System

Abstract

A system is provided.SOLUTION: A system, comprising: means for uploading photographs of a room taken by a user to a server; means for analyzing the uploaded photographs to generate a full-sized three dimensional space model; means for collecting and analyzing interior desires from the user; means for generating a proposed furniture arrangement design based on the collected desires; and means for providing the generated proposed design 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] It is extremely difficult for ordinary consumers, real estate agents, and furniture manufacturers to establish an ideal interior design and create specific plans. Selecting furniture that precisely matches the dimensions of a room requires specialized knowledge, which is time-consuming and laborious. Furthermore, trial and error in interior design is costly, so simulations in realistic environments are needed. Furthermore, when commercially available furniture does not perfectly meet a user's needs, the process of preparing custom-made furniture is extremely complicated. The objective of this invention is to solve these problems and provide users with a simple and effective means for interior design planning. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides an interior design support system having the following configuration. The system includes a means for uploading photos of a room taken by a user to a server, a means for analyzing the uploaded photos to generate a life-size three-dimensional space model, a means for collecting and analyzing interior design requests from the user, a means for generating a furniture layout design proposal based on the collected requests, and a means for providing the generated design proposal to the user. Specifically, the means for analyzing the uploaded photos to generate a life-size three-dimensional space model uses spatial recognition artificial intelligence to analyze multiple photos and construct a room framework. The means for generating a furniture layout design proposal uses visual recognition artificial intelligence to select furniture that matches the user's requests and place it in the three-dimensional space model. This allows the user to realistically simulate furniture placement and interior design in a virtual space and easily establish an optimal plan.

[0006] A "user" is an individual or corporation that uses the system to plan the interior design of a room.

[0007] "Server" means the central system that receives and processes data uploaded by users.

[0008] A "terminal" is a device such as a smartphone or tablet that a user owns, and is used to take photographs and send and receive data.

[0009] "Photos" are image data taken by a user to establish the interior design of a room.

[0010] A "three-dimensional spatial model" is a life-size, three-dimensional digital model created based on photographic data analyzed by spatial recognition artificial intelligence.

[0011] "Spatial recognition artificial intelligence" is a technology that analyzes photographic data to generate a three-dimensional spatial model.

[0012] "Conversational artificial intelligence" is a technology that collects and analyzes interior design requests from users in natural language.

[0013] "Visual recognition artificial intelligence" is a technology that processes things based on visual data, such as selecting furniture and arranging it in a three-dimensional space model.

[0014] "Generative AI" is a technology that designs new furniture based on user requests or generates optimal pieces from existing furniture.

[0015] The "database" is a system that stores information on commercially available furniture for use as reference by the visual recognition artificial intelligence.

[0016] A "virtual space" is a simulated digital environment that reflects design proposals within a three-dimensional spatial model.

[0017] "Custom-made furniture" is new furniture designed by generative AI and custom-made by partner factories.

[0018] A "preview" is a diagram or image that is displayed so that the user can check the generated design proposal.

[0019] A "partner factory" is an external manufacturer that produces custom-made furniture based on instructions from the server. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] The present invention is a system that supports interior design planning by allowing users to take photos of a room using a smartphone or tablet, and then generating a life-size 3D space model from the photos. Specific embodiments for implementing the present invention are described below.

[0042] System Overview

[0043] The system of the present invention begins when a user takes a photo of a room and uploads it to a server. The server analyzes the uploaded photo and uses spatial recognition AI to generate a life-size 3D space model. Based on this 3D space model, a conversational AI collects the user's interior design requests, and a visual recognition AI generates a furniture layout design proposal based on those requests. The final design proposal is provided to the user as a virtual preview.

[0044] Program processing description

[0045] 1. Take and upload a photo

[0046] User: Takes photos of the room from multiple angles using a smartphone or tablet, allowing data on the entire room to be collected.

[0047] Device: Uploads the photos you take to the cloud server.

[0048] 2. Generating 3D models

[0049] Server: To analyze the received photos, spatial recognition AI is used to extract feature points and edge information from the photos. Based on this, the skeleton of the room is constructed and a life-size 3D space model is generated.

[0050] 3. Collecting user requests

[0051] Server: Based on the generated 3D space model, the conversational AI interactively collects information about the user's interior design needs, such as preferred style, color, and budget.

[0052] 4. Generating interior design ideas

[0053] Server: Visual recognition AI searches for furniture in the database based on the collected requests. The selected furniture is placed in a 3D space model and generated as a design proposal.

[0054] 5. Providing a Virtual Preview

[0055] Server: Provides users with a virtual preview of the completed design, which can be viewed in 360 degrees and zoomed in on the device, allowing users to check the details.

[0056] User: Checks the preview and, if necessary, requests for changes or additions are sent to the server again via the conversational AI.

[0057] 6. Proposing additional options and creating custom furniture

[0058] Server: In response to the user's additional requests, the visual recognition AI and generation AI regenerate new design proposals. If the user is not satisfied with commercially available furniture, the generation AI will propose a new furniture design and place an order for custom-made furniture with a partner factory.

[0059] Partner Factory: Produces custom-made furniture based on instructions from the server and delivers it to the user.

[0060] Specific examples

[0061] For example, if a user is thinking about redecorating their living room, they can take a photo of the room with their smartphone and upload it to a cloud server via the app. The server then analyzes the uploaded photo and generates a life-size 3D model of the space.

[0062] The server then uses conversational AI to collect user preferences, such as a "natural style" and a "budget of under 200,000 yen." Based on the collected preferences, the visual recognition AI selects appropriate furniture from a database and places it within the 3D space model. The generated design proposal is provided to the user as a virtual preview, which they can check on their smartphone. If necessary, any changes or additions can be sent to the server, and another interior design proposal is generated.

[0063] Finally, if the user requests custom-made furniture, the server uses generative AI to propose a new furniture design and sends instructions to a partner factory to manufacture it. The partner factory then produces the furniture and delivers it to the user.

[0064] In this way, the system of the present invention helps users to plan their ideal interior design effectively and easily.

[0065] The processing flow will be explained below.

[0066] Step 1:

[0067] Users: Use a smartphone or tablet to take photos from multiple points in the room. It is recommended to take photos from multiple angles and positions to cover the entire room.

[0068] Step 2:

[0069] Device: Upload all photos you take to the cloud server. When uploading, necessary metadata (date and time of photo, location, etc.) is also sent.

[0070] Step 3:

[0071] Server: The cloud server receives the uploaded photos and instructs the spatially aware AI to begin analysis.

[0072] Step 4:

[0073] Server: Spatial AI analyzes each photo and extracts feature points and edge information. Based on this data, it constructs the skeleton of the room and generates a life-size 3D model of the space.

[0074] Step 5:

[0075] Server: Saves the generated 3D space model in a database and notifies the user of its completion.

[0076] Step 6:

[0077] Server: Uses conversational AI to gather user requests through interactive question-based inquiry, such as "What style do you prefer?" or "What is your budget?"

[0078] Step 7:

[0079] Users: Through a conversational interface, they input detailed requirements in natural language, such as preferred interior style and color, budget, and specific use.

[0080] Step 8:

[0081] Server: Analyzes the collected request data and stores it as the base data for generating interior design proposals.

[0082] Step 9:

[0083] Server: Uses visual recognition AI to search for items that match the user's requirements from a database of commercially available furniture.

[0084] Step 10:

[0085] Server: Visually aware AI selects and places the most suitable furniture in a 3D spatial model, taking into account style, color, and location based on the user's requirements.

[0086] Step 11:

[0087] Server: Renders a 3D spatial model of the placed furniture and generates images for a virtual preview.

[0088] Step 12:

[0089] On the device: The device displays the preview image received from the server for the user to check, and also provides functions such as zooming and 360-degree view.

[0090] Step 13:

[0091] User: Checks the virtual preview and, if necessary, sends any changes or additions to the server via the terminal.

[0092] Step 14:

[0093] Server: Based on the changes or additions requested, the visual recognition AI and generation AI will regenerate the interior design proposal. If necessary, new furniture designs will be proposed and placed in the virtual space.

[0094] Step 15:

[0095] Server: Regenerate a new preview image and resend it to the device.

[0096] Step 16:

[0097] Terminal: Show the regenerated preview image to the user.

[0098] Step 17:

[0099] User: After viewing the final preview, if they would like to have the furniture custom-made, they notify the server.

[0100] Step 18:

[0101] Server: Sends production instructions for custom-made furniture to partner factories. The instructions include the necessary design data and material information.

[0102] Step 19:

[0103] Partner Factory: Based on instructions received from the server, the factory starts production of custom-made furniture and delivers it to the user once completed.

[0104] In this way, the system of the present invention allows users to easily plan their ideal interior and ultimately supports them in the creation of custom-made furniture.

[0105] Example 1

[0106] 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."

[0107] In today's world, there is a growing need for systems that allow individual users to more efficiently and accurately plan the decoration of their living spaces. However, conventional methods, such as creating a life-size 3D model of a room and then arranging furniture and equipment based on that model, are extremely complex and time-consuming, often requiring specialized knowledge. Furthermore, due to insufficient functionality for automatically generating and proposing design proposals that meet the user's needs, the end product often falls short of the user's expectations. Therefore, there is a need for a system that allows users to easily create their own room decoration plans and quickly confirm and modify the designs based on their needs.

[0108] 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.

[0109] In this invention, the server includes means for transmitting a video of a room taken by a user to an information processing device, means for analyzing the transmitted video and generating a life-size three-dimensional space model, means for collecting and analyzing requests for decoration from the user, means for generating a design proposal for equipment layout based on the collected requests, and means for providing the generated design proposal to the user. This allows the user to easily create a three-dimensional model of the entire room and quickly confirm and modify the decorative design based on the model to meet their needs.

[0110] "User" refers to an individual or organization that uses the system to plan room decoration.

[0111] "Room footage" includes image or video data taken by the user from multiple angles within the room.

[0112] "Information processing device" refers to a device that processes and stores data, including a cloud server and its peripheral devices.

[0113] "Life-size 3D space model" refers to a digital 3D model that is the same size as a real room.

[0114] "Spatial recognition machine learning" refers to an algorithm technology for recognizing spatial information from input images and videos and generating three-dimensional models.

[0115] "Decorative requests" are the user's preferences and requirements regarding the interior of the room, including style, color, budget, etc.

[0116] "Equipment layout design proposal" refers to a plan that arranges furniture and equipment selected based on the user's requests within a three-dimensional space model.

[0117] "Visual recognition machine learning" refers to algorithm technology that uses image processing technology to recognize, select, and place objects.

[0118] "Generated design proposal" refers to a furniture and equipment layout plan that is automatically created based on the user's requests.

[0119] "Means to provide" refers to the technical means for displaying or notifying the generated design proposal in a form that can be confirmed by the user.

[0120] This invention is a system that allows a user to take a video of a room using a smartphone or tablet, generate a life-size three-dimensional space model from the video, and assist in decoration planning. Specific embodiments for implementing the invention are described below.

[0121] Program processing description

[0122] This system uses the following hardware and software:

[0123] Hardware: smartphones, tablets, cloud servers

[0124] Software: OpenCV, TensorFlow, YOLOv5, Unity, Natural Language Processing models (e.g. GPT-3)

[0125] The user uses a smartphone or tablet to capture video from multiple angles of a room, thereby acquiring information about the entire room. The captured video is then uploaded from the device to a cloud server, where a cloud storage service such as Amazon S3 is used.

[0126] The server analyzes the received video. Specifically, it uses spatial recognition machine learning (e.g., an object detection model using TensorFlow) to extract feature points and edge information from the video. Based on this, it understands the relative positions of each image and generates the skeleton of the room. Furthermore, it uses the distance information from the images to construct a life-size 3D spatial model.

[0127] To gather the user's decorating preferences, the server uses conversational AI (e.g., GPT-3) to interactively ask questions such as, "How would you like to style this room?", "Do you have a preferred color scheme?", and "What is your budget?", and collects detailed data based on those questions.

[0128] The server uses visual recognition machine learning (e.g., YOLOv5) to search the database for furniture and equipment that meets the user's requirements. The selected furniture and equipment are placed in the generated 3D space model and a design proposal is generated. In this process, an algorithm is used that takes into account the balance of furniture placement and the overall aesthetics of the room.

[0129] The resulting design proposal is provided to the user as a virtual preview, which is generated using 3D rendering software (e.g., Unity). The preview can be viewed on a smartphone or tablet, and users can view it in 360 degrees and zoom in and out.

[0130] Users can check the virtual preview and send any changes or additions they require to the server. The server receives the changes and adds new designs using visual recognition machine learning and generative AI models (e.g., DALL·E). If users are not satisfied with commercially available furniture, the server uses the generative AI model to propose a new furniture design and sends instructions to a partner factory to create the custom-made furniture. The partner factory creates the custom-made furniture based on the instructions from the server and delivers it to the user once it is completed. Users are notified of the delivery progress, and installation services may also be provided after delivery.

[0131] Specific examples

[0132] For example, a user considering redecorating their living room can take a video of the living room with their smartphone and upload it to a cloud server via the app. The server analyzes the uploaded video and generates a life-size 3D space model. The server then uses conversational AI to collect user preferences, such as a "natural style" and a "budget of under 200,000 yen." Based on the collected preferences, visual recognition machine learning selects appropriate furniture and fixtures from a database and places them within the 3D space model. The generated design proposal is provided to the user as a virtual preview, which the user can check on their smartphone. If necessary, changes or additions can be made and the server generates another interior design proposal. Finally, if the user requests custom-made furniture, the server uses generative AI to propose a new furniture design and instructs a partner factory to manufacture it. The partner factory then produces the furniture and delivers it to the user.

[0133] Prompt Sentence Examples

[0134] "I'm thinking about the interior design of a living room. I want a natural style, and my budget is under 200,000 yen. Please generate a design plan with appropriate furniture arrangement based on this."

[0135] In this way, the system of the present invention helps users to plan their ideal interior efficiently and accurately.

[0136] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0137] Step 1:

[0138] User: Uses a smartphone or tablet to capture images of a room from multiple angles. The input is an image of the entire room, which allows data on the room's walls, floors, furniture, etc. to be acquired. The output is the captured data from multiple angles.

[0139] Specifically, the app guides the user to the optimal shooting position and angle, and then multiple photos and videos are taken.

[0140] Step 2:

[0141] Terminal: Uploads the captured video to a cloud server. The input is the video data stored on a smartphone or tablet, and the output is the uploaded data stored in cloud storage.

[0142] Specifically, the app allows users to select video data and transfer it to a cloud server via the internet. Upload progress is displayed on the screen.

[0143] Step 3:

[0144] Server: Analyzes uploaded videos. The input is multiple video data stored in cloud storage, and the output is analysis data including feature points and edge information extracted from the video.

[0145] Specifically, it uses spatial recognition machine learning (e.g., an object detection model using TensorFlow) to analyze video data and extract feature points and edges from each frame. Based on this, it calculates the relative positions of each image and generates the skeleton of the room.

[0146] Step 4:

[0147] Server: Generates a life-size 3D space model. The input is the analyzed feature points and edge information, and the output is a life-size 3D space model.

[0148] Specifically, the server uses an algorithm to reconstruct three-dimensional space based on feature points and edge information, and generates a life-size model of the room's skeletal structure.

[0149] Step 5:

[0150] Server: Collects decoration requests from users. The input is a life-size 3D space model and an interactive interface, and the output is the user's request data.

[0151] Specifically, it uses conversational AI (e.g., GPT-3) to interact with users, asking questions such as, "What style do you like?", "Do you have a favorite color scheme?", and "What is your budget?", and collects their responses.

[0152] Step 6:

[0153] Server: Generates a design proposal for facility layout based on the collected requirements. The input is the user's requirement data and a 3D space model, and the output is a design proposal for the decoration plan.

[0154] Specifically, it uses visual recognition machine learning (e.g., YOLOv5) to search a database for furniture and equipment that matches the user's requirements and place them within a three-dimensional space model.

[0155] Step 7:

[0156] Server: Provides the generated design proposal to the user as a virtual preview. The input is the design proposal data, and the output is virtual preview data that can be displayed on the user's terminal.

[0157] Specifically, the design proposals generated using 3D rendering software (e.g., Unity) are visualized in a virtual space, allowing users to view them in 360 degrees and zoom in and out via their smartphones or tablets.

[0158] Step 8:

[0159] User: Checks the virtual preview and sends requests for changes or additions to the server as needed. The input is the user's feedback data, and the output is the updated request data.

[0160] Specifically, the user checks the virtual preview, inputs any necessary changes or additions, and submits the request.

[0161] Step 9:

[0162] Server: Receives requests for changes or additions and generates new design proposals. The input is the updated request data, and the output is the new design proposal.

[0163] Specifically, the design proposal is regenerated using visual recognition machine learning and a generative AI model (e.g., DALL·E) to create updated data for the virtual preview.

[0164] Step 10:

[0165] Server: When a user requests custom-made furniture, the server sends production instructions to a partner factory. The input is new furniture design data, and the output is production instruction data for the partner factory.

[0166] Specifically, the system uses generative AI models to propose new furniture designs, then sends production instructions to partner factories, which then deliver the furniture to the user after production is complete.

[0167] (Application example 1)

[0168] 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."

[0169] Currently, planning an interior design requires consulting with experts and going through a wide range of furniture selection and purchasing procedures, which takes a lot of time and effort. Furthermore, it is difficult to visualize how the furniture will be arranged in the actual space, which often leads to dissatisfaction after purchase. For this reason, there is a need for a system that allows users to easily and effectively design interiors, select the optimal furniture arrangement, and purchase directly from an online shopping site.

[0170] 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.

[0171] In this invention, the server includes means for transmitting images of a room taken by a user to an information processing device, means for processing the transmitted images to generate a life-size three-dimensional space model, means for collecting and analyzing interior design requests from the user, means for generating furniture layout proposals based on the collected requests, means for presenting the generated proposals to the user, and means for the user to confirm the proposed proposals and process orders in the virtual space in conjunction with information from an online shopping site. This enables the user to generate a three-dimensional space model based on photos of their home, interactively try out interior designs, and select and purchase the optimal furniture.

[0172] 1. "Room images taken by users" are photos of rooms taken by users using electronic devices such as smartphones or tablets.

[0173] 2. "Information processing device" means a device for inputting, processing, storing, and outputting data, including servers and cloud services.

[0174] 3. "Transmission" is the act of moving data or information from one point to another.

[0175] 4. A "life-size three-dimensional spatial model" is a three-dimensional spatial model generated from photographed images, which reflects the actual size and shape of a room.

[0176] 5. "Spatial recognition artificial intelligence" is an artificial intelligence system that analyzes images and data and uses them to recognize and construct three-dimensional space.

[0177] 6. "Interior design requests" refers to the user's wishes and demands regarding the design and furniture arrangement of the room.

[0178] 7. "Collection and analysis" refers to the act of gathering data and information, examining it, and extracting the necessary results and characteristics.

[0179] 8. "Furniture Arrangement Proposal" refers to a design proposal showing the appropriate furniture arrangement for a room based on the collected requests.

[0180] 9. "Means for processing orders in a virtual space in conjunction with information from an online shopping site" refers to the function that allows users to check interior designs in a virtual space and then order furniture directly through an online shopping site.

[0181] This invention relates to a system that generates a life-size three-dimensional space model based on images of a room taken by a user, plans an interior design, and ultimately proposes an optimal furniture layout. Specific embodiments for carrying out the invention are described below.

[0182] Program Generation

[0183] 1. User authentication and login function

[0184] The server uses Firebase Authentication to securely authenticate the user: the user opens the app and logs in with their email or social media account.

[0185] 2. Photo taking and uploading function

[0186] The user takes multiple images of the room using a smartphone camera and uploads them to cloud storage (e.g., Google Cloud Storage). The device then sends these image data to the cloud.

[0187] 3. 3D space model generation function

[0188] The server receives the uploaded images and analyzes them using spatial recognition AI (e.g., OpenCV, TensorFlow). Specifically, it extracts feature points and edge information from the images and generates an accurate 3D spatial model of the room based on that information.

[0189] 4. User request collection and analysis function

[0190] The server uses conversational AI (e.g., Dialogflow) to interactively collect user requests. For example, a user might input a request such as, "A natural style, within a budget of 200,000 yen." This request is analyzed on the server.

[0191] 5. Interior proposal generation function

[0192] The server uses visual recognition AI (e.g., YOLO, Pytorch) to select items that match the user's requests from the furniture information in the database and place them in a 3D space model. The generated interior proposals are then rendered in the virtual space using a 3D rendering engine (e.g., Unity).

[0193] 6. Virtual Preview and Ordering Features

[0194] Users can use their smartphones to view the generated interior design proposals in 360 degrees, checking out the details, and if they find furniture they like, they can link it to the online shopping site information in the virtual space and place an order right away.

[0195] Specific examples

[0196] For example, a user thinking about redecorating their living room can take a photo of the living room with their smartphone and upload it to a cloud server via the app. The server analyzes the uploaded image and generates a life-size 3D space model. Conversational AI is then used to gather the user's main requirements. Specifically, the user inputs requirements such as "natural style" and "budget under 200,000 yen" into the conversational AI. Based on this information, the visual recognition AI selects appropriate furniture from a database and places it within the 3D space model. The user can then view the resulting interior design in a virtual space, seamlessly purchasing the furniture.

[0197] Prompt Sentence Examples

[0198] markdown

[0199] You are creating an interior design application. This application allows users to take photos of a room, generates a 3D model from the photos, and suggests interior coordination. Design your program to meet the following requirements:

[0200] Uses Firebase Authentication for user authentication.

[0201] Supports taking photos and uploading to the cloud (using Google Cloud Storage).

[0202] Generate 3D spatial models using spatial recognition AI (OpenCV and TensorFlow).

[0203] Collect user requests using conversational AI (Dialogflow).

[0204] Interior design ideas are generated using visual recognition AI (YOLO and Pytorch).

[0205] The generated design proposals are displayed in a 360-degree view using Unity.

[0206] Please provide the following steps in detail:

[0207] 1. Initial Setup and User Login

[0208] 2. Take a photo and upload it

[0209] 3. 3D model generation

[0210] 4. Collecting user requests

[0211] 5. Interior design proposal generation

[0212] 6. Virtual Preview and Online Shopping Integration

[0213] thank you.

[0214] In this way, the present invention provides a specific method for users to effectively realize their ideal interior design.

[0215] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0216] Step 1:

[0217] Users take photos of their rooms using their smartphones or tablets, capturing data on the entire room from multiple angles. This is the input. They then upload these photos to cloud storage through the app. This is the output.

[0218] Step 2:

[0219] The device sends image data to the server to upload the photos of the room taken by the user to cloud storage. This requires an internet connection. The input is the room image taken by the user, and the output is the image data stored in the cloud storage.

[0220] Step 3:

[0221] The server receives and downloads image data from cloud storage. It then uses spatial recognition AI (e.g., OpenCV, TensorFlow) to analyze the image's feature points and edge information. This is the input. Based on this analysis, a life-size 3D spatial model is generated and stored on the server. This is the output.

[0222] Step 4:

[0223] The server uses conversational artificial intelligence (e.g., Dialogflow) to interactively collect interior design requests from the user. For example, the user might input requests such as "natural style" and "budget under 200,000 yen." This is the input. The collected requests are analyzed and the necessary information is saved on the server. This is the output.

[0224] Step 5:

[0225] The server uses visual recognition AI (e.g., YOLO, Pytorch) to search for furniture items in the database that match the user's requirements. This is the input. The selected furniture is placed in the generated 3D space model. This is the output.

[0226] Step 6:

[0227] The server uses a 3D rendering engine (e.g. Unity) to render the generated interior proposal in a virtual space. This is the input. The user can view this virtual preview in a 360-degree view on their smartphone. This is the output.

[0228] Step 7:

[0229] The user uses a smartphone to view a virtual preview and check the details. Next, they select the furniture they like and place an order in the virtual space, linking it with the information on the online shopping site. This is the input. Finally, the information on the furniture purchased by the user is sent to the online shopping site, and the order is confirmed. This is the output.

[0230] By following the steps outlined above, users can generate a three-dimensional spatial model based on images of their home, interactively experiment with interior designs, and select and purchase the most suitable furniture.

[0231] 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.

[0232] The present invention combines an emotion engine with a system that allows users to take photos of a room with a smartphone or tablet, generate a life-size 3D space model from the photo, and assist in interior design planning. Specific embodiments for implementing the present invention are described below.

[0233] System Overview

[0234] The system of the present invention begins when a user takes a photo of a room and uploads it to a server. The server analyzes the uploaded photo and uses spatial recognition AI to generate a life-size 3D space model. Based on this 3D space model, a conversational AI collects the user's interior design requests, and a visual recognition AI generates a furniture layout design proposal based on those requests. An emotion engine then analyzes the user's emotions and adjusts the interior design based on those emotions. The final design proposal is provided to the user as a virtual preview.

[0235] Program processing description

[0236] 1. Take and upload a photo

[0237] Users: Use a smartphone or tablet to take photos from multiple points in the room. It is recommended to take photos from multiple angles and positions to cover the entire room.

[0238] Device: Uploads the photos to the cloud server. When uploading, necessary metadata (date and time of photo, location, etc.) is also sent.

[0239] 2. Generating 3D models

[0240] Server: To analyze the received photos, spatial recognition AI is used to extract feature points and edge information from the photos, and based on this, a skeleton of the room is constructed and a life-size 3D spatial model is generated.

[0241] Server: Saves the generated 3D spatial model in a database and notifies the user of its completion.

[0242] 3. Collecting user requests

[0243] Server: Uses conversational AI to gather user requests through interactive question-based inquiry, such as "What style do you prefer?" or "What is your budget?"

[0244] Users: Through a conversational interface, they input detailed requirements in natural language, such as preferred interior style and color, budget, and specific use.

[0245] 4. Collecting and analyzing user sentiment

[0246] Server: The emotion engine analyzes the user's voice and facial expression data to identify their emotions. The emotion data is used to adjust the interior design.

[0247] 5. Generating interior design ideas

[0248] Server: Visual recognition AI searches for furniture in the database based on the collected requests. The selected furniture is placed in a 3D space model and generated as a design proposal. At this time, color and style are adjusted based on the analysis results of the emotion engine.

[0249] 6. Providing Virtual Previews

[0250] Server: Provides users with a virtual preview of the completed design, which can be viewed in 360 degrees and zoomed in on the device, allowing users to see the details.

[0251] User: Check the preview and, if necessary, make any changes or additions and send them back to the server via the device.

[0252] 7. Proposing additional options and creating custom furniture

[0253] Server: In response to the user's additional requests, the visual recognition AI and generation AI regenerate new design proposals. If the user is not satisfied with commercially available furniture, the generation AI proposes a new furniture design and places an order for custom-made furniture with a partner factory.

[0254] Partner Factory: Produces custom-made furniture based on instructions from the server and delivers it to the user.

[0255] Specific examples

[0256] For example, if a user is thinking about redecorating their living room, they can take a photo of the room with their smartphone and upload it to a cloud server via the app. The server then analyzes the uploaded photo and generates a life-size 3D model of the space.

[0257] The server then uses conversational AI to collect user preferences, such as "natural style" and "budget under 200,000 yen." Based on the preferences, visual recognition AI selects appropriate furniture from a database and places it within the 3D space model. The server also uses an emotion engine to analyze the user's facial expressions and tone of voice to determine whether the user is relaxed or stressed. Based on the results of this analysis, the server adjusts the colors and style. For example, if the user feels like relaxing, it will suggest furniture and designs in calming colors.

[0258] The generated design proposal is provided to the user as a virtual preview, which they can check on their smartphone. If necessary, they can submit any changes or additions they wish to make to the server, and the interior design proposal is generated again.

[0259] Finally, if the user requests custom-made furniture, the server uses generative AI to propose a new furniture design and sends instructions to a partner factory to manufacture it. The partner factory then produces the furniture and delivers it to the user.

[0260] In this way, the system of the present invention helps users to plan their ideal interiors effectively and easily. Furthermore, by using the emotion engine, it is possible to provide more personalized interior designs that take into consideration the user's emotions.

[0261] The processing flow will be explained below.

[0262] Step 1:

[0263] Users: Use a smartphone or tablet to take photos from multiple points in the room. It is recommended to take photos from multiple angles and positions to cover the entire room.

[0264] Step 2:

[0265] Device: Upload all photos you take to the cloud server. When uploading, necessary metadata (date and time of photo, location, etc.) is also sent.

[0266] Step 3:

[0267] Server: The cloud server receives the uploaded photos and instructs the spatially aware AI to begin analysis.

[0268] Step 4:

[0269] Server: Spatial recognition AI analyzes each photo and extracts feature points and edge information. Based on this data, it constructs the skeleton of the room and generates a life-size 3D spatial model.

[0270] Step 5:

[0271] Server: Saves the generated 3D space model in a database and notifies the user of its completion.

[0272] Step 6:

[0273] Server: Using conversational AI, collects user requests through interactive question format, such as "What style do you prefer?" or "What is your budget?"

[0274] Step 7:

[0275] Users: Through a conversational interface, they input detailed requirements in natural language, such as preferred interior style and color, budget, and specific use.

[0276] Step 8:

[0277] Server: Analyzes the collected request data and stores it as basic data for generating interior design proposals.

[0278] Step 9:

[0279] Server: The emotion engine analyzes the user's emotions from their voice and facial expressions. For example, it determines their stress level from their tone of voice and speaking rate, and uses facial expression recognition technology to understand their emotional state.

[0280] Step 10:

[0281] Server: Reflects the analyzed emotional data in the design proposal. For example, if the user wants to relax, select calm colors and a soft design.

[0282] Step 11:

[0283] Server: Visual recognition AI searches a database of commercially available furniture for items that match the user's needs and emotions.

[0284] Step 12:

[0285] Server: Visual recognition AI selects the most suitable furniture and places it in a 3D spatial model, taking into account style, color, and location based on the user's requirements.

[0286] Step 13:

[0287] Server: Renders a 3D spatial model of the placed furniture and generates an image for a virtual preview.

[0288] Step 14:

[0289] Terminal: Displays the preview image received from the server so that the user can check it. It also provides functions such as zooming and 360-degree view.

[0290] Step 15:

[0291] User: Checks the virtual preview and, if necessary, requests for changes or additions are sent back to the server via the terminal.

[0292] Step 16:

[0293] Server: Based on the changes or additions requested, the visual recognition AI and generation AI will regenerate the interior design proposal. If necessary, new furniture designs will be proposed and placed in the virtual space.

[0294] Step 17:

[0295] Server: Regenerate a new preview image and resend it to the device.

[0296] Step 18:

[0297] Terminal: Show the regenerated preview image to the user.

[0298] Step 19:

[0299] User: After checking the final preview, if the user wishes to have the furniture made to order, the user notifies the server.

[0300] Step 20:

[0301] Server: Sends production instructions for custom-made furniture to partner factories. The instructions include the necessary design data and material information.

[0302] Step 21:

[0303] Partner Factory: Based on instructions received from the server, the factory starts production of custom-made furniture and delivers it to the user once completed.

[0304] In this way, the system of the present invention can more personalized interior design based on the user's emotions, helping the user to easily plan their ideal interior.

[0305] Example 2

[0306] 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."

[0307] Conventional interior planning systems require users to measure the space in their rooms and manually arrange furniture, which is time-consuming and laborious. Additionally, it is difficult to provide interior designs that reflect the user's emotions and desires, which makes it difficult to achieve highly satisfying planning.

[0308] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for uploading photos of a room taken by a user to the server, means for analyzing the uploaded photos and generating a life-size three-dimensional space model, means for collecting and analyzing interior design requests from the user in the form of interactive questions, means for generating design proposals for furniture layout based on the collected requests and emotional data analyzed from the user's voice and facial expressions, means for providing the generated design proposals to the user as virtual previews and regenerating design proposals in response to additional requests, and means for proposing designs for custom-made furniture and instructing its production, if the user so desires. This allows the user to receive emotionally sensitive interior design proposals simply by uploading photos of their room, thereby realizing efficient and high-quality interior planning.

[0309] A "server" is a computer system connected to a network, which processes data in response to requests from users and provides services.

[0310] "User" refers to an individual or corporation that uses the system, and who accesses the system and provides input via a device such as a smartphone or tablet.

[0311] "Photo upload" refers to the process of sending image data taken by a user to a server via the Internet for storage and analysis.

[0312] A "three-dimensional space model" refers to three-dimensional digital data that reproduces a room or space in actual size, allowing for simulation of interior design and spatial layout.

[0313] An "interactive question format" is a method for collecting information through dialogue with the user, in which the next question is dynamically generated based on the user's response.

[0314] "Collected requests" refers to information obtained from users that specifically expresses their wishes and conditions regarding interior design.

[0315] "Emotional data analyzed from voice and facial expressions" is data that indicates the emotional state of the user, obtained by analyzing the user's voice and facial expressions.

[0316] "Furniture layout design proposal" refers to a plan for the arrangement and placement of furniture within a room, proposed based on collected desires and emotional data.

[0317] "Virtual Preview" refers to a virtual reality environment that allows users to visually check the completed design proposal, and is a function that users can view and manipulate through an interface.

[0318] "Made-to-order furniture" refers to custom-made furniture that is designed and manufactured based on the specific requirements of the user.

[0319] MODE FOR CARRYING OUT THE INVENTION

[0320] The present invention provides a system that allows users to take photos of a room using a smartphone or tablet, generate a life-size 3D space model from the photos, and assist in interior design planning. Specific embodiments for implementing the present invention are described below.

[0321] Work Environment

[0322] Hardware: Smartphones, tablets, server computers

[0323] Software: spatially aware artificial intelligence (AI), visual recognition AI, conversational AI, sentiment analysis engines, database management systems, 3D modeling tools (e.g., Unity, Unreal Engine), speech recognition and facial expression recognition libraries (e.g., Google Cloud Speech-to-Text, Microsoft Azure Face API)

[0324] Program processing

[0325] Taking and uploading photos

[0326] User: The user uses a smartphone or tablet to take photos of the room from multiple angles, for example, from all four corners of the room, including the ceiling and floor.

[0327] Device: The photos are uploaded to a cloud server, along with metadata such as the date and time the photo was taken, location information, and device information.

[0328] 3D model generation

[0329] Server: The server receives the uploaded photos and uses spatial recognition AI to extract feature points and edge information from the photos, constructing the skeleton of the room and generating a life-size 3D space model, using libraries such as OpenCV and TensorFlow.

[0330] Server: The generated 3D spatial model is saved in a database and the user is notified of its completion.

[0331] Collecting user requests

[0332] Server: Uses conversational AI to ask users interactive questions such as "What style do you prefer?" and "What is your budget?" using tools such as Dialogflow and Amazon Lex.

[0333] User: In response to questions, the interface inputs preferred interior style and color, budget, and specific use in natural language.

[0334] Collecting and analyzing user sentiment

[0335] Device: Voice and facial expression data is collected on the device when the user responds, using the camera and microphone.

[0336] Server: The emotion analysis engine analyzes the collected data and identifies the user's emotions, for example, using Google Cloud Speech-to-Text for voice analysis and Microsoft Azure Face API for facial expression analysis.

[0337] Generate interior design ideas

[0338] Server: Visual recognition AI searches for suitable furniture in the database based on the user's request.

[0339] Server: Places the selected furniture in the 3D space model. Uses a 3D engine such as Unity or Unreal Engine.

[0340] Server: Adjusts colors and layout based on the results of the sentiment analysis engine. For example, if the user is looking to relax, it will suggest furniture and designs with calming colors.

[0341] Virtual preview available

[0342] Server: Generates a virtual preview of the completed design and provides it to the user, with 360-degree view and zoom capabilities.

[0343] Terminal: The user checks the virtual preview and resubmits any changes or additions via the terminal, if necessary.

[0344] Proposing additional options and creating custom furniture

[0345] Server: In response to the user's additional requests, the visual recognition AI and generation AI regenerate new design proposals. If new furniture is needed, the AI ​​also generates the design.

[0346] Partner Factory: Produces custom-made furniture based on instructions from the server and delivers it to the user.

[0347] Specific examples

[0348] For example, say a user is considering redecorating their living room. The user takes photos of the living room from multiple angles with their smartphone and uploads them to a cloud server via an app. The server analyzes the uploaded photos and generates a life-size 3D space model. The server then uses conversational AI to collect the user's preferences, such as "natural style" and "budget under 200,000 yen." Next, visual recognition AI selects appropriate furniture from a database based on these preferences and places them in the 3D space model.

[0349] The server then uses an emotion analysis engine to analyze the user's facial expressions and tone of voice to determine whether they are relaxed or stressed. Based on the results of this analysis, the server adjusts colors and style to suggest the optimal interior design. The generated design proposal is provided to the user as a virtual preview, which can be viewed on their smartphone. If necessary, the user can submit any changes or additions they wish to the server, and a new design proposal will be generated.

[0350] Finally, if the user wants custom-made furniture, the server uses generative AI to propose a new furniture design and instructs a partner factory to manufacture it. The partner factory then delivers the manufactured furniture to the user.

[0351] Example prompt statement

[0352] Please suggest a natural style living room for a budget of under 200,000 yen. I'd like it to have an airy cafe-like feel.

[0353] Design a relaxing, modern bedroom for under $1,500.

[0354] I would like to decorate the dining room with a warm interior for my family. My budget is within 300,000 yen.

[0355] Through the above prompts, the user can provide detailed requirements to the server and receive advanced interior design proposals.

[0356] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0357] Step 1: Take and upload a photo

[0358] User: The user uses a smartphone or tablet to take photos of the room from multiple angles, including the four corners, ceiling, and floor.

[0359] Input: A photo of the room taken by the user (JPEG, PNG, or other image format).

[0360] Device: Uploads the captured photo to the cloud server, along with metadata such as the date and time of the photo, location information, and device information.

[0361] Output: Room photos and metadata uploaded to a cloud server.

[0362] Step 2: Generate a 3D spatial model

[0363] Server: The server receives the uploaded photos and uses spatial recognition AI to extract feature points and edge information from the photos. For example, it performs image analysis using OpenCV or TensorFlow libraries.

[0364] Input: Uploaded room photo and metadata.

[0365] Server: A 3D modeling tool is used to construct the skeleton of the room based on the feature points and edge information of the photograph, and generate a life-size 3D spatial model.

[0366] Output: A generated full-scale 3D space model.

[0367] Server: Saves the generated 3D spatial model in a database and notifies the user of its completion.

[0368] Step 3: Gather user requests

[0369] Server: Uses conversational AI to gather interior design requirements from users through interactive question-and-answer sessions, such as "What style do you prefer?" and "What is your budget?"

[0370] Input: Interior design requests from the user.

[0371] Users: Through a conversational interface, they input their preferred interior style and color, budget, and specific use in natural language.

[0372] Output: Collected demand data.

[0373] Step 4: Collect and analyze user sentiment

[0374] Device: Uses a camera and microphone to collect the user's voice and facial expression data.

[0375] Input: Voice and facial expression data provided by the user through the interface.

[0376] Server: The emotion analysis engine analyzes the collected data and identifies the user's emotions, using, for example, Google Cloud Speech-to-Text for voice analysis and Microsoft Azure Face API for facial expression analysis.

[0377] Output: Parsed emotion data.

[0378] Step 5: Generate interior design ideas

[0379] Server: Visual recognition AI searches for suitable furniture in the database based on the user's request.

[0380] Input: Collected desire and sentiment data.

[0381] Server: Places selected furniture within the 3D space model and generates design proposals. Uses 3D engines such as Unity or Unreal Engine.

[0382] Server: Adjust colors and layout based on sentiment analysis, for example, choosing calming colors to enhance relaxation.

[0383] Output: Generated interior design proposals.

[0384] Step 6: Provide a virtual preview

[0385] Server: Generates a virtual preview of the completed design, with 360-degree view and zoom capabilities.

[0386] Input: Generated interior design proposal.

[0387] Device: Provides users with a virtual preview, allowing them to view details on their smartphone or tablet.

[0388] Output: User feedback and addition requests.

[0389] Step 7: Propose additional options and create custom furniture

[0390] Server: Based on the user's additional requests, the visual recognition AI and generation AI regenerate new design proposals. If necessary, new furniture designs are also generated.

[0391] Input: Additional data requested by the user.

[0392] Server: Sends instructions to partner factories to produce custom-made furniture. The factories produce the furniture based on the generated design.

[0393] Partner factory: delivers the completed furniture to the user.

[0394] Output: Finished custom-made furniture.

[0395] (Application example 2)

[0396] 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."

[0397] In modern brick-and-mortar store management, attractive interior design is an important factor in increasing customer purchasing motivation. However, planning interior designs can be difficult for store owners and designers without specialized knowledge. Providing designs that take customer emotions into consideration is even more complicated. The present invention aims to address these challenges by enabling users to easily and effectively plan store interior designs and provide more personalized designs based on emotions.

[0398] 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.

[0399] In this invention, the server includes means for uploading photos of rooms taken by users to the server, means for analyzing the uploaded photos and generating a life-size three-dimensional space model, means for collecting and analyzing interior design requests from users, means for generating furniture layout design proposals based on the collected requests, means for providing the generated design proposals to the user, means for collecting and analyzing emotion data and reflecting the emotion data in the interior design, means for using conversational artificial intelligence to collect user requests, and means for simulating the interior design of a store based on the generated three-dimensional space model. This allows users to easily simulate and realize an interior design that suits their own requests and emotions.

[0400] "Photos of a room taken by a user" are image data showing the state of a room, taken by a user using an electronic device such as a smartphone or tablet.

[0401] A "server" is a remote computer system that receives and processes data sent by a user.

[0402] A "life-size three-dimensional space model" is a three-dimensional virtual space model constructed based on actual dimensions and photographs of a room.

[0403] "Spatial awareness artificial intelligence" is a general term for algorithms and technologies that analyze visual data such as images and videos and recognize the location of objects and spatial structures.

[0404] "Interior design requirements" refers to basic information and requests such as the user's desired interior decoration style, color, layout, budget, etc.

[0405] "Conversational AI" is a general term for algorithms and technologies that allow users to ask and answer questions interactively in natural language, and collect and provide information.

[0406] A "furniture layout design proposal" is a design plan that shows how furniture and decorations should be arranged within a life-size three-dimensional space model based on requests collected from users.

[0407] "Visual recognition artificial intelligence" is a general term for algorithms and techniques for identifying objects in images and video data and analyzing their features.

[0408] "Emotion data" is data that indicates the emotional state of the user, such as joy, anger, sadness, or happiness, obtained from the user's voice or facial expression.

[0409] "Reflecting in the interior design" means proposing the optimal interior design based on the collected data and requests, and reflecting it in the actual model.

[0410] "Simulating the interior design of a store" involves virtually testing the design and layout of a store on a three-dimensional spatial model to confirm its actual appearance and functionality.

[0411] This invention is a system that supports interior design and renovation planning for brick-and-mortar stores. It aims to take photos of the current state of the store using a smartphone or tablet, generate a virtual 3D model, and simulate the interior design. Furthermore, it uses an emotion engine to provide interior designs based on customer emotions.

[0412] System Configuration

[0413] User: Uses a smartphone or tablet to take photos of the current state of the store. By taking photos from multiple locations, data is collected for the entire room.

[0414] On your device, take a photo and upload it to a cloud server, along with the date, time, location, and other metadata.

[0415] server:

[0416] Using spatial recognition AI, the system analyzes uploaded photos and generates a life-size 3D space model by extracting feature points and edge information from the photo and constructing the skeleton of the room.

[0417] It uses conversational AI to interactively gather interior design preferences from users, such as asking questions like "What style do you prefer?" and "What is your budget?"

[0418] Using visual recognition AI, the system selects furniture from a database that matches the user's needs and places it in a 3D space model. The generated design proposals are constructed based on the information in the database.

[0419] The emotion engine analyzes the user's voice and facial expression data to identify their emotions, and adjusts the design's color and style based on the analysis results.

[0420] The final design is then presented to the user as a virtual preview, which can be viewed in 360 degrees and zoomed in and out via the device, allowing the user to check the details.

[0421] Program processing

[0422] Spatial recognition artificial intelligence extracts feature points and edge information from the photo and generates a life-size three-dimensional spatial model.

[0423] Conversational AI collects interior design requests from users in an interactive format.

[0424] Visual recognition AI selects appropriate furniture based on the user's requests and places it in a three-dimensional space model.

[0425] The emotion engine analyzes the user's emotions and adjusts the color and style of the design.

[0426] A virtual preview is provided to users, allowing them to see the details using a 360-degree view and zoom capabilities.

[0427] Hardware and software used

[0428] Smartphones and tablets: Provides shooting and uploading functions.

[0429] Cloud server: Analyzes and stores data, and generates three-dimensional spatial models.

[0430] Spatial recognition AI, conversational AI, visual recognition AI, emotion engine: software technologies to realize each function.

[0431] Specific examples

[0432] For example, if a store owner wants to change the interior design of their store, they take multiple photos of the interior with their smartphone and upload them to a cloud server. The server analyzes the photos and generates a life-size 3D spatial model, then uses conversational AI to collect the owner's preferences, such as "modern style" and "budget under 500,000 yen." Visual recognition AI selects appropriate furniture from a database and places it within the 3D spatial model. Furthermore, an emotion engine analyzes the user's emotional data and adjusts colors and style to make customers feel relaxed. The final generated design is provided as a virtual preview, which the user can view on their smartphone.

[0433] Prompt Sentence Examples

[0434] Generate a 3D model using the images: ['shop1.jpg', 'shop2.jpg']. Create a design that includes preferences: {'style': 'modern', 'budget': 50000}

[0435] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0436] Step 1:

[0437] Users use their smartphones or tablets to take photos from multiple locations within the store, allowing data to be collected from the entire room.

[0438] (Input: Multiple photos of the store, Output: Photos uploaded to the cloud server)

[0439] Step 2:

[0440] The device uploads the photos it takes to a cloud server, along with the date, time, location, and other metadata.

[0441] (Input: captured photos and metadata, Output: photos and metadata stored on the cloud server)

[0442] Step 3:

[0443] The server uses spatial recognition AI to analyze the uploaded photos and generate a life-size 3D spatial model by extracting feature points and edge information from the photos and constructing the skeleton of the room.

[0444] (Input: saved photo, Output: 3D space model)

[0445] Step 4:

[0446] The server uses conversational AI to interactively gather interior design requests from users, such as asking questions like "What style do you prefer?" or "What is your budget?", and users input their answers in natural language.

[0447] (Input: interactive questions and user responses, Output: collected interior design requests)

[0448] Step 5:

[0449] The server uses visual recognition AI to select furniture from a database that matches the user's needs and place it in a 3D space model. The generated design proposal is based on the information in the database.

[0450] (Input: collected interior design requests and a 3D space model, output: a 3D space model with furniture arranged)

[0451] Step 6:

[0452] The server uses an emotion engine to analyze the user's voice and facial expression data to identify their emotions, and adjusts the color and style of the design based on the analysis results.

[0453] (Input: User's voice and facial expression data, Output: Design proposal with adjusted color and style)

[0454] Step 7:

[0455] The server generates a virtual preview and provides it to the user. This preview can be viewed in 360 degrees and zoomed in and out through the device, allowing the user to check the details. The user can check the preview and, if necessary, submit changes or additions to the server again.

[0456] (Input: adjusted design proposal, Output: virtual preview provided to user)

[0457] 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.

[0458] 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.

[0459] 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.

[0460] [Second embodiment]

[0461] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0462] 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.

[0463] 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).

[0464] 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.

[0465] 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.

[0466] 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).

[0467] 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.

[0468] 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.

[0469] 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.

[0470] 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.

[0471] 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.

[0472] 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."

[0473] The present invention is a system that supports interior design planning by allowing users to take photos of a room using a smartphone or tablet, and then generating a life-size 3D space model from the photos. Specific embodiments for implementing the present invention are described below.

[0474] System Overview

[0475] The system of the present invention begins when a user takes a photo of a room and uploads it to a server. The server analyzes the uploaded photo and uses spatial recognition AI to generate a life-size 3D space model. Based on this 3D space model, a conversational AI collects the user's interior design requests, and a visual recognition AI generates a furniture layout design proposal based on those requests. The final design proposal is provided to the user as a virtual preview.

[0476] Program processing description

[0477] 1. Take and upload a photo

[0478] User: Takes photos of the room from multiple angles using a smartphone or tablet, allowing data on the entire room to be collected.

[0479] Device: Uploads the photos you take to the cloud server.

[0480] 2. Generating 3D models

[0481] Server: To analyze the received photos, spatial recognition AI is used to extract feature points and edge information from the photos. Based on this, the skeleton of the room is constructed and a life-size 3D space model is generated.

[0482] 3. Collecting user requests

[0483] Server: Based on the generated 3D space model, the conversational AI interactively collects information about the user's interior design needs, such as preferred style, color, and budget.

[0484] 4. Generating interior design ideas

[0485] Server: Visual recognition AI searches for furniture in the database based on the collected requests. The selected furniture is placed in a 3D space model and generated as a design proposal.

[0486] 5. Providing a Virtual Preview

[0487] Server: Provides users with a virtual preview of the completed design, which can be viewed in 360 degrees and zoomed in on the device, allowing users to check the details.

[0488] User: Checks the preview and, if necessary, requests for changes or additions are sent to the server again via the conversational AI.

[0489] 6. Proposing additional options and creating custom furniture

[0490] Server: In response to the user's additional requests, the visual recognition AI and generation AI regenerate new design proposals. If the user is not satisfied with commercially available furniture, the generation AI will propose a new furniture design and place an order for custom-made furniture with a partner factory.

[0491] Partner Factory: Produces custom-made furniture based on instructions from the server and delivers it to the user.

[0492] Specific examples

[0493] For example, if a user is thinking about redecorating their living room, they can take a photo of the room with their smartphone and upload it to a cloud server via the app. The server then analyzes the uploaded photo and generates a life-size 3D model of the space.

[0494] The server then uses conversational AI to collect user preferences, such as a "natural style" and a "budget of under 200,000 yen." Based on the collected preferences, the visual recognition AI selects appropriate furniture from a database and places it within the 3D space model. The generated design proposal is provided to the user as a virtual preview, which they can check on their smartphone. If necessary, any changes or additions can be sent to the server, and another interior design proposal is generated.

[0495] Finally, if the user requests custom-made furniture, the server uses generative AI to propose a new furniture design and sends instructions to a partner factory to manufacture it. The partner factory then produces the furniture and delivers it to the user.

[0496] In this way, the system of the present invention helps users to plan their ideal interior design effectively and easily.

[0497] The processing flow will be explained below.

[0498] Step 1:

[0499] Users: Use a smartphone or tablet to take photos from multiple points in the room. It is recommended to take photos from multiple angles and positions to cover the entire room.

[0500] Step 2:

[0501] Device: Upload all photos you take to the cloud server. When uploading, necessary metadata (date and time of photo, location, etc.) is also sent.

[0502] Step 3:

[0503] Server: The cloud server receives the uploaded photos and instructs the spatially aware AI to begin analysis.

[0504] Step 4:

[0505] Server: Spatial AI analyzes each photo and extracts feature points and edge information. Based on this data, it constructs the skeleton of the room and generates a life-size 3D model of the space.

[0506] Step 5:

[0507] Server: Saves the generated 3D space model in a database and notifies the user of its completion.

[0508] Step 6:

[0509] Server: Uses conversational AI to gather user requests through interactive question-based inquiry, such as "What style do you prefer?" or "What is your budget?"

[0510] Step 7:

[0511] Users: Through a conversational interface, they input detailed requirements in natural language, such as preferred interior style and color, budget, and specific use.

[0512] Step 8:

[0513] Server: Analyzes the collected request data and stores it as the base data for generating interior design proposals.

[0514] Step 9:

[0515] Server: Uses visual recognition AI to search for items that match the user's requirements from a database of commercially available furniture.

[0516] Step 10:

[0517] Server: Visually aware AI selects and places the most suitable furniture in a 3D spatial model, taking into account style, color, and location based on the user's requirements.

[0518] Step 11:

[0519] Server: Renders a 3D spatial model of the placed furniture and generates images for a virtual preview.

[0520] Step 12:

[0521] On the device: The device displays the preview image received from the server for the user to check, and also provides functions such as zooming and 360-degree view.

[0522] Step 13:

[0523] User: Checks the virtual preview and, if necessary, sends any changes or additions to the server via the terminal.

[0524] Step 14:

[0525] Server: Based on the changes or additions requested, the visual recognition AI and generation AI will regenerate the interior design proposal. If necessary, new furniture designs will be proposed and placed in the virtual space.

[0526] Step 15:

[0527] Server: Regenerate a new preview image and resend it to the device.

[0528] Step 16:

[0529] Terminal: Show the regenerated preview image to the user.

[0530] Step 17:

[0531] User: After viewing the final preview, if they would like to have the furniture custom-made, they notify the server.

[0532] Step 18:

[0533] Server: Sends production instructions for custom-made furniture to partner factories. The instructions include the necessary design data and material information.

[0534] Step 19:

[0535] Partner Factory: Based on instructions received from the server, the factory starts production of custom-made furniture and delivers it to the user once completed.

[0536] In this way, the system of the present invention allows users to easily plan their ideal interior and ultimately supports them in the creation of custom-made furniture.

[0537] Example 1

[0538] 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."

[0539] In today's world, there is a growing need for systems that allow individual users to more efficiently and accurately plan the decoration of their living spaces. However, conventional methods, such as creating a life-size 3D model of a room and then arranging furniture and equipment based on that model, are extremely complex and time-consuming, often requiring specialized knowledge. Furthermore, due to insufficient functionality for automatically generating and proposing design proposals that meet the user's needs, the end product often falls short of the user's expectations. Therefore, there is a need for a system that allows users to easily create their own room decoration plans and quickly confirm and modify the designs based on their needs.

[0540] 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.

[0541] In this invention, the server includes means for transmitting a video of a room taken by a user to an information processing device, means for analyzing the transmitted video and generating a life-size three-dimensional space model, means for collecting and analyzing requests for decoration from the user, means for generating a design proposal for equipment layout based on the collected requests, and means for providing the generated design proposal to the user. This allows the user to easily create a three-dimensional model of the entire room and quickly confirm and modify the decorative design based on the model to meet their needs.

[0542] "User" refers to an individual or organization that uses the system to plan room decoration.

[0543] "Room footage" includes image or video data taken by the user from multiple angles within the room.

[0544] "Information processing device" refers to a device that processes and stores data, including a cloud server and its peripheral devices.

[0545] "Life-size 3D space model" refers to a digital 3D model that is the same size as a real room.

[0546] "Spatial recognition machine learning" refers to an algorithm technology for recognizing spatial information from input images and videos and generating three-dimensional models.

[0547] "Decorative requests" are the user's preferences and requirements regarding the interior of the room, including style, color, budget, etc.

[0548] "Equipment layout design proposal" refers to a plan that arranges furniture and equipment selected based on the user's requests within a three-dimensional space model.

[0549] "Visual recognition machine learning" refers to algorithm technology that uses image processing technology to recognize, select, and place objects.

[0550] "Generated design proposal" refers to a furniture and equipment layout plan that is automatically created based on the user's requests.

[0551] "Means to provide" refers to the technical means for displaying or notifying the generated design proposal in a form that can be confirmed by the user.

[0552] This invention is a system that allows a user to take a video of a room using a smartphone or tablet, generate a life-size three-dimensional space model from the video, and assist in decoration planning. Specific embodiments for implementing the invention are described below.

[0553] Program processing description

[0554] This system uses the following hardware and software:

[0555] Hardware: smartphones, tablets, cloud servers

[0556] Software: OpenCV, TensorFlow, YOLOv5, Unity, Natural Language Processing models (e.g. GPT-3)

[0557] The user uses a smartphone or tablet to capture video from multiple angles of a room, thereby acquiring information about the entire room. The captured video is then uploaded from the device to a cloud server, where a cloud storage service such as Amazon S3 is used.

[0558] The server analyzes the received video. Specifically, it uses spatial recognition machine learning (e.g., an object detection model using TensorFlow) to extract feature points and edge information from the video. Based on this, it understands the relative positions of each image and generates the skeleton of the room. Furthermore, it uses the distance information from the images to construct a life-size 3D spatial model.

[0559] To gather the user's decorating preferences, the server uses conversational AI (e.g., GPT-3) to interactively ask questions such as, "How would you like to style this room?", "Do you have a preferred color scheme?", and "What is your budget?", and collects detailed data based on those questions.

[0560] The server uses visual recognition machine learning (e.g., YOLOv5) to search the database for furniture and equipment that meets the user's requirements. The selected furniture and equipment are placed in the generated 3D space model and a design proposal is generated. In this process, an algorithm is used that takes into account the balance of furniture placement and the overall aesthetics of the room.

[0561] The resulting design proposal is provided to the user as a virtual preview, which is generated using 3D rendering software (e.g., Unity). The preview can be viewed on a smartphone or tablet, and users can view it in 360 degrees and zoom in and out.

[0562] Users can check the virtual preview and send any changes or additions they require to the server. The server receives the changes and adds new designs using visual recognition machine learning and generative AI models (e.g., DALL·E). If users are not satisfied with commercially available furniture, the server uses the generative AI model to propose a new furniture design and sends instructions to a partner factory to create the custom-made furniture. The partner factory creates the custom-made furniture based on the instructions from the server and delivers it to the user once it is completed. Users are notified of the delivery progress, and installation services may also be provided after delivery.

[0563] Specific examples

[0564] For example, a user considering redecorating their living room can take a video of the living room with their smartphone and upload it to a cloud server via the app. The server analyzes the uploaded video and generates a life-size 3D space model. The server then uses conversational AI to collect user preferences, such as a "natural style" and a "budget of under 200,000 yen." Based on the collected preferences, visual recognition machine learning selects appropriate furniture and fixtures from a database and places them within the 3D space model. The generated design proposal is provided to the user as a virtual preview, which the user can check on their smartphone. If necessary, changes or additions can be made and the server generates another interior design proposal. Finally, if the user requests custom-made furniture, the server uses generative AI to propose a new furniture design and instructs a partner factory to manufacture it. The partner factory then produces the furniture and delivers it to the user.

[0565] Prompt Sentence Examples

[0566] "I'm thinking about the interior design of a living room. I want a natural style, and my budget is under 200,000 yen. Please generate a design plan with appropriate furniture arrangement based on this."

[0567] In this way, the system of the present invention helps users to plan their ideal interior efficiently and accurately.

[0568] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0569] Step 1:

[0570] User: Uses a smartphone or tablet to capture images of a room from multiple angles. The input is an image of the entire room, which allows data on the room's walls, floors, furniture, etc. to be acquired. The output is the captured data from multiple angles.

[0571] Specifically, the app guides the user to the optimal shooting position and angle, and then multiple photos and videos are taken.

[0572] Step 2:

[0573] Terminal: Uploads the captured video to a cloud server. The input is the video data stored on a smartphone or tablet, and the output is the uploaded data stored in cloud storage.

[0574] Specifically, the app allows users to select video data and transfer it to a cloud server via the internet. Upload progress is displayed on the screen.

[0575] Step 3:

[0576] Server: Analyzes uploaded videos. The input is multiple video data stored in cloud storage, and the output is analysis data including feature points and edge information extracted from the video.

[0577] Specifically, it uses spatial recognition machine learning (e.g., an object detection model using TensorFlow) to analyze video data and extract feature points and edges from each frame. Based on this, it calculates the relative positions of each image and generates the skeleton of the room.

[0578] Step 4:

[0579] Server: Generates a life-size 3D space model. The input is the analyzed feature points and edge information, and the output is a life-size 3D space model.

[0580] Specifically, the server uses an algorithm to reconstruct three-dimensional space based on feature points and edge information, and generates a life-size model of the room's skeletal structure.

[0581] Step 5:

[0582] Server: Collects decoration requests from users. The input is a life-size 3D space model and an interactive interface, and the output is the user's request data.

[0583] Specifically, it uses conversational AI (e.g., GPT-3) to interact with users, asking questions such as, "What style do you like?", "Do you have a favorite color scheme?", and "What is your budget?", and collects their responses.

[0584] Step 6:

[0585] Server: Generates a design proposal for facility layout based on the collected requirements. The input is the user's requirement data and a 3D space model, and the output is a design proposal for the decoration plan.

[0586] Specifically, it uses visual recognition machine learning (e.g., YOLOv5) to search a database for furniture and equipment that matches the user's requirements and place them within a three-dimensional space model.

[0587] Step 7:

[0588] Server: Provides the generated design proposal to the user as a virtual preview. The input is the design proposal data, and the output is virtual preview data that can be displayed on the user's terminal.

[0589] Specifically, the design proposals generated using 3D rendering software (e.g., Unity) are visualized in a virtual space, allowing users to view them in 360 degrees and zoom in and out via their smartphones or tablets.

[0590] Step 8:

[0591] User: Checks the virtual preview and sends requests for changes or additions to the server as needed. The input is the user's feedback data, and the output is the updated request data.

[0592] Specifically, the user checks the virtual preview, inputs any necessary changes or additions, and submits the request.

[0593] Step 9:

[0594] Server: Receives requests for changes or additions and generates new design proposals. The input is the updated request data, and the output is the new design proposal.

[0595] Specifically, the design proposal is regenerated using visual recognition machine learning and a generative AI model (e.g., DALL·E) to create updated data for the virtual preview.

[0596] Step 10:

[0597] Server: When a user requests custom-made furniture, the server sends production instructions to a partner factory. The input is new furniture design data, and the output is production instruction data for the partner factory.

[0598] Specifically, the system uses generative AI models to propose new furniture designs, then sends production instructions to partner factories, which then deliver the furniture to the user after production is complete.

[0599] (Application example 1)

[0600] 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."

[0601] Currently, planning an interior design requires consulting with experts and going through a wide range of furniture selection and purchasing procedures, which takes a lot of time and effort. Furthermore, it is difficult to visualize how the furniture will be arranged in the actual space, which often leads to dissatisfaction after purchase. For this reason, there is a need for a system that allows users to easily and effectively design interiors, select the optimal furniture arrangement, and purchase directly from an online shopping site.

[0602] 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.

[0603] In this invention, the server includes means for transmitting images of a room taken by a user to an information processing device, means for processing the transmitted images to generate a life-size three-dimensional space model, means for collecting and analyzing interior design requests from the user, means for generating furniture layout proposals based on the collected requests, means for presenting the generated proposals to the user, and means for the user to confirm the proposed proposals and process orders in the virtual space in conjunction with information from an online shopping site. This enables the user to generate a three-dimensional space model based on photos of their home, interactively try out interior designs, and select and purchase the optimal furniture.

[0604] 1. "Room images taken by users" are photos of rooms taken by users using electronic devices such as smartphones or tablets.

[0605] 2. "Information processing device" means a device for inputting, processing, storing, and outputting data, including servers and cloud services.

[0606] 3. "Transmission" is the act of moving data or information from one point to another.

[0607] 4. A "life-size three-dimensional spatial model" is a three-dimensional spatial model generated from photographed images, which reflects the actual size and shape of a room.

[0608] 5. "Spatial recognition artificial intelligence" is an artificial intelligence system that analyzes images and data and uses them to recognize and construct three-dimensional space.

[0609] 6. "Interior design requests" refers to the user's wishes and demands regarding the design and furniture arrangement of the room.

[0610] 7. "Collection and analysis" refers to the act of gathering data and information, examining it, and extracting the necessary results and characteristics.

[0611] 8. "Furniture Arrangement Proposal" refers to a design proposal showing the appropriate furniture arrangement for a room based on the collected requests.

[0612] 9. "Means for processing orders in a virtual space in conjunction with information from an online shopping site" refers to the function that allows users to check interior designs in a virtual space and then order furniture directly through an online shopping site.

[0613] This invention relates to a system that generates a life-size three-dimensional space model based on images of a room taken by a user, plans an interior design, and ultimately proposes an optimal furniture layout. Specific embodiments for carrying out the invention are described below.

[0614] Program Generation

[0615] 1. User authentication and login function

[0616] The server uses Firebase Authentication to securely authenticate the user: the user opens the app and logs in with their email or social media account.

[0617] 2. Photo taking and uploading function

[0618] The user takes multiple images of the room using a smartphone camera and uploads them to cloud storage (e.g., Google Cloud Storage). The device then sends these image data to the cloud.

[0619] 3. 3D space model generation function

[0620] The server receives the uploaded images and analyzes them using spatial recognition AI (e.g., OpenCV, TensorFlow). Specifically, it extracts feature points and edge information from the images and generates an accurate 3D spatial model of the room based on that information.

[0621] 4. User request collection and analysis function

[0622] The server uses conversational AI (e.g., Dialogflow) to interactively collect user requests. For example, a user might input a request such as, "A natural style, within a budget of 200,000 yen." This request is analyzed on the server.

[0623] 5. Interior proposal generation function

[0624] The server uses visual recognition AI (e.g., YOLO, Pytorch) to select items that match the user's requests from the furniture information in the database and place them in a 3D space model. The generated interior proposals are then rendered in the virtual space using a 3D rendering engine (e.g., Unity).

[0625] 6. Virtual Preview and Ordering Features

[0626] Users can use their smartphones to view the generated interior design proposals in 360 degrees, checking out the details, and if they find furniture they like, they can link it to the online shopping site information in the virtual space and place an order right away.

[0627] Specific examples

[0628] For example, a user thinking about redecorating their living room can take a photo of the living room with their smartphone and upload it to a cloud server via the app. The server analyzes the uploaded image and generates a life-size 3D space model. Conversational AI is then used to gather the user's main requirements. Specifically, the user inputs requirements such as "natural style" and "budget under 200,000 yen" into the conversational AI. Based on this information, the visual recognition AI selects appropriate furniture from a database and places it within the 3D space model. The user can then view the resulting interior design in a virtual space, seamlessly purchasing the furniture.

[0629] Prompt Sentence Examples

[0630] markdown

[0631] You are creating an interior design application. This application allows users to take photos of a room, generates a 3D model from the photos, and suggests interior coordination. Design your program to meet the following requirements:

[0632] Uses Firebase Authentication for user authentication.

[0633] Supports taking photos and uploading to the cloud (using Google Cloud Storage).

[0634] Generate 3D spatial models using spatial recognition AI (OpenCV and TensorFlow).

[0635] Collect user requests using conversational AI (Dialogflow).

[0636] Interior design ideas are generated using visual recognition AI (YOLO and Pytorch).

[0637] The generated design proposals are displayed in a 360-degree view using Unity.

[0638] Please provide the following steps in detail:

[0639] 1. Initial Setup and User Login

[0640] 2. Take a photo and upload it

[0641] 3. 3D model generation

[0642] 4. Collecting user requests

[0643] 5. Interior design proposal generation

[0644] 6. Virtual Preview and Online Shopping Integration

[0645] thank you.

[0646] In this way, the present invention provides a specific method for users to effectively realize their ideal interior design.

[0647] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0648] Step 1:

[0649] Users take photos of their rooms using their smartphones or tablets, capturing data on the entire room from multiple angles. This is the input. They then upload these photos to cloud storage through the app. This is the output.

[0650] Step 2:

[0651] The device sends image data to the server to upload the photos of the room taken by the user to cloud storage. This requires an internet connection. The input is the room image taken by the user, and the output is the image data stored in the cloud storage.

[0652] Step 3:

[0653] The server receives and downloads image data from cloud storage. It then uses spatial recognition AI (e.g., OpenCV, TensorFlow) to analyze the image's feature points and edge information. This is the input. Based on this analysis, a life-size 3D spatial model is generated and stored on the server. This is the output.

[0654] Step 4:

[0655] The server uses conversational artificial intelligence (e.g., Dialogflow) to interactively collect interior design requests from the user. For example, the user might input requests such as "natural style" and "budget under 200,000 yen." This is the input. The collected requests are analyzed and the necessary information is saved on the server. This is the output.

[0656] Step 5:

[0657] The server uses visual recognition AI (e.g., YOLO, Pytorch) to search for furniture items in the database that match the user's requirements. This is the input. The selected furniture is placed in the generated 3D space model. This is the output.

[0658] Step 6:

[0659] The server uses a 3D rendering engine (e.g. Unity) to render the generated interior proposal in a virtual space. This is the input. The user can view this virtual preview in a 360-degree view on their smartphone. This is the output.

[0660] Step 7:

[0661] The user uses a smartphone to view a virtual preview and check the details. Next, they select the furniture they like and place an order in the virtual space, linking it with the information on the online shopping site. This is the input. Finally, the information on the furniture purchased by the user is sent to the online shopping site, and the order is confirmed. This is the output.

[0662] By following the steps outlined above, users can generate a three-dimensional spatial model based on images of their home, interactively experiment with interior designs, and select and purchase the most suitable furniture.

[0663] 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.

[0664] The present invention combines an emotion engine with a system that allows users to take photos of a room with a smartphone or tablet, generate a life-size 3D space model from the photo, and assist in interior design planning. Specific embodiments for implementing the present invention are described below.

[0665] System Overview

[0666] The system of the present invention begins when a user takes a photo of a room and uploads it to a server. The server analyzes the uploaded photo and uses spatial recognition AI to generate a life-size 3D space model. Based on this 3D space model, a conversational AI collects the user's interior design requests, and a visual recognition AI generates a furniture layout design proposal based on those requests. An emotion engine then analyzes the user's emotions and adjusts the interior design based on those emotions. The final design proposal is provided to the user as a virtual preview.

[0667] Program processing description

[0668] 1. Take and upload a photo

[0669] Users: Use a smartphone or tablet to take photos from multiple points in the room. It is recommended to take photos from multiple angles and positions to cover the entire room.

[0670] Device: Uploads the photos to the cloud server. When uploading, necessary metadata (date and time of photo, location, etc.) is also sent.

[0671] 2. Generating 3D models

[0672] Server: To analyze the received photos, spatial recognition AI is used to extract feature points and edge information from the photos, and based on this, a skeleton of the room is constructed and a life-size 3D spatial model is generated.

[0673] Server: Saves the generated 3D spatial model in a database and notifies the user of its completion.

[0674] 3. Collecting user requests

[0675] Server: Uses conversational AI to gather user requests through interactive question-based inquiry, such as "What style do you prefer?" or "What is your budget?"

[0676] Users: Through a conversational interface, they input detailed requirements in natural language, such as preferred interior style and color, budget, and specific use.

[0677] 4. Collecting and analyzing user sentiment

[0678] Server: The emotion engine analyzes the user's voice and facial expression data to identify their emotions. The emotion data is used to adjust the interior design.

[0679] 5. Generating interior design ideas

[0680] Server: Visual recognition AI searches for furniture in the database based on the collected requests. The selected furniture is placed in a 3D space model and generated as a design proposal. At this time, color and style are adjusted based on the analysis results of the emotion engine.

[0681] 6. Providing Virtual Previews

[0682] Server: Provides users with a virtual preview of the completed design, which can be viewed in 360 degrees and zoomed in on the device, allowing users to see the details.

[0683] User: Check the preview and, if necessary, make any changes or additions and send them back to the server via the device.

[0684] 7. Proposing additional options and creating custom furniture

[0685] Server: In response to the user's additional requests, the visual recognition AI and generation AI regenerate new design proposals. If the user is not satisfied with commercially available furniture, the generation AI proposes a new furniture design and places an order for custom-made furniture with a partner factory.

[0686] Partner Factory: Produces custom-made furniture based on instructions from the server and delivers it to the user.

[0687] Specific examples

[0688] For example, if a user is thinking about redecorating their living room, they can take a photo of the room with their smartphone and upload it to a cloud server via the app. The server then analyzes the uploaded photo and generates a life-size 3D model of the space.

[0689] The server then uses conversational AI to collect user preferences, such as "natural style" and "budget under 200,000 yen." Based on the preferences, visual recognition AI selects appropriate furniture from a database and places it within the 3D space model. The server also uses an emotion engine to analyze the user's facial expressions and tone of voice to determine whether the user is relaxed or stressed. Based on the results of this analysis, the server adjusts the colors and style. For example, if the user feels like relaxing, it will suggest furniture and designs in calming colors.

[0690] The generated design proposal is provided to the user as a virtual preview, which they can check on their smartphone. If necessary, they can submit any changes or additions they wish to make to the server, and the interior design proposal is generated again.

[0691] Finally, if the user requests custom-made furniture, the server uses generative AI to propose a new furniture design and sends instructions to a partner factory to manufacture it. The partner factory then produces the furniture and delivers it to the user.

[0692] In this way, the system of the present invention helps users to plan their ideal interiors effectively and easily. Furthermore, by using the emotion engine, it is possible to provide more personalized interior designs that take into consideration the user's emotions.

[0693] The processing flow will be explained below.

[0694] Step 1:

[0695] Users: Use a smartphone or tablet to take photos from multiple points in the room. It is recommended to take photos from multiple angles and positions to cover the entire room.

[0696] Step 2:

[0697] Device: Upload all photos you take to the cloud server. When uploading, necessary metadata (date and time of photo, location, etc.) is also sent.

[0698] Step 3:

[0699] Server: The cloud server receives the uploaded photos and instructs the spatially aware AI to begin analysis.

[0700] Step 4:

[0701] Server: Spatial recognition AI analyzes each photo and extracts feature points and edge information. Based on this data, it constructs the skeleton of the room and generates a life-size 3D spatial model.

[0702] Step 5:

[0703] Server: Saves the generated 3D space model in a database and notifies the user of its completion.

[0704] Step 6:

[0705] Server: Using conversational AI, collects user requests through interactive question format, such as "What style do you prefer?" or "What is your budget?"

[0706] Step 7:

[0707] Users: Through a conversational interface, they input detailed requirements in natural language, such as preferred interior style and color, budget, and specific use.

[0708] Step 8:

[0709] Server: Analyzes the collected request data and stores it as basic data for generating interior design proposals.

[0710] Step 9:

[0711] Server: The emotion engine analyzes the user's emotions from their voice and facial expressions. For example, it determines their stress level from their tone of voice and speaking rate, and uses facial expression recognition technology to understand their emotional state.

[0712] Step 10:

[0713] Server: Reflects the analyzed emotional data in the design proposal. For example, if the user wants to relax, select calm colors and a soft design.

[0714] Step 11:

[0715] Server: Visual recognition AI searches a database of commercially available furniture for items that match the user's needs and emotions.

[0716] Step 12:

[0717] Server: Visual recognition AI selects the most suitable furniture and places it in a 3D spatial model, taking into account style, color, and location based on the user's requirements.

[0718] Step 13:

[0719] Server: Renders a 3D spatial model of the placed furniture and generates an image for a virtual preview.

[0720] Step 14:

[0721] Terminal: Displays the preview image received from the server so that the user can check it. It also provides functions such as zooming and 360-degree view.

[0722] Step 15:

[0723] User: Checks the virtual preview and, if necessary, requests for changes or additions are sent back to the server via the terminal.

[0724] Step 16:

[0725] Server: Based on the changes or additions requested, the visual recognition AI and generation AI will regenerate the interior design proposal. If necessary, new furniture designs will be proposed and placed in the virtual space.

[0726] Step 17:

[0727] Server: Regenerate a new preview image and resend it to the device.

[0728] Step 18:

[0729] Terminal: Show the regenerated preview image to the user.

[0730] Step 19:

[0731] User: After checking the final preview, if the user wishes to have the furniture made to order, the user notifies the server.

[0732] Step 20:

[0733] Server: Sends production instructions for custom-made furniture to partner factories. The instructions include the necessary design data and material information.

[0734] Step 21:

[0735] Partner Factory: Based on instructions received from the server, the factory starts production of custom-made furniture and delivers it to the user once completed.

[0736] In this way, the system of the present invention can more personalized interior design based on the user's emotions, helping the user to easily plan their ideal interior.

[0737] Example 2

[0738] 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."

[0739] Conventional interior planning systems require users to measure the space in their rooms and manually arrange furniture, which is time-consuming and laborious. Additionally, it is difficult to provide interior designs that reflect the user's emotions and desires, which makes it difficult to achieve highly satisfying planning.

[0740] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for uploading photos of a room taken by a user to the server, means for analyzing the uploaded photos and generating a life-size three-dimensional space model, means for collecting and analyzing interior design requests from the user in the form of interactive questions, means for generating design proposals for furniture layout based on the collected requests and emotional data analyzed from the user's voice and facial expressions, means for providing the generated design proposals to the user as virtual previews and regenerating design proposals in response to additional requests, and means for proposing designs for custom-made furniture and instructing its production, if the user so desires. This allows the user to receive emotionally sensitive interior design proposals simply by uploading photos of their room, thereby realizing efficient and high-quality interior planning.

[0741] A "server" is a computer system connected to a network, which processes data in response to requests from users and provides services.

[0742] "User" refers to an individual or corporation that uses the system, and who accesses the system and provides input via a device such as a smartphone or tablet.

[0743] "Photo upload" refers to the process of sending image data taken by a user to a server via the Internet for storage and analysis.

[0744] A "three-dimensional space model" refers to three-dimensional digital data that reproduces a room or space in actual size, allowing for simulation of interior design and spatial layout.

[0745] An "interactive question format" is a method for collecting information through dialogue with the user, in which the next question is dynamically generated based on the user's response.

[0746] "Collected requests" refers to information obtained from users that specifically expresses their wishes and conditions regarding interior design.

[0747] "Emotional data analyzed from voice and facial expressions" is data that indicates the emotional state of the user, obtained by analyzing the user's voice and facial expressions.

[0748] "Furniture layout design proposal" refers to a plan for the arrangement and placement of furniture within a room, proposed based on collected desires and emotional data.

[0749] "Virtual Preview" refers to a virtual reality environment that allows users to visually check the completed design proposal, and is a function that users can view and manipulate through an interface.

[0750] "Made-to-order furniture" refers to custom-made furniture that is designed and manufactured based on the specific requirements of the user.

[0751] MODE FOR CARRYING OUT THE INVENTION

[0752] The present invention provides a system that allows users to take photos of a room using a smartphone or tablet, generate a life-size 3D space model from the photos, and assist in interior design planning. Specific embodiments for implementing the present invention are described below.

[0753] Work Environment

[0754] Hardware: Smartphones, tablets, server computers

[0755] Software: spatially aware artificial intelligence (AI), visual recognition AI, conversational AI, sentiment analysis engines, database management systems, 3D modeling tools (e.g., Unity, Unreal Engine), speech recognition and facial expression recognition libraries (e.g., Google Cloud Speech-to-Text, Microsoft Azure Face API)

[0756] Program processing

[0757] Taking and uploading photos

[0758] User: The user uses a smartphone or tablet to take photos of the room from multiple angles, for example, from all four corners of the room, including the ceiling and floor.

[0759] Device: The photos are uploaded to a cloud server, along with metadata such as the date and time the photo was taken, location information, and device information.

[0760] 3D model generation

[0761] Server: The server receives the uploaded photos and uses spatial recognition AI to extract feature points and edge information from the photos, constructing the skeleton of the room and generating a life-size 3D space model, using libraries such as OpenCV and TensorFlow.

[0762] Server: The generated 3D spatial model is saved in a database and the user is notified of its completion.

[0763] Collecting user requests

[0764] Server: Uses conversational AI to ask users interactive questions such as "What style do you prefer?" and "What is your budget?" using tools such as Dialogflow and Amazon Lex.

[0765] User: In response to questions, the interface inputs preferred interior style and color, budget, and specific use in natural language.

[0766] Collecting and analyzing user sentiment

[0767] Device: Voice and facial expression data is collected on the device when the user responds, using the camera and microphone.

[0768] Server: The emotion analysis engine analyzes the collected data and identifies the user's emotions, for example, using Google Cloud Speech-to-Text for voice analysis and Microsoft Azure Face API for facial expression analysis.

[0769] Generate interior design ideas

[0770] Server: Visual recognition AI searches for suitable furniture in the database based on the user's request.

[0771] Server: Places the selected furniture in the 3D space model. Uses a 3D engine such as Unity or Unreal Engine.

[0772] Server: Adjusts colors and layout based on the results of the sentiment analysis engine. For example, if the user is looking to relax, it will suggest furniture and designs with calming colors.

[0773] Virtual preview available

[0774] Server: Generates a virtual preview of the completed design and provides it to the user, with 360-degree view and zoom capabilities.

[0775] Terminal: The user checks the virtual preview and resubmits any changes or additions via the terminal, if necessary.

[0776] Proposing additional options and creating custom furniture

[0777] Server: In response to the user's additional requests, the visual recognition AI and generation AI regenerate new design proposals. If new furniture is needed, the AI ​​also generates the design.

[0778] Partner Factory: Produces custom-made furniture based on instructions from the server and delivers it to the user.

[0779] Specific examples

[0780] For example, say a user is considering redecorating their living room. The user takes photos of the living room from multiple angles with their smartphone and uploads them to a cloud server via an app. The server analyzes the uploaded photos and generates a life-size 3D space model. The server then uses conversational AI to collect the user's preferences, such as "natural style" and "budget under 200,000 yen." Next, visual recognition AI selects appropriate furniture from a database based on these preferences and places them in the 3D space model.

[0781] The server then uses an emotion analysis engine to analyze the user's facial expressions and tone of voice to determine whether they are relaxed or stressed. Based on the results of this analysis, the server adjusts colors and style to suggest the optimal interior design. The generated design proposal is provided to the user as a virtual preview, which can be viewed on their smartphone. If necessary, the user can submit any changes or additions they wish to the server, and a new design proposal will be generated.

[0782] Finally, if the user wants custom-made furniture, the server uses generative AI to propose a new furniture design and instructs a partner factory to manufacture it. The partner factory then delivers the manufactured furniture to the user.

[0783] Example prompt statement

[0784] Please suggest a natural style living room for a budget of under 200,000 yen. I'd like it to have an airy cafe-like feel.

[0785] Design a relaxing, modern bedroom for under $1,500.

[0786] I would like to decorate the dining room with a warm interior for my family. My budget is within 300,000 yen.

[0787] Through the above prompts, the user can provide detailed requirements to the server and receive advanced interior design proposals.

[0788] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0789] Step 1: Take and upload a photo

[0790] User: The user uses a smartphone or tablet to take photos of the room from multiple angles, including the four corners, ceiling, and floor.

[0791] Input: A photo of the room taken by the user (JPEG, PNG, or other image format).

[0792] Device: Uploads the captured photo to the cloud server, along with metadata such as the date and time of the photo, location information, and device information.

[0793] Output: Room photos and metadata uploaded to a cloud server.

[0794] Step 2: Generate a 3D spatial model

[0795] Server: The server receives the uploaded photos and uses spatial recognition AI to extract feature points and edge information from the photos. For example, it performs image analysis using OpenCV or TensorFlow libraries.

[0796] Input: Uploaded room photo and metadata.

[0797] Server: A 3D modeling tool is used to construct the skeleton of the room based on the feature points and edge information of the photograph, and generate a life-size 3D spatial model.

[0798] Output: A generated full-scale 3D space model.

[0799] Server: Saves the generated 3D spatial model in a database and notifies the user of its completion.

[0800] Step 3: Gather user requests

[0801] Server: Uses conversational AI to gather interior design requirements from users through interactive question-and-answer sessions, such as "What style do you prefer?" and "What is your budget?"

[0802] Input: Interior design requests from the user.

[0803] Users: Through a conversational interface, they input their preferred interior style and color, budget, and specific use in natural language.

[0804] Output: Collected demand data.

[0805] Step 4: Collect and analyze user sentiment

[0806] Device: Uses a camera and microphone to collect the user's voice and facial expression data.

[0807] Input: Voice and facial expression data provided by the user through the interface.

[0808] Server: The emotion analysis engine analyzes the collected data and identifies the user's emotions, using, for example, Google Cloud Speech-to-Text for voice analysis and Microsoft Azure Face API for facial expression analysis.

[0809] Output: Parsed emotion data.

[0810] Step 5: Generate interior design ideas

[0811] Server: Visual recognition AI searches for suitable furniture in the database based on the user's request.

[0812] Input: Collected desire and sentiment data.

[0813] Server: Places selected furniture within the 3D space model and generates design proposals. Uses 3D engines such as Unity or Unreal Engine.

[0814] Server: Adjust colors and layout based on sentiment analysis, for example, choosing calming colors to enhance relaxation.

[0815] Output: Generated interior design proposals.

[0816] Step 6: Provide a virtual preview

[0817] Server: Generates a virtual preview of the completed design, with 360-degree view and zoom capabilities.

[0818] Input: Generated interior design proposal.

[0819] Device: Provides users with a virtual preview, allowing them to view details on their smartphone or tablet.

[0820] Output: User feedback and addition requests.

[0821] Step 7: Propose additional options and create custom furniture

[0822] Server: Based on the user's additional requests, the visual recognition AI and generation AI regenerate new design proposals. If necessary, new furniture designs are also generated.

[0823] Input: Additional data requested by the user.

[0824] Server: Sends instructions to partner factories to produce custom-made furniture. The factories produce the furniture based on the generated design.

[0825] Partner factory: delivers the completed furniture to the user.

[0826] Output: Finished custom-made furniture.

[0827] (Application example 2)

[0828] 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."

[0829] In modern brick-and-mortar store management, attractive interior design is an important factor in increasing customer purchasing motivation. However, planning interior designs can be difficult for store owners and designers without specialized knowledge. Providing designs that take customer emotions into consideration is even more complicated. The present invention aims to address these challenges by enabling users to easily and effectively plan store interior designs and provide more personalized designs based on emotions.

[0830] 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.

[0831] In this invention, the server includes means for uploading photos of rooms taken by users to the server, means for analyzing the uploaded photos and generating a life-size three-dimensional space model, means for collecting and analyzing interior design requests from users, means for generating furniture layout design proposals based on the collected requests, means for providing the generated design proposals to the user, means for collecting and analyzing emotion data and reflecting the emotion data in the interior design, means for using conversational artificial intelligence to collect user requests, and means for simulating the interior design of a store based on the generated three-dimensional space model. This allows users to easily simulate and realize an interior design that suits their own requests and emotions.

[0832] "Photos of a room taken by a user" are image data showing the state of a room, taken by a user using an electronic device such as a smartphone or tablet.

[0833] A "server" is a remote computer system that receives and processes data sent by a user.

[0834] A "life-size three-dimensional space model" is a three-dimensional virtual space model constructed based on actual dimensions and photographs of a room.

[0835] "Spatial awareness artificial intelligence" is a general term for algorithms and technologies that analyze visual data such as images and videos and recognize the location of objects and spatial structures.

[0836] "Interior design requirements" refers to basic information and requests such as the user's desired interior decoration style, color, layout, budget, etc.

[0837] "Conversational AI" is a general term for algorithms and technologies that allow users to ask and answer questions interactively in natural language, and collect and provide information.

[0838] A "furniture layout design proposal" is a design plan that shows how furniture and decorations should be arranged within a life-size three-dimensional space model based on requests collected from users.

[0839] "Visual recognition artificial intelligence" is a general term for algorithms and techniques for identifying objects in images and video data and analyzing their features.

[0840] "Emotion data" is data that indicates the emotional state of the user, such as joy, anger, sadness, or happiness, obtained from the user's voice or facial expression.

[0841] "Reflecting in the interior design" means proposing the optimal interior design based on the collected data and requests, and reflecting it in the actual model.

[0842] "Simulating the interior design of a store" involves virtually testing the design and layout of a store on a three-dimensional spatial model to confirm its actual appearance and functionality.

[0843] This invention is a system that supports interior design and renovation planning for brick-and-mortar stores. It aims to take photos of the current state of the store using a smartphone or tablet, generate a virtual 3D model, and simulate the interior design. Furthermore, it uses an emotion engine to provide interior designs based on customer emotions.

[0844] System Configuration

[0845] User: Uses a smartphone or tablet to take photos of the current state of the store. By taking photos from multiple locations, data is collected for the entire room.

[0846] On your device, take a photo and upload it to a cloud server, along with the date, time, location, and other metadata.

[0847] server:

[0848] Using spatial recognition AI, the system analyzes uploaded photos and generates a life-size 3D space model by extracting feature points and edge information from the photo and constructing the skeleton of the room.

[0849] It uses conversational AI to interactively gather interior design preferences from users, such as asking questions like "What style do you prefer?" and "What is your budget?"

[0850] Using visual recognition AI, the system selects furniture from a database that matches the user's needs and places it in a 3D space model. The generated design proposals are constructed based on the information in the database.

[0851] The emotion engine analyzes the user's voice and facial expression data to identify their emotions, and adjusts the design's color and style based on the analysis results.

[0852] The final design is then presented to the user as a virtual preview, which can be viewed in 360 degrees and zoomed in and out via the device, allowing the user to check the details.

[0853] Program processing

[0854] Spatial recognition artificial intelligence extracts feature points and edge information from the photo and generates a life-size three-dimensional spatial model.

[0855] Conversational AI collects interior design requests from users in an interactive format.

[0856] Visual recognition AI selects appropriate furniture based on the user's requests and places it in a three-dimensional space model.

[0857] The emotion engine analyzes the user's emotions and adjusts the color and style of the design.

[0858] A virtual preview is provided to users, allowing them to see the details using a 360-degree view and zoom capabilities.

[0859] Hardware and software used

[0860] Smartphones and tablets: Provides shooting and uploading functions.

[0861] Cloud server: Analyzes and stores data, and generates three-dimensional spatial models.

[0862] Spatial recognition AI, conversational AI, visual recognition AI, emotion engine: software technologies to realize each function.

[0863] Specific examples

[0864] For example, if a store owner wants to change the interior design of their store, they take multiple photos of the interior with their smartphone and upload them to a cloud server. The server analyzes the photos and generates a life-size 3D spatial model, then uses conversational AI to collect the owner's preferences, such as "modern style" and "budget under 500,000 yen." Visual recognition AI selects appropriate furniture from a database and places it within the 3D spatial model. Furthermore, an emotion engine analyzes the user's emotional data and adjusts colors and style to make customers feel relaxed. The final generated design is provided as a virtual preview, which the user can view on their smartphone.

[0865] Prompt Sentence Examples

[0866] Generate a 3D model using the images: ['shop1.jpg', 'shop2.jpg']. Create a design that includes preferences: {'style': 'modern', 'budget': 50000}

[0867] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0868] Step 1:

[0869] Users use their smartphones or tablets to take photos from multiple locations within the store, allowing data to be collected from the entire room.

[0870] (Input: Multiple photos of the store, Output: Photos uploaded to the cloud server)

[0871] Step 2:

[0872] The device uploads the photos it takes to a cloud server, along with the date, time, location, and other metadata.

[0873] (Input: captured photos and metadata, Output: photos and metadata stored on the cloud server)

[0874] Step 3:

[0875] The server uses spatial recognition AI to analyze the uploaded photos and generate a life-size 3D spatial model by extracting feature points and edge information from the photos and constructing the skeleton of the room.

[0876] (Input: saved photo, Output: 3D space model)

[0877] Step 4:

[0878] The server uses conversational AI to interactively gather interior design requests from users, such as asking questions like "What style do you prefer?" or "What is your budget?", and users input their answers in natural language.

[0879] (Input: interactive questions and user responses, Output: collected interior design requests)

[0880] Step 5:

[0881] The server uses visual recognition AI to select furniture from a database that matches the user's needs and place it in a 3D space model. The generated design proposal is based on the information in the database.

[0882] (Input: collected interior design requests and a 3D space model, output: a 3D space model with furniture arranged)

[0883] Step 6:

[0884] The server uses an emotion engine to analyze the user's voice and facial expression data to identify their emotions, and adjusts the color and style of the design based on the analysis results.

[0885] (Input: User's voice and facial expression data, Output: Design proposal with adjusted color and style)

[0886] Step 7:

[0887] The server generates a virtual preview and provides it to the user. This preview can be viewed in 360 degrees and zoomed in and out through the device, allowing the user to check the details. The user can check the preview and, if necessary, submit changes or additions to the server again.

[0888] (Input: adjusted design proposal, Output: virtual preview provided to user)

[0889] 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.

[0890] 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.

[0891] 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.

[0892] [Third embodiment]

[0893] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0894] 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.

[0895] 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).

[0896] 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.

[0897] 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.

[0898] 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).

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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.

[0903] 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.

[0904] 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."

[0905] The present invention is a system that supports interior design planning by allowing users to take photos of a room using a smartphone or tablet, and then generating a life-size 3D space model from the photos. Specific embodiments for implementing the present invention are described below.

[0906] System Overview

[0907] The system of the present invention begins when a user takes a photo of a room and uploads it to a server. The server analyzes the uploaded photo and uses spatial recognition AI to generate a life-size 3D space model. Based on this 3D space model, a conversational AI collects the user's interior design requests, and a visual recognition AI generates a furniture layout design proposal based on those requests. The final design proposal is provided to the user as a virtual preview.

[0908] Program processing description

[0909] 1. Take and upload a photo

[0910] User: Takes photos of the room from multiple angles using a smartphone or tablet, allowing data on the entire room to be collected.

[0911] Device: Uploads the photos you take to the cloud server.

[0912] 2. Generating 3D models

[0913] Server: To analyze the received photos, spatial recognition AI is used to extract feature points and edge information from the photos. Based on this, the skeleton of the room is constructed and a life-size 3D space model is generated.

[0914] 3. Collecting user requests

[0915] Server: Based on the generated 3D space model, the conversational AI interactively collects information about the user's interior design needs, such as preferred style, color, and budget.

[0916] 4. Generating interior design ideas

[0917] Server: Visual recognition AI searches for furniture in the database based on the collected requests. The selected furniture is placed in a 3D space model and generated as a design proposal.

[0918] 5. Providing a Virtual Preview

[0919] Server: Provides users with a virtual preview of the completed design, which can be viewed in 360 degrees and zoomed in on the device, allowing users to check the details.

[0920] User: Checks the preview and, if necessary, requests for changes or additions are sent to the server again via the conversational AI.

[0921] 6. Proposing additional options and creating custom furniture

[0922] Server: In response to the user's additional requests, the visual recognition AI and generation AI regenerate new design proposals. If the user is not satisfied with commercially available furniture, the generation AI will propose a new furniture design and place an order for custom-made furniture with a partner factory.

[0923] Partner Factory: Produces custom-made furniture based on instructions from the server and delivers it to the user.

[0924] Specific examples

[0925] For example, if a user is thinking about redecorating their living room, they can take a photo of the room with their smartphone and upload it to a cloud server via the app. The server then analyzes the uploaded photo and generates a life-size 3D model of the space.

[0926] The server then uses conversational AI to collect user preferences, such as a "natural style" and a "budget of under 200,000 yen." Based on the collected preferences, the visual recognition AI selects appropriate furniture from a database and places it within the 3D space model. The generated design proposal is provided to the user as a virtual preview, which they can check on their smartphone. If necessary, any changes or additions can be sent to the server, and another interior design proposal is generated.

[0927] Finally, if the user requests custom-made furniture, the server uses generative AI to propose a new furniture design and sends instructions to a partner factory to manufacture it. The partner factory then produces the furniture and delivers it to the user.

[0928] In this way, the system of the present invention helps users to plan their ideal interior design effectively and easily.

[0929] The processing flow will be explained below.

[0930] Step 1:

[0931] Users: Use a smartphone or tablet to take photos from multiple points in the room. It is recommended to take photos from multiple angles and positions to cover the entire room.

[0932] Step 2:

[0933] Device: Upload all photos you take to the cloud server. When uploading, necessary metadata (date and time of photo, location, etc.) is also sent.

[0934] Step 3:

[0935] Server: The cloud server receives the uploaded photos and instructs the spatially aware AI to begin analysis.

[0936] Step 4:

[0937] Server: Spatial AI analyzes each photo and extracts feature points and edge information. Based on this data, it constructs the skeleton of the room and generates a life-size 3D model of the space.

[0938] Step 5:

[0939] Server: Saves the generated 3D space model in a database and notifies the user of its completion.

[0940] Step 6:

[0941] Server: Uses conversational AI to gather user requests through interactive question-based inquiry, such as "What style do you prefer?" or "What is your budget?"

[0942] Step 7:

[0943] Users: Through a conversational interface, they input detailed requirements in natural language, such as preferred interior style and color, budget, and specific use.

[0944] Step 8:

[0945] Server: Analyzes the collected request data and stores it as the base data for generating interior design proposals.

[0946] Step 9:

[0947] Server: Uses visual recognition AI to search for items that match the user's requirements from a database of commercially available furniture.

[0948] Step 10:

[0949] Server: Visually aware AI selects and places the most suitable furniture in a 3D spatial model, taking into account style, color, and location based on the user's requirements.

[0950] Step 11:

[0951] Server: Renders a 3D spatial model of the placed furniture and generates images for a virtual preview.

[0952] Step 12:

[0953] On the device: The device displays the preview image received from the server for the user to check, and also provides functions such as zooming and 360-degree view.

[0954] Step 13:

[0955] User: Checks the virtual preview and, if necessary, sends any changes or additions to the server via the terminal.

[0956] Step 14:

[0957] Server: Based on the changes or additions requested, the visual recognition AI and generation AI will regenerate the interior design proposal. If necessary, new furniture designs will be proposed and placed in the virtual space.

[0958] Step 15:

[0959] Server: Regenerate a new preview image and resend it to the device.

[0960] Step 16:

[0961] Terminal: Show the regenerated preview image to the user.

[0962] Step 17:

[0963] User: After viewing the final preview, if they would like to have the furniture custom-made, they notify the server.

[0964] Step 18:

[0965] Server: Sends production instructions for custom-made furniture to partner factories. The instructions include the necessary design data and material information.

[0966] Step 19:

[0967] Partner Factory: Based on instructions received from the server, the factory starts production of custom-made furniture and delivers it to the user once completed.

[0968] In this way, the system of the present invention allows users to easily plan their ideal interior and ultimately supports them in the creation of custom-made furniture.

[0969] Example 1

[0970] 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."

[0971] In today's world, there is a growing need for systems that allow individual users to more efficiently and accurately plan the decoration of their living spaces. However, conventional methods, such as creating a life-size 3D model of a room and then arranging furniture and equipment based on that model, are extremely complex and time-consuming, often requiring specialized knowledge. Furthermore, due to insufficient functionality for automatically generating and proposing design proposals that meet the user's needs, the end product often falls short of the user's expectations. Therefore, there is a need for a system that allows users to easily create their own room decoration plans and quickly confirm and modify the designs based on their needs.

[0972] 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.

[0973] In this invention, the server includes means for transmitting a video of a room taken by a user to an information processing device, means for analyzing the transmitted video and generating a life-size three-dimensional space model, means for collecting and analyzing requests for decoration from the user, means for generating a design proposal for equipment layout based on the collected requests, and means for providing the generated design proposal to the user. This allows the user to easily create a three-dimensional model of the entire room and quickly confirm and modify the decorative design based on the model to meet their needs.

[0974] "User" refers to an individual or organization that uses the system to plan room decoration.

[0975] "Room footage" includes image or video data taken by the user from multiple angles within the room.

[0976] "Information processing device" refers to a device that processes and stores data, including a cloud server and its peripheral devices.

[0977] "Life-size 3D space model" refers to a digital 3D model that is the same size as a real room.

[0978] "Spatial recognition machine learning" refers to an algorithm technology for recognizing spatial information from input images and videos and generating three-dimensional models.

[0979] "Decorative requests" are the user's preferences and requirements regarding the interior of the room, including style, color, budget, etc.

[0980] "Equipment layout design proposal" refers to a plan that arranges furniture and equipment selected based on the user's requests within a three-dimensional space model.

[0981] "Visual recognition machine learning" refers to algorithm technology that uses image processing technology to recognize, select, and place objects.

[0982] "Generated design proposal" refers to a furniture and equipment layout plan that is automatically created based on the user's requests.

[0983] "Means to provide" refers to the technical means for displaying or notifying the generated design proposal in a form that can be confirmed by the user.

[0984] This invention is a system that allows a user to take a video of a room using a smartphone or tablet, generate a life-size three-dimensional space model from the video, and assist in decoration planning. Specific embodiments for implementing the invention are described below.

[0985] Program processing description

[0986] This system uses the following hardware and software:

[0987] Hardware: smartphones, tablets, cloud servers

[0988] Software: OpenCV, TensorFlow, YOLOv5, Unity, Natural Language Processing models (e.g. GPT-3)

[0989] The user uses a smartphone or tablet to capture video from multiple angles of a room, thereby acquiring information about the entire room. The captured video is then uploaded from the device to a cloud server, where a cloud storage service such as Amazon S3 is used.

[0990] The server analyzes the received video. Specifically, it uses spatial recognition machine learning (e.g., an object detection model using TensorFlow) to extract feature points and edge information from the video. Based on this, it understands the relative positions of each image and generates the skeleton of the room. Furthermore, it uses the distance information from the images to construct a life-size 3D spatial model.

[0991] To gather the user's decorating preferences, the server uses conversational AI (e.g., GPT-3) to interactively ask questions such as, "How would you like to style this room?", "Do you have a preferred color scheme?", and "What is your budget?", and collects detailed data based on those questions.

[0992] The server uses visual recognition machine learning (e.g., YOLOv5) to search the database for furniture and equipment that meets the user's requirements. The selected furniture and equipment are placed in the generated 3D space model and a design proposal is generated. In this process, an algorithm is used that takes into account the balance of furniture placement and the overall aesthetics of the room.

[0993] The resulting design proposal is provided to the user as a virtual preview, which is generated using 3D rendering software (e.g., Unity). The preview can be viewed on a smartphone or tablet, and users can view it in 360 degrees and zoom in and out.

[0994] Users can check the virtual preview and send any changes or additions they require to the server. The server receives the changes and adds new designs using visual recognition machine learning and generative AI models (e.g., DALL·E). If users are not satisfied with commercially available furniture, the server uses the generative AI model to propose a new furniture design and sends instructions to a partner factory to create the custom-made furniture. The partner factory creates the custom-made furniture based on the instructions from the server and delivers it to the user once it is completed. Users are notified of the delivery progress, and installation services may also be provided after delivery.

[0995] Specific examples

[0996] For example, a user considering redecorating their living room can take a video of the living room with their smartphone and upload it to a cloud server via the app. The server analyzes the uploaded video and generates a life-size 3D space model. The server then uses conversational AI to collect user preferences, such as a "natural style" and a "budget of under 200,000 yen." Based on the collected preferences, visual recognition machine learning selects appropriate furniture and fixtures from a database and places them within the 3D space model. The generated design proposal is provided to the user as a virtual preview, which the user can check on their smartphone. If necessary, changes or additions can be made and the server generates another interior design proposal. Finally, if the user requests custom-made furniture, the server uses generative AI to propose a new furniture design and instructs a partner factory to manufacture it. The partner factory then produces the furniture and delivers it to the user.

[0997] Prompt Sentence Examples

[0998] "I'm thinking about the interior design of a living room. I want a natural style, and my budget is under 200,000 yen. Please generate a design plan with appropriate furniture arrangement based on this."

[0999] In this way, the system of the present invention helps users to plan their ideal interior efficiently and accurately.

[1000] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1001] Step 1:

[1002] User: Uses a smartphone or tablet to capture images of a room from multiple angles. The input is an image of the entire room, which allows data on the room's walls, floors, furniture, etc. to be acquired. The output is the captured data from multiple angles.

[1003] Specifically, the app guides the user to the optimal shooting position and angle, and then multiple photos and videos are taken.

[1004] Step 2:

[1005] Terminal: Uploads the captured video to a cloud server. The input is the video data stored on a smartphone or tablet, and the output is the uploaded data stored in cloud storage.

[1006] Specifically, the app allows users to select video data and transfer it to a cloud server via the internet. Upload progress is displayed on the screen.

[1007] Step 3:

[1008] Server: Analyzes uploaded videos. The input is multiple video data stored in cloud storage, and the output is analysis data including feature points and edge information extracted from the video.

[1009] Specifically, it uses spatial recognition machine learning (e.g., an object detection model using TensorFlow) to analyze video data and extract feature points and edges from each frame. Based on this, it calculates the relative positions of each image and generates the skeleton of the room.

[1010] Step 4:

[1011] Server: Generates a life-size 3D space model. The input is the analyzed feature points and edge information, and the output is a life-size 3D space model.

[1012] Specifically, the server uses an algorithm to reconstruct three-dimensional space based on feature points and edge information, and generates a life-size model of the room's skeletal structure.

[1013] Step 5:

[1014] Server: Collects decoration requests from users. The input is a life-size 3D space model and an interactive interface, and the output is the user's request data.

[1015] Specifically, it uses conversational AI (e.g., GPT-3) to interact with users, asking questions such as, "What style do you like?", "Do you have a favorite color scheme?", and "What is your budget?", and collects their responses.

[1016] Step 6:

[1017] Server: Generates a design proposal for facility layout based on the collected requirements. The input is the user's requirement data and a 3D space model, and the output is a design proposal for the decoration plan.

[1018] Specifically, it uses visual recognition machine learning (e.g., YOLOv5) to search a database for furniture and equipment that matches the user's requirements and place them within a three-dimensional space model.

[1019] Step 7:

[1020] Server: Provides the generated design proposal to the user as a virtual preview. The input is the design proposal data, and the output is virtual preview data that can be displayed on the user's terminal.

[1021] Specifically, the design proposals generated using 3D rendering software (e.g., Unity) are visualized in a virtual space, allowing users to view them in 360 degrees and zoom in and out via their smartphones or tablets.

[1022] Step 8:

[1023] User: Checks the virtual preview and sends requests for changes or additions to the server as needed. The input is the user's feedback data, and the output is the updated request data.

[1024] Specifically, the user checks the virtual preview, inputs any necessary changes or additions, and submits the request.

[1025] Step 9:

[1026] Server: Receives requests for changes or additions and generates new design proposals. The input is the updated request data, and the output is the new design proposal.

[1027] Specifically, the design proposal is regenerated using visual recognition machine learning and a generative AI model (e.g., DALL·E) to create updated data for the virtual preview.

[1028] Step 10:

[1029] Server: When a user requests custom-made furniture, the server sends production instructions to a partner factory. The input is new furniture design data, and the output is production instruction data for the partner factory.

[1030] Specifically, the system uses generative AI models to propose new furniture designs, then sends production instructions to partner factories, which then deliver the furniture to the user after production is complete.

[1031] (Application example 1)

[1032] 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."

[1033] Currently, planning an interior design requires consulting with experts and going through a wide range of furniture selection and purchasing procedures, which takes a lot of time and effort. Furthermore, it is difficult to visualize how the furniture will be arranged in the actual space, which often leads to dissatisfaction after purchase. For this reason, there is a need for a system that allows users to easily and effectively design interiors, select the optimal furniture arrangement, and purchase directly from an online shopping site.

[1034] 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.

[1035] In this invention, the server includes means for transmitting images of a room taken by a user to an information processing device, means for processing the transmitted images to generate a life-size three-dimensional space model, means for collecting and analyzing interior design requests from the user, means for generating furniture layout proposals based on the collected requests, means for presenting the generated proposals to the user, and means for the user to confirm the proposed proposals and process orders in the virtual space in conjunction with information from an online shopping site. This enables the user to generate a three-dimensional space model based on photos of their home, interactively try out interior designs, and select and purchase the optimal furniture.

[1036] 1. "Room images taken by users" are photos of rooms taken by users using electronic devices such as smartphones or tablets.

[1037] 2. "Information processing device" means a device for inputting, processing, storing, and outputting data, including servers and cloud services.

[1038] 3. "Transmission" is the act of moving data or information from one point to another.

[1039] 4. A "life-size three-dimensional spatial model" is a three-dimensional spatial model generated from photographed images, which reflects the actual size and shape of a room.

[1040] 5. "Spatial recognition artificial intelligence" is an artificial intelligence system that analyzes images and data and uses them to recognize and construct three-dimensional space.

[1041] 6. "Interior design requests" refers to the user's wishes and demands regarding the design and furniture arrangement of the room.

[1042] 7. "Collection and analysis" refers to the act of gathering data and information, examining it, and extracting the necessary results and characteristics.

[1043] 8. "Furniture Arrangement Proposal" refers to a design proposal showing the appropriate furniture arrangement for a room based on the collected requests.

[1044] 9. "Means for processing orders in a virtual space in conjunction with information from an online shopping site" refers to the function that allows users to check interior designs in a virtual space and then order furniture directly through an online shopping site.

[1045] This invention relates to a system that generates a life-size three-dimensional space model based on images of a room taken by a user, plans an interior design, and ultimately proposes an optimal furniture layout. Specific embodiments for carrying out the invention are described below.

[1046] Program Generation

[1047] 1. User authentication and login function

[1048] The server uses Firebase Authentication to securely authenticate the user: the user opens the app and logs in with their email or social media account.

[1049] 2. Photo taking and uploading function

[1050] The user takes multiple images of the room using a smartphone camera and uploads them to cloud storage (e.g., Google Cloud Storage). The device then sends these image data to the cloud.

[1051] 3. 3D space model generation function

[1052] The server receives the uploaded images and analyzes them using spatial recognition AI (e.g., OpenCV, TensorFlow). Specifically, it extracts feature points and edge information from the images and generates an accurate 3D spatial model of the room based on that information.

[1053] 4. User request collection and analysis function

[1054] The server uses conversational AI (e.g., Dialogflow) to interactively collect user requests. For example, a user might input a request such as, "A natural style, within a budget of 200,000 yen." This request is analyzed on the server.

[1055] 5. Interior proposal generation function

[1056] The server uses visual recognition AI (e.g., YOLO, Pytorch) to select items that match the user's requests from the furniture information in the database and place them in a 3D space model. The generated interior proposals are then rendered in the virtual space using a 3D rendering engine (e.g., Unity).

[1057] 6. Virtual Preview and Ordering Features

[1058] Users can use their smartphones to view the generated interior design proposals in 360 degrees, checking out the details, and if they find furniture they like, they can link it to the online shopping site information in the virtual space and place an order right away.

[1059] Specific examples

[1060] For example, a user thinking about redecorating their living room can take a photo of the living room with their smartphone and upload it to a cloud server via the app. The server analyzes the uploaded image and generates a life-size 3D space model. Conversational AI is then used to gather the user's main requirements. Specifically, the user inputs requirements such as "natural style" and "budget under 200,000 yen" into the conversational AI. Based on this information, the visual recognition AI selects appropriate furniture from a database and places it within the 3D space model. The user can then view the resulting interior design in a virtual space, seamlessly purchasing the furniture.

[1061] Prompt Sentence Examples

[1062] markdown

[1063] You are creating an interior design application. This application allows users to take photos of a room, generates a 3D model from the photos, and suggests interior coordination. Design your program to meet the following requirements:

[1064] Uses Firebase Authentication for user authentication.

[1065] Supports taking photos and uploading to the cloud (using Google Cloud Storage).

[1066] Generate 3D spatial models using spatial recognition AI (OpenCV and TensorFlow).

[1067] Collect user requests using conversational AI (Dialogflow).

[1068] Interior design ideas are generated using visual recognition AI (YOLO and Pytorch).

[1069] The generated design proposals are displayed in a 360-degree view using Unity.

[1070] Please provide the following steps in detail:

[1071] 1. Initial Setup and User Login

[1072] 2. Take a photo and upload it

[1073] 3. 3D model generation

[1074] 4. Collecting user requests

[1075] 5. Interior design proposal generation

[1076] 6. Virtual Preview and Online Shopping Integration

[1077] thank you.

[1078] In this way, the present invention provides a specific method for users to effectively realize their ideal interior design.

[1079] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1080] Step 1:

[1081] Users take photos of their rooms using their smartphones or tablets, capturing data on the entire room from multiple angles. This is the input. They then upload these photos to cloud storage through the app. This is the output.

[1082] Step 2:

[1083] The device sends image data to the server to upload the photos of the room taken by the user to cloud storage. This requires an internet connection. The input is the room image taken by the user, and the output is the image data stored in the cloud storage.

[1084] Step 3:

[1085] The server receives and downloads image data from cloud storage. It then uses spatial recognition AI (e.g., OpenCV, TensorFlow) to analyze the image's feature points and edge information. This is the input. Based on this analysis, a life-size 3D spatial model is generated and stored on the server. This is the output.

[1086] Step 4:

[1087] The server uses conversational artificial intelligence (e.g., Dialogflow) to interactively collect interior design requests from the user. For example, the user might input requests such as "natural style" and "budget under 200,000 yen." This is the input. The collected requests are analyzed and the necessary information is saved on the server. This is the output.

[1088] Step 5:

[1089] The server uses visual recognition AI (e.g., YOLO, Pytorch) to search for furniture items in the database that match the user's requirements. This is the input. The selected furniture is placed in the generated 3D space model. This is the output.

[1090] Step 6:

[1091] The server uses a 3D rendering engine (e.g. Unity) to render the generated interior proposal in a virtual space. This is the input. The user can view this virtual preview in a 360-degree view on their smartphone. This is the output.

[1092] Step 7:

[1093] The user uses a smartphone to view a virtual preview and check the details. Next, they select the furniture they like and place an order in the virtual space, linking it with the information on the online shopping site. This is the input. Finally, the information on the furniture purchased by the user is sent to the online shopping site, and the order is confirmed. This is the output.

[1094] By following the steps outlined above, users can generate a three-dimensional spatial model based on images of their home, interactively experiment with interior designs, and select and purchase the most suitable furniture.

[1095] 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.

[1096] The present invention combines an emotion engine with a system that allows users to take photos of a room with a smartphone or tablet, generate a life-size 3D space model from the photo, and assist in interior design planning. Specific embodiments for implementing the present invention are described below.

[1097] System Overview

[1098] The system of the present invention begins when a user takes a photo of a room and uploads it to a server. The server analyzes the uploaded photo and uses spatial recognition AI to generate a life-size 3D space model. Based on this 3D space model, a conversational AI collects the user's interior design requests, and a visual recognition AI generates a furniture layout design proposal based on those requests. An emotion engine then analyzes the user's emotions and adjusts the interior design based on those emotions. The final design proposal is provided to the user as a virtual preview.

[1099] Program processing description

[1100] 1. Take and upload a photo

[1101] Users: Use a smartphone or tablet to take photos from multiple points in the room. It is recommended to take photos from multiple angles and positions to cover the entire room.

[1102] Device: Uploads the photos to the cloud server. When uploading, necessary metadata (date and time of photo, location, etc.) is also sent.

[1103] 2. Generating 3D models

[1104] Server: To analyze the received photos, spatial recognition AI is used to extract feature points and edge information from the photos, and based on this, a skeleton of the room is constructed and a life-size 3D spatial model is generated.

[1105] Server: Saves the generated 3D spatial model in a database and notifies the user of its completion.

[1106] 3. Collecting user requests

[1107] Server: Uses conversational AI to gather user requests through interactive question-based inquiry, such as "What style do you prefer?" or "What is your budget?"

[1108] Users: Through a conversational interface, they input detailed requirements in natural language, such as preferred interior style and color, budget, and specific use.

[1109] 4. Collecting and analyzing user sentiment

[1110] Server: The emotion engine analyzes the user's voice and facial expression data to identify their emotions. The emotion data is used to adjust the interior design.

[1111] 5. Generating interior design ideas

[1112] Server: Visual recognition AI searches for furniture in the database based on the collected requests. The selected furniture is placed in a 3D space model and generated as a design proposal. At this time, color and style are adjusted based on the analysis results of the emotion engine.

[1113] 6. Providing Virtual Previews

[1114] Server: Provides users with a virtual preview of the completed design, which can be viewed in 360 degrees and zoomed in on the device, allowing users to see the details.

[1115] User: Check the preview and, if necessary, make any changes or additions and send them back to the server via the device.

[1116] 7. Proposing additional options and creating custom furniture

[1117] Server: In response to the user's additional requests, the visual recognition AI and generation AI regenerate new design proposals. If the user is not satisfied with commercially available furniture, the generation AI proposes a new furniture design and places an order for custom-made furniture with a partner factory.

[1118] Partner Factory: Produces custom-made furniture based on instructions from the server and delivers it to the user.

[1119] Specific examples

[1120] For example, if a user is thinking about redecorating their living room, they can take a photo of the room with their smartphone and upload it to a cloud server via the app. The server then analyzes the uploaded photo and generates a life-size 3D model of the space.

[1121] The server then uses conversational AI to collect user preferences, such as "natural style" and "budget under 200,000 yen." Based on the preferences, visual recognition AI selects appropriate furniture from a database and places it within the 3D space model. The server also uses an emotion engine to analyze the user's facial expressions and tone of voice to determine whether the user is relaxed or stressed. Based on the results of this analysis, the server adjusts the colors and style. For example, if the user feels like relaxing, it will suggest furniture and designs in calming colors.

[1122] The generated design proposal is provided to the user as a virtual preview, which they can check on their smartphone. If necessary, they can submit any changes or additions they wish to make to the server, and the interior design proposal is generated again.

[1123] Finally, if the user requests custom-made furniture, the server uses generative AI to propose a new furniture design and sends instructions to a partner factory to manufacture it. The partner factory then produces the furniture and delivers it to the user.

[1124] In this way, the system of the present invention helps users to plan their ideal interiors effectively and easily. Furthermore, by using the emotion engine, it is possible to provide more personalized interior designs that take into consideration the user's emotions.

[1125] The processing flow will be explained below.

[1126] Step 1:

[1127] Users: Use a smartphone or tablet to take photos from multiple points in the room. It is recommended to take photos from multiple angles and positions to cover the entire room.

[1128] Step 2:

[1129] Device: Upload all photos you take to the cloud server. When uploading, necessary metadata (date and time of photo, location, etc.) is also sent.

[1130] Step 3:

[1131] Server: The cloud server receives the uploaded photos and instructs the spatially aware AI to begin analysis.

[1132] Step 4:

[1133] Server: Spatial recognition AI analyzes each photo and extracts feature points and edge information. Based on this data, it constructs the skeleton of the room and generates a life-size 3D spatial model.

[1134] Step 5:

[1135] Server: Saves the generated 3D space model in a database and notifies the user of its completion.

[1136] Step 6:

[1137] Server: Using conversational AI, collects user requests through interactive question format, such as "What style do you prefer?" or "What is your budget?"

[1138] Step 7:

[1139] Users: Through a conversational interface, they input detailed requirements in natural language, such as preferred interior style and color, budget, and specific use.

[1140] Step 8:

[1141] Server: Analyzes the collected request data and stores it as basic data for generating interior design proposals.

[1142] Step 9:

[1143] Server: The emotion engine analyzes the user's emotions from their voice and facial expressions. For example, it determines their stress level from their tone of voice and speaking rate, and uses facial expression recognition technology to understand their emotional state.

[1144] Step 10:

[1145] Server: Reflects the analyzed emotional data in the design proposal. For example, if the user wants to relax, select calm colors and a soft design.

[1146] Step 11:

[1147] Server: Visual recognition AI searches a database of commercially available furniture for items that match the user's needs and emotions.

[1148] Step 12:

[1149] Server: Visual recognition AI selects the most suitable furniture and places it in a 3D spatial model, taking into account style, color, and location based on the user's requirements.

[1150] Step 13:

[1151] Server: Renders a 3D spatial model of the placed furniture and generates an image for a virtual preview.

[1152] Step 14:

[1153] Terminal: Displays the preview image received from the server so that the user can check it. It also provides functions such as zooming and 360-degree view.

[1154] Step 15:

[1155] User: Checks the virtual preview and, if necessary, requests for changes or additions are sent back to the server via the terminal.

[1156] Step 16:

[1157] Server: Based on the changes or additions requested, the visual recognition AI and generation AI will regenerate the interior design proposal. If necessary, new furniture designs will be proposed and placed in the virtual space.

[1158] Step 17:

[1159] Server: Regenerate a new preview image and resend it to the device.

[1160] Step 18:

[1161] Terminal: Show the regenerated preview image to the user.

[1162] Step 19:

[1163] User: After checking the final preview, if the user wishes to have the furniture made to order, the user notifies the server.

[1164] Step 20:

[1165] Server: Sends production instructions for custom-made furniture to partner factories. The instructions include the necessary design data and material information.

[1166] Step 21:

[1167] Partner Factory: Based on instructions received from the server, the factory starts production of custom-made furniture and delivers it to the user once completed.

[1168] In this way, the system of the present invention can more personalized interior design based on the user's emotions, helping the user to easily plan their ideal interior.

[1169] Example 2

[1170] 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."

[1171] Conventional interior planning systems require users to measure the space in their rooms and manually arrange furniture, which is time-consuming and laborious. Additionally, it is difficult to provide interior designs that reflect the user's emotions and desires, which makes it difficult to achieve highly satisfying planning.

[1172] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for uploading photos of a room taken by a user to the server, means for analyzing the uploaded photos and generating a life-size three-dimensional space model, means for collecting and analyzing interior design requests from the user in the form of interactive questions, means for generating design proposals for furniture layout based on the collected requests and emotional data analyzed from the user's voice and facial expressions, means for providing the generated design proposals to the user as virtual previews and regenerating design proposals in response to additional requests, and means for proposing designs for custom-made furniture and instructing its production, if the user so desires. This allows the user to receive emotionally sensitive interior design proposals simply by uploading photos of their room, thereby realizing efficient and high-quality interior planning.

[1173] A "server" is a computer system connected to a network, which processes data in response to requests from users and provides services.

[1174] "User" refers to an individual or corporation that uses the system, and who accesses the system and provides input via a device such as a smartphone or tablet.

[1175] "Photo upload" refers to the process of sending image data taken by a user to a server via the Internet for storage and analysis.

[1176] A "three-dimensional space model" refers to three-dimensional digital data that reproduces a room or space in actual size, allowing for simulation of interior design and spatial layout.

[1177] An "interactive question format" is a method for collecting information through dialogue with the user, in which the next question is dynamically generated based on the user's response.

[1178] "Collected requests" refers to information obtained from users that specifically expresses their wishes and conditions regarding interior design.

[1179] "Emotional data analyzed from voice and facial expressions" is data that indicates the emotional state of the user, obtained by analyzing the user's voice and facial expressions.

[1180] "Furniture layout design proposal" refers to a plan for the arrangement and placement of furniture within a room, proposed based on collected desires and emotional data.

[1181] "Virtual Preview" refers to a virtual reality environment that allows users to visually check the completed design proposal, and is a function that users can view and manipulate through an interface.

[1182] "Made-to-order furniture" refers to custom-made furniture that is designed and manufactured based on the specific requirements of the user.

[1183] MODE FOR CARRYING OUT THE INVENTION

[1184] The present invention provides a system that allows users to take photos of a room using a smartphone or tablet, generate a life-size 3D space model from the photos, and assist in interior design planning. Specific embodiments for implementing the present invention are described below.

[1185] Work Environment

[1186] Hardware: Smartphones, tablets, server computers

[1187] Software: spatially aware artificial intelligence (AI), visual recognition AI, conversational AI, sentiment analysis engines, database management systems, 3D modeling tools (e.g., Unity, Unreal Engine), speech recognition and facial expression recognition libraries (e.g., Google Cloud Speech-to-Text, Microsoft Azure Face API)

[1188] Program processing

[1189] Taking and uploading photos

[1190] User: The user uses a smartphone or tablet to take photos of the room from multiple angles, for example, from all four corners of the room, including the ceiling and floor.

[1191] Device: The photos are uploaded to a cloud server, along with metadata such as the date and time the photo was taken, location information, and device information.

[1192] 3D model generation

[1193] Server: The server receives the uploaded photos and uses spatial recognition AI to extract feature points and edge information from the photos, constructing the skeleton of the room and generating a life-size 3D space model, using libraries such as OpenCV and TensorFlow.

[1194] Server: The generated 3D spatial model is saved in a database and the user is notified of its completion.

[1195] Collecting user requests

[1196] Server: Uses conversational AI to ask users interactive questions such as "What style do you prefer?" and "What is your budget?" using tools such as Dialogflow and Amazon Lex.

[1197] User: In response to questions, the interface inputs preferred interior style and color, budget, and specific use in natural language.

[1198] Collecting and analyzing user sentiment

[1199] Device: Voice and facial expression data is collected on the device when the user responds, using the camera and microphone.

[1200] Server: The emotion analysis engine analyzes the collected data and identifies the user's emotions, for example, using Google Cloud Speech-to-Text for voice analysis and Microsoft Azure Face API for facial expression analysis.

[1201] Generate interior design ideas

[1202] Server: Visual recognition AI searches for suitable furniture in the database based on the user's request.

[1203] Server: Places the selected furniture in the 3D space model. Uses a 3D engine such as Unity or Unreal Engine.

[1204] Server: Adjusts colors and layout based on the results of the sentiment analysis engine. For example, if the user is looking to relax, it will suggest furniture and designs with calming colors.

[1205] Virtual preview available

[1206] Server: Generates a virtual preview of the completed design and provides it to the user, with 360-degree view and zoom capabilities.

[1207] Terminal: The user checks the virtual preview and resubmits any changes or additions via the terminal, if necessary.

[1208] Proposing additional options and creating custom furniture

[1209] Server: In response to the user's additional requests, the visual recognition AI and generation AI regenerate new design proposals. If new furniture is needed, the AI ​​also generates the design.

[1210] Partner Factory: Produces custom-made furniture based on instructions from the server and delivers it to the user.

[1211] Specific examples

[1212] For example, say a user is considering redecorating their living room. The user takes photos of the living room from multiple angles with their smartphone and uploads them to a cloud server via an app. The server analyzes the uploaded photos and generates a life-size 3D space model. The server then uses conversational AI to collect the user's preferences, such as "natural style" and "budget under 200,000 yen." Next, visual recognition AI selects appropriate furniture from a database based on these preferences and places them in the 3D space model.

[1213] The server then uses an emotion analysis engine to analyze the user's facial expressions and tone of voice to determine whether they are relaxed or stressed. Based on the results of this analysis, the server adjusts colors and style to suggest the optimal interior design. The generated design proposal is provided to the user as a virtual preview, which can be viewed on their smartphone. If necessary, the user can submit any changes or additions they wish to the server, and a new design proposal will be generated.

[1214] Finally, if the user wants custom-made furniture, the server uses generative AI to propose a new furniture design and instructs a partner factory to manufacture it. The partner factory then delivers the manufactured furniture to the user.

[1215] Example prompt statement

[1216] Please suggest a natural style living room for a budget of under 200,000 yen. I'd like it to have an airy cafe-like feel.

[1217] Design a relaxing, modern bedroom for under $1,500.

[1218] I would like to decorate the dining room with a warm interior for my family. My budget is within 300,000 yen.

[1219] Through the above prompts, the user can provide detailed requirements to the server and receive advanced interior design proposals.

[1220] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1221] Step 1: Take and upload a photo

[1222] User: The user uses a smartphone or tablet to take photos of the room from multiple angles, including the four corners, ceiling, and floor.

[1223] Input: A photo of the room taken by the user (JPEG, PNG, or other image format).

[1224] Device: Uploads the captured photo to the cloud server, along with metadata such as the date and time of the photo, location information, and device information.

[1225] Output: Room photos and metadata uploaded to a cloud server.

[1226] Step 2: Generate a 3D spatial model

[1227] Server: The server receives the uploaded photos and uses spatial recognition AI to extract feature points and edge information from the photos. For example, it performs image analysis using OpenCV or TensorFlow libraries.

[1228] Input: Uploaded room photo and metadata.

[1229] Server: A 3D modeling tool is used to construct the skeleton of the room based on the feature points and edge information of the photograph, and generate a life-size 3D spatial model.

[1230] Output: A generated full-scale 3D space model.

[1231] Server: Saves the generated 3D spatial model in a database and notifies the user of its completion.

[1232] Step 3: Gather user requests

[1233] Server: Uses conversational AI to gather interior design requirements from users through interactive question-and-answer sessions, such as "What style do you prefer?" and "What is your budget?"

[1234] Input: Interior design requests from the user.

[1235] Users: Through a conversational interface, they input their preferred interior style and color, budget, and specific use in natural language.

[1236] Output: Collected demand data.

[1237] Step 4: Collect and analyze user sentiment

[1238] Device: Uses a camera and microphone to collect the user's voice and facial expression data.

[1239] Input: Voice and facial expression data provided by the user through the interface.

[1240] Server: The emotion analysis engine analyzes the collected data and identifies the user's emotions, using, for example, Google Cloud Speech-to-Text for voice analysis and Microsoft Azure Face API for facial expression analysis.

[1241] Output: Parsed emotion data.

[1242] Step 5: Generate interior design ideas

[1243] Server: Visual recognition AI searches for suitable furniture in the database based on the user's request.

[1244] Input: Collected desire and sentiment data.

[1245] Server: Places selected furniture within the 3D space model and generates design proposals. Uses 3D engines such as Unity or Unreal Engine.

[1246] Server: Adjust colors and layout based on sentiment analysis, for example, choosing calming colors to enhance relaxation.

[1247] Output: Generated interior design proposals.

[1248] Step 6: Provide a virtual preview

[1249] Server: Generates a virtual preview of the completed design, with 360-degree view and zoom capabilities.

[1250] Input: Generated interior design proposal.

[1251] Device: Provides users with a virtual preview, allowing them to view details on their smartphone or tablet.

[1252] Output: User feedback and addition requests.

[1253] Step 7: Propose additional options and create custom furniture

[1254] Server: Based on the user's additional requests, the visual recognition AI and generation AI regenerate new design proposals. If necessary, new furniture designs are also generated.

[1255] Input: Additional data requested by the user.

[1256] Server: Sends instructions to partner factories to produce custom-made furniture. The factories produce the furniture based on the generated design.

[1257] Partner factory: delivers the completed furniture to the user.

[1258] Output: Finished custom-made furniture.

[1259] (Application example 2)

[1260] 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."

[1261] In modern brick-and-mortar store management, attractive interior design is an important factor in increasing customer purchasing motivation. However, planning interior designs can be difficult for store owners and designers without specialized knowledge. Providing designs that take customer emotions into consideration is even more complicated. The present invention aims to address these challenges by enabling users to easily and effectively plan store interior designs and provide more personalized designs based on emotions.

[1262] 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.

[1263] In this invention, the server includes means for uploading photos of rooms taken by users to the server, means for analyzing the uploaded photos and generating a life-size three-dimensional space model, means for collecting and analyzing interior design requests from users, means for generating furniture layout design proposals based on the collected requests, means for providing the generated design proposals to the user, means for collecting and analyzing emotion data and reflecting the emotion data in the interior design, means for using conversational artificial intelligence to collect user requests, and means for simulating the interior design of a store based on the generated three-dimensional space model. This allows users to easily simulate and realize an interior design that suits their own requests and emotions.

[1264] "Photos of a room taken by a user" are image data showing the state of a room, taken by a user using an electronic device such as a smartphone or tablet.

[1265] A "server" is a remote computer system that receives and processes data sent by a user.

[1266] A "life-size three-dimensional space model" is a three-dimensional virtual space model constructed based on actual dimensions and photographs of a room.

[1267] "Spatial awareness artificial intelligence" is a general term for algorithms and technologies that analyze visual data such as images and videos and recognize the location of objects and spatial structures.

[1268] "Interior design requirements" refers to basic information and requests such as the user's desired interior decoration style, color, layout, budget, etc.

[1269] "Conversational AI" is a general term for algorithms and technologies that allow users to ask and answer questions interactively in natural language, and collect and provide information.

[1270] A "furniture layout design proposal" is a design plan that shows how furniture and decorations should be arranged within a life-size three-dimensional space model based on requests collected from users.

[1271] "Visual recognition artificial intelligence" is a general term for algorithms and techniques for identifying objects in images and video data and analyzing their features.

[1272] "Emotion data" is data that indicates the emotional state of the user, such as joy, anger, sadness, or happiness, obtained from the user's voice or facial expression.

[1273] "Reflecting in the interior design" means proposing the optimal interior design based on the collected data and requests, and reflecting it in the actual model.

[1274] "Simulating the interior design of a store" involves virtually testing the design and layout of a store on a three-dimensional spatial model to confirm its actual appearance and functionality.

[1275] This invention is a system that supports interior design and renovation planning for brick-and-mortar stores. It aims to take photos of the current state of the store using a smartphone or tablet, generate a virtual 3D model, and simulate the interior design. Furthermore, it uses an emotion engine to provide interior designs based on customer emotions.

[1276] System Configuration

[1277] User: Uses a smartphone or tablet to take photos of the current state of the store. By taking photos from multiple locations, data is collected for the entire room.

[1278] On your device, take a photo and upload it to a cloud server, along with the date, time, location, and other metadata.

[1279] server:

[1280] Using spatial recognition AI, the system analyzes uploaded photos and generates a life-size 3D space model by extracting feature points and edge information from the photo and constructing the skeleton of the room.

[1281] It uses conversational AI to interactively gather interior design preferences from users, such as asking questions like "What style do you prefer?" and "What is your budget?"

[1282] Using visual recognition AI, the system selects furniture from a database that matches the user's needs and places it in a 3D space model. The generated design proposals are constructed based on the information in the database.

[1283] The emotion engine analyzes the user's voice and facial expression data to identify their emotions, and adjusts the design's color and style based on the analysis results.

[1284] The final design is then presented to the user as a virtual preview, which can be viewed in 360 degrees and zoomed in and out via the device, allowing the user to check the details.

[1285] Program processing

[1286] Spatial recognition artificial intelligence extracts feature points and edge information from the photo and generates a life-size three-dimensional spatial model.

[1287] Conversational AI collects interior design requests from users in an interactive format.

[1288] Visual recognition AI selects appropriate furniture based on the user's requests and places it in a three-dimensional space model.

[1289] The emotion engine analyzes the user's emotions and adjusts the color and style of the design.

[1290] A virtual preview is provided to users, allowing them to see the details using a 360-degree view and zoom capabilities.

[1291] Hardware and software used

[1292] Smartphones and tablets: Provides shooting and uploading functions.

[1293] Cloud server: Analyzes and stores data, and generates three-dimensional spatial models.

[1294] Spatial recognition AI, conversational AI, visual recognition AI, emotion engine: software technologies to realize each function.

[1295] Specific examples

[1296] For example, if a store owner wants to change the interior design of their store, they take multiple photos of the interior with their smartphone and upload them to a cloud server. The server analyzes the photos and generates a life-size 3D spatial model, then uses conversational AI to collect the owner's preferences, such as "modern style" and "budget under 500,000 yen." Visual recognition AI selects appropriate furniture from a database and places it within the 3D spatial model. Furthermore, an emotion engine analyzes the user's emotional data and adjusts colors and style to make customers feel relaxed. The final generated design is provided as a virtual preview, which the user can view on their smartphone.

[1297] Prompt Sentence Examples

[1298] Generate a 3D model using the images: ['shop1.jpg', 'shop2.jpg']. Create a design that includes preferences: {'style': 'modern', 'budget': 50000}

[1299] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1300] Step 1:

[1301] Users use their smartphones or tablets to take photos from multiple locations within the store, allowing data to be collected from the entire room.

[1302] (Input: Multiple photos of the store, Output: Photos uploaded to the cloud server)

[1303] Step 2:

[1304] The device uploads the photos it takes to a cloud server, along with the date, time, location, and other metadata.

[1305] (Input: captured photos and metadata, Output: photos and metadata stored on the cloud server)

[1306] Step 3:

[1307] The server uses spatial recognition AI to analyze the uploaded photos and generate a life-size 3D spatial model by extracting feature points and edge information from the photos and constructing the skeleton of the room.

[1308] (Input: saved photo, Output: 3D space model)

[1309] Step 4:

[1310] The server uses conversational AI to interactively gather interior design requests from users, such as asking questions like "What style do you prefer?" or "What is your budget?", and users input their answers in natural language.

[1311] (Input: interactive questions and user responses, Output: collected interior design requests)

[1312] Step 5:

[1313] The server uses visual recognition AI to select furniture from a database that matches the user's needs and place it in a 3D space model. The generated design proposal is based on the information in the database.

[1314] (Input: collected interior design requests and a 3D space model, output: a 3D space model with furniture arranged)

[1315] Step 6:

[1316] The server uses an emotion engine to analyze the user's voice and facial expression data to identify their emotions, and adjusts the color and style of the design based on the analysis results.

[1317] (Input: User's voice and facial expression data, Output: Design proposal with adjusted color and style)

[1318] Step 7:

[1319] The server generates a virtual preview and provides it to the user. This preview can be viewed in 360 degrees and zoomed in and out through the device, allowing the user to check the details. The user can check the preview and, if necessary, submit changes or additions to the server again.

[1320] (Input: adjusted design proposal, Output: virtual preview provided to user)

[1321] 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.

[1322] 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.

[1323] 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.

[1324] [Fourth embodiment]

[1325] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1326] 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.

[1327] 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).

[1328] 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.

[1329] 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.

[1330] 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).

[1331] 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.

[1332] 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.

[1333] 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.

[1334] 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.

[1335] 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.

[1336] 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.

[1337] 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."

[1338] The present invention is a system that supports interior design planning by allowing users to take photos of a room using a smartphone or tablet, and then generating a life-size 3D space model from the photos. Specific embodiments for implementing the present invention are described below.

[1339] System Overview

[1340] The system of the present invention begins when a user takes a photo of a room and uploads it to a server. The server analyzes the uploaded photo and uses spatial recognition AI to generate a life-size 3D space model. Based on this 3D space model, a conversational AI collects the user's interior design requests, and a visual recognition AI generates a furniture layout design proposal based on those requests. The final design proposal is provided to the user as a virtual preview.

[1341] Program processing description

[1342] 1. Take and upload a photo

[1343] User: Takes photos of the room from multiple angles using a smartphone or tablet, allowing data on the entire room to be collected.

[1344] Device: Uploads the photos you take to the cloud server.

[1345] 2. Generating 3D models

[1346] Server: To analyze the received photos, spatial recognition AI is used to extract feature points and edge information from the photos. Based on this, the skeleton of the room is constructed and a life-size 3D space model is generated.

[1347] 3. Collecting user requests

[1348] Server: Based on the generated 3D space model, the conversational AI interactively collects information about the user's interior design needs, such as preferred style, color, and budget.

[1349] 4. Generating interior design ideas

[1350] Server: Visual recognition AI searches for furniture in the database based on the collected requests. The selected furniture is placed in a 3D space model and generated as a design proposal.

[1351] 5. Providing a Virtual Preview

[1352] Server: Provides users with a virtual preview of the completed design, which can be viewed in 360 degrees and zoomed in on the device, allowing users to check the details.

[1353] User: Checks the preview and, if necessary, requests for changes or additions are sent to the server again via the conversational AI.

[1354] 6. Proposing additional options and creating custom furniture

[1355] Server: In response to the user's additional requests, the visual recognition AI and generation AI regenerate new design proposals. If the user is not satisfied with commercially available furniture, the generation AI will propose a new furniture design and place an order for custom-made furniture with a partner factory.

[1356] Partner Factory: Produces custom-made furniture based on instructions from the server and delivers it to the user.

[1357] Specific examples

[1358] For example, if a user is thinking about redecorating their living room, they can take a photo of the room with their smartphone and upload it to a cloud server via the app. The server then analyzes the uploaded photo and generates a life-size 3D model of the space.

[1359] The server then uses conversational AI to collect user preferences, such as a "natural style" and a "budget of under 200,000 yen." Based on the collected preferences, the visual recognition AI selects appropriate furniture from a database and places it within the 3D space model. The generated design proposal is provided to the user as a virtual preview, which they can check on their smartphone. If necessary, any changes or additions can be sent to the server, and another interior design proposal is generated.

[1360] Finally, if the user requests custom-made furniture, the server uses generative AI to propose a new furniture design and sends instructions to a partner factory to manufacture it. The partner factory then produces the furniture and delivers it to the user.

[1361] In this way, the system of the present invention helps users to plan their ideal interior design effectively and easily.

[1362] The processing flow will be explained below.

[1363] Step 1:

[1364] Users: Use a smartphone or tablet to take photos from multiple points in the room. It is recommended to take photos from multiple angles and positions to cover the entire room.

[1365] Step 2:

[1366] Device: Upload all photos you take to the cloud server. When uploading, necessary metadata (date and time of photo, location, etc.) is also sent.

[1367] Step 3:

[1368] Server: The cloud server receives the uploaded photos and instructs the spatially aware AI to begin analysis.

[1369] Step 4:

[1370] Server: Spatial AI analyzes each photo and extracts feature points and edge information. Based on this data, it constructs the skeleton of the room and generates a life-size 3D model of the space.

[1371] Step 5:

[1372] Server: Saves the generated 3D space model in a database and notifies the user of its completion.

[1373] Step 6:

[1374] Server: Uses conversational AI to gather user requests through interactive question-based inquiry, such as "What style do you prefer?" or "What is your budget?"

[1375] Step 7:

[1376] Users: Through a conversational interface, they input detailed requirements in natural language, such as preferred interior style and color, budget, and specific use.

[1377] Step 8:

[1378] Server: Analyzes the collected request data and stores it as the base data for generating interior design proposals.

[1379] Step 9:

[1380] Server: Uses visual recognition AI to search for items that match the user's requirements from a database of commercially available furniture.

[1381] Step 10:

[1382] Server: Visually aware AI selects and places the most suitable furniture in a 3D spatial model, taking into account style, color, and location based on the user's requirements.

[1383] Step 11:

[1384] Server: Renders a 3D spatial model of the placed furniture and generates images for a virtual preview.

[1385] Step 12:

[1386] On the device: The device displays the preview image received from the server for the user to check, and also provides functions such as zooming and 360-degree view.

[1387] Step 13:

[1388] User: Checks the virtual preview and, if necessary, sends any changes or additions to the server via the terminal.

[1389] Step 14:

[1390] Server: Based on the changes or additions requested, the visual recognition AI and generation AI will regenerate the interior design proposal. If necessary, new furniture designs will be proposed and placed in the virtual space.

[1391] Step 15:

[1392] Server: Regenerate a new preview image and resend it to the device.

[1393] Step 16:

[1394] Terminal: Show the regenerated preview image to the user.

[1395] Step 17:

[1396] User: After viewing the final preview, if they would like to have the furniture custom-made, they notify the server.

[1397] Step 18:

[1398] Server: Sends production instructions for custom-made furniture to partner factories. The instructions include the necessary design data and material information.

[1399] Step 19:

[1400] Partner Factory: Based on instructions received from the server, the factory starts production of custom-made furniture and delivers it to the user once completed.

[1401] In this way, the system of the present invention allows users to easily plan their ideal interior and ultimately supports them in the creation of custom-made furniture.

[1402] Example 1

[1403] 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."

[1404] In today's world, there is a growing need for systems that allow individual users to more efficiently and accurately plan the decoration of their living spaces. However, conventional methods, such as creating a life-size 3D model of a room and then arranging furniture and equipment based on that model, are extremely complex and time-consuming, often requiring specialized knowledge. Furthermore, due to insufficient functionality for automatically generating and proposing design proposals that meet the user's needs, the end product often falls short of the user's expectations. Therefore, there is a need for a system that allows users to easily create their own room decoration plans and quickly confirm and modify the designs based on their needs.

[1405] 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.

[1406] In this invention, the server includes means for transmitting a video of a room taken by a user to an information processing device, means for analyzing the transmitted video and generating a life-size three-dimensional space model, means for collecting and analyzing requests for decoration from the user, means for generating a design proposal for equipment layout based on the collected requests, and means for providing the generated design proposal to the user. This allows the user to easily create a three-dimensional model of the entire room and quickly confirm and modify the decorative design based on the model to meet their needs.

[1407] "User" refers to an individual or organization that uses the system to plan room decoration.

[1408] "Room footage" includes image or video data taken by the user from multiple angles within the room.

[1409] "Information processing device" refers to a device that processes and stores data, including a cloud server and its peripheral devices.

[1410] "Life-size 3D space model" refers to a digital 3D model that is the same size as a real room.

[1411] "Spatial recognition machine learning" refers to an algorithm technology for recognizing spatial information from input images and videos and generating three-dimensional models.

[1412] "Decorative requests" are the user's preferences and requirements regarding the interior of the room, including style, color, budget, etc.

[1413] "Equipment layout design proposal" refers to a plan that arranges furniture and equipment selected based on the user's requests within a three-dimensional space model.

[1414] "Visual recognition machine learning" refers to algorithm technology that uses image processing technology to recognize, select, and place objects.

[1415] "Generated design proposal" refers to a furniture and equipment layout plan that is automatically created based on the user's requests.

[1416] "Means to provide" refers to the technical means for displaying or notifying the generated design proposal in a form that can be confirmed by the user.

[1417] This invention is a system that allows a user to take a video of a room using a smartphone or tablet, generate a life-size three-dimensional space model from the video, and assist in decoration planning. Specific embodiments for implementing the invention are described below.

[1418] Program processing description

[1419] This system uses the following hardware and software:

[1420] Hardware: smartphones, tablets, cloud servers

[1421] Software: OpenCV, TensorFlow, YOLOv5, Unity, Natural Language Processing models (e.g. GPT-3)

[1422] The user uses a smartphone or tablet to capture video from multiple angles of a room, thereby acquiring information about the entire room. The captured video is then uploaded from the device to a cloud server, where a cloud storage service such as Amazon S3 is used.

[1423] The server analyzes the received video. Specifically, it uses spatial recognition machine learning (e.g., an object detection model using TensorFlow) to extract feature points and edge information from the video. Based on this, it understands the relative positions of each image and generates the skeleton of the room. Furthermore, it uses the distance information from the images to construct a life-size 3D spatial model.

[1424] To gather the user's decorating preferences, the server uses conversational AI (e.g., GPT-3) to interactively ask questions such as, "How would you like to style this room?", "Do you have a preferred color scheme?", and "What is your budget?", and collects detailed data based on those questions.

[1425] The server uses visual recognition machine learning (e.g., YOLOv5) to search the database for furniture and equipment that meets the user's requirements. The selected furniture and equipment are placed in the generated 3D space model and a design proposal is generated. In this process, an algorithm is used that takes into account the balance of furniture placement and the overall aesthetics of the room.

[1426] The resulting design proposal is provided to the user as a virtual preview, which is generated using 3D rendering software (e.g., Unity). The preview can be viewed on a smartphone or tablet, and users can view it in 360 degrees and zoom in and out.

[1427] Users can check the virtual preview and send any changes or additions they require to the server. The server receives the changes and adds new designs using visual recognition machine learning and generative AI models (e.g., DALL·E). If users are not satisfied with commercially available furniture, the server uses the generative AI model to propose a new furniture design and sends instructions to a partner factory to create the custom-made furniture. The partner factory creates the custom-made furniture based on the instructions from the server and delivers it to the user once it is completed. Users are notified of the delivery progress, and installation services may also be provided after delivery.

[1428] Specific examples

[1429] For example, a user considering redecorating their living room can take a video of the living room with their smartphone and upload it to a cloud server via the app. The server analyzes the uploaded video and generates a life-size 3D space model. The server then uses conversational AI to collect user preferences, such as a "natural style" and a "budget of under 200,000 yen." Based on the collected preferences, visual recognition machine learning selects appropriate furniture and fixtures from a database and places them within the 3D space model. The generated design proposal is provided to the user as a virtual preview, which the user can check on their smartphone. If necessary, changes or additions can be made and the server generates another interior design proposal. Finally, if the user requests custom-made furniture, the server uses generative AI to propose a new furniture design and instructs a partner factory to manufacture it. The partner factory then produces the furniture and delivers it to the user.

[1430] Prompt Sentence Examples

[1431] "I'm thinking about the interior design of a living room. I want a natural style, and my budget is under 200,000 yen. Please generate a design plan with appropriate furniture arrangement based on this."

[1432] In this way, the system of the present invention helps users to plan their ideal interior efficiently and accurately.

[1433] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1434] Step 1:

[1435] User: Uses a smartphone or tablet to capture images of a room from multiple angles. The input is an image of the entire room, which allows data on the room's walls, floors, furniture, etc. to be acquired. The output is the captured data from multiple angles.

[1436] Specifically, the app guides the user to the optimal shooting position and angle, and then multiple photos and videos are taken.

[1437] Step 2:

[1438] Terminal: Uploads the captured video to a cloud server. The input is the video data stored on a smartphone or tablet, and the output is the uploaded data stored in cloud storage.

[1439] Specifically, the app allows users to select video data and transfer it to a cloud server via the internet. Upload progress is displayed on the screen.

[1440] Step 3:

[1441] Server: Analyzes uploaded videos. The input is multiple video data stored in cloud storage, and the output is analysis data including feature points and edge information extracted from the video.

[1442] Specifically, it uses spatial recognition machine learning (e.g., an object detection model using TensorFlow) to analyze video data and extract feature points and edges from each frame. Based on this, it calculates the relative positions of each image and generates the skeleton of the room.

[1443] Step 4:

[1444] Server: Generates a life-size 3D space model. The input is the analyzed feature points and edge information, and the output is a life-size 3D space model.

[1445] Specifically, the server uses an algorithm to reconstruct three-dimensional space based on feature points and edge information, and generates a life-size model of the room's skeletal structure.

[1446] Step 5:

[1447] Server: Collects decoration requests from users. The input is a life-size 3D space model and an interactive interface, and the output is the user's request data.

[1448] Specifically, it uses conversational AI (e.g., GPT-3) to interact with users, asking questions such as, "What style do you like?", "Do you have a favorite color scheme?", and "What is your budget?", and collects their responses.

[1449] Step 6:

[1450] Server: Generates a design proposal for facility layout based on the collected requirements. The input is the user's requirement data and a 3D space model, and the output is a design proposal for the decoration plan.

[1451] Specifically, it uses visual recognition machine learning (e.g., YOLOv5) to search a database for furniture and equipment that matches the user's requirements and place them within a three-dimensional space model.

[1452] Step 7:

[1453] Server: Provides the generated design proposal to the user as a virtual preview. The input is the design proposal data, and the output is virtual preview data that can be displayed on the user's terminal.

[1454] Specifically, the design proposals generated using 3D rendering software (e.g., Unity) are visualized in a virtual space, allowing users to view them in 360 degrees and zoom in and out via their smartphones or tablets.

[1455] Step 8:

[1456] User: Checks the virtual preview and sends requests for changes or additions to the server as needed. The input is the user's feedback data, and the output is the updated request data.

[1457] Specifically, the user checks the virtual preview, inputs any necessary changes or additions, and submits the request.

[1458] Step 9:

[1459] Server: Receives requests for changes or additions and generates new design proposals. The input is the updated request data, and the output is the new design proposal.

[1460] Specifically, the design proposal is regenerated using visual recognition machine learning and a generative AI model (e.g., DALL·E) to create updated data for the virtual preview.

[1461] Step 10:

[1462] Server: When a user requests custom-made furniture, the server sends production instructions to a partner factory. The input is new furniture design data, and the output is production instruction data for the partner factory.

[1463] Specifically, the system uses generative AI models to propose new furniture designs, then sends production instructions to partner factories, which then deliver the furniture to the user after production is complete.

[1464] (Application example 1)

[1465] 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."

[1466] Currently, planning an interior design requires consulting with experts and going through a wide range of furniture selection and purchasing procedures, which takes a lot of time and effort. Furthermore, it is difficult to visualize how the furniture will be arranged in the actual space, which often leads to dissatisfaction after purchase. For this reason, there is a need for a system that allows users to easily and effectively design interiors, select the optimal furniture arrangement, and purchase directly from an online shopping site.

[1467] 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.

[1468] In this invention, the server includes means for transmitting images of a room taken by a user to an information processing device, means for processing the transmitted images to generate a life-size three-dimensional space model, means for collecting and analyzing interior design requests from the user, means for generating furniture layout proposals based on the collected requests, means for presenting the generated proposals to the user, and means for the user to confirm the proposed proposals and process orders in the virtual space in conjunction with information from an online shopping site. This enables the user to generate a three-dimensional space model based on photos of their home, interactively try out interior designs, and select and purchase the optimal furniture.

[1469] 1. "Room images taken by users" are photos of rooms taken by users using electronic devices such as smartphones or tablets.

[1470] 2. "Information processing device" means a device for inputting, processing, storing, and outputting data, including servers and cloud services.

[1471] 3. "Transmission" is the act of moving data or information from one point to another.

[1472] 4. A "life-size three-dimensional spatial model" is a three-dimensional spatial model generated from photographed images, which reflects the actual size and shape of a room.

[1473] 5. "Spatial recognition artificial intelligence" is an artificial intelligence system that analyzes images and data and uses them to recognize and construct three-dimensional space.

[1474] 6. "Interior design requests" refers to the user's wishes and demands regarding the design and furniture arrangement of the room.

[1475] 7. "Collection and analysis" refers to the act of gathering data and information, examining it, and extracting the necessary results and characteristics.

[1476] 8. "Furniture Arrangement Proposal" refers to a design proposal showing the appropriate furniture arrangement for a room based on the collected requests.

[1477] 9. "Means for processing orders in a virtual space in conjunction with information from an online shopping site" refers to the function that allows users to check interior designs in a virtual space and then order furniture directly through an online shopping site.

[1478] This invention relates to a system that generates a life-size three-dimensional space model based on images of a room taken by a user, plans an interior design, and ultimately proposes an optimal furniture layout. Specific embodiments for carrying out the invention are described below.

[1479] Program Generation

[1480] 1. User authentication and login function

[1481] The server uses Firebase Authentication to securely authenticate the user: the user opens the app and logs in with their email or social media account.

[1482] 2. Photo taking and uploading function

[1483] The user takes multiple images of the room using a smartphone camera and uploads them to cloud storage (e.g., Google Cloud Storage). The device then sends these image data to the cloud.

[1484] 3. 3D space model generation function

[1485] The server receives the uploaded images and analyzes them using spatial recognition AI (e.g., OpenCV, TensorFlow). Specifically, it extracts feature points and edge information from the images and generates an accurate 3D spatial model of the room based on that information.

[1486] 4. User request collection and analysis function

[1487] The server uses conversational AI (e.g., Dialogflow) to interactively collect user requests. For example, a user might input a request such as, "A natural style, within a budget of 200,000 yen." This request is analyzed on the server.

[1488] 5. Interior proposal generation function

[1489] The server uses visual recognition AI (e.g., YOLO, Pytorch) to select items that match the user's requests from the furniture information in the database and place them in a 3D space model. The generated interior proposals are then rendered in the virtual space using a 3D rendering engine (e.g., Unity).

[1490] 6. Virtual Preview and Ordering Features

[1491] Users can use their smartphones to view the generated interior design proposals in 360 degrees, checking out the details, and if they find furniture they like, they can link it to the online shopping site information in the virtual space and place an order right away.

[1492] Specific examples

[1493] For example, a user thinking about redecorating their living room can take a photo of the living room with their smartphone and upload it to a cloud server via the app. The server analyzes the uploaded image and generates a life-size 3D space model. Conversational AI is then used to gather the user's main requirements. Specifically, the user inputs requirements such as "natural style" and "budget under 200,000 yen" into the conversational AI. Based on this information, the visual recognition AI selects appropriate furniture from a database and places it within the 3D space model. The user can then view the resulting interior design in a virtual space, seamlessly purchasing the furniture.

[1494] Prompt Sentence Examples

[1495] markdown

[1496] You are creating an interior design application. This application allows users to take photos of a room, generates a 3D model from the photos, and suggests interior coordination. Design your program to meet the following requirements:

[1497] Uses Firebase Authentication for user authentication.

[1498] Supports taking photos and uploading to the cloud (using Google Cloud Storage).

[1499] Generate 3D spatial models using spatial recognition AI (OpenCV and TensorFlow).

[1500] Collect user requests using conversational AI (Dialogflow).

[1501] Interior design ideas are generated using visual recognition AI (YOLO and Pytorch).

[1502] The generated design proposals are displayed in a 360-degree view using Unity.

[1503] Please provide the following steps in detail:

[1504] 1. Initial Setup and User Login

[1505] 2. Take a photo and upload it

[1506] 3. 3D model generation

[1507] 4. Collecting user requests

[1508] 5. Interior design proposal generation

[1509] 6. Virtual Preview and Online Shopping Integration

[1510] thank you.

[1511] In this way, the present invention provides a specific method for users to effectively realize their ideal interior design.

[1512] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1513] Step 1:

[1514] Users take photos of their rooms using their smartphones or tablets, capturing data on the entire room from multiple angles. This is the input. They then upload these photos to cloud storage through the app. This is the output.

[1515] Step 2:

[1516] The device sends image data to the server to upload the photos of the room taken by the user to cloud storage. This requires an internet connection. The input is the room image taken by the user, and the output is the image data stored in the cloud storage.

[1517] Step 3:

[1518] The server receives and downloads image data from cloud storage. It then uses spatial recognition AI (e.g., OpenCV, TensorFlow) to analyze the image's feature points and edge information. This is the input. Based on this analysis, a life-size 3D spatial model is generated and stored on the server. This is the output.

[1519] Step 4:

[1520] The server uses conversational artificial intelligence (e.g., Dialogflow) to interactively collect interior design requests from the user. For example, the user might input requests such as "natural style" and "budget under 200,000 yen." This is the input. The collected requests are analyzed and the necessary information is saved on the server. This is the output.

[1521] Step 5:

[1522] The server uses visual recognition AI (e.g., YOLO, Pytorch) to search for furniture items in the database that match the user's requirements. This is the input. The selected furniture is placed in the generated 3D space model. This is the output.

[1523] Step 6:

[1524] The server uses a 3D rendering engine (e.g. Unity) to render the generated interior proposal in a virtual space. This is the input. The user can view this virtual preview in a 360-degree view on their smartphone. This is the output.

[1525] Step 7:

[1526] The user uses a smartphone to view a virtual preview and check the details. Next, they select the furniture they like and place an order in the virtual space, linking it with the information on the online shopping site. This is the input. Finally, the information on the furniture purchased by the user is sent to the online shopping site, and the order is confirmed. This is the output.

[1527] By following the steps outlined above, users can generate a three-dimensional spatial model based on images of their home, interactively experiment with interior designs, and select and purchase the most suitable furniture.

[1528] 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.

[1529] The present invention combines an emotion engine with a system that allows users to take photos of a room with a smartphone or tablet, generate a life-size 3D space model from the photo, and assist in interior design planning. Specific embodiments for implementing the present invention are described below.

[1530] System Overview

[1531] The system of the present invention begins when a user takes a photo of a room and uploads it to a server. The server analyzes the uploaded photo and uses spatial recognition AI to generate a life-size 3D space model. Based on this 3D space model, a conversational AI collects the user's interior design requests, and a visual recognition AI generates a furniture layout design proposal based on those requests. An emotion engine then analyzes the user's emotions and adjusts the interior design based on those emotions. The final design proposal is provided to the user as a virtual preview.

[1532] Program processing description

[1533] 1. Take and upload a photo

[1534] Users: Use a smartphone or tablet to take photos from multiple points in the room. It is recommended to take photos from multiple angles and positions to cover the entire room.

[1535] Device: Uploads the photos to the cloud server. When uploading, necessary metadata (date and time of photo, location, etc.) is also sent.

[1536] 2. Generating 3D models

[1537] Server: To analyze the received photos, spatial recognition AI is used to extract feature points and edge information from the photos, and based on this, a skeleton of the room is constructed and a life-size 3D spatial model is generated.

[1538] Server: Saves the generated 3D spatial model in a database and notifies the user of its completion.

[1539] 3. Collecting user requests

[1540] Server: Uses conversational AI to gather user requests through interactive question-based inquiry, such as "What style do you prefer?" or "What is your budget?"

[1541] Users: Through a conversational interface, they input detailed requirements in natural language, such as preferred interior style and color, budget, and specific use.

[1542] 4. Collecting and analyzing user sentiment

[1543] Server: The emotion engine analyzes the user's voice and facial expression data to identify their emotions. The emotion data is used to adjust the interior design.

[1544] 5. Generating interior design ideas

[1545] Server: Visual recognition AI searches for furniture in the database based on the collected requests. The selected furniture is placed in a 3D space model and generated as a design proposal. At this time, color and style are adjusted based on the analysis results of the emotion engine.

[1546] 6. Providing Virtual Previews

[1547] Server: Provides users with a virtual preview of the completed design, which can be viewed in 360 degrees and zoomed in on the device, allowing users to see the details.

[1548] User: Check the preview and, if necessary, make any changes or additions and send them back to the server via the device.

[1549] 7. Proposing additional options and creating custom furniture

[1550] Server: In response to the user's additional requests, the visual recognition AI and generation AI regenerate new design proposals. If the user is not satisfied with commercially available furniture, the generation AI proposes a new furniture design and places an order for custom-made furniture with a partner factory.

[1551] Partner Factory: Produces custom-made furniture based on instructions from the server and delivers it to the user.

[1552] Specific examples

[1553] For example, if a user is thinking about redecorating their living room, they can take a photo of the room with their smartphone and upload it to a cloud server via the app. The server then analyzes the uploaded photo and generates a life-size 3D model of the space.

[1554] The server then uses conversational AI to collect user preferences, such as "natural style" and "budget under 200,000 yen." Based on the preferences, visual recognition AI selects appropriate furniture from a database and places it within the 3D space model. The server also uses an emotion engine to analyze the user's facial expressions and tone of voice to determine whether the user is relaxed or stressed. Based on the results of this analysis, the server adjusts the colors and style. For example, if the user feels like relaxing, it will suggest furniture and designs in calming colors.

[1555] The generated design proposal is provided to the user as a virtual preview, which they can check on their smartphone. If necessary, they can submit any changes or additions they wish to make to the server, and the interior design proposal is generated again.

[1556] Finally, if the user requests custom-made furniture, the server uses generative AI to propose a new furniture design and sends instructions to a partner factory to manufacture it. The partner factory then produces the furniture and delivers it to the user.

[1557] In this way, the system of the present invention helps users to plan their ideal interiors effectively and easily. Furthermore, by using the emotion engine, it is possible to provide more personalized interior designs that take into consideration the user's emotions.

[1558] The processing flow will be explained below.

[1559] Step 1:

[1560] Users: Use a smartphone or tablet to take photos from multiple points in the room. It is recommended to take photos from multiple angles and positions to cover the entire room.

[1561] Step 2:

[1562] Device: Upload all photos you take to the cloud server. When uploading, necessary metadata (date and time of photo, location, etc.) is also sent.

[1563] Step 3:

[1564] Server: The cloud server receives the uploaded photos and instructs the spatially aware AI to begin analysis.

[1565] Step 4:

[1566] Server: Spatial recognition AI analyzes each photo and extracts feature points and edge information. Based on this data, it constructs the skeleton of the room and generates a life-size 3D spatial model.

[1567] Step 5:

[1568] Server: Saves the generated 3D space model in a database and notifies the user of its completion.

[1569] Step 6:

[1570] Server: Using conversational AI, collects user requests through interactive question format, such as "What style do you prefer?" or "What is your budget?"

[1571] Step 7:

[1572] Users: Through a conversational interface, they input detailed requirements in natural language, such as preferred interior style and color, budget, and specific use.

[1573] Step 8:

[1574] Server: Analyzes the collected request data and stores it as basic data for generating interior design proposals.

[1575] Step 9:

[1576] Server: The emotion engine analyzes the user's emotions from their voice and facial expressions. For example, it determines their stress level from their tone of voice and speaking rate, and uses facial expression recognition technology to understand their emotional state.

[1577] Step 10:

[1578] Server: Reflects the analyzed emotional data in the design proposal. For example, if the user wants to relax, select calm colors and a soft design.

[1579] Step 11:

[1580] Server: Visual recognition AI searches a database of commercially available furniture for items that match the user's needs and emotions.

[1581] Step 12:

[1582] Server: Visual recognition AI selects the most suitable furniture and places it in a 3D spatial model, taking into account style, color, and location based on the user's requirements.

[1583] Step 13:

[1584] Server: Renders a 3D spatial model of the placed furniture and generates an image for a virtual preview.

[1585] Step 14:

[1586] Terminal: Displays the preview image received from the server so that the user can check it. It also provides functions such as zooming and 360-degree view.

[1587] Step 15:

[1588] User: Checks the virtual preview and, if necessary, requests for changes or additions are sent back to the server via the terminal.

[1589] Step 16:

[1590] Server: Based on the changes or additions requested, the visual recognition AI and generation AI will regenerate the interior design proposal. If necessary, new furniture designs will be proposed and placed in the virtual space.

[1591] Step 17:

[1592] Server: Regenerate a new preview image and resend it to the device.

[1593] Step 18:

[1594] Terminal: Show the regenerated preview image to the user.

[1595] Step 19:

[1596] User: After checking the final preview, if the user wishes to have the furniture made to order, the user notifies the server.

[1597] Step 20:

[1598] Server: Sends production instructions for custom-made furniture to partner factories. The instructions include the necessary design data and material information.

[1599] Step 21:

[1600] Partner Factory: Based on instructions received from the server, the factory starts production of custom-made furniture and delivers it to the user once completed.

[1601] In this way, the system of the present invention can more personalized interior design based on the user's emotions, helping the user to easily plan their ideal interior.

[1602] Example 2

[1603] 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."

[1604] Conventional interior planning systems require users to measure the space in their rooms and manually arrange furniture, which is time-consuming and laborious. Additionally, it is difficult to provide interior designs that reflect the user's emotions and desires, which makes it difficult to achieve highly satisfying planning.

[1605] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for uploading photos of a room taken by a user to the server, means for analyzing the uploaded photos and generating a life-size three-dimensional space model, means for collecting and analyzing interior design requests from the user in the form of interactive questions, means for generating design proposals for furniture layout based on the collected requests and emotional data analyzed from the user's voice and facial expressions, means for providing the generated design proposals to the user as virtual previews and regenerating design proposals in response to additional requests, and means for proposing designs for custom-made furniture and instructing its production, if the user so desires. This allows the user to receive emotionally sensitive interior design proposals simply by uploading photos of their room, thereby realizing efficient and high-quality interior planning.

[1606] A "server" is a computer system connected to a network, which processes data in response to requests from users and provides services.

[1607] "User" refers to an individual or corporation that uses the system, and who accesses the system and provides input via a device such as a smartphone or tablet.

[1608] "Photo upload" refers to the process of sending image data taken by a user to a server via the Internet for storage and analysis.

[1609] A "three-dimensional space model" refers to three-dimensional digital data that reproduces a room or space in actual size, allowing for simulation of interior design and spatial layout.

[1610] An "interactive question format" is a method for collecting information through dialogue with the user, in which the next question is dynamically generated based on the user's response.

[1611] "Collected requests" refers to information obtained from users that specifically expresses their wishes and conditions regarding interior design.

[1612] "Emotional data analyzed from voice and facial expressions" is data that indicates the emotional state of the user, obtained by analyzing the user's voice and facial expressions.

[1613] "Furniture layout design proposal" refers to a plan for the arrangement and placement of furniture within a room, proposed based on collected desires and emotional data.

[1614] "Virtual Preview" refers to a virtual reality environment that allows users to visually check the completed design proposal, and is a function that users can view and manipulate through an interface.

[1615] "Made-to-order furniture" refers to custom-made furniture that is designed and manufactured based on the specific requirements of the user.

[1616] MODE FOR CARRYING OUT THE INVENTION

[1617] The present invention provides a system that allows users to take photos of a room using a smartphone or tablet, generate a life-size 3D space model from the photos, and assist in interior design planning. Specific embodiments for implementing the present invention are described below.

[1618] Work Environment

[1619] Hardware: Smartphones, tablets, server computers

[1620] Software: spatially aware artificial intelligence (AI), visual recognition AI, conversational AI, sentiment analysis engines, database management systems, 3D modeling tools (e.g., Unity, Unreal Engine), speech recognition and facial expression recognition libraries (e.g., Google Cloud Speech-to-Text, Microsoft Azure Face API)

[1621] Program processing

[1622] Taking and uploading photos

[1623] User: The user uses a smartphone or tablet to take photos of the room from multiple angles, for example, from all four corners of the room, including the ceiling and floor.

[1624] Device: The photos are uploaded to a cloud server, along with metadata such as the date and time the photo was taken, location information, and device information.

[1625] 3D model generation

[1626] Server: The server receives the uploaded photos and uses spatial recognition AI to extract feature points and edge information from the photos, constructing the skeleton of the room and generating a life-size 3D space model, using libraries such as OpenCV and TensorFlow.

[1627] Server: The generated 3D spatial model is saved in a database and the user is notified of its completion.

[1628] Collecting user requests

[1629] Server: Uses conversational AI to ask users interactive questions such as "What style do you prefer?" and "What is your budget?" using tools such as Dialogflow and Amazon Lex.

[1630] User: In response to questions, the interface inputs preferred interior style and color, budget, and specific use in natural language.

[1631] Collecting and analyzing user sentiment

[1632] Device: Voice and facial expression data is collected on the device when the user responds, using the camera and microphone.

[1633] Server: The emotion analysis engine analyzes the collected data and identifies the user's emotions, for example, using Google Cloud Speech-to-Text for voice analysis and Microsoft Azure Face API for facial expression analysis.

[1634] Generate interior design ideas

[1635] Server: Visual recognition AI searches for suitable furniture in the database based on the user's request.

[1636] Server: Places the selected furniture in the 3D space model. Uses a 3D engine such as Unity or Unreal Engine.

[1637] Server: Adjusts colors and layout based on the results of the sentiment analysis engine. For example, if the user is looking to relax, it will suggest furniture and designs with calming colors.

[1638] Virtual preview available

[1639] Server: Generates a virtual preview of the completed design and provides it to the user, with 360-degree view and zoom capabilities.

[1640] Terminal: The user checks the virtual preview and resubmits any changes or additions via the terminal, if necessary.

[1641] Proposing additional options and creating custom furniture

[1642] Server: In response to the user's additional requests, the visual recognition AI and generation AI regenerate new design proposals. If new furniture is needed, the AI ​​also generates the design.

[1643] Partner Factory: Produces custom-made furniture based on instructions from the server and delivers it to the user.

[1644] Specific examples

[1645] For example, say a user is considering redecorating their living room. The user takes photos of the living room from multiple angles with their smartphone and uploads them to a cloud server via an app. The server analyzes the uploaded photos and generates a life-size 3D space model. The server then uses conversational AI to collect the user's preferences, such as "natural style" and "budget under 200,000 yen." Next, visual recognition AI selects appropriate furniture from a database based on these preferences and places them in the 3D space model.

[1646] The server then uses an emotion analysis engine to analyze the user's facial expressions and tone of voice to determine whether they are relaxed or stressed. Based on the results of this analysis, the server adjusts colors and style to suggest the optimal interior design. The generated design proposal is provided to the user as a virtual preview, which can be viewed on their smartphone. If necessary, the user can submit any changes or additions they wish to the server, and a new design proposal will be generated.

[1647] Finally, if the user wants custom-made furniture, the server uses generative AI to propose a new furniture design and instructs a partner factory to manufacture it. The partner factory then delivers the manufactured furniture to the user.

[1648] Example prompt statement

[1649] Please suggest a natural style living room for a budget of under 200,000 yen. I'd like it to have an airy cafe-like feel.

[1650] Design a relaxing, modern bedroom for under $1,500.

[1651] I would like to decorate the dining room with a warm interior for my family. My budget is within 300,000 yen.

[1652] Through the above prompts, the user can provide detailed requirements to the server and receive advanced interior design proposals.

[1653] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1654] Step 1: Take and upload a photo

[1655] User: The user uses a smartphone or tablet to take photos of the room from multiple angles, including the four corners, ceiling, and floor.

[1656] Input: A photo of the room taken by the user (JPEG, PNG, or other image format).

[1657] Device: Uploads the captured photo to the cloud server, along with metadata such as the date and time of the photo, location information, and device information.

[1658] Output: Room photos and metadata uploaded to a cloud server.

[1659] Step 2: Generate a 3D spatial model

[1660] Server: The server receives the uploaded photos and uses spatial recognition AI to extract feature points and edge information from the photos. For example, it performs image analysis using OpenCV or TensorFlow libraries.

[1661] Input: Uploaded room photo and metadata.

[1662] Server: A 3D modeling tool is used to construct the skeleton of the room based on the feature points and edge information of the photograph, and generate a life-size 3D spatial model.

[1663] Output: A generated full-scale 3D space model.

[1664] Server: Saves the generated 3D spatial model in a database and notifies the user of its completion.

[1665] Step 3: Gather user requests

[1666] Server: Uses conversational AI to gather interior design requirements from users through interactive question-and-answer sessions, such as "What style do you prefer?" and "What is your budget?"

[1667] Input: Interior design requests from the user.

[1668] Users: Through a conversational interface, they input their preferred interior style and color, budget, and specific use in natural language.

[1669] Output: Collected demand data.

[1670] Step 4: Collect and analyze user sentiment

[1671] Device: Uses a camera and microphone to collect the user's voice and facial expression data.

[1672] Input: Voice and facial expression data provided by the user through the interface.

[1673] Server: The emotion analysis engine analyzes the collected data and identifies the user's emotions, using, for example, Google Cloud Speech-to-Text for voice analysis and Microsoft Azure Face API for facial expression analysis.

[1674] Output: Parsed emotion data.

[1675] Step 5: Generate interior design ideas

[1676] Server: Visual recognition AI searches for suitable furniture in the database based on the user's request.

[1677] Input: Collected desire and sentiment data.

[1678] Server: Places selected furniture within the 3D space model and generates design proposals. Uses 3D engines such as Unity or Unreal Engine.

[1679] Server: Adjust colors and layout based on sentiment analysis, for example, choosing calming colors to enhance relaxation.

[1680] Output: Generated interior design proposals.

[1681] Step 6: Provide a virtual preview

[1682] Server: Generates a virtual preview of the completed design, with 360-degree view and zoom capabilities.

[1683] Input: Generated interior design proposal.

[1684] Device: Provides users with a virtual preview, allowing them to view details on their smartphone or tablet.

[1685] Output: User feedback and addition requests.

[1686] Step 7: Propose additional options and create custom furniture

[1687] Server: Based on the user's additional requests, the visual recognition AI and generation AI regenerate new design proposals. If necessary, new furniture designs are also generated.

[1688] Input: Additional data requested by the user.

[1689] Server: Sends instructions to partner factories to produce custom-made furniture. The factories produce the furniture based on the generated design.

[1690] Partner factory: delivers the completed furniture to the user.

[1691] Output: Finished custom-made furniture.

[1692] (Application example 2)

[1693] 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."

[1694] In modern brick-and-mortar store management, attractive interior design is an important factor in increasing customer purchasing motivation. However, planning interior designs can be difficult for store owners and designers without specialized knowledge. Providing designs that take customer emotions into consideration is even more complicated. The present invention aims to address these challenges by enabling users to easily and effectively plan store interior designs and provide more personalized designs based on emotions.

[1695] 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.

[1696] In this invention, the server includes means for uploading photos of rooms taken by users to the server, means for analyzing the uploaded photos and generating a life-size three-dimensional space model, means for collecting and analyzing interior design requests from users, means for generating furniture layout design proposals based on the collected requests, means for providing the generated design proposals to the user, means for collecting and analyzing emotion data and reflecting the emotion data in the interior design, means for using conversational artificial intelligence to collect user requests, and means for simulating the interior design of a store based on the generated three-dimensional space model. This allows users to easily simulate and realize an interior design that suits their own requests and emotions.

[1697] "Photos of a room taken by a user" are image data showing the state of a room, taken by a user using an electronic device such as a smartphone or tablet.

[1698] A "server" is a remote computer system that receives and processes data sent by a user.

[1699] A "life-size three-dimensional space model" is a three-dimensional virtual space model constructed based on actual dimensions and photographs of a room.

[1700] "Spatial awareness artificial intelligence" is a general term for algorithms and technologies that analyze visual data such as images and videos and recognize the location of objects and spatial structures.

[1701] "Interior design requirements" refers to basic information and requests such as the user's desired interior decoration style, color, layout, budget, etc.

[1702] "Conversational AI" is a general term for algorithms and technologies that allow users to ask and answer questions interactively in natural language, and collect and provide information.

[1703] A "furniture layout design proposal" is a design plan that shows how furniture and decorations should be arranged within a life-size three-dimensional space model based on requests collected from users.

[1704] "Visual recognition artificial intelligence" is a general term for algorithms and techniques for identifying objects in images and video data and analyzing their features.

[1705] "Emotion data" is data that indicates the emotional state of the user, such as joy, anger, sadness, or happiness, obtained from the user's voice or facial expression.

[1706] "Reflecting in the interior design" means proposing the optimal interior design based on the collected data and requests, and reflecting it in the actual model.

[1707] "Simulating the interior design of a store" involves virtually testing the design and layout of a store on a three-dimensional spatial model to confirm its actual appearance and functionality.

[1708] This invention is a system that supports interior design and renovation planning for brick-and-mortar stores. It aims to take photos of the current state of the store using a smartphone or tablet, generate a virtual 3D model, and simulate the interior design. Furthermore, it uses an emotion engine to provide interior designs based on customer emotions.

[1709] System Configuration

[1710] User: Uses a smartphone or tablet to take photos of the current state of the store. By taking photos from multiple locations, data is collected for the entire room.

[1711] On your device, take a photo and upload it to a cloud server, along with the date, time, location, and other metadata.

[1712] server:

[1713] Using spatial recognition AI, the system analyzes uploaded photos and generates a life-size 3D space model by extracting feature points and edge information from the photo and constructing the skeleton of the room.

[1714] It uses conversational AI to interactively gather interior design preferences from users, such as asking questions like "What style do you prefer?" and "What is your budget?"

[1715] Using visual recognition AI, the system selects furniture from a database that matches the user's needs and places it in a 3D space model. The generated design proposals are constructed based on the information in the database.

[1716] The emotion engine analyzes the user's voice and facial expression data to identify their emotions, and adjusts the design's color and style based on the analysis results.

[1717] The final design is then presented to the user as a virtual preview, which can be viewed in 360 degrees and zoomed in and out via the device, allowing the user to check the details.

[1718] Program processing

[1719] Spatial recognition artificial intelligence extracts feature points and edge information from the photo and generates a life-size three-dimensional spatial model.

[1720] Conversational AI collects interior design requests from users in an interactive format.

[1721] Visual recognition AI selects appropriate furniture based on the user's requests and places it in a three-dimensional space model.

[1722] The emotion engine analyzes the user's emotions and adjusts the color and style of the design.

[1723] A virtual preview is provided to users, allowing them to see the details using a 360-degree view and zoom capabilities.

[1724] Hardware and software used

[1725] Smartphones and tablets: Provides shooting and uploading functions.

[1726] Cloud server: Analyzes and stores data, and generates three-dimensional spatial models.

[1727] Spatial recognition AI, conversational AI, visual recognition AI, emotion engine: software technologies to realize each function.

[1728] Specific examples

[1729] For example, if a store owner wants to change the interior design of their store, they take multiple photos of the interior with their smartphone and upload them to a cloud server. The server analyzes the photos and generates a life-size 3D spatial model, then uses conversational AI to collect the owner's preferences, such as "modern style" and "budget under 500,000 yen." Visual recognition AI selects appropriate furniture from a database and places it within the 3D spatial model. Furthermore, an emotion engine analyzes the user's emotional data and adjusts colors and style to make customers feel relaxed. The final generated design is provided as a virtual preview, which the user can view on their smartphone.

[1730] Prompt Sentence Examples

[1731] Generate a 3D model using the images: ['shop1.jpg', 'shop2.jpg']. Create a design that includes preferences: {'style': 'modern', 'budget': 50000}

[1732] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1733] Step 1:

[1734] Users use their smartphones or tablets to take photos from multiple locations within the store, allowing data to be collected from the entire room.

[1735] (Input: Multiple photos of the store, Output: Photos uploaded to the cloud server)

[1736] Step 2:

[1737] The device uploads the photos it takes to a cloud server, along with the date, time, location, and other metadata.

[1738] (Input: captured photos and metadata, Output: photos and metadata stored on the cloud server)

[1739] Step 3:

[1740] The server uses spatial recognition AI to analyze the uploaded photos and generate a life-size 3D spatial model by extracting feature points and edge information from the photos and constructing the skeleton of the room.

[1741] (Input: saved photo, Output: 3D space model)

[1742] Step 4:

[1743] The server uses conversational AI to interactively gather interior design requests from users, such as asking questions like "What style do you prefer?" or "What is your budget?", and users input their answers in natural language.

[1744] (Input: interactive questions and user responses, Output: collected interior design requests)

[1745] Step 5:

[1746] The server uses visual recognition AI to select furniture from a database that matches the user's needs and place it in a 3D space model. The generated design proposal is based on the information in the database.

[1747] (Input: collected interior design requests and a 3D space model, output: a 3D space model with furniture arranged)

[1748] Step 6:

[1749] The server uses an emotion engine to analyze the user's voice and facial expression data to identify their emotions, and adjusts the color and style of the design based on the analysis results.

[1750] (Input: User's voice and facial expression data, Output: Design proposal with adjusted color and style)

[1751] Step 7:

[1752] The server generates a virtual preview and provides it to the user. This preview can be viewed in 360 degrees and zoomed in and out through the device, allowing the user to check the details. The user can check the preview and, if necessary, submit changes or additions to the server again.

[1753] (Input: adjusted design proposal, Output: virtual preview provided to user)

[1754] 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.

[1755] 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.

[1756] 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.

[1757] 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.

[1758] 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.

[1759] 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.

[1760] 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).

[1761] 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.

[1762] 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."

[1763] 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.

[1764] 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).

[1765] 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.

[1766] 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.

[1767] 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.

[1768] 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.

[1769] 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.

[1770] 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.

[1771] 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.

[1772] 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.

[1773] 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.

[1774] 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.

[1775] The following is further disclosed regarding the above embodiment.

[1776] (Claim 1)

[1777] A means for users to upload photos of their rooms to the server,

[1778] A means for analyzing uploaded photos and generating a life-size three-dimensional space model;

[1779] A means of collecting and analyzing interior design requests from users,

[1780] A means for generating a furniture layout design proposal based on the collected requests;

[1781] A means for providing the generated design proposal to the user;

[1782] A system including:

[1783] (Claim 2)

[1784] The system of claim 1, wherein the means for analyzing uploaded photos and generating a life-size three-dimensional space model uses spatially aware artificial intelligence to analyze multiple photos and construct a skeleton of the room.

[1785] (Claim 3)

[1786] The system of claim 1, wherein the means for generating a furniture layout design proposal uses visual recognition artificial intelligence to select furniture that meets the user's requirements and place it in the three-dimensional space model.

[1787] "Example 1"

[1788] (Claim 1)

[1789] means for transmitting a video of a room taken by a user to an information processing device;

[1790] means for analyzing the transmitted video and generating a life-size three-dimensional space model;

[1791] A means of collecting and analyzing user requests regarding decoration,

[1792] A means for generating a design proposal for facility layout based on the collected requests;

[1793] A means for providing the generated design proposal to a user;

[1794] A system including:

[1795] (Claim 2)

[1796] The system of claim 1, wherein the means for analyzing the transmitted video and generating a life-size three-dimensional space model uses spatial recognition machine learning to analyze multiple videos and construct...

Claims

1. A means for users to upload photos of their rooms to the server, A means for analyzing uploaded photos and generating a life-size three-dimensional space model; A means of collecting and analyzing interior design requests from users, A means for generating a furniture layout design proposal based on the collected requests; A means for providing the generated design proposal to the user; A system including:

2. The system of claim 1, wherein the means for analyzing uploaded photographs and generating a life-size three-dimensional space model uses spatially aware artificial intelligence to analyze multiple photographs and construct a skeleton of a room.

3. 2. The system of claim 1, wherein the means for generating a furniture layout design proposal uses visual recognition artificial intelligence to select furniture that meets the user's requirements and place it in the three-dimensional space model.

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