methods for proposing interior design and floor plans" refers to algorithms and software that propose optimal interior designs and floor plans based on the user's emotions and preferences.

"Social media" refers to a platform on the internet for users to share information.

A "real estate information website" refers to a website or online service that provides detailed information about properties.

Modes for carrying out the invention

This invention is a system that automatically generates house blueprints based on user preference and desired information, and further adjusts the blueprints to take user emotions into consideration. This system consists of a server, a terminal, and the user.

System Configuration

1. User actions

Users save their favorite house images from social media on the internet and upload them to the system.

Users input their preferences into the system, including family structure, desired address, price range, whether renovations are needed, and whether they prefer a house or apartment.

2. Server Processing

The server receives residential images uploaded by users and extracts image features using image recognition algorithms (e.g., TensorFlow, OpenCV).

The server analyzes the user's input using a natural language processing engine (e.g., GPT-3) and organizes it as structured data.

The server integrates data obtained from image recognition algorithms and natural language processing engines to generate blueprints for a house.

The server uses an emotion engine to recognize the user's emotions and analyzes them from the user's facial expressions and entered text.

The server adjusts the design based on the user's perceived emotions. For example, if the user is feeling happy, it suggests bright color schemes and an open layout.

The server generates the final, adjusted blueprint and provides it to the user.

Hardware and software to be used

Hardware: High-performance servers (e.g., servers with NVIDIA GPUs)

Software: Image recognition algorithms (e.g., TensorFlow, OpenCV), natural language processing engines (e.g., GPT-3), emotion recognition engines

Specific examples

For example, suppose a user uploads a modern house design image saved from Instagram to the system and inputs their desired information, such as family size (four people), desired address (Tokyo), price range (under 50 million yen), whether renovation is desired (yes), and whether they prefer a house or apartment (house).

The server first uses an image recognition algorithm to extract modern design features from residential images. Next, it analyzes the user's preferences using a natural language processing engine to generate floor plans suitable for a family of four, designs appropriate for residential areas in Tokyo, specifications feasible within a budget of 50 million yen, designs that consider renovations, and plans for detached houses.

Furthermore, if the emotion engine recognizes the user's emotion as "joy," the server will suggest interior design with bright colors and an open living room. Finally, the server integrates this information to provide the user with a blueprint for their ideal home.

Example of a prompt message

The user provided images of modern-designed houses saved from social media, along with their family size (4 people), desired address (Tokyo), price range (under 50 million yen), whether renovations were desired (yes), and whether they preferred a house or apartment (house). Based on this information, please generate a house design. Additionally, if the user's emotion is "joy," please suggest a bright interior color scheme and an open floor plan.

In this way, the system can automatically generate house designs based on the user's preferences and desires, and further adjust the designs to take the user's emotions into consideration.

The flow of the specific processing in Example 1 will be explained using Figure 17.

Step 1:

Users save their preferred house images and input them into the system.

Specifically, the user downloads images of houses they like from social media on the internet and uploads those images through the system's interface.

Input: Residential images obtained from social media

Output: Residential images uploaded to the system

Step 2:

The user enters their desired information into the system.

In terms of specific operations, the user inputs desired information such as family structure, desired address, price range, whether renovations are needed, and whether they prefer a house or apartment, through the system interface.

Input: Family composition, desired address, price range, whether renovation is required, preferred type of property (house or apartment), etc.

Output: Desired information entered into the system

Step 3:

The server analyzes residential images using an image recognition algorithm.

In terms of specific operation, the server receives uploaded images of houses and extracts image features using image recognition algorithms (e.g., TensorFlow, OpenCV). For example, it analyzes information such as the building's exterior, interior style, and color scheme.

Input: Residential images uploaded to the system

Output: Image feature data (building exterior, interior style, color tone, etc.)

Step 4:

The server uses a natural language processing engine to analyze the requested information.

In terms of specific operations, the server analyzes the user's input using a natural language processing engine (e.g., GPT-3) and organizes it as structured data. For example, it understands information such as family structure, desired address, and price range, and stores it in a database.

Input: Desired information entered into the system

Output: Structured desired information data

Step 5:

The server integrates the analysis results and generates the house blueprints.

In terms of specific operations, the server integrates data obtained from image recognition algorithms and natural language processing engines to generate blueprints for a house. For example, it designs the floor plan, interior, and exterior based on the user's preferences and desires.

Input: Image feature data, structured desired information data

Output: House design plans

Step 6:

The server uses an emotion engine to recognize the user's emotions.

Specifically, the server uses an emotion engine to recognize the user's emotions. It analyzes the text and facial expression data entered by the user into the system to determine the user's current emotional state.

Input: User text input, facial expression data

Output: User emotional state data

Step 7:

The server adjusts the design based on the user's emotions.

Specifically, the server adjusts the design based on the user's perceived emotions. For example, if the user is feeling happy, it suggests bright color schemes and an open layout. Conversely, if the user is feeling depressed, it suggests calm color schemes and a layout that prioritizes privacy.

Input: User emotional state data, house blueprints

Output: Adjusted house blueprints

Step 8:

The server provides the user with the final blueprint.

Specifically, the server generates a final, adjusted blueprint and provides it to the user. The user can review the blueprint through the system interface and provide feedback as needed.

Input: Adjusted house blueprints

Output: Final design drawings provided to the user

(Application Example 1)

Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

Traditional residential design systems could generate blueprints based on user preferences and requests, but they lacked the ability to adjust the blueprints to reflect the user's emotions. Furthermore, it was difficult to provide real-time, emotionally responsive suggestions when users consulted with design and renovation companies in person. This resulted in a challenge in increasing user satisfaction.

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

This invention includes a server that receives user preference and desired information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that proposes a house design based on the generated design; a server that recognizes the user's emotions and adjusts the house design based on those emotions; and a server that analyzes the user's facial expressions using a smart device and recognizes their emotions. This enables the generation and proposal of house design plans that reflect the user's emotions in real time.

"User preference information" refers to images related to housing obtained by users from social media such as Instagram, as well as information indicating the user's personal preferences.

"Desired information" refers to information that indicates the user's specific requests, such as desired family structure, address, price range, whether or not renovations are needed, and whether they prefer a detached house or an apartment.

"Real estate information" refers to detailed information about a property, such as its location, price, floor plan, and year of construction, obtained from real estate information websites.

An "automatic blueprint generation method" refers to a system or algorithm for automatically generating blueprints for a house based on user preference information, desired information, and real estate information.

A "proposal method" refers to a system or algorithm for proposing housing designs to users based on generated blueprints.

An "emotion recognition system" is a system or algorithm that recognizes a user's emotions and adjusts the design plans of a house based on those emotions.

A "smart device" is a device, such as a smartphone or smart glasses, that analyzes a user's facial expressions and recognizes their emotions.

"Facial expression analysis means" refers to a system or algorithm that uses a smart device to analyze a user's facial expressions and recognize their emotions.

The system for implementing this invention automatically generates a house design based on the user's preference information, desired information, and real estate information, and adjusts the design based on the user's emotions. Specific embodiments are shown below.

System Configuration

1. Hardware:

Smart devices: Devices such as smartphones and smart glasses that analyze a user's facial expressions and recognize their emotions.

Server: A server used for data processing and blueprint generation.

2. Software:

Emotion Recognition Model: A machine learning model that analyzes a user's facial expressions and recognizes their emotions (e.g., an emotion recognition model using Keras).

Blueprint Generation Algorithm: An algorithm that automatically generates house blueprints based on user preferences, desired features, and real estate information.

Data Acquisition Module: A module for acquiring necessary information from social media and real estate information websites.

Processing Flow

1. Input of user preference and desired information:

Users upload images of houses they've saved from social media like Instagram to the app and enter information such as family composition, desired address, price range, whether renovations are planned, and whether it's a house or apartment.

2. Obtaining real estate information:

The server retrieves detailed information such as property location, price, floor plan, and year of construction from real estate information websites.

3. Emotion recognition:

Using smart devices, we analyze the user's facial expressions and recognize the user's emotions using an emotion recognition model.

4. Automatic generation of blueprints:

The server automatically generates house blueprints using a blueprint generation algorithm, based on user preference information, desired property information, and recognized emotional information.

5. Proposal:

Based on the generated blueprints, we propose housing designs to the user. This includes suggesting interior designs and floor plans that align with the user's preferences.

Specific examples

For example, a user uploads an image saved from Instagram to the app and enters their desired address and price range. The app analyzes the user's facial expressions through smart glasses and recognizes their emotions. Based on this information, the server automatically generates a design plan and proposes it to the user.

Example of a prompt message

Users upload images of houses they've saved from Instagram and enter information such as family composition, desired address, price range, whether renovations are needed, and whether it's a house or apartment. The system analyzes facial expressions through smart glasses to recognize emotions. Based on this information, it automatically generates an optimal house design.

In this way, a housing design assistant system based on the user's emotions and preferences can be realized.

The flow of the specific processing in Application Example 1 will be explained using Figure 18.

Step 1:

Users upload images of houses they have saved from social media such as Instagram to their devices.

Input: Image file of a house

Output: Uploaded image data

Specific operation: The user uploads saved images of their home to the application using their smartphone or smart glasses. The application then sends the image data to the server.

Step 2:

The user enters their desired information into the terminal, including family structure, preferred address, price range, whether renovations are needed, and whether they prefer a house or apartment.

Input: Family composition, address, price range, whether renovations were done, housing type

Output: User's requested information data

Specific operation: The user enters the desired information into the application's input form and presses the submit button. The application sends the entered data to the server.

Step 3:

The server retrieves detailed information such as property location, price, floor plan, and year of construction from real estate information websites.

Input: API of a real estate information website

Output: Real estate information data

Specific operation: The server calls the API of a real estate information website to retrieve the necessary real estate information. The retrieved data is stored in an internal database.

Step 4:

The device uses a smart device to analyze the user's facial expressions and recognizes the user's emotions using an emotion recognition model.

Input: User's facial expression image

Output: User sentiment data

Specific operation: The smart device's camera captures the user's facial expressions and sends the image data to the server. The server analyzes the facial image using an emotion recognition model to recognize the user's emotions.

Step 5:

The server automatically generates house blueprints using a blueprint generation algorithm based on user preference information, desired properties, real estate information, and emotional information.

Input: User preference information, desired information, real estate information, emotional information

Output: Automatically generated house blueprints

Specific operation: The server integrates all input data and executes the blueprint generation algorithm. The algorithm generates blueprints that take into account the user's emotions regarding interior design and floor plans.

Step 6:

The server generates blueprints and then proposes housing designs to the user.

Input: Automatically generated house blueprints

Output: Suggestions for the user

Specific operation: The server presents the generated blueprints to the user and offers suggestions for interior design and floor plans that align with the user's emotions. The user can review the suggestions through the application.

(Example 2)

Next, we will describe Example 2 of the 2nd embodiment. 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."

Conventional residential design systems could generate blueprints based on user preferences and requests, but they lacked the ability to consider user emotions when making proposals. Furthermore, they struggled to effectively utilize real estate information for residential design, making it difficult to provide optimal proposals to users. There was a growing need to improve user satisfaction by offering housing proposals that resonated with users' emotions.

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

This invention includes a server that receives user preference and desire information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that proposes a house based on the generated design; and a server that analyzes the user's emotions and adjusts the house proposal based on the analysis results. This makes it possible to propose an optimal house design that not only reflects the user's preferences and desires but also their emotions.

"User preference information" refers to information that indicates a user's personal preferences and desires regarding their home.

"Desired information" refers to information that indicates the specific conditions and requirements that the user desires for a home.

"Real estate information" refers to information that includes detailed data on land and buildings, such as their shape, location, and price.

A "design drawing" is a diagram showing the layout, structure, window orientation, and size of a house.

A "proposal" refers to a specific plan regarding the design and layout of a house, provided to the user.

"Analyzing emotions" means identifying and evaluating a user's emotional state based on their facial expressions, behavior, and input data.

"Adjusting" means modifying and optimizing the proposed content based on the analysis results.

This invention is a system that automatically generates house designs and provides optimal housing proposals, taking into account the user's preferences, desires, real estate information, and emotions. A specific embodiment of this system is described below.

System Configuration

1. Obtaining real estate information

The server retrieves land and building information from real estate information websites. In this process, the server uses specific APIs to collect data. For example, it might utilize the API of a real estate information website.

2. Data Analysis and Processing

The server analyzes the acquired land and building information, extracting data such as shape and location conditions. This is done using a data analysis library.

3. Automatic generation of residential building plans

The server automatically generates house blueprints based on the analyzed data. Specifically, it considers the shape and location of the land and proposes window orientations, sizes, and floor plans. This process utilizes the API of design software.

4. Adjustment by the Emotional Engine

The device analyzes the user's emotions using an emotion engine. This emotion engine utilizes an emotion analysis API. If the user is feeling happy, the device suggests a luxurious home. Conversely, if the user is feeling depressed, it suggests a simple and functional home.

Specific examples

Example 1: Obtaining real estate information

The server uses the API of a real estate information website to retrieve land information for a specific area. For example, it sends an API request like the following:

"Retrieve land information for San Francisco and generate blueprints that suggest luxurious homes if the user is happy, and simple homes if they are depressed."

Example 2: Data Analysis and Processing

The server analyzes the acquired data using a data analysis library. For example, it extracts information about the land's shape and location, preparing it for use in the next step.

Example 3: Automatic generation of residential building plans

The server generates design drawings using the design software's API. For example, it considers the shape and location of the land and proposes window orientations, sizes, and floor plans.

Example 4: Adjustment by an emotional engine

The device uses an emotion analysis API to analyze the user's emotions. For example, if the user is happy, it suggests a luxurious house; if they are depressed, it suggests a simple house.

In this way, it becomes possible to propose optimal house designs that not only reflect the user's preferences and desires, but also their emotions. This system can improve user satisfaction.

The flow of the specific processing in Example 2 will be explained using Figure 19.

Step 1:

The server retrieves land and building information from real estate information websites. Specifically, the server sends an API request and receives JSON data containing land information for a specified area. The input is the parameters of the API request (e.g., area, property type), and the output is the retrieved real estate information in JSON data.

Step 2:

The server converts the retrieved JSON data into a data frame using a data analysis library. Specifically, the server uses the pandas library to read the JSON data and convert it into a data frame. The input is the retrieved JSON data, and the output is real estate information in data frame format.

Step 3:

The server extracts the necessary data from the data frame. Specifically, it extracts information such as land shape and location conditions and prepares it for use in the next step. The input is real estate information in data frame format, and the output is the extracted data on land shape and location conditions.

Step 4:

The server generates house blueprints using the design software's API. Specifically, it considers the land's shape and location to suggest window orientations and sizes, floor plans, and other details. The input is extracted data on land shape and location, and the output is the generated house blueprints.

Step 5:

The device analyzes the user's emotions using an emotion analysis API. Specifically, it identifies and evaluates the emotional state from the user's facial expressions, actions, and input data. Input consists of the user's images and text data, and output is the analyzed emotion data.

Step 6:

The device adjusts housing suggestions based on the analysis results. Specifically, it suggests a luxurious house if the user is happy, and a simple house if they are depressed. The input is the analyzed emotion data and the generated house blueprints, and the output is the adjusted housing suggestion.

In this way, by clearly defining the specific actions, inputs, and outputs performed at each processing step, the program's processing flow was explained in detail.

(Application Example 2)

Next, we will describe Application Example 2 of Form 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."

Conventional housing design systems could automatically generate blueprints based on user preferences, desires, and real estate information, but they lacked the ability to adjust proposals to reflect user emotions or display blueprints using virtual reality. Therefore, it was difficult to provide housing proposals that resonated with users' emotions or offer a more realistic experience. This resulted in a challenge in increasing user satisfaction.

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

This invention includes a server that receives user preference and desire information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that makes house proposals based on the generated design; a server that receives user emotion information as input and adjusts the house proposals based on this information; and a server that displays the generated design using a virtual reality display device. This enables the adjustment of house proposals according to the user's emotions and provides a realistic experience using virtual reality.

"User preference information" refers to information that indicates a user's personal preferences and desires regarding their home.

"Desired information" refers to information that indicates the specific conditions and requirements that the user desires for a home.

"Real estate information" refers to information that includes detailed data about land and buildings.

A "house design drawing" is a detailed drawing showing the layout, structure, and facilities of a house.

"Emotional information" refers to information that indicates the user's current emotional state.

A "virtual reality display device" is a device used to visually display a virtual space.

"Automatic generation methods" refer to devices or programs that have the function of automatically creating design drawings based on input information.

A "means for adjusting suggestions" refers to a device or program that has the function of modifying and optimizing suggestion content based on user sentiment information.

"Means of display" refers to devices or programs that have the function of visually presenting the generated design drawings to the user.

The system for implementing this invention automatically generates a house design based on the user's preference information, desired information, real estate information, and emotional information, and presents it to the user using a virtual reality display device. Specific embodiments are described below.

System Configuration

The system consists of the following main components:

1. User Terminal: A device such as a smartphone or head-mounted display collects user input information and displays it in virtual reality.

2. Server: Receives user preference information, desired information, real estate information, and emotional information, and automatically generates house blueprints.

3. Emotion Engine: Analyzes user emotion information and adjusts housing recommendations accordingly.

4. Design Generation Engine: Generates house blueprints based on real estate information, user preferences, and desired information.

5. Virtual Reality Display Device: Displays the generated blueprint in a virtual reality space.

Program processing

The server processes the data using the following steps:

1. Data Acquisition: Acquire preference information, desired information, and real estate information from the user's device. Acquire user sentiment information using the sentiment engine.

2. Design Drawing Generation: Using a design generation engine, the design drawings for the house are automatically generated based on the acquired information.

3. Proposal Adjustment: Using an emotion engine, housing proposals are adjusted based on the user's emotional information.

4. Virtual Reality Display: The generated design drawings are presented to the user using a virtual reality display device.

Hardware and software to be used

Hardware: Smartphones, head-mounted displays, servers

Software: EmotionEngine, DesignGenerator, VRDisplay (Virtual Reality Display Engine)

Specific examples

For example, when a user experiences designing a house using a smartphone, a luxurious house is suggested when the user is happy, and a simple, functional house is suggested when the user is depressed. The user wears a head-mounted display and can realistically experience the design blueprints generated in a virtual reality space.

Example of a prompt message

"Please generate blueprints for luxurious homes that will be presented to users when they are happy."

"Please generate blueprints for a simple and functional home that will be suitable for users who are feeling down."

In this way, we can provide housing proposals tailored to the user's emotions and realistic experiences using virtual reality.

The flow of the specific processing in Application Example 2 will be explained using Figure 20.

Step 1:

The user terminal receives user preference and desired information as input. Users input their housing preferences and specific requests using smartphones or head-mounted displays. This information is then transmitted to the server.

Input: User preference information, desired information

Output: User preference information and desired information sent to the server.

Step 2:

The server retrieves real estate information from real estate information websites. The server accesses the sites via APIs and collects detailed data about land and buildings.

Input: URL of the real estate information website

Output: Acquired real estate information

Step 3:

The user terminal uses an emotion engine to acquire the user's emotional information. It analyzes the user's facial expressions and voice to detect their current emotional state. This emotional information is then transmitted to the server.

Input: User's facial expressions, voice

Output: Emotional information sent to the server

Step 4:

The server uses a design generation engine to automatically generate house blueprints based on acquired preference information, desired information, and real estate information. The design generation engine analyzes this information to design the optimal floor plan and structure.

Input: User preference information, desired information, real estate information

Output: Generated house blueprints

Step 5:

The server uses an emotion engine to adjust housing suggestions based on the user's emotional information. For example, if the user is happy, it suggests a luxurious house; if they are depressed, it suggests a simple and functional house.

Input: Generated house blueprints, emotional information

Output: Proposed adjusted housing design

Step 6:

The server uses a virtual reality display to present the generated blueprints to the user. The user wears a head-mounted display and can realistically experience the blueprints within the virtual reality space.

Input: Adapted housing proposal

Output: Residential blueprints displayed in a virtual reality space.

In this way, we can provide housing proposals tailored to the user's emotions and realistic experiences using virtual reality.

(Example 3)

Next, we will describe Embodiment 3 of Example 3. 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."

Conventional residential design systems have difficulty automatically generating blueprints that reflect user preferences and desires, and generating blueprints that take real estate information into account is also time-consuming. Furthermore, they cannot generate blueprints that take into account the user's emotional state, thus failing to enhance user psychological satisfaction. There is a need for a system that solves these problems and automatically generates residential blueprints that meet the diverse needs of users.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

This invention includes a server that receives user preference and desire information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; and a server that recognizes the user's emotional state and adjusts the house design according to that emotional state. This enables the automatic generation of a house design that takes into account the user's preferences, desires, real estate information, and even their emotional state.

"User preference information" refers to information about the designs and styles that users prefer, and is obtained from sources such as social media.

"Desired information" refers to information about the user's specific requirements, such as the desired location, budget, and family structure of their home.

"Real estate information" refers to information about land and buildings obtained from real estate information websites.

"Emotional state" refers to information that indicates the user's current psychological state and is recognized by the emotion engine.

A "design drawing" is a diagram showing the structure, layout, window orientation, and size of a house.

"Automatic generation" refers to the process where a system automatically creates a blueprint based on user input, property information, and emotional state.

A "proposal" is the process of presenting a specific house design and layout to the user based on the generated blueprints.

Modes for carrying out the invention

This invention is a system that automatically generates and proposes house designs to users based on their preferences, desires, real estate information, and emotional state. A specific embodiment of this system is described below.

Hardware and software to be used

Hardware: Servers, user terminals (PCs, smartphones)

Software: Generative AI models (e.g., GPT-4), emotion recognition engine, database management system (e.g., MySQL), real estate information site API

System Operation Overview

1. Entering User Information

Users use their devices to input information such as images saved from Instagram, family composition, desired housing location (within Tokyo), and budget (50 million yen).

Specific example input: "We are a family of four, looking for a modern-looking detached house in Tokyo, with a budget of 50 million yen."

2. Data Transmission and Analysis

The terminal sends the entered information to the server.

The server analyzes the received information and inputs it as a prompt message into the generating AI model.

Specific prompt example: "The user has saved an image of a modern exterior from Instagram. They are a family of four looking for a detached house in Tokyo with a budget of 50 million yen. Please generate a design plan based on this information."

3. Generating Blueprints

The server inputs prompt messages into the generated AI model and generates the blueprint.

The server uses land and building information in Tokyo obtained from real estate information websites to suggest window orientations, sizes, floor plans, and other details.

4. Emotion Recognition and Adjustment of the Blueprint

The server uses an emotion engine to recognize the user's emotional state.

The server adjusts the blueprint based on the results of the emotion engine.

Specific prompt example: "The user is delighted to have a new job. Please generate blueprints for a luxurious house with large windows, a spacious living room, and high-quality interior materials."

5. Provision of design drawings

The server sends the generated blueprint to the user's terminal.

The terminal displays the received blueprints to the user.

Specific examples

User input: "We are a family of four, looking for a modern-looking detached house in Tokyo, with a budget of 50 million yen."

Prompt: "The user has saved an image of a modern exterior from Instagram. They are a family of four looking for a detached house in Tokyo with a budget of 50 million yen. Please generate a design plan based on this information."

Emotion recognition prompt message: "The user is happy to have a new job. Please generate blueprints for a luxurious house with large windows, a spacious living room, and high-quality interior materials."

This system enables the automatic generation of residential blueprints that take into account the user's preferences, desired information, real estate information, and even their emotional state. This allows for residential designs that meet the diverse needs of users. The specific processing flow in Example 3 will be explained using Figure 21.

Step 1:

Entering user information

The user uses their device to input information such as images saved from Instagram, family composition, desired housing location (within Tokyo), and budget (50 million yen).

Input: User preference information, desired information

Output: The entered information is saved to the device.

Specific action: The user enters information into the input form on the device and clicks the "Submit" button.

Step 2:

Data transmission and analysis

The terminal sends the entered information to the server.

Input: Information entered by the user

Output: User information sent to the server

Specific operation: The terminal uses an HTTP request to send input data to the server.

The server analyzes the received information and inputs it as a prompt message into the generating AI model.

Input: User information sent from the device

Output: Prompt text to input to the generated AI model

Specific operation: The server analyzes the data and generates a prompt message like the following: "The user has saved an image of a modern exterior from Instagram. They are a family of four looking for a detached house in Tokyo with a budget of 50 million yen. Please generate a design plan based on this information."

Step 3:

Blueprint generation

The server inputs prompt messages into the generated AI model and generates the blueprint.

Input: Prompt text

Output: Generated design drawing data

Specific operation: The server sends prompt messages to the generated AI model (e.g., GPT-4) and receives the blueprint data returned from the model.

The server uses land and building information in Tokyo obtained from real estate information websites to suggest window orientations, sizes, floor plans, and other details.

Input: Information obtained from a real estate information website.

Output: Proposed window orientation and size, floor plan, etc.

Specific operation: The server uses an API to retrieve real estate information and reflects it in the design plans.

Step 4:

Emotion recognition and blueprint adjustment

The server uses an emotion engine to recognize the user's emotional state.

Input: User sentiment data

Output: Recognized emotional state

Specific operation: The server sends user emotion data to the emotion engine and analyzes the emotional state.

The server adjusts the blueprint based on the results of the emotion engine.

Input: Recognized emotional state

Output: Adjusted blueprint

Specific operation: The server receives the result from the emotion engine and generates a prompt message like the following: "The user is happy to have a new job. Please generate blueprints for a luxurious house with large windows, a spacious living room, and high-quality interior materials."

Step 5:

Provision of design drawings

The server sends the generated blueprint to the user's terminal.

Input: Generated design data

Output: Blueprint sent to the user's terminal

Specific operation: The server sends the design data as an HTTP response to the user's terminal.

The terminal displays the received blueprints to the user.

Input: Design drawing data sent from the server

Output: Blueprint displayed to the user

Specific operation: The terminal analyzes the received design data and displays it on the user interface.

(Application Example 3)

Next, we will describe Application Example 3 of Form Example 3. 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."

Conventional residential design systems struggled to automatically generate blueprints that reflected user preferences and desires, and their proposals based on real estate information were limited. Furthermore, they lacked the means to generate blueprints that considered the user's emotional state, and to visually confirm the generated blueprints as 3D models. This made it difficult to increase user satisfaction.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

This invention includes a server that receives user preference and desire information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that makes house proposals based on the generated design; a server that receives the user's emotional state as input and automatically generates a house design based on this; and a server that displays the generated design as a 3D model. This enables the automatic generation of house design plans that reflect the user's preferences, desires, and emotional state, as well as visual confirmation.

"User preference information" refers to information about the designs and styles that users prefer, and is obtained from social media and image storage services.

"Desired information" refers to information about the conditions and specifications of a home that the user desires, including family structure, budget, and location.

"Real estate information" refers to information about land and buildings obtained from real estate information websites, including details such as price, area, and location.

"Emotional state" refers to information that indicates the user's current emotions and mood, including feelings such as joy and sadness.

A "design plan" is a drawing that shows the structure, layout, and design of a house, and is automatically generated based on the user's preferences, desires, real estate information, and emotional state.

A "3D model" is a three-dimensional visual model created based on design drawings, allowing users to view the exterior and interior of a house in three dimensions.

"Automatic generation" refers to the process by which a system automatically creates design drawings and 3D models based on user input and acquired data.

A "proposal" is the act of showing the user specific features of a house, such as the orientation and size of windows and the floor plan, based on the generated design drawings.

The system for implementing this invention automatically generates a house design based on the user's preferences, desires, real estate information, and emotional state, and displays it as a 3D model. A specific embodiment is shown below.

System Configuration

The system consists of user terminals, a server, and a display device. User terminals include smartphones and head-mounted displays (HMDs). The server performs data processing and automatically generates design drawings using a generated AI model. The display device is used to display the generated design drawings as 3D models.

Hardware and software to be used

Hardware: Smartphones, head-mounted displays (HMDs), servers

Software: Python, OpenAI API, 3D modeling libraries

Data processing and data calculation

1. Receiving user input:

The user terminal receives user preference information (e.g., images obtained from social media), desired information (e.g., family structure, budget, location), and emotional state (e.g., joy or sadness) as input.

2. Obtaining real estate information:

The server retrieves information about land and buildings in Tokyo from real estate information websites. This includes details such as price, area, and location.

3. Automatic generation of blueprints:

The server uses a generative AI model (e.g., OpenAI's GPT-3) to automatically generate house blueprints based on user input and acquired real estate information. An example of a prompt message for the generative AI model is as follows:

User input: {'image': 'modern_house_image.jpg', 'family_size': 4, 'location': 'Tokyo', 'budget': 5000, 'emotion': 'happy'}

Real estate data: {'Land information': '...', 'Building information': '...'}

Please generate the blueprint.

4. Creating and displaying 3D models:

The server creates a 3D model using a 3D modeling library based on the generated blueprint. The created 3D model is then displayed on the user's device (smartphone or HMD).

Specific examples

If a user provides an image of a modern exterior saved from Instagram, enters that they are a family of four looking for a detached house in Tokyo with a budget of 50 million yen, the server will automatically generate a blueprint for a modern-looking detached house in Tokyo based on this information. Furthermore, when a user is feeling happy about getting a new job, the emotion engine recognizes this happiness and automatically generates a blueprint for a luxurious home. This blueprint will feature large windows, a spacious living room, and high-quality interior materials.

In this way, it becomes possible to automatically generate and visually confirm house designs that reflect the user's preferences, desires, and emotional state.

The flow of the specific processing in Application Example 3 will be explained using Figure 22.

Step 1:

Users input preference information (e.g., images from social media), desired information (e.g., family structure, budget, location), and emotional state (e.g., joy, sadness) using a smartphone or head-mounted display (HMD).

Input: User preference information, desired information, emotional state

Output: User input data

Specific action: The user enters information into the application's input form and presses the submit button.

Step 2:

The server retrieves information about land and buildings in Tokyo from real estate information websites.

Input: User's desired information (e.g., location)

Output: Real estate information data

Specific operation: The server uses an API to send requests to real estate information websites and retrieve the necessary data.

Step 3:

The server uses an AI model (e.g., OpenAI's GPT-3) to automatically generate house blueprints based on user input and acquired real estate information.

Input: User input data, real estate information data

Output: House design drawing data

Specific operation: The server sends prompt messages to the generated AI model to generate the blueprint. An example of a prompt message is as follows:

User input: {'image': 'modern_house_image.jpg', 'family_size': 4, 'location': 'Tokyo', 'budget': 5000, 'emotion': 'happy'}

Real estate data: {'Land information': '...', 'Building information': '...'}

Please generate the blueprints.

Step 4:

The server creates a 3D model using a 3D modeling library based on the generated blueprint.

Input: House design drawing data

Output: 3D model data

Specific operation: The server inputs design data into the 3D modeling library and generates a 3D model.

Step 5:

The server sends the generated 3D model to the user's terminal, and the user's terminal displays it.

Input: 3D model data

Output: 3D model displayed on the user's terminal

Specific operation: The server sends 3D model data to the user's terminal, and the user's terminal displays it on a display device (smartphone or HMD).

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

The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is generative AI such as ChatGPT (Internet Search <URL: https://openai.com/blog/chatgpt>). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives prompts containing instructions, as well as inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions provided by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization.

Another example of generative AI is Gemini (Internet search engine <URL: https://gemini.google.com/?hl=ja>).

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

[Third Embodiment]

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

As shown in Figure 5, the data processing system 310 comprises a data processing device 12 and a headset-type terminal 314. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

In the headset terminal 314, the processor 46 performs the reception and output processing. The storage 50 stores the reception and output program 60. The processor 46 reads the reception and output program 60 from the storage 50 and executes the read program 60 on the RAM 48. The reception and output processing is realized by the processor 46 acting as a control unit 46A according to the reception and output program 60 executed on the RAM 48.

Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be explained.

"Example of Morphology 1"

In one embodiment of the system, a user saves images of their preferred homes from social media such as Instagram and inputs the image information into this system. The user also inputs their family structure, desired address, price range, whether or not they want renovations, and whether they prefer a house or apartment. Based on this preference and desired information, the system automatically generates a blueprint for the house.

"Example of Morphology 2"

Furthermore, this system acquires land and building information from real estate information websites and automatically generates house blueprints based on this information. Specifically, it proposes window orientations, sizes, and floor plans, taking into account the shape of the land and building, location conditions, etc.

"Example of Morphology 3"

As a concrete example, if a user inputs an image of a modern exterior saved from Instagram, along with information such as a family of four, a desire for a detached house in Tokyo, and a budget of 50 million yen, this system will automatically generate a blueprint for a detached house in Tokyo with a modern exterior based on this information. Furthermore, based on land and building information in Tokyo obtained from real estate information websites, it will suggest window orientations and sizes, floor plans, and other details.

The following describes the processing flow for each example form.

"Example of Morphology 1"

Step 1: The user saves images of their preferred homes from social media such as Instagram.

Step 2: The user inputs the saved image information into this system.

Step 3: The user enters their family structure, desired address, price range, whether or not they want renovations, and other desired information such as a house or apartment into the system.

Step 4: This system automatically generates house blueprints based on these preference and desired information.

"Example of Morphology 2"

Step 1: This system retrieves land and building information from real estate information websites.

Step 2: This system automatically generates house plans based on the acquired land and building information. Specifically, it proposes window orientations, sizes, and floor plans, taking into account the shape of the land and building, location conditions, etc.

"Example of Morphology 3"

Step 1: The user enters a modern exterior image saved from Instagram, along with information such as being a family of four, wanting a detached house in Tokyo, and having a budget of 50 million yen.

Step 2: Based on this information, the system automatically generates a blueprint for a modern-looking detached house in Tokyo.

Step 3: This system uses land and building information in Tokyo obtained from real estate information websites to suggest window orientations, sizes, floor plans, etc.

(Example 1)

Next, we will describe Embodiment 1 of Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

Conventional residential design systems struggled to automatically generate blueprints that reflected users' preferences and desires. Furthermore, a lack of effective means to utilize image information obtained from social media meant that specific user preferences could not be reflected in the design. Additionally, when proposing housing designs based on the generated blueprints, it was difficult to create proposals that truly met the user's needs.

The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes means for receiving user preference information and desired information as input and automatically generating a house design based on this information; means for receiving image information saved by the user as input and analyzing this information to identify the user's preferences; means for generating and inputting prompt statements to the generation AI model; and means for making house proposals based on the generated design. This enables the automatic generation of house design plans that reflect the user's specific preferences and desires, and allows for the proposal of houses that match the user's wishes.

"User preference information" refers to information about the housing designs and styles that users prefer, and includes images and text data obtained from social media.

"Desired information" refers to the specific conditions and requirements of the user's desired housing, including family structure, desired address, price range, whether renovations are needed, and whether it's a detached house or an apartment.

"Image information" refers to image data of houses that users obtain from social media or other sources and upload to the system.

A "generative AI model" is an artificial intelligence model that automatically generates house blueprints based on user preferences and desired information, utilizing natural language processing and image analysis technologies.

A "prompt message" is an instruction given to the AI generation model, and it is generated based on the user's preferences and desired information.

A "house design plan" refers to a design drawing of a house generated based on the user's preferences and desired information, including specific layouts and designs.

A "housing proposal" refers to a housing design proposal made to the user based on the generated housing blueprints, and includes content that matches the user's wishes.

This invention is based on the premise that users save images of their preferred homes from social media such as Instagram and input that image information into the system. Users also input their family structure, desired address, price range, whether or not they want renovations, and whether they want a house or an apartment. Based on this information, the system automatically generates a blueprint for the house.

Hardware and software to be used

The server receives image information and request information entered by the user and stores it in a database. Specifically, it uses the following hardware and software:

Server: High-performance cloud server (e.g., Amazon Web Services, Google Cloud Platform)

Database: Relational database (e.g., MySQL, PostgreSQL)

Generative AI models: AI models using natural language processing and image analysis techniques (e.g., OpenAI's GPT-4, DALL-E)

Image analysis software: Computer vision technology (e.g., TensorFlow, PyTorch)

Data processing and data calculation

The server receives image files uploaded by the user and requested information, and stores this data in a database. Based on the stored data, it generates prompt messages for a generative AI model and inputs them. The generative AI model then generates a house design based on these prompt messages and provides it to the user via the server.

Specific examples

As a concrete example, suppose a user saves an image of a modern-designed house from Instagram and enters the following desired information:

Family composition: 4 people (2 adults, 2 children)

Desired address: Tokyo

Price range: Under 50 million yen

Renovation status: Yes

House or apartment: House

Based on this information, the server inputs the following prompt message into the AI model:

Based on images of modern-designed homes saved by the user from Instagram, please generate a blueprint for a home that matches the following requirements.

Family composition: 4 people (2 adults, 2 children)

Desired address: Tokyo

Price range: Under 50 million yen

Renovation status: Yes

House or apartment: House

The AI model generates a house design tailored to the user's preferences based on this prompt and provides it to the user via the server. The user can review the received design on their device and, if necessary, modify their preferences to generate a new design.

In this way, it becomes possible to automatically generate house designs that reflect the user's specific preferences and desires, and to propose houses that match the user's wishes.

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

Step 1:

Users save images of their favorite houses from social media such as Instagram. Users use their smartphones or computers to find and save images of their favorite houses from social media such as Instagram. The input is image data obtained from social media, and the output is an image file saved on the user's device.

Step 2:

The user uploads saved images to the system. The user accesses the system's website or application and uploads saved images of their home. Specifically, they click the upload button and select the saved image file. The input is the image file saved on the user's device, and the output is the image data sent to the server.

Step 3:

The user enters their desired information, such as family structure, preferred address, price range, whether renovations are needed, and whether they prefer a house or apartment. The user enters their desired information into the system's input form, including family structure (e.g., 2 adults, 2 children), preferred address (e.g., Tokyo), price range (e.g., under 50 million yen), whether renovations are needed (e.g., yes), and whether they prefer a house or apartment (e.g., house). The input is the desired information entered by the user, and the output is the desired information sent to the server.

Step 4:

The server receives image and request information from the user and stores it in the database. The server receives uploaded image files and input request information from the user and stores this data in the database. When saving, the data is managed in association with the user ID. The input is the image and request information sent by the user, and the output is the data stored in the database.

Step 5:

The server generates prompt messages for the AI model based on the stored data and inputs them. The server generates prompt messages for the AI model based on image information and desired information stored in the database. For example, it generates prompt messages like the following:

Based on images of modern-designed homes saved by the user from Instagram, please generate a blueprint for a home that matches the following requirements.

Family composition: 4 people (2 adults, 2 children)

Desired address: Tokyo

Price range: Under 50 million yen

Renovation status: Yes

House or apartment: House

The input consists of image information and desired information stored in a database, while the output is the prompt text input to the generating AI model.

Step 6:

The generative AI model generates a house design based on the prompt text. The generative AI model (e.g., OpenAI's GPT-4 or DALL-E) generates the house design based on the prompt text sent from the server. The generative AI model utilizes image analysis and natural language processing techniques to create a design that meets the user's preferences. The input is the prompt text, and the output is the generated house design.

Step 7:

The server sends the generated blueprint to the user's terminal. The server sends the blueprint received from the generated AI model to the user's terminal. The user can download or view the blueprint through the system's website or application. The input is the generated house blueprint, and the output is the blueprint sent to the user's terminal.

Step 8:

The user reviews the blueprint. The user reviews the blueprint received on their device. They can check if the blueprint meets their requirements and, if necessary, modify the required information to generate a new blueprint. The input is the blueprint sent to the user's device, and the output is the user's review result.

(Application Example 1)

Next, we will describe Application Example 1 of Configuration 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."

Traditional home design systems struggled to automatically generate blueprints that reflected user preferences and desires. In particular, if a user had a specific image in mind, specialized knowledge was required to translate that image into a blueprint. Furthermore, real-time blueprint generation and display were difficult when users viewed home designs in physical showrooms. This led to decreased user satisfaction and delays in home purchase decisions.

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

This invention includes a server that receives user preference and desired information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that proposes houses based on the generated design; a server that analyzes house images taken by the user and extracts their features; a server that generates prompt text based on the extracted features and the user's desired information and generates a house design using a generation AI model; and a server that presents the generated design to the user via a display device. This enables the real-time generation of house design plans that reflect the user's specific image, allowing for on-site verification at a physical location.

"User preference information" refers to information that indicates a user's personal preferences and design tastes regarding housing.

"Desired information" refers to information that indicates the user's specific requests and conditions regarding housing (family structure, desired address, price range, whether renovations are needed, whether it's a detached house or an apartment, etc.).

"Real estate information" refers to information that provides detailed data about a real estate property (location, price, floor plan, year built, etc.).

A "house design drawing" is a diagram that specifically shows the structure, layout, and design of a house.

"Automatic generation methods" refer to devices or programs that have the function of automatically creating design drawings using algorithms or AI models based on input information.

"Means of making proposals" refers to devices or programs that have the function of making housing proposals to users based on the generated blueprints.

"Residential images" refer to photographs or image data of houses taken by users.

"Means of analysis" refers to devices or programs that have the function of analyzing input image data and extracting its features.

A "means for extracting features" refers to a device or program that has the function of extracting important features from data obtained through image analysis.

A "prompt" is a text-based input message used to give instructions to a generative AI model.

A "generative AI model" is an artificial intelligence model that generates blueprints and other data based on input prompts.

A "display device" is a device (such as a display or VR headset) used to visually present generated design drawings to the user.

The system for carrying out this invention includes means for receiving user preference information and desired information as input and automatically generating a house design based on this information; means for receiving real estate information as input and automatically generating a house design based on this information; means for making house proposals based on the generated design; means for analyzing house images taken by the user and extracting their features; means for generating prompt sentences based on the extracted features and the user's desired information, and generating a house design using a generation AI model; and means for presenting the generated design to the user through a display device.

Hardware and software to be used

Hardware

Smartphone or tablet (iOS or Android): Used by the user to take pictures of the house and enter information.

Smart glasses (e.g., Google Glass): Used to analyze images taken by the user in real time.

Large-screen display or VR headset (e.g., Oculus Rift): Used to present the generated blueprints to the user.

Software

Image recognition libraries (e.g., OpenCV): Used to analyze residential images taken by the user and extract their features.

Generative AI models (e.g., GPT-4, DALL-E): Used to generate house blueprints based on prompt messages.

Database (e.g., Firebase): Used to store and manage user preference information, desired information, and real estate information.

Frontend frameworks (e.g., React Native): Used to build user interfaces.

Processing Flow

1. User Input

Users use their smartphones or tablets to take photos of their preferred homes and upload them to the application. They also input information such as family composition, desired address, price range, whether renovations are needed, and whether they prefer a house or apartment.

2. Image Analysis

The server uses an image recognition library (OpenCV) to analyze uploaded residential images and extract their features.

3. Generating the prompt message

The server generates prompt messages for the generative AI model (GPT-4) based on the extracted features and user preferences.

Example of a prompt message

Based on the images and desired information provided by the user, please generate a house design that meets the following conditions:

Family composition: 4 people

Desired address: Tokyo

Price range: Under 50 million yen

Renovation: Yes

Detached house

Modern design

4. Generating Blueprints

The server uses a generative AI model (DALL-E) to generate house blueprints based on the prompt text.

5. Displaying Results

The generated blueprints are presented to the user via a large screen display or VR headset. Users can then view their ideal home design in real time at a physical store.

Specific examples

For example, a user uploads an image of a modern living room taken with their smartphone and enters their desired information as follows: "Family size: 4 people, Desired address: Tokyo, Price range: under 50 million yen, Renovation: Yes, Detached house." Based on this information, the server generates a blueprint for a modern detached house in Tokyo, suitable for a family of four, within a 50 million yen budget, and presents it to the user.

In this way, users can view their ideal home design in real time at a physical store.

The flow of the specific processing in Application Example 1 will be explained using Figure 12.

Step 1:

Users use their smartphones or tablets to take photos of their preferred homes and upload them to the application. They also input information such as family composition, desired address, price range, whether renovations are needed, and whether they prefer a house or apartment.

Input: Housing images, family composition, desired address, price range, whether renovation is needed, desired type of property (house or apartment), etc.

Output: Uploaded house images and desired information

Step 2:

The server uses an image recognition library (OpenCV) to analyze uploaded residential images and extract their features.

Input: Uploaded house image

Output: Extracted image features

Specific operation: Using an image recognition library, perform edge detection and color analysis on residential images, and extract features.

Step 3:

The server generates prompt messages for the generative AI model (GPT-4) based on the extracted features and user preferences.

Input: Extracted image features, desired information

Output: Generated prompt message

Specific operation: Combine the extracted features and desired information to create prompt sentences for input into the generative AI model.

Step 4:

The server uses a generative AI model (DALL-E) to generate house blueprints based on the prompt text.

Input: Generated prompt text

Output: Generated house blueprints

Specific operation: The system takes prompt text as input to a generation AI model and generates a house design.

Step 5:

The server presents the generated blueprints to the user via a large screen display or VR headset.

Input: Generated house blueprints

Output: Design drawings presented to the user

Specific operation: The generated design drawings are sent to a display or VR headset, allowing the user to visually confirm them.

In this way, users can view their ideal home design in real time at a physical store.

(Example 2)

Next, we will describe Embodiment 2 of Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

Traditional residential design systems had the problem of requiring a great deal of time and effort to reflect user preferences and desired information. Furthermore, efficiently acquiring real estate information and automatically generating optimal residential designs based on that information was difficult. As a result, it was difficult to quickly provide residential designs that satisfied users.

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

In this invention, the server includes means for acquiring land and building information from a real estate information provision site, means for storing the acquired information in a data frame and processing the data, means for generating prompt sentences to be input into a generation AI model based on the processed data, means for automatically generating house blueprints using the generation AI model, and means for making house proposals based on the generated blueprints. This makes it possible to quickly and efficiently provide house blueprints that reflect the user's preferences and desired information.

A "real estate information website" is a website that provides information about land and buildings.

"Land and building information" refers to detailed data such as the shape of the land, the location of the building, the orientation and size of the windows, and the floor plan.

A "data frame" is a two-dimensional data structure consisting of rows and columns, used for storing and processing data.

"Data processing" refers to the process of transforming acquired data into a format that is easy to analyze and use, and includes processes such as imputing missing values and normalizing data.

A "generative AI model" is a model that uses artificial intelligence to perform specific tasks; in this case, it is used to automatically generate blueprints for houses.

A "prompt message" is an instruction message used to input data into a generation AI model, and it is generated based on information about the land and buildings.

A "house design drawing" is a diagram showing the layout and structure of a house, and forms the basis of the building plan.

A "housing proposal" refers to suggestions regarding the design and layout of a house, made to the user based on the generated blueprints.

Modes for carrying out the invention

This invention relates to a system that acquires land and building information from real estate information websites and automatically generates house blueprints based on this information. A specific embodiment of this system is described below.

1. Program Generation

The server generates a program to retrieve land and building information from real estate information websites. This program is developed using Python and performs web scraping using libraries such as BeautifulSoup and Selenium.

2. Data Acquisition and Processing

The server executes the generated program and retrieves land and building information from real estate information websites. Specifically, it collects data such as land shape, building location, window orientation and size, and floor plan. The collected data is stored in a dataframe using the Pandas library, and necessary data processing is performed. For example, missing value imputation and data normalization are performed.

3. Generating prompt sentences for the generative AI model

The server generates prompt statements to input into the AI model based on the processed data. These prompt statements include information such as the shape of the land and building, location conditions, window orientation, size, and floor plan.

Example of a prompt message:

Based on land information in Shibuya Ward, Tokyo, please generate a house design plan considering the following conditions.

Land shape: Rectangle

Building location: South-facing

Window orientation: South-facing

Window size: Large

Floor plan: 3LDK

4. Generating House Design Plans

The server inputs the generated prompt text into a generation AI model (for example, a generation AI model) and generates a house design. The generation AI model then proposes the optimal design based on the prompt text.

5. Displaying Results

The server displays the generated house design plans on the user's terminal. The user can review the proposed design plans and request revisions as needed.

Specific examples

If a user requests to generate a house design based on land information in Shibuya Ward, Tokyo, the server will input the following prompt into the AI model:

Example of a prompt message:

Based on land information in Shibuya Ward, Tokyo, please generate a house design plan considering the following conditions.

Land shape: Rectangle

Building location: South-facing

Window orientation: South-facing

Window size: Large

Floor plan: 3LDK

The AI model generates a house design based on this prompt. The proposed design is displayed on the user's device for review.

In this way, the present invention makes it possible to quickly and efficiently provide residential design plans that reflect the user's preferences and desired information.

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

Program execution flow

Step 1: Obtaining real estate information

The server retrieves land and building information from real estate information websites. Specifically, it uses Python and libraries such as BeautifulSoup and Selenium to perform web scraping.

Input: URL of the real estate information website

Output: Land and building information (e.g., land shape, building location, window orientation, size, floor plan)

Specific actions:

The server uses Selenium to automatically control the browser and access the specified URL. Next, it uses BeautifulSoup to parse the HTML and extract the necessary information.

Step 2: Data Processing

The server stores the retrieved real estate information in a dataframe using the Pandas library and performs necessary data processing. For example, it might impute missing values or normalize the data.

Input: Land and building information

Output: Processed data frame

Specific actions:

The server converts the retrieved data into a DataFrame using the Pandas `read_html` function. Next, it imputes missing values using the `fillna` function and normalizes the data using the `normalize` function.

Step 3: Generating prompts for the generative AI model

The server generates prompt statements to input into the AI model based on the processed data. These prompt statements include information such as the shape of the land and building, location conditions, window orientation, size, and floor plan.

Input: Processed data frame

Output: Prompt message

Specific actions:

The server extracts the necessary information from the data frame and generates a prompt message like the following:

Based on land information in Shibuya Ward, Tokyo, please generate a house design plan considering the following conditions.

Land shape: Rectangle

Building location: South-facing

Window orientation: South-facing

Window size: Large

Floor plan: 3LDK

Step 4: Generating the House Design Plan

The server inputs the generated prompt text into a generation AI model (for example, a generation AI model) and generates a house design. The generation AI model then proposes the optimal design based on the prompt text.

Input: Prompt text

Output: House design plans

Specific actions:

The server sends prompts to the API for generating AI models and receives proposed blueprints in return. For example, it might use the `openai.Completion.create` function to send prompts.

Step 5: Displaying Results

The server displays the generated house design plans on the user's terminal. The user can review the proposed design plans and request revisions as needed.

Input: House blueprints

Output: Blueprint displayed on the user's terminal

Specific actions:

The server formats the generated blueprint in HTML format and sends it to the user's terminal. The user then views the blueprint through their browser.

(Application Example 2)

Next, we will describe Application Example 2 of Form 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."

Conventional residential design systems have difficulty reflecting user preferences and desires, and their automatic generation of design plans based on real estate information has been limited. Furthermore, interior design for physical stores requires specialized knowledge and is not easily accessible to the average user. Therefore, there is a need for a system that allows users to easily design homes and stores that meet their specific needs.

In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving user preference information and desired information as input and automatically generating house blueprints based on this information; means for receiving real estate information as input and automatically generating house blueprints based on this information; means for making house proposals based on the generated blueprints; means for automatically generating interior designs for physical stores based on information obtained from real estate information provision sites; means for creating prompt statements for generating blueprints using a generation AI model; and means for generating actual blueprints using the generation AI model. This makes it easy for users to design houses and stores that meet their preferences.

"User preference information" refers to information about the style, functions, and design that users desire in the design of their homes or shops.

"Desired information" refers to information about the specific conditions and requirements that users desire in the design of their homes or shops.

"Real estate information" refers to information about the shape, location, and area of land and buildings.

A "design drawing" is a diagram showing the layout of a house or shop, including the direction and size of windows, and the arrangement of interior furnishings.

A "real estate information website" is a website that provides information about land and buildings on the internet.

"Interior design" refers to planning the layout and design of the interior spaces of shops and residences.

A "generative AI model" is an algorithm or program that uses artificial intelligence to automatically generate blueprints and designs.

A "prompt statement" is an instruction statement used to input data into a generative AI model, containing conditions and requirements for generating blueprints or designs.

The system for carrying out this invention includes a server, a user terminal, and a generative AI model. Specific embodiments of this system are described below.

First, the user terminal receives user preference and desired information as input. This information concerns the style, functions, and design the user desires for the design of their home or store. The user terminal then transmits this information to the server.

Next, the server retrieves real estate information from a real estate information website. This real estate information includes details such as the shape of the land and building, location conditions, and area. Based on the retrieved real estate information, the server automatically generates blueprints for houses and shops.

The server uses a generative AI model to create prompt statements for generating blueprints. These prompt statements include user preference information, desired information, and real estate information. For example, prompt statements like the following are generated:

"Land shape: Rectangle, Location: Urban area, Window orientation: South-facing, Lighting position: Center of ceiling"

Based on the generated prompt text, the generative AI model creates the actual design drawing. For example, OpenAI's GPT-3 is used as the generative AI model. This model receives the prompt text as input and outputs the design drawing.

The generated blueprints are sent from the server to the user's terminal and presented to the user. The user can then review the design of the house or shop based on the presented blueprints and make modifications as needed.

This system allows users to easily design houses and shops that meet their specific needs. Specifically, users can easily generate and review design plans using their smartphones or computers. This enables them to realize their desired designs even without specialized knowledge.

The above describes a specific embodiment for carrying out this invention.

The flow of the specific processing in Application Example 2 will be explained using Figure 14.

Step 1:

The user terminal receives user preference and desired information as input. Users use smartphones or computers to input information regarding their desired style, function, and design for residential or commercial buildings. The entered information is stored on the user terminal.

Step 2:

The user terminal sends the entered preference and desired information to the server. The transmitted information is stored in the server's database.

Step 3:

The server retrieves real estate information from real estate information websites. The server uses an API to send requests to these websites, obtaining information such as the shape, location, and area of the land and buildings. The retrieved real estate information is stored on the server.

Step 4:

The server generates prompt statements to create a blueprint based on the acquired real estate information and the user's preferences and requests. These prompt statements include information such as the land shape, location, window orientation, and lighting position. For example, a prompt statement like "Land shape: Rectangle, Location: Urban area, Window orientation: South-facing, Lighting position: Center of ceiling" might be generated.

Step 5:

The server uses a generative AI model to generate a blueprint using prompt text as input. For example, OpenAI's GPT-3 is used as the generative AI model. The generative AI model, upon receiving prompt text as input, outputs a blueprint. The generated blueprint is saved on the server.

Step 6:

The server sends the generated blueprint to the user's terminal. The user's terminal displays the received blueprint to the user. The user can then view the blueprint on their smartphone or computer screen.

Step 7:

Users can review the provided blueprints for houses and shops, and make modifications as needed. The modified information is then sent back to the server, and the blueprints are regenerated. This allows users to realize a design that meets their specific needs.

(Example 3)

Next, we will describe Embodiment 3 of Embodiment Example 3. 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."

Conventional residential design systems have difficulty automatically generating blueprints that reflect user preferences and desires, and have been unable to provide optimal design proposals based on real estate information. Therefore, there is a need to efficiently provide residential designs that meet user needs.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

This invention includes a server that receives user preference and desire information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that makes house design proposals based on the generated design; a server that sends prompt text and images to a generation AI model to generate a design; a server that acquires land and building information from a real estate information website; a server that makes optimal design proposals based on the acquired real estate information; and a server that displays the generated design and proposal content to the user. This makes it possible to automatically generate house design plans that reflect the user's preferences and desires, and to make optimal design proposals based on real estate information.

"User preference information" refers to information indicating a user's personal preferences regarding design, style, and functionality in residential design.

"Desired information" refers to specific conditions and requirements that users have in mind when designing a house, such as family structure, budget, and location.

"Real estate information" refers to detailed data about land and buildings, such as price, area, and surrounding environment.

A "generative AI model" is an algorithm or system that uses artificial intelligence to generate design plans based on user input.

A "prompt" is a text-based input message used to give instructions to a generative AI model.

A "design drawing" is a diagram showing the structure, layout, and design of a house.

A "real estate information website" is an online platform that provides information about land and buildings.

"Optimal design proposal" refers to proposing the most suitable residential design based on the user's preferences, desires, and real estate information.

"Means of display" refers to methods or devices for visually providing users with generated design drawings or proposed content.

This invention is a system that automatically generates house blueprints based on user preference and desired information, and provides optimal design suggestions based on real estate information. A specific embodiment of this system is described below.

System Configuration

This system consists of three main elements: a server, a terminal, and a user. The server receives input information from the user, generates design plans using a generative AI model, and provides optimal design suggestions based on real estate information. The terminal provides an interface for the user to input information and check the results. The user accesses the system through the terminal and inputs the necessary information.

Hardware and software to be used

Server: A server with high-performance computing capabilities is required. Specifically, a cloud-based server (e.g., Amazon Web Services or Google Cloud Platform) should be used.

Terminal: A device used by a user to input information. This includes internet-connected devices such as personal computers, tablets, and smartphones.

Generative AI Model: We use advanced artificial intelligence models such as OpenAI's GPT-4 and DALL-E as generative AI models.

Real estate information websites: Use online platforms (e.g., SUUMO or At Home) to obtain real estate information.

Data processing and calculations

1. User Information Input: The user accesses a form on a web browser using their device and uploads a modern-looking image saved from Instagram. Next, they enter their family composition (four people), desired location (Tokyo), and budget (50 million yen).

2. Prompt Generation: The server receives the information entered by the user and generates prompts for input into the generated AI model. Specifically, it generates prompts like the following:

"Based on images of modern exteriors saved by users on Instagram, please automatically generate blueprints for a detached house in Tokyo suitable for a family of four, with a budget of under 50 million yen."

3. Sending to the Generating AI Model: The server sends the generated prompt text and the user-uploaded image to the Generating AI model. This transmission is done via API.

4. Blueprint Generation: The generation AI model generates a blueprint for a modern-looking detached house in Tokyo based on the received prompt text and image. The blueprint includes details such as window orientation and size, and floor plan. The generated blueprint is returned to the server.

5. Acquisition of Real Estate Information: The server uses the API of a real estate information provider website to acquire information on land and buildings in Tokyo. The acquired information includes land prices, building area, and surrounding environment.

6. Optimal Design Proposal: Based on the acquired real estate information, the server proposes optimal window orientations, sizes, and layouts for the generated design drawings. For example, by proposing a design drawing with many south-facing windows, it provides a living environment with plenty of sunlight.

7. Displaying Results: The server displays the final design and proposal to the user. The user can review this information in a web browser and make modifications or regenerations as needed. An interactive UI is used for display to ensure user-friendliness.

Specific examples

If the user enters the following information:

Images of modern exteriors saved from Instagram

family of 4

I'm looking for a detached house in Tokyo.

The budget is 50 million yen.

The server generates the following prompt:

"Based on images of modern exteriors saved by users on Instagram, please automatically generate blueprints for a detached house in Tokyo suitable for a family of four, with a budget of under 50 million yen."

The AI model generates a design drawing based on this prompt text and image, and the server proposes the optimal window orientation, size, and floor plan based on data obtained from a real estate information website. The specific processing flow in Example 3 is explained using Figure 15.

Step 1:

The user enters information.

The user accesses a form on a web browser using their device and uploads a modern-looking image saved from Instagram. Next, they enter their family size (four people), desired location (Tokyo), and budget (50 million yen). This information is then sent to the server.

Input: Images saved from Instagram, family composition, desired region, budget

Output: User preference information and desired information

Step 2:

The server generates the prompt message.

The server receives the information entered by the user and generates prompt messages for input into the generated AI model. Specifically, it generates prompt messages like the following:

"Based on images of modern exteriors saved by users on Instagram, please automatically generate blueprints for a detached house in Tokyo suitable for a family of four, with a budget of under 50 million yen."

Input: User preference information and desired information

Output: Prompt text to send to the generated AI model

Step 3:

The server sends prompt text and images to the generated AI model.

The server sends the generated prompt text and the user-uploaded image to the AI model. This transmission is done via API.

Input: Prompt text, user-uploaded image

Output: Input data for the generated AI model

Step 4:

The generative AI model generates the blueprint.

The AI model generates a blueprint of a modern-looking detached house in Tokyo based on the received prompt text and image. The blueprint includes details such as window orientation and size, and floor plan. The generated blueprint is then returned to the server.

Input: Prompt text, user-uploaded image

Output: Generated blueprint

Step 5:

The server retrieves real estate information.

The server uses the API of a real estate information website to retrieve information on land and buildings in Tokyo. The retrieved information includes land prices, building area, and surrounding environment.

Input: API of a real estate information website

Output: Acquired real estate information

Step 6:

The server will propose the optimal design.

The server uses the acquired real estate information to suggest optimal window orientations, sizes, and layouts for the generated blueprints. For example, by suggesting blueprints with many south-facing windows, it provides a living environment with plenty of sunlight.

Input: Generated blueprints, acquired property information

Output: Optimal design proposal

Step 7:

The server displays the results to the user.

The server displays the final design blueprint and proposal to the user. Users can view this information in a web browser and modify or regenerate it as needed. An interactive UI is used for display to ensure user-friendliness.

Input: Final design drawings, proposed content

Output: Results displayed to the user

(Application Example 3)

Next, we will describe Application Example 3 of Form Example 3. 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."

Conventional residential design systems struggled to automatically generate blueprints that reflected user preferences and desires, and were unable to effectively utilize real estate information to propose blueprints. Furthermore, they could not leverage images saved by users or information from social media, making it difficult to provide blueprints that met specific user needs. This resulted in a challenge in increasing user satisfaction.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

This invention includes a server that receives user preference and desired information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that proposes a house design based on the generated design; a server that generates prompt text based on images saved by the user and generates a design using a generation AI model; and a server that proposes window orientation, size, floor plan, etc., based on land and building information obtained from a real estate information website. This enables the automatic generation of design plans tailored to the user's specific needs and the effective utilization of real estate information.

"User preference information" refers to information about the design and style that users desire, and is obtained from sources such as social media and saved images.

"Desired information" refers to information about the conditions and requirements of a home that the user desires, including specific preferences such as family structure, budget, and location.

A "house design plan" is a drawing that shows the exterior, floor plan, window orientation, size, etc., of a house, and is automatically generated based on the user's preferences and requests.

"Real estate information" refers to information about land and buildings, and is obtained from real estate information websites and other sources.

A "generative AI model" is a model that uses artificial intelligence to generate design plans or proposals from specific input information.

A "prompt message" is an instruction message used to input into a generative AI model, and it is generated based on the user's preferences and desired information.

A "real estate information website" is a website that provides information about land and buildings, and users can access and obtain this information.

"Window orientation, size, and floor plan" are important elements in residential design and are proposed based on the user's wishes and real estate information.

The system for implementing this invention includes means for receiving user preference information and desired information as input and automatically generating house blueprints based on this information; means for receiving real estate information as input and automatically generating house blueprints based on this information; means for making house proposals based on the generated blueprints; means for generating prompt text based on images saved by the user and generating blueprints using a generation AI model; and means for proposing window orientation, size, floor plan, etc., based on land and building information obtained from real estate information provision sites.

System Program

The server receives preference and request information entered by the user via a device such as a smartphone or computer. This includes images obtained from social media such as Instagram, as well as information such as family structure, budget, and desired location. Next, the server generates prompt statements based on this information. These prompt statements are input into a generation AI model, which serves as instructions for generating house blueprints.

Based on the generated prompt text, the AI model automatically generates a blueprint for a house. This blueprint reflects the user's preferences and desires, including specific exterior details and floor plans. Furthermore, the server suggests window orientations and sizes, floor plans, etc., based on land and building information obtained from real estate information websites.

Hardware and software to be used

This system uses the following hardware and software:

Hardware: Servers, user terminals (smartphones, PCs, etc.)

Software: Generative AI model (e.g., OpenAI's GPT-3), prompt generation program, image acquisition program (e.g., requests, PIL)

Specific examples

For example, if the user enters the following conditions:

Instagram image URL: https://example.com/instagram_image.jpg

Family composition: 4 people family

Desired area: Tokyo

Budget: 50 million yen

The generated prompt will look like this:

Based on images of modern exteriors saved by the user on Instagram, we need a design for a detached house in Tokyo for a family of four with a budget of 50 million yen. Please generate a blueprint for a detached house with a modern exterior based on this information.

When this prompt is entered into the AI model, the model automatically generates a house design that meets the user's preferences. The generated design reflects the user's desired modern exterior and a floor plan suitable for their family structure. Furthermore, based on land and building information obtained from real estate information websites, the model also provides specific suggestions regarding window orientation and size, floor plan, and other details.

The flow of the specific processing in Application Example 3 will be explained using Figure 16.

Step 1:

Users input their preferences and desired information from devices such as smartphones and computers. This includes image URLs obtained from social media such as Instagram, as well as information such as family structure, budget, and desired region. The entered information is sent to the server.

Input: Instagram image URL, family composition, budget, desired region

Output: User preference and desired information sent to the server.

Step 2:

The server receives preference and request information from the user. Based on this information, it performs data processing to generate prompt messages. Specifically, it retrieves image URLs and converts information such as family structure, budget, and desired region into text format.

Input: User preference information and desired information

Output: Data for prompt message generation

Step 3:

The server downloads the image based on the retrieved image URL and loads it as an image object. This process uses the requests library and the PIL library.

Input: Image URL

Output: Image object

Step 4:

The server generates prompt text based on image objects and user information in text format. The generated prompt text serves as instructions for inputting into the AI model.

Input: Image object, user information in text format

Output: Prompt message

Step 5:

The server inputs the generated prompt text into a generative AI model to automatically generate a house design. This process uses generative AI models such as OpenAI's GPT-3.

Input: Prompt text

Output: Automatically generated house blueprints

Step 6:

The server retrieves land and building information from real estate information websites. This information includes window orientation and size, floor plan, etc.

Input: URL of the real estate information website

Output: Land and building information

Step 7:

Based on the acquired real estate information, the server provides specific suggestions regarding window orientation, size, and floor plan for the generated house blueprints.

Input: Land and building information, automatically generated house blueprints

Output: Residential blueprints including specific proposals

Step 8:

The server sends the final design to the user's terminal and provides a proposal to the user. The user can review the proposed design and make modifications or regenerate it as needed.

Input: Residential blueprints including specific proposals

Output: Final design drawings sent to the user's terminal.

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

"Example of Morphology 1"

One embodiment of the present invention incorporates an emotion engine that recognizes the user's emotions. This emotion engine recognizes the user's emotions and adjusts the house design based on those emotions. Specifically, when the user is feeling happy, it suggests bright-colored interiors and an open floor plan. Conversely, when the user is feeling depressed, it suggests calm-colored interiors and a floor plan that prioritizes privacy.

"Example of Morphology 2"

Furthermore, the emotion engine adjusts housing suggestions based on the user's emotions. Specifically, when the user is feeling happy, it suggests luxurious homes. Conversely, when the user is feeling down, it suggests simple and functional homes.

"Example of Morphology 3"

As a concrete example, when a user is happy about getting a new job, the emotion engine recognizes that happiness and automatically generates blueprints for a luxurious house. These blueprints feature large windows, a spacious living room, and high-quality interior materials. Conversely, when a user is depressed after losing their job, the emotion engine recognizes that depression and automatically generates blueprints for a simple, functional house. These blueprints have only the bare minimum number of rooms and functions, keeping costs down.

The following describes the processing flow for each example form.

"Example of Morphology 1"

Step 1: The emotion engine recognizes the user's emotions.

Step 2: The emotion engine adjusts the house design based on the user's emotions. Specifically, when the user is feeling happy, it suggests bright interior colors and an open floor plan.

Step 3: On the other hand, when the user is feeling down, suggest interiors with calming color schemes and floor plans that prioritize privacy.

"Example of Morphology 2"

Step 1: The emotion engine recognizes the user's emotions.

Step 2: The emotion engine adjusts housing suggestions based on the user's emotions. Specifically, when the user is feeling happy, it suggests luxurious homes.

Step 3: On the other hand, when the user is feeling down, propose a simple and functional home.

"Example of Morphology 3"

Step 1: When a user feels joy after acquiring a new job, the emotion engine recognizes that joy.

Step 2: The emotion engine automatically generates blueprints for a luxurious house. These blueprints feature large windows, a spacious living room, and high-quality interior materials.

Step 3: On the other hand, when a user is depressed after losing their job, the emotion engine recognizes that depression.

Step 4: The emotion engine automatically generates a simple and functional house design. This design includes the minimum necessary number of rooms and functions, keeping costs down.

(Example 1)

Next, we will describe Embodiment 1 of Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

Conventional residential design systems struggled to automatically generate blueprints that reflected user preferences and desires. Furthermore, they failed to adjust blueprints to accommodate user emotions, resulting in lower user satisfaction. Additionally, they lacked effective means of utilizing information from social media and real estate information websites. This led to challenges in creating residential designs that met diverse user needs.

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

This invention includes a server that receives user preference and desire information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that proposes a house design based on the generated design; a server that recognizes the user's emotions and adjusts the house design based on those emotions; and a server that proposes interior design and floor plans based on the user's emotions. This enables the automatic generation of house design plans that reflect the user's preferences and desires, and further allows for adjustments to the design plans that take the user's emotions into consideration, thereby increasing user satisfaction. Furthermore, by effectively utilizing information from social media and real estate information websites, it is possible to realize house designs that meet the diverse needs of users.

"User preference information" refers to images and text information about housing that users have obtained from social media and other sources on the internet.

"Desired information" refers to specific requests entered by the user, such as family structure, desired address, price range, whether renovations are needed, and whether they prefer a house or apartment.

"Real estate information" refers to detailed property information obtained from real estate information websites and other databases.

"House design plans" refer to floor plans, interior and exterior drawings of a house that are automatically generated based on the user's preferences, desired features, and real estate information.

"Means of recognizing emotions" refers to algorithms and software that analyze a user's facial expressions and entered text to determine their emotional state.

"Means for adjusting the blueprint" refers to algorithms and software used to modify or correct the content of a house's blueprint based on recognized user emotions.

"Methods for proposing interior design and floor plans" refers to algorithms and software that propose optimal interior designs and floor plans based on the user's emotions and preferences.

"Social media" refers to a platform on the internet for users to share information.

A "real estate information website" refers to a website or online service that provides detailed information about properties.

Modes for carrying out the invention

This invention is a system that automatically generates house blueprints based on user preference and desired information, and further adjusts the blueprints to take the user's emotions into consideration. This system consists of a server, a terminal, and the user.

System Configuration

1. User actions

Users save their favorite house images from social media on the internet and upload them to the system.

Users input their preferences into the system, including family structure, desired address, price range, whether renovations are needed, and whether they prefer a house or apartment.

2. Server Processing

The server receives residential images uploaded by users and extracts image features using image recognition algorithms (e.g., TensorFlow, OpenCV).

The server analyzes the user's input using a natural language processing engine (e.g., GPT-3) and organizes it as structured data.

The server integrates data obtained from image recognition algorithms and natural language processing engines to generate blueprints for a house.

The server uses an emotion engine to recognize the user's emotions and analyzes them from the user's facial expressions and entered text.

The server adjusts the design based on the user's perceived emotions. For example, if the user is feeling happy, it suggests bright color schemes and an open layout.

The server generates the final, adjusted blueprint and provides it to the user.

Hardware and software to be used

Hardware: High-performance servers (e.g., servers with NVIDIA GPUs)

Software: Image recognition algorithms (e.g., TensorFlow, OpenCV), natural language processing engines (e.g., GPT-3), emotion recognition engines

Specific examples

For example, suppose a user uploads a modern house design image saved from Instagram to the system and inputs their desired information, such as family size (four people), desired address (Tokyo), price range (under 50 million yen), whether renovation is desired (yes), and whether they prefer a house or apartment (house).

The server first uses an image recognition algorithm to extract modern design features from residential images. Next, it analyzes the user's preferences using a natural language processing engine to generate floor plans suitable for a family of four, designs appropriate for residential areas in Tokyo, specifications feasible within a budget of 50 million yen, designs that consider renovations, and plans for detached houses.

Furthermore, if the emotion engine recognizes the user's emotion as "joy," the server will suggest interior design with bright colors and an open living room. Finally, the server integrates this information to provide the user with a blueprint for their ideal home.

Example of a prompt message

The user provided images of modern-designed houses saved from social media, along with their family size (4 people), desired address (Tokyo), price range (under 50 million yen), whether renovations were desired (yes), and whether they preferred a house or apartment (house). Based on this information, please generate a house design. Additionally, if the user's emotion is "joy," please suggest a bright interior color scheme and an open floor plan.

In this way, the system can automatically generate house designs based on the user's preferences and desires, and further adjust the designs to take the user's emotions into consideration.

The flow of the specific processing in Example 1 will be explained using Figure 17.

Step 1:

Users save their preferred house images and input them into the system.

Specifically, the user downloads images of houses they like from social media on the internet and uploads those images through the system's interface.

Input: Residential images obtained from social media

Output: Residential images uploaded to the system

Step 2:

The user enters their desired information into the system.

In terms of specific operations, the user inputs desired information such as family structure, desired address, price range, whether renovations are needed, and whether they prefer a house or apartment, through the system interface.

Input: Family composition, desired address, price range, whether renovation is required, preferred type of property (house or apartment), etc.

Output: Desired information entered into the system

Step 3:

The server analyzes residential images using an image recognition algorithm.

In terms of specific operation, the server receives uploaded images of houses and extracts image features using image recognition algorithms (e.g., TensorFlow, OpenCV). For example, it analyzes information such as the building's exterior, interior style, and color scheme.

Input: Residential images uploaded to the system

Output: Image feature data (building exterior, interior style, color tone, etc.)

Step 4:

The server uses a natural language processing engine to analyze the requested information.

In terms of specific operations, the server analyzes the user's input using a natural language processing engine (e.g., GPT-3) and organizes it as structured data. For example, it understands information such as family structure, desired address, and price range, and stores it in a database.

Input: Desired information entered into the system

Output: Structured desired information data

Step 5:

The server integrates the analysis results and generates the house blueprints.

In terms of specific operations, the server integrates data obtained from image recognition algorithms and natural language processing engines to generate blueprints for a house. For example, it designs the floor plan, interior, and exterior based on the user's preferences and desires.

Input: Image feature data, structured desired information data

Output: House design plans

Step 6:

The server uses an emotion engine to recognize the user's emotions.

Specifically, the server uses an emotion engine to recognize the user's emotions. It analyzes the text and facial expression data entered by the user into the system to determine the user's current emotional state.

Input: User text input, facial expression data

Output: User emotional state data

Step 7:

The server adjusts the design based on the user's emotions.

Specifically, the server adjusts the design based on the user's perceived emotions. For example, if the user is feeling happy, it suggests bright color schemes and an open layout. Conversely, if the user is feeling depressed, it suggests calm color schemes and a layout that prioritizes privacy.

Input: User emotional state data, house blueprints

Output: Adjusted house blueprints

Step 8:

The server provides the user with the final blueprint.

Specifically, the server generates a final, adjusted blueprint and provides it to the user. The user can review the blueprint through the system interface and provide feedback as needed.

Input: Adjusted house blueprints

Output: Final design drawings provided to the user

(Application Example 1)

Next, we will describe Application Example 1 of Configuration 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."

Traditional residential design systems could generate blueprints based on user preferences and requests, but they lacked the ability to adjust the blueprints to reflect the user's emotions. Furthermore, it was difficult to provide real-time, emotionally responsive suggestions when users consulted with design and renovation companies in person. This resulted in a challenge in increasing user satisfaction.

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

This invention includes a server that receives user preference and desired information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that proposes a house design based on the generated design; a server that recognizes the user's emotions and adjusts the house design based on those emotions; and a server that analyzes the user's facial expressions using a smart device and recognizes their emotions. This enables the generation and proposal of house design plans that reflect the user's emotions in real time.

"User preference information" refers to images related to housing obtained by users from social media such as Instagram, as well as information indicating the user's personal preferences.

"Desired information" refers to information that indicates the user's specific requests, such as desired family structure, address, price range, whether or not renovations are needed, and whether they prefer a detached house or an apartment.

"Real estate information" refers to detailed information about a property, such as its location, price, floor plan, and year of construction, obtained from real estate information websites.

An "automatic blueprint generation method" refers to a system or algorithm for automatically generating blueprints for a house based on user preference information, desired information, and real estate information.

A "proposal method" refers to a system or algorithm for proposing housing designs to users based on generated blueprints.

An "emotion recognition system" is a system or algorithm that recognizes a user's emotions and adjusts the design plans of a house based on those emotions.

A "smart device" is a device, such as a smartphone or smart glasses, that analyzes a user's facial expressions and recognizes their emotions.

"Facial expression analysis means" refers to a system or algorithm that uses a smart device to analyze a user's facial expressions and recognize their emotions.

The system for implementing this invention automatically generates a house design based on the user's preference information, desired information, and real estate information, and adjusts the design based on the user's emotions. Specific embodiments are shown below.

System Configuration

1. Hardware:

Smart devices: Devices such as smartphones and smart glasses that analyze a user's facial expressions and recognize their emotions.

Server: A server used for data processing and blueprint generation.

2. Software:

Emotion Recognition Model: A machine learning model that analyzes a user's facial expressions and recognizes their emotions (e.g., an emotion recognition model using Keras).

Blueprint Generation Algorithm: An algorithm that automatically generates house blueprints based on user preferences, desired features, and real estate information.

Data Acquisition Module: A module for acquiring necessary information from social media and real estate information websites.

Processing Flow

1. Input of user preference and desired information:

Users upload images of houses they've saved from social media like Instagram to the app and enter information such as family composition, desired address, price range, whether renovations are planned, and whether it's a house or apartment.

2. Obtaining real estate information:

The server retrieves detailed information such as property location, price, floor plan, and year of construction from real estate information websites.

3. Emotion recognition:

Using smart devices, we analyze the user's facial expressions and recognize the user's emotions using an emotion recognition model.

4. Automatic generation of blueprints:

The server automatically generates house blueprints using a blueprint generation algorithm, based on user preference information, desired property information, and recognized emotional information.

5. Proposal:

Based on the generated blueprints, we propose housing designs to the user. This includes suggesting interior designs and floor plans that align with the user's preferences.

Specific examples

For example, a user uploads an image saved from Instagram to the app and enters their desired address and price range. The app analyzes the user's facial expressions through smart glasses and recognizes their emotions. Based on this information, the server automatically generates a design plan and proposes it to the user.

Example of a prompt message

Users upload images of houses they've saved from Instagram and enter information such as family composition, desired address, price range, whether renovations are needed, and whether it's a house or apartment. The system analyzes facial expressions through smart glasses to recognize emotions. Based on this information, it automatically generates an optimal house design.

In this way, a housing design assistant system based on the user's emotions and preferences can be realized.

The flow of the specific processing in Application Example 1 will be explained using Figure 18.

Step 1:

Users upload images of houses they have saved from social media such as Instagram to their devices.

Input: Image file of a house

Output: Uploaded image data

Specific operation: The user uploads saved images of their home to the application using their smartphone or smart glasses. The application then sends the image data to the server.

Step 2:

The user enters their desired information into the terminal, including family structure, preferred address, price range, whether renovations are needed, and whether they prefer a house or apartment.

Input: Family composition, address, price range, whether renovations were done, housing type

Output: User's requested information data

Specific operation: The user enters the desired information into the application's input form and presses the submit button. The application sends the entered data to the server.

Step 3:

The server retrieves detailed information such as property location, price, floor plan, and year of construction from real estate information websites.

Input: API of a real estate information website

Output: Real estate information data

Specific operation: The server calls the API of a real estate information website to retrieve the necessary real estate information. The retrieved data is stored in an internal database.

Step 4:

The device uses a smart device to analyze the user's facial expressions and recognizes the user's emotions using an emotion recognition model.

Input: User's facial expression image

Output: User sentiment data

Specific operation: The smart device's camera captures the user's facial expressions and sends the image data to the server. The server analyzes the facial image using an emotion recognition model to recognize the user's emotions.

Step 5:

The server automatically generates house blueprints using a blueprint generation algorithm based on user preference information, desired properties, real estate information, and emotional information.

Input: User preference information, desired information, real estate information, emotional information

Output: Automatically generated house blueprints

Specific operation: The server integrates all input data and executes the blueprint generation algorithm. The algorithm generates blueprints that take into account the user's emotions regarding interior design and floor plans.

Step 6:

The server generates blueprints and then proposes housing designs to the user.

Input: Automatically generated house blueprints

Output: Suggestions for the user

Specific operation: The server presents the generated blueprints to the user and offers suggestions for interior design and floor plans that align with the user's emotions. The user can review the suggestions through the application.

(Example 2)

Next, we will describe Embodiment 2 of Embodiment Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

Conventional residential design systems could generate blueprints based on user preferences and requests, but they lacked the ability to consider user emotions when making proposals. Furthermore, they struggled to effectively utilize real estate information for residential design, making it difficult to provide optimal proposals to users. There was a growing need to improve user satisfaction by offering housing proposals that resonated with users' emotions.

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

This invention includes a server that receives user preference and desire information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that proposes a house based on the generated design; and a server that analyzes the user's emotions and adjusts the house proposal based on the analysis results. This makes it possible to propose an optimal house design that not only reflects the user's preferences and desires but also their emotions.

"User preference information" refers to information that indicates a user's personal preferences and desires regarding their home.

"Desired information" refers to information that indicates the specific conditions and requirements that the user desires for a home.

"Real estate information" refers to information that includes detailed data on land and buildings, such as their shape, location, and price.

A "design drawing" is a diagram showing the layout, structure, window orientation, and size of a house.

A "proposal" refers to a specific plan regarding the design and layout of a house, provided to the user.

"Analyzing emotions" means identifying and evaluating a user's emotional state based on their facial expressions, behavior, and input data.

"Adjusting" means modifying and optimizing the proposed content based on the analysis results.

This invention is a system that automatically generates house designs and provides optimal housing proposals, taking into account the user's preferences, desires, real estate information, and emotions. A specific embodiment of this system is described below.

System Configuration

1. Obtaining real estate information

The server retrieves land and building information from real estate information websites. In this process, the server uses specific APIs to collect data. For example, it might utilize the API of a real estate information website.

2. Data Analysis and Processing

The server analyzes the acquired land and building information, extracting data such as shape and location conditions. This is done using a data analysis library.

3. Automatic generation of residential building plans

The server automatically generates house blueprints based on the analyzed data. Specifically, it considers the shape and location of the land and proposes window orientations, sizes, and floor plans. This process utilizes the API of design software.

4. Adjustment by the Emotional Engine

The device analyzes the user's emotions using an emotion engine. This emotion engine utilizes an emotion analysis API. If the user is feeling happy, the device suggests a luxurious home. Conversely, if the user is feeling depressed, it suggests a simple and functional home.

Specific examples

Example 1: Obtaining real estate information

The server uses the API of a real estate information website to retrieve land information for a specific area. For example, it sends an API request like the following:

"Retrieve land information for San Francisco and generate blueprints that suggest luxurious homes if the user is happy, and simple homes if they are depressed."

Example 2: Data Analysis and Processing

The server analyzes the acquired data using a data analysis library. For example, it extracts information about the land's shape and location, preparing it for use in the next step.

Example 3: Automatic generation of residential building plans

The server generates design drawings using the design software's API. For example, it considers the shape and location of the land and proposes window orientations, sizes, and floor plans.

Example 4: Adjustment by an emotional engine

The device uses an emotion analysis API to analyze the user's emotions. For example, if the user is happy, it suggests a luxurious house; if they are depressed, it suggests a simple house.

In this way, it becomes possible to propose optimal house designs that not only reflect the user's preferences and desires, but also their emotions. This system can improve user satisfaction.

The flow of the specific processing in Example 2 will be explained using Figure 19.

Step 1:

The server retrieves land and building information from real estate information websites. Specifically, the server sends an API request and receives JSON data containing land information for a specified area. The input is the parameters of the API request (e.g., area, property type), and the output is the retrieved real estate information in JSON data.

Step 2:

The server converts the retrieved JSON data into a data frame using a data analysis library. Specifically, the server uses the pandas library to read the JSON data and convert it into a data frame. The input is the retrieved JSON data, and the output is real estate information in data frame format.

Step 3:

The server extracts the necessary data from the data frame. Specifically, it extracts information such as land shape and location conditions and prepares it for use in the next step. The input is real estate information in data frame format, and the output is the extracted data on land shape and location conditions.

Step 4:

The server generates house blueprints using the design software's API. Specifically, it considers the land's shape and location to suggest window orientations and sizes, floor plans, and other details. The input is extracted data on land shape and location, and the output is the generated house blueprints.

Step 5:

The device analyzes the user's emotions using an emotion analysis API. Specifically, it identifies and evaluates the emotional state from the user's facial expressions, actions, and input data. Input consists of the user's images and text data, and output is the analyzed emotion data.

Step 6:

The device adjusts housing suggestions based on the analysis results. Specifically, it suggests a luxurious house if the user is happy, and a simple house if they are depressed. The input is the analyzed emotion data and the generated house blueprints, and the output is the adjusted housing suggestion.

In this way, by clearly defining the specific actions, inputs, and outputs performed at each processing step, the program's processing flow was explained in detail.

(Application Example 2)

Next, we will describe Application Example 2 of Form 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."

Conventional housing design systems could automatically generate blueprints based on user preferences, desires, and real estate information, but they lacked the ability to adjust proposals to reflect user emotions or display blueprints using virtual reality. Therefore, it was difficult to provide housing proposals that resonated with users' emotions or offer a more realistic experience. This resulted in a challenge in increasing user satisfaction.

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

This invention includes a server that receives user preference and desire information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that makes house proposals based on the generated design; a server that receives user emotion information as input and adjusts the house proposals based on this information; and a server that displays the generated design using a virtual reality display device. This enables the adjustment of house proposals according to the user's emotions and provides a realistic experience using virtual reality.

"User preference information" refers to information that indicates a user's personal preferences and desires regarding their home.

"Desired information" refers to information that indicates the specific conditions and requirements that the user desires for a home.

"Real estate information" refers to information that includes detailed data about land and buildings.

A "house design drawing" is a detailed drawing showing the layout, structure, and facilities of a house.

"Emotional information" refers to information that indicates the user's current emotional state.

A "virtual reality display device" is a device used to visually display a virtual space.

"Automatic generation methods" refer to devices or programs that have the function of automatically creating design drawings based on input information.

A "means for adjusting suggestions" refers to a device or program that has the function of modifying and optimizing suggestion content based on user sentiment information.

"Means of display" refers to devices or programs that have the function of visually presenting the generated design drawings to the user.

The system for implementing this invention automatically generates a house design based on the user's preference information, desired information, real estate information, and emotional information, and presents it to the user using a virtual reality display device. Specific embodiments are described below.

System Configuration

The system consists of the following main components:

1. User Terminal: A device such as a smartphone or head-mounted display collects user input information and displays it in virtual reality.

2. Server: Receives user preference information, desired information, real estate information, and emotional information, and automatically generates house blueprints.

3. Emotion Engine: Analyzes user emotion information and adjusts housing recommendations accordingly.

4. Design Generation Engine: Generates house blueprints based on real estate information, user preferences, and desired information.

5. Virtual Reality Display Device: Displays the generated blueprints in a virtual reality space.

Program processing

The server processes the data using the following steps:

1. Data Acquisition: Acquire preference information, desired information, and real estate information from the user's device. Acquire user sentiment information using the sentiment engine.

2. Design Drawing Generation: Using a design generation engine, the design drawings for the house are automatically generated based on the acquired information.

3. Proposal Adjustment: Using an emotion engine, housing proposals are adjusted based on the user's emotional information.

4. Virtual Reality Display: The generated design drawings are presented to the user using a virtual reality display device.

Hardware and software to be used

Hardware: Smartphones, head-mounted displays, servers

Software: EmotionEngine, DesignGenerator, VRDisplay (Virtual Reality Display Engine)

Specific examples

For example, when a user experiences designing a house using a smartphone, a luxurious house is suggested when the user is happy, and a simple, functional house is suggested when the user is depressed. The user wears a head-mounted display and can realistically experience the design blueprints generated in a virtual reality space.

Example of a prompt message

"Please generate blueprints for luxurious homes that will be presented to users when they are happy."

"Please generate blueprints for a simple and functional home that will be suitable for users who are feeling down."

In this way, we can provide housing proposals tailored to the user's emotions and realistic experiences using virtual reality.

The flow of the specific processing in Application Example 2 will be explained using Figure 20.

Step 1:

The user terminal receives user preference and desired information as input. Users input their housing preferences and specific requests using smartphones or head-mounted displays. This information is then transmitted to the server.

Input: User preference information, desired information

Output: User preference information and desired information sent to the server.

Step 2:

The server retrieves real estate information from real estate information websites. The server accesses the sites via APIs and collects detailed data about land and buildings.

Input: URL of the real estate information website

Output: Acquired real estate information

Step 3:

The user terminal uses an emotion engine to acquire the user's emotional information. It analyzes the user's facial expressions and voice to detect their current emotional state. This emotional information is then transmitted to the server.

Input: User's facial expressions, voice

Output: Emotional information sent to the server

Step 4:

The server uses a design generation engine to automatically generate house blueprints based on acquired preference information, desired information, and real estate information. The design generation engine analyzes this information to design the optimal floor plan and structure.

Input: User preference information, desired information, real estate information

Output: Generated house blueprints

Step 5:

The server uses an emotion engine to adjust housing suggestions based on the user's emotional information. For example, if the user is happy, it suggests a luxurious house; if they are depressed, it suggests a simple and functional house.

Input: Generated house blueprints, emotional information

Output: Proposed adjusted housing design

Step 6:

The server uses a virtual reality display to present the generated blueprints to the user. The user wears a head-mounted display and can realistically experience the blueprints within the virtual reality space.

Input: Adapted housing proposal

Output: Residential blueprints displayed in a virtual reality space.

In this way, we can provide housing proposals tailored to the user's emotions and realistic experiences using virtual reality.

(Example 3)

Next, we will describe Embodiment 3 of Embodiment Example 3. 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."

Conventional residential design systems have difficulty automatically generating blueprints that reflect user preferences and desires, and generating blueprints that take real estate information into account is also time-consuming. Furthermore, they cannot generate blueprints that take into account the user's emotional state, thus failing to enhance user psychological satisfaction. There is a need for a system that solves these problems and automatically generates residential blueprints that meet the diverse needs of users.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

This invention includes a server that receives user preference and desire information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; and a server that recognizes the user's emotional state and adjusts the house design according to that emotional state. This enables the automatic generation of a house design that takes into account the user's preferences, desires, real estate information, and even their emotional state.

"User preference information" refers to information about the designs and styles that users prefer, and is obtained from sources such as social media.

"Desired information" refers to information about the user's specific requirements, such as the desired location, budget, and family structure of their home.

"Real estate information" refers to information about land and buildings obtained from real estate information websites.

"Emotional state" refers to information that indicates the user's current psychological state and is recognized by the emotion engine.

A "design drawing" is a diagram showing the structure, layout, window orientation, and size of a house.

"Automatic generation" refers to the process where a system automatically creates a blueprint based on user input, property information, and emotional state.

A "proposal" is the process of presenting a specific house design and layout to the user based on the generated blueprints.

Modes for carrying out the invention

This invention is a system that automatically generates and proposes house designs to users based on their preferences, desires, real estate information, and emotional state. A specific embodiment of this system is described below.

Hardware and software to be used

Hardware: Servers, user terminals (PCs, smartphones)

Software: Generative AI models (e.g., GPT-4), emotion recognition engine, database management system (e.g., MySQL), real estate information site API

System Operation Overview

1. Entering User Information

Users use their devices to input information such as images saved from Instagram, family composition, desired housing location (within Tokyo), and budget (50 million yen).

Specific example input: "We are a family of four, looking for a modern-looking detached house in Tokyo, with a budget of 50 million yen."

2. Data Transmission and Analysis

The terminal sends the entered information to the server.

The server analyzes the received information and inputs it as a prompt message into the generating AI model.

Specific prompt example: "The user has saved an image of a modern exterior from Instagram. They are a family of four looking for a detached house in Tokyo with a budget of 50 million yen. Please generate a design plan based on this information."

3. Generating Blueprints

The server inputs prompt messages into the generated AI model and generates the blueprint.

The server uses land and building information in Tokyo obtained from real estate information websites to suggest window orientations, sizes, floor plans, and other details.

4. Emotion Recognition and Adjustment of the Blueprint

The server uses an emotion engine to recognize the user's emotional state.

The server adjusts the blueprint based on the results of the emotion engine.

Specific prompt example: "The user is delighted to have a new job. Please generate blueprints for a luxurious house with large windows, a spacious living room, and high-quality interior materials."

5. Provision of design drawings

The server sends the generated blueprint to the user's terminal.

The terminal displays the received blueprints to the user.

Specific examples

User input: "We are a family of four, looking for a modern-looking detached house in Tokyo, with a budget of 50 million yen."

Prompt: "The user has saved an image of a modern exterior from Instagram. They are a family of four looking for a detached house in Tokyo with a budget of 50 million yen. Please generate a design plan based on this information."

Emotion recognition prompt message: "The user is happy to have a new job. Please generate blueprints for a luxurious house with large windows, a spacious living room, and high-quality interior materials."

This system enables the automatic generation of residential blueprints that take into account the user's preferences, desired information, real estate information, and even their emotional state. This allows for residential designs that meet the diverse needs of users. The specific processing flow in Example 3 will be explained using Figure 21.

Step 1:

Entering user information

The user uses their device to input information such as images saved from Instagram, family composition, desired housing location (within Tokyo), and budget (50 million yen).

Input: User preference information, desired information

Output: The entered information is saved to the device.

Specific action: The user enters information into the input form on the device and clicks the "Submit" button.

Step 2:

Data transmission and analysis

The terminal sends the entered information to the server.

Input: Information entered by the user

Output: User information sent to the server

Specific operation: The terminal uses an HTTP request to send input data to the server.

The server analyzes the received information and inputs it as a prompt message into the generating AI model.

Input: User information sent from the device

Output: Prompt text to input to the generated AI model

Specific operation: The server analyzes the data and generates a prompt message like the following: "The user has saved an image of a modern exterior from Instagram. They are a family of four looking for a detached house in Tokyo with a budget of 50 million yen. Please generate a design plan based on this information."

Step 3:

Blueprint generation

The server inputs prompt messages into the generated AI model and generates the blueprint.

Input: Prompt text

Output: Generated design drawing data

Specific operation: The server sends prompt messages to the generated AI model (e.g., GPT-4) and receives the blueprint data returned from the model.

The server uses land and building information in Tokyo obtained from real estate information websites to suggest window orientations, sizes, floor plans, and other details.

Input: Information obtained from a real estate information website.

Output: Proposed window orientation and size, floor plan, etc.

Specific operation: The server uses an API to retrieve real estate information and reflects it in the design plans.

Step 4:

Emotion recognition and blueprint adjustment

The server uses an emotion engine to recognize the user's emotional state.

Input: User sentiment data

Output: Recognized emotional state

Specific operation: The server sends user emotion data to the emotion engine and analyzes the emotional state.

The server adjusts the blueprint based on the results of the emotion engine.

Input: Recognized emotional state

Output: Adjusted blueprint

Specific operation: The server receives the result from the emotion engine and generates a prompt message like the following: "The user is happy to have a new job. Please generate blueprints for a luxurious house with large windows, a spacious living room, and high-quality interior materials."

Step 5:

Provision of design drawings

The server sends the generated blueprint to the user's terminal.

Input: Generated design data

Output: Blueprint sent to the user's terminal

Specific operation: The server sends the design data as an HTTP response to the user's terminal.

The terminal displays the received blueprints to the user.

Input: Design drawing data sent from the server

Output: Blueprint displayed to the user

Specific operation: The terminal analyzes the received design data and displays it on the user interface.

(Application Example 3)

Next, we will describe Application Example 3 of Form Example 3. 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."

Conventional residential design systems struggled to automatically generate blueprints that reflected user preferences and desires, and their proposals based on real estate information were limited. Furthermore, they lacked the means to generate blueprints that considered the user's emotional state, and to visually confirm the generated blueprints as 3D models. This made it difficult to increase user satisfaction.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

This invention includes a server that receives user preference and desire information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that makes house proposals based on the generated design; a server that receives the user's emotional state as input and automatically generates a house design based on this; and a server that displays the generated design as a 3D model. This enables the automatic generation of house design plans that reflect the user's preferences, desires, and emotional state, as well as visual confirmation.

"User preference information" refers to information about the designs and styles that users prefer, and is obtained from social media and image storage services.

"Desired information" refers to information about the conditions and specifications of a home that the user desires, including family structure, budget, and location.

"Real estate information" refers to information about land and buildings obtained from real estate information websites, including details such as price, area, and location.

"Emotional state" refers to information that indicates the user's current emotions and mood, including feelings such as joy and sadness.

A "design plan" is a drawing that shows the structure, layout, and design of a house, and is automatically generated based on the user's preferences, desires, real estate information, and emotional state.

A "3D model" is a three-dimensional visual model created based on design drawings, allowing users to view the exterior and interior of a house in three dimensions.

"Automatic generation" refers to the process by which a system automatically creates design drawings and 3D models based on user input and acquired data.

A "proposal" is the act of showing the user specific features of a house, such as the orientation and size of windows and the floor plan, based on the generated design drawings.

The system for implementing this invention automatically generates a house design based on the user's preferences, desires, real estate information, and emotional state, and displays it as a 3D model. A specific embodiment is shown below.

System Configuration

The system consists of user terminals, a server, and a display device. User terminals include smartphones and head-mounted displays (HMDs). The server performs data processing and automatically generates design drawings using a generated AI model. The display device is used to display the generated design drawings as 3D models.

Hardware and software to be used

Hardware: Smartphones, head-mounted displays (HMDs), servers

Software: Python, OpenAI API, 3D modeling libraries

Data processing and data calculation

1. Receiving user input:

The user terminal receives user preference information (e.g., images obtained from social media), desired information (e.g., family structure, budget, location), and emotional state (e.g., joy or sadness) as input.

2. Obtaining real estate information:

The server retrieves information about land and buildings in Tokyo from real estate information websites. This includes details such as price, area, and location.

3. Automatic generation of blueprints:

The server uses a generative AI model (e.g., OpenAI's GPT-3) to automatically generate house blueprints based on user input and acquired real estate information. An example of a prompt message for the generative AI model is as follows:

User input: {'image': 'modern_house_image.jpg', 'family_size': 4, 'location': 'Tokyo', 'budget': 5000, 'emotion': 'happy'}

Real estate data: {'Land information': '...', 'Building information': '...'}

Please generate the blueprint.

4. Creating and displaying 3D models:

The server creates a 3D model using a 3D modeling library based on the generated blueprint. The created 3D model is then displayed on the user's device (smartphone or HMD).

Specific examples

If a user provides an image of a modern exterior saved from Instagram, enters that they are a family of four looking for a detached house in Tokyo with a budget of 50 million yen, the server will automatically generate a blueprint for a modern-looking detached house in Tokyo based on this information. Furthermore, when a user is feeling happy about getting a new job, the emotion engine recognizes this happiness and automatically generates a blueprint for a luxurious home. This blueprint will feature large windows, a spacious living room, and high-quality interior materials.

In this way, it becomes possible to automatically generate and visually confirm house designs that reflect the user's preferences, desires, and emotional state.

The flow of the specific processing in Application Example 3 will be explained using Figure 22.

Step 1:

Users input preference information (e.g., images from social media), desired information (e.g., family structure, budget, location), and emotional state (e.g., joy, sadness) using a smartphone or head-mounted display (HMD).

Input: User preference information, desired information, emotional state

Output: User input data

Specific action: The user enters information into the application's input form and presses the submit button.

Step 2:

The server retrieves information about land and buildings in Tokyo from real estate information websites.

Input: User's desired information (e.g., location)

Output: Real estate information data

Specific operation: The server uses an API to send requests to real estate information websites and retrieve the necessary data.

Step 3:

The server uses an AI model (e.g., OpenAI's GPT-3) to automatically generate house blueprints based on user input and acquired real estate information.

Input: User input data, real estate information data

Output: House design drawing data

Specific operation: The server sends prompt messages to the generated AI model to generate the blueprint. An example of a prompt message is as follows:

User input: {'image': 'modern_house_image.jpg', 'family_size': 4, 'location': 'Tokyo', 'budget': 5000, 'emotion': 'happy'}

Real estate data: {'Land information': '...', 'Building information': '...'}

Please generate the blueprints.

Step 4:

The server creates a 3D model using a 3D modeling library based on the generated blueprint.

Input: House design drawing data

Output: 3D model data

Specific operation: The server inputs design data into the 3D modeling library and generates a 3D model.

Step 5:

The server sends the generated 3D model to the user's terminal, and the user's terminal displays it.

Input: 3D model data

Output: 3D model displayed on the user's terminal

Specific operation: The server sends 3D model data to the user's terminal, and the user's terminal displays it on a display device (smartphone or HMD).

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

The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is generative AI such as ChatGPT (Internet Search <URL: https://openai.com/blog/chatgpt>). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives prompts containing instructions, as well as inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions provided by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization.

Another example of generative AI is Gemini (Internet search engine <URL: https://gemini.google.com/?hl=ja>).

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

[Fourth Embodiment]

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

As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

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

The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and movements of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

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

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

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

In robot 414, the processor 46 performs the reception and output processing. The storage 50 stores the reception and output program 60. The processor 46 reads the reception and output program 60 from the storage 50 and executes the read program 60 on the RAM 48. The reception and output processing is realized by the processor 46 acting as a control unit 46A according to the reception and output program 60 executed on the RAM 48.

Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be explained.

"Example of Morphology 1"

In one embodiment of the system, a user saves images of their preferred homes from social media such as Instagram and inputs the image information into this system. The user also inputs their family structure, desired address, price range, whether or not they want renovations, and whether they prefer a house or apartment. Based on this preference and desired information, the system automatically generates a blueprint for the house.

"Example of Morphology 2"

Furthermore, this system acquires land and building information from real estate information websites and automatically generates house blueprints based on this information. Specifically, it proposes window orientations, sizes, and floor plans, taking into account the shape of the land and building, location conditions, etc.

"Example of Morphology 3"

As a concrete example, if a user inputs an image of a modern exterior saved from Instagram, along with information such as a family of four, a desire for a detached house in Tokyo, and a budget of 50 million yen, this system will automatically generate a blueprint for a detached house in Tokyo with a modern exterior based on this information. Furthermore, based on land and building information in Tokyo obtained from real estate information websites, it will suggest window orientations and sizes, floor plans, and other details.

The following describes the processing flow for each example form.

"Example of Morphology 1"

Step 1: The user saves images of their preferred homes from social media such as Instagram.

Step 2: The user inputs the saved image information into this system.

Step 3: The user enters their family structure, desired address, price range, whether or not they want renovations, and other desired information such as a house or apartment into the system.

Step 4: This system automatically generates house blueprints based on these preference and desired information.

"Example of Morphology 2"

Step 1: This system retrieves land and building information from real estate information websites.

Step 2: This system automatically generates house plans based on the acquired land and building information. Specifically, it proposes window orientations, sizes, and floor plans, taking into account the shape of the land and building, location conditions, etc.

"Example of Morphology 3"

Step 1: The user enters a modern exterior image saved from Instagram, along with information such as being a family of four, wanting a detached house in Tokyo, and having a budget of 50 million yen.

Step 2: Based on this information, the system automatically generates a blueprint for a modern-looking detached house in Tokyo.

Step 3: This system uses land and building information in Tokyo obtained from real estate information websites to suggest window orientations, sizes, floor plans, etc.

(Example 1)

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

Conventional residential design systems struggled to automatically generate blueprints that reflected users' preferences and desires. Furthermore, a lack of effective means to utilize image information obtained from social media meant that specific user preferences could not be reflected in the design. Additionally, when proposing housing designs based on the generated blueprints, it was difficult to create proposals that truly met the user's needs.

The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes means for receiving user preference information and desired information as input and automatically generating a house design based on this information; means for receiving image information saved by the user as input and analyzing this information to identify the user's preferences; means for generating and inputting prompt statements to the generation AI model; and means for making house proposals based on the generated design. This enables the automatic generation of house design plans that reflect the user's specific preferences and desires, and allows for the proposal of houses that match the user's wishes.

"User preference information" refers to information about the housing designs and styles that users prefer, and includes images and text data obtained from social media.

"Desired information" refers to the specific conditions and requirements of the user's desired housing, including family structure, desired address, price range, whether renovations are needed, and whether it's a detached house or an apartment.

"Image information" refers to image data of houses that users obtain from social media or other sources and upload to the system.

A "generative AI model" is an artificial intelligence model that automatically generates house blueprints based on user preferences and desired information, utilizing natural language processing and image analysis technologies.

A "prompt message" is an instruction given to the AI generation model, and it is generated based on the user's preferences and desired information.

A "house design plan" refers to a design drawing of a house generated based on the user's preferences and desired information, including specific layouts and designs.

A "housing proposal" refers to a housing design proposal made to the user based on the generated housing blueprints, and includes content that matches the user's wishes.

This invention is based on the premise that users save images of their preferred homes from social media such as Instagram and input that image information into the system. Users also input their family structure, desired address, price range, whether or not they want renovations, and whether they want a house or an apartment. Based on this information, the system automatically generates a blueprint for the house.

Hardware and software to be used

The server receives image information and request information entered by the user and stores it in a database. Specifically, it uses the following hardware and software:

Server: High-performance cloud server (e.g., Amazon Web Services, Google Cloud Platform)

Database: Relational database (e.g., MySQL, PostgreSQL)

Generative AI models: AI models using natural language processing and image analysis techniques (e.g., OpenAI's GPT-4, DALL-E)

Image analysis software: Computer vision technology (e.g., TensorFlow, PyTorch)

Data processing and data calculation

The server receives image files uploaded by the user and requested information, and stores this data in a database. Based on the stored data, it generates prompt messages for a generative AI model and inputs them. The generative AI model then generates a house design based on these prompt messages and provides it to the user via the server.

Specific examples

As a concrete example, suppose a user saves an image of a modern-designed house from Instagram and enters the following desired information:

Family composition: 4 people (2 adults, 2 children)

Desired address: Tokyo

Price range: Under 50 million yen

Renovation status: Yes

House or apartment: House

Based on this information, the server inputs the following prompt message into the AI model:

Based on images of modern-designed homes saved by the user from Instagram, please generate a blueprint for a home that matches the following requirements.

Family composition: 4 people (2 adults, 2 children)

Desired address: Tokyo

Price range: Under 50 million yen

Renovation status: Yes

House or apartment: House

The AI model generates a house design tailored to the user's preferences based on this prompt and provides it to the user via the server. The user can review the received design on their device and, if necessary, modify their preferences to generate a new design.

In this way, it becomes possible to automatically generate house designs that reflect the user's specific preferences and desires, and to propose houses that match the user's wishes.

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

Step 1:

Users save images of their favorite houses from social media such as Instagram. Users use their smartphones or computers to find and save images of their favorite houses from social media such as Instagram. The input is image data obtained from social media, and the output is an image file saved on the user's device.

Step 2:

The user uploads saved images to the system. The user accesses the system's website or application and uploads saved images of their home. Specifically, they click the upload button and select the saved image file. The input is the image file saved on the user's device, and the output is the image data sent to the server.

Step 3:

The user enters their desired information, such as family structure, preferred address, price range, whether renovations are needed, and whether they prefer a house or apartment. The user enters their desired information into the system's input form, including family structure (e.g., 2 adults, 2 children), preferred address (e.g., Tokyo), price range (e.g., under 50 million yen), whether renovations are needed (e.g., yes), and whether they prefer a house or apartment (e.g., house). The input is the desired information entered by the user, and the output is the desired information sent to the server.

Step 4:

The server receives image and request information from the user and stores it in the database. The server receives uploaded image files and input request information from the user and stores this data in the database. When saving, the data is managed in association with the user ID. The input is the image and request information sent by the user, and the output is the data stored in the database.

Step 5:

The server generates prompt messages for the AI model based on the stored data and inputs them. The server generates prompt messages for the AI model based on image information and desired information stored in the database. For example, it generates prompt messages like the following:

Based on images of modern-designed homes saved by the user from Instagram, please generate a blueprint for a home that matches the following requirements.

Family composition: 4 people (2 adults, 2 children)

Desired address: Tokyo

Price range: Under 50 million yen

Renovation status: Yes

House or apartment: House

The input consists of image information and desired information stored in a database, while the output is the prompt text input to the generating AI model.

Step 6:

The generative AI model generates a house design based on the prompt text. The generative AI model (e.g., OpenAI's GPT-4 or DALL-E) generates the house design based on the prompt text sent from the server. The generative AI model utilizes image analysis and natural language processing techniques to create a design that meets the user's preferences. The input is the prompt text, and the output is the generated house design.

Step 7:

The server sends the generated blueprint to the user's terminal. The server sends the blueprint received from the generated AI model to the user's terminal. The user can download or view the blueprint through the system's website or application. The input is the generated house blueprint, and the output is the blueprint sent to the user's terminal.

Step 8:

The user reviews the blueprint. The user reviews the blueprint received on their device. They can check if the blueprint meets their requirements and, if necessary, modify the required information to generate a new blueprint. The input is the blueprint sent to the user's device, and the output is the user's review result.

(Application Example 1)

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

Traditional home design systems struggled to automatically generate blueprints that reflected user preferences and desires. In particular, if a user had a specific image in mind, specialized knowledge was required to translate that image into a blueprint. Furthermore, real-time blueprint generation and display were difficult when users viewed home designs in physical showrooms. This led to decreased user satisfaction and delays in home purchase decisions.

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

This invention includes a server that receives user preference and desired information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that proposes houses based on the generated design; a server that analyzes house images taken by the user and extracts their features; a server that generates prompt text based on the extracted features and the user's desired information and generates a house design using a generation AI model; and a server that presents the generated design to the user via a display device. This enables the real-time generation of house design plans that reflect the user's specific image, allowing for on-site verification at a physical location.

"User preference information" refers to information that indicates a user's personal preferences and design tastes regarding housing.

"Desired information" refers to information that indicates the user's specific requests and conditions regarding housing (family structure, desired address, price range, whether renovations are needed, whether it's a detached house or an apartment, etc.).

"Real estate information" refers to information that provides detailed data about a real estate property (location, price, floor plan, year built, etc.).

A "house design drawing" is a diagram that specifically shows the structure, layout, and design of a house.

"Automatic generation methods" refer to devices or programs that have the function of automatically creating design drawings using algorithms or AI models based on input information.

"Means of making proposals" refers to devices or programs that have the function of making housing proposals to users based on the generated blueprints.

"Residential images" refer to photographs or image data of houses taken by users.

"Means of analysis" refer to devices or programs that have the function of analyzing input image data and extracting its features.

A "means for extracting features" refers to a device or program that has the function of extracting important features from data obtained through image analysis.

A "prompt" is a text-based input message used to give instructions to a generative AI model.

A "generative AI model" is an artificial intelligence model that generates blueprints and other data based on input prompts.

A "display device" is a device (such as a display or VR headset) used to visually present generated design drawings to the user.

The system for carrying out this invention includes means for receiving user preference information and desired information as input and automatically generating a house design based on this information; means for receiving real estate information as input and automatically generating a house design based on this information; means for making house proposals based on the generated design; means for analyzing house images taken by the user and extracting their features; means for generating prompt sentences based on the extracted features and the user's desired information, and generating a house design using a generation AI model; and means for presenting the generated design to the user through a display device.

Hardware and software to be used

Hardware

Smartphone or tablet (iOS or Android): Used by the user to take pictures of the house and enter information.

Smart glasses (e.g., Google Glass): Used to analyze images taken by the user in real time.

Large-screen display or VR headset (e.g., Oculus Rift): Used to present the generated blueprints to the user.

Software

Image recognition libraries (e.g., OpenCV): Used to analyze residential images taken by the user and extract their features.

Generative AI models (e.g., GPT-4, DALL-E): Used to generate house blueprints based on prompt messages.

Database (e.g., Firebase): Used to store and manage user preference information, desired information, and real estate information.

Frontend frameworks (e.g., React Native): Used to build user interfaces.

Processing Flow

1. User Input

Users use their smartphones or tablets to take photos of their preferred homes and upload them to the application. They also input information such as family composition, desired address, price range, whether renovations are needed, and whether they prefer a house or apartment.

2. Image Analysis

The server uses an image recognition library (OpenCV) to analyze uploaded residential images and extract their features.

3. Generating the prompt message

The server generates prompt messages for the generative AI model (GPT-4) based on the extracted features and user preferences.

Example of a prompt message

Based on the images and desired information provided by the user, please generate a house design that meets the following conditions:

Family composition: 4 people

Desired address: Tokyo

Price range: Under 50 million yen

Renovation: Yes

Detached house

Modern design

4. Generating Blueprints

The server uses a generative AI model (DALL-E) to generate house blueprints based on the prompt text.

5. Displaying Results

The generated blueprints are presented to the user via a large screen display or VR headset. Users can then view their ideal home design in real time at a physical store.

Specific examples

For example, a user uploads an image of a modern living room taken with their smartphone and enters their desired information as follows: "Family size: 4 people, Desired address: Tokyo, Price range: under 50 million yen, Renovation: Yes, Detached house." Based on this information, the server generates a blueprint for a modern detached house in Tokyo, suitable for a family of four, within a 50 million yen budget, and presents it to the user.

In this way, users can view their ideal home design in real time at a physical store.

The flow of the specific processing in Application Example 1 will be explained using Figure 12.

Step 1:

Users use their smartphones or tablets to take photos of their preferred homes and upload them to the application. They also input information such as family composition, desired address, price range, whether renovations are needed, and whether they prefer a house or apartment.

Input: Housing images, family composition, desired address, price range, whether renovation is needed, desired type of property (house or apartment), etc.

Output: Uploaded house images and desired information

Step 2:

The server uses an image recognition library (OpenCV) to analyze uploaded residential images and extract their features.

Input: Uploaded house image

Output: Extracted image features

Specific operation: Using an image recognition library, perform edge detection and color analysis on residential images, and extract features.

Step 3:

The server generates prompt messages for the generative AI model (GPT-4) based on the extracted features and user preferences.

Input: Extracted image features, desired information

Output: Generated prompt message

Specific operation: Combine the extracted features and desired information to create prompt sentences for input into the generative AI model.

Step 4:

The server uses a generative AI model (DALL-E) to generate house blueprints based on the prompt text.

Input: Generated prompt text

Output: Generated house blueprints

Specific operation: The system takes prompt text as input to a generation AI model and generates a house design.

Step 5:

The server presents the generated blueprints to the user via a large screen display or VR headset.

Input: Generated house blueprints

Output: Design drawings presented to the user

Specific operation: The generated design drawings are sent to a display or VR headset, allowing the user to visually confirm them.

In this way, users can view their ideal home design in real time at a physical store.

(Example 2)

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

Traditional residential design systems had the problem of requiring a great deal of time and effort to reflect user preferences and desired information. Furthermore, efficiently acquiring real estate information and automatically generating optimal residential designs based on that information was difficult. As a result, it was difficult to quickly provide residential designs that satisfied users.

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

In this invention, the server includes means for acquiring land and building information from a real estate information provision site, means for storing the acquired information in a data frame and processing the data, means for generating prompt sentences to be input into a generation AI model based on the processed data, means for automatically generating house blueprints using the generation AI model, and means for making house proposals based on the generated blueprints. This makes it possible to quickly and efficiently provide house blueprints that reflect the user's preferences and desired information.

A "real estate information website" is a website that provides information about land and buildings.

"Land and building information" refers to detailed data such as the shape of the land, the location of the building, the orientation and size of the windows, and the floor plan.

A "data frame" is a two-dimensional data structure consisting of rows and columns, used for storing and processing data.

"Data processing" refers to the process of transforming acquired data into a format that is easy to analyze and use, and includes processes such as imputing missing values and normalizing data.

A "generative AI model" is a model that uses artificial intelligence to perform a specific task; in this case, it is used to automatically generate blueprints for a house.

A "prompt message" is an instruction message used to input data into a generation AI model, and it is generated based on information about the land and buildings.

A "house design drawing" is a diagram showing the layout and structure of a house, and forms the basis of the building plan.

A "housing proposal" refers to suggestions regarding the design and layout of a house, made to the user based on the generated blueprints.

Modes for carrying out the invention

This invention relates to a system that acquires land and building information from real estate information websites and automatically generates house blueprints based on this information. A specific embodiment of this system is described below.

1. Program Generation

The server generates a program to retrieve land and building information from real estate information websites. This program is developed using Python and performs web scraping using libraries such as BeautifulSoup and Selenium.

2. Data Acquisition and Processing

The server executes the generated program and retrieves land and building information from real estate information websites. Specifically, it collects data such as land shape, building location, window orientation and size, and floor plan. The collected data is stored in a dataframe using the Pandas library, and necessary data processing is performed. For example, missing value imputation and data normalization are performed.

3. Generating prompt sentences for the generative AI model

The server generates prompt statements to input into the AI model based on the processed data. These prompt statements include information such as the shape of the land and building, location conditions, window orientation, size, and floor plan.

Example of a prompt message:

Based on land information in Shibuya Ward, Tokyo, please generate a house design plan considering the following conditions.

Land shape: Rectangle

Building location: South-facing

Window orientation: South-facing

Window size: Large

Floor plan: 3LDK

4. Generating House Design Plans

The server inputs the generated prompt text into a generation AI model (for example, a generation AI model) and generates a blueprint for a house. The generation AI model then proposes the optimal blueprint based on the prompt text.

5. Displaying Results

The server displays the generated house design plans on the user's terminal. The user can review the proposed design plans and request revisions as needed.

Specific examples

If a user requests to generate a house design based on land information in Shibuya Ward, Tokyo, the server will input the following prompt into the AI model:

Example of a prompt message:

Based on land information in Shibuya Ward, Tokyo, please generate a house design plan considering the following conditions.

Land shape: Rectangle

Building location: South-facing

Window orientation: South-facing

Window size: Large

Floor plan: 3LDK

The AI model generates a house design based on this prompt. The proposed design is displayed on the user's device for review.

In this way, the present invention makes it possible to quickly and efficiently provide residential design plans that reflect the user's preferences and desired information.

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

Program execution flow

Step 1: Obtaining real estate information

The server retrieves land and building information from real estate information websites. Specifically, it uses Python and libraries such as BeautifulSoup and Selenium to perform web scraping.

Input: URL of the real estate information website

Output: Land and building information (e.g., land shape, building location, window orientation, size, floor plan)

Specific actions:

The server uses Selenium to automatically control the browser and access the specified URL. Next, it uses BeautifulSoup to parse the HTML and extract the necessary information.

Step 2: Data Processing

The server stores the retrieved real estate information in a dataframe using the Pandas library and performs necessary data processing. For example, it might impute missing values or normalize the data.

Input: Land and building information

Output: Processed data frame

Specific actions:

The server converts the retrieved data into a DataFrame using the Pandas `read_html` function. Next, it imputes missing values using the `fillna` function and normalizes the data using the `normalize` function.

Step 3: Generating prompts for the generative AI model

The server generates prompt statements to input into the AI model based on the processed data. These prompt statements include information such as the shape of the land and building, location conditions, window orientation, size, and floor plan.

Input: Processed data frame

Output: Prompt message

Specific actions:

The server extracts the necessary information from the data frame and generates a prompt message like the following:

Based on land information in Shibuya Ward, Tokyo, please generate a house design plan considering the following conditions.

Land shape: Rectangle

Building location: South-facing

Window orientation: South-facing

Window size: Large

Floor plan: 3LDK

Step 4: Generating the House Design Plan

The server inputs the generated prompt text into a generation AI model (for example, a generation AI model) and generates a blueprint for a house. The generation AI model then proposes the optimal blueprint based on the prompt text.

Input: Prompt text

Output: House design plans

Specific actions:

The server sends prompts to the API for generating AI models and receives proposed blueprints in return. For example, it might use the `openai.Completion.create` function to send prompts.

Step 5: Displaying Results

The server displays the generated house design plans on the user's terminal. The user can review the proposed design plans and request revisions as needed.

Input: House blueprints

Output: Blueprint displayed on the user's terminal

Specific actions:

The server formats the generated blueprint in HTML format and sends it to the user's terminal. The user then views the blueprint through their browser.

(Application Example 2)

Next, we will describe Application Example 2 of Form 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."

Conventional residential design systems have difficulty reflecting user preferences and desires, and their automatic generation of design plans based on real estate information has been limited. Furthermore, interior design for physical stores requires specialized knowledge and is not easily accessible to the average user. Therefore, there is a need for a system that allows users to easily design homes and stores that meet their specific needs.

In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving user preference information and desired information as input and automatically generating house blueprints based on this information; means for receiving real estate information as input and automatically generating house blueprints based on this information; means for making house proposals based on the generated blueprints; means for automatically generating interior designs for physical stores based on information obtained from real estate information provision sites; means for creating prompt statements for generating blueprints using a generation AI model; and means for generating actual blueprints using the generation AI model. This makes it easy for users to design houses and stores that meet their preferences.

"User preference information" refers to information about the style, functions, and design that users desire in the design of their homes or shops.

"Desired information" refers to information about the specific conditions and requirements that users desire in the design of their homes or shops.

"Real estate information" refers to information about the shape, location, and area of land and buildings.

A "design drawing" is a diagram showing the layout of a house or shop, including the direction and size of windows, and the arrangement of interior furnishings.

A "real estate information website" is a website that provides information about land and buildings on the internet.

"Interior design" refers to planning the layout and design of the interior spaces of shops and residences.

A "generative AI model" is an algorithm or program that uses artificial intelligence to automatically generate blueprints and designs.

A "prompt statement" is an instruction text used to input data into a generative AI model, containing conditions and requirements for generating blueprints or designs.

The system for carrying out this invention includes a server, a user terminal, and a generative AI model. Specific embodiments of this system are described below.

First, the user terminal receives user preference and desired information as input. This information concerns the style, functions, and design the user desires for the design of their home or store. The user terminal then transmits this information to the server.

Next, the server retrieves real estate information from a real estate information website. This real estate information includes details such as the shape of the land and building, location conditions, and area. Based on the retrieved real estate information, the server automatically generates blueprints for houses and shops.

The server uses a generative AI model to create prompt statements for generating blueprints. These prompt statements include user preference information, desired information, and real estate information. For example, prompt statements like the following are generated:

"Land shape: Rectangle, Location: Urban area, Window orientation: South-facing, Lighting position: Center of ceiling"

Based on the generated prompt text, the generative AI model creates the actual design drawing. For example, OpenAI's GPT-3 is used as the generative AI model. This model receives the prompt text as input and outputs the design drawing.

The generated blueprints are sent from the server to the user's terminal and presented to the user. The user can then review the design of the house or shop based on the presented blueprints and make modifications as needed.

This system allows users to easily design houses and shops that meet their specific needs. Specifically, users can easily generate and review design plans using their smartphones or computers. This enables them to realize their desired designs even without specialized knowledge.

The above describes a specific embodiment for carrying out this invention.

The flow of the specific processing in Application Example 2 will be explained using Figure 14.

Step 1:

The user terminal receives user preference and desired information as input. Users use smartphones or computers to input information regarding their desired style, function, and design for residential or commercial buildings. The entered information is stored on the user terminal.

Step 2:

The user terminal sends the entered preference and desired information to the server. The transmitted information is stored in the server's database.

Step 3:

The server retrieves real estate information from real estate information websites. The server uses an API to send requests to these websites, obtaining information such as the shape, location, and area of the land and buildings. The retrieved real estate information is stored on the server.

Step 4:

The server generates prompt statements to create a blueprint based on the acquired real estate information and the user's preferences and requests. These prompt statements include information such as the land shape, location, window orientation, and lighting position. For example, a prompt statement like "Land shape: Rectangle, Location: Urban area, Window orientation: South-facing, Lighting position: Center of ceiling" might be generated.

Step 5:

The server uses a generative AI model to generate a blueprint using prompt text as input. For example, OpenAI's GPT-3 is used as the generative AI model. The generative AI model, upon receiving prompt text as input, outputs a blueprint. The generated blueprint is saved on the server.

Step 6:

The server sends the generated blueprint to the user's terminal. The user's terminal displays the received blueprint to the user. The user can then view the blueprint on their smartphone or computer screen.

Step 7:

Users can review the provided blueprints for houses and shops, and make modifications as needed. The modified information is then sent back to the server, and the blueprints are regenerated. This allows users to realize a design that meets their specific needs.

(Example 3)

Next, we will describe Embodiment 3 of Example 3. In the following description, the data processing device 12 will be referred to as the "server," and the robot 414 will be referred to as the "terminal."

Conventional residential design systems have difficulty automatically generating blueprints that reflect user preferences and desires, and have been unable to provide optimal design proposals based on real estate information. Therefore, there is a need to efficiently provide residential designs that meet user needs.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

This invention includes a server that receives user preference and desire information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that makes house design proposals based on the generated design; a server that sends prompt text and images to a generation AI model to generate a design; a server that acquires land and building information from a real estate information website; a server that makes optimal design proposals based on the acquired real estate information; and a server that displays the generated design and proposal content to the user. This makes it possible to automatically generate house design plans that reflect the user's preferences and desires, and to make optimal design proposals based on real estate information.

"User preference information" refers to information indicating a user's personal preferences regarding design, style, and functionality in residential design.

"Desired information" refers to specific conditions and requirements that users have in mind when designing a house, such as family structure, budget, and location.

"Real estate information" refers to detailed data about land and buildings, such as price, area, and surrounding environment.

A "generative AI model" is an algorithm or system that uses artificial intelligence to generate design plans based on user input.

A "prompt" is a text-based input message used to give instructions to a generative AI model.

A "design drawing" is a diagram showing the structure, layout, and design of a house.

A "real estate information website" is an online platform that provides information about land and buildings.

"Optimal design proposal" refers to proposing the most suitable residential design based on the user's preferences, desires, and real estate information.

"Means of display" refers to methods or devices for visually providing users with generated design drawings or proposed content.

This invention is a system that automatically generates house blueprints based on user preference and desired information, and provides optimal design suggestions based on real estate information. A specific embodiment of this system is described below.

System Configuration

This system consists of three main elements: a server, a terminal, and a user. The server receives input information from the user, generates design plans using a generative AI model, and provides optimal design suggestions based on real estate information. The terminal provides an interface for the user to input information and check the results. The user accesses the system through the terminal and inputs the necessary information.

Hardware and software to be used

Server: A server with high-performance computing capabilities is required. Specifically, a cloud-based server (e.g., Amazon Web Services or Google Cloud Platform) should be used.

Terminal: A device used by a user to input information. This includes internet-connected devices such as personal computers, tablets, and smartphones.

Generative AI Model: We use advanced artificial intelligence models such as OpenAI's GPT-4 and DALL-E as generative AI models.

Real estate information websites: Use online platforms (e.g., SUUMO or At Home) to obtain real estate information.

Data processing and calculations

1. User Information Input: The user accesses a form on a web browser using their device and uploads a modern-looking image saved from Instagram. Next, they enter their family composition (four people), desired location (Tokyo), and budget (50 million yen).

2. Prompt Generation: The server receives the information entered by the user and generates prompts for input into the generated AI model. Specifically, it generates prompts like the following:

"Based on images of modern exteriors saved by users on Instagram, please automatically generate blueprints for a detached house in Tokyo suitable for a family of four, with a budget of under 50 million yen."

3. Sending to the Generating AI Model: The server sends the generated prompt text and the user-uploaded image to the Generating AI model. This transmission is done via API.

4. Blueprint Generation: The generation AI model generates a blueprint for a modern-looking detached house in Tokyo based on the received prompt text and image. The blueprint includes details such as window orientation and size, and floor plan. The generated blueprint is returned to the server.

5. Acquisition of Real Estate Information: The server uses the API of a real estate information provider website to acquire information on land and buildings in Tokyo. The acquired information includes land prices, building area, and surrounding environment.

6. Optimal Design Proposal: Based on the acquired real estate information, the server proposes optimal window orientations, sizes, and layouts for the generated design drawings. For example, by proposing a design drawing with many south-facing windows, it provides a living environment with plenty of sunlight.

7. Displaying Results: The server displays the final design and proposal to the user. The user can review this information in a web browser and make modifications or regenerations as needed. An interactive UI is used for display to ensure user-friendliness.

Specific examples

If the user enters the following information:

Images of modern exteriors saved from Instagram

family of 4

I'm looking for a detached house in Tokyo.

The budget is 50 million yen.

The server generates the following prompt:

"Based on images of modern exteriors saved by users on Instagram, please automatically generate blueprints for a detached house in Tokyo suitable for a family of four, with a budget of under 50 million yen."

The AI model generates a design drawing based on this prompt text and image, and the server proposes the optimal window orientation, size, and floor plan based on data obtained from a real estate information website. The specific processing flow in Example 3 is explained using Figure 15.

Step 1:

The user enters information.

The user accesses a form on a web browser using their device and uploads a modern-looking image saved from Instagram. Next, they enter their family size (four people), desired location (Tokyo), and budget (50 million yen). This information is then sent to the server.

Input: Images saved from Instagram, family composition, desired region, budget

Output: User preference information and desired information

Step 2:

The server generates the prompt message.

The server receives the information entered by the user and generates prompt messages for input into the generated AI model. Specifically, it generates prompt messages like the following:

"Based on images of modern exteriors saved by users on Instagram, please automatically generate blueprints for a detached house in Tokyo suitable for a family of four, with a budget of under 50 million yen."

Input: User preference information and desired information

Output: Prompt text to send to the generated AI model

Step 3:

The server sends prompt text and images to the generated AI model.

The server sends the generated prompt text and the user-uploaded image to the AI model. This transmission is done via API.

Input: Prompt text, user-uploaded image

Output: Input data for the generated AI model

Step 4:

The generative AI model generates the blueprint.

The AI model generates a blueprint for a modern-looking detached house in Tokyo based on the received prompt text and image. The blueprint includes details such as window orientation and size, and floor plan. The generated blueprint is then returned to the server.

Input: Prompt text, user-uploaded image

Output: Generated blueprint

Step 5:

The server retrieves real estate information.

The server uses the API of a real estate information website to retrieve information on land and buildings in Tokyo. The retrieved information includes land prices, building area, and surrounding environment.

Input: API of a real estate information website

Output: Acquired real estate information

Step 6:

The server will propose the optimal design.

The server uses the acquired real estate information to suggest optimal window orientations, sizes, and layouts for the generated blueprints. For example, by suggesting blueprints with many south-facing windows, it provides a living environment with plenty of sunlight.

Input: Generated blueprints, acquired property information

Output: Optimal design proposal

Step 7:

The server displays the results to the user.

The server displays the final design blueprint and proposal to the user. Users can view this information in a web browser and modify or regenerate it as needed. An interactive UI is used for display to ensure user-friendliness.

Input: Final design drawings, proposed content

Output: Results displayed to the user

(Application Example 3)

Next, we will explain Application Example 3 of Configuration Example 3. In the following explanation, the data processing device 12 will be referred to as a "server," and the robot 414 will be referred to as a "terminal."

Conventional residential design systems struggled to automatically generate blueprints that reflected user preferences and desires, and were unable to effectively utilize real estate information to propose blueprints. Furthermore, they could not leverage images saved by users or information from social media, making it difficult to provide blueprints that met specific user needs. This resulted in a challenge in increasing user satisfaction.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

This invention includes a server that receives user preference and desired information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that proposes a house design based on the generated design; a server that generates prompt text based on images saved by the user and generates a design using a generation AI model; and a server that proposes window orientation, size, floor plan, etc., based on land and building information obtained from a real estate information website. This enables the automatic generation of design plans tailored to the user's specific needs and the effective utilization of real estate information.

"User preference information" refers to information about the design and style that users desire, and is obtained from sources such as social media and saved images.

"Desired information" refers to information about the conditions and requirements of a home that the user desires, including specific preferences such as family structure, budget, and location.

A "house design plan" is a drawing that shows the exterior, floor plan, window orientation, size, etc., of a house, and is automatically generated based on the user's preferences and requests.

"Real estate information" refers to information about land and buildings, and is obtained from real estate information websites and other sources.

A "generative AI model" is a model that uses artificial intelligence to generate design plans or proposals from specific input information.

A "prompt message" is an instruction message used to input into a generative AI model, and it is generated based on the user's preferences and desired information.

A "real estate information website" is a website that provides information about land and buildings, and users can access and obtain this information.

"Window orientation, size, and floor plan" are important elements in residential design and are proposed based on the user's wishes and real estate information.

The system for implementing this invention includes means for receiving user preference information and desired information as input and automatically generating house blueprints based on this information; means for receiving real estate information as input and automatically generating house blueprints based on this information; means for making house proposals based on the generated blueprints; means for generating prompt text based on images saved by the user and generating blueprints using a generation AI model; and means for proposing window orientation, size, floor plan, etc., based on land and building information obtained from real estate information provision sites.

System Program

The server receives preference and request information entered by the user via a device such as a smartphone or computer. This includes images obtained from social media such as Instagram, as well as information such as family structure, budget, and desired location. Next, the server generates prompt statements based on this information. These prompt statements are input into a generation AI model, which serves as instructions for generating house blueprints.

Based on the generated prompt text, the AI model automatically generates a blueprint for a house. This blueprint reflects the user's preferences and desires, including specific exterior details and floor plans. Furthermore, the server suggests window orientations and sizes, floor plans, etc., based on land and building information obtained from real estate information websites.

Hardware and software to be used

This system uses the following hardware and software:

Hardware: Servers, user terminals (smartphones, PCs, etc.)

Software: Generative AI models (e.g., OpenAI's GPT-3), prompt generation programs, image acquisition programs (e.g., requests, PIL)

Specific examples

For example, if the user enters the following conditions:

Instagram image URL: https://example.com/instagram_image.jpg

Family structure: 4 people family

Desired area: Tokyo

Budget: 50 million yen

The generated prompt will look like this:

Based on images of modern exteriors saved by the user on Instagram, we need a design for a detached house in Tokyo for a family of four with a budget of 50 million yen. Please generate a blueprint for a detached house with a modern exterior based on this information.

When this prompt is entered into the AI model, the model automatically generates a house design that meets the user's preferences. The generated design reflects the user's desired modern exterior and a floor plan suitable for their family structure. Furthermore, based on land and building information obtained from real estate information websites, the model also provides specific suggestions regarding window orientation and size, floor plan, and other details.

The flow of the specific processing in Application Example 3 will be explained using Figure 16.

Step 1:

Users input their preferences and desired information from devices such as smartphones and computers. This includes image URLs obtained from social media such as Instagram, as well as information such as family structure, budget, and desired region. The entered information is sent to the server.

Input: Instagram image URL, family composition, budget, desired region

Output: User preference and desired information sent to the server.

Step 2:

The server receives preference and request information from the user. Based on this information, it performs data processing to generate prompt messages. Specifically, it retrieves image URLs and converts information such as family structure, budget, and desired region into text format.

Input: User preference information and desired information

Output: Data for prompt message generation

Step 3:

The server downloads the image based on the retrieved image URL and loads it as an image object. This process uses the requests library and the PIL library.

Input: Image URL

Output: Image object

Step 4:

The server generates prompt text based on image objects and user information in text format. The generated prompt text serves as instructions for inputting into the AI model.

Input: Image object, user information in text format

Output: Prompt message

Step 5:

The server inputs the generated prompt text into a generative AI model to automatically generate a house design. This process uses generative AI models such as OpenAI's GPT-3.

Input: Prompt text

Output: Automatically generated house blueprints

Step 6:

The server retrieves land and building information from real estate information websites. This information includes window orientation and size, floor plan, etc.

Input: URL of the real estate information website

Output: Land and building information

Step 7:

Based on the acquired real estate information, the server provides specific suggestions regarding window orientation, size, and floor plan for the generated house blueprints.

Input: Land and building information, automatically generated house blueprints

Output: Residential blueprints including specific proposals

Step 8:

The server sends the final design to the user's terminal and provides a proposal to the user. The user can review the proposed design and make modifications or regenerate it as needed.

Input: Residential blueprints including specific proposals

Output: Final design drawings sent to the user's terminal.

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

"Example of Morphology 1"

One embodiment of the present invention incorporates an emotion engine that recognizes the user's emotions. This emotion engine recognizes the user's emotions and adjusts the house design based on those emotions. Specifically, when the user is feeling happy, it suggests bright-colored interiors and an open floor plan. Conversely, when the user is feeling depressed, it suggests calm-colored interiors and a floor plan that prioritizes privacy.

"Example of Morphology 2"

Furthermore, the emotion engine adjusts housing suggestions based on the user's emotions. Specifically, when the user is feeling happy, it suggests luxurious homes. Conversely, when the user is feeling down, it suggests simple and functional homes.

"Example of Morphology 3"

As a concrete example, when a user is happy about getting a new job, the emotion engine recognizes that happiness and automatically generates blueprints for a luxurious house. These blueprints feature large windows, a spacious living room, and high-quality interior materials. Conversely, when a user is depressed after losing their job, the emotion engine recognizes that depression and automatically generates blueprints for a simple, functional house. These blueprints have only the bare minimum number of rooms and functions, keeping costs down.

The following describes the processing flow for each example form.

"Example of Morphology 1"

Step 1: The emotion engine recognizes the user's emotions.

Step 2: The emotion engine adjusts the house design based on the user's emotions. Specifically, when the user is feeling happy, it suggests bright interior colors and an open floor plan.

Step 3: On the other hand, when the user is feeling down, suggest interiors with calming color schemes and floor plans that prioritize privacy.

"Example of Morphology 2"

Step 1: The emotion engine recognizes the user's emotions.

Step 2: The emotion engine adjusts housing suggestions based on the user's emotions. Specifically, when the user is feeling happy, it suggests luxurious homes.

Step 3: On the other hand, when the user is feeling down, propose a simple and functional home.

"Example of Morphology 3"

Step 1: When a user feels joy from acquiring a new job, the emotion engine recognizes that joy.

Step 2: The emotion engine automatically generates blueprints for a luxurious house. These blueprints feature large windows, a spacious living room, and high-quality interior materials.

Step 3: On the other hand, when a user is depressed after losing their job, the emotion engine recognizes that depression.

Step 4: The emotion engine automatically generates a simple and functional house design. This design includes the minimum necessary number of rooms and functions, keeping costs down.

(Example 1)

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

Conventional residential design systems struggled to automatically generate blueprints that reflected user preferences and desires. Furthermore, they failed to adjust blueprints to accommodate user emotions, resulting in lower user satisfaction. Additionally, they lacked effective means of utilizing information from social media and real estate information websites. This led to challenges in creating residential designs that met diverse user needs.

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

This invention includes a server that receives user preference and desire information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that proposes a house design based on the generated design; a server that recognizes the user's emotions and adjusts the house design based on those emotions; and a server that proposes interior design and floor plans based on the user's emotions. This enables the automatic generation of house design plans that reflect the user's preferences and desires, and further allows for adjustments to the design plans that take the user's emotions into consideration, thereby increasing user satisfaction. Furthermore, by effectively utilizing information from social media and real estate information websites, it is possible to realize house designs that meet the diverse needs of users.

"User preference information" refers to images and text information about housing that users have obtained from social media and other sources on the internet.

"Desired information" refers to specific requests entered by the user, such as family structure, desired address, price range, whether renovations are needed, and whether they prefer a house or apartment.

"Real estate information" refers to detailed property information obtained from real estate information websites and other databases.

"House design plans" refer to floor plans, interior and exterior drawings of a house that are automatically generated based on the user's preferences, desired features, and real estate information.

"Means of recognizing emotions" refers to algorithms and software that analyze a user's facial expressions and entered text to determine their emotional state.

"Means for adjusting the blueprint" refers to algorithms and software used to modify or correct the content of a house's blueprint based on recognized user emotions.

"Methods for proposing interior design and floor plans" refers to algorithms and software that propose optimal interior designs and floor plans based on the user's emotions and preferences.

"Social media" refers to a platform on the internet for users to share information.

A "real estate information website" refers to a website or online service that provides detailed information about properties.

Modes for carrying out the invention

This invention is a system that automatically generates house blueprints based on user preference and desired information, and further adjusts the blueprints to take user emotions into consideration. This system consists of a server, a terminal, and the user.

System Configuration

1. User actions

Users save their favorite house images from social media on the internet and upload them to the system.

Users input their preferences into the system, including family structure, desired address, price range, whether renovations are needed, and whether they prefer a house or apartment.

2. Server Processing

The server receives residential images uploaded by users and extracts image features using image recognition algorithms (e.g., TensorFlow, OpenCV).

The server analyzes the user's input using a natural language processing engine (e.g., GPT-3) and organizes it as structured data.

The server integrates data obtained from image recognition algorithms and natural language processing engines to generate blueprints for a house.

The server uses an emotion engine to recognize the user's emotions and analyzes them from the user's facial expressions and entered text.

The server adjusts the design based on the user's perceived emotions. For example, if the user is feeling happy, it suggests bright color schemes and an open layout.

The server generates the final, adjusted blueprint and provides it to the user.

Hardware and software to be used

Hardware: High-performance servers (e.g., servers with NVIDIA GPUs)

Software: Image recognition algorithms (e.g., TensorFlow, OpenCV), natural language processing engines (e.g., GPT-3), emotion recognition engines

Specific examples

For example, suppose a user uploads a modern house design image saved from Instagram to the system and inputs their desired information, such as family size (four people), desired address (Tokyo), price range (under 50 million yen), whether renovation is desired (yes), and whether they prefer a house or apartment (house).

The server first uses an image recognition algorithm to extract modern design features from residential images. Next, it analyzes the user's preferences using a natural language processing engine to generate floor plans suitable for a family of four, designs appropriate for residential areas in Tokyo, specifications feasible within a budget of 50 million yen, designs that consider renovations, and plans for detached houses.

Furthermore, if the emotion engine recognizes the user's emotion as "joy," the server will suggest interior design with bright colors and an open living room. Finally, the server integrates this information to provide the user with a blueprint for their ideal home.

Example of a prompt message

The user provided images of modern-designed houses saved from social media, along with their family size (4 people), desired address (Tokyo), price range (under 50 million yen), whether renovations were desired (yes), and whether they preferred a house or apartment (house). Based on this information, please generate a house design. Additionally, if the user's emotion is "joy," please suggest a bright interior color scheme and an open floor plan.

In this way, the system can automatically generate house designs based on the user's preferences and desires, and further adjust the designs to take the user's emotions into consideration.

The flow of the specific processing in Example 1 will be explained using Figure 17.

Step 1:

Users save their preferred house images and input them into the system.

Specifically, the user downloads images of houses they like from social media on the internet and uploads those images through the system's interface.

Input: Residential images obtained from social media

Output: Residential images uploaded to the system

Step 2:

The user enters their desired information into the system.

In terms of specific operations, the user inputs desired information such as family structure, desired address, price range, whether renovations are needed, and whether they prefer a house or apartment, through the system interface.

Input: Family composition, desired address, price range, whether renovation is required, preferred type of property (house or apartment), etc.

Output: Desired information entered into the system

Step 3:

The server analyzes residential images using an image recognition algorithm.

In terms of specific operation, the server receives uploaded images of houses and extracts image features using image recognition algorithms (e.g., TensorFlow, OpenCV). For example, it analyzes information such as the building's exterior, interior style, and color scheme.

Input: Residential images uploaded to the system

Output: Image feature data (building exterior, interior style, color tone, etc.)

Step 4:

The server uses a natural language processing engine to analyze the requested information.

In terms of specific operations, the server analyzes the user's input using a natural language processing engine (e.g., GPT-3) and organizes it as structured data. For example, it understands information such as family structure, desired address, and price range, and stores it in a database.

Input: Desired information entered into the system

Output: Structured desired information data

Step 5:

The server integrates the analysis results and generates the house blueprints.

In terms of specific operations, the server integrates data obtained from image recognition algorithms and natural language processing engines to generate blueprints for a house. For example, it designs the floor plan, interior, and exterior based on the user's preferences and desires.

Input: Image feature data, structured desired information data

Output: House design plans

Step 6:

The server uses an emotion engine to recognize the user's emotions.

Specifically, the server uses an emotion engine to recognize the user's emotions. It analyzes the text and facial expression data entered by the user into the system to determine the user's current emotional state.

Input: User text input, facial expression data

Output: User emotional state data

Step 7:

The server adjusts the design based on the user's emotions.

Specifically, the server adjusts the design based on the user's perceived emotions. For example, if the user is feeling happy, it suggests bright color schemes and an open layout. Conversely, if the user is feeling depressed, it suggests calm color schemes and a layout that prioritizes privacy.

Input: User emotional state data, house blueprints

Output: Adjusted house blueprints

Step 8:

The server provides the user with the final blueprint.

Specifically, the server generates a final, adjusted blueprint and provides it to the user. The user can review the blueprint through the system interface and provide feedback as needed.

Input: Adjusted house blueprints

Output: Final design drawings provided to the user

(Application Example 1)

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

Traditional residential design systems could generate blueprints based on user preferences and requests, but they lacked the ability to adjust the blueprints to reflect the user's emotions. Furthermore, it was difficult to provide real-time, emotionally responsive suggestions when users consulted with design and renovation companies in person. This resulted in a challenge in increasing user satisfaction.

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

This invention includes a server that receives user preference and desired information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that proposes a house design based on the generated design; a server that recognizes the user's emotions and adjusts the house design based on those emotions; and a server that analyzes the user's facial expressions using a smart device and recognizes their emotions. This enables the generation and proposal of house design plans that reflect the user's emotions in real time.

"User preference information" refers to images related to housing obtained by users from social media such as Instagram, as well as information indicating the user's personal preferences.

"Desired information" refers to information that indicates the user's specific requests, such as desired family structure, address, price range, whether or not renovations are needed, and whether they prefer a detached house or an apartment.

"Real estate information" refers to detailed information about a property, such as its location, price, floor plan, and year of construction, obtained from real estate information websites.

An "automatic blueprint generation method" refers to a system or algorithm for automatically generating blueprints for a house based on user preference information, desired information, and real estate information.

A "proposal method" refers to a system or algorithm for proposing housing designs to users based on generated blueprints.

An "emotion recognition system" is a system or algorithm that recognizes a user's emotions and adjusts the design plans of a house based on those emotions.

A "smart device" is a device, such as a smartphone or smart glasses, that analyzes a user's facial expressions and recognizes their emotions.

"Facial expression analysis means" refers to a system or algorithm that uses a smart device to analyze a user's facial expressions and recognize their emotions.

The system for implementing this invention automatically generates a house design based on the user's preference information, desired information, and real estate information, and adjusts the design based on the user's emotions. Specific embodiments are shown below.

System Configuration

1. Hardware:

Smart devices: Devices such as smartphones and smart glasses that analyze a user's facial expressions and recognize their emotions.

Server: A server used for data processing and blueprint generation.

2. Software:

Emotion Recognition Model: A machine learning model that analyzes a user's facial expressions and recognizes their emotions (e.g., an emotion recognition model using Keras).

Blueprint Generation Algorithm: An algorithm that automatically generates house blueprints based on user preferences, desired features, and real estate information.

Data Acquisition Module: A module for acquiring necessary information from social media and real estate information websites.

Processing Flow

1. Input of user preference and desired information:

Users upload images of houses they've saved from social media like Instagram to the app and enter information such as family composition, desired address, price range, whether renovations are planned, and whether it's a house or apartment.

2. Obtaining real estate information:

The server retrieves detailed information such as property location, price, floor plan, and year of construction from real estate information websites.

3. Emotion recognition:

Using smart devices, we analyze the user's facial expressions and recognize the user's emotions using an emotion recognition model.

4. Automatic generation of blueprints:

The server automatically generates house blueprints using a blueprint generation algorithm, based on user preference information, desired property information, and recognized emotional information.

5. Proposal:

Based on the generated blueprints, we propose housing designs to the user. This includes suggesting interior designs and floor plans that align with the user's preferences.

Specific examples

For example, a user uploads an image saved from Instagram to the app and enters their desired address and price range. The app analyzes the user's facial expressions through smart glasses and recognizes their emotions. Based on this information, the server automatically generates a design plan and proposes it to the user.

Example of a prompt message

Users upload images of houses they've saved from Instagram and enter information such as family composition, desired address, price range, whether renovations are needed, and whether it's a house or apartment. The system analyzes facial expressions through smart glasses to recognize emotions. Based on this information, it automatically generates an optimal house design.

In this way, a housing design assistant system based on the user's emotions and preferences can be realized.

The flow of the specific processing in Application Example 1 will be explained using Figure 18.

Step 1:

Users upload images of houses they have saved from social media such as Instagram to their devices.

Input: Image file of a house

Output: Uploaded image data

Specific operation: The user uploads saved images of their home to the application using their smartphone or smart glasses. The application then sends the image data to the server.

Step 2:

The user enters their desired information into the terminal, including family structure, preferred address, price range, whether renovations are needed, and whether they prefer a house or apartment.

Input: Family composition, address, price range, whether renovations were done, housing type

Output: User's requested information data

Specific operation: The user enters the desired information into the application's input form and presses the submit button. The application sends the entered data to the server.

Step 3:

The server retrieves detailed information such as property location, price, floor plan, and year of construction from real estate information websites.

Input: API of a real estate information website

Output: Real estate information data

Specific operation: The server calls the API of a real estate information website to retrieve the necessary real estate information. The retrieved data is stored in an internal database.

Step 4:

The device uses a smart device to analyze the user's facial expressions and recognizes the user's emotions using an emotion recognition model.

Input: User's facial expression image

Output: User sentiment data

Specific operation: The smart device's camera captures the user's facial expressions and sends the image data to the server. The server analyzes the facial image using an emotion recognition model to recognize the user's emotions.

Step 5:

The server automatically generates house blueprints using a blueprint generation algorithm based on user preference information, desired properties, real estate information, and emotional information.

Input: User preference information, desired information, real estate information, emotional information

Output: Automatically generated house blueprints

Specific operation: The server integrates all input data and executes the blueprint generation algorithm. The algorithm generates blueprints that take into account the user's emotions regarding interior design and floor plans.

Step 6:

The server generates blueprints and then proposes housing designs to the user.

Input: Automatically generated house blueprints

Output: Suggestions for the user

Specific operation: The server presents the generated blueprints to the user and offers suggestions for interior design and floor plans that align with their emotions. The user can review the suggestions through the application.

(Example 2)

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

Conventional residential design systems could generate blueprints based on user preferences and requests, but they lacked the ability to consider user emotions when making proposals. Furthermore, they struggled to effectively utilize real estate information for residential design, making it difficult to provide optimal proposals to users. There was a growing need to improve user satisfaction by offering housing proposals that resonated with users' emotions.

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

This invention includes a server that receives user preference and desire information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that proposes a house based on the generated design; and a server that analyzes the user's emotions and adjusts the house proposal based on the analysis results. This makes it possible to propose an optimal house design that not only reflects the user's preferences and desires but also their emotions.

"User preference information" refers to information that indicates a user's personal preferences and desires regarding their home.

"Desired information" refers to information that indicates the specific conditions and requirements that the user desires for a home.

"Real estate information" refers to information that includes detailed data on land and buildings, such as their shape, location, and price.

A "design drawing" is a diagram showing the layout, structure, window orientation, and size of a house.

A "proposal" refers to a specific plan regarding the design and layout of a house, provided to the user.

"Analyzing emotions" means identifying and evaluating a user's emotional state based on their facial expressions, behavior, and input data.

"Adjusting" means modifying and optimizing the proposed content based on the analysis results.

This invention is a system that automatically generates house designs and provides optimal housing proposals, taking into account the user's preferences, desires, real estate information, and emotions. A specific embodiment of this system is described below.

System Configuration

1. Obtaining real estate information

The server retrieves land and building information from real estate information websites. In this process, the server uses specific APIs to collect data. For example, it might utilize the API of a real estate information website.

2. Data Analysis and Processing

The server analyzes the acquired land and building information, extracting data such as shape and location conditions. This is done using a data analysis library.

3. Automatic generation of residential building plans

The server automatically generates house blueprints based on the analyzed data. Specifically, it considers the shape and location of the land and proposes window orientations, sizes, and floor plans. This process utilizes the API of design software.

4. Adjustment by the Emotional Engine

The device analyzes the user's emotions using an emotion engine. This emotion engine utilizes an emotion analysis API. If the user is feeling happy, the device suggests a luxurious home. Conversely, if the user is feeling depressed, it suggests a simple and functional home.

Specific examples

Example 1: Obtaining real estate information

The server uses the API of a real estate information website to retrieve land information for a specific area. For example, it sends an API request like the following:

"Retrieve land information for San Francisco and generate blueprints that suggest luxurious homes if the user is happy, and simple homes if they are depressed."

Example 2: Data Analysis and Processing

The server analyzes the acquired data using a data analysis library. For example, it extracts information about the land's shape and location, preparing it for use in the next step.

Example 3: Automatic generation of residential building plans

The server generates design drawings using the design software's API. For example, it considers the shape and location of the land and proposes window orientations, sizes, and floor plans.

Example 4: Adjustment by an emotional engine

The device uses an emotion analysis API to analyze the user's emotions. For example, if the user is happy, it suggests a luxurious house; if they are depressed, it suggests a simple house.

In this way, it becomes possible to propose optimal house designs that not only reflect the user's preferences and desires, but also their emotions. This system can improve user satisfaction.

The flow of the specific processing in Example 2 will be explained using Figure 19.

Step 1:

The server retrieves land and building information from real estate information websites. Specifically, the server sends an API request and receives JSON data containing land information for a specified area. The input is the parameters of the API request (e.g., area, property type), and the output is the retrieved real estate information in JSON data.

Step 2:

The server converts the retrieved JSON data into a DataFrame using a data analysis library. Specifically, the server uses the pandas library to read the JSON data and convert it into a DataFrame. The input is the retrieved JSON data, and the output is real estate information in DataFrame format.

Step 3:

The server extracts the necessary data from the data frame. Specifically, it extracts information such as land shape and location conditions and prepares it for use in the next step. The input is real estate information in data frame format, and the output is the extracted data on land shape and location conditions.

Step 4:

The server generates house blueprints using the design software's API. Specifically, it considers the land's shape and location to suggest window orientations and sizes, floor plans, and other details. The input is extracted data on land shape and location, and the output is the generated house blueprints.

Step 5:

The device analyzes the user's emotions using an emotion analysis API. Specifically, it identifies and evaluates the emotional state from the user's facial expressions, actions, and input data. Input consists of the user's images and text data, and output is the analyzed emotion data.

Step 6:

The device adjusts housing suggestions based on the analysis results. Specifically, it suggests a luxurious house if the user is happy, and a simple house if they are depressed. The input is the analyzed emotion data and the generated house blueprints, and the output is the adjusted housing suggestion.

In this way, by clearly defining the specific actions, inputs, and outputs performed at each processing step, the system's program processing flow was explained in detail.

(Application Example 2)

Next, we will describe Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server," and the robot 414 as a "terminal."

Conventional housing design systems could automatically generate blueprints based on user preferences, desires, and real estate information, but they lacked the ability to adjust proposals to reflect user emotions or display blueprints using virtual reality. Therefore, it was difficult to provide housing proposals that resonated with users' emotions or offer a more realistic experience. This resulted in a challenge in increasing user satisfaction.

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

This invention includes a server that receives user preference and desire information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that makes house proposals based on the generated design; a server that receives user emotion information as input and adjusts the house proposals based on this information; and a server that displays the generated design using a virtual reality display device. This enables the adjustment of house proposals according to the user's emotions and provides a realistic experience using virtual reality.

"User preference information" refers to information that indicates a user's personal preferences and desires regarding their home.

"Desired information" refers to information that indicates the specific conditions and requirements that the user desires for a home.

"Real estate information" refers to information that includes detailed data about land and buildings.

A "house design drawing" is a detailed drawing showing the layout, structure, and facilities of a house.

"Emotional information" refers to information that indicates the user's current emotional state.

A "virtual reality display device" is a device used to visually display a virtual space.

"Automatic generation methods" refer to devices or programs that have the function of automatically creating design drawings based on input information.

A "means for adjusting suggestions" refers to a device or program that has the function of modifying and optimizing suggestion content based on user sentiment information.

"Means of display" refers to devices or programs that have the function of visually presenting the generated design drawings to the user.

The system for implementing this invention automatically generates a house design based on the user's preference information, desired information, real estate information, and emotional information, and presents it to the user using a virtual reality display device. Specific embodiments are described below.

System Configuration

The system consists of the following main components:

1. User Terminal: A device such as a smartphone or head-mounted display collects user input information and displays it in virtual reality.

2. Server: Receives user preference information, desired information, real estate information, and emotional information, and automatically generates house blueprints.

3. Emotion Engine: Analyzes user emotion information and adjusts housing recommendations accordingly.

4. Design Generation Engine: Generates house blueprints based on real estate information, user preferences, and desired information.

5. Virtual Reality Display Device: Displays the generated blueprint in a virtual reality space.

Program processing

The server processes the data using the following steps:

1. Data Acquisition: Acquire preference information, desired information, and real estate information from the user's device. Acquire user sentiment information using the sentiment engine.

2. Design Drawing Generation: Using a design generation engine, the design drawings for the house are automatically generated based on the acquired information.

3. Proposal Adjustment: Using an emotion engine, housing proposals are adjusted based on the user's emotional information.

4. Virtual Reality Display: The generated design drawings are presented to the user using a virtual reality display device.

Hardware and software to be used

Hardware: Smartphones, head-mounted displays, servers

Software: EmotionEngine, DesignGenerator, VRDisplay (Virtual Reality Display Engine)

Specific examples

For example, when a user experiences designing a house using a smartphone, a luxurious house is suggested when the user is happy, and a simple, functional house is suggested when the user is depressed. The user wears a head-mounted display and can realistically experience the design blueprints generated in a virtual reality space.

Example of a prompt message

"Please generate blueprints for luxurious homes that will be presented to users when they are happy."

"Please generate blueprints for a simple and functional home that will be suitable for users who are feeling down."

In this way, we can provide housing proposals tailored to the user's emotions and realistic experiences using virtual reality.

The flow of the specific processing in Application Example 2 will be explained using Figure 20.

Step 1:

The user terminal receives user preference and desired information as input. Users input their housing preferences and specific requests using smartphones or head-mounted displays. This information is then transmitted to the server.

Input: User preference information, desired information

Output: User preference information and desired information sent to the server.

Step 2:

The server retrieves real estate information from real estate information websites. The server accesses the sites via APIs and collects detailed data about land and buildings.

Input: URL of the real estate information website

Output: Acquired real estate information

Step 3:

The user terminal uses an emotion engine to acquire the user's emotional information. It analyzes the user's facial expressions and voice to detect their current emotional state. This emotional information is then transmitted to the server.

Input: User's facial expressions, voice

Output: Emotional information sent to the server

Step 4:

The server uses a design generation engine to automatically generate house blueprints based on acquired preference information, desired information, and real estate information. The design generation engine analyzes this information to design the optimal floor plan and structure.

Input: User preference information, desired information, real estate information

Output: Generated house blueprints

Step 5:

The server uses an emotion engine to adjust housing suggestions based on the user's emotional information. For example, if the user is happy, it suggests a luxurious house; if they are depressed, it suggests a simple and functional house.

Input: Generated house blueprints, emotional information

Output: Proposed adjusted housing design

Step 6:

The server uses a virtual reality display to present the generated blueprints to the user. The user wears a head-mounted display and can realistically experience the blueprints within the virtual reality space.

Input: Adapted housing proposal

Output: Residential blueprints displayed in a virtual reality space.

In this way, we can provide housing proposals tailored to the user's emotions and realistic experiences using virtual reality.

(Example 3)

Next, we will describe Embodiment 3 of Example 3. In the following description, the data processing device 12 will be referred to as the "server," and the robot 414 will be referred to as the "terminal."

Conventional residential design systems have difficulty automatically generating blueprints that reflect user preferences and desires, and generating blueprints that take real estate information into account is also time-consuming. Furthermore, they cannot generate blueprints that take into account the user's emotional state, thus failing to enhance user psychological satisfaction. There is a need for a system that solves these problems and automatically generates residential blueprints that meet the diverse needs of users.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

This invention includes a server that receives user preference and desire information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; and a server that recognizes the user's emotional state and adjusts the house design according to that emotional state. This enables the automatic generation of a house design that takes into account the user's preferences, desires, real estate information, and even their emotional state.

"User preference information" refers to information about the designs and styles that users prefer, and is obtained from sources such as social media.

"Desired information" refers to information about the user's specific requirements, such as the desired location, budget, and family structure of their home.

"Real estate information" refers to information about land and buildings obtained from real estate information websites.

"Emotional state" refers to information that indicates the user's current psychological state and is recognized by the emotion engine.

A "design drawing" is a diagram showing the structure, layout, window orientation, and size of a house.

"Automatic generation" refers to the process where a system automatically creates a blueprint based on user input, property information, and emotional state.

A "proposal" is the process of presenting a specific house design and layout to the user based on the generated blueprints.

Modes for carrying out the invention

This invention is a system that automatically generates and proposes house designs to users based on their preferences, desires, real estate information, and emotional state. A specific embodiment of this system is described below.

Hardware and software to be used

Hardware: Servers, user terminals (PCs, smartphones)

Software: Generative AI models (e.g., GPT-4), emotion recognition engine, database management system (e.g., MySQL), real estate information site API

System Operation Overview

1. Entering User Information

Users use their devices to input information such as images saved from Instagram, family composition, desired housing location (within Tokyo), and budget (50 million yen).

Specific example input: "We are a family of four, looking for a modern-looking detached house in Tokyo, with a budget of 50 million yen."

2. Data Transmission and Analysis

The terminal sends the entered information to the server.

The server analyzes the received information and inputs it as a prompt message into the generating AI model.

Specific prompt example: "The user has saved an image of a modern exterior from Instagram. They are a family of four looking for a detached house in Tokyo with a budget of 50 million yen. Please generate a design plan based on this information."

3. Generating Blueprints

The server inputs prompt messages into the generated AI model and generates the blueprint.

The server uses land and building information in Tokyo obtained from real estate information websites to suggest window orientations, sizes, floor plans, and other details.

4. Emotion Recognition and Adjustment of the Blueprint

The server uses an emotion engine to recognize the user's emotional state.

The server adjusts the blueprint based on the results of the emotion engine.

Specific prompt example: "The user is delighted to have a new job. Please generate blueprints for a luxurious house with large windows, a spacious living room, and high-quality interior materials."

5. Provision of design drawings

The server sends the generated blueprint to the user's terminal.

The terminal displays the received blueprints to the user.

Specific examples

User input: "We are a family of four, looking for a modern-looking detached house in Tokyo, with a budget of 50 million yen."

Prompt: "The user has saved an image of a modern exterior from Instagram. They are a family of four looking for a detached house in Tokyo with a budget of 50 million yen. Please generate a design plan based on this information."

Emotion recognition prompt message: "The user is happy to have a new job. Please generate blueprints for a luxurious house with large windows, a spacious living room, and high-quality interior materials."

This system enables the automatic generation of residential blueprints that take into account the user's preferences, desired information, real estate information, and even their emotional state. This allows for residential designs that meet the diverse needs of users. The specific processing flow in Example 3 will be explained using Figure 21.

Step 1:

Entering user information

The user uses their device to input information such as images saved from Instagram, family composition, desired housing location (within Tokyo), and budget (50 million yen).

Input: User preference information, desired information

Output: The entered information is saved to the device.

Specific action: The user enters information into the input form on the device and clicks the "Submit" button.

Step 2:

Data transmission and analysis

The terminal sends the entered information to the server.

Input: Information entered by the user

Output: User information sent to the server

Specific operation: The terminal uses an HTTP request to send input data to the server.

The server analyzes the received information and inputs it as a prompt message into the generating AI model.

Input: User information sent from the device

Output: Prompt text to input to the generated AI model

Specific operation: The server analyzes the data and generates a prompt message like the following: "The user has saved an image of a modern exterior from Instagram. They are a family of four looking for a detached house in Tokyo with a budget of 50 million yen. Please generate a design plan based on this information."

Step 3:

Blueprint generation

The server inputs prompt messages into the generated AI model and generates the blueprint.

Input: Prompt text

Output: Generated design drawing data

Specific operation: The server sends prompt messages to the generated AI model (e.g., GPT-4) and receives the blueprint data returned from the model.

The server uses land and building information in Tokyo obtained from real estate information websites to suggest window orientations, sizes, floor plans, and other details.

Input: Information obtained from a real estate information website.

Output: Proposed window orientation and size, floor plan, etc.

Specific operation: The server uses an API to retrieve real estate information and reflects it in the design plans.

Step 4:

Emotion recognition and blueprint adjustment

The server uses an emotion engine to recognize the user's emotional state.

Input: User sentiment data

Output: Recognized emotional state

Specific operation: The server sends user emotion data to the emotion engine and analyzes the emotional state.

The server adjusts the blueprint based on the results of the emotion engine.

Input: Recognized emotional state

Output: Adjusted blueprint

Specific operation: The server receives the result from the emotion engine and generates a prompt message like the following: "The user is happy to have a new job. Please generate blueprints for a luxurious house with large windows, a spacious living room, and high-quality interior materials."

Step 5:

Provision of design drawings

The server sends the generated blueprint to the user's terminal.

Input: Generated design data

Output: Blueprint sent to the user's terminal

Specific operation: The server sends the design data as an HTTP response to the user's terminal.

The terminal displays the received blueprints to the user.

Input: Design drawing data sent from the server

Output: Blueprint displayed to the user

Specific operation: The terminal analyzes the received design data and displays it on the user interface.

(Application Example 3)

Next, we will explain Application Example 3 of Configuration Example 3. In the following explanation, the data processing device 12 will be referred to as a "server," and the robot 414 will be referred to as a "terminal."

Conventional residential design systems struggled to automatically generate blueprints that reflected user preferences and desires, and their proposals based on real estate information were limited. Furthermore, they lacked the means to generate blueprints that considered the user's emotional state, and to visually confirm the generated blueprints as 3D models. This made it difficult to increase user satisfaction.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

This invention includes a server that receives user preference and desire information as input and automatically generates a house design based on this information; a server that receives real estate information as input and automatically generates a house design based on this information; a server that makes house proposals based on the generated design; a server that receives the user's emotional state as input and automatically generates a house design based on this; and a server that displays the generated design as a 3D model. This enables the automatic generation of house design plans that reflect the user's preferences, desires, and emotional state, as well as visual confirmation.

"User preference information" refers to information about the designs and styles that users prefer, and is obtained from social media and image storage services.

"Desired information" refers to information about the conditions and specifications of a home that the user desires, including family structure, budget, and location.

"Real estate information" refers to information about land and buildings obtained from real estate information websites, including details such as price, area, and location.

"Emotional state" refers to information that indicates the user's current emotions and mood, including feelings such as joy and sadness.

A "design plan" is a drawing that shows the structure, layout, and design of a house, and is automatically generated based on the user's preferences, desires, real estate information, and emotional state.

A "3D model" is a three-dimensional visual model created based on design drawings, allowing users to view the exterior and interior of a house in three dimensions.

"Automatic generation" refers to the process by which a system automatically creates design drawings and 3D models based on user input and acquired data.

A "proposal" is the act of showing the user specific features of a house, such as the orientation and size of windows and the floor plan, based on the generated design drawings.

The system for implementing this invention automatically generates a house design based on the user's preferences, desires, real estate information, and emotional state, and displays it as a 3D model. A specific embodiment is shown below.

System Configuration

The system consists of user terminals, a server, and a display device. User terminals include smartphones and head-mounted displays (HMDs). The server performs data processing and automatically generates design drawings using a generated AI model. The display device is used to display the generated design drawings as 3D models.

Hardware and software to be used

Hardware: Smartphones, head-mounted displays (HMDs), servers

Software: Python, OpenAI API, 3D modeling libraries

Data processing and data calculation

1. Receiving user input:

The user terminal receives user preference information (e.g., images obtained from social media), desired information (e.g., family structure, budget, location), and emotional state (e.g., joy or sadness) as input.

2. Obtaining real estate information:

The server retrieves information about land and buildings in Tokyo from real estate information websites. This includes details such as price, area, and location.

3. Automatic generation of blueprints:

The server uses a generative AI model (e.g., OpenAI's GPT-3) to automatically generate house blueprints based on user input and acquired real estate information. An example of a prompt message for the generative AI model is as follows:

User input: {'image': 'modern_house_image.jpg', 'family_size': 4, 'location': 'Tokyo', 'budget': 5000, 'emotion': 'happy'}

Real estate data: {'Land information': '...', 'Building information': '...'}

Please generate the blueprint.

4. Creating and displaying 3D models:

The server creates a 3D model using a 3D modeling library based on the generated blueprint. The created 3D model is then displayed on the user's device (smartphone or HMD).

Specific examples

If a user provides an image of a modern exterior saved from Instagram, enters that they are a family of four looking for a detached house in Tokyo with a budget of 50 million yen, the server will automatically generate a blueprint for a modern-looking detached house in Tokyo based on this information. Furthermore, when a user is feeling happy about getting a new job, the emotion engine recognizes this happiness and automatically generates a blueprint for a luxurious home. This blueprint will feature large windows, a spacious living room, and high-quality interior materials.

In this way, it becomes possible to automatically generate and visually confirm house designs that reflect the user's preferences, desires, and emotional state.

The flow of the specific processing in Application Example 3 will be explained using Figure 22.

Step 1:

Users input preference information (e.g., images from social media), desired information (e.g., family structure, budget, location), and emotional state (e.g., joy, sadness) using a smartphone or head-mounted display (HMD).

Input: User preference information, desired information, emotional state

Output: User input data

Specific action: The user enters information into the application's input form and presses the submit button.

Step 2:

The server retrieves information about land and buildings in Tokyo from real estate information websites.

Input: User's desired information (e.g., location)

Output: Real estate information data

Specific operation: The server uses an API to send requests to real estate information websites and retrieve the necessary data.

Step 3:

The server uses an AI model (e.g., OpenAI's GPT-3) to automatically generate house blueprints based on user input and acquired real estate information.

Input: User input data, real estate information data

Output: House design drawing data

Specific operation: The server sends prompt messages to the generated AI model to generate the blueprint. An example of a prompt message is as follows:

User input: {'image': 'modern_house_image.jpg', 'family_size': 4, 'location': 'Tokyo', 'budget': 5000, 'emotion': 'happy'}

Real estate data: {'Land information': '...', 'Building information': '...'}

Please generate the blueprints.

Step 4:

The server creates a 3D model using a 3D modeling library based on the generated blueprint.

Input: House design drawing data

Output: 3D model data

Specific operation: The server inputs design data into the 3D modeling library and generates a 3D model.

Step 5:

The server sends the generated 3D model to the user's terminal, and the user's terminal displays it.

Input: 3D model data

Output: 3D model displayed on the user's terminal

Specific operation: The server sends 3D model data to the user's terminal, and the user's terminal displays it on a display device (smartphone or HMD).

The specific processing unit 290 transmits the result of the specific processing to the robot 414. The robot 414's control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input regarding the result of the specific processing. The control unit 46A receives the audio indicating user input acquired by the microphone 238.

The data is transmitted to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is generative AI such as ChatGPT (Internet Search <URL: https://openai.com/blog/chatgpt>). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives prompts containing instructions, as well as inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions provided by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization.

Another example of generative AI is Gemini (Internet search engine <URL: https://gemini.google.com/?hl=ja>).

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

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

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

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

The inner part of the Emotion Map 400 represents inner thoughts, while the outer part represents actions. Therefore, the further you go from the outer edge of the Emotion Map 400, the more visible (expressed through actions) your emotions become.

Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort; when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort; when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a Brain Physiological Signal Analysis System for Speech Emotion Recognition and Emotion, Tokushima University, Doctoral Dissertation: https://ci.nii.ac.jp/naid/500000375379). The left half of the emotion map displays emotions belonging to the "response" domain, where sensation is dominant. The right half of the emotion map displays emotions belonging to the "situation" domain, where situational awareness is dominant.

The emotion map defines two emotions that promote learning. One is the negative emotion around the middle of "repentance" or "reflection" on the situation side. This is when the robot experiences negative emotions such as, "I never want to feel this way again," or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. This is when the robot feels positive emotions such as, "I want more," or "I want to know more."

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

In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, involving multiple computers, including computer 22.

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

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.

Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, nor is it necessary to store the entirety of the specific processing program 56 in the storage device 32. It is acceptable to store only a portion of the specific processing program 56.

The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which are processors with circuit configurations specifically designed to perform particular processing.

Each processor has built-in or connected memory, and each processor uses memory to perform specific processes.

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

Examples of configurations using a single processor include, firstly, a configuration where one or more CPUs and software are combined to form a single processor, which functions as a hardware resource for executing specific processing. Secondly, a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources for executing specific processing, on a single IC chip, as exemplified by SoC (System-on-a-chip). Thus, specific processing is realized using one or more of the above-mentioned types of processors as hardware resources.

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

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

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

The following is further disclosed regarding the embodiments described above.

(Claim 1)
A system that includes means for receiving user preference information and desired information as input and automatically generating house blueprints based on this information, means for receiving real estate information as input and automatically generating house blueprints based on this information, and means for making house proposals based on the generated blueprints.

(Claim 2)
The system according to claim 1, wherein the user preference information is information obtained from social media such as Instagram.

(Claim 3)
The system according to claim 1, wherein the real estate information is information obtained from a real estate information provision site.

(Claim 4)
The system according to claim 1, further comprising an emotion engine that recognizes the emotions of the user.

(Claim 5)
The system according to claim 4, wherein the emotion engine includes means for adjusting the design of a house based on the user's emotions.

(Claim 6)
The system according to claim 4, wherein the emotion engine includes means for adjusting housing suggestions based on the user's emotions.

"Example 1"

(Claim 1)
A means of receiving user preference and desired information as input and automatically generating house blueprints based on this information,
A means of receiving image information saved by the user as input, analyzing this information to identify the user's preferences,
A means of generating prompt sentences for a generative AI model and inputting them,
A means of proposing a house based on the generated blueprints,
A system that includes this.

(Claim 2)
The system according to claim 1, wherein the user preference information is information obtained from social media.

(Claim 3)
The system according to claim 1, wherein the generation AI model generates a house design drawing based on a prompt message.

"Application Example 1"

(Claim 1)
A means of receiving user preference and desired information as input and automatically generating house blueprints based on this information,
A means of receiving real estate information as input and automatically generating house blueprints based on this information,
A means of proposing a house based on the generated blueprints,
A method for analyzing residential images taken by users and extracting their features,
A means for generating prompt text based on extracted features and user preference information, and for generating house blueprints using a generation AI model,
A system including means for presenting generated design drawings to the user via a display device.

(Claim 2)
The system according to claim 1, wherein the user preference information is information obtained from social media.

(Claim 3)
The system according to claim 1, wherein the real estate information is information obtained from a real estate information provision site.

"Example 2"

(Claim 1)
Methods for obtaining land and building information from real estate information websites,
A means of storing acquired information in a data frame and performing data processing,
A means for generating prompt sentences to be input into an AI model based on processed data,
A method for automatically generating house blueprints using a generative AI model,
A means of proposing a house based on the generated blueprints,
A system that includes this.

(Claim 2)
The system according to claim 1, wherein the real estate information is information obtained from a real estate information provision site.

(Claim 3)
The system according to claim 1, wherein the generation AI model is a means for automatically generating a house design drawing using the generation AI model.

"Application Example 2"

(Claim 1)
A means of receiving user preference and desired information as input and automatically generating house blueprints based on this information,
A means of receiving real estate information as input and automatically generating house blueprints based on this information,
A means of proposing a house based on the generated blueprints,
A method for automatically generating interior designs for physical stores based on information obtained from real estate information websites,
A means for creating prompt statements to generate blueprints using a generative AI model,
A means of generating actual blueprints using a generative AI model,
A system that includes this.

(Claim 2)
The system according to claim 1, wherein the user preference information is information obtained from social media.

(Claim 3)
The system according to claim 1, wherein the real estate information is information obtained from a real estate information provision site.

"Example 3"

(Claim 1)
A means of receiving user preference and desired information as input and automatically generating house blueprints based on this information,
A means of receiving real estate information as input and automatically generating house blueprints based on this information,
A means of proposing a house based on the generated blueprints,
A means for sending prompt text and images to a generative AI model to generate a blueprint,
Methods for obtaining land and building information from real estate information websites,
A means of providing optimal design proposals based on acquired real estate information,
A means of displaying the generated design drawings and proposed content to the user,
A system that includes this.

(Claim 2)
The system according to claim 1, wherein the user preference information is information obtained from social media.

(Claim 3)
The system according to claim 1, wherein the real estate information is information obtained from a real estate information provision site.

"Application Example 3"

(Claim 1)
A means of receiving user preference and desired information as input and automatically generating house blueprints based on this information,
A means of receiving real estate information as input and automatically generating house blueprints based on this information,
A means of proposing a house based on the generated blueprints,
A means for generating prompt text based on images saved by the user and generating a design drawing using a generation AI model,
A method of proposing window orientation, size, floor plan, etc., based on land and building information obtained from real estate information websites,
A system that includes this.

(Claim 2)
The system according to claim 1, wherein the user preference information is information obtained from social media.

(Claim 3)
The system according to claim 1, wherein the real estate information is information obtained from a real estate information provision site.

"Example 1 of combining an emotion engine"

(Claim 1)
A means of receiving user preference and desired information as input and automatically generating house blueprints based on this information,
A means of receiving real estate information as input and automatically generating house blueprints based on this information,
A means of proposing a house based on the generated blueprints,
A means of recognizing the user's emotions and adjusting the house design based on those emotions,
A method for proposing interior design and floor plans based on user emotions,
A system that includes this.

(Claim 2)
The system according to claim 1, wherein the user preference information is information obtained from social media.

(Claim 3)
The system according to claim 1, wherein the real estate information is information obtained from a real estate information provision site.

"Application Example 1 of Combining Emotional Engines"

(Claim 1)
A means of receiving user preference and desired information as input and automatically generating house blueprints based on this information,
A means of receiving real estate information as input and automatically generating house blueprints based on this information,
A means of proposing a house based on the generated blueprints,
A means of recognizing the user's emotions and adjusting the house design based on those emotions,
A means of analyzing a user's facial expressions using a smart device and recognizing their emotions,
A system that includes this.

(Claim 2)
The system according to claim 1, wherein the user preference information is information obtained from social media.

(Claim 3)
The system according to claim 1, wherein the real estate information is information obtained from a real estate information provision site.

"Example 2 of combining an emotion engine"

(Claim 1)
A means of receiving user preference and desired information as input and automatically generating house blueprints based on this information,
A means of receiving real estate information as input and automatically generating house blueprints based on this information,
A means of proposing a house based on the generated blueprints,
A means of analyzing user emotions and adjusting housing proposals based on the analysis results,
A system that includes this.

(Claim 2)
The system according to claim 1, wherein the user preference information is information obtained from social media.

(Claim 3)
The system according to claim 1, wherein the real estate information is information obtained from a real estate information provision site.

"Application Example 2 of Combining Emotional Engines"

(Claim 1)
A means of receiving user preference and desired information as input and automatically generating house blueprints based on this information,
A means of receiving real estate information as input and automatically generating house blueprints based on this information,
A means of proposing a house based on the generated blueprints,
A means of receiving user emotional information as input and adjusting housing proposals based on this information,
A means for displaying a design drawing generated using a virtual reality display device,
A system that includes this.

(Claim 2)
The system according to claim 1, wherein the user preference information is information obtained from social media.

(Claim 3)
The system according to claim 1, wherein the real estate information is information obtained from a real estate information provision site.

"Example 3 of combining an emotion engine"

(Claim 1)
A means of receiving user preference and desired information as input and automatically generating house blueprints based on this information,
A means of receiving real estate information as input and automatically generating house blueprints based on this information,
A means for recognizing the user's emotional state and adjusting the house design according to that emotional state,
A means of proposing a house based on the generated blueprints,
A system that includes this.

(Claim 2)
The system according to claim 1, wherein the user preference information is information obtained from social media.

(Claim 3)
The system according to claim 1, wherein the real estate information is information obtained from a real estate information provision site.

"Application Example 3 of Combining Emotional Engines"

(Claim 1)
A means of receiving user preference and desired information as input and automatically generating house blueprints based on this information,
A means of receiving real estate information as input and automatically generating house blueprints based on this information,
A means of proposing a house based on the generated blueprints,
A means for receiving the user's emotional state as input and automatically generating a house design based on it,
A means of displaying the generated blueprint as a 3D model,
A system that includes this.

(Claim 2)
The system according to claim 1, wherein the user preference information is information obtained from social media.

(Claim 3)
The system according to claim 1, wherein the real estate information is information obtained from a real estate information provision site.

(Claim 1)
A generation means that, by inputting specific prompt sentences into an AI model, automatically generates a design drawing of the house based on user housing-related information, including the user's preferred housing design, style, and conditions, and real estate information.
A means for proposing the house based on the generated design drawings,
The emotion engine that recognizes the user's emotions,
A means for adjusting the interior and floor plan included in the design drawings of the house based on the aforementioned emotions,
A means for displaying the adjusted design drawing using a virtual reality display device,
including

(Claim 2)
The generation means is the system according to claim 1, which obtains user housing-related information from social media.

(Claim 3)
The system according to claim 1, wherein the generation means obtains the real estate information from a real estate information provision site.

10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset terminal 414 Robot

Claims (3)

A means for analyzing a residential image uploaded by a user using image recognition and extracting features, and a prompt generation means for generating a specific prompt sentence based on the extracted features and the user's desired information.
A generation means that, by inputting the aforementioned specific prompt sentence into a generation AI model, automatically generates a design drawing of the house based on user housing-related information, including the user's preferred housing design, style, and conditions, and real estate information.
A means for proposing the house based on the generated design drawings,
The emotion engine that recognizes the user's emotions,
A means for adjusting the interior and floor plan included in the design drawings of the house based on the aforementioned emotions,
A means for displaying the adjusted design drawing using a virtual reality display device,
A system that includes this. The system according to claim 1, wherein the generation means acquires the user's housing-related information obtained by the user from social media. The system according to claim 1, wherein the generation means obtains the real estate information from a real estate information provision site.