System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to upload an image of an article of clothing; means for transmitting the image to a server; means for the server to analyze the image and extract and store features of the article of clothing in a database; and means for automatically updating a digital closet of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Managing clothing in modern society is complicated, making it difficult for users to find the right outfits and styles. It's also difficult to keep track of the clothing they own, often resulting in duplicate purchases and wasteful spending. Furthermore, with the spread of online shopping, it's difficult to check how new clothes coordinate with their existing wardrobe when purchasing them. Traditional clothing management applications fail to properly reflect a user's body type or preferred style, and their AI-based coordination suggestions are limited. A system that addresses these challenges and helps users efficiently manage their clothing and find their optimal style is needed. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means: A means is provided for users to upload images of clothing and send them from their terminal to a server. A means is provided for the server to analyze the received images, extract the characteristics of the clothing, and store them in a database, thereby automatically updating the user's digital closet. A means is also provided for users to upload images of their body and send them to the server, thereby providing a means for the server to analyze the user's body characteristics and understand their style based on the user's profile. Furthermore, a means is provided for users to register clothing they own or are considering purchasing, and a means is provided for the server to generate optimal outfits based on the registered information and provide the generated outfits to the user. This system allows users to efficiently manage their clothing and receive style suggestions.

[0006] "User" refers to consumers and individuals who use this system.

[0007] "Clothing" refers to fashion items such as clothes and accessories worn by users.

[0008] "Image" means a digital visual of an article of clothing or a photograph of a person's body uploaded by a User.

[0009] "Terminal" refers to an electronic device such as a smartphone, tablet, or PC, which serves as the interface for users to operate the system.

[0010] "Server" refers to a remote computer system for analyzing and storing images and data.

[0011] "Analysis" refers to the process by which the server extracts specific features or information from the image.

[0012] "Features" refers to information such as color, style, and brand extracted from clothing and body photos.

[0013] "Database" refers to a collection of information stored on a server, including clothing items and user profile information.

[0014] A "digital closet" refers to a virtual closet that centrally manages data on the clothing items owned by a user.

[0015] "Physical characteristics" refers to physical characteristics used to suggest styles, such as the user's face shape, body type, and height.

[0016] "Profile" refers to data about an individual user, including the user's physical characteristics, style preferences, etc.

[0017] "Coordination" refers to style suggestions generated by combining multiple clothing items.

[0018] "Online shopping site" refers to a website where clothing can be purchased over the Internet.

[0019] "API integration" refers to an interface for sharing functions and data between different systems.

[0020] "Buy Link" refers to a URL that provides direct access to an online shopping site for the clothing item selected by the User. [Brief explanation of the drawings]

[0021] [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

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] 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).

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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."

[0042] This invention is a system that allows users to efficiently manage their clothing and suggests the most suitable style. This system is realized by users uploading images of their clothing, which the server analyzes. The system also identifies the user's face, body type, and style preferences, and suggests coordination ideas with existing clothing and new clothing to be purchased.

[0043] Overall system configuration

[0044] This system consists of a user, a terminal, and a server. Users operate the system using a terminal such as a smartphone or PC to manage their clothing, register their physical characteristics, simulate styles, and link with online shopping.

[0045] Program processing

[0046] 1. Clothing Image Recognition and Management

[0047] Users take photos of their clothing and upload them to the app via their device. The device then sends the uploaded images to a server, which analyzes them. This analysis uses AI technology to extract characteristics such as the color, pattern, style, and brand of the clothing. The server then stores this information in a database and automatically updates the user's digital closet.

[0048] Example: If a user uploads a photo of a red dress, the server analyzes the image, generates tags for "red," "dress," and "brand Y," and adds them to the user's digital closet.

[0049] 2. Face and body recognition and style preference understanding

[0050] Users upload photos of their face and entire body. The device sends these images to a server, which uses AI models to analyze the user's face shape and body type. This allows the server to understand the user's physical characteristics and update their profile. Users can also provide information about their preferred styles and brands in a questionnaire format. This information is stored on the server and used to suggest styles to the user.

[0051] Example: If a user uploads a full-body photo and replies, "I like casual, light-colored clothing," the server adds information to their profile such as "height 170cm," "slim build," "casual," and "light colors."

[0052] 3. AI Coordination Simulation

[0053] Through the system, users select clothing items they own or plan to purchase. The device sends this selection information to the server, which then uses AI technology to generate the optimal outfit. The generated outfit is presented to the user through the app screen.

[0054] Example: If a user selects "white shirt," "blue jeans," and "sneakers," the server will suggest a combination of "white shirt + blue jeans + sneakers" and display it to the user.

[0055] 4. AI Fashion Consultation Room

[0056] The user enters a fashion-related question into the chat box. The device sends this input to the server, which analyzes the question using natural language processing (NLP). The server generates an appropriate answer based on the analysis results and provides it to the user via the device.

[0057] Example: A user asks, "What outerwear is recommended for fall?" and the server responds, "Casual jackets and hoodies are suitable for fall."

[0058] 5. Online shopping integration

[0059] The user selects the clothing item they are considering purchasing within the app. The device sends the selected item information to the server, and the server obtains detailed information and a purchase link via the online shopping site's API. The user can then check the item within the app and access the purchase page.

[0060] Example: When a user wants to buy a new coat and enters that information into the simulation, the server suggests outfits for the new coat and a scarf they already own, and also displays a link to an online shopping site to purchase the coat.

[0061] The above is a specific embodiment of the system of the present invention, which allows users to efficiently manage their clothing and receive style suggestions.

[0062] The processing flow will be explained below.

[0063] Clothing image recognition and management

[0064] Step 1:

[0065] The user takes a photo of the clothing item they own using the camera on their smartphone or PC.

[0066] Step 2:

[0067] The device temporarily stores the captured photo and asks the user for confirmation through the user interface.

[0068] Step 3:

[0069] After the user confirms, the device sends the photo to the server.

[0070] Step 4:

[0071] The server then feeds the received images into an analytical AI model, which extracts features such as color, pattern, style, and brand.

[0072] Step 5:

[0073] The server stores the extracted features in a database and generates a corresponding item ID.

[0074] Step 6:

[0075] The server updates the user's digital closet to reflect the added item information.

[0076] Step 7:

[0077] Users can see new items added to their digital closet through the app's UI.

[0078] Face and body recognition and style preference identification

[0079] Step 1:

[0080] The user uses the app to take a photo of their face and a full-body photo.

[0081] Step 2:

[0082] The device temporarily stores the captured photo and asks the user for confirmation through the user interface.

[0083] Step 3:

[0084] After the user confirms, the device sends the photo to the server.

[0085] Step 4:

[0086] The images received by the server are input into an analytical AI model to extract the user's facial and body features.

[0087] Step 5:

[0088] The server stores the extracted features in a database and updates the user's profile information.

[0089] Step 6:

[0090] Users provide information about their preferred fashion styles, brands, colors, etc. in a survey format within the app.

[0091] Step 7:

[0092] The server stores the user's style tendency in a database based on the provided style information.

[0093] AI Coordination Simulation

[0094] Step 1:

[0095] The user opens the simulation screen and selects clothing items they own or are considering purchasing.

[0096] Step 2:

[0097] The terminal temporarily stores the selected item information and asks the user for confirmation through the user interface.

[0098] Step 3:

[0099] After the user confirms, the terminal transmits the item information to the server.

[0100] Step 4:

[0101] Based on the information received by the server, an AI model for simulation is executed.

[0102] Step 5:

[0103] The server generates the optimal coordinates and stores the coordinate results in a database.

[0104] Step 6:

[0105] The server transmits the generated coordination results to the terminal.

[0106] Step 7:

[0107] Through the app's UI, users can check the outfits suggested by the AI ​​and make evaluations or changes.

[0108] AI Fashion Consultation Room

[0109] Step 1:

[0110] Users can enter their fashion-related questions or concerns into the chat box within the app.

[0111] Step 2:

[0112] The terminal sends the entered text to the server.

[0113] Step 3:

[0114] The server inputs the received text into a natural language processing (NLP) model for analysis.

[0115] Step 4:

[0116] The server understands the meaning of the question and generates an appropriate answer.

[0117] Step 5:

[0118] The server generates a response and sends it to the terminal.

[0119] Step 6:

[0120] Users can review the answers and ask further questions or provide feedback as needed through the app's UI.

[0121] Online shopping integration

[0122] Step 1:

[0123] The user selects the item they are considering purchasing through the app.

[0124] Step 2:

[0125] The terminal temporarily stores the selected item information and asks the user for confirmation through the user interface.

[0126] Step 3:

[0127] After the user confirms, the terminal transmits the item information to the server.

[0128] Step 4:

[0129] The server retrieves detailed information, price, and stock status of the selected item via the online shopping site's API.

[0130] Step 5:

[0131] Based on the information received by the server, it runs a simulation AI model and generates an outfit that combines the items the customer is considering purchasing with clothing they already own.

[0132] Step 6:

[0133] The server sends the generated coordination results and a purchase link to the terminal.

[0134] Step 7:

[0135] Through the app's UI, users can check the AI's suggested outfits and purchase links, and proceed with the purchase process if necessary.

[0136] Example 1

[0137] 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."

[0138] Managing clothing and suggesting outfits takes a lot of time and effort, placing a significant burden on users. Furthermore, there is a lack of systems that can suggest optimal outfits that reflect a user's individual physical characteristics and style preferences. Furthermore, there is a lack of efficient methods for providing detailed information and links to purchase items that users are considering purchasing. There is a need for a system that can solve these issues and enable users to efficiently manage clothing and receive style suggestions.

[0139] 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.

[0140] In this invention, the server includes: means for a user to upload images of clothing items; means for transmitting the images to the server; means for the server to analyze the images, extract clothing features, and store the extracted features in a database; means for automatically updating the user's digital closet; means for a user to upload body images; means for transmitting the body images to the server; means for the server to analyze the body images and identify the user's body features and style preferences; means for transmitting information about clothing items selected by the user to the server; means for the server to generate optimal outfits using AI technology; means for providing the generated outfits to the user; means for the user to input fashion-related questions into a chat box; means for transmitting the input questions to the server; means for the server to generate appropriate answers using natural language processing technology and provide them to the user; means for the user to select clothing items for online shopping; means for the server to obtain detailed information and purchase links from an API of an online shopping site; and means for providing the purchase links to the user. This allows users to efficiently manage their clothing and receive suggestions for optimal outfits based on their individual body features and style preferences. In addition, by linking with online shopping, it will be possible to efficiently obtain detailed information and purchase links for items being considered for purchase.

[0141] "User" refers to an individual who uses the system to manage clothing items and receive coordination suggestions.

[0142] "Means for uploading images of clothing items" refers to a function that allows users to import photos of clothing items into the system.

[0143] "Server" refers to the central system that receives and analyzes data sent by users, and processes and manages various information.

[0144] "Means for transmitting images to a server" refers to a communication function for transferring image data captured by a user to a server.

[0145] "Analyzing an image" refers to the process by which the server extracts detailed information from the image it receives.

[0146] "Database" refers to a digital repository for storing and managing extracted clothing characteristics.

[0147] "User's digital closet" refers to a function that centrally manages information about the clothing items owned by the user in digital format.

[0148] "Means for uploading body images" refers to a function that allows users to upload photos of their bodies into the system.

[0149] "Physical characteristics" refers to the user's own physical characteristics, such as the user's body shape and facial shape.

[0150] "Style preferences" refer to the fashion trends and brands that a user prefers.

[0151] "Means for transmitting clothing information" refers to a communication function for transferring data on clothing items selected by the user to the server.

[0152] "AI technology" refers to technology that uses artificial intelligence to analyze data and generate coordination.

[0153] "Means for generating coordination" refers to a function that uses AI technology to suggest clothing combinations.

[0154] "Means for providing to the user" refers to a function for displaying the generated coordination and answers on the user's terminal.

[0155] "Chat box" refers to an interface that allows users to enter questions and send them to the server.

[0156] "Natural language processing technology" refers to artificial intelligence technology that enables a server to understand input text and generate appropriate responses.

[0157] "Means for selecting clothing items for online shopping" refers to a function that allows a user to select clothing items within the system that they are considering purchasing.

[0158] "API" refers to an interface for exchanging data between different software programs.

[0159] "Detailed information" refers to specific information about the purchase, such as the characteristics, price, and availability of the clothing item.

[0160] "Purchase Link" refers to the access URL to an online shopping site for purchasing the clothing item selected by the user.

[0161] The present invention relates to a system that allows users to efficiently manage their clothing and suggests optimal styles. This system allows users to upload images of their clothing and body, and analyzes them to optimize the user's style.

[0162] Overall system configuration

[0163] This system consists of a user, a device, and a server. Users operate the system using devices such as smartphones or PCs. Major operations include managing clothing items, registering physical characteristics, simulating styles, and linking with online shopping.

[0164] Clothing image recognition and management

[0165] Users first take a photo of their clothing and upload it to the app via their device. The device then sends the image to a server, which analyzes it. This analysis process uses AI technology to extract characteristics from the image, such as the color, pattern, style, and brand of the clothing. Once the analysis is complete, the server stores this information in a database and automatically updates the user's digital closet.

[0166] Example: If a user uploads a photo of a red dress, the server analyzes the image and generates tags for "red," "dress," and "brand X," which are added to the user's digital closet.

[0167] Face and body recognition and style preference identification

[0168] Next, users upload photos of their face and body. The device sends these images to a server, which uses AI models to analyze the images and detect the user's face shape and body type. The information obtained by the server is reflected in the user's profile and used to suggest style recommendations. Users can also answer a questionnaire about their preferred styles and brands, and this information is also stored on the server.

[0169] Example: If a user uploads a full-body photo and answers a survey saying, "I like casual, light-colored clothing," the server adds information such as "height 170cm," "slim build," "casual," and "light colors" to the profile.

[0170] AI Coordination Simulation

[0171] Users select clothing items they already own or plan to purchase and run a simulation. Information about the selected items is sent from the device to the server, which then uses AI technology to generate the optimal outfit. The generated outfit is then presented to the user on the app screen.

[0172] Example: If a user selects "white shirt," "blue jeans," and "sneakers," the server will suggest a coordination of "white shirt + blue jeans + sneakers" and display it on the user's device.

[0173] AI Fashion Consultation Room

[0174] Users enter fashion-related questions into the chat box and send them from their devices to the server, which uses natural language processing technology to analyze the questions and generate appropriate answers, which are then provided to the user's devices.

[0175] Example: When a user types a question into a chat box such as "What outerwear is recommended for autumn?", the server responds with "Casual jackets and hoodies are suitable for autumn" and displays it to the user.

[0176] Online shopping integration

[0177] The user selects the clothing item they are considering purchasing within the app, and their device sends that information to the server. The server then obtains detailed information and a purchase link via the online shopping site's API and provides it to the user. The user can then check the details within the app and access the purchase page.

[0178] Example: When a user selects a new coat to purchase, the server suggests coordinating the new coat with a scarf they already own and displays a link to purchase the item.

[0179] This system allows users to efficiently manage their clothing and receive style suggestions optimized for their physical characteristics and preferences. The online shopping function also allows for a smooth purchasing process.

[0180] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0181] Step 1: Take and upload your clothing image

[0182] 1. The user takes a photo of the clothing item using a smartphone or PC.

[0183] 2. The device displays a UI (user interface) for uploading the captured image to the app.

[0184] 3. When the user presses the upload button, the device sends the image to the server.

[0185] Specific operation: A user takes a photo of a red dress with their smartphone and taps the "upload" button in the app. The device then sends the image to the server.

[0186] Input: Clothing image

[0187] Output: Notification of completion of transmission to the server

[0188] Step 2: Image analysis and database update

[0189] 1. The server analyzes the images received using artificial intelligence (AI) technology.

[0190] 2. The server extracts features such as color, pattern, style, and brand of the clothing from the image.

[0191] 3. The server stores the extracted feature information in a database and automatically updates the user's digital closet.

[0192] What it does: The server analyzes the image, creates tags such as "red," "dress," and "Brand X," and adds these tags to the database.

[0193] Input: User-uploaded clothing images

[0194] Output: Analyzed clothing feature information (color, pattern, style, brand)

[0195] Step 3: Face and body recognition and profile update

[0196] 1. The user takes a photo of their face or whole body and uploads it via their device.

[0197] 2. The server uses an AI model to analyze the face shape and body type based on the images received.

[0198] 3. The server updates the user's profile based on the analysis results. It also collects information about the user's preferred styles and brands through a questionnaire and stores that information on the server.

[0199] Specific operation: The server performs analysis and adds characteristics such as the user's height of 170 cm and slim build to the profile. If the user answers a questionnaire saying that they like casual, light-colored clothing, this information is also added to the profile.

[0200] Input: User's face photo, full-body photo, survey results

[0201] Output: Updated user profile

[0202] Step 4: AI Coordination Simulation

[0203] 1. The user selects clothing items they own or new items they plan to purchase within the app.

[0204] 2. The device sends the selected item information to the server.

[0205] 3. The server uses AI technology to generate the optimal coordination and sends the results to the device.

[0206] 4. The terminal displays the generated coordinates on the screen to the user.

[0207] Specific operation: The user selects "white shirt," "blue jeans," and "sneakers," and taps the simulation button. The server generates an outfit called "white shirt + blue jeans + sneakers" and displays it on the user's device.

[0208] Input: Clothing information selected by the user

[0209] Output: Generated outfit suggestions

[0210] Step 5: AI Fashion Consultation

[0211] 1. The user types a fashion-related question into the chat box within the app.

[0212] 2. The device sends the entered question to the server.

[0213] 3. The server uses natural language processing (NLP) techniques to analyze the question and generate an appropriate answer.

[0214] 4. The server generates a response and sends it to the terminal, which displays it to the user.

[0215] Specific operation: The user enters a question in the chat box, "What outerwear is recommended for autumn?" and taps the send button. The server analyzes the question and replies, "A casual jacket or hoodie is suitable for autumn."

[0216] Input: User fashion question

[0217] Output: The generated answer

[0218] Step 6: Online shopping integration

[0219] 1. The user selects the clothing item they are considering purchasing within the app.

[0220] 2. The device sends the selected item information to the server.

[0221] 3. The server retrieves details and a purchase link via the online shopping site's API.

[0222] 4. The server sends the retrieved details and purchase link to the device, which displays them to the user.

[0223] Specific operation: When a user tries to purchase a "new coat" and selects that item and enters it into the simulation, the server proposes a combination of "new coat + existing scarf" and a purchase link, which are displayed on the user's device.

[0224] Input: Information about the clothing item selected by the user

[0225] Output: More information and purchase links

[0226] This allows users to efficiently manage their clothing and receive style suggestions, and also makes online shopping smoother.

[0227] (Application example 1)

[0228] 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."

[0229] In order to effectively manage the clothing items users currently own or are considering purchasing, and to provide optimal fashion coordination, users have traditionally had to try on the items in person. However, this is time-consuming and expensive, and often results in items not fitting their preferences or body type. Therefore, there is a need for a system that allows users to easily try on items in a virtual environment.

[0230] 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.

[0231] In this invention, the server includes means for a user to upload an image of a clothing item, means for transmitting the image to the server, means for the server to analyze the image, extract characteristics of the clothing item, and store the characteristics in a database, means for automatically updating the user's digital closet, means for the user to select a new clothing item and provide a virtual try-on, and means for displaying a purchase link based on the virtual try-on, thereby enabling the user to easily try on the clothing item virtually from home, confirm the best style, and purchase it immediately.

[0232] A "user" is an individual who uses this system to manage clothing items and receive coordination suggestions.

[0233] "Clothing images" are images that users take of clothing they own or are considering purchasing with a smartphone or other device and upload to the app.

[0234] A "server" is a computer system that analyzes image data sent from users and manages various databases.

[0235] The "database" is a storage device that stores the characteristics of clothing analyzed by the server and the user's physical information.

[0236] A "digital closet" is an electronic closet that manages a digital list of clothing items owned by a user and is provided to the user through an application.

[0237] A "new clothing item" is a clothing item that the user does not yet own and is considering purchasing.

[0238] "Virtual try-on" is a function that virtually simulates the appearance of clothing based on the user's physical data and provides it to the user.

[0239] A "purchase link" is a link that allows users to purchase clothing items they like after virtual try-on directly online.

[0240] "Body images" are images that users take of their face or whole body and upload to the app.

[0241] A "profile" is personal data that includes a user's physical characteristics, preferred style, past purchase history, and so on.

[0242] "Coordination" refers to proposing the optimal fashion style by combining the user's clothing items or clothing items the user is considering purchasing.

[0243] This invention is a system that allows users to efficiently manage their clothing and suggest outfits. The system is designed to allow users to easily upload their clothing and body data using a device such as a smartphone, and to simulate optimal fashion styles via a server.

[0244] Overall system configuration

[0245] This system consists of a user, a terminal, and a server. The user operates the system using the terminal to upload images of clothing, register physical characteristics, try on the clothing virtually, and link it to online shopping.

[0246] Program processing

[0247] 1. Clothing Image Recognition and Management

[0248] Users take photos of their clothing items using a smartphone or other device and upload them to the app.

[0249] The uploaded image is sent from the device to the server.

[0250] The server analyzes the received images using AI technology (e.g., TensorFlow) to extract characteristics such as the color, pattern, style, and brand of the clothing.

[0251] The extracted information is stored in a database (e.g., MongoDB) and automatically updates the user's digital closet.

[0252] 2. Face and body recognition and style preference understanding

[0253] Users take a photo of their face or whole body and upload it to the app.

[0254] The uploaded image is sent from the device to the server.

[0255] The server analyzes these images using AI models (e.g., OpenPose, DeepFace) to extract the user's facial shape and body features.

[0256] Based on the user's profile, the system reflects information about preferred styles and brands.

[0257] 3. AI Coordination Simulation

[0258] Through the system, users select clothing items they already own or items they are considering purchasing.

[0259] The server uses an AI algorithm to generate the optimal outfit based on the selected information.

[0260] The generated outfits are combined with the user's body data as a virtual try-on and presented to the user in real time.

[0261] 4. Virtual try-on

[0262] The selected item is superimposed onto the user's photo and displayed in the app, as if they were actually trying it on.

[0263] Users can use this feature to see if a new style suits them.

[0264] 5. Linking with online shopping

[0265] If you find an item you like, you can access the online shopping site directly from the app and proceed with the purchase.

[0266] The purchase link is generated by the server and provided to the user.

[0267] Specific examples

[0268] If a user wants to coordinate a "blue shirt" they own with a "black jacket" they found in an online shop:

[0269] When a user uploads a photo of these two items to the app, the server analyzes the photo and generates a virtual try-on image that combines the "blue shirt" and "black jacket" based on the user's facial and body data.

[0270] This allows users to easily check whether a new jacket will suit them.

[0271] If you like it, you can purchase the jacket directly from the online shop via the purchase link.

[0272] Prompt Sentence Examples

[0273] "I'd like to virtually try on a new jacket and see if it goes with my blue shirt. How can I do that?"

[0274] "I like the outfit I created and would like to purchase this jacket online. Can you give me a link to purchase it?"

[0275] Through the above steps, the system of the present invention allows users to easily manage clothing items and try on outfits from home, confirm the best style, and then purchase the items.

[0276] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0277] Step 1: Upload an image

[0278] Subject: User

[0279] Users take photos of clothing or their own bodies using a device such as a smartphone and upload them to the app. As input, the user provides image data, and as output, the image files are saved on the device.

[0280] Step 2: Send image

[0281] Subject: Terminal

[0282] The terminal sends the uploaded image to the server. In this process, the terminal sends the image file to the server using the HTTP protocol or similar. The uploaded image data is the input, and the image data arrives at the server as the output.

[0283] Step 3: Image analysis

[0284] Subject: Server

[0285] The server analyzes the received image data using AI technology (e.g., TensorFlow). Image recognition algorithms are used for the analysis to extract features such as the color, pattern, style, and brand of the clothing. The input is the image data, and the output is the extracted feature data.

[0286] Step 4: Save Data

[0287] Subject: Server

[0288] The server stores the extracted feature data in a database (e.g., MongoDB). This process adds a new entry to the database. The input is the feature data, and the output is the updated database.

[0289] Step 5: Update your digital closet

[0290] Subject: Server

[0291] The server updates the user's digital closet based on the new entries in the database. The input is the newly added feature data, and the output is the updated digital closet information.

[0292] Step 6: Body Data Analysis

[0293] Subject: Server

[0294] The server analyzes the face and body images uploaded by the user using an AI model (e.g., OpenPose, DeepFace). The input is image data of the body, and the output is data on the user's physical characteristics.

[0295] Step 7: Update your profile

[0296] Subject: Server

[0297] The server updates the user's profile with the physical feature data and adds preferred style and brand information to the profile. The input is the physical feature data and the user's style information, and the output is an updated user profile.

[0298] Step 8: Item Selection

[0299] Subject: User

[0300] Users select clothing items they own or are considering purchasing through the system. The selected clothing information is provided to the app as input, and the selection information is sent to the server as output.

[0301] Step 9: Coordinate Generation

[0302] Subject: Server

[0303] The server uses an AI algorithm to generate the optimal outfit based on the selection information. The input is the selection information and the user profile, and the output is the generated outfit data.

[0304] Step 10: Generate a virtual try-on

[0305] Subject: Server

[0306] The server generates a virtual try-on image by combining the user's body data and clothing items based on the generated coordinate data. The inputs are coordinate data and body feature data, and the output is a virtual try-on image.

[0307] Step 11: Displaying try-on images

[0308] Subject: Terminal

[0309] The terminal displays the virtual try-on image received from the server to the user. The virtual try-on image data is input, and the image is displayed on the terminal screen as output.

[0310] Step 12: View the purchase link

[0311] Subject: Server

[0312] The server generates a purchase link for the item the user likes during the virtual try-on and sends it to the device. The input is the try-on image and purchase information, and the output is the purchase link.

[0313] Step 13: Online Shopping

[0314] Subject: User

[0315] A user clicks on a purchase link displayed on the device to purchase an item on an online shopping site. The input is the purchase link, and the output is the online shop's purchase page.

[0316] 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.

[0317] The present invention is a system that allows users to efficiently manage their clothing and suggests optimal styles. This system is combined with an emotion engine that recognizes the user's emotions, and adds a function to suggest clothing items based on the user's emotional state.

[0318] Overall system configuration

[0319] This system is composed of a user, a device, a server, and an emotion engine. Users operate the system using devices such as smartphones or PCs to manage their clothing, register their physical characteristics, simulate styles, and link with online shopping. The emotion engine also analyzes the user's facial expressions and behavior to make suggestions based on their emotional state.

[0320] Program processing

[0321] 1. Clothing Image Recognition and Management

[0322] Users take photos of their clothing and upload them to the app via their device. The device then sends the uploaded images to a server, which analyzes them. This analysis uses AI technology to extract characteristics such as the color, pattern, style, and brand of the clothing. The server then stores this information in a database and automatically updates the user's digital closet.

[0323] Example: If a user uploads a photo of a red dress, the server analyzes the image, generates tags for "red," "dress," and "brand Y," and adds them to the user's digital closet.

[0324] 2. Face and body recognition and style preference understanding

[0325] Users upload photos of their face and entire body. The device sends these images to a server, which uses AI models to analyze the user's face shape and body type. This allows the server to understand the user's physical characteristics and update their profile. Users can also provide information about their preferred styles and brands in a questionnaire format. This information is stored on the server and used to suggest styles to the user.

[0326] Example: If a user uploads a full-body photo and replies, "I like casual, light-colored clothing," the server adds information to their profile such as "height 170cm," "slim build," "casual," and "light colors."

[0327] 3. AI Coordination Simulation

[0328] Through the system, users select clothing items they own or plan to purchase. The device sends this selection information to the server, which then uses AI technology to generate the optimal outfit. The generated outfit is presented to the user through the app screen.

[0329] Example: If a user selects "white shirt," "blue jeans," and "sneakers," the server will suggest a combination of "white shirt + blue jeans + sneakers" and display it to the user.

[0330] 4. AI Fashion Consultation Room

[0331] The user enters a fashion-related question into the chat box. The device sends this input to the server, which analyzes the question using natural language processing (NLP). The server generates an appropriate answer based on the analysis results and provides it to the user via the device.

[0332] Example: A user asks, "What outerwear is recommended for fall?" and the server responds, "Casual jackets and hoodies are suitable for fall."

[0333] 5. Online shopping integration

[0334] The user selects the clothing item they are considering purchasing within the app. The device sends the selected item information to the server, and the server obtains detailed information and a purchase link via the online shopping site's API. The user can then check the item within the app and access the purchase page.

[0335] Example: When a user wants to buy a new coat and enters that information into the simulation, the server suggests outfits for the new coat and a scarf they already own, and also displays a link to an online shopping site to purchase the coat.

[0336] 6. Suggestions linked to emotion engine

[0337] When a user uses the system, the emotion engine analyzes the user's facial expressions and voice through the camera and microphone to recognize emotions in real time. The server receives data from the emotion engine and adjusts clothing recommendations based on the user's emotional state. For example, it suggests casual clothes if the user wants to relax, and formal clothes if the user has an important meeting coming up.

[0338] Example: If the emotion engine recognizes that the user is feeling stressed, the server will suggest "relaxing casual wear" and coordinate clothing that will ease the user's mood.

[0339] Overall effect

[0340] This system allows users to efficiently manage their clothing and receive style suggestions that best suit their emotional state. Furthermore, by linking with online shopping, users can smoothly purchase new clothing. The addition of an emotion engine makes it possible to make suggestions based on the user's real-time emotions, providing a more personalized experience.

[0341] The processing flow will be explained below.

[0342] Clothing image recognition and management

[0343] Step 1:

[0344] The user takes a photo of the clothing item they own using the camera on their smartphone or PC.

[0345] Step 2:

[0346] The device temporarily stores the captured photo and asks the user for confirmation through the user interface.

[0347] Step 3:

[0348] After the user reviews and approves the photo, the device sends the photo to the server.

[0349] Step 4:

[0350] The server then feeds the received images into an analytical AI model, which extracts features such as color, pattern, style, and brand.

[0351] Step 5:

[0352] The server stores the extracted features in a database and generates a corresponding item ID.

[0353] Step 6:

[0354] The server updates the user's digital closet to reflect the added item information.

[0355] Step 7:

[0356] Users can see new items added to their digital closet through the app's UI.

[0357] Face and body recognition and style preference identification

[0358] Step 1:

[0359] The user uses the app to take a photo of their face and a full-body photo.

[0360] Step 2:

[0361] The device temporarily stores the captured photo and asks the user for confirmation through the user interface.

[0362] Step 3:

[0363] After the user reviews and approves the photo, the device sends the photo to the server.

[0364] Step 4:

[0365] The images received by the server are input into an analytical AI model to extract the user's facial and body features.

[0366] Step 5:

[0367] The server stores the extracted features in a database and updates the user's profile information.

[0368] Step 6:

[0369] Users provide information about their preferred fashion styles, brands, colors, etc. in a survey format within the app.

[0370] Step 7:

[0371] The server stores the user's style tendency in a database based on the provided style information.

[0372] AI Coordination Simulation

[0373] Step 1:

[0374] The user opens the simulation screen and selects clothing items they own or are considering purchasing.

[0375] Step 2:

[0376] The terminal temporarily stores the selected item information and asks the user for confirmation through the user interface.

[0377] Step 3:

[0378] After the user checks and approves the item information, the terminal sends the information to the server.

[0379] Step 4:

[0380] Based on the information received by the server, an AI model for simulation is executed.

[0381] Step 5:

[0382] The server generates the optimal coordinates and stores the coordinate results in a database.

[0383] Step 6:

[0384] The server transmits the generated coordination results to the terminal.

[0385] Step 7:

[0386] Through the app's UI, users can check the outfits suggested by the AI ​​and make evaluations or changes.

[0387] AI Fashion Consultation Room

[0388] Step 1:

[0389] Users can enter their fashion-related questions or concerns into the chat box within the app.

[0390] Step 2:

[0391] The terminal sends the entered text to the server.

[0392] Step 3:

[0393] The server inputs the received text into a natural language processing (NLP) model for analysis.

[0394] Step 4:

[0395] The server understands the meaning of the question and generates an appropriate answer.

[0396] Step 5:

[0397] The server generates a response and sends it to the terminal.

[0398] Step 6:

[0399] Users can review the answers and ask further questions or provide feedback as needed through the app's UI.

[0400] Online shopping integration

[0401] Step 1:

[0402] The user selects the item they are considering purchasing through the app.

[0403] Step 2:

[0404] The terminal temporarily stores the selected item information and asks the user for confirmation through the user interface.

[0405] Step 3:

[0406] After the user checks and approves the item information, the terminal sends the information to the server.

[0407] Step 4:

[0408] The server retrieves detailed information, price, and stock status of the selected item via the online shopping site's API.

[0409] Step 5:

[0410] Based on the information received by the server, it runs a simulation AI model and generates an outfit that combines the items the customer is considering purchasing with clothing they already own.

[0411] Step 6:

[0412] The server sends the generated coordination results and a purchase link to the terminal.

[0413] Step 7:

[0414] Through the app's UI, users can check the AI's suggested outfits and purchase links, and proceed with the purchase process if necessary.

[0415] Suggestions linked with emotion engine

[0416] Step 1:

[0417] When a user uses the system, the emotion engine analyzes the user's facial expressions and voice through the camera and microphone, recognizing emotions in real time.

[0418] Step 2:

[0419] The server receives the data from the emotion engine and stores the user's emotional state in a database.

[0420] Step 3:

[0421] The server adjusts the suggested clothing styles based on the user's emotional state.

[0422] Step 4:

[0423] The user receives style suggestions from the system, for example, casual attire if the user wants to relax, or formal attire if the user has an important meeting coming up.

[0424] Step 5:

[0425] The user checks clothing suggestions according to emotions and selects or purchases clothing items as needed.

[0426] Example 2

[0427] 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."

[0428] Conventional clothing management systems make it difficult for users to efficiently manage their clothing inventory, particularly lacking the ability to suggest optimal styles based on the user's physical characteristics and emotional state. Furthermore, they lack sufficient integration with online shopping, making it difficult for users to purchase new clothing. Furthermore, they lack adequate support for fashion consultations and style suggestions, which can lead to reduced user satisfaction. There is a need for a system that can solve these problems and provide a more personalized experience.

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

[0430] In this invention, the server includes a means for users to upload images of clothing, a means for users to upload images of their bodies, a means for users to input fashion-related questions, and a means for an emotion recognition engine to analyze the user's facial expressions and voice and recognize their emotional state. This allows users to efficiently manage their clothing and receive optimal style suggestions based on their physical characteristics and emotional state. Furthermore, by linking the system with online shopping, users can easily purchase new clothing and receive fashion advice and style suggestions, thereby improving user satisfaction.

[0431] "Clothing" refers to all fashion items such as clothes and accessories that users wear on a daily basis.

[0432] A "terminal" is a device that allows a user to access and operate the system, and specifically includes a smartphone, personal computer, tablet, etc.

[0433] A "server" refers to a computer system that receives input data from a user, analyzes it, and generates and provides appropriate information.

[0434] "Image" refers to a digital photograph or illustration that visually records an item of clothing, a user's body, or the like.

[0435] A "database" refers to a collection of data that systematically stores analyzed information and is managed so that it can be easily searched and updated.

[0436] The "digital closet" refers to a part of the system that has the function of managing information about clothing owned by a user in digital form and visually displaying it.

[0437] A "profile" refers to data that compiles and records personal information about a user, such as physical characteristics, style preferences, and brand preferences.

[0438] "Coordination" refers to a style or fashion proposal created by combining multiple clothing items.

[0439] "Natural language processing technology" refers to technology that enables computers to understand and generate human language, and includes algorithms and models used to analyze text and understand its meaning.

[0440] An "online shopping site" refers to a type of website where you can browse and purchase products over the Internet.

[0441] "API" refers to a defined interface that allows different software applications to communicate with each other and utilize each other's functionality.

[0442] An "emotion recognition engine" refers to a technology or system that analyzes a user's facial expressions and voice to recognize their emotional state at that time.

[0443] A "generative AI model" refers to an artificial intelligence model that allows a computer to generate new information based on given input data, and is particularly used for natural language generation and image generation.

[0444] A "prompt" is a textual instruction or question that a user enters to obtain information or trigger an action using a generative AI model.

[0445] The present invention is a system that allows users to efficiently manage their clothing and suggests the most suitable style, and by combining it with an emotion engine, it is possible to suggest clothing based on the user's emotional state.

[0446] 1. Overall system configuration

[0447] This system consists of a user, a device, a server, and an emotion engine. Users operate the system using devices such as smartphones or personal computers. Operations include managing clothing items, registering physical characteristics, simulating styles, and linking with online shopping.

[0448] 2. Clothing Management

[0449] Users take photos of their clothing and upload them to the app via their device. The device then sends the uploaded images to a server. The server then uses AI technologies such as TensorFlow and YOLO to analyze the images, extracting characteristics such as the color, pattern, style, and brand of the clothing, and stores them in a database. The user's digital closet is then automatically updated.

[0450] For example, if a user uploads a photo of a red dress, the server will analyze the image, generate tags for "red," "dress," and "brand X," and add them to the database.

[0451] 3. Registering physical features and understanding style

[0452] Users upload photos of their face and body. The device sends these images to a server. The server uses FaceNet and OpenPose to analyze the user's face shape and body type. The analyzed information is recorded in a profile and used to suggest styles to the user. Users also fill out a questionnaire to enter their preferred styles and brands, and this information is also saved in their profile.

[0453] For example, if a user uploads a full-body photo and replies that they like casual, light-colored clothing, the server will add information such as "height 170cm," "slim build," "casual," and "light colors" to their profile.

[0454] 4. AI Coordination Simulation

[0455] The user selects clothing items they own or items they plan to purchase. The device sends this selection information to the server, which then uses a generative AI model (e.g., GPT-4) to generate the optimal outfit. The generated outfit is presented to the user through the app screen.

[0456] As a specific example, if a user selects "white shirt," "blue jeans," and "sneakers," the server will use GPT-4 to suggest a combination of "white shirt + blue jeans + sneakers" and display it in the user's app.

[0457] 5. AI Fashion Consultation Room

[0458] The user enters a fashion-related question into the chat box. The device sends this input to the server. The server uses natural language processing techniques (e.g., BERT or GPT-3) to analyze the question and generate an appropriate answer. The device then displays this answer in the chat box.

[0459] For example, if a user asks, "What outerwear is recommended for autumn?", the server will analyze using BERT and answer, "Casual jackets and hoodies are suitable for autumn."

[0460] 6. Online shopping integration

[0461] The user selects the clothing item they are considering purchasing within the app. The device sends the selected item information to the server, which then uses the API of the online shopping site to obtain detailed information and a purchase link, which it then provides to the user.

[0462] As a concrete example, if a user wants to buy a new coat, the server will suggest a coordination of "new coat + existing scarf" and also display a link to an online shopping site to purchase the coat.

[0463] 7. Suggestions linked to emotion engine

[0464] When a user uses the system, the emotion engine analyzes the user's facial expressions and voice through the camera and microphone. The server receives the data from the emotion engine and adjusts clothing recommendations based on the user's emotional state.

[0465] As a specific example, if the emotion engine recognizes that the user is feeling stressed, the server will suggest "relaxing casual wear" and coordinate clothing that will ease the user's mood.

[0466] Prompt Sentence Examples

[0467] The prompt statements are as follows:

[0468] "Please tell me what outfits would suit my body type. I've uploaded a full-body photo."

[0469] "Please suggest a style that suits your mood today."

[0470] "What are some casual outfit combinations suitable for autumn?"

[0471] The above system configuration allows users to efficiently manage their clothing and enjoy a more personalized fashion experience according to their emotional state.

[0472] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0473] The flow of this system's program processing

[0474] Processing Steps

[0475] Step 1: Take and upload your clothing image

[0476] The user takes a photo of the clothing item they own and uploads it to the app using their device. The user takes a photo of the clothing item using the camera app on their smartphone and sends the image file by clicking the upload button in the app. The input is the image of the clothing item, and the output is the image sent to the server.

[0477] Step 2: Sending images

[0478] The device sends the uploaded image data to the server. The device uses an HTTP POST request to send the image file selected by the user to the server. The input is the image file, and the output is the image data sent to the server.

[0479] Step 3: Analyze the images

[0480] The server analyzes the received images using AI techniques such as TensorFlow and YOLO to extract the clothing features. The server converts the images into tensor format and feeds them into an AI model to extract features such as color, pattern, style, and brand. The input is the received image data, and the output is extracted feature data.

[0481] Step 4: Save feature data

[0482] The server stores the analysis results in a database and automatically updates the user's digital closet. The server inserts the extracted feature data into the database in an appropriate format. The input is the feature data, and the output is the feature data stored in the database.

[0483] Step 5: Upload a body image

[0484] The user takes a face or full-body photo and uploads the image to the server using the device. The user takes a face or full-body photo using the camera app on their smartphone and uploads the image using the profile update function of the app. The input is the user's body image, and the output is the image sent to the server.

[0485] Step 6: Submit a body image

[0486] The device sends the user's body image to the server. The image file is sent to the server using an HTTP POST request. The input is the body image, and the output is the image data sent to the server.

[0487] Step 7: Analyze physical characteristics

[0488] The server analyzes the user's facial shape and body shape using FaceNet and OpenPose. The images are fed into an AI model, which extracts facial and body features. The input is the received body image, and the output is the analyzed body feature data.

[0489] Step 8: Register your preferred style

[0490] The user enters their preferred style and brand in a questionnaire format. This information is sent from the device to the server. The input is the user's preference information, and the output is the information sent to the server.

[0491] Step 9: Save style information

[0492] The server stores the entered style and brand information based on the profile. It inserts the data in the appropriate format for storage in the database. The input is the user's preferences, and the output is the information added to the profile.

[0493] Step 10: Submit your clothing registration information

[0494] The user selects clothing items they own or plan to purchase. The device sends this information to the server. The input is the selected clothing information, and the output is the information sent to the server.

[0495] Step 11: Coordinate generation

[0496] The server uses a generative AI model (e.g., GPT-4) to generate the optimal outfit based on the submitted item information. The AI ​​model is fed with information and calculates the appropriate combination. The input is the submitted clothing information, and the output is the generated outfit.

[0497] Step 12: Display your outfit

[0498] The generated outfit is presented to the user through the app screen. The device displays the information received from the server on a GUI. The input is the generated outfit information, and the output is the outfit displayed to the user.

[0499] Step 13: Enter your fashion questions

[0500] The user types a fashion question into the chat box. The device receives the question from the text input field. The input is the user's question, and the output is the question sent to the server.

[0501] Step 14: Submit your question

[0502] The terminal sends the entered question to the server. The question text is sent using an HTTP request. The input is the question text, and the output is the text sent to the server.

[0503] Step 15: Parsing the Question

[0504] The server analyzes the question using natural language processing techniques (e.g., BERT or GPT-3), tokenizes the text data, and analyzes its meaning. The input is the received question text, and the output is the analyzed semantic information.

[0505] Step 16: Generate and communicate answers

[0506] The server generates an appropriate answer based on the analysis results and sends it to the terminal. The input is the analyzed question data, and the output is the generated answer text. The terminal displays this answer in the user's chat box.

[0507] Step 17: Select items for online shopping

[0508] The user selects the clothing item they are considering purchasing within the app. The device sends the selection information to the server. The input is the item information selected by the user, and the output is the item information sent to the server.

[0509] Step 18: Obtaining Purchase Information

[0510] The server uses the API of the online shopping site to get details and a purchase link. It sends an API request and parses the response. The input is the selected item information, and the output is the retrieved details and link.

[0511] Step 19: View Purchase Links

[0512] The acquired purchase link is sent to the user's device and displayed within the app. The input is the acquired link information, and the output is the purchase link displayed to the user.

[0513] Step 20: Emotion Recognition

[0514] When a user uses the system, the emotion engine analyzes the user's facial expressions and voice through the camera and microphone to recognize their emotional state. The input is camera and microphone data acquired in real time, and the output is analyzed emotion data.

[0515] Step 21: Sending Emotion Data

[0516] The device sends emotion recognition data to the server. The input is the analyzed emotion data, and the output is the data sent to the server.

[0517] Step 22: Emotion-Based Suggestions

[0518] The server receives the emotional data and tailors clothing suggestions based on the user's emotional state, with the emotional data as input and the tailored suggestions as output, resulting in a display of clothing suggestions suited to the user.

[0519] The system operates according to the above processing steps, allowing the user to efficiently manage clothing and receive optimal style suggestions.

[0520] (Application example 2)

[0521] 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."

[0522] Conventional digital closet systems have the ability to manage a user's clothing and provide style suggestions, but they lack the ability to provide real-time suggestions that take into account the user's emotional state. Furthermore, users have limited means to check the fit and appearance of clothing without actually trying them on, which can make purchasing decisions difficult. Therefore, there is a need for a system that can suggest clothing items based on the user's emotional state and enable virtual try-on.

[0523] 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.

[0524] In this invention, the server includes means for a user to upload images of clothing items, means for transmitting the images to the server, means for the server to analyze the images, extract clothing features, and store them in a database, means for automatically updating the user's digital closet, means for adjusting clothing suggestions based on the user's emotional state using an emotion engine that analyzes the user's emotions, and means for using smart glasses or a head-mounted display for the user to have a virtual try-on experience. This allows the user to receive clothing suggestions that are optimal for their emotional state and to check the fit and appearance of the clothing in real time without actually trying it on.

[0525] The "means for uploading images of clothing" is an interface that allows users to import photos of clothing they own into the system.

[0526] "Means for transmitting said images to a server" refers to a method or protocol for transferring uploaded images to a server over the Internet.

[0527] The "means for extracting characteristics of clothing items and storing them in a database" refers to the process of using image analysis technology to extract information such as the color, pattern, style, and brand of clothing items and storing it in a database.

[0528] The "means for automatically updating a user's digital closet" is a system that uses the extracted clothing features to keep the user's clothing information up to date.

[0529] An "emotion engine that analyzes user emotions" is an algorithm or mechanism that analyzes the user's facial expressions, tone of voice, etc., and determines their emotional state.

[0530] "Means for adjusting clothing suggestions based on the user's emotional state" refers to a process for selecting the most suitable clothing items and outfits for a user based on the analyzed emotional information of the user.

[0531] "Means for using smart glasses or head-mounted displays to provide a virtual try-on experience" refers to devices that utilize augmented reality or virtual reality technology to allow users to try on clothing in a virtual space.

[0532] The "means for uploading body images" is an interface that allows users to upload full-body and facial photos into the system.

[0533] "Means of extracting the user's physical characteristics and understanding their style based on their profile" refers to the process of analyzing information such as body type and face shape from uploaded body images and suggesting styles that suit the user's preferences.

[0534] The "means for generating optimal outfits" refers to an algorithm that allows the AI ​​to consider the optimal combination based on the clothing items selected by the user and registered information.

[0535] The "means for providing the generated coordinated outfit to the user" refers to an interface or display method for showing the generated optimal clothing set to the user.

[0536] The present invention is a system that allows users to manage their clothing and provide optimal styles based on their emotional state. This system analyzes the user's emotional state in real time and suggests optimal outfits for the user through a virtual try-on experience using smart glasses or a head-mounted display. Specific embodiments of the system are described below.

[0537] 1. Clothing Image Recognition and Management

[0538] Users take photos of their clothing and upload them to the system via their device. The images are then sent over the internet to a cloud server where they are analyzed using AI technology. This analysis uses the Google Cloud Vision API to extract characteristics such as the color, pattern, style, and brand of the clothing. The extracted information is stored in a database, and the user's digital closet is automatically updated.

[0539] 2. Face and body recognition and style preference understanding

[0540] Users take full-body and facial images and upload them to the system via smart glasses or a head-mounted display. These images are sent to a cloud server and analyzed by an AI model using TensorFlow. This recognizes the user's face shape and body type, and updates their profile. In addition, users provide information about their preferred styles and brands in a questionnaire format.

[0541] 3. AI Coordination Simulation

[0542] When a user selects clothing items they own or are considering purchasing, the device sends this selection information to a cloud server, which then uses AI technology to generate the optimal outfit. The generated outfit is displayed to the user in real time via smart glasses or a head-mounted display. Users can check the fit and appearance of the clothing in a virtual space without actually trying it on.

[0543] 4. Proposals linked to emotion engine

[0544] When a user uses the system, the emotion engine analyzes the user's facial expressions and voice through the camera and microphone to recognize emotions in real time. This emotion analysis uses the Microsoft Azure Emotion API. The cloud server receives data from the emotion engine and adjusts clothing recommendations based on the user's emotional state. For example, if the system recognizes that the user is feeling stressed, it will suggest relaxing, casual clothing.

[0545] Specific examples

[0546] If a user takes a photo of a red dress and uploads it to the app, they could use a prompt like this:

[0547] Example prompt: "I want to upload and manage my red dress in an app."

[0548] If a user takes a full-body photo and registers body shape information, the following prompt sentence can be used:

[0549] Example prompt: "Take a full-body photo and register your body shape information."

[0550] To suggest relaxing casual clothing based on emotional state, you can use prompts such as:

[0551] Example prompt: "Analyze your emotional state and suggest relaxing casual wear."

[0552] This allows users to receive recommendations for clothing that are optimal for their emotional state, and allows them to check the fit and appearance of the clothing in real time without actually trying it on.

[0553] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0554] Step 1:

[0555] The user takes a photo of the clothing item and uploads it to the device. At this time, the user takes a picture of the clothing item they own using a device such as a smartphone or PC. The image is then imported into the system using the device's upload function.

[0556] Input: Clothing photo

[0557] Output: Uploaded image file on device

[0558] What happens: A user takes a photo of a red dress with their smartphone camera and uploads it to the app.

[0559] Step 2:

[0560] The device sends the uploaded image to a cloud server, which then transfers the image over the internet and prepares it for analysis on the server side.

[0561] Input: Uploaded image file

[0562] Output: Image data transferred to the server

[0563] Specific behavior: The app automatically sends the image to the cloud server and notifies the user that the image has been sent.

[0564] Step 3:

[0565] The server analyzes the image and extracts the clothing's characteristics. An image analysis engine (using Google Cloud Vision API) on the cloud server processes the image data and extracts characteristics such as the clothing's color, pattern, style, and brand.

[0566] Input: Image data transferred to the server

[0567] Output: Extracted clothing feature data

[0568] Specific operation: The server detects the characteristics of the clothing item (e.g., red, dress, brand Y) and stores the results in a database.

[0569] Step 4:

[0570] Automatically updates the user's digital closet: The extracted clothing feature information is stored in a database, and the user's digital closet is updated to the latest version.

[0571] Input: Clothing feature data

[0572] Output: Updated digital closet

[0573] What happens: A new item (a red dress) is added to the database and the user's digital closet is automatically updated.

[0574] Step 5:

[0575] The user takes a photo of their entire body or face and uploads it to the device. The user uses smart glasses or a head-mounted display to take a photo of their entire body or face.

[0576] Input: A full-body or face image of the user

[0577] Output: Uploaded image file on device

[0578] What happens: The user takes a full-body photo with the camera on their smart glasses and uploads it to the app.

[0579] Step 6:

[0580] The device sends the uploaded full-body or facial images to a cloud server, which then transfers the images over the internet and prepares them for analysis on the server side.

[0581] Input: Uploaded full-body or face image

[0582] Output: Image data of the whole body and face sent to the server

[0583] Specific behavior: The app automatically sends the image to the cloud server and notifies the user that the image has been sent.

[0584] Step 7:

[0585] The server analyzes the image and extracts the user's physical features. An image analysis engine (using TensorFlow) on the cloud server processes the image data and extracts the user's facial shape and body shape features.

[0586] Input: Image data of the whole body and face transferred to the server

[0587] Output: Extracted user's body feature data

[0588] What it does: The server detects the user's height, body type, and facial features and updates the user's profile.

[0589] Step 8:

[0590] Users provide information about their preferred styles and brands in the form of a questionnaire. Users answer the questionnaire via their smartphone or PC, and the information is stored on the server.

[0591] Input: User's style and brand preferences

[0592] Output: Updated user profile

[0593] Specific operation: The user answers a questionnaire on the app, saying, "I like casual, light-colored clothes," and this information is stored on the server.

[0594] Step 9:

[0595] The user selects clothing items they own or are considering purchasing. The user selects items from the virtual closet and the selection information is sent to the server.

[0596] Input: Data about clothing items selected by the user

[0597] Output: Selection information transmitted to the server

[0598] Specific behavior: The user selects a white shirt, blue jeans, and sneakers from a virtual closet.

[0599] Step 10:

[0600] The server generates the optimal outfit based on the selection information, and the AI ​​coordination engine on the cloud server proposes the optimal outfit based on the selection information and saved profile data.

[0601] Input: Selection information and profile data

[0602] Output: Proposed optimal coordinates

[0603] Specific operation: The server generates an outfit consisting of "white shirt + blue jeans + sneakers" and notifies the user.

[0604] Step 11:

[0605] Users use smart glasses or a head-mounted display to have a virtual try-on experience. The generated outfits are displayed on the device in real time, and the user can try them on in a virtual space.

[0606] Input: Suggested optimal coordinates

[0607] Output: Real-time display of virtual try-on experience

[0608] Specific operation: The user puts on the smart glasses and checks the generated outfit in the virtual space.

[0609] Step 12:

[0610] The emotion engine analyzes the user's facial expressions and voice through the camera and microphone to recognize emotions in real time, and this emotional information is sent to the server, which then adjusts clothing recommendations based on the user's emotional state.

[0611] Input: User facial and voice data

[0612] Output: Recognized emotional state and tailored clothing recommendations

[0613] Specific operation: The emotion engine recognizes that the user is feeling stressed from the camera footage, and the server suggests casual clothing that will help them relax.

[0614] 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.

[0615] 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.

[0616] 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.

[0617] [Second embodiment]

[0618] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0619] 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.

[0620] 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).

[0621] 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.

[0622] 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.

[0623] 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).

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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."

[0630] This invention is a system that allows users to efficiently manage their clothing and suggests the most suitable style. This system is realized by users uploading images of their clothing, which the server analyzes. The system also identifies the user's face, body type, and style preferences, and suggests coordination ideas with existing clothing and new clothing to be purchased.

[0631] Overall system configuration

[0632] This system consists of a user, a terminal, and a server. Users operate the system using a terminal such as a smartphone or PC to manage their clothing, register their physical characteristics, simulate styles, and link with online shopping.

[0633] Program processing

[0634] 1. Clothing Image Recognition and Management

[0635] Users take photos of their clothing and upload them to the app via their device. The device then sends the uploaded images to a server, which analyzes them. This analysis uses AI technology to extract characteristics such as the color, pattern, style, and brand of the clothing. The server then stores this information in a database and automatically updates the user's digital closet.

[0636] Example: If a user uploads a photo of a red dress, the server analyzes the image, generates tags for "red," "dress," and "brand Y," and adds them to the user's digital closet.

[0637] 2. Face and body recognition and style preference understanding

[0638] Users upload photos of their face and entire body. The device sends these images to a server, which uses AI models to analyze the user's face shape and body type. This allows the server to understand the user's physical characteristics and update their profile. Users can also provide information about their preferred styles and brands in a questionnaire format. This information is stored on the server and used to suggest styles to the user.

[0639] Example: If a user uploads a full-body photo and replies, "I like casual, light-colored clothing," the server adds information to their profile such as "height 170cm," "slim build," "casual," and "light colors."

[0640] 3. AI Coordination Simulation

[0641] Through the system, users select clothing items they own or plan to purchase. The device sends this selection information to the server, which then uses AI technology to generate the optimal outfit. The generated outfit is presented to the user through the app screen.

[0642] Example: If a user selects "white shirt," "blue jeans," and "sneakers," the server will suggest a combination of "white shirt + blue jeans + sneakers" and display it to the user.

[0643] 4. AI Fashion Consultation Room

[0644] The user enters a fashion-related question into the chat box. The device sends this input to the server, which analyzes the question using natural language processing (NLP). The server generates an appropriate answer based on the analysis results and provides it to the user via the device.

[0645] Example: A user asks, "What outerwear is recommended for fall?" and the server responds, "Casual jackets and hoodies are suitable for fall."

[0646] 5. Online shopping integration

[0647] The user selects the clothing item they are considering purchasing within the app. The device sends the selected item information to the server, and the server obtains detailed information and a purchase link via the online shopping site's API. The user can then check the item within the app and access the purchase page.

[0648] Example: When a user wants to buy a new coat and enters that information into the simulation, the server suggests outfits for the new coat and a scarf they already own, and also displays a link to an online shopping site to purchase the coat.

[0649] The above is a specific embodiment of the system of the present invention, which allows users to efficiently manage their clothing and receive style suggestions.

[0650] The processing flow will be explained below.

[0651] Clothing image recognition and management

[0652] Step 1:

[0653] The user takes a photo of the clothing item they own using the camera on their smartphone or PC.

[0654] Step 2:

[0655] The device temporarily stores the captured photo and asks the user for confirmation through the user interface.

[0656] Step 3:

[0657] After the user confirms, the device sends the photo to the server.

[0658] Step 4:

[0659] The server then feeds the received images into an analytical AI model, which extracts features such as color, pattern, style, and brand.

[0660] Step 5:

[0661] The server stores the extracted features in a database and generates a corresponding item ID.

[0662] Step 6:

[0663] The server updates the user's digital closet to reflect the added item information.

[0664] Step 7:

[0665] Users can see new items added to their digital closet through the app's UI.

[0666] Face and body recognition and style preference identification

[0667] Step 1:

[0668] The user uses the app to take a photo of their face and a full-body photo.

[0669] Step 2:

[0670] The device temporarily stores the captured photo and asks the user for confirmation through the user interface.

[0671] Step 3:

[0672] After the user confirms, the device sends the photo to the server.

[0673] Step 4:

[0674] The images received by the server are input into an analytical AI model to extract the user's facial and body features.

[0675] Step 5:

[0676] The server stores the extracted features in a database and updates the user's profile information.

[0677] Step 6:

[0678] Users provide information about their preferred fashion styles, brands, colors, etc. in a survey format within the app.

[0679] Step 7:

[0680] The server stores the user's style tendency in a database based on the provided style information.

[0681] AI Coordination Simulation

[0682] Step 1:

[0683] The user opens the simulation screen and selects clothing items they own or are considering purchasing.

[0684] Step 2:

[0685] The terminal temporarily stores the selected item information and asks the user for confirmation through the user interface.

[0686] Step 3:

[0687] After the user confirms, the terminal transmits the item information to the server.

[0688] Step 4:

[0689] Based on the information received by the server, an AI model for simulation is executed.

[0690] Step 5:

[0691] The server generates the optimal coordinates and stores the coordinate results in a database.

[0692] Step 6:

[0693] The server transmits the generated coordination results to the terminal.

[0694] Step 7:

[0695] Through the app's UI, users can check the outfits suggested by the AI ​​and make evaluations or changes.

[0696] AI Fashion Consultation Room

[0697] Step 1:

[0698] Users can enter their fashion-related questions or concerns into the chat box within the app.

[0699] Step 2:

[0700] The terminal sends the entered text to the server.

[0701] Step 3:

[0702] The server inputs the received text into a natural language processing (NLP) model for analysis.

[0703] Step 4:

[0704] The server understands the meaning of the question and generates an appropriate answer.

[0705] Step 5:

[0706] The server generates a response and sends it to the terminal.

[0707] Step 6:

[0708] Users can review the answers and ask further questions or provide feedback as needed through the app's UI.

[0709] Online shopping integration

[0710] Step 1:

[0711] The user selects the item they are considering purchasing through the app.

[0712] Step 2:

[0713] The terminal temporarily stores the selected item information and asks the user for confirmation through the user interface.

[0714] Step 3:

[0715] After the user confirms, the terminal transmits the item information to the server.

[0716] Step 4:

[0717] The server retrieves detailed information, price, and stock status of the selected item via the online shopping site's API.

[0718] Step 5:

[0719] Based on the information received by the server, it runs a simulation AI model and generates an outfit that combines the items the customer is considering purchasing with clothing they already own.

[0720] Step 6:

[0721] The server sends the generated coordination results and a purchase link to the terminal.

[0722] Step 7:

[0723] Through the app's UI, users can check the AI's suggested outfits and purchase links, and proceed with the purchase process if necessary.

[0724] Example 1

[0725] 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."

[0726] Managing clothing and suggesting outfits takes a lot of time and effort, placing a significant burden on users. Furthermore, there is a lack of systems that can suggest optimal outfits that reflect a user's individual physical characteristics and style preferences. Furthermore, there is a lack of efficient methods for providing detailed information and links to purchase items that users are considering purchasing. There is a need for a system that can solve these issues and enable users to efficiently manage clothing and receive style suggestions.

[0727] 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.

[0728] In this invention, the server includes: means for a user to upload images of clothing items; means for transmitting the images to the server; means for the server to analyze the images, extract clothing features, and store the extracted features in a database; means for automatically updating the user's digital closet; means for a user to upload body images; means for transmitting the body images to the server; means for the server to analyze the body images and identify the user's body features and style preferences; means for transmitting information about clothing items selected by the user to the server; means for the server to generate optimal outfits using AI technology; means for providing the generated outfits to the user; means for the user to input fashion-related questions into a chat box; means for transmitting the input questions to the server; means for the server to generate appropriate answers using natural language processing technology and provide them to the user; means for the user to select clothing items for online shopping; means for the server to obtain detailed information and purchase links from an API of an online shopping site; and means for providing the purchase links to the user. This allows users to efficiently manage their clothing and receive suggestions for optimal outfits based on their individual body features and style preferences. In addition, by linking with online shopping, it will be possible to efficiently obtain detailed information and purchase links for items being considered for purchase.

[0729] "User" refers to an individual who uses the system to manage clothing items and receive coordination suggestions.

[0730] "Means for uploading images of clothing items" refers to a function that allows users to import photos of clothing items into the system.

[0731] "Server" refers to the central system that receives and analyzes data sent by users, and processes and manages various information.

[0732] "Means for transmitting images to a server" refers to a communication function for transferring image data captured by a user to a server.

[0733] "Analyzing an image" refers to the process by which the server extracts detailed information from the image it receives.

[0734] "Database" refers to a digital repository for storing and managing extracted clothing characteristics.

[0735] "User's digital closet" refers to a function that centrally manages information about the clothing items owned by the user in digital format.

[0736] "Means for uploading body images" refers to a function that allows users to upload photos of their bodies into the system.

[0737] "Physical characteristics" refers to the user's own physical characteristics, such as the user's body shape and facial shape.

[0738] "Style preferences" refer to the fashion trends and brands that a user prefers.

[0739] "Means for transmitting clothing information" refers to a communication function for transferring data on clothing items selected by the user to the server.

[0740] "AI technology" refers to technology that uses artificial intelligence to analyze data and generate coordination.

[0741] "Means for generating coordination" refers to a function that uses AI technology to suggest clothing combinations.

[0742] "Means for providing to the user" refers to a function for displaying the generated coordination and answers on the user's terminal.

[0743] "Chat box" refers to an interface that allows users to enter questions and send them to the server.

[0744] "Natural language processing technology" refers to artificial intelligence technology that enables a server to understand input text and generate appropriate responses.

[0745] "Means for selecting clothing items for online shopping" refers to a function that allows a user to select clothing items within the system that they are considering purchasing.

[0746] "API" refers to an interface for exchanging data between different software programs.

[0747] "Detailed information" refers to specific information about the purchase, such as the characteristics, price, and availability of the clothing item.

[0748] "Purchase Link" refers to the access URL to an online shopping site for purchasing the clothing item selected by the user.

[0749] The present invention relates to a system that allows users to efficiently manage their clothing and suggests optimal styles. This system allows users to upload images of their clothing and body, and analyzes them to optimize the user's style.

[0750] Overall system configuration

[0751] This system consists of a user, a device, and a server. Users operate the system using devices such as smartphones or PCs. Major operations include managing clothing items, registering physical characteristics, simulating styles, and linking with online shopping.

[0752] Clothing image recognition and management

[0753] Users first take a photo of their clothing and upload it to the app via their device. The device then sends the image to a server, which analyzes it. This analysis process uses AI technology to extract characteristics from the image, such as the color, pattern, style, and brand of the clothing. Once the analysis is complete, the server stores this information in a database and automatically updates the user's digital closet.

[0754] Example: If a user uploads a photo of a red dress, the server analyzes the image and generates tags for "red," "dress," and "brand X," which are added to the user's digital closet.

[0755] Face and body recognition and style preference identification

[0756] Next, users upload photos of their face and body. The device sends these images to a server, which uses AI models to analyze the images and detect the user's face shape and body type. The information obtained by the server is reflected in the user's profile and used to suggest style recommendations. Users can also answer a questionnaire about their preferred styles and brands, and this information is also stored on the server.

[0757] Example: If a user uploads a full-body photo and answers a survey saying, "I like casual, light-colored clothing," the server adds information such as "height 170cm," "slim build," "casual," and "light colors" to the profile.

[0758] AI Coordination Simulation

[0759] Users select clothing items they already own or plan to purchase and run a simulation. Information about the selected items is sent from the device to the server, which then uses AI technology to generate the optimal outfit. The generated outfit is then presented to the user on the app screen.

[0760] Example: If a user selects "white shirt," "blue jeans," and "sneakers," the server will suggest a coordination of "white shirt + blue jeans + sneakers" and display it on the user's device.

[0761] AI Fashion Consultation Room

[0762] Users enter fashion-related questions into the chat box and send them from their devices to the server, which uses natural language processing technology to analyze the questions and generate appropriate answers, which are then provided to the user's devices.

[0763] Example: When a user types a question into a chat box such as "What outerwear is recommended for autumn?", the server responds with "Casual jackets and hoodies are suitable for autumn" and displays it to the user.

[0764] Online shopping integration

[0765] The user selects the clothing item they are considering purchasing within the app, and their device sends that information to the server. The server then obtains detailed information and a purchase link via the online shopping site's API and provides it to the user. The user can then check the details within the app and access the purchase page.

[0766] Example: When a user selects a new coat to purchase, the server suggests coordinating the new coat with a scarf they already own and displays a link to purchase the item.

[0767] This system allows users to efficiently manage their clothing and receive style suggestions optimized for their physical characteristics and preferences. The online shopping function also allows for a smooth purchasing process.

[0768] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0769] Step 1: Take and upload your clothing image

[0770] 1. The user takes a photo of the clothing item using a smartphone or PC.

[0771] 2. The device displays a UI (user interface) for uploading the captured image to the app.

[0772] 3. When the user presses the upload button, the device sends the image to the server.

[0773] Specific operation: A user takes a photo of a red dress with their smartphone and taps the "upload" button in the app. The device then sends the image to the server.

[0774] Input: Clothing image

[0775] Output: Notification of completion of transmission to the server

[0776] Step 2: Image analysis and database update

[0777] 1. The server analyzes the images received using artificial intelligence (AI) technology.

[0778] 2. The server extracts features such as color, pattern, style, and brand of the clothing from the image.

[0779] 3. The server stores the extracted feature information in a database and automatically updates the user's digital closet.

[0780] What it does: The server analyzes the image, creates tags such as "red," "dress," and "Brand X," and adds these tags to the database.

[0781] Input: User-uploaded clothing images

[0782] Output: Analyzed clothing feature information (color, pattern, style, brand)

[0783] Step 3: Face and body recognition and profile update

[0784] 1. The user takes a photo of their face or whole body and uploads it via their device.

[0785] 2. The server uses an AI model to analyze the face shape and body type based on the images received.

[0786] 3. The server updates the user's profile based on the analysis results. It also collects information about the user's preferred styles and brands through a questionnaire and stores that information on the server.

[0787] Specific operation: The server performs analysis and adds characteristics such as the user's height of 170 cm and slim build to the profile. If the user answers a questionnaire saying that they like casual, light-colored clothing, this information is also added to the profile.

[0788] Input: User's face photo, full-body photo, survey results

[0789] Output: Updated user profile

[0790] Step 4: AI Coordination Simulation

[0791] 1. The user selects clothing items they own or new items they plan to purchase within the app.

[0792] 2. The device sends the selected item information to the server.

[0793] 3. The server uses AI technology to generate the optimal coordination and sends the results to the device.

[0794] 4. The terminal displays the generated coordinates on the screen to the user.

[0795] Specific operation: The user selects "white shirt," "blue jeans," and "sneakers," and taps the simulation button. The server generates an outfit called "white shirt + blue jeans + sneakers" and displays it on the user's device.

[0796] Input: Clothing information selected by the user

[0797] Output: Generated outfit suggestions

[0798] Step 5: AI Fashion Consultation

[0799] 1. The user types a fashion-related question into the chat box within the app.

[0800] 2. The device sends the entered question to the server.

[0801] 3. The server uses natural language processing (NLP) techniques to analyze the question and generate an appropriate answer.

[0802] 4. The server generates a response and sends it to the terminal, which displays it to the user.

[0803] Specific operation: The user enters a question in the chat box, "What outerwear is recommended for autumn?" and taps the send button. The server analyzes the question and replies, "A casual jacket or hoodie is suitable for autumn."

[0804] Input: User fashion question

[0805] Output: The generated answer

[0806] Step 6: Online shopping integration

[0807] 1. The user selects the clothing item they are considering purchasing within the app.

[0808] 2. The device sends the selected item information to the server.

[0809] 3. The server retrieves details and a purchase link via the online shopping site's API.

[0810] 4. The server sends the retrieved details and purchase link to the device, which displays them to the user.

[0811] Specific operation: When a user tries to purchase a "new coat" and selects that item and enters it into the simulation, the server proposes a combination of "new coat + existing scarf" and a purchase link, which are displayed on the user's device.

[0812] Input: Information about the clothing item selected by the user

[0813] Output: More information and purchase links

[0814] This allows users to efficiently manage their clothing and receive style suggestions, and also makes online shopping smoother.

[0815] (Application example 1)

[0816] 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."

[0817] In order to effectively manage the clothing items users currently own or are considering purchasing, and to provide optimal fashion coordination, users have traditionally had to try on the items in person. However, this is time-consuming and expensive, and often results in items not fitting their preferences or body type. Therefore, there is a need for a system that allows users to easily try on items in a virtual environment.

[0818] 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.

[0819] In this invention, the server includes means for a user to upload an image of a clothing item, means for transmitting the image to the server, means for the server to analyze the image, extract characteristics of the clothing item, and store the characteristics in a database, means for automatically updating the user's digital closet, means for the user to select a new clothing item and provide a virtual try-on, and means for displaying a purchase link based on the virtual try-on, thereby enabling the user to easily try on the clothing item virtually from home, confirm the best style, and purchase it immediately.

[0820] A "user" is an individual who uses this system to manage clothing items and receive coordination suggestions.

[0821] "Clothing images" are images that users take of clothing they own or are considering purchasing with a smartphone or other device and upload to the app.

[0822] A "server" is a computer system that analyzes image data sent from users and manages various databases.

[0823] The "database" is a storage device that stores the characteristics of clothing analyzed by the server and the user's physical information.

[0824] A "digital closet" is an electronic closet that manages a digital list of clothing items owned by a user and is provided to the user through an application.

[0825] A "new clothing item" is a clothing item that the user does not yet own and is considering purchasing.

[0826] "Virtual try-on" is a function that virtually simulates the appearance of clothing based on the user's physical data and provides it to the user.

[0827] A "purchase link" is a link that allows users to purchase clothing items they like after virtual try-on directly online.

[0828] "Body images" are images that users take of their face or whole body and upload to the app.

[0829] A "profile" is personal data that includes a user's physical characteristics, preferred style, past purchase history, and so on.

[0830] "Coordination" refers to proposing the optimal fashion style by combining the user's clothing items or clothing items the user is considering purchasing.

[0831] This invention is a system that allows users to efficiently manage their clothing and suggest outfits. The system is designed to allow users to easily upload their clothing and body data using a device such as a smartphone, and to simulate optimal fashion styles via a server.

[0832] Overall system configuration

[0833] This system consists of a user, a terminal, and a server. The user operates the system using the terminal to upload images of clothing, register physical characteristics, try on the clothing virtually, and link it to online shopping.

[0834] Program processing

[0835] 1. Clothing Image Recognition and Management

[0836] Users take photos of their clothing items using a smartphone or other device and upload them to the app.

[0837] The uploaded image is sent from the device to the server.

[0838] The server analyzes the received images using AI technology (e.g., TensorFlow) to extract characteristics such as the color, pattern, style, and brand of the clothing.

[0839] The extracted information is stored in a database (e.g., MongoDB) and automatically updates the user's digital closet.

[0840] 2. Face and body recognition and style preference understanding

[0841] Users take a photo of their face or whole body and upload it to the app.

[0842] The uploaded image is sent from the device to the server.

[0843] The server analyzes these images using AI models (e.g., OpenPose, DeepFace) to extract the user's facial shape and body features.

[0844] Based on the user's profile, the system reflects information about preferred styles and brands.

[0845] 3. AI Coordination Simulation

[0846] Through the system, users select clothing items they already own or items they are considering purchasing.

[0847] The server uses an AI algorithm to generate the optimal outfit based on the selected information.

[0848] The generated outfits are combined with the user's body data as a virtual try-on and presented to the user in real time.

[0849] 4. Virtual try-on

[0850] The selected item is superimposed onto the user's photo and displayed in the app, as if they were actually trying it on.

[0851] Users can use this feature to see if a new style suits them.

[0852] 5. Linking with online shopping

[0853] If you find an item you like, you can access the online shopping site directly from the app and proceed with the purchase.

[0854] The purchase link is generated by the server and provided to the user.

[0855] Specific examples

[0856] If a user wants to coordinate a "blue shirt" they own with a "black jacket" they found in an online shop:

[0857] When a user uploads a photo of these two items to the app, the server analyzes the photo and generates a virtual try-on image that combines the "blue shirt" and "black jacket" based on the user's facial and body data.

[0858] This allows users to easily check whether a new jacket will suit them.

[0859] If you like it, you can purchase the jacket directly from the online shop via the purchase link.

[0860] Prompt Sentence Examples

[0861] "I'd like to virtually try on a new jacket and see if it goes with my blue shirt. How can I do that?"

[0862] "I like the outfit I created and would like to purchase this jacket online. Can you give me a link to purchase it?"

[0863] Through the above steps, the system of the present invention allows users to easily manage clothing items and try on outfits from home, confirm the best style, and then purchase the items.

[0864] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0865] Step 1: Upload an image

[0866] Subject: User

[0867] Users take photos of clothing or their own bodies using a device such as a smartphone and upload them to the app. As input, the user provides image data, and as output, the image files are saved on the device.

[0868] Step 2: Send image

[0869] Subject: Terminal

[0870] The terminal sends the uploaded image to the server. In this process, the terminal sends the image file to the server using the HTTP protocol or similar. The uploaded image data is the input, and the image data arrives at the server as the output.

[0871] Step 3: Image analysis

[0872] Subject: Server

[0873] The server analyzes the received image data using AI technology (e.g., TensorFlow). Image recognition algorithms are used for the analysis to extract features such as the color, pattern, style, and brand of the clothing. The input is the image data, and the output is the extracted feature data.

[0874] Step 4: Save Data

[0875] Subject: Server

[0876] The server stores the extracted feature data in a database (e.g., MongoDB). This process adds a new entry to the database. The input is the feature data, and the output is the updated database.

[0877] Step 5: Update your digital closet

[0878] Subject: Server

[0879] The server updates the user's digital closet based on the new entries in the database. The input is the newly added feature data, and the output is the updated digital closet information.

[0880] Step 6: Body Data Analysis

[0881] Subject: Server

[0882] The server analyzes the face and body images uploaded by the user using an AI model (e.g., OpenPose, DeepFace). The input is image data of the body, and the output is data on the user's physical characteristics.

[0883] Step 7: Update your profile

[0884] Subject: Server

[0885] The server updates the user's profile with the physical feature data and adds preferred style and brand information to the profile. The input is the physical feature data and the user's style information, and the output is an updated user profile.

[0886] Step 8: Item Selection

[0887] Subject: User

[0888] Users select clothing items they own or are considering purchasing through the system. The selected clothing information is provided to the app as input, and the selection information is sent to the server as output.

[0889] Step 9: Coordinate Generation

[0890] Subject: Server

[0891] The server uses an AI algorithm to generate the optimal outfit based on the selection information. The input is the selection information and the user profile, and the output is the generated outfit data.

[0892] Step 10: Generate a virtual try-on

[0893] Subject: Server

[0894] The server generates a virtual try-on image by combining the user's body data and clothing items based on the generated coordinate data. The inputs are coordinate data and body feature data, and the output is a virtual try-on image.

[0895] Step 11: Displaying try-on images

[0896] Subject: Terminal

[0897] The terminal displays the virtual try-on image received from the server to the user. The virtual try-on image data is input, and the image is displayed on the terminal screen as output.

[0898] Step 12: View the purchase link

[0899] Subject: Server

[0900] The server generates a purchase link for the item the user likes during the virtual try-on and sends it to the device. The input is the try-on image and purchase information, and the output is the purchase link.

[0901] Step 13: Online Shopping

[0902] Subject: User

[0903] A user clicks on a purchase link displayed on the device to purchase an item on an online shopping site. The input is the purchase link, and the output is the online shop's purchase page.

[0904] 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.

[0905] The present invention is a system that allows users to efficiently manage their clothing and suggests optimal styles. This system is combined with an emotion engine that recognizes the user's emotions, and adds a function to suggest clothing items based on the user's emotional state.

[0906] Overall system configuration

[0907] This system is composed of a user, a device, a server, and an emotion engine. Users operate the system using devices such as smartphones or PCs to manage their clothing, register their physical characteristics, simulate styles, and link with online shopping. The emotion engine also analyzes the user's facial expressions and behavior to make suggestions based on their emotional state.

[0908] Program processing

[0909] 1. Clothing Image Recognition and Management

[0910] Users take photos of their clothing and upload them to the app via their device. The device then sends the uploaded images to a server, which analyzes them. This analysis uses AI technology to extract characteristics such as the color, pattern, style, and brand of the clothing. The server then stores this information in a database and automatically updates the user's digital closet.

[0911] Example: If a user uploads a photo of a red dress, the server analyzes the image, generates tags for "red," "dress," and "brand Y," and adds them to the user's digital closet.

[0912] 2. Face and body recognition and style preference understanding

[0913] Users upload photos of their face and entire body. The device sends these images to a server, which uses AI models to analyze the user's face shape and body type. This allows the server to understand the user's physical characteristics and update their profile. Users can also provide information about their preferred styles and brands in a questionnaire format. This information is stored on the server and used to suggest styles to the user.

[0914] Example: If a user uploads a full-body photo and replies, "I like casual, light-colored clothing," the server adds information to their profile such as "height 170cm," "slim build," "casual," and "light colors."

[0915] 3. AI Coordination Simulation

[0916] Through the system, users select clothing items they own or plan to purchase. The device sends this selection information to the server, which then uses AI technology to generate the optimal outfit. The generated outfit is presented to the user through the app screen.

[0917] Example: If a user selects "white shirt," "blue jeans," and "sneakers," the server will suggest a combination of "white shirt + blue jeans + sneakers" and display it to the user.

[0918] 4. AI Fashion Consultation Room

[0919] The user enters a fashion-related question into the chat box. The device sends this input to the server, which analyzes the question using natural language processing (NLP). The server generates an appropriate answer based on the analysis results and provides it to the user via the device.

[0920] Example: A user asks, "What outerwear is recommended for fall?" and the server responds, "Casual jackets and hoodies are suitable for fall."

[0921] 5. Online shopping integration

[0922] The user selects the clothing item they are considering purchasing within the app. The device sends the selected item information to the server, and the server obtains detailed information and a purchase link via the online shopping site's API. The user can then check the item within the app and access the purchase page.

[0923] Example: When a user wants to buy a new coat and enters that information into the simulation, the server suggests outfits for the new coat and a scarf they already own, and also displays a link to an online shopping site to purchase the coat.

[0924] 6. Suggestions linked to emotion engine

[0925] When a user uses the system, the emotion engine analyzes the user's facial expressions and voice through the camera and microphone to recognize emotions in real time. The server receives data from the emotion engine and adjusts clothing recommendations based on the user's emotional state. For example, it suggests casual clothes if the user wants to relax, and formal clothes if the user has an important meeting coming up.

[0926] Example: If the emotion engine recognizes that the user is feeling stressed, the server will suggest "relaxing casual wear" and coordinate clothing that will ease the user's mood.

[0927] Overall effect

[0928] This system allows users to efficiently manage their clothing and receive style suggestions that best suit their emotional state. Furthermore, by linking with online shopping, users can smoothly purchase new clothing. The addition of an emotion engine makes it possible to make suggestions based on the user's real-time emotions, providing a more personalized experience.

[0929] The processing flow will be explained below.

[0930] Clothing image recognition and management

[0931] Step 1:

[0932] The user takes a photo of the clothing item they own using the camera on their smartphone or PC.

[0933] Step 2:

[0934] The device temporarily stores the captured photo and asks the user for confirmation through the user interface.

[0935] Step 3:

[0936] After the user reviews and approves the photo, the device sends the photo to the server.

[0937] Step 4:

[0938] The server then feeds the received images into an analytical AI model, which extracts features such as color, pattern, style, and brand.

[0939] Step 5:

[0940] The server stores the extracted features in a database and generates a corresponding item ID.

[0941] Step 6:

[0942] The server updates the user's digital closet to reflect the added item information.

[0943] Step 7:

[0944] Users can see new items added to their digital closet through the app's UI.

[0945] Face and body recognition and style preference identification

[0946] Step 1:

[0947] The user uses the app to take a photo of their face and a full-body photo.

[0948] Step 2:

[0949] The device temporarily stores the captured photo and asks the user for confirmation through the user interface.

[0950] Step 3:

[0951] After the user reviews and approves the photo, the device sends the photo to the server.

[0952] Step 4:

[0953] The images received by the server are input into an analytical AI model to extract the user's facial and body features.

[0954] Step 5:

[0955] The server stores the extracted features in a database and updates the user's profile information.

[0956] Step 6:

[0957] Users provide information about their preferred fashion styles, brands, colors, etc. in a survey format within the app.

[0958] Step 7:

[0959] The server stores the user's style tendency in a database based on the provided style information.

[0960] AI Coordination Simulation

[0961] Step 1:

[0962] The user opens the simulation screen and selects clothing items they own or are considering purchasing.

[0963] Step 2:

[0964] The terminal temporarily stores the selected item information and asks the user for confirmation through the user interface.

[0965] Step 3:

[0966] After the user checks and approves the item information, the terminal sends the information to the server.

[0967] Step 4:

[0968] Based on the information received by the server, an AI model for simulation is executed.

[0969] Step 5:

[0970] The server generates the optimal coordinates and stores the coordinate results in a database.

[0971] Step 6:

[0972] The server transmits the generated coordination results to the terminal.

[0973] Step 7:

[0974] Through the app's UI, users can check the outfits suggested by the AI ​​and make evaluations or changes.

[0975] AI Fashion Consultation Room

[0976] Step 1:

[0977] Users can enter their fashion-related questions or concerns into the chat box within the app.

[0978] Step 2:

[0979] The terminal sends the entered text to the server.

[0980] Step 3:

[0981] The server inputs the received text into a natural language processing (NLP) model for analysis.

[0982] Step 4:

[0983] The server understands the meaning of the question and generates an appropriate answer.

[0984] Step 5:

[0985] The server generates a response and sends it to the terminal.

[0986] Step 6:

[0987] Users can review the answers and ask further questions or provide feedback as needed through the app's UI.

[0988] Online shopping integration

[0989] Step 1:

[0990] The user selects the item they are considering purchasing through the app.

[0991] Step 2:

[0992] The terminal temporarily stores the selected item information and asks the user for confirmation through the user interface.

[0993] Step 3:

[0994] After the user checks and approves the item information, the terminal sends the information to the server.

[0995] Step 4:

[0996] The server retrieves detailed information, price, and stock status of the selected item via the online shopping site's API.

[0997] Step 5:

[0998] Based on the information received by the server, it runs a simulation AI model and generates an outfit that combines the items the customer is considering purchasing with clothing they already own.

[0999] Step 6:

[1000] The server sends the generated coordination results and a purchase link to the terminal.

[1001] Step 7:

[1002] Through the app's UI, users can check the AI's suggested outfits and purchase links, and proceed with the purchase process if necessary.

[1003] Suggestions linked with emotion engine

[1004] Step 1:

[1005] When a user uses the system, the emotion engine analyzes the user's facial expressions and voice through the camera and microphone, recognizing emotions in real time.

[1006] Step 2:

[1007] The server receives the data from the emotion engine and stores the user's emotional state in a database.

[1008] Step 3:

[1009] The server adjusts the suggested clothing styles based on the user's emotional state.

[1010] Step 4:

[1011] The user receives style suggestions from the system, for example, casual attire if the user wants to relax, or formal attire if the user has an important meeting coming up.

[1012] Step 5:

[1013] The user checks clothing suggestions according to emotions and selects or purchases clothing items as needed.

[1014] Example 2

[1015] 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."

[1016] Conventional clothing management systems make it difficult for users to efficiently manage their clothing inventory, particularly lacking the ability to suggest optimal styles based on the user's physical characteristics and emotional state. Furthermore, they lack sufficient integration with online shopping, making it difficult for users to purchase new clothing. Furthermore, they lack adequate support for fashion consultations and style suggestions, which can lead to reduced user satisfaction. There is a need for a system that can solve these problems and provide a more personalized experience.

[1017] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1018] In this invention, the server includes a means for users to upload images of clothing, a means for users to upload images of their bodies, a means for users to input fashion-related questions, and a means for an emotion recognition engine to analyze the user's facial expressions and voice and recognize their emotional state. This allows users to efficiently manage their clothing and receive optimal style suggestions based on their physical characteristics and emotional state. Furthermore, by linking the system with online shopping, users can easily purchase new clothing and receive fashion advice and style suggestions, thereby improving user satisfaction.

[1019] "Clothing" refers to all fashion items such as clothes and accessories that users wear on a daily basis.

[1020] A "terminal" is a device that allows a user to access and operate the system, and specifically includes a smartphone, personal computer, tablet, etc.

[1021] A "server" refers to a computer system that receives input data from a user, analyzes it, and generates and provides appropriate information.

[1022] "Image" refers to a digital photograph or illustration that visually records an item of clothing, a user's body, or the like.

[1023] A "database" refers to a collection of data that systematically stores analyzed information and is managed so that it can be easily searched and updated.

[1024] The "digital closet" refers to a part of the system that has the function of managing information about clothing owned by a user in digital form and visually displaying it.

[1025] A "profile" refers to data that compiles and records personal information about a user, such as physical characteristics, style preferences, and brand preferences.

[1026] "Coordination" refers to a style or fashion proposal created by combining multiple clothing items.

[1027] "Natural language processing technology" refers to technology that enables computers to understand and generate human language, and includes algorithms and models used to analyze text and understand its meaning.

[1028] An "online shopping site" refers to a type of website where you can browse and purchase products over the Internet.

[1029] "API" refers to a defined interface that allows different software applications to communicate with each other and utilize each other's functionality.

[1030] An "emotion recognition engine" refers to a technology or system that analyzes a user's facial expressions and voice to recognize their emotional state at that time.

[1031] A "generative AI model" refers to an artificial intelligence model that allows a computer to generate new information based on given input data, and is particularly used for natural language generation and image generation.

[1032] A "prompt" is a textual instruction or question that a user enters to obtain information or trigger an action using a generative AI model.

[1033] The present invention is a system that allows users to efficiently manage their clothing and suggests the most suitable style, and by combining it with an emotion engine, it is possible to suggest clothing based on the user's emotional state.

[1034] 1. Overall system configuration

[1035] This system consists of a user, a device, a server, and an emotion engine. Users operate the system using devices such as smartphones or personal computers. Operations include managing clothing items, registering physical characteristics, simulating styles, and linking with online shopping.

[1036] 2. Clothing Management

[1037] Users take photos of their clothing and upload them to the app via their device. The device then sends the uploaded images to a server. The server then uses AI technologies such as TensorFlow and YOLO to analyze the images, extracting characteristics such as the color, pattern, style, and brand of the clothing, and stores them in a database. The user's digital closet is then automatically updated.

[1038] For example, if a user uploads a photo of a red dress, the server will analyze the image, generate tags for "red," "dress," and "brand X," and add them to the database.

[1039] 3. Registering physical features and understanding style

[1040] Users upload photos of their face and body. The device sends these images to a server. The server uses FaceNet and OpenPose to analyze the user's face shape and body type. The analyzed information is recorded in a profile and used to suggest styles to the user. Users also fill out a questionnaire to enter their preferred styles and brands, and this information is also saved in their profile.

[1041] For example, if a user uploads a full-body photo and replies that they like casual, light-colored clothing, the server will add information such as "height 170cm," "slim build," "casual," and "light colors" to their profile.

[1042] 4. AI Coordination Simulation

[1043] The user selects clothing items they own or items they plan to purchase. The device sends this selection information to the server, which then uses a generative AI model (e.g., GPT-4) to generate the optimal outfit. The generated outfit is presented to the user through the app screen.

[1044] As a specific example, if a user selects "white shirt," "blue jeans," and "sneakers," the server will use GPT-4 to suggest a combination of "white shirt + blue jeans + sneakers" and display it in the user's app.

[1045] 5. AI Fashion Consultation Room

[1046] The user enters a fashion-related question into the chat box. The device sends this input to the server. The server uses natural language processing techniques (e.g., BERT or GPT-3) to analyze the question and generate an appropriate answer. The device then displays this answer in the chat box.

[1047] For example, if a user asks, "What outerwear is recommended for autumn?", the server will analyze using BERT and answer, "Casual jackets and hoodies are suitable for autumn."

[1048] 6. Online shopping integration

[1049] The user selects the clothing item they are considering purchasing within the app. The device sends the selected item information to the server, which then uses the API of the online shopping site to obtain detailed information and a purchase link, which it then provides to the user.

[1050] As a concrete example, if a user wants to buy a new coat, the server will suggest a coordination of "new coat + existing scarf" and also display a link to an online shopping site to purchase the coat.

[1051] 7. Suggestions linked to emotion engine

[1052] When a user uses the system, the emotion engine analyzes the user's facial expressions and voice through the camera and microphone. The server receives the data from the emotion engine and adjusts clothing recommendations based on the user's emotional state.

[1053] As a specific example, if the emotion engine recognizes that the user is feeling stressed, the server will suggest "relaxing casual wear" and coordinate clothing that will ease the user's mood.

[1054] Prompt Sentence Examples

[1055] The prompt statements are as follows:

[1056] "Please tell me what outfits would suit my body type. I've uploaded a full-body photo."

[1057] "Please suggest a style that suits your mood today."

[1058] "What are some casual outfit combinations suitable for autumn?"

[1059] The above system configuration allows users to efficiently manage their clothing and enjoy a more personalized fashion experience according to their emotional state.

[1060] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1061] The flow of this system's program processing

[1062] Processing Steps

[1063] Step 1: Take and upload your clothing image

[1064] The user takes a photo of the clothing item they own and uploads it to the app using their device. The user takes a photo of the clothing item using the camera app on their smartphone and sends the image file by clicking the upload button in the app. The input is the image of the clothing item, and the output is the image sent to the server.

[1065] Step 2: Sending images

[1066] The device sends the uploaded image data to the server. The device uses an HTTP POST request to send the image file selected by the user to the server. The input is the image file, and the output is the image data sent to the server.

[1067] Step 3: Analyze the images

[1068] The server analyzes the received images using AI techniques such as TensorFlow and YOLO to extract the clothing features. The server converts the images into tensor format and feeds them into an AI model to extract features such as color, pattern, style, and brand. The input is the received image data, and the output is extracted feature data.

[1069] Step 4: Save feature data

[1070] The server stores the analysis results in a database and automatically updates the user's digital closet. The server inserts the extracted feature data into the database in an appropriate format. The input is the feature data, and the output is the feature data stored in the database.

[1071] Step 5: Upload a body image

[1072] The user takes a face or full-body photo and uploads the image to the server using the device. The user takes a face or full-body photo using the camera app on their smartphone and uploads the image using the profile update function of the app. The input is the user's body image, and the output is the image sent to the server.

[1073] Step 6: Submit a body image

[1074] The device sends the user's body image to the server. The image file is sent to the server using an HTTP POST request. The input is the body image, and the output is the image data sent to the server.

[1075] Step 7: Analyze physical characteristics

[1076] The server analyzes the user's facial shape and body shape using FaceNet and OpenPose. The images are fed into an AI model, which extracts facial and body features. The input is the received body image, and the output is the analyzed body feature data.

[1077] Step 8: Register your preferred style

[1078] The user enters their preferred style and brand in a questionnaire format. This information is sent from the device to the server. The input is the user's preference information, and the output is the information sent to the server.

[1079] Step 9: Save style information

[1080] The server stores the entered style and brand information based on the profile. It inserts the data in the appropriate format for storage in the database. The input is the user's preferences, and the output is the information added to the profile.

[1081] Step 10: Submit your clothing registration information

[1082] The user selects clothing items they own or plan to purchase. The device sends this information to the server. The input is the selected clothing information, and the output is the information sent to the server.

[1083] Step 11: Coordinate generation

[1084] The server uses a generative AI model (e.g., GPT-4) to generate the optimal outfit based on the submitted item information. The AI ​​model is fed with information and calculates the appropriate combination. The input is the submitted clothing information, and the output is the generated outfit.

[1085] Step 12: Display your outfit

[1086] The generated outfit is presented to the user through the app screen. The device displays the information received from the server on a GUI. The input is the generated outfit information, and the output is the outfit displayed to the user.

[1087] Step 13: Enter your fashion questions

[1088] The user types a fashion question into the chat box. The device receives the question from the text input field. The input is the user's question, and the output is the question sent to the server.

[1089] Step 14: Submit your question

[1090] The terminal sends the entered question to the server. The question text is sent using an HTTP request. The input is the question text, and the output is the text sent to the server.

[1091] Step 15: Parsing the Question

[1092] The server analyzes the question using natural language processing techniques (e.g., BERT or GPT-3), tokenizes the text data, and analyzes its meaning. The input is the received question text, and the output is the analyzed semantic information.

[1093] Step 16: Generate and communicate answers

[1094] The server generates an appropriate answer based on the analysis results and sends it to the terminal. The input is the analyzed question data, and the output is the generated answer text. The terminal displays this answer in the user's chat box.

[1095] Step 17: Select items for online shopping

[1096] The user selects the clothing item they are considering purchasing within the app. The device sends the selection information to the server. The input is the item information selected by the user, and the output is the item information sent to the server.

[1097] Step 18: Obtaining Purchase Information

[1098] The server uses the API of the online shopping site to get details and a purchase link. It sends an API request and parses the response. The input is the selected item information, and the output is the retrieved details and link.

[1099] Step 19: View Purchase Links

[1100] The acquired purchase link is sent to the user's device and displayed within the app. The input is the acquired link information, and the output is the purchase link displayed to the user.

[1101] Step 20: Emotion Recognition

[1102] When a user uses the system, the emotion engine analyzes the user's facial expressions and voice through the camera and microphone to recognize their emotional state. The input is camera and microphone data acquired in real time, and the output is analyzed emotion data.

[1103] Step 21: Sending Emotion Data

[1104] The device sends emotion recognition data to the server. The input is the analyzed emotion data, and the output is the data sent to the server.

[1105] Step 22: Emotion-Based Suggestions

[1106] The server receives the emotional data and tailors clothing suggestions based on the user's emotional state, with the emotional data as input and the tailored suggestions as output, resulting in a display of clothing suggestions suited to the user.

[1107] The system operates according to the above processing steps, allowing the user to efficiently manage clothing and receive optimal style suggestions.

[1108] (Application example 2)

[1109] 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."

[1110] Conventional digital closet systems have the ability to manage a user's clothing and provide style suggestions, but they lack the ability to provide real-time suggestions that take into account the user's emotional state. Furthermore, users have limited means to check the fit and appearance of clothing without actually trying them on, which can make purchasing decisions difficult. Therefore, there is a need for a system that can suggest clothing items based on the user's emotional state and enable virtual try-on.

[1111] 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.

[1112] In this invention, the server includes means for a user to upload images of clothing items, means for transmitting the images to the server, means for the server to analyze the images, extract clothing features, and store them in a database, means for automatically updating the user's digital closet, means for adjusting clothing suggestions based on the user's emotional state using an emotion engine that analyzes the user's emotions, and means for using smart glasses or a head-mounted display for the user to have a virtual try-on experience. This allows the user to receive clothing suggestions that are optimal for their emotional state and to check the fit and appearance of the clothing in real time without actually trying it on.

[1113] The "means for uploading images of clothing" is an interface that allows users to import photos of clothing they own into the system.

[1114] "Means for transmitting said images to a server" refers to a method or protocol for transferring uploaded images to a server over the Internet.

[1115] The "means for extracting characteristics of clothing items and storing them in a database" refers to the process of using image analysis technology to extract information such as the color, pattern, style, and brand of clothing items and storing it in a database.

[1116] The "means for automatically updating a user's digital closet" is a system that uses the extracted clothing features to keep the user's clothing information up to date.

[1117] An "emotion engine that analyzes user emotions" is an algorithm or mechanism that analyzes the user's facial expressions, tone of voice, etc., and determines their emotional state.

[1118] "Means for adjusting clothing suggestions based on the user's emotional state" refers to a process for selecting the most suitable clothing items and outfits for a user based on the analyzed emotional information of the user.

[1119] "Means for using smart glasses or head-mounted displays to provide a virtual try-on experience" refers to devices that utilize augmented reality or virtual reality technology to allow users to try on clothing in a virtual space.

[1120] The "means for uploading body images" is an interface that allows users to upload full-body and facial photos into the system.

[1121] "Means of extracting the user's physical characteristics and understanding their style based on their profile" refers to the process of analyzing information such as body type and face shape from uploaded body images and suggesting styles that suit the user's preferences.

[1122] The "means for generating optimal outfits" refers to an algorithm that allows the AI ​​to consider the optimal combination based on the clothing items selected by the user and registered information.

[1123] The "means for providing the generated coordinated outfit to the user" refers to an interface or display method for showing the generated optimal clothing set to the user.

[1124] The present invention is a system that allows users to manage their clothing and provide optimal styles based on their emotional state. This system analyzes the user's emotional state in real time and suggests optimal outfits for the user through a virtual try-on experience using smart glasses or a head-mounted display. Specific embodiments of the system are described below.

[1125] 1. Clothing Image Recognition and Management

[1126] Users take photos of their clothing and upload them to the system via their device. The images are then sent over the internet to a cloud server where they are analyzed using AI technology. This analysis uses the Google Cloud Vision API to extract characteristics such as the color, pattern, style, and brand of the clothing. The extracted information is stored in a database, and the user's digital closet is automatically updated.

[1127] 2. Face and body recognition and style preference understanding

[1128] Users take full-body and facial images and upload them to the system via smart glasses or a head-mounted display. These images are sent to a cloud server and analyzed by an AI model using TensorFlow. This recognizes the user's face shape and body type, and updates their profile. In addition, users provide information about their preferred styles and brands in a questionnaire format.

[1129] 3. AI Coordination Simulation

[1130] When a user selects clothing items they own or are considering purchasing, the device sends this selection information to a cloud server, which then uses AI technology to generate the optimal outfit. The generated outfit is displayed to the user in real time via smart glasses or a head-mounted display. Users can check the fit and appearance of the clothing in a virtual space without actually trying it on.

[1131] 4. Proposals linked to emotion engine

[1132] When a user uses the system, the emotion engine analyzes the user's facial expressions and voice through the camera and microphone to recognize emotions in real time. This emotion analysis uses the Microsoft Azure Emotion API. The cloud server receives data from the emotion engine and adjusts clothing recommendations based on the user's emotional state. For example, if the system recognizes that the user is feeling stressed, it will suggest relaxing, casual clothing.

[1133] Specific examples

[1134] If a user takes a photo of a red dress and uploads it to the app, they could use a prompt like this:

[1135] Example prompt: "I want to upload and manage my red dress in an app."

[1136] If a user takes a full-body photo and registers body shape information, the following prompt sentence can be used:

[1137] Example prompt: "Take a full-body photo and register your body shape information."

[1138] To suggest relaxing casual clothing based on emotional state, you can use prompts such as:

[1139] Example prompt: "Analyze your emotional state and suggest relaxing casual wear."

[1140] This allows users to receive recommendations for clothing that are optimal for their emotional state, and allows them to check the fit and appearance of the clothing in real time without actually trying it on.

[1141] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1142] Step 1:

[1143] The user takes a photo of the clothing item and uploads it to the device. At this time, the user takes a picture of the clothing item they own using a device such as a smartphone or PC. The image is then imported into the system using the device's upload function.

[1144] Input: Clothing photo

[1145] Output: Uploaded image file on device

[1146] What happens: A user takes a photo of a red dress with their smartphone camera and uploads it to the app.

[1147] Step 2:

[1148] The device sends the uploaded image to a cloud server, which then transfers the image over the internet and prepares it for analysis on the server side.

[1149] Input: Uploaded image file

[1150] Output: Image data transferred to the server

[1151] Specific behavior: The app automatically sends the image to the cloud server and notifies the user that the image has been sent.

[1152] Step 3:

[1153] The server analyzes the image and extracts the clothing's characteristics. An image analysis engine (using Google Cloud Vision API) on the cloud server processes the image data and extracts characteristics such as the clothing's color, pattern, style, and brand.

[1154] Input: Image data transferred to the server

[1155] Output: Extracted clothing feature data

[1156] Specific operation: The server detects the characteristics of the clothing item (e.g., red, dress, brand Y) and stores the results in a database.

[1157] Step 4:

[1158] Automatically updates the user's digital closet: The extracted clothing feature information is stored in a database, and the user's digital closet is updated to the latest version.

[1159] Input: Clothing feature data

[1160] Output: Updated digital closet

[1161] What happens: A new item (a red dress) is added to the database and the user's digital closet is automatically updated.

[1162] Step 5:

[1163] The user takes a photo of their entire body or face and uploads it to the device. The user uses smart glasses or a head-mounted display to take a photo of their entire body or face.

[1164] Input: A full-body or face image of the user

[1165] Output: Uploaded image file on device

[1166] What happens: The user takes a full-body photo with the camera on their smart glasses and uploads it to the app.

[1167] Step 6:

[1168] The device sends the uploaded full-body or facial images to a cloud server, which then transfers the images over the internet and prepares them for analysis on the server side.

[1169] Input: Uploaded full-body or face image

[1170] Output: Image data of the whole body and face sent to the server

[1171] Specific behavior: The app automatically sends the image to the cloud server and notifies the user that the image has been sent.

[1172] Step 7:

[1173] The server analyzes the image and extracts the user's physical features. An image analysis engine (using TensorFlow) on the cloud server processes the image data and extracts the user's facial shape and body shape features.

[1174] Input: Image data of the whole body and face transferred to the server

[1175] Output: Extracted user's body feature data

[1176] What it does: The server detects the user's height, body type, and facial features and updates the user's profile.

[1177] Step 8:

[1178] Users provide information about their preferred styles and brands in the form of a questionnaire. Users answer the questionnaire via their smartphone or PC, and the information is stored on the server.

[1179] Input: User's style and brand preferences

[1180] Output: Updated user profile

[1181] Specific operation: The user answers a questionnaire on the app, saying, "I like casual, light-colored clothes," and this information is stored on the server.

[1182] Step 9:

[1183] The user selects clothing items they own or are considering purchasing. The user selects items from the virtual closet and the selection information is sent to the server.

[1184] Input: Data about clothing items selected by the user

[1185] Output: Selection information transmitted to the server

[1186] Specific behavior: The user selects a white shirt, blue jeans, and sneakers from a virtual closet.

[1187] Step 10:

[1188] The server generates the optimal outfit based on the selection information, and the AI ​​coordination engine on the cloud server proposes the optimal outfit based on the selection information and saved profile data.

[1189] Input: Selection information and profile data

[1190] Output: Proposed optimal coordinates

[1191] Specific operation: The server generates an outfit consisting of "white shirt + blue jeans + sneakers" and notifies the user.

[1192] Step 11:

[1193] Users use smart glasses or a head-mounted display to have a virtual try-on experience. The generated outfits are displayed on the device in real time, and the user can try them on in a virtual space.

[1194] Input: Suggested optimal coordinates

[1195] Output: Real-time display of virtual try-on experience

[1196] Specific operation: The user puts on the smart glasses and checks the generated outfit in the virtual space.

[1197] Step 12:

[1198] The emotion engine analyzes the user's facial expressions and voice through the camera and microphone to recognize emotions in real time, and this emotional information is sent to the server, which then adjusts clothing recommendations based on the user's emotional state.

[1199] Input: User facial and voice data

[1200] Output: Recognized emotional state and tailored clothing recommendations

[1201] Specific operation: The emotion engine recognizes that the user is feeling stressed from the camera footage, and the server suggests casual clothing that will help them relax.

[1202] 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.

[1203] 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.

[1204] 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.

[1205] [Third embodiment]

[1206] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1207] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1208] 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).

[1209] 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.

[1210] 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.

[1211] 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).

[1212] 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.

[1213] 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.

[1214] 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.

[1215] 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.

[1216] 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.

[1217] 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."

[1218] This invention is a system that allows users to efficiently manage their clothing and suggests the most suitable style. This system is realized by users uploading images of their clothing, which the server analyzes. The system also identifies the user's face, body type, and style preferences, and suggests coordination ideas with existing clothing and new clothing to be purchased.

[1219] Overall system configuration

[1220] This system consists of a user, a terminal, and a server. Users operate the system using a terminal such as a smartphone or PC to manage their clothing, register their physical characteristics, simulate styles, and link with online shopping.

[1221] Program processing

[1222] 1. Clothing Image Recognition and Management

[1223] Users take photos of their clothing and upload them to the app via their device. The device then sends the uploaded images to a server, which analyzes them. This analysis uses AI technology to extract characteristics such as the color, pattern, style, and brand of the clothing. The server then stores this information in a database and automatically updates the user's digital closet.

[1224] Example: If a user uploads a photo of a red dress, the server analyzes the image, generates tags for "red," "dress," and "brand Y," and adds them to the user's digital closet.

[1225] 2. Face and body recognition and style preference understanding

[1226] Users upload photos of their face and entire body. The device sends these images to a server, which uses AI models to analyze the user's face shape and body type. This allows the server to understand the user's physical characteristics and update their profile. Users can also provide information about their preferred styles and brands in a questionnaire format. This information is stored on the server and used to suggest styles to the user.

[1227] Example: If a user uploads a full-body photo and replies, "I like casual, light-colored clothing," the server adds information to their profile such as "height 170cm," "slim build," "casual," and "light colors."

[1228] 3. AI Coordination Simulation

[1229] Through the system, users select clothing items they own or plan to purchase. The device sends this selection information to the server, which then uses AI technology to generate the optimal outfit. The generated outfit is presented to the user through the app screen.

[1230] Example: If a user selects "white shirt," "blue jeans," and "sneakers," the server will suggest a combination of "white shirt + blue jeans + sneakers" and display it to the user.

[1231] 4. AI Fashion Consultation Room

[1232] The user enters a fashion-related question into the chat box. The device sends this input to the server, which analyzes the question using natural language processing (NLP). The server generates an appropriate answer based on the analysis results and provides it to the user via the device.

[1233] Example: A user asks, "What outerwear is recommended for fall?" and the server responds, "Casual jackets and hoodies are suitable for fall."

[1234] 5. Online shopping integration

[1235] The user selects the clothing item they are considering purchasing within the app. The device sends the selected item information to the server, and the server obtains detailed information and a purchase link via the online shopping site's API. The user can then check the item within the app and access the purchase page.

[1236] Example: When a user wants to buy a new coat and enters that information into the simulation, the server suggests outfits for the new coat and a scarf they already own, and also displays a link to an online shopping site to purchase the coat.

[1237] The above is a specific embodiment of the system of the present invention, which allows users to efficiently manage their clothing and receive style suggestions.

[1238] The processing flow will be explained below.

[1239] Clothing image recognition and management

[1240] Step 1:

[1241] The user takes a photo of the clothing item they own using the camera on their smartphone or PC.

[1242] Step 2:

[1243] The device temporarily stores the captured photo and asks the user for confirmation through the user interface.

[1244] Step 3:

[1245] After the user confirms, the device sends the photo to the server.

[1246] Step 4:

[1247] The server then feeds the received images into an analytical AI model, which extracts features such as color, pattern, style, and brand.

[1248] Step 5:

[1249] The server stores the extracted features in a database and generates a corresponding item ID.

[1250] Step 6:

[1251] The server updates the user's digital closet to reflect the added item information.

[1252] Step 7:

[1253] Users can see new items added to their digital closet through the app's UI.

[1254] Face and body recognition and style preference identification

[1255] Step 1:

[1256] The user uses the app to take a photo of their face and a full-body photo.

[1257] Step 2:

[1258] The device temporarily stores the captured photo and asks the user for confirmation through the user interface.

[1259] Step 3:

[1260] After the user confirms, the device sends the photo to the server.

[1261] Step 4:

[1262] The images received by the server are input into an analytical AI model to extract the user's facial and body features.

[1263] Step 5:

[1264] The server stores the extracted features in a database and updates the user's profile information.

[1265] Step 6:

[1266] Users provide information about their preferred fashion styles, brands, colors, etc. in a survey format within the app.

[1267] Step 7:

[1268] The server stores the user's style tendency in a database based on the provided style information.

[1269] AI Coordination Simulation

[1270] Step 1:

[1271] The user opens the simulation screen and selects clothing items they own or are considering purchasing.

[1272] Step 2:

[1273] The terminal temporarily stores the selected item information and asks the user for confirmation through the user interface.

[1274] Step 3:

[1275] After the user confirms, the terminal transmits the item information to the server.

[1276] Step 4:

[1277] Based on the information received by the server, an AI model for simulation is executed.

[1278] Step 5:

[1279] The server generates the optimal coordinates and stores the coordinate results in a database.

[1280] Step 6:

[1281] The server transmits the generated coordination results to the terminal.

[1282] Step 7:

[1283] Through the app's UI, users can check the outfits suggested by the AI ​​and make evaluations or changes.

[1284] AI Fashion Consultation Room

[1285] Step 1:

[1286] Users can enter their fashion-related questions or concerns into the chat box within the app.

[1287] Step 2:

[1288] The terminal sends the entered text to the server.

[1289] Step 3:

[1290] The server inputs the received text into a natural language processing (NLP) model for analysis.

[1291] Step 4:

[1292] The server understands the meaning of the question and generates an appropriate answer.

[1293] Step 5:

[1294] The server generates a response and sends it to the terminal.

[1295] Step 6:

[1296] Users can review the answers and ask further questions or provide feedback as needed through the app's UI.

[1297] Online shopping integration

[1298] Step 1:

[1299] The user selects the item they are considering purchasing through the app.

[1300] Step 2:

[1301] The terminal temporarily stores the selected item information and asks the user for confirmation through the user interface.

[1302] Step 3:

[1303] After the user confirms, the terminal transmits the item information to the server.

[1304] Step 4:

[1305] The server retrieves detailed information, price, and stock status of the selected item via the online shopping site's API.

[1306] Step 5:

[1307] Based on the information received by the server, it runs a simulation AI model and generates an outfit that combines the items the customer is considering purchasing with clothing they already own.

[1308] Step 6:

[1309] The server sends the generated coordination results and a purchase link to the terminal.

[1310] Step 7:

[1311] Through the app's UI, users can check the AI's suggested outfits and purchase links, and proceed with the purchase process if necessary.

[1312] Example 1

[1313] 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."

[1314] Managing clothing and suggesting outfits takes a lot of time and effort, placing a significant burden on users. Furthermore, there is a lack of systems that can suggest optimal outfits that reflect a user's individual physical characteristics and style preferences. Furthermore, there is a lack of efficient methods for providing detailed information and links to purchase items that users are considering purchasing. There is a need for a system that can solve these issues and enable users to efficiently manage clothing and receive style suggestions.

[1315] 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.

[1316] In this invention, the server includes: means for a user to upload images of clothing items; means for transmitting the images to the server; means for the server to analyze the images, extract clothing features, and store the extracted features in a database; means for automatically updating the user's digital closet; means for a user to upload body images; means for transmitting the body images to the server; means for the server to analyze the body images and identify the user's body features and style preferences; means for transmitting information about clothing items selected by the user to the server; means for the server to generate optimal outfits using AI technology; means for providing the generated outfits to the user; means for the user to input fashion-related questions into a chat box; means for transmitting the input questions to the server; means for the server to generate appropriate answers using natural language processing technology and provide them to the user; means for the user to select clothing items for online shopping; means for the server to obtain detailed information and purchase links from an API of an online shopping site; and means for providing the purchase links to the user. This allows users to efficiently manage their clothing and receive suggestions for optimal outfits based on their individual body features and style preferences. In addition, by linking with online shopping, it will be possible to efficiently obtain detailed information and purchase links for items being considered for purchase.

[1317] "User" refers to an individual who uses the system to manage clothing items and receive coordination suggestions.

[1318] "Means for uploading images of clothing items" refers to a function that allows users to import photos of clothing items into the system.

[1319] "Server" refers to the central system that receives and analyzes data sent by users, and processes and manages various information.

[1320] "Means for transmitting images to a server" refers to a communication function for transferring image data captured by a user to a server.

[1321] "Analyzing an image" refers to the process by which the server extracts detailed information from the image it receives.

[1322] "Database" refers to a digital repository for storing and managing extracted clothing characteristics.

[1323] "User's digital closet" refers to a function that centrally manages information about the clothing items owned by the user in digital format.

[1324] "Means for uploading body images" refers to a function that allows users to upload photos of their bodies into the system.

[1325] "Physical characteristics" refers to the user's own physical characteristics, such as the user's body shape and facial shape.

[1326] "Style preferences" refer to the fashion trends and brands that a user prefers.

[1327] "Means for transmitting clothing information" refers to a communication function for transferring data on clothing items selected by the user to the server.

[1328] "AI technology" refers to technology that uses artificial intelligence to analyze data and generate coordination.

[1329] "Means for generating coordination" refers to a function that uses AI technology to suggest clothing combinations.

[1330] "Means for providing to the user" refers to a function for displaying the generated coordination and answers on the user's terminal.

[1331] "Chat box" refers to an interface that allows users to enter questions and send them to the server.

[1332] "Natural language processing technology" refers to artificial intelligence technology that enables a server to understand input text and generate appropriate responses.

[1333] "Means for selecting clothing items for online shopping" refers to a function that allows a user to select clothing items within the system that they are considering purchasing.

[1334] "API" refers to an interface for exchanging data between different software programs.

[1335] "Detailed information" refers to specific information about the purchase, such as the characteristics, price, and availability of the clothing item.

[1336] "Purchase Link" refers to the access URL to an online shopping site for purchasing the clothing item selected by the user.

[1337] The present invention relates to a system that allows users to efficiently manage their clothing and suggests optimal styles. This system allows users to upload images of their clothing and body, and analyzes them to optimize the user's style.

[1338] Overall system configuration

[1339] This system consists of a user, a device, and a server. Users operate the system using devices such as smartphones or PCs. Major operations include managing clothing items, registering physical characteristics, simulating styles, and linking with online shopping.

[1340] Clothing image recognition and management

[1341] Users first take a photo of their clothing and upload it to the app via their device. The device then sends the image to a server, which analyzes it. This analysis process uses AI technology to extract characteristics from the image, such as the color, pattern, style, and brand of the clothing. Once the analysis is complete, the server stores this information in a database and automatically updates the user's digital closet.

[1342] Example: If a user uploads a photo of a red dress, the server analyzes the image and generates tags for "red," "dress," and "brand X," which are added to the user's digital closet.

[1343] Face and body recognition and style preference identification

[1344] Next, users upload photos of their face and body. The device sends these images to a server, which uses AI models to analyze the images and detect the user's face shape and body type. The information obtained by the server is reflected in the user's profile and used to suggest style recommendations. Users can also answer a questionnaire about their preferred styles and brands, and this information is also stored on the server.

[1345] Example: If a user uploads a full-body photo and answers a survey saying, "I like casual, light-colored clothing," the server adds information such as "height 170cm," "slim build," "casual," and "light colors" to the profile.

[1346] AI Coordination Simulation

[1347] Users select clothing items they already own or plan to purchase and run a simulation. Information about the selected items is sent from the device to the server, which then uses AI technology to generate the optimal outfit. The generated outfit is then presented to the user on the app screen.

[1348] Example: If a user selects "white shirt," "blue jeans," and "sneakers," the server will suggest a coordination of "white shirt + blue jeans + sneakers" and display it on the user's device.

[1349] AI Fashion Consultation Room

[1350] Users enter fashion-related questions into the chat box and send them from their devices to the server, which uses natural language processing technology to analyze the questions and generate appropriate answers, which are then provided to the user's devices.

[1351] Example: When a user types a question into a chat box such as "What outerwear is recommended for autumn?", the server responds with "Casual jackets and hoodies are suitable for autumn" and displays it to the user.

[1352] Online shopping integration

[1353] The user selects the clothing item they are considering purchasing within the app, and their device sends that information to the server. The server then obtains detailed information and a purchase link via the online shopping site's API and provides it to the user. The user can then check the details within the app and access the purchase page.

[1354] Example: When a user selects a new coat to purchase, the server suggests coordinating the new coat with a scarf they already own and displays a link to purchase the item.

[1355] This system allows users to efficiently manage their clothing and receive style suggestions optimized for their physical characteristics and preferences. The online shopping function also allows for a smooth purchasing process.

[1356] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1357] Step 1: Take and upload your clothing image

[1358] 1. The user takes a photo of the clothing item using a smartphone or PC.

[1359] 2. The device displays a UI (user interface) for uploading the captured image to the app.

[1360] 3. When the user presses the upload button, the device sends the image to the server.

[1361] Specific operation: A user takes a photo of a red dress with their smartphone and taps the "upload" button in the app. The device then sends the image to the server.

[1362] Input: Clothing image

[1363] Output: Notification of completion of transmission to the server

[1364] Step 2: Image analysis and database update

[1365] 1. The server analyzes the images received using artificial intelligence (AI) technology.

[1366] 2. The server extracts features such as color, pattern, style, and brand of the clothing from the image.

[1367] 3. The server stores the extracted feature information in a database and automatically updates the user's digital closet.

[1368] What it does: The server analyzes the image, creates tags such as "red," "dress," and "Brand X," and adds these tags to the database.

[1369] Input: User-uploaded clothing images

[1370] Output: Analyzed clothing feature information (color, pattern, style, brand)

[1371] Step 3: Face and body recognition and profile update

[1372] 1. The user takes a photo of their face or whole body and uploads it via their device.

[1373] 2. The server uses an AI model to analyze the face shape and body type based on the images received.

[1374] 3. The server updates the user's profile based on the analysis results. It also collects information about the user's preferred styles and brands through a questionnaire and stores that information on the server.

[1375] Specific operation: The server performs analysis and adds characteristics such as the user's height of 170 cm and slim build to the profile. If the user answers a questionnaire saying that they like casual, light-colored clothing, this information is also added to the profile.

[1376] Input: User's face photo, full-body photo, survey results

[1377] Output: Updated user profile

[1378] Step 4: AI Coordination Simulation

[1379] 1. The user selects clothing items they own or new items they plan to purchase within the app.

[1380] 2. The device sends the selected item information to the server.

[1381] 3. The server uses AI technology to generate the optimal coordination and sends the results to the device.

[1382] 4. The terminal displays the generated coordinates on the screen to the user.

[1383] Specific operation: The user selects "white shirt," "blue jeans," and "sneakers," and taps the simulation button. The server generates an outfit called "white shirt + blue jeans + sneakers" and displays it on the user's device.

[1384] Input: Clothing information selected by the user

[1385] Output: Generated outfit suggestions

[1386] Step 5: AI Fashion Consultation

[1387] 1. The user types a fashion-related question into the chat box within the app.

[1388] 2. The device sends the entered question to the server.

[1389] 3. The server uses natural language processing (NLP) techniques to analyze the question and generate an appropriate answer.

[1390] 4. The server generates a response and sends it to the terminal, which displays it to the user.

[1391] Specific operation: The user enters a question in the chat box, "What outerwear is recommended for autumn?" and taps the send button. The server analyzes the question and replies, "A casual jacket or hoodie is suitable for autumn."

[1392] Input: User fashion question

[1393] Output: The generated answer

[1394] Step 6: Online shopping integration

[1395] 1. The user selects the clothing item they are considering purchasing within the app.

[1396] 2. The device sends the selected item information to the server.

[1397] 3. The server retrieves details and a purchase link via the online shopping site's API.

[1398] 4. The server sends the retrieved details and purchase link to the device, which displays them to the user.

[1399] Specific operation: When a user tries to purchase a "new coat" and selects that item and enters it into the simulation, the server proposes a combination of "new coat + existing scarf" and a purchase link, which are displayed on the user's device.

[1400] Input: Information about the clothing item selected by the user

[1401] Output: More information and purchase links

[1402] This allows users to efficiently manage their clothing and receive style suggestions, and also makes online shopping smoother.

[1403] (Application example 1)

[1404] 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."

[1405] In order to effectively manage the clothing items users currently own or are considering purchasing, and to provide optimal fashion coordination, users have traditionally had to try on the items in person. However, this is time-consuming and expensive, and often results in items not fitting their preferences or body type. Therefore, there is a need for a system that allows users to easily try on items in a virtual environment.

[1406] 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.

[1407] In this invention, the server includes means for a user to upload an image of a clothing item, means for transmitting the image to the server, means for the server to analyze the image, extract characteristics of the clothing item, and store the characteristics in a database, means for automatically updating the user's digital closet, means for the user to select a new clothing item and provide a virtual try-on, and means for displaying a purchase link based on the virtual try-on, thereby enabling the user to easily try on the clothing item virtually from home, confirm the best style, and purchase it immediately.

[1408] A "user" is an individual who uses this system to manage clothing items and receive coordination suggestions.

[1409] "Clothing images" are images that users take of clothing they own or are considering purchasing with a smartphone or other device and upload to the app.

[1410] A "server" is a computer system that analyzes image data sent from users and manages various databases.

[1411] The "database" is a storage device that stores the characteristics of clothing analyzed by the server and the user's physical information.

[1412] A "digital closet" is an electronic closet that manages a digital list of clothing items owned by a user and is provided to the user through an application.

[1413] A "new clothing item" is a clothing item that the user does not yet own and is considering purchasing.

[1414] "Virtual try-on" is a function that virtually simulates the appearance of clothing based on the user's physical data and provides it to the user.

[1415] A "purchase link" is a link that allows users to purchase clothing items they like after virtual try-on directly online.

[1416] "Body images" are images that users take of their face or whole body and upload to the app.

[1417] A "profile" is personal data that includes a user's physical characteristics, preferred style, past purchase history, and so on.

[1418] "Coordination" refers to proposing the optimal fashion style by combining the user's clothing items or clothing items the user is considering purchasing.

[1419] This invention is a system that allows users to efficiently manage their clothing and suggest outfits. The system is designed to allow users to easily upload their clothing and body data using a device such as a smartphone, and to simulate optimal fashion styles via a server.

[1420] Overall system configuration

[1421] This system consists of a user, a terminal, and a server. The user operates the system using the terminal to upload images of clothing, register physical characteristics, try on the clothing virtually, and link it to online shopping.

[1422] Program processing

[1423] 1. Clothing Image Recognition and Management

[1424] Users take photos of their clothing items using a smartphone or other device and upload them to the app.

[1425] The uploaded image is sent from the device to the server.

[1426] The server analyzes the received images using AI technology (e.g., TensorFlow) to extract characteristics such as the color, pattern, style, and brand of the clothing.

[1427] The extracted information is stored in a database (e.g., MongoDB) and automatically updates the user's digital closet.

[1428] 2. Face and body recognition and style preference understanding

[1429] Users take a photo of their face or whole body and upload it to the app.

[1430] The uploaded image is sent from the device to the server.

[1431] The server analyzes these images using AI models (e.g., OpenPose, DeepFace) to extract the user's facial shape and body features.

[1432] Based on the user's profile, the system reflects information about preferred styles and brands.

[1433] 3. AI Coordination Simulation

[1434] Through the system, users select clothing items they already own or items they are considering purchasing.

[1435] The server uses an AI algorithm to generate the optimal outfit based on the selected information.

[1436] The generated outfits are combined with the user's body data as a virtual try-on and presented to the user in real time.

[1437] 4. Virtual try-on

[1438] The selected item is superimposed onto the user's photo and displayed in the app, as if they were actually trying it on.

[1439] Users can use this feature to see if a new style suits them.

[1440] 5. Linking with online shopping

[1441] If you find an item you like, you can access the online shopping site directly from the app and proceed with the purchase.

[1442] The purchase link is generated by the server and provided to the user.

[1443] Specific examples

[1444] If a user wants to coordinate a "blue shirt" they own with a "black jacket" they found in an online shop:

[1445] When a user uploads a photo of these two items to the app, the server analyzes the photo and generates a virtual try-on image that combines the "blue shirt" and "black jacket" based on the user's facial and body data.

[1446] This allows users to easily check whether a new jacket will suit them.

[1447] If you like it, you can purchase the jacket directly from the online shop via the purchase link.

[1448] Prompt Sentence Examples

[1449] "I'd like to virtually try on a new jacket and see if it goes with my blue shirt. How can I do that?"

[1450] "I like the outfit I created and would like to purchase this jacket online. Can you give me a link to purchase it?"

[1451] Through the above steps, the system of the present invention allows users to easily manage clothing items and try on outfits from home, confirm the best style, and then purchase the items.

[1452] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1453] Step 1: Upload an image

[1454] Subject: User

[1455] Users take photos of clothing or their own bodies using a device such as a smartphone and upload them to the app. As input, the user provides image data, and as output, the image files are saved on the device.

[1456] Step 2: Send image

[1457] Subject: Terminal

[1458] The terminal sends the uploaded image to the server. In this process, the terminal sends the image file to the server using the HTTP protocol or similar. The uploaded image data is the input, and the image data arrives at the server as the output.

[1459] Step 3: Image analysis

[1460] Subject: Server

[1461] The server analyzes the received image data using AI technology (e.g., TensorFlow). Image recognition algorithms are used for the analysis to extract features such as the color, pattern, style, and brand of the clothing. The input is the image data, and the output is the extracted feature data.

[1462] Step 4: Save Data

[1463] Subject: Server

[1464] The server stores the extracted feature data in a database (e.g., MongoDB). This process adds a new entry to the database. The input is the feature data, and the output is the updated database.

[1465] Step 5: Update your digital closet

[1466] Subject: Server

[1467] The server updates the user's digital closet based on the new entries in the database. The input is the newly added feature data, and the output is the updated digital closet information.

[1468] Step 6: Body Data Analysis

[1469] Subject: Server

[1470] The server analyzes the face and body images uploaded by the user using an AI model (e.g., OpenPose, DeepFace). The input is image data of the body, and the output is data on the user's physical characteristics.

[1471] Step 7: Update your profile

[1472] Subject: Server

[1473] The server updates the user's profile with the physical feature data and adds preferred style and brand information to the profile. The input is the physical feature data and the user's style information, and the output is an updated user profile.

[1474] Step 8: Item Selection

[1475] Subject: User

[1476] Users select clothing items they own or are considering purchasing through the system. The selected clothing information is provided to the app as input, and the selection information is sent to the server as output.

[1477] Step 9: Coordinate Generation

[1478] Subject: Server

[1479] The server uses an AI algorithm to generate the optimal outfit based on the selection information. The input is the selection information and the user profile, and the output is the generated outfit data.

[1480] Step 10: Generate a virtual try-on

[1481] Subject: Server

[1482] The server generates a virtual try-on image by combining the user's body data and clothing items based on the generated coordinate data. The inputs are coordinate data and body feature data, and the output is a virtual try-on image.

[1483] Step 11: Displaying try-on images

[1484] Subject: Terminal

[1485] The terminal displays the virtual try-on image received from the server to the user. The virtual try-on image data is input, and the image is displayed on the terminal screen as output.

[1486] Step 12: View the purchase link

[1487] Subject: Server

[1488] The server generates a purchase link for the item the user likes during the virtual try-on and sends it to the device. The input is the try-on image and purchase information, and the output is the purchase link.

[1489] Step 13: Online Shopping

[1490] Subject: User

[1491] A user clicks on a purchase link displayed on the device to purchase an item on an online shopping site. The input is the purchase link, and the output is the online shop's purchase page.

[1492] 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.

[1493] The present invention is a system that allows users to efficiently manage their clothing and suggests optimal styles. This system is combined with an emotion engine that recognizes the user's emotions, and adds a function to suggest clothing items based on the user's emotional state.

[1494] Overall system configuration

[1495] This system is composed of a user, a device, a server, and an emotion engine. Users operate the system using devices such as smartphones or PCs to manage their clothing, register their physical characteristics, simulate styles, and link with online shopping. The emotion engine also analyzes the user's facial expressions and behavior to make suggestions based on their emotional state.

[1496] Program processing

[1497] 1. Clothing Image Recognition and Management

[1498] Users take photos of their clothing and upload them to the app via their device. The device then sends the uploaded images to a server, which analyzes them. This analysis uses AI technology to extract characteristics such as the color, pattern, style, and brand of the clothing. The server then stores this information in a database and automatically updates the user's digital closet.

[1499] Example: If a user uploads a photo of a red dress, the server analyzes the image, generates tags for "red," "dress," and "brand Y," and adds them to the user's digital closet.

[1500] 2. Face and body recognition and style preference understanding

[1501] Users upload photos of their face and entire body. The device sends these images to a server, which uses AI models to analyze the user's face shape and body type. This allows the server to understand the user's physical characteristics and update their profile. Users can also provide information about their preferred styles and brands in a questionnaire format. This information is stored on the server and used to suggest styles to the user.

[1502] Example: If a user uploads a full-body photo and replies, "I like casual, light-colored clothing," the server adds information to their profile such as "height 170cm," "slim build," "casual," and "light colors."

[1503] 3. AI Coordination Simulation

[1504] Through the system, users select clothing items they own or plan to purchase. The device sends this selection information to the server, which then uses AI technology to generate the optimal outfit. The generated outfit is presented to the user through the app screen.

[1505] Example: If a user selects "white shirt," "blue jeans," and "sneakers," the server will suggest a combination of "white shirt + blue jeans + sneakers" and display it to the user.

[1506] 4. AI Fashion Consultation Room

[1507] The user enters a fashion-related question into the chat box. The device sends this input to the server, which analyzes the question using natural language processing (NLP). The server generates an appropriate answer based on the analysis results and provides it to the user via the device.

[1508] Example: A user asks, "What outerwear is recommended for fall?" and the server responds, "Casual jackets and hoodies are suitable for fall."

[1509] 5. Online shopping integration

[1510] The user selects the clothing item they are considering purchasing within the app. The device sends the selected item information to the server, and the server obtains detailed information and a purchase link via the online shopping site's API. The user can then check the item within the app and access the purchase page.

[1511] Example: When a user wants to buy a new coat and enters that information into the simulation, the server suggests outfits for the new coat and a scarf they already own, and also displays a link to an online shopping site to purchase the coat.

[1512] 6. Suggestions linked to emotion engine

[1513] When a user uses the system, the emotion engine analyzes the user's facial expressions and voice through the camera and microphone to recognize emotions in real time. The server receives data from the emotion engine and adjusts clothing recommendations based on the user's emotional state. For example, it suggests casual clothes if the user wants to relax, and formal clothes if the user has an important meeting coming up.

[1514] Example: If the emotion engine recognizes that the user is feeling stressed, the server will suggest "relaxing casual wear" and coordinate clothing that will ease the user's mood.

[1515] Overall effect

[1516] This system allows users to efficiently manage their clothing and receive style suggestions that best suit their emotional state. Furthermore, by linking with online shopping, users can smoothly purchase new clothing. The addition of an emotion engine makes it possible to make suggestions based on the user's real-time emotions, providing a more personalized experience.

[1517] The processing flow will be explained below.

[1518] Clothing image recognition and management

[1519] Step 1:

[1520] The user takes a photo of the clothing item they own using the camera on their smartphone or PC.

[1521] Step 2:

[1522] The device temporarily stores the captured photo and asks the user for confirmation through the user interface.

[1523] Step 3:

[1524] After the user reviews and approves the photo, the device sends the photo to the server.

[1525] Step 4:

[1526] The server then feeds the received images into an analytical AI model, which extracts features such as color, pattern, style, and brand.

[1527] Step 5:

[1528] The server stores the extracted features in a database and generates a corresponding item ID.

[1529] Step 6:

[1530] The server updates the user's digital closet to reflect the added item information.

[1531] Step 7:

[1532] Users can see new items added to their digital closet through the app's UI.

[1533] Face and body recognition and style preference identification

[1534] Step 1:

[1535] The user uses the app to take a photo of their face and a full-body photo.

[1536] Step 2:

[1537] The device temporarily stores the captured photo and asks the user for confirmation through the user interface.

[1538] Step 3:

[1539] After the user reviews and approves the photo, the device sends the photo to the server.

[1540] Step 4:

[1541] The images received by the server are input into an analytical AI model to extract the user's facial and body features.

[1542] Step 5:

[1543] The server stores the extracted features in a database and updates the user's profile information.

[1544] Step 6:

[1545] Users provide information about their preferred fashion styles, brands, colors, etc. in a survey format within the app.

[1546] Step 7:

[1547] The server stores the user's style tendency in a database based on the provided style information.

[1548] AI Coordination Simulation

[1549] Step 1:

[1550] The user opens the simulation screen and selects clothing items they own or are considering purchasing.

[1551] Step 2:

[1552] The terminal temporarily stores the selected item information and asks the user for confirmation through the user interface.

[1553] Step 3:

[1554] After the user checks and approves the item information, the terminal sends the information to the server.

[1555] Step 4:

[1556] Based on the information received by the server, an AI model for simulation is executed.

[1557] Step 5:

[1558] The server generates the optimal coordinates and stores the coordinate results in a database.

[1559] Step 6:

[1560] The server transmits the generated coordination results to the terminal.

[1561] Step 7:

[1562] Through the app's UI, users can check the outfits suggested by the AI ​​and make evaluations or changes.

[1563] AI Fashion Consultation Room

[1564] Step 1:

[1565] Users can enter their fashion-related questions or concerns into the chat box within the app.

[1566] Step 2:

[1567] The terminal sends the entered text to the server.

[1568] Step 3:

[1569] The server inputs the received text into a natural language processing (NLP) model for analysis.

[1570] Step 4:

[1571] The server understands the meaning of the question and generates an appropriate answer.

[1572] Step 5:

[1573] The server generates a response and sends it to the terminal.

[1574] Step 6:

[1575] Users can review the answers and ask further questions or provide feedback as needed through the app's UI.

[1576] Online shopping integration

[1577] Step 1:

[1578] The user selects the item they are considering purchasing through the app.

[1579] Step 2:

[1580] The terminal temporarily stores the selected item information and asks the user for confirmation through the user interface.

[1581] Step 3:

[1582] After the user checks and approves the item information, the terminal sends the information to the server.

[1583] Step 4:

[1584] The server retrieves detailed information, price, and stock status of the selected item via the online shopping site's API.

[1585] Step 5:

[1586] Based on the information received by the server, it runs a simulation AI model and generates an outfit that combines the items the customer is considering purchasing with clothing they already own.

[1587] Step 6:

[1588] The server sends the generated coordination results and a purchase link to the terminal.

[1589] Step 7:

[1590] Through the app's UI, users can check the AI's suggested outfits and purchase links, and proceed with the purchase process if necessary.

[1591] Suggestions linked with emotion engine

[1592] Step 1:

[1593] When a user uses the system, the emotion engine analyzes the user's facial expressions and voice through the camera and microphone, recognizing emotions in real time.

[1594] Step 2:

[1595] The server receives the data from the emotion engine and stores the user's emotional state in a database.

[1596] Step 3:

[1597] The server adjusts the suggested clothing styles based on the user's emotional state.

[1598] Step 4:

[1599] The user receives style suggestions from the system, for example, casual attire if the user wants to relax, or formal attire if the user has an important meeting coming up.

[1600] Step 5:

[1601] The user checks clothing suggestions according to emotions and selects or purchases clothing items as needed.

[1602] Example 2

[1603] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1604] Conventional clothing management systems make it difficult for users to efficiently manage their clothing inventory, particularly lacking the ability to suggest optimal styles based on the user's physical characteristics and emotional state. Furthermore, they lack sufficient integration with online shopping, making it difficult for users to purchase new clothing. Furthermore, they lack adequate support for fashion consultations and style suggestions, which can lead to reduced user satisfaction. There is a need for a system that can solve these problems and provide a more personalized experience.

[1605] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1606] In this invention, the server includes a means for users to upload images of clothing, a means for users to upload images of their bodies, a means for users to input fashion-related questions, and a means for an emotion recognition engine to analyze the user's facial expressions and voice and recognize their emotional state. This allows users to efficiently manage their clothing and receive optimal style suggestions based on their physical characteristics and emotional state. Furthermore, by linking the system with online shopping, users can easily purchase new clothing and receive fashion advice and style suggestions, thereby improving user satisfaction.

[1607] "Clothing" refers to all fashion items such as clothes and accessories that users wear on a daily basis.

[1608] A "terminal" is a device that allows a user to access and operate the system, and specifically includes a smartphone, personal computer, tablet, etc.

[1609] A "server" refers to a computer system that receives input data from a user, analyzes it, and generates and provides appropriate information.

[1610] "Image" refers to a digital photograph or illustration that visually records an item of clothing, a user's body, or the like.

[1611] A "database" refers to a collection of data that systematically stores analyzed information and is managed so that it can be easily searched and updated.

[1612] The "digital closet" refers to a part of the system that has the function of managing information about clothing owned by a user in digital form and visually displaying it.

[1613] A "profile" refers to data that compiles and records personal information about a user, such as physical characteristics, style preferences, and brand preferences.

[1614] "Coordination" refers to a style or fashion proposal created by combining multiple clothing items.

[1615] "Natural language processing technology" refers to technology that enables computers to understand and generate human language, and includes algorithms and models used to analyze text and understand its meaning.

[1616] An "online shopping site" refers to a type of website where you can browse and purchase products over the Internet.

[1617] "API" refers to a defined interface that allows different software applications to communicate with each other and utilize each other's functionality.

[1618] An "emotion recognition engine" refers to a technology or system that analyzes a user's facial expressions and voice to recognize their emotional state at that time.

[1619] A "generative AI model" refers to an artificial intelligence model that allows a computer to generate new information based on given input data, and is particularly used for natural language generation and image generation.

[1620] A "prompt" is a textual instruction or question that a user enters to obtain information or trigger an action using a generative AI model.

[1621] The present invention is a system that allows users to efficiently manage their clothing and suggests the most suitable style, and by combining it with an emotion engine, it is possible to suggest clothing based on the user's emotional state.

[1622] 1. Overall system configuration

[1623] This system consists of a user, a device, a server, and an emotion engine. Users operate the system using devices such as smartphones or personal computers. Operations include managing clothing items, registering physical characteristics, simulating styles, and linking with online shopping.

[1624] 2. Clothing Management

[1625] Users take photos of their clothing and upload them to the app via their device. The device then sends the uploaded images to a server. The server then uses AI technologies such as TensorFlow and YOLO to analyze the images, extracting characteristics such as the color, pattern, style, and brand of the clothing, and stores them in a database. The user's digital closet is then automatically updated.

[1626] For example, if a user uploads a photo of a red dress, the server will analyze the image, generate tags for "red," "dress," and "brand X," and add them to the database.

[1627] 3. Registering physical features and understanding style

[1628] Users upload photos of their face and body. The device sends these images to a server. The server uses FaceNet and OpenPose to analyze the user's face shape and body type. The analyzed information is recorded in a profile and used to suggest styles to the user. Users also fill out a questionnaire to enter their preferred styles and brands, and this information is also saved in their profile.

[1629] For example, if a user uploads a full-body photo and replies that they like casual, light-colored clothing, the server will add information such as "height 170cm," "slim build," "casual," and "light colors" to their profile.

[1630] 4. AI Coordination Simulation

[1631] The user selects clothing items they own or items they plan to purchase. The device sends this selection information to the server, which then uses a generative AI model (e.g., GPT-4) to generate the optimal outfit. The generated outfit is presented to the user through the app screen.

[1632] As a specific example, if a user selects "white shirt," "blue jeans," and "sneakers," the server will use GPT-4 to suggest a combination of "white shirt + blue jeans + sneakers" and display it in the user's app.

[1633] 5. AI Fashion Consultation Room

[1634] The user enters a fashion-related question into the chat box. The device sends this input to the server. The server uses natural language processing techniques (e.g., BERT or GPT-3) to analyze the question and generate an appropriate answer. The device then displays this answer in the chat box.

[1635] For example, if a user asks, "What outerwear is recommended for autumn?", the server will analyze using BERT and answer, "Casual jackets and hoodies are suitable for autumn."

[1636] 6. Online shopping integration

[1637] The user selects the clothing item they are considering purchasing within the app. The device sends the selected item information to the server, which then uses the API of the online shopping site to obtain detailed information and a purchase link, which it then provides to the user.

[1638] As a concrete example, if a user wants to buy a new coat, the server will suggest a coordination of "new coat + existing scarf" and also display a link to an online shopping site to purchase the coat.

[1639] 7. Suggestions linked to emotion engine

[1640] When a user uses the system, the emotion engine analyzes the user's facial expressions and voice through the camera and microphone. The server receives the data from the emotion engine and adjusts clothing recommendations based on the user's emotional state.

[1641] As a specific example, if the emotion engine recognizes that the user is feeling stressed, the server will suggest "relaxing casual wear" and coordinate clothing that will ease the user's mood.

[1642] Prompt Sentence Examples

[1643] The prompt statements are as follows:

[1644] "Please tell me what outfits would suit my body type. I've uploaded a full-body photo."

[1645] "Please suggest a style that suits your mood today."

[1646] "What are some casual outfit combinations suitable for autumn?"

[1647] The above system configuration allows users to efficiently manage their clothing and enjoy a more personalized fashion experience according to their emotional state.

[1648] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1649] The flow of this system's program processing

[1650] Processing Steps

[1651] Step 1: Take and upload your clothing image

[1652] The user takes a photo of the clothing item they own and uploads it to the app using their device. The user takes a photo of the clothing item using the camera app on their smartphone and sends the image file by clicking the upload button in the app. The input is the image of the clothing item, and the output is the image sent to the server.

[1653] Step 2: Sending images

[1654] The device sends the uploaded image data to the server. The device uses an HTTP POST request to send the image file selected by the user to the server. The input is the image file, and the output is the image data sent to the server.

[1655] Step 3: Analyze the images

[1656] The server analyzes the received images using AI techniques such as TensorFlow and YOLO to extract the clothing features. The server converts the images into tensor format and feeds them into an AI model to extract features such as color, pattern, style, and brand. The input is the received image data, and the output is extracted feature data.

[1657] Step 4: Save feature data

[1658] The server stores the analysis results in a database and automatically updates the user's digital closet. The server inserts the extracted feature data into the database in an appropriate format. The input is the feature data, and the output is the feature data stored in the database.

[1659] Step 5: Upload a body image

[1660] The user takes a face or full-body photo and uploads the image to the server using the device. The user takes a face or full-body photo using the camera app on their smartphone and uploads the image using the profile update function of the app. The input is the user's body image, and the output is the image sent to the server.

[1661] Step 6: Submit a body image

[1662] The device sends the user's body image to the server. The image file is sent to the server using an HTTP POST request. The input is the body image, and the output is the image data sent to the server.

[1663] Step 7: Analyze physical characteristics

[1664] The server analyzes the user's facial shape and body shape using FaceNet and OpenPose. The images are fed into an AI model, which extracts facial and body features. The input is the received body image, and the output is the analyzed body feature data.

[1665] Step 8: Register your preferred style

[1666] The user enters their preferred style and brand in a questionnaire format. This information is sent from the device to the server. The input is the user's preference information, and the output is the information sent to the server.

[1667] Step 9: Save style information

[1668] The server stores the entered style and brand information based on the profile. It inserts the data in the appropriate format for storage in the database. The input is the user's preferences, and the output is the information added to the profile.

[1669] Step 10: Submit your clothing registration information

[1670] The user selects clothing items they own or plan to purchase. The device sends this information to the server. The input is the selected clothing information, and the output is the information sent to the server.

[1671] Step 11: Coordinate generation

[1672] The server uses a generative AI model (e.g., GPT-4) to generate the optimal outfit based on the submitted item information. The AI ​​model is fed with information and calculates the appropriate combination. The input is the submitted clothing information, and the output is the generated outfit.

[1673] Step 12: Display your outfit

[1674] The generated outfit is presented to the user through the app screen. The device displays the information received from the server on a GUI. The input is the generated outfit information, and the output is the outfit displayed to the user.

[1675] Step 13: Enter your fashion questions

[1676] The user types a fashion question into the chat box. The device receives the question from the text input field. The input is the user's question, and the output is the question sent to the server.

[1677] Step 14: Submit your question

[1678] The terminal sends the entered question to the server. The question text is sent using an HTTP request. The input is the question text, and the output is the text sent to the server.

[1679] Step 15: Parsing the Question

[1680] The server analyzes the question using natural language processing techniques (e.g., BERT or GPT-3), tokenizes the text data, and analyzes its meaning. The input is the received question text, and the output is the analyzed semantic information.

[1681] Step 16: Generate and communicate answers

[1682] The server generates an appropriate answer based on the analysis results and sends it to the terminal. The input is the analyzed question data, and the output is the generated answer text. The terminal displays this answer in the user's chat box.

[1683] Step 17: Select items for online shopping

[1684] The user selects the clothing item they are considering purchasing within the app. The device sends the selection information to the server. The input is the item information selected by the user, and the output is the item information sent to the server.

[1685] Step 18: Obtaining Purchase Information

[1686] The server uses the API of the online shopping site to get details and a purchase link. It sends an API request and parses the response. The input is the selected item information, and the output is the retrieved details and link.

[1687] Step 19: View Purchase Links

[1688] The acquired purchase link is sent to the user's device and displayed within the app. The input is the acquired link information, and the output is the purchase link displayed to the user.

[1689] Step 20: Emotion Recognition

[1690] When a user uses the system, the emotion engine analyzes the user's facial expressions and voice through the camera and microphone to recognize their emotional state. The input is camera and microphone data acquired in real time, and the output is analyzed emotion data.

[1691] Step 21: Sending Emotion Data

[1692] The device sends emotion recognition data to the server. The input is the analyzed emotion data, and the output is the data sent to the server.

[1693] Step 22: Emotion-Based Suggestions

[1694] The server receives the emotional data and tailors clothing suggestions based on the user's emotional state, with the emotional data as input and the tailored suggestions as output, resulting in a display of clothing suggestions suited to the user.

[1695] The system operates according to the above processing steps, allowing the user to efficiently manage clothing and receive optimal style suggestions.

[1696] (Application example 2)

[1697] 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."

[1698] Conventional digital closet systems have the ability to manage a user's clothing and provide style suggestions, but they lack the ability to provide real-time suggestions that take into account the user's emotional state. Furthermore, users have limited means to check the fit and appearance of clothing without actually trying them on, which can make purchasing decisions difficult. Therefore, there is a need for a system that can suggest clothing items based on the user's emotional state and enable virtual try-on.

[1699] 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.

[1700] In this invention, the server includes means for a user to upload images of clothing items, means for transmitting the images to the server, means for the server to analyze the images, extract clothing features, and store them in a database, means for automatically updating the user's digital closet, means for adjusting clothing suggestions based on the user's emotional state using an emotion engine that analyzes the user's emotions, and means for using smart glasses or a head-mounted display for the user to have a virtual try-on experience. This allows the user to receive clothing suggestions that are optimal for their emotional state and to check the fit and appearance of the clothing in real time without actually trying it on.

[1701] The "means for uploading images of clothing" is an interface that allows users to import photos of clothing they own into the system.

[1702] "Means for transmitting said images to a server" refers to a method or protocol for transferring uploaded images to a server over the Internet.

[1703] The "means for extracting characteristics of clothing items and storing them in a database" refers to the process of using image analysis technology to extract information such as the color, pattern, style, and brand of clothing items and storing it in a database.

[1704] The "means for automatically updating a user's digital closet" is a system that uses the extracted clothing features to keep the user's clothing information up to date.

[1705] An "emotion engine that analyzes user emotions" is an algorithm or mechanism that analyzes the user's facial expressions, tone of voice, etc., and determines their emotional state.

[1706] "Means for adjusting clothing suggestions based on the user's emotional state" refers to a process for selecting the most suitable clothing items and outfits for a user based on the analyzed emotional information of the user.

[1707] "Means for using smart glasses or head-mounted displays to provide a virtual try-on experience" refers to devices that utilize augmented reality or virtual reality technology to allow users to try on clothing in a virtual space.

[1708] The "means for uploading body images" is an interface that allows users to upload full-body and facial photos into the system.

[1709] "Means of extracting the user's physical characteristics and understanding their style based on their profile" refers to the process of analyzing information such as body type and face shape from uploaded body images and suggesting styles that suit the user's preferences.

[1710] The "means for generating optimal outfits" refers to an algorithm that allows the AI ​​to consider the optimal combination based on the clothing items selected by the user and registered information.

[1711] The "means for providing the generated coordinated outfit to the user" refers to an interface or display method for showing the generated optimal clothing set to the user.

[1712] The present invention is a system that allows users to manage their clothing and provide optimal styles based on their emotional state. This system analyzes the user's emotional state in real time and suggests optimal outfits for the user through a virtual try-on experience using smart glasses or a head-mounted display. Specific embodiments of the system are described below.

[1713] 1. Clothing Image Recognition and Management

[1714] Users take photos of their clothing and upload them to the system via their device. The images are then sent over the internet to a cloud server where they are analyzed using AI technology. This analysis uses the Google Cloud Vision API to extract characteristics such as the color, pattern, style, and brand of the clothing. The extracted information is stored in a database, and the user's digital closet is automatically updated.

[1715] 2. Face and body recognition and style preference understanding

[1716] Users take full-body and facial images and upload them to the system via smart glasses or a head-mounted display. These images are sent to a cloud server and analyzed by an AI model using TensorFlow. This recognizes the user's face shape and body type, and updates their profile. In addition, users provide information about their preferred styles and brands in a questionnaire format.

[1717] 3. AI Coordination Simulation

[1718] When a user selects clothing items they own or are considering purchasing, the device sends this selection information to a cloud server, which then uses AI technology to generate the optimal outfit. The generated outfit is displayed to the user in real time via smart glasses or a head-mounted display. Users can check the fit and appearance of the clothing in a virtual space without actually trying it on.

[1719] 4. Proposals linked to emotion engine

[1720] When a user uses the system, the emotion engine analyzes the user's facial expressions and voice through the camera and microphone to recognize emotions in real time. This emotion analysis uses the Microsoft Azure Emotion API. The cloud server receives data from the emotion engine and adjusts clothing recommendations based on the user's emotional state. For example, if the system recognizes that the user is feeling stressed, it will suggest relaxing, casual clothing.

[1721] Specific examples

[1722] If a user takes a photo of a red dress and uploads it to the app, they could use a prompt like this:

[1723] Example prompt: "I want to upload and manage my red dress in an app."

[1724] If a user takes a full-body photo and registers body shape information, the following prompt sentence can be used:

[1725] Example prompt: "Take a full-body photo and register your body shape information."

[1726] To suggest relaxing casual clothing based on emotional state, you can use prompts such as:

[1727] Example prompt: "Analyze your emotional state and suggest relaxing casual wear."

[1728] This allows users to receive recommendations for clothing that are optimal for their emotional state, and allows them to check the fit and appearance of the clothing in real time without actually trying it on.

[1729] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1730] Step 1:

[1731] The user takes a photo of the clothing item and uploads it to the device. At this time, the user takes a picture of the clothing item they own using a device such as a smartphone or PC. The image is then imported into the system using the device's upload function.

[1732] Input: Clothing photo

[1733] Output: Uploaded image file on device

[1734] What happens: A user takes a photo of a red dress with their smartphone camera and uploads it to the app.

[1735] Step 2:

[1736] The device sends the uploaded image to a cloud server, which then transfers the image over the internet and prepares it for analysis on the server side.

[1737] Input: Uploaded image file

[1738] Output: Image data transferred to the server

[1739] Specific behavior: The app automatically sends the image to the cloud server and notifies the user that the image has been sent.

[1740] Step 3:

[1741] The server analyzes the image and extracts the clothing's characteristics. An image analysis engine (using Google Cloud Vision API) on the cloud server processes the image data and extracts characteristics such as the clothing's color, pattern, style, and brand.

[1742] Input: Image data transferred to the server

[1743] Output: Extracted clothing feature data

[1744] Specific operation: The server detects the characteristics of the clothing item (e.g., red, dress, brand Y) and stores the results in a database.

[1745] Step 4:

[1746] Automatically updates the user's digital closet: The extracted clothing feature information is stored in a database, and the user's digital closet is updated to the latest version.

[1747] Input: Clothing feature data

[1748] Output: Updated digital closet

[1749] What happens: A new item (a red dress) is added to the database and the user's digital closet is automatically updated.

[1750] Step 5:

[1751] The user takes a photo of their entire body or face and uploads it to the device. The user uses smart glasses or a head-mounted display to take a photo of their entire body or face.

[1752] Input: A full-body or face image of the user

[1753] Output: Uploaded image file on device

[1754] What happens: The user takes a full-body photo with the camera on their smart glasses and uploads it to the app.

[1755] Step 6:

[1756] The device sends the uploaded full-body or facial images to a cloud server, which then transfers the images over the internet and prepares them for analysis on the server side.

[1757] Input: Uploaded full-body or face image

[1758] Output: Image data of the whole body and face sent to the server

[1759] Specific behavior: The app automatically sends the image to the cloud server and notifies the user that the image has been sent.

[1760] Step 7:

[1761] The server analyzes the image and extracts the user's physical features. An image analysis engine (using TensorFlow) on the cloud server processes the image data and extracts the user's facial shape and body shape features.

[1762] Input: Image data of the whole body and face transferred to the server

[1763] Output: Extracted user's body feature data

[1764] What it does: The server detects the user's height, body type, and facial features and updates the user's profile.

[1765] Step 8:

[1766] Users provide information about their preferred styles and brands in the form of a questionnaire. Users answer the questionnaire via their smartphone or PC, and the information is stored on the server.

[1767] Input: User's style and brand preferences

[1768] Output: Updated user profile

[1769] Specific operation: The user answers a questionnaire on the app, saying, "I like casual, light-colored clothes," and this information is stored on the server.

[1770] Step 9:

[1771] The user selects clothing items they own or are considering purchasing. The user selects items from the virtual closet and the selection information is sent to the server.

[1772] Input: Data about clothing items selected by the user

[1773] Output: Selection information transmitted to the server

[1774] Specific behavior: The user selects a white shirt, blue jeans, and sneakers from a virtual closet.

[1775] Step 10:

[1776] The server generates the optimal outfit based on the selection information, and the AI ​​coordination engine on the cloud server proposes the optimal outfit based on the selection information and saved profile data.

[1777] Input: Selection information and profile data

[1778] Output: Proposed optimal coordinates

[1779] Specific operation: The server generates an outfit consisting of "white shirt + blue jeans + sneakers" and notifies the user.

[1780] Step 11:

[1781] Users use smart glasses or a head-mounted display to have a virtual try-on experience. The generated outfits are displayed on the device in real time, and the user can try them on in a virtual space.

[1782] Input: Suggested optimal coordinates

[1783] Output: Real-time display of virtual try-on experience

[1784] Specific operation: The user puts on the smart glasses and checks the generated outfit in the virtual space.

[1785] Step 12:

[1786] The emotion engine analyzes the user's facial expressions and voice through the camera and microphone to recognize emotions in real time, and this emotional information is sent to the server, which then adjusts clothing recommendations based on the user's emotional state.

[1787] Input: User facial and voice data

[1788] Output: Recognized emotional state and tailored clothing recommendations

[1789] Specific operation: The emotion engine recognizes that the user is feeling stressed from the camera footage, and the server suggests casual clothing that will help them relax.

[1790] 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.

[1791] 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.

[1792] 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.

[1793] [Fourth embodiment]

[1794] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1795] 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.

[1796] 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).

[1797] 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.

[1798] 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.

[1799] 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).

[1800] 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.

[1801] 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.

[1802] 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.

[1803] 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.

[1804] 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.

[1805] 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.

[1806] 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."

[1807] This invention is a system that allows users to efficiently manage their clothing and suggests the most suitable style. This system is realized by users uploading images of their clothing, which the server analyzes. The system also identifies the user's face, body type, and style preferences, and suggests coordination ideas with existing clothing and new clothing to be purchased.

[1808] Overall system configuration

[1809] This system consists of a user, a terminal, and a server. Users operate the system using a terminal such as a smartphone or PC to manage their clothing, register their physical characteristics, simulate styles, and link with online shopping.

[1810] Program processing

[1811] 1. Clothing Image Recognition and Management

[1812] Users take photos of their clothing and upload them to the app via their device. The device then sends the uploaded images to a server, which analyzes them. This analysis uses AI technology to extract characteristics such as the color, pattern, style, and brand of the clothing. The server then stores this information in a database and automatically updates the user's digital closet.

[1813] Example: If a user uploads a photo of a red dress, the server analyzes the image, generates tags for "red," "dress," and "brand Y," and adds them to the user's digital closet.

[1814] 2. Face and body recognition and style preference understanding

[1815] Users upload photos of their face and entire body. The device sends these images to a server, which uses AI models to analyze the user's face shape and body type. This allows the server to understand the user's physical characteristics and update their profile. Users can also provide information about their preferred styles and brands in a questionnaire format. This information is stored on the server and used to suggest styles to the user.

[1816] Example: If a user uploads a full-body photo and replies, "I like casual, light-colored clothing," the server adds information to their profile such as "height 170cm," "slim build," "casual," and "light colors."

[1817] 3. AI Coordination Simulation

[1818] Through the system, users select clothing items they own or plan to purchase. The device sends this selection information to the server, which then uses AI technology to generate the optimal outfit. The generated outfit is presented to the user through the app screen.

[1819] Example: If a user selects "white shirt," "blue jeans," and "sneakers," the server will suggest a combination of "white shirt + blue jeans + sneakers" and display it to the user.

[1820] 4. AI Fashion Consultation Room

[1821] The user enters a fashion-related question into the chat box. The device sends this input to the server, which analyzes the question using natural language processing (NLP). The server generates an appropriate answer based on the analysis results and provides it to the user via the device.

[1822] Example: A user asks, "What outerwear is recommended for fall?" and the server responds, "Casual jackets and hoodies are suitable for fall."

[1823] 5. Online shopping integration

[1824] The user selects the clothing item they are considering purchasing within the app. The device sends the selected item information to the server, and the server obtains detailed information and a purchase link via the online shopping site's API. The user can then check the item within the app and access the purchase page.

[1825] Example: When a user wants to buy a new coat and enters that information into the simulation, the server suggests outfits for the new coat and a scarf they already own, and also displays a link to an online shopping site to purchase the coat.

[1826] The above is a specific embodiment of the system of the present invention, which allows users to efficiently manage their clothing and receive style suggestions.

[1827] The processing flow will be explained below.

[1828] Clothing image recognition and management

[1829] Step 1:

[1830] The user takes a photo of the clothing item they own using the camera on their smartphone or PC.

[1831] Step 2:

[1832] The device temporarily stores the captured photo and asks the user for confirmation through the user interface.

[1833] Step 3:

[1834] After the user confirms, the device sends the photo to the server.

[1835] Step 4:

[1836] The server then feeds the received images into an analytical AI model, which extracts features such as color, pattern, style, and brand.

[1837] Step 5:

[1838] The server stores the extracted features in a database and generates a corresponding item ID.

[1839] Step 6:

[1840] The server updates the user's digital closet to reflect the added item information.

[1841] Step 7:

[1842] Users can see new items added to their digital closet through the app's UI.

[1843] Face and body recognition and style preference identification

[1844] Step 1:

[1845] The user uses the app to take a photo of their face and a full-body photo.

[1846] Step 2:

[1847] The device temporarily stores the captured photo and asks the user for confirmation through the user interface.

[1848] Step 3:

[1849] After the user confirms, the device sends the photo to the server.

[1850] Step 4:

[1851] The images received by the server are input into an analytical AI model to extract the user's facial and body features.

[1852] Step 5:

[1853] The server stores the extracted features in a database and updates the user's profile information.

[1854] Step 6:

[1855] Users provide information about their preferred fashion styles, brands, colors, etc. in a survey format within the app.

[1856] Step 7:

[1857] The server stores the user's style tendency in a database based on the provided style information.

[1858] AI Coordination Simulation

[1859] Step 1:

[1860] The user opens the simulation screen and selects clothing items they own or are considering purchasing.

[1861] Step 2:

[1862] The terminal temporarily stores the selected item information and asks the user for confirmation through the user interface.

[1863] Step 3:

[1864] After the user confirms, the terminal transmits the item information to the server.

[1865] Step 4:

[1866] Based on the information received by the server, an AI model for simulation is executed.

[1867] Step 5:

[1868] The server generates the optimal coordinates and stores the coordinate results in a database.

[1869] Step 6:

[1870] The server transmits the generated coordination results to the terminal.

[1871] Step 7:

[1872] Through the app's UI, users can check the outfits suggested by the AI ​​and make evaluations or changes.

[1873] AI Fashion Consultation Room

[1874] Step 1:

[1875] Users can enter their fashion-related questions or concerns into the chat box within the app.

[1876] Step 2:

[1877] The terminal sends the entered text to the server.

[1878] Step 3:

[1879] The server inputs the received text into a natural language processing (NLP) model for analysis.

[1880] Step 4:

[1881] The server understands the meaning of the question and generates an appropriate answer.

[1882] Step 5:

[1883] The server generates a response and sends it to the terminal.

[1884] Step 6:

[1885] Users can review the answers and ask further questions or provide feedback as needed through the app's UI.

[1886] Online shopping integration

[1887] Step 1:

[1888] The user selects the item they are considering purchasing through the app.

[1889] Step 2:

[1890] The terminal temporarily stores the selected item information and asks the user for confirmation through the user interface.

[1891] Step 3:

[1892] After the user confirms, the terminal transmits the item information to the server.

[1893] Step 4:

[1894] The server retrieves detailed information, price, and stock status of the selected item via the online shopping site's API.

[1895] Step 5:

[1896] Based on the information received by the server, it runs a simulation AI model and generates an outfit that combines the items the customer is considering purchasing with clothing they already own.

[1897] Step 6:

[1898] The server sends the generated coordination results and a purchase link to the terminal.

[1899] Step 7:

[1900] Through the app's UI, users can check the AI's suggested outfits and purchase links, and proceed with the purchase process if necessary.

[1901] Example 1

[1902] 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."

[1903] Managing clothing and suggesting outfits takes a lot of time and effort, placing a significant burden on users. Furthermore, there is a lack of systems that can suggest optimal outfits that reflect a user's individual physical characteristics and style preferences. Furthermore, there is a lack of efficient methods for providing detailed information and links to purchase items that users are considering purchasing. There is a need for a system that can solve these issues and enable users to efficiently manage clothing and receive style suggestions.

[1904] 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.

[1905] In this invention, the server includes: means for a user to upload images of clothing items; means for transmitting the images to the server; means for the server to analyze the images, extract clothing features, and store the extracted features in a database; means for automatically updating the user's digital closet; means for a user to upload body images; means for transmitting the body images to the server; means for the server to analyze the body images and identify the user's body features and style preferences; means for transmitting information about clothing items selected by the user to the server; means for the server to generate optimal outfits using AI technology; means for providing the generated outfits to the user; means for the user to input fashion-related questions into a chat box; means for transmitting the input questions to the server; means for the server to generate appropriate answers using natural language processing technology and provide them to the user; means for the user to select clothing items for online shopping; means for the server to obtain detailed information and purchase links from an API of an online shopping site; and means for providing the purchase links to the user. This allows users to efficiently manage their clothing and receive suggestions for optimal outfits based on their individual body features and style preferences. In addition, by linking with online shopping, it will be possible to efficiently obtain detailed information and purchase links for items being considered for purchase.

[1906] "User" refers to an individual who uses the system to manage clothing items and receive coordination suggestions.

[1907] "Means for uploading images of clothing items" refers to a function that allows users to import photos of clothing items into the system.

[1908] "Server" refers to the central system that receives and analyzes data sent by users, and processes and manages various information.

[1909] "Means for transmitting images to a server" refers to a communication function for transferring image data captured by a user to a server.

[1910] "Analyzing an image" refers to the process by which the server extracts detailed information from the image it receives.

[1911] "Database" refers to a digital repository for storing and managing extracted clothing characteristics.

[1912] "User's digital closet" refers to a function that centrally manages information about the clothing items owned by the user in digital format.

[1913] "Means for uploading body images" refers to a function that allows users to upload photos of their bodies into the system.

[1914] "Physical characteristics" refers to the user's own physical characteristics, such as the user's body shape and facial shape.

[1915] "Style preferences" refer to the fashion trends and brands that a user prefers.

[1916] "Means for transmitting clothing information" refers to a communication function for transferring data on clothing items selected by the user to the server.

[1917] "AI technology" refers to technology that uses artificial intelligence to analyze data and generate coordination.

[1918] "Means for generating coordination" refers to a function that uses AI technology to suggest clothing combinations.

[1919] "Means for providing to the user" refers to a function for displaying the generated coordination and answers on the user's terminal.

[1920] "Chat box" refers to an interface that allows users to enter questions and send them to the server.

[1921] "Natural language processing technology" refers to artificial intelligence technology that enables a server to understand input text and generate appropriate responses.

[1922] "Means for selecting clothing items for online shopping" refers to a function that allows a user to select clothing items within the system that they are considering purchasing.

[1923] "API" refers to an interface for exchanging data between different software programs.

[1924] "Detailed information" refers to specific information about the purchase, such as the characteristics, price, and availability of the clothing item.

[1925] "Purchase Link" refers to the access URL to an online shopping site for purchasing the clothing item selected by the user.

[1926] The present invention relates to a system that allows users to efficiently manage their clothing and suggests optimal styles. This system allows users to upload images of their clothing and body, and analyzes them to optimize the user's style.

[1927] Overall system configuration

[1928] This system consists of a user, a device, and a server. Users operate the system using devices such as smartphones or PCs. Major operations include managing clothing items, registering physical characteristics, simulating styles, and linking with online shopping.

[1929] Clothing image recognition and management

[1930] Users first take a photo of their clothing and upload it to the app via their device. The device then sends the image to a server, which analyzes it. This analysis process uses AI technology to extract characteristics from the image, such as the color, pattern, style, and brand of the clothing. Once the analysis is complete, the server stores this information in a database and automatically updates the user's digital closet.

[1931] Example: If a user uploads a photo of a red dress, the server analyzes the image and generates tags for "red," "dress," and "brand X," which are added to the user's digital closet.

[1932] Face and body recognition and style preference identification

[1933] Next, users upload photos of their face and body. The device sends these images to a server, which uses AI models to analyze the images and detect the user's face shape and body type. The information obtained by the server is reflected in the user's profile and used to suggest style recommendations. Users can also answer a questionnaire about their preferred styles and brands, and this information is also stored on the server.

[1934] Example: If a user uploads a full-body photo and answers a survey saying, "I like casual, light-colored clothing," the server adds information such as "height 170cm," "slim build," "casual," and "light colors" to the profile.

[1935] AI Coordination Simulation

[1936] Users select clothing items they already own or plan to purchase and run a simulation. Information about the selected items is sent from the device to the server, which then uses AI technology to generate the optimal outfit. The generated outfit is then presented to the user on the app screen.

[1937] Example: If a user selects "white shirt," "blue jeans," and "sneakers," the server will suggest a coordination of "white shirt + blue jeans + sneakers" and display it on the user's device.

[1938] AI Fashion Consultation Room

[1939] Users enter fashion-related questions into the chat box and send them from their devices to the server, which uses natural language processing technology to analyze the questions and generate appropriate answers, which are then provided to the user's devices.

[1940] Example: When a user types a question into a chat box such as "What outerwear is recommended for autumn?", the server responds with "Casual jackets and hoodies are suitable for autumn" and displays it to the user.

[1941] Online shopping integration

[1942] The user selects the clothing item they are considering purchasing within the app, and their device sends that information to the server. The server then obtains detailed information and a purchase link via the online shopping site's API and provides it to the user. The user can then check the details within the app and access the purchase page.

[1943] Example: When a user selects a new coat to purchase, the server suggests coordinating the new coat with a scarf they already own and displays a link to purchase the item.

[1944] This system allows users to efficiently manage their clothing and receive style suggestions optimized for their physical characteristics and preferences. The online shopping function also allows for a smooth purchasing process.

[1945] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1946] Step 1: Take and upload your clothing image

[1947] 1. The user takes a photo of the clothing item using a smartphone or PC.

[1948] 2. The device displays a UI (user interface) for uploading the captured image to the app.

[1949] 3. When the user presses the upload button, the device sends the image to the server.

[1950] Specific operation: A user takes a photo of a red dress with their smartphone and taps the "upload" button in the app. The device then sends the image to the server.

[1951] Input: Clothing image

[1952] Output: Notification of completion of transmission to the server

[1953] Step 2: Image analysis and database update

[1954] 1. The server analyzes the images received using artificial intelligence (AI) technology.

[1955] 2. The server extracts features such as color, pattern, style, and brand of the clothing from the image.

[1956] 3. The server stores the extracted feature information in a database and automatically updates the user's digital closet.

[1957] What it does: The server analyzes the image, creates tags such as "red," "dress," and "Brand X," and adds these tags to the database.

[1958] Input: User-uploaded clothing images

[1959] Output: Analyzed clothing feature information (color, pattern, style, brand)

[1960] Step 3: Face and body recognition and profile update

[1961] 1. The user takes a photo of their face or whole body and uploads it via their device.

[1962] 2. The server uses an AI model to analyze the face shape and body type based on the images received.

[1963] 3. The server updates the user's profile based on the analysis results. It also collects information about the user's preferred styles and brands through a questionnaire and stores that information on the server.

[1964] Specific operation: The server performs analysis and adds characteristics such as the user's height of 170 cm and slim build to the profile. If the user answers a questionnaire saying that they like casual, light-colored clothing, this information is also added to the profile.

[1965] Input: User's face photo, full-body photo, survey results

[1966] Output: Updated user profile

[1967] Step 4: AI Coordination Simulation

[1968] 1. The user selects clothing items they own or new items they plan to purchase within the app.

[1969] 2. The device sends the selected item information to the server.

[1970] 3. The server uses AI technology to generate the optimal coordination and sends the results to the device.

[1971] 4. The terminal displays the generated coordinates on the screen to the user.

[1972] Specific operation: The user selects "white shirt," "blue jeans," and "sneakers," and taps the simulation button. The server generates an outfit called "white shirt + blue jeans + sneakers" and displays it on the user's device.

[1973] Input: Clothing information selected by the user

[1974] Output: Generated outfit suggestions

[1975] Step 5: AI Fashion Consultation

[1976] 1. The user types a fashion-related question into the chat box within the app.

[1977] 2. The device sends the entered question to the server.

[1978] 3. The server uses natural language processing (NLP) techniques to analyze the question and generate an appropriate answer.

[1979] 4. The server generates a response and sends it to the terminal, which displays it to the user.

[1980] Specific operation: The user enters a question in the chat box, "What outerwear is recommended for autumn?" and taps the send button. The server analyzes the question and replies, "A casual jacket or hoodie is suitable for autumn."

[1981] Input: User fashion question

[1982] Output: The generated answer

[1983] Step 6: Online shopping integration

[1984] 1. The user selects the clothing item they are considering purchasing within the app.

[1985] 2. The device sends the selected item information to the server.

[1986] 3. The server retrieves details and a purchase link via the online shopping site's API.

[1987] 4. The server sends the retrieved details and purchase link to the device, which displays them to the user.

[1988] Specific operation: When a user tries to purchase a "new coat" and selects that item and enters it into the simulation, the server proposes a combination of "new coat + existing scarf" and a purchase link, which are displayed on the user's device.

[1989] Input: Information about the clothing item selected by the user

[1990] Output: More information and purchase links

[1991] This allows users to efficiently manage their clothing and receive style suggestions, and also makes online shopping smoother.

[1992] (Application example 1)

[1993] 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."

[1994] In order to effectively manage the clothing items users currently own or are considering purchasing, and to provide optimal fashion coordination, users have traditionally had to try on the items in person. However, this is time-consuming and expensive, and often results in items not fitting their preferences or body type. Therefore, there is a need for a system that allows users to easily try on items in a virtual environment.

[1995] 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.

[1996] In this invention, the server includes means for a user to upload an image of a clothing item, means for transmitting the image to the server, means for the server to analyze the image, extract characteristics of the clothing item, and store the characteristics in a database, means for automatically updating the user's digital closet, means for the user to select a new clothing item and provide a virtual try-on, and means for displaying a purchase link based on the virtual try-on, thereby enabling the user to easily try on the clothing item virtually from home, confirm the best style, and purchase it immediately.

[1997] A "user" is an individual who uses this system to manage clothing items and receive coordination suggestions.

[1998] "Clothing images" are images that users take of clothing they own or are considering purchasing with a smartphone or other device and upload to the app.

[1999] A "server" is a computer system that analyzes image data sent from users and manages various databases.

[2000] The "database" is a storage device that stores the characteristics of clothing analyzed by the server and the user's physical information.

[2001] A "digital closet" is an electronic closet that manages a digital list of clothing items owned by a user and is provided to the user through an application.

[2002] A "new clothing item" is a clothing item that the user does not yet own and is considering purchasing.

[2003] "Virtual try-on" is a function that virtually simulates the appearance of clothing based on the user's physical data and provides it to the user.

[2004] A "purchase link" is a link that allows users to purchase clothing items they like after virtual try-on directly online.

[2005] "Body images" are images that users take of their face or whole body and upload to the app.

[2006] A "profile" is personal data that includes a user's physical characteristics, preferred style, past purchase history, and so on.

[2007] "Coordination" refers to proposing the optimal fashion style by combining the user's clothing items or clothing items the user is considering purchasing.

[2008] This invention is a system that allows users to efficiently manage their clothing and suggest outfits. The system is designed to allow users to easily upload their clothing and body data using a device such as a smartphone, and to simulate optimal fashion styles via a server.

[2009] Overall system configuration

[2010] This system consists of a user, a terminal, and a server. The user operates the system using the terminal to upload images of clothing, register physical characteristics, try on the clothing virtually, and link it to online shopping.

[2011] Program processing

[2012] 1. Clothing Image Recognition and Management

[2013] Users take photos of their clothing items using a smartphone or other device and upload them to the app.

[2014] The uploaded image is sent from the device to the server.

[2015] The server analyzes the received images using AI technology (e.g., TensorFlow) to extract characteristics such as the color, pattern, style, and brand of the clothing.

[2016] The extracted information is stored in a database (e.g., MongoDB) and automatically updates the user's digital closet.

[2017] 2. Face and body recognition and style preference understanding

[2018] Users take a photo of their face or whole body and upload it to the app.

[2019] The uploaded image is sent from the device to the server.

[2020] The server analyzes these images using AI models (e.g., OpenPose, DeepFace) to extract the user's facial shape and body features.

[2021] Based on the user's profile, the system reflects information about preferred styles and brands.

[2022] 3. AI Coordination Simulation

[2023] Through the system, users select clothing items they already own or items they are considering purchasing.

[2024] The server uses an AI algorithm to generate the optimal outfit based on the selected information.

[2025] The generated outfits are combined with the user's body data as a virtual try-on and presented to the user in real time.

[2026] 4. Virtual try-on

[2027] The selected item is superimposed onto the user's photo and displayed in the app, as if they were actually trying it on.

[2028] Users can use this feature to see if a new style suits them.

[2029] 5. Linking with online shopping

[2030] If you find an item you like, you can access the online shopping site directly from the app and proceed with the purchase.

[2031] The purchase link is generated by the server and provided to the user.

[2032] Specific examples

[2033] If a user wants to coordinate a "blue shirt" they own with a "black jacket" they found in an online shop:

[2034] When a user uploads a photo of these two items to the app, the server analyzes the photo and generates a virtual try-on image that combines the "blue shirt" and "black jacket" based on the user's facial and body data.

[2035] This allows users to easily check whether a new jacket will suit them.

[2036] If you like it, you can purchase the jacket directly from the online shop via the purchase link.

[2037] Prompt Sentence Examples

[2038] "I'd like to virtually try on a new jacket and see if it goes with my blue shirt. How can I do that?"

[2039] "I like the outfit I created and would like to purchase this jacket online. Can you give me a link to purchase it?"

[2040] Through the above steps, the system of the present invention allows users to easily manage clothing items and try on outfits from home, confirm the best style, and then purchase the items.

[2041] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2042] Step 1: Upload an image

[2043] Subject: User

[2044] Users take photos of clothing or their own bodies using a device such as a smartphone and upload them to the app. As input, the user provides image data, and as output, the image files are saved on the device.

[2045] Step 2: Send image

[2046] Subject: Terminal

[2047] The terminal sends the uploaded image to the server. In this process, the terminal sends the image file to the server using the HTTP protocol or similar. The uploaded image data is the input, and the image data arrives at the server as the output.

[2048] Step 3: Image analysis

[2049] Subject: Server

[2050] The server analyzes the received image data using AI technology (e.g., TensorFlow). Image recognition algorithms are used for the analysis to extract features such as the color, pattern, style, and brand of the clothing. The input is the image data, and the output is the extracted feature data.

[2051] Step 4: Save Data

[2052] Subject: Server

[2053] The server stores the extracted feature data in a database (e.g., MongoDB). This process adds a new entry to the database. The input is the feature data, and the output is the updated database.

[2054] Step 5: Update your digital closet

[2055] Subject: Server

[2056] The server updates the user's digital closet based on the new entries in the database. The input is the newly added feature data, and the output is the updated digital closet information.

[2057] Step 6: Body Data Analysis

[2058] Subject: Server

[2059] The server analyzes the face and body images uploaded by the user using an AI model (e.g., OpenPose, DeepFace). The input is image data of the body, and the output is data on the user's physical characteristics.

[2060] Step 7: Update your profile

[2061] Subject: Server

[2062] The server updates the user's profile with the physical feature data and adds preferred style and brand information to the profile. The input is the physical feature data and the user's style information, and the output is an updated user profile.

[2063] Step 8: Item Selection

[2064] Subject: User

[2065] Users select clothing items they own or are considering purchasing through the system. The selected clothing information is provided to the app as input, and the selection information is sent to the server as output.

[2066] Step 9: Coordinate Generation

[2067] Subject: Server

[2068] The server uses an AI algorithm to generate the optimal outfit based on the selection information. The input is the selection information and the user profile, and the output is the generated outfit data.

[2069] Step 10: Generate a virtual try-on

[2070] Subject: Server

[2071] The server generates a virtual try-on image by combining the user's body data and clothing items based on the generated coordinate data. The inputs are coordinate data and body feature data, and the output is a virtual try-on image.

[2072] Step 11: Displaying try-on images

[2073] Subject: Terminal

[2074] The terminal displays the virtual try-on image received from the server to the user. The virtual try-on image data is input, and the image is displayed on the terminal screen as output.

[2075] Step 12: View the purchase link

[2076] Subject: Server

[2077] The server generates a purchase link for the item the user likes during the virtual try-on and sends it to the device. The input is the try-on image and purchase information, and the output is the purchase link.

[2078] Step 13: Online Shopping

[2079] Subject: User

[2080] A user clicks on a purchase link displayed on the device to purchase an item on an online shopping site. The input is the purchase link, and the output is the online shop's purchase page.

[2081] 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.

[2082] The present invention is a system that allows users to efficiently manage their clothing and suggests optimal styles. This system is combined with an emotion engine that recognizes the user's emotions, and adds a function to suggest clothing items based on the user's emotional state.

[2083] Overall system configuration

[2084] This system is composed of a user, a device, a server, and an emotion engine. Users operate the system using devices such as smartphones or PCs to manage their clothing, register their physical characteristics, simulate styles, and link with online shopping. The emotion engine also analyzes the user's facial expressions and behavior to make suggestions based on their emotional state.

[2085] Program processing

[2086] 1. Clothing Image Recognition and Management

[2087] Users take photos of their clothing and upload them to the app via their device. The device then sends the uploaded images to a server, which analyzes them. This analysis uses AI technology to extract characteristics such as the color, pattern, style, and brand of the clothing. The server then stores this information in a database and automatically updates the user's digital closet.

[2088] Example: If a user uploads a photo of a red dress, the server analyzes the image, generates tags for "red," "dress," and "brand Y," and adds them to the user's digital closet.

[2089] 2. Face and body recognition and style preference understanding

[2090] Users upload photos of their face and entire body. The device sends these images to a server, which uses AI models to analyze the user's face shape and body type. This allows the server to understand the user's physical characteristics and update their profile. Users can also provide information about their preferred styles and brands in a questionnaire format. This information is stored on the server and used to suggest styles to the user.

[2091] Example: If a user uploads a full-body photo and replies, "I like casual, light-colored clothing," the server adds information to their profile such as "height 170cm," "slim build," "casual," and "light colors."

[2092] 3. AI Coordination Simulation

[2093] Through the system, users select clothing items they own or plan to purchase. The device sends this selection information to the server, which then uses AI technology to generate the optimal outfit. The generated outfit is presented to the user through the app screen.

[2094] Example: If a user selects "white shirt," "blue jeans," and "sneakers," the server will suggest a combination of "white shirt + blue jeans + sneakers" and display it to the user.

[2095] 4. AI Fashion Consultation Room

[2096] The user enters a fashion-related question into the chat box. The device sends this input to the server, which analyzes the question using natural language processing (NLP). The server generates an appropriate answer based on the analysis results and provides it to the user via the device.

[2097] Example: A user asks, "What outerwear is recommended for fall?" and the server responds, "Casual jackets and hoodies are suitable for fall."

[2098] 5. Online shopping integration

[2099] The user selects the clothing item they are considering purchasing within the app. The device sends the selected item information to the server, and the server obtains detailed information and a purchase link via the online shopping site's API. The user can then check the item within the app and access the purchase page.

[2100] Example: When a user wants to buy a new coat and enters that information into the simulation, the server suggests outfits for the new coat and a scarf they already own, and also displays a link to an online shopping site to purchase the coat.

[2101] 6. Suggestions linked to emotion engine

[2102] When a user uses the system, the emotion engine analyzes the user's facial expressions and voice through the camera and microphone to recognize emotions in real time. The server receives data from the emotion engine and adjusts clothing recommendations based on the user's emotional state. For example, it suggests casual clothes if the user wants to relax, and formal clothes if the user has an important meeting coming up.

[2103] Example: If the emotion engine recognizes that the user is feeling stressed, the server will suggest "relaxing casual wear" and coordinate clothing that will ease the user's mood.

[2104] Overall effect

[2105] This system allows users to efficiently manage their clothing and receive style suggestions that best suit their emotional state. Furthermore, by linking with online shopping, users can smoothly purchase new clothing. The addition of an emotion engine makes it possible to make suggestions based on the user's real-time emotions, providing a more personalized experience.

[2106] The processing flow will be explained below.

[2107] Clothing image recognition and management

[2108] Step 1:

[2109] The user takes a photo of the clothing item they own using the camera on their smartphone or PC.

[2110] Step 2:

[2111] The device temporarily stores the captured photo and asks the user for confirmation through the user interface.

[2112] Step 3:

[2113] After the user reviews and approves the photo, the device sends the photo to the server.

[2114] Step 4:

[2115] The server then feeds the received images into an analytical AI model, which extracts features such as color, pattern, style, and brand.

[2116] Step 5:

[2117] The server stores the extracted features in a database and generates a corresponding item ID.

[2118] Step 6:

[2119] The server updates the user's digital closet to reflect the added item information.

[2120] Step 7:

[2121] Users can see new items added to their digital closet through the app's UI.

[2122] Face and body recognition and style preference identification

[2123] Step 1:

[2124] The user uses the app to take a photo of their face and a full-body photo.

[2125] Step 2:

[2126] The device temporarily stores the captured photo and asks the user for confirmation through the user interface.

[2127] Step 3:

[2128] After the user reviews and approves the photo, the device sends the photo to the server.

[2129] Step 4:

[2130] The images received by the server are input into an analytical AI model to extract the user's facial and body features.

[2131] Step 5:

[2132] The server stores the extracted features in a database and updates the user's profile information.

[2133] Step 6:

[2134] Users provide information about their preferred fashion styles, brands, colors, etc. in a survey format within the app.

[2135] Step 7:

[2136] The server stores the user's style tendency in a database based on the provided style information.

[2137] AI Coordination Simulation

[2138] Step 1:

[2139] The user opens the simulation screen and selects clothing items they own or are considering purchasing.

[2140] Step 2:

[2141] The terminal temporarily stores the selected item information and asks the user for confirmation through the user interface.

[2142] Step 3:

[2143] After the user checks and approves the item information, the terminal sends the information to the server.

[2144] Step 4:

[2145] Based on the information received by the server, an AI model for simulation is executed.

[2146] Step 5:

[2147] The server generates the optimal coordinates and stores the coordinate results in a database.

[2148] Step 6:

[2149] The server transmits the generated coordination results to the terminal.

[2150] Step 7:

[2151] Through the app's UI, users can check the outfits suggested by the AI ​​and make evaluations or changes.

[2152] AI Fashion Consultation Room

[2153] Step 1:

[2154] Users can enter their fashion-related questions or concerns into the chat box within the app.

[2155] Step 2:

[2156] The terminal sends the entered text to the server.

[2157] Step 3:

[2158] The server inputs the received text into a natural language processing (NLP) model for analysis.

[2159] Step 4:

[2160] The server understands the meaning of the question and generates an appropriate answer.

[2161] Step 5:

[2162] The server generates a response and sends it to the terminal.

[2163] Step 6:

[2164] Users can review the answers and ask further questions or provide feedback as needed through the app's UI.

[2165] Online shopping integration

[2166] Step 1:

[2167] The user selects the item they are considering purchasing through the app.

[2168] Step 2:

[2169] The terminal temporarily stores the selected item information and asks the user for confirmation through the user interface.

[2170] Step 3:

[2171] After the user checks and approves the item information, the terminal sends the information to the server.

[2172] Step 4:

[2173] The server retrieves detailed information, price, and stock status of the selected item via the online shopping site's API.

[2174] Step 5:

[2175] Based on the information received by the server, it runs a simulation AI model and generates an outfit that combines the items the customer is considering purchasing with clothing they already own.

[2176] Step 6:

[2177] The server sends the generated coordination results and a purchase link to the terminal.

[2178] Step 7:

[2179] Through the app's UI, users can check the AI's suggested outfits and purchase links, and proceed with the purchase process if necessary.

[2180] Suggestions linked with emotion engine

[2181] Step 1:

[2182] When a user uses the system, the emotion engine analyzes the user's facial expressions and voice through the camera and microphone, recognizing emotions in real time.

[2183] Step 2:

[2184] The server receives the data from the emotion engine and stores the user's emotional state in a database.

[2185] Step 3:

[2186] The server adjusts the suggested clothing styles based on the user's emotional state.

[2187] Step 4:

[2188] The user receives style suggestions from the system, for example, casual attire if the user wants to relax, or formal atti...

Claims

1. a means for a user to upload an image of an article of clothing; means for transmitting the image to a server; A server analyzes the image, extracts features of the clothing item, and stores the extracted features in a database; a means for automatically updating a user's digital closet; A system including:

2. a means for a user to upload an image of their body; means for transmitting the image to a server; A server analyzes the image, extracts the user's physical features, and determines the user's style based on the user's profile; The system of claim 1 , comprising:

3. A means for a user to register clothing items that the user owns or is considering purchasing; A means for the server to generate an optimal coordination based on the registered information; means for providing the generated coordinates to a user; The system of claim 1 , comprising:

Images