System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving an image of a room; means for analyzing the received image to determine a layout and furniture arrangement of the room; means for presenting a plurality of interior style options to a user; means for suggesting an optimal layout and furniture arrangement based on a user selection; means for listing required items based on the suggested layout and retrieving lowest price information for each item from an internet shopping site; means for displaying the suggested layout and item list; and means for receiving user feedback and adjusting the suggestions based thereon.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] In today's world, the demand for interior design is increasing. However, for ordinary users without specialized knowledge, creating an appropriate layout and furniture arrangement is difficult and often takes time and effort. It is also difficult to make optimal choices in terms of price during the interior design process. Furthermore, hiring a professional designer can be expensive. There is a need for a system that can solve these problems and allow users to style the interior of a room easily and effectively. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides the following means. First, the system includes a means for receiving an image of a room. Next, the system includes a means for analyzing the received image and identifying the room layout and furniture arrangement. Further, the system provides a means for presenting a plurality of interior style options to the user. The system also includes a means for proposing an optimal layout and furniture arrangement based on the user's selection. The system provides a means for listing necessary items based on the proposed layout and searching online shopping sites for the lowest prices for each item. The system includes a means for displaying the proposed layout and item list, and a means for receiving user feedback and adjusting the proposal based on that feedback. The system also includes a means for providing the user with a purchase link to the online shopping site. These means realize a system that allows users to easily optimize the interior of their own rooms.

[0006] An "image" is digital data of visual information captured using a computer, camera, or other device.

[0007] "Analysis" is the process of breaking down input data or information, identifying its components, and adding meaning to them.

[0008] "Layout" is the design of how furniture and equipment are arranged in a room or space.

[0009] An "interior style" is an expression of interior decoration based on a particular design theme or aesthetic sense.

[0010] "Proposal" refers to presenting the optimal method or configuration for a specific purpose.

[0011] An "item list" is a list of furniture and decorations required to realize the proposed layout.

[0012] "Cheapest price" refers to the lowest price in the market for a particular product.

[0013] "Feedback" refers to the opinions and reactions users give to information and suggestions they receive.

[0014] An "online shopping site" is a website where you can purchase products over the Internet.

[0015] A "Buy Link" is a URL or hyperlink that takes you directly to a specific product page. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0024] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0037] This invention relates to a system that automatically optimizes the interior design and layout of a room. The system utilizes AI technology to suggest appropriate interior styling and find the best products from online shopping sites, simply by uploading an image of the room.

[0038] Overall system flow

[0039] 1. Upload an image

[0040] Users upload images of their rooms taken from their own devices to the system, and the devices send the images to the system's server.

[0041] 2. Image Analysis

[0042] The server analyzes the received images using AI algorithms to determine information such as the room layout, furniture placement, and dimensions.

[0043] 3. Choosing an interior style

[0044] Based on the analysis results, the server presents the user with multiple interior style options (e.g., modern, mid-century, Scandinavian, etc.), and the user selects their preferred style from the presented style options.

[0045] 4. Layout and item suggestions

[0046] Based on the user's selection, the server proposes an optimal interior layout and furniture arrangement. Based on the proposed layout, it also lists the necessary items and searches online shopping sites for the lowest prices for those items.

[0047] 5. Display of Suggestions

[0048] The server sends the generated layout proposal and item list to the user's device. The user can review the proposal through their device and request changes if necessary. The item list also includes a purchase link for each product.

[0049] 6. User Feedback and Adjustments

[0050] The user can provide feedback on the suggestions, and the server will readjust the suggestions based on the feedback, repeating the process until an optimal layout is achieved.

[0051] Specific examples

[0052] Example 1: Uploading and analyzing an image

[0053] User A takes a photo of their living room with their smartphone and uploads it to a dedicated app. The device sends the image to the server, which then receives it and analyzes it using an AI algorithm. The server then identifies furniture such as sofas, tables, and televisions, and determines their positions and the dimensions of the room.

[0054] Example 2: Style selection and suggestions

[0055] The server presents User A with interior style options such as "Modern," "Mid-century," and "Scandinavian." User A selects the "Modern" style and sends the information to the server. The server generates an optimal layout based on the "Modern" style and creates a list of the necessary furniture and decorations. The list also includes the lowest price information for each item.

[0056] Example 3: Viewing suggestions and providing feedback

[0057] The server sends the proposed layout and item list to User A's device. User A reviews the proposal on his / her device and feels that the proposed carpet color does not suit his / her taste, so he / she provides feedback to the server. The server then suggests new carpet options based on the feedback, and this process is repeated until User A is satisfied.

[0058] This allows users to easily create interiors that suit their tastes, even if they do not have specialized knowledge.In addition, the lowest price information is provided, so users can make selections that fit their budget.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] Users take photos of their rooms with their devices, which then upload the image files to the system via a dedicated application or web interface.

[0062] Step 2:

[0063] The device sends images of the room selected by the user to the system's server, which stores the received images and prepares them for analysis.

[0064] Step 3:

[0065] The server then analyzes the received images of the room using AI algorithms. Specifically, the image analysis module identifies furniture and decorations, and determines their locations and the dimensions of the room.

[0066] Step 4:

[0067] The server generates multiple interior style options based on the analysis, including modern, mid-century, and Scandinavian styles.

[0068] Step 5:

[0069] The server sends the generated interior style options to the user's terminal, and the user selects a preferred style from the style options displayed on the terminal.

[0070] Step 6:

[0071] The user selects the interior style option of their choice and inputs the selection information into the terminal, which then transmits the input information to the server.

[0072] Step 7:

[0073] The server then proposes optimal interior layouts and furniture arrangements based on the user's selections, with the proposals being customized according to the selected style.

[0074] Step 8:

[0075] The server creates a list of required items based on the proposed layout, searches online shopping sites for the lowest prices for each item, and retrieves price information and purchase links.

[0076] Step 9:

[0077] The server sends the generated layout proposal and item list to the user's terminal, where the user can confirm the proposal contents.

[0078] Step 10:

[0079] Users can provide feedback on suggestions, for example, they may request that the color or type of a particular item be changed.

[0080] Step 11:

[0081] The device sends the user's feedback information to the server, which then readjusts the suggestions based on the feedback.

[0082] Step 12:

[0083] The server then sends the new, adjusted layout proposal and item list back to the user's device, and this process is repeated until the user is satisfied.

[0084] Step 13:

[0085] If the user is satisfied with the final proposal, he or she can click on a purchase link included in the item list to purchase the product on the online shopping site. The terminal provides access to the online shopping site based on the user's click.

[0086] Example 1

[0087] 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."

[0088] In modern life, interior styling is an important concern for many people, but choosing the right interior design can be difficult, requiring specialized knowledge and time. It can also be time-consuming to search for products individually and find the best price. Furthermore, if a proposed layout or items do not suit a user's preferences, there is also the problem that re-proposals to adjust them are not smoothly carried out.

[0089] 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.

[0090] In this invention, the server includes: means for receiving room images; means for analyzing the received images to identify the room layout and furniture arrangement; means for presenting multiple interior style options to the user using a generative AI model based on the analysis results; means for proposing an optimal layout and furniture arrangement based on the user's selection; means for listing necessary items based on the proposed layout and searching e-commerce sites for the lowest prices for each item; means for displaying the proposed layout and item list; and means for receiving user feedback and adjusting the proposals based on the feedback. This allows users to easily achieve interior styling without specialized knowledge and purchase products at optimal prices. Furthermore, the proposals can be smoothly readjusted, increasing user satisfaction.

[0091] A "room image" is a still image of the interior of a room that is taken by a user using a terminal.

[0092] "Means for receiving images" refers to a series of functions by which the server receives images of the room sent by the user via the terminal.

[0093] "Means for analyzing images" refers to a function that uses AI algorithms to process received images of a room and identify the room layout and furniture arrangement.

[0094] "Interior style options" refers to style options for the interior of a room, and refers to multiple styles presented to the user, such as modern and mid-century.

[0095] A "generative AI model" is an artificial intelligence model that incorporates natural language processing and image generation technologies and is used to present interior style options to users.

[0096] The "optimal layout" is a furniture and decoration arrangement plan for the room proposed by the server based on the analyzed room information and the interior style selected by the user.

[0097] "E-commerce Site" means an online shopping platform that sells products over the Internet.

[0098] An "item list" is a list of furniture and decorations needed to decorate a room based on the proposed layout.

[0099] "Means for receiving feedback" is a function by which the server receives opinions and requests regarding the proposals sent by users.

[0100] "Means for adjusting the proposal content" refers to the function of reconstructing the layout and item list presented by the server based on feedback received from the user and re-proposing them.

[0101] This invention relates to a system that automatically optimizes interior styling using room images. This system utilizes AI technology to suggest appropriate interior styling and find optimal products from e-commerce sites simply by uploading a room image. Specific hardware and software configurations and their processing are described below.

[0102] Hardware and software used

[0103] Hardware

[0104] 1. Device: A device such as a smartphone, tablet, or computer that allows users to take and upload images of a room.

[0105] 2. Server: A high-performance computer for proposal generation, image analysis, and database management.

[0106] software

[0107] 1. Image analysis algorithm: Analyzes room layout and furniture placement using TENSORFLOW (registered trademark) and OpenCV.

[0108] 2. Generative AI model: An artificial intelligence model with natural language processing and image generation technologies, such as GPT-4 (registered trademark) and DALL-E.

[0109] 3. Online Shopping API: An API for obtaining product information from e-commerce sites, such as Amazon API and Rakuten API.

[0110] Specific processing of the program

[0111] Uploading an image

[0112] The user takes a picture of the room with their own device and uploads it using the system's dedicated application. The device then sends this image data to the server as an HTTP request.

[0113] Image analysis

[0114] The server temporarily stores the received images and analyzes them using image analysis algorithms such as TensorFlow and OpenCV. The analysis identifies the furniture and decorations present in the room, their arrangement, and the dimensions of the room. The analysis results are stored in a database.

[0115] Choosing an interior style

[0116] The server uses a generative AI model (e.g., GPT-4) to generate multiple interior style options based on the image analysis results, including sample images and descriptions of the interior styles. The generated options are sent to the device and presented to the user.

[0117] Layout and item suggestions

[0118] Based on the interior style selected by the user, the server proposes the optimal layout and furniture placement using a layout generation algorithm. The server then lists the necessary furniture and decorations and uses an online shopping API to search for the lowest prices for each item.

[0119] View Suggestions

[0120] The server sends the generated layout proposal and item list to the terminal, which displays it on the user's terminal, allowing the user to check the proposal and provide feedback.

[0121] Feedback and Resubmissions

[0122] The server receives the user's feedback and adjusts the suggestions accordingly, repeating the process as necessary until it generates a suggestion that satisfies the user.

[0123] Examples of concrete examples and prompts

[0124] Example 1: Image upload and analysis

[0125] User A takes a photo of their living room with their smartphone and uploads it through a dedicated application. The device sends the image to the server, which analyzes it using AI algorithms. The server identifies furniture such as sofas, tables, and televisions, and determines their positions and the dimensions of the room.

[0126] Example 2: Style selection and suggestions

[0127] The server uses a generative AI model to present User A with interior style options such as "modern," "midcentury," and "Scandinavian." User A selects the "modern" style and sends that information to the server. The server generates an optimal layout based on the "modern" style and creates a list of the necessary furniture and decorations. The list includes the lowest price information for each item.

[0128] Prompt Sentence Examples

[0129] I've uploaded an image of a living room. I'd like you to suggest interior style options from this image. Options include "modern," "midcentury," and "Scandinavian."

[0130]

[0131] The user has chosen a "Modern" style. Based on this style, suggest the optimal layout and furniture list. Also get the lowest prices using information from e-commerce sites.

[0132] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0133] Step 1:

[0134] Uploading an image

[0135] User

[0136] The user takes a picture of the room with their device and opens a dedicated application.

[0137] Terminal

[0138] The device provides an option for the user to upload images taken by the user through a user interface. When the user selects an image and presses the upload button, the device sends the selected image data to the server in an HTTP request.

[0139] input

[0140] Room image data

[0141] output

[0142] Image data sent to the server

[0143] Step 2:

[0144] Image analysis

[0145] server

[0146] The server writes the received image data to disk for temporary storage. The server then analyzes the image using an image analysis algorithm (e.g., TensorFlow or OpenCV). This algorithm identifies the furniture and decorations present in the room and determines their positions and dimensions. The analysis results are saved in a database as room layout data.

[0147] input

[0148] Received room image data

[0149] output

[0150] Room layout data (furniture position and dimensions)

[0151] Step 3:

[0152] Choosing an interior style

[0153] server

[0154] The server generates multiple interior style options using a generative AI model (e.g., GPT-4 or DALL-E) based on the layout data obtained as a result of image analysis. The generated style options include sample images and descriptions of the interior styles. The generated data is sent to the device for presentation to the user.

[0155] input

[0156] Room layout data

[0157] output

[0158] Interior style options (sample images and descriptions)

[0159] Step 4:

[0160] User-selected styles

[0161] Terminal

[0162] The terminal displays the interior style options received from the server on a user interface, which provides option buttons for selecting from multiple style options.

[0163] User

[0164] The user selects the style of their choice from the presented style options and transmits the selection to the server via the terminal.

[0165] input

[0166] Interior Style Options

[0167] output

[0168] User-selected style information

[0169] Step 5:

[0170] Layout and item suggestions

[0171] server

[0172] The server generates an optimal interior layout based on the selected style information received from the user. It uses a layout generation algorithm to create the optimal layout while adjusting the furniture placement. It also lists the necessary furniture and decorations based on the proposed layout. It then uses online shopping APIs (such as the Amazon API or Rakuten API) to search for the lowest price information for each item.

[0173] input

[0174] User-selected style information, room layout data

[0175] output

[0176] Optimal layout plan, item list (including lowest price information)

[0177] Step 6:

[0178] View Suggestions

[0179] server

[0180] The server transmits the generated layout plan and item list to the terminal.

[0181] Terminal

[0182] The terminal displays the data received from the server on the user interface, allowing the user to check the proposed layout and item list through the terminal.

[0183] input

[0184] Optimal layout plan, item list

[0185] output

[0186] An interface where users can check the suggestions

[0187] Step 7:

[0188] Feedback and Resubmissions

[0189] User

[0190] Users can send feedback about the suggestions to the server via their devices, including specific requests such as "I don't like the color of the carpet."

[0191] server

[0192] The server receives feedback from the user, readjusts its suggestions based on that feedback, and again uses the generative AI model to generate new options and re-suggest them to the user.

[0193] input

[0194] User Feedback

[0195] output

[0196] Adjusted layout and item list

[0197] The above specific processing steps allow users to easily realize interior styling without specialized knowledge and purchase products at the optimal price. Furthermore, the proposed content can be smoothly readjusted, increasing user satisfaction.

[0198] (Application example 1)

[0199] 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."

[0200] Interior design is an important element in modern life, and styling that suits individual tastes is required. However, for ordinary users without specialized knowledge, it takes a lot of effort and time to make their rooms look good. Furthermore, with so many interior design products available on the market, it can be difficult to find the perfect product. Furthermore, there is a lack of tools for users to actually visualize the proposed interior style, which can cause anxiety about the final selection.

[0201] 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.

[0202] In this invention, the server includes means for receiving room images, means for analyzing the received images to identify the room layout and furniture arrangement, means for presenting a plurality of interior style options to the user, means for proposing an optimal layout and furniture arrangement based on the user's selection, means for listing necessary items based on the proposed layout and searching an e-commerce site for the lowest price information for each item, means for displaying the proposed layout and item list, means for receiving user feedback and adjusting the proposal based thereon, and means for visualizing the proposed layout using virtual reality technology, thereby enabling the user to easily optimize the interior style of their own room and actually visualize and check it.

[0203] A "room image" is a photo or video taken by a user that shows the interior of a room.

[0204] The "receiving means" is a device or system that has the function of receiving image data sent by a user from a terminal.

[0205] "Room layout" refers to the relative positions and structure of furniture and decorations within a room.

[0206] "Furniture arrangement" is information about the position of each piece of furniture in the room.

[0207] The "analysis means" refers to AI algorithms and software that process image data and identify room layouts and furniture placement.

[0208] "Interior style options" are interior design options based on a specific design theme or aesthetic concept.

[0209] The "presentation means" refers to a display device or user interface for displaying information to the user.

[0210] "Optimal layout and furniture arrangement" refers to the state of a room arranged in the most efficient and aesthetic way based on the user's selections.

[0211] A "means for proposing" is a system that has the functionality to generate layout and placement proposals based on the analysis results and user selections.

[0212] "Means for listing items" refers to a device or program that extracts necessary furniture and decorative items based on the proposed layout and organizes them in a list format.

[0213] "Lowest price information" is the lowest price information for a specific product in the market.

[0214] An "e-commerce site" is an online platform for buying and selling goods and services over the Internet.

[0215] A "searching means" is a system that has the functionality to obtain information from an e-commerce site based on specified conditions.

[0216] The "display means" refers to a device or software that displays the proposed layout and item list so that the user can check it.

[0217] "Means for receiving and adjusting feedback" refers to a system for readjusting the content of proposals by reflecting user evaluations and requests.

[0218] "Virtual reality technology" is a technology that uses computer graphics to generate a virtual environment that is close to reality, allowing users to experience that environment.

[0219] A "visualization means" is a system that uses 3D graphics or virtual reality technology to visually display the proposed layout.

[0220] The present invention relates to a system for automatically optimizing the interior and layout of a room. Hereinafter, an embodiment of the present invention will be described in detail.

[0221] System configuration

[0222] This system is realized using user terminals, servers, cloud computing infrastructure, and virtual reality (VR) devices (such as smartphones and head-mounted displays).

[0223] Receiving and analyzing room images

[0224] The user uploads images of the room they have taken to the server. Specifically, they use a smartphone app to send photos showing the interior of the room to the server. The server then analyzes the images using a deep learning framework such as TensorFlow or PyTorch to perform image analysis on the cloud and identify the room layout and furniture arrangement.

[0225] Selecting an interior style and proposing the optimal layout

[0226] Based on the analysis results, the server presents the user with multiple interior style options, such as "modern," "midcentury," and "Scandinavian." Based on the style information selected by the user, the AI ​​model proposes the optimal layout and furniture arrangement. This AI model uses, for example, a machine learning algorithm.

[0227] Make a list of the items you need and search for the best prices

[0228] Based on the proposed layout, the required items are listed. For each item, the server searches e-commerce sites for the lowest price. This process is performed using web scraping technology and API integration.

[0229] Proposal display and VR visualization

[0230] The server sends the optimized layout and item list to the user's device, where the user can review the proposal and visualize the proposed layout using virtual reality (VR) technology, for example, in game engines such as Unity or Unreal Engine.

[0231] User feedback and suggestions adjustments

[0232] Users can provide feedback on the suggestions, which is sent to the server, which analyzes the feedback and refines the suggestions. The process is automated, and the cycle of suggestions and feedback is repeated until an optimal layout is achieved that satisfies the user.

[0233] Examples and prompts

[0234] Specific examples

[0235] User: 30 years old, female, living alone, prefers modern design.

[0236] Room size: 10 tatami mats, one-room apartment.

[0237] Prompt Sentence Examples

[0238] "I'm a 30-year-old woman living in a 10-tatami studio apartment. I like modern design and am looking for interior decor that suits my room. I've uploaded a photo of my room, so please suggest furniture placement and items."

[0239] In this way, users can easily optimize the interior of their rooms and check it virtually, even if they do not have specialized knowledge.

[0240] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0241] Step 1:

[0242] Users use their smartphones to take pictures of the interior of the room and upload them to the server via a dedicated application.

[0243] Input: Room image (JPEG or PNG format)

[0244] Output: Image data is sent to the server

[0245] Step 2:

[0246] The server processes the received image data in a cloud environment and performs analysis using TensorFlow or PyTorch, which identifies the room layout and furniture positions.

[0247] Input: Received image data

[0248] Output: Room layout information and furniture placement information

[0249] Step 3:

[0250] Based on the analysis results, the server presents the user with multiple interior style options (e.g., modern, mid-century, Scandinavian).

[0251] Input: Room layout information and furniture placement information

[0252] Output: A list of interior style options

[0253] Step 4:

[0254] The user selects a preferred style from the presented interior style options and transmits the selection data to the server.

[0255] Input: User selected interior style

[0256] Output: The selected style information is sent to the server.

[0257] Step 5:

[0258] Based on the user's selections, the server uses AI models to suggest optimal layouts and furniture placements.

[0259] Input: Selected style information, room layout information

[0260] Output: Optimal layout and furniture arrangement proposals

[0261] Step 6:

[0262] Based on the proposed layout and furniture, the server creates a list of required items and searches e-commerce sites for the lowest prices for each item.

[0263] Input: Optimal layout and furniture arrangement

[0264] Output: List of required items and lowest price information

[0265] Step 7:

[0266] The server transmits the proposed layout and item list to the user terminal and displays it for the user to check.

[0267] Input: List of required items and lowest price information

[0268] Output: Layout and item list displayed on the user's device

[0269] Step 8:

[0270] Users can visually check the proposed layout using virtual reality technology, specifically by using a smartphone or head-mounted display to experience the room in a VR environment generated through Unity or Unreal Engine.

[0271] Input: Proposed layout and item list

[0272] Output: Virtual reality visualization of the room

[0273] Step 9:

[0274] The user provides feedback on the suggestions and sends it to the server, which analyzes the feedback and adjusts the suggestions.

[0275] Input: User feedback

[0276] Output: Adjusted proposal

[0277] Step 10:

[0278] The server then automatically re-proposes and generates new layouts and item lists based on the feedback received. This process is repeated until the user is satisfied.

[0279] Input: User feedback

[0280] Output: Revised layout proposal and item list

[0281] 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.

[0282] This invention relates to a system that automatically optimizes the interior design and layout of a room and adjusts the suggestions by recognizing the user's emotions. The system utilizes AI technology and an emotion engine to suggest appropriate interior styling and find the best products from online shopping sites, simply by the user uploading an image of the room.

[0283] Overall system flow

[0284] 1. Upload an image

[0285] Users upload images of their rooms taken from their own devices to the system, and the devices send the images to the system's server.

[0286] 2. Image Analysis

[0287] The server analyzes the received images using AI algorithms to determine information such as the room layout, furniture placement, and dimensions.

[0288] 3. Choosing an interior style

[0289] Based on the analysis results, the server presents the user with multiple interior style options (e.g., modern, mid-century, Scandinavian, etc.), and the user selects their preferred style from the presented style options.

[0290] 4. Layout and item suggestions

[0291] Based on the user's selection, the server proposes an optimal interior layout and furniture arrangement. Based on the proposed layout, it also lists the necessary items and searches online shopping sites for the lowest prices for those items.

[0292] 5. Display of Suggestions

[0293] The server sends the generated layout proposal and item list to the user's device. The user can review the proposal through their device and request changes as needed. The item list also includes a purchase link for each product.

[0294] 6. User Emotion Recognition by Emotion Engine

[0295] When presenting the proposal content to the user, the server uses an emotion engine to recognize the user's emotions, thereby making it possible to evaluate the user's emotions.

[0296] 7. User feedback and sentiment ratings

[0297] The user can provide feedback on the suggestions, and the server receives the feedback and uses an emotion engine to assess the user's emotional state.

[0298] 8. Adjust your offers based on emotions

[0299] The server readjusts the suggestions based on the user's feedback and emotional state. For example, if the user looks dissatisfied, the server changes the suggestions and re-presents them. This process is repeated until the user is satisfied.

[0300] Specific examples

[0301] Example 1: Uploading and analyzing an image

[0302] User A takes a photo of their living room with their smartphone and uploads it to a dedicated app. The device sends the image to the server, which then receives it and analyzes it using an AI algorithm. The server then identifies furniture such as sofas, tables, and televisions, and determines their positions and the dimensions of the room.

[0303] Example 2: Style selection and suggestions

[0304] The server presents User A with interior style options such as "Modern," "Mid-century," and "Scandinavian." User A selects the "Modern" style and sends the information to the server. The server generates an optimal layout based on the "Modern" style and creates a list of the necessary furniture and decorations. The list also includes the lowest price information for each item.

[0305] Example 3: Displaying Suggestions and Emotion Recognition

[0306] The server sends the proposed layout and item list to User A's device. User A checks the proposal on the device, and the emotion engine recognizes his / her facial expressions and voice via the device's camera. The server evaluates User A's emotional state based on the results of the emotion engine.

[0307] Example 4: Feedback and emotion-based adjustments

[0308] User A sends feedback requesting a change in the color of the carpet. The device sends the feedback along with emotional data obtained from User A's facial expressions and voice to the server. The server analyzes the feedback and emotional data and readjusts the suggestion. For example, if User A looks dissatisfied, the server will improve the suggestion and present it again. This process is repeated until User A looks satisfied.

[0309] This allows users to receive personalized interior design suggestions based on their emotions, even if they do not have specialized knowledge, and easily create an interior design that suits their tastes.In addition, the lowest price information is provided, so users can make selections that fit their budget.

[0310] The processing flow will be explained below.

[0311] Step 1:

[0312] Users take photos of their rooms with their devices, which then upload the image files to the system via a dedicated application or web interface.

[0313] Step 2:

[0314] The device sends images of the room selected by the user to the system's server, which stores the received images and prepares them for analysis.

[0315] Step 3:

[0316] The server then analyzes the received images of the room using AI algorithms. Specifically, the image analysis module identifies furniture and decorations, and determines their locations and the dimensions of the room.

[0317] Step 4:

[0318] The server generates multiple interior style options based on the analysis, including modern, mid-century, and Scandinavian styles.

[0319] Step 5:

[0320] The server sends the generated interior style options to the user's terminal, and the user selects a preferred style from the style options displayed on the terminal.

[0321] Step 6:

[0322] The user selects the interior style option of their choice and inputs the selection information into the terminal, which then transmits the input information to the server.

[0323] Step 7:

[0324] The server then proposes optimal interior layouts and furniture arrangements based on the user's selections, with the proposals being customized according to the selected style.

[0325] Step 8:

[0326] The server creates a list of required items based on the proposed layout, searches online shopping sites for the lowest prices for each item, and retrieves price information and purchase links.

[0327] Step 9:

[0328] The server sends the generated layout proposal and item list to the user's terminal, where the user can confirm the proposal contents.

[0329] Step 10:

[0330] While the user is checking the layout proposal, the server activates a mechanism that uses the device's camera and microphone to enable the emotion engine to recognize the user's emotions. The emotion engine performs facial expression recognition and voice analysis to evaluate the user's emotional state.

[0331] Step 11:

[0332] Users can provide feedback on suggestions, for example, they may request that the color or type of a particular item be changed.

[0333] Step 12:

[0334] The device transmits the user's feedback and recognized emotion data to the server, which evaluates the feedback as well as the user's emotional state to help tailor the suggestions.

[0335] Step 13:

[0336] The server analyzes the feedback and emotion data and re-adjusts the suggestions. For example, if the user looks dissatisfied, the server will improve the suggestions and re-present them.

[0337] Step 14:

[0338] The server then sends the new, adjusted layout proposal and item list back to the user's device, and this process is repeated until the user is satisfied.

[0339] Step 15:

[0340] If the user is satisfied with the final proposal, he or she can click on a purchase link included in the item list to purchase the product on the online shopping site. The terminal provides access to the online shopping site based on the user's click.

[0341] Example 2

[0342] 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."

[0343] Conventional interior design suggestion systems have difficulty making personalized suggestions that take user emotions into account, and the suggestions often do not fully match the user's preferences. Furthermore, optimal product selection and layout adjustments are performed manually, which is time-consuming and laborious for the user. Therefore, there is a need for a system that can automatically adjust interior design suggestions based on the user's emotions and select optimal products.

[0344] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0345] In this invention, the server includes a means for receiving images of a room, a means for analyzing the received images using an AI algorithm to identify the layout and furniture arrangement of the room, and a means for presenting multiple interior style options to the user, thereby enabling personalized interior suggestions that take the user's emotions into consideration.

[0346] A "room image" is digital data containing visual information about the interior of a room that is taken by a user and uploaded to the system.

[0347] An "AI algorithm" is a calculation procedure that uses artificial intelligence technology, and is a program used to perform processes such as image analysis and pattern recognition.

[0348] "Interior style options" are multiple interior design choices presented to the user that affect the layout of the room and the placement of furniture.

[0349] The "optimal layout" is a layout that is generated based on the style selected by the user and the analysis results, and in which the arrangement of furniture and decorations is most efficient and aesthetically pleasing.

[0350] An "emotion engine" is software or hardware that analyzes a user's facial expressions and voice data and recognizes their emotional state.

[0351] An "online shopping site" is an e-commerce website where products can be purchased over the Internet.

[0352] "Feedback" is information including opinions and requests provided by users regarding the proposed content.

[0353] "Re-adjusting suggestions" is the process of modifying existing interior suggestions, taking into account user feedback and sentiment data.

[0354] This invention relates to a system that automatically optimizes the interior design and layout of a room and adjusts the suggestions based on the user's emotions. The system utilizes AI technology and an emotion engine to suggest appropriate interior styling and find the best products from online shopping sites, simply by uploading an image of the room.

[0355] Hardware and software used

[0356] 1. Hardware

[0357] Server: A high-performance computer that handles image analysis and emotion recognition.

[0358] Device: A device such as a smartphone, tablet, or computer where users can upload images and view suggestions.

[0359] Camera and microphone: Built into the device to capture the user's facial expressions and voice.

[0360] 2. Software

[0361] AI algorithms: TensorFlow, PyTorch, and other algorithms are used to analyze images and optimize interior styling.

[0362] Emotion engine: Recognizes user emotions in real time using Microsoft® Azure® Emotion API and other technologies.

[0363] Online shopping sites: Websites that provide product data, including Amazon and Rakuten.

[0364] Specific operation of the system

[0365] First, the user takes a picture of the room using the device's camera and uploads it to the system via a dedicated app. The device then sends the image data to the server, which uses TensorFlow to analyze the image and determine the room layout and furniture arrangement.

[0366] Based on the analysis results, the server presents the user with multiple interior style options (e.g., modern, mid-century, Scandinavian, etc.) The user selects their preferred style from the presented style options and their selection is sent to the server.

[0367] The server generates an optimal layout based on the selected style, creates a list of necessary furniture and decorations, and searches online shopping sites for the lowest prices for each item and adds them to the item list.

[0368] The generated layout proposal and item list are sent from the server to the user's device, where the user can check the proposal. The proposal also includes a purchase link for each product, allowing the user to purchase the product directly.

[0369] When reviewing the suggestions, the device's camera and microphone capture the user's facial expressions and voice, and send the data to the emotion engine. The emotion engine analyzes this and evaluates the user's emotions. The evaluation results are returned to the server, which then readjusts the suggestions based on the user's emotions. If the user provides feedback, that feedback is also sent to the server and analyzed along with the emotion data.

[0370] If readjustment is required, the server generates a new proposal and sends it again to the user's terminal, and this process is repeated until the user is satisfied.

[0371] Examples of concrete examples and prompts

[0372] Specific examples

[0373] 1. Image upload and analysis

[0374] Users take a photo of their living room with their smartphone and upload the image using a dedicated app.

[0375] The device sends the image to the server, which then analyzes it using AI algorithms.

[0376] The server identifies furniture such as sofas, tables, and televisions, and determines their locations and room dimensions.

[0377] 2. Style selection and suggestions

[0378] The server presents the user with interior style options such as "modern," "midcentury," and "Scandinavian."

[0379] The user selects the "Modern" style and the selection is sent to the server.

[0380] The server generates an optimal layout based on the "modern" style and creates a list of required furniture and decorations.

[0381] 3. Suggestion Display and Emotion Recognition

[0382] The server transmits the proposal content to the user's terminal, and the user confirms the proposal content.

[0383] When confirming the proposal, the device's camera and microphone capture the user's facial expressions and voice, and the emotion engine evaluates the user's emotions.

[0384] 4. Adjusting based on feedback and emotions

[0385] The user provides feedback on the proposal and sends it to the server along with emotion data.

[0386] The server analyzes the feedback and sentiment data and readjusts its suggestions.

[0387] Prompt Sentence Examples

[0388] "Can I upload an image of my living room and get suggestions for the best layout and furniture items for a modern style?"

[0389] "Could you please evaluate your feelings based on this proposal and resubmit your proposal?"

[0390] This allows users to receive interior design suggestions tailored to their preferences, even if they do not have specialized knowledge, and to easily select the best products according to their budget.

[0391] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0392] Step 1: Upload an image

[0393] Specific operation: The user takes a picture of the room using the device's camera and uploads this image from a dedicated app.

[0394] Input: Image data of a room taken by a user.

[0395] Output: Image data sent from the device to the server.

[0396] Details: The device accepts user operations, and when the upload button is pressed, the image data is sent to the cloud server. The server receives the sent image data and passes it on to the next processing step.

[0397] Step 2: Analyze the images

[0398] Specific operation: The server analyzes the received image using an AI algorithm.

[0399] Input: Image data received from the user.

[0400] Output: Room layout information and furniture placement data as analysis results.

[0401] Details: The server uses TensorFlow to run image analysis models to extract furniture (e.g. sofas, tables, TVs, etc.), their placement, and dimensions from the images. The results are stored in a database for further processing.

[0402] Step 3: Choose your interior style

[0403] Specific operation: The server presents the user with multiple interior style options based on the analysis results.

[0404] Input: Room layout information and furniture placement data as a result of image analysis.

[0405] Output: Interior style options presented to the user.

[0406] Details: The server retrieves the analysis results from the database, generates style options based on the user's preferences (e.g., modern, mid-century, Scandinavian, etc.), and sends them to the user's device for display. The user selects their preferred style, and their selection is sent to the server.

[0407] Step 4: Layout and item suggestions

[0408] Specific behavior: The server generates the optimal layout based on the style selected by the user.

[0409] Input: User selected interior style options.

[0410] Output: Optimal layout information and required item list.

[0411] Details: The server uses a layout algorithm based on the selected style to calculate the optimal furniture placement. It then lists the required furniture and decorations and searches online shopping sites for the lowest prices for each item. The generated layout and item list are sent to the user's device.

[0412] Step 5: Viewing Proposals

[0413] Specific operation: The server sends the generated layout and item list to the user's device.

[0414] Input: Optimal layout information and required item list.

[0415] Output: The suggestions that are displayed on the user's device.

[0416] Details: The server generates the proposed content (layout image and item list) in HTML format and sends all data to the user's device. The user can view the content in the device's browser or app.

[0417] Step 6: Recognizing user emotions with the emotion engine

[0418] Specific operation: The server uses an emotion engine to recognize the user's emotions.

[0419] Input: User's facial expression data and voice data.

[0420] Output: Evaluation result of the user's emotional state.

[0421] Details: When displaying the suggestions, the device's camera and microphone capture the user's facial expressions and voice in real time and send the data to the emotion engine, which analyzes the user's emotions and sends the evaluation results to the server.

[0422] Step 7: User feedback and sentiment assessment

[0423] Specific Action: The user provides feedback on the proposal.

[0424] Input: User feedback data and emotion data obtained from facial expressions and voice.

[0425] Output: Analysis results based on feedback and sentiment data.

[0426] Details: Users enter feedback through a dedicated form or voice input, and the device sends the data to the server. The server analyzes the feedback and emotion data and extracts the information necessary for the next adjustment of the proposal.

[0427] Step 8: Adjust your offer based on emotion

[0428] Specific behavior: The server readjusts its suggestions based on the sentiment assessment and feedback.

[0429] Input: Feedback data and emotion rating data.

[0430] Output: The reworked proposal.

[0431] Details: The server restructures its suggestions based on emotional data and specific feedback indicating dissatisfaction. If necessary, it runs the AI ​​algorithm again to generate a new layout and item list, and sends the improved suggestions back to the user's device. This process is repeated until the user is satisfied.

[0432] (Application example 2)

[0433] 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."

[0434] Conventional interior design suggestion systems have the problem of not being able to provide personalized suggestions based on the user's emotions and preferences. Furthermore, they are unable to provide interior design suggestions linked to real-time emotion recognition, limiting the user experience. Furthermore, when suggesting interior styles and layouts, they lack the ability to dynamically generate prompts, making effective interaction with the user difficult.

[0435] 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.

[0436] In this invention, the server includes means for receiving images, means for analyzing images to identify the room layout and furniture arrangement, means for adjusting suggestions based on the user's emotions, means for recognizing the user's emotions from the smart device and adjusting the suggestions, and means for generating prompt sentences using a generative AI model to dynamically make suggestions to the user. This enables personalized suggestions based on the user's emotions and feedback, enabling effective interaction with the user in real time.

[0437] The "means for receiving images of the room" is a function that allows a user to send images taken by a smart device to a server.

[0438] "Means for analyzing received images to determine the layout and furniture placement of the room" refers to a function that uses AI algorithms to identify elements in the image and determine the location of furniture and the dimensions of the room.

[0439] The "means for presenting a plurality of interior style options to the user" is a function for presenting several interior styles to the user as options based on the results of room analysis.

[0440] The "means for proposing the optimal layout and furniture arrangement based on the user's selection" is a function for automatically generating and proposing the optimal layout and furniture arrangement based on the interior style selected by the user.

[0441] "A means for listing necessary items based on a proposed layout and searching online shopping sites for the lowest price information for each item" is a function for listing furniture and decorations required for a proposed layout and obtaining the lowest price information for those items.

[0442] The "means for displaying the proposed layout and item list" is a function for visually displaying the generated layout and item list on the user's smart device.

[0443] "Means for receiving user feedback and adjusting the proposed content based on that" is a function for receiving evaluations and requests for changes from users and readjusting the proposed content based on that.

[0444] "Means for recognizing a user's emotions from a smart device and adjusting suggestions" refers to a function that uses the camera and microphone of a smart device to analyze the user's facial expressions and voice and adjust suggestions according to their emotional state.

[0445] "Means for generating prompt sentences using a generative AI model and dynamically making suggestions to the user" refers to a function for dynamically generating prompt sentences for suggestions or questions to the user using a generative AI model and presenting them to the user.

[0446] In order to implement the present invention, the following system configuration and process are employed.

[0447] First, a user takes an image of the room using a smart device (e.g., a smartphone or smart glasses) and uploads it to the system. The user's device then sends the image to the system's server. The server then analyzes the received image using AI algorithms to identify information such as the room layout, furniture placement, and dimensions. OpenCV and advanced image recognition technology are used for image analysis.

[0448] Based on the analysis results, the server presents multiple interior style options (e.g., modern, mid-century, Scandinavian, etc.) to the user. The user selects their preferred style from the presented style options through their smart device. The selected style information is then sent to the server.

[0449] The server then proposes an optimal interior layout and furniture arrangement based on the user's selection. Furthermore, based on the proposed layout, it lists the necessary items and searches online shopping sites for the lowest prices on those items, allowing users to easily obtain the items they need within their budget.

[0450] The proposals are displayed on the smart device's display, and the user can check the proposed layout and item list. The system uses the user's smart device's camera and microphone to recognize the user's facial expressions and tone of voice in real time. The "facial_emotion_recognition" library is used for emotion recognition.

[0451] The server evaluates the user's emotional state and adjusts the suggestions as needed. It also receives feedback from the user and adjusts the suggestions accordingly. This process continues until the user is satisfied. The server utilizes the user's emotional data along with the feedback to provide more personalized suggestions.

[0452] Furthermore, a generative AI model (such as GPT-3 (registered trademark) 5) is used to dynamically generate prompts for suggestions or questions for the user and present them to the user. The "transformers" library is used to generate these prompts. As a specific example, if a user selects the "modern" style and requests a change in carpet color, the prompt is generated as follows:

[0453] example:

[0454] "Please suggest a carpet color that would be suitable for a modern style living room."

[0455] The prompt sentences thus suggested are displayed as appropriate suggestions to the user.

[0456] Overall, the system enables personalized interior design suggestions based on user emotions and feedback, enabling effective interaction in real time.

[0457] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0458] Step 1:

[0459] Users can take pictures of their rooms using their smart devices and upload them to the system.

[0460] Input: Room image (JPEG or PNG format)

[0461] Specific operation: The user's device uses the camera app to take a picture of the room and sends it to the server via the image upload function.

[0462] Output: Image data of the room sent to the server

[0463] Step 2:

[0464] The server analyzes the received images using an AI algorithm (e.g., OpenCV) to determine information such as the room layout, furniture placement, and dimensions.

[0465] Input: Image data of the room

[0466] What it does: The server uses image analysis software to identify the type, location, and size of furniture in the image and saves this as metadata.

[0467] Output: Room layout data and furniture placement information

[0468] Step 3:

[0469] Based on the image analysis results, the server presents the user with multiple interior style options (e.g., modern, mid-century, Scandinavian).

[0470] Input: Room layout data and furniture placement information

[0471] What it does: The server selects appropriate style options from the interior style database and presents them to the user's smart device.

[0472] Output: Interior style options presented to the user

[0473] Step 4:

[0474] The user selects the preferred style from the presented interior style options and transmits it to the server.

[0475] Input: Interior style option selection information

[0476] Specific operation: The user's device sends the selection information to the server.

[0477] Output: User's interior style selection sent to the server

[0478] Step 5:

[0479] The server proposes the optimal interior layout and furniture placement based on the user's selection, lists the necessary items, and searches online shopping sites for the lowest prices for those items.

[0480] Input: User's interior style selection information

[0481] How it works: Based on the selected style, the server uses AI algorithms to generate the optimal layout, creates a list of furniture and decorations, and uses online shopping APIs to retrieve the lowest prices.

[0482] Output: Proposed interior layout and item list, along with the lowest price information

[0483] Step 6:

[0484] The proposed layout and item list are displayed on the user's smart device.

[0485] Input: Proposed interior layout, item list, and lowest price information

[0486] Specific operation: The server sends the generated suggestions to the user's device, which then displays them visually.

[0487] Output: The suggestion displayed on the user's device

[0488] Step 7:

[0489] The user's smart device uses a camera and microphone to recognize emotions in real time from the user's facial expressions and tone of voice.

[0490] Input: User facial expressions and tone of voice

[0491] Specific operation: The device inputs data acquired by the camera and microphone into an emotion recognition library (e.g., facial_emotion_recognition) to identify the emotional state.

[0492] Output: Recognized user emotion data

[0493] Step 8:

[0494] The server adjusts the suggestions based on the user's emotional state.

[0495] Input: Recognized user emotion data

[0496] What it does: The server analyzes the emotion data and, if necessary, readjusts and re-presents the suggestions.

[0497] Output: Adjusted proposal

[0498] Step 9:

[0499] The user provides feedback on the proposal content, and the terminal transmits the feedback together with emotion data to the server.

[0500] Input: User feedback and sentiment data

[0501] Specific operation: The device sends feedback and emotion data to the server.

[0502] Output: Feedback and emotion data sent to the server

[0503] Step 10:

[0504] The server readjusts the suggestions based on the feedback and emotional data and continues making suggestions until the user is satisfied.

[0505] Input: Feedback and emotion data

[0506] What it does: The server analyzes the feedback and emotion data, and then refines the suggestions and presents them to the user. This refinement involves generating prompts using a generative AI model and presenting them as new suggestions.

[0507] Output: A refined proposal that is iterated on until the user is satisfied.

[0508] Examples of specific prompts include the following:

[0509] "Please suggest a carpet color that would be suitable for a modern style living room."

[0510] 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.

[0511] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0512] 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.

[0513] [Second embodiment]

[0514] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0515] 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.

[0516] 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).

[0517] 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.

[0518] 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.

[0519] 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).

[0520] 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. 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.

[0521] 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.

[0522] 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.

[0523] 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.

[0524] 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.

[0525] 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."

[0526] This invention relates to a system that automatically optimizes the interior design and layout of a room. The system utilizes AI technology to suggest appropriate interior styling and find the best products from online shopping sites, simply by uploading an image of the room.

[0527] Overall system flow

[0528] 1. Upload an image

[0529] Users upload images of their rooms taken from their own devices to the system, and the devices send the images to the system's server.

[0530] 2. Image Analysis

[0531] The server analyzes the received images using AI algorithms to determine information such as the room layout, furniture placement, and dimensions.

[0532] 3. Choosing an interior style

[0533] Based on the analysis results, the server presents the user with multiple interior style options (e.g., modern, mid-century, Scandinavian, etc.), and the user selects their preferred style from the presented style options.

[0534] 4. Layout and item suggestions

[0535] Based on the user's selection, the server proposes an optimal interior layout and furniture arrangement. Based on the proposed layout, it also lists the necessary items and searches online shopping sites for the lowest prices for those items.

[0536] 5. Display of Suggestions

[0537] The server sends the generated layout proposal and item list to the user's device. The user can review the proposal through their device and request changes if necessary. The item list also includes a purchase link for each product.

[0538] 6. User Feedback and Adjustments

[0539] The user can provide feedback on the suggestions, and the server will readjust the suggestions based on the feedback, repeating the process until an optimal layout is achieved.

[0540] Specific examples

[0541] Example 1: Uploading and analyzing an image

[0542] User A takes a photo of their living room with their smartphone and uploads it to a dedicated app. The device sends the image to the server, which then receives it and analyzes it using an AI algorithm. The server then identifies furniture such as sofas, tables, and televisions, and determines their positions and the dimensions of the room.

[0543] Example 2: Style selection and suggestions

[0544] The server presents User A with interior style options such as "Modern," "Mid-century," and "Scandinavian." User A selects the "Modern" style and sends the information to the server. The server generates an optimal layout based on the "Modern" style and creates a list of the necessary furniture and decorations. The list also includes the lowest price information for each item.

[0545] Example 3: Viewing suggestions and providing feedback

[0546] The server sends the proposed layout and item list to User A's device. User A reviews the proposal on his / her device and feels that the proposed carpet color does not suit his / her taste, so he / she provides feedback to the server. The server then suggests new carpet options based on the feedback, and this process is repeated until User A is satisfied.

[0547] This allows users to easily create interiors that suit their tastes, even if they do not have specialized knowledge.In addition, the lowest price information is provided, so users can make selections that fit their budget.

[0548] The processing flow will be explained below.

[0549] Step 1:

[0550] Users take photos of their rooms with their devices, which then upload the image files to the system via a dedicated application or web interface.

[0551] Step 2:

[0552] The device sends images of the room selected by the user to the system's server, which stores the received images and prepares them for analysis.

[0553] Step 3:

[0554] The server then analyzes the received images of the room using AI algorithms. Specifically, the image analysis module identifies furniture and decorations, and determines their locations and the dimensions of the room.

[0555] Step 4:

[0556] The server generates multiple interior style options based on the analysis, including modern, mid-century, and Scandinavian styles.

[0557] Step 5:

[0558] The server sends the generated interior style options to the user's terminal, and the user selects a preferred style from the style options displayed on the terminal.

[0559] Step 6:

[0560] The user selects the interior style option of their choice and inputs the selection information into the terminal, which then transmits the input information to the server.

[0561] Step 7:

[0562] The server then proposes optimal interior layouts and furniture arrangements based on the user's selections, with the proposals being customized according to the selected style.

[0563] Step 8:

[0564] The server creates a list of required items based on the proposed layout, searches online shopping sites for the lowest prices for each item, and retrieves price information and purchase links.

[0565] Step 9:

[0566] The server sends the generated layout proposal and item list to the user's terminal, where the user can confirm the proposal contents.

[0567] Step 10:

[0568] Users can provide feedback on suggestions, for example, they may request that the color or type of a particular item be changed.

[0569] Step 11:

[0570] The device sends the user's feedback information to the server, which then readjusts the suggestions based on the feedback.

[0571] Step 12:

[0572] The server then sends the new, adjusted layout proposal and item list back to the user's device, and this process is repeated until the user is satisfied.

[0573] Step 13:

[0574] If the user is satisfied with the final proposal, he or she can click on a purchase link included in the item list to purchase the product on the online shopping site. The terminal provides access to the online shopping site based on the user's click.

[0575] Example 1

[0576] 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."

[0577] In modern life, interior styling is an important concern for many people, but choosing the right interior design can be difficult, requiring specialized knowledge and time. It can also be time-consuming to search for products individually and find the best price. Furthermore, if a proposed layout or items do not suit a user's preferences, there is also the problem that re-proposals to adjust them are not smoothly carried out.

[0578] 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.

[0579] In this invention, the server includes: means for receiving room images; means for analyzing the received images to identify the room layout and furniture arrangement; means for presenting multiple interior style options to the user using a generative AI model based on the analysis results; means for proposing an optimal layout and furniture arrangement based on the user's selection; means for listing necessary items based on the proposed layout and searching e-commerce sites for the lowest prices for each item; means for displaying the proposed layout and item list; and means for receiving user feedback and adjusting the proposals based on the feedback. This allows users to easily achieve interior styling without specialized knowledge and purchase products at optimal prices. Furthermore, the proposals can be smoothly readjusted, increasing user satisfaction.

[0580] A "room image" is a still image of the interior of a room that is taken by a user using a terminal.

[0581] "Means for receiving images" refers to a series of functions by which the server receives images of the room sent by the user via the terminal.

[0582] "Means for analyzing images" refers to a function that uses AI algorithms to process received images of a room and identify the room layout and furniture arrangement.

[0583] "Interior style options" refers to style options for the interior of a room, and refers to multiple styles presented to the user, such as modern and mid-century.

[0584] A "generative AI model" is an artificial intelligence model that incorporates natural language processing and image generation technologies and is used to present interior style options to users.

[0585] The "optimal layout" is a furniture and decoration arrangement plan for the room proposed by the server based on the analyzed room information and the interior style selected by the user.

[0586] "E-commerce Site" means an online shopping platform that sells products over the Internet.

[0587] An "item list" is a list of furniture and decorations needed to decorate a room based on the proposed layout.

[0588] "Means for receiving feedback" is a function by which the server receives opinions and requests regarding the proposals sent by users.

[0589] "Means for adjusting the proposal content" refers to the function of reconstructing the layout and item list presented by the server based on feedback received from the user and re-proposing them.

[0590] This invention relates to a system that automatically optimizes interior styling using room images. This system utilizes AI technology to suggest appropriate interior styling and find optimal products from e-commerce sites simply by uploading a room image. Specific hardware and software configurations and their processing are described below.

[0591] Hardware and software used

[0592] Hardware

[0593] 1. Device: A device such as a smartphone, tablet, or computer that allows users to take and upload images of a room.

[0594] 2. Server: A high-performance computer for proposal generation, image analysis, and database management.

[0595] software

[0596] 1. Image analysis algorithms: Analyze room layout and furniture placement using TensorFlow and OpenCV.

[0597] 2. Generative AI models: Artificial intelligence models that incorporate natural language processing and image generation technologies, such as GPT-4 and DALL-E.

[0598] 3. Online Shopping API: An API for obtaining product information from e-commerce sites, such as Amazon API and Rakuten API.

[0599] Specific processing of the program

[0600] Uploading an image

[0601] The user takes a picture of the room with their own device and uploads it using the system's dedicated application. The device then sends this image data to the server as an HTTP request.

[0602] Image analysis

[0603] The server temporarily stores the received images and analyzes them using image analysis algorithms such as TensorFlow and OpenCV. The analysis identifies the furniture and decorations present in the room, their arrangement, and the dimensions of the room. The analysis results are stored in a database.

[0604] Choosing an interior style

[0605] The server uses a generative AI model (e.g., GPT-4) to generate multiple interior style options based on the image analysis results, including sample images and descriptions of the interior styles. The generated options are sent to the device and presented to the user.

[0606] Layout and item suggestions

[0607] Based on the interior style selected by the user, the server proposes the optimal layout and furniture placement using a layout generation algorithm. The server then lists the necessary furniture and decorations and uses an online shopping API to search for the lowest prices for each item.

[0608] View Suggestions

[0609] The server sends the generated layout proposal and item list to the terminal, which displays it on the user's terminal, allowing the user to check the proposal and provide feedback.

[0610] Feedback and Resubmissions

[0611] The server receives the user's feedback and adjusts the suggestions accordingly, repeating the process as necessary until it generates a suggestion that satisfies the user.

[0612] Examples of concrete examples and prompts

[0613] Example 1: Image upload and analysis

[0614] User A takes a photo of their living room with their smartphone and uploads it through a dedicated application. The device sends the image to the server, which analyzes it using AI algorithms. The server identifies furniture such as sofas, tables, and televisions, and determines their positions and the dimensions of the room.

[0615] Example 2: Style selection and suggestions

[0616] The server uses a generative AI model to present User A with interior style options such as "modern," "midcentury," and "Scandinavian." User A selects the "modern" style and sends that information to the server. The server generates an optimal layout based on the "modern" style and creates a list of the necessary furniture and decorations. The list includes the lowest price information for each item.

[0617] Prompt Sentence Examples

[0618] I've uploaded an image of a living room. I'd like you to suggest interior style options from this image. Options include "modern," "midcentury," and "Scandinavian."

[0619]

[0620] The user has chosen a "Modern" style. Based on this style, suggest the optimal layout and furniture list. Also get the lowest prices using information from e-commerce sites.

[0621] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0622] Step 1:

[0623] Uploading an image

[0624] User

[0625] The user takes a picture of the room with their device and opens a dedicated application.

[0626] Terminal

[0627] The device provides an option for the user to upload images taken by the user through a user interface. When the user selects an image and presses the upload button, the device sends the selected image data to the server in an HTTP request.

[0628] input

[0629] Room image data

[0630] output

[0631] Image data sent to the server

[0632] Step 2:

[0633] Image analysis

[0634] server

[0635] The server writes the received image data to disk for temporary storage. The server then analyzes the image using an image analysis algorithm (e.g., TensorFlow or OpenCV). This algorithm identifies the furniture and decorations present in the room and determines their positions and dimensions. The analysis results are saved in a database as room layout data.

[0636] input

[0637] Received room image data

[0638] output

[0639] Room layout data (furniture position and dimensions)

[0640] Step 3:

[0641] Choosing an interior style

[0642] server

[0643] The server generates multiple interior style options using a generative AI model (e.g., GPT-4 or DALL-E) based on the layout data obtained as a result of image analysis. The generated style options include sample images and descriptions of the interior styles. The generated data is sent to the device for presentation to the user.

[0644] input

[0645] Room layout data

[0646] output

[0647] Interior style options (sample images and descriptions)

[0648] Step 4:

[0649] User-selected styles

[0650] Terminal

[0651] The terminal displays the interior style options received from the server on a user interface, which provides option buttons for selecting from multiple style options.

[0652] User

[0653] The user selects the style of their choice from the presented style options and transmits the selection to the server via the terminal.

[0654] input

[0655] Interior Style Options

[0656] output

[0657] User-selected style information

[0658] Step 5:

[0659] Layout and item suggestions

[0660] server

[0661] The server generates an optimal interior layout based on the selected style information received from the user. It uses a layout generation algorithm to create the optimal layout while adjusting the furniture placement. It also lists the necessary furniture and decorations based on the proposed layout. It then uses online shopping APIs (such as the Amazon API or Rakuten API) to search for the lowest price information for each item.

[0662] input

[0663] User-selected style information, room layout data

[0664] output

[0665] Optimal layout plan, item list (including lowest price information)

[0666] Step 6:

[0667] View Suggestions

[0668] server

[0669] The server transmits the generated layout plan and item list to the terminal.

[0670] Terminal

[0671] The terminal displays the data received from the server on the user interface, allowing the user to check the proposed layout and item list through the terminal.

[0672] input

[0673] Optimal layout plan, item list

[0674] output

[0675] An interface where users can check the suggestions

[0676] Step 7:

[0677] Feedback and Resubmissions

[0678] User

[0679] Users can send feedback about the suggestions to the server via their devices, including specific requests such as "I don't like the color of the carpet."

[0680] server

[0681] The server receives feedback from the user, readjusts its suggestions based on that feedback, and again uses the generative AI model to generate new options and re-suggest them to the user.

[0682] input

[0683] User Feedback

[0684] output

[0685] Adjusted layout and item list

[0686] The above specific processing steps allow users to easily realize interior styling without specialized knowledge and purchase products at the optimal price. Furthermore, the proposed content can be smoothly readjusted, increasing user satisfaction.

[0687] (Application example 1)

[0688] 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."

[0689] Interior design is an important element in modern life, and styling that suits individual tastes is required. However, for ordinary users without specialized knowledge, it takes a lot of effort and time to make their rooms look good. Furthermore, with so many interior design products available on the market, it can be difficult to find the perfect product. Furthermore, there is a lack of tools for users to actually visualize the proposed interior style, which can cause anxiety about the final selection.

[0690] 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.

[0691] In this invention, the server includes means for receiving room images, means for analyzing the received images to identify the room layout and furniture arrangement, means for presenting a plurality of interior style options to the user, means for proposing an optimal layout and furniture arrangement based on the user's selection, means for listing necessary items based on the proposed layout and searching an e-commerce site for the lowest price information for each item, means for displaying the proposed layout and item list, means for receiving user feedback and adjusting the proposal based thereon, and means for visualizing the proposed layout using virtual reality technology, thereby enabling the user to easily optimize the interior style of their own room and actually visualize and check it.

[0692] A "room image" is a photo or video taken by a user that shows the interior of a room.

[0693] The "receiving means" is a device or system that has the function of receiving image data sent by a user from a terminal.

[0694] "Room layout" refers to the relative positions and structure of furniture and decorations within a room.

[0695] "Furniture arrangement" is information about the position of each piece of furniture in the room.

[0696] The "analysis means" refers to AI algorithms and software that process image data and identify room layouts and furniture placement.

[0697] "Interior style options" are interior design options based on a specific design theme or aesthetic concept.

[0698] The "presentation means" refers to a display device or user interface for displaying information to the user.

[0699] "Optimal layout and furniture arrangement" refers to the state of a room arranged in the most efficient and aesthetic way based on the user's selections.

[0700] A "means for proposing" is a system that has the functionality to generate layout and placement proposals based on the analysis results and user selections.

[0701] "Means for listing items" refers to a device or program that extracts necessary furniture and decorative items based on the proposed layout and organizes them in a list format.

[0702] "Lowest price information" is the lowest price information for a specific product in the market.

[0703] An "e-commerce site" is an online platform for buying and selling goods and services over the Internet.

[0704] A "searching means" is a system that has the functionality to obtain information from an e-commerce site based on specified conditions.

[0705] The "display means" refers to a device or software that displays the proposed layout and item list so that the user can check it.

[0706] "Means for receiving and adjusting feedback" refers to a system for readjusting the content of proposals by reflecting user evaluations and requests.

[0707] "Virtual reality technology" is a technology that uses computer graphics to generate a virtual environment that is close to reality, allowing users to experience that environment.

[0708] A "visualization means" is a system that uses 3D graphics or virtual reality technology to visually display the proposed layout.

[0709] The present invention relates to a system for automatically optimizing the interior and layout of a room. Hereinafter, an embodiment of the present invention will be described in detail.

[0710] System configuration

[0711] This system is realized using user terminals, servers, cloud computing infrastructure, and virtual reality (VR) devices (such as smartphones and head-mounted displays).

[0712] Receiving and analyzing room images

[0713] The user uploads images of the room they have taken to the server. Specifically, they use a smartphone app to send photos showing the interior of the room to the server. The server then analyzes the images using a deep learning framework such as TensorFlow or PyTorch to perform image analysis on the cloud and identify the room layout and furniture arrangement.

[0714] Selecting an interior style and proposing the optimal layout

[0715] Based on the analysis results, the server presents the user with multiple interior style options, such as "modern," "midcentury," and "Scandinavian." Based on the style information selected by the user, the AI ​​model proposes the optimal layout and furniture arrangement. This AI model uses, for example, a machine learning algorithm.

[0716] Make a list of the items you need and search for the best prices

[0717] Based on the proposed layout, the required items are listed. For each item, the server searches e-commerce sites for the lowest price. This process is performed using web scraping technology and API integration.

[0718] Proposal display and VR visualization

[0719] The server sends the optimized layout and item list to the user's device, where the user can review the proposal and visualize the proposed layout using virtual reality (VR) technology, for example, in game engines such as Unity or Unreal Engine.

[0720] User feedback and suggestions adjustments

[0721] Users can provide feedback on the suggestions, which is sent to the server, which analyzes the feedback and refines the suggestions. The process is automated, and the cycle of suggestions and feedback is repeated until an optimal layout is achieved that satisfies the user.

[0722] Examples and prompts

[0723] Specific examples

[0724] User: 30 years old, female, living alone, prefers modern design.

[0725] Room size: 10 tatami mats, one-room apartment.

[0726] Prompt Sentence Examples

[0727] "I'm a 30-year-old woman living in a 10-tatami studio apartment. I like modern design and am looking for interior decor that suits my room. I've uploaded a photo of my room, so please suggest furniture placement and items."

[0728] In this way, users can easily optimize the interior of their rooms and check it virtually, even if they do not have specialized knowledge.

[0729] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0730] Step 1:

[0731] Users use their smartphones to take pictures of the interior of the room and upload them to the server via a dedicated application.

[0732] Input: Room image (JPEG or PNG format)

[0733] Output: Image data is sent to the server

[0734] Step 2:

[0735] The server processes the received image data in a cloud environment and performs analysis using TensorFlow or PyTorch, which identifies the room layout and furniture positions.

[0736] Input: Received image data

[0737] Output: Room layout information and furniture placement information

[0738] Step 3:

[0739] Based on the analysis results, the server presents the user with multiple interior style options (e.g., modern, mid-century, Scandinavian).

[0740] Input: Room layout information and furniture placement information

[0741] Output: A list of interior style options

[0742] Step 4:

[0743] The user selects a preferred style from the presented interior style options and transmits the selection data to the server.

[0744] Input: User selected interior style

[0745] Output: The selected style information is sent to the server.

[0746] Step 5:

[0747] Based on the user's selections, the server uses AI models to suggest optimal layouts and furniture placements.

[0748] Input: Selected style information, room layout information

[0749] Output: Optimal layout and furniture arrangement proposals

[0750] Step 6:

[0751] Based on the proposed layout and furniture, the server creates a list of required items and searches e-commerce sites for the lowest prices for each item.

[0752] Input: Optimal layout and furniture arrangement

[0753] Output: List of required items and lowest price information

[0754] Step 7:

[0755] The server transmits the proposed layout and item list to the user terminal and displays it for the user to check.

[0756] Input: List of required items and lowest price information

[0757] Output: Layout and item list displayed on the user's device

[0758] Step 8:

[0759] Users can visually check the proposed layout using virtual reality technology, specifically by using a smartphone or head-mounted display to experience the room in a VR environment generated through Unity or Unreal Engine.

[0760] Input: Proposed layout and item list

[0761] Output: Virtual reality visualization of the room

[0762] Step 9:

[0763] The user provides feedback on the suggestions and sends it to the server, which analyzes the feedback and adjusts the suggestions.

[0764] Input: User feedback

[0765] Output: Adjusted proposal

[0766] Step 10:

[0767] The server then automatically re-proposes and generates new layouts and item lists based on the feedback received. This process is repeated until the user is satisfied.

[0768] Input: User feedback

[0769] Output: Revised layout proposal and item list

[0770] 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.

[0771] This invention relates to a system that automatically optimizes the interior design and layout of a room and adjusts the suggestions by recognizing the user's emotions. The system utilizes AI technology and an emotion engine to suggest appropriate interior styling and find the best products from online shopping sites, simply by the user uploading an image of the room.

[0772] Overall system flow

[0773] 1. Upload an image

[0774] Users upload images of their rooms taken from their own devices to the system, and the devices send the images to the system's server.

[0775] 2. Image Analysis

[0776] The server analyzes the received images using AI algorithms to determine information such as the room layout, furniture placement, and dimensions.

[0777] 3. Choosing an interior style

[0778] Based on the analysis results, the server presents the user with multiple interior style options (e.g., modern, mid-century, Scandinavian, etc.), and the user selects their preferred style from the presented style options.

[0779] 4. Layout and item suggestions

[0780] Based on the user's selection, the server proposes an optimal interior layout and furniture arrangement. Based on the proposed layout, it also lists the necessary items and searches online shopping sites for the lowest prices for those items.

[0781] 5. Display of Suggestions

[0782] The server sends the generated layout proposal and item list to the user's device. The user can review the proposal through their device and request changes if necessary. The item list also includes a purchase link for each product.

[0783] 6. User Emotion Recognition by Emotion Engine

[0784] When presenting the proposal content to the user, the server uses an emotion engine to recognize the user's emotions, thereby making it possible to evaluate the user's emotions.

[0785] 7. User feedback and sentiment ratings

[0786] The user can provide feedback on the suggestions, and the server receives the feedback and uses an emotion engine to assess the user's emotional state.

[0787] 8. Adjust your offers based on emotions

[0788] The server readjusts the suggestions based on the user's feedback and emotional state. For example, if the user looks dissatisfied, the server changes the suggestions and re-presents them. This process is repeated until the user is satisfied.

[0789] Specific examples

[0790] Example 1: Uploading and analyzing an image

[0791] User A takes a photo of their living room with their smartphone and uploads it to a dedicated app. The device sends the image to the server, which then receives it and analyzes it using an AI algorithm. The server then identifies furniture such as sofas, tables, and televisions, and determines their positions and the dimensions of the room.

[0792] Example 2: Style selection and suggestions

[0793] The server presents User A with interior style options such as "Modern," "Mid-century," and "Scandinavian." User A selects the "Modern" style and sends the information to the server. The server generates an optimal layout based on the "Modern" style and creates a list of the necessary furniture and decorations. The list also includes the lowest price information for each item.

[0794] Example 3: Displaying Suggestions and Emotion Recognition

[0795] The server sends the proposed layout and item list to User A's device. User A checks the proposal on the device, and the emotion engine recognizes his / her facial expressions and voice via the device's camera. The server evaluates User A's emotional state based on the results of the emotion engine.

[0796] Example 4: Feedback and emotion-based adjustments

[0797] User A sends feedback requesting a change in the color of the carpet. The device sends the feedback along with emotional data obtained from User A's facial expressions and voice to the server. The server analyzes the feedback and emotional data and readjusts the suggestion. For example, if User A looks dissatisfied, the server will improve the suggestion and present it again. This process is repeated until User A looks satisfied.

[0798] This allows users to receive personalized interior design suggestions based on their emotions, even if they do not have specialized knowledge, and easily create an interior design that suits their tastes.In addition, the lowest price information is provided, so users can make selections that fit their budget.

[0799] The processing flow will be explained below.

[0800] Step 1:

[0801] Users take photos of their rooms with their devices, which then upload the image files to the system via a dedicated application or web interface.

[0802] Step 2:

[0803] The device sends images of the room selected by the user to the system's server, which stores the received images and prepares them for analysis.

[0804] Step 3:

[0805] The server then analyzes the received images of the room using AI algorithms. Specifically, the image analysis module identifies furniture and decorations, and determines their locations and the dimensions of the room.

[0806] Step 4:

[0807] The server generates multiple interior style options based on the analysis, including modern, mid-century, and Scandinavian styles.

[0808] Step 5:

[0809] The server sends the generated interior style options to the user's terminal, and the user selects a preferred style from the style options displayed on the terminal.

[0810] Step 6:

[0811] The user selects the interior style option of their choice and inputs the selection information into the terminal, which then transmits the input information to the server.

[0812] Step 7:

[0813] The server then proposes optimal interior layouts and furniture arrangements based on the user's selections, with the proposals being customized according to the selected style.

[0814] Step 8:

[0815] The server creates a list of required items based on the proposed layout, searches online shopping sites for the lowest prices for each item, and retrieves price information and purchase links.

[0816] Step 9:

[0817] The server sends the generated layout proposal and item list to the user's terminal, where the user can confirm the proposal contents.

[0818] Step 10:

[0819] While the user is checking the layout proposal, the server activates a mechanism that uses the device's camera and microphone to enable the emotion engine to recognize the user's emotions. The emotion engine performs facial expression recognition and voice analysis to evaluate the user's emotional state.

[0820] Step 11:

[0821] Users can provide feedback on suggestions, for example, they may request that the color or type of a particular item be changed.

[0822] Step 12:

[0823] The device transmits the user's feedback and recognized emotion data to the server, which evaluates the feedback as well as the user's emotional state to help tailor the suggestions.

[0824] Step 13:

[0825] The server analyzes the feedback and emotion data and re-adjusts the suggestions. For example, if the user looks dissatisfied, the server will improve the suggestions and re-present them.

[0826] Step 14:

[0827] The server then sends the new, adjusted layout proposal and item list back to the user's device, and this process is repeated until the user is satisfied.

[0828] Step 15:

[0829] If the user is satisfied with the final proposal, he or she can click on a purchase link included in the item list to purchase the product on the online shopping site. The terminal provides access to the online shopping site based on the user's click.

[0830] Example 2

[0831] 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."

[0832] Conventional interior design suggestion systems have difficulty making personalized suggestions that take user emotions into account, and the suggestions often do not fully match the user's preferences. Furthermore, optimal product selection and layout adjustments are performed manually, which is time-consuming and laborious for the user. Therefore, there is a need for a system that can automatically adjust interior design suggestions based on the user's emotions and select optimal products.

[0833] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0834] In this invention, the server includes a means for receiving images of a room, a means for analyzing the received images using an AI algorithm to identify the layout and furniture arrangement of the room, and a means for presenting multiple interior style options to the user, thereby enabling personalized interior suggestions that take the user's emotions into consideration.

[0835] A "room image" is digital data containing visual information about the interior of a room that is taken by a user and uploaded to the system.

[0836] An "AI algorithm" is a calculation procedure that uses artificial intelligence technology, and is a program used to perform processes such as image analysis and pattern recognition.

[0837] "Interior style options" are multiple interior design choices presented to the user that affect the layout of the room and the placement of furniture.

[0838] The "optimal layout" is a layout that is generated based on the style selected by the user and the analysis results, and in which the arrangement of furniture and decorations is most efficient and aesthetically pleasing.

[0839] An "emotion engine" is software or hardware that analyzes a user's facial expressions and voice data and recognizes their emotional state.

[0840] An "online shopping site" is an e-commerce website where products can be purchased over the Internet.

[0841] "Feedback" is information including opinions and requests provided by users regarding the proposed content.

[0842] "Re-adjusting suggestions" is the process of modifying existing interior suggestions, taking into account user feedback and sentiment data.

[0843] This invention relates to a system that automatically optimizes the interior design and layout of a room and adjusts the suggestions based on the user's emotions. The system utilizes AI technology and an emotion engine to suggest appropriate interior styling and find the best products from online shopping sites, simply by uploading an image of the room.

[0844] Hardware and software used

[0845] 1. Hardware

[0846] Server: A high-performance computer that handles image analysis and emotion recognition.

[0847] Device: A device such as a smartphone, tablet, or computer where users can upload images and view suggestions.

[0848] Camera and microphone: Built into the device to capture the user's facial expressions and voice.

[0849] 2. Software

[0850] AI algorithms: TensorFlow, PyTorch, and other algorithms are used to analyze images and optimize interior styling.

[0851] Emotion engine: Recognizes user emotions in real time using Microsoft Azure Emotion API and other tools.

[0852] Online shopping sites: Websites that provide product data, including Amazon and Rakuten.

[0853] Specific operation of the system

[0854] First, the user takes a picture of the room using the device's camera and uploads it to the system via a dedicated app. The device then sends the image data to the server, which uses TensorFlow to analyze the image and determine the room layout and furniture arrangement.

[0855] Based on the analysis results, the server presents the user with multiple interior style options (e.g., modern, mid-century, Scandinavian, etc.) The user selects their preferred style from the presented style options and their selection is sent to the server.

[0856] The server generates an optimal layout based on the selected style, creates a list of necessary furniture and decorations, and searches online shopping sites for the lowest prices for each item and adds them to the item list.

[0857] The generated layout proposal and item list are sent from the server to the user's device, where the user can check the proposal. The proposal also includes a purchase link for each product, allowing the user to purchase the product directly.

[0858] When reviewing the suggestions, the device's camera and microphone capture the user's facial expressions and voice, and send the data to the emotion engine. The emotion engine analyzes this and evaluates the user's emotions. The evaluation results are returned to the server, which then readjusts the suggestions based on the user's emotions. If the user provides feedback, that feedback is also sent to the server and analyzed along with the emotion data.

[0859] If readjustment is required, the server generates a new proposal and sends it again to the user's terminal, and this process is repeated until the user is satisfied.

[0860] Examples of concrete examples and prompts

[0861] Specific examples

[0862] 1. Image upload and analysis

[0863] Users take a photo of their living room with their smartphone and upload the image using a dedicated app.

[0864] The device sends the image to the server, which then analyzes it using AI algorithms.

[0865] The server identifies furniture such as sofas, tables, and televisions, and determines their locations and room dimensions.

[0866] 2. Style selection and suggestions

[0867] The server presents the user with interior style options such as "modern," "midcentury," and "Scandinavian."

[0868] The user selects the "Modern" style and the selection is sent to the server.

[0869] The server generates an optimal layout based on the "modern" style and creates a list of required furniture and decorations.

[0870] 3. Suggestion Display and Emotion Recognition

[0871] The server transmits the proposal content to the user's terminal, and the user confirms the proposal content.

[0872] When confirming the proposal, the device's camera and microphone capture the user's facial expressions and voice, and the emotion engine evaluates the user's emotions.

[0873] 4. Adjusting based on feedback and emotions

[0874] The user provides feedback on the proposal and sends it to the server along with emotion data.

[0875] The server analyzes the feedback and sentiment data and readjusts its suggestions.

[0876] Prompt Sentence Examples

[0877] "Can I upload an image of my living room and get suggestions for the best layout and furniture items for a modern style?"

[0878] "Could you please evaluate your feelings based on this proposal and resubmit your proposal?"

[0879] This allows users to receive interior design suggestions tailored to their preferences, even if they do not have specialized knowledge, and to easily select the best products according to their budget.

[0880] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0881] Step 1: Upload an image

[0882] Specific operation: The user takes a picture of the room using the device's camera and uploads this image from a dedicated app.

[0883] Input: Image data of a room taken by a user.

[0884] Output: Image data sent from the device to the server.

[0885] Details: The device accepts user operations, and when the upload button is pressed, the image data is sent to the cloud server. The server receives the sent image data and passes it on to the next processing step.

[0886] Step 2: Analyze the images

[0887] Specific operation: The server analyzes the received image using an AI algorithm.

[0888] Input: Image data received from the user.

[0889] Output: Room layout information and furniture placement data as analysis results.

[0890] Details: The server uses TensorFlow to run image analysis models to extract furniture (e.g. sofas, tables, TVs, etc.), their placement, and dimensions from the images. The results are stored in a database for further processing.

[0891] Step 3: Choose your interior style

[0892] Specific operation: The server presents the user with multiple interior style options based on the analysis results.

[0893] Input: Room layout information and furniture placement data as a result of image analysis.

[0894] Output: Interior style options presented to the user.

[0895] Details: The server retrieves the analysis results from the database, generates style options based on the user's preferences (e.g., modern, mid-century, Scandinavian, etc.), and sends them to the user's device for display. The user selects their preferred style, and their selection is sent to the server.

[0896] Step 4: Layout and item suggestions

[0897] Specific behavior: The server generates the optimal layout based on the style selected by the user.

[0898] Input: User selected interior style options.

[0899] Output: Optimal layout information and required item list.

[0900] Details: The server uses a layout algorithm based on the selected style to calculate the optimal furniture placement. It then lists the required furniture and decorations and searches online shopping sites for the lowest prices for each item. The generated layout and item list are sent to the user's device.

[0901] Step 5: Viewing Proposals

[0902] Specific operation: The server sends the generated layout and item list to the user's device.

[0903] Input: Optimal layout information and required item list.

[0904] Output: The suggestions that are displayed on the user's device.

[0905] Details: The server generates the proposed content (layout image and item list) in HTML format and sends all data to the user's device. The user can view the content in the device's browser or app.

[0906] Step 6: Recognizing user emotions with the emotion engine

[0907] Specific operation: The server uses an emotion engine to recognize the user's emotions.

[0908] Input: User's facial expression data and voice data.

[0909] Output: Evaluation result of the user's emotional state.

[0910] Details: When displaying the suggestions, the device's camera and microphone capture the user's facial expressions and voice in real time and send the data to the emotion engine, which analyzes the user's emotions and sends the evaluation results to the server.

[0911] Step 7: User feedback and sentiment assessment

[0912] Specific Action: The user provides feedback on the proposal.

[0913] Input: User feedback data and emotion data obtained from facial expressions and voice.

[0914] Output: Analysis results based on feedback and sentiment data.

[0915] Details: Users enter feedback through a dedicated form or voice input, and the device sends the data to the server. The server analyzes the feedback and emotion data and extracts the information necessary for the next adjustment of the proposal.

[0916] Step 8: Adjust your offer based on emotion

[0917] Specific behavior: The server readjusts its suggestions based on the sentiment assessment and feedback.

[0918] Input: Feedback data and emotion rating data.

[0919] Output: The reworked proposal.

[0920] Details: The server restructures its suggestions based on emotional data and specific feedback indicating dissatisfaction. If necessary, it runs the AI ​​algorithm again to generate a new layout and item list, and sends the improved suggestions back to the user's device. This process is repeated until the user is satisfied.

[0921] (Application example 2)

[0922] 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."

[0923] Conventional interior design suggestion systems have the problem of not being able to provide personalized suggestions based on the user's emotions and preferences. Furthermore, they are unable to provide interior design suggestions linked to real-time emotion recognition, limiting the user experience. Furthermore, when suggesting interior styles and layouts, they lack the ability to dynamically generate prompts, making effective interaction with the user difficult.

[0924] 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.

[0925] In this invention, the server includes means for receiving images, means for analyzing images to identify the room layout and furniture arrangement, means for adjusting suggestions based on the user's emotions, means for recognizing the user's emotions from the smart device and adjusting the suggestions, and means for generating prompt sentences using a generative AI model to dynamically make suggestions to the user. This enables personalized suggestions based on the user's emotions and feedback, enabling effective interaction with the user in real time.

[0926] The "means for receiving images of the room" is a function that allows a user to send images taken by a smart device to a server.

[0927] "Means for analyzing received images to determine the layout and furniture placement of the room" refers to a function that uses AI algorithms to identify elements in the image and determine the location of furniture and the dimensions of the room.

[0928] The "means for presenting a plurality of interior style options to the user" is a function for presenting several interior styles to the user as options based on the results of room analysis.

[0929] The "means for proposing the optimal layout and furniture arrangement based on the user's selection" is a function for automatically generating and proposing the optimal layout and furniture arrangement based on the interior style selected by the user.

[0930] "A means for listing necessary items based on a proposed layout and searching online shopping sites for the lowest price information for each item" is a function for listing furniture and decorations required for a proposed layout and obtaining the lowest price information for those items.

[0931] The "means for displaying the proposed layout and item list" is a function for visually displaying the generated layout and item list on the user's smart device.

[0932] "Means for receiving user feedback and adjusting the proposed content based on that" is a function for receiving evaluations and requests for changes from users and readjusting the proposed content based on that.

[0933] "Means for recognizing a user's emotions from a smart device and adjusting suggestions" refers to a function that uses the camera and microphone of a smart device to analyze the user's facial expressions and voice and adjust suggestions according to their emotional state.

[0934] "Means for generating prompt sentences using a generative AI model and dynamically making suggestions to the user" refers to a function for dynamically generating prompt sentences for suggestions or questions to the user using a generative AI model and presenting them to the user.

[0935] In order to implement the present invention, the following system configuration and process are employed.

[0936] First, a user takes an image of the room using a smart device (e.g., a smartphone or smart glasses) and uploads it to the system. The user's device then sends the image to the system's server. The server then analyzes the received image using AI algorithms to identify information such as the room layout, furniture placement, and dimensions. OpenCV and advanced image recognition technology are used for image analysis.

[0937] Based on the analysis results, the server presents multiple interior style options (e.g., modern, mid-century, Scandinavian, etc.) to the user. The user selects their preferred style from the presented style options through their smart device. The selected style information is then sent to the server.

[0938] The server then proposes an optimal interior layout and furniture arrangement based on the user's selection. Furthermore, based on the proposed layout, it lists the necessary items and searches online shopping sites for the lowest prices on those items, allowing users to easily obtain the items they need within their budget.

[0939] The proposals are displayed on the smart device's display, and the user can check the proposed layout and item list. The system uses the user's smart device's camera and microphone to recognize the user's facial expressions and tone of voice in real time. The "facial_emotion_recognition" library is used for emotion recognition.

[0940] The server evaluates the user's emotional state and adjusts the suggestions as needed. It also receives feedback from the user and adjusts the suggestions accordingly. This process continues until the user is satisfied. The server utilizes the user's emotional data along with the feedback to provide more personalized suggestions.

[0941] Furthermore, a generative AI model (such as GPT-3.5) is used to dynamically generate prompts for suggestions or questions for the user and present them to the user. The "transformers" library is used to generate these prompts. As a concrete example, if a user selects the "modern" style and wants to change the carpet color, the prompt is generated as follows:

[0942] example:

[0943] "Please suggest a carpet color that would be suitable for a modern style living room."

[0944] The prompt sentences thus suggested are displayed as appropriate suggestions to the user.

[0945] Overall, the system enables personalized interior design suggestions based on user emotions and feedback, enabling effective interaction in real time.

[0946] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0947] Step 1:

[0948] Users can take pictures of their rooms using their smart devices and upload them to the system.

[0949] Input: Room image (JPEG or PNG format)

[0950] Specific operation: The user's device uses the camera app to take a picture of the room and sends it to the server via the image upload function.

[0951] Output: Image data of the room sent to the server

[0952] Step 2:

[0953] The server analyzes the received images using an AI algorithm (e.g., OpenCV) to determine information such as the room layout, furniture placement, and dimensions.

[0954] Input: Image data of the room

[0955] What it does: The server uses image analysis software to identify the type, location, and size of furniture in the image and saves this as metadata.

[0956] Output: Room layout data and furniture placement information

[0957] Step 3:

[0958] Based on the image analysis results, the server presents the user with multiple interior style options (e.g., modern, mid-century, Scandinavian).

[0959] Input: Room layout data and furniture placement information

[0960] What it does: The server selects appropriate style options from the interior style database and presents them to the user's smart device.

[0961] Output: Interior style options presented to the user

[0962] Step 4:

[0963] The user selects the preferred style from the presented interior style options and transmits it to the server.

[0964] Input: Interior style option selection information

[0965] Specific operation: The user's device sends the selection information to the server.

[0966] Output: User's interior style selection sent to the server

[0967] Step 5:

[0968] The server proposes the optimal interior layout and furniture placement based on the user's selection, lists the necessary items, and searches online shopping sites for the lowest prices for those items.

[0969] Input: User's interior style selection information

[0970] How it works: Based on the selected style, the server uses AI algorithms to generate the optimal layout, creates a list of furniture and decorations, and uses online shopping APIs to retrieve the lowest prices.

[0971] Output: Proposed interior layout and item list, along with the lowest price information

[0972] Step 6:

[0973] The proposed layout and item list are displayed on the user's smart device.

[0974] Input: Proposed interior layout, item list, and lowest price information

[0975] Specific operation: The server sends the generated suggestions to the user's device, which then displays them visually.

[0976] Output: The suggestion displayed on the user's device

[0977] Step 7:

[0978] The user's smart device uses a camera and microphone to recognize emotions in real time from the user's facial expressions and tone of voice.

[0979] Input: User facial expressions and tone of voice

[0980] Specific operation: The device inputs data acquired by the camera and microphone into an emotion recognition library (e.g., facial_emotion_recognition) to identify the emotional state.

[0981] Output: Recognized user emotion data

[0982] Step 8:

[0983] The server adjusts the suggestions based on the user's emotional state.

[0984] Input: Recognized user emotion data

[0985] What it does: The server analyzes the emotion data and, if necessary, readjusts and re-presents the suggestions.

[0986] Output: Adjusted proposal

[0987] Step 9:

[0988] The user provides feedback on the proposal content, and the terminal transmits the feedback together with emotion data to the server.

[0989] Input: User feedback and sentiment data

[0990] Specific operation: The device sends feedback and emotion data to the server.

[0991] Output: Feedback and emotion data sent to the server

[0992] Step 10:

[0993] The server readjusts the suggestions based on the feedback and emotional data and continues making suggestions until the user is satisfied.

[0994] Input: Feedback and emotion data

[0995] What it does: The server analyzes the feedback and emotion data, and then refines the suggestions and presents them to the user. This refinement involves generating prompts using a generative AI model and presenting them as new suggestions.

[0996] Output: A refined proposal that is iterated on until the user is satisfied.

[0997] Examples of specific prompts include the following:

[0998] "Please suggest a carpet color that would be suitable for a modern style living room."

[0999] 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.

[1000] 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.

[1001] 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.

[1002] [Third embodiment]

[1003] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1004] 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.

[1005] 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).

[1006] 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.

[1007] 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.

[1008] 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).

[1009] 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. 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.

[1010] 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.

[1011] 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.

[1012] 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.

[1013] 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.

[1014] 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."

[1015] This invention relates to a system that automatically optimizes the interior design and layout of a room. The system utilizes AI technology to suggest appropriate interior styling and find the best products from online shopping sites, simply by uploading an image of the room.

[1016] Overall system flow

[1017] 1. Upload an image

[1018] Users upload images of their rooms taken from their own devices to the system, and the devices send the images to the system's server.

[1019] 2. Image Analysis

[1020] The server analyzes the received images using AI algorithms to determine information such as the room layout, furniture placement, and dimensions.

[1021] 3. Choosing an interior style

[1022] Based on the analysis results, the server presents the user with multiple interior style options (e.g., modern, mid-century, Scandinavian, etc.), and the user selects their preferred style from the presented style options.

[1023] 4. Layout and item suggestions

[1024] Based on the user's selection, the server proposes an optimal interior layout and furniture arrangement. Based on the proposed layout, it also lists the necessary items and searches online shopping sites for the lowest prices for those items.

[1025] 5. Display of Suggestions

[1026] The server sends the generated layout proposal and item list to the user's device. The user can review the proposal through their device and request changes if necessary. The item list also includes a purchase link for each product.

[1027] 6. User Feedback and Adjustments

[1028] The user can provide feedback on the suggestions, and the server will readjust the suggestions based on the feedback, repeating the process until an optimal layout is achieved.

[1029] Specific examples

[1030] Example 1: Uploading and analyzing an image

[1031] User A takes a photo of their living room with their smartphone and uploads it to a dedicated app. The device sends the image to the server, which then receives it and analyzes it using an AI algorithm. The server then identifies furniture such as sofas, tables, and televisions, and determines their positions and the dimensions of the room.

[1032] Example 2: Style selection and suggestions

[1033] The server presents User A with interior style options such as "Modern," "Mid-century," and "Scandinavian." User A selects the "Modern" style and sends the information to the server. The server generates an optimal layout based on the "Modern" style and creates a list of the necessary furniture and decorations. The list also includes the lowest price information for each item.

[1034] Example 3: Viewing suggestions and providing feedback

[1035] The server sends the proposed layout and item list to User A's device. User A reviews the proposal on his / her device and feels that the proposed carpet color does not suit his / her taste, so he / she provides feedback to the server. The server then suggests new carpet options based on the feedback, and this process is repeated until User A is satisfied.

[1036] This allows users to easily create interiors that suit their tastes, even if they do not have specialized knowledge.In addition, the lowest price information is provided, so users can make selections that fit their budget.

[1037] The processing flow will be explained below.

[1038] Step 1:

[1039] Users take photos of their rooms with their devices, which then upload the image files to the system via a dedicated application or web interface.

[1040] Step 2:

[1041] The device sends images of the room selected by the user to the system's server, which stores the received images and prepares them for analysis.

[1042] Step 3:

[1043] The server then analyzes the received images of the room using AI algorithms. Specifically, the image analysis module identifies furniture and decorations, and determines their locations and the dimensions of the room.

[1044] Step 4:

[1045] The server generates multiple interior style options based on the analysis, including modern, mid-century, and Scandinavian styles.

[1046] Step 5:

[1047] The server sends the generated interior style options to the user's terminal, and the user selects a preferred style from the style options displayed on the terminal.

[1048] Step 6:

[1049] The user selects the interior style option of their choice and inputs the selection information into the terminal, which then transmits the input information to the server.

[1050] Step 7:

[1051] The server then proposes optimal interior layouts and furniture arrangements based on the user's selections, with the proposals being customized according to the selected style.

[1052] Step 8:

[1053] The server creates a list of required items based on the proposed layout, searches online shopping sites for the lowest prices for each item, and retrieves price information and purchase links.

[1054] Step 9:

[1055] The server sends the generated layout proposal and item list to the user's terminal, where the user can confirm the proposal contents.

[1056] Step 10:

[1057] Users can provide feedback on suggestions, for example, they may request that the color or type of a particular item be changed.

[1058] Step 11:

[1059] The device sends the user's feedback information to the server, which then readjusts the suggestions based on the feedback.

[1060] Step 12:

[1061] The server then sends the new, adjusted layout proposal and item list back to the user's device, and this process is repeated until the user is satisfied.

[1062] Step 13:

[1063] If the user is satisfied with the final proposal, he or she can click on a purchase link included in the item list to purchase the product on the online shopping site. The terminal provides access to the online shopping site based on the user's click.

[1064] Example 1

[1065] 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."

[1066] In modern life, interior styling is an important concern for many people, but choosing the right interior design can be difficult, requiring specialized knowledge and time. It can also be time-consuming to search for products individually and find the best price. Furthermore, if a proposed layout or items do not suit a user's preferences, there is also the problem that re-proposals to adjust them are not smoothly carried out.

[1067] 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.

[1068] In this invention, the server includes: means for receiving room images; means for analyzing the received images to identify the room layout and furniture arrangement; means for presenting multiple interior style options to the user using a generative AI model based on the analysis results; means for proposing an optimal layout and furniture arrangement based on the user's selection; means for listing necessary items based on the proposed layout and searching e-commerce sites for the lowest prices for each item; means for displaying the proposed layout and item list; and means for receiving user feedback and adjusting the proposals based on the feedback. This allows users to easily achieve interior styling without specialized knowledge and purchase products at optimal prices. Furthermore, the proposals can be smoothly readjusted, increasing user satisfaction.

[1069] A "room image" is a still image of the interior of a room that is taken by a user using a terminal.

[1070] "Means for receiving images" refers to a series of functions by which the server receives images of the room sent by the user via the terminal.

[1071] "Means for analyzing images" refers to a function that uses AI algorithms to process received images of a room and identify the room layout and furniture arrangement.

[1072] "Interior style options" refers to style options for the interior of a room, and refers to multiple styles presented to the user, such as modern and mid-century.

[1073] A "generative AI model" is an artificial intelligence model that incorporates natural language processing and image generation technologies and is used to present interior style options to users.

[1074] The "optimal layout" is a furniture and decoration arrangement plan for the room proposed by the server based on the analyzed room information and the interior style selected by the user.

[1075] "E-commerce Site" means an online shopping platform that sells products over the Internet.

[1076] An "item list" is a list of furniture and decorations needed to decorate a room based on the proposed layout.

[1077] "Means for receiving feedback" is a function by which the server receives opinions and requests regarding the proposals sent by users.

[1078] "Means for adjusting the proposal content" refers to the function of reconstructing the layout and item list presented by the server based on feedback received from the user and re-proposing them.

[1079] This invention relates to a system that automatically optimizes interior styling using room images. This system utilizes AI technology to suggest appropriate interior styling and find optimal products from e-commerce sites simply by uploading a room image. Specific hardware and software configurations and their processing are described below.

[1080] Hardware and software used

[1081] Hardware

[1082] 1. Device: A device such as a smartphone, tablet, or computer that allows users to take and upload images of a room.

[1083] 2. Server: A high-performance computer for proposal generation, image analysis, and database management.

[1084] software

[1085] 1. Image analysis algorithms: Analyze room layout and furniture placement using TensorFlow and OpenCV.

[1086] 2. Generative AI models: Artificial intelligence models that incorporate natural language processing and image generation technologies, such as GPT-4 and DALL-E.

[1087] 3. Online Shopping API: An API for obtaining product information from e-commerce sites, such as Amazon API and Rakuten API.

[1088] Specific processing of the program

[1089] Uploading an image

[1090] The user takes a picture of the room with their own device and uploads it using the system's dedicated application. The device then sends this image data to the server as an HTTP request.

[1091] Image analysis

[1092] The server temporarily stores the received images and analyzes them using image analysis algorithms such as TensorFlow and OpenCV. The analysis identifies the furniture and decorations present in the room, their arrangement, and the dimensions of the room. The analysis results are stored in a database.

[1093] Choosing an interior style

[1094] The server uses a generative AI model (e.g., GPT-4) to generate multiple interior style options based on the image analysis results, including sample images and descriptions of the interior styles. The generated options are sent to the device and presented to the user.

[1095] Layout and item suggestions

[1096] Based on the interior style selected by the user, the server proposes the optimal layout and furniture placement using a layout generation algorithm. The server then lists the necessary furniture and decorations and uses an online shopping API to search for the lowest prices for each item.

[1097] View Suggestions

[1098] The server sends the generated layout proposal and item list to the terminal, which displays it on the user's terminal, allowing the user to check the proposal and provide feedback.

[1099] Feedback and Resubmissions

[1100] The server receives the user's feedback and adjusts the suggestions accordingly, repeating the process as necessary until it generates a suggestion that satisfies the user.

[1101] Examples of concrete examples and prompts

[1102] Example 1: Image upload and analysis

[1103] User A takes a photo of their living room with their smartphone and uploads it through a dedicated application. The device sends the image to the server, which analyzes it using AI algorithms. The server identifies furniture such as sofas, tables, and televisions, and determines their positions and the dimensions of the room.

[1104] Example 2: Style selection and suggestions

[1105] The server uses a generative AI model to present User A with interior style options such as "modern," "midcentury," and "Scandinavian." User A selects the "modern" style and sends that information to the server. The server generates an optimal layout based on the "modern" style and creates a list of the necessary furniture and decorations. The list includes the lowest price information for each item.

[1106] Prompt Sentence Examples

[1107] I've uploaded an image of a living room. I'd like you to suggest interior style options from this image. Options include "modern," "midcentury," and "Scandinavian."

[1108]

[1109] The user has chosen a "Modern" style. Based on this style, suggest the optimal layout and furniture list. Also get the lowest prices using information from e-commerce sites.

[1110] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1111] Step 1:

[1112] Uploading an image

[1113] User

[1114] The user takes a picture of the room with their device and opens a dedicated application.

[1115] Terminal

[1116] The device provides an option for the user to upload images taken by the user through a user interface. When the user selects an image and presses the upload button, the device sends the selected image data to the server in an HTTP request.

[1117] input

[1118] Room image data

[1119] output

[1120] Image data sent to the server

[1121] Step 2:

[1122] Image analysis

[1123] server

[1124] The server writes the received image data to disk for temporary storage. The server then analyzes the image using an image analysis algorithm (e.g., TensorFlow or OpenCV). This algorithm identifies the furniture and decorations present in the room and determines their positions and dimensions. The analysis results are saved in a database as room layout data.

[1125] input

[1126] Received room image data

[1127] output

[1128] Room layout data (furniture position and dimensions)

[1129] Step 3:

[1130] Choosing an interior style

[1131] server

[1132] The server generates multiple interior style options using a generative AI model (e.g., GPT-4 or DALL-E) based on the layout data obtained as a result of image analysis. The generated style options include sample images and descriptions of the interior styles. The generated data is sent to the device for presentation to the user.

[1133] input

[1134] Room layout data

[1135] output

[1136] Interior style options (sample images and descriptions)

[1137] Step 4:

[1138] User-selected styles

[1139] Terminal

[1140] The terminal displays the interior style options received from the server on a user interface, which provides option buttons for selecting from multiple style options.

[1141] User

[1142] The user selects the style of their choice from the presented style options and transmits the selection to the server via the terminal.

[1143] input

[1144] Interior Style Options

[1145] output

[1146] User-selected style information

[1147] Step 5:

[1148] Layout and item suggestions

[1149] server

[1150] The server generates an optimal interior layout based on the selected style information received from the user. It uses a layout generation algorithm to create the optimal layout while adjusting the furniture placement. It also lists the necessary furniture and decorations based on the proposed layout. It then uses online shopping APIs (such as the Amazon API or Rakuten API) to search for the lowest price information for each item.

[1151] input

[1152] User-selected style information, room layout data

[1153] output

[1154] Optimal layout plan, item list (including lowest price information)

[1155] Step 6:

[1156] View Suggestions

[1157] server

[1158] The server transmits the generated layout plan and item list to the terminal.

[1159] Terminal

[1160] The terminal displays the data received from the server on the user interface, allowing the user to check the proposed layout and item list through the terminal.

[1161] input

[1162] Optimal layout plan, item list

[1163] output

[1164] An interface where users can check the suggestions

[1165] Step 7:

[1166] Feedback and Resubmissions

[1167] User

[1168] Users can send feedback about the suggestions to the server via their devices, including specific requests such as "I don't like the color of the carpet."

[1169] server

[1170] The server receives feedback from the user, readjusts its suggestions based on that feedback, and again uses the generative AI model to generate new options and re-suggest them to the user.

[1171] input

[1172] User Feedback

[1173] output

[1174] Adjusted layout and item list

[1175] The above specific processing steps allow users to easily realize interior styling without specialized knowledge and purchase products at the optimal price. Furthermore, the proposed content can be smoothly readjusted, increasing user satisfaction.

[1176] (Application example 1)

[1177] 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."

[1178] Interior design is an important element in modern life, and styling that suits individual tastes is required. However, for ordinary users without specialized knowledge, it takes a lot of effort and time to arrange their rooms in a way that looks good. Furthermore, with so many interior design products available on the market, it can be difficult to find the perfect product. Furthermore, there is a lack of ways for users to actually visualize the proposed interior style, which can cause anxiety about the final selection.

[1179] 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.

[1180] In this invention, the server includes means for receiving room images, means for analyzing the received images to identify the room layout and furniture arrangement, means for presenting a plurality of interior style options to the user, means for proposing an optimal layout and furniture arrangement based on the user's selection, means for listing necessary items based on the proposed layout and searching an e-commerce site for the lowest price information for each item, means for displaying the proposed layout and item list, means for receiving user feedback and adjusting the proposal based thereon, and means for visualizing the proposed layout using virtual reality technology, thereby enabling the user to easily optimize the interior style of their own room and actually visualize and check it.

[1181] A "room image" is a photo or video taken by a user that shows the interior of a room.

[1182] The "receiving means" is a device or system that has the function of receiving image data sent by a user from a terminal.

[1183] "Room layout" refers to the relative positions and structure of furniture and decorations within a room.

[1184] "Furniture arrangement" is information about the position of each piece of furniture in the room.

[1185] The "analysis means" refers to AI algorithms and software that process image data and identify room layouts and furniture placement.

[1186] "Interior style options" are interior design options based on a specific design theme or aesthetic concept.

[1187] The "presentation means" refers to a display device or user interface for displaying information to the user.

[1188] "Optimal layout and furniture arrangement" refers to the state of a room arranged in the most efficient and aesthetic way based on the user's selections.

[1189] A "means for proposing" is a system that has the functionality to generate layout and placement proposals based on the analysis results and user selections.

[1190] "Means for listing items" refers to a device or program that extracts necessary furniture and decorative items based on the proposed layout and organizes them in a list format.

[1191] "Lowest price information" is the lowest price information for a specific product in the market.

[1192] An "e-commerce site" is an online platform for buying and selling goods and services over the Internet.

[1193] A "searching means" is a system that has the functionality to obtain information from an e-commerce site based on specified conditions.

[1194] The "display means" refers to a device or software that displays the proposed layout and item list so that the user can check it.

[1195] "Means for receiving and adjusting feedback" refers to a system for readjusting the content of proposals by reflecting user evaluations and requests.

[1196] "Virtual reality technology" is a technology that uses computer graphics to generate a virtual environment that is close to reality, allowing users to experience that environment.

[1197] A "visualization means" is a system that uses 3D graphics or virtual reality technology to visually display the proposed layout.

[1198] The present invention relates to a system for automatically optimizing the interior and layout of a room. Hereinafter, an embodiment of the present invention will be described in detail.

[1199] System configuration

[1200] This system is realized using user terminals, servers, cloud computing infrastructure, and virtual reality (VR) devices (such as smartphones and head-mounted displays).

[1201] Receiving and analyzing room images

[1202] The user uploads images of the room they have taken to the server. Specifically, they use a smartphone app to send photos showing the interior of the room to the server. The server then analyzes the images using a deep learning framework such as TensorFlow or PyTorch to perform image analysis on the cloud and identify the room layout and furniture arrangement.

[1203] Selecting an interior style and proposing the optimal layout

[1204] Based on the analysis results, the server presents the user with multiple interior style options, such as "modern," "midcentury," and "Scandinavian." Based on the style information selected by the user, the AI ​​model proposes the optimal layout and furniture arrangement. This AI model uses, for example, a machine learning algorithm.

[1205] Make a list of the items you need and search for the best prices

[1206] Based on the proposed layout, the required items are listed. For each item, the server searches e-commerce sites for the lowest price. This process is performed using web scraping technology and API integration.

[1207] Proposal display and VR visualization

[1208] The server sends the optimized layout and item list to the user's device, where the user can review the proposal and visualize the proposed layout using virtual reality (VR) technology, for example, in game engines such as Unity or Unreal Engine.

[1209] User feedback and suggestions adjustments

[1210] Users can provide feedback on the suggestions, which is sent to the server, which analyzes the feedback and refines the suggestions. The process is automated, and the cycle of suggestions and feedback continues until an optimal layout is achieved that satisfies the user.

[1211] Examples and prompts

[1212] Specific examples

[1213] User: 30 years old, female, living alone, prefers modern design.

[1214] Room size: 10 tatami mats, one-room apartment.

[1215] Prompt Sentence Examples

[1216] "I'm a 30-year-old woman living in a 10-tatami studio apartment. I like modern design and am looking for interior decor that suits my room. I've uploaded a photo of my room, so please suggest furniture placement and items."

[1217] In this way, users can easily optimize the interior of their rooms and check it virtually, even if they do not have specialized knowledge.

[1218] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1219] Step 1:

[1220] Users use their smartphones to take pictures of the interior of the room and upload them to the server via a dedicated application.

[1221] Input: Room image (JPEG or PNG format)

[1222] Output: Image data is sent to the server

[1223] Step 2:

[1224] The server processes the received image data in a cloud environment and performs analysis using TensorFlow or PyTorch, which identifies the room layout and furniture positions.

[1225] Input: Received image data

[1226] Output: Room layout information and furniture placement information

[1227] Step 3:

[1228] Based on the analysis results, the server presents the user with multiple interior style options (e.g., modern, mid-century, Scandinavian).

[1229] Input: Room layout information and furniture placement information

[1230] Output: A list of interior style options

[1231] Step 4:

[1232] The user selects a preferred style from the presented interior style options and transmits the selection data to the server.

[1233] Input: User selected interior style

[1234] Output: The selected style information is sent to the server.

[1235] Step 5:

[1236] Based on the user's selections, the server uses AI models to suggest optimal layouts and furniture placements.

[1237] Input: Selected style information, room layout information

[1238] Output: Optimal layout and furniture arrangement proposals

[1239] Step 6:

[1240] Based on the proposed layout and furniture, the server creates a list of required items and searches e-commerce sites for the lowest prices for each item.

[1241] Input: Optimal layout and furniture arrangement

[1242] Output: List of required items and lowest price information

[1243] Step 7:

[1244] The server transmits the proposed layout and item list to the user terminal and displays it for the user to check.

[1245] Input: List of required items and lowest price information

[1246] Output: Layout and item list displayed on the user's device

[1247] Step 8:

[1248] Users can visually check the proposed layout using virtual reality technology, specifically by using a smartphone or head-mounted display to experience the room in a VR environment generated through Unity or Unreal Engine.

[1249] Input: Proposed layout and item list

[1250] Output: Virtual reality visualization of the room

[1251] Step 9:

[1252] The user provides feedback on the suggestions and sends it to the server, which analyzes the feedback and adjusts the suggestions.

[1253] Input: User feedback

[1254] Output: Adjusted proposal

[1255] Step 10:

[1256] The server then automatically re-proposes and generates new layouts and item lists based on the feedback received. This process is repeated until the user is satisfied.

[1257] Input: User feedback

[1258] Output: Revised layout proposal and item list

[1259] 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.

[1260] This invention relates to a system that automatically optimizes the interior design and layout of a room and adjusts the suggestions by recognizing the user's emotions. The system utilizes AI technology and an emotion engine to suggest appropriate interior styling and find the best products from online shopping sites, simply by the user uploading an image of the room.

[1261] Overall system flow

[1262] 1. Upload an image

[1263] Users upload images of their rooms taken from their own devices to the system, and the devices send the images to the system's server.

[1264] 2. Image Analysis

[1265] The server analyzes the received images using AI algorithms to determine information such as the room layout, furniture placement, and dimensions.

[1266] 3. Choosing an interior style

[1267] Based on the analysis results, the server presents the user with multiple interior style options (e.g., modern, mid-century, Scandinavian, etc.), and the user selects their preferred style from the presented style options.

[1268] 4. Layout and item suggestions

[1269] Based on the user's selection, the server proposes an optimal interior layout and furniture arrangement. Based on the proposed layout, it also lists the necessary items and searches online shopping sites for the lowest prices for those items.

[1270] 5. Display of Suggestions

[1271] The server sends the generated layout proposal and item list to the user's device. The user can review the proposal through their device and request changes if necessary. The item list also includes a purchase link for each product.

[1272] 6. User Emotion Recognition by Emotion Engine

[1273] When presenting the proposal content to the user, the server uses an emotion engine to recognize the user's emotions, thereby making it possible to evaluate the user's emotions.

[1274] 7. User feedback and sentiment ratings

[1275] The user can provide feedback on the suggestions, and the server receives the feedback and uses an emotion engine to assess the user's emotional state.

[1276] 8. Adjust your offers based on emotions

[1277] The server readjusts the suggestions based on the user's feedback and emotional state. For example, if the user looks dissatisfied, the server changes the suggestions and re-presents them. This process is repeated until the user is satisfied.

[1278] Specific examples

[1279] Example 1: Uploading and analyzing an image

[1280] User A takes a photo of their living room with their smartphone and uploads it to a dedicated app. The device sends the image to the server, which then receives it and analyzes it using an AI algorithm. The server then identifies furniture such as sofas, tables, and televisions, and determines their positions and the dimensions of the room.

[1281] Example 2: Style selection and suggestions

[1282] The server presents User A with interior style options such as "Modern," "Mid-century," and "Scandinavian." User A selects the "Modern" style and sends the information to the server. The server generates an optimal layout based on the "Modern" style and creates a list of the necessary furniture and decorations. The list also includes the lowest price information for each item.

[1283] Example 3: Displaying Suggestions and Emotion Recognition

[1284] The server sends the proposed layout and item list to User A's device. User A checks the proposal on the device, and the emotion engine recognizes his / her facial expressions and voice via the device's camera. The server evaluates User A's emotional state based on the results of the emotion engine.

[1285] Example 4: Feedback and emotion-based adjustments

[1286] User A sends feedback requesting a change in the color of the carpet. The device sends the feedback along with emotional data obtained from User A's facial expressions and voice to the server. The server analyzes the feedback and emotional data and readjusts the suggestion. For example, if User A looks dissatisfied, the server will improve the suggestion and present it again. This process is repeated until User A looks satisfied.

[1287] This allows users to receive personalized interior design suggestions based on their emotions, even if they do not have specialized knowledge, and easily create an interior design that suits their tastes.In addition, the lowest price information is provided, so users can make selections that fit their budget.

[1288] The processing flow will be explained below.

[1289] Step 1:

[1290] Users take photos of their rooms with their devices, which then upload the image files to the system via a dedicated application or web interface.

[1291] Step 2:

[1292] The device sends images of the room selected by the user to the system's server, which stores the received images and prepares them for analysis.

[1293] Step 3:

[1294] The server then analyzes the received images of the room using AI algorithms. Specifically, the image analysis module identifies furniture and decorations, and determines their locations and the dimensions of the room.

[1295] Step 4:

[1296] The server generates multiple interior style options based on the analysis, including modern, mid-century, and Scandinavian styles.

[1297] Step 5:

[1298] The server sends the generated interior style options to the user's terminal, and the user selects a preferred style from the style options displayed on the terminal.

[1299] Step 6:

[1300] The user selects the interior style option of their choice and inputs the selection information into the terminal, which then transmits the input information to the server.

[1301] Step 7:

[1302] The server then proposes optimal interior layouts and furniture arrangements based on the user's selections, with the proposals being customized according to the selected style.

[1303] Step 8:

[1304] The server creates a list of required items based on the proposed layout, searches online shopping sites for the lowest prices for each item, and retrieves price information and purchase links.

[1305] Step 9:

[1306] The server sends the generated layout proposal and item list to the user's terminal, where the user can confirm the proposal contents.

[1307] Step 10:

[1308] While the user is checking the layout proposal, the server activates a mechanism that uses the device's camera and microphone to enable the emotion engine to recognize the user's emotions. The emotion engine performs facial expression recognition and voice analysis to evaluate the user's emotional state.

[1309] Step 11:

[1310] Users can provide feedback on suggestions, for example, they may request that the color or type of a particular item be changed.

[1311] Step 12:

[1312] The device transmits the user's feedback and recognized emotion data to the server, which evaluates the feedback as well as the user's emotional state to help tailor the suggestions.

[1313] Step 13:

[1314] The server analyzes the feedback and emotion data and re-adjusts the suggestions. For example, if the user looks dissatisfied, the server will improve the suggestions and re-present them.

[1315] Step 14:

[1316] The server then sends the new, adjusted layout proposal and item list back to the user's device, and this process is repeated until the user is satisfied.

[1317] Step 15:

[1318] If the user is satisfied with the final proposal, he or she can click on a purchase link included in the item list to purchase the product on the online shopping site. The terminal provides access to the online shopping site based on the user's click.

[1319] Example 2

[1320] 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."

[1321] Conventional interior design suggestion systems have difficulty making personalized suggestions that take user emotions into account, and the suggestions often do not fully match the user's preferences. Furthermore, optimal product selection and layout adjustments are performed manually, which is time-consuming and laborious for the user. Therefore, there is a need for a system that can automatically adjust interior design suggestions based on the user's emotions and select optimal products.

[1322] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1323] In this invention, the server includes a means for receiving images of a room, a means for analyzing the received images using an AI algorithm to identify the layout and furniture arrangement of the room, and a means for presenting multiple interior style options to the user, thereby enabling personalized interior suggestions that take the user's emotions into consideration.

[1324] A "room image" is digital data containing visual information about the interior of a room that is taken by a user and uploaded to the system.

[1325] An "AI algorithm" is a calculation procedure that uses artificial intelligence technology, and is a program used to perform processes such as image analysis and pattern recognition.

[1326] "Interior style options" are multiple interior design choices presented to the user that affect the layout of the room and the placement of furniture.

[1327] The "optimal layout" is a layout that is generated based on the style selected by the user and the analysis results, and in which the arrangement of furniture and decorations is most efficient and aesthetically pleasing.

[1328] An "emotion engine" is software or hardware that analyzes a user's facial expressions and voice data and recognizes their emotional state.

[1329] An "online shopping site" is an e-commerce website where products can be purchased over the Internet.

[1330] "Feedback" is information including opinions and requests provided by users regarding the proposed content.

[1331] "Re-adjusting suggestions" is the process of modifying existing interior suggestions, taking into account user feedback and sentiment data.

[1332] This invention relates to a system that automatically optimizes the interior design and layout of a room and adjusts the suggestions based on the user's emotions. The system utilizes AI technology and an emotion engine to suggest appropriate interior styling and find the best products from online shopping sites, simply by uploading an image of the room.

[1333] Hardware and software used

[1334] 1. Hardware

[1335] Server: A high-performance computer that handles image analysis and emotion recognition.

[1336] Device: A device such as a smartphone, tablet, or computer where users can upload images and view suggestions.

[1337] Camera and microphone: Built into the device to capture the user's facial expressions and voice.

[1338] 2. Software

[1339] AI algorithms: TensorFlow, PyTorch, and other algorithms are used to analyze images and optimize interior styling.

[1340] Emotion engine: Recognizes user emotions in real time using Microsoft Azure Emotion API and other tools.

[1341] Online shopping sites: Websites that provide product data, including Amazon and Rakuten.

[1342] Specific operation of the system

[1343] First, the user takes a picture of the room using the device's camera and uploads it to the system via a dedicated app. The device then sends the image data to the server, which uses TensorFlow to analyze the image and determine the room layout and furniture arrangement.

[1344] Based on the analysis results, the server presents the user with multiple interior style options (e.g., modern, mid-century, Scandinavian, etc.) The user selects their preferred style from the presented style options and their selection is sent to the server.

[1345] The server generates an optimal layout based on the selected style, creates a list of necessary furniture and decorations, and searches online shopping sites for the lowest prices for each item and adds them to the item list.

[1346] The generated layout proposal and item list are sent from the server to the user's device, where the user can check the proposal. The proposal also includes a purchase link for each product, allowing the user to purchase the product directly.

[1347] When reviewing the suggestions, the device's camera and microphone capture the user's facial expressions and voice, and send the data to the emotion engine. The emotion engine analyzes this and evaluates the user's emotions. The evaluation results are returned to the server, which then readjusts the suggestions based on the user's emotions. If the user provides feedback, that feedback is also sent to the server and analyzed along with the emotion data.

[1348] If readjustment is required, the server generates a new proposal and sends it again to the user's terminal, and this process is repeated until the user is satisfied.

[1349] Examples of concrete examples and prompts

[1350] Specific examples

[1351] 1. Image upload and analysis

[1352] Users take a photo of their living room with their smartphone and upload the image using a dedicated app.

[1353] The device sends the image to the server, which then analyzes it using AI algorithms.

[1354] The server identifies furniture such as sofas, tables, and televisions, and determines their locations and room dimensions.

[1355] 2. Style selection and suggestions

[1356] The server presents the user with interior style options such as "modern," "midcentury," and "Scandinavian."

[1357] The user selects the "Modern" style and the selection is sent to the server.

[1358] The server generates an optimal layout based on the "modern" style and creates a list of required furniture and decorations.

[1359] 3. Suggestion Display and Emotion Recognition

[1360] The server transmits the proposal content to the user's terminal, and the user confirms the proposal content.

[1361] When confirming the proposal, the device's camera and microphone capture the user's facial expressions and voice, and the emotion engine evaluates the user's emotions.

[1362] 4. Adjusting based on feedback and emotions

[1363] The user provides feedback on the proposal and sends it to the server along with emotion data.

[1364] The server analyzes the feedback and sentiment data and readjusts its suggestions.

[1365] Prompt Sentence Examples

[1366] "Can I upload an image of my living room and get suggestions for the best layout and furniture items for a modern style?"

[1367] "Could you please evaluate your feelings based on this proposal and resubmit your proposal?"

[1368] This allows users to receive interior design suggestions tailored to their preferences, even if they do not have specialized knowledge, and to easily select the best products according to their budget.

[1369] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1370] Step 1: Upload an image

[1371] Specific operation: The user takes a picture of the room using the device's camera and uploads this image from a dedicated app.

[1372] Input: Image data of a room taken by a user.

[1373] Output: Image data sent from the device to the server.

[1374] Details: The device accepts user operations, and when the upload button is pressed, the image data is sent to the cloud server. The server receives the sent image data and passes it on to the next processing step.

[1375] Step 2: Analyze the images

[1376] Specific operation: The server analyzes the received image using an AI algorithm.

[1377] Input: Image data received from the user.

[1378] Output: Room layout information and furniture placement data as analysis results.

[1379] Details: The server uses TensorFlow to run image analysis models to extract furniture (e.g. sofas, tables, TVs, etc.), their placement, and dimensions from the images. The results are stored in a database for further processing.

[1380] Step 3: Choose your interior style

[1381] Specific operation: The server presents the user with multiple interior style options based on the analysis results.

[1382] Input: Room layout information and furniture placement data as a result of image analysis.

[1383] Output: Interior style options presented to the user.

[1384] Details: The server retrieves the analysis results from the database, generates style options based on the user's preferences (e.g., modern, mid-century, Scandinavian, etc.), and sends them to the user's device for display. The user selects their preferred style, and their selection is sent to the server.

[1385] Step 4: Layout and item suggestions

[1386] Specific behavior: The server generates the optimal layout based on the style selected by the user.

[1387] Input: User selected interior style options.

[1388] Output: Optimal layout information and required item list.

[1389] Details: The server uses a layout algorithm based on the selected style to calculate the optimal furniture placement. It then lists the required furniture and decorations and searches online shopping sites for the lowest prices for each item. The generated layout and item list are sent to the user's device.

[1390] Step 5: Viewing Proposals

[1391] Specific operation: The server sends the generated layout and item list to the user's device.

[1392] Input: Optimal layout information and required item list.

[1393] Output: The suggestions that are displayed on the user's device.

[1394] Details: The server generates the proposed content (layout image and item list) in HTML format and sends all data to the user's device. The user can view the content in the device's browser or app.

[1395] Step 6: Recognizing user emotions with the emotion engine

[1396] Specific operation: The server uses an emotion engine to recognize the user's emotions.

[1397] Input: User's facial expression data and voice data.

[1398] Output: Evaluation result of the user's emotional state.

[1399] Details: When displaying the suggestions, the device's camera and microphone capture the user's facial expressions and voice in real time and send the data to the emotion engine, which analyzes the user's emotions and sends the evaluation results to the server.

[1400] Step 7: User feedback and sentiment assessment

[1401] Specific Action: The user provides feedback on the proposal.

[1402] Input: User feedback data and emotion data obtained from facial expressions and voice.

[1403] Output: Analysis results based on feedback and sentiment data.

[1404] Details: Users enter feedback through a dedicated form or voice input, and the device sends the data to the server. The server analyzes the feedback and emotion data and extracts the information necessary for the next adjustment of the proposal.

[1405] Step 8: Adjust your offer based on emotion

[1406] Specific behavior: The server readjusts its suggestions based on the sentiment assessment and feedback.

[1407] Input: Feedback data and emotion rating data.

[1408] Output: The reworked proposal.

[1409] Details: The server restructures its suggestions based on emotional data and specific feedback indicating dissatisfaction. If necessary, it runs the AI ​​algorithm again to generate a new layout and item list, and sends the improved suggestions back to the user's device. This process is repeated until the user is satisfied.

[1410] (Application example 2)

[1411] 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."

[1412] Conventional interior design suggestion systems have the problem of not being able to provide personalized suggestions based on the user's emotions and preferences. Furthermore, they are unable to provide interior design suggestions linked to real-time emotion recognition, limiting the user experience. Furthermore, when suggesting interior styles and layouts, they lack the ability to dynamically generate prompts, making effective interaction with the user difficult.

[1413] 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.

[1414] In this invention, the server includes means for receiving images, means for analyzing images to identify the room layout and furniture arrangement, means for adjusting suggestions based on the user's emotions, means for recognizing the user's emotions from the smart device and adjusting the suggestions, and means for generating prompt sentences using a generative AI model to dynamically make suggestions to the user. This enables personalized suggestions based on the user's emotions and feedback, enabling effective interaction with the user in real time.

[1415] The "means for receiving images of the room" is a function that allows a user to send images taken by a smart device to a server.

[1416] "Means for analyzing received images to determine the layout and furniture placement of the room" refers to a function that uses AI algorithms to identify elements in the image and determine the location of furniture and the dimensions of the room.

[1417] The "means for presenting a plurality of interior style options to the user" is a function for presenting several interior styles to the user as options based on the results of room analysis.

[1418] The "means for proposing the optimal layout and furniture arrangement based on the user's selection" is a function for automatically generating and proposing the optimal layout and furniture arrangement based on the interior style selected by the user.

[1419] "A means for listing necessary items based on a proposed layout and searching online shopping sites for the lowest price information for each item" is a function for listing furniture and decorations required for a proposed layout and obtaining the lowest price information for those items.

[1420] The "means for displaying the proposed layout and item list" is a function for visually displaying the generated layout and item list on the user's smart device.

[1421] "Means for receiving user feedback and adjusting the proposed content based on that" is a function for receiving evaluations and requests for changes from users and readjusting the proposed content based on that.

[1422] "Means for recognizing a user's emotions from a smart device and adjusting suggestions" refers to a function that uses the camera and microphone of a smart device to analyze the user's facial expressions and voice and adjust suggestions according to their emotional state.

[1423] "Means for generating prompt sentences using a generative AI model and dynamically making suggestions to the user" refers to a function for dynamically generating prompt sentences for suggestions or questions to the user using a generative AI model and presenting them to the user.

[1424] In order to implement the present invention, the following system configuration and process are employed.

[1425] First, a user takes an image of the room using a smart device (e.g., a smartphone or smart glasses) and uploads it to the system. The user's device then sends the image to the system's server. The server then analyzes the received image using AI algorithms to identify information such as the room layout, furniture placement, and dimensions. OpenCV and advanced image recognition technology are used for image analysis.

[1426] Based on the analysis results, the server presents multiple interior style options (e.g., modern, mid-century, Scandinavian, etc.) to the user. The user selects their preferred style from the presented style options through their smart device. The selected style information is then sent to the server.

[1427] The server then proposes an optimal interior layout and furniture arrangement based on the user's selection. Furthermore, based on the proposed layout, it lists the necessary items and searches online shopping sites for the lowest prices on those items, allowing users to easily obtain the items they need within their budget.

[1428] The proposals are displayed on the smart device's display, and the user can check the proposed layout and item list. The system uses the user's smart device's camera and microphone to recognize the user's facial expressions and tone of voice in real time. The "facial_emotion_recognition" library is used for emotion recognition.

[1429] The server evaluates the user's emotional state and adjusts the suggestions as needed. It also receives feedback from the user and adjusts the suggestions accordingly. This process continues until the user is satisfied. The server utilizes the user's emotional data along with the feedback to provide more personalized suggestions.

[1430] Furthermore, a generative AI model (such as GPT-3.5) is used to dynamically generate prompts for suggestions or questions for the user and present them to the user. The "transformers" library is used to generate these prompts. As a concrete example, if a user selects the "modern" style and wants to change the carpet color, the prompt is generated as follows:

[1431] example:

[1432] "Please suggest a carpet color that would be suitable for a modern style living room."

[1433] The prompt sentences thus suggested are displayed as appropriate suggestions to the user.

[1434] Overall, the system enables personalized interior design suggestions based on user emotions and feedback, enabling effective interaction in real time.

[1435] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1436] Step 1:

[1437] Users can take pictures of their rooms using their smart devices and upload them to the system.

[1438] Input: Room image (JPEG or PNG format)

[1439] Specific operation: The user's device uses the camera app to take a picture of the room and sends it to the server via the image upload function.

[1440] Output: Image data of the room sent to the server

[1441] Step 2:

[1442] The server analyzes the received images using an AI algorithm (e.g., OpenCV) to determine information such as the room layout, furniture placement, and dimensions.

[1443] Input: Image data of the room

[1444] What it does: The server uses image analysis software to identify the type, location, and size of furniture in the image and saves this as metadata.

[1445] Output: Room layout data and furniture placement information

[1446] Step 3:

[1447] Based on the image analysis results, the server presents the user with multiple interior style options (e.g., modern, mid-century, Scandinavian).

[1448] Input: Room layout data and furniture placement information

[1449] What it does: The server selects appropriate style options from the interior style database and presents them to the user's smart device.

[1450] Output: Interior style options presented to the user

[1451] Step 4:

[1452] The user selects the preferred style from the presented interior style options and transmits it to the server.

[1453] Input: Interior style option selection information

[1454] Specific operation: The user's device sends the selection information to the server.

[1455] Output: User's interior style selection sent to the server

[1456] Step 5:

[1457] The server proposes the optimal interior layout and furniture placement based on the user's selection, lists the necessary items, and searches online shopping sites for the lowest prices for those items.

[1458] Input: User's interior style selection information

[1459] How it works: Based on the selected style, the server uses AI algorithms to generate the optimal layout, creates a list of furniture and decorations, and uses online shopping APIs to retrieve the lowest prices.

[1460] Output: Proposed interior layout and item list, along with the lowest price information

[1461] Step 6:

[1462] The proposed layout and item list are displayed on the user's smart device.

[1463] Input: Proposed interior layout, item list, and lowest price information

[1464] Specific operation: The server sends the generated suggestions to the user's device, which then displays them visually.

[1465] Output: The suggestion displayed on the user's device

[1466] Step 7:

[1467] The user's smart device uses a camera and microphone to recognize emotions in real time from the user's facial expressions and tone of voice.

[1468] Input: User facial expressions and tone of voice

[1469] Specific operation: The device inputs data acquired by the camera and microphone into an emotion recognition library (e.g., facial_emotion_recognition) to identify the emotional state.

[1470] Output: Recognized user emotion data

[1471] Step 8:

[1472] The server adjusts the suggestions based on the user's emotional state.

[1473] Input: Recognized user emotion data

[1474] What it does: The server analyzes the emotion data and, if necessary, readjusts and re-presents the suggestions.

[1475] Output: Adjusted proposal

[1476] Step 9:

[1477] The user provides feedback on the proposal content, and the terminal transmits the feedback together with emotion data to the server.

[1478] Input: User feedback and sentiment data

[1479] Specific operation: The device sends feedback and emotion data to the server.

[1480] Output: Feedback and emotion data sent to the server

[1481] Step 10:

[1482] The server readjusts the suggestions based on the feedback and emotional data and continues making suggestions until the user is satisfied.

[1483] Input: Feedback and emotion data

[1484] What it does: The server analyzes the feedback and emotion data, and then refines the suggestions and presents them to the user. This refinement involves generating prompts using a generative AI model and presenting them as new suggestions.

[1485] Output: A refined proposal that is iterated on until the user is satisfied.

[1486] Examples of specific prompts include the following:

[1487] "Please suggest a carpet color that would be suitable for a modern style living room."

[1488] 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.

[1489] 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.

[1490] 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.

[1491] [Fourth embodiment]

[1492] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1493] 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.

[1494] 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).

[1495] 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.

[1496] 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.

[1497] 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).

[1498] 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. 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.

[1499] 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.

[1500] 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.

[1501] 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.

[1502] 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.

[1503] 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.

[1504] 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."

[1505] This invention relates to a system that automatically optimizes the interior design and layout of a room. The system utilizes AI technology to suggest appropriate interior styling and find the best products from online shopping sites, simply by uploading an image of the room.

[1506] Overall system flow

[1507] 1. Upload an image

[1508] Users upload images of their rooms taken from their own devices to the system, and the devices send the images to the system's server.

[1509] 2. Image Analysis

[1510] The server analyzes the received images using AI algorithms to determine information such as the room layout, furniture placement, and dimensions.

[1511] 3. Choosing an interior style

[1512] Based on the analysis results, the server presents the user with multiple interior style options (e.g., modern, mid-century, Scandinavian, etc.), and the user selects their preferred style from the presented style options.

[1513] 4. Layout and item suggestions

[1514] Based on the user's selection, the server proposes an optimal interior layout and furniture arrangement. Based on the proposed layout, it also lists the necessary items and searches online shopping sites for the lowest prices for those items.

[1515] 5. Display of Suggestions

[1516] The server sends the generated layout proposal and item list to the user's device. The user can review the proposal through their device and request changes if necessary. The item list also includes a purchase link for each product.

[1517] 6. User Feedback and Adjustments

[1518] The user can provide feedback on the suggestions, and the server will readjust the suggestions based on the feedback, repeating the process until an optimal layout is achieved.

[1519] Specific examples

[1520] Example 1: Uploading and analyzing an image

[1521] User A takes a photo of their living room with their smartphone and uploads it to a dedicated app. The device sends the image to the server, which then receives it and analyzes it using an AI algorithm. The server then identifies furniture such as sofas, tables, and televisions, and determines their positions and the dimensions of the room.

[1522] Example 2: Style selection and suggestions

[1523] The server presents User A with interior style options such as "Modern," "Mid-century," and "Scandinavian." User A selects the "Modern" style and sends the information to the server. The server generates an optimal layout based on the "Modern" style and creates a list of the necessary furniture and decorations. The list also includes the lowest price information for each item.

[1524] Example 3: Viewing suggestions and providing feedback

[1525] The server sends the proposed layout and item list to User A's device. User A reviews the proposal on his / her device and feels that the proposed carpet color does not suit his / her taste, so he / she provides feedback to the server. The server then suggests new carpet options based on the feedback, and this process is repeated until User A is satisfied.

[1526] This allows users to easily create interiors that suit their tastes, even if they do not have specialized knowledge.In addition, the lowest price information is provided, so users can make selections that fit their budget.

[1527] The processing flow will be explained below.

[1528] Step 1:

[1529] Users take photos of their rooms with their devices, which then upload the image files to the system via a dedicated application or web interface.

[1530] Step 2:

[1531] The device sends images of the room selected by the user to the system's server, which stores the received images and prepares them for analysis.

[1532] Step 3:

[1533] The server then analyzes the received images of the room using AI algorithms. Specifically, the image analysis module identifies furniture and decorations, and determines their locations and the dimensions of the room.

[1534] Step 4:

[1535] The server generates multiple interior style options based on the analysis, including modern, mid-century, and Scandinavian styles.

[1536] Step 5:

[1537] The server sends the generated interior style options to the user's terminal, and the user selects a preferred style from the style options displayed on the terminal.

[1538] Step 6:

[1539] The user selects the interior style option of their choice and inputs the selection information into the terminal, which then transmits the input information to the server.

[1540] Step 7:

[1541] The server then proposes optimal interior layouts and furniture arrangements based on the user's selections, with the proposals being customized according to the selected style.

[1542] Step 8:

[1543] The server creates a list of required items based on the proposed layout, searches online shopping sites for the lowest prices for each item, and retrieves price information and purchase links.

[1544] Step 9:

[1545] The server sends the generated layout proposal and item list to the user's terminal, where the user can confirm the proposal contents.

[1546] Step 10:

[1547] Users can provide feedback on suggestions, for example, they may request that the color or type of a particular item be changed.

[1548] Step 11:

[1549] The device sends the user's feedback information to the server, which then readjusts the suggestions based on the feedback.

[1550] Step 12:

[1551] The server then sends the new, adjusted layout proposal and item list back to the user's device, and this process is repeated until the user is satisfied.

[1552] Step 13:

[1553] If the user is satisfied with the final proposal, he or she can click on a purchase link included in the item list to purchase the product on the online shopping site. The terminal provides access to the online shopping site based on the user's click.

[1554] Example 1

[1555] 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."

[1556] In modern life, interior styling is an important concern for many people, but choosing the right interior design can be difficult, requiring specialized knowledge and time. It can also be time-consuming to search for products individually and find the best price. Furthermore, if a proposed layout or items do not suit a user's preferences, there is also the problem that re-proposals to adjust them are not smoothly carried out.

[1557] 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.

[1558] In this invention, the server includes: means for receiving room images; means for analyzing the received images to identify the room layout and furniture arrangement; means for presenting multiple interior style options to the user using a generative AI model based on the analysis results; means for proposing an optimal layout and furniture arrangement based on the user's selection; means for listing necessary items based on the proposed layout and searching e-commerce sites for the lowest prices for each item; means for displaying the proposed layout and item list; and means for receiving user feedback and adjusting the proposals based on the feedback. This allows users to easily achieve interior styling without specialized knowledge and purchase products at optimal prices. Furthermore, the proposals can be smoothly readjusted, increasing user satisfaction.

[1559] A "room image" is a still image of the interior of a room that is taken by a user using a terminal.

[1560] "Means for receiving images" refers to a series of functions by which the server receives images of the room sent by the user via the terminal.

[1561] "Means for analyzing images" refers to a function that uses AI algorithms to process received images of a room and identify the room layout and furniture arrangement.

[1562] "Interior style options" refers to style options for the interior of a room, and refers to multiple styles presented to the user, such as modern and mid-century.

[1563] A "generative AI model" is an artificial intelligence model that incorporates natural language processing and image generation technologies and is used to present interior style options to users.

[1564] The "optimal layout" is a furniture and decoration arrangement plan for the room proposed by the server based on the analyzed room information and the interior style selected by the user.

[1565] "E-commerce Site" means an online shopping platform that sells products over the Internet.

[1566] An "item list" is a list of furniture and decorations needed to decorate a room based on the proposed layout.

[1567] "Means for receiving feedback" is a function by which the server receives opinions and requests regarding the proposals sent by users.

[1568] "Means for adjusting the proposal content" refers to the function of reconstructing the layout and item list presented by the server based on feedback received from the user and re-proposing them.

[1569] This invention relates to a system that automatically optimizes interior styling using room images. This system utilizes AI technology to suggest appropriate interior styling and find optimal products from e-commerce sites simply by uploading a room image. Specific hardware and software configurations and their processing are described below.

[1570] Hardware and software used

[1571] Hardware

[1572] 1. Device: A device such as a smartphone, tablet, or computer that allows users to take and upload images of a room.

[1573] 2. Server: A high-performance computer for proposal generation, image analysis, and database management.

[1574] software

[1575] 1. Image analysis algorithms: Analyze room layout and furniture placement using TensorFlow and OpenCV.

[1576] 2. Generative AI models: Artificial intelligence models that incorporate natural language processing and image generation technologies, such as GPT-4 and DALL-E.

[1577] 3. Online Shopping API: An API for obtaining product information from e-commerce sites, such as Amazon API and Rakuten API.

[1578] Specific processing of the program

[1579] Uploading an image

[1580] The user takes a picture of the room with their own device and uploads it using the system's dedicated application. The device then sends this image data to the server as an HTTP request.

[1581] Image analysis

[1582] The server temporarily stores the received images and analyzes them using image analysis algorithms such as TensorFlow and OpenCV. The analysis identifies the furniture and decorations present in the room, their arrangement, and the dimensions of the room. The analysis results are stored in a database.

[1583] Choosing an interior style

[1584] The server uses a generative AI model (e.g., GPT-4) to generate multiple interior style options based on the image analysis results, including sample images and descriptions of the interior styles. The generated options are sent to the device and presented to the user.

[1585] Layout and item suggestions

[1586] Based on the interior style selected by the user, the server proposes the optimal layout and furniture placement using a layout generation algorithm. The server then lists the necessary furniture and decorations and uses an online shopping API to search for the lowest prices for each item.

[1587] View Suggestions

[1588] The server sends the generated layout proposal and item list to the terminal, which displays it on the user's terminal, allowing the user to check the proposal and provide feedback.

[1589] Feedback and Resubmissions

[1590] The server receives the user's feedback and adjusts the suggestions accordingly, repeating the process as necessary until it generates a suggestion that satisfies the user.

[1591] Examples of concrete examples and prompts

[1592] Example 1: Image upload and analysis

[1593] User A takes a photo of their living room with their smartphone and uploads it through a dedicated application. The device sends the image to the server, which analyzes it using AI algorithms. The server identifies furniture such as sofas, tables, and televisions, and determines their positions and the dimensions of the room.

[1594] Example 2: Style selection and suggestions

[1595] The server uses a generative AI model to present User A with interior style options such as "modern," "midcentury," and "Scandinavian." User A selects the "modern" style and sends that information to the server. The server generates an optimal layout based on the "modern" style and creates a list of the necessary furniture and decorations. The list includes the lowest price information for each item.

[1596] Prompt Sentence Examples

[1597] I've uploaded an image of a living room. I'd like you to suggest interior style options from this image. Options include "modern," "midcentury," and "Scandinavian."

[1598]

[1599] The user has chosen a "Modern" style. Based on this style, suggest the optimal layout and furniture list. Also get the lowest prices using information from e-commerce sites.

[1600] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1601] Step 1:

[1602] Uploading an image

[1603] User

[1604] The user takes a picture of the room with their device and opens a dedicated application.

[1605] Terminal

[1606] The device provides an option for the user to upload images taken by the user through a user interface. When the user selects an image and presses the upload button, the device sends the selected image data to the server in an HTTP request.

[1607] input

[1608] Room image data

[1609] output

[1610] Image data sent to the server

[1611] Step 2:

[1612] Image analysis

[1613] server

[1614] The server writes the received image data to disk for temporary storage. The server then analyzes the image using an image analysis algorithm (e.g., TensorFlow or OpenCV). This algorithm identifies the furniture and decorations present in the room and determines their positions and dimensions. The analysis results are saved in a database as room layout data.

[1615] input

[1616] Received room image data

[1617] output

[1618] Room layout data (furniture position and dimensions)

[1619] Step 3:

[1620] Choosing an interior style

[1621] server

[1622] The server generates multiple interior style options using a generative AI model (e.g., GPT-4 or DALL-E) based on the layout data obtained as a result of image analysis. The generated style options include sample images and descriptions of the interior styles. The generated data is sent to the device for presentation to the user.

[1623] input

[1624] Room layout data

[1625] output

[1626] Interior style options (sample images and descriptions)

[1627] Step 4:

[1628] User-selected styles

[1629] Terminal

[1630] The terminal displays the interior style options received from the server on a user interface, which provides option buttons for selecting from multiple style options.

[1631] User

[1632] The user selects the style of their choice from the presented style options and transmits the selection to the server via the terminal.

[1633] input

[1634] Interior Style Options

[1635] output

[1636] User-selected style information

[1637] Step 5:

[1638] Layout and item suggestions

[1639] server

[1640] The server generates an optimal interior layout based on the selected style information received from the user. It uses a layout generation algorithm to create the optimal layout while adjusting the furniture placement. It also lists the necessary furniture and decorations based on the proposed layout. It then uses online shopping APIs (such as the Amazon API or Rakuten API) to search for the lowest price information for each item.

[1641] input

[1642] User-selected style information, room layout data

[1643] output

[1644] Optimal layout plan, item list (including lowest price information)

[1645] Step 6:

[1646] View Suggestions

[1647] server

[1648] The server transmits the generated layout plan and item list to the terminal.

[1649] Terminal

[1650] The terminal displays the data received from the server on the user interface, allowing the user to check the proposed layout and item list through the terminal.

[1651] input

[1652] Optimal layout plan, item list

[1653] output

[1654] An interface where users can check the suggestions

[1655] Step 7:

[1656] Feedback and Resubmissions

[1657] User

[1658] Users can send feedback about the suggestions to the server via their devices, including specific requests such as "I don't like the color of the carpet."

[1659] server

[1660] The server receives feedback from the user, readjusts its suggestions based on that feedback, and again uses the generative AI model to generate new options and re-suggest them to the user.

[1661] input

[1662] User Feedback

[1663] output

[1664] Adjusted layout and item list

[1665] The above specific processing steps allow users to easily realize interior styling without specialized knowledge and purchase products at the optimal price. Furthermore, the proposed content can be smoothly readjusted, increasing user satisfaction.

[1666] (Application example 1)

[1667] 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."

[1668] Interior design is an important element in modern life, and styling that suits individual tastes is required. However, for ordinary users without specialized knowledge, it takes a lot of effort and time to make their rooms look good. Furthermore, with so many interior design products available on the market, it can be difficult to find the perfect product. Furthermore, there is a lack of tools for users to actually visualize the proposed interior style, which can cause anxiety about the final selection.

[1669] 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.

[1670] In this invention, the server includes means for receiving room images, means for analyzing the received images to identify the room layout and furniture arrangement, means for presenting a plurality of interior style options to the user, means for proposing an optimal layout and furniture arrangement based on the user's selection, means for listing necessary items based on the proposed layout and searching an e-commerce site for the lowest price information for each item, means for displaying the proposed layout and item list, means for receiving user feedback and adjusting the proposal based thereon, and means for visualizing the proposed layout using virtual reality technology, thereby enabling the user to easily optimize the interior style of their own room and actually visualize and check it.

[1671] A "room image" is a photo or video taken by a user that shows the interior of a room.

[1672] The "receiving means" is a device or system that has the function of receiving image data sent by a user from a terminal.

[1673] "Room layout" refers to the relative positions and structure of furniture and decorations within a room.

[1674] "Furniture arrangement" is information about the position of each piece of furniture in the room.

[1675] The "analysis means" refers to AI algorithms and software that process image data and identify room layouts and furniture placement.

[1676] "Interior style options" are interior design options based on a specific design theme or aesthetic concept.

[1677] The "presentation means" refers to a display device or user interface for displaying information to the user.

[1678] "Optimal layout and furniture arrangement" refers to the state of a room arranged in the most efficient and aesthetic way based on the user's selections.

[1679] A "means for proposing" is a system that has the functionality to generate layout and placement proposals based on the analysis results and user selections.

[1680] "Means for listing items" refers to a device or program that extracts necessary furniture and decorative items based on the proposed layout and organizes them in a list format.

[1681] "Lowest price information" is the lowest price information for a specific product in the market.

[1682] An "e-commerce site" is an online platform for buying and selling goods and services over the Internet.

[1683] A "searching means" is a system that has the functionality to obtain information from an e-commerce site based on specified conditions.

[1684] The "display means" refers to a device or software that displays the proposed layout and item list so that the user can check it.

[1685] "Means for receiving and adjusting feedback" refers to a system for readjusting the content of proposals by reflecting user evaluations and requests.

[1686] "Virtual reality technology" is a technology that uses computer graphics to generate a virtual environment that is close to reality, allowing users to experience that environment.

[1687] A "visualization means" is a system that uses 3D graphics or virtual reality technology to visually display the proposed layout.

[1688] The present invention relates to a system for automatically optimizing the interior and layout of a room. Hereinafter, an embodiment of the present invention will be described in detail.

[1689] System configuration

[1690] This system is realized using user terminals, servers, cloud computing infrastructure, and virtual reality (VR) devices (such as smartphones and head-mounted displays).

[1691] Receiving and analyzing room images

[1692] The user uploads images of the room they have taken to the server. Specifically, they use a smartphone app to send photos showing the interior of the room to the server. The server then analyzes the images using a deep learning framework such as TensorFlow or PyTorch to perform image analysis on the cloud and identify the room layout and furniture arrangement.

[1693] Selecting an interior style and proposing the optimal layout

[1694] Based on the analysis results, the server presents the user with multiple interior style options, such as "modern," "midcentury," and "Scandinavian." Based on the style information selected by the user, the AI ​​model proposes the optimal layout and furniture arrangement. This AI model uses, for example, a machine learning algorithm.

[1695] Make a list of the items you need and search for the best prices

[1696] Based on the proposed layout, the required items are listed. For each item, the server searches e-commerce sites for the lowest price. This process is performed using web scraping technology and API integration.

[1697] Proposal display and VR visualization

[1698] The server sends the optimized layout and item list to the user's device, where the user can review the proposal and visualize the proposed layout using virtual reality (VR) technology, for example, in game engines such as Unity or Unreal Engine.

[1699] User feedback and suggestions adjustments

[1700] Users can provide feedback on the suggestions, which is sent to the server, which analyzes the feedback and refines the suggestions. The process is automated, and the cycle of suggestions and feedback is repeated until an optimal layout is achieved that satisfies the user.

[1701] Examples and prompts

[1702] Specific examples

[1703] User: 30 years old, female, living alone, prefers modern design.

[1704] Room size: 10 tatami mats, one-room apartment.

[1705] Prompt Sentence Examples

[1706] "I'm a 30-year-old woman living in a 10-tatami studio apartment. I like modern design and am looking for interior decor that suits my room. I've uploaded a photo of my room, so please suggest furniture placement and items."

[1707] In this way, users can easily optimize the interior of their rooms and check it virtually, even if they do not have specialized knowledge.

[1708] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1709] Step 1:

[1710] Users use their smartphones to take pictures of the interior of the room and upload them to the server via a dedicated application.

[1711] Input: Room image (JPEG or PNG format)

[1712] Output: Image data is sent to the server

[1713] Step 2:

[1714] The server processes the received image data in a cloud environment and performs analysis using TensorFlow or PyTorch, which identifies the room layout and furniture positions.

[1715] Input: Received image data

[1716] Output: Room layout information and furniture placement information

[1717] Step 3:

[1718] Based on the analysis results, the server presents the user with multiple interior style options (e.g., modern, mid-century, Scandinavian).

[1719] Input: Room layout information and furniture placement information

[1720] Output: A list of interior style options

[1721] Step 4:

[1722] The user selects a preferred style from the presented interior style options and transmits the selection data to the server.

[1723] Input: User selected interior style

[1724] Output: The selected style information is sent to the server.

[1725] Step 5:

[1726] Based on the user's selections, the server uses AI models to suggest optimal layouts and furniture placements.

[1727] Input: Selected style information, room layout information

[1728] Output: Optimal layout and furniture arrangement proposals

[1729] Step 6:

[1730] Based on the proposed layout and furniture, the server creates a list of required items and searches e-commerce sites for the lowest prices for each item.

[1731] Input: Optimal layout and furniture arrangement

[1732] Output: List of required items and lowest price information

[1733] Step 7:

[1734] The server transmits the proposed layout and item list to the user terminal and displays it for the user to check.

[1735] Input: List of required items and lowest price information

[1736] Output: Layout and item list displayed on the user's device

[1737] Step 8:

[1738] Users can visually check the proposed layout using virtual reality technology, specifically by using a smartphone or head-mounted display to experience the room in a VR environment generated through Unity or Unreal Engine.

[1739] Input: Proposed layout and item list

[1740] Output: Virtual reality visualization of the room

[1741] Step 9:

[1742] The user provides feedback on the suggestions and sends it to the server, which analyzes the feedback and adjusts the suggestions.

[1743] Input: User feedback

[1744] Output: Adjusted proposal

[1745] Step 10:

[1746] The server then automatically re-proposes and generates new layouts and item lists based on the feedback received. This process is repeated until the user is satisfied.

[1747] Input: User feedback

[1748] Output: Revised layout proposal and item list

[1749] 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.

[1750] This invention relates to a system that automatically optimizes the interior design and layout of a room and adjusts the suggestions by recognizing the user's emotions. The system utilizes AI technology and an emotion engine to suggest appropriate interior styling and find the best products from online shopping sites, simply by the user uploading an image of the room.

[1751] Overall system flow

[1752] 1. Upload an image

[1753] Users upload images of their rooms taken from their own devices to the system, and the devices send the images to the system's server.

[1754] 2. Image Analysis

[1755] The server analyzes the received images using AI algorithms to determine information such as the room layout, furniture placement, and dimensions.

[1756] 3. Choosing an interior style

[1757] Based on the analysis results, the server presents the user with multiple interior style options (e.g., modern, mid-century, Scandinavian, etc.), and the user selects their preferred style from the presented style options.

[1758] 4. Layout and item suggestions

[1759] Based on the user's selection, the server proposes an optimal interior layout and furniture arrangement. Based on the proposed layout, it also lists the necessary items and searches online shopping sites for the lowest prices for those items.

[1760] 5. Display of Suggestions

[1761] The server sends the generated layout proposal and item list to the user's device. The user can review the proposal through their device and request changes if necessary. The item list also includes a purchase link for each product.

[1762] 6. User Emotion Recognition by Emotion Engine

[1763] When presenting the proposal content to the user, the server uses an emotion engine to recognize the user's emotions, thereby making it possible to evaluate the user's emotions.

[1764] 7. User feedback and sentiment ratings

[1765] The user can provide feedback on the suggestions, and the server receives the feedback and uses an emotion engine to assess the user's emotional state.

[1766] 8. Adjust your offers based on emotions

[1767] The server readjusts the suggestions based on the user's feedback and emotional state. For example, if the user looks dissatisfied, the server changes the suggestions and re-presents them. This process is repeated until the user is satisfied.

[1768] Specific examples

[1769] Example 1: Uploading and analyzing an image

[1770] User A takes a photo of their living room with their smartphone and uploads it to a dedicated app. The device sends the image to the server, which then receives it and analyzes it using an AI algorithm. The server then identifies furniture such as sofas, tables, and televisions, and determines their positions and the dimensions of the room.

[1771] Example 2: Style selection and suggestions

[1772] The server presents User A with interior style options such as "Modern," "Mid-century," and "Scandinavian." User A selects the "Modern" style and sends the information to the server. The server generates an optimal layout based on the "Modern" style and creates a list of the necessary furniture and decorations. The list also includes the lowest price information for each item.

[1773] Example 3: Displaying Suggestions and Emotion Recognition

[1774] The server sends the proposed layout and item list to User A's device. User A checks the proposal on the device, and the emotion engine recognizes his / her facial expressions and voice via the device's camera. The server evaluates User A's emotional state based on the results of the emotion engine.

[1775] Example 4: Feedback and emotion-based adjustments

[1776] User A sends feedback requesting a change in the color of the carpet. The device sends the feedback along with emotional data obtained from User A's facial expressions and voice to the server. The server analyzes the feedback and emotional data and readjusts the suggestion. For example, if User A looks dissatisfied, the server will improve the suggestion and present it again. This process is repeated until User A looks satisfied.

[1777] This allows users to receive personalized interior design suggestions based on their emotions, even if they do not have specialized knowledge, and easily create an interior design that suits their tastes.In addition, the lowest price information is provided, so users can make selections that fit their budget.

[1778] The processing flow will be explained below.

[1779] Step 1:

[1780] Users take photos of their rooms with their devices, which then upload the image files to the system via a dedicated application or web interface.

[1781] Step 2:

[1782] The device sends images of the room selected by the user to the system's server, which stores the received images and prepares them for analysis.

[1783] Step 3:

[1784] The server then analyzes the received images of the room using AI algorithms. Specifically, the image analysis module identifies furniture and decorations, and determines their locations and the dimensions of the room.

[1785] Step 4:

[1786] The server generates multiple interior style options based on the analysis, including modern, mid-century, and Scandinavian styles.

[1787] Step 5:

[1788] The server sends the generated interior style options to the user's terminal, and the user selects a preferred style from the style options displayed on the terminal.

[1789] Step 6:

[1790] The user selects the interior style option of their choice and inputs the selection information into the terminal, which then transmits the input information to the server.

[1791] Step 7:

[1792] The server then proposes optimal interior layouts and furniture arrangements based on the user's selections, with the proposals being customized according to the selected style.

[1793] Step 8:

[1794] The server creates a list of required items based on the proposed layout, searches online shopping sites for the lowest prices for each item, and retrieves price information and purchase links.

[1795] Step 9:

[1796] The server sends the generated layout proposal and item list to the user's terminal, where the user can confirm the proposal contents.

[1797] Step 10:

[1798] While the user is checking the layout proposal, the server activates a mechanism that uses the device's camera and microphone to enable the emotion engine to recognize the user's emotions. The emotion engine performs facial expression recognition and voice analysis to evaluate the user's emotional state.

[1799] Step 11:

[1800] Users can provide feedback on suggestions, for example, they may request that the color or type of a particular item be changed.

[1801] Step 12:

[1802] The device transmits the user's feedback and recognized emotion data to the server, which evaluates the feedback as well as the user's emotional state to help tailor the suggestions.

[1803] Step 13:

[1804] The server analyzes the feedback and emotion data and re-adjusts the suggestions. For example, if the user looks dissatisfied, the server will improve the suggestions and re-present them.

[1805] Step 14:

[1806] The server then sends the new, adjusted layout proposal and item list back to the user's device, and this process is repeated until the user is satisfied.

[1807] Step 15:

[1808] If the user is satisfied with the final proposal, he or she can click on a purchase link included in the item list to purchase the product on the online shopping site. The terminal provides access to the online shopping site based on the user's click.

[1809] Example 2

[1810] 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."

[1811] Conventional interior design suggestion systems have difficulty making personalized suggestions that take user emotions into account, and the suggestions often do not fully match the user's preferences. Furthermore, optimal product selection and layout adjustments are performed manually, which is time-consuming and laborious for the user. Therefore, there is a need for a system that can automatically adjust interior design suggestions based on the user's emotions and select optimal products.

[1812] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1813] In this invention, the server includes a means for receiving images of a room, a means for analyzing the received images using an AI algorithm to identify the layout and furniture arrangement of the room, and a means for presenting multiple interior style options to the user, thereby enabling personalized interior suggestions that take the user's emotions into consideration.

[1814] A "room image" is digital data containing visual information about the interior of a room that is taken by a user and uploaded to the system.

[1815] An "AI algorithm" is a calculation procedure that uses artificial intelligence technology, and is a program used to perform processes such as image analysis and pattern recognition.

[1816] "Interior style options" are multiple interior design choices presented to the user that affect the layout of the room and the placement of furniture.

[1817] The "optimal layout" is a layout that is generated based on the style selected by the user and the analysis results, and in which the arrangement of furniture and decorations is most efficient and aesthetically pleasing.

[1818] An "emotion engine" is software or hardware that analyzes a user's facial expressions and voice data and recognizes their emotional state.

[1819] An "online shopping site" is an e-commerce website where products can be purchased over the Internet.

[1820] "Feedback" is information including opinions and requests provided by users regarding the proposed content.

[1821] "Re-adjusting suggestions" is the process of modifying existing interior suggestions, taking into account user feedback and sentiment data.

[1822] This invention relates to a system that automatically optimizes the interior design and layout of a room and adjusts the suggestions based on the user's emotions. The system utilizes AI technology and an emotion engine to suggest appropriate interior styling and find the best products from online shopping sites, simply by uploading an image of the room.

[1823] Hardware and software used

[1824] 1. Hardware

[1825] Server: A high-performance computer that handles image analysis and emotion recognition.

[1826] Device: A device such as a smartphone, tablet, or computer where users can upload images and view suggestions.

[1827] Camera and microphone: Built into the device to capture the user's facial expressions and voice.

[1828] 2. Software

[1829] AI algorithms: TensorFlow, PyTorch, and other algorithms are used to analyze images and optimize interior styling.

[1830] Emotion engine: Recognizes user emotions in real time using Microsoft Azure Emotion API and other tools.

[1831] Online shopping sites: Websites that provide product data, including Amazon and Rakuten.

[1832] Specific operation of the system

[1833] First, the user takes a picture of the room using the device's camera and uploads it to the system via a dedicated app. The device then sends the image data to the server, which uses TensorFlow to analyze the image and determine the room layout and furniture arrangement.

[1834] Based on the analysis results, the server presents the user with multiple interior style options (e.g., modern, mid-century, Scandinavian, etc.) The user selects their preferred style from the presented style options and their selection is sent to the server.

[1835] The server generates an optimal layout based on the selected style, creates a list of necessary furniture and decorations, and searches online shopping sites for the lowest prices for each item and adds them to the item list.

[1836] The generated layout proposal and item list are sent from the server to the user's device, where the user can check the proposal. The proposal also includes a purchase link for each product, allowing the user to purchase the product directly.

[1837] When reviewing the suggestions, the device's camera and microphone capture the user's facial expressions and voice, and send the data to the emotion engine. The emotion engine analyzes this and evaluates the user's emotions. The evaluation results are returned to the server, which then readjusts the suggestions based on the user's emotions. If the user provides feedback, that feedback is also sent to the server and analyzed along with the emotion data.

[1838] If readjustment is required, the server generates a new proposal and sends it again to the user's terminal, and this process is repeated until the user is satisfied.

[1839] Examples of concrete examples and prompts

[1840] Specific examples

[1841] 1. Image upload and analysis

[1842] Users take a photo of their living room with their smartphone and upload the image using a dedicated app.

[1843] The device sends the image to the server, which then analyzes it using AI algorithms.

[1844] The server identifies furniture such as sofas, tables, and televisions, and determines their locations and room dimensions.

[1845] 2. Style selection and suggestions

[1846] The server presents the user with interior style options such as "modern," "midcentury," and "Scandinavian."

[1847] The user selects the "Modern" style and the selection is sent to the server.

[1848] The server generates an optimal layout based on the "modern" style and creates a list of required furniture and decorations.

[1849] 3. Suggestion Display and Emotion Recognition

[1850] The server transmits the proposal content to the user's terminal, and the user confirms the proposal content.

[1851] When confirming the proposal, the device's camera and microphone capture the user's facial expressions and voice, and the emotion engine evaluates the user's emotions.

[1852] 4. Adjusting based on feedback and emotions

[1853] The user provides feedback on the proposal and sends it to the server along with emotion data.

[1854] The server analyzes the feedback and sentiment data and readjusts its suggestions.

[1855] Prompt Sentence Examples

[1856] "Can I upload an image of my living room and get suggestions for the best layout and furniture items for a modern style?"

[1857] "Could you please evaluate your feelings based on this proposal and resubmit your proposal?"

[1858] This allows users to receive interior design suggestions tailored to their preferences, even if they do not have specialized knowledge, and to easily select the best products according to their budget.

[1859] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1860] Step 1: Upload an image

[1861] Specific operation: The user takes a picture of the room using the device's camera and uploads this image from a dedicated app.

[1862] Input: Image data of a room taken by a user.

[1863] Output: Image data sent from the device to the server.

[1864] Details: The device accepts user operations, and when the upload button is pressed, the image data is sent to the cloud server. The server receives the sent image data and passes it on to the next processing step.

[1865] Step 2: Analyze the images

[1866] Specific operation: The server analyzes the received image using an AI algorithm.

[1867] Input: Image data received from the user.

[1868] Output: Room layout information and furniture placement data as analysis results.

[1869] Details: The server uses TensorFlow to run image analysis models to extract furniture (e.g. sofas, tables, TVs, etc.), their placement, and dimensions from the images. The results are stored in a database for further processing.

[1870] Step 3: Choose your interior style

[1871] Specific operation: The server presents the user with multiple interior style options based on the analysis results.

[1872] Input: Room layout information and furniture placement data as a result of image analysis.

[1873] Output: Interior style options presented to the user.

[1874] Details: The server retrieves the analysis results from the database, generates style options based on the user's preferences (e.g., modern, mid-century, Scandinavian, etc.), and sends them to the user's device for display. The user selects their preferred style, and their selection is sent to the server.

[1875] Step 4: Layout and item suggestions

[1876] Specific behavior: The server generates the optimal layout based on the style selected by the user.

[1877] Input: User selected interior style options.

[1878] Output: Optimal layout information and required item list.

[1879] Details: The server uses a layout algorithm based on the selected style to calculate the optimal furniture placement. It then lists the required furniture and decorations and searches online shopping sites for the lowest prices for each item. The generated layout and item list are sent to the user's device.

[1880] Step 5: Viewing Proposals

[1881] Specific operation: The server sends the generated layout and item list to the user's device.

[1882] Input: Optimal layout information and required item list.

[1883] Output: The suggestions that are displayed on the user's device.

[1884] Details: The server generates the proposed content (layout image and item list) in HTML format and sends all data to the user's device. The user can view the content in the device's browser or app.

[1885] Step 6: Recognizing user emotions with the emotion engine

[1886] Specific operation: The server uses an emotion engine to recognize the user's emotions.

[1887] Input: User's facial expression data and voice data.

[1888] Output: Evaluation result of the user's emotional state.

[1889] Details: When displaying the suggestions, the device's camera and microphone capture the user's facial expressions and voice in real time and send the data to the emotion engine, which analyzes the user's emotions and sends the evaluation results to the server.

[1890] Step 7: User feedback and sentiment assessment

[1891] Specific Action: The user provides feedback on the proposal.

[1892] Input: User feedback data and emotion data obtained from facial expressions and voice.

[1893] Output: Analysis results based on feedback and sentiment data.

[1894] Details: Users enter feedback through a dedicated form or voice input, and the device sends the data to the server. The server analyzes the feedback and emotion data and extracts the information necessary for the next adjustment of the proposal.

[1895] Step 8: Adjust your offer based on emotion

[1896] Specific behavior: The server readjusts its suggestions based on the sentiment assessment and feedback.

[1897] Input: Feedback data and emotion rating data.

[1898] Output: The reworked proposal.

[1899] Details: The server restructures its suggestions based on emotional data and specific feedback indicating dissatisfaction. If necessary, it runs the AI ​​algorithm again to generate a new layout and item list, and sends the improved suggestions back to the user's device. This process is repeated until the user is satisfied.

[1900] (Application example 2)

[1901] 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."

[1902] Conventional interior design suggestion systems have the problem of not being able to provide personalized suggestions based on the user's emotions and preferences. Furthermore, they are unable to provide interior design suggestions linked to real-time emotion recognition, limiting the user experience. Furthermore, when suggesting interior styles and layouts, they lack the ability to dynamically generate prompts, making effective interaction with the user difficult.

[1903] 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.

[1904] In this invention, the server includes means for receiving images, means for analyzing images to identify the room layout and furniture arrangement, means for adjusting suggestions based on the user's emotions, means for recognizing the user's emotions from the smart device and adjusting the suggestions, and means for generating prompt sentences using a generative AI model to dynamically make suggestions to the user. This enables personalized suggestions based on the user's emotions and feedback, enabling effective interaction with the user in real time.

[1905] The "means for receiving images of the room" is a function that allows a user to send images taken by a smart device to a server.

[1906] "Means for analyzing received images to determine the layout and furniture placement of the room" refers to a function that uses AI algorithms to identify elements in the image and determine the location of furniture and the dimensions of the room.

[1907] The "means for presenting a plurality of interior style options to the user" is a function for presenting several interior styles to the user as options based on the results of room analysis.

[1908] The "means for proposing the optimal layout and furniture arrangement based on the user's selection" is a function for automatically generating and proposing the optimal layout and furniture arrangement based on the interior style selected by the user.

[1909] "A means for listing necessary items based on a proposed layout and searching online shopping sites for the lowest price information for each item" is a function for listing furniture and decorations required for a proposed layout and obtaining the lowest price information for those items.

[1910] The "means for displaying the proposed layout and item list" is a function for visually displaying the generated layout and item list on the user's smart device.

[1911] "Means for receiving user feedback and adjusting the proposed content based on that" is a function for receiving evaluations and requests for changes from users and readjusting the proposed content based on that.

[1912] "Means for recognizing a user's emotions from a smart device and adjusting suggestions" refers to a function that uses the camera and microphone of a smart device to analyze the user's facial expressions and voice and adjust suggestions according to their emotional state.

[1913] "Means for generating prompt sentences using a generative AI model and dynamically making suggestions to the user" refers to a function for dynamically generating prompt sentences for suggestions or questions to the user using a generative AI model and presenting them to the user.

[1914] In order to implement the present invention, the following system configuration and process are employed.

[1915] First, a user takes an image of the room using a smart device (e.g., a smartphone or smart glasses) and uploads it to the system. The user's device then sends the image to the system's server. The server then analyzes the received image using AI algorithms to identify information such as the room layout, furniture placement, and dimensions. OpenCV and advanced image recognition technology are used for image analysis.

[1916] Based on the analysis results, the server presents multiple interior style options (e.g., modern, mid-century, Scandinavian, etc.) to the user. The user selects their preferred style from the presented style options through their smart device. The selected style information is then sent to the server.

[1917] The server then proposes an optimal interior layout and furniture arrangement based on the user's selection. Furthermore, based on the proposed layout, it lists the necessary items and searches online shopping sites for the lowest prices on those items, allowing users to easily obtain the items they need within their budget.

[1918] The proposals are displayed on the smart device's display, and the user can check the proposed layout and item list. The system uses the user's smart device's camera and microphone to recognize the user's facial expressions and tone of voice in real time. The "facial_emotion_recognition" library is used for emotion recognition.

[1919] The server evaluates the user's emotional state and adjusts the suggestions as needed. It also receives feedback from the user and adjusts the suggestions accordingly. This process continues until the user is satisfied. The server utilizes the user's emotional data along with the feedback to provide more personalized suggestions.

[1920] Furthermore, a generative AI model (such as GPT-3.5) is used to dynamically generate prompts for suggestions or questions for the user and present them to the user. The "transformers" library is used to generate these prompts. As a concrete example, if a user selects the "modern" style and wants to change the carpet color, the prompt is generated as follows:

[1921] example:

[1922] "Please suggest a carpet color that would be suitable for a modern style living room."

[1923] The prompt sentences thus suggested are displayed as appropriate suggestions to the user.

[1924] Overall, the system enables personalized interior design suggestions based on user emotions and feedback, enabling effective interaction in real time.

[1925] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1926] Step 1:

[1927] Users can take pictures of their rooms using their smart devices and upload them to the system.

[1928] Input: Room image (JPEG or PNG format)

[1929] Specific operation: The user's device uses the camera app to take a picture of the room and sends it to the server via the image upload function.

[1930] Output: Image data of the room sent to the server

[1931] Step 2:

[1932] The server analyzes the received images using an AI algorithm (e.g., OpenCV) to determine information such as the room layout, furniture placement, and dimensions.

[1933] Input: Image data of the room

[1934] What it does: The server uses image analysis software to identify the type, location, and size of furniture in the image and saves this as metadata.

[1935] Output: Room layout data and furniture placement information

[1936] Step 3:

[1937] Based on the image analysis results, the server presents the user with multiple interior style options (e.g., modern, mid-century, Scandinavian).

[1938] Input: Room layout data and furniture placement information

[1939] What it does: The server selects appropriate style options from the interior style database and presents them to the user's smart device.

[1940] Output: Interior style options presented to the user

[1941] Step 4:

[1942] The user selects the preferred style from the presented interior style options and transmits it to the server.

[1943] Input: Interior style option selection information

[1944] Specific operation: The user's device sends the selection information to the server.

[1945] Output: User's interior style selection sent to the server

[1946] Step 5:

[1947] The server proposes the optimal interior layout and furniture placement based on the user's selection, lists the necessary items, and searches online shopping sites for the lowest prices for those items.

[1948] Input: User's interior style selection information

[1949] How it works: Based on the selected style, the server uses AI algorithms to generate the optimal layout, creates a list of furniture and decorations, and uses online shopping APIs to retrieve the lowest prices.

[1950] Output: Proposed interior layout and item list, along with the lowest price information

[1951] Step 6:

[1952] The proposed layout and item list are displayed on the user's smart device.

[1953] Input: Proposed interior layout, item list, and lowest price information

[1954] Specific operation: The server sends the generated suggestions to the user's device, which then displays them visually.

[1955] Output: The suggestion displayed on the user's device

[1956] Step 7:

[1957] The user's smart device uses a camera and microphone to recognize emotions in real time from the user's facial expressions and tone of voice.

[1958] Input: User facial expressions and tone of voice

[1959] Specific operation: The device inputs data acquired by the camera and microphone into an emotion recognition library (e.g., facial_emotion_recognition) to identify the emotional state.

[1960] Output: Recognized user emotion data

[1961] Step 8:

[1962] The server adjusts the suggestions based on the user's emotional state.

[1963] Input: Recognized user emotion data

[1964] What it does: The server analyzes the emotion data and, if necessary, readjusts and re-presents the suggestions.

[1965] Output: Adjusted proposal

[1966] Step 9:

[1967] The user provides feedback on the proposal content, and the terminal transmits the feedback together with emotion data to the server.

[1968] Input: User feedback and sentiment data

[1969] Specific operation: The device sends feedback and emotion data to the server.

[1970] Output: Feedback and emotion data sent to the server

[1971] Step 10:

[1972] The server readjusts the suggestions based on the feedback and emotional data and continues making suggestions until the user is satisfied.

[1973] Input: Feedback and emotion data

[1974] What it does: The server analyzes the feedback and emotion data, and then refines the suggestions and presents them to the user. This refinement involves generating prompts using a generative AI model and presenting them as new suggestions.

[1975] Output: A refined proposal that is iterated on until the user is satisfied.

[1976] Examples of specific prompts include the following:

[1977] "Please suggest a carpet color that would be suitable for a modern style living room."

[1978] 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.

[1979] 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.

[1980] 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.

[1981] 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.

[1982] 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.

[1983] 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.

[1984] 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).

[1985] 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.

[1986] 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."

[1987] 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.

[1988] 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).

[1989] 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.

[1990] 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.

[1991] 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.

[1992] 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.

[1993] 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.

[1994] 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.

[1995] 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.

[1996] 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.

[1997] 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.

[1998] 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.

[1999] The following is further disclosed regarding the above embodiment.

[2000] (Claim 1)

[2001] means for receiving an image of a room;

[2002] means for analyzing the received images to determine the layout and furniture arrangement of the room;

[2003] means for presenting a plurality of interior style options to a user;

[2004] means for suggesting an optimal layout and furniture arrangement based on the user's selection;

[2005] A method to list the necessary items based on the proposed layout and search online shopping sites for the lowest prices for each item.

[2006] means for displaying the proposed layout and item list;

[2007] a means of receiving user feedback and adjusting the suggestions accordingly;

[2008] A system including:

[2009] (Claim 2)

[2010] 10. The system of claim 1, further comprising means for providing a purchase link to an online shopping site for the suggested item list.

[2011] (Claim 3)

[2012] 10. The system of claim 1, further comprising means for automatically making re-suggestions after receiving feedback.

[2013] "Example 1"

[2014] (Claim 1)

[2015] means for receiving an image of a room;

[2016] means for analyzing the received images to determine the layout and furniture arrangement of the room;

[2017] A means for presenting multiple interior style options to the user using a generative AI model based on the analysis results; and

[2018] means for suggesting an optimal layout and furniture arrangement based on the user's selection;

[2019] A means to list the necessary items based on the proposed layout and search for the lowest price information for each item on e-commerce sites;

[2020] means for displaying the proposed layout and item list;

[2021] a means of receiving user feedback and adjusting the suggestions accordingly;

[2022] A system including:

[2023] (Claim 2)

[2024] 10. The system of claim 1, further comprising: means for providing a purchase link to an e-commerce site for the suggested item listing.

[2025] (Claim 3)

[2026] 10. The system of claim 1, further comprising means for automatically making re-suggestions after receiving feedback.

[2027] "Application Example 1"

[2028] (Claim 1)

[2029] means for receiving an image of a room;

[2030] means for analyzing the received images to determine the layout and furniture arrangement of the room;

[2031] means for presenting a plurality of interior style options to a user;

[2032] means for suggesting an optimal layout and furniture arrangement based on the user's selection;

[2033] A means to list the necessary items based on the proposed layout and search for the lowest price information for each item on e-commerce sites;

[2034] means for displaying the proposed layout and item list;

[2035] a means of receiving user feedback and adjusting the suggestions accordingly;

[2036] a means for visualizing the proposed layout using virtual reality technology; and

[2037] A system including:

[2038] (Claim 2)

[2039] 10. The system of claim 1, further comprising: means for providing a purchase link to an e-commerce site for the suggested item listing.

[2040] (Claim 3)

[2041] 10. The system of claim 1, further comprising means for automatically making re-suggestions after receiving feedback.

[2042] "Example 2: Combining Emotion Engines"

[2043] (Claim 1)

[2044] means for receiving an image of a room;

[2045] A means for analyzing the received images using an AI algorithm to identify the layout of the room and the placement of furniture;

[2046] means for presenting a plurality of interior style options to a user;

[2047] means for generating an optimal layout and furniture arrangement based on user selections;

[2048] A method to list the items you need based on the proposed layout and search online shopping sites for the lowest prices for each item.

[2049] means for displaying the proposed layout and item list on a user's terminal;

[2050] a means for recognizing a user's emotion using an emotion engine and adjusting the content of the suggestions;

[2051] a means of receiving user feedback and re-adjusting the suggestions based on that feedback;

[2052] A system including:

[2053] (Claim 2)

[2054] 10. The system of claim 1, further comprising: means for providing a purchase link to an online shopping site for the suggested item list.

[2055] (Claim 3)

[2056] 10. The system of claim 1, further comprising means for automatically making re-suggestions after receiving feedback and sentiment ratings.

[2057] "Application example 2 when combining emotion engines"

[2058] (Claim 1)

[2059] means for receiving an image of a room;

[2060] means for analyzing the received images to determine the layout and furniture arrangement of the room;

[2061] means for presenting a plurality of interior style options to a user;

[2062] means for suggesting an optimal layout and furniture arrangement based on the user's selection;

[2063] A method to list the necessary items based on the proposed layout and search online shopping sites for the lowest prices for each item.

[2064] means for displaying the proposed layout and item list;

[2065] a means of receiving user feedback and adjusting the suggestions accordingly;

[2066] A means for recognizing user emotions from a smart device and adjusting suggestions;

[2067] a means for generating prompts using a generative AI model to dynamically provide suggestions to a user;

[2068] A system including:

[2069] (Claim 2)

[2070] 10. The system of claim 1, further comprising means for providing a purchase link to an online shopping site for the suggested item list.

[2071] (Claim 3)

[2072] 10. The system of claim 1, further comprising means for automatically making re-suggestions after receiving feedback. [Explanation of symbols]

[2073] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving an image of a room; means for analyzing the received images to determine the layout and furniture arrangement of the room; means for presenting a plurality of interior style options to a user; means for suggesting an optimal layout and furniture arrangement based on the user's selection; A method to list the necessary items based on the proposed layout and search online shopping sites for the lowest prices for each item. means for displaying the proposed layout and item list; a means of receiving user feedback and adjusting the suggestions accordingly; A system including:

2. 2. The system of claim 1, further comprising means for providing a purchase link to an online shopping site for the suggested item list.

3. The system of claim 1 further comprising means for automatically making re-suggestions after receiving feedback.

Images