System

Abstract

A system is provided.SOLUTION: This system includes a means for allowing a user to input the image data and dimension data of his or her own living space, a means for analyzing the inputted image data and dimension data, and for generating an optimal interior design plan, a means for visualizing the generated design plan in an augmented reality and virtual reality environment in real time, and a means for learning the selection and operation of the user, and for reflecting them in the next proposal.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] Traditional interior design requires specialized knowledge and experience, making it difficult for average consumers to access. Furthermore, when purchasing furniture and decorative items, it is not possible to actually see them in person, creating a gap between expectations and reality. Furthermore, there is a lack of personalized proposals tailored to users' preferences and needs, making it difficult to quickly provide designs that satisfy users. To address these issues, there is a need for a system that allows users to easily design their ideal interior and see the changes in real time in a virtual environment. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for a user to input image data and dimensional data of their living space, a means for analyzing the input image data and dimensional data to generate optimal interior design proposals, a means for visualizing the generated design proposals in real time in an augmented reality and virtual reality environment, and a means for learning the user's selections and operations and reflecting them in future proposals. This allows users to easily create interior designs without specialized knowledge and to check changes in real time in the virtual environment. Furthermore, personalized proposals based on the user's preferences and needs can be made, resulting in a more satisfying design experience.

[0006] "User" refers to an individual or group that uses the system to design their ideal interior.

[0007] "Living space" means a space where a user lives, including rooms and specific areas within a home.

[0008] "Image data" refers to photographs and image files of living spaces that users upload to the system.

[0009] "Dimensional data" refers to the measurements of each element of the living space (walls, floors, ceilings, furniture, etc.) entered by the user.

[0010] "Means" refers to specific methods or processes for achieving a particular purpose.

[0011] "Optimal interior design proposals" refer to the most appropriate interior layout and decoration suggestions generated by AI based on the user's image data and dimensional data.

[0012] "Augmented reality (AR)" refers to a technology that displays virtual information overlaid on the real-world environment.

[0013] "Virtual reality (VR)" refers to a technology that immerses users in a computer-generated virtual space.

[0014] "Real-time" refers to responding immediately to operations and inputs and displaying results without delay.

[0015] "Visualization" refers to the process of visually representing data or information.

[0016] "Selection" means the act of a User choosing one or more options or settings from those offered on the System.

[0017] "Operation" refers to the actions a user takes to control the system through the interface.

[0018] "Learning" refers to the process by which the system collects user behavior data and analyzes it for future suggestions and service improvements.

[0019] "Personalized suggestions" refers to providing interior design ideas that are individually tailored to the user based on their preferences and past choices. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0023] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0024] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0025] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0026] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0028] [First embodiment]

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

[0030] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0037] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] The system of the present invention allows users to input image data and dimensional data of their living space through an interface, and generates and provides optimal interior design proposals based on that data. The system runs on a web browser and includes the following main components:

[0042] 1. User Registration and Login

[0043] Users: Create an account or log in with an existing account through a web browser.

[0044] Terminal: displays the registration or login form, receives input from the user, and sends it to the server.

[0045] Server: Validates the credentials and authenticates the user, saving the information in a database for new users or generating and returning an authentication token for existing users.

[0046] 2. Upload photos and dimensions of your room

[0047] User: Take a photo of your living space and measure the dimensions of your living space.

[0048] Terminal: Provides an interface for uploading photos and entering dimensions, and sends the entered data to the server.

[0049] Server: Analyzes the received photos and dimension data and stores them in a database.

[0050] 3. Proposing the best design

[0051] Server: Analyzes stored photos and dimension data using AI to generate optimal interior design proposals.

[0052] Terminal: Visually displays the generated design proposals to the user.

[0053] 4. Real-time simulation using AR / VR

[0054] Users: Review proposed designs and virtually rearrange furniture and decorations.

[0055] Terminal: Provides an AR / VR interface, repositions itself in real time based on user interaction, and provides visual feedback.

[0056] Server: Generates AR / VR data as needed and sends it to the device.

[0057] 5. Learning your preferences

[0058] Device: Collects user selections and operation history and sends them to the server.

[0059] Server: The collected data is fed back to the AI ​​model and reflected in the next proposal.

[0060] Specific examples

[0061] 1. Users upload photos and dimensions of their rooms

[0062] User: "Take a photo of my living room, measure the dimensions, and upload it to the app."

[0063] Device: "The user has entered a photo and dimensions of their living room. This data will be sent to the server."

[0064] Server: "Data received, AI will begin analysis."

[0065] 2. Proposing the best design

[0066] Server: "Analyze the photos and dimensions to generate the optimal interior design. The resulting design has been sent to your device."

[0067] Device: "The AI ​​will display living room design ideas to the user."

[0068] 3. AR / VR Simulation

[0069] User: "View the proposed design in AR mode and adjust the sofa position."

[0070] Device: "User repositioned the couch. View the new position in AR."

[0071] Server: "We will accumulate new placement data and reflect it in our next proposal."

[0072] This allows users to easily design their ideal interior without specialist knowledge and see the changes in real time in a virtual environment, enabling personalized suggestions based on the user's preferences and resulting in a more satisfying design experience.

[0073] The processing flow will be explained below.

[0074] Step 1: User Registration

[0075] User: Clicks the "Create a new account" button to open the registration form.

[0076] On your device: Display a form to enter your name, email address, and password.

[0077] User: Enter the required information and click the "Submit" button.

[0078] Terminal: Sends the entered information to the server.

[0079] Server: Checks the credentials and saves the new user's information to the database. If the save is successful, returns a success message to the device.

[0080] Step 2: Log in

[0081] User: Enter your email address and password and click the Login button.

[0082] On your device: Send your credentials to the server.

[0083] Server: Checks the credentials against a database and, if correct, generates an authentication token and sends it to the device.

[0084] Terminal: Receives the authentication token and redirects the user to the dashboard.

[0085] Step 3: Upload photos of your room

[0086] User: Take a photo of your living space with your smartphone or camera.

[0087] Terminal: Display the photo upload interface.

[0088] User: Select the photo you have taken and click the upload button.

[0089] Device: Sends photo data to the server.

[0090] Server: Saves the received photos to storage and returns a save completion message to the device.

[0091] Step 4: Enter dimensional data

[0092] User: Use a tape measure to measure the dimensions of the room.

[0093] Terminal: Displays the dimension input form.

[0094] User: Enter the measured dimensions and click the submit button.

[0095] Terminal: Sends the entered dimension data to the server.

[0096] Server: Stores the received dimension data in a database.

[0097] Step 5: Generate optimal design proposals

[0098] Server: Passes the saved photos and dimensional data to the AI ​​module and begins analysis.

[0099] AI module: Analyzes image data and dimensional data to generate optimal interior design proposals.

[0100] Server: Sends the generated design proposals to the device and displays the results.

[0101] Step 6: View in AR / VR mode

[0102] User: Clicks the "AR Mode" or "VR Mode" button.

[0103] Device: Launch camera view and overlay the virtual interior design onto the real space.

[0104] User: Take action to adjust the position of virtual furniture and decorations.

[0105] Device: Updates the AR / VR display in real time based on user actions.

[0106] Step 7: Learning user preferences

[0107] Device: Collects user selections, layout changes, and other operational history.

[0108] Terminal: Sends collected data to the server.

[0109] Server: The received user data is fed back to the AI ​​module and reflected in the next proposal.

[0110] With such detailed processing for each step, users can easily design their ideal interior and check it in real time in the virtual space, improving the user experience and providing optimal design suggestions.

[0111] Example 1

[0112] 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."

[0113] Currently available interior design systems on the market require users to provide photos and dimensions of their living space. However, the subsequent design proposals and feedback to users lack real-time and personalized features. Furthermore, users have no way to virtually change the proposed design and visually confirm the results, which can lead to low user satisfaction. Therefore, there is a need for a system that allows users to easily find interior designs based on their preferences and view and adjust them in real time.

[0114] 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.

[0115] In this invention, the server includes a means for a user to input image data and dimensional data of their living space, a means using a generative AI model to analyze the input image data and dimensional data and generate optimal interior design proposals, a means for visualizing the generated design proposals in real time in an augmented reality and virtual reality environment, and a means using a machine learning algorithm to learn the user's selections and operations and reflect them in future proposals, thereby enabling users to easily find interior designs based on their preferences and check and adjust them in real time.

[0116] "User" means any person or entity that uses the System.

[0117] "Living space" refers to the place or room where the user lives.

[0118] "Image data" refers to digital data including photographs and images taken by users.

[0119] "Dimensional Data" refers to data regarding the size of a room or furniture measured by the user.

[0120] A "generative AI model" refers to an artificial intelligence model that analyzes input data and generates optimal interior design proposals.

[0121] "Augmented reality" refers to technology that adds digital information to real-world visual information.

[0122] "Virtual reality" refers to technology that allows you to experience a completely digitally created environment as if it were real.

[0123] "Machine learning algorithm" refers to an algorithm that allows the system to automatically learn from collected data and reflect that knowledge in its next proposal.

[0124] "Proposal" refers to showing the user the interior design ideas generated by the system.

[0125] The system of the present invention allows users to input image data and dimensional data of their living space, and generates optimal interior design proposals based on that data, and then proposes and visualizes them.The system runs on a web browser and includes the user, a terminal, and a server as its main components.

[0126] Detailed component description

[0127] 1. User Registration and Login

[0128] Users: Open a web browser and access the account creation page. Enter your name, email address, and password and press the "Register" button. Existing users should enter their email address and password and press the "Login" button.

[0129] On the device: Display a registration or login form, validate the data using JavaScript form validation, and submit it to the server.

[0130] Server: Store new user information in a database (e.g. MySQL, PostgreSQL). For existing users, verify the credentials, generate a JWT (JSON Web Token) and return it to the user.

[0131] 2. Upload photos and dimensions of your room

[0132] User: Take a photo of your living space using a smartphone or digital camera, measure the room dimensions, select the photo on the application's upload page, enter the dimensions, and press the "Upload" button.

[0133] Terminal: Receives image file and dimension data, displays a preview of the image selected by the user, and sends the data to the server using the FormData API.

[0134] Server: Stores uploaded image files in the file system or cloud storage (e.g., Amazon S3), stores dimensional data in a database, and performs image analysis and extracts features using OpenCV.

[0135] 3. Proposing the best design

[0136] Server: Generates a prompt into an AI engine (e.g., TensorFlow, PyTorch) that prompts for the input of the saved photo and dimension data. An example of a generated prompt is "Generate a design proposal for this living space." The AI ​​model analyzes this and generates the optimal interior design proposal based on the input data. The generated design proposal is saved as an image or 3D model.

[0137] Terminal: Receives the response from the server and uses Three.js to visually display the generated design proposal to the user.

[0138] 4. Real-time simulation using AR / VR

[0139] User: Press a button on the web page to switch to AR / VR mode, view proposed designs, and virtually rearrange furniture and decorations.

[0140] Device: Use Unity or Unreal Engine to provide an AR / VR interface, repositioning furniture in real time based on user interaction and providing visual feedback.

[0141] Server: Optionally, store the new placement data in the backend for future recommendations.

[0142] 5. Learning your preferences

[0143] Terminal: Collects user operation history and selections and sends them to the server.

[0144] Server: Analyzes the received data using a collaborative filtering algorithm to learn the user's preferences, which is then used to improve the accuracy of the next recommendation.

[0145] Specific examples

[0146] When a user uploads photos and dimensions of a living space, they:

[0147] 1. User: "Take a photo of my living room, measure the dimensions, and upload it to the app."

[0148] 2. Device: "The user has entered a photo and dimensions of their living room. Send this data to the server."

[0149] 3. Server: "The data is received and the AI ​​begins analysis."

[0150] When proposing the optimal design, we act as follows:

[0151] 1. Server: "Analyzes the photos and dimensional data, generates optimal interior design proposals, and sends the generated design proposals to the device."

[0152] 2. Device: "Displays AI-suggested living room design ideas to the user."

[0153] This allows users to easily find their ideal interior design without specialist knowledge, and check and adjust it in real time. It also provides personalized suggestions based on the user's preferences, providing a highly satisfying design experience.

[0154] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0155] Step 1:

[0156] User registration and login

[0157] User: Open a web browser and access the account creation page. Enter your name, email address, and password, then click the "Register" button. Existing users should enter their email address and password and click the "Login" button.

[0158] Input: Name, Email Address, Password (if new user). Email Address, Password (if existing user).

[0159] Terminal: Receives the input data and performs JavaScript form validation (e.g., using the JQuery Validation Plugin). After validation is passed, the data is sent to the server.

[0160] Output: The input data that passes validation.

[0161] Server: Stores the received data in a database (e.g., MySQL, PostgreSQL), and performs registration processing for new users. For existing users, checks the entered credentials against the information in the database. If they match, generates a JWT (JSON Web Token) and returns it to the user.

[0162] Output: Saves new user information or issues a JWT token for an existing user.

[0163] Step 2:

[0164] Upload room photos and dimensions

[0165] User: Take a photo of your living space using a smartphone or digital camera, measure the dimensions of the room, select the photo on the application's upload page, enter the dimensions, and press the "Upload" button.

[0166] Input: Photo files of the living space, room dimensions.

[0167] Terminal: Receives image file and dimension data, displays a preview of the image, and sends the data to the server using the FormData API.

[0168] Output: Image file and dimension data sent to the server.

[0169] Server: Stores image files in cloud storage (e.g., Amazon S3) or a file system. Stores dimensional data in a database. Additionally, analyzes the images using OpenCV and extracts image features.

[0170] Output: Saved image files with dimensional data and extracted image features.

[0171] Step 3:

[0172] Proposing optimal design ideas

[0173] Server: Generates a prompt to input the saved photo and dimension data into an AI engine (e.g., TensorFlow, PyTorch). Example: "Generate design ideas for this living space." The data along with the prompt is input into the AI ​​model to generate optimal interior design ideas. The generated design ideas are saved as images or 3D models.

[0174] Input: Saved photo and dimensional data, prompt text.

[0175] Output: Generated interior design proposals (images or 3D models).

[0176] On the device: The design proposals received from the server are visually displayed to the user using Three.js.

[0177] Output: A displayed interior design proposal.

[0178] Step 4:

[0179] Real-time simulation using AR / VR

[0180] Users: Press a button on the web page to switch to AR / VR mode, view proposed designs, and virtually rearrange furniture and decorations.

[0181] Input: Proposed design ideas, user actions (rearrangement of furniture and decorations).

[0182] Device: Provides an AR / VR interface using Unity or Unreal Engine. Rearranges furniture in real time based on user actions and provides visual feedback. For example, if a user drags a sofa to change its position, the sofa's position on the screen is instantly updated.

[0183] Output: Real-time updated furniture placement.

[0184] Server: Optionally, store the new placement data in the backend and use it for future recommendations.

[0185] Output: The new saved configuration data.

[0186] Step 5:

[0187] Learning user preferences

[0188] Terminal: Collects user operation history and selections and sends them to the server.

[0189] Input: User operation history, selection data.

[0190] Output: Operation history and selection data sent to the server.

[0191] Server: Analyzes the received data using a collaborative filtering algorithm to learn the user's preferences, thereby improving the accuracy of the next suggestions.

[0192] Output: Trained user model, data for next proposal.

[0193] As described above, the present invention is a system that allows users to easily find their ideal interior design, check and adjust it in real time, and provides personalized suggestions based on the user's preferences, resulting in a highly satisfying design experience.

[0194] (Application example 1)

[0195] 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."

[0196] Conventional interior design proposal systems have the drawback of making it difficult for users to check the actual layout image in real time when selecting interior items, and they cannot purchase the proposed products on the spot. Furthermore, the accuracy of the system's ability to learn the user's preferences and choices and reflect them in future proposals is insufficient. This makes it difficult for users to select interior items that will satisfy them.

[0197] 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.

[0198] In this invention, the server includes: a means for a user to input image data and dimensional data of their living space; a means for analyzing the input image data and dimensional data to generate optimal interior design proposals; a means for visualizing the generated design proposals in an augmented reality and virtual reality environment in real time and projecting them onto the user's living space; a means for learning the user's selections and operations and reflecting them in future proposals; and a means for connecting with an online platform to purchase the proposed products on the spot. This allows users to visually confirm the interior layout of their living space in real time and select interior designs based on their preferences. Furthermore, the ability to purchase products on the spot provides a consistent user experience from selection to purchase.

[0199] "Means for users to input image data and dimensional data of their living space" refers to an interface or application that allows users to take a photograph of the current state of their living space using a camera or measuring device and input the image data and dimensional data.

[0200] "Means for analyzing input image data and dimensional data to generate optimal interior design proposals" refers to software or systems that use AI or algorithms to analyze image data and dimensional data of living spaces provided by users, and automatically propose the optimal interior design for that space.

[0201] "Means of visualizing the generated design proposals in real time in an augmented reality or virtual reality environment and projecting them into the user's living space" refers to AR (augmented reality) and VR (virtual reality) technologies that allow interior design proposals generated by AI to be displayed in the user's field of vision in real time, allowing them to be viewed superimposed on the real space.

[0202] "Means of learning user choices and operations and reflecting them in the next proposal" refers to functions and algorithms that record the choices and changes made by the user on the system, and allow the AI ​​to learn from that data to help improve the accuracy of the next interior design proposal.

[0203] "Means for connecting with online platforms to purchase proposed products on the spot" refers to a system that provides links and purchasing procedures for directly purchasing furniture and decorative items included in the interior design proposals that users have confirmed from affiliated online shops and e-commerce sites.

[0204] This invention is a system that allows users to input image data and dimensional data of their living space, and generates and proposes optimal interior designs based on that data. To realize this system, the following hardware and software are required:

[0205] Hardware:

[0206] Smart glasses (or smartphone): A device for capturing images of the current state of the living space and acquiring image data.

[0207] Server: A high-performance computing device that analyzes image and dimensional data and generates design proposals.

[0208] software:

[0209] OpenCV: A library for real-time video processing acquired from smart glasses and smartphones.

[0210] Requests: A library for communicating with the server to send and receive data.

[0211] AI model: A generative model that analyzes data entered by the user and generates optimal interior designs.

[0212] Processing flow

[0213] 1. Input of image and dimension data

[0214] Users use smart glasses or a smartphone to take pictures of their living space and input dimensional data, which is then sent from the device to a server.

[0215] 2. Generate design proposals

[0216] The server then uses the received image and dimension data to generate optimal interior design proposals using an AI model that has been trained on a large amount of design data in advance.

[0217] 3. Real-time visualization

[0218] The design proposals generated by the server are displayed in real time in the user's field of vision via smart glasses or a smartphone, allowing the user to see how the design will look in their living space.

[0219] 4. Collaboration with online platforms

[0220] If a user likes a suggested product, they can connect to the online platform and complete the purchase process on the spot.

[0221] 5. Learning user choices and actions

[0222] The choices and actions taken by the user are logged and fed back to the AI ​​model, which then incorporates this data into the next recommendation, allowing for more personalized suggestions.

[0223] Specific examples

[0224] Example 1:

[0225] User: "I'm going to use my smart glasses to take a picture of my living room and enter the dimensions."

[0226] System: "Receives data and generates optimal interior design proposals."

[0227] Result: "I'll use AR mode to view the generated design and consider the placement of the sofa."

[0228] Example 2:

[0229] User: "I'll choose the sofa I like from the suggested interior designs and buy it right away."

[0230] System: "We will connect with your online platform to begin the purchase process."

[0231] Example prompts

[0232] User ID: 'user123'

[0233] Living space image: 'image123.jpg'

[0234] Living space dimensions: {'length': 500, 'width': 400, 'height': 300}

[0235] Suggestion: 'Optimal furniture arrangement for living room'

[0236] Through these steps, users can see how the interior layout of their living space will look in real time and purchase the products they like on the spot. Furthermore, the next proposal will reflect the user's selections and operation history, providing more personalized design suggestions.

[0237] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0238] Step 1:

[0239] The user takes an image of the living space and inputs dimensional data. The input data is acquired using smart glasses or a smartphone. The input consists of image data and dimensional data of the living space. The output is the input data ready to be transmitted.

[0240] Specifically, the user takes a photo of their living room using a camera, measures the dimensions using a tape measure or a dedicated app, enters the data into their smart glasses or smartphone, and then presses a button to send the information to the server.

[0241] Step 2:

[0242] The image data and dimension data acquired by the terminal are sent to the server. The input is the image data and dimension data received from the user, and the output is the data sent to the server.

[0243] Specifically, the device recognizes the user's actions and uses the API to send data to the server, which is done using an HTTP POST request.

[0244] Step 3:

[0245] The server analyzes the received image data and dimension data and generates optimal interior design proposals using a generative AI model. The input is the image data and dimension data sent from the device, and the output is the generated interior design proposal.

[0246] Specifically, after receiving the data, the server provides the input data to the AI ​​model, which analyzes it and generates optimal design proposals taking into account the layout and dimensions of the living space.

[0247] Step 4:

[0248] The server sends the interior design proposals it generates to the device. The input is the design proposal generated by the AI ​​model, and the output is the design proposal data that can be displayed on the device.

[0249] Specifically, the server sends design proposals in a format such as JSON to the device, which then receives and processes them.

[0250] Step 5:

[0251] The device displays interior design proposals in real time within the user's field of view. The input is the design proposal sent from the server, and the output is a real-time image displayed in the user's field of view.

[0252] Specifically, users can view design proposals overlaid on the real world through smart glasses or smartphone displays, with the placement of furniture and decorations projected onto the real world using AR technology.

[0253] Step 6:

[0254] When a user wants to purchase the suggested furniture or decorative item on the spot, they are connected to an online platform, where the input is the user's purchasing decision information and design proposals, and the output is links and processes to proceed to the purchase process.

[0255] Specifically, users can select a product they like and click on the link displayed on their device to be redirected to the relevant online shop and proceed with the purchase.

[0256] Step 7:

[0257] The device sends the user's selections and operation history to the server, which analyzes the data and reflects it in the next proposal. The input is the user's selections and operation history data, and the output is learning data for the AI ​​model.

[0258] Specifically, the device records user operation data and periodically sends it to a server. The server then stores the data and feeds it back into the AI ​​model. This allows the data to be reflected in the next interior design proposal, enabling more personalized suggestions.

[0259] 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.

[0260] The system of the present invention allows users to input image data and dimensional data of their living space through an interface, and generates and provides optimal interior design proposals based on that data. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system can provide personalized interior design proposals that take the user's emotions into consideration. The system runs on a web browser and includes the following main components:

[0261] 1. User Registration and Login

[0262] Users: Create an account or log in with an existing account through a web browser.

[0263] Terminal: displays the registration or login form, receives input from the user, and sends it to the server.

[0264] Server: Validates the credentials and authenticates the user, saving the information in a database for new users or generating and returning an authentication token for existing users.

[0265] 2. Upload photos and dimensions of your room

[0266] User: Take a photo of your living space and measure the dimensions of your living space.

[0267] Terminal: Provides an interface for uploading photos and entering dimensions, and sends the entered data to the server.

[0268] Server: Analyzes the received photos and dimension data and stores them in a database.

[0269] 3. Proposing the best design

[0270] Server: Analyzes stored photos and dimension data using AI to generate optimal interior design proposals.

[0271] Terminal: Visually displays the generated design proposals to the user.

[0272] 4. Incorporating an Emotional Engine

[0273] User: Provides facial expressions and voice to the system in real time while accessing the system.

[0274] Device: Activates the emotion engine and collects the user's facial expressions and voice via the camera and microphone.

[0275] Emotion Engine: Analyzes user emotions in real time using facial expression recognition and voice analysis technology.

[0276] Server: Receives emotion data obtained from the emotion engine and feeds it back to the AI ​​model. It generates personalized design proposals based on the emotion data.

[0277] 5. Real-time simulation using AR / VR

[0278] Users: Review proposed designs and virtually rearrange furniture and decorations.

[0279] Terminal: Provides an AR / VR interface, repositions itself in real time based on user interaction, and provides visual feedback.

[0280] Server: Generates AR / VR data as needed and sends it to the device.

[0281] 6. Learning your preferences

[0282] Device: Collects user selections and operation history and sends them to the server.

[0283] Server: The collected data is fed back to the AI ​​model and reflected in the next proposal.

[0284] Specific examples

[0285] 1. Users upload photos and dimensions of their rooms

[0286] User: "Take a photo of my living room, measure the dimensions, and upload it to the app."

[0287] Device: "The user has entered a photo and dimensions of their living room. This data will be sent to the server."

[0288] Server: "Data received, AI will begin analysis."

[0289] 2. Proposing the best design

[0290] Server: "Analyze the photos and dimensions to generate the optimal interior design. The resulting design has been sent to your device."

[0291] Device: "The AI ​​will display living room design ideas to the user."

[0292] 3. Incorporating an Emotional Engine

[0293] User: "As I review the design proposal, I provide facial expressions and voice for the system to analyze my emotions."

[0294] Device: "Collects your facial expressions and voice through a camera and microphone."

[0295] Emotion Engine: "Analyzes user emotions from collected data."

[0296] Server: "Feedback emotional data into the AI ​​model to personalize design suggestions."

[0297] 4. AR / VR Simulation

[0298] User: "Check out the design in AR mode and adjust the sofa position."

[0299] Device: "User repositioned the couch. View the new position in AR."

[0300] Server: "We will accumulate new placement data and reflect it in our next proposal."

[0301] Through these processing steps, users can easily design their ideal interior and receive emotionally sensitive suggestions, improving the user experience and providing optimal design suggestions.

[0302] The processing flow will be explained below.

[0303] Step 1: User Registration

[0304] User: Clicks the "Create a new account" button to open the registration form.

[0305] On your device: Display a form to enter your name, email address, and password.

[0306] User: Enter the required information and click the "Submit" button.

[0307] Terminal: Sends the entered information to the server.

[0308] Server: Checks the credentials and saves the new user's information to the database. If the save is successful, returns a success message to the device.

[0309] Step 2: Log in

[0310] User: Enter your email address and password and click the Login button.

[0311] On your device: Send your credentials to the server.

[0312] Server: Checks the credentials against a database and, if correct, generates an authentication token and sends it to the device.

[0313] Terminal: Receives the authentication token and redirects the user to the dashboard.

[0314] Step 3: Upload photos of your room

[0315] User: Take a photo of your living space with your smartphone or camera.

[0316] Terminal: Display the photo upload interface.

[0317] User: Select the photo you have taken and click the upload button.

[0318] Device: Sends photo data to the server.

[0319] Server: Saves the received photos to storage and returns a save completion message to the device.

[0320] Step 4: Enter dimensional data

[0321] User: Use a tape measure to measure the dimensions of the room.

[0322] Terminal: Displays the dimension input form.

[0323] User: Enter the measured dimensions and click the submit button.

[0324] Terminal: Sends the entered dimension data to the server.

[0325] Server: Stores the received dimension data in a database.

[0326] Step 5: Generate optimal design proposals

[0327] Server: Passes the saved photos and dimensional data to the AI ​​module and begins analysis.

[0328] AI module: Analyzes image data and dimensional data to generate optimal interior design proposals.

[0329] Server: Sends the generated design proposals to the device and displays the results.

[0330] Step 6: Activating the Emotion Engine

[0331] User: Clicks the "Emotion Recognition Mode" button.

[0332] Device: Activates the emotion engine and uses the camera and microphone to collect the user's facial expressions and voice in real time.

[0333] Emotion engine: Analyzes collected data and assesses the user's emotional state.

[0334] Server: Receives emotion data from the emotion engine and reflects it in the design proposal.

[0335] Step 7: Update your design proposal based on emotions

[0336] Server: Uses the sentiment analysis results from the emotion engine to update personalized design suggestions.

[0337] On the device: Updated design ideas are presented to the user in real time.

[0338] Step 8: View in AR / VR mode

[0339] User: Clicks the "AR Mode" or "VR Mode" button.

[0340] Device: Launch camera view and overlay the virtual interior design onto the real space.

[0341] User: Take action to adjust the position of virtual furniture and decorations.

[0342] Device: Updates the AR / VR display in real time based on user actions.

[0343] Step 9: Learning user preferences

[0344] Device: Collects user selections, layout changes, and other operational history.

[0345] Terminal: Sends collected data to the server.

[0346] Server: The received user data is fed back to the AI ​​module and reflected in the next proposal.

[0347] Such steps will enable personalized interior design suggestions based on the user's emotional state, significantly improving the user experience.

[0348] Example 2

[0349] 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."

[0350] Conventional interior design systems typically input image data and dimensional data of a user's living space to generate optimal design proposals. However, these systems lacked personalization based on the user's emotions and preferences, resulting in an insufficient user experience. Furthermore, it was difficult to make proposals that reflected changes in emotions in real time when the user was reviewing the design proposals. Furthermore, the system lacked the ability to learn from the user's selections and operation history and reflect these in future proposals.

[0351] The specification processing by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for a user to input image data and dimensional data of his or her living space, a means for analyzing the input image data and dimensional data to generate optimal interior design proposals, a means for visualizing the generated design proposals in real time in an augmented reality and virtual reality environment, a means for collecting and analyzing user emotion data and reflecting the analysis in the design proposals, and a means for learning the user's selections and operations and reflecting the learning in the next proposal. This enables personalized interior design proposals that take into account the user's emotions and preferences, significantly improving the user experience.

[0352] "User" refers to any individual or legal entity that uses the System.

[0353] "Living space" means the indoor space where the user lives.

[0354] "Image data" refers to visual data stored in the form of photographs or digital images.

[0355] "Dimensional data" means data that expresses the physical length, width, height, etc. of a living space numerically.

[0356] "Interface" refers to the input and output means by which a user interacts with a system.

[0357] "Server" means a computer system that stores, analyzes, and processes data.

[0358] "Analysis" refers to the process of performing calculations and evaluations based on input data to extract meaningful information.

[0359] "Design proposal" refers to specific proposals regarding interior layout and decoration.

[0360] "Augmented reality" and "virtual reality" refer to technologies that overlay digital information on the real world and create fully digital virtual environments, respectively.

[0361] "Visualization" means presenting a digital design proposal in a form that can be seen by the user.

[0362] "Emotional data" refers to data about the user's emotional state analyzed from their facial expressions and voice.

[0363] "Learning" refers to the data processing that allows the system to record the user's choices and operation history and reflect them in the next proposal.

[0364] The system of the present invention allows users to input image data and dimensional data of their living space through an interface, and generates and provides optimal interior design proposals based on that data. Furthermore, it analyzes the user's emotional data and makes it possible to propose personalized designs based on those emotions.

[0365] Key Components of the System

[0366] User registration and login

[0367] Users can create an account or log in with an existing account through a web browser.

[0368] The terminal displays a registration or login form, receives input from the user, and sends it to the server.

[0369] The server validates the credentials and authenticates the user, either storing the information in a database for new users or generating and returning an authentication token for existing users.

[0370] Upload room photos and dimensions

[0371] Users take photos of their living space and measure its dimensions.

[0372] The terminal provides an interface for uploading photos and inputting dimensions, and transmits the input data to the server.

[0373] The server analyzes the received photos and dimensional data and stores them in a database.

[0374] Proposing optimal design ideas

[0375] The server analyzes the stored photos and dimension data using an AI model (e.g., a generative AI model) to generate optimal interior design proposals.

[0376] The device visually displays the generated design proposals to the user.

[0377] Emotional Data Analysis

[0378] While accessing the system, users provide facial expressions and voices to the system in real time.

[0379] The device activates an emotion engine and collects the user's facial expressions and voice through the camera and microphone.

[0380] The emotion engine uses facial expression recognition and voice analysis technology to analyze the user's emotional data in real time.

[0381] The server receives the emotional data obtained from the emotion engine and feeds it back to the AI ​​model, which then generates personalized design proposals based on the emotional data.

[0382] Real-time simulation using AR / VR

[0383] Users can review proposed designs and virtually rearrange furniture and decorations.

[0384] The device provides an AR / VR interface, changes position in real time based on user interaction, and provides visual feedback.

[0385] The server generates AR / VR data as needed and sends it to the device.

[0386] Learning user preferences

[0387] The device collects the user's selections and operation history and sends them to the server.

[0388] The server feeds the collected data back into the AI ​​model and reflects it in the next proposal.

[0389] Specific examples

[0390] Users upload photos and dimensions of their rooms

[0391] User: "Take a photo of my living room, measure the dimensions, and upload it to the app."

[0392] Device: "The user has entered a photo and dimensions of their living room. This data will be sent to the server."

[0393] Server: "Data received, AI will begin analysis."

[0394] Proposing optimal design ideas

[0395] Server: "Analyze the photos and dimensions to generate the optimal interior design. The resulting design has been sent to your device."

[0396] Device: "The AI ​​will display living room design ideas to the user."

[0397] Emotional Data Analysis

[0398] User: "As I review the design proposal, I provide facial expressions and voice for the system to analyze my emotions."

[0399] Device: "Collects your facial expressions and voice through a camera and microphone."

[0400] Emotion Engine: "Analyzes user emotions from collected data."

[0401] Server: "Feedback emotional data into the AI ​​model to personalize design suggestions."

[0402] AR / VR simulation

[0403] User: "Check out the design in AR mode and adjust the sofa position."

[0404] Device: "User repositioned the couch. View the new position in AR."

[0405] Server: "We will accumulate new placement data and reflect it in our next proposal."

[0406] With the above configuration, users can easily design their ideal interior and receive emotionally-sensitive suggestions, greatly improving the user experience and providing optimal design suggestions.

[0407] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0408] Step 1:

[0409] A user accesses the system through a web browser and creates an account or logs in. The input data is the user's user ID and password.

[0410] Input: Username, Password

[0411] Specific behavior:

[0412] User: Open a web browser and enter the required information (username, password) on the login page.

[0413] Terminal: Converts the input data into JSON format and sends an HTTPS request to the server.

[0414] Server: Checks the input data against the database, and if there is a match, generates and returns an authentication token.

[0415] Output: Authentication token

[0416] Step 2:

[0417] The user takes a photo of the living space and inputs the dimensions of the living space into the application.

[0418] Input: Living space photos, dimensions

[0419] Specific behavior:

[0420] User: Launches the application, takes a photo of the living space, enters the dimensions, and clicks the "Submit" button.

[0421] Terminal: Bundle the photo file and dimension data and send an HTTP POST request to the server.

[0422] Server: Stores the received image files in the storage system and records the dimensional data in the database.

[0423] Output: Image files stored in storage, dimensional data stored in database

[0424] Step 3:

[0425] The server generates interior design proposals using a generative AI model based on the stored image data and dimension data.

[0426] Input: Image data, dimensional data

[0427] Specific behavior:

[0428] Server: Input the image data and dimension data into the AI ​​model and start the analysis. Enter the prompt "Analyze this data and generate the optimal interior design proposal."

[0429] Generative AI model: Analyzes input data and generates optimal design proposals.

[0430] Server: Converts the generated design proposal into a data format and sends it to the device.

[0431] Output: Design proposal (image data)

[0432] Step 4:

[0433] Users can review the proposed design and provide feedback, and real-time emotional data (facial expressions and voice) from users is also collected.

[0434] Input: Design proposal (image data)

[0435] Specific behavior:

[0436] User: Views proposed design ideas and gives feedback, providing voice and facial expressions using a camera and microphone.

[0437] Device: Collects the user's facial expressions and voice through the camera and microphone and sends them to the emotion engine.

[0438] Emotion engine: Analyzes collected data and determines user emotions.

[0439] Server: Receives emotional data and reflects the feedback in the design proposal.

[0440] Output: Personalized design proposal incorporating your feedback

[0441] Step 5:

[0442] Users can view the generated design proposals in AR / VR mode and rearrange furniture and decorations.

[0443] Input: Personalized design ideas

[0444] Specific behavior:

[0445] Users can view the generated design proposals using a smartphone or AR / VR device and rearrange furniture and decorations in real time.

[0446] Terminal: Provides the AR / VR interface, changes position based on user interaction, and provides visual feedback.

[0447] Server: Generates the necessary AR / VR data and sends it to the device.

[0448] Output: User-modified deployment data

[0449] Step 6:

[0450] The system collects user selections and operation history and uses them for future suggestions.

[0451] Input: User selections and operation history

[0452] Specific behavior:

[0453] Terminal: Records user interaction data (e.g., choices, repositioning, viewing time) and transmits it to the server.

[0454] Server: Stores the received data in a database and feeds it back into the generative AI model. This information is reflected in the next interior design proposal.

[0455] Output: Design proposal reflected in next proposal

[0456] (Application example 2)

[0457] 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."

[0458] Conventional electronic payment services offer a uniform set of payment options without considering the user's emotional state, resulting in a uniform user experience. As a result, it is not possible to determine whether the user is enjoying shopping, and it is not possible to provide optimal payment suggestions.

[0459] 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.

[0460] In this invention, the server includes means for a user to input image data and dimensional data of his / her living space, means for analyzing the input image data and dimensional data to generate optimal interior design proposals, means for visualizing the generated design proposals in real time in an augmented reality and virtual reality environment, means for learning the user's selections and operations and reflecting them in subsequent proposals, and means for detecting the user's emotions and suggesting customized options based on the emotions, thereby enabling personalized payment proposals according to the user's emotions.

[0461] "User" means any individual or entity that accesses and uses the System or Application.

[0462] "Living space image data" refers to digital data that users have taken of the interior of their homes, including photos and videos.

[0463] "Dimensional data" refers to numerical data measuring the dimensions of living spaces and the furniture and decorations within them.

[0464] "Analysis" refers to the process of extracting information based on input data and performing the necessary processing and calculations.

[0465] An "interior design proposal" refers to a proposal for the placement and layout of furniture and decorations in a living space.

[0466] "Augmented reality (AR)" refers to a technology that overlays virtual information onto a real-world environment.

[0467] "Virtual reality (VR)" refers to a technology that allows you to experience a virtual three-dimensional space generated by a computer.

[0468] "Visualization" refers to the process of presenting information or data in a visual form.

[0469] "Selections and actions" refers to interface choices and inputs that users make within the system.

[0470] "Learning" refers to the process by which a system uses past data and operational history to improve future suggestions and decisions.

[0471] "Emotion detection" refers to technology that analyzes a user's facial expressions and voice to recognize their emotional state at that time.

[0472] "Customized options" refers to choices and suggestions tailored to a user's individual circumstances and emotions.

[0473] The present invention relates to a system for enabling users to enjoy emotion-based customized payment options in electronic payment services. Hereinafter, embodiments of the present invention will be described in detail.

[0474] System Overview

[0475] The system allows users to use a camera and microphone to provide facial expressions and voice recordings to a server, which then analyzes the data to recognize emotions and suggests optimal payment options based on those.

[0476] Hardware and software used

[0477] Camera: Hardware for capturing the user's facial expressions.

[0478] Microphone: Hardware used to capture the user's voice.

[0479] Server: The central processing unit for analyzing data, recognizing emotions, and suggesting options.

[0480] OpenCV: Software used to perform face recognition using image processing libraries.

[0481] Keras: Software used to implement emotion recognition models using deep learning libraries.

[0482] Emotion Recognition Model: A pre-trained model for recognizing user emotions.

[0483] How to operate the system

[0484] 1. User registration and login:

[0485] Users create an account through a web browser or log in with an existing account, and the server validates the credentials and generates and returns an authentication token.

[0486] 2. Collecting Emotional Data:

[0487] The device uses a camera and microphone to collect the user's facial expressions and voice in real time, and the emotion engine uses this data to analyze the user's emotions.

[0488] 3. Emotion-based suggestions:

[0489] The server analyzes the emotional data obtained from the emotion engine and accordingly suggests optimal payment options (e.g., a 5% discount coupon or a 10% cashback). These options are customized according to the user's emotional state.

[0490] Examples of concrete examples and prompts

[0491] For example, if a user is shopping online and the system recognizes their emotion of happiness, it will suggest a 5% discount coupon option. Conversely, if the user shows emotion of sadness, it will suggest a 10% cashback option.

[0492] Example prompts to input to a generative AI model:

[0493] “Implement a system that uses emotion recognition while users are shopping to offer customized payment options based on their emotions. If they look happy, offer a 5% discount coupon; if they look sad, offer a 10% cash back.”

[0494] This format is expected to improve the user experience and provide optimal payment suggestions based on emotions.

[0495] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0496] Step 1:

[0497] User Registration and Login

[0498] Input: User enters credentials (username, password)

[0499] What it does: The device displays a registration or login form and receives input from the user.

[0500] Data processing: The device sends the credentials to the server.

[0501] Output: The server validates the credentials and generates an authentication token to return to the user.

[0502] Step 2:

[0503] Collecting Emotional Data

[0504] Input: User facial and voice data captured using a camera and microphone

[0505] How it works: The device collects the user's facial expressions and voice in real time through the camera and microphone.

[0506] Data processing: The collected data is sent to the emotion engine, where emotions are analyzed using facial expression recognition and voice analysis technology.

[0507] Output: Real-time emotion data is generated as the analysis result.

[0508] Step 3:

[0509] Generating Sentiment-Based Recommendations

[0510] Input: Emotion data obtained from the emotion engine

[0511] How it works: The server generates optimal payment options for the user based on the emotion data, for example, a 5% discount coupon for happy emotions and a 10% cashback for sad emotions.

[0512] Data processing: Emotional data is fed back into the AI ​​model to generate customized suggestions.

[0513] Output: The payment options to offer to the user.

[0514] Step 4:

[0515] Visual representation of suggestions

[0516] Input: Payment option data sent from the server

[0517] How it works: The terminal visually displays customized payment options to the user.

[0518] Data processing: Adjust the design to suit the display format.

[0519] Output: The payment options visually presented to the user.

[0520] Step 5:

[0521] Collecting user feedback

[0522] Input: User selections and action history

[0523] How it works: The device records the user's choices and actions and sends them to the server.

[0524] Data processing: The server feeds the collected data back into the AI ​​model and reflects it in the next proposal.

[0525] Output: Next suggestion data based on user preferences and operation trends.

[0526] Step 6:

[0527] Data accumulation and learning

[0528] Input: Feedback data and user operation history data

[0529] How it works: The server stores the collected data in a database and provides feedback to the AI ​​model.

[0530] Data processing: Analyze the collected data and continue learning to reflect it in future proposals.

[0531] Output: Improved accuracy of suggestions from next time onwards.

[0532] 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.

[0533] 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.

[0534] 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.

[0535] [Second embodiment]

[0536] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0537] 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.

[0538] 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).

[0539] 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.

[0540] 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.

[0541] 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).

[0542] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0543] 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.

[0544] 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.

[0545] 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.

[0546] 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.

[0547] 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."

[0548] The system of the present invention allows users to input image data and dimensional data of their living space through an interface, and generates and provides optimal interior design proposals based on that data. The system runs on a web browser and includes the following main components:

[0549] 1. User Registration and Login

[0550] Users: Create an account or log in with an existing account through a web browser.

[0551] Terminal: displays the registration or login form, receives input from the user, and sends it to the server.

[0552] Server: Validates the credentials and authenticates the user, saving the information in a database for new users or generating and returning an authentication token for existing users.

[0553] 2. Upload photos and dimensions of your room

[0554] User: Take a photo of your living space and measure the dimensions of your living space.

[0555] Terminal: Provides an interface for uploading photos and entering dimensions, and sends the entered data to the server.

[0556] Server: Analyzes the received photos and dimension data and stores them in a database.

[0557] 3. Proposing the best design

[0558] Server: Analyzes stored photos and dimension data using AI to generate optimal interior design proposals.

[0559] Terminal: Visually displays the generated design proposals to the user.

[0560] 4. Real-time simulation using AR / VR

[0561] Users: Review proposed designs and virtually rearrange furniture and decorations.

[0562] Terminal: Provides an AR / VR interface, repositions itself in real time based on user interaction, and provides visual feedback.

[0563] Server: Generates AR / VR data as needed and sends it to the device.

[0564] 5. Learning your preferences

[0565] Device: Collects user selections and operation history and sends them to the server.

[0566] Server: The collected data is fed back to the AI ​​model and reflected in the next proposal.

[0567] Specific examples

[0568] 1. Users upload photos and dimensions of their rooms

[0569] User: "Take a photo of my living room, measure the dimensions, and upload it to the app."

[0570] Device: "The user has entered a photo and dimensions of their living room. This data will be sent to the server."

[0571] Server: "Data received, AI will begin analysis."

[0572] 2. Proposing the best design

[0573] Server: "Analyze the photos and dimensions to generate the optimal interior design. The resulting design has been sent to your device."

[0574] Device: "The AI ​​will display living room design ideas to the user."

[0575] 3. AR / VR Simulation

[0576] User: "View the proposed design in AR mode and adjust the sofa position."

[0577] Device: "User repositioned the couch. View the new position in AR."

[0578] Server: "We will accumulate new placement data and reflect it in our next proposal."

[0579] This allows users to easily design their ideal interior without specialist knowledge and see the changes in real time in a virtual environment, enabling personalized suggestions based on the user's preferences and resulting in a more satisfying design experience.

[0580] The processing flow will be explained below.

[0581] Step 1: User Registration

[0582] User: Clicks the "Create a new account" button to open the registration form.

[0583] On your device: Display a form to enter your name, email address, and password.

[0584] User: Enter the required information and click the "Submit" button.

[0585] Terminal: Sends the entered information to the server.

[0586] Server: Checks the credentials and saves the new user's information to the database. If the save is successful, returns a success message to the device.

[0587] Step 2: Log in

[0588] User: Enter your email address and password and click the Login button.

[0589] On your device: Send your credentials to the server.

[0590] Server: Checks the credentials against a database and, if correct, generates an authentication token and sends it to the device.

[0591] Terminal: Receives the authentication token and redirects the user to the dashboard.

[0592] Step 3: Upload photos of your room

[0593] User: Take a photo of your living space with your smartphone or camera.

[0594] Terminal: Display the photo upload interface.

[0595] User: Select the photo you have taken and click the upload button.

[0596] Device: Sends photo data to the server.

[0597] Server: Saves the received photos to storage and returns a save completion message to the device.

[0598] Step 4: Enter dimensional data

[0599] User: Use a tape measure to measure the dimensions of the room.

[0600] Terminal: Displays the dimension input form.

[0601] User: Enter the measured dimensions and click the submit button.

[0602] Terminal: Sends the entered dimension data to the server.

[0603] Server: Stores the received dimension data in a database.

[0604] Step 5: Generate optimal design proposals

[0605] Server: Passes the saved photos and dimensional data to the AI ​​module and begins analysis.

[0606] AI module: Analyzes image data and dimensional data to generate optimal interior design proposals.

[0607] Server: Sends the generated design proposals to the device and displays the results.

[0608] Step 6: View in AR / VR mode

[0609] User: Clicks the "AR Mode" or "VR Mode" button.

[0610] Device: Launch camera view and overlay the virtual interior design onto the real space.

[0611] User: Take action to adjust the position of virtual furniture and decorations.

[0612] Device: Updates the AR / VR display in real time based on user actions.

[0613] Step 7: Learning user preferences

[0614] Device: Collects user selections, layout changes, and other operational history.

[0615] Terminal: Sends collected data to the server.

[0616] Server: The received user data is fed back to the AI ​​module and reflected in the next proposal.

[0617] With such detailed processing for each step, users can easily design their ideal interior and check it in real time in the virtual space, improving the user experience and providing optimal design suggestions.

[0618] Example 1

[0619] 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."

[0620] Currently available interior design systems on the market require users to provide photos and dimensions of their living space. However, the subsequent design proposals and feedback to users lack real-time and personalized features. Furthermore, users have no way to virtually change the proposed design and visually confirm the results, which can lead to low user satisfaction. Therefore, there is a need for a system that allows users to easily find interior designs based on their preferences and view and adjust them in real time.

[0621] 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.

[0622] In this invention, the server includes a means for a user to input image data and dimensional data of their living space, a means using a generative AI model to analyze the input image data and dimensional data and generate optimal interior design proposals, a means for visualizing the generated design proposals in real time in an augmented reality and virtual reality environment, and a means using a machine learning algorithm to learn the user's selections and operations and reflect them in future proposals, thereby enabling users to easily find interior designs based on their preferences and check and adjust them in real time.

[0623] "User" means any person or entity that uses the System.

[0624] "Living space" refers to the place or room where the user lives.

[0625] "Image data" refers to digital data including photographs and images taken by users.

[0626] "Dimensional Data" refers to data regarding the size of a room or furniture measured by the user.

[0627] A "generative AI model" refers to an artificial intelligence model that analyzes input data and generates optimal interior design proposals.

[0628] "Augmented reality" refers to technology that adds digital information to real-world visual information.

[0629] "Virtual reality" refers to technology that allows you to experience a completely digitally created environment as if it were real.

[0630] "Machine learning algorithm" refers to an algorithm that allows the system to automatically learn from collected data and reflect that knowledge in its next proposal.

[0631] "Proposal" refers to showing the user the interior design ideas generated by the system.

[0632] The system of the present invention allows users to input image data and dimensional data of their living space, and generates optimal interior design proposals based on that data, and then proposes and visualizes them.The system runs on a web browser and includes the user, a terminal, and a server as its main components.

[0633] Detailed component description

[0634] 1. User Registration and Login

[0635] Users: Open a web browser and access the account creation page. Enter your name, email address, and password and press the "Register" button. Existing users should enter their email address and password and press the "Login" button.

[0636] On the device: Display a registration or login form, validate the data using JavaScript form validation, and submit it to the server.

[0637] Server: Store new user information in a database (e.g. MySQL, PostgreSQL). For existing users, verify the credentials, generate a JWT (JSON Web Token) and return it to the user.

[0638] 2. Upload photos and dimensions of your room

[0639] User: Take a photo of your living space using a smartphone or digital camera, measure the room dimensions, select the photo on the application's upload page, enter the dimensions, and press the "Upload" button.

[0640] Terminal: Receives image file and dimension data, displays a preview of the image selected by the user, and sends the data to the server using the FormData API.

[0641] Server: Stores uploaded image files in the file system or cloud storage (e.g., Amazon S3), stores dimensional data in a database, and performs image analysis and extracts features using OpenCV.

[0642] 3. Proposing the best design

[0643] Server: Generates a prompt into an AI engine (e.g., TensorFlow, PyTorch) that prompts for the input of the saved photo and dimension data. An example of a generated prompt is "Generate a design proposal for this living space." The AI ​​model analyzes this and generates the optimal interior design proposal based on the input data. The generated design proposal is saved as an image or 3D model.

[0644] Terminal: Receives the response from the server and uses Three.js to visually display the generated design proposal to the user.

[0645] 4. Real-time simulation using AR / VR

[0646] User: Press a button on the web page to switch to AR / VR mode, view proposed designs, and virtually rearrange furniture and decorations.

[0647] Device: Use Unity or Unreal Engine to provide an AR / VR interface, repositioning furniture in real time based on user interaction and providing visual feedback.

[0648] Server: Optionally, store the new placement data in the backend for future recommendations.

[0649] 5. Learning your preferences

[0650] Terminal: Collects user operation history and selections and sends them to the server.

[0651] Server: Analyzes the received data using a collaborative filtering algorithm to learn the user's preferences, which is then used to improve the accuracy of the next recommendation.

[0652] Specific examples

[0653] When a user uploads photos and dimensions of a living space, they:

[0654] 1. User: "Take a photo of my living room, measure the dimensions, and upload it to the app."

[0655] 2. Device: "The user has entered a photo and dimensions of their living room. Send this data to the server."

[0656] 3. Server: "The data is received and the AI ​​begins analysis."

[0657] When proposing the optimal design, we act as follows:

[0658] 1. Server: "Analyzes the photos and dimensional data, generates optimal interior design proposals, and sends the generated design proposals to the device."

[0659] 2. Device: "Displays AI-suggested living room design ideas to the user."

[0660] This allows users to easily find their ideal interior design without specialist knowledge, and check and adjust it in real time. It also provides personalized suggestions based on the user's preferences, providing a highly satisfying design experience.

[0661] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0662] Step 1:

[0663] User registration and login

[0664] User: Open a web browser and access the account creation page. Enter your name, email address, and password, then click the "Register" button. Existing users should enter their email address and password and click the "Login" button.

[0665] Input: Name, Email Address, Password (if new user). Email Address, Password (if existing user).

[0666] Terminal: Receives the input data and performs JavaScript form validation (e.g., using the JQuery Validation Plugin). After validation is passed, the data is sent to the server.

[0667] Output: The input data that passes validation.

[0668] Server: Stores the received data in a database (e.g., MySQL, PostgreSQL), and performs registration processing for new users. For existing users, checks the entered credentials against the information in the database. If they match, generates a JWT (JSON Web Token) and returns it to the user.

[0669] Output: Saves new user information or issues a JWT token for an existing user.

[0670] Step 2:

[0671] Upload room photos and dimensions

[0672] User: Take a photo of your living space using a smartphone or digital camera, measure the dimensions of the room, select the photo on the application's upload page, enter the dimensions, and press the "Upload" button.

[0673] Input: Photo files of the living space, room dimensions.

[0674] Terminal: Receives image file and dimension data, displays a preview of the image, and sends the data to the server using the FormData API.

[0675] Output: Image file and dimension data sent to the server.

[0676] Server: Stores image files in cloud storage (e.g., Amazon S3) or a file system. Stores dimensional data in a database. Additionally, analyzes the images using OpenCV and extracts image features.

[0677] Output: Saved image files with dimensional data and extracted image features.

[0678] Step 3:

[0679] Proposing optimal design ideas

[0680] Server: Generates a prompt to input the saved photo and dimension data into an AI engine (e.g., TensorFlow, PyTorch). Example: "Generate design ideas for this living space." The data along with the prompt is input into the AI ​​model to generate optimal interior design ideas. The generated design ideas are saved as images or 3D models.

[0681] Input: Saved photo and dimensional data, prompt text.

[0682] Output: Generated interior design proposals (images or 3D models).

[0683] On the device: The design proposals received from the server are visually displayed to the user using Three.js.

[0684] Output: A displayed interior design proposal.

[0685] Step 4:

[0686] Real-time simulation using AR / VR

[0687] Users: Press a button on the web page to switch to AR / VR mode, view proposed designs, and virtually rearrange furniture and decorations.

[0688] Input: Proposed design ideas, user actions (rearrangement of furniture and decorations).

[0689] Device: Provides an AR / VR interface using Unity or Unreal Engine. Rearranges furniture in real time based on user actions and provides visual feedback. For example, if a user drags a sofa to change its position, the sofa's position on the screen is instantly updated.

[0690] Output: Real-time updated furniture placement.

[0691] Server: Optionally, store the new placement data in the backend and use it for future recommendations.

[0692] Output: The new saved configuration data.

[0693] Step 5:

[0694] Learning user preferences

[0695] Terminal: Collects user operation history and selections and sends them to the server.

[0696] Input: User operation history, selection data.

[0697] Output: Operation history and selection data sent to the server.

[0698] Server: Analyzes the received data using a collaborative filtering algorithm to learn the user's preferences, thereby improving the accuracy of the next suggestions.

[0699] Output: Trained user model, data for next proposal.

[0700] As described above, the present invention is a system that allows users to easily find their ideal interior design, check and adjust it in real time, and provides personalized suggestions based on the user's preferences, resulting in a highly satisfying design experience.

[0701] (Application example 1)

[0702] 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."

[0703] Conventional interior design proposal systems have the drawback of making it difficult for users to check the actual layout image in real time when selecting interior items, and they cannot purchase the proposed products on the spot. Furthermore, the accuracy of the system's ability to learn the user's preferences and choices and reflect them in future proposals is insufficient. This makes it difficult for users to select interior items that will satisfy them.

[0704] 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.

[0705] In this invention, the server includes: a means for a user to input image data and dimensional data of their living space; a means for analyzing the input image data and dimensional data to generate optimal interior design proposals; a means for visualizing the generated design proposals in an augmented reality and virtual reality environment in real time and projecting them onto the user's living space; a means for learning the user's selections and operations and reflecting them in future proposals; and a means for connecting with an online platform to purchase the proposed products on the spot. This allows users to visually confirm the interior layout of their living space in real time and select interior designs based on their preferences. Furthermore, the ability to purchase products on the spot provides a consistent user experience from selection to purchase.

[0706] "Means for users to input image data and dimensional data of their living space" refers to an interface or application that allows users to take a photograph of the current state of their living space using a camera or measuring device and input the image data and dimensional data.

[0707] "Means for analyzing input image data and dimensional data to generate optimal interior design proposals" refers to software or systems that use AI or algorithms to analyze image data and dimensional data of living spaces provided by users, and automatically propose the optimal interior design for that space.

[0708] "Means of visualizing the generated design proposals in real time in an augmented reality or virtual reality environment and projecting them into the user's living space" refers to AR (augmented reality) and VR (virtual reality) technologies that allow interior design proposals generated by AI to be displayed in the user's field of vision in real time, allowing them to be viewed superimposed on the real space.

[0709] "Means of learning user choices and operations and reflecting them in the next proposal" refers to functions and algorithms that record the choices and changes made by the user on the system, and allow the AI ​​to learn from that data to help improve the accuracy of the next interior design proposal.

[0710] "Means for connecting with online platforms to purchase proposed products on the spot" refers to a system that provides links and purchasing procedures for directly purchasing furniture and decorative items included in the interior design proposals that users have confirmed from affiliated online shops and e-commerce sites.

[0711] This invention is a system that allows users to input image data and dimensional data of their living space, and generates and proposes optimal interior designs based on that data. To realize this system, the following hardware and software are required:

[0712] Hardware:

[0713] Smart glasses (or smartphone): A device for capturing images of the current state of the living space and acquiring image data.

[0714] Server: A high-performance computing device that analyzes image and dimensional data and generates design proposals.

[0715] software:

[0716] OpenCV: A library for real-time video processing acquired from smart glasses and smartphones.

[0717] Requests: A library for communicating with the server to send and receive data.

[0718] AI model: A generative model that analyzes data entered by the user and generates optimal interior designs.

[0719] Processing flow

[0720] 1. Input of image and dimension data

[0721] Users use smart glasses or a smartphone to take pictures of their living space and input dimensional data, which is then sent from the device to a server.

[0722] 2. Generate design proposals

[0723] The server then uses the received image and dimension data to generate optimal interior design proposals using an AI model that has been trained on a large amount of design data in advance.

[0724] 3. Real-time visualization

[0725] The design proposals generated by the server are displayed in real time in the user's field of vision via smart glasses or a smartphone, allowing the user to see how the design will look in their living space.

[0726] 4. Collaboration with online platforms

[0727] If a user likes a suggested product, they can connect to the online platform and complete the purchase process on the spot.

[0728] 5. Learning user choices and actions

[0729] The choices and actions taken by the user are logged and fed back to the AI ​​model, which then incorporates this data into the next recommendation, allowing for more personalized suggestions.

[0730] Specific examples

[0731] Example 1:

[0732] User: "I'm going to use my smart glasses to take a picture of my living room and enter the dimensions."

[0733] System: "Receives data and generates optimal interior design proposals."

[0734] Result: "I'll use AR mode to view the generated design and consider the placement of the sofa."

[0735] Example 2:

[0736] User: "I'll choose the sofa I like from the suggested interior designs and buy it right away."

[0737] System: "We will connect with your online platform to begin the purchase process."

[0738] Example prompts

[0739] User ID: 'user123'

[0740] Living space image: 'image123.jpg'

[0741] Living space dimensions: {'length': 500, 'width': 400, 'height': 300}

[0742] Suggestion: 'Optimal furniture arrangement for living room'

[0743] Through these steps, users can see how the interior layout of their living space will look in real time and purchase the products they like on the spot. Furthermore, the next proposal will reflect the user's selections and operation history, providing more personalized design suggestions.

[0744] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0745] Step 1:

[0746] The user takes an image of the living space and inputs dimensional data. The input data is acquired using smart glasses or a smartphone. The input consists of image data and dimensional data of the living space. The output is the input data ready to be transmitted.

[0747] Specifically, the user takes a photo of their living room using a camera, measures the dimensions using a tape measure or a dedicated app, enters the data into their smart glasses or smartphone, and then presses a button to send the information to the server.

[0748] Step 2:

[0749] The image data and dimension data acquired by the terminal are sent to the server. The input is the image data and dimension data received from the user, and the output is the data sent to the server.

[0750] Specifically, the device recognizes the user's actions and uses the API to send data to the server, which is done using an HTTP POST request.

[0751] Step 3:

[0752] The server analyzes the received image data and dimension data and generates optimal interior design proposals using a generative AI model. The input is the image data and dimension data sent from the device, and the output is the generated interior design proposal.

[0753] Specifically, after receiving the data, the server provides the input data to the AI ​​model, which analyzes it and generates optimal design proposals taking into account the layout and dimensions of the living space.

[0754] Step 4:

[0755] The server sends the interior design proposals it generates to the device. The input is the design proposal generated by the AI ​​model, and the output is the design proposal data that can be displayed on the device.

[0756] Specifically, the server sends design proposals in a format such as JSON to the device, which then receives and processes them.

[0757] Step 5:

[0758] The device displays interior design proposals in real time within the user's field of view. The input is the design proposal sent from the server, and the output is a real-time image displayed in the user's field of view.

[0759] Specifically, users can view design proposals overlaid on the real world through smart glasses or smartphone displays, with the placement of furniture and decorations projected onto the real world using AR technology.

[0760] Step 6:

[0761] When a user wants to purchase the suggested furniture or decorative item on the spot, they are connected to an online platform, where the input is the user's purchasing decision information and design proposals, and the output is links and processes to proceed to the purchase process.

[0762] Specifically, users can select a product they like and click on the link displayed on their device to be redirected to the relevant online shop and proceed with the purchase.

[0763] Step 7:

[0764] The device sends the user's selections and operation history to the server, which analyzes the data and reflects it in the next proposal. The input is the user's selections and operation history data, and the output is learning data for the AI ​​model.

[0765] Specifically, the device records user operation data and periodically sends it to a server. The server then stores the data and feeds it back into the AI ​​model. This allows the data to be reflected in the next interior design proposal, enabling more personalized suggestions.

[0766] 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.

[0767] The system of the present invention allows users to input image data and dimensional data of their living space through an interface, and generates and provides optimal interior design proposals based on that data. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system can provide personalized interior design proposals that take the user's emotions into consideration. The system runs on a web browser and includes the following main components:

[0768] 1. User Registration and Login

[0769] Users: Create an account or log in with an existing account through a web browser.

[0770] Terminal: displays the registration or login form, receives input from the user, and sends it to the server.

[0771] Server: Validates the credentials and authenticates the user, saving the information in a database for new users or generating and returning an authentication token for existing users.

[0772] 2. Upload photos and dimensions of your room

[0773] User: Take a photo of your living space and measure the dimensions of your living space.

[0774] Terminal: Provides an interface for uploading photos and entering dimensions, and sends the entered data to the server.

[0775] Server: Analyzes the received photos and dimension data and stores them in a database.

[0776] 3. Proposing the best design

[0777] Server: Analyzes stored photos and dimension data using AI to generate optimal interior design proposals.

[0778] Terminal: Visually displays the generated design proposals to the user.

[0779] 4. Incorporating an Emotional Engine

[0780] User: Provides facial expressions and voice to the system in real time while accessing the system.

[0781] Device: Activates the emotion engine and collects the user's facial expressions and voice via the camera and microphone.

[0782] Emotion Engine: Analyzes user emotions in real time using facial expression recognition and voice analysis technology.

[0783] Server: Receives emotion data obtained from the emotion engine and feeds it back to the AI ​​model. It generates personalized design proposals based on the emotion data.

[0784] 5. Real-time simulation using AR / VR

[0785] Users: Review proposed designs and virtually rearrange furniture and decorations.

[0786] Terminal: Provides an AR / VR interface, repositions itself in real time based on user interaction, and provides visual feedback.

[0787] Server: Generates AR / VR data as needed and sends it to the device.

[0788] 6. Learning your preferences

[0789] Device: Collects user selections and operation history and sends them to the server.

[0790] Server: The collected data is fed back to the AI ​​model and reflected in the next proposal.

[0791] Specific examples

[0792] 1. Users upload photos and dimensions of their rooms

[0793] User: "Take a photo of my living room, measure the dimensions, and upload it to the app."

[0794] Device: "The user has entered a photo and dimensions of their living room. This data will be sent to the server."

[0795] Server: "Data received, AI will begin analysis."

[0796] 2. Proposing the best design

[0797] Server: "Analyze the photos and dimensions to generate the optimal interior design. The resulting design has been sent to your device."

[0798] Device: "The AI ​​will display living room design ideas to the user."

[0799] 3. Incorporating an Emotional Engine

[0800] User: "As I review the design proposal, I provide facial expressions and voice for the system to analyze my emotions."

[0801] Device: "Collects your facial expressions and voice through a camera and microphone."

[0802] Emotion Engine: "Analyzes user emotions from collected data."

[0803] Server: "Feedback emotional data into the AI ​​model to personalize design suggestions."

[0804] 4. AR / VR Simulation

[0805] User: "Check out the design in AR mode and adjust the sofa position."

[0806] Device: "User repositioned the couch. View the new position in AR."

[0807] Server: "We will accumulate new placement data and reflect it in our next proposal."

[0808] Through these processing steps, users can easily design their ideal interior and receive emotionally sensitive suggestions, improving the user experience and providing optimal design suggestions.

[0809] The processing flow will be explained below.

[0810] Step 1: User Registration

[0811] User: Clicks the "Create a new account" button to open the registration form.

[0812] On your device: Display a form to enter your name, email address, and password.

[0813] User: Enter the required information and click the "Submit" button.

[0814] Terminal: Sends the entered information to the server.

[0815] Server: Checks the credentials and saves the new user's information to the database. If the save is successful, returns a success message to the device.

[0816] Step 2: Log in

[0817] User: Enter your email address and password and click the Login button.

[0818] On your device: Send your credentials to the server.

[0819] Server: Checks the credentials against a database and, if correct, generates an authentication token and sends it to the device.

[0820] Terminal: Receives the authentication token and redirects the user to the dashboard.

[0821] Step 3: Upload photos of your room

[0822] User: Take a photo of your living space with your smartphone or camera.

[0823] Terminal: Display the photo upload interface.

[0824] User: Select the photo you have taken and click the upload button.

[0825] Device: Sends photo data to the server.

[0826] Server: Saves the received photos to storage and returns a save completion message to the device.

[0827] Step 4: Enter dimensional data

[0828] User: Use a tape measure to measure the dimensions of the room.

[0829] Terminal: Displays the dimension input form.

[0830] User: Enter the measured dimensions and click the submit button.

[0831] Terminal: Sends the entered dimension data to the server.

[0832] Server: Stores the received dimension data in a database.

[0833] Step 5: Generate optimal design proposals

[0834] Server: Passes the saved photos and dimensional data to the AI ​​module and begins analysis.

[0835] AI module: Analyzes image data and dimensional data to generate optimal interior design proposals.

[0836] Server: Sends the generated design proposals to the device and displays the results.

[0837] Step 6: Activating the Emotion Engine

[0838] User: Clicks the "Emotion Recognition Mode" button.

[0839] Device: Activates the emotion engine and uses the camera and microphone to collect the user's facial expressions and voice in real time.

[0840] Emotion engine: Analyzes collected data and assesses the user's emotional state.

[0841] Server: Receives emotion data from the emotion engine and reflects it in the design proposal.

[0842] Step 7: Update your design proposal based on emotions

[0843] Server: Uses the sentiment analysis results from the emotion engine to update personalized design suggestions.

[0844] On the device: Updated design ideas are presented to the user in real time.

[0845] Step 8: View in AR / VR mode

[0846] User: Clicks the "AR Mode" or "VR Mode" button.

[0847] Device: Launch camera view and overlay the virtual interior design onto the real space.

[0848] User: Take action to adjust the position of virtual furniture and decorations.

[0849] Device: Updates the AR / VR display in real time based on user actions.

[0850] Step 9: Learning user preferences

[0851] Device: Collects user selections, layout changes, and other operational history.

[0852] Terminal: Sends collected data to the server.

[0853] Server: The received user data is fed back to the AI ​​module and reflected in the next proposal.

[0854] Such steps will enable personalized interior design suggestions based on the user's emotional state, significantly improving the user experience.

[0855] Example 2

[0856] 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."

[0857] Conventional interior design systems typically input image data and dimensional data of a user's living space to generate optimal design proposals. However, these systems lacked personalization based on the user's emotions and preferences, resulting in an insufficient user experience. Furthermore, it was difficult to make proposals that reflected changes in emotions in real time when the user was reviewing the design proposals. Furthermore, the system lacked the ability to learn from the user's selections and operation history and reflect these in future proposals.

[0858] The specification processing by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for a user to input image data and dimensional data of his or her living space, a means for analyzing the input image data and dimensional data to generate optimal interior design proposals, a means for visualizing the generated design proposals in real time in an augmented reality and virtual reality environment, a means for collecting and analyzing user emotion data and reflecting the analysis in the design proposals, and a means for learning the user's selections and operations and reflecting the learning in the next proposal. This enables personalized interior design proposals that take into account the user's emotions and preferences, significantly improving the user experience.

[0859] "User" refers to any individual or legal entity that uses the System.

[0860] "Living space" means the indoor space where the user lives.

[0861] "Image data" refers to visual data stored in the form of photographs or digital images.

[0862] "Dimensional data" means data that expresses the physical length, width, height, etc. of a living space numerically.

[0863] "Interface" refers to the input and output means by which a user interacts with a system.

[0864] "Server" means a computer system that stores, analyzes, and processes data.

[0865] "Analysis" refers to the process of performing calculations and evaluations based on input data to extract meaningful information.

[0866] "Design proposal" refers to specific proposals regarding interior layout and decoration.

[0867] "Augmented reality" and "virtual reality" refer to technologies that overlay digital information on the real world and create fully digital virtual environments, respectively.

[0868] "Visualization" means presenting a digital design proposal in a form that can be seen by the user.

[0869] "Emotional data" refers to data about the user's emotional state analyzed from their facial expressions and voice.

[0870] "Learning" refers to the data processing that allows the system to record the user's choices and operation history and reflect them in the next proposal.

[0871] The system of the present invention allows users to input image data and dimensional data of their living space through an interface, and generates and provides optimal interior design proposals based on that data. Furthermore, it analyzes the user's emotional data and makes it possible to propose personalized designs based on those emotions.

[0872] Key Components of the System

[0873] User registration and login

[0874] Users can create an account or log in with an existing account through a web browser.

[0875] The terminal displays a registration or login form, receives input from the user, and sends it to the server.

[0876] The server validates the credentials and authenticates the user, either storing the information in a database for new users or generating and returning an authentication token for existing users.

[0877] Upload room photos and dimensions

[0878] Users take photos of their living space and measure its dimensions.

[0879] The terminal provides an interface for uploading photos and inputting dimensions, and transmits the input data to the server.

[0880] The server analyzes the received photos and dimensional data and stores them in a database.

[0881] Proposing optimal design ideas

[0882] The server analyzes the stored photos and dimension data using an AI model (e.g., a generative AI model) to generate optimal interior design proposals.

[0883] The device visually displays the generated design proposals to the user.

[0884] Emotional Data Analysis

[0885] While accessing the system, users provide facial expressions and voices to the system in real time.

[0886] The device activates an emotion engine and collects the user's facial expressions and voice through the camera and microphone.

[0887] The emotion engine uses facial expression recognition and voice analysis technology to analyze the user's emotional data in real time.

[0888] The server receives the emotional data obtained from the emotion engine and feeds it back to the AI ​​model, which then generates personalized design proposals based on the emotional data.

[0889] Real-time simulation using AR / VR

[0890] Users can review proposed designs and virtually rearrange furniture and decorations.

[0891] The device provides an AR / VR interface, changes position in real time based on user interaction, and provides visual feedback.

[0892] The server generates AR / VR data as needed and sends it to the device.

[0893] Learning user preferences

[0894] The device collects the user's selections and operation history and sends them to the server.

[0895] The server feeds the collected data back into the AI ​​model and reflects it in the next proposal.

[0896] Specific examples

[0897] Users upload photos and dimensions of their rooms

[0898] User: "Take a photo of my living room, measure the dimensions, and upload it to the app."

[0899] Device: "The user has entered a photo and dimensions of their living room. This data will be sent to the server."

[0900] Server: "Data received, AI will begin analysis."

[0901] Proposing optimal design ideas

[0902] Server: "Analyze the photos and dimensions to generate the optimal interior design. The resulting design has been sent to your device."

[0903] Device: "The AI ​​will display living room design ideas to the user."

[0904] Emotional Data Analysis

[0905] User: "As I review the design proposal, I provide facial expressions and voice for the system to analyze my emotions."

[0906] Device: "Collects your facial expressions and voice through a camera and microphone."

[0907] Emotion Engine: "Analyzes user emotions from collected data."

[0908] Server: "Feedback emotional data into the AI ​​model to personalize design suggestions."

[0909] AR / VR simulation

[0910] User: "Check out the design in AR mode and adjust the sofa position."

[0911] Device: "User repositioned the couch. View the new position in AR."

[0912] Server: "We will accumulate new placement data and reflect it in our next proposal."

[0913] With the above configuration, users can easily design their ideal interior and receive emotionally-sensitive suggestions, greatly improving the user experience and providing optimal design suggestions.

[0914] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0915] Step 1:

[0916] A user accesses the system through a web browser and creates an account or logs in. The input data is the user's user ID and password.

[0917] Input: Username, Password

[0918] Specific behavior:

[0919] User: Open a web browser and enter the required information (username, password) on the login page.

[0920] Terminal: Converts the input data into JSON format and sends an HTTPS request to the server.

[0921] Server: Checks the input data against the database, and if there is a match, generates and returns an authentication token.

[0922] Output: Authentication token

[0923] Step 2:

[0924] The user takes a photo of the living space and inputs the dimensions of the living space into the application.

[0925] Input: Living space photos, dimensions

[0926] Specific behavior:

[0927] User: Launches the application, takes a photo of the living space, enters the dimensions, and clicks the "Submit" button.

[0928] Terminal: Bundle the photo file and dimension data and send an HTTP POST request to the server.

[0929] Server: Stores the received image files in the storage system and records the dimensional data in the database.

[0930] Output: Image files stored in storage, dimensional data stored in database

[0931] Step 3:

[0932] The server generates interior design proposals using a generative AI model based on the stored image data and dimension data.

[0933] Input: Image data, dimensional data

[0934] Specific behavior:

[0935] Server: Input the image data and dimension data into the AI ​​model and start the analysis. Enter the prompt "Analyze this data and generate the optimal interior design proposal."

[0936] Generative AI model: Analyzes input data and generates optimal design proposals.

[0937] Server: Converts the generated design proposal into a data format and sends it to the device.

[0938] Output: Design proposal (image data)

[0939] Step 4:

[0940] Users can review the proposed design and provide feedback, and real-time emotional data (facial expressions and voice) from users is also collected.

[0941] Input: Design proposal (image data)

[0942] Specific behavior:

[0943] User: Views proposed design ideas and gives feedback, providing voice and facial expressions using a camera and microphone.

[0944] Device: Collects the user's facial expressions and voice through the camera and microphone and sends them to the emotion engine.

[0945] Emotion engine: Analyzes collected data and determines user emotions.

[0946] Server: Receives emotional data and reflects the feedback in the design proposal.

[0947] Output: Personalized design proposal incorporating your feedback

[0948] Step 5:

[0949] Users can view the generated design proposals in AR / VR mode and rearrange furniture and decorations.

[0950] Input: Personalized design ideas

[0951] Specific behavior:

[0952] Users can view the generated design proposals using a smartphone or AR / VR device and rearrange furniture and decorations in real time.

[0953] Terminal: Provides the AR / VR interface, changes position based on user interaction, and provides visual feedback.

[0954] Server: Generates the necessary AR / VR data and sends it to the device.

[0955] Output: User-modified deployment data

[0956] Step 6:

[0957] The system collects user selections and operation history and uses them for future suggestions.

[0958] Input: User selections and operation history

[0959] Specific behavior:

[0960] Terminal: Records user interaction data (e.g., choices, repositioning, viewing time) and transmits it to the server.

[0961] Server: Stores the received data in a database and feeds it back into the generative AI model. This information is reflected in the next interior design proposal.

[0962] Output: Design proposal reflected in next proposal

[0963] (Application example 2)

[0964] 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."

[0965] Conventional electronic payment services offer a uniform set of payment options without considering the user's emotional state, resulting in a uniform user experience. As a result, it is not possible to determine whether the user is enjoying shopping, and it is not possible to provide optimal payment suggestions.

[0966] 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.

[0967] In this invention, the server includes means for a user to input image data and dimensional data of his / her living space, means for analyzing the input image data and dimensional data to generate optimal interior design proposals, means for visualizing the generated design proposals in real time in an augmented reality and virtual reality environment, means for learning the user's selections and operations and reflecting them in subsequent proposals, and means for detecting the user's emotions and suggesting customized options based on the emotions, thereby enabling personalized payment proposals according to the user's emotions.

[0968] "User" means any individual or entity that accesses and uses the System or Application.

[0969] "Living space image data" refers to digital data that users have taken of the interior of their homes, including photos and videos.

[0970] "Dimensional data" refers to numerical data measuring the dimensions of living spaces and the furniture and decorations within them.

[0971] "Analysis" refers to the process of extracting information based on input data and performing the necessary processing and calculations.

[0972] An "interior design proposal" refers to a proposal for the placement and layout of furniture and decorations in a living space.

[0973] "Augmented reality (AR)" refers to a technology that overlays virtual information onto a real-world environment.

[0974] "Virtual reality (VR)" refers to a technology that allows you to experience a virtual three-dimensional space generated by a computer.

[0975] "Visualization" refers to the process of presenting information or data in a visual form.

[0976] "Selections and actions" refers to interface choices and inputs that users make within the system.

[0977] "Learning" refers to the process by which a system uses past data and operational history to improve future suggestions and decisions.

[0978] "Emotion detection" refers to technology that analyzes a user's facial expressions and voice to recognize their emotional state at that time.

[0979] "Customized options" refers to choices and suggestions tailored to a user's individual circumstances and emotions.

[0980] The present invention relates to a system for enabling users to enjoy emotion-based customized payment options in electronic payment services. Hereinafter, embodiments of the present invention will be described in detail.

[0981] System Overview

[0982] The system allows users to use a camera and microphone to provide facial expressions and voice recordings to a server, which then analyzes the data to recognize emotions and suggests optimal payment options based on those.

[0983] Hardware and software used

[0984] Camera: Hardware for capturing the user's facial expressions.

[0985] Microphone: Hardware used to capture the user's voice.

[0986] Server: The central processing unit for analyzing data, recognizing emotions, and suggesting options.

[0987] OpenCV: Software used to perform face recognition using image processing libraries.

[0988] Keras: Software used to implement emotion recognition models using deep learning libraries.

[0989] Emotion Recognition Model: A pre-trained model for recognizing user emotions.

[0990] How to operate the system

[0991] 1. User registration and login:

[0992] Users create an account through a web browser or log in with an existing account, and the server validates the credentials and generates and returns an authentication token.

[0993] 2. Collecting Emotional Data:

[0994] The device uses a camera and microphone to collect the user's facial expressions and voice in real time, and the emotion engine uses this data to analyze the user's emotions.

[0995] 3. Emotion-based suggestions:

[0996] The server analyzes the emotional data obtained from the emotion engine and accordingly suggests optimal payment options (e.g., a 5% discount coupon or a 10% cashback). These options are customized according to the user's emotional state.

[0997] Examples of concrete examples and prompts

[0998] For example, if a user is shopping online and the system recognizes their emotion of happiness, it will suggest a 5% discount coupon option. Conversely, if the user shows emotion of sadness, it will suggest a 10% cashback option.

[0999] Example prompts to input to a generative AI model:

[1000] “Implement a system that uses emotion recognition while users are shopping to offer customized payment options based on their emotions. If they look happy, offer a 5% discount coupon; if they look sad, offer a 10% cash back.”

[1001] This format is expected to improve the user experience and provide optimal payment suggestions based on emotions.

[1002] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1003] Step 1:

[1004] User Registration and Login

[1005] Input: User enters credentials (username, password)

[1006] What it does: The device displays a registration or login form and receives input from the user.

[1007] Data processing: The device sends the credentials to the server.

[1008] Output: The server validates the credentials and generates an authentication token to return to the user.

[1009] Step 2:

[1010] Collecting Emotional Data

[1011] Input: User facial and voice data captured using a camera and microphone

[1012] How it works: The device collects the user's facial expressions and voice in real time through the camera and microphone.

[1013] Data processing: The collected data is sent to the emotion engine, where emotions are analyzed using facial expression recognition and voice analysis technology.

[1014] Output: Real-time emotion data is generated as the analysis result.

[1015] Step 3:

[1016] Generating Sentiment-Based Recommendations

[1017] Input: Emotion data obtained from the emotion engine

[1018] How it works: The server generates optimal payment options for the user based on the emotion data, for example, a 5% discount coupon for happy emotions and a 10% cashback for sad emotions.

[1019] Data processing: Emotional data is fed back into the AI ​​model to generate customized suggestions.

[1020] Output: The payment options to offer to the user.

[1021] Step 4:

[1022] Visual representation of suggestions

[1023] Input: Payment option data sent from the server

[1024] How it works: The terminal visually displays customized payment options to the user.

[1025] Data processing: Adjust the design to suit the display format.

[1026] Output: The payment options visually presented to the user.

[1027] Step 5:

[1028] Collecting user feedback

[1029] Input: User selections and action history

[1030] How it works: The device records the user's choices and actions and sends them to the server.

[1031] Data processing: The server feeds the collected data back into the AI ​​model and reflects it in the next proposal.

[1032] Output: Next suggestion data based on user preferences and operation trends.

[1033] Step 6:

[1034] Data accumulation and learning

[1035] Input: Feedback data and user operation history data

[1036] How it works: The server stores the collected data in a database and provides feedback to the AI ​​model.

[1037] Data processing: Analyze the collected data and continue learning to reflect it in future proposals.

[1038] Output: Improved accuracy of suggestions from next time onwards.

[1039] 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.

[1040] 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.

[1041] 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.

[1042] [Third embodiment]

[1043] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1044] 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.

[1045] 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).

[1046] 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.

[1047] 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.

[1048] 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).

[1049] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1050] 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.

[1051] 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.

[1052] 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.

[1053] 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.

[1054] 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."

[1055] The system of the present invention allows users to input image data and dimensional data of their living space through an interface, and generates and provides optimal interior design proposals based on that data. The system runs on a web browser and includes the following main components:

[1056] 1. User Registration and Login

[1057] Users: Create an account or log in with an existing account through a web browser.

[1058] Terminal: displays the registration or login form, receives input from the user, and sends it to the server.

[1059] Server: Validates the credentials and authenticates the user, saving the information in a database for new users or generating and returning an authentication token for existing users.

[1060] 2. Upload photos and dimensions of your room

[1061] User: Take a photo of your living space and measure the dimensions of your living space.

[1062] Terminal: Provides an interface for uploading photos and entering dimensions, and sends the entered data to the server.

[1063] Server: Analyzes the received photos and dimension data and stores them in a database.

[1064] 3. Proposing the best design

[1065] Server: Analyzes stored photos and dimension data using AI to generate optimal interior design proposals.

[1066] Terminal: Visually displays the generated design proposals to the user.

[1067] 4. Real-time simulation using AR / VR

[1068] Users: Review proposed designs and virtually rearrange furniture and decorations.

[1069] Terminal: Provides an AR / VR interface, repositions itself in real time based on user interaction, and provides visual feedback.

[1070] Server: Generates AR / VR data as needed and sends it to the device.

[1071] 5. Learning your preferences

[1072] Device: Collects user selections and operation history and sends them to the server.

[1073] Server: The collected data is fed back to the AI ​​model and reflected in the next proposal.

[1074] Specific examples

[1075] 1. Users upload photos and dimensions of their rooms

[1076] User: "Take a photo of my living room, measure the dimensions, and upload it to the app."

[1077] Device: "The user has entered a photo and dimensions of their living room. This data will be sent to the server."

[1078] Server: "Data received, AI will begin analysis."

[1079] 2. Proposing the best design

[1080] Server: "Analyze the photos and dimensions to generate the optimal interior design. The resulting design has been sent to your device."

[1081] Device: "The AI ​​will display living room design ideas to the user."

[1082] 3. AR / VR Simulation

[1083] User: "View the proposed design in AR mode and adjust the sofa position."

[1084] Device: "User repositioned the couch. View the new position in AR."

[1085] Server: "We will accumulate new placement data and reflect it in our next proposal."

[1086] This allows users to easily design their ideal interior without specialist knowledge and see the changes in real time in a virtual environment, enabling personalized suggestions based on the user's preferences and resulting in a more satisfying design experience.

[1087] The processing flow will be explained below.

[1088] Step 1: User Registration

[1089] User: Clicks the "Create a new account" button to open the registration form.

[1090] On your device: Display a form to enter your name, email address, and password.

[1091] User: Enter the required information and click the "Submit" button.

[1092] Terminal: Sends the entered information to the server.

[1093] Server: Checks the credentials and saves the new user's information to the database. If the save is successful, returns a success message to the device.

[1094] Step 2: Log in

[1095] User: Enter your email address and password and click the Login button.

[1096] On your device: Send your credentials to the server.

[1097] Server: Checks the credentials against a database and, if correct, generates an authentication token and sends it to the device.

[1098] Terminal: Receives the authentication token and redirects the user to the dashboard.

[1099] Step 3: Upload photos of your room

[1100] User: Take a photo of your living space with your smartphone or camera.

[1101] Terminal: Display the photo upload interface.

[1102] User: Select the photo you have taken and click the upload button.

[1103] Device: Sends photo data to the server.

[1104] Server: Saves the received photos to storage and returns a save completion message to the device.

[1105] Step 4: Enter dimensional data

[1106] User: Use a tape measure to measure the dimensions of the room.

[1107] Terminal: Displays the dimension input form.

[1108] User: Enter the measured dimensions and click the submit button.

[1109] Terminal: Sends the entered dimension data to the server.

[1110] Server: Stores the received dimension data in a database.

[1111] Step 5: Generate optimal design proposals

[1112] Server: Passes the saved photos and dimensional data to the AI ​​module and begins analysis.

[1113] AI module: Analyzes image data and dimensional data to generate optimal interior design proposals.

[1114] Server: Sends the generated design proposals to the device and displays the results.

[1115] Step 6: View in AR / VR mode

[1116] User: Clicks the "AR Mode" or "VR Mode" button.

[1117] Device: Launch camera view and overlay the virtual interior design onto the real space.

[1118] User: Take action to adjust the position of virtual furniture and decorations.

[1119] Device: Updates the AR / VR display in real time based on user actions.

[1120] Step 7: Learning user preferences

[1121] Device: Collects user selections, layout changes, and other operational history.

[1122] Terminal: Sends collected data to the server.

[1123] Server: The received user data is fed back to the AI ​​module and reflected in the next proposal.

[1124] With such detailed processing for each step, users can easily design their ideal interior and check it in real time in the virtual space, improving the user experience and providing optimal design suggestions.

[1125] Example 1

[1126] 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."

[1127] Currently available interior design systems on the market require users to provide photos and dimensions of their living space. However, the subsequent design proposals and feedback to users lack real-time and personalized features. Furthermore, users have no way to virtually change the proposed design and visually confirm the results, which can lead to low user satisfaction. Therefore, there is a need for a system that allows users to easily find interior designs based on their preferences and view and adjust them in real time.

[1128] 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.

[1129] In this invention, the server includes a means for a user to input image data and dimensional data of their living space, a means using a generative AI model to analyze the input image data and dimensional data and generate optimal interior design proposals, a means for visualizing the generated design proposals in real time in an augmented reality and virtual reality environment, and a means using a machine learning algorithm to learn the user's selections and operations and reflect them in future proposals, thereby enabling users to easily find interior designs based on their preferences and check and adjust them in real time.

[1130] "User" means any person or entity that uses the System.

[1131] "Living space" refers to the place or room where the user lives.

[1132] "Image data" refers to digital data including photographs and images taken by users.

[1133] "Dimensional Data" refers to data regarding the size of a room or furniture measured by the user.

[1134] A "generative AI model" refers to an artificial intelligence model that analyzes input data and generates optimal interior design proposals.

[1135] "Augmented reality" refers to technology that adds digital information to real-world visual information.

[1136] "Virtual reality" refers to technology that allows you to experience a completely digitally created environment as if it were real.

[1137] "Machine learning algorithm" refers to an algorithm that allows the system to automatically learn from collected data and reflect that knowledge in its next proposal.

[1138] "Proposal" refers to showing the user the interior design ideas generated by the system.

[1139] The system of the present invention allows users to input image data and dimensional data of their living space, and generates optimal interior design proposals based on that data, and then proposes and visualizes them.The system runs on a web browser and includes the user, a terminal, and a server as its main components.

[1140] Detailed component description

[1141] 1. User Registration and Login

[1142] Users: Open a web browser and access the account creation page. Enter your name, email address, and password and press the "Register" button. Existing users should enter their email address and password and press the "Login" button.

[1143] On the device: Display a registration or login form, validate the data using JavaScript form validation, and submit it to the server.

[1144] Server: Store new user information in a database (e.g. MySQL, PostgreSQL). For existing users, verify the credentials, generate a JWT (JSON Web Token) and return it to the user.

[1145] 2. Upload photos and dimensions of your room

[1146] User: Take a photo of your living space using a smartphone or digital camera, measure the room dimensions, select the photo on the application's upload page, enter the dimensions, and press the "Upload" button.

[1147] Terminal: Receives image file and dimension data, displays a preview of the image selected by the user, and sends the data to the server using the FormData API.

[1148] Server: Stores uploaded image files in the file system or cloud storage (e.g., Amazon S3), stores dimensional data in a database, and performs image analysis and extracts features using OpenCV.

[1149] 3. Proposing the best design

[1150] Server: Generates a prompt into an AI engine (e.g., TensorFlow, PyTorch) that prompts for the input of the saved photo and dimension data. An example of a generated prompt is "Generate a design proposal for this living space." The AI ​​model analyzes this and generates the optimal interior design proposal based on the input data. The generated design proposal is saved as an image or 3D model.

[1151] Terminal: Receives the response from the server and uses Three.js to visually display the generated design proposal to the user.

[1152] 4. Real-time simulation using AR / VR

[1153] User: Press a button on the web page to switch to AR / VR mode, view proposed designs, and virtually rearrange furniture and decorations.

[1154] Device: Use Unity or Unreal Engine to provide an AR / VR interface, repositioning furniture in real time based on user interaction and providing visual feedback.

[1155] Server: Optionally, store the new placement data in the backend for future recommendations.

[1156] 5. Learning your preferences

[1157] Terminal: Collects user operation history and selections and sends them to the server.

[1158] Server: Analyzes the received data using a collaborative filtering algorithm to learn the user's preferences, which is then used to improve the accuracy of the next recommendation.

[1159] Specific examples

[1160] When a user uploads photos and dimensions of a living space, they:

[1161] 1. User: "Take a photo of my living room, measure the dimensions, and upload it to the app."

[1162] 2. Device: "The user has entered a photo and dimensions of their living room. Send this data to the server."

[1163] 3. Server: "The data is received and the AI ​​begins analysis."

[1164] When proposing the optimal design, we act as follows:

[1165] 1. Server: "Analyzes the photos and dimensional data, generates optimal interior design proposals, and sends the generated design proposals to the device."

[1166] 2. Device: "Displays AI-suggested living room design ideas to the user."

[1167] This allows users to easily find their ideal interior design without specialist knowledge, and check and adjust it in real time. It also provides personalized suggestions based on the user's preferences, providing a highly satisfying design experience.

[1168] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1169] Step 1:

[1170] User registration and login

[1171] User: Open a web browser and access the account creation page. Enter your name, email address, and password, then click the "Register" button. Existing users should enter their email address and password and click the "Login" button.

[1172] Input: Name, Email Address, Password (if new user). Email Address, Password (if existing user).

[1173] Terminal: Receives the input data and performs JavaScript form validation (e.g., using the JQuery Validation Plugin). After validation is passed, the data is sent to the server.

[1174] Output: The input data that passes validation.

[1175] Server: Stores the received data in a database (e.g., MySQL, PostgreSQL), and performs registration processing for new users. For existing users, checks the entered credentials against the information in the database. If they match, generates a JWT (JSON Web Token) and returns it to the user.

[1176] Output: Saves new user information or issues a JWT token for an existing user.

[1177] Step 2:

[1178] Upload room photos and dimensions

[1179] User: Take a photo of your living space using a smartphone or digital camera, measure the dimensions of the room, select the photo on the application's upload page, enter the dimensions, and press the "Upload" button.

[1180] Input: Photo files of the living space, room dimensions.

[1181] Terminal: Receives image file and dimension data, displays a preview of the image, and sends the data to the server using the FormData API.

[1182] Output: Image file and dimension data sent to the server.

[1183] Server: Stores image files in cloud storage (e.g., Amazon S3) or a file system. Stores dimensional data in a database. Additionally, analyzes the images using OpenCV and extracts image features.

[1184] Output: Saved image files with dimensional data and extracted image features.

[1185] Step 3:

[1186] Proposing optimal design ideas

[1187] Server: Generates a prompt to input the saved photo and dimension data into an AI engine (e.g., TensorFlow, PyTorch). Example: "Generate design ideas for this living space." The data along with the prompt is input into the AI ​​model to generate optimal interior design ideas. The generated design ideas are saved as images or 3D models.

[1188] Input: Saved photo and dimensional data, prompt text.

[1189] Output: Generated interior design proposals (images or 3D models).

[1190] On the device: The design proposals received from the server are visually displayed to the user using Three.js.

[1191] Output: A displayed interior design proposal.

[1192] Step 4:

[1193] Real-time simulation using AR / VR

[1194] Users: Press a button on the web page to switch to AR / VR mode, view proposed designs, and virtually rearrange furniture and decorations.

[1195] Input: Proposed design ideas, user actions (rearrangement of furniture and decorations).

[1196] Device: Provides an AR / VR interface using Unity or Unreal Engine. Rearranges furniture in real time based on user actions and provides visual feedback. For example, if a user drags a sofa to change its position, the sofa's position on the screen is instantly updated.

[1197] Output: Real-time updated furniture placement.

[1198] Server: Optionally, store the new placement data in the backend and use it for future recommendations.

[1199] Output: The new saved configuration data.

[1200] Step 5:

[1201] Learning user preferences

[1202] Terminal: Collects user operation history and selections and sends them to the server.

[1203] Input: User operation history, selection data.

[1204] Output: Operation history and selection data sent to the server.

[1205] Server: Analyzes the received data using a collaborative filtering algorithm to learn the user's preferences, thereby improving the accuracy of the next suggestions.

[1206] Output: Trained user model, data for next proposal.

[1207] As described above, the present invention is a system that allows users to easily find their ideal interior design, check and adjust it in real time, and provides personalized suggestions based on the user's preferences, resulting in a highly satisfying design experience.

[1208] (Application example 1)

[1209] 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."

[1210] Conventional interior design proposal systems have the drawback of making it difficult for users to check the actual layout image in real time when selecting interior items, and they cannot purchase the proposed products on the spot. Furthermore, the accuracy of the system's ability to learn the user's preferences and choices and reflect them in future proposals is insufficient. This makes it difficult for users to select interior items that will satisfy them.

[1211] 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.

[1212] In this invention, the server includes: a means for a user to input image data and dimensional data of their living space; a means for analyzing the input image data and dimensional data to generate optimal interior design proposals; a means for visualizing the generated design proposals in an augmented reality and virtual reality environment in real time and projecting them onto the user's living space; a means for learning the user's selections and operations and reflecting them in future proposals; and a means for connecting with an online platform to purchase the proposed products on the spot. This allows users to visually confirm the interior layout of their living space in real time and select interior designs based on their preferences. Furthermore, the ability to purchase products on the spot provides a consistent user experience from selection to purchase.

[1213] "Means for users to input image data and dimensional data of their living space" refers to an interface or application that allows users to take a photograph of the current state of their living space using a camera or measuring device and input the image data and dimensional data.

[1214] "Means for analyzing input image data and dimensional data to generate optimal interior design proposals" refers to software or systems that use AI or algorithms to analyze image data and dimensional data of living spaces provided by users, and automatically propose the optimal interior design for that space.

[1215] "Means of visualizing the generated design proposals in real time in an augmented reality or virtual reality environment and projecting them into the user's living space" refers to AR (augmented reality) and VR (virtual reality) technologies that allow interior design proposals generated by AI to be displayed in the user's field of vision in real time, allowing them to be viewed superimposed on the real space.

[1216] "Means of learning user choices and operations and reflecting them in the next proposal" refers to functions and algorithms that record the choices and changes made by the user on the system, and allow the AI ​​to learn from that data to help improve the accuracy of the next interior design proposal.

[1217] "Means for connecting with online platforms to purchase proposed products on the spot" refers to a system that provides links and purchasing procedures for directly purchasing furniture and decorative items included in the interior design proposals that users have confirmed from affiliated online shops and e-commerce sites.

[1218] This invention is a system that allows users to input image data and dimensional data of their living space, and generates and proposes optimal interior designs based on that data. To realize this system, the following hardware and software are required:

[1219] Hardware:

[1220] Smart glasses (or smartphone): A device for capturing images of the current state of the living space and acquiring image data.

[1221] Server: A high-performance computing device that analyzes image and dimensional data and generates design proposals.

[1222] software:

[1223] OpenCV: A library for real-time video processing acquired from smart glasses and smartphones.

[1224] Requests: A library for communicating with the server to send and receive data.

[1225] AI model: A generative model that analyzes data entered by the user and generates optimal interior designs.

[1226] Processing flow

[1227] 1. Input of image and dimension data

[1228] Users use smart glasses or a smartphone to take pictures of their living space and input dimensional data, which is then sent from the device to a server.

[1229] 2. Generate design proposals

[1230] The server then uses the received image and dimension data to generate optimal interior design proposals using an AI model that has been trained on a large amount of design data in advance.

[1231] 3. Real-time visualization

[1232] The design proposals generated by the server are displayed in real time in the user's field of vision via smart glasses or a smartphone, allowing the user to see how the design will look in their living space.

[1233] 4. Collaboration with online platforms

[1234] If a user likes a suggested product, they can connect to the online platform and complete the purchase process on the spot.

[1235] 5. Learning user choices and actions

[1236] The choices and actions taken by the user are logged and fed back to the AI ​​model, which then incorporates this data into the next recommendation, allowing for more personalized suggestions.

[1237] Specific examples

[1238] Example 1:

[1239] User: "I'm going to use my smart glasses to take a picture of my living room and enter the dimensions."

[1240] System: "Receives data and generates optimal interior design proposals."

[1241] Result: "I'll use AR mode to view the generated design and consider the placement of the sofa."

[1242] Example 2:

[1243] User: "I'll choose the sofa I like from the suggested interior designs and buy it right away."

[1244] System: "We will connect with your online platform to begin the purchase process."

[1245] Example prompts

[1246] User ID: 'user123'

[1247] Living space image: 'image123.jpg'

[1248] Living space dimensions: {'length': 500, 'width': 400, 'height': 300}

[1249] Suggestion: 'Optimal furniture arrangement for living room'

[1250] Through these steps, users can see how the interior layout of their living space will look in real time and purchase the products they like on the spot. Furthermore, the next proposal will reflect the user's selections and operation history, providing more personalized design suggestions.

[1251] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1252] Step 1:

[1253] The user takes an image of the living space and inputs dimensional data. The input data is acquired using smart glasses or a smartphone. The input consists of image data and dimensional data of the living space. The output is the input data ready to be transmitted.

[1254] Specifically, the user takes a photo of their living room using a camera, measures the dimensions using a tape measure or a dedicated app, enters the data into their smart glasses or smartphone, and then presses a button to send the information to the server.

[1255] Step 2:

[1256] The image data and dimension data acquired by the terminal are sent to the server. The input is the image data and dimension data received from the user, and the output is the data sent to the server.

[1257] Specifically, the device recognizes the user's actions and uses the API to send data to the server, which is done using an HTTP POST request.

[1258] Step 3:

[1259] The server analyzes the received image data and dimension data and generates optimal interior design proposals using a generative AI model. The input is the image data and dimension data sent from the device, and the output is the generated interior design proposal.

[1260] Specifically, after receiving the data, the server provides the input data to the AI ​​model, which analyzes it and generates optimal design proposals taking into account the layout and dimensions of the living space.

[1261] Step 4:

[1262] The server sends the interior design proposals it generates to the device. The input is the design proposal generated by the AI ​​model, and the output is the design proposal data that can be displayed on the device.

[1263] Specifically, the server sends design proposals in a format such as JSON to the device, which then receives and processes them.

[1264] Step 5:

[1265] The device displays interior design proposals in real time within the user's field of view. The input is the design proposal sent from the server, and the output is a real-time image displayed in the user's field of view.

[1266] Specifically, users can view design proposals overlaid on the real world through smart glasses or smartphone displays, with the placement of furniture and decorations projected onto the real world using AR technology.

[1267] Step 6:

[1268] When a user wants to purchase the suggested furniture or decorative item on the spot, they are connected to an online platform, where the input is the user's purchasing decision information and design proposals, and the output is links and processes to proceed to the purchase process.

[1269] Specifically, users can select a product they like and click on the link displayed on their device to be redirected to the relevant online shop and proceed with the purchase.

[1270] Step 7:

[1271] The device sends the user's selections and operation history to the server, which analyzes the data and reflects it in the next proposal. The input is the user's selections and operation history data, and the output is learning data for the AI ​​model.

[1272] Specifically, the device records user operation data and periodically sends it to a server. The server then stores the data and feeds it back into the AI ​​model. This allows the data to be reflected in the next interior design proposal, enabling more personalized suggestions.

[1273] 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.

[1274] The system of the present invention allows users to input image data and dimensional data of their living space through an interface, and generates and provides optimal interior design proposals based on that data. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system can provide personalized interior design proposals that take the user's emotions into consideration. The system runs on a web browser and includes the following main components:

[1275] 1. User Registration and Login

[1276] Users: Create an account or log in with an existing account through a web browser.

[1277] Terminal: displays the registration or login form, receives input from the user, and sends it to the server.

[1278] Server: Validates the credentials and authenticates the user, saving the information in a database for new users or generating and returning an authentication token for existing users.

[1279] 2. Upload photos and dimensions of your room

[1280] User: Take a photo of your living space and measure the dimensions of your living space.

[1281] Terminal: Provides an interface for uploading photos and entering dimensions, and sends the entered data to the server.

[1282] Server: Analyzes the received photos and dimension data and stores them in a database.

[1283] 3. Proposing the best design

[1284] Server: Analyzes stored photos and dimension data using AI to generate optimal interior design proposals.

[1285] Terminal: Visually displays the generated design proposals to the user.

[1286] 4. Incorporating an Emotional Engine

[1287] User: Provides facial expressions and voice to the system in real time while accessing the system.

[1288] Device: Activates the emotion engine and collects the user's facial expressions and voice via the camera and microphone.

[1289] Emotion Engine: Analyzes user emotions in real time using facial expression recognition and voice analysis technology.

[1290] Server: Receives emotion data obtained from the emotion engine and feeds it back to the AI ​​model. It generates personalized design proposals based on the emotion data.

[1291] 5. Real-time simulation using AR / VR

[1292] Users: Review proposed designs and virtually rearrange furniture and decorations.

[1293] Terminal: Provides an AR / VR interface, repositions itself in real time based on user interaction, and provides visual feedback.

[1294] Server: Generates AR / VR data as needed and sends it to the device.

[1295] 6. Learning your preferences

[1296] Device: Collects user selections and operation history and sends them to the server.

[1297] Server: The collected data is fed back to the AI ​​model and reflected in the next proposal.

[1298] Specific examples

[1299] 1. Users upload photos and dimensions of their rooms

[1300] User: "Take a photo of my living room, measure the dimensions, and upload it to the app."

[1301] Device: "The user has entered a photo and dimensions of their living room. This data will be sent to the server."

[1302] Server: "Data received, AI will begin analysis."

[1303] 2. Proposing the best design

[1304] Server: "Analyze the photos and dimensions to generate the optimal interior design. The resulting design has been sent to your device."

[1305] Device: "The AI ​​will display living room design ideas to the user."

[1306] 3. Incorporating an Emotional Engine

[1307] User: "As I review the design proposal, I provide facial expressions and voice for the system to analyze my emotions."

[1308] Device: "Collects your facial expressions and voice through a camera and microphone."

[1309] Emotion Engine: "Analyzes user emotions from collected data."

[1310] Server: "Feedback emotional data into the AI ​​model to personalize design suggestions."

[1311] 4. AR / VR Simulation

[1312] User: "Check out the design in AR mode and adjust the sofa position."

[1313] Device: "User repositioned the couch. View the new position in AR."

[1314] Server: "We will accumulate new placement data and reflect it in our next proposal."

[1315] Through these processing steps, users can easily design their ideal interior and receive emotionally sensitive suggestions, improving the user experience and providing optimal design suggestions.

[1316] The processing flow will be explained below.

[1317] Step 1: User Registration

[1318] User: Clicks the "Create a new account" button to open the registration form.

[1319] On your device: Display a form to enter your name, email address, and password.

[1320] User: Enter the required information and click the "Submit" button.

[1321] Terminal: Sends the entered information to the server.

[1322] Server: Checks the credentials and saves the new user's information to the database. If the save is successful, returns a success message to the device.

[1323] Step 2: Log in

[1324] User: Enter your email address and password and click the Login button.

[1325] On your device: Send your credentials to the server.

[1326] Server: Checks the credentials against a database and, if correct, generates an authentication token and sends it to the device.

[1327] Terminal: Receives the authentication token and redirects the user to the dashboard.

[1328] Step 3: Upload photos of your room

[1329] User: Take a photo of your living space with your smartphone or camera.

[1330] Terminal: Display the photo upload interface.

[1331] User: Select the photo you have taken and click the upload button.

[1332] Device: Sends photo data to the server.

[1333] Server: Saves the received photos to storage and returns a save completion message to the device.

[1334] Step 4: Enter dimensional data

[1335] User: Use a tape measure to measure the dimensions of the room.

[1336] Terminal: Displays the dimension input form.

[1337] User: Enter the measured dimensions and click the submit button.

[1338] Terminal: Sends the entered dimension data to the server.

[1339] Server: Stores the received dimension data in a database.

[1340] Step 5: Generate optimal design proposals

[1341] Server: Passes the saved photos and dimensional data to the AI ​​module and begins analysis.

[1342] AI module: Analyzes image data and dimensional data to generate optimal interior design proposals.

[1343] Server: Sends the generated design proposals to the device and displays the results.

[1344] Step 6: Activating the Emotion Engine

[1345] User: Clicks the "Emotion Recognition Mode" button.

[1346] Device: Activates the emotion engine and uses the camera and microphone to collect the user's facial expressions and voice in real time.

[1347] Emotion engine: Analyzes collected data and assesses the user's emotional state.

[1348] Server: Receives emotion data from the emotion engine and reflects it in the design proposal.

[1349] Step 7: Update your design proposal based on emotions

[1350] Server: Uses the sentiment analysis results from the emotion engine to update personalized design suggestions.

[1351] On the device: Updated design ideas are presented to the user in real time.

[1352] Step 8: View in AR / VR mode

[1353] User: Clicks the "AR Mode" or "VR Mode" button.

[1354] Device: Launch camera view and overlay the virtual interior design onto the real space.

[1355] User: Take action to adjust the position of virtual furniture and decorations.

[1356] Device: Updates the AR / VR display in real time based on user actions.

[1357] Step 9: Learning user preferences

[1358] Device: Collects user selections, layout changes, and other operational history.

[1359] Terminal: Sends collected data to the server.

[1360] Server: The received user data is fed back to the AI ​​module and reflected in the next proposal.

[1361] Such steps will enable personalized interior design suggestions based on the user's emotional state, significantly improving the user experience.

[1362] Example 2

[1363] 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."

[1364] Conventional interior design systems typically input image data and dimensional data of a user's living space to generate optimal design proposals. However, these systems lacked personalization based on the user's emotions and preferences, resulting in an insufficient user experience. Furthermore, it was difficult to make proposals that reflected changes in emotions in real time when the user was reviewing the design proposals. Furthermore, the system lacked the ability to learn from the user's selections and operation history and reflect these in future proposals.

[1365] The specification processing by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for a user to input image data and dimensional data of his or her living space, a means for analyzing the input image data and dimensional data to generate optimal interior design proposals, a means for visualizing the generated design proposals in real time in an augmented reality and virtual reality environment, a means for collecting and analyzing user emotion data and reflecting the analysis in the design proposals, and a means for learning the user's selections and operations and reflecting the learning in the next proposal. This enables personalized interior design proposals that take into account the user's emotions and preferences, significantly improving the user experience.

[1366] "User" refers to any individual or legal entity that uses the System.

[1367] "Living space" means the indoor space where the user lives.

[1368] "Image data" refers to visual data stored in the form of photographs or digital images.

[1369] "Dimensional data" means data that expresses the physical length, width, height, etc. of a living space numerically.

[1370] "Interface" refers to the input and output means by which a user interacts with a system.

[1371] "Server" means a computer system that stores, analyzes, and processes data.

[1372] "Analysis" refers to the process of performing calculations and evaluations based on input data to extract meaningful information.

[1373] "Design proposal" refers to specific proposals regarding interior layout and decoration.

[1374] "Augmented reality" and "virtual reality" refer to technologies that overlay digital information on the real world and create fully digital virtual environments, respectively.

[1375] "Visualization" means presenting a digital design proposal in a form that can be seen by the user.

[1376] "Emotional data" refers to data about the user's emotional state analyzed from their facial expressions and voice.

[1377] "Learning" refers to the data processing that allows the system to record the user's choices and operation history and reflect them in the next proposal.

[1378] The system of the present invention allows users to input image data and dimensional data of their living space through an interface, and generates and provides optimal interior design proposals based on that data. Furthermore, it analyzes the user's emotional data and makes it possible to propose personalized designs based on those emotions.

[1379] Key Components of the System

[1380] User registration and login

[1381] Users can create an account or log in with an existing account through a web browser.

[1382] The terminal displays a registration or login form, receives input from the user, and sends it to the server.

[1383] The server validates the credentials and authenticates the user, either storing the information in a database for new users or generating and returning an authentication token for existing users.

[1384] Upload room photos and dimensions

[1385] Users take photos of their living space and measure its dimensions.

[1386] The terminal provides an interface for uploading photos and inputting dimensions, and transmits the input data to the server.

[1387] The server analyzes the received photos and dimensional data and stores them in a database.

[1388] Proposing optimal design ideas

[1389] The server analyzes the stored photos and dimension data using an AI model (e.g., a generative AI model) to generate optimal interior design proposals.

[1390] The device visually displays the generated design proposals to the user.

[1391] Emotional Data Analysis

[1392] While accessing the system, users provide facial expressions and voices to the system in real time.

[1393] The device activates an emotion engine and collects the user's facial expressions and voice through the camera and microphone.

[1394] The emotion engine uses facial expression recognition and voice analysis technology to analyze the user's emotional data in real time.

[1395] The server receives the emotional data obtained from the emotion engine and feeds it back to the AI ​​model, which then generates personalized design proposals based on the emotional data.

[1396] Real-time simulation using AR / VR

[1397] Users can review proposed designs and virtually rearrange furniture and decorations.

[1398] The device provides an AR / VR interface, changes position in real time based on user interaction, and provides visual feedback.

[1399] The server generates AR / VR data as needed and sends it to the device.

[1400] Learning user preferences

[1401] The device collects the user's selections and operation history and sends them to the server.

[1402] The server feeds the collected data back into the AI ​​model and reflects it in the next proposal.

[1403] Specific examples

[1404] Users upload photos and dimensions of their rooms

[1405] User: "Take a photo of my living room, measure the dimensions, and upload it to the app."

[1406] Device: "The user has entered a photo and dimensions of their living room. This data will be sent to the server."

[1407] Server: "Data received, AI will begin analysis."

[1408] Proposing optimal design ideas

[1409] Server: "Analyze the photos and dimensions to generate the optimal interior design. The resulting design has been sent to your device."

[1410] Device: "The AI ​​will display living room design ideas to the user."

[1411] Emotional Data Analysis

[1412] User: "As I review the design proposal, I provide facial expressions and voice for the system to analyze my emotions."

[1413] Device: "Collects your facial expressions and voice through a camera and microphone."

[1414] Emotion Engine: "Analyzes user emotions from collected data."

[1415] Server: "Feedback emotional data into the AI ​​model to personalize design suggestions."

[1416] AR / VR simulation

[1417] User: "Check out the design in AR mode and adjust the sofa position."

[1418] Device: "User repositioned the couch. View the new position in AR."

[1419] Server: "We will accumulate new placement data and reflect it in our next proposal."

[1420] With the above configuration, users can easily design their ideal interior and receive emotionally-sensitive suggestions, greatly improving the user experience and providing optimal design suggestions.

[1421] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1422] Step 1:

[1423] A user accesses the system through a web browser and creates an account or logs in. The input data is the user's user ID and password.

[1424] Input: Username, Password

[1425] Specific behavior:

[1426] User: Open a web browser and enter the required information (username, password) on the login page.

[1427] Terminal: Converts the input data into JSON format and sends an HTTPS request to the server.

[1428] Server: Checks the input data against the database, and if there is a match, generates and returns an authentication token.

[1429] Output: Authentication token

[1430] Step 2:

[1431] The user takes a photo of the living space and inputs the dimensions of the living space into the application.

[1432] Input: Living space photos, dimensions

[1433] Specific behavior:

[1434] User: Launches the application, takes a photo of the living space, enters the dimensions, and clicks the "Submit" button.

[1435] Terminal: Bundle the photo file and dimension data and send an HTTP POST request to the server.

[1436] Server: Stores the received image files in the storage system and records the dimensional data in the database.

[1437] Output: Image files stored in storage, dimensional data stored in database

[1438] Step 3:

[1439] The server generates interior design proposals using a generative AI model based on the stored image data and dimension data.

[1440] Input: Image data, dimensional data

[1441] Specific behavior:

[1442] Server: Input the image data and dimension data into the AI ​​model and start the analysis. Enter the prompt "Analyze this data and generate the optimal interior design proposal."

[1443] Generative AI model: Analyzes input data and generates optimal design proposals.

[1444] Server: Converts the generated design proposal into a data format and sends it to the device.

[1445] Output: Design proposal (image data)

[1446] Step 4:

[1447] Users can review the proposed design and provide feedback, and real-time emotional data (facial expressions and voice) from users is also collected.

[1448] Input: Design proposal (image data)

[1449] Specific behavior:

[1450] User: Views proposed design ideas and gives feedback, providing voice and facial expressions using a camera and microphone.

[1451] Device: Collects the user's facial expressions and voice through the camera and microphone and sends them to the emotion engine.

[1452] Emotion engine: Analyzes collected data and determines user emotions.

[1453] Server: Receives emotional data and reflects the feedback in the design proposal.

[1454] Output: Personalized design proposal incorporating your feedback

[1455] Step 5:

[1456] Users can view the generated design proposals in AR / VR mode and rearrange furniture and decorations.

[1457] Input: Personalized design ideas

[1458] Specific behavior:

[1459] Users can view the generated design proposals using a smartphone or AR / VR device and rearrange furniture and decorations in real time.

[1460] Terminal: Provides the AR / VR interface, changes position based on user interaction, and provides visual feedback.

[1461] Server: Generates the necessary AR / VR data and sends it to the device.

[1462] Output: User-modified deployment data

[1463] Step 6:

[1464] The system collects user selections and operation history and uses them for future suggestions.

[1465] Input: User selections and operation history

[1466] Specific behavior:

[1467] Terminal: Records user interaction data (e.g., choices, repositioning, viewing time) and transmits it to the server.

[1468] Server: Stores the received data in a database and feeds it back into the generative AI model. This information is reflected in the next interior design proposal.

[1469] Output: Design proposal reflected in next proposal

[1470] (Application example 2)

[1471] 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."

[1472] Conventional electronic payment services offer a uniform set of payment options without considering the user's emotional state, resulting in a uniform user experience. As a result, it is not possible to determine whether the user is enjoying shopping, and it is not possible to provide optimal payment suggestions.

[1473] 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.

[1474] In this invention, the server includes means for a user to input image data and dimensional data of his / her living space, means for analyzing the input image data and dimensional data to generate optimal interior design proposals, means for visualizing the generated design proposals in real time in an augmented reality and virtual reality environment, means for learning the user's selections and operations and reflecting them in subsequent proposals, and means for detecting the user's emotions and suggesting customized options based on the emotions, thereby enabling personalized payment proposals according to the user's emotions.

[1475] "User" means any individual or entity that accesses and uses the System or Application.

[1476] "Living space image data" refers to digital data that users have taken of the interior of their homes, including photos and videos.

[1477] "Dimensional data" refers to numerical data measuring the dimensions of living spaces and the furniture and decorations within them.

[1478] "Analysis" refers to the process of extracting information based on input data and performing the necessary processing and calculations.

[1479] An "interior design proposal" refers to a proposal for the placement and layout of furniture and decorations in a living space.

[1480] "Augmented reality (AR)" refers to a technology that overlays virtual information onto a real-world environment.

[1481] "Virtual reality (VR)" refers to a technology that allows you to experience a virtual three-dimensional space generated by a computer.

[1482] "Visualization" refers to the process of presenting information or data in a visual form.

[1483] "Selections and actions" refers to interface choices and inputs that users make within the system.

[1484] "Learning" refers to the process by which a system uses past data and operational history to improve future suggestions and decisions.

[1485] "Emotion detection" refers to technology that analyzes a user's facial expressions and voice to recognize their emotional state at that time.

[1486] "Customized options" refers to choices and suggestions tailored to a user's individual circumstances and emotions.

[1487] The present invention relates to a system for enabling users to enjoy emotion-based customized payment options in electronic payment services. Hereinafter, embodiments of the present invention will be described in detail.

[1488] System Overview

[1489] The system allows users to use a camera and microphone to provide facial expressions and voice recordings to a server, which then analyzes the data to recognize emotions and suggests optimal payment options based on those.

[1490] Hardware and software used

[1491] Camera: Hardware for capturing the user's facial expressions.

[1492] Microphone: Hardware used to capture the user's voice.

[1493] Server: The central processing unit for analyzing data, recognizing emotions, and suggesting options.

[1494] OpenCV: Software used to perform face recognition using image processing libraries.

[1495] Keras: Software used to implement emotion recognition models using deep learning libraries.

[1496] Emotion Recognition Model: A pre-trained model for recognizing user emotions.

[1497] How to operate the system

[1498] 1. User registration and login:

[1499] Users create an account through a web browser or log in with an existing account, and the server validates the credentials and generates and returns an authentication token.

[1500] 2. Collecting Emotional Data:

[1501] The device uses a camera and microphone to collect the user's facial expressions and voice in real time, and the emotion engine uses this data to analyze the user's emotions.

[1502] 3. Emotion-based suggestions:

[1503] The server analyzes the emotional data obtained from the emotion engine and accordingly suggests optimal payment options (e.g., a 5% discount coupon or a 10% cashback). These options are customized according to the user's emotional state.

[1504] Examples of concrete examples and prompts

[1505] For example, if a user is shopping online and the system recognizes their emotion of happiness, it will suggest a 5% discount coupon option. Conversely, if the user shows emotion of sadness, it will suggest a 10% cashback option.

[1506] Example prompts to input to a generative AI model:

[1507] “Implement a system that uses emotion recognition while users are shopping to offer customized payment options based on their emotions. If they look happy, offer a 5% discount coupon; if they look sad, offer a 10% cash back.”

[1508] This format is expected to improve the user experience and provide optimal payment suggestions based on emotions.

[1509] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1510] Step 1:

[1511] User Registration and Login

[1512] Input: User enters credentials (username, password)

[1513] What it does: The device displays a registration or login form and receives input from the user.

[1514] Data processing: The device sends the credentials to the server.

[1515] Output: The server validates the credentials and generates an authentication token to return to the user.

[1516] Step 2:

[1517] Collecting Emotional Data

[1518] Input: User facial and voice data captured using a camera and microphone

[1519] How it works: The device collects the user's facial expressions and voice in real time through the camera and microphone.

[1520] Data processing: The collected data is sent to the emotion engine, where emotions are analyzed using facial expression recognition and voice analysis technology.

[1521] Output: Real-time emotion data is generated as the analysis result.

[1522] Step 3:

[1523] Generating Sentiment-Based Recommendations

[1524] Input: Emotion data obtained from the emotion engine

[1525] How it works: The server generates optimal payment options for the user based on the emotion data, for example, a 5% discount coupon for happy emotions and a 10% cashback for sad emotions.

[1526] Data processing: Emotional data is fed back into the AI ​​model to generate customized suggestions.

[1527] Output: The payment options to offer to the user.

[1528] Step 4:

[1529] Visual representation of suggestions

[1530] Input: Payment option data sent from the server

[1531] How it works: The terminal visually displays customized payment options to the user.

[1532] Data processing: Adjust the design to suit the display format.

[1533] Output: The payment options visually presented to the user.

[1534] Step 5:

[1535] Collecting user feedback

[1536] Input: User selections and action history

[1537] How it works: The device records the user's choices and actions and sends them to the server.

[1538] Data processing: The server feeds the collected data back into the AI ​​model and reflects it in the next proposal.

[1539] Output: Next suggestion data based on user preferences and operation trends.

[1540] Step 6:

[1541] Data accumulation and learning

[1542] Input: Feedback data and user operation history data

[1543] How it works: The server stores the collected data in a database and provides feedback to the AI ​​model.

[1544] Data processing: Analyze the collected data and continue learning to reflect it in future proposals.

[1545] Output: Improved accuracy of suggestions from next time onwards.

[1546] 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.

[1547] 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.

[1548] 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.

[1549] [Fourth embodiment]

[1550] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1551] 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.

[1552] 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).

[1553] 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.

[1554] 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.

[1555] 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).

[1556] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1557] 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.

[1558] 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.

[1559] 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.

[1560] 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.

[1561] 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.

[1562] 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."

[1563] The system of the present invention allows users to input image data and dimensional data of their living space through an interface, and generates and provides optimal interior design proposals based on that data. The system runs on a web browser and includes the following main components:

[1564] 1. User Registration and Login

[1565] Users: Create an account or log in with an existing account through a web browser.

[1566] Terminal: displays the registration or login form, receives input from the user, and sends it to the server.

[1567] Server: Validates the credentials and authenticates the user, saving the information in a database for new users or generating and returning an authentication token for existing users.

[1568] 2. Upload photos and dimensions of your room

[1569] User: Take a photo of your living space and measure the dimensions of your living space.

[1570] Terminal: Provides an interface for uploading photos and entering dimensions, and sends the entered data to the server.

[1571] Server: Analyzes the received photos and dimension data and stores them in a database.

[1572] 3. Proposing the best design

[1573] Server: Analyzes stored photos and dimension data using AI to generate optimal interior design proposals.

[1574] Terminal: Visually displays the generated design proposals to the user.

[1575] 4. Real-time simulation using AR / VR

[1576] Users: Review proposed designs and virtually rearrange furniture and decorations.

[1577] Terminal: Provides an AR / VR interface, repositions itself in real time based on user interaction, and provides visual feedback.

[1578] Server: Generates AR / VR data as needed and sends it to the device.

[1579] 5. Learning your preferences

[1580] Device: Collects user selections and operation history and sends them to the server.

[1581] Server: The collected data is fed back to the AI ​​model and reflected in the next proposal.

[1582] Specific examples

[1583] 1. Users upload photos and dimensions of their rooms

[1584] User: "Take a photo of my living room, measure the dimensions, and upload it to the app."

[1585] Device: "The user has entered a photo and dimensions of their living room. This data will be sent to the server."

[1586] Server: "Data received, AI will begin analysis."

[1587] 2. Proposing the best design

[1588] Server: "Analyze the photos and dimensions to generate the optimal interior design. The resulting design has been sent to your device."

[1589] Device: "The AI ​​will display living room design ideas to the user."

[1590] 3. AR / VR Simulation

[1591] User: "View the proposed design in AR mode and adjust the sofa position."

[1592] Device: "User repositioned the couch. View the new position in AR."

[1593] Server: "We will accumulate new placement data and reflect it in our next proposal."

[1594] This allows users to easily design their ideal interior without specialist knowledge and see the changes in real time in a virtual environment, enabling personalized suggestions based on the user's preferences and resulting in a more satisfying design experience.

[1595] The processing flow will be explained below.

[1596] Step 1: User Registration

[1597] User: Clicks the "Create a new account" button to open the registration form.

[1598] On your device: Display a form to enter your name, email address, and password.

[1599] User: Enter the required information and click the "Submit" button.

[1600] Terminal: Sends the entered information to the server.

[1601] Server: Checks the credentials and saves the new user's information to the database. If the save is successful, returns a success message to the device.

[1602] Step 2: Log in

[1603] User: Enter your email address and password and click the Login button.

[1604] On your device: Send your credentials to the server.

[1605] Server: Checks the credentials against a database and, if correct, generates an authentication token and sends it to the device.

[1606] Terminal: Receives the authentication token and redirects the user to the dashboard.

[1607] Step 3: Upload photos of your room

[1608] User: Take a photo of your living space with your smartphone or camera.

[1609] Terminal: Display the photo upload interface.

[1610] User: Select the photo you have taken and click the upload button.

[1611] Device: Sends photo data to the server.

[1612] Server: Saves the received photos to storage and returns a save completion message to the device.

[1613] Step 4: Enter dimensional data

[1614] User: Use a tape measure to measure the dimensions of the room.

[1615] Terminal: Displays the dimension input form.

[1616] User: Enter the measured dimensions and click the submit button.

[1617] Terminal: Sends the entered dimension data to the server.

[1618] Server: Stores the received dimension data in a database.

[1619] Step 5: Generate optimal design proposals

[1620] Server: Passes the saved photos and dimensional data to the AI ​​module and begins analysis.

[1621] AI module: Analyzes image data and dimensional data to generate optimal interior design proposals.

[1622] Server: Sends the generated design proposals to the device and displays the results.

[1623] Step 6: View in AR / VR mode

[1624] User: Clicks the "AR Mode" or "VR Mode" button.

[1625] Device: Launch camera view and overlay the virtual interior design onto the real space.

[1626] User: Take action to adjust the position of virtual furniture and decorations.

[1627] Device: Updates the AR / VR display in real time based on user actions.

[1628] Step 7: Learning user preferences

[1629] Device: Collects user selections, layout changes, and other operational history.

[1630] Terminal: Sends collected data to the server.

[1631] Server: The received user data is fed back to the AI ​​module and reflected in the next proposal.

[1632] With such detailed processing for each step, users can easily design their ideal interior and check it in real time in the virtual space, improving the user experience and providing optimal design suggestions.

[1633] Example 1

[1634] 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."

[1635] Currently available interior design systems on the market require users to provide photos and dimensions of their living space. However, the subsequent design proposals and feedback to users lack real-time and personalized features. Furthermore, users have no way to virtually change the proposed design and visually confirm the results, which can lead to low user satisfaction. Therefore, there is a need for a system that allows users to easily find interior designs based on their preferences and view and adjust them in real time.

[1636] 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.

[1637] In this invention, the server includes a means for a user to input image data and dimensional data of their living space, a means using a generative AI model to analyze the input image data and dimensional data and generate optimal interior design proposals, a means for visualizing the generated design proposals in real time in an augmented reality and virtual reality environment, and a means using a machine learning algorithm to learn the user's selections and operations and reflect them in future proposals, thereby enabling users to easily find interior designs based on their preferences and check and adjust them in real time.

[1638] "User" means any person or entity that uses the System.

[1639] "Living space" refers to the place or room where the user lives.

[1640] "Image data" refers to digital data including photographs and images taken by users.

[1641] "Dimensional Data" refers to data regarding the size of a room or furniture measured by the user.

[1642] A "generative AI model" refers to an artificial intelligence model that analyzes input data and generates optimal interior design proposals.

[1643] "Augmented reality" refers to technology that adds digital information to real-world visual information.

[1644] "Virtual reality" refers to technology that allows you to experience a completely digitally created environment as if it were real.

[1645] "Machine learning algorithm" refers to an algorithm that allows the system to automatically learn from collected data and reflect that knowledge in its next proposal.

[1646] "Proposal" refers to showing the user the interior design ideas generated by the system.

[1647] The system of the present invention allows users to input image data and dimensional data of their living space, and generates optimal interior design proposals based on that data, and then proposes and visualizes them.The system runs on a web browser and includes the user, a terminal, and a server as its main components.

[1648] Detailed component description

[1649] 1. User Registration and Login

[1650] Users: Open a web browser and access the account creation page. Enter your name, email address, and password and press the "Register" button. Existing users should enter their email address and password and press the "Login" button.

[1651] On the device: Display a registration or login form, validate the data using JavaScript form validation, and submit it to the server.

[1652] Server: Store new user information in a database (e.g. MySQL, PostgreSQL). For existing users, verify the credentials, generate a JWT (JSON Web Token) and return it to the user.

[1653] 2. Upload photos and dimensions of your room

[1654] User: Take a photo of your living space using a smartphone or digital camera, measure the room dimensions, select the photo on the application's upload page, enter the dimensions, and press the "Upload" button.

[1655] Terminal: Receives image file and dimension data, displays a preview of the image selected by the user, and sends the data to the server using the FormData API.

[1656] Server: Stores uploaded image files in the file system or cloud storage (e.g., Amazon S3), stores dimensional data in a database, and performs image analysis and extracts features using OpenCV.

[1657] 3. Proposing the best design

[1658] Server: Generates a prompt into an AI engine (e.g., TensorFlow, PyTorch) that prompts for the input of the saved photo and dimension data. An example of a generated prompt is "Generate a design proposal for this living space." The AI ​​model analyzes this and generates the optimal interior design proposal based on the input data. The generated design proposal is saved as an image or 3D model.

[1659] Terminal: Receives the response from the server and uses Three.js to visually display the generated design proposal to the user.

[1660] 4. Real-time simulation using AR / VR

[1661] User: Press a button on the web page to switch to AR / VR mode, view proposed designs, and virtually rearrange furniture and decorations.

[1662] Device: Use Unity or Unreal Engine to provide an AR / VR interface, repositioning furniture in real time based on user interaction and providing visual feedback.

[1663] Server: Optionally, store the new placement data in the backend for future recommendations.

[1664] 5. Learning your preferences

[1665] Terminal: Collects user operation history and selections and sends them to the server.

[1666] Server: Analyzes the received data using a collaborative filtering algorithm to learn the user's preferences, which is then used to improve the accuracy of the next recommendation.

[1667] Specific examples

[1668] When a user uploads photos and dimensions of a living space, they:

[1669] 1. User: "Take a photo of my living room, measure the dimensions, and upload it to the app."

[1670] 2. Device: "The user has entered a photo and dimensions of their living room. Send this data to the server."

[1671] 3. Server: "The data is received and the AI ​​begins analysis."

[1672] When proposing the optimal design, we act as follows:

[1673] 1. Server: "Analyzes the photos and dimensional data, generates optimal interior design proposals, and sends the generated design proposals to the device."

[1674] 2. Device: "Displays AI-suggested living room design ideas to the user."

[1675] This allows users to easily find their ideal interior design without specialist knowledge, and check and adjust it in real time. It also provides personalized suggestions based on the user's preferences, providing a highly satisfying design experience.

[1676] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1677] Step 1:

[1678] User registration and login

[1679] User: Open a web browser and access the account creation page. Enter your name, email address, and password, then click the "Register" button. Existing users should enter their email address and password and click the "Login" button.

[1680] Input: Name, Email Address, Password (if new user). Email Address, Password (if existing user).

[1681] Terminal: Receives the input data and performs JavaScript form validation (e.g., using the JQuery Validation Plugin). After validation is passed, the data is sent to the server.

[1682] Output: The input data that passes validation.

[1683] Server: Stores the received data in a database (e.g., MySQL, PostgreSQL), and performs registration processing for new users. For existing users, checks the entered credentials against the information in the database. If they match, generates a JWT (JSON Web Token) and returns it to the user.

[1684] Output: Saves new user information or issues a JWT token for an existing user.

[1685] Step 2:

[1686] Upload room photos and dimensions

[1687] User: Take a photo of your living space using a smartphone or digital camera, measure the dimensions of the room, select the photo on the application's upload page, enter the dimensions, and press the "Upload" button.

[1688] Input: Photo files of the living space, room dimensions.

[1689] Terminal: Receives image file and dimension data, displays a preview of the image, and sends the data to the server using the FormData API.

[1690] Output: Image file and dimension data sent to the server.

[1691] Server: Stores image files in cloud storage (e.g., Amazon S3) or a file system. Stores dimensional data in a database. Additionally, analyzes the images using OpenCV and extracts image features.

[1692] Output: Saved image files with dimensional data and extracted image features.

[1693] Step 3:

[1694] Proposing optimal design ideas

[1695] Server: Generates a prompt to input the saved photo and dimension data into an AI engine (e.g., TensorFlow, PyTorch). Example: "Generate design ideas for this living space." The data along with the prompt is input into the AI ​​model to generate optimal interior design ideas. The generated design ideas are saved as images or 3D models.

[1696] Input: Saved photo and dimensional data, prompt text.

[1697] Output: Generated interior design proposals (images or 3D models).

[1698] On the device: The design proposals received from the server are visually displayed to the user using Three.js.

[1699] Output: A displayed interior design proposal.

[1700] Step 4:

[1701] Real-time simulation using AR / VR

[1702] Users: Press a button on the web page to switch to AR / VR mode, view proposed designs, and virtually rearrange furniture and decorations.

[1703] Input: Proposed design ideas, user actions (rearrangement of furniture and decorations).

[1704] Device: Provides an AR / VR interface using Unity or Unreal Engine. Rearranges furniture in real time based on user actions and provides visual feedback. For example, if a user drags a sofa to change its position, the sofa's position on the screen is instantly updated.

[1705] Output: Real-time updated furniture placement.

[1706] Server: Optionally, store the new placement data in the backend and use it for future recommendations.

[1707] Output: The new saved configuration data.

[1708] Step 5:

[1709] Learning user preferences

[1710] Terminal: Collects user operation history and selections and sends them to the server.

[1711] Input: User operation history, selection data.

[1712] Output: Operation history and selection data sent to the server.

[1713] Server: Analyzes the received data using a collaborative filtering algorithm to learn the user's preferences, thereby improving the accuracy of the next suggestions.

[1714] Output: Trained user model, data for next proposal.

[1715] As described above, the present invention is a system that allows users to easily find their ideal interior design, check and adjust it in real time, and provides personalized suggestions based on the user's preferences, resulting in a highly satisfying design experience.

[1716] (Application example 1)

[1717] 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."

[1718] Conventional interior design proposal systems have the drawback of making it difficult for users to check the actual layout image in real time when selecting interior items, and they cannot purchase the proposed products on the spot. Furthermore, the accuracy of the system's ability to learn the user's preferences and choices and reflect them in future proposals is insufficient. This makes it difficult for users to select interior items that will satisfy them.

[1719] 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.

[1720] In this invention, the server includes: a means for a user to input image data and dimensional data of their living space; a means for analyzing the input image data and dimensional data to generate optimal interior design proposals; a means for visualizing the generated design proposals in an augmented reality and virtual reality environment in real time and projecting them onto the user's living space; a means for learning the user's selections and operations and reflecting them in future proposals; and a means for connecting with an online platform to purchase the proposed products on the spot. This allows users to visually confirm the interior layout of their living space in real time and select interior designs based on their preferences. Furthermore, the ability to purchase products on the spot provides a consistent user experience from selection to purchase.

[1721] "Means for users to input image data and dimensional data of their living space" refers to an interface or application that allows users to take a photograph of the current state of their living space using a camera or measuring device and input the image data and dimensional data.

[1722] "Means for analyzing input image data and dimensional data to generate optimal interior design proposals" refers to software or systems that use AI or algorithms to analyze image data and dimensional data of living spaces provided by users, and automatically propose the optimal interior design for that space.

[1723] "Means of visualizing the generated design proposals in real time in an augmented reality or virtual reality environment and projecting them into the user's living space" refers to AR (augmented reality) and VR (virtual reality) technologies that allow interior design proposals generated by AI to be displayed in the user's field of vision in real time, allowing them to be viewed superimposed on the real space.

[1724] "Means of learning user choices and operations and reflecting them in the next proposal" refers to functions and algorithms that record the choices and changes made by the user on the system, and allow the AI ​​to learn from that data to help improve the accuracy of the next interior design proposal.

[1725] "Means for connecting with online platforms to purchase proposed products on the spot" refers to a system that provides links and purchasing procedures for directly purchasing furniture and decorative items included in the interior design proposals that users have confirmed from affiliated online shops and e-commerce sites.

[1726] This invention is a system that allows users to input image data and dimensional data of their living space, and generates and proposes optimal interior designs based on that data. To realize this system, the following hardware and software are required:

[1727] Hardware:

[1728] Smart glasses (or smartphone): A device for capturing images of the current state of the living space and acquiring image data.

[1729] Server: A high-performance computing device that analyzes image and dimensional data and generates design proposals.

[1730] software:

[1731] OpenCV: A library for real-time video processing acquired from smart glasses and smartphones.

[1732] Requests: A library for communicating with the server to send and receive data.

[1733] AI model: A generative model that analyzes data entered by the user and generates optimal interior designs.

[1734] Processing flow

[1735] 1. Input of image and dimension data

[1736] Users use smart glasses or a smartphone to take pictures of their living space and input dimensional data, which is then sent from the device to a server.

[1737] 2. Generate design proposals

[1738] The server then uses the received image and dimension data to generate optimal interior design proposals using an AI model that has been trained on a large amount of design data in advance.

[1739] 3. Real-time visualization

[1740] The design proposals generated by the server are displayed in real time in the user's field of vision via smart glasses or a smartphone, allowing the user to see how the design will look in their living space.

[1741] 4. Collaboration with online platforms

[1742] If a user likes a suggested product, they can connect to the online platform and complete the purchase process on the spot.

[1743] 5. Learning user choices and actions

[1744] The choices and actions taken by the user are logged and fed back to the AI ​​model, which then incorporates this data into the next recommendation, allowing for more personalized suggestions.

[1745] Specific examples

[1746] Example 1:

[1747] User: "I'm going to use my smart glasses to take a picture of my living room and enter the dimensions."

[1748] System: "Receives data and generates optimal interior design proposals."

[1749] Result: "I'll use AR mode to view the generated design and consider the placement of the sofa."

[1750] Example 2:

[1751] User: "I'll choose the sofa I like from the suggested interior designs and buy it right away."

[1752] System: "We will connect with your online platform to begin the purchase process."

[1753] Example prompts

[1754] User ID: 'user123'

[1755] Living space image: 'image123.jpg'

[1756] Living space dimensions: {'length': 500, 'width': 400, 'height': 300}

[1757] Suggestion: 'Optimal furniture arrangement for living room'

[1758] Through these steps, users can see how the interior layout of their living space will look in real time and purchase the products they like on the spot. Furthermore, the next proposal will reflect the user's selections and operation history, providing more personalized design suggestions.

[1759] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1760] Step 1:

[1761] The user takes an image of the living space and inputs dimensional data. The input data is acquired using smart glasses or a smartphone. The input consists of image data and dimensional data of the living space. The output is the input data ready to be transmitted.

[1762] Specifically, the user takes a photo of their living room using a camera, measures the dimensions using a tape measure or a dedicated app, enters the data into their smart glasses or smartphone, and then presses a button to send the information to the server.

[1763] Step 2:

[1764] The image data and dimension data acquired by the terminal are sent to the server. The input is the image data and dimension data received from the user, and the output is the data sent to the server.

[1765] Specifically, the device recognizes the user's actions and uses the API to send data to the server, which is done using an HTTP POST request.

[1766] Step 3:

[1767] The server analyzes the received image data and dimension data and generates optimal interior design proposals using a generative AI model. The input is the image data and dimension data sent from the device, and the output is the generated interior design proposal.

[1768] Specifically, after receiving the data, the server provides the input data to the AI ​​model, which analyzes it and generates optimal design proposals taking into account the layout and dimensions of the living space.

[1769] Step 4:

[1770] The server sends the interior design proposals it generates to the device. The input is the design proposal generated by the AI ​​model, and the output is the design proposal data that can be displayed on the device.

[1771] Specifically, the server sends design proposals in a format such as JSON to the device, which then receives and processes them.

[1772] Step 5:

[1773] The device displays interior design proposals in real time within the user's field of view. The input is the design proposal sent from the server, and the output is a real-time image displayed in the user's field of view.

[1774] Specifically, users can view design proposals overlaid on the real world through smart glasses or smartphone displays, with the placement of furniture and decorations projected onto the real world using AR technology.

[1775] Step 6:

[1776] When a user wants to purchase the suggested furniture or decorative item on the spot, they are connected to an online platform, where the input is the user's purchasing decision information and design proposals, and the output is links and processes to proceed to the purchase process.

[1777] Specifically, users can select a product they like and click on the link displayed on their device to be redirected to the relevant online shop and proceed with the purchase.

[1778] Step 7:

[1779] The device sends the user's selections and operation history to the server, which analyzes the data and reflects it in the next proposal. The input is the user's selections and operation history data, and the output is learning data for the AI ​​model.

[1780] Specifically, the device records user operation data and periodically sends it to a server. The server then stores the data and feeds it back into the AI ​​model. This allows the data to be reflected in the next interior design proposal, enabling more personalized suggestions.

[1781] 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.

[1782] The system of the present invention allows users to input image data and dimensional data of their living space through an interface, and generates and provides optimal interior design proposals based on that data. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system can provide personalized interior design proposals that take the user's emotions into consideration. The system runs on a web browser and includes the following main components:

[1783] 1. User Registration and Login

[1784] Users: Create an account or log in with an existing account through a web browser.

[1785] Terminal: displays the registration or login form, receives input from the user, and sends it to the server.

[1786] Server: Validates the credentials and authenticates the user, saving the information in a database for new users or generating and returning an authentication token for existing users.

[1787] 2. Upload photos and dimensions of your room

[1788] User: Take a photo of your living space and measure the dimensions of your living space.

[1789] Terminal: Provides an interface for uploading photos and entering dimensions, and sends the entered data to the server.

[1790] Server: Analyzes the received photos and dimension data and stores them in a database.

[1791] 3. Proposing the best design

[1792] Server: Analyzes stored photos and dimension data using AI to generate optimal interior design proposals.

[1793] Terminal: Visually displays the generated design proposals to the user.

[1794] 4. Incorporating an Emotional Engine

[1795] User: Provides facial expressions and voice to the system in real time while accessing the system.

[1796] Device: Activates the emotion engine and collects the user's facial expressions and voice via the camera and microphone.

[1797] Emotion Engine: Analyzes user emotions in real time using facial expression recognition and voice analysis technology.

[1798] Server: Receives emotion data obtained from the emotion engine and feeds it back to the AI ​​model. It generates personalized design proposals based on the emotion data.

[1799] 5. Real-time simulation using AR / VR

[1800] Users: Review proposed designs and virtually rearrange furniture and decorations.

[1801] Terminal: Provides an AR / VR interface, repositions itself in real time based on user interaction, and provides visual feedback.

[1802] Server: Generates AR / VR data as needed and sends it to the device.

[1803] 6. Learning your preferences

[1804] Device: Collects user selections and operation history and sends them to the server.

[1805] Server: The collected data is fed back to the AI ​​model and reflected in the next proposal.

[1806] Specific examples

[1807] 1. Users upload photos and dimensions of their rooms

[1808] User: "Take a photo of my living room, measure the dimensions, and upload it to the app."

[1809] Device: "The user has entered a photo and dimensions of their living room. This data will be sent to the server."

[1810] Server: "Data received, AI will begin analysis."

[1811] 2. Proposing the best design

[1812] Server: "Analyze the photos and dimensions to generate the optimal interior design. The resulting design has been sent to your device."

[1813] Device: "The AI ​​will display living room design ideas to the user."

[1814] 3. Incorporating an Emotional Engine

[1815] User: "As I review the design proposal, I provide facial expressions and voice for the system to analyze my emotions."

[1816] Device: "Collects your facial expressions and voice through a camera and microphone."

[1817] Emotion Engine: "Analyzes user emotions from collected data."

[1818] Server: "Feedback emotional data into the AI ​​model to personalize design suggestions."

[1819] 4. AR / VR Simulation

[1820] User: "Check out the design in AR mode and adjust the sofa position."

[1821] Device: "User repositioned the couch. View the new position in AR."

[1822] Server: "We will accumulate new placement data and reflect it in our next proposal."

[1823] Through these processing steps, users can easily design their ideal interior and receive emotionally sensitive suggestions, improving the user experience and providing optimal design suggestions.

[1824] The processing flow will be explained below.

[1825] Step 1: User Registration

[1826] User: Clicks the "Create a new account" button to open the registration form.

[1827] On your device: Display a form to enter your name, email address, and password.

[1828] User: Enter the required information and click the "Submit" button.

[1829] Terminal: Sends the entered information to the server.

[1830] Server: Checks the credentials and saves the new user's information to the database. If the save is successful, returns a success message to the device.

[1831] Step 2: Log in

[1832] User: Enter your email address and password and click the Login button.

[1833] On your device: Send your credentials to the server.

[1834] Server: Checks the credentials against a database and, if correct, generates an authentication token and sends it to the device.

[1835] Terminal: Receives the authentication token and redirects the user to the dashboard.

[1836] Step 3: Upload photos of your room

[1837] User: Take a photo of your living space with your smartphone or camera.

[1838] Terminal: Display the photo upload interface.

[1839] User: Select the photo you have taken and click the upload button.

[1840] Device: Sends photo data to the server.

[1841] Server: Saves the received photos to storage and returns a save completion message to the device.

[1842] Step 4: Enter dimensional data

[1843] User: Use a tape measure to measure the dimensions of the room.

[1844] Terminal: Displays the dimension input form.

[1845] User: Enter the measured dimensions and click the submit button.

[1846] Terminal: Sends the entered dimension data to the server.

[1847] Server: Stores the received dimension data in a database.

[1848] Step 5: Generate optimal design proposals

[1849] Server: Passes the saved photos and dimensional data to the AI ​​module and begins analysis.

[1850] AI module: Analyzes image data and dimensional data to generate optimal interior design proposals.

[1851] Server: Sends the generated design proposals to the device and displays the results.

[1852] Step 6: Activating the Emotion Engine

[1853] User: Clicks the "Emotion Recognition Mode" button.

[1854] Device: Activates the emotion engine and uses the camera and microphone to collect the user's facial expressions and voice in real time.

[1855] Emotion engine: Analyzes collected data and assesses the user's emotional state.

[1856] Server: Receives emotion data from the emotion engine and reflects it in the design proposal.

[1857] Step 7: Update your design proposal based on emotions

[1858] Server: Uses the sentiment analysis results from the emotion engine to update personalized design suggestions.

[1859] On the device: Updated design ideas are presented to the user in real time.

[1860] Step 8: View in AR / VR mode

[1861] User: Clicks the "AR Mode" or "VR Mode" button.

[1862] Device: Launch camera view and overlay the virtual interior design onto the real space.

[1863] User: Take action to adjust the position of virtual furniture and decorations.

[1864] Device: Updates the AR / VR display in real time based on user actions.

[1865] Step 9: Learning user preferences

[1866] Device: Collects user selections, layout changes, and other operational history.

[1867] Terminal: Sends collected data to the server.

[1868] Server: The received user data is fed back to the AI ​​module and reflected in the next proposal.

[1869] Such steps will enable personalized interior design suggestions based on the user's emotional state, significantly improving the user experience.

[1870] Example 2

[1871] 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."

[1872] Conventional interior design systems typically input image data and dimensional data of a user's living space to generate optimal design proposals. However, these systems lacked personalization based on the user's emotions and preferences, resulting in an insufficient user experience. Furthermore, it was difficult to make proposals that reflected changes in emotions in real time when the user was reviewing the design proposals. Furthermore, the system lacked the ability to learn from the user's selections and operation history and reflect these in future proposals.

[1873] The specification processing by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for a user to input image data and dimensional data of his or her living space, a means for analyzing the input image data and dimensional data to generate optimal interior design proposals, a means for visualizing the generated design proposals in real time in an augmented reality and virtual reality environment, a means for collecting and analyzing user emotion data and reflecting the analysis in the design proposals, and a means for learning the user's selections and operations and reflecting the learning in the next proposal. This enables personalized interior design proposals that take into account the user's emotions and preferences, significantly improving the user experience.

[1874] "User" refers to any individual or legal entity that uses the System.

[1875] "Living space" means the indoor space where the user lives.

[1876] "Image data" refers to visual data stored in the form of photographs or digital images.

[1877] "Dimensional data" means data that expresses the physical length, width, height, etc. of a living space numerically.

[1878] "Interface" refers to the input and output means by which a user interacts with a system.

[1879] "Server" means a computer system that stores, analyzes, and processes data.

[1880] "Analysis" refers to the process of performing calculations and evaluations based on input data to extract meaningful information.

[1881] "Design proposal" refers to specific proposals regarding interior layout and decoration.

[1882] "Augmented reality" and "virtual reality" refer to technologies that overlay digital information on the real world and create fully digital virtual environments, respectively.

[1883] "Visualization" means presenting a digital design proposal in a form that can be seen by the user.

[1884] "Emotional data" refers to data about the user's emotional state analyzed from their facial expressions and voice.

[1885] "Learning" refers to the data processing that allows the system to record the user's choices and operation history and reflect them in the next proposal.

[1886] The system of the present invention allows users to input image data and dimensional data of their living space through an interface, and generates and provides optimal interior design proposals based on that data. Furthermore, it analyzes the user's emotional data and makes it possible to propose personalized designs based on those emotions.

[1887] Key Components of the System

[1888] User registration and login

[1889] Users can create an account or log in with an existing account through a web browser.

[1890] The terminal displays a registration or login form, receives input from the user, and sends it to the server.

[1891] The server validates the credentials and authenticates the user, either storing the information in a database for new users or generating and returning an authentication token for existing users.

[1892] Upload room photos and dimensions

[1893] Users take photos of their living space and measure its dimensions.

[1894] The terminal provides an interface for uploading photos and inputting dimensions, and transmits the input data to the server.

[1895] The server analyzes the received photos and dimensional data and stores them in a database.

[1896] Proposing optimal design ideas

[1897] The server analyzes the stored photos and dimension data using an AI model (e.g., a generative AI model) to generate optimal interior design proposals.

[1898] The device visually displays the generated design proposals to the user.

[1899] Emotional Data Analysis

[1900] While accessing the system, users provide facial expressions and voices to the system in real time.

[1901] The device activates an emotion engine and collects the user's facial expressions and voice through the camera and microphone.

[1902] The emotion engine uses facial expression recognition and voice analysis technology to analyze the user's emotional data in real time.

[1903] The server receives the emotional data obtained from the emotion engine and feeds it back to the AI ​​model, which then generates personalized design proposals based on the emotional data.

[1904] Real-time simulation using AR / VR

[1905] Users can review proposed designs and virtually rearrange furniture and decorations.

[1906] The device provides an AR / VR interface, changes position in real time based on user interaction, and provides visual feedback.

[1907] The server generates AR / VR data as needed and sends it to the device.

[1908] Learning user preferences

[1909] The device collects the user's selections and operation history and sends them to the server.

[1910] The server feeds the collected data back into the AI ​​model and reflects it in the next proposal.

[1911] Specific examples

[1912] Users upload photos and dimensions of their rooms

[1913] User: "Take a photo of my living room, measure the dimensions, and upload it to the app."

[1914] Device: "The user has entered a photo and dimensions of their living room. This data will be sent to the server."

[1915] Server: "Data received, AI will begin analysis."

[1916] Proposing optimal design ideas

[1917] Server: "Analyze the photos and dimensions to generate the optimal interior design. The resulting design has been sent to your device."

[1918] Device: "The AI ​​will display living room design ideas to the user."

[1919] Emotional Data Analysis

[1920] User: "As I review the design proposal, I provide facial expressions and voice for the system to analyze my emotions."

[1921] Device: "Collects your facial expressions and voice through a camera and microphone."

[1922] Emotion Engine: "Analyzes user emotions from collected data."

[1923] Server: "Feedback emotional data into the AI ​​model to personalize design suggestions."

[1924] AR / VR simulation

[1925] User: "Check out the design in AR mode and adjust the sofa position."

[1926] Device: "User repositioned the couch. View the new position in AR."

[1927] Server: "We will accumulate new placement data and reflect it in our next proposal."

[1928] With the above configuration, users can easily design their ideal interior and receive emotionally-sensitive suggestions, greatly improving the user experience and providing optimal design suggestions.

[1929] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1930] Step 1:

[1931] A user accesses the system through a web browser and creates an account or logs in. The input data is the user's user ID and password.

[1932] Input: Username, Password

[1933] Specific behavior:

[1934] User: Open a web browser and enter the required information (username, password) on the login page.

[1935] Terminal: Converts the input data into JSON format and sends an HTTPS request to the server.

[1936] Server: Checks the input data against the database, and if there is a match, generates and returns an authentication token.

[1937] Output: Authentication token

[1938] Step 2:

[1939] The user takes a photo of the living space and inputs the dimensions of the living space into the application.

[1940] Input: Living space photos, dimensions

[1941] Specific behavior:

[1942] User: Launches the application, takes a photo of the living space, enters the dimensions, and clicks the "Submit" button.

[1943] Terminal: Bundle the photo file and dimension data and send an HTTP POST request to the server.

[1944] Server: Stores the received image files in the storage system and records the dimensional data in the database.

[1945] Output: Image files stored in storage, dimensional data stored in database

[1946] Step 3:

[1947] The server generates interior design proposals using a generative AI model based on the stored image data and dimension data.

[1948] Input: Image data, dimensional data

[1949] Specific behavior:

[1950] Server: Input the image data and dimension data into the AI ​​model and start the analysis. Enter the prompt "Analyze this data and generate the optimal interior design proposal."

[1951] Generative AI model: Analyzes input data and generates optimal design proposals.

[1952] Server: Converts the generated design proposal into a data format and sends it to the device.

[1953] Output: Design proposal (image data)

[1954] Step 4:

[1955] Users can review the proposed design and provide feedback, and real-time emotional data (facial expressions and voice) from users is also collected.

[1956] Input: Design proposal (image data)

[1957] Specific behavior:

[1958] User: Views proposed design ideas and gives feedback, providing voice and facial expressions using a camera and microphone.

[1959] Device: Collects the user's facial expressions and voice through the camera and microphone and sends them to the emotion engine.

[1960] Emotion engine: Analyzes collected data and determines user emotions.

[1961] Server: Receives emotional data and reflects the feedback in the design proposal.

[1962] Output: Personalized design proposal incorporating your feedback

[1963] Step 5:

[1964] Users can view the generated design proposals in AR / VR mode and rearrange furniture and decorations.

[1965] Input: Personalized design ideas

[1966] Specific behavior:

[1967] Users can view the generated design proposals using a smartphone or AR / VR device and rearrange furniture and decorations in real time.

[1968] Terminal: Provides the AR / VR interface, changes position based on user interaction, and provides visual feedback.

[1969] Server: Generates the necessary AR / VR data and sends it to the device.

[1970] Output: User-modified deployment data

[1971] Step 6:

[1972] The system collects user selections and operation history and uses them for future suggestions.

[1973] Input: User selections and operation history

[1974] Specific behavior:

[1975] Terminal: Records user interaction data (e.g., choices, repositioning, viewing time) and transmits it to the server.

[1976] Server: Stores the received data in a database and feeds it back into the generative AI model. This information is reflected in the next interior design proposal.

[1977] Output: Design proposal reflected in next proposal

[1978] (Application example 2)

[1979] 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."

[1980] Conventional electronic payment services offer a uniform set of payment options without considering the user's emotional state, resulting in a uniform user experience. As a result, it is not possible to determine whether the user is enjoying shopping, and it is not possible to provide optimal payment suggestions.

[1981] 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.

[1982] In this invention, the server includes means for a user to input image data and dimensional data of his / her living space, means for analyzing the input image data and dimensional data to generate optimal interior design proposals, means for visualizing the generated design proposals in real time in an augmented reality and virtual reality environment, means for learning the user's selections and operations and reflecting them in subsequent proposals, and means for detecting the user's emotions and suggesting customized options based on the emotions, thereby enabling personalized payment proposals according to the user's emotions.

[1983] "User" means any individual or entity that accesses and uses the System or Application.

[1984] "Living space image data" refers to digital data that users have taken of the interior of their homes, including photos and videos.

[1985] "Dimensional data" refers to numerical data measuring the dimensions of living spaces and the furniture and decorations within them.

[1986] "Analysis" refers to the process of extracting information based on input data and performing the necessary processing and calculations.

[1987] An "interior design proposal" refers to a proposal for the placement and layout of furniture and decorations in a living space.

[1988] "Augmented reality (AR)" refers to a technology that overlays virtual information onto a real-world environment.

[1989] "Virtual reality (VR)" refers to a technology that allows you to experience a virtual three-dimensional space generated by a computer.

[1990] "Visualization" refers to the process of presenting information or data in a visual form.

[1991] "Selections and actions" refers to interface choices and inputs that users make within the system.

[1992] "Learning" refers to the process by which a system uses past data and operational history to improve future suggestions and decisions.

[1993] "Emotion detection" refers to technology that analyzes a user's facial expressions and voice to recognize their emotional state at that time.

[1994] "Customized options" refers to choices and suggestions tailored to a user's individual circumstances and emotions.

[1995] The present invention relates to a system for enabling users to enjoy emotion-based customized payment options in electronic payment services. Hereinafter, embodiments of the present invention will be described in detail.

[1996] System Overview

[1997] The system allows users to use a camera and microphone to provide facial expressions and voice recordings to a server, which then analyzes the data to recognize emotions and suggests optimal payment options based on those.

[1998] Hardware and software used

[1999] Camera: Hardware for capturing the user's facial expressions.

[2000] Microphone: Hardware used to capture the user's voice.

[2001] Server: The central processing unit for analyzing data, recognizing emotions, and suggesting options.

[2002] OpenCV: Software used to perform face recognition using image processing libraries.

[2003] Keras: Software used to implement emotion recognition models using deep learning libraries.

[2004] Emotion Recognition Model: A pre-trained model for recognizing user emotions.

[2005] How to operate the system

[2006] 1. User registration and login:

[2007] Users create an account through a web browser or log in with an existing account, and the server validates the credentials and generates and returns an authentication token.

[2008] 2. Collecting Emotional Data:

[2009] The device uses a camera and microphone to collect the user's facial expressions and voice in real time, and the emotion engine uses this data to analyze the user's emotions.

[2010] 3. Emotion-based suggestions:

[2011] The server analyzes the emotional data obtained from the emotion engine and accordingly suggests optimal payment options (e.g., a 5% discount coupon or a 10% cashback). These options are customized according to the user's emotional state.

[2012] Examples of concrete examples and prompts

[2013] For example, if a user is shopping online and the system recognizes their emotion of happiness, it will suggest a 5% discount coupon option. Conversely, if the user shows emotion of sadness, it will suggest a 10% cashback option.

[2014] Example prompts to input to a generative AI model:

[2015] “Implement a system that uses emotion recognition while users are shopping to offer customized payment options based on their emotions. If they look happy, offer a 5% discount coupon; if they look sad, offer a 10% cash back.”

[2016] This format is expected to improve the user experience and provide optimal payment suggestions based on emotions.

[2017] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2018] Step 1:

[2019] User Registration and Login

[2020] Input: User enters credentials (username, password)

[2021] What it does: The device displays a registration or login form and receives input from the user.

[2022] Data processing: The device sends the credentials to the server.

[2023] Output: The server validates the credentials and generates an authentication token to return to the user.

[2024] Step 2:

[2025] Collecting Emotional Data

[2026] Input: User facial and voice data captured using a camera and microphone

[2027] How it works: The device collects the user's facial expressions and voice in real time through the camera and microphone.

[2028] Data processing: The collected data is sent to the emotion engine, where emotions are analyzed using facial expression recognition and voice analysis technology.

[2029] Output: Real-time emotion data is generated as the analysis result.

[2030] Step 3:

[2031] Generating Sentiment-Based Recommendations

[2032] Input: Emotion data obtained from the emotion engine

[2033] How it works: The server generates optimal payment options for the user based on the emotion data, for example, a 5% discount coupon for happy emotions and a 10% cashback for sad emotions.

[2034] Data processing: Emotional data is fed back into the AI ​​model to generate customized suggestions.

[2035] Output: The payment options to offer to the user.

[2036] Step 4:

[2037] Visual representation of suggestions

[2038] Input: Payment option data sent from the server

[2039] How it works: The terminal visually displays customized payment options to the user.

[2040] Data processing: Adjust the design to suit the display format.

[2041] Output: The payment options visually presented to the user.

[2042] Step 5:

[2043] Collecting user feedback

[2044] Input: User selections and action history

[2045] How it works: The device records the user's choices and actions and sends them to the server.

[2046] Data processing: The server feeds the collected data back into the AI ​​model and reflects it in the next proposal.

[2047] Output: Next suggestion data based on user preferences and operation trends.

[2048] Step 6:

[2049] Data accumulation and learning

[2050] Input: Feedback data and user operation history data

[2051] How it works: The server stores the collected data in a database and provides feedback to the AI ​​model.

[2052] Data processing: Analyze the collected data and continue learning to reflect it in future proposals.

[2053] Output: Improved accuracy of suggestions from next time onwards.

[2054] 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.

[2055] 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.

[2056] 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.

[2057] 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.

[2058] 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.

[2059] 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.

[2060] 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).

[2061] 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.

[2062] 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."

[2063] 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.

[2064] 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).

[2065] 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.

[2066] 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.

[2067] 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.

[2068] 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.

[2069] 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.

[2070] 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.

[2071] 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.

[2072] 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.

[2073] 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.

[2074] 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.

[2075] The following is further disclosed regarding the above embodiment.

[2076] (Claim 1)

[2077] a means for a user to input image data and dimensional data of his or her living space;

[2078] A means for analyzing input image data and dimension data to generate optimal interior design proposals;

[2079] a means for visualizing the generated design proposals in real time in an augmented reality and virtual reality environment; and

[2080] A way to learn from user choices and actions and reflect them in future suggestions;

[2081] A system including:

[2082] (Claim 2)

[2083] 10. The system of claim 1, further comprising means for a user to virtually rearrange the furniture and decorative items through the interface.

[2084] (Claim 3)

[2085] 2. The system according to claim 1, further comprising means for transmitting data on changes to furniture and decorations input by the user through the interface to the server, and for the server to analyze the data and reflect it in the next proposal.

[2086] "Example 1"

[2087] (Claim 1)

[2088] a means for a user to input image data and dimensional data of his or her living space;

[2089] A method using a generative AI model that analyzes input image data and dimensional data to generate optimal interior design proposals;

[2090] a means for visualizing the generated design proposals in real time in an augmented reality and virtual reality environment; and

[2091] A means using machine learning algorithms to learn user choices and actions and reflect them in future suggestions;

[2092] A system including:

[2093] (Claim 2)

[2094] 10. The system of claim 1, further comprising means for a user to virtually rearrange the furniture and decorative items through the interface.

[2095] (Claim 3)

[2096] 10. The system of claim 1, further comprising means for transmitting data on changes to furniture and decorations input by a user through the interface to a server, the server analyzing the data and incorporating the data into subsequent suggestions using a machine learning algorithm.

[2097] "Application Example 1"

[2098] (Claim 1)

[2099] a means for a user to input image data and dimensional data of his or her living space;

[2100] A means for analyzing input image data and dimension data to generate optimal interior design proposals;

[2101] A means for visualizing the generated design proposals in real time in an augmented reality and virtual reality environment and projecting them into the user's living space; and

[2102] A way to learn from user choices and actions and reflect them in future suggestions;

[2103] Linking with online platforms allows customers to purchase suggested products on the spot,

[2104] A system including:

[2105] (Claim 2)

[2106] 10. The system of claim 1, further comprising means for a user to virtually rearrange furniture and decorative items through the interface and view the arrangement in real time.

[2107] (Claim 3)

[2108] 2. The system according to claim 1, further comprising means for transmitting data on changes to furniture and decorations input by the user through the interface to the server, and for the server to analyze the data and reflect it in the next proposal.

[2109] "Example 2: Combining Emotion Engines"

[2110] (Claim 1)

[2111] a means for a user to input image data and dimensional data of his or her living space;

[2112] A means for analyzing input image data and dimension data to generate optimal interior design proposals;

[2113] a means for visualizing the generated design proposals in real time in an augmented reality and virtual reality environment; and

[2114] A means to collect and analyze user emotional data and reflect it in design proposals,

[2115] A way to learn from user choices and actions and reflect them in future suggestions;

[2116] A system including:

[2117] (Claim 2)

[2118] 10. The system of claim 1, further comprising means for a user to virtually rearrange the furniture and decorative items through the interface.

[2119] (Claim 3)

[2120] 2. The system according to claim 1, further comprising means for transmitting data on changes to furniture and decorations input by the user through the interface to the server, and for the server to analyze the data and reflect it in the next proposal.

[2121] "Application example 2 when combining emotion engines"

[2122] (Claim 1)

[2123] a means for a user to input image data and dimensional data of his or her living space;

[2124] A means for analyzing input image data and dimension data to generate optimal interior design proposals;

[2125] a means for visualizing the generated design proposals in real time in an augmented reality and virtual reality environment; and

[2126] A way to learn from user choices and actions and reflect them in future suggestions;

[2127] A means for detecting user emotions and suggesting customized options based on the emotions;

[2128] A system including:

[2129] (Claim 2)

[2130] 10. The system of claim 1, further comprising means for a user to virtually rearrange the furniture and decorative items through the interface.

[2131] (Claim 3)

[2132] 2. The system according to claim 1, further comprising means for transmitting data on changes to furniture and decorations input by the user through the interface to the server, and for the server to analyze the data and reflect it in the next proposal. [Explanation of symbols]

[2133] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for a user to input image data and dimensional data of his or her living space; A means for analyzing input image data and dimension data to generate optimal interior design proposals; a means for visualizing the generated design proposals in real time in an augmented reality and virtual reality environment; and A way to learn from user choices and actions and reflect them in future suggestions; A system including:

2. The system of claim 1 further comprising means for a user to virtually rearrange the furniture and decorations through the interface.

3. 2. The system according to claim 1, further comprising means for transmitting data on changes to furniture and decorations input by the user through the interface to the server, and for the server to analyze the data and reflect it in the next proposal.

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