System

Abstract

A system is provided.SOLUTION: A system, comprising: means for obtaining floor plan information input by a user; means for analyzing the obtained floor plan information to determine room dimensions; means for determining furnishings dimensions based on the obtained furnishings information; means for generating a 3D model based on the determined room dimensions and furnishings dimensions; means for rendering an interior perspective based on the generated 3D model; and means for providing the rendered interior perspective to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] When purchasing furniture, there is a need for a way to prevent mistakes such as finding that the furniture is too large for the room or is different from what was imagined after purchase. Especially for expensive furniture, such mistakes can lead to major returns and repurchases, which can be a significant burden in both time and money. Furthermore, there is a lack of methods for generating interior perspectives that are easy to use and do not require specialized knowledge. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for acquiring floor plan information input by a user, analyzing the floor plan information to identify room dimensions, and a means for identifying furniture dimensions from the furniture information input by the user. The system also includes a means for generating a 3D model based on the identified room dimensions and furniture dimensions, and for rendering the 3D model to generate an interior perspective. Furthermore, the generated interior perspective is provided to the user, allowing the user to realistically check the compatibility and placement of furniture before purchasing. This reduces post-purchase mistakes and improves user satisfaction.

[0006] "Floor plan information" is data illustrating the floor plan of a building or room, and includes the position of walls, the shape and dimensions of rooms, and so on.

[0007] "Furniture information" refers to detailed data about furniture, such as the furniture's name, URL, size, shape, and color.

[0008] "Dimensions" are numerical values ​​such as length, width, and depth that indicate the size of an object.

[0009] A "3D model" is digital data of an object or space reproduced in three dimensions on a computer.

[0010] "Rendering" is the process of generating an image on a computer based on a 3D model.

[0011] An "interior perspective" is an image that visually represents the design and layout of an interior space in three dimensions. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0013] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0016] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0017] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0018] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0025] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0032] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0033] This invention relates to a system that automatically generates interior perspective drawings of a room based on floor plan information and furniture information entered by a user. To implement this system, a program is required for communication between a server, a terminal, and a user, and for data processing. The specific program processing and its operation are described below.

[0034] Data entry and submission

[0035] The user uses their device to input specific floor plan information and its scale. They also input the name and URL of the furniture they are considering purchasing. For example, the user selects the product "Modern Sofa" and enters its URL, "http: / / example.com / modern-sofa." The device combines these input data into a single request and sends it to the server.

[0036] Parsing floor plan information

[0037] The server reads the received floor plan information and uses image processing algorithms to analyze the shape and dimensions of the room. This identifies the location of walls and the shape of the room, and calculates specific dimensions (e.g., 4m x 5m). Scale information is also taken into account and converted into actual dimensions.

[0038] Get furniture information

[0039] The server then accesses the URL provided by the user to retrieve detailed information about the furniture. This is done using web scraping technology and APIs to collect data such as the furniture's dimensions (e.g., width 2m, depth 0.8m, height 0.85m) and shape. The retrieved data is then formatted into a standard format.

[0040] 3D model generation and rendering

[0041] The server integrates the analyzed room dimensions and furniture size data and uses 3D modeling software to create a 3D model of the entire room. Based on the room layout, furniture is placed in appropriate locations. The server then renders this 3D model to generate a highly accurate interior perspective.

[0042] Interior perspective provided

[0043] The image data of the generated interior perspective is sent from the server to the terminal. The terminal displays the received interior perspective to the user, allowing the user to check it realistically. This allows the user to check whether the furniture is suitable for the room and whether it matches their image before purchasing.

[0044] In this way, the system of the present invention allows users to easily generate realistic interior perspectives and use them as a reference when selecting furniture. To give a concrete example, if a user wants to place a new sofa in their living room, they can simply input the floor plan and sofa information to check in advance whether the sofa will fit the room. This process can prevent mistakes after the purchase.

[0045] The processing flow will be explained below.

[0046] Step 1:

[0047] The user inputs a floor plan file and its scale on their device. They also input the name and URL of the furniture they are considering purchasing. For example, the user inputs the name "Modern Sofa" and the URL "http: / / example.com / modern-sofa".

[0048] Step 2:

[0049] The terminal compiles the floor plan file, scale, furniture name, and URL entered by the user into a single request and sends it to the server.

[0050] Step 3:

[0051] The server reads the received floor plan file and uses image processing algorithms to analyze the shape and dimensions of the room, recognizing the position of walls and the shape of the room, and determining the dimensions of the room (e.g., 4m x 5m).

[0052] Step 4:

[0053] The server uses the scale information provided by the user to convert the identified room dimensions into real-world dimensions.

[0054] Step 5:

[0055] The server accesses the URL entered by the user and obtains detailed information about the furniture. Using web scraping technology and APIs, the server collects furniture dimensions (e.g., width 2m, depth 0.8m, height 0.85m) and shape data.

[0056] Step 6:

[0057] The server converts the acquired furniture data into a standard format and integrates it with the room dimension data.

[0058] Step 7:

[0059] The server uses 3D modeling software to generate a 3D model of the entire room, then places the furniture in the appropriate locations based on the room's layout.

[0060] Step 8:

[0061] The server renders the generated 3D model and creates a highly accurate interior perspective.

[0062] Step 9:

[0063] The server sends the rendered interior perspective image data to the terminal.

[0064] Step 10:

[0065] The terminal displays the received interior perspective to the user, allowing the user to check whether the sofa is suitable for the room and matches their image.

[0066] In this way, users can reduce the chances of making mistakes when choosing furniture and choose the perfect interior.

[0067] Example 1

[0068] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0069] In recent years, there has been a demand for tools to simulate interior layouts. However, current systems require users to manually input floor plans and furniture information, making them cumbersome to use. Furthermore, the generated interior perspectives lack precision and real-time performance, which means they cannot fully reflect the nuances of actual furniture layouts. This often leads to failure after furniture purchase due to differences in layout or size compared to expectations.

[0070] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0071] In this invention, the server includes means for acquiring floor plan information input by a user, means for acquiring furniture information input by a user, means for analyzing the acquired floor plan information to identify room dimensions, means for identifying furniture dimensions based on the acquired furniture information, means for integrating the room dimensions and furniture dimensions to generate a 3D model, means for rendering an interior perspective based on the 3D model, means for providing the rendered interior perspective to a user's terminal, and means for the terminal to display the provided interior perspective to the user. This allows a user to automatically generate a high-precision interior perspective based on the information they input and check it in real time.

[0072] The "means for acquiring" is a function for receiving data entered by a user and converting it into a format that can be used within the system.

[0073] "Means of analysis" refers to the function of interpreting information based on acquired data and extracting details such as specific dimensions and shapes.

[0074] The "means of identification" is a function for clarifying the exact dimensions and position from the analyzed information.

[0075] "Means for generating a three-dimensional model" refers to a function for constructing a three-dimensional virtual space based on two-dimensional or text-format data.

[0076] "Rendering means" is a function for visually expressing the generated three-dimensional model and outputting it as a concrete image.

[0077] The "means of providing to the user's terminal" is a function for transmitting data generated by the server via a network to the device used by the user.

[0078] The "means for displaying by the terminal" is a function for visually showing the received data to the user via a user interface.

[0079] "Floor plan information" refers to drawings showing the shape and dimensions of a room and related data.

[0080] "Furniture information" is data about the dimensions, shape, and other attributes of a particular piece of furniture.

[0081] "URL" is an abbreviation for Uniform Resource Locator, and is an address for identifying the location of a specific resource.

[0082] "Interior perspective" is an image or rendering that shows the visual representation of furniture arranged in an actual room.

[0083] This invention relates to a system that automatically generates interior perspective drawings based on floor plan and furniture information entered by a user. This system requires a server, terminals, and users to communicate with each other and a program to process data.

[0084] Hardware and software used

[0085] The following hardware and software are used to implement this system.

[0086] Server: A central system that processes and analyzes data

[0087] Terminal: The device the user uses to provide input information

[0088] Image processing algorithms: Image analysis libraries such as OpenCV

[0089] Web scraping technology: Web data extraction tools such as BeautifulSoup

[0090] 3D modeling software: 3D model generation tools such as Blender

[0091] Program processing overview

[0092] Data entry and submission

[0093] The user uses their device to input specific floor plan information and its scale. They also input the name and URL of the furniture they are considering purchasing. For example, the user selects the product "Modern Sofa" and enters its URL, "http: / / example.com / modern-sofa." The device combines these input data into a single request and sends it to the server.

[0094] Parsing floor plan information

[0095] The server reads the received floor plan information and analyzes the shape and dimensions of the room using an image processing algorithm (e.g., OpenCV). This identifies the position of walls and the shape of the room, and calculates specific dimensions (e.g., 4m x 5m). Scale information is also taken into account and converted into actual dimensions.

[0096] Get furniture information

[0097] The server then accesses the URL provided by the user to retrieve detailed information about the furniture. This is done using web scraping techniques (e.g., BeautifulSoup) or APIs to collect the furniture's dimensions (e.g., width 2m, depth 0.8m, height 0.85m) and shape data. The retrieved data is then formatted into a standard format.

[0098] 3D model generation and rendering

[0099] The server integrates the analyzed room dimensions and furniture size data and creates a 3D model of the entire room using 3D modeling software (e.g., Blender). Based on the room layout, furniture is placed in appropriate locations. The server then renders this 3D model to generate a highly accurate interior perspective.

[0100] Interior perspective provided

[0101] The generated image data of the interior perspective is sent from the server to the terminal. The terminal displays the received interior perspective to the user, allowing the user to check it realistically. This allows the user to check whether the furniture is suitable for the room and matches their image before purchasing.

[0102] Specific examples

[0103] For example, imagine a user wants to place a new sofa in their living room. The user inputs the floor plan of the living room and the "Modern Sofa" information. Based on this, the system generates a highly accurate interior perspective and displays it to the user. This process helps users avoid the disappointment of finding that the sofa doesn't fit the room after purchase.

[0104] Example prompts for generative AI models

[0105] "I want to place a new modern sofa in my living room. Please generate an interior perspective using the floor plan and furniture information below.

[0106] Floor plan information: Living room (4m x 5m)

[0107] Furniture information: Modern Sofa (http: / / example.com / modern-sofa)

[0108] Thank you."

[0109] In this way, this system allows users to easily generate realistic interior perspectives and use them as a reference when selecting furniture. The server and device work together to process data based on the information entered by the user, and provide results in real time.

[0110] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0111] Divide the processing flow of the system program into processing steps

[0112] Step 1:

[0113] The user uses their device to input floor plan information and its scale. Specifically, the user uploads the floor plan as an image file using the device and inputs the scale information into a text field. For example, the user inputs the floor plan of the living room and the scale of "1:50". This input information is stored on the device for further processing.

[0114] Step 2:

[0115] The user enters furniture information. The user enters the name of the furniture they are considering purchasing and its URL into the terminal. For example, the user enters the furniture name "Modern Sofa" and its URL "http: / / example.com / modern-sofa." This input information is also saved on the terminal for subsequent processing.

[0116] Step 3:

[0117] The device combines the input floor plan information and furniture information into a single request and sends it to the server. Specifically, the device packages the input floor plan information (image file) and furniture information (name and URL) in JSON format and sends it to the server via an HTTP request. Once the input data arrives at the server, it proceeds to the next step.

[0118] Step 4:

[0119] The server reads the received floor plan information and performs image processing. The server uses the OpenCV library to analyze the received floor plan image. Specifically, it detects lines in the image and identifies the shape of the room and the location of walls. The results of this analysis (e.g., the shape and dimensions of the room) are saved for further processing.

[0120] Step 5:

[0121] The server uses the scale information to convert the analysis results to actual size. For example, if a scale of "1:50" is entered, a 4cm line actually corresponds to 2m, so the room dimensions are converted based on that. The result of this conversion (for example, the dimensions of a 4m x 5m room) is then passed on to the next step.

[0122] Step 6:

[0123] The server accesses the furniture information URL and retrieves detailed information about the furniture using web scraping or an API. Specifically, the server uses BeautifulSoup to parse the HTML in the URL and extract detailed information such as the furniture's height, width, and depth. This extracted data is formatted into a standard format (e.g., JSON) and saved for further processing.

[0124] Step 7:

[0125] The server combines the room's dimensional data with detailed furniture information to generate a 3D model. The server then uses 3D modeling software such as Blender to create a 3D model of the entire room. Specifically, it generates the room's outline based on the room's dimensional data, and places the 3D furniture models within the room based on the extracted furniture information.

[0126] Step 8:

[0127] The server renders the generated 3D model. The server uses Blender's rendering function to generate a highly accurate interior perspective. The rendering results (image data) are saved for further processing.

[0128] Step 9:

[0129] The server sends the rendered interior perspective image data to the device. The server sends the image data as an HTTP response, and the device receives it. This received data proceeds to the next step.

[0130] Step 10:

[0131] The device displays the received interior perspective to the user. Specifically, the rendered image of the interior perspective is displayed via the device's user interface. The user can check this in real time to confirm whether the furniture is suitable for the room and matches their image before purchasing.

[0132] (Application example 1)

[0133] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0134] When users arrange furniture in their rooms, it is difficult to visually confirm how the furniture will be placed in the room before purchasing it. Furthermore, moving actual furniture around to trial and error the interior layout is time-consuming. Therefore, there is a need for a support system that helps users smoothly select and arrange furniture.

[0135] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0136] In this invention, the server includes means for acquiring floor plan information input by a user, means for acquiring furniture information input by a user, means for analyzing the acquired floor plan information to identify room dimensions, means for identifying furniture dimensions based on the acquired furniture information, means for generating a 3D model based on the identified room dimensions and furniture dimensions, means for rendering an interior perspective based on the generated 3D model, means for providing the rendered interior perspective to a user, and means for inputting the floor plan information and furniture information as prompts and automatically generating an interior perspective using a generative AI model, thereby enabling a user to realistically see how furniture will be arranged in a room before purchasing it.

[0137] The "means for acquiring floor plan information entered by the user" is a function by which the system receives floor plan information indicating the layout of the user's own room via the network.

[0138] The "means for acquiring furniture information entered by the user" is a function by which the system receives detailed information about the furniture that the user is considering purchasing.

[0139] The "means for analyzing the acquired floor plan information and identifying the dimensions of the room" is a function for analyzing the received floor plan information and identifying the overall shape and dimensions of the room.

[0140] The "means for identifying the dimensions of the furniture based on the acquired furniture information" is a function for identifying the size of the furniture based on the furniture information provided by the user.

[0141] The "means for generating a 3D model based on the specified room dimensions and furniture dimensions" refers to a function for generating a three-dimensional model using 3D modeling software using the dimensions of the room and furniture.

[0142] The "means for rendering an interior perspective based on the generated 3D model" is a function for drawing a highly accurate interior perspective based on the generated three-dimensional model.

[0143] The "means for providing the rendered interior perspective to the user" is a function for transmitting the rendered interior perspective to the user's device and displaying it.

[0144] "Means for inputting the floor plan information and furniture information as prompts and automatically generating an interior perspective using a generative AI model" refers to a function for automatically creating an interior perspective using a generative AI model based on the floor plan information and furniture information provided by the user.

[0145] A system for implementing this invention provides a function for generating interior perspectives of a room based on floor plan information and furniture information entered by a user. Specifically, the system has a function for automatically generating interior perspectives by inputting floor plan information and furniture information as prompts and using a generative AI model. Detailed embodiments of the system of the present invention are described below.

[0146] System Program

[0147] 1. Getting user input data:

[0148] The user inputs floor plan information and furniture information using their own device (such as a smartphone). The floor plan information is provided as an image file, and the furniture information is input in the form of a web link (URL). For example, the user inputs URLs such as "http: / / example.com / floorplan.png" or "http: / / example.com / modern-sofa."

[0149] 2. Parsing floor plan information:

[0150] The server receives the input floor plan image and uses image recognition software (e.g., OpenCV) to identify the shape and dimensions of the room. The analysis results are calculated as specific dimensional information such as the width and height of the room.

[0151] 3. Get furniture information:

[0152] The server accesses the furniture URL provided by the user and retrieves detailed information (such as dimensions) of the furniture using web scraping or API, thereby collecting data such as the width, height, and depth of the furniture.

[0153] 4. Generate 3D model:

[0154] The analyzed room dimensions are combined with the acquired furniture size information to generate a 3D model of the room using 3D modeling software (e.g., Blender, Open3D). The furniture is then placed in the designated locations, and the overall layout is determined.

[0155] 5. Interior perspective rendering:

[0156] Based on the generated 3D model, high-precision interior perspectives are created using rendering software, and realistic effects such as lighting and shadows are also added at this stage.

[0157] 6. Use of generative AI models:

[0158] The server inputs the floor plan information and furniture information into the AI ​​model as prompts, and automatically generates the generated interior perspective. Specifically, the prompt uses the following text: "Please automatically generate a 3D interior perspective of the room based on the following information: Floor plan URL: 'http: / / example.com / floorplan.png' Furniture URL: 'http: / / example.com / modern-sofa'."

[0159] 7. User Offerings:

[0160] The rendered interior perspective is sent from the server to the user's device and displayed on a smartphone app, allowing users to visually check how furniture will be arranged in their room before purchasing it.

[0161] Hardware and software used

[0162] Hardware: User's smartphone, cloud server

[0163] Software: Flask (web server framework), OpenCV (image processing library), Blender or Open3D (3D modeling and rendering software), web scraping tool

[0164] Specific examples

[0165] A user uses an online shopping site's app to input the floor plan of their living room and information about the sofa they are considering purchasing. The app then sends the input information to a server, which then generates an interior perspective based on the floor plan and furniture information. The generated interior perspective is displayed to the user through the app, allowing the user to see in advance how the sofa will be placed in their living room.

[0166] Prompt Sentence Examples

[0167] "Please automatically generate a 3D interior perspective of the room based on the following information. Floor plan URL: 'http: / / example.com / floorplan.png' Furniture URL: 'http: / / example.com / modern-sofa'"

[0168] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0169] Step 1:

[0170] The user inputs floor plan information and furniture information using their own terminal. The input floor plan information is provided as an image file, and the furniture information is provided as a URL. This information is then sent to the server by the terminal.

[0171] Step 2:

[0172] The server retrieves the floor plan information it has received. The floor plan information (input) is in image file format, and a Python image processing library (e.g., OpenCV) is used to read the image and identify the shape and dimensions of the room. The analysis results (output) are specific dimensional information such as the width and height of the room.

[0173] Step 3:

[0174] The server retrieves the furniture information it receives. The furniture information (input) is in URL format, and the server retrieves detailed furniture information from the specified URL using web scraping or a web API. This allows it to collect dimensional data (output) such as the width, height, and depth of the furniture.

[0175] Step 4:

[0176] The server generates a 3D model based on floor plan information and furniture information. Specifically, it uses a 3D modeling tool such as Open3D to create a 3D model of the room and furniture. At this time, the room dimension data (input) and furniture dimension data (input) are integrated to generate a 3D model (output) with the furniture arranged in the 3D space.

[0177] Step 5:

[0178] The server renders the interior perspective based on the generated 3D model, and uses Blender or other rendering software to create a highly accurate interior perspective (output) with realistic lighting and shadow effects.

[0179] Step 6:

[0180] The server provides the rendered interior perspective to the user. The generated interior perspective image (input) is sent from the server to the user's device, which displays it in a form that the user can visually confirm. The user can check this interior perspective in real time and consider furniture placement.

[0181] Step 7:

[0182] The server inputs floor plan information and furniture information as prompts into the generative AI model, which then automatically generates an interior perspective. Specifically, the prompt "Please automatically generate a 3D interior perspective of the room based on the following information: Floor plan URL: 'http: / / example.com / floorplan.png' Furniture URL: 'http: / / example.com / modern-sofa'" is input into the generative AI model, and the interior perspective (output) generated by the model is obtained. This generated interior perspective is also provided to the user's device.

[0183] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0184] This invention relates to a system that automatically generates interior perspectives based on floor plan information and furniture information, and combines it with an emotion engine that recognizes user emotions to provide more user-friendly and personalized interior design suggestions. To implement this system, a program is required for the server, terminals, and users to communicate with each other and process data. The specific program processing and its operation are described below.

[0185] Data entry and submission

[0186] The user uses their own device to input specific floor plan information and its scale. They also input the name and URL of the furniture they are considering purchasing, as well as facial and voice data for use by the emotion engine. For example, suppose a user selects a product called "Modern Sofa" and enters its URL, "http: / / example.com / modern-sofa." This input data is compiled by the device into a single request and sent to the server.

[0187] Parsing floor plan information

[0188] The server reads the received floor plan information and uses image processing algorithms to analyze the shape and dimensions of the room. This identifies the location of walls and the shape of the room, and calculates specific dimensions (e.g., 4m x 5m). Scale information is also taken into account and converted into actual dimensions.

[0189] Get furniture information

[0190] The server then accesses the URL provided by the user to retrieve detailed information about the furniture. This is done using web scraping technology and APIs to collect data such as the furniture's dimensions (e.g., width 2m, depth 0.8m, height 0.85m) and shape. The retrieved data is then formatted into a standard format.

[0191] emotion recognition

[0192] The server uses an emotion engine to analyze the facial expression and voice data sent by the user, and recognizes the user's emotions (e.g., joy, surprise, satisfaction, etc.).

[0193] 3D model generation and rendering

[0194] The server combines the analyzed room dimensions, furniture size data, and the recognized user emotion data. It uses 3D modeling software to generate a 3D model of the entire room and arranges the furniture in appropriate locations based on the room layout. The server then renders this 3D model and generates a highly accurate interior perspective. Based on the emotion data, it adjusts the presentation of the interior perspective (e.g., color tone and flexibility of placement).

[0195] Interior perspective provided

[0196] The generated image data of the interior perspective is sent from the server to the terminal. The terminal displays the received interior perspective to the user, allowing the user to check it realistically. This allows the user to check whether the furniture is suitable for the room and matches their image before purchasing. In addition, the displayed interior perspective is personalized based on the user's emotions, resulting in a more satisfying proposal.

[0197] In this way, the system of the present invention allows users to easily generate realistic interior perspectives to help them select furniture, and further improves user satisfaction with personalized suggestions based on emotion recognition. As a specific example, when a user is choosing interior decor for a new living room, the emotion engine recognizes the user's reactions and provides the optimal perspective based on those reactions, helping the user make the most satisfying choice.

[0198] The processing flow will be explained below.

[0199] Step 1:

[0200] The user inputs a floor plan file and its scale on their device. For example, they input "floor_plan.png" and the scale "1:100." They also input the name of the furniture they are considering purchasing, "Modern Sofa," and the URL, "http: / / example.com / modern-sofa."

[0201] Step 2:

[0202] Users provide facial expression and voice data for use by the emotion engine by recording their current facial expressions and voice using a webcam and microphone, or by uploading previously collected data.

[0203] Step 3:

[0204] The device compiles the floor plan file, scale, furniture name, URL, and emotion data entered by the user into a single request and sends it to the server.

[0205] Step 4:

[0206] The server reads the received floor plan file and uses image processing algorithms to analyze the shape and dimensions of the room, identifying the location of walls and the shape of the room, and calculating specific dimensions (e.g., 4m x 5m).

[0207] Step 5:

[0208] The server uses the scale information provided by the user to convert the analyzed room dimensions into real-world dimensions.

[0209] Step 6:

[0210] The server accesses the URL of the furniture item entered by the user and retrieves detailed information. Using web scraping technology and APIs, the server collects the furniture's dimensions (2m width, 0.8m depth, 0.85m height) and shape data. This data is then formatted into a standard format.

[0211] Step 7:

[0212] The server uses the received facial expression and voice data to analyze it using an emotion engine, which then recognizes the user's emotions (happiness, surprise, satisfaction, etc.).

[0213] Step 8:

[0214] The server combines the analyzed room dimensions, furniture size data, and the recognized user emotion data, then generates a 3D model of the entire room using 3D modeling software, and places the furniture in appropriate locations based on the room layout.

[0215] Step 9:

[0216] The server renders the generated 3D model and creates a highly accurate interior perspective. Based on the emotion data, the content of the interior perspective (e.g., adjusting color, brightness, and layout) is customized to suit the user's preferences.

[0217] Step 10:

[0218] The server sends the rendered interior perspective image data to the terminal, which then displays the received interior perspective to the user.

[0219] Step 11:

[0220] Users can check the displayed interior perspective and decide whether the furniture they are considering purchasing is suitable for the room and matches their image.In addition, interior perspectives that reflect the user's emotions allow for a more satisfying consideration.

[0221] In this way, users can reduce the chances of making mistakes when choosing furniture and choose the perfect interior.

[0222] Example 2

[0223] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0224] Conventional interior design support systems generate interior perspectives based on floor plan and furniture information entered by the user, but they are unable to provide personalized suggestions that take the user's emotions into account. This results in reduced user satisfaction and makes it difficult to select the optimal interior design. Furthermore, the lack of a function to personalize interior perspectives using emotion recognition led to a demand for a more user-friendly system.

[0225] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0226] In this invention, the server includes means for acquiring floor plan information input by a user, means for acquiring furniture information input by a user, means for analyzing the acquired floor plan information to identify room dimensions, means for identifying furniture dimensions based on the acquired furniture information, means for generating a 3D model based on the identified room dimensions and furniture dimensions, means for rendering an interior perspective based on the generated 3D model, means for acquiring facial expression data or voice data of the user, means for analyzing the acquired facial expression data or voice data to recognize the user's emotion, means for personalizing the rendered interior perspective based on the recognized emotion data, and means for providing the personalized interior perspective to the user. This makes it possible to propose interior perspectives that take the user's emotions into consideration and provide a user with a higher level of satisfaction.

[0227] A "user" is an individual or organization that uses the system to input floor plan and furniture information.

[0228] "Floor plan information" is drawing data that includes information such as the shape and dimensions of the room, and the positions of walls and doors.

[0229] "Furniture information" is information including the name, size, shape, and URL of the furniture.

[0230] "Facial expression data" is image or video data showing the facial expression of the user.

[0231] "Voice data" refers to data that records the user's voice.

[0232] A "server" is a central device that receives requests from users and processes data accordingly.

[0233] A "3D model" is digital data that represents the shape of a room and the arrangement of furniture in three dimensions.

[0234] An "interior perspective" is an image that shows the interior design of a room, generated based on a 3D model.

[0235] An "emotion engine" is a program that analyzes facial expression data and voice data to recognize the user's emotions.

[0236] "Personalization" refers to individually adjusting the interior perspective based on the user's emotional data.

[0237] This invention relates to a system that automatically generates interior perspectives based on floor plan information and furniture information, and combines it with an emotion engine that recognizes user emotions to provide more user-friendly and personalized interior design suggestions. To implement this system, a program is required for the server, terminals, and users to communicate with each other and process data. The specific program processing and its operation are described below.

[0238] First, the user uses their device to input floor plan information and its scale. This floor plan information is uploaded as a JPEG image, for example, and the scale is specified as "1:100." The user also enters the name of the furniture they are considering, "Modern Sofa," and its URL, "http: / / example.com / modern-sofa," and provides smiling facial expression data via their webcam. The device then compiles this data into a single request and sends it to the server.

[0239] The server uses image processing libraries such as OpenCV and TensorFlow to analyze the received floor plan file. Image processing algorithms are used to detect the positions of walls, doors, and windows, and to identify the shape and dimensions of the room. For example, it may determine that the room is rectangular, measuring 4m x 5m. The server also converts the units on the floor plan into actual dimensions based on the scale information.

[0240] The server then accesses the URL provided by the user and uses web scraping techniques (such as BeautifulSoup) or APIs to obtain detailed information about the furniture. For example, the size data for a "Modern Sofa" measuring 2m wide, 0.8m deep, and 0.85m high is collected. The collected data is then formatted into a standard format (such as JSON) for subsequent processing.

[0241] The server also uses emotion engines such as Face++ and Microsoft Azure Emotion API to analyze the received facial and voice data. The server recognizes emotions such as joy, surprise, and satisfaction from the user's facial expressions and stores the results in a database.

[0242] The server combines the room dimension data, furniture size data, and the user's recognized emotion data. It uses 3D modeling software (such as Blender) to generate a 3D model of the entire room and arranges the furniture in appropriate locations. For example, if the user's emotion is recognized as "joy," the server creates an interior perspective using bright colors. It then renders this 3D model to generate a highly accurate interior perspective image.

[0243] Finally, the server sends the image data of the generated interior perspective to the device. The device displays the received interior perspective to the user, allowing the user to check it in real time. This allows the user to determine whether the furniture is suitable for the room and whether it matches their image. A personalized interior perspective based on emotional data allows the user to make a more satisfying choice.

[0244] As a concrete example, when a user selects the interior of a new living room, the system works as follows: The user selects "Modern Sofa" and enters the URL "http: / / example.com / modern-sofa." When the user smiles at the camera, the server analyzes the smile and recognizes that the user is "satisfied." Based on this information, the server generates an interior perspective that reflects the layout and color tones that will most likely satisfy the user.

[0245] Example prompt sentence:

[0246] "Enter your floor plan information, provide details about the furniture you're considering, and create a layout for your new living room. Smile for the camera and enjoy the interior design experience."

[0247] This system allows users to easily generate realistic interior perspectives and use them as a reference when selecting furniture. Furthermore, by utilizing emotion recognition, it can provide personalized interior design suggestions, improving user satisfaction.

[0248] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0249] Step 1:

[0250] The user uses their own device to input floor plan information and its scale. Specifically, they upload a floor plan of, say, a living room as an image file (JPEG, PNG, etc.) and specify the scale as "1:100," for example. The input at this point is the floor plan image file and scale information, and the output is request data that sends this information to the server.

[0251] Step 2:

[0252] The user enters the name of the furniture they are considering purchasing, "Modern Sofa," and its URL, "http: / / example.com / modern-sofa," into their device. In addition, the user provides facial expression data of a smile via the camera. The input is the furniture name, URL, and facial expression data, and the output is a request data that compiles these.

[0253] Step 3:

[0254] The terminal sends the floor plan information, furniture information, and facial expression data entered by the user to the server as a single request. The input is the request data from the user, and the output is that this request is sent to the server.

[0255] Step 4:

[0256] The server analyzes the received floor plan file. Specifically, it uses image processing libraries such as OpenCV and TensorFlow to identify the shape and dimensions of the room, as well as the locations of walls, doors, and windows. The input is an image file of the floor plan and scale information, and the output is specific dimensional data of the room, including the locations of walls, doors, and windows.

[0257] Step 5:

[0258] The server converts the units on the floor plan into actual dimensions based on the scale information. For example, if a scale of 1:100 is input, 1 cm corresponds to 1 m. This conversion outputs the actual room dimension data.

[0259] Step 6:

[0260] The server accesses the furniture URL provided by the user and uses web scraping technology (e.g., BeautifulSoup) or APIs to obtain detailed information about the furniture. The input is the furniture URL, and the output is the furniture's size (width, depth, height) and shape data.

[0261] Step 7:

[0262] The server formats the acquired furniture information into a standard format (e.g., JSON). The formatted data is used in subsequent processing. The input is the acquired furniture details, and the output is the data formatted in the standard format.

[0263] Step 8:

[0264] The server uses an emotion engine to analyze the facial expression data sent by the user. Specifically, it uses Face++ or Microsoft Azure Emotion API to recognize the user's emotions from the facial expression data. The input is the user's facial expression data, and the output is the recognized emotion data (happiness, surprise, satisfaction, etc.).

[0265] Step 9:

[0266] The server integrates the room dimension data, furniture size data, and emotion data. Based on this, it uses 3D modeling software (e.g., Blender) to generate a 3D model of the entire room and arranges the furniture in appropriate locations. The input is the integrated data, and the output is a 3D model of the entire room.

[0267] Step 10:

[0268] The server renders the generated 3D model and generates a highly accurate interior perspective image. It also personalizes color tones and furniture placement based on the user's emotional data. The input is the 3D model and emotional data, and the output is personalized interior perspective image data.

[0269] Step 11:

[0270] The server sends the generated interior perspective image data to the terminal. The input is the interior perspective image data, and the output is that it is sent to the user's terminal.

[0271] Step 12:

[0272] The terminal displays the received interior perspective to the user. The user can then view the displayed interior perspective and check whether the furniture is suitable for the room. The input is image data of the interior perspective sent from the server, and the output is an interface that allows the user to visually check it.

[0273] (Application example 2)

[0274] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0275] Conventional interior design proposal systems generate interior perspectives based on floor plan and furniture information provided by the user, but they lack personalization based on the user's emotions and preferences. This often leaves users dissatisfied with the proposals. Furthermore, it is difficult to grasp in advance the atmosphere and feel of the furniture arrangement, which can reduce purchasing motivation.

[0276] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0277] In this invention, the server includes means for acquiring floor plan information input by a user, means for acquiring furniture information input by a user, means for recognizing emotions based on the user's facial expression data and voice data, means for analyzing the acquired floor plan information to identify room dimensions, means for identifying furniture dimensions based on the acquired furniture information, means for generating a 3D model based on the identified room dimensions, furniture dimensions, and the recognized emotion, means for rendering an interior perspective based on the generated 3D model, and means for providing the rendered interior perspective to the user. This enables personalized interior suggestions based on the user's emotions, thereby improving user satisfaction and purchasing motivation.

[0278] "Floor plan information" is data about the shape and dimensions of a room provided by the user, and is basic information for identifying the specific dimensions of the room through analysis.

[0279] "Furniture information" refers to data about furniture provided by the user, including detailed information about the furniture, including its size and shape.

[0280] "Emotion recognition" is a technology that analyzes a user's facial expression data and voice data to identify the user's emotional state (for example, joy, surprise, satisfaction, etc.).

[0281] A "3D model" is a digital representation of a three-dimensional interior space generated based on analyzed floor plan information, furniture information, and recognized emotional data.

[0282] An "interior perspective" is a visual image rendered based on the generated 3D model, and is an interior design proposal provided to the user.

[0283] A "server" is a central computing device that receives, analyzes, and processes data sent from user terminals.

[0284] A "terminal" is a device operated by a user, and is a device for inputting and transmitting floor plan information, furniture information, facial expression data, and voice data.

[0285] "Rendering" is the process of representing the generated 3D model as a visual image and providing it to the user.

[0286] This invention is a system that recognizes a user's emotions and automatically generates personalized interior design proposals based on floor plan and furniture information provided by the user. To realize this system, multiple components, such as a server, a terminal, and an emotion engine, work together.

[0287] The user's device inputs floor plan information, furniture information, and facial expression and voice data for emotion recognition. The floor plan information is acquired as an image file, and the furniture information is acquired via a URL. This input data is compiled by the device and sent to the server as a single request.

[0288] The server first analyzes the floor plan information sent to it and identifies the shape and dimensions of the room. This process uses image processing software such as OpenCV. Next, it accesses the URL provided by the user and uses web scraping technology and APIs to obtain detailed information about the furniture. This data is then converted into a standard format.

[0289] Furthermore, the server uses an emotion engine to analyze the user's facial expression data and voice data to recognize the user's emotional state. This analysis is performed using emotion recognition software such as EmotionEngine.

[0290] The system combines the identified room dimensions, furniture size data, and the user's recognized emotion data to generate a 3D model of the entire room using 3D modeling software. This 3D model is then rendered with high precision using a rendering engine to generate an optimal interior perspective for the user. The perspective content, including color tone and placement, is adjusted based on the user's emotion.

[0291] Finally, the generated image data of the interior perspective is sent from the server to the terminal. The terminal receives this data and displays it to the user. This allows the user to confirm whether the furniture is suitable for the room and matches their image before purchasing. In addition, the displayed perspective is personalized based on the user's emotions, further increasing user satisfaction.

[0292] For example, when a user is choosing interior decor for a new living room, the emotion engine recognizes the user's reactions and provides the optimal perspective based on those reactions. This system allows the user to make a more satisfying interior design choice.

[0293] Example prompt for a generative AI model:

[0294] "Enter the floor plan and furniture information of the room selected by the user, as well as emotional data, and generate a 3D model to propose a room layout. Recognize the emotional data from the user's facial expressions and voice, and create the optimal interior perspective based on those emotions."

[0295] This invention makes it possible to propose personalized interiors based on emotion recognition, thereby improving user satisfaction and purchasing motivation.

[0296] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0297] Step 1:

[0298] The user uses a terminal to input floor plan information, furniture information, facial expression data, and voice data. The input floor plan information is specified as an image file, and the furniture information is provided as a URL. Facial expression data and voice data for emotion recognition are also input as files. This data is compiled by the terminal into a single request and sent to the server. The input is an image file of the floor plan, a URL for the furniture, an image file of the facial expression data, and an audio file of the voice data, and the output is the data compiled into a single request.

[0299] Step 2:

[0300] The server obtains floor plan information from the received request and analyzes it using image processing algorithms such as OpenCV. The analysis identifies the shape and dimensions of the room and calculates specific dimensional information. The input is an image file of the floor plan, and the output is the room dimensional information.

[0301] Step 3:

[0302] The server accesses the URL provided by the user and uses web scraping technology or APIs to obtain detailed information about the furniture. The obtained data is converted into a standard format, and the size and shape of the furniture are identified. The input is the furniture URL, and the output is information about the size and shape of the furniture.

[0303] Step 4:

[0304] The server analyzes the facial expression data and voice data using an emotion engine (e.g., EmotionEngine) to recognize the user's emotions. This analysis identifies the user's emotional state (e.g., joy, surprise, satisfaction, etc.). The input is an image file of the facial expression data and an audio file of the voice data, and the output is information on the recognized emotions.

[0305] Step 5:

[0306] The server integrates the identified room dimensions, furniture size data, and the recognized user emotion data, and generates a 3D model of the entire room using 3D modeling software. The input is the room dimension data, furniture size data, and emotion information, and the output is the 3D model. This model includes the overall layout of the room and the arrangement of the furniture.

[0307] Step 6:

[0308] The server uses a rendering engine to render the generated 3D model with high precision, generating an optimal interior perspective for the user. The perspective content is adjusted based on the user's emotions, including color tone and placement. The input is the 3D model, and the output is the rendered image data of the interior perspective.

[0309] Step 7:

[0310] The server sends image data of the generated interior perspective to the terminal. The terminal receives this data and displays it to the user. This allows the user to check whether the furniture is suitable for the room and matches their image before purchasing. The displayed perspective is personalized based on the user's emotions, improving user satisfaction. The input is image data of the rendered interior perspective, and the output is the interior perspective displayed on the user's terminal.

[0311] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0312] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0313] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0314] [Second embodiment]

[0315] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0316] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0317] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0318] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0319] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0320] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0321] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0322] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0323] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0324] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0325] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0326] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0327] This invention relates to a system that automatically generates interior perspective drawings of a room based on floor plan information and furniture information entered by a user. To implement this system, a program is required for communication between a server, a terminal, and a user, and for data processing. The specific program processing and its operation are described below.

[0328] Data entry and submission

[0329] The user uses their device to input specific floor plan information and its scale. They also input the name and URL of the furniture they are considering purchasing. For example, the user selects the product "Modern Sofa" and enters its URL, "http: / / example.com / modern-sofa." The device combines these input data into a single request and sends it to the server.

[0330] Parsing floor plan information

[0331] The server reads the received floor plan information and uses image processing algorithms to analyze the shape and dimensions of the room. This identifies the location of walls and the shape of the room, and calculates specific dimensions (e.g., 4m x 5m). Scale information is also taken into account and converted into actual dimensions.

[0332] Get furniture information

[0333] The server then accesses the URL provided by the user to retrieve detailed information about the furniture. This is done using web scraping technology and APIs to collect data such as the furniture's dimensions (e.g., width 2m, depth 0.8m, height 0.85m) and shape. The retrieved data is then formatted into a standard format.

[0334] 3D model generation and rendering

[0335] The server integrates the analyzed room dimensions and furniture size data and uses 3D modeling software to create a 3D model of the entire room. Based on the room layout, furniture is placed in appropriate locations. The server then renders this 3D model to generate a highly accurate interior perspective.

[0336] Interior perspective provided

[0337] The image data of the generated interior perspective is sent from the server to the terminal. The terminal displays the received interior perspective to the user, allowing the user to check it realistically. This allows the user to check whether the furniture is suitable for the room and whether it matches their image before purchasing.

[0338] In this way, the system of the present invention allows users to easily generate realistic interior perspectives and use them as a reference when selecting furniture. To give a concrete example, if a user wants to place a new sofa in their living room, they can simply input the floor plan and sofa information to check in advance whether the sofa will fit the room. This process can prevent mistakes after the purchase.

[0339] The processing flow will be explained below.

[0340] Step 1:

[0341] The user inputs a floor plan file and its scale on their device. They also input the name and URL of the furniture they are considering purchasing. For example, the user inputs the name "Modern Sofa" and the URL "http: / / example.com / modern-sofa".

[0342] Step 2:

[0343] The terminal compiles the floor plan file, scale, furniture name, and URL entered by the user into a single request and sends it to the server.

[0344] Step 3:

[0345] The server reads the received floor plan file and uses image processing algorithms to analyze the shape and dimensions of the room, recognizing the position of walls and the shape of the room, and determining the dimensions of the room (e.g., 4m x 5m).

[0346] Step 4:

[0347] The server uses the scale information provided by the user to convert the identified room dimensions into real-world dimensions.

[0348] Step 5:

[0349] The server accesses the URL entered by the user and obtains detailed information about the furniture. Using web scraping technology and APIs, the server collects furniture dimensions (e.g., width 2m, depth 0.8m, height 0.85m) and shape data.

[0350] Step 6:

[0351] The server converts the acquired furniture data into a standard format and integrates it with the room dimension data.

[0352] Step 7:

[0353] The server uses 3D modeling software to generate a 3D model of the entire room, then places the furniture in the appropriate locations based on the room's layout.

[0354] Step 8:

[0355] The server renders the generated 3D model and creates a highly accurate interior perspective.

[0356] Step 9:

[0357] The server sends the rendered interior perspective image data to the terminal.

[0358] Step 10:

[0359] The terminal displays the received interior perspective to the user, allowing the user to check whether the sofa is suitable for the room and matches their image.

[0360] In this way, users can reduce the chances of making mistakes when choosing furniture and choose the perfect interior.

[0361] Example 1

[0362] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0363] In recent years, there has been a demand for tools to simulate interior layouts. However, current systems require users to manually input floor plans and furniture information, making them cumbersome to use. Furthermore, the generated interior perspectives lack precision and real-time performance, which means they cannot fully reflect the nuances of actual furniture layouts. This often leads to failure after furniture purchase due to differences in layout or size compared to expectations.

[0364] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0365] In this invention, the server includes means for acquiring floor plan information input by a user, means for acquiring furniture information input by a user, means for analyzing the acquired floor plan information to identify room dimensions, means for identifying furniture dimensions based on the acquired furniture information, means for integrating the room dimensions and furniture dimensions to generate a 3D model, means for rendering an interior perspective based on the 3D model, means for providing the rendered interior perspective to a user's terminal, and means for the terminal to display the provided interior perspective to the user. This allows a user to automatically generate a high-precision interior perspective based on the information they input and check it in real time.

[0366] The "means for acquiring" is a function for receiving data entered by a user and converting it into a format that can be used within the system.

[0367] "Means of analysis" refers to the function of interpreting information based on acquired data and extracting details such as specific dimensions and shapes.

[0368] The "means of identification" is a function for clarifying the exact dimensions and position from the analyzed information.

[0369] "Means for generating a three-dimensional model" refers to a function for constructing a three-dimensional virtual space based on two-dimensional or text-format data.

[0370] "Rendering means" is a function for visually expressing the generated three-dimensional model and outputting it as a concrete image.

[0371] The "means of providing to the user's terminal" is a function for transmitting data generated by the server via a network to the device used by the user.

[0372] The "means for displaying by the terminal" is a function for visually showing the received data to the user via a user interface.

[0373] "Floor plan information" refers to drawings showing the shape and dimensions of a room and related data.

[0374] "Furniture information" is data about the dimensions, shape, and other attributes of a particular piece of furniture.

[0375] "URL" is an abbreviation for Uniform Resource Locator, and is an address for identifying the location of a specific resource.

[0376] "Interior perspective" is an image or rendering that shows the visual representation of furniture arranged in an actual room.

[0377] This invention relates to a system that automatically generates interior perspective drawings based on floor plan and furniture information entered by a user. This system requires a server, terminals, and users to communicate with each other and a program to process data.

[0378] Hardware and software used

[0379] The following hardware and software are used to implement this system.

[0380] Server: A central system that processes and analyzes data

[0381] Terminal: The device the user uses to provide input information

[0382] Image processing algorithms: Image analysis libraries such as OpenCV

[0383] Web scraping technology: Web data extraction tools such as BeautifulSoup

[0384] 3D modeling software: 3D model generation tools such as Blender

[0385] Program processing overview

[0386] Data entry and submission

[0387] The user uses their device to input specific floor plan information and its scale. They also input the name and URL of the furniture they are considering purchasing. For example, the user selects the product "Modern Sofa" and enters its URL, "http: / / example.com / modern-sofa." The device combines these input data into a single request and sends it to the server.

[0388] Parsing floor plan information

[0389] The server reads the received floor plan information and analyzes the shape and dimensions of the room using an image processing algorithm (e.g., OpenCV). This identifies the position of walls and the shape of the room, and calculates specific dimensions (e.g., 4m x 5m). Scale information is also taken into account and converted into actual dimensions.

[0390] Get furniture information

[0391] The server then accesses the URL provided by the user to retrieve detailed information about the furniture. This is done using web scraping techniques (e.g., BeautifulSoup) or APIs to collect the furniture's dimensions (e.g., width 2m, depth 0.8m, height 0.85m) and shape data. The retrieved data is then formatted into a standard format.

[0392] 3D model generation and rendering

[0393] The server integrates the analyzed room dimensions and furniture size data and creates a 3D model of the entire room using 3D modeling software (e.g., Blender). Based on the room layout, furniture is placed in appropriate locations. The server then renders this 3D model to generate a highly accurate interior perspective.

[0394] Interior perspective provided

[0395] The generated image data of the interior perspective is sent from the server to the terminal. The terminal displays the received interior perspective to the user, allowing the user to check it realistically. This allows the user to check whether the furniture is suitable for the room and matches their image before purchasing.

[0396] Specific examples

[0397] For example, imagine a user wants to place a new sofa in their living room. The user inputs the floor plan of the living room and the "Modern Sofa" information. Based on this, the system generates a highly accurate interior perspective and displays it to the user. This process helps users avoid the disappointment of finding that the sofa doesn't fit the room after purchase.

[0398] Example prompts for generative AI models

[0399] "I want to place a new modern sofa in my living room. Please generate an interior perspective using the floor plan and furniture information below.

[0400] Floor plan information: Living room (4m x 5m)

[0401] Furniture information: Modern Sofa (http: / / example.com / modern-sofa)

[0402] Thank you."

[0403] In this way, this system allows users to easily generate realistic interior perspectives and use them as a reference when selecting furniture. The server and device work together to process data based on the information entered by the user, and provide results in real time.

[0404] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0405] Divide the processing flow of the system program into processing steps

[0406] Step 1:

[0407] The user uses their device to input floor plan information and its scale. Specifically, the user uploads the floor plan as an image file using the device and inputs the scale information into a text field. For example, the user inputs the floor plan of the living room and the scale of "1:50". This input information is stored on the device for further processing.

[0408] Step 2:

[0409] The user enters furniture information. The user enters the name of the furniture they are considering purchasing and its URL into the terminal. For example, the user enters the furniture name "Modern Sofa" and its URL "http: / / example.com / modern-sofa." This input information is also saved on the terminal for subsequent processing.

[0410] Step 3:

[0411] The device combines the input floor plan information and furniture information into a single request and sends it to the server. Specifically, the device packages the input floor plan information (image file) and furniture information (name and URL) in JSON format and sends it to the server via an HTTP request. Once the input data arrives at the server, it proceeds to the next step.

[0412] Step 4:

[0413] The server reads the received floor plan information and performs image processing. The server uses the OpenCV library to analyze the received floor plan image. Specifically, it detects lines in the image and identifies the shape of the room and the location of walls. The results of this analysis (e.g., the shape and dimensions of the room) are saved for further processing.

[0414] Step 5:

[0415] The server uses the scale information to convert the analysis results to actual size. For example, if a scale of "1:50" is entered, a 4cm line actually corresponds to 2m, so the room dimensions are converted based on that. The result of this conversion (for example, the dimensions of a 4m x 5m room) is then passed on to the next step.

[0416] Step 6:

[0417] The server accesses the furniture information URL and retrieves detailed information about the furniture using web scraping or an API. Specifically, the server uses BeautifulSoup to parse the HTML in the URL and extract detailed information such as the furniture's height, width, and depth. This extracted data is formatted into a standard format (e.g., JSON) and saved for further processing.

[0418] Step 7:

[0419] The server combines the room's dimensional data with detailed furniture information to generate a 3D model. The server then uses 3D modeling software such as Blender to create a 3D model of the entire room. Specifically, it generates the room's outline based on the room's dimensional data, and places the 3D furniture models within the room based on the extracted furniture information.

[0420] Step 8:

[0421] The server renders the generated 3D model. The server uses Blender's rendering function to generate a highly accurate interior perspective. The rendering results (image data) are saved for further processing.

[0422] Step 9:

[0423] The server sends the rendered interior perspective image data to the device. The server sends the image data as an HTTP response, and the device receives it. This received data proceeds to the next step.

[0424] Step 10:

[0425] The device displays the received interior perspective to the user. Specifically, the rendered image of the interior perspective is displayed via the device's user interface. The user can check this in real time to confirm whether the furniture is suitable for the room and matches their image before purchasing.

[0426] (Application example 1)

[0427] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0428] When users arrange furniture in their rooms, it is difficult to visually confirm how the furniture will be placed in the room before purchasing it. Furthermore, moving actual furniture around to trial and error the interior layout is time-consuming. Therefore, there is a need for a support system that helps users smoothly select and arrange furniture.

[0429] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0430] In this invention, the server includes means for acquiring floor plan information input by a user, means for acquiring furniture information input by a user, means for analyzing the acquired floor plan information to identify room dimensions, means for identifying furniture dimensions based on the acquired furniture information, means for generating a 3D model based on the identified room dimensions and furniture dimensions, means for rendering an interior perspective based on the generated 3D model, means for providing the rendered interior perspective to a user, and means for inputting the floor plan information and furniture information as prompts and automatically generating an interior perspective using a generative AI model, thereby enabling a user to realistically see how furniture will be arranged in a room before purchasing it.

[0431] The "means for acquiring floor plan information entered by the user" is a function by which the system receives floor plan information indicating the layout of the user's own room via the network.

[0432] The "means for acquiring furniture information entered by the user" is a function by which the system receives detailed information about the furniture that the user is considering purchasing.

[0433] The "means for analyzing the acquired floor plan information and identifying the dimensions of the room" is a function for analyzing the received floor plan information and identifying the overall shape and dimensions of the room.

[0434] The "means for identifying the dimensions of the furniture based on the acquired furniture information" is a function for identifying the size of the furniture based on the furniture information provided by the user.

[0435] The "means for generating a 3D model based on the specified room dimensions and furniture dimensions" refers to a function for generating a three-dimensional model using 3D modeling software using the dimensions of the room and furniture.

[0436] The "means for rendering an interior perspective based on the generated 3D model" is a function for drawing a highly accurate interior perspective based on the generated three-dimensional model.

[0437] The "means for providing the rendered interior perspective to the user" is a function for transmitting the rendered interior perspective to the user's device and displaying it.

[0438] "Means for inputting the floor plan information and furniture information as prompts and automatically generating an interior perspective using a generative AI model" refers to a function for automatically creating an interior perspective using a generative AI model based on the floor plan information and furniture information provided by the user.

[0439] A system for implementing this invention provides a function for generating interior perspectives of a room based on floor plan information and furniture information entered by a user. Specifically, the system has a function for automatically generating interior perspectives by inputting floor plan information and furniture information as prompts and using a generative AI model. Detailed embodiments of the system of the present invention are described below.

[0440] System Program

[0441] 1. Getting user input data:

[0442] The user inputs floor plan information and furniture information using their own device (such as a smartphone). The floor plan information is provided as an image file, and the furniture information is input in the form of a web link (URL). For example, the user inputs URLs such as "http: / / example.com / floorplan.png" or "http: / / example.com / modern-sofa."

[0443] 2. Parsing floor plan information:

[0444] The server receives the input floor plan image and uses image recognition software (e.g., OpenCV) to identify the shape and dimensions of the room. The analysis results are calculated as specific dimensional information such as the width and height of the room.

[0445] 3. Get furniture information:

[0446] The server accesses the furniture URL provided by the user and retrieves detailed information (such as dimensions) of the furniture using web scraping or API, thereby collecting data such as the width, height, and depth of the furniture.

[0447] 4. Generate 3D model:

[0448] The analyzed room dimensions are combined with the acquired furniture size information to generate a 3D model of the room using 3D modeling software (e.g., Blender, Open3D). The furniture is then placed in the designated locations, and the overall layout is determined.

[0449] 5. Interior perspective rendering:

[0450] Based on the generated 3D model, high-precision interior perspectives are created using rendering software, and realistic effects such as lighting and shadows are also added at this stage.

[0451] 6. Use of generative AI models:

[0452] The server inputs the floor plan information and furniture information into the AI ​​model as prompts, and automatically generates the generated interior perspective. Specifically, the prompt uses the following text: "Please automatically generate a 3D interior perspective of the room based on the following information: Floor plan URL: 'http: / / example.com / floorplan.png' Furniture URL: 'http: / / example.com / modern-sofa'."

[0453] 7. User Offerings:

[0454] The rendered interior perspective is sent from the server to the user's device and displayed on a smartphone app, allowing users to visually check how furniture will be arranged in their room before purchasing it.

[0455] Hardware and software used

[0456] Hardware: User's smartphone, cloud server

[0457] Software: Flask (web server framework), OpenCV (image processing library), Blender or Open3D (3D modeling and rendering software), web scraping tool

[0458] Specific examples

[0459] A user uses an online shopping site's app to input the floor plan of their living room and information about the sofa they are considering purchasing. The app then sends the input information to a server, which then generates an interior perspective based on the floor plan and furniture information. The generated interior perspective is displayed to the user through the app, allowing the user to see in advance how the sofa will be placed in their living room.

[0460] Prompt Sentence Examples

[0461] "Please automatically generate a 3D interior perspective of the room based on the following information. Floor plan URL: 'http: / / example.com / floorplan.png' Furniture URL: 'http: / / example.com / modern-sofa'"

[0462] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0463] Step 1:

[0464] The user inputs floor plan information and furniture information using their own terminal. The input floor plan information is provided as an image file, and the furniture information is provided as a URL. This information is then sent to the server by the terminal.

[0465] Step 2:

[0466] The server retrieves the floor plan information it has received. The floor plan information (input) is in image file format, and a Python image processing library (e.g., OpenCV) is used to read the image and identify the shape and dimensions of the room. The analysis results (output) are specific dimensional information such as the width and height of the room.

[0467] Step 3:

[0468] The server retrieves the furniture information it receives. The furniture information (input) is in URL format, and the server retrieves detailed furniture information from the specified URL using web scraping or a web API. This allows it to collect dimensional data (output) such as the width, height, and depth of the furniture.

[0469] Step 4:

[0470] The server generates a 3D model based on floor plan information and furniture information. Specifically, it uses a 3D modeling tool such as Open3D to create a 3D model of the room and furniture. At this time, the room dimension data (input) and furniture dimension data (input) are integrated to generate a 3D model (output) with the furniture arranged in the 3D space.

[0471] Step 5:

[0472] The server renders the interior perspective based on the generated 3D model, and uses Blender or other rendering software to create a highly accurate interior perspective (output) with realistic lighting and shadow effects.

[0473] Step 6:

[0474] The server provides the rendered interior perspective to the user. The generated interior perspective image (input) is sent from the server to the user's device, which displays it in a form that the user can visually confirm. The user can check this interior perspective in real time and consider furniture placement.

[0475] Step 7:

[0476] The server inputs floor plan information and furniture information as prompts into the generative AI model, which then automatically generates an interior perspective. Specifically, the prompt "Please automatically generate a 3D interior perspective of the room based on the following information: Floor plan URL: 'http: / / example.com / floorplan.png' Furniture URL: 'http: / / example.com / modern-sofa'" is input into the generative AI model, and the interior perspective (output) generated by the model is obtained. This generated interior perspective is also provided to the user's device.

[0477] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0478] This invention relates to a system that automatically generates interior perspectives based on floor plan information and furniture information, and combines it with an emotion engine that recognizes user emotions to provide more user-friendly and personalized interior design suggestions. To implement this system, a program is required for the server, terminals, and users to communicate with each other and process data. The specific program processing and its operation are described below.

[0479] Data entry and submission

[0480] The user uses their own device to input specific floor plan information and its scale. They also input the name and URL of the furniture they are considering purchasing, as well as facial and voice data for use by the emotion engine. For example, suppose a user selects a product called "Modern Sofa" and enters its URL, "http: / / example.com / modern-sofa." This input data is compiled by the device into a single request and sent to the server.

[0481] Parsing floor plan information

[0482] The server reads the received floor plan information and uses image processing algorithms to analyze the shape and dimensions of the room. This identifies the location of walls and the shape of the room, and calculates specific dimensions (e.g., 4m x 5m). Scale information is also taken into account and converted into actual dimensions.

[0483] Get furniture information

[0484] The server then accesses the URL provided by the user to retrieve detailed information about the furniture. This is done using web scraping technology and APIs to collect data such as the furniture's dimensions (e.g., width 2m, depth 0.8m, height 0.85m) and shape. The retrieved data is then formatted into a standard format.

[0485] emotion recognition

[0486] The server uses an emotion engine to analyze the facial expression and voice data sent by the user, and recognizes the user's emotions (e.g., joy, surprise, satisfaction, etc.).

[0487] 3D model generation and rendering

[0488] The server combines the analyzed room dimensions, furniture size data, and the recognized user emotion data. It uses 3D modeling software to generate a 3D model of the entire room and arranges the furniture in appropriate locations based on the room layout. The server then renders this 3D model and generates a highly accurate interior perspective. Based on the emotion data, it adjusts the presentation of the interior perspective (e.g., color tone and flexibility of placement).

[0489] Interior perspective provided

[0490] The generated image data of the interior perspective is sent from the server to the terminal. The terminal displays the received interior perspective to the user, allowing the user to check it realistically. This allows the user to check whether the furniture is suitable for the room and matches their image before purchasing. In addition, the displayed interior perspective is personalized based on the user's emotions, resulting in a more satisfying proposal.

[0491] In this way, the system of the present invention allows users to easily generate realistic interior perspectives to help them select furniture, and further improves user satisfaction with personalized suggestions based on emotion recognition. As a specific example, when a user is choosing interior decor for a new living room, the emotion engine recognizes the user's reactions and provides the optimal perspective based on those reactions, helping the user make the most satisfying choice.

[0492] The processing flow will be explained below.

[0493] Step 1:

[0494] The user inputs a floor plan file and its scale on their device. For example, they input "floor_plan.png" and the scale "1:100." They also input the name of the furniture they are considering purchasing, "Modern Sofa," and the URL, "http: / / example.com / modern-sofa."

[0495] Step 2:

[0496] Users provide facial expression and voice data for use by the emotion engine by recording their current facial expressions and voice using a webcam and microphone, or by uploading previously collected data.

[0497] Step 3:

[0498] The device compiles the floor plan file, scale, furniture name, URL, and emotion data entered by the user into a single request and sends it to the server.

[0499] Step 4:

[0500] The server reads the received floor plan file and uses image processing algorithms to analyze the shape and dimensions of the room, identifying the location of walls and the shape of the room, and calculating specific dimensions (e.g., 4m x 5m).

[0501] Step 5:

[0502] The server uses the scale information provided by the user to convert the analyzed room dimensions into real-world dimensions.

[0503] Step 6:

[0504] The server accesses the URL of the furniture item entered by the user and retrieves detailed information. Using web scraping technology and APIs, the server collects the furniture's dimensions (2m width, 0.8m depth, 0.85m height) and shape data. This data is then formatted into a standard format.

[0505] Step 7:

[0506] The server uses the received facial expression and voice data to analyze it using an emotion engine, which then recognizes the user's emotions (happiness, surprise, satisfaction, etc.).

[0507] Step 8:

[0508] The server combines the analyzed room dimensions, furniture size data, and the recognized user emotion data, then generates a 3D model of the entire room using 3D modeling software, and places the furniture in appropriate locations based on the room layout.

[0509] Step 9:

[0510] The server renders the generated 3D model and creates a highly accurate interior perspective. Based on the emotion data, the content of the interior perspective (e.g., adjusting color, brightness, and layout) is customized to suit the user's preferences.

[0511] Step 10:

[0512] The server sends the rendered interior perspective image data to the terminal, which then displays the received interior perspective to the user.

[0513] Step 11:

[0514] Users can check the displayed interior perspective and decide whether the furniture they are considering purchasing is suitable for the room and matches their image.In addition, interior perspectives that reflect the user's emotions allow for a more satisfying consideration.

[0515] In this way, users can reduce the chances of making mistakes when choosing furniture and choose the perfect interior.

[0516] Example 2

[0517] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0518] Conventional interior design support systems generate interior perspectives based on floor plan and furniture information entered by the user, but they are unable to provide personalized suggestions that take the user's emotions into account. This results in reduced user satisfaction and makes it difficult to select the optimal interior design. Furthermore, the lack of a function to personalize interior perspectives using emotion recognition led to a demand for a more user-friendly system.

[0519] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0520] In this invention, the server includes means for acquiring floor plan information input by a user, means for acquiring furniture information input by a user, means for analyzing the acquired floor plan information to identify room dimensions, means for identifying furniture dimensions based on the acquired furniture information, means for generating a 3D model based on the identified room dimensions and furniture dimensions, means for rendering an interior perspective based on the generated 3D model, means for acquiring facial expression data or voice data of the user, means for analyzing the acquired facial expression data or voice data to recognize the user's emotion, means for personalizing the rendered interior perspective based on the recognized emotion data, and means for providing the personalized interior perspective to the user. This makes it possible to propose interior perspectives that take the user's emotions into consideration and provide a user with a higher level of satisfaction.

[0521] A "user" is an individual or organization that uses the system to input floor plan and furniture information.

[0522] "Floor plan information" is drawing data that includes information such as the shape and dimensions of the room, and the positions of walls and doors.

[0523] "Furniture information" is information including the name, size, shape, and URL of the furniture.

[0524] "Facial expression data" is image or video data showing the facial expression of the user.

[0525] "Voice data" refers to data that records the user's voice.

[0526] A "server" is a central device that receives requests from users and processes data accordingly.

[0527] A "3D model" is digital data that represents the shape of a room and the arrangement of furniture in three dimensions.

[0528] An "interior perspective" is an image that shows the interior design of a room, generated based on a 3D model.

[0529] An "emotion engine" is a program that analyzes facial expression data and voice data to recognize the user's emotions.

[0530] "Personalization" refers to individually adjusting the interior perspective based on the user's emotional data.

[0531] This invention relates to a system that automatically generates interior perspectives based on floor plan information and furniture information, and combines it with an emotion engine that recognizes user emotions to provide more user-friendly and personalized interior design suggestions. To implement this system, a program is required for the server, terminals, and users to communicate with each other and process data. The specific program processing and its operation are described below.

[0532] First, the user uses their device to input floor plan information and its scale. This floor plan information is uploaded as a JPEG image, for example, and the scale is specified as "1:100." The user also enters the name of the furniture they are considering, "Modern Sofa," and its URL, "http: / / example.com / modern-sofa," and provides smiling facial expression data via their webcam. The device then compiles this data into a single request and sends it to the server.

[0533] The server uses image processing libraries such as OpenCV and TensorFlow to analyze the received floor plan file. Image processing algorithms are used to detect the positions of walls, doors, and windows, and to identify the shape and dimensions of the room. For example, it may determine that the room is rectangular, measuring 4m x 5m. The server also converts the units on the floor plan into actual dimensions based on the scale information.

[0534] The server then accesses the URL provided by the user and uses web scraping techniques (such as BeautifulSoup) or APIs to obtain detailed information about the furniture. For example, the size data for a "Modern Sofa" measuring 2m wide, 0.8m deep, and 0.85m high is collected. The collected data is then formatted into a standard format (such as JSON) for subsequent processing.

[0535] The server also uses emotion engines such as Face++ and Microsoft Azure Emotion API to analyze the received facial and voice data. The server recognizes emotions such as joy, surprise, and satisfaction from the user's facial expressions and stores the results in a database.

[0536] The server combines the room dimension data, furniture size data, and the user's recognized emotion data. It uses 3D modeling software (such as Blender) to generate a 3D model of the entire room and arranges the furniture in appropriate locations. For example, if the user's emotion is recognized as "joy," the server creates an interior perspective using bright colors. It then renders this 3D model to generate a highly accurate interior perspective image.

[0537] Finally, the server sends the image data of the generated interior perspective to the device. The device displays the received interior perspective to the user, allowing the user to check it in real time. This allows the user to determine whether the furniture is suitable for the room and whether it matches their image. A personalized interior perspective based on emotional data allows the user to make a more satisfying choice.

[0538] As a concrete example, when a user selects the interior of a new living room, the system works as follows: The user selects "Modern Sofa" and enters the URL "http: / / example.com / modern-sofa." When the user smiles at the camera, the server analyzes the smile and recognizes that the user is "satisfied." Based on this information, the server generates an interior perspective that reflects the layout and color tones that will most likely satisfy the user.

[0539] Example prompt sentence:

[0540] "Enter your floor plan information, provide details about the furniture you're considering, and create a layout for your new living room. Smile for the camera and enjoy the interior design experience."

[0541] This system allows users to easily generate realistic interior perspectives and use them as a reference when selecting furniture. Furthermore, by utilizing emotion recognition, it can provide personalized interior design suggestions, improving user satisfaction.

[0542] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0543] Step 1:

[0544] The user uses their own device to input floor plan information and its scale. Specifically, they upload a floor plan of, say, a living room as an image file (JPEG, PNG, etc.) and specify the scale as "1:100," for example. The input at this point is the floor plan image file and scale information, and the output is request data that sends this information to the server.

[0545] Step 2:

[0546] The user enters the name of the furniture they are considering purchasing, "Modern Sofa," and its URL, "http: / / example.com / modern-sofa," into their device. In addition, the user provides facial expression data of a smile via the camera. The input is the furniture name, URL, and facial expression data, and the output is a request data that compiles these.

[0547] Step 3:

[0548] The terminal sends the floor plan information, furniture information, and facial expression data entered by the user to the server as a single request. The input is the request data from the user, and the output is that this request is sent to the server.

[0549] Step 4:

[0550] The server analyzes the received floor plan file. Specifically, it uses image processing libraries such as OpenCV and TensorFlow to identify the shape and dimensions of the room, as well as the locations of walls, doors, and windows. The input is an image file of the floor plan and scale information, and the output is specific dimensional data of the room, including the locations of walls, doors, and windows.

[0551] Step 5:

[0552] The server converts the units on the floor plan into actual dimensions based on the scale information. For example, if a scale of 1:100 is input, 1 cm corresponds to 1 m. This conversion outputs the actual room dimension data.

[0553] Step 6:

[0554] The server accesses the furniture URL provided by the user and uses web scraping technology (e.g., BeautifulSoup) or APIs to obtain detailed information about the furniture. The input is the furniture URL, and the output is the furniture's size (width, depth, height) and shape data.

[0555] Step 7:

[0556] The server formats the acquired furniture information into a standard format (e.g., JSON). The formatted data is used in subsequent processing. The input is the acquired furniture details, and the output is the data formatted in the standard format.

[0557] Step 8:

[0558] The server uses an emotion engine to analyze the facial expression data sent by the user. Specifically, it uses Face++ or Microsoft Azure Emotion API to recognize the user's emotions from the facial expression data. The input is the user's facial expression data, and the output is the recognized emotion data (happiness, surprise, satisfaction, etc.).

[0559] Step 9:

[0560] The server integrates the room dimension data, furniture size data, and emotion data. Based on this, it uses 3D modeling software (e.g., Blender) to generate a 3D model of the entire room and arranges the furniture in appropriate locations. The input is the integrated data, and the output is a 3D model of the entire room.

[0561] Step 10:

[0562] The server renders the generated 3D model and generates a highly accurate interior perspective image. It also personalizes color tones and furniture placement based on the user's emotional data. The input is the 3D model and emotional data, and the output is personalized interior perspective image data.

[0563] Step 11:

[0564] The server sends the generated interior perspective image data to the terminal. The input is the interior perspective image data, and the output is that it is sent to the user's terminal.

[0565] Step 12:

[0566] The terminal displays the received interior perspective to the user. The user can then view the displayed interior perspective and check whether the furniture is suitable for the room. The input is image data of the interior perspective sent from the server, and the output is an interface that allows the user to visually check it.

[0567] (Application example 2)

[0568] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0569] Conventional interior design proposal systems generate interior perspectives based on floor plan and furniture information provided by the user, but they lack personalization based on the user's emotions and preferences. This often leaves users dissatisfied with the proposals. Furthermore, it is difficult to grasp in advance the atmosphere and feel of the furniture arrangement, which can reduce purchasing motivation.

[0570] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0571] In this invention, the server includes means for acquiring floor plan information input by a user, means for acquiring furniture information input by a user, means for recognizing emotions based on the user's facial expression data and voice data, means for analyzing the acquired floor plan information to identify room dimensions, means for identifying furniture dimensions based on the acquired furniture information, means for generating a 3D model based on the identified room dimensions, furniture dimensions, and the recognized emotion, means for rendering an interior perspective based on the generated 3D model, and means for providing the rendered interior perspective to the user. This enables personalized interior suggestions based on the user's emotions, thereby improving user satisfaction and purchasing motivation.

[0572] "Floor plan information" is data about the shape and dimensions of a room provided by the user, and is basic information for identifying the specific dimensions of the room through analysis.

[0573] "Furniture information" refers to data about furniture provided by the user, including detailed information about the furniture, including its size and shape.

[0574] "Emotion recognition" is a technology that analyzes a user's facial expression data and voice data to identify the user's emotional state (for example, joy, surprise, satisfaction, etc.).

[0575] A "3D model" is a digital representation of a three-dimensional interior space generated based on analyzed floor plan information, furniture information, and recognized emotional data.

[0576] An "interior perspective" is a visual image rendered based on the generated 3D model, and is an interior design proposal provided to the user.

[0577] A "server" is a central computing device that receives, analyzes, and processes data sent from user terminals.

[0578] A "terminal" is a device operated by a user, and is a device for inputting and transmitting floor plan information, furniture information, facial expression data, and voice data.

[0579] "Rendering" is the process of representing the generated 3D model as a visual image and providing it to the user.

[0580] This invention is a system that recognizes a user's emotions and automatically generates personalized interior design proposals based on floor plan and furniture information provided by the user. To realize this system, multiple components, such as a server, a terminal, and an emotion engine, work together.

[0581] The user's device inputs floor plan information, furniture information, and facial expression and voice data for emotion recognition. The floor plan information is acquired as an image file, and the furniture information is acquired via a URL. This input data is compiled by the device and sent to the server as a single request.

[0582] The server first analyzes the floor plan information sent to it and identifies the shape and dimensions of the room. This process uses image processing software such as OpenCV. Next, it accesses the URL provided by the user and uses web scraping technology and APIs to obtain detailed information about the furniture. This data is then converted into a standard format.

[0583] Furthermore, the server uses an emotion engine to analyze the user's facial expression data and voice data to recognize the user's emotional state. This analysis is performed using emotion recognition software such as EmotionEngine.

[0584] The system combines the identified room dimensions, furniture size data, and the user's recognized emotion data to generate a 3D model of the entire room using 3D modeling software. This 3D model is then rendered with high precision using a rendering engine to generate an optimal interior perspective for the user. The perspective content, including color tone and placement, is adjusted based on the user's emotion.

[0585] Finally, the generated image data of the interior perspective is sent from the server to the terminal. The terminal receives this data and displays it to the user. This allows the user to confirm whether the furniture is suitable for the room and matches their image before purchasing. In addition, the displayed perspective is personalized based on the user's emotions, further increasing user satisfaction.

[0586] For example, when a user is choosing interior decor for a new living room, the emotion engine recognizes the user's reactions and provides the optimal perspective based on those reactions. This system allows the user to make a more satisfying interior design choice.

[0587] Example prompt for a generative AI model:

[0588] "Enter the floor plan and furniture information of the room selected by the user, as well as emotional data, and generate a 3D model to propose a room layout. Recognize the emotional data from the user's facial expressions and voice, and create the optimal interior perspective based on those emotions."

[0589] This invention makes it possible to propose personalized interiors based on emotion recognition, thereby improving user satisfaction and purchasing motivation.

[0590] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0591] Step 1:

[0592] The user uses a terminal to input floor plan information, furniture information, facial expression data, and voice data. The input floor plan information is specified as an image file, and the furniture information is provided as a URL. Facial expression data and voice data for emotion recognition are also input as files. This data is compiled by the terminal into a single request and sent to the server. The input is an image file of the floor plan, a URL for the furniture, an image file of the facial expression data, and an audio file of the voice data, and the output is the data compiled into a single request.

[0593] Step 2:

[0594] The server obtains floor plan information from the received request and analyzes it using image processing algorithms such as OpenCV. The analysis identifies the shape and dimensions of the room and calculates specific dimensional information. The input is an image file of the floor plan, and the output is the room dimensional information.

[0595] Step 3:

[0596] The server accesses the URL provided by the user and uses web scraping technology or APIs to obtain detailed information about the furniture. The obtained data is converted into a standard format, and the size and shape of the furniture are identified. The input is the furniture URL, and the output is information about the size and shape of the furniture.

[0597] Step 4:

[0598] The server analyzes the facial expression data and voice data using an emotion engine (e.g., EmotionEngine) to recognize the user's emotions. This analysis identifies the user's emotional state (e.g., joy, surprise, satisfaction, etc.). The input is an image file of the facial expression data and an audio file of the voice data, and the output is information on the recognized emotions.

[0599] Step 5:

[0600] The server integrates the identified room dimensions, furniture size data, and the recognized user emotion data, and generates a 3D model of the entire room using 3D modeling software. The input is the room dimension data, furniture size data, and emotion information, and the output is the 3D model. This model includes the overall layout of the room and the arrangement of the furniture.

[0601] Step 6:

[0602] The server uses a rendering engine to render the generated 3D model with high precision, generating an optimal interior perspective for the user. The perspective content is adjusted based on the user's emotions, including color tone and placement. The input is the 3D model, and the output is the rendered image data of the interior perspective.

[0603] Step 7:

[0604] The server sends image data of the generated interior perspective to the terminal. The terminal receives this data and displays it to the user. This allows the user to check whether the furniture is suitable for the room and matches their image before purchasing. The displayed perspective is personalized based on the user's emotions, improving user satisfaction. The input is image data of the rendered interior perspective, and the output is the interior perspective displayed on the user's terminal.

[0605] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0606] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0607] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0608] [Third embodiment]

[0609] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0610] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0611] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0612] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0613] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0614] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0615] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0616] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0617] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0618] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0619] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0620] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0621] This invention relates to a system that automatically generates interior perspective drawings of a room based on floor plan information and furniture information entered by a user. To implement this system, a program is required for communication between a server, a terminal, and a user, and for data processing. The specific program processing and its operation are described below.

[0622] Data entry and submission

[0623] The user uses their device to input specific floor plan information and its scale. They also input the name and URL of the furniture they are considering purchasing. For example, the user selects the product "Modern Sofa" and enters its URL, "http: / / example.com / modern-sofa." The device combines these input data into a single request and sends it to the server.

[0624] Parsing floor plan information

[0625] The server reads the received floor plan information and uses image processing algorithms to analyze the shape and dimensions of the room. This identifies the location of walls and the shape of the room, and calculates specific dimensions (e.g., 4m x 5m). Scale information is also taken into account and converted into actual dimensions.

[0626] Get furniture information

[0627] The server then accesses the URL provided by the user to retrieve detailed information about the furniture. This is done using web scraping technology and APIs to collect data such as the furniture's dimensions (e.g., width 2m, depth 0.8m, height 0.85m) and shape. The retrieved data is then formatted into a standard format.

[0628] 3D model generation and rendering

[0629] The server integrates the analyzed room dimensions and furniture size data and uses 3D modeling software to create a 3D model of the entire room. Based on the room layout, furniture is placed in appropriate locations. The server then renders this 3D model to generate a highly accurate interior perspective.

[0630] Interior perspective provided

[0631] The image data of the generated interior perspective is sent from the server to the terminal. The terminal displays the received interior perspective to the user, allowing the user to check it realistically. This allows the user to check whether the furniture is suitable for the room and whether it matches their image before purchasing.

[0632] In this way, the system of the present invention allows users to easily generate realistic interior perspectives and use them as a reference when selecting furniture. To give a concrete example, if a user wants to place a new sofa in their living room, they can simply input the floor plan and sofa information to check in advance whether the sofa will fit the room. This process can prevent mistakes after the purchase.

[0633] The processing flow will be explained below.

[0634] Step 1:

[0635] The user inputs a floor plan file and its scale on their device. They also input the name and URL of the furniture they are considering purchasing. For example, the user inputs the name "Modern Sofa" and the URL "http: / / example.com / modern-sofa".

[0636] Step 2:

[0637] The terminal compiles the floor plan file, scale, furniture name, and URL entered by the user into a single request and sends it to the server.

[0638] Step 3:

[0639] The server reads the received floor plan file and uses image processing algorithms to analyze the shape and dimensions of the room, recognizing the position of walls and the shape of the room, and determining the dimensions of the room (e.g., 4m x 5m).

[0640] Step 4:

[0641] The server uses the scale information provided by the user to convert the identified room dimensions into real-world dimensions.

[0642] Step 5:

[0643] The server accesses the URL entered by the user and obtains detailed information about the furniture. Using web scraping technology and APIs, the server collects furniture dimensions (e.g., width 2m, depth 0.8m, height 0.85m) and shape data.

[0644] Step 6:

[0645] The server converts the acquired furniture data into a standard format and integrates it with the room dimension data.

[0646] Step 7:

[0647] The server uses 3D modeling software to generate a 3D model of the entire room, then places the furniture in the appropriate locations based on the room's layout.

[0648] Step 8:

[0649] The server renders the generated 3D model and creates a highly accurate interior perspective.

[0650] Step 9:

[0651] The server sends the rendered interior perspective image data to the terminal.

[0652] Step 10:

[0653] The terminal displays the received interior perspective to the user, allowing the user to check whether the sofa is suitable for the room and matches their image.

[0654] In this way, users can reduce the chances of making mistakes when choosing furniture and choose the perfect interior.

[0655] Example 1

[0656] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0657] In recent years, there has been a demand for tools to simulate interior layouts. However, current systems require users to manually input floor plans and furniture information, making them cumbersome to use. Furthermore, the generated interior perspectives lack precision and real-time performance, which means they cannot fully reflect the nuances of actual furniture layouts. This often leads to failure after furniture purchase due to differences in layout or size compared to expectations.

[0658] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0659] In this invention, the server includes means for acquiring floor plan information input by a user, means for acquiring furniture information input by a user, means for analyzing the acquired floor plan information to identify room dimensions, means for identifying furniture dimensions based on the acquired furniture information, means for integrating the room dimensions and furniture dimensions to generate a 3D model, means for rendering an interior perspective based on the 3D model, means for providing the rendered interior perspective to a user's terminal, and means for the terminal to display the provided interior perspective to the user. This allows a user to automatically generate a high-precision interior perspective based on the information they input and check it in real time.

[0660] The "means for acquiring" is a function for receiving data entered by a user and converting it into a format that can be used within the system.

[0661] "Means of analysis" refers to the function of interpreting information based on acquired data and extracting details such as specific dimensions and shapes.

[0662] The "means of identification" is a function for clarifying the exact dimensions and position from the analyzed information.

[0663] "Means for generating a three-dimensional model" refers to a function for constructing a three-dimensional virtual space based on two-dimensional or text-format data.

[0664] "Rendering means" is a function for visually expressing the generated three-dimensional model and outputting it as a concrete image.

[0665] The "means of providing to the user's terminal" is a function for transmitting data generated by the server via a network to the device used by the user.

[0666] The "means for displaying by the terminal" is a function for visually showing the received data to the user via a user interface.

[0667] "Floor plan information" refers to drawings showing the shape and dimensions of a room and related data.

[0668] "Furniture information" is data about the dimensions, shape, and other attributes of a particular piece of furniture.

[0669] "URL" is an abbreviation for Uniform Resource Locator, and is an address for identifying the location of a specific resource.

[0670] "Interior perspective" is an image or rendering that shows the visual representation of furniture arranged in an actual room.

[0671] This invention relates to a system that automatically generates interior perspective drawings based on floor plan and furniture information entered by a user. This system requires a server, terminals, and users to communicate with each other and a program to process data.

[0672] Hardware and software used

[0673] The following hardware and software are used to implement this system.

[0674] Server: A central system that processes and analyzes data

[0675] Terminal: The device the user uses to provide input information

[0676] Image processing algorithms: Image analysis libraries such as OpenCV

[0677] Web scraping technology: Web data extraction tools such as BeautifulSoup

[0678] 3D modeling software: 3D model generation tools such as Blender

[0679] Program processing overview

[0680] Data entry and submission

[0681] The user uses their device to input specific floor plan information and its scale. They also input the name and URL of the furniture they are considering purchasing. For example, the user selects the product "Modern Sofa" and enters its URL, "http: / / example.com / modern-sofa." The device combines these input data into a single request and sends it to the server.

[0682] Parsing floor plan information

[0683] The server reads the received floor plan information and analyzes the shape and dimensions of the room using an image processing algorithm (e.g., OpenCV). This identifies the position of walls and the shape of the room, and calculates specific dimensions (e.g., 4m x 5m). Scale information is also taken into account and converted into actual dimensions.

[0684] Get furniture information

[0685] The server then accesses the URL provided by the user to retrieve detailed information about the furniture. This is done using web scraping techniques (e.g., BeautifulSoup) or APIs to collect the furniture's dimensions (e.g., width 2m, depth 0.8m, height 0.85m) and shape data. The retrieved data is then formatted into a standard format.

[0686] 3D model generation and rendering

[0687] The server integrates the analyzed room dimensions and furniture size data and creates a 3D model of the entire room using 3D modeling software (e.g., Blender). Based on the room layout, furniture is placed in appropriate locations. The server then renders this 3D model to generate a highly accurate interior perspective.

[0688] Interior perspective provided

[0689] The generated image data of the interior perspective is sent from the server to the terminal. The terminal displays the received interior perspective to the user, allowing the user to check it realistically. This allows the user to check whether the furniture is suitable for the room and matches their image before purchasing.

[0690] Specific examples

[0691] For example, imagine a user wants to place a new sofa in their living room. The user inputs the floor plan of the living room and the "Modern Sofa" information. Based on this, the system generates a highly accurate interior perspective and displays it to the user. This process helps users avoid the disappointment of finding that the sofa doesn't fit the room after purchase.

[0692] Example prompts for generative AI models

[0693] "I want to place a new modern sofa in my living room. Please generate an interior perspective using the floor plan and furniture information below.

[0694] Floor plan information: Living room (4m x 5m)

[0695] Furniture information: Modern Sofa (http: / / example.com / modern-sofa)

[0696] Thank you."

[0697] In this way, this system allows users to easily generate realistic interior perspectives and use them as a reference when selecting furniture. The server and device work together to process data based on the information entered by the user, and provide results in real time.

[0698] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0699] Divide the processing flow of the system program into processing steps

[0700] Step 1:

[0701] The user uses their device to input floor plan information and its scale. Specifically, the user uploads the floor plan as an image file using the device and inputs the scale information into a text field. For example, the user inputs the floor plan of the living room and the scale of "1:50". This input information is stored on the device for further processing.

[0702] Step 2:

[0703] The user enters furniture information. The user enters the name of the furniture they are considering purchasing and its URL into the terminal. For example, the user enters the furniture name "Modern Sofa" and its URL "http: / / example.com / modern-sofa." This input information is also saved on the terminal for subsequent processing.

[0704] Step 3:

[0705] The device combines the input floor plan information and furniture information into a single request and sends it to the server. Specifically, the device packages the input floor plan information (image file) and furniture information (name and URL) in JSON format and sends it to the server via an HTTP request. Once the input data arrives at the server, it proceeds to the next step.

[0706] Step 4:

[0707] The server reads the received floor plan information and performs image processing. The server uses the OpenCV library to analyze the received floor plan image. Specifically, it detects lines in the image and identifies the shape of the room and the location of walls. The results of this analysis (e.g., the shape and dimensions of the room) are saved for further processing.

[0708] Step 5:

[0709] The server uses the scale information to convert the analysis results to actual size. For example, if a scale of "1:50" is entered, a 4cm line actually corresponds to 2m, so the room dimensions are converted based on that. The result of this conversion (for example, the dimensions of a 4m x 5m room) is then passed on to the next step.

[0710] Step 6:

[0711] The server accesses the furniture information URL and retrieves detailed information about the furniture using web scraping or an API. Specifically, the server uses BeautifulSoup to parse the HTML in the URL and extract detailed information such as the furniture's height, width, and depth. This extracted data is formatted into a standard format (e.g., JSON) and saved for further processing.

[0712] Step 7:

[0713] The server combines the room's dimensional data with detailed furniture information to generate a 3D model. The server then uses 3D modeling software such as Blender to create a 3D model of the entire room. Specifically, it generates the room's outline based on the room's dimensional data, and places the 3D furniture models within the room based on the extracted furniture information.

[0714] Step 8:

[0715] The server renders the generated 3D model. The server uses Blender's rendering function to generate a highly accurate interior perspective. The rendering results (image data) are saved for further processing.

[0716] Step 9:

[0717] The server sends the rendered interior perspective image data to the device. The server sends the image data as an HTTP response, and the device receives it. This received data proceeds to the next step.

[0718] Step 10:

[0719] The device displays the received interior perspective to the user. Specifically, the rendered image of the interior perspective is displayed via the device's user interface. The user can check this in real time to confirm whether the furniture is suitable for the room and matches their image before purchasing.

[0720] (Application example 1)

[0721] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0722] When users arrange furniture in their rooms, it is difficult to visually confirm how the furniture will be placed in the room before purchasing it. Furthermore, moving actual furniture around to trial and error the interior layout is time-consuming. Therefore, there is a need for a support system that helps users smoothly select and arrange furniture.

[0723] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0724] In this invention, the server includes means for acquiring floor plan information input by a user, means for acquiring furniture information input by a user, means for analyzing the acquired floor plan information to identify room dimensions, means for identifying furniture dimensions based on the acquired furniture information, means for generating a 3D model based on the identified room dimensions and furniture dimensions, means for rendering an interior perspective based on the generated 3D model, means for providing the rendered interior perspective to a user, and means for inputting the floor plan information and furniture information as prompts and automatically generating an interior perspective using a generative AI model, thereby enabling a user to realistically see how furniture will be arranged in a room before purchasing it.

[0725] The "means for acquiring floor plan information entered by the user" is a function by which the system receives floor plan information indicating the layout of the user's own room via the network.

[0726] The "means for acquiring furniture information entered by the user" is a function by which the system receives detailed information about the furniture that the user is considering purchasing.

[0727] The "means for analyzing the acquired floor plan information and identifying the dimensions of the room" is a function for analyzing the received floor plan information and identifying the overall shape and dimensions of the room.

[0728] The "means for identifying the dimensions of the furniture based on the acquired furniture information" is a function for identifying the size of the furniture based on the furniture information provided by the user.

[0729] The "means for generating a 3D model based on the specified room dimensions and furniture dimensions" refers to a function for generating a three-dimensional model using 3D modeling software using the dimensions of the room and furniture.

[0730] The "means for rendering an interior perspective based on the generated 3D model" is a function for drawing a highly accurate interior perspective based on the generated three-dimensional model.

[0731] The "means for providing the rendered interior perspective to the user" is a function for transmitting the rendered interior perspective to the user's device and displaying it.

[0732] "Means for inputting the floor plan information and furniture information as prompts and automatically generating an interior perspective using a generative AI model" refers to a function for automatically creating an interior perspective using a generative AI model based on the floor plan information and furniture information provided by the user.

[0733] A system for implementing this invention provides a function for generating interior perspectives of a room based on floor plan information and furniture information entered by a user. Specifically, the system has a function for automatically generating interior perspectives by inputting floor plan information and furniture information as prompts and using a generative AI model. Detailed embodiments of the system of the present invention are described below.

[0734] System Program

[0735] 1. Getting user input data:

[0736] The user inputs floor plan information and furniture information using their own device (such as a smartphone). The floor plan information is provided as an image file, and the furniture information is input in the form of a web link (URL). For example, the user inputs URLs such as "http: / / example.com / floorplan.png" or "http: / / example.com / modern-sofa."

[0737] 2. Parsing floor plan information:

[0738] The server receives the input floor plan image and uses image recognition software (e.g., OpenCV) to identify the shape and dimensions of the room. The analysis results are calculated as specific dimensional information such as the width and height of the room.

[0739] 3. Get furniture information:

[0740] The server accesses the furniture URL provided by the user and retrieves detailed information (such as dimensions) of the furniture using web scraping or API, thereby collecting data such as the width, height, and depth of the furniture.

[0741] 4. Generate 3D model:

[0742] The analyzed room dimensions are combined with the acquired furniture size information to generate a 3D model of the room using 3D modeling software (e.g., Blender, Open3D). The furniture is then placed in the designated locations, and the overall layout is determined.

[0743] 5. Interior perspective rendering:

[0744] Based on the generated 3D model, high-precision interior perspectives are created using rendering software, and realistic effects such as lighting and shadows are also added at this stage.

[0745] 6. Use of generative AI models:

[0746] The server inputs the floor plan information and furniture information into the AI ​​model as prompts, and automatically generates the generated interior perspective. Specifically, the prompt uses the following text: "Please automatically generate a 3D interior perspective of the room based on the following information: Floor plan URL: 'http: / / example.com / floorplan.png' Furniture URL: 'http: / / example.com / modern-sofa'."

[0747] 7. User Offerings:

[0748] The rendered interior perspective is sent from the server to the user's device and displayed on a smartphone app, allowing users to visually check how furniture will be arranged in their room before purchasing it.

[0749] Hardware and software used

[0750] Hardware: User's smartphone, cloud server

[0751] Software: Flask (web server framework), OpenCV (image processing library), Blender or Open3D (3D modeling and rendering software), web scraping tool

[0752] Specific examples

[0753] A user uses an online shopping site's app to input the floor plan of their living room and information about the sofa they are considering purchasing. The app then sends the input information to a server, which then generates an interior perspective based on the floor plan and furniture information. The generated interior perspective is displayed to the user through the app, allowing the user to see in advance how the sofa will be placed in their living room.

[0754] Prompt Sentence Examples

[0755] "Please automatically generate a 3D interior perspective of the room based on the following information. Floor plan URL: 'http: / / example.com / floorplan.png' Furniture URL: 'http: / / example.com / modern-sofa'"

[0756] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0757] Step 1:

[0758] The user inputs floor plan information and furniture information using their own terminal. The input floor plan information is provided as an image file, and the furniture information is provided as a URL. This information is then sent to the server by the terminal.

[0759] Step 2:

[0760] The server retrieves the floor plan information it has received. The floor plan information (input) is in image file format, and a Python image processing library (e.g., OpenCV) is used to read the image and identify the shape and dimensions of the room. The analysis results (output) are specific dimensional information such as the width and height of the room.

[0761] Step 3:

[0762] The server retrieves the furniture information it receives. The furniture information (input) is in URL format, and the server retrieves detailed furniture information from the specified URL using web scraping or a web API. This allows it to collect dimensional data (output) such as the width, height, and depth of the furniture.

[0763] Step 4:

[0764] The server generates a 3D model based on floor plan information and furniture information. Specifically, it uses a 3D modeling tool such as Open3D to create a 3D model of the room and furniture. At this time, the room dimension data (input) and furniture dimension data (input) are integrated to generate a 3D model (output) with the furniture arranged in the 3D space.

[0765] Step 5:

[0766] The server renders the interior perspective based on the generated 3D model, and uses Blender or other rendering software to create a highly accurate interior perspective (output) with realistic lighting and shadow effects.

[0767] Step 6:

[0768] The server provides the rendered interior perspective to the user. The generated interior perspective image (input) is sent from the server to the user's device, which displays it in a form that the user can visually confirm. The user can check this interior perspective in real time and consider furniture placement.

[0769] Step 7:

[0770] The server inputs floor plan information and furniture information as prompts into the generative AI model, which then automatically generates an interior perspective. Specifically, the prompt "Please automatically generate a 3D interior perspective of the room based on the following information: Floor plan URL: 'http: / / example.com / floorplan.png' Furniture URL: 'http: / / example.com / modern-sofa'" is input into the generative AI model, and the interior perspective (output) generated by the model is obtained. This generated interior perspective is also provided to the user's device.

[0771] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0772] This invention relates to a system that automatically generates interior perspectives based on floor plan information and furniture information, and combines it with an emotion engine that recognizes user emotions to provide more user-friendly and personalized interior design suggestions. To implement this system, a program is required for the server, terminals, and users to communicate with each other and process data. The specific program processing and its operation are described below.

[0773] Data entry and submission

[0774] The user uses their own device to input specific floor plan information and its scale. They also input the name and URL of the furniture they are considering purchasing, as well as facial and voice data for use by the emotion engine. For example, suppose a user selects a product called "Modern Sofa" and enters its URL, "http: / / example.com / modern-sofa." This input data is compiled by the device into a single request and sent to the server.

[0775] Parsing floor plan information

[0776] The server reads the received floor plan information and uses image processing algorithms to analyze the shape and dimensions of the room. This identifies the location of walls and the shape of the room, and calculates specific dimensions (e.g., 4m x 5m). Scale information is also taken into account and converted into actual dimensions.

[0777] Get furniture information

[0778] The server then accesses the URL provided by the user to retrieve detailed information about the furniture. This is done using web scraping technology and APIs to collect data such as the furniture's dimensions (e.g., width 2m, depth 0.8m, height 0.85m) and shape. The retrieved data is then formatted into a standard format.

[0779] emotion recognition

[0780] The server uses an emotion engine to analyze the facial expression and voice data sent by the user, and recognizes the user's emotions (e.g., joy, surprise, satisfaction, etc.).

[0781] 3D model generation and rendering

[0782] The server combines the analyzed room dimensions, furniture size data, and the recognized user emotion data. It uses 3D modeling software to generate a 3D model of the entire room and arranges the furniture in appropriate locations based on the room layout. The server then renders this 3D model and generates a highly accurate interior perspective. Based on the emotion data, it adjusts the presentation of the interior perspective (e.g., color tone and flexibility of placement).

[0783] Interior perspective provided

[0784] The generated image data of the interior perspective is sent from the server to the terminal. The terminal displays the received interior perspective to the user, allowing the user to check it realistically. This allows the user to check whether the furniture is suitable for the room and matches their image before purchasing. In addition, the displayed interior perspective is personalized based on the user's emotions, resulting in a more satisfying proposal.

[0785] In this way, the system of the present invention allows users to easily generate realistic interior perspectives to help them select furniture, and further improves user satisfaction with personalized suggestions based on emotion recognition. As a specific example, when a user is choosing interior decor for a new living room, the emotion engine recognizes the user's reactions and provides the optimal perspective based on those reactions, helping the user make the most satisfying choice.

[0786] The processing flow will be explained below.

[0787] Step 1:

[0788] The user inputs a floor plan file and its scale on their device. For example, they input "floor_plan.png" and the scale "1:100." They also input the name of the furniture they are considering purchasing, "Modern Sofa," and the URL, "http: / / example.com / modern-sofa."

[0789] Step 2:

[0790] Users provide facial expression and voice data for use by the emotion engine by recording their current facial expressions and voice using a webcam and microphone, or by uploading previously collected data.

[0791] Step 3:

[0792] The device compiles the floor plan file, scale, furniture name, URL, and emotion data entered by the user into a single request and sends it to the server.

[0793] Step 4:

[0794] The server reads the received floor plan file and uses image processing algorithms to analyze the shape and dimensions of the room, identifying the location of walls and the shape of the room, and calculating specific dimensions (e.g., 4m x 5m).

[0795] Step 5:

[0796] The server uses the scale information provided by the user to convert the analyzed room dimensions into real-world dimensions.

[0797] Step 6:

[0798] The server accesses the URL of the furniture item entered by the user and retrieves detailed information. Using web scraping technology and APIs, the server collects the furniture's dimensions (2m width, 0.8m depth, 0.85m height) and shape data. This data is then formatted into a standard format.

[0799] Step 7:

[0800] The server uses the received facial expression and voice data to analyze it using an emotion engine, which then recognizes the user's emotions (happiness, surprise, satisfaction, etc.).

[0801] Step 8:

[0802] The server combines the analyzed room dimensions, furniture size data, and the recognized user emotion data, then generates a 3D model of the entire room using 3D modeling software, and places the furniture in appropriate locations based on the room layout.

[0803] Step 9:

[0804] The server renders the generated 3D model and creates a highly accurate interior perspective. Based on the emotion data, the content of the interior perspective (e.g., adjusting color, brightness, and layout) is customized to suit the user's preferences.

[0805] Step 10:

[0806] The server sends the rendered interior perspective image data to the terminal, which then displays the received interior perspective to the user.

[0807] Step 11:

[0808] Users can check the displayed interior perspective and decide whether the furniture they are considering purchasing is suitable for the room and matches their image.In addition, interior perspectives that reflect the user's emotions allow for a more satisfying consideration.

[0809] In this way, users can reduce the chances of making mistakes when choosing furniture and choose the perfect interior.

[0810] Example 2

[0811] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0812] Conventional interior design support systems generate interior perspectives based on floor plan and furniture information entered by the user, but they are unable to provide personalized suggestions that take the user's emotions into account. This results in reduced user satisfaction and makes it difficult to select the optimal interior design. Furthermore, the lack of a function to personalize interior perspectives using emotion recognition led to a demand for a more user-friendly system.

[0813] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0814] In this invention, the server includes means for acquiring floor plan information input by a user, means for acquiring furniture information input by a user, means for analyzing the acquired floor plan information to identify room dimensions, means for identifying furniture dimensions based on the acquired furniture information, means for generating a 3D model based on the identified room dimensions and furniture dimensions, means for rendering an interior perspective based on the generated 3D model, means for acquiring facial expression data or voice data of the user, means for analyzing the acquired facial expression data or voice data to recognize the user's emotion, means for personalizing the rendered interior perspective based on the recognized emotion data, and means for providing the personalized interior perspective to the user. This makes it possible to propose interior perspectives that take the user's emotions into consideration and provide a user with a higher level of satisfaction.

[0815] A "user" is an individual or organization that uses the system to input floor plan and furniture information.

[0816] "Floor plan information" is drawing data that includes information such as the shape and dimensions of the room, and the positions of walls and doors.

[0817] "Furniture information" is information including the name, size, shape, and URL of the furniture.

[0818] "Facial expression data" is image or video data showing the facial expression of the user.

[0819] "Voice data" refers to data that records the user's voice.

[0820] A "server" is a central device that receives requests from users and processes data accordingly.

[0821] A "3D model" is digital data that represents the shape of a room and the arrangement of furniture in three dimensions.

[0822] An "interior perspective" is an image that shows the interior design of a room, generated based on a 3D model.

[0823] An "emotion engine" is a program that analyzes facial expression data and voice data to recognize the user's emotions.

[0824] "Personalization" refers to individually adjusting the interior perspective based on the user's emotional data.

[0825] This invention relates to a system that automatically generates interior perspectives based on floor plan information and furniture information, and combines it with an emotion engine that recognizes user emotions to provide more user-friendly and personalized interior design suggestions. To implement this system, a program is required for the server, terminals, and users to communicate with each other and process data. The specific program processing and its operation are described below.

[0826] First, the user uses their device to input floor plan information and its scale. This floor plan information is uploaded as a JPEG image, for example, and the scale is specified as "1:100." The user also enters the name of the furniture they are considering, "Modern Sofa," and its URL, "http: / / example.com / modern-sofa," and provides smiling facial expression data via their webcam. The device then compiles this data into a single request and sends it to the server.

[0827] The server uses image processing libraries such as OpenCV and TensorFlow to analyze the received floor plan file. Image processing algorithms are used to detect the positions of walls, doors, and windows, and to identify the shape and dimensions of the room. For example, it may determine that the room is rectangular, measuring 4m x 5m. The server also converts the units on the floor plan into actual dimensions based on the scale information.

[0828] The server then accesses the URL provided by the user and uses web scraping techniques (such as BeautifulSoup) or APIs to obtain detailed information about the furniture. For example, the size data for a "Modern Sofa" measuring 2m wide, 0.8m deep, and 0.85m high is collected. The collected data is then formatted into a standard format (such as JSON) for subsequent processing.

[0829] The server also uses emotion engines such as Face++ and Microsoft Azure Emotion API to analyze the received facial and voice data. The server recognizes emotions such as joy, surprise, and satisfaction from the user's facial expressions and stores the results in a database.

[0830] The server combines the room dimension data, furniture size data, and the user's recognized emotion data. It uses 3D modeling software (such as Blender) to generate a 3D model of the entire room and arranges the furniture in appropriate locations. For example, if the user's emotion is recognized as "joy," the server creates an interior perspective using bright colors. It then renders this 3D model to generate a highly accurate interior perspective image.

[0831] Finally, the server sends the image data of the generated interior perspective to the device. The device displays the received interior perspective to the user, allowing the user to check it in real time. This allows the user to determine whether the furniture is suitable for the room and whether it matches their image. A personalized interior perspective based on emotional data allows the user to make a more satisfying choice.

[0832] As a concrete example, when a user selects the interior of a new living room, the system works as follows: The user selects "Modern Sofa" and enters the URL "http: / / example.com / modern-sofa." When the user smiles at the camera, the server analyzes the smile and recognizes that the user is "satisfied." Based on this information, the server generates an interior perspective that reflects the layout and color tones that will most likely satisfy the user.

[0833] Example prompt sentence:

[0834] "Enter your floor plan information, provide details about the furniture you're considering, and create a layout for your new living room. Smile for the camera and enjoy the interior design experience."

[0835] This system allows users to easily generate realistic interior perspectives and use them as a reference when selecting furniture. Furthermore, by utilizing emotion recognition, it can provide personalized interior design suggestions, improving user satisfaction.

[0836] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0837] Step 1:

[0838] The user uses their own device to input floor plan information and its scale. Specifically, they upload a floor plan of, say, a living room as an image file (JPEG, PNG, etc.) and specify the scale as "1:100," for example. The input at this point is the floor plan image file and scale information, and the output is request data that sends this information to the server.

[0839] Step 2:

[0840] The user enters the name of the furniture they are considering purchasing, "Modern Sofa," and its URL, "http: / / example.com / modern-sofa," into their device. In addition, the user provides facial expression data of a smile via the camera. The input is the furniture name, URL, and facial expression data, and the output is a request data that compiles these.

[0841] Step 3:

[0842] The terminal sends the floor plan information, furniture information, and facial expression data entered by the user to the server as a single request. The input is the request data from the user, and the output is that this request is sent to the server.

[0843] Step 4:

[0844] The server analyzes the received floor plan file. Specifically, it uses image processing libraries such as OpenCV and TensorFlow to identify the shape and dimensions of the room, as well as the locations of walls, doors, and windows. The input is an image file of the floor plan and scale information, and the output is specific dimensional data of the room, including the locations of walls, doors, and windows.

[0845] Step 5:

[0846] The server converts the units on the floor plan into actual dimensions based on the scale information. For example, if a scale of 1:100 is input, 1 cm corresponds to 1 m. This conversion outputs the actual room dimension data.

[0847] Step 6:

[0848] The server accesses the furniture URL provided by the user and uses web scraping technology (e.g., BeautifulSoup) or APIs to obtain detailed information about the furniture. The input is the furniture URL, and the output is the furniture's size (width, depth, height) and shape data.

[0849] Step 7:

[0850] The server formats the acquired furniture information into a standard format (e.g., JSON). The formatted data is used in subsequent processing. The input is the acquired furniture details, and the output is the data formatted in the standard format.

[0851] Step 8:

[0852] The server uses an emotion engine to analyze the facial expression data sent by the user. Specifically, it uses Face++ or Microsoft Azure Emotion API to recognize the user's emotions from the facial expression data. The input is the user's facial expression data, and the output is the recognized emotion data (happiness, surprise, satisfaction, etc.).

[0853] Step 9:

[0854] The server integrates the room dimension data, furniture size data, and emotion data. Based on this, it uses 3D modeling software (e.g., Blender) to generate a 3D model of the entire room and arranges the furniture in appropriate locations. The input is the integrated data, and the output is a 3D model of the entire room.

[0855] Step 10:

[0856] The server renders the generated 3D model and generates a highly accurate interior perspective image. It also personalizes color tones and furniture placement based on the user's emotional data. The input is the 3D model and emotional data, and the output is personalized interior perspective image data.

[0857] Step 11:

[0858] The server sends the generated interior perspective image data to the terminal. The input is the interior perspective image data, and the output is that it is sent to the user's terminal.

[0859] Step 12:

[0860] The terminal displays the received interior perspective to the user. The user can then view the displayed interior perspective and check whether the furniture is suitable for the room. The input is image data of the interior perspective sent from the server, and the output is an interface that allows the user to visually check it.

[0861] (Application example 2)

[0862] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0863] Conventional interior design proposal systems generate interior perspectives based on floor plan and furniture information provided by the user, but they lack personalization based on the user's emotions and preferences. This often leaves users dissatisfied with the proposals. Furthermore, it is difficult to grasp in advance the atmosphere and feel of the furniture arrangement, which can reduce purchasing motivation.

[0864] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0865] In this invention, the server includes means for acquiring floor plan information input by a user, means for acquiring furniture information input by a user, means for recognizing emotions based on the user's facial expression data and voice data, means for analyzing the acquired floor plan information to identify room dimensions, means for identifying furniture dimensions based on the acquired furniture information, means for generating a 3D model based on the identified room dimensions, furniture dimensions, and the recognized emotion, means for rendering an interior perspective based on the generated 3D model, and means for providing the rendered interior perspective to the user. This enables personalized interior suggestions based on the user's emotions, thereby improving user satisfaction and purchasing motivation.

[0866] "Floor plan information" is data about the shape and dimensions of a room provided by the user, and is basic information for identifying the specific dimensions of the room through analysis.

[0867] "Furniture information" refers to data about furniture provided by the user, including detailed information about the furniture, including its size and shape.

[0868] "Emotion recognition" is a technology that analyzes a user's facial expression data and voice data to identify the user's emotional state (for example, joy, surprise, satisfaction, etc.).

[0869] A "3D model" is a digital representation of a three-dimensional interior space generated based on analyzed floor plan information, furniture information, and recognized emotional data.

[0870] An "interior perspective" is a visual image rendered based on the generated 3D model, and is an interior design proposal provided to the user.

[0871] A "server" is a central computing device that receives, analyzes, and processes data sent from user terminals.

[0872] A "terminal" is a device operated by a user, and is a device for inputting and transmitting floor plan information, furniture information, facial expression data, and voice data.

[0873] "Rendering" is the process of representing the generated 3D model as a visual image and providing it to the user.

[0874] This invention is a system that recognizes a user's emotions and automatically generates personalized interior design proposals based on floor plan and furniture information provided by the user. To realize this system, multiple components, such as a server, a terminal, and an emotion engine, work together.

[0875] The user's device inputs floor plan information, furniture information, and facial expression and voice data for emotion recognition. The floor plan information is acquired as an image file, and the furniture information is acquired via a URL. This input data is compiled by the device and sent to the server as a single request.

[0876] The server first analyzes the floor plan information sent to it and identifies the shape and dimensions of the room. This process uses image processing software such as OpenCV. Next, it accesses the URL provided by the user and uses web scraping technology and APIs to obtain detailed information about the furniture. This data is then converted into a standard format.

[0877] Furthermore, the server uses an emotion engine to analyze the user's facial expression data and voice data to recognize the user's emotional state. This analysis is performed using emotion recognition software such as EmotionEngine.

[0878] The system combines the identified room dimensions, furniture size data, and the user's recognized emotion data to generate a 3D model of the entire room using 3D modeling software. This 3D model is then rendered with high precision using a rendering engine to generate an optimal interior perspective for the user. The perspective content, including color tone and placement, is adjusted based on the user's emotion.

[0879] Finally, the generated image data of the interior perspective is sent from the server to the terminal. The terminal receives this data and displays it to the user. This allows the user to confirm whether the furniture is suitable for the room and matches their image before purchasing. In addition, the displayed perspective is personalized based on the user's emotions, further increasing user satisfaction.

[0880] For example, when a user is choosing interior decor for a new living room, the emotion engine recognizes the user's reactions and provides the optimal perspective based on those reactions. This system allows the user to make a more satisfying interior design choice.

[0881] Example prompt for a generative AI model:

[0882] "Enter the floor plan and furniture information of the room selected by the user, as well as emotional data, and generate a 3D model to propose a room layout. Recognize the emotional data from the user's facial expressions and voice, and create the optimal interior perspective based on those emotions."

[0883] This invention makes it possible to propose personalized interiors based on emotion recognition, thereby improving user satisfaction and purchasing motivation.

[0884] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0885] Step 1:

[0886] The user uses a terminal to input floor plan information, furniture information, facial expression data, and voice data. The input floor plan information is specified as an image file, and the furniture information is provided as a URL. Facial expression data and voice data for emotion recognition are also input as files. This data is compiled by the terminal into a single request and sent to the server. The input is an image file of the floor plan, a URL for the furniture, an image file of the facial expression data, and an audio file of the voice data, and the output is the data compiled into a single request.

[0887] Step 2:

[0888] The server obtains floor plan information from the received request and analyzes it using image processing algorithms such as OpenCV. The analysis identifies the shape and dimensions of the room and calculates specific dimensional information. The input is an image file of the floor plan, and the output is the room dimensional information.

[0889] Step 3:

[0890] The server accesses the URL provided by the user and uses web scraping technology or APIs to obtain detailed information about the furniture. The obtained data is converted into a standard format, and the size and shape of the furniture are identified. The input is the furniture URL, and the output is information about the size and shape of the furniture.

[0891] Step 4:

[0892] The server analyzes the facial expression data and voice data using an emotion engine (e.g., EmotionEngine) to recognize the user's emotions. This analysis identifies the user's emotional state (e.g., joy, surprise, satisfaction, etc.). The input is an image file of the facial expression data and an audio file of the voice data, and the output is information on the recognized emotions.

[0893] Step 5:

[0894] The server integrates the identified room dimensions, furniture size data, and the recognized user emotion data, and generates a 3D model of the entire room using 3D modeling software. The input is the room dimension data, furniture size data, and emotion information, and the output is the 3D model. This model includes the overall layout of the room and the arrangement of the furniture.

[0895] Step 6:

[0896] The server uses a rendering engine to render the generated 3D model with high precision, generating an optimal interior perspective for the user. The perspective content is adjusted based on the user's emotions, including color tone and placement. The input is the 3D model, and the output is the rendered image data of the interior perspective.

[0897] Step 7:

[0898] The server sends image data of the generated interior perspective to the terminal. The terminal receives this data and displays it to the user. This allows the user to check whether the furniture is suitable for the room and matches their image before purchasing. The displayed perspective is personalized based on the user's emotions, improving user satisfaction. The input is image data of the rendered interior perspective, and the output is the interior perspective displayed on the user's terminal.

[0899] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0900] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0901] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0902] [Fourth embodiment]

[0903] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0904] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0905] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0906] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0907] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0908] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0909] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0910] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0911] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0912] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0913] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0914] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0915] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0916] This invention relates to a system that automatically generates interior perspective drawings of a room based on floor plan information and furniture information entered by a user. To implement this system, a program is required for communication between a server, a terminal, and a user, and for data processing. The specific program processing and its operation are described below.

[0917] Data entry and submission

[0918] The user uses their device to input specific floor plan information and its scale. They also input the name and URL of the furniture they are considering purchasing. For example, the user selects the product "Modern Sofa" and enters its URL, "http: / / example.com / modern-sofa." The device combines these input data into a single request and sends it to the server.

[0919] Parsing floor plan information

[0920] The server reads the received floor plan information and uses image processing algorithms to analyze the shape and dimensions of the room. This identifies the location of walls and the shape of the room, and calculates specific dimensions (e.g., 4m x 5m). Scale information is also taken into account and converted into actual dimensions.

[0921] Get furniture information

[0922] The server then accesses the URL provided by the user to retrieve detailed information about the furniture. This is done using web scraping technology and APIs to collect data such as the furniture's dimensions (e.g., width 2m, depth 0.8m, height 0.85m) and shape. The retrieved data is then formatted into a standard format.

[0923] 3D model generation and rendering

[0924] The server integrates the analyzed room dimensions and furniture size data and uses 3D modeling software to create a 3D model of the entire room. Based on the room layout, furniture is placed in appropriate locations. The server then renders this 3D model to generate a highly accurate interior perspective.

[0925] Interior perspective provided

[0926] The image data of the generated interior perspective is sent from the server to the terminal. The terminal displays the received interior perspective to the user, allowing the user to check it realistically. This allows the user to check whether the furniture is suitable for the room and whether it matches their image before purchasing.

[0927] In this way, the system of the present invention allows users to easily generate realistic interior perspectives and use them as a reference when selecting furniture. To give a concrete example, if a user wants to place a new sofa in their living room, they can simply input the floor plan and sofa information to check in advance whether the sofa will fit the room. This process can prevent mistakes after the purchase.

[0928] The processing flow will be explained below.

[0929] Step 1:

[0930] The user inputs a floor plan file and its scale on their device. They also input the name and URL of the furniture they are considering purchasing. For example, the user inputs the name "Modern Sofa" and the URL "http: / / example.com / modern-sofa".

[0931] Step 2:

[0932] The terminal compiles the floor plan file, scale, furniture name, and URL entered by the user into a single request and sends it to the server.

[0933] Step 3:

[0934] The server reads the received floor plan file and uses image processing algorithms to analyze the shape and dimensions of the room, recognizing the position of walls and the shape of the room, and determining the dimensions of the room (e.g., 4m x 5m).

[0935] Step 4:

[0936] The server uses the scale information provided by the user to convert the identified room dimensions into real-world dimensions.

[0937] Step 5:

[0938] The server accesses the URL entered by the user and obtains detailed information about the furniture. Using web scraping technology and APIs, the server collects furniture dimensions (e.g., width 2m, depth 0.8m, height 0.85m) and shape data.

[0939] Step 6:

[0940] The server converts the acquired furniture data into a standard format and integrates it with the room dimension data.

[0941] Step 7:

[0942] The server uses 3D modeling software to generate a 3D model of the entire room, then places the furniture in the appropriate locations based on the room's layout.

[0943] Step 8:

[0944] The server renders the generated 3D model and creates a highly accurate interior perspective.

[0945] Step 9:

[0946] The server sends the rendered interior perspective image data to the terminal.

[0947] Step 10:

[0948] The terminal displays the received interior perspective to the user, allowing the user to check whether the sofa is suitable for the room and matches their image.

[0949] In this way, users can reduce the chances of making mistakes when choosing furniture and choose the perfect interior.

[0950] Example 1

[0951] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0952] In recent years, there has been a demand for tools to simulate interior layouts. However, current systems require users to manually input floor plans and furniture information, making them cumbersome to use. Furthermore, the generated interior perspectives lack precision and real-time performance, which means they cannot fully reflect the nuances of actual furniture layouts. This often leads to failure after furniture purchase due to differences in layout or size compared to expectations.

[0953] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0954] In this invention, the server includes means for acquiring floor plan information input by a user, means for acquiring furniture information input by a user, means for analyzing the acquired floor plan information to identify room dimensions, means for identifying furniture dimensions based on the acquired furniture information, means for integrating the room dimensions and furniture dimensions to generate a 3D model, means for rendering an interior perspective based on the 3D model, means for providing the rendered interior perspective to a user's terminal, and means for the terminal to display the provided interior perspective to the user. This allows a user to automatically generate a high-precision interior perspective based on the information they input and check it in real time.

[0955] The "means for acquiring" is a function for receiving data entered by a user and converting it into a format that can be used within the system.

[0956] "Means of analysis" refers to the function of interpreting information based on acquired data and extracting details such as specific dimensions and shapes.

[0957] The "means of identification" is a function for clarifying the exact dimensions and position from the analyzed information.

[0958] "Means for generating a three-dimensional model" refers to a function for constructing a three-dimensional virtual space based on two-dimensional or text-format data.

[0959] "Rendering means" is a function for visually expressing the generated three-dimensional model and outputting it as a concrete image.

[0960] The "means of providing to the user's terminal" is a function for transmitting data generated by the server via a network to the device used by the user.

[0961] The "means for displaying by the terminal" is a function for visually showing the received data to the user via a user interface.

[0962] "Floor plan information" refers to drawings showing the shape and dimensions of a room and related data.

[0963] "Furniture information" is data about the dimensions, shape, and other attributes of a particular piece of furniture.

[0964] "URL" is an abbreviation for Uniform Resource Locator, and is an address for identifying the location of a specific resource.

[0965] "Interior perspective" is an image or rendering that shows the visual representation of furniture arranged in an actual room.

[0966] This invention relates to a system that automatically generates interior perspective drawings based on floor plan and furniture information entered by a user. This system requires a server, terminals, and users to communicate with each other and a program to process data.

[0967] Hardware and software used

[0968] The following hardware and software are used to implement this system.

[0969] Server: A central system that processes and analyzes data

[0970] Terminal: The device the user uses to provide input information

[0971] Image processing algorithms: Image analysis libraries such as OpenCV

[0972] Web scraping technology: Web data extraction tools such as BeautifulSoup

[0973] 3D modeling software: 3D model generation tools such as Blender

[0974] Program processing overview

[0975] Data entry and submission

[0976] The user uses their device to input specific floor plan information and its scale. They also input the name and URL of the furniture they are considering purchasing. For example, the user selects the product "Modern Sofa" and enters its URL, "http: / / example.com / modern-sofa." The device combines these input data into a single request and sends it to the server.

[0977] Parsing floor plan information

[0978] The server reads the received floor plan information and analyzes the shape and dimensions of the room using an image processing algorithm (e.g., OpenCV). This identifies the position of walls and the shape of the room, and calculates specific dimensions (e.g., 4m x 5m). Scale information is also taken into account and converted into actual dimensions.

[0979] Get furniture information

[0980] The server then accesses the URL provided by the user to retrieve detailed information about the furniture. This is done using web scraping techniques (e.g., BeautifulSoup) or APIs to collect the furniture's dimensions (e.g., width 2m, depth 0.8m, height 0.85m) and shape data. The retrieved data is then formatted into a standard format.

[0981] 3D model generation and rendering

[0982] The server integrates the analyzed room dimensions and furniture size data and creates a 3D model of the entire room using 3D modeling software (e.g., Blender). Based on the room layout, furniture is placed in appropriate locations. The server then renders this 3D model to generate a highly accurate interior perspective.

[0983] Interior perspective provided

[0984] The generated image data of the interior perspective is sent from the server to the terminal. The terminal displays the received interior perspective to the user, allowing the user to check it realistically. This allows the user to check whether the furniture is suitable for the room and matches their image before purchasing.

[0985] Specific examples

[0986] For example, imagine a user wants to place a new sofa in their living room. The user inputs the floor plan of the living room and the "Modern Sofa" information. Based on this, the system generates a highly accurate interior perspective and displays it to the user. This process helps users avoid the disappointment of finding that the sofa doesn't fit the room after purchase.

[0987] Example prompts for generative AI models

[0988] "I want to place a new modern sofa in my living room. Please generate an interior perspective using the floor plan and furniture information below.

[0989] Floor plan information: Living room (4m x 5m)

[0990] Furniture information: Modern Sofa (http: / / example.com / modern-sofa)

[0991] Thank you."

[0992] In this way, this system allows users to easily generate realistic interior perspectives and use them as a reference when selecting furniture. The server and device work together to process data based on the information entered by the user, and provide results in real time.

[0993] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0994] Divide the processing flow of the system program into processing steps

[0995] Step 1:

[0996] The user uses their device to input floor plan information and its scale. Specifically, the user uploads the floor plan as an image file using the device and inputs the scale information into a text field. For example, the user inputs the floor plan of the living room and the scale of "1:50". This input information is stored on the device for further processing.

[0997] Step 2:

[0998] The user enters furniture information. The user enters the name of the furniture they are considering purchasing and its URL into the terminal. For example, the user enters the furniture name "Modern Sofa" and its URL "http: / / example.com / modern-sofa." This input information is also saved on the terminal for subsequent processing.

[0999] Step 3:

[1000] The device combines the input floor plan information and furniture information into a single request and sends it to the server. Specifically, the device packages the input floor plan information (image file) and furniture information (name and URL) in JSON format and sends it to the server via an HTTP request. Once the input data arrives at the server, it proceeds to the next step.

[1001] Step 4:

[1002] The server reads the received floor plan information and performs image processing. The server uses the OpenCV library to analyze the received floor plan image. Specifically, it detects lines in the image and identifies the shape of the room and the location of walls. The results of this analysis (e.g., the shape and dimensions of the room) are saved for further processing.

[1003] Step 5:

[1004] The server uses the scale information to convert the analysis results to actual size. For example, if a scale of "1:50" is entered, a 4cm line actually corresponds to 2m, so the room dimensions are converted based on that. The result of this conversion (for example, the dimensions of a 4m x 5m room) is then passed on to the next step.

[1005] Step 6:

[1006] The server accesses the furniture information URL and retrieves detailed information about the furniture using web scraping or an API. Specifically, the server uses BeautifulSoup to parse the HTML in the URL and extract detailed information such as the furniture's height, width, and depth. This extracted data is formatted into a standard format (e.g., JSON) and saved for further processing.

[1007] Step 7:

[1008] The server combines the room's dimensional data with detailed furniture information to generate a 3D model. The server then uses 3D modeling software such as Blender to create a 3D model of the entire room. Specifically, it generates the room's outline based on the room's dimensional data, and places the 3D furniture models within the room based on the extracted furniture information.

[1009] Step 8:

[1010] The server renders the generated 3D model. The server uses Blender's rendering function to generate a highly accurate interior perspective. The rendering results (image data) are saved for further processing.

[1011] Step 9:

[1012] The server sends the rendered interior perspective image data to the device. The server sends the image data as an HTTP response, and the device receives it. This received data proceeds to the next step.

[1013] Step 10:

[1014] The device displays the received interior perspective to the user. Specifically, the rendered image of the interior perspective is displayed via the device's user interface. The user can check this in real time to confirm whether the furniture is suitable for the room and matches their image before purchasing.

[1015] (Application example 1)

[1016] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1017] When users arrange furniture in their rooms, it is difficult to visually confirm how the furniture will be placed in the room before purchasing it. Furthermore, moving actual furniture around to trial and error the interior layout is time-consuming. Therefore, there is a need for a support system that helps users smoothly select and arrange furniture.

[1018] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1019] In this invention, the server includes means for acquiring floor plan information input by a user, means for acquiring furniture information input by a user, means for analyzing the acquired floor plan information to identify room dimensions, means for identifying furniture dimensions based on the acquired furniture information, means for generating a 3D model based on the identified room dimensions and furniture dimensions, means for rendering an interior perspective based on the generated 3D model, means for providing the rendered interior perspective to a user, and means for inputting the floor plan information and furniture information as prompts and automatically generating an interior perspective using a generative AI model, thereby enabling a user to realistically see how furniture will be arranged in a room before purchasing it.

[1020] The "means for acquiring floor plan information entered by the user" is a function by which the system receives floor plan information indicating the layout of the user's own room via the network.

[1021] The "means for acquiring furniture information entered by the user" is a function by which the system receives detailed information about the furniture that the user is considering purchasing.

[1022] The "means for analyzing the acquired floor plan information and identifying the dimensions of the room" is a function for analyzing the received floor plan information and identifying the overall shape and dimensions of the room.

[1023] The "means for identifying the dimensions of the furniture based on the acquired furniture information" is a function for identifying the size of the furniture based on the furniture information provided by the user.

[1024] The "means for generating a 3D model based on the specified room dimensions and furniture dimensions" refers to a function for generating a three-dimensional model using 3D modeling software using the dimensions of the room and furniture.

[1025] The "means for rendering an interior perspective based on the generated 3D model" is a function for drawing a highly accurate interior perspective based on the generated three-dimensional model.

[1026] The "means for providing the rendered interior perspective to the user" is a function for transmitting the rendered interior perspective to the user's device and displaying it.

[1027] "Means for inputting the floor plan information and furniture information as prompts and automatically generating an interior perspective using a generative AI model" refers to a function for automatically creating an interior perspective using a generative AI model based on the floor plan information and furniture information provided by the user.

[1028] A system for implementing this invention provides a function for generating interior perspectives of a room based on floor plan information and furniture information entered by a user. Specifically, the system has a function for automatically generating interior perspectives by inputting floor plan information and furniture information as prompts and using a generative AI model. Detailed embodiments of the system of the present invention are described below.

[1029] System Program

[1030] 1. Getting user input data:

[1031] The user inputs floor plan information and furniture information using their own device (such as a smartphone). The floor plan information is provided as an image file, and the furniture information is input in the form of a web link (URL). For example, the user inputs URLs such as "http: / / example.com / floorplan.png" or "http: / / example.com / modern-sofa."

[1032] 2. Parsing floor plan information:

[1033] The server receives the input floor plan image and uses image recognition software (e.g., OpenCV) to identify the shape and dimensions of the room. The analysis results are calculated as specific dimensional information such as the width and height of the room.

[1034] 3. Get furniture information:

[1035] The server accesses the furniture URL provided by the user and retrieves detailed information (such as dimensions) of the furniture using web scraping or API, thereby collecting data such as the width, height, and depth of the furniture.

[1036] 4. Generate 3D model:

[1037] The analyzed room dimensions are combined with the acquired furniture size information to generate a 3D model of the room using 3D modeling software (e.g., Blender, Open3D). The furniture is then placed in the designated locations, and the overall layout is determined.

[1038] 5. Interior perspective rendering:

[1039] Based on the generated 3D model, high-precision interior perspectives are created using rendering software, and realistic effects such as lighting and shadows are also added at this stage.

[1040] 6. Use of generative AI models:

[1041] The server inputs the floor plan information and furniture information into the AI ​​model as prompts, and automatically generates the generated interior perspective. Specifically, the prompt uses the following text: "Please automatically generate a 3D interior perspective of the room based on the following information: Floor plan URL: 'http: / / example.com / floorplan.png' Furniture URL: 'http: / / example.com / modern-sofa'."

[1042] 7. User Offerings:

[1043] The rendered interior perspective is sent from the server to the user's device and displayed on a smartphone app, allowing users to visually check how furniture will be arranged in their room before purchasing it.

[1044] Hardware and software used

[1045] Hardware: User's smartphone, cloud server

[1046] Software: Flask (web server framework), OpenCV (image processing library), Blender or Open3D (3D modeling and rendering software), web scraping tool

[1047] Specific examples

[1048] A user uses an online shopping site's app to input the floor plan of their living room and information about the sofa they are considering purchasing. The app then sends the input information to a server, which then generates an interior perspective based on the floor plan and furniture information. The generated interior perspective is displayed to the user through the app, allowing the user to see in advance how the sofa will be placed in their living room.

[1049] Prompt Sentence Examples

[1050] "Please automatically generate a 3D interior perspective of the room based on the following information. Floor plan URL: 'http: / / example.com / floorplan.png' Furniture URL: 'http: / / example.com / modern-sofa'"

[1051] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1052] Step 1:

[1053] The user inputs floor plan information and furniture information using their own terminal. The input floor plan information is provided as an image file, and the furniture information is provided as a URL. This information is then sent to the server by the terminal.

[1054] Step 2:

[1055] The server retrieves the floor plan information it has received. The floor plan information (input) is in image file format, and a Python image processing library (e.g., OpenCV) is used to read the image and identify the shape and dimensions of the room. The analysis results (output) are specific dimensional information such as the width and height of the room.

[1056] Step 3:

[1057] The server retrieves the furniture information it receives. The furniture information (input) is in URL format, and the server retrieves detailed furniture information from the specified URL using web scraping or a web API. This allows it to collect dimensional data (output) such as the width, height, and depth of the furniture.

[1058] Step 4:

[1059] The server generates a 3D model based on floor plan information and furniture information. Specifically, it uses a 3D modeling tool such as Open3D to create a 3D model of the room and furniture. At this time, the room dimension data (input) and furniture dimension data (input) are integrated to generate a 3D model (output) with the furniture arranged in the 3D space.

[1060] Step 5:

[1061] The server renders the interior perspective based on the generated 3D model, and uses Blender or other rendering software to create a highly accurate interior perspective (output) with realistic lighting and shadow effects.

[1062] Step 6:

[1063] The server provides the rendered interior perspective to the user. The generated interior perspective image (input) is sent from the server to the user's device, which displays it in a form that the user can visually confirm. The user can check this interior perspective in real time and consider furniture placement.

[1064] Step 7:

[1065] The server inputs floor plan information and furniture information as prompts into the generative AI model, which then automatically generates an interior perspective. Specifically, the prompt "Please automatically generate a 3D interior perspective of the room based on the following information: Floor plan URL: 'http: / / example.com / floorplan.png' Furniture URL: 'http: / / example.com / modern-sofa'" is input into the generative AI model, and the interior perspective (output) generated by the model is obtained. This generated interior perspective is also provided to the user's device.

[1066] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1067] This invention relates to a system that automatically generates interior perspectives based on floor plan information and furniture information, and combines it with an emotion engine that recognizes user emotions to provide more user-friendly and personalized interior design suggestions. To implement this system, a program is required for the server, terminals, and users to communicate with each other and process data. The specific program processing and its operation are described below.

[1068] Data entry and submission

[1069] The user uses their own device to input specific floor plan information and its scale. They also input the name and URL of the furniture they are considering purchasing, as well as facial and voice data for use by the emotion engine. For example, suppose a user selects a product called "Modern Sofa" and enters its URL, "http: / / example.com / modern-sofa." This input data is compiled by the device into a single request and sent to the server.

[1070] Parsing floor plan information

[1071] The server reads the received floor plan information and uses image processing algorithms to analyze the shape and dimensions of the room. This identifies the location of walls and the shape of the room, and calculates specific dimensions (e.g., 4m x 5m). Scale information is also taken into account and converted into actual dimensions.

[1072] Get furniture information

[1073] The server then accesses the URL provided by the user to retrieve detailed information about the furniture. This is done using web scraping technology and APIs to collect data such as the furniture's dimensions (e.g., width 2m, depth 0.8m, height 0.85m) and shape. The retrieved data is then formatted into a standard format.

[1074] emotion recognition

[1075] The server uses an emotion engine to analyze the facial expression and voice data sent by the user, and recognizes the user's emotions (e.g., joy, surprise, satisfaction, etc.).

[1076] 3D model generation and rendering

[1077] The server combines the analyzed room dimensions, furniture size data, and the recognized user emotion data. It uses 3D modeling software to generate a 3D model of the entire room and arranges the furniture in appropriate locations based on the room layout. The server then renders this 3D model and generates a highly accurate interior perspective. Based on the emotion data, it adjusts the presentation of the interior perspective (e.g., color tone and flexibility of placement).

[1078] Interior perspective provided

[1079] The generated image data of the interior perspective is sent from the server to the terminal. The terminal displays the received interior perspective to the user, allowing the user to check it realistically. This allows the user to check whether the furniture is suitable for the room and matches their image before purchasing. In addition, the displayed interior perspective is personalized based on the user's emotions, resulting in a more satisfying proposal.

[1080] In this way, the system of the present invention allows users to easily generate realistic interior perspectives to help them select furniture, and further improves user satisfaction with personalized suggestions based on emotion recognition. As a specific example, when a user is choosing interior decor for a new living room, the emotion engine recognizes the user's reactions and provides the optimal perspective based on those reactions, helping the user make the most satisfying choice.

[1081] The processing flow will be explained below.

[1082] Step 1:

[1083] The user inputs a floor plan file and its scale on their device. For example, they input "floor_plan.png" and the scale "1:100." They also input the name of the furniture they are considering purchasing, "Modern Sofa," and the URL, "http: / / example.com / modern-sofa."

[1084] Step 2:

[1085] Users provide facial expression and voice data for use by the emotion engine by recording their current facial expressions and voice using a webcam and microphone, or by uploading previously collected data.

[1086] Step 3:

[1087] The device compiles the floor plan file, scale, furniture name, URL, and emotion data entered by the user into a single request and sends it to the server.

[1088] Step 4:

[1089] The server reads the received floor plan file and uses image processing algorithms to analyze the shape and dimensions of the room, identifying the location of walls and the shape of the room, and calculating specific dimensions (e.g., 4m x 5m).

[1090] Step 5:

[1091] The server uses the scale information provided by the user to convert the analyzed room dimensions into real-world dimensions.

[1092] Step 6:

[1093] The server accesses the URL of the furniture item entered by the user and retrieves detailed information. Using web scraping technology and APIs, the server collects the furniture's dimensions (2m width, 0.8m depth, 0.85m height) and shape data. This data is then formatted into a standard format.

[1094] Step 7:

[1095] The server uses the received facial expression and voice data to analyze it using an emotion engine, which then recognizes the user's emotions (happiness, surprise, satisfaction, etc.).

[1096] Step 8:

[1097] The server combines the analyzed room dimensions, furniture size data, and the recognized user emotion data, then generates a 3D model of the entire room using 3D modeling software, and places the furniture in appropriate locations based on the room layout.

[1098] Step 9:

[1099] The server renders the generated 3D model and creates a highly accurate interior perspective. Based on the emotion data, the content of the interior perspective (e.g., adjusting color, brightness, and layout) is customized to suit the user's preferences.

[1100] Step 10:

[1101] The server sends the rendered interior perspective image data to the terminal, which then displays the received interior perspective to the user.

[1102] Step 11:

[1103] Users can check the displayed interior perspective and decide whether the furniture they are considering purchasing is suitable for the room and matches their image.In addition, interior perspectives that reflect the user's emotions allow for a more satisfying consideration.

[1104] In this way, users can reduce the chances of making mistakes when choosing furniture and choose the perfect interior.

[1105] Example 2

[1106] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1107] Conventional interior design support systems generate interior perspectives based on floor plan and furniture information entered by the user, but they are unable to provide personalized suggestions that take the user's emotions into account. This results in reduced user satisfaction and makes it difficult to select the optimal interior design. Furthermore, the lack of a function to personalize interior perspectives using emotion recognition led to a demand for a more user-friendly system.

[1108] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1109] In this invention, the server includes means for acquiring floor plan information input by a user, means for acquiring furniture information input by a user, means for analyzing the acquired floor plan information to identify room dimensions, means for identifying furniture dimensions based on the acquired furniture information, means for generating a 3D model based on the identified room dimensions and furniture dimensions, means for rendering an interior perspective based on the generated 3D model, means for acquiring facial expression data or voice data of the user, means for analyzing the acquired facial expression data or voice data to recognize the user's emotion, means for personalizing the rendered interior perspective based on the recognized emotion data, and means for providing the personalized interior perspective to the user. This makes it possible to propose interior perspectives that take the user's emotions into consideration and provide a user with a higher level of satisfaction.

[1110] A "user" is an individual or organization that uses the system to input floor plan and furniture information.

[1111] "Floor plan information" is drawing data that includes information such as the shape and dimensions of the room, and the positions of walls and doors.

[1112] "Furniture information" is information including the name, size, shape, and URL of the furniture.

[1113] "Facial expression data" is image or video data showing the facial expression of the user.

[1114] "Voice data" refers to data that records the user's voice.

[1115] A "server" is a central device that receives requests from users and processes data accordingly.

[1116] A "3D model" is digital data that represents the shape of a room and the arrangement of furniture in three dimensions.

[1117] An "interior perspective" is an image that shows the interior design of a room, generated based on a 3D model.

[1118] An "emotion engine" is a program that analyzes facial expression data and voice data to recognize the user's emotions.

[1119] "Personalization" refers to individually adjusting the interior perspective based on the user's emotional data.

[1120] This invention relates to a system that automatically generates interior perspectives based on floor plan information and furniture information, and combines it with an emotion engine that recognizes user emotions to provide more user-friendly and personalized interior design suggestions. To implement this system, a program is required for the server, terminals, and users to communicate with each other and process data. The specific program processing and its operation are described below.

[1121] First, the user uses their device to input floor plan information and its scale. This floor plan information is uploaded as a JPEG image, for example, and the scale is specified as "1:100." The user also enters the name of the furniture they are considering, "Modern Sofa," and its URL, "http: / / example.com / modern-sofa," and provides smiling facial expression data via their webcam. The device then compiles this data into a single request and sends it to the server.

[1122] The server uses image processing libraries such as OpenCV and TensorFlow to analyze the received floor plan file. Image processing algorithms are used to detect the positions of walls, doors, and windows, and to identify the shape and dimensions of the room. For example, it may determine that the room is rectangular, measuring 4m x 5m. The server also converts the units on the floor plan into actual dimensions based on the scale information.

[1123] The server then accesses the URL provided by the user and uses web scraping techniques (such as BeautifulSoup) or APIs to obtain detailed information about the furniture. For example, the size data for a "Modern Sofa" measuring 2m wide, 0.8m deep, and 0.85m high is collected. The collected data is then formatted into a standard format (such as JSON) for subsequent processing.

[1124] The server also uses emotion engines such as Face++ and Microsoft Azure Emotion API to analyze the received facial and voice data. The server recognizes emotions such as joy, surprise, and satisfaction from the user's facial expressions and stores the results in a database.

[1125] The server combines the room dimension data, furniture size data, and the user's recognized emotion data. It uses 3D modeling software (such as Blender) to generate a 3D model of the entire room and arranges the furniture in appropriate locations. For example, if the user's emotion is recognized as "joy," the server creates an interior perspective using bright colors. It then renders this 3D model to generate a highly accurate interior perspective image.

[1126] Finally, the server sends the image data of the generated interior perspective to the device. The device displays the received interior perspective to the user, allowing the user to check it in real time. This allows the user to determine whether the furniture is suitable for the room and whether it matches their image. A personalized interior perspective based on emotional data allows the user to make a more satisfying choice.

[1127] As a concrete example, when a user selects the interior of a new living room, the system works as follows: The user selects "Modern Sofa" and enters the URL "http: / / example.com / modern-sofa." When the user smiles at the camera, the server analyzes the smile and recognizes that the user is "satisfied." Based on this information, the server generates an interior perspective that reflects the layout and color tones that will most likely satisfy the user.

[1128] Example prompt sentence:

[1129] "Enter your floor plan information, provide details about the furniture you're considering, and create a layout for your new living room. Smile for the camera and enjoy the interior design experience."

[1130] This system allows users to easily generate realistic interior perspectives and use them as a reference when selecting furniture. Furthermore, by utilizing emotion recognition, it can provide personalized interior design suggestions, improving user satisfaction.

[1131] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1132] Step 1:

[1133] The user uses their own device to input floor plan information and its scale. Specifically, they upload a floor plan of, say, a living room as an image file (JPEG, PNG, etc.) and specify the scale as "1:100," for example. The input at this point is the floor plan image file and scale information, and the output is request data that sends this information to the server.

[1134] Step 2:

[1135] The user enters the name of the furniture they are considering purchasing, "Modern Sofa," and its URL, "http: / / example.com / modern-sofa," into their device. In addition, the user provides facial expression data of a smile via the camera. The input is the furniture name, URL, and facial expression data, and the output is a request data that compiles these.

[1136] Step 3:

[1137] The terminal sends the floor plan information, furniture information, and facial expression data entered by the user to the server as a single request. The input is the request data from the user, and the output is that this request is sent to the server.

[1138] Step 4:

[1139] The server analyzes the received floor plan file. Specifically, it uses image processing libraries such as OpenCV and TensorFlow to identify the shape and dimensions of the room, as well as the locations of walls, doors, and windows. The input is an image file of the floor plan and scale information, and the output is specific dimensional data of the room, including the locations of walls, doors, and windows.

[1140] Step 5:

[1141] The server converts the units on the floor plan into actual dimensions based on the scale information. For example, if a scale of 1:100 is input, 1 cm corresponds to 1 m. This conversion outputs the actual room dimension data.

[1142] Step 6:

[1143] The server accesses the furniture URL provided by the user and uses web scraping technology (e.g., BeautifulSoup) or APIs to obtain detailed information about the furniture. The input is the furniture URL, and the output is the furniture's size (width, depth, height) and shape data.

[1144] Step 7:

[1145] The server formats the acquired furniture information into a standard format (e.g., JSON). The formatted data is used in subsequent processing. The input is the acquired furniture details, and the output is the data formatted in the standard format.

[1146] Step 8:

[1147] The server uses an emotion engine to analyze the facial expression data sent by the user. Specifically, it uses Face++ or Microsoft Azure Emotion API to recognize the user's emotions from the facial expression data. The input is the user's facial expression data, and the output is the recognized emotion data (happiness, surprise, satisfaction, etc.).

[1148] Step 9:

[1149] The server integrates the room dimension data, furniture size data, and emotion data. Based on this, it uses 3D modeling software (e.g., Blender) to generate a 3D model of the entire room and arranges the furniture in appropriate locations. The input is the integrated data, and the output is a 3D model of the entire room.

[1150] Step 10:

[1151] The server renders the generated 3D model and generates a highly accurate interior perspective image. It also personalizes color tones and furniture placement based on the user's emotional data. The input is the 3D model and emotional data, and the output is personalized interior perspective image data.

[1152] Step 11:

[1153] The server sends the generated interior perspective image data to the terminal. The input is the interior perspective image data, and the output is that it is sent to the user's terminal.

[1154] Step 12:

[1155] The terminal displays the received interior perspective to the user. The user can then view the displayed interior perspective and check whether the furniture is suitable for the room. The input is image data of the interior perspective sent from the server, and the output is an interface that allows the user to visually check it.

[1156] (Application example 2)

[1157] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1158] Conventional interior design proposal systems generate interior perspectives based on floor plan and furniture information provided by the user, but they lack personalization based on the user's emotions and preferences. This often leaves users dissatisfied with the proposals. Furthermore, it is difficult to grasp in advance the atmosphere and feel of the furniture arrangement, which can reduce purchasing motivation.

[1159] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1160] In this invention, the server includes means for acquiring floor plan information input by a user, means for acquiring furniture information input by a user, means for recognizing emotions based on the user's facial expression data and voice data, means for analyzing the acquired floor plan information to identify room dimensions, means for identifying furniture dimensions based on the acquired furniture information, means for generating a 3D model based on the identified room dimensions, furniture dimensions, and the recognized emotion, means for rendering an interior perspective based on the generated 3D model, and means for providing the rendered interior perspective to the user. This enables personalized interior suggestions based on the user's emotions, thereby improving user satisfaction and purchasing motivation.

[1161] "Floor plan information" is data about the shape and dimensions of a room provided by the user, and is basic information for identifying the specific dimensions of the room through analysis.

[1162] "Furniture information" refers to data about furniture provided by the user, including detailed information about the furniture, including its size and shape.

[1163] "Emotion recognition" is a technology that analyzes a user's facial expression data and voice data to identify the user's emotional state (for example, joy, surprise, satisfaction, etc.).

[1164] A "3D model" is a digital representation of a three-dimensional interior space generated based on analyzed floor plan information, furniture information, and recognized emotional data.

[1165] An "interior perspective" is a visual image rendered based on the generated 3D model, and is an interior design proposal provided to the user.

[1166] A "server" is a central computing device that receives, analyzes, and processes data sent from user terminals.

[1167] A "terminal" is a device operated by a user, and is a device for inputting and transmitting floor plan information, furniture information, facial expression data, and voice data.

[1168] "Rendering" is the process of representing the generated 3D model as a visual image and providing it to the user.

[1169] This invention is a system that recognizes a user's emotions and automatically generates personalized interior design proposals based on floor plan and furniture information provided by the user. To realize this system, multiple components, such as a server, a terminal, and an emotion engine, work together.

[1170] The user's device inputs floor plan information, furniture information, and facial expression and voice data for emotion recognition. The floor plan information is acquired as an image file, and the furniture information is acquired via a URL. This input data is compiled by the device and sent to the server as a single request.

[1171] The server first analyzes the floor plan information sent to it and identifies the shape and dimensions of the room. This process uses image processing software such as OpenCV. Next, it accesses the URL provided by the user and uses web scraping technology and APIs to obtain detailed information about the furniture. This data is then converted into a standard format.

[1172] Furthermore, the server uses an emotion engine to analyze the user's facial expression data and voice data to recognize the user's emotional state. This analysis is performed using emotion recognition software such as EmotionEngine.

[1173] The system combines the identified room dimensions, furniture size data, and the user's recognized emotion data to generate a 3D model of the entire room using 3D modeling software. This 3D model is then rendered with high precision using a rendering engine to generate an optimal interior perspective for the user. The perspective content, including color tone and placement, is adjusted based on the user's emotion.

[1174] Finally, the generated image data of the interior perspective is sent from the server to the terminal. The terminal receives this data and displays it to the user. This allows the user to confirm whether the furniture is suitable for the room and matches their image before purchasing. In addition, the displayed perspective is personalized based on the user's emotions, further increasing user satisfaction.

[1175] For example, when a user is choosing interior decor for a new living room, the emotion engine recognizes the user's reactions and provides the optimal perspective based on those reactions. This system allows the user to make a more satisfying interior design choice.

[1176] Example prompt for a generative AI model:

[1177] "Enter the floor plan and furniture information of the room selected by the user, as well as emotional data, and generate a 3D model to propose a room layout. Recognize the emotional data from the user's facial expressions and voice, and create the optimal interior perspective based on those emotions."

[1178] This invention makes it possible to propose personalized interiors based on emotion recognition, thereby improving user satisfaction and purchasing motivation.

[1179] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1180] Step 1:

[1181] The user uses a terminal to input floor plan information, furniture information, facial expression data, and voice data. The input floor plan information is specified as an image file, and the furniture information is provided as a URL. Facial expression data and voice data for emotion recognition are also input as files. This data is compiled by the terminal into a single request and sent to the server. The input is an image file of the floor plan, a URL for the furniture, an image file of the facial expression data, and an audio file of the voice data, and the output is the data compiled into a single request.

[1182] Step 2:

[1183] The server obtains floor plan information from the received request and analyzes it using image processing algorithms such as OpenCV. The analysis identifies the shape and dimensions of the room and calculates specific dimensional information. The input is an image file of the floor plan, and the output is the room dimensional information.

[1184] Step 3:

[1185] The server accesses the URL provided by the user and uses web scraping technology or APIs to obtain detailed information about the furniture. The obtained data is converted into a standard format, and the size and shape of the furniture are identified. The input is the furniture URL, and the output is information about the size and shape of the furniture.

[1186] Step 4:

[1187] The server analyzes the facial expression data and voice data using an emotion engine (e.g., EmotionEngine) to recognize the user's emotions. This analysis identifies the user's emotional state (e.g., joy, surprise, satisfaction, etc.). The input is an image file of the facial expression data and an audio file of the voice data, and the output is information on the recognized emotions.

[1188] Step 5:

[1189] The server integrates the identified room dimensions, furniture size data, and the recognized user emotion data, and generates a 3D model of the entire room using 3D modeling software. The input is the room dimension data, furniture size data, and emotion information, and the output is the 3D model. This model includes the overall layout of the room and the arrangement of the furniture.

[1190] Step 6:

[1191] The server uses a rendering engine to render the generated 3D model with high precision, generating an optimal interior perspective for the user. The perspective content is adjusted based on the user's emotions, including color tone and placement. The input is the 3D model, and the output is the rendered image data of the interior perspective.

[1192] Step 7:

[1193] The server sends image data of the generated interior perspective to the terminal. The terminal receives this data and displays it to the user. This allows the user to check whether the furniture is suitable for the room and matches their image before purchasing. The displayed perspective is personalized based on the user's emotions, improving user satisfaction. The input is image data of the rendered interior perspective, and the output is the interior perspective displayed on the user's terminal.

[1194] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1195] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1196] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1197] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1198] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1199] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1200] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1201] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1202] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1203] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1204] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1205] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1206] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1207] 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.

[1208] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1209] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1210] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1211] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1212] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1213] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1214] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1215] The following is further disclosed regarding the above embodiment.

[1216] (Claim 1)

[1217] A means for acquiring floor plan information input by a user;

[1218] A means for acquiring furniture information input by a user;

[1219] means for analyzing the acquired floor plan information to identify room dimensions;

[1220] a means for identifying the dimensions of the furniture based on the acquired furniture information;

[1221] means for generating a 3D model based on the identified room dimensions and furniture dimensions;

[1222] A means for rendering an interior perspective based on the generated 3D model;

[1223] means for providing the rendered interior perspective to a user;

[1224] A system including:

[1225] (Claim 2)

[1226] 2. The system according to claim 1, further comprising means for acquiring the floor plan information input by the user as an image file.

[1227] (Claim 3)

[1228] The system of claim 1, further comprising: means for obtaining the furniture information input by the user from a URL.

[1229] "Example 1"

[1230] (Claim 1)

[1231] A means for acquiring floor plan information input by a user;

[1232] A means for acquiring furniture information input by a user;

[1233] means for analyzing the acquired floor plan information to identify room dimensions;

[1234] a means for identifying the dimensions of the furniture based on the acquired furniture information;

[1235] means for integrating the dimensions of the room and the dimensions of the furniture to generate a three-dimensional model;

[1236] means for rendering an interior perspective based on the three-dimensional model;

[1237] means for providing the rendered interior perspective to a user's terminal;

[1238] a means for displaying the provided interior perspective to a user on the terminal;

[1239] A system including:

[1240] (Claim 2)

[1241] 10. The system of claim 1, further comprising means for capturing the floor plan information entered by the user as a photograph or image file.

[1242] (Claim 3)

[1243] 10. The system of claim 1, further comprising means for obtaining user-entered furniture information from a URL and collecting detailed information using web scraping or an API.

[1244] "Application Example 1"

[1245] (Claim 1)

[1246] A means for acquiring floor plan information input by a user;

[1247] A means for acquiring furniture information input by a user;

[1248] means for analyzing the acquired floor plan information to identify room dimensions;

[1249] a means for identifying the dimensions of the furniture based on the acquired furniture information;

[1250] means for generating a 3D model based on the identified room dimensions and furniture dimensions;

[1251] A means for rendering an interior perspective based on the generated 3D model;

[1252] means for providing the rendered interior perspective to a user;

[1253] a means for inputting the floor plan information and furniture information as prompts and automatically generating an interior perspective using a generative AI model;

[1254] A system including:

[1255] (Claim 2)

[1256] 2. The system according to claim 1, further comprising means for acquiring the floor plan information input by the user as an image file.

[1257] (Claim 3)

[1258] The system of claim 1, further comprising: means for obtaining the furniture information input by the user from a URL.

[1259] "Example 2: Combining Emotion Engines"

[1260] (Claim 1)

[1261] A means for acquiring floor plan information input by a user;

[1262] A means for acquiring furniture information input by a user;

[1263] means for analyzing the acquired floor plan information to identify room dimensions;

[1264] a means for identifying the dimensions of the furniture based on the acquired furniture information;

[1265] means for generating a 3D model based on the identified room dimensions and furniture dimensions;

[1266] A means for rendering an interior perspective based on the generated 3D model;

[1267] means for acquiring facial expression data or voice data of the user;

[1268] means for recognizing a user's emotion by analyzing the acquired facial expression data or voice data;

[1269] means for personalizing the rendered interior perspective based on the recognized emotion data;

[1270] means for providing the personalized interior perspective to a user;

[1271] A system including:

[1272] (Claim 2)

[1273] 2. The system according to claim 1, further comprising means for acquiring the floor plan information input by the user as an image file.

[1274] (Claim 3)

[1275] The system of claim 1, further comprising: means for obtaining the furniture information input by the user from a URL.

[1276] "Application example 2 when combining emotion engines"

[1277] (Claim 1)

[1278] A means for acquiring floor plan information input by a user;

[1279] A means for acquiring furniture information input by a user;

[1280] means for analyzing the acquired floor plan information to identify room dimensions;

[1281] a means for identifying the dimensions of the furniture based on the acquired furniture information;

[1282] A means for recognizing emotions based on facial expression data and voice data of a user;

[1283] means for generating a 3D model based on the identified room dimensions, furniture dimensions and the recognized emotion;

[1284] A means for rendering an interior perspective based on the generated 3D model;

[1285] means for providing the rendered interior perspective to a user;

[1286] A system including:

[1287] (Claim 2)

[1288] 2. The system according to claim 1, further comprising means for acquiring the floor plan information input by the user as an image file.

[1289] (Claim 3)

[1290] The system of claim 1, further comprising: means for obtaining the furniture information input by the user from a URL. [Explanation of symbols]

[1291] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for acquiring floor plan information input by a user; A means for acquiring furniture information input by a user; means for analyzing the acquired floor plan information to identify room dimensions; a means for identifying the dimensions of the furniture based on the acquired furniture information; means for generating a 3D model based on the identified room dimensions and furniture dimensions; A means for rendering an interior perspective based on the generated 3D model; means for providing the rendered interior perspective to a user; A system including:

2. The system according to claim 1 , further comprising means for acquiring the floor plan information input by the user as an image file.

3. The system according to claim 1 , further comprising means for acquiring the furniture information input by the user from a URL.

Images