System

Abstract

A system is provided.SOLUTION: This system includes a means for inputting an image photographed by a user, a means for transmitting the image inputted by a terminal to a server, a means for analyzing the image received by the server and generating merchandise information matched with the atmosphere of a room, a means for transmitting the merchandise information generated by the server to the terminal, and a means for displaying the merchandise information to the user by the terminal.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] While the number of interior design products has increased in recent years, users often spend a lot of time searching for products that suit their living space. Furthermore, purchased products often do not match the atmosphere of the room, resulting in waste. Furthermore, organizing living spaces, space-saving storage, and decluttering unnecessary items are also important issues. However, individually collecting and applying this information is cumbersome and difficult for users without specialized knowledge. The present invention aims to solve these problems by providing a system that allows users to quickly select optimal interior design products and effectively organize their living space. [Means for solving the problem]

[0005] The present invention is a system including a means for inputting an image taken by a user, a means for the terminal to transmit the input image to a server, a means for the server to analyze the received image and generate product information that matches the ambiance of the room, a means for the server to transmit the generated product information to the terminal, and a means for the terminal to display the product information to the user. The system also includes a means for the user and the server to collect detailed needs through a chat-style discussion, a means for the server to suggest optimal lifestyles based on the information collected from the user, a means for the server to suggest space-saving storage methods and methods for decluttering unnecessary items, and a means for the terminal to display the suggestions received from the server to the user and for the user to proceed with purchasing the suggested products as needed. The present invention allows users to efficiently select interior products that are optimal for their living space and create a tidy and comfortable living environment.

[0006] A "user" in the present invention is a person who uses the system, inputs images with the aim of improving their living space, and receives suggestions.

[0007] A "terminal" is a device used by a user, such as a smartphone, tablet, or computer, that has the functions of inputting photos, communicating with a server, and displaying proposal content.

[0008] A "server" is a device or system that operates on the cloud or on-premise and performs image analysis, product information generation, chat functions, and proposal generation and transmission.

[0009] "Image analysis" is the process of processing received images and extracting features such as the room's atmosphere, furniture arrangement, and color.

[0010] "Product information" is information about interior products and furniture that match the atmosphere of the user's room, and includes attributes such as color, design, and price.

[0011] "Suggestion" refers to providing optimal product information and living space improvement measures based on analyzed images and dialogue with the user.

[0012] "Chat-style discussion" is a method of communication between the server and the user in text format, and is used to gather detailed information about the user's needs, interests, and preferences.

[0013] "Lifestyle suggestions" are overall improvements to living spaces, including advice on the best interior products and furniture placements based on the user's needs.

[0014] A "space-saving storage method" is a storage technique that makes effective use of limited space.

[0015] The "decluttering method" is a method for organizing unused and unnecessary items and making efficient use of living space.

[0016] The "purchase procedure" refers to the steps a user takes when actually purchasing a suggested product from an online store or the like. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] This invention is a system that allows users to easily select the best interior products for their living space and realize effective storage methods. This system consists of three main components: the user, the terminal, and the server.

[0039] First, users use a device such as a smartphone, tablet, or computer to input a photo of their room into the system. To input photos, they use a dedicated app or a web browser. The user selects a photo and clicks the upload button.

[0040] Next, the device sends the selected photo to the cloud server. The device issues an HTTP POST request to upload the photo data to the server. After the photo is sent to the server, the server receives it and stores it in its storage.

[0041] The server analyzes the received photos using an image analysis algorithm. Image analysis uses techniques such as object recognition and color extraction to extract features such as the room's atmosphere and furniture arrangement. After analyzing the features, the server generates optimal interior product information from a product database.

[0042] Next, the server sends the generated product information to the terminal. The proposal content is generated in a data format such as JSON and sent as an HTTP response. The terminal displays the received product information to the user. The displayed content includes the product image, name, color, price, etc.

[0043] The user then chats with the server via their device to discuss their needs and preferences. For example, they can express specific requests such as "I prefer a more natural look" or "I want storage space under the bed."

[0044] Based on the information collected through chat-style discussions, the server generates lifestyle suggestions that best suit the user's needs, including color and design that matches the room's atmosphere, temporary storage ideas, and how to declutter unnecessary items.

[0045] The server can also suggest space-saving storage solutions and ways to get rid of unnecessary items, such as placing storage boxes under the bed or recycling items you haven't used in over a year.

[0046] Finally, the user can check the suggestions on the device and, if necessary, purchase the suggested products. The device also provides a purchase link to the online store, allowing the user to easily purchase the products.

[0047] For example, if a user is suggested blue cushions and white shelves, and then requests more natural items, the server will again suggest natural-colored cushions and wooden shelves. It will also make specific suggestions, such as "putting storage boxes under the bed" as a space-saving storage solution. In this way, users can find interior and storage solutions optimized for their lifestyle.

[0048] This system allows users to quickly select the best interior for their living space, effectively organize their room, and create a comfortable living environment.

[0049] The processing flow will be explained below.

[0050] Step 1:

[0051] The user takes a photo of the room and inputs it into the device. The user then selects the photo from a dedicated app or web browser and clicks the upload button.

[0052] Step 2:

[0053] The device sends the entered photo to the server. The device issues an HTTP POST request to upload the photo data to the server.

[0054] Step 3:

[0055] The server receives the photos and stores them in storage. The server stores the uploaded photos in the appropriate directory.

[0056] Step 4:

[0057] The server analyzes the received photos and uses image analysis algorithms to extract features such as the room's atmosphere and furniture arrangement.

[0058] Step 5:

[0059] The server generates product information based on the features extracted from the image, searches a product database, and selects the most suitable interior product.

[0060] Step 6:

[0061] The server generates product information and sends it to the terminal. The proposal content is generated in a data format such as JSON and sent as an HTTP response.

[0062] Step 7:

[0063] The terminal displays the received product information to the user, including product images, names, colors, prices, etc.

[0064] Step 8:

[0065] Users can chat with the server through their devices and provide detailed information about their needs, interests, and preferences.

[0066] Step 9:

[0067] The server analyzes the content of the chat with the user and generates lifestyle suggestions that best suit the user's needs. The server updates product information and layout based on the collected information.

[0068] Step 10:

[0069] The server also suggests space-saving storage solutions and ways to get rid of unnecessary items, such as placing storage boxes under the bed or recycling items you haven't used in over a year.

[0070] Step 11:

[0071] The user reviews the final offer on the device and, if necessary, completes the purchase process for the proposed product. The device provides a purchase link to the online store.

[0072] Step 12:

[0073] The user clicks on the provided purchase link and completes the purchase process at the specified online store.

[0074] Example 1

[0075] 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."

[0076] In modern life, selecting the best interior products for a user's living space and implementing effective storage solutions is a time-consuming and laborious task. It is particularly difficult to find products that match the user's tastes and preferences and the atmosphere of the room. Furthermore, there is a demand not only for help selecting interior products but also for suggestions on specific storage solutions and lifestyles. The present invention aims to provide a system that solves these problems.

[0077] 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.

[0078] In this invention, the server includes means for analyzing received images, extracting room features using object recognition and color extraction technology, and generating product information based on the features, means for collecting detailed needs through chat-style discussions with users, and means for proposing optimal lifestyles based on the collected information, thereby enabling users to easily select interior products that are best suited to their living space and realize effective storage methods.

[0079] "User" refers to an individual or group that uses this system to select interior products that suit their living space and receive suggestions.

[0080] "Terminal" refers to an electronic device, such as a smartphone, tablet, or computer, that a user uses to access the system and input or transmit images.

[0081] A "server" refers to a computer system that receives data sent from a terminal, analyzes and processes it, and returns the results to the terminal.

[0082] An "image analysis algorithm" refers to a calculation method used to analyze image data received by a server and extract features using techniques such as object recognition and color extraction.

[0083] "Product information" refers to information about interior products that are optimal for the user's living space, generated based on image analysis and detailed user needs. Specifically, this information includes product images, names, colors, prices, etc.

[0084] "Chat-style discussion" refers to a method in which users and servers exchange information in real time through text messages, which allows detailed information about users' needs, interests, and preferences to be collected.

[0085] "Optimal lifestyle suggestions" refers to specific advice provided by the server based on the analysis results and chat data to make life more comfortable, such as interior products, storage methods, and design ideas that suit the user's living space.

[0086] "Object recognition" refers to the technology whereby image analysis algorithms identify different objects in an image and determine what each object is.

[0087] "Color extraction technology" refers to a technology in which an image analysis algorithm identifies colors within an image and analyzes the distribution and characteristics of those colors.

[0088] This invention is a system that allows users to easily select the best interior products for their living space and realize effective storage methods. This system consists of three main components: the user, the terminal, and the server.

[0089] Users can use devices such as smartphones, tablets, and computers to input photos of their rooms into the system. To input photos, they use a dedicated app or web browser. For example, a user can take a photo of their living room using the camera app on their smartphone, open the app, and upload the photo.

[0090] Next, the device sends the photo data selected by the user to the cloud server using an HTTP POST request. Specifically, the device encodes the photo data as an HTTP POST request and sends it to the specified URL. For example, the device sends the photo data to "https: / / example.com / upload."

[0091] The server receives the photos sent from the device and stores them in storage. It then runs image analysis algorithms to analyze the content of the photos. This analysis uses object recognition and color extraction techniques. For example, the server uses an image analysis library (such as OpenCV or TensorFlow) to identify the sofa, table, and wall colors in the photo.

[0092] The server extracts and generates optimal interior product information from a product database based on the analysis results. The generated data is expressed in a data format such as JSON and sent to the terminal as an HTTP response. Specifically, the server generates information on, for example, a blue cushion or a white shelf and sends it to the terminal in JSON format.

[0093] The device visually displays the received product information to the user, including product images, names, colors, and prices. For example, the device might display "Blue cushion - ¥3,000" or "White shelf - ¥5,000" to the user.

[0094] Furthermore, users can hold chat-style discussions with the server via their devices, providing detailed information about their needs and preferences. For example, a user might request, "I prefer a more natural look."

[0095] The server generates optimal lifestyle suggestions based on the information collected through chat discussions. For example, the server generates information on natural-colored cushions and wooden shelves, and sends it back to the device.

[0096] The user finally reviews the suggested products and proceeds with the purchase if necessary. The device provides a purchase link to the online store, allowing the user to easily purchase the product. For example, the user can click on a natural-colored cushion displayed in the app and press the "Purchase" button to proceed with the purchase at the online store.

[0097] Example prompt sentence:

[0098] "I'd like you to suggest interior design products based on a photo of my living room."

[0099] In this way, users can get interior and storage solutions that are optimized for their lifestyle.

[0100] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0101] Step 1:

[0102] Users take photos of their rooms using devices such as smartphones, tablets, or computers, and enter them into the system via a dedicated app or web browser.

[0103] Specific operation: The user opens the camera app on their smartphone and takes a full-size photo of their living room. Then, they open the dedicated app and click the "Upload photo" button.

[0104] Input: A photo of the room taken by the user.

[0105] Output: The device is ready to input the user's photo into the system.

[0106] Step 2:

[0107] The device sends the photo data uploaded by the user to the cloud server using an HTTP POST request.

[0108] Specific operation: The device encodes the photo data as an HTTP POST request and sends it to "https: / / example.com / upload".

[0109] Input: Photo data of the user's room.

[0110] Output: The photo data is sent to the cloud server.

[0111] Step 3:

[0112] The server receives the photos sent from the terminal and stores them in storage.

[0113] Specific operation: The server receives the request and saves the photo data in the storage system as " / photos / user12345.jpg".

[0114] Input: Photo data sent from the device.

[0115] Output: Photo files saved on the server.

[0116] Step 4:

[0117] The server then runs the stored photos through image analysis algorithms, which use object recognition and color extraction techniques.

[0118] Specific operation: The server uses an image analysis library (e.g., OpenCV or TensorFlow) to identify sofas, tables, wall colors, etc. in the photo.

[0119] Input: Photo files stored on the server.

[0120] Output: Analysis result data including object recognition and color information.

[0121] Step 5:

[0122] The server generates optimal interior product information from a product database based on the analysis results. The generated data is expressed in a data format such as JSON and sent to the terminal as an HTTP response.

[0123] Specific operation: The server selects the blue cushion and the white shelf from the analysis results and generates JSON data such as {"product": {"name": "blue cushion", "color": "blue", "price": 3000}}.

[0124] Input: Analysis result data.

[0125] Output: JSON data of the best interior product information.

[0126] Step 6:

[0127] The server sends the generated product information to the terminal as an HTTP response.

[0128] What happens: The server sends the JSON data in the HTTP response as {"status": 200, "data": {"products": [{"name": "blue cushion", "color": "blue", "price": 3000}, {"name": "white shelf", "color": "white", "price": 5000}]}}

[0129] Input: JSON data of product information.

[0130] Output: Product information sent to the device.

[0131] Step 7:

[0132] The terminal visually displays the received product information to the user, including product images, names, colors, prices, etc.

[0133] Specific operation: The device parses the received JSON data and displays it on the user interface as "Blue cushion - ¥3000" or "White shelf - ¥5000".

[0134] Input: Product information sent from the server.

[0135] Output: Product information displayed in a user interface.

[0136] Step 8:

[0137] Users can hold discussions with the server in chat format through their terminals, providing detailed information about their needs, hobbies, and preferences.

[0138] Specific Actions: The user opens the chat interface, types the text message "I like it more natural," and sends it.

[0139] Input: The user's text message.

[0140] Output: Detailed user needs information sent to the server.

[0141] Step 9:

[0142] The server generates optimal lifestyle suggestions that meet the user's needs based on the information collected through chat discussions.

[0143] Specific operation: The server analyzes the user's request data ("I prefer a more natural feel"), recreates information about natural-colored cushions and wooden shelves, and sends it to the terminal in JSON format.

[0144] Input: User needs information collected through chat discussions.

[0145] Output: Updated interior product information and lifestyle suggestions.

[0146] Step 10:

[0147] The user checks the proposed interior products again and proceeds with the purchase procedure if necessary.

[0148] Specific operation: The user clicks on the natural-colored cushion product displayed in the app and presses the "Purchase" button. The device then opens a purchase link to the online store and assists with the purchase process.

[0149] Input: Interior products selected by the user.

[0150] Output: Purchase completed at online store.

[0151] (Application example 1)

[0152] 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."

[0153] Conventional interior design suggestion systems have the problem that it is difficult for users to select and confirm the placement of products before actually placing them in a room. It is also difficult to effectively reflect the user's detailed needs and preferences. This often leads to problems after purchase, such as the product not meeting expectations or not matching the atmosphere of the room.

[0154] 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.

[0155] In this invention, the server includes means for inputting an image taken by a user, means for the terminal to transmit the input image to the server, means for the server to analyze the received image and generate product information that matches the atmosphere of the room, means for the server to transmit the generated product information to the terminal, means for the terminal to display the product information to the user, and means for the terminal or head-mounted display to display the generated product information in a virtual environment so that the user can check and customize it in real time. This allows the user to directly check the arrangement and atmosphere of the product in the virtual environment before actually purchasing it, and to customize it in real time to meet their detailed needs.

[0156] "Means for inputting images taken by the user" refers to a method by which a user can use a device such as a smartphone or tablet to import photos or videos of their own room into the system.

[0157] "Means by which the terminal transmits input images to the server" refers to the protocol or technology used to transfer image data from the user's terminal to the server.

[0158] "Means for analyzing the images received by the server and generating product information that matches the atmosphere of the room" refers to the process in which the server analyzes the image data received using image analysis technology and generates interior product information that is optimal for the user's room based on the results.

[0159] The "means for transmitting product information generated by the server to the terminal" is a method for transmitting interior product information generated by the server to the user's terminal.

[0160] The "means for the terminal to display product information to the user" refers to a function that visually presents the product information received by the user's terminal to the user through an interface such as a screen.

[0161] "Means for a terminal or head-mounted display to display generated product information in a virtual environment, allowing users to check and customize in real time" refers to a method that allows users to use a smartphone or head-mounted display to check suggested interior products in a virtual reality space as if they were actually placed in a room, and change their placement, color, size, etc. in real time.

[0162] The "means for gathering detailed needs through discussion in chat format" is a process for gathering specific requests and preferences of a user through an interactive chat between the user and the system.

[0163] The "means for proposing the optimal lifestyle" is a means for proposing the interior design, storage methods, and lifestyle that are most suitable for the user's room based on collected information about the user.

[0164] This invention is a system that allows users to select the interior products that best suit their living space and realize effective storage methods. This system consists of three main components: the user, the terminal, and the server. The following hardware and software are used to realize the system.

[0165] 1. Hardware

[0166] Smartphones and tablets: Devices that allow users to take photos of the room and upload them to the system.

[0167] Server: A central system that performs image analysis and data processing.

[0168] Head-mounted display (HMD): A device that allows users to view product placement in a virtual environment, such as the Oculus Quest 2.

[0169] 2. Software

[0170] Unity: A game engine for building virtual environments.

[0171] TensorFlow: A machine learning framework for image analysis.

[0172] Flask: Used as a server-side framework.

[0173] OpenCV: Used as an image processing library.

[0174] System Embodiments

[0175] First, users input photos of their rooms into the system using a device such as a smartphone or tablet. The photos are then uploaded to the system using a dedicated app or a web browser. The uploaded photos are then sent from the device to the server as an HTTP POST request.

[0176] The server analyzes the received images using an image analysis algorithm. It uses OpenCV and TensorFlow to extract the room's features using object recognition and color extraction techniques. Based on the analysis results, the server generates optimal interior product information from a product database.

[0177] The product information generated by the server is sent to the user's device in a data format such as JSON. The device visually displays the received product information to the user. The displayed content includes product images, names, colors, prices, etc. The user can also use a head-mounted display (HMD) to view the product information generated in the virtual environment in real time and customize its placement and color.

[0178] Users can access product information using their devices or HMDs and communicate their detailed needs and preferences to the server through voice or text chat. Through this chat-style discussion, the server generates more detailed interior design proposals based on the user's requirements.

[0179] For example, if a user requests a "natural-looking bed," the system will suggest a bed made of natural materials based on that request. The user can also use the virtual environment to see the bed arrangement and change the size and color in real time, if necessary. Here are some example prompts:

[0180] Prompt Sentence Examples

[0181] "Please suggest a bed with a natural feel."

[0182] "I want to see the bed placement in a virtual environment."

[0183] I would like the interior to be more brightly colored.

[0184] In this way, users can get interior and storage solutions that are optimized for their lifestyle.

[0185] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0186] Step 1:

[0187] Users can take photos of their rooms using a smartphone or tablet and upload them to the system via a dedicated app or web browser. The data uploaded by the user is an image file, and the image data is entered into the system.

[0188] Step 2:

[0189] The device sends the uploaded image to the server. An HTTP POST request is issued and the image data is transferred to the server. The server temporarily stores the received image data. The input to this process is the user's image data, and the output is the image data sent to the server.

[0190] Step 3:

[0191] The server analyzes the received image data using an image analysis algorithm. At this time, the server uses OpenCV and TensorFlow to perform object recognition and color extraction, and extract the room's features. The input is image data, and the output is data containing the room's features. Specifically, the position, color, shape, etc. of furniture are identified.

[0192] Step 4:

[0193] The server searches a product database based on the analysis results and generates interior product information that matches the room's atmosphere. The generated product information includes product images, names, colors, prices, etc. The input is the feature data of the room, and the output is interior product information.

[0194] Step 5:

[0195] The server sends the generated product information to the user's device. The product information is generated in a data format such as JSON and sent to the device as an HTTP response. The input to this process is the generated product information, and the output is the product information sent to the user's device.

[0196] Step 6:

[0197] The terminal displays product information to the user. Product images, names, colors, prices, etc. are visually displayed on the screen of a smartphone or tablet. The input is product information received from the server, and the output is an interface visually presented to the user.

[0198] Step 7:

[0199] The user checks product information and uses a head-mounted display (HMD) to arrange and customize products in a virtual environment in real time. Product information is displayed on the HMD and can be viewed in a 3D environment. The user can change the arrangement and color using voice or gestures. The input is product information, and the output is product arrangement information in the virtual environment.

[0200] Step 8:

[0201] The user and the server hold a chat-style discussion. The user inputs specific requests and preferences via text or voice, and the server generates more detailed interior design proposals in response. The input is the user's requests and preferences, and the output is further customized proposal information.

[0202] For example, if a user inputs "Please suggest a bed with a natural feel," the server will re-suggest a bed made of natural materials and allow the user to check its placement in the virtual environment. As a concrete example of this operation, a natural wooden bed is displayed in a 3D environment, and the user can change its placement and color in real time.

[0203] Example prompt sentence:

[0204] "Please suggest a bed with a natural feel."

[0205] "I want to see the bed placement in a virtual environment."

[0206] I would like the interior to be more brightly colored.

[0207] 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.

[0208] This invention is a system that allows users to easily select the best interior products for their living space and realize effective storage methods, and it also has the function of adjusting the suggestions by combining it with an emotion engine that recognizes the user's emotions. This system consists of four main components: the user, the terminal, the server, and the emotion engine.

[0209] First, users use a device such as a smartphone, tablet, or computer to input a photo of their room into the system. To input photos, they use a dedicated app or a web browser. The user selects a photo and clicks the upload button.

[0210] Next, the device sends the selected photo to the cloud server. The device issues an HTTP POST request to upload the photo data to the server. After the photo is sent to the server, the server receives it and stores it in its storage.

[0211] The server analyzes the received photos using an image analysis algorithm. Image analysis uses techniques such as object recognition and color extraction to extract features such as the room's atmosphere and furniture arrangement. After analyzing the features, the server generates optimal interior product information from a product database.

[0212] Next, the server sends the generated product information to the terminal. The proposal content is generated in a data format such as JSON and sent as an HTTP response. The terminal displays the received product information to the user. The displayed content includes the product image, name, color, price, etc.

[0213] The device also incorporates an emotion engine that recognizes emotions from facial expressions and input data as the user operates the device. The server uses the emotion engine to analyze the user's emotions and adjusts product information and suggestions based on the analysis results. For example, if the user has a dissatisfied expression, the server can update the suggestions to offer more desirable options.

[0214] The user then engages in a chat-style discussion with the server via their device. During this discussion, the user provides detailed information about their needs and preferences. For example, they can express specific requests such as "I prefer a more natural look" or "I want storage space under the bed." The emotion engine continues to monitor the user's emotions in real time and reflects them in the chat content.

[0215] Based on the information collected through chat-style discussions, the server generates lifestyle recommendations that best suit the user's needs. These recommendations include colors and designs that match the room's atmosphere, temporary storage ideas, and how to declutter unnecessary items. The emotion engine tracks the user's emotional changes and retains data on long-term emotional trends, which can then be used to provide further customized recommendations.

[0216] The server can also suggest space-saving storage solutions and ways to get rid of unnecessary items, such as placing storage boxes under the bed or recycling items that haven't been used for over a year. The emotion engine also considers how likely the suggestions are to be accepted, providing information in a way that satisfies the user.

[0217] Finally, the user can check the final proposal on the terminal and, if necessary, purchase the proposed product. The terminal also provides a purchase link to the online store, allowing the user to easily purchase the product.

[0218] For example, if a user is suggested blue cushions and white shelves, and then requests more natural items, the server will again suggest natural-colored cushions and wooden shelves. It will also make specific suggestions, such as "putting storage boxes under the bed" as a space-saving storage solution. In this way, users can find interior and storage solutions optimized for their lifestyle.

[0219] This system allows users to quickly select the best interior design for their living space, effectively organize their room, and create a comfortable living environment.The use of an emotion engine makes it possible to make more personalized suggestions based on the user's emotions, thereby improving user satisfaction.

[0220] The processing flow will be explained below.

[0221] Step 1:

[0222] The user takes a photo of the room and inputs it into the device. The user then selects the photo from a dedicated app or web browser and clicks the upload button.

[0223] Step 2:

[0224] The device sends the entered photo to the server. The device issues an HTTP POST request to upload the photo data to the server.

[0225] Step 3:

[0226] The server receives the photos and stores them in storage. The server stores the uploaded photos in the appropriate directory.

[0227] Step 4:

[0228] The server analyzes the received photos and uses image analysis algorithms to extract features such as the room's atmosphere and furniture arrangement.

[0229] Step 5:

[0230] The server generates product information based on the features extracted from the image, searches a product database, and selects the most suitable interior product.

[0231] Step 6:

[0232] The server generates product information and sends it to the terminal. The proposal content is generated in a data format such as JSON and sent as an HTTP response.

[0233] Step 7:

[0234] The terminal displays the received product information to the user, including product images, names, colors, prices, etc.

[0235] Step 8:

[0236] Users can chat with the server through their devices and provide detailed information about their needs, interests, and preferences.

[0237] Step 9:

[0238] The emotion engine recognizes the user's emotions in real time. While the user is typing chat content or checking product information, the emotion engine analyzes emotions from the user's facial expressions and input data.

[0239] Step 10:

[0240] The server generates lifestyle suggestions that best suit the user's needs based on the content of the chat and the analysis results from the emotion engine, including product information, layout, storage ideas, and methods for decluttering unnecessary items.

[0241] Step 11:

[0242] The server sends the final proposal to the device, taking into account the analysis results of the emotion engine and adjusting the proposal to be most appealing to the user.

[0243] Step 12:

[0244] The terminal displays the final proposal to the user, who then checks the information and, if necessary, completes the purchase procedure for the proposed product.

[0245] Step 13:

[0246] The device provides a purchase link to the online store, and the user clicks the link to complete the purchase procedure at the specified online store.

[0247] For example, if a user is suggested blue cushions and a white shelf, and the user requests more natural items, the server will re-suggest natural-colored cushions and wooden shelves based on the user's chat input and the analysis results of the emotion engine. It will also make specific suggestions such as "putting storage boxes under the bed" as a space-saving storage method. In this way, users can obtain interior and storage solutions optimized for their lifestyle.

[0248] Example 2

[0249] 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."

[0250] Conventional interior design selection systems required users to manually select products, making them inefficient and particularly difficult to customize based on user emotions. Furthermore, because product suggestions could not reflect individual needs or emotions, improving user satisfaction was a difficult challenge.

[0251] The identification process by the identification 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 means for analyzing a received image and generating product information based on the characteristics of the room, means for recognizing the user's emotion using an emotion engine and adjusting the product information based on the emotion, and means for transmitting the generated product information to the terminal. This makes it possible to propose products that correspond to the user's emotion and individual needs.

[0252] "User" refers to the person who uses the system. Generally, this refers to an individual who wants to select interior products and receive suggestions suitable for their living space.

[0253] "Terminal" refers to a device operated by a user, such as a smartphone, tablet, or computer.

[0254] A "server" is a computer system that performs the central processing of the system, and is responsible for important processes such as analyzing received data and generating proposals.

[0255] "Images" refers to photos and drawings taken by the user and input through the device. They are primarily used to analyze the interior of a room.

[0256] "Product Information" refers to data about interior products generated by the server, including details such as images, names, colors, and prices.

[0257] An "emotion engine" refers to a combination of software and hardware that recognizes a user's emotions and adjusts the system's output based on those emotions.

[0258] "Chat-style" refers to a conversation-like interaction between the user and the system, where the user inputs specific requests and feedback, and the system responds accordingly.

[0259] "Discussion" refers to the process in which the user and the server interact to discuss detailed requests and conditions. It proceeds like an everyday conversation, digging deeper into the user's needs.

[0260] This system allows users to easily select the best interior products for their living space and realize effective storage solutions. It also has a function that adjusts the recommendations by combining it with an emotion engine that recognizes the user's emotions. This system consists of four main components: the user, the terminal, the server, and the emotion engine.

[0261] First, users use a device such as a smartphone, tablet, or computer to input a photo of their room into the system. To input photos, they use a dedicated app or a web browser. The user selects a photo and clicks the upload button.

[0262] Next, the device sends the selected photo to the cloud server. The device issues an HTTP POST request to upload the photo data to the server. After the photo is sent to the server, the server receives it and stores it in its storage.

[0263] The server analyzes the received photos using an image analysis algorithm. Image analysis uses techniques such as object recognition and color extraction to extract features such as the room's atmosphere and furniture arrangement. After analyzing the features, the server generates optimal interior product information from a product database.

[0264] Next, the server sends the generated product information to the terminal. The proposal content is generated in a data format such as JSON and sent as an HTTP response. The terminal displays the received product information to the user. The displayed content includes the product image, name, color, price, etc.

[0265] The device also incorporates an emotion engine that recognizes emotions from facial expressions and input data as the user operates the device. The server uses the emotion engine to analyze the user's emotions and adjusts product information and suggestions based on the analysis results. For example, if the user has a dissatisfied expression, the server can update the suggestions to offer more desirable options.

[0266] The user then engages in a chat-style discussion with the server via their device. During this discussion, the user provides detailed information about their needs and preferences. For example, they can express specific requests such as "I prefer a more natural look" or "I want storage space under the bed." The emotion engine continues to monitor the user's emotions in real time and reflects them in the chat content.

[0267] Based on the information collected through chat-style discussions, the server generates lifestyle recommendations that best suit the user's needs. These recommendations include colors and designs that match the room's atmosphere, temporary storage ideas, and how to declutter unnecessary items. The emotion engine tracks the user's emotional changes and retains data on long-term emotional trends, which can then be used to provide further customized recommendations.

[0268] The server can also suggest space-saving storage solutions and ways to get rid of unnecessary items, such as placing storage boxes under the bed or recycling items that haven't been used for over a year. The emotion engine also considers how likely the suggestions are to be accepted, providing information in a way that satisfies the user.

[0269] Finally, the user can check the final proposal on the terminal and, if necessary, purchase the proposed product. The terminal also provides a purchase link to the online store, allowing the user to easily purchase the product.

[0270] For example, if a user is suggested blue cushions and white shelves, and then requests more natural items, the server will again suggest natural-colored cushions and wooden shelves. It will also make specific suggestions, such as "putting storage boxes under the bed" as a space-saving storage solution. In this way, users can find interior and storage solutions optimized for their lifestyle.

[0271] Example prompts to input to a generative AI model:

[0272] 1. "Analyze photos of user A's room and suggest suitable interior products."

[0273] 2. "Analyze the room layout from the images uploaded by user B and suggest the optimal furniture."

[0274] 3. "Please identify User C's emotions and provide products that match their preferences."

[0275] The above is an embodiment of the present invention, and this system allows users to quickly select the best interior for their living space, effectively organize their room, and create a comfortable living environment. Furthermore, the use of an emotion engine makes it possible to make more personalized suggestions based on the user's emotions, thereby improving satisfaction.

[0276] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0277] Step 1:

[0278] The user takes a photo of the room and inputs it into the system via a terminal.

[0279] Specifically, a user uses a smartphone, tablet, computer, or other device to select a photo using a dedicated app or web browser and clicks the upload button. The input data is an image file of the room, and the output is a state in which this image is ready to be sent to the server.

[0280] Step 2:

[0281] The terminal transmits the input image to the server.

[0282] Specifically, the device issues an HTTP POST request to upload the selected photo data to the server. The input is an image file of the room, and the output is the state in which the photo data has been sent to the server.

[0283] Step 3:

[0284] The server analyzes the received images and generates product information based on the characteristics of the room.

[0285] Specifically, the server stores the received image data in storage and analyzes it using an image analysis algorithm (TensorFlow, OpenCV, etc.). Image analysis involves extracting features such as object recognition, color extraction, and furniture placement. The input is the uploaded image data of the room, and the output is the analyzed feature data.

[0286] Step 4:

[0287] The server searches for the most suitable interior product from a product database and generates product information.

[0288] Specifically, the system uses the analyzed feature data to issue a query to a product database to obtain information on interior products that match the characteristics of the room. Based on the obtained product information, the system selects and generates optimal interior product candidates. The input is the feature data, and the output is the selected interior product information.

[0289] Step 5:

[0290] The server transmits the generated product information to the terminal.

[0291] Specifically, product information is generated in a data format such as JSON and sent to the terminal as an HTTP response. The input is the generated interior product information, and the output is the transmitted product information data.

[0292] Step 6:

[0293] The terminal displays the product information to the user.

[0294] Specifically, the device parses the received product information and displays detailed information such as product images, names, colors, and prices on the app screen for the user. The user can view this information. The input is the product information data sent from the server, and the output is the displayed product information.

[0295] Step 7:

[0296] The emotion engine recognizes the user's emotions.

[0297] Specifically, an emotion engine (e.g., Smile Detector API) uses the device's front camera to analyze the user's facial expressions in real time and infer their emotions. The input is the user's facial expression data, and the output is the inferred emotion data.

[0298] Step 8:

[0299] The server adjusts the suggestions based on the emotion.

[0300] Specifically, the system receives emotional data obtained from the emotion engine and updates the content of the suggestions in real time. For example, if the user looks dissatisfied, the server modifies the suggestions to select more appropriate products. The input is emotional data, and the output is adjusted product suggestion information.

[0301] Step 9:

[0302] The user and the server hold discussions in chat format.

[0303] Specifically, the user uses the chatbot function on their device to input their specific requests and preferences, and the server provides information in response. The user expresses specific requests such as "I like a natural feel" or "I want storage under the bed." The input is the user's desired data, and the output is additional information generated by the server.

[0304] Step 10:

[0305] The server generates a final proposal and sends it to the device.

[0306] Specifically, the system combines the information obtained from the discussion with the user's emotional data to generate a final proposal. This proposal includes color and design that matches the room's atmosphere, storage ideas, and more. The generated proposal is sent to the device in JSON format or similar. The input is the user's requests and emotional data, and the output is the final proposal data.

[0307] Step 11:

[0308] The terminal displays the final offer and the user completes the purchase.

[0309] Specifically, the terminal displays the final offer received from the server to the user and provides a purchase link to the online store. The user can click the link to complete the purchase procedure on the store page. The input is the final offer data, and the output is the displayed final offer and the purchase link.

[0310] (Application example 2)

[0311] 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."

[0312] The objective of the present invention is to solve the problem that conventional methods require time and effort when users select the best interior products for their living space. It is also necessary to solve the problem that systems that can adjust product suggestions taking into account the user's feelings when they are dissatisfied with the suggested products are insufficient. It is important to enable users to receive more personalized suggestions in real time when selecting interior products in a physical store.

[0313] 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.

[0314] In this invention, the server includes means for inputting an image taken by a user, means for the terminal to transmit the input image to the server, means for the server to analyze the received image and generate product information that matches the ambiance of the room, means for the terminal to transmit the generated product information to the user, means for the terminal to display the product information to the user, means for using smart glasses to display the product information in the user's field of view using augmented reality, and means for recognizing the user's emotions and adjusting the product information based on the emotions. This allows the user to select appropriate interior products in a short time and further receive personalized suggestions based on their emotions.

[0315] "User" refers to a person who uses the system to select the interior products that best suit their living space.

[0316] A "terminal" is a device operated by a user, and includes smart glasses, smartphones, tablets, etc.

[0317] A "server" is a computer system that runs on the cloud and performs image analysis and generates and provides product information.

[0318] "Image analysis" refers to the technology in which the server analyzes images received and extracts features such as the atmosphere of the room and the arrangement of furniture.

[0319] "Product information" refers to data such as images, names, colors, and prices related to interior products.

[0320] "Smart glasses" are wearable devices with augmented reality (AR) capabilities that display information in the user's field of vision.

[0321] "Augmented reality" refers to a technology that displays computer-generated information overlaid on real-world scenery.

[0322] "Emotion analysis" refers to the technology of recognizing and analyzing a user's emotions from their facial expressions and input data.

[0323] "Chat format" refers to a format in which a user and a server have a text-based conversation.

[0324] "Lifestyle suggestions" refers to the act of proposing interior products, storage methods, colors, and designs that are best suited to the user's living space.

[0325] This invention is a system that allows users to efficiently select the best interior products for their living space and find effective storage methods. The system consists of four main components: the user, the terminal, the server, and the emotion engine.

[0326] First, the user takes a photo of their room using a device such as smart glasses, a smartphone, or a tablet, and inputs it into the system. To input the photo, a dedicated application or a web browser is used. After the user takes a photo, they click the upload button, which sends the photo to the cloud server. The device then issues an HTTP POST request, uploading the photo data to the server.

[0327] The server then analyzes the received photos. It uses ImageAnalyzer to analyze the images and extract features such as the room's atmosphere and furniture arrangement. Specifically, it uses algorithms for object recognition and color extraction. Based on the extracted features, it then generates optimal interior product information from a product database.

[0328] The server sends the generated product information to the terminal. The suggestions are generated in a data format such as JSON and sent as an HTTP response. The terminal displays the received product information to the user. Product information includes product images, names, colors, prices, etc. When using smart glasses, the suggested products are displayed in the user's field of vision using augmented reality (AR) technology.

[0329] The device also incorporates an emotion engine that recognizes emotions from facial expressions and input data as the user operates the device. The server uses the emotion engine to analyze the user's emotions and adjusts product information and suggestions based on the analysis results. For example, if the user looks dissatisfied, the server can update the suggestions to offer more desirable options.

[0330] Users can discuss their needs and preferences in a chat format with the server via their devices. For example, they can express specific requests such as "I prefer a more natural look" or "I want storage space under the bed." The emotion engine monitors the user's emotions in real time and reflects them in the chat content.

[0331] The server uses information collected from chat-style discussions to generate lifestyle recommendations tailored to the user's needs, including color and design suggestions that match the room's atmosphere, temporary storage ideas, and how to organize unnecessary items. The emotion engine tracks the user's emotional changes and retains data on long-term emotional trends, allowing it to provide further customized recommendations.

[0332] The server can also suggest space-saving storage solutions and ways to organize unnecessary items, such as placing storage boxes under the bed or recycling items that haven't been used for over a year. The emotion engine adjusts the suggestions to make them more likely to be accepted by the user.

[0333] The user can review the final proposal on the device and, if necessary, purchase the proposed product. The device also provides a purchase link to the online store, allowing the user to easily purchase the product.

[0334] For example, if a user is suggested blue cushions and a white shelf, and then requests more natural items, the server will again suggest natural-colored cushions and wooden shelves. It will also make specific suggestions, such as "putting storage boxes under the bed" as a space-saving storage method. In this way, users can find interior and storage solutions optimized for their lifestyle.

[0335] Examples of prompts to be input into the generative AI model are, "Please enter the interior product category the user is looking for. For example, 'furniture in natural tones' or 'elegant storage solutions'." and "furniture in natural tones."

[0336] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0337] Step 1:

[0338] A user takes a photo of their room using smart glasses, a smartphone, or a tablet, which provides image data of the user's living space. The input data is the image of the room captured by the end device. This image is used for further processing.

[0339] Step 2:

[0340] The device sends the captured image data to the server. The device issues an HTTP POST request to send the photo data to the cloud server. The input data is the image captured by the user, and the output data is the image data stored in the server's storage. Through this transmission process, the server receives the photo of the user's room.

[0341] Step 3:

[0342] The server analyzes the received images. It uses ImageAnalyzer to analyze the images and extracts the features of the room using algorithms such as object recognition and color extraction. The input data is the image data stored on the server, and the output data is the room features obtained from the analysis. Specifically, the server's image analysis algorithm processes the image data.

[0343] Step 4:

[0344] The server generates optimal interior product information from a product database based on the extracted features. The generated product information includes product images, names, colors, and prices. The input data are the analyzed features, and the output data is the generated product information. The server searches the product database and collects and generates appropriate product information.

[0345] Step 5:

[0346] The server sends the generated product information to the terminal. The proposal content is sent in JSON format, and the terminal receives it as an HTTP response. The input data is the generated product information, and the output data is the product information sent to the terminal.

[0347] Step 6:

[0348] The terminal displays the received product information to the user. When smart glasses are used, the product information is displayed in the user's field of view using augmented reality (AR) technology. The input data is the product information received from the server, and the output data is the product information displayed in the user's field of view. The user can visually confirm the presented information.

[0349] Step 7:

[0350] The emotion engine recognizes and analyzes emotions from the user's facial expressions and input data. The emotion engine collects and analyzes the user's emotional data in real time. The input data is the user's facial expressions and input actions, and the output data is the analyzed emotional information.

[0351] Step 8:

[0352] The server adjusts the product information based on the analyzed emotion information. For example, if the user has a dissatisfied expression, it updates the suggestions to provide more favorable options. The input data is the emotion information obtained from the emotion engine, and the output data is the adjusted product information. Specifically, the server reanalyzes the emotion data and product data to generate new suggestions.

[0353] Step 9:

[0354] Users can discuss with the server in chat format through their terminals. They can provide detailed information about their needs, interests, and preferences. Input data is the user's text input, and output data is the server's response. The chat interface conducts the conversation in real time.

[0355] Step 10:

[0356] The server generates optimal lifestyle suggestions based on information collected from chat-style discussions. Suggestions include colors and designs that match the room's atmosphere, temporary storage ideas, and methods for organizing unnecessary items. The input data is the chat history and information provided by the user, and the output data is the newly generated suggestions.

[0357] An example of a prompt sentence to input to the generative AI model is:

[0358] "Enter the interior product category the user is looking for, for example, 'furniture in natural tones' or 'elegant storage solutions'." or 'furniture in natural tones'.

[0359] 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.

[0360] 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.

[0361] 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.

[0362] [Second embodiment]

[0363] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0364] 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.

[0365] 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).

[0366] 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.

[0367] 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.

[0368] 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).

[0369] 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.

[0370] 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.

[0371] 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.

[0372] 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.

[0373] 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.

[0374] 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."

[0375] This invention is a system that allows users to easily select the best interior products for their living space and realize effective storage methods. This system consists of three main components: the user, the terminal, and the server.

[0376] First, users use a device such as a smartphone, tablet, or computer to input a photo of their room into the system. To input photos, they use a dedicated app or a web browser. The user selects a photo and clicks the upload button.

[0377] Next, the device sends the selected photo to the cloud server. The device issues an HTTP POST request to upload the photo data to the server. After the photo is sent to the server, the server receives it and stores it in its storage.

[0378] The server analyzes the received photos using an image analysis algorithm. Image analysis uses techniques such as object recognition and color extraction to extract features such as the room's atmosphere and furniture arrangement. After analyzing the features, the server generates optimal interior product information from a product database.

[0379] Next, the server sends the generated product information to the terminal. The proposal content is generated in a data format such as JSON and sent as an HTTP response. The terminal displays the received product information to the user. The displayed content includes the product image, name, color, price, etc.

[0380] The user then chats with the server via their device to discuss their needs and preferences. For example, they can express specific requests such as "I prefer a more natural look" or "I want storage space under the bed."

[0381] Based on the information collected through chat-style discussions, the server generates lifestyle suggestions that best suit the user's needs, including color and design that matches the room's atmosphere, temporary storage ideas, and how to declutter unnecessary items.

[0382] The server can also suggest space-saving storage solutions and ways to get rid of unnecessary items, such as placing storage boxes under the bed or recycling items you haven't used in over a year.

[0383] Finally, the user can check the suggestions on the device and, if necessary, purchase the suggested products. The device also provides a purchase link to the online store, allowing the user to easily purchase the products.

[0384] For example, if a user is suggested blue cushions and white shelves, and then requests more natural items, the server will again suggest natural-colored cushions and wooden shelves. It will also make specific suggestions, such as "putting storage boxes under the bed" as a space-saving storage solution. In this way, users can find interior and storage solutions optimized for their lifestyle.

[0385] This system allows users to quickly select the best interior for their living space, effectively organize their room, and create a comfortable living environment.

[0386] The processing flow will be explained below.

[0387] Step 1:

[0388] The user takes a photo of the room and inputs it into the device. The user then selects the photo from a dedicated app or web browser and clicks the upload button.

[0389] Step 2:

[0390] The device sends the entered photo to the server. The device issues an HTTP POST request to upload the photo data to the server.

[0391] Step 3:

[0392] The server receives the photos and stores them in storage. The server stores the uploaded photos in the appropriate directory.

[0393] Step 4:

[0394] The server analyzes the received photos and uses image analysis algorithms to extract features such as the room's atmosphere and furniture arrangement.

[0395] Step 5:

[0396] The server generates product information based on the features extracted from the image, searches a product database, and selects the most suitable interior product.

[0397] Step 6:

[0398] The server generates product information and sends it to the terminal. The proposal content is generated in a data format such as JSON and sent as an HTTP response.

[0399] Step 7:

[0400] The terminal displays the received product information to the user, including product images, names, colors, prices, etc.

[0401] Step 8:

[0402] Users can chat with the server through their devices and provide detailed information about their needs, interests, and preferences.

[0403] Step 9:

[0404] The server analyzes the content of the chat with the user and generates lifestyle suggestions that best suit the user's needs. The server updates product information and layout based on the collected information.

[0405] Step 10:

[0406] The server also suggests space-saving storage solutions and ways to get rid of unnecessary items, such as placing storage boxes under the bed or recycling items you haven't used in over a year.

[0407] Step 11:

[0408] The user reviews the final offer on the device and, if necessary, completes the purchase process for the proposed product. The device provides a purchase link to the online store.

[0409] Step 12:

[0410] The user clicks on the provided purchase link and completes the purchase process at the specified online store.

[0411] Example 1

[0412] 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."

[0413] In modern life, selecting the best interior products for a user's living space and implementing effective storage solutions is a time-consuming and laborious task. It is particularly difficult to find products that match the user's tastes and preferences and the atmosphere of the room. Furthermore, there is a demand not only for help selecting interior products but also for suggestions on specific storage solutions and lifestyles. The present invention aims to provide a system that solves these problems.

[0414] 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.

[0415] In this invention, the server includes means for analyzing received images, extracting room features using object recognition and color extraction technology, and generating product information based on the features, means for collecting detailed needs through chat-style discussions with users, and means for proposing optimal lifestyles based on the collected information, thereby enabling users to easily select interior products that are best suited to their living space and realize effective storage methods.

[0416] "User" refers to an individual or group that uses this system to select interior products that suit their living space and receive suggestions.

[0417] "Terminal" refers to an electronic device, such as a smartphone, tablet, or computer, that a user uses to access the system and input or transmit images.

[0418] A "server" refers to a computer system that receives data sent from a terminal, analyzes and processes it, and returns the results to the terminal.

[0419] An "image analysis algorithm" refers to a calculation method used to analyze image data received by a server and extract features using techniques such as object recognition and color extraction.

[0420] "Product information" refers to information about interior products that are optimal for the user's living space, generated based on image analysis and detailed user needs. Specifically, this information includes product images, names, colors, prices, etc.

[0421] "Chat-style discussion" refers to a method in which users and servers exchange information in real time through text messages, which allows detailed information about users' needs, interests, and preferences to be collected.

[0422] "Optimal lifestyle suggestions" refers to specific advice provided by the server based on the analysis results and chat data to make life more comfortable, such as interior products, storage methods, and design ideas that suit the user's living space.

[0423] "Object recognition" refers to the technology whereby image analysis algorithms identify different objects in an image and determine what each object is.

[0424] "Color extraction technology" refers to a technology in which an image analysis algorithm identifies colors within an image and analyzes the distribution and characteristics of those colors.

[0425] This invention is a system that allows users to easily select the best interior products for their living space and realize effective storage methods. This system consists of three main components: the user, the terminal, and the server.

[0426] Users can use devices such as smartphones, tablets, and computers to input photos of their rooms into the system. To input photos, they use a dedicated app or web browser. For example, a user can take a photo of their living room using the camera app on their smartphone, open the app, and upload the photo.

[0427] Next, the device sends the photo data selected by the user to the cloud server using an HTTP POST request. Specifically, the device encodes the photo data as an HTTP POST request and sends it to the specified URL. For example, the device sends the photo data to "https: / / example.com / upload."

[0428] The server receives the photos sent from the device and stores them in storage. It then runs image analysis algorithms to analyze the content of the photos. This analysis uses object recognition and color extraction techniques. For example, the server uses an image analysis library (such as OpenCV or TensorFlow) to identify the sofa, table, and wall colors in the photo.

[0429] The server extracts and generates optimal interior product information from a product database based on the analysis results. The generated data is expressed in a data format such as JSON and sent to the terminal as an HTTP response. Specifically, the server generates information on, for example, a blue cushion or a white shelf and sends it to the terminal in JSON format.

[0430] The device visually displays the received product information to the user, including product images, names, colors, and prices. For example, the device might display "Blue cushion - ¥3,000" or "White shelf - ¥5,000" to the user.

[0431] Furthermore, users can hold chat-style discussions with the server via their devices, providing detailed information about their needs and preferences. For example, a user might request, "I prefer a more natural look."

[0432] The server generates optimal lifestyle suggestions based on the information collected through chat discussions. For example, the server generates information on natural-colored cushions and wooden shelves, and sends it back to the device.

[0433] The user finally reviews the suggested products and proceeds with the purchase if necessary. The device provides a purchase link to the online store, allowing the user to easily purchase the product. For example, the user can click on a natural-colored cushion displayed in the app and press the "Purchase" button to proceed with the purchase at the online store.

[0434] Example prompt sentence:

[0435] "I'd like you to suggest interior design products based on a photo of my living room."

[0436] In this way, users can get interior and storage solutions that are optimized for their lifestyle.

[0437] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0438] Step 1:

[0439] Users take photos of their rooms using devices such as smartphones, tablets, or computers, and enter them into the system via a dedicated app or web browser.

[0440] Specific operation: The user opens the camera app on their smartphone and takes a full-size photo of their living room. Then, they open the dedicated app and click the "Upload photo" button.

[0441] Input: A photo of the room taken by the user.

[0442] Output: The device is ready to input the user's photo into the system.

[0443] Step 2:

[0444] The device sends the photo data uploaded by the user to the cloud server using an HTTP POST request.

[0445] Specific operation: The device encodes the photo data as an HTTP POST request and sends it to "https: / / example.com / upload".

[0446] Input: Photo data of the user's room.

[0447] Output: The photo data is sent to the cloud server.

[0448] Step 3:

[0449] The server receives the photos sent from the terminal and stores them in storage.

[0450] Specific operation: The server receives the request and saves the photo data in the storage system as " / photos / user12345.jpg".

[0451] Input: Photo data sent from the device.

[0452] Output: Photo files saved on the server.

[0453] Step 4:

[0454] The server then runs the stored photos through image analysis algorithms, which use object recognition and color extraction techniques.

[0455] Specific operation: The server uses an image analysis library (e.g., OpenCV or TensorFlow) to identify sofas, tables, wall colors, etc. in the photo.

[0456] Input: Photo files stored on the server.

[0457] Output: Analysis result data including object recognition and color information.

[0458] Step 5:

[0459] The server generates optimal interior product information from a product database based on the analysis results. The generated data is expressed in a data format such as JSON and sent to the terminal as an HTTP response.

[0460] Specific operation: The server selects the blue cushion and the white shelf from the analysis results and generates JSON data such as {"product": {"name": "blue cushion", "color": "blue", "price": 3000}}.

[0461] Input: Analysis result data.

[0462] Output: JSON data of the best interior product information.

[0463] Step 6:

[0464] The server sends the generated product information to the terminal as an HTTP response.

[0465] What happens: The server sends the JSON data in the HTTP response as {"status": 200, "data": {"products": [{"name": "blue cushion", "color": "blue", "price": 3000}, {"name": "white shelf", "color": "white", "price": 5000}]}}

[0466] Input: JSON data of product information.

[0467] Output: Product information sent to the device.

[0468] Step 7:

[0469] The terminal visually displays the received product information to the user, including product images, names, colors, prices, etc.

[0470] Specific operation: The device parses the received JSON data and displays it on the user interface as "Blue cushion - ¥3000" or "White shelf - ¥5000".

[0471] Input: Product information sent from the server.

[0472] Output: Product information displayed in a user interface.

[0473] Step 8:

[0474] Users can hold discussions with the server in chat format through their terminals, providing detailed information about their needs, hobbies, and preferences.

[0475] Specific Actions: The user opens the chat interface, types the text message "I like it more natural," and sends it.

[0476] Input: The user's text message.

[0477] Output: Detailed user needs information sent to the server.

[0478] Step 9:

[0479] The server generates optimal lifestyle suggestions that meet the user's needs based on the information collected through chat discussions.

[0480] Specific operation: The server analyzes the user's request data ("I prefer a more natural feel"), recreates information about natural-colored cushions and wooden shelves, and sends it to the terminal in JSON format.

[0481] Input: User needs information collected through chat discussions.

[0482] Output: Updated interior product information and lifestyle suggestions.

[0483] Step 10:

[0484] The user checks the proposed interior products again and proceeds with the purchase procedure if necessary.

[0485] Specific operation: The user clicks on the natural-colored cushion product displayed in the app and presses the "Purchase" button. The device then opens a purchase link to the online store and assists with the purchase process.

[0486] Input: Interior products selected by the user.

[0487] Output: Purchase completed at online store.

[0488] (Application example 1)

[0489] 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."

[0490] Conventional interior design suggestion systems have the problem that it is difficult for users to select and confirm the placement of products before actually placing them in a room. It is also difficult to effectively reflect the user's detailed needs and preferences. This often leads to problems after purchase, such as the product not meeting expectations or not matching the atmosphere of the room.

[0491] 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.

[0492] In this invention, the server includes means for inputting an image taken by a user, means for the terminal to transmit the input image to the server, means for the server to analyze the received image and generate product information that matches the atmosphere of the room, means for the server to transmit the generated product information to the terminal, means for the terminal to display the product information to the user, and means for the terminal or head-mounted display to display the generated product information in a virtual environment so that the user can check and customize it in real time. This allows the user to directly check the arrangement and atmosphere of the product in the virtual environment before actually purchasing it, and to customize it in real time to meet their detailed needs.

[0493] "Means for inputting images taken by the user" refers to a method by which a user can use a device such as a smartphone or tablet to import photos or videos of their own room into the system.

[0494] "Means by which the terminal transmits input images to the server" refers to the protocol or technology used to transfer image data from the user's terminal to the server.

[0495] "Means for analyzing the images received by the server and generating product information that matches the atmosphere of the room" refers to the process in which the server analyzes the image data received using image analysis technology and generates interior product information that is optimal for the user's room based on the results.

[0496] The "means for transmitting product information generated by the server to the terminal" is a method for transmitting interior product information generated by the server to the user's terminal.

[0497] The "means for the terminal to display product information to the user" refers to a function that visually presents the product information received by the user's terminal to the user through an interface such as a screen.

[0498] "Means for a terminal or head-mounted display to display generated product information in a virtual environment, allowing users to check and customize in real time" refers to a method that allows users to use a smartphone or head-mounted display to check suggested interior products in a virtual reality space as if they were actually placed in a room, and change their placement, color, size, etc. in real time.

[0499] The "means for gathering detailed needs through discussion in chat format" is a process for gathering specific requests and preferences of a user through an interactive chat between the user and the system.

[0500] The "means for proposing the optimal lifestyle" is a means for proposing the interior design, storage methods, and lifestyle that are most suitable for the user's room based on collected information about the user.

[0501] This invention is a system that allows users to select the interior products that best suit their living space and realize effective storage methods. This system consists of three main components: the user, the terminal, and the server. The following hardware and software are used to realize the system.

[0502] 1. Hardware

[0503] Smartphones and tablets: Devices that allow users to take photos of the room and upload them to the system.

[0504] Server: A central system that performs image analysis and data processing.

[0505] Head-mounted display (HMD): A device that allows users to view product placement in a virtual environment, such as the Oculus Quest 2.

[0506] 2. Software

[0507] Unity: A game engine for building virtual environments.

[0508] TensorFlow: A machine learning framework for image analysis.

[0509] Flask: Used as a server-side framework.

[0510] OpenCV: Used as an image processing library.

[0511] System Embodiments

[0512] First, users input photos of their rooms into the system using a device such as a smartphone or tablet. The photos are then uploaded to the system using a dedicated app or a web browser. The uploaded photos are then sent from the device to the server as an HTTP POST request.

[0513] The server analyzes the received images using an image analysis algorithm. It uses OpenCV and TensorFlow to extract the room's features using object recognition and color extraction techniques. Based on the analysis results, the server generates optimal interior product information from a product database.

[0514] The product information generated by the server is sent to the user's device in a data format such as JSON. The device visually displays the received product information to the user. The displayed content includes product images, names, colors, prices, etc. The user can also use a head-mounted display (HMD) to view the product information generated in the virtual environment in real time and customize its placement and color.

[0515] Users can access product information using their devices or HMDs and communicate their detailed needs and preferences to the server through voice or text chat. Through this chat-style discussion, the server generates more detailed interior design proposals based on the user's requirements.

[0516] For example, if a user requests a "natural-looking bed," the system will suggest a bed made of natural materials based on that request. The user can also use the virtual environment to see the bed arrangement and change the size and color in real time, if necessary. Here are some example prompts:

[0517] Prompt Sentence Examples

[0518] "Please suggest a bed with a natural feel."

[0519] "I want to see the bed placement in a virtual environment."

[0520] I would like the interior to be more brightly colored.

[0521] In this way, users can get interior and storage solutions that are optimized for their lifestyle.

[0522] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0523] Step 1:

[0524] Users can take photos of their rooms using a smartphone or tablet and upload them to the system via a dedicated app or web browser. The data uploaded by the user is an image file, and the image data is entered into the system.

[0525] Step 2:

[0526] The device sends the uploaded image to the server. An HTTP POST request is issued and the image data is transferred to the server. The server temporarily stores the received image data. The input to this process is the user's image data, and the output is the image data sent to the server.

[0527] Step 3:

[0528] The server analyzes the received image data using an image analysis algorithm. At this time, the server uses OpenCV and TensorFlow to perform object recognition and color extraction, and extract the room's features. The input is image data, and the output is data containing the room's features. Specifically, the position, color, shape, etc. of furniture are identified.

[0529] Step 4:

[0530] The server searches a product database based on the analysis results and generates interior product information that matches the room's atmosphere. The generated product information includes product images, names, colors, prices, etc. The input is the feature data of the room, and the output is interior product information.

[0531] Step 5:

[0532] The server sends the generated product information to the user's device. The product information is generated in a data format such as JSON and sent to the device as an HTTP response. The input to this process is the generated product information, and the output is the product information sent to the user's device.

[0533] Step 6:

[0534] The terminal displays product information to the user. Product images, names, colors, prices, etc. are visually displayed on the screen of a smartphone or tablet. The input is product information received from the server, and the output is an interface visually presented to the user.

[0535] Step 7:

[0536] The user checks product information and uses a head-mounted display (HMD) to arrange and customize products in a virtual environment in real time. Product information is displayed on the HMD and can be viewed in a 3D environment. The user can change the arrangement and color using voice or gestures. The input is product information, and the output is product arrangement information in the virtual environment.

[0537] Step 8:

[0538] The user and the server hold a chat-style discussion. The user inputs specific requests and preferences via text or voice, and the server generates more detailed interior design proposals in response. The input is the user's requests and preferences, and the output is further customized proposal information.

[0539] For example, if a user inputs "Please suggest a bed with a natural feel," the server will re-suggest a bed made of natural materials and allow the user to check its placement in the virtual environment. As a concrete example of this operation, a natural wooden bed is displayed in a 3D environment, and the user can change its placement and color in real time.

[0540] Example prompt sentence:

[0541] "Please suggest a bed with a natural feel."

[0542] "I want to see the bed placement in a virtual environment."

[0543] I would like the interior to be more brightly colored.

[0544] 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.

[0545] This invention is a system that allows users to easily select the best interior products for their living space and realize effective storage methods, and it also has the function of adjusting the suggestions by combining it with an emotion engine that recognizes the user's emotions. This system consists of four main components: the user, the terminal, the server, and the emotion engine.

[0546] First, users use a device such as a smartphone, tablet, or computer to input a photo of their room into the system. To input photos, they use a dedicated app or a web browser. The user selects a photo and clicks the upload button.

[0547] Next, the device sends the selected photo to the cloud server. The device issues an HTTP POST request to upload the photo data to the server. After the photo is sent to the server, the server receives it and stores it in its storage.

[0548] The server analyzes the received photos using an image analysis algorithm. Image analysis uses techniques such as object recognition and color extraction to extract features such as the room's atmosphere and furniture arrangement. After analyzing the features, the server generates optimal interior product information from a product database.

[0549] Next, the server sends the generated product information to the terminal. The proposal content is generated in a data format such as JSON and sent as an HTTP response. The terminal displays the received product information to the user. The displayed content includes the product image, name, color, price, etc.

[0550] The device also incorporates an emotion engine that recognizes emotions from facial expressions and input data as the user operates the device. The server uses the emotion engine to analyze the user's emotions and adjusts product information and suggestions based on the analysis results. For example, if the user has a dissatisfied expression, the server can update the suggestions to offer more desirable options.

[0551] The user then engages in a chat-style discussion with the server via their device. During this discussion, the user provides detailed information about their needs and preferences. For example, they can express specific requests such as "I prefer a more natural look" or "I want storage space under the bed." The emotion engine continues to monitor the user's emotions in real time and reflects them in the chat content.

[0552] Based on the information collected through chat-style discussions, the server generates lifestyle recommendations that best suit the user's needs. These recommendations include colors and designs that match the room's atmosphere, temporary storage ideas, and how to declutter unnecessary items. The emotion engine tracks the user's emotional changes and retains data on long-term emotional trends, which can then be used to provide further customized recommendations.

[0553] The server can also suggest space-saving storage solutions and ways to get rid of unnecessary items, such as placing storage boxes under the bed or recycling items that haven't been used for over a year. The emotion engine also considers how likely the suggestions are to be accepted, providing information in a way that satisfies the user.

[0554] Finally, the user can check the final proposal on the terminal and, if necessary, purchase the proposed product. The terminal also provides a purchase link to the online store, allowing the user to easily purchase the product.

[0555] For example, if a user is suggested blue cushions and white shelves, and then requests more natural items, the server will again suggest natural-colored cushions and wooden shelves. It will also make specific suggestions, such as "putting storage boxes under the bed" as a space-saving storage solution. In this way, users can find interior and storage solutions optimized for their lifestyle.

[0556] This system allows users to quickly select the best interior design for their living space, effectively organize their room, and create a comfortable living environment.The use of an emotion engine makes it possible to make more personalized suggestions based on the user's emotions, thereby improving user satisfaction.

[0557] The processing flow will be explained below.

[0558] Step 1:

[0559] The user takes a photo of the room and inputs it into the device. The user then selects the photo from a dedicated app or web browser and clicks the upload button.

[0560] Step 2:

[0561] The device sends the entered photo to the server. The device issues an HTTP POST request to upload the photo data to the server.

[0562] Step 3:

[0563] The server receives the photos and stores them in storage. The server stores the uploaded photos in the appropriate directory.

[0564] Step 4:

[0565] The server analyzes the received photos and uses image analysis algorithms to extract features such as the room's atmosphere and furniture arrangement.

[0566] Step 5:

[0567] The server generates product information based on the features extracted from the image, searches a product database, and selects the most suitable interior product.

[0568] Step 6:

[0569] The server generates product information and sends it to the terminal. The proposal content is generated in a data format such as JSON and sent as an HTTP response.

[0570] Step 7:

[0571] The terminal displays the received product information to the user, including product images, names, colors, prices, etc.

[0572] Step 8:

[0573] Users can chat with the server through their devices and provide detailed information about their needs, interests, and preferences.

[0574] Step 9:

[0575] The emotion engine recognizes the user's emotions in real time. While the user is typing chat content or checking product information, the emotion engine analyzes emotions from the user's facial expressions and input data.

[0576] Step 10:

[0577] The server generates lifestyle suggestions that best suit the user's needs based on the content of the chat and the analysis results from the emotion engine, including product information, layout, storage ideas, and methods for decluttering unnecessary items.

[0578] Step 11:

[0579] The server sends the final proposal to the device, taking into account the analysis results of the emotion engine and adjusting the proposal to be most appealing to the user.

[0580] Step 12:

[0581] The terminal displays the final proposal to the user, who then checks the information and, if necessary, completes the purchase procedure for the proposed product.

[0582] Step 13:

[0583] The device provides a purchase link to the online store, and the user clicks the link to complete the purchase procedure at the specified online store.

[0584] For example, if a user is suggested blue cushions and a white shelf, and the user requests more natural items, the server will re-suggest natural-colored cushions and wooden shelves based on the user's chat input and the analysis results of the emotion engine. It will also make specific suggestions such as "putting storage boxes under the bed" as a space-saving storage method. In this way, users can obtain interior and storage solutions optimized for their lifestyle.

[0585] Example 2

[0586] 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."

[0587] Conventional interior design selection systems required users to manually select products, making them inefficient and particularly difficult to customize based on user emotions. Furthermore, because product suggestions could not reflect individual needs or emotions, improving user satisfaction was a difficult challenge.

[0588] The identification process by the identification 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 means for analyzing a received image and generating product information based on the characteristics of the room, means for recognizing the user's emotion using an emotion engine and adjusting the product information based on the emotion, and means for transmitting the generated product information to the terminal. This makes it possible to propose products that correspond to the user's emotion and individual needs.

[0589] "User" refers to the person who uses the system. Generally, this refers to an individual who wants to select interior products and receive suggestions suitable for their living space.

[0590] "Terminal" refers to a device operated by a user, such as a smartphone, tablet, or computer.

[0591] A "server" is a computer system that performs the central processing of the system, and is responsible for important processes such as analyzing received data and generating proposals.

[0592] "Images" refers to photos and drawings taken by the user and input through the device. They are primarily used to analyze the interior of a room.

[0593] "Product Information" refers to data about interior products generated by the server, including details such as images, names, colors, and prices.

[0594] An "emotion engine" refers to a combination of software and hardware that recognizes a user's emotions and adjusts the system's output based on those emotions.

[0595] "Chat-style" refers to a conversation-like interaction between the user and the system, where the user inputs specific requests and feedback, and the system responds accordingly.

[0596] "Discussion" refers to the process in which the user and the server interact to discuss detailed requests and conditions. It proceeds like an everyday conversation, digging deeper into the user's needs.

[0597] This system allows users to easily select the best interior products for their living space and realize effective storage solutions. It also has a function that adjusts the recommendations by combining it with an emotion engine that recognizes the user's emotions. This system consists of four main components: the user, the terminal, the server, and the emotion engine.

[0598] First, users use a device such as a smartphone, tablet, or computer to input a photo of their room into the system. To input photos, they use a dedicated app or a web browser. The user selects a photo and clicks the upload button.

[0599] Next, the device sends the selected photo to the cloud server. The device issues an HTTP POST request to upload the photo data to the server. After the photo is sent to the server, the server receives it and stores it in its storage.

[0600] The server analyzes the received photos using an image analysis algorithm. Image analysis uses techniques such as object recognition and color extraction to extract features such as the room's atmosphere and furniture arrangement. After analyzing the features, the server generates optimal interior product information from a product database.

[0601] Next, the server sends the generated product information to the terminal. The proposal content is generated in a data format such as JSON and sent as an HTTP response. The terminal displays the received product information to the user. The displayed content includes the product image, name, color, price, etc.

[0602] The device also incorporates an emotion engine that recognizes emotions from facial expressions and input data as the user operates the device. The server uses the emotion engine to analyze the user's emotions and adjusts product information and suggestions based on the analysis results. For example, if the user has a dissatisfied expression, the server can update the suggestions to offer more desirable options.

[0603] The user then engages in a chat-style discussion with the server via their device. During this discussion, the user provides detailed information about their needs and preferences. For example, they can express specific requests such as "I prefer a more natural look" or "I want storage space under the bed." The emotion engine continues to monitor the user's emotions in real time and reflects them in the chat content.

[0604] Based on the information collected through chat-style discussions, the server generates lifestyle recommendations that best suit the user's needs. These recommendations include colors and designs that match the room's atmosphere, temporary storage ideas, and how to declutter unnecessary items. The emotion engine tracks the user's emotional changes and retains data on long-term emotional trends, which can then be used to provide further customized recommendations.

[0605] The server can also suggest space-saving storage solutions and ways to get rid of unnecessary items, such as placing storage boxes under the bed or recycling items that haven't been used for over a year. The emotion engine also considers how likely the suggestions are to be accepted, providing information in a way that satisfies the user.

[0606] Finally, the user can check the final proposal on the terminal and, if necessary, purchase the proposed product. The terminal also provides a purchase link to the online store, allowing the user to easily purchase the product.

[0607] For example, if a user is suggested blue cushions and white shelves, and then requests more natural items, the server will again suggest natural-colored cushions and wooden shelves. It will also make specific suggestions, such as "putting storage boxes under the bed" as a space-saving storage solution. In this way, users can find interior and storage solutions optimized for their lifestyle.

[0608] Example prompts to input to a generative AI model:

[0609] 1. "Analyze photos of user A's room and suggest suitable interior products."

[0610] 2. "Analyze the room layout from the images uploaded by user B and suggest the optimal furniture."

[0611] 3. "Please identify User C's emotions and provide products that match their preferences."

[0612] The above is an embodiment of the present invention, and this system allows users to quickly select the best interior for their living space, effectively organize their room, and create a comfortable living environment. Furthermore, the use of an emotion engine makes it possible to make more personalized suggestions based on the user's emotions, thereby improving satisfaction.

[0613] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0614] Step 1:

[0615] The user takes a photo of the room and inputs it into the system via a terminal.

[0616] Specifically, a user uses a smartphone, tablet, computer, or other device to select a photo using a dedicated app or web browser and clicks the upload button. The input data is an image file of the room, and the output is a state in which this image is ready to be sent to the server.

[0617] Step 2:

[0618] The terminal transmits the input image to the server.

[0619] Specifically, the device issues an HTTP POST request to upload the selected photo data to the server. The input is an image file of the room, and the output is the state in which the photo data has been sent to the server.

[0620] Step 3:

[0621] The server analyzes the received images and generates product information based on the characteristics of the room.

[0622] Specifically, the server stores the received image data in storage and analyzes it using an image analysis algorithm (TensorFlow, OpenCV, etc.). Image analysis involves extracting features such as object recognition, color extraction, and furniture placement. The input is the uploaded image data of the room, and the output is the analyzed feature data.

[0623] Step 4:

[0624] The server searches for the most suitable interior product from a product database and generates product information.

[0625] Specifically, the system uses the analyzed feature data to issue a query to a product database to obtain information on interior products that match the characteristics of the room. Based on the obtained product information, the system selects and generates optimal interior product candidates. The input is the feature data, and the output is the selected interior product information.

[0626] Step 5:

[0627] The server transmits the generated product information to the terminal.

[0628] Specifically, product information is generated in a data format such as JSON and sent to the terminal as an HTTP response. The input is the generated interior product information, and the output is the transmitted product information data.

[0629] Step 6:

[0630] The terminal displays the product information to the user.

[0631] Specifically, the device parses the received product information and displays detailed information such as product images, names, colors, and prices on the app screen for the user. The user can view this information. The input is the product information data sent from the server, and the output is the displayed product information.

[0632] Step 7:

[0633] The emotion engine recognizes the user's emotions.

[0634] Specifically, an emotion engine (e.g., Smile Detector API) uses the device's front camera to analyze the user's facial expressions in real time and infer their emotions. The input is the user's facial expression data, and the output is the inferred emotion data.

[0635] Step 8:

[0636] The server adjusts the suggestions based on the emotion.

[0637] Specifically, the system receives emotional data obtained from the emotion engine and updates the content of the suggestions in real time. For example, if the user looks dissatisfied, the server modifies the suggestions to select more appropriate products. The input is emotional data, and the output is adjusted product suggestion information.

[0638] Step 9:

[0639] The user and the server hold discussions in chat format.

[0640] Specifically, the user uses the chatbot function on their device to input their specific requests and preferences, and the server provides information in response. The user expresses specific requests such as "I like a natural feel" or "I want storage under the bed." The input is the user's desired data, and the output is additional information generated by the server.

[0641] Step 10:

[0642] The server generates a final proposal and sends it to the device.

[0643] Specifically, the system combines the information obtained from the discussion with the user's emotional data to generate a final proposal. This proposal includes color and design that matches the room's atmosphere, storage ideas, and more. The generated proposal is sent to the device in JSON format or similar. The input is the user's requests and emotional data, and the output is the final proposal data.

[0644] Step 11:

[0645] The terminal displays the final offer and the user completes the purchase.

[0646] Specifically, the terminal displays the final offer received from the server to the user and provides a purchase link to the online store. The user can click the link to complete the purchase procedure on the store page. The input is the final offer data, and the output is the displayed final offer and the purchase link.

[0647] (Application example 2)

[0648] 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."

[0649] The objective of the present invention is to solve the problem that conventional methods require time and effort when users select the best interior products for their living space. It is also necessary to solve the problem that systems that can adjust product suggestions taking into account the user's feelings when they are dissatisfied with the suggested products are insufficient. It is important to enable users to receive more personalized suggestions in real time when selecting interior products in a physical store.

[0650] 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.

[0651] In this invention, the server includes means for inputting an image taken by a user, means for the terminal to transmit the input image to the server, means for the server to analyze the received image and generate product information that matches the ambiance of the room, means for the terminal to transmit the generated product information to the user, means for the terminal to display the product information to the user, means for using smart glasses to display the product information in the user's field of view using augmented reality, and means for recognizing the user's emotions and adjusting the product information based on the emotions. This allows the user to select appropriate interior products in a short time and further receive personalized suggestions based on their emotions.

[0652] "User" refers to a person who uses the system to select the interior products that best suit their living space.

[0653] A "terminal" is a device operated by a user, and includes smart glasses, smartphones, tablets, etc.

[0654] A "server" is a computer system that runs on the cloud and performs image analysis and generates and provides product information.

[0655] "Image analysis" refers to the technology in which the server analyzes images received and extracts features such as the atmosphere of the room and the arrangement of furniture.

[0656] "Product information" refers to data such as images, names, colors, and prices related to interior products.

[0657] "Smart glasses" are wearable devices with augmented reality (AR) capabilities that display information in the user's field of vision.

[0658] "Augmented reality" refers to a technology that displays computer-generated information overlaid on real-world scenery.

[0659] "Emotion analysis" refers to the technology of recognizing and analyzing a user's emotions from their facial expressions and input data.

[0660] "Chat format" refers to a format in which a user and a server have a text-based conversation.

[0661] "Lifestyle suggestions" refers to the act of proposing interior products, storage methods, colors, and designs that are best suited to the user's living space.

[0662] This invention is a system that allows users to efficiently select the best interior products for their living space and find effective storage methods. The system consists of four main components: the user, the terminal, the server, and the emotion engine.

[0663] First, the user takes a photo of their room using a device such as smart glasses, a smartphone, or a tablet, and inputs it into the system. To input the photo, a dedicated application or a web browser is used. After the user takes a photo, they click the upload button, which sends the photo to the cloud server. The device then issues an HTTP POST request, uploading the photo data to the server.

[0664] The server then analyzes the received photos. It uses ImageAnalyzer to analyze the images and extract features such as the room's atmosphere and furniture arrangement. Specifically, it uses algorithms for object recognition and color extraction. Based on the extracted features, it then generates optimal interior product information from a product database.

[0665] The server sends the generated product information to the terminal. The suggestions are generated in a data format such as JSON and sent as an HTTP response. The terminal displays the received product information to the user. Product information includes product images, names, colors, prices, etc. When using smart glasses, the suggested products are displayed in the user's field of vision using augmented reality (AR) technology.

[0666] The device also incorporates an emotion engine that recognizes emotions from facial expressions and input data as the user operates the device. The server uses the emotion engine to analyze the user's emotions and adjusts product information and suggestions based on the analysis results. For example, if the user looks dissatisfied, the server can update the suggestions to offer more desirable options.

[0667] Users can discuss their needs and preferences in a chat format with the server via their devices. For example, they can express specific requests such as "I prefer a more natural look" or "I want storage space under the bed." The emotion engine monitors the user's emotions in real time and reflects them in the chat content.

[0668] The server uses information collected from chat-style discussions to generate lifestyle recommendations tailored to the user's needs, including color and design suggestions that match the room's atmosphere, temporary storage ideas, and how to organize unnecessary items. The emotion engine tracks the user's emotional changes and retains data on long-term emotional trends, allowing it to provide further customized recommendations.

[0669] The server can also suggest space-saving storage solutions and ways to organize unnecessary items, such as placing storage boxes under the bed or recycling items that haven't been used for over a year. The emotion engine adjusts the suggestions to make them more likely to be accepted by the user.

[0670] The user can review the final proposal on the device and, if necessary, purchase the proposed product. The device also provides a purchase link to the online store, allowing the user to easily purchase the product.

[0671] For example, if a user is suggested blue cushions and a white shelf, and then requests more natural items, the server will again suggest natural-colored cushions and wooden shelves. It will also make specific suggestions, such as "putting storage boxes under the bed" as a space-saving storage method. In this way, users can find interior and storage solutions optimized for their lifestyle.

[0672] Examples of prompts to be input into the generative AI model are, "Please enter the interior product category the user is looking for. For example, 'furniture in natural tones' or 'elegant storage solutions'." and "furniture in natural tones."

[0673] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0674] Step 1:

[0675] A user takes a photo of their room using smart glasses, a smartphone, or a tablet, which provides image data of the user's living space. The input data is the image of the room captured by the end device. This image is used for further processing.

[0676] Step 2:

[0677] The device sends the captured image data to the server. The device issues an HTTP POST request to send the photo data to the cloud server. The input data is the image captured by the user, and the output data is the image data stored in the server's storage. Through this transmission process, the server receives the photo of the user's room.

[0678] Step 3:

[0679] The server analyzes the received images. It uses ImageAnalyzer to analyze the images and extracts the features of the room using algorithms such as object recognition and color extraction. The input data is the image data stored on the server, and the output data is the room features obtained from the analysis. Specifically, the server's image analysis algorithm processes the image data.

[0680] Step 4:

[0681] The server generates optimal interior product information from a product database based on the extracted features. The generated product information includes product images, names, colors, and prices. The input data are the analyzed features, and the output data is the generated product information. The server searches the product database and collects and generates appropriate product information.

[0682] Step 5:

[0683] The server sends the generated product information to the terminal. The proposal content is sent in JSON format, and the terminal receives it as an HTTP response. The input data is the generated product information, and the output data is the product information sent to the terminal.

[0684] Step 6:

[0685] The terminal displays the received product information to the user. When smart glasses are used, the product information is displayed in the user's field of view using augmented reality (AR) technology. The input data is the product information received from the server, and the output data is the product information displayed in the user's field of view. The user can visually confirm the presented information.

[0686] Step 7:

[0687] The emotion engine recognizes and analyzes emotions from the user's facial expressions and input data. The emotion engine collects and analyzes the user's emotional data in real time. The input data is the user's facial expressions and input actions, and the output data is the analyzed emotional information.

[0688] Step 8:

[0689] The server adjusts the product information based on the analyzed emotion information. For example, if the user has a dissatisfied expression, it updates the suggestions to provide more favorable options. The input data is the emotion information obtained from the emotion engine, and the output data is the adjusted product information. Specifically, the server reanalyzes the emotion data and product data to generate new suggestions.

[0690] Step 9:

[0691] Users can discuss with the server in chat format through their terminals. They can provide detailed information about their needs, interests, and preferences. Input data is the user's text input, and output data is the server's response. The chat interface conducts the conversation in real time.

[0692] Step 10:

[0693] The server generates optimal lifestyle suggestions based on information collected from chat-style discussions. Suggestions include colors and designs that match the room's atmosphere, temporary storage ideas, and methods for organizing unnecessary items. The input data is the chat history and information provided by the user, and the output data is the newly generated suggestions.

[0694] An example of a prompt sentence to input to the generative AI model is:

[0695] "Enter the interior product category the user is looking for, for example, 'furniture in natural tones' or 'elegant storage solutions'." or 'furniture in natural tones'.

[0696] 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.

[0697] 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.

[0698] 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.

[0699] [Third embodiment]

[0700] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0701] 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.

[0702] 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).

[0703] 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.

[0704] 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.

[0705] 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).

[0706] 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.

[0707] 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.

[0708] 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.

[0709] 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.

[0710] 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.

[0711] 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."

[0712] This invention is a system that allows users to easily select the best interior products for their living space and realize effective storage methods. This system consists of three main components: the user, the terminal, and the server.

[0713] First, users use a device such as a smartphone, tablet, or computer to input a photo of their room into the system. To input photos, they use a dedicated app or a web browser. The user selects a photo and clicks the upload button.

[0714] Next, the device sends the selected photo to the cloud server. The device issues an HTTP POST request to upload the photo data to the server. After the photo is sent to the server, the server receives it and stores it in its storage.

[0715] The server analyzes the received photos using an image analysis algorithm. Image analysis uses techniques such as object recognition and color extraction to extract features such as the room's atmosphere and furniture arrangement. After analyzing the features, the server generates optimal interior product information from a product database.

[0716] Next, the server sends the generated product information to the terminal. The proposal content is generated in a data format such as JSON and sent as an HTTP response. The terminal displays the received product information to the user. The displayed content includes the product image, name, color, price, etc.

[0717] The user then chats with the server via their device to discuss their needs and preferences. For example, they can express specific requests such as "I prefer a more natural look" or "I want storage space under the bed."

[0718] Based on the information collected through chat-style discussions, the server generates lifestyle suggestions that best suit the user's needs, including color and design that matches the room's atmosphere, temporary storage ideas, and how to declutter unnecessary items.

[0719] The server can also suggest space-saving storage solutions and ways to get rid of unnecessary items, such as placing storage boxes under the bed or recycling items you haven't used in over a year.

[0720] Finally, the user can check the suggestions on the device and, if necessary, purchase the suggested products. The device also provides a purchase link to the online store, allowing the user to easily purchase the products.

[0721] For example, if a user is suggested blue cushions and white shelves, and then requests more natural items, the server will again suggest natural-colored cushions and wooden shelves. It will also make specific suggestions, such as "putting storage boxes under the bed" as a space-saving storage solution. In this way, users can find interior and storage solutions optimized for their lifestyle.

[0722] This system allows users to quickly select the best interior for their living space, effectively organize their room, and create a comfortable living environment.

[0723] The processing flow will be explained below.

[0724] Step 1:

[0725] The user takes a photo of the room and inputs it into the device. The user then selects the photo from a dedicated app or web browser and clicks the upload button.

[0726] Step 2:

[0727] The device sends the entered photo to the server. The device issues an HTTP POST request to upload the photo data to the server.

[0728] Step 3:

[0729] The server receives the photos and stores them in storage. The server stores the uploaded photos in the appropriate directory.

[0730] Step 4:

[0731] The server analyzes the received photos and uses image analysis algorithms to extract features such as the room's atmosphere and furniture arrangement.

[0732] Step 5:

[0733] The server generates product information based on the features extracted from the image, searches a product database, and selects the most suitable interior product.

[0734] Step 6:

[0735] The server generates product information and sends it to the terminal. The proposal content is generated in a data format such as JSON and sent as an HTTP response.

[0736] Step 7:

[0737] The terminal displays the received product information to the user, including product images, names, colors, prices, etc.

[0738] Step 8:

[0739] Users can chat with the server through their devices and provide detailed information about their needs, interests, and preferences.

[0740] Step 9:

[0741] The server analyzes the content of the chat with the user and generates lifestyle suggestions that best suit the user's needs. The server updates product information and layout based on the collected information.

[0742] Step 10:

[0743] The server also suggests space-saving storage solutions and ways to get rid of unnecessary items, such as placing storage boxes under the bed or recycling items you haven't used in over a year.

[0744] Step 11:

[0745] The user reviews the final offer on the device and, if necessary, completes the purchase process for the proposed product. The device provides a purchase link to the online store.

[0746] Step 12:

[0747] The user clicks on the provided purchase link and completes the purchase process at the specified online store.

[0748] Example 1

[0749] 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."

[0750] In modern life, selecting the best interior products for a user's living space and implementing effective storage solutions is a time-consuming and laborious task. It is particularly difficult to find products that match the user's tastes and preferences and the atmosphere of the room. Furthermore, there is a demand not only for help selecting interior products but also for suggestions on specific storage solutions and lifestyles. The present invention aims to provide a system that solves these problems.

[0751] 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.

[0752] In this invention, the server includes means for analyzing received images, extracting room features using object recognition and color extraction technology, and generating product information based on the features, means for collecting detailed needs through chat-style discussions with users, and means for proposing optimal lifestyles based on the collected information, thereby enabling users to easily select interior products that are best suited to their living space and realize effective storage methods.

[0753] "User" refers to an individual or group that uses this system to select interior products that suit their living space and receive suggestions.

[0754] "Terminal" refers to an electronic device, such as a smartphone, tablet, or computer, that a user uses to access the system and input or transmit images.

[0755] A "server" refers to a computer system that receives data sent from a terminal, analyzes and processes it, and returns the results to the terminal.

[0756] An "image analysis algorithm" refers to a calculation method used to analyze image data received by a server and extract features using techniques such as object recognition and color extraction.

[0757] "Product information" refers to information about interior products that are optimal for the user's living space, generated based on image analysis and detailed user needs. Specifically, this information includes product images, names, colors, prices, etc.

[0758] "Chat-style discussion" refers to a method in which users and servers exchange information in real time through text messages, which allows detailed information about users' needs, interests, and preferences to be collected.

[0759] "Optimal lifestyle suggestions" refers to specific advice provided by the server based on the analysis results and chat data to make life more comfortable, such as interior products, storage methods, and design ideas that suit the user's living space.

[0760] "Object recognition" refers to the technology whereby image analysis algorithms identify different objects in an image and determine what each object is.

[0761] "Color extraction technology" refers to a technology in which an image analysis algorithm identifies colors within an image and analyzes the distribution and characteristics of those colors.

[0762] This invention is a system that allows users to easily select the best interior products for their living space and realize effective storage methods. This system consists of three main components: the user, the terminal, and the server.

[0763] Users can use devices such as smartphones, tablets, and computers to input photos of their rooms into the system. To input photos, they use a dedicated app or web browser. For example, a user can take a photo of their living room using the camera app on their smartphone, open the app, and upload the photo.

[0764] Next, the device sends the photo data selected by the user to the cloud server using an HTTP POST request. Specifically, the device encodes the photo data as an HTTP POST request and sends it to the specified URL. For example, the device sends the photo data to "https: / / example.com / upload."

[0765] The server receives the photos sent from the device and stores them in storage. It then runs image analysis algorithms to analyze the content of the photos. This analysis uses object recognition and color extraction techniques. For example, the server uses an image analysis library (such as OpenCV or TensorFlow) to identify the sofa, table, and wall colors in the photo.

[0766] The server extracts and generates optimal interior product information from a product database based on the analysis results. The generated data is expressed in a data format such as JSON and sent to the terminal as an HTTP response. Specifically, the server generates information on, for example, a blue cushion or a white shelf and sends it to the terminal in JSON format.

[0767] The device visually displays the received product information to the user, including product images, names, colors, and prices. For example, the device might display "Blue cushion - ¥3,000" or "White shelf - ¥5,000" to the user.

[0768] Furthermore, users can hold chat-style discussions with the server via their devices, providing detailed information about their needs and preferences. For example, a user might request, "I prefer a more natural look."

[0769] The server generates optimal lifestyle suggestions based on the information collected through chat discussions. For example, the server generates information on natural-colored cushions and wooden shelves, and sends it back to the device.

[0770] The user finally reviews the suggested products and proceeds with the purchase if necessary. The device provides a purchase link to the online store, allowing the user to easily purchase the product. For example, the user can click on a natural-colored cushion displayed in the app and press the "Purchase" button to proceed with the purchase at the online store.

[0771] Example prompt sentence:

[0772] "I'd like you to suggest interior design products based on a photo of my living room."

[0773] In this way, users can get interior and storage solutions that are optimized for their lifestyle.

[0774] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0775] Step 1:

[0776] Users take photos of their rooms using devices such as smartphones, tablets, or computers, and enter them into the system via a dedicated app or web browser.

[0777] Specific operation: The user opens the camera app on their smartphone and takes a full-size photo of their living room. Then, they open the dedicated app and click the "Upload photo" button.

[0778] Input: A photo of the room taken by the user.

[0779] Output: The device is ready to input the user's photo into the system.

[0780] Step 2:

[0781] The device sends the photo data uploaded by the user to the cloud server using an HTTP POST request.

[0782] Specific operation: The device encodes the photo data as an HTTP POST request and sends it to "https: / / example.com / upload".

[0783] Input: Photo data of the user's room.

[0784] Output: The photo data is sent to the cloud server.

[0785] Step 3:

[0786] The server receives the photos sent from the terminal and stores them in storage.

[0787] Specific operation: The server receives the request and saves the photo data in the storage system as " / photos / user12345.jpg".

[0788] Input: Photo data sent from the device.

[0789] Output: Photo files saved on the server.

[0790] Step 4:

[0791] The server then runs the stored photos through image analysis algorithms, which use object recognition and color extraction techniques.

[0792] Specific operation: The server uses an image analysis library (e.g., OpenCV or TensorFlow) to identify sofas, tables, wall colors, etc. in the photo.

[0793] Input: Photo files stored on the server.

[0794] Output: Analysis result data including object recognition and color information.

[0795] Step 5:

[0796] The server generates optimal interior product information from a product database based on the analysis results. The generated data is expressed in a data format such as JSON and sent to the terminal as an HTTP response.

[0797] Specific operation: The server selects the blue cushion and the white shelf from the analysis results and generates JSON data such as {"product": {"name": "blue cushion", "color": "blue", "price": 3000}}.

[0798] Input: Analysis result data.

[0799] Output: JSON data of the best interior product information.

[0800] Step 6:

[0801] The server sends the generated product information to the terminal as an HTTP response.

[0802] What happens: The server sends the JSON data in the HTTP response as {"status": 200, "data": {"products": [{"name": "blue cushion", "color": "blue", "price": 3000}, {"name": "white shelf", "color": "white", "price": 5000}]}}

[0803] Input: JSON data of product information.

[0804] Output: Product information sent to the device.

[0805] Step 7:

[0806] The terminal visually displays the received product information to the user, including product images, names, colors, prices, etc.

[0807] Specific operation: The device parses the received JSON data and displays it on the user interface as "Blue cushion - ¥3000" or "White shelf - ¥5000".

[0808] Input: Product information sent from the server.

[0809] Output: Product information displayed in a user interface.

[0810] Step 8:

[0811] Users can hold discussions with the server in chat format through their terminals, providing detailed information about their needs, hobbies, and preferences.

[0812] Specific Actions: The user opens the chat interface, types the text message "I like it more natural," and sends it.

[0813] Input: The user's text message.

[0814] Output: Detailed user needs information sent to the server.

[0815] Step 9:

[0816] The server generates optimal lifestyle suggestions that meet the user's needs based on the information collected through chat discussions.

[0817] Specific operation: The server analyzes the user's request data ("I prefer a more natural feel"), recreates information about natural-colored cushions and wooden shelves, and sends it to the terminal in JSON format.

[0818] Input: User needs information collected through chat discussions.

[0819] Output: Updated interior product information and lifestyle suggestions.

[0820] Step 10:

[0821] The user checks the proposed interior products again and proceeds with the purchase procedure if necessary.

[0822] Specific operation: The user clicks on the natural-colored cushion product displayed in the app and presses the "Purchase" button. The device then opens a purchase link to the online store and assists with the purchase process.

[0823] Input: Interior products selected by the user.

[0824] Output: Purchase completed at online store.

[0825] (Application example 1)

[0826] 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."

[0827] Conventional interior design suggestion systems have the problem that it is difficult for users to select and confirm the placement of products before actually placing them in a room. It is also difficult to effectively reflect the user's detailed needs and preferences. This often leads to problems after purchase, such as the product not meeting expectations or not matching the atmosphere of the room.

[0828] 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.

[0829] In this invention, the server includes means for inputting an image taken by a user, means for the terminal to transmit the input image to the server, means for the server to analyze the received image and generate product information that matches the atmosphere of the room, means for the server to transmit the generated product information to the terminal, means for the terminal to display the product information to the user, and means for the terminal or head-mounted display to display the generated product information in a virtual environment so that the user can check and customize it in real time. This allows the user to directly check the arrangement and atmosphere of the product in the virtual environment before actually purchasing it, and to customize it in real time to meet their detailed needs.

[0830] "Means for inputting images taken by the user" refers to a method by which a user can use a device such as a smartphone or tablet to import photos or videos of their own room into the system.

[0831] "Means by which the terminal transmits input images to the server" refers to the protocol or technology used to transfer image data from the user's terminal to the server.

[0832] "Means for analyzing the images received by the server and generating product information that matches the atmosphere of the room" refers to the process in which the server analyzes the image data received using image analysis technology and generates interior product information that is optimal for the user's room based on the results.

[0833] The "means for transmitting product information generated by the server to the terminal" is a method for transmitting interior product information generated by the server to the user's terminal.

[0834] The "means for the terminal to display product information to the user" refers to a function that visually presents the product information received by the user's terminal to the user through an interface such as a screen.

[0835] "Means for a terminal or head-mounted display to display generated product information in a virtual environment, allowing users to check and customize in real time" refers to a method that allows users to use a smartphone or head-mounted display to check suggested interior products in a virtual reality space as if they were actually placed in a room, and change their placement, color, size, etc. in real time.

[0836] The "means for gathering detailed needs through discussion in chat format" is a process for gathering specific requests and preferences of a user through an interactive chat between the user and the system.

[0837] The "means for proposing the optimal lifestyle" is a means for proposing the interior design, storage methods, and lifestyle that are most suitable for the user's room based on collected information about the user.

[0838] This invention is a system that allows users to select the interior products that best suit their living space and realize effective storage methods. This system consists of three main components: the user, the terminal, and the server. The following hardware and software are used to realize the system.

[0839] 1. Hardware

[0840] Smartphones and tablets: Devices that allow users to take photos of the room and upload them to the system.

[0841] Server: A central system that performs image analysis and data processing.

[0842] Head-mounted display (HMD): A device that allows users to view product placement in a virtual environment, such as the Oculus Quest 2.

[0843] 2. Software

[0844] Unity: A game engine for building virtual environments.

[0845] TensorFlow: A machine learning framework for image analysis.

[0846] Flask: Used as a server-side framework.

[0847] OpenCV: Used as an image processing library.

[0848] System Embodiments

[0849] First, users input photos of their rooms into the system using a device such as a smartphone or tablet. The photos are then uploaded to the system using a dedicated app or a web browser. The uploaded photos are then sent from the device to the server as an HTTP POST request.

[0850] The server analyzes the received images using an image analysis algorithm. It uses OpenCV and TensorFlow to extract the room's features using object recognition and color extraction techniques. Based on the analysis results, the server generates optimal interior product information from a product database.

[0851] The product information generated by the server is sent to the user's device in a data format such as JSON. The device visually displays the received product information to the user. The displayed content includes product images, names, colors, prices, etc. The user can also use a head-mounted display (HMD) to view the product information generated in the virtual environment in real time and customize its placement and color.

[0852] Users can access product information using their devices or HMDs and communicate their detailed needs and preferences to the server through voice or text chat. Through this chat-style discussion, the server generates more detailed interior design proposals based on the user's requirements.

[0853] For example, if a user requests a "natural-looking bed," the system will suggest a bed made of natural materials based on that request. The user can also use the virtual environment to see the bed arrangement and change the size and color in real time, if necessary. Here are some example prompts:

[0854] Prompt Sentence Examples

[0855] "Please suggest a bed with a natural feel."

[0856] "I want to see the bed placement in a virtual environment."

[0857] I would like the interior to be more brightly colored.

[0858] In this way, users can get interior and storage solutions that are optimized for their lifestyle.

[0859] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0860] Step 1:

[0861] Users can take photos of their rooms using a smartphone or tablet and upload them to the system via a dedicated app or web browser. The data uploaded by the user is an image file, and the image data is entered into the system.

[0862] Step 2:

[0863] The device sends the uploaded image to the server. An HTTP POST request is issued and the image data is transferred to the server. The server temporarily stores the received image data. The input to this process is the user's image data, and the output is the image data sent to the server.

[0864] Step 3:

[0865] The server analyzes the received image data using an image analysis algorithm. At this time, the server uses OpenCV and TensorFlow to perform object recognition and color extraction, and extract the room's features. The input is image data, and the output is data containing the room's features. Specifically, the position, color, shape, etc. of furniture are identified.

[0866] Step 4:

[0867] The server searches a product database based on the analysis results and generates interior product information that matches the room's atmosphere. The generated product information includes product images, names, colors, prices, etc. The input is the feature data of the room, and the output is interior product information.

[0868] Step 5:

[0869] The server sends the generated product information to the user's device. The product information is generated in a data format such as JSON and sent to the device as an HTTP response. The input to this process is the generated product information, and the output is the product information sent to the user's device.

[0870] Step 6:

[0871] The terminal displays product information to the user. Product images, names, colors, prices, etc. are visually displayed on the screen of a smartphone or tablet. The input is product information received from the server, and the output is an interface visually presented to the user.

[0872] Step 7:

[0873] The user checks product information and uses a head-mounted display (HMD) to arrange and customize products in a virtual environment in real time. Product information is displayed on the HMD and can be viewed in a 3D environment. The user can change the arrangement and color using voice or gestures. The input is product information, and the output is product arrangement information in the virtual environment.

[0874] Step 8:

[0875] The user and the server hold a chat-style discussion. The user inputs specific requests and preferences via text or voice, and the server generates more detailed interior design proposals in response. The input is the user's requests and preferences, and the output is further customized proposal information.

[0876] For example, if a user inputs "Please suggest a bed with a natural feel," the server will re-suggest a bed made of natural materials and allow the user to check its placement in the virtual environment. As a concrete example of this operation, a natural wooden bed is displayed in a 3D environment, and the user can change its placement and color in real time.

[0877] Example prompt sentence:

[0878] "Please suggest a bed with a natural feel."

[0879] "I want to see the bed placement in a virtual environment."

[0880] I would like the interior to be more brightly colored.

[0881] 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.

[0882] This invention is a system that allows users to easily select the best interior products for their living space and realize effective storage methods, and it also has the function of adjusting the suggestions by combining it with an emotion engine that recognizes the user's emotions. This system consists of four main components: the user, the terminal, the server, and the emotion engine.

[0883] First, users use a device such as a smartphone, tablet, or computer to input a photo of their room into the system. To input photos, they use a dedicated app or a web browser. The user selects a photo and clicks the upload button.

[0884] Next, the device sends the selected photo to the cloud server. The device issues an HTTP POST request to upload the photo data to the server. After the photo is sent to the server, the server receives it and stores it in its storage.

[0885] The server analyzes the received photos using an image analysis algorithm. Image analysis uses techniques such as object recognition and color extraction to extract features such as the room's atmosphere and furniture arrangement. After analyzing the features, the server generates optimal interior product information from a product database.

[0886] Next, the server sends the generated product information to the terminal. The proposal content is generated in a data format such as JSON and sent as an HTTP response. The terminal displays the received product information to the user. The displayed content includes the product image, name, color, price, etc.

[0887] The device also incorporates an emotion engine that recognizes emotions from facial expressions and input data as the user operates the device. The server uses the emotion engine to analyze the user's emotions and adjusts product information and suggestions based on the analysis results. For example, if the user has a dissatisfied expression, the server can update the suggestions to offer more desirable options.

[0888] The user then engages in a chat-style discussion with the server via their device. During this discussion, the user provides detailed information about their needs and preferences. For example, they can express specific requests such as "I prefer a more natural look" or "I want storage space under the bed." The emotion engine continues to monitor the user's emotions in real time and reflects them in the chat content.

[0889] Based on the information collected through chat-style discussions, the server generates lifestyle recommendations that best suit the user's needs. These recommendations include colors and designs that match the room's atmosphere, temporary storage ideas, and how to declutter unnecessary items. The emotion engine tracks the user's emotional changes and retains data on long-term emotional trends, which can then be used to provide further customized recommendations.

[0890] The server can also suggest space-saving storage solutions and ways to get rid of unnecessary items, such as placing storage boxes under the bed or recycling items that haven't been used for over a year. The emotion engine also considers how likely the suggestions are to be accepted, providing information in a way that satisfies the user.

[0891] Finally, the user can check the final proposal on the terminal and, if necessary, purchase the proposed product. The terminal also provides a purchase link to the online store, allowing the user to easily purchase the product.

[0892] For example, if a user is suggested blue cushions and white shelves, and then requests more natural items, the server will again suggest natural-colored cushions and wooden shelves. It will also make specific suggestions, such as "putting storage boxes under the bed" as a space-saving storage solution. In this way, users can find interior and storage solutions optimized for their lifestyle.

[0893] This system allows users to quickly select the best interior design for their living space, effectively organize their room, and create a comfortable living environment.The use of an emotion engine makes it possible to make more personalized suggestions based on the user's emotions, thereby improving user satisfaction.

[0894] The processing flow will be explained below.

[0895] Step 1:

[0896] The user takes a photo of the room and inputs it into the device. The user then selects the photo from a dedicated app or web browser and clicks the upload button.

[0897] Step 2:

[0898] The device sends the entered photo to the server. The device issues an HTTP POST request to upload the photo data to the server.

[0899] Step 3:

[0900] The server receives the photos and stores them in storage. The server stores the uploaded photos in the appropriate directory.

[0901] Step 4:

[0902] The server analyzes the received photos and uses image analysis algorithms to extract features such as the room's atmosphere and furniture arrangement.

[0903] Step 5:

[0904] The server generates product information based on the features extracted from the image, searches a product database, and selects the most suitable interior product.

[0905] Step 6:

[0906] The server generates product information and sends it to the terminal. The proposal content is generated in a data format such as JSON and sent as an HTTP response.

[0907] Step 7:

[0908] The terminal displays the received product information to the user, including product images, names, colors, prices, etc.

[0909] Step 8:

[0910] Users can chat with the server through their devices and provide detailed information about their needs, interests, and preferences.

[0911] Step 9:

[0912] The emotion engine recognizes the user's emotions in real time. While the user is typing chat content or checking product information, the emotion engine analyzes emotions from the user's facial expressions and input data.

[0913] Step 10:

[0914] The server generates lifestyle suggestions that best suit the user's needs based on the content of the chat and the analysis results from the emotion engine, including product information, layout, storage ideas, and methods for decluttering unnecessary items.

[0915] Step 11:

[0916] The server sends the final proposal to the device, taking into account the analysis results of the emotion engine and adjusting the proposal to be most appealing to the user.

[0917] Step 12:

[0918] The terminal displays the final proposal to the user, who then checks the information and, if necessary, completes the purchase procedure for the proposed product.

[0919] Step 13:

[0920] The device provides a purchase link to the online store, and the user clicks the link to complete the purchase procedure at the specified online store.

[0921] For example, if a user is suggested blue cushions and a white shelf, and the user requests more natural items, the server will re-suggest natural-colored cushions and wooden shelves based on the user's chat input and the analysis results of the emotion engine. It will also make specific suggestions such as "putting storage boxes under the bed" as a space-saving storage method. In this way, users can obtain interior and storage solutions optimized for their lifestyle.

[0922] Example 2

[0923] 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."

[0924] Conventional interior design selection systems required users to manually select products, making them inefficient and particularly difficult to customize based on user emotions. Furthermore, because product suggestions could not reflect individual needs or emotions, improving user satisfaction was a difficult challenge.

[0925] The identification process by the identification 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 means for analyzing a received image and generating product information based on the characteristics of the room, means for recognizing the user's emotion using an emotion engine and adjusting the product information based on the emotion, and means for transmitting the generated product information to the terminal. This makes it possible to propose products that correspond to the user's emotion and individual needs.

[0926] "User" refers to the person who uses the system. Generally, this refers to an individual who wants to select interior products and receive suggestions suitable for their living space.

[0927] "Terminal" refers to a device operated by a user, such as a smartphone, tablet, or computer.

[0928] A "server" is a computer system that performs the central processing of the system, and is responsible for important processes such as analyzing received data and generating proposals.

[0929] "Images" refers to photos and drawings taken by the user and input through the device. They are primarily used to analyze the interior of a room.

[0930] "Product Information" refers to data about interior products generated by the server, including details such as images, names, colors, and prices.

[0931] An "emotion engine" refers to a combination of software and hardware that recognizes a user's emotions and adjusts the system's output based on those emotions.

[0932] "Chat-style" refers to a conversation-like interaction between the user and the system, where the user inputs specific requests and feedback, and the system responds accordingly.

[0933] "Discussion" refers to the process in which the user and the server interact to discuss detailed requests and conditions. It proceeds like an everyday conversation, digging deeper into the user's needs.

[0934] This system allows users to easily select the best interior products for their living space and realize effective storage solutions. It also has a function that adjusts the recommendations by combining it with an emotion engine that recognizes the user's emotions. This system consists of four main components: the user, the terminal, the server, and the emotion engine.

[0935] First, users use a device such as a smartphone, tablet, or computer to input a photo of their room into the system. To input photos, they use a dedicated app or a web browser. The user selects a photo and clicks the upload button.

[0936] Next, the device sends the selected photo to the cloud server. The device issues an HTTP POST request to upload the photo data to the server. After the photo is sent to the server, the server receives it and stores it in its storage.

[0937] The server analyzes the received photos using an image analysis algorithm. Image analysis uses techniques such as object recognition and color extraction to extract features such as the room's atmosphere and furniture arrangement. After analyzing the features, the server generates optimal interior product information from a product database.

[0938] Next, the server sends the generated product information to the terminal. The proposal content is generated in a data format such as JSON and sent as an HTTP response. The terminal displays the received product information to the user. The displayed content includes the product image, name, color, price, etc.

[0939] The device also incorporates an emotion engine that recognizes emotions from facial expressions and input data as the user operates the device. The server uses the emotion engine to analyze the user's emotions and adjusts product information and suggestions based on the analysis results. For example, if the user has a dissatisfied expression, the server can update the suggestions to offer more desirable options.

[0940] The user then engages in a chat-style discussion with the server via their device. During this discussion, the user provides detailed information about their needs and preferences. For example, they can express specific requests such as "I prefer a more natural look" or "I want storage space under the bed." The emotion engine continues to monitor the user's emotions in real time and reflects them in the chat content.

[0941] Based on the information collected through chat-style discussions, the server generates lifestyle recommendations that best suit the user's needs. These recommendations include colors and designs that match the room's atmosphere, temporary storage ideas, and how to declutter unnecessary items. The emotion engine tracks the user's emotional changes and retains data on long-term emotional trends, which can then be used to provide further customized recommendations.

[0942] The server can also suggest space-saving storage solutions and ways to get rid of unnecessary items, such as placing storage boxes under the bed or recycling items that haven't been used for over a year. The emotion engine also considers how likely the suggestions are to be accepted, providing information in a way that satisfies the user.

[0943] Finally, the user can check the final proposal on the terminal and, if necessary, purchase the proposed product. The terminal also provides a purchase link to the online store, allowing the user to easily purchase the product.

[0944] For example, if a user is suggested blue cushions and white shelves, and then requests more natural items, the server will again suggest natural-colored cushions and wooden shelves. It will also make specific suggestions, such as "putting storage boxes under the bed" as a space-saving storage solution. In this way, users can find interior and storage solutions optimized for their lifestyle.

[0945] Example prompts to input to a generative AI model:

[0946] 1. "Analyze photos of user A's room and suggest suitable interior products."

[0947] 2. "Analyze the room layout from the images uploaded by user B and suggest the optimal furniture."

[0948] 3. "Please identify User C's emotions and provide products that match their preferences."

[0949] The above is an embodiment of the present invention, and this system allows users to quickly select the best interior for their living space, effectively organize their room, and create a comfortable living environment. Furthermore, the use of an emotion engine makes it possible to make more personalized suggestions based on the user's emotions, thereby improving satisfaction.

[0950] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0951] Step 1:

[0952] The user takes a photo of the room and inputs it into the system via a terminal.

[0953] Specifically, a user uses a smartphone, tablet, computer, or other device to select a photo using a dedicated app or web browser and clicks the upload button. The input data is an image file of the room, and the output is a state in which this image is ready to be sent to the server.

[0954] Step 2:

[0955] The terminal transmits the input image to the server.

[0956] Specifically, the device issues an HTTP POST request to upload the selected photo data to the server. The input is an image file of the room, and the output is the state in which the photo data has been sent to the server.

[0957] Step 3:

[0958] The server analyzes the received images and generates product information based on the characteristics of the room.

[0959] Specifically, the server stores the received image data in storage and analyzes it using an image analysis algorithm (TensorFlow, OpenCV, etc.). Image analysis involves extracting features such as object recognition, color extraction, and furniture placement. The input is the uploaded image data of the room, and the output is the analyzed feature data.

[0960] Step 4:

[0961] The server searches for the most suitable interior product from a product database and generates product information.

[0962] Specifically, the system uses the analyzed feature data to issue a query to a product database to obtain information on interior products that match the characteristics of the room. Based on the obtained product information, the system selects and generates optimal interior product candidates. The input is the feature data, and the output is the selected interior product information.

[0963] Step 5:

[0964] The server transmits the generated product information to the terminal.

[0965] Specifically, product information is generated in a data format such as JSON and sent to the terminal as an HTTP response. The input is the generated interior product information, and the output is the transmitted product information data.

[0966] Step 6:

[0967] The terminal displays the product information to the user.

[0968] Specifically, the device parses the received product information and displays detailed information such as product images, names, colors, and prices on the app screen for the user. The user can view this information. The input is the product information data sent from the server, and the output is the displayed product information.

[0969] Step 7:

[0970] The emotion engine recognizes the user's emotions.

[0971] Specifically, an emotion engine (e.g., Smile Detector API) uses the device's front camera to analyze the user's facial expressions in real time and infer their emotions. The input is the user's facial expression data, and the output is the inferred emotion data.

[0972] Step 8:

[0973] The server adjusts the suggestions based on the emotion.

[0974] Specifically, the system receives emotional data obtained from the emotion engine and updates the content of the suggestions in real time. For example, if the user looks dissatisfied, the server modifies the suggestions to select more appropriate products. The input is emotional data, and the output is adjusted product suggestion information.

[0975] Step 9:

[0976] The user and the server hold discussions in chat format.

[0977] Specifically, the user uses the chatbot function on their device to input their specific requests and preferences, and the server provides information in response. The user expresses specific requests such as "I like a natural feel" or "I want storage under the bed." The input is the user's desired data, and the output is additional information generated by the server.

[0978] Step 10:

[0979] The server generates a final proposal and sends it to the device.

[0980] Specifically, the system combines the information obtained from the discussion with the user's emotional data to generate a final proposal. This proposal includes color and design that matches the room's atmosphere, storage ideas, and more. The generated proposal is sent to the device in JSON format or similar. The input is the user's requests and emotional data, and the output is the final proposal data.

[0981] Step 11:

[0982] The terminal displays the final offer and the user completes the purchase.

[0983] Specifically, the terminal displays the final offer received from the server to the user and provides a purchase link to the online store. The user can click the link to complete the purchase procedure on the store page. The input is the final offer data, and the output is the displayed final offer and the purchase link.

[0984] (Application example 2)

[0985] 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."

[0986] The objective of the present invention is to solve the problem that conventional methods require time and effort when users select the best interior products for their living space. It is also necessary to solve the problem that systems that can adjust product suggestions taking into account the user's feelings when they are dissatisfied with the suggested products are insufficient. It is important to enable users to receive more personalized suggestions in real time when selecting interior products in a physical store.

[0987] 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.

[0988] In this invention, the server includes means for inputting an image taken by a user, means for the terminal to transmit the input image to the server, means for the server to analyze the received image and generate product information that matches the ambiance of the room, means for the terminal to transmit the generated product information to the user, means for the terminal to display the product information to the user, means for using smart glasses to display the product information in the user's field of view using augmented reality, and means for recognizing the user's emotions and adjusting the product information based on the emotions. This allows the user to select appropriate interior products in a short time and further receive personalized suggestions based on their emotions.

[0989] "User" refers to a person who uses the system to select the interior products that best suit their living space.

[0990] A "terminal" is a device operated by a user, and includes smart glasses, smartphones, tablets, etc.

[0991] A "server" is a computer system that runs on the cloud and performs image analysis and generates and provides product information.

[0992] "Image analysis" refers to the technology in which the server analyzes images received and extracts features such as the atmosphere of the room and the arrangement of furniture.

[0993] "Product information" refers to data such as images, names, colors, and prices related to interior products.

[0994] "Smart glasses" are wearable devices with augmented reality (AR) capabilities that display information in the user's field of vision.

[0995] "Augmented reality" refers to a technology that displays computer-generated information overlaid on real-world scenery.

[0996] "Emotion analysis" refers to the technology of recognizing and analyzing a user's emotions from their facial expressions and input data.

[0997] "Chat format" refers to a format in which a user and a server have a text-based conversation.

[0998] "Lifestyle suggestions" refers to the act of proposing interior products, storage methods, colors, and designs that are best suited to the user's living space.

[0999] This invention is a system that allows users to efficiently select the best interior products for their living space and find effective storage methods. The system consists of four main components: the user, the terminal, the server, and the emotion engine.

[1000] First, the user takes a photo of their room using a device such as smart glasses, a smartphone, or a tablet, and inputs it into the system. To input the photo, a dedicated application or a web browser is used. After the user takes a photo, they click the upload button, which sends the photo to the cloud server. The device then issues an HTTP POST request, uploading the photo data to the server.

[1001] The server then analyzes the received photos. It uses ImageAnalyzer to analyze the images and extract features such as the room's atmosphere and furniture arrangement. Specifically, it uses algorithms for object recognition and color extraction. Based on the extracted features, it then generates optimal interior product information from a product database.

[1002] The server sends the generated product information to the terminal. The suggestions are generated in a data format such as JSON and sent as an HTTP response. The terminal displays the received product information to the user. Product information includes product images, names, colors, prices, etc. When using smart glasses, the suggested products are displayed in the user's field of vision using augmented reality (AR) technology.

[1003] The device also incorporates an emotion engine that recognizes emotions from facial expressions and input data as the user operates the device. The server uses the emotion engine to analyze the user's emotions and adjusts product information and suggestions based on the analysis results. For example, if the user looks dissatisfied, the server can update the suggestions to offer more desirable options.

[1004] Users can discuss their needs and preferences in a chat format with the server via their devices. For example, they can express specific requests such as "I prefer a more natural look" or "I want storage space under the bed." The emotion engine monitors the user's emotions in real time and reflects them in the chat content.

[1005] The server uses information collected from chat-style discussions to generate lifestyle recommendations tailored to the user's needs, including color and design suggestions that match the room's atmosphere, temporary storage ideas, and how to organize unnecessary items. The emotion engine tracks the user's emotional changes and retains data on long-term emotional trends, allowing it to provide further customized recommendations.

[1006] The server can also suggest space-saving storage solutions and ways to organize unnecessary items, such as placing storage boxes under the bed or recycling items that haven't been used for over a year. The emotion engine adjusts the suggestions to make them more likely to be accepted by the user.

[1007] The user can review the final proposal on the device and, if necessary, purchase the proposed product. The device also provides a purchase link to the online store, allowing the user to easily purchase the product.

[1008] For example, if a user is suggested blue cushions and a white shelf, and then requests more natural items, the server will again suggest natural-colored cushions and wooden shelves. It will also make specific suggestions, such as "putting storage boxes under the bed" as a space-saving storage method. In this way, users can find interior and storage solutions optimized for their lifestyle.

[1009] Examples of prompts to be input into the generative AI model are, "Please enter the interior product category the user is looking for. For example, 'furniture in natural tones' or 'elegant storage solutions'." and "furniture in natural tones."

[1010] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1011] Step 1:

[1012] A user takes a photo of their room using smart glasses, a smartphone, or a tablet, which provides image data of the user's living space. The input data is the image of the room captured by the end device. This image is used for further processing.

[1013] Step 2:

[1014] The device sends the captured image data to the server. The device issues an HTTP POST request to send the photo data to the cloud server. The input data is the image captured by the user, and the output data is the image data stored in the server's storage. Through this transmission process, the server receives the photo of the user's room.

[1015] Step 3:

[1016] The server analyzes the received images. It uses ImageAnalyzer to analyze the images and extracts the features of the room using algorithms such as object recognition and color extraction. The input data is the image data stored on the server, and the output data is the room features obtained from the analysis. Specifically, the server's image analysis algorithm processes the image data.

[1017] Step 4:

[1018] The server generates optimal interior product information from a product database based on the extracted features. The generated product information includes product images, names, colors, and prices. The input data are the analyzed features, and the output data is the generated product information. The server searches the product database and collects and generates appropriate product information.

[1019] Step 5:

[1020] The server sends the generated product information to the terminal. The proposal content is sent in JSON format, and the terminal receives it as an HTTP response. The input data is the generated product information, and the output data is the product information sent to the terminal.

[1021] Step 6:

[1022] The terminal displays the received product information to the user. When smart glasses are used, the product information is displayed in the user's field of view using augmented reality (AR) technology. The input data is the product information received from the server, and the output data is the product information displayed in the user's field of view. The user can visually confirm the presented information.

[1023] Step 7:

[1024] The emotion engine recognizes and analyzes emotions from the user's facial expressions and input data. The emotion engine collects and analyzes the user's emotional data in real time. The input data is the user's facial expressions and input actions, and the output data is the analyzed emotional information.

[1025] Step 8:

[1026] The server adjusts the product information based on the analyzed emotion information. For example, if the user has a dissatisfied expression, it updates the suggestions to provide more favorable options. The input data is the emotion information obtained from the emotion engine, and the output data is the adjusted product information. Specifically, the server reanalyzes the emotion data and product data to generate new suggestions.

[1027] Step 9:

[1028] Users can discuss with the server in chat format through their terminals. They can provide detailed information about their needs, interests, and preferences. Input data is the user's text input, and output data is the server's response. The chat interface conducts the conversation in real time.

[1029] Step 10:

[1030] The server generates optimal lifestyle suggestions based on information collected from chat-style discussions. Suggestions include colors and designs that match the room's atmosphere, temporary storage ideas, and methods for organizing unnecessary items. The input data is the chat history and information provided by the user, and the output data is the newly generated suggestions.

[1031] An example of a prompt sentence to input to the generative AI model is:

[1032] "Enter the interior product category the user is looking for, for example, 'furniture in natural tones' or 'elegant storage solutions'." or 'furniture in natural tones'.

[1033] 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.

[1034] 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.

[1035] 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.

[1036] [Fourth embodiment]

[1037] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1038] 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.

[1039] 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).

[1040] 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.

[1041] 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.

[1042] 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).

[1043] 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.

[1044] 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.

[1045] 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.

[1046] 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.

[1047] 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.

[1048] 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.

[1049] 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."

[1050] This invention is a system that allows users to easily select the best interior products for their living space and realize effective storage methods. This system consists of three main components: the user, the terminal, and the server.

[1051] First, users use a device such as a smartphone, tablet, or computer to input a photo of their room into the system. To input photos, they use a dedicated app or a web browser. The user selects a photo and clicks the upload button.

[1052] Next, the device sends the selected photo to the cloud server. The device issues an HTTP POST request to upload the photo data to the server. After the photo is sent to the server, the server receives it and stores it in its storage.

[1053] The server analyzes the received photos using an image analysis algorithm. Image analysis uses techniques such as object recognition and color extraction to extract features such as the room's atmosphere and furniture arrangement. After analyzing the features, the server generates optimal interior product information from a product database.

[1054] Next, the server sends the generated product information to the terminal. The proposal content is generated in a data format such as JSON and sent as an HTTP response. The terminal displays the received product information to the user. The displayed content includes the product image, name, color, price, etc.

[1055] The user then chats with the server via their device to discuss their needs and preferences. For example, they can express specific requests such as "I prefer a more natural look" or "I want storage space under the bed."

[1056] Based on the information collected through chat-style discussions, the server generates lifestyle suggestions that best suit the user's needs, including color and design that matches the room's atmosphere, temporary storage ideas, and how to declutter unnecessary items.

[1057] The server can also suggest space-saving storage solutions and ways to get rid of unnecessary items, such as placing storage boxes under the bed or recycling items you haven't used in over a year.

[1058] Finally, the user can check the suggestions on the device and, if necessary, purchase the suggested products. The device also provides a purchase link to the online store, allowing the user to easily purchase the products.

[1059] For example, if a user is suggested blue cushions and white shelves, and then requests more natural items, the server will again suggest natural-colored cushions and wooden shelves. It will also make specific suggestions, such as "putting storage boxes under the bed" as a space-saving storage solution. In this way, users can find interior and storage solutions optimized for their lifestyle.

[1060] This system allows users to quickly select the best interior for their living space, effectively organize their room, and create a comfortable living environment.

[1061] The processing flow will be explained below.

[1062] Step 1:

[1063] The user takes a photo of the room and inputs it into the device. The user then selects the photo from a dedicated app or web browser and clicks the upload button.

[1064] Step 2:

[1065] The device sends the entered photo to the server. The device issues an HTTP POST request to upload the photo data to the server.

[1066] Step 3:

[1067] The server receives the photos and stores them in storage. The server stores the uploaded photos in the appropriate directory.

[1068] Step 4:

[1069] The server analyzes the received photos and uses image analysis algorithms to extract features such as the room's atmosphere and furniture arrangement.

[1070] Step 5:

[1071] The server generates product information based on the features extracted from the image, searches a product database, and selects the most suitable interior product.

[1072] Step 6:

[1073] The server generates product information and sends it to the terminal. The proposal content is generated in a data format such as JSON and sent as an HTTP response.

[1074] Step 7:

[1075] The terminal displays the received product information to the user, including product images, names, colors, prices, etc.

[1076] Step 8:

[1077] Users can chat with the server through their devices and provide detailed information about their needs, interests, and preferences.

[1078] Step 9:

[1079] The server analyzes the content of the chat with the user and generates lifestyle suggestions that best suit the user's needs. The server updates product information and layout based on the collected information.

[1080] Step 10:

[1081] The server also suggests space-saving storage solutions and ways to get rid of unnecessary items, such as placing storage boxes under the bed or recycling items you haven't used in over a year.

[1082] Step 11:

[1083] The user reviews the final offer on the device and, if necessary, completes the purchase process for the proposed product. The device provides a purchase link to the online store.

[1084] Step 12:

[1085] The user clicks on the provided purchase link and completes the purchase process at the specified online store.

[1086] Example 1

[1087] 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."

[1088] In modern life, selecting the best interior products for a user's living space and implementing effective storage solutions is a time-consuming and laborious task. It is particularly difficult to find products that match the user's tastes and preferences and the atmosphere of the room. Furthermore, there is a demand not only for help selecting interior products but also for suggestions on specific storage solutions and lifestyles. The present invention aims to provide a system that solves these problems.

[1089] 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.

[1090] In this invention, the server includes means for analyzing received images, extracting room features using object recognition and color extraction technology, and generating product information based on the features, means for collecting detailed needs through chat-style discussions with users, and means for proposing optimal lifestyles based on the collected information, thereby enabling users to easily select interior products that are best suited to their living space and realize effective storage methods.

[1091] "User" refers to an individual or group that uses this system to select interior products that suit their living space and receive suggestions.

[1092] "Terminal" refers to an electronic device, such as a smartphone, tablet, or computer, that a user uses to access the system and input or transmit images.

[1093] A "server" refers to a computer system that receives data sent from a terminal, analyzes and processes it, and returns the results to the terminal.

[1094] An "image analysis algorithm" refers to a calculation method used to analyze image data received by a server and extract features using techniques such as object recognition and color extraction.

[1095] "Product information" refers to information about interior products that are optimal for the user's living space, generated based on image analysis and detailed user needs. Specifically, this information includes product images, names, colors, prices, etc.

[1096] "Chat-style discussion" refers to a method in which users and servers exchange information in real time through text messages, which allows detailed information about users' needs, interests, and preferences to be collected.

[1097] "Optimal lifestyle suggestions" refers to specific advice provided by the server based on the analysis results and chat data to make life more comfortable, such as interior products, storage methods, and design ideas that suit the user's living space.

[1098] "Object recognition" refers to the technology whereby image analysis algorithms identify different objects in an image and determine what each object is.

[1099] "Color extraction technology" refers to a technology in which an image analysis algorithm identifies colors within an image and analyzes the distribution and characteristics of those colors.

[1100] This invention is a system that allows users to easily select the best interior products for their living space and realize effective storage methods. This system consists of three main components: the user, the terminal, and the server.

[1101] Users can use devices such as smartphones, tablets, and computers to input photos of their rooms into the system. To input photos, they use a dedicated app or web browser. For example, a user can take a photo of their living room using the camera app on their smartphone, open the app, and upload the photo.

[1102] Next, the device sends the photo data selected by the user to the cloud server using an HTTP POST request. Specifically, the device encodes the photo data as an HTTP POST request and sends it to the specified URL. For example, the device sends the photo data to "https: / / example.com / upload."

[1103] The server receives the photos sent from the device and stores them in storage. It then runs image analysis algorithms to analyze the content of the photos. This analysis uses object recognition and color extraction techniques. For example, the server uses an image analysis library (such as OpenCV or TensorFlow) to identify the sofa, table, and wall colors in the photo.

[1104] The server extracts and generates optimal interior product information from a product database based on the analysis results. The generated data is expressed in a data format such as JSON and sent to the terminal as an HTTP response. Specifically, the server generates information on, for example, a blue cushion or a white shelf and sends it to the terminal in JSON format.

[1105] The device visually displays the received product information to the user, including product images, names, colors, and prices. For example, the device might display "Blue cushion - ¥3,000" or "White shelf - ¥5,000" to the user.

[1106] Furthermore, users can hold chat-style discussions with the server via their devices, providing detailed information about their needs and preferences. For example, a user might request, "I prefer a more natural look."

[1107] The server generates optimal lifestyle suggestions based on the information collected through chat discussions. For example, the server generates information on natural-colored cushions and wooden shelves, and sends it back to the device.

[1108] The user finally reviews the suggested products and proceeds with the purchase if necessary. The device provides a purchase link to the online store, allowing the user to easily purchase the product. For example, the user can click on a natural-colored cushion displayed in the app and press the "Purchase" button to proceed with the purchase at the online store.

[1109] Example prompt sentence:

[1110] "I'd like you to suggest interior design products based on a photo of my living room."

[1111] In this way, users can get interior and storage solutions that are optimized for their lifestyle.

[1112] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1113] Step 1:

[1114] Users take photos of their rooms using devices such as smartphones, tablets, or computers, and enter them into the system via a dedicated app or web browser.

[1115] Specific operation: The user opens the camera app on their smartphone and takes a full-size photo of their living room. Then, they open the dedicated app and click the "Upload photo" button.

[1116] Input: A photo of the room taken by the user.

[1117] Output: The device is ready to input the user's photo into the system.

[1118] Step 2:

[1119] The device sends the photo data uploaded by the user to the cloud server using an HTTP POST request.

[1120] Specific operation: The device encodes the photo data as an HTTP POST request and sends it to "https: / / example.com / upload".

[1121] Input: Photo data of the user's room.

[1122] Output: The photo data is sent to the cloud server.

[1123] Step 3:

[1124] The server receives the photos sent from the terminal and stores them in storage.

[1125] Specific operation: The server receives the request and saves the photo data in the storage system as " / photos / user12345.jpg".

[1126] Input: Photo data sent from the device.

[1127] Output: Photo files saved on the server.

[1128] Step 4:

[1129] The server then runs the stored photos through image analysis algorithms, which use object recognition and color extraction techniques.

[1130] Specific operation: The server uses an image analysis library (e.g., OpenCV or TensorFlow) to identify sofas, tables, wall colors, etc. in the photo.

[1131] Input: Photo files stored on the server.

[1132] Output: Analysis result data including object recognition and color information.

[1133] Step 5:

[1134] The server generates optimal interior product information from a product database based on the analysis results. The generated data is expressed in a data format such as JSON and sent to the terminal as an HTTP response.

[1135] Specific operation: The server selects the blue cushion and the white shelf from the analysis results and generates JSON data such as {"product": {"name": "blue cushion", "color": "blue", "price": 3000}}.

[1136] Input: Analysis result data.

[1137] Output: JSON data of the best interior product information.

[1138] Step 6:

[1139] The server sends the generated product information to the terminal as an HTTP response.

[1140] What happens: The server sends the JSON data in the HTTP response as {"status": 200, "data": {"products": [{"name": "blue cushion", "color": "blue", "price": 3000}, {"name": "white shelf", "color": "white", "price": 5000}]}}

[1141] Input: JSON data of product information.

[1142] Output: Product information sent to the device.

[1143] Step 7:

[1144] The terminal visually displays the received product information to the user, including product images, names, colors, prices, etc.

[1145] Specific operation: The device parses the received JSON data and displays it on the user interface as "Blue cushion - ¥3000" or "White shelf - ¥5000".

[1146] Input: Product information sent from the server.

[1147] Output: Product information displayed in a user interface.

[1148] Step 8:

[1149] Users can hold discussions with the server in chat format through their terminals, providing detailed information about their needs, hobbies, and preferences.

[1150] Specific Actions: The user opens the chat interface, types the text message "I like it more natural," and sends it.

[1151] Input: The user's text message.

[1152] Output: Detailed user needs information sent to the server.

[1153] Step 9:

[1154] The server generates optimal lifestyle suggestions that meet the user's needs based on the information collected through chat discussions.

[1155] Specific operation: The server analyzes the user's request data ("I prefer a more natural feel"), recreates information about natural-colored cushions and wooden shelves, and sends it to the terminal in JSON format.

[1156] Input: User needs information collected through chat discussions.

[1157] Output: Updated interior product information and lifestyle suggestions.

[1158] Step 10:

[1159] The user checks the proposed interior products again and proceeds with the purchase procedure if necessary.

[1160] Specific operation: The user clicks on the natural-colored cushion product displayed in the app and presses the "Purchase" button. The device then opens a purchase link to the online store and assists with the purchase process.

[1161] Input: Interior products selected by the user.

[1162] Output: Purchase completed at online store.

[1163] (Application example 1)

[1164] 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."

[1165] Conventional interior design suggestion systems have the problem that it is difficult for users to select and confirm the placement of products before actually placing them in a room. It is also difficult to effectively reflect the user's detailed needs and preferences. This often leads to problems after purchase, such as the product not meeting expectations or not matching the atmosphere of the room.

[1166] 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.

[1167] In this invention, the server includes means for inputting an image taken by a user, means for the terminal to transmit the input image to the server, means for the server to analyze the received image and generate product information that matches the atmosphere of the room, means for the server to transmit the generated product information to the terminal, means for the terminal to display the product information to the user, and means for the terminal or head-mounted display to display the generated product information in a virtual environment so that the user can check and customize it in real time. This allows the user to directly check the arrangement and atmosphere of the product in the virtual environment before actually purchasing it, and to customize it in real time to meet their detailed needs.

[1168] "Means for inputting images taken by the user" refers to a method by which a user can use a device such as a smartphone or tablet to import photos or videos of their own room into the system.

[1169] "Means by which the terminal transmits input images to the server" refers to the protocol or technology used to transfer image data from the user's terminal to the server.

[1170] "Means for analyzing the images received by the server and generating product information that matches the atmosphere of the room" refers to the process in which the server analyzes the image data received using image analysis technology and generates interior product information that is optimal for the user's room based on the results.

[1171] The "means for transmitting product information generated by the server to the terminal" is a method for transmitting interior product information generated by the server to the user's terminal.

[1172] The "means for the terminal to display product information to the user" refers to a function that visually presents the product information received by the user's terminal to the user through an interface such as a screen.

[1173] "Means for a terminal or head-mounted display to display generated product information in a virtual environment, allowing users to check and customize in real time" refers to a method that allows users to use a smartphone or head-mounted display to check suggested interior products in a virtual reality space as if they were actually placed in a room, and change their placement, color, size, etc. in real time.

[1174] The "means for gathering detailed needs through discussion in chat format" is a process for gathering specific requests and preferences of a user through an interactive chat between the user and the system.

[1175] The "means for proposing the optimal lifestyle" is a means for proposing the interior design, storage methods, and lifestyle that are most suitable for the user's room based on collected information about the user.

[1176] This invention is a system that allows users to select the interior products that best suit their living space and realize effective storage methods. This system consists of three main components: the user, the terminal, and the server. The following hardware and software are used to realize the system.

[1177] 1. Hardware

[1178] Smartphones and tablets: Devices that allow users to take photos of the room and upload them to the system.

[1179] Server: A central system that performs image analysis and data processing.

[1180] Head-mounted display (HMD): A device that allows users to view product placement in a virtual environment, such as the Oculus Quest 2.

[1181] 2. Software

[1182] Unity: A game engine for building virtual environments.

[1183] TensorFlow: A machine learning framework for image analysis.

[1184] Flask: Used as a server-side framework.

[1185] OpenCV: Used as an image processing library.

[1186] System Embodiments

[1187] First, users input photos of their rooms into the system using a device such as a smartphone or tablet. The photos are then uploaded to the system using a dedicated app or a web browser. The uploaded photos are then sent from the device to the server as an HTTP POST request.

[1188] The server analyzes the received images using an image analysis algorithm. It uses OpenCV and TensorFlow to extract the room's features using object recognition and color extraction techniques. Based on the analysis results, the server generates optimal interior product information from a product database.

[1189] The product information generated by the server is sent to the user's device in a data format such as JSON. The device visually displays the received product information to the user. The displayed content includes product images, names, colors, prices, etc. The user can also use a head-mounted display (HMD) to view the product information generated in the virtual environment in real time and customize its placement and color.

[1190] Users can access product information using their devices or HMDs and communicate their detailed needs and preferences to the server through voice or text chat. Through this chat-style discussion, the server generates more detailed interior design proposals based on the user's requirements.

[1191] For example, if a user requests a "natural-looking bed," the system will suggest a bed made of natural materials based on that request. The user can also use the virtual environment to see the bed arrangement and change the size and color in real time, if necessary. Here are some example prompts:

[1192] Prompt Sentence Examples

[1193] "Please suggest a bed with a natural feel."

[1194] "I want to see the bed placement in a virtual environment."

[1195] I would like the interior to be more brightly colored.

[1196] In this way, users can get interior and storage solutions that are optimized for their lifestyle.

[1197] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1198] Step 1:

[1199] Users can take photos of their rooms using a smartphone or tablet and upload them to the system via a dedicated app or web browser. The data uploaded by the user is an image file, and the image data is entered into the system.

[1200] Step 2:

[1201] The device sends the uploaded image to the server. An HTTP POST request is issued and the image data is transferred to the server. The server temporarily stores the received image data. The input to this process is the user's image data, and the output is the image data sent to the server.

[1202] Step 3:

[1203] The server analyzes the received image data using an image analysis algorithm. At this time, the server uses OpenCV and TensorFlow to perform object recognition and color extraction, and extract the room's features. The input is image data, and the output is data containing the room's features. Specifically, the position, color, shape, etc. of furniture are identified.

[1204] Step 4:

[1205] The server searches a product database based on the analysis results and generates interior product information that matches the room's atmosphere. The generated product information includes product images, names, colors, prices, etc. The input is the feature data of the room, and the output is interior product information.

[1206] Step 5:

[1207] The server sends the generated product information to the user's device. The product information is generated in a data format such as JSON and sent to the device as an HTTP response. The input to this process is the generated product information, and the output is the product information sent to the user's device.

[1208] Step 6:

[1209] The terminal displays product information to the user. Product images, names, colors, prices, etc. are visually displayed on the screen of a smartphone or tablet. The input is product information received from the server, and the output is an interface visually presented to the user.

[1210] Step 7:

[1211] The user checks product information and uses a head-mounted display (HMD) to arrange and customize products in a virtual environment in real time. Product information is displayed on the HMD and can be viewed in a 3D environment. The user can change the arrangement and color using voice or gestures. The input is product information, and the output is product arrangement information in the virtual environment.

[1212] Step 8:

[1213] The user and the server hold a chat-style discussion. The user inputs specific requests and preferences via text or voice, and the server generates more detailed interior design proposals in response. The input is the user's requests and preferences, and the output is further customized proposal information.

[1214] For example, if a user inputs "Please suggest a bed with a natural feel," the server will re-suggest a bed made of natural materials and allow the user to check its placement in the virtual environment. As a concrete example of this operation, a natural wooden bed is displayed in a 3D environment, and the user can change its placement and color in real time.

[1215] Example prompt sentence:

[1216] "Please suggest a bed with a natural feel."

[1217] "I want to see the bed placement in a virtual environment."

[1218] I would like the interior to be more brightly colored.

[1219] 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.

[1220] This invention is a system that allows users to easily select the best interior products for their living space and realize effective storage methods, and it also has the function of adjusting the suggestions by combining it with an emotion engine that recognizes the user's emotions. This system consists of four main components: the user, the terminal, the server, and the emotion engine.

[1221] First, users use a device such as a smartphone, tablet, or computer to input a photo of their room into the system. To input photos, they use a dedicated app or a web browser. The user selects a photo and clicks the upload button.

[1222] Next, the device sends the selected photo to the cloud server. The device issues an HTTP POST request to upload the photo data to the server. After the photo is sent to the server, the server receives it and stores it in its storage.

[1223] The server analyzes the received photos using an image analysis algorithm. Image analysis uses techniques such as object recognition and color extraction to extract features such as the room's atmosphere and furniture arrangement. After analyzing the features, the server generates optimal interior product information from a product database.

[1224] Next, the server sends the generated product information to the terminal. The proposal content is generated in a data format such as JSON and sent as an HTTP response. The terminal displays the received product information to the user. The displayed content includes the product image, name, color, price, etc.

[1225] The device also incorporates an emotion engine that recognizes emotions from facial expressions and input data as the user operates the device. The server uses the emotion engine to analyze the user's emotions and adjusts product information and suggestions based on the analysis results. For example, if the user has a dissatisfied expression, the server can update the suggestions to offer more desirable options.

[1226] The user then engages in a chat-style discussion with the server via their device. During this discussion, the user provides detailed information about their needs and preferences. For example, they can express specific requests such as "I prefer a more natural look" or "I want storage space under the bed." The emotion engine continues to monitor the user's emotions in real time and reflects them in the chat content.

[1227] Based on the information collected through chat-style discussions, the server generates lifestyle recommendations that best suit the user's needs. These recommendations include colors and designs that match the room's atmosphere, temporary storage ideas, and how to declutter unnecessary items. The emotion engine tracks the user's emotional changes and retains data on long-term emotional trends, which can then be used to provide further customized recommendations.

[1228] The server can also suggest space-saving storage solutions and ways to get rid of unnecessary items, such as placing storage boxes under the bed or recycling items that haven't been used for over a year. The emotion engine also considers how likely the suggestions are to be accepted, providing information in a way that satisfies the user.

[1229] Finally, the user can check the final proposal on the terminal and, if necessary, purchase the proposed product. The terminal also provides a purchase link to the online store, allowing the user to easily purchase the product.

[1230] For example, if a user is suggested blue cushions and white shelves, and then requests more natural items, the server will again suggest natural-colored cushions and wooden shelves. It will also make specific suggestions, such as "putting storage boxes under the bed" as a space-saving storage solution. In this way, users can find interior and storage solutions optimized for their lifestyle.

[1231] This system allows users to quickly select the best interior design for their living space, effectively organize their room, and create a comfortable living environment.The use of an emotion engine makes it possible to make more personalized suggestions based on the user's emotions, thereby improving user satisfaction.

[1232] The processing flow will be explained below.

[1233] Step 1:

[1234] The user takes a photo of the room and inputs it into the device. The user then selects the photo from a dedicated app or web browser and clicks the upload button.

[1235] Step 2:

[1236] The device sends the entered photo to the server. The device issues an HTTP POST request to upload the photo data to the server.

[1237] Step 3:

[1238] The server receives the photos and stores them in storage. The server stores the uploaded photos in the appropriate directory.

[1239] Step 4:

[1240] The server analyzes the received photos and uses image analysis algorithms to extract features such as the room's atmosphere and furniture arrangement.

[1241] Step 5:

[1242] The server generates product information based on the features extracted from the image, searches a product database, and selects the most suitable interior product.

[1243] Step 6:

[1244] The server generates product information and sends it to the terminal. The proposal content is generated in a data format such as JSON and sent as an HTTP response.

[1245] Step 7:

[1246] The terminal displays the received product information to the user, including product images, names, colors, prices, etc.

[1247] Step 8:

[1248] Users can chat with the server through their devices and provide detailed information about their needs, interests, and preferences.

[1249] Step 9:

[1250] The emotion engine recognizes the user's emotions in real time. While the user is typing chat content or checking product information, the emotion engine analyzes emotions from the user's facial expressions and input data.

[1251] Step 10:

[1252] The server generates lifestyle suggestions that best suit the user's needs based on the content of the chat and the analysis results from the emotion engine, including product information, layout, storage ideas, and methods for decluttering unnecessary items.

[1253] Step 11:

[1254] The server sends the final proposal to the device, taking into account the analysis results of the emotion engine and adjusting the proposal to be most appealing to the user.

[1255] Step 12:

[1256] The terminal displays the final proposal to the user, who then checks the information and, if necessary, completes the purchase procedure for the proposed product.

[1257] Step 13:

[1258] The device provides a purchase link to the online store, and the user clicks the link to complete the purchase procedure at the specified online store.

[1259] For example, if a user is suggested blue cushions and a white shelf, and the user requests more natural items, the server will re-suggest natural-colored cushions and wooden shelves based on the user's chat input and the analysis results of the emotion engine. It will also make specific suggestions such as "putting storage boxes under the bed" as a space-saving storage method. In this way, users can obtain interior and storage solutions optimized for their lifestyle.

[1260] Example 2

[1261] 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."

[1262] Conventional interior design selection systems required users to manually select products, making them inefficient and particularly difficult to customize based on user emotions. Furthermore, because product suggestions could not reflect individual needs or emotions, improving user satisfaction was a difficult challenge.

[1263] The identification process by the identification 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 means for analyzing a received image and generating product information based on the characteristics of the room, means for recognizing the user's emotion using an emotion engine and adjusting the product information based on the emotion, and means for transmitting the generated product information to the terminal. This makes it possible to propose products that correspond to the user's emotion and individual needs.

[1264] "User" refers to the person who uses the system. Generally, this refers to an individual who wants to select interior products and receive suggestions suitable for their living space.

[1265] "Terminal" refers to a device operated by a user, such as a smartphone, tablet, or computer.

[1266] A "server" is a computer system that performs the central processing of the system, and is responsible for important processes such as analyzing received data and generating proposals.

[1267] "Images" refers to photos and drawings taken by the user and input through the device. They are primarily used to analyze the interior of a room.

[1268] "Product Information" refers to data about interior products generated by the server, including details such as images, names, colors, and prices.

[1269] An "emotion engine" refers to a combination of software and hardware that recognizes a user's emotions and adjusts the system's output based on those emotions.

[1270] "Chat-style" refers to a conversation-like interaction between the user and the system, where the user inputs specific requests and feedback, and the system responds accordingly.

[1271] "Discussion" refers to the process in which the user and the server interact to discuss detailed requests and conditions. It proceeds like an everyday conversation, digging deeper into the user's needs.

[1272] This system allows users to easily select the best interior products for their living space and realize effective storage solutions. It also has a function that adjusts the recommendations by combining it with an emotion engine that recognizes the user's emotions. This system consists of four main components: the user, the terminal, the server, and the emotion engine.

[1273] First, users use a device such as a smartphone, tablet, or computer to input a photo of their room into the system. To input photos, they use a dedicated app or a web browser. The user selects a photo and clicks the upload button.

[1274] Next, the device sends the selected photo to the cloud server. The device issues an HTTP POST request to upload the photo data to the server. After the photo is sent to the server, the server receives it and stores it in its storage.

[1275] The server analyzes the received photos using an image analysis algorithm. Image analysis uses techniques such as object recognition and color extraction to extract features such as the room's atmosphere and furniture arrangement. After analyzing the features, the server generates optimal interior product information from a product database.

[1276] Next, the server sends the generated product information to the terminal. The proposal content is generated in a data format such as JSON and sent as an HTTP response. The terminal displays the received product information to the user. The displayed content includes the product image, name, color, price, etc.

[1277] The device also incorporates an emotion engine that recognizes emotions from facial expressions and input data as the user operates the device. The server uses the emotion engine to analyze the user's emotions and adjusts product information and suggestions based on the analysis results. For example, if the user has a dissatisfied expression, the server can update the suggestions to offer more desirable options.

[1278] The user then engages in a chat-style discussion with the server via their device. During this discussion, the user provides detailed information about their needs and preferences. For example, they can express specific requests such as "I prefer a more natural look" or "I want storage space under the bed." The emotion engine continues to monitor the user's emotions in real time and reflects them in the chat content.

[1279] Based on the information collected through chat-style discussions, the server generates lifestyle recommendations that best suit the user's needs. These recommendations include colors and designs that match the room's atmosphere, temporary storage ideas, and how to declutter unnecessary items. The emotion engine tracks the user's emotional changes and retains data on long-term emotional trends, which can then be used to provide further customized recommendations.

[1280] The server can also suggest space-saving storage solutions and ways to get rid of unnecessary items, such as placing storage boxes under the bed or recycling items that haven't been used for over a year. The emotion engine also considers how likely the suggestions are to be accepted, providing information in a way that satisfies the user.

[1281] Finally, the user can check the final proposal on the terminal and, if necessary, purchase the proposed product. The terminal also provides a purchase link to the online store, allowing the user to easily purchase the product.

[1282] For example, if a user is suggested blue cushions and white shelves, and then requests more natural items, the server will again suggest natural-colored cushions and wooden shelves. It will also make specific suggestions, such as "putting storage boxes under the bed" as a space-saving storage solution. In this way, users can find interior and storage solutions optimized for their lifestyle.

[1283] Example prompts to input to a generative AI model:

[1284] 1. "Analyze photos of user A's room and suggest suitable interior products."

[1285] 2. "Analyze the room layout from the images uploaded by user B and suggest the optimal furniture."

[1286] 3. "Please identify User C's emotions and provide products that match their preferences."

[1287] The above is an embodiment of the present invention, and this system allows users to quickly select the best interior for their living space, effectively organize their room, and create a comfortable living environment. Furthermore, the use of an emotion engine makes it possible to make more personalized suggestions based on the user's emotions, thereby improving satisfaction.

[1288] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1289] Step 1:

[1290] The user takes a photo of the room and inputs it into the system via a terminal.

[1291] Specifically, a user uses a smartphone, tablet, computer, or other device to select a photo using a dedicated app or web browser and clicks the upload button. The input data is an image file of the room, and the output is a state in which this image is ready to be sent to the server.

[1292] Step 2:

[1293] The terminal transmits the input image to the server.

[1294] Specifically, the device issues an HTTP POST request to upload the selected photo data to the server. The input is an image file of the room, and the output is the state in which the photo data has been sent to the server.

[1295] Step 3:

[1296] The server analyzes the received images and generates product information based on the characteristics of the room.

[1297] Specifically, the server stores the received image data in storage and analyzes it using an image analysis algorithm (TensorFlow, OpenCV, etc.). Image analysis involves extracting features such as object recognition, color extraction, and furniture placement. The input is the uploaded image data of the room, and the output is the analyzed feature data.

[1298] Step 4:

[1299] The server searches for the most suitable interior product from a product database and generates product information.

[1300] Specifically, the system uses the analyzed feature data to issue a query to a product database to obtain information on interior products that match the characteristics of the room. Based on the obtained product information, the system selects and generates optimal interior product candidates. The input is the feature data, and the output is the selected interior product information.

[1301] Step 5:

[1302] The server transmits the generated product information to the terminal.

[1303] Specifically, product information is generated in a data format such as JSON and sent to the terminal as an HTTP response. The input is the generated interior product information, and the output is the transmitted product information data.

[1304] Step 6:

[1305] The terminal displays the product information to the user.

[1306] Specifically, the device parses the received product information and displays detailed information such as product images, names, colors, and prices on the app screen for the user. The user can view this information. The input is the product information data sent from the server, and the output is the displayed product information.

[1307] Step 7:

[1308] The emotion engine recognizes the user's emotions.

[1309] Specifically, an emotion engine (e.g., Smile Detector API) uses the device's front camera to analyze the user's facial expressions in real time and infer their emotions. The input is the user's facial expression data, and the output is the inferred emotion data.

[1310] Step 8:

[1311] The server adjusts the suggestions based on the emotion.

[1312] Specifically, the system receives emotional data obtained from the emotion engine and updates the content of the suggestions in real time. For example, if the user looks dissatisfied, the server modifies the suggestions to select more appropriate products. The input is emotional data, and the output is adjusted product suggestion information.

[1313] Step 9:

[1314] The user and the server hold discussions in chat format.

[1315] Specifically, the user uses the chatbot function on their device to input their specific requests and preferences, and the server provides information in response. The user expresses specific requests such as "I like a natural feel" or "I want storage under the bed." The input is the user's desired data, and the output is additional information generated by the server.

[1316] Step 10:

[1317] The server generates a final proposal and sends it to the device.

[1318] Specifically, the system combines the information obtained from the discussion with the user's emotional data to generate a final proposal. This proposal includes color and design that matches the room's atmosphere, storage ideas, and more. The generated proposal is sent to the device in JSON format or similar. The input is the user's requests and emotional data, and the output is the final proposal data.

[1319] Step 11:

[1320] The terminal displays the final offer and the user completes the purchase.

[1321] Specifically, the terminal displays the final offer received from the server to the user and provides a purchase link to the online store. The user can click the link to complete the purchase procedure on the store page. The input is the final offer data, and the output is the displayed final offer and the purchase link.

[1322] (Application example 2)

[1323] 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."

[1324] The objective of the present invention is to solve the problem that conventional methods require time and effort when users select the best interior products for their living space. It is also necessary to solve the problem that systems that can adjust product suggestions taking into account the user's feelings when they are dissatisfied with the suggested products are insufficient. It is important to enable users to receive more personalized suggestions in real time when selecting interior products in a physical store.

[1325] 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.

[1326] In this invention, the server includes means for inputting an image taken by a user, means for the terminal to transmit the input image to the server, means for the server to analyze the received image and generate product information that matches the ambiance of the room, means for the terminal to transmit the generated product information to the user, means for the terminal to display the product information to the user, means for using smart glasses to display the product information in the user's field of view using augmented reality, and means for recognizing the user's emotions and adjusting the product information based on the emotions. This allows the user to select appropriate interior products in a short time and further receive personalized suggestions based on their emotions.

[1327] "User" refers to a person who uses the system to select the interior products that best suit their living space.

[1328] A "terminal" is a device operated by a user, and includes smart glasses, smartphones, tablets, etc.

[1329] A "server" is a computer system that runs on the cloud and performs image analysis and generates and provides product information.

[1330] "Image analysis" refers to the technology in which the server analyzes images received and extracts features such as the atmosphere of the room and the arrangement of furniture.

[1331] "Product information" refers to data such as images, names, colors, and prices related to interior products.

[1332] "Smart glasses" are wearable devices with augmented reality (AR) capabilities that display information in the user's field of vision.

[1333] "Augmented reality" refers to a technology that displays computer-generated information overlaid on real-world scenery.

[1334] "Emotion analysis" refers to the technology of recognizing and analyzing a user's emotions from their facial expressions and input data.

[1335] "Chat format" refers to a format in which a user and a server have a text-based conversation.

[1336] "Lifestyle suggestions" refers to the act of proposing interior products, storage methods, colors, and designs that are best suited to the user's living space.

[1337] This invention is a system that allows users to efficiently select the best interior products for their living space and find effective storage methods. The system consists of four main components: the user, the terminal, the server, and the emotion engine.

[1338] First, the user takes a photo of their room using a device such as smart glasses, a smartphone, or a tablet, and inputs it into the system. To input the photo, a dedicated application or a web browser is used. After the user takes a photo, they click the upload button, which sends the photo to the cloud server. The device then issues an HTTP POST request, uploading the photo data to the server.

[1339] The server then analyzes the received photos. It uses ImageAnalyzer to analyze the images and extract features such as the room's atmosphere and furniture arrangement. Specifically, it uses algorithms for object recognition and color extraction. Based on the extracted features, it then generates optimal interior product information from a product database.

[1340] The server sends the generated product information to the terminal. The suggestions are generated in a data format such as JSON and sent as an HTTP response. The terminal displays the received product information to the user. Product information includes product images, names, colors, prices, etc. When using smart glasses, the suggested products are displayed in the user's field of vision using augmented reality (AR) technology.

[1341] The device also incorporates an emotion engine that recognizes emotions from facial expressions and input data as the user operates the device. The server uses the emotion engine to analyze the user's emotions and adjusts product information and suggestions based on the analysis results. For example, if the user looks dissatisfied, the server can update the suggestions to offer more desirable options.

[1342] Users can discuss their needs and preferences in a chat format with the server via their devices. For example, they can express specific requests such as "I prefer a more natural look" or "I want storage space under the bed." The emotion engine monitors the user's emotions in real time and reflects them in the chat content.

[1343] The server uses information collected from chat-style discussions to generate lifestyle recommendations tailored to the user's needs, including color and design suggestions that match the room's atmosphere, temporary storage ideas, and how to organize unnecessary items. The emotion engine tracks the user's emotional changes and retains data on long-term emotional trends, allowing it to provide further customized recommendations.

[1344] The server can also suggest space-saving storage solutions and ways to organize unnecessary items, such as placing storage boxes under the bed or recycling items that haven't been used for over a year. The emotion engine adjusts the suggestions to make them more likely to be accepted by the user.

[1345] The user can review the final proposal on the device and, if necessary, purchase the proposed product. The device also provides a purchase link to the online store, allowing the user to easily purchase the product.

[1346] For example, if a user is suggested blue cushions and a white shelf, and then requests more natural items, the server will again suggest natural-colored cushions and wooden shelves. It will also make specific suggestions, such as "putting storage boxes under the bed" as a space-saving storage method. In this way, users can find interior and storage solutions optimized for their lifestyle.

[1347] Examples of prompts to be input into the generative AI model are, "Please enter the interior product category the user is looking for. For example, 'furniture in natural tones' or 'elegant storage solutions'." and "furniture in natural tones."

[1348] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1349] Step 1:

[1350] A user takes a photo of their room using smart glasses, a smartphone, or a tablet, which provides image data of the user's living space. The input data is the image of the room captured by the end device. This image is used for further processing.

[1351] Step 2:

[1352] The device sends the captured image data to the server. The device issues an HTTP POST request to send the photo data to the cloud server. The input data is the image captured by the user, and the output data is the image data stored in the server's storage. Through this transmission process, the server receives the photo of the user's room.

[1353] Step 3:

[1354] The server analyzes the received images. It uses ImageAnalyzer to analyze the images and extracts the features of the room using algorithms such as object recognition and color extraction. The input data is the image data stored on the server, and the output data is the room features obtained from the analysis. Specifically, the server's image analysis algorithm processes the image data.

[1355] Step 4:

[1356] The server generates optimal interior product information from a product database based on the extracted features. The generated product information includes product images, names, colors, and prices. The input data are the analyzed features, and the output data is the generated product information. The server searches the product database and collects and generates appropriate product information.

[1357] Step 5:

[1358] The server sends the generated product information to the terminal. The proposal content is sent in JSON format, and the terminal receives it as an HTTP response. The input data is the generated product information, and the output data is the product information sent to the terminal.

[1359] Step 6:

[1360] The terminal displays the received product information to the user. When smart glasses are used, the product information is displayed in the user's field of view using augmented reality (AR) technology. The input data is the product information received from the server, and the output data is the product information displayed in the user's field of view. The user can visually confirm the presented information.

[1361] Step 7:

[1362] The emotion engine recognizes and analyzes emotions from the user's facial expressions and input data. The emotion engine collects and analyzes the user's emotional data in real time. The input data is the user's facial expressions and input actions, and the output data is the analyzed emotional information.

[1363] Step 8:

[1364] The server adjusts the product information based on the analyzed emotion information. For example, if the user has a dissatisfied expression, it updates the suggestions to provide more favorable options. The input data is the emotion information obtained from the emotion engine, and the output data is the adjusted product information. Specifically, the server reanalyzes the emotion data and product data to generate new suggestions.

[1365] Step 9:

[1366] Users can discuss with the server in chat format through their terminals. They can provide detailed information about their needs, interests, and preferences. Input data is the user's text input, and output data is the server's response. The chat interface conducts the conversation in real time.

[1367] Step 10:

[1368] The server generates optimal lifestyle suggestions based on information collected from chat-style discussions. Suggestions include colors and designs that match the room's atmosphere, temporary storage ideas, and methods for organizing unnecessary items. The input data is the chat history and information provided by the user, and the output data is the newly generated suggestions.

[1369] An example of a prompt sentence to input to the generative AI model is:

[1370] "Enter the interior product category the user is looking for, for example, 'furniture in natural tones' or 'elegant storage solutions'." or 'furniture in natural tones'.

[1371] 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.

[1372] 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.

[1373] 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.

[1374] 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.

[1375] 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.

[1376] 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.

[1377] 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).

[1378] 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.

[1379] 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."

[1380] 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.

[1381] 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).

[1382] 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.

[1383] 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.

[1384] 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.

[1385] 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.

[1386] 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.

[1387] 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.

[1388] 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.

[1389] 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.

[1390] 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.

[1391] 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.

[1392] The following is further disclosed regarding the above embodiment.

[1393] (Claim 1)

[1394] A means for inputting an image taken by a user;

[1395] A means for transmitting the input image from the terminal to the server;

[1396] A means for analyzing the image received by the server and generating product information that matches the atmosphere of the room;

[1397] means for transmitting the product information generated by the server to the terminal;

[1398] a means for the terminal to display product information to a user;

[1399] A system including:

[1400] (Claim 2)

[1401] 2. The system according to claim 1, further comprising means for collecting detailed needs through a discussion in a chat format between the user and the server.

[1402] (Claim 3)

[1403] 10. The system according to claim 1, further comprising means for making an optimum lifestyle suggestion based on the information collected by the server from the user.

[1404] (Claim 4)

[1405] The system according to claim 1, further comprising means for the server to suggest space-saving storage methods and methods for decluttering unnecessary items.

[1406] (Claim 5)

[1407] 2. The system according to claim 1, further comprising means for displaying the proposals received from the server to the user, and for the user to carry out a procedure for purchasing the proposed products as necessary.

[1408] "Example 1"

[1409] (Claim 1)

[1410] A means for inputting an image taken by a user;

[1411] A means for transmitting the input image from the terminal to the server;

[1412] A means for analyzing the image received by the server, extracting features of the room using object recognition and color extraction techniques, and generating product information based on the features;

[1413] means for transmitting the product information generated by the server to the terminal;

[1414] a means for the terminal to display product information to a user;

[1415] A system including:

[1416] (Claim 2)

[1417] 2. The system according to claim 1, further comprising means for collecting detailed needs through a discussion in a chat format between the user and the server.

[1418] (Claim 3)

[1419] 10. The system according to claim 1, further comprising means for making an optimum lifestyle suggestion based on the information collected by the server from the user.

[1420] "Application Example 1"

[1421] (Claim 1)

[1422] A means for inputting an image taken by a user;

[1423] A means for transmitting the input image from the terminal to the server;

[1424] A means for analyzing the image received by the server and generating product information that matches the atmosphere of the room;

[1425] means for transmitting the product information generated by the server to the terminal;

[1426] a means for the terminal to display product information to a user;

[1427] A terminal or a head-mounted display displays the generated product information in a virtual environment, allowing the user to check and customize the information in real time;

[1428] A system including:

[1429] (Claim 2)

[1430] 2. The system according to claim 1, further comprising means for collecting detailed needs through a discussion in a chat format between the user and the server.

[1431] (Claim 3)

[1432] 10. The system according to claim 1, further comprising means for making an optimum lifestyle suggestion based on the information collected by the server from the user.

[1433] "Example 2: Combining Emotion Engines"

[1434] (Claim 1)

[1435] A means for inputting an image taken by a user;

[1436] A means for transmitting the input image from the terminal to the server;

[1437] A means for analyzing the image received by the server and generating product information based on the characteristics of the room;

[1438] a means for recognizing a user's emotion using an emotion engine and adjusting product information based on the emotion;

[1439] means for transmitting the product information generated by the server to the terminal;

[1440] a means for the terminal to display product information to a user;

[1441] A system including:

[1442] (Claim 2)

[1443] 2. The system according to claim 1, further comprising means for collecting detailed requests through a chat-style interaction between the user and the server.

[1444] (Claim 3)

[1445] 2. The system according to claim 1, further comprising means for suggesting an optimal living environment based on the information collected by the server from the user.

[1446] "Application example 2 when combining emotion engines"

[1447] New Claims

[1448] (Claim 1)

[1449] A means for inputting an image taken by a user;

[1450] A means for transmitting the input image from the terminal to the server;

[1451] A means for analyzing the image received by the server and generating product information that matches the atmosphere of the room;

[1452] means for transmitting the product information generated by the server to the terminal;

[1453] a means for the terminal to display product information to a user;

[1454] a means for displaying product information in augmented reality in the user's field of view using smart glasses;

[1455] means for recognizing a user's emotion and adjusting product information based on the emotion;

[1456] A system including:

[1457] (Claim 2)

[1458] 2. The system according to claim 1, further comprising means for collecting detailed needs through a discussion in a chat format between the user and the server.

[1459] (Claim 3)

[1460] 10. The system according to claim 1, further comprising means for making an optimum lifestyle suggestion based on the information collected by the server from the user. [Explanation of symbols]

[1461] 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 inputting an image taken by a user; A means for transmitting the input image from the terminal to the server; A means for analyzing the image received by the server and generating product information that matches the atmosphere of the room; means for transmitting the product information generated by the server to the terminal; a means for the terminal to display product information to a user; A system including:

2. 2. The system according to claim 1, further comprising means for collecting detailed needs through a chat-style discussion between the user and the server.

3. 2. The system according to claim 1, further comprising means for providing an optimum lifestyle suggestion based on the information collected by the server from the user.

4. The system according to claim 1, further comprising means for the server to suggest space-saving storage methods and methods for decluttering unnecessary items.

5. 2. The system according to claim 1, further comprising means for displaying the proposals received from the server to the user, and for the user to carry out a procedure for purchasing the proposed products as necessary.

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