System

Abstract

A system is provided.SOLUTION: A system, comprising: means for inputting body shape information of a user; means for taking pictures of the whole body of the user from a plurality of angles and uploading the pictures; means for collecting information of clothes on sale; means for synthesizing the clothes with a generated 3D model of the user; and means for presenting a fitting image to a user device.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] Nowadays, many working people are too busy to go to stores, and are increasingly purchasing clothing online. However, when they actually purchase something, the size or image may differ from what they expected, causing anxiety and dissatisfaction. Furthermore, since they cannot try on the clothes, they cannot check how the overall outfit will look. This often forces them to return the item after purchase, resulting in wasted time and money for both the user and the seller. The present invention aims to solve these problems. [Means for solving the problem]

[0005] The present invention provides a means for users to input their own body type information and upload full-body photos taken from multiple angles. It also provides a means for collecting data on clothing sold in online stores, which generates a 3D model of the user based on the user's body type information and photographic data. By providing a means for overlaying clothing onto the 3D model, the user can accurately view fitting images on their own device. Furthermore, by providing a means for users to request coordination suggestions from a salesperson, generate fitting images of the suggested coordination, and display them on the user's device, users can enjoy creating a total outfit that suits them online.

[0006] "User's body type information" refers to the user's specific body measurements, such as the user's height, weight, and three sizes.

[0007] "Full-body photo" refers to photo data of the front, back, and side taken by the user from multiple angles.

[0008] "Clothing data" refers to detailed information about the clothing sold in the online store, such as images, sizes, materials, and colors.

[0009] A "3D model" is a high-resolution three-dimensional representation generated based on a user's body shape information and full-body photograph data.

[0010] "Try-on image" refers to a virtual image of the clothing being worn, generated by combining the selected clothing with a 3D model of the user.

[0011] "Coordination suggestions" refer to combinations of multiple clothes and accessories that a salesperson suggests based on the user's body type information and preferences.

[0012] The term "salesperson" refers to an online fashion advisor who suggests outfits to the user. [Brief explanation of the drawings]

[0013] [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

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] 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).

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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."

[0034] This invention is a virtual try-on system that uses a user's body type information and full-body photos taken from multiple angles to generate a fitting image tailored to the user. The system begins by the user entering body type information and uploading a full-body photo using their own device. The system then utilizes a generative AI model based on the collected clothing data to synthesize the clothing onto the user's 3D model and present a virtual fitting image. Furthermore, the user can receive coordination suggestions from sales staff, making the process of selecting and purchasing clothing even more enjoyable.

[0035] 1. Enter your body type information and upload a full-body photo

[0036] Users start the application and enter their body information, such as height, weight, and measurements, and also upload full-body photos taken from the front, back, and side.

[0037] The terminal transmits the body type information entered by the user and the whole-body photograph taken to the server.

[0038] 2. Collection of clothing data

[0039] The server collects data on clothing sold online (images, sizes, materials, colors, etc.) using APIs or web scraping technology from partner online stores and stores it in a database.

[0040] 3. 3D Model Generation and Synthesis

[0041] The server analyzes the user's body shape information and uploaded full-body photos to generate a high-resolution 3D model of the user.

[0042] The server uses a generative AI model to synthesize the user's chosen clothing onto the 3D model, using deep learning techniques to realistically recreate the fit and texture of the clothing.

[0043] 4. Showing images of the clothes when they are tried on

[0044] The server transmits the generated try-on images to the user's terminal as images taken from multiple angles.

[0045] The terminal displays the received try-on images on a user interface.

[0046] The user can check the displayed try-on images and, if necessary, request images from different angles or images of other clothes being tried on.

[0047] 5. Coordination suggestions and purchase procedures

[0048] If the user desires a coordination suggestion, the user clicks the "Request coordination suggestion" button.

[0049] The server sends a request to a salesperson who is responsible for suggesting a coordination.

[0050] The salesperson proposes appropriate outfits based on the user's body type and preferences, and sends feedback to the server.

[0051] The server generates a try-on image of the proposed outfit and presents it to the user's terminal.

[0052] The user checks the suggested coordination and, if he / she decides to purchase, clicks the "Purchase" button.

[0053] The terminal sends the purchase information to the server and connects to the online store's payment system.

[0054] This invention allows users to easily select clothes that fit their body type and experience the virtual fitting process as if they were actually trying them on. Furthermore, they can smoothly complete the online purchase process while checking suggested outfits.

[0055] The processing flow will be explained below.

[0056] Step 1:

[0057] Users start the application and enter their body information, such as height, weight, and measurements, and also upload full-body photos taken from the front, back, and side.

[0058] Step 2:

[0059] The terminal transmits the body type information and full-body photograph input by the user to the server.

[0060] Step 3:

[0061] The server analyzes the received body shape information and full-body photo data to generate a high-resolution 3D model of the user, using image recognition algorithms.

[0062] Step 4:

[0063] The server uses APIs or web scraping technology from affiliated online stores to collect data on the clothes being sold (images, sizes, materials, colors, etc.) and stores it in a database.

[0064] Step 5:

[0065] The server then synthesizes the clothing selected by the user onto the generated 3D model of the user, using a generative AI model to realistically recreate the fit and texture of the clothing.

[0066] Step 6:

[0067] The server transmits the generated try-on images to the user's terminal as images taken from multiple angles.

[0068] Step 7:

[0069] The terminal displays the received try-on images on a user interface.

[0070] Step 8:

[0071] The user checks the displayed try-on images and requests try-on images from different angles or try-on images of other clothes as needed.

[0072] Step 9:

[0073] If the user desires a coordination suggestion, the user clicks the "Request coordination suggestion" button.

[0074] Step 10:

[0075] The server sends a request to a salesperson who is responsible for suggesting a coordination.

[0076] Step 11:

[0077] The salesperson proposes appropriate outfits based on the user's body type and preferences, and sends feedback to the server.

[0078] Step 12:

[0079] The server generates a try-on image of the proposed outfit and sends it to the user's terminal.

[0080] Step 13:

[0081] The terminal displays the received try-on image of the coordinated outfit on a user interface.

[0082] Step 14:

[0083] The user checks the suggested coordination and, if he / she decides to purchase, clicks the "Purchase" button.

[0084] Step 15:

[0085] The terminal transmits the purchase information to the server.

[0086] Step 16:

[0087] The server records the purchase information and connects to the online store's payment system.

[0088] Step 17:

[0089] The server notifies the user of the order confirmation and delivery information.

[0090] Step 18:

[0091] The terminal displays the order confirmation and shipping information to the user.

[0092] Example 1

[0093] 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."

[0094] Conventional online shopping has the problem that users cannot accurately understand how clothes fit or how the design will look on them unless they actually try them on. Furthermore, coordination suggestions tailored to the user's preferences and the ability to check how the clothes will look when they are tried on are insufficient, which discourages users from making a purchase. This has led to a decline in user satisfaction with online shopping.

[0095] 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.

[0096] In this invention, the server includes a means for inputting the user's body type information, a means for taking and uploading full-body photos of the user from multiple angles, a means for collecting data on clothing sold online, a means for combining clothing with the generated high-resolution 3D model of the user, a means for transmitting the generated try-on images to the user's device, and a means for displaying the try-on images on the user's device. This allows the user to easily select clothing that fits their body type and experience the virtual try-on experience as if they were actually trying on the clothes. Furthermore, the user can request coordination suggestions from a salesperson and check the suggested try-on images, which increases the user's motivation to purchase.

[0097] "User" refers to an individual who utilizes the System to input body type information and take and upload a full-body photograph.

[0098] "Body type information" refers to detailed data about the user's body, such as height, weight, and measurements.

[0099] A "full-body photo" refers to an image of the user's body taken from multiple angles, including the front, back, and side.

[0100] "Online clothing data" refers to information such as clothing images, sizes, materials, and colors collected from online sales sites and stores.

[0101] "High-resolution 3D model" refers to a three-dimensional computer graphics model that reproduces the user's body in detail, generated based on the user's body shape information and a full-body photograph.

[0102] "Generative AI model" refers to an artificial intelligence model trained using deep learning techniques to realistically synthesize clothing onto a user's 3D model.

[0103] "Try-on image" refers to an image created by using a generative AI model to synthesize selected clothing onto a user's 3D model.

[0104] "User's device" refers to an electronic device used by a user, such as a smartphone, tablet, or PC.

[0105] "Coordination suggestions" refer to combinations of multiple clothes recommended by a salesperson based on the user's body type information and preferences.

[0106] A "salesperson" refers to a person in charge of making coordination suggestions to users through the system.

[0107] The present invention is a virtual try-on system that uses a user's body type information and full-body photographs taken from multiple angles to generate a fitting image tailored to the user. In this system, a program is executed in the following procedure.

[0108] First, the user launches a dedicated application on their smartphone or PC and enters their body information, including height, weight, and measurements. Then, the user takes full-body photos of the front, back, and side and uploads them to the application. This uploaded data is then sent to the server via the device.

[0109] The device securely transmits input and uploaded data to the server using the HTTPS protocol. The server receives this data and uses OpenCV, an image analysis technology, to analyze the user's body shape with high accuracy. Based on the analyzed data, it uses deep learning frameworks such as TensorFlow and PyTorch to generate a high-resolution 3D model.

[0110] After generating a 3D model for the user, the server uses APIs or web scraping technology from partner online stores to collect data on clothing sold online, including images of the clothing, sizes, materials, colors, etc. This data is stored in a database and used to synthesize clothing onto the user's 3D model.

[0111] The server then synthesizes the clothing selected by the user onto the generated 3D model using a generative AI model. The generative AI model is trained using deep learning technology to realistically reproduce the fit and texture of the clothing. Deep learning algorithms using TensorFlow and PyTorch are used in this process.

[0112] The generated try-on images are taken from multiple angles and sent to the user's device as JPEG or PNG image files. The device displays the received try-on images on the application's user interface. The user can review the displayed try-on images and request images from different angles or try-on images of other clothes as needed.

[0113] Furthermore, if the user wishes to receive a coordination suggestion, they click the "Request Coordination Suggestion" button. The server sends a request to the salesperson in charge of coordination suggestions, who then suggests an appropriate coordination based on the user's body type information and preferences, and sends feedback to the server. Based on this feedback, the server generates a new fitting image and displays it on the user's device. Finally, the user checks the suggested coordination and clicks the "Purchase" button if they decide to purchase. This purchase information is sent to the server via the device, and the purchase procedure is carried out in conjunction with the online store's payment system.

[0114] Specific examples

[0115] For example, if a user wants to try on "blue jeans" and a "white T-shirt,"

[0116] 1. The user enters their height as 170cm, weight as 65kg, measurements as 90-75-90, and uploads full-body photos of the front, back, and side.

[0117] 2. The device sends this information to the server.

[0118] 3. The server uses web scraping technology to collect data on "blue jeans" and "white T-shirts" and stores it in a database.

[0119] 4. The server uses image analysis technology to generate a high-resolution 3D model of the user, and then uses a generative AI model to synthesize jeans and a T-shirt onto the 3D model.

[0120] 5. The server generates images of the clothes being tried on from multiple angles and sends them to the user's device.

[0121] 6. The user can check the newly generated try-on image and then decide to "purchase this outfit."

[0122] Prompt Sentence Examples

[0123] "Enter your height, weight, and measurements, then upload full-body photos taken from the front, back, and side."

[0124] This system allows users to easily find clothes that fit their body type, and experience the virtual fitting process as if they were actually trying them on. Users can also request outfit suggestions and check the suggested try-on images, making online clothing shopping smoother and more satisfying.

[0125] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0126] Step 1:

[0127] The user launches an application.

[0128] Specific operation: The user taps or clicks on the dedicated application on their smartphone or PC to launch it and proceeds to the first screen.

[0129] Input: None

[0130] Output: The application launches and displays a screen where the user can enter their body information.

[0131] Step 2:

[0132] The user inputs body type information.

[0133] Specific operation: The user enters body information such as height, weight, and measurements into the application form. The user also prepares full-body photos taken from the front, back, and side.

[0134] Input: Body information such as height, weight, and measurements

[0135] Output: Display of input body shape information and full-body photo

[0136] Step 3:

[0137] The user uploads a full-body photo.

[0138] Specific operation: The user uploads full-body photos of the front, back, and side using the upload function within the application.

[0139] Input: Full-body photos of the front, back, and side

[0140] Output: Full body photo upload complete message

[0141] Step 4:

[0142] The terminal sends the input data to the server.

[0143] Specific operation: The device sends the user's body shape information and a full-body photo to the server via the HTTPS protocol.

[0144] Input: User's body type information and full-body photo

[0145] Output: The data is sent to the server and awaits processing.

[0146] Step 5:

[0147] The server analyzes body shape information and full-body photos.

[0148] Specific operation: The server performs image analysis using OpenCV and extracts the features necessary to create a 3D model based on the user's body shape information.

[0149] Input: Body type information and full-body photo

[0150] Output: Feature-extracted data

[0151] Step 6:

[0152] The server generates a high-resolution 3D model.

[0153] Specific operation: The server uses TensorFlow and PyTorch for deep learning to generate a high-resolution 3D model based on the user's body shape.

[0154] Input: Feature-extracted data

[0155] Output: High-resolution 3D model

[0156] Step 7:

[0157] A server collects data about clothing sold online.

[0158] Specific operation: The server calls the API of partner online stores or uses web scraping to collect the latest clothing data (images, sizes, materials, colors, etc.).

[0159] Input: API endpoint of partner online store or scraping target URL

[0160] Output: Latest clothing data stored in the clothing database

[0161] Step 8:

[0162] The server uses a generative AI model to synthesize the clothing onto a 3D model.

[0163] Specific operation: The server uses a generative AI model to synthesize clothing onto the user's 3D model based on the collected clothing data.

[0164] Input: 3D model and clothing data

[0165] Output: Try-on images from multiple angles

[0166] Step 9:

[0167] The server transmits the generated try-on images to the terminal.

[0168] How it works: The server generates try-on images in JPEG or PNG format and sends them to the user's device. High-speed data transfer via a CDN may also be used.

[0169] Input: Try-on image

[0170] Output: Try-on image sent to the device

[0171] Step 10:

[0172] The terminal displays the try-on image on the user interface.

[0173] Specific operation: The device displays the received try-on images on the application's user interface, allowing the user to operate them.

[0174] Input: Try-on image

[0175] Output: Try-on image displayed on the user interface

[0176] Step 11:

[0177] The user checks the try-on images and sends a request if necessary.

[0178] Specific operation: The user checks the try-on images, and if they want to request images from different angles or images of other clothes being tried on, they click the corresponding button in the application.

[0179] Input: User request

[0180] Output: The request is sent to the server.

[0181] Step 12:

[0182] The user requests outfit suggestions.

[0183] Specific operation: If the user wants outfit suggestions, he / she clicks the "Request outfit suggestions" button.

[0184] Input: User's outfit suggestion request

[0185] Output: The request is sent to the server.

[0186] Step 13:

[0187] The server sends the request to the salesperson.

[0188] Specific operation: The server sends a coordination proposal request to the salesperson, along with the necessary user information.

[0189] Input: User request

[0190] Output: The request is sent to the salesperson

[0191] Step 14:

[0192] Salespeople provide feedback on suggestions.

[0193] Specific operation: The salesperson considers the appropriate outfit based on the user's body type and preferences, and sends feedback to the server.

[0194] Input: User's body type information and preferences

[0195] Output: Feedback of outfit suggestions

[0196] Step 15:

[0197] The server generates an image of a coordinated outfit to try on.

[0198] Specific operation: The server generates new try-on images based on feedback from the salesperson and sends them to the user's device.

[0199] Input: Feedback on outfit suggestions

[0200] Output: Coordinate try-on image sent to the device

[0201] Step 16:

[0202] The user reviews the offer and decides to purchase.

[0203] Specific operation: The user checks the newly generated outfit try-on image and clicks the "Purchase" button if they like it.

[0204] Input: Coordination try-on image

[0205] Output: Purchase request sent to server

[0206] Step 17:

[0207] The device sends purchase information to the server and connects with the online store.

[0208] Specific operation: The terminal sends purchase information to the server, and the server works with the online store's payment system to complete the purchase process.

[0209] Input: Purchase information

[0210] Output: The purchase is completed and a confirmation is sent to the user.

[0211] (Application example 1)

[0212] 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."

[0213] Traditionally, trying on clothes in physical stores was time-consuming and labor-intensive. Furthermore, the types of clothing that could be tried on were limited, making it difficult to try out many options. Furthermore, online shopping presents the problem of frequent returns and exchanges after purchase, since users are unable to see how the actual product will look. To solve these problems, a system is needed that allows users to easily try on clothes and select the best fit for their body type.

[0214] 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.

[0215] In this invention, the server includes: means for inputting a user's body type information; means for taking and uploading full-body photos of the user from multiple angles; means for collecting data on available items; means for combining items with the generated 3D model of the user; means for displaying virtual try-on images on the user's device; means for the user to input body type information and upload a full-body photo using their own device; means for collecting and storing data on items sold online using APIs or web scraping technology of affiliated online stores; means for analyzing the user's body type information and the uploaded full-body photo to generate a high-resolution 3D model of the user; and means for generating try-on images from multiple angles and sending them to the user's device. This allows users to smoothly try on clothes virtually in a physical store, shortening the time it takes to actually try on clothes and combining the convenience of online shopping with the experience of a physical store.

[0216] "User's body type information" refers to the user's physical measurements and characteristics, such as height, weight, and three sizes.

[0217] A "full-body photo" is a photo that shows the user's entire body, and includes multiple photos taken from the front, back, and side.

[0218] "Purchasable Item Data" means information about products such as clothing and accessories sold through affiliated online stores and physical stores, including attributes such as images, sizes, materials, and colors.

[0219] "3D Model" refers to a high-resolution three-dimensional digital model generated based on a user's body shape information and a full-body photograph.

[0220] "Means for synthesizing items" refers to the process of virtually applying selected clothing and accessories to the generated 3D model of the user to generate a realistic try-on image.

[0221] "Virtual try-on image" refers to an image that combines selected clothing and accessories with a user's 3D model, and simulates trying on the clothes from multiple angles.

[0222] "API of affiliated online stores" refers to an application programming interface for automatically obtaining data on products sold by affiliated online stores.

[0223] "Web scraping technology" refers to technology that automatically extracts data from websites.

[0224] "Means for generating a high-resolution 3D model" refers to the process of analyzing a user's body shape information and full-body photograph to create a highly accurate three-dimensional digital model.

[0225] The "means for requesting coordination suggestions" refers to a function that allows a user to request a salesperson to suggest a coordination based on the user's preferences.

[0226] "Performing a virtual try-on" refers to the act of allowing a user to try on an item on a digital model instead of actually trying it on.

[0227] This invention is a system that enables virtual try-on using a user's body type information and full-body photo to improve the fitting experience in physical stores. This system is built using the user's device (smartphone), a series of servers, and APIs or web scraping technologies of affiliated online stores.

[0228] Generating a Program

[0229] The system uses the following hardware and software:

[0230] User device (smartphone): Android or iOS device with camera function

[0231] Server: Data processing and execution of generative AI models (using TensorFlow or PyTorch)

[0232] Partner online store APIs or web scraping technologies (Beautiful Soup, Scrapy, etc.)

[0233] Database: MySQL or PostgreSQL

[0234] Program processing overview

[0235] The server first receives the user's body type information and full-body photo from their device, and then uses image analysis technology to generate a high-resolution 3D model of the user. This 3D model is precisely created using deep learning, recreating a realistic fit to the user's body type.

[0236] The server then uses clothing data collected from partner online stores to synthesize the selected clothing onto the user's 3D model. This synthesis process utilizes generative AI models to accurately reproduce the texture and fit of the clothing. Try-on images taken from multiple angles are generated and sent to the user's device.

[0237] The user can check the virtual try-on images on the device and, if they like them, proceed to the purchase process. If the user would like a coordination suggestion, they can send a request to the salesperson. The coordination suggested by the salesperson is again generated as a virtual try-on image and presented to the user.

[0238] Examples and prompts

[0239] To use the API of a partner online store to collect clothing data, run the following command:

[0240] curl -X GET "https: / / netstore.com / api / v1 / products" -H "Authorization: Bearer YOUR_API_KEY"

[0241] Alternatively, if you use web scraping technology, here's a script that uses Python's Beautiful Soup to extract product data from a specified URL:

[0242] from bs4 import BeautifulSoup

[0243] import requests

[0244] url = "https: / / netstore.com / products"

[0245] response = requests.get(url)

[0246] soup = BeautifulSoup(response.content, 'html.parser')

[0247] for product in soup.find_all('div', class_='product'):

[0248] product_name = product.find('h2').get_text()

[0249] product_price = product.find('span', class_='price').get_text()

[0250] print(product_name, product_price)

[0251] This system allows users to seamlessly try on clothes virtually in a physical store, shortening the time spent trying them on in person and combining the convenience of online with the experience of a physical store.

[0252] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0253] Step 1:

[0254] The user inputs their own body type information into the terminal. The user inputs physical attributes such as height, weight, and three sizes into the terminal, and the input data is sent to the server. At this point, the input is the user's physical numerical data, and the output is raw data sent to the server.

[0255] Step 2:

[0256] The user takes full-body photos from multiple angles and uploads them via the device. The user takes photos from the front, back, and side using the device's camera and sends the image data to the server. At this point, the input is multiple image files, and the output is image data stored on the server.

[0257] Step 3:

[0258] The server uses the API of a partner online store or web scraping technology to collect data on the items being sold. Specifically, the server sends a request to an API endpoint and receives product information in response. Alternatively, it parses a web page to obtain the required product data. At this point, the input is the API request or HTML content, and the output is a set of product data.

[0259] Step 4:

[0260] The server analyzes the user's body shape information and full-body photo to generate a high-resolution 3D model. The collected image data and body shape information are analyzed using a deep learning model (using TensorFlow and PyTorch) to create a highly accurate 3D model of the user. At this point, the input is body shape information and image data, and the output is a 3D model.

[0261] Step 5:

[0262] The server uses a generative AI model to synthesize the selected item onto a 3D model. The collected product data is then synthesized onto the 3D model, creating a virtual try-on image. The generative AI model accurately reproduces the texture and fit of the clothing. At this point, the input is the 3D model and product data, and the output is a try-on image.

[0263] Step 6:

[0264] The server sends the generated try-on images, photographed from multiple angles, to the user's device. These images are displayed on the user's device, allowing the user to check the virtual try-on image. At this point, the input is the try-on image data, and the output is the virtual try-on image displayed on the user's device.

[0265] Step 7:

[0266] When a user requests a coordination suggestion, they send a request from their terminal to the server. The server receives this request and notifies the salesperson in charge. At this point, the input is the request from the user, and the output is a notification to the salesperson.

[0267] Step 8:

[0268] The salesperson creates a coordination suggestion and sends feedback to the server, which then generates a virtual try-on image of the suggested outfit based on the user's 3D model. At this point, the input is the coordination suggestion from the salesperson, and the output is the new try-on image.

[0269] Step 9:

[0270] The server sends the generated coordinated try-on image to the user's device, where the user confirms it. If the user decides to purchase, they click the "Purchase" button on their device, and the purchase information is sent to the server. The input at this point is the virtual try-on image and the user's purchase decision, and the output is the progress of the purchase procedure.

[0271] 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.

[0272] This invention combines a virtual fitting system that generates a 3D model based on the user's body type information and full-body photographs from multiple angles, and then synthesizes clothing onto it, with an emotion engine that recognizes the user's emotions. This system can suggest clothing and outfits that correspond to the user's emotions, providing a more personalized shopping experience.

[0273] 1. Enter your body type information and upload a full-body photo

[0274] Users start the application, input their body information (height, weight, three sizes, etc.), and upload full-body photos taken from multiple angles.

[0275] The terminal transmits this body type information and a full-body photo to the server.

[0276] 2. Collection of clothing data

[0277] The server uses API or web scraping technology to collect data on the clothes being sold (images, sizes, materials, colors, etc.) from partner online stores and stores it in a database.

[0278] 3. 3D model generation and clothing synthesis

[0279] The server analyzes the user's body shape information and full-body photo data to generate a high-resolution 3D model of the user, using the latest image recognition algorithms.

[0280] The server then synthesizes the selected garment onto the 3D model using a generative AI model, a process that leverages deep learning techniques to realistically recreate the garment's fit and texture.

[0281] 4. Showing images of the clothes when they are tried on

[0282] The server transmits the generated try-on images to the user's terminal as images taken from multiple angles.

[0283] The terminal displays the received try-on images on a user interface.

[0284] The user checks the displayed try-on images and requests try-on images from different angles or requests to try on other clothes, if necessary.

[0285] 5. Emotion Recognition by Emotion Engine

[0286] The device uses an emotion engine to analyze the user's facial expressions and voice to identify their emotional state, using a camera and microphone to capture data in real time.

[0287] The server evaluates the user's reaction to the try-on images based on the emotion data from the emotion engine.

[0288] 6. Coordination proposals and adjustments

[0289] If the user desires a coordination suggestion, the user clicks the "Request coordination suggestion" button.

[0290] The server sends a request to a salesperson who is responsible for suggesting a coordination.

[0291] The salesperson proposes an appropriate outfit based on the user's body type information, preferences, and emotional data, and sends feedback to the server.

[0292] The server generates images of the suggested outfits to try on and displays them on the user's device, and adjusts the suggested outfits in real time based on the user's emotional data.

[0293] 7. Purchase Procedure

[0294] The user checks the suggested coordination and, if he / she decides to purchase, clicks the "Purchase" button.

[0295] The terminal sends the purchase information to the server and connects to the online store's payment system.

[0296] The server records the purchase information and notifies the user of the order confirmation and shipping information.

[0297] The terminal displays the order confirmation and shipping information to the user.

[0298] The system of the present invention allows users to easily select clothes that fit their body type and experience the virtual fitting process as if they were actually trying them on. Furthermore, by utilizing an emotion engine to make suggestions based on the user's emotional state, a more personalized shopping experience is realized. This series of processes increases user satisfaction and improves the online shopping experience.

[0299] The processing flow will be explained below.

[0300] Step 1:

[0301] Users start the application and enter their body information, such as height, weight, and measurements, and then upload full-body photos taken from the front, back, and side to the application.

[0302] Step 2:

[0303] The terminal transmits the body type information and full-body photograph input by the user to the server.

[0304] Step 3:

[0305] The server analyzes the received body shape information and full-body photo data and uses image recognition algorithms to generate a high-resolution 3D model of the user.

[0306] Step 4:

[0307] The server uses API or web scraping technology to collect data on the clothes being sold (images, sizes, materials, colors, etc.) from partner online stores and stores it in a database.

[0308] Step 5:

[0309] The server then synthesizes the clothing selected by the user onto the generated 3D model of the user, using a generative AI model to realistically recreate the fit and texture of the clothing.

[0310] Step 6:

[0311] The server transmits the generated try-on images to the user's terminal as images taken from multiple angles.

[0312] Step 7:

[0313] The terminal displays the received try-on images on a user interface.

[0314] Step 8:

[0315] The user checks the displayed try-on images and requests images from different angles or requests to try on other clothes as needed.

[0316] Step 9:

[0317] The device captures the user's facial expressions with a camera or collects their voice with a microphone, and sends this data to the emotion engine.

[0318] Step 10:

[0319] The server uses an emotion engine to analyze the user's facial expressions and voice to identify the user's emotional state, for example, a smiling user is identified as "satisfied" and a frowning user is identified as "anxious."

[0320] Step 11:

[0321] The server uses the emotional state data from the emotion engine to determine the next action, for example, continuing with the proposal if the user is satisfied, or presenting a different option if the user is anxious.

[0322] Step 12:

[0323] If the user desires a coordination suggestion, the user clicks the "Request coordination suggestion" button.

[0324] Step 13:

[0325] The server sends a request to a salesperson who is responsible for suggesting a coordination.

[0326] Step 14:

[0327] The salesperson proposes an appropriate outfit based on the user's body type information, preferences, and emotional data, and sends feedback to the server.

[0328] Step 15:

[0329] The server generates images of the suggested outfits to try on and sends them to the user's device, and also adjusts the suggested outfits in real time based on the user's emotional data.

[0330] Step 16:

[0331] The terminal displays the received try-on image of the coordinated outfit on a user interface.

[0332] Step 17:

[0333] The user checks the suggested coordination and, if he / she decides to purchase, clicks the "Purchase" button.

[0334] Step 18:

[0335] The terminal transmits the purchase information to the server.

[0336] Step 19:

[0337] The server records the purchase information and notifies the user of the order confirmation and shipping information.

[0338] Step 20:

[0339] The terminal displays the order confirmation and shipping information to the user.

[0340] Example 2

[0341] 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."

[0342] In recent years, the spread of online shopping has led to an increasing demand for remote try-on and personalized shopping experiences. However, current systems have difficulty fully reproducing the fit and texture of clothing when trying on in person, and are unable to make recommendations based on the user's emotions. Therefore, there is a need for a more realistic virtual try-on system to improve the user experience.

[0343] 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.

[0344] In this invention, the server includes means for inputting the user's body type information, means for taking and uploading full-body images of the user from multiple angles, means for collecting data on clothing available for sale, means for combining clothing items with the generated three-dimensional model of the user, means for presenting images of the items to be tried on at the user's terminal, means for analyzing the user's facial expressions and voice to recognize emotions, and means for adjusting suggestions based on emotions. This allows the user to virtually try on clothes that fit their body type and receive personalized suggestions based on their emotional state.

[0345] "Means for inputting user's body type information" refers to an interface and system for inputting data related to the user's body type, such as height, weight, and measurements.

[0346] "Means for taking and uploading full-body images of the user from multiple angles" refers to a camera and upload function for taking images of the user's entire body from different angles and uploading the image data to the system.

[0347] "Means of collecting data on clothing for sale" refers to technology used to collect and store data such as images, sizes, materials, and colors of clothing for sale from online stores and other sources.

[0348] "Means for synthesizing clothing onto a generated three-dimensional model of a user" refers to an algorithm and system for realistically synthesizing selected clothing onto a three-dimensional model generated based on the user's body shape information and full-body image.

[0349] "Means for presenting try-on images on a user's device" refers to an interface and system for displaying the generated try-on images on the screen of a user's device (smartphone, tablet, PC, etc.).

[0350] "Means for analyzing a user's facial expressions and voice to recognize emotions" refers to algorithms and systems for analyzing a user's facial expressions and voice data acquired using devices such as cameras and microphones, and identifying the user's emotional state.

[0351] "Means for adjusting suggestions based on emotions" refers to technologies and systems that allow suggested clothing and outfits to be changed or adjusted in real time based on the user's emotional data.

[0352] This virtual fitting system generates a high-resolution 3D model based on the user's physique information and full-body image, and then realistically combines clothing onto it. It also analyzes the user's emotions from their facial expressions and voice, and adjusts clothing suggestions accordingly.

[0353] The system uses the following hardware and software:

[0354] An interface for inputting the user's body shape information (such as a smartphone or PC)

[0355] A camera that takes a full-body image of the user (such as a camera built into a smartphone)

[0356] API or web scraping tool to collect data on the clothes being sold

[0357] Generative AI models for generating 3D models (e.g., deep learning models built in Python)

[0358] A user interface that displays synthesized images of clothes to try on

[0359] Emotion recognition engine for analyzing the user's facial expressions and voice (e.g., emotion analysis API)

[0360] The system performs the following operations:

[0361] The user enters their body type information through the application and takes and uploads full-body images from multiple angles. The device then sends this data to the server. The server then collects data on clothing sold by online stores using APIs or web scraping technology and stores it in a database. The server then analyzes the user's body type information and full-body images and uses a generative AI model to generate a high-resolution 3D model of the user. The server then combines the clothing selected by the user into the 3D model to generate a realistic try-on image. This try-on image is then sent to the user's device and displayed.

[0362] The device also uses a camera and microphone to capture the user's facial expressions and voice in real time, and an emotion recognition engine to analyze their emotions. The server then uses this emotional data to adjust the clothing and outfit suggestions in real time.

[0363] As a concrete example, suppose a user inputs their height of 170cm, weight of 65kg, and measurements (B:95cm, W:75cm, H:95cm), and uploads full-body images from the front and side. Based on this, the server generates a 3D model of the user and synthesizes a navy jacket. An example of a prompt at this stage is as follows: "Based on the user's height of 170cm, weight of 65kg, and measurements (B:95, W:75, H:95), generate a 3D model based on the front and side full-body photos and synthesize a high-resolution navy jacket. Also, recognize positive emotions from the user's smiling face and suggest further outfit recommendations."

[0364] The system allows users to virtually try on clothes that fit their body type and receive personalized suggestions based on their emotional state.

[0365] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0366] Program processing flow

[0367] Step 1:

[0368] The user starts the application and inputs body type information.

[0369] Input: User's body information (height, weight, three sizes, etc.)

[0370] Output: Body shape information is saved in the system.

[0371] Specific operation: The user enters body type information into the application's input fields and clicks the "Submit" button. This information is sent to the server via the terminal.

[0372] Step 2:

[0373] The user takes full-body images from multiple angles and uploads them.

[0374] Input: Full-body images of the user taken from the front and side

[0375] Output: The whole body image is saved in the system.

[0376] Specific operation: The user takes a full-body image of themselves from multiple angles using the smartphone camera, selects the image in the application, and clicks the "Upload" button. The image data is sent to the server via the device.

[0377] Step 3:

[0378] The server collects clothing data from affiliated online stores.

[0379] Input: Online store API information or web page URL

[0380] Output: Garment data (image, size, material, color, etc.) is saved in a database.

[0381] How it works: The server automatically collects clothing data from online stores using APIs and web scraping tools. During data collection, it also checks for duplicates and missing data.

[0382] Step 4:

[0383] The server analyzes the user's body shape information and full-body image, and generates a three-dimensional model using a generative AI model.

[0384] Input: User's body shape information and full-body image

[0385] Output: A high-resolution 3D model of the user

[0386] How it works: The server uses deep learning algorithms to analyze the user's body shape information and full-body image to generate a 3D model, utilizing GPUs to accelerate the calculations in this process.

[0387] Step 5:

[0388] The server synthesizes the user's selected clothing onto the three-dimensional model.

[0389] Input: 3D model of the user and selected clothing data

[0390] Output: Composite fitting image

[0391] How it works: The server uses a generative AI model to synthesize the selected clothing onto the user's 3D model in a way that realistically reproduces the fit and texture, resulting in a try-on image.

[0392] Step 6:

[0393] The server transmits the generated try-on images to the user's terminal.

[0394] Input: Synthesized try-on image

[0395] Output: Try-on image displayed on the user's device

[0396] Specific operation: The try-on images are encoded as images taken from multiple angles and sent from the server to the device, which then displays them on the user interface.

[0397] Step 7:

[0398] The device captures the user's facial expressions and voice and analyzes them using an emotion recognition engine.

[0399] Input: User's facial and voice data

[0400] Output: User emotion data

[0401] Specific operation: The device uses a camera and microphone to capture the user's facial expressions and voice in real time, and then analyzes this data with an emotion recognition engine to identify the user's emotional state.

[0402] Step 8:

[0403] The server analyzes the emotional data and adjusts the suggestions in real time.

[0404] Input: User emotion data

[0405] Output: Adjusted proposal

[0406] Specific operation: Based on the emotional data, the server changes and adjusts the fitting images and coordination suggestions in real time to make optimal suggestions.

[0407] Step 9:

[0408] The user selects the suggested outfit and completes the purchase procedure.

[0409] Input: User's purchasing decision information

[0410] Output: Purchase confirmation and shipping information

[0411] Specific operation: When the user clicks the "Purchase" button, the device sends this information to the server. The server then links the purchase information to the online store's payment system and completes the purchase process. The user is then notified of order confirmation and delivery information.

[0412] This allows users to virtually try on clothes that fit their body type, receive personalized suggestions based on their emotions, and enjoy an optimal shopping experience.

[0413] (Application example 2)

[0414] 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."

[0415] Conventional virtual try-on systems can generate fitting images based on the user's body shape information, but they cannot make suggestions that take the user's emotions into account. This makes it difficult to improve the user's satisfaction with the clothing and outfits they select. Furthermore, the lack of coordination adjustments based on real-time emotional feedback makes it difficult to fully meet the user's individual needs.

[0416] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting the user's physique information, a means for taking and uploading full-body photos of the user from multiple angles, a means for collecting data on clothing for sale, a means for combining clothing with the generated high-resolution 3D model of the user, a means for analyzing the user's emotions and suggesting clothing and outfits based on the emotion data, and a means for displaying try-on images on the user's device. This enables personalized clothing and outfit suggestions based on the user's emotions, improving user satisfaction. Furthermore, by adjusting the suggestions based on real-time emotion feedback, the system can more appropriately meet the user's individual needs.

[0417] "User's body type information" is data related to the user's body dimensions such as height, weight, and three sizes.

[0418] A "full-body photo" is an image of the user taken from multiple angles (front, back, side, etc.).

[0419] "Data on clothing for sale" refers to detailed information such as images, sizes, materials, and colors of clothing collected from affiliated online stores.

[0420] A "high-resolution 3D model" is a 3D model that is generated based on the user's body shape information and a full-body photograph and is rendered in great detail.

[0421] "Means for synthesizing clothing" refers to a technology that uses data on clothing for sale to realistically display clothing on the generated high-resolution 3D model.

[0422] "Means for analyzing emotions and suggesting clothing and outfits based on emotional data" refers to technology that analyzes a user's facial expressions and voice, and then suggests the most suitable clothing and outfits for the user based on the emotional data obtained from that.

[0423] The "means for presenting a try-on image" is a technology for displaying the generated virtual try-on image on the user's terminal.

[0424] "Emotion data" is data that represents an emotional state such as joy, surprise, or dissatisfaction obtained from the analysis results of the user's facial expressions and voice.

[0425] "Coordination" refers to the user's clothing combination and includes the selection process.

[0426] "Feedback" is the process of adjusting and re-proposing suggestions based on the user's emotional data.

[0427] This invention is a virtual fitting system that combines an emotion engine that recognizes the user's emotions with a 3D model that is generated based on the user's body type information and full-body photographs taken from multiple angles, and then synthesizes clothing onto the 3D model. This system can suggest clothing and outfits that correspond to the user's emotions, providing a more personalized shopping experience.

[0428] The server realizes this system by integrating the following technologies. First, the server provides a means for inputting the user's body type information. This allows the user to enter information such as their height, weight, and three sizes. In addition, the server provides a means for uploading full-body photos taken from multiple angles. This allows the user to upload photos of their front, side, and back.

[0429] The server collects data on clothing sold from online stores and other data sources. This data includes detailed information such as clothing images, sizes, materials, and colors. The server then uses state-of-the-art image recognition algorithms (e.g., TensorFlow) to analyze the user's body shape and full-body photo data to generate a high-resolution 3D model. Deep learning techniques are then used to synthesize clothing onto this 3D model, generating realistic try-on images.

[0430] Furthermore, it has a means to analyze the user's facial expressions and voice using an emotion engine (e.g., Affectiva API) to obtain emotional data. It also incorporates a means to suggest optimal clothing and outfits for the user based on the emotional data. This makes it possible to make appropriate suggestions in real time based on the user's emotions.

[0431] A means of displaying try-on images on the user's device is also in place, allowing the user to view the generated virtual try-on image from multiple angles. After the try-on image is displayed, the user can provide feedback on their emotions on the spot and adjust their outfit as needed.

[0432] For example, a user enters their height and weight into the app and uploads three full-body photos (front, back, and side). After the photo shoot is complete, if the user is happy based on emotion analysis, the system will suggest outfits with bright colors and the latest fashions. On the other hand, if the user is dissatisfied, the system will adjust to suggest new outfits.

[0433] An example of a prompt is as follows:

[0434] The user is 170cm tall and weighs 65kg. They have uploaded photos of the front, back, and side. Sentiment analysis indicates that the user is currently feeling "joy." Based on this, suggest multiple outfits in bright colors and the latest fashions.

[0435] In this way, users can choose the best clothing for their body type and get the same feeling of trying them on in real life through a virtual try-on experience. Real-time emotional feedback provides a more personalized shopping experience.

[0436] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0437] Step 1:

[0438] The user launches the application, inputs their body type information (height, weight, measurements, etc.), and uploads full-body photos taken from multiple angles (front, back, and side). The input data consists of body type information and photo data, which are sent from the device to the server. As a result, data related to the user's body type and full-body photos is stored on the server.

[0439] Step 2:

[0440] The server collects data about clothing sold (images, sizes, materials, colors, etc.) from online stores and other data sources. This collection is done using APIs or web scraping technology. The acquired data is stored in a database on the server. The input is clothing data, and the output is clothing data stored in the database.

[0441] Step 3:

[0442] The server uses the latest image recognition algorithms (e.g., TensorFlow) to analyze the user's body shape information and full-body photo data and generate a high-resolution 3D model. During this process, each user's photo is pixel-analyzed to create the shape of the 3D model. The input for this step is the user's body shape information and photo data, and the output is a high-resolution 3D model.

[0443] Step 4:

[0444] The server uses a generative AI model to synthesize commercially available clothing onto the generated 3D model. Using deep learning technology, the server processes the data to realistically reproduce the fit and texture of the clothing. The input is a high-resolution 3D model and clothing data, and the output is a try-on image.

[0445] Step 5:

[0446] The device uses an emotion engine (e.g., Affectiva API) to analyze the user's facial expressions and voice in real time and acquire emotional data. The captured facial and voice data are input, and analyzed emotional data is output. In this step, the device acquires data using the camera and microphone.

[0447] Step 6:

[0448] The server then proposes optimal clothing and outfits to the user based on the emotional data. The input is the emotional data, and the output is outfit suggestions tailored to the user's preferences. These suggestions are made dynamically and adjusted in real time.

[0449] Step 7:

[0450] The user's terminal displays the try-on images sent from the server. The input is the try-on image, and the output is the try-on image displayed on the user interface. The user can check the displayed try-on image and request images from different angles or to try on other clothes as needed.

[0451] Step 8:

[0452] If the user likes the suggested outfit, they proceed with the purchase. The user clicks the "Purchase" button, and the terminal sends the purchase information to the server. The input is the purchase information, and the output is a purchase confirmation and delivery information. The server connects with the online store's payment system, records the order, and sends a confirmation to the user.

[0453] This allows users to virtually try on clothes that fit their body type, receive personalized coordination suggestions based on emotional data, and finally make a purchase.

[0454] 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.

[0455] 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.

[0456] 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.

[0457] [Second embodiment]

[0458] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0459] 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.

[0460] 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).

[0461] 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.

[0462] 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.

[0463] 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).

[0464] 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.

[0465] 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.

[0466] 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.

[0467] 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.

[0468] 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.

[0469] 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."

[0470] This invention is a virtual try-on system that uses a user's body type information and full-body photos taken from multiple angles to generate a fitting image tailored to the user. The system begins by the user entering body type information and uploading a full-body photo using their own device. The system then utilizes a generative AI model based on the collected clothing data to synthesize the clothing onto the user's 3D model and present a virtual fitting image. Furthermore, the user can receive coordination suggestions from sales staff, making the process of selecting and purchasing clothing even more enjoyable.

[0471] 1. Enter your body type information and upload a full-body photo

[0472] Users start the application and enter their body information, such as height, weight, and measurements, and also upload full-body photos taken from the front, back, and side.

[0473] The terminal transmits the body type information entered by the user and the whole-body photograph taken to the server.

[0474] 2. Collection of clothing data

[0475] The server collects data on clothing sold online (images, sizes, materials, colors, etc.) using APIs or web scraping technology from partner online stores and stores it in a database.

[0476] 3. 3D Model Generation and Synthesis

[0477] The server analyzes the user's body shape information and uploaded full-body photos to generate a high-resolution 3D model of the user.

[0478] The server uses a generative AI model to synthesize the user's chosen clothing onto the 3D model, using deep learning techniques to realistically recreate the fit and texture of the clothing.

[0479] 4. Showing images of the clothes when they are tried on

[0480] The server transmits the generated try-on images to the user's terminal as images taken from multiple angles.

[0481] The terminal displays the received try-on images on a user interface.

[0482] The user can check the displayed try-on images and, if necessary, request images from different angles or images of other clothes being tried on.

[0483] 5. Coordination suggestions and purchase procedures

[0484] If the user desires a coordination suggestion, the user clicks the "Request coordination suggestion" button.

[0485] The server sends a request to a salesperson who is responsible for suggesting a coordination.

[0486] The salesperson proposes appropriate outfits based on the user's body type and preferences, and sends feedback to the server.

[0487] The server generates a try-on image of the proposed outfit and presents it to the user's terminal.

[0488] The user checks the suggested coordination and, if he / she decides to purchase, clicks the "Purchase" button.

[0489] The terminal sends the purchase information to the server and connects to the online store's payment system.

[0490] This invention allows users to easily select clothes that fit their body type and experience the virtual fitting process as if they were actually trying them on. Furthermore, they can smoothly complete the online purchase process while checking suggested outfits.

[0491] The processing flow will be explained below.

[0492] Step 1:

[0493] Users start the application and enter their body information, such as height, weight, and measurements, and also upload full-body photos taken from the front, back, and side.

[0494] Step 2:

[0495] The terminal transmits the body type information and full-body photograph input by the user to the server.

[0496] Step 3:

[0497] The server analyzes the received body shape information and full-body photo data to generate a high-resolution 3D model of the user, using image recognition algorithms.

[0498] Step 4:

[0499] The server uses APIs or web scraping technology from affiliated online stores to collect data on the clothes being sold (images, sizes, materials, colors, etc.) and stores it in a database.

[0500] Step 5:

[0501] The server then synthesizes the clothing selected by the user onto the generated 3D model of the user, using a generative AI model to realistically recreate the fit and texture of the clothing.

[0502] Step 6:

[0503] The server transmits the generated try-on images to the user's terminal as images taken from multiple angles.

[0504] Step 7:

[0505] The terminal displays the received try-on images on a user interface.

[0506] Step 8:

[0507] The user checks the displayed try-on images and requests try-on images from different angles or try-on images of other clothes as needed.

[0508] Step 9:

[0509] If the user desires a coordination suggestion, the user clicks the "Request coordination suggestion" button.

[0510] Step 10:

[0511] The server sends a request to a salesperson who is responsible for suggesting a coordination.

[0512] Step 11:

[0513] The salesperson proposes appropriate outfits based on the user's body type and preferences, and sends feedback to the server.

[0514] Step 12:

[0515] The server generates a try-on image of the proposed outfit and sends it to the user's terminal.

[0516] Step 13:

[0517] The terminal displays the received try-on image of the coordinated outfit on a user interface.

[0518] Step 14:

[0519] The user checks the suggested coordination and, if he / she decides to purchase, clicks the "Purchase" button.

[0520] Step 15:

[0521] The terminal transmits the purchase information to the server.

[0522] Step 16:

[0523] The server records the purchase information and connects to the online store's payment system.

[0524] Step 17:

[0525] The server notifies the user of the order confirmation and delivery information.

[0526] Step 18:

[0527] The terminal displays the order confirmation and shipping information to the user.

[0528] Example 1

[0529] 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."

[0530] Conventional online shopping has the problem that users cannot accurately understand how clothes fit or how the design will look on them unless they actually try them on. Furthermore, coordination suggestions tailored to the user's preferences and the ability to check how the clothes will look when they are tried on are insufficient, which discourages users from making a purchase. This has led to a decline in user satisfaction with online shopping.

[0531] 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.

[0532] In this invention, the server includes a means for inputting the user's body type information, a means for taking and uploading full-body photos of the user from multiple angles, a means for collecting data on clothing sold online, a means for combining clothing with the generated high-resolution 3D model of the user, a means for transmitting the generated try-on images to the user's device, and a means for displaying the try-on images on the user's device. This allows the user to easily select clothing that fits their body type and experience the virtual try-on experience as if they were actually trying on the clothes. Furthermore, the user can request coordination suggestions from a salesperson and check the suggested try-on images, which increases the user's motivation to purchase.

[0533] "User" refers to an individual who utilizes the System to input body type information and take and upload a full-body photograph.

[0534] "Body type information" refers to detailed data about the user's body, such as height, weight, and measurements.

[0535] A "full-body photo" refers to an image of the user's body taken from multiple angles, including the front, back, and side.

[0536] "Online clothing data" refers to information such as clothing images, sizes, materials, and colors collected from online sales sites and stores.

[0537] "High-resolution 3D model" refers to a three-dimensional computer graphics model that reproduces the user's body in detail, generated based on the user's body shape information and a full-body photograph.

[0538] "Generative AI model" refers to an artificial intelligence model trained using deep learning techniques to realistically synthesize clothing onto a user's 3D model.

[0539] "Try-on image" refers to an image created by using a generative AI model to synthesize selected clothing onto a user's 3D model.

[0540] "User's device" refers to an electronic device used by a user, such as a smartphone, tablet, or PC.

[0541] "Coordination suggestions" refer to combinations of multiple clothes recommended by a salesperson based on the user's body type information and preferences.

[0542] A "salesperson" refers to a person in charge of making coordination suggestions to users through the system.

[0543] The present invention is a virtual try-on system that uses a user's body type information and full-body photographs taken from multiple angles to generate a fitting image tailored to the user. In this system, a program is executed in the following procedure.

[0544] First, the user launches a dedicated application on their smartphone or PC and enters their body information, including height, weight, and measurements. Then, the user takes full-body photos of the front, back, and side and uploads them to the application. This uploaded data is then sent to the server via the device.

[0545] The device securely transmits input and uploaded data to the server using the HTTPS protocol. The server receives this data and uses OpenCV, an image analysis technology, to analyze the user's body shape with high accuracy. Based on the analyzed data, it uses deep learning frameworks such as TensorFlow and PyTorch to generate a high-resolution 3D model.

[0546] After generating a 3D model for the user, the server uses APIs or web scraping technology from partner online stores to collect data on clothing sold online, including images of the clothing, sizes, materials, colors, etc. This data is stored in a database and used to synthesize clothing onto the user's 3D model.

[0547] The server then synthesizes the clothing selected by the user onto the generated 3D model using a generative AI model. The generative AI model is trained using deep learning technology to realistically reproduce the fit and texture of the clothing. Deep learning algorithms using TensorFlow and PyTorch are used in this process.

[0548] The generated try-on images are taken from multiple angles and sent to the user's device as JPEG or PNG image files. The device displays the received try-on images on the application's user interface. The user can review the displayed try-on images and request images from different angles or try-on images of other clothes as needed.

[0549] Furthermore, if the user wishes to receive a coordination suggestion, they click the "Request Coordination Suggestion" button. The server sends a request to the salesperson in charge of coordination suggestions, who then suggests an appropriate coordination based on the user's body type information and preferences, and sends feedback to the server. Based on this feedback, the server generates a new fitting image and displays it on the user's device. Finally, the user checks the suggested coordination and clicks the "Purchase" button if they decide to purchase. This purchase information is sent to the server via the device, and the purchase procedure is carried out in conjunction with the online store's payment system.

[0550] Specific examples

[0551] For example, if a user wants to try on "blue jeans" and a "white T-shirt,"

[0552] 1. The user enters their height as 170cm, weight as 65kg, measurements as 90-75-90, and uploads full-body photos of the front, back, and side.

[0553] 2. The device sends this information to the server.

[0554] 3. The server uses web scraping technology to collect data on "blue jeans" and "white T-shirts" and stores it in a database.

[0555] 4. The server uses image analysis technology to generate a high-resolution 3D model of the user, and then uses a generative AI model to synthesize jeans and a T-shirt onto the 3D model.

[0556] 5. The server generates images of the clothes being tried on from multiple angles and sends them to the user's device.

[0557] 6. The user can check the newly generated try-on image and then decide to "purchase this outfit."

[0558] Prompt Sentence Examples

[0559] "Enter your height, weight, and measurements, then upload full-body photos taken from the front, back, and side."

[0560] This system allows users to easily find clothes that fit their body type, and experience the virtual fitting process as if they were actually trying them on. Users can also request outfit suggestions and check the suggested try-on images, making online clothing shopping smoother and more satisfying.

[0561] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0562] Step 1:

[0563] The user launches an application.

[0564] Specific operation: The user taps or clicks on the dedicated application on their smartphone or PC to launch it and proceeds to the first screen.

[0565] Input: None

[0566] Output: The application launches and displays a screen where the user can enter their body information.

[0567] Step 2:

[0568] The user inputs body type information.

[0569] Specific operation: The user enters body information such as height, weight, and measurements into the application form. The user also prepares full-body photos taken from the front, back, and side.

[0570] Input: Body information such as height, weight, and measurements

[0571] Output: Display of input body shape information and full-body photo

[0572] Step 3:

[0573] The user uploads a full-body photo.

[0574] Specific operation: The user uploads full-body photos of the front, back, and side using the upload function within the application.

[0575] Input: Full-body photos of the front, back, and side

[0576] Output: Full body photo upload complete message

[0577] Step 4:

[0578] The terminal sends the input data to the server.

[0579] Specific operation: The device sends the user's body shape information and a full-body photo to the server via the HTTPS protocol.

[0580] Input: User's body type information and full-body photo

[0581] Output: The data is sent to the server and awaits processing.

[0582] Step 5:

[0583] The server analyzes body shape information and full-body photos.

[0584] Specific operation: The server performs image analysis using OpenCV and extracts the features necessary to create a 3D model based on the user's body shape information.

[0585] Input: Body type information and full-body photo

[0586] Output: Feature-extracted data

[0587] Step 6:

[0588] The server generates a high-resolution 3D model.

[0589] Specific operation: The server uses TensorFlow and PyTorch for deep learning to generate a high-resolution 3D model based on the user's body shape.

[0590] Input: Feature-extracted data

[0591] Output: High-resolution 3D model

[0592] Step 7:

[0593] A server collects data about clothing sold online.

[0594] Specific operation: The server calls the API of partner online stores or uses web scraping to collect the latest clothing data (images, sizes, materials, colors, etc.).

[0595] Input: API endpoint of partner online store or scraping target URL

[0596] Output: Latest clothing data stored in the clothing database

[0597] Step 8:

[0598] The server uses a generative AI model to synthesize the clothing onto a 3D model.

[0599] Specific operation: The server uses a generative AI model to synthesize clothing onto the user's 3D model based on the collected clothing data.

[0600] Input: 3D model and clothing data

[0601] Output: Try-on images from multiple angles

[0602] Step 9:

[0603] The server transmits the generated try-on images to the terminal.

[0604] How it works: The server generates try-on images in JPEG or PNG format and sends them to the user's device. High-speed data transfer via a CDN may also be used.

[0605] Input: Try-on image

[0606] Output: Try-on image sent to the device

[0607] Step 10:

[0608] The terminal displays the try-on image on the user interface.

[0609] Specific operation: The device displays the received try-on images on the application's user interface, allowing the user to operate them.

[0610] Input: Try-on image

[0611] Output: Try-on image displayed on the user interface

[0612] Step 11:

[0613] The user checks the try-on images and sends a request if necessary.

[0614] Specific operation: The user checks the try-on images, and if they want to request images from different angles or images of other clothes being tried on, they click the corresponding button in the application.

[0615] Input: User request

[0616] Output: The request is sent to the server.

[0617] Step 12:

[0618] The user requests outfit suggestions.

[0619] Specific operation: If the user wants outfit suggestions, he / she clicks the "Request outfit suggestions" button.

[0620] Input: User's outfit suggestion request

[0621] Output: The request is sent to the server.

[0622] Step 13:

[0623] The server sends the request to the salesperson.

[0624] Specific operation: The server sends a coordination proposal request to the salesperson, along with the necessary user information.

[0625] Input: User request

[0626] Output: The request is sent to the salesperson

[0627] Step 14:

[0628] Salespeople provide feedback on suggestions.

[0629] Specific operation: The salesperson considers the appropriate outfit based on the user's body type and preferences, and sends feedback to the server.

[0630] Input: User's body type information and preferences

[0631] Output: Feedback of outfit suggestions

[0632] Step 15:

[0633] The server generates an image of a coordinated outfit to try on.

[0634] Specific operation: The server generates new try-on images based on feedback from the salesperson and sends them to the user's device.

[0635] Input: Feedback on outfit suggestions

[0636] Output: Coordinate try-on image sent to the device

[0637] Step 16:

[0638] The user reviews the offer and decides to purchase.

[0639] Specific operation: The user checks the newly generated outfit try-on image and clicks the "Purchase" button if they like it.

[0640] Input: Coordination try-on image

[0641] Output: Purchase request sent to server

[0642] Step 17:

[0643] The device sends purchase information to the server and connects with the online store.

[0644] Specific operation: The terminal sends purchase information to the server, and the server works with the online store's payment system to complete the purchase process.

[0645] Input: Purchase information

[0646] Output: The purchase is completed and a confirmation is sent to the user.

[0647] (Application example 1)

[0648] 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."

[0649] Traditionally, trying on clothes in physical stores was time-consuming and labor-intensive. Furthermore, the types of clothing that could be tried on were limited, making it difficult to try out many options. Furthermore, online shopping presents the problem of frequent returns and exchanges after purchase, since users are unable to see how the actual product will look. To solve these problems, a system is needed that allows users to easily try on clothes and select the best fit for their body type.

[0650] 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.

[0651] In this invention, the server includes: means for inputting a user's body type information; means for taking and uploading full-body photos of the user from multiple angles; means for collecting data on available items; means for combining items with the generated 3D model of the user; means for displaying virtual try-on images on the user's device; means for the user to input body type information and upload a full-body photo using their own device; means for collecting and storing data on items sold online using APIs or web scraping technology of affiliated online stores; means for analyzing the user's body type information and the uploaded full-body photo to generate a high-resolution 3D model of the user; and means for generating try-on images from multiple angles and sending them to the user's device. This allows users to smoothly try on clothes virtually in a physical store, shortening the time it takes to actually try on clothes and combining the convenience of online shopping with the experience of a physical store.

[0652] "User's body type information" refers to the user's physical measurements and characteristics, such as height, weight, and three sizes.

[0653] A "full-body photo" is a photo that shows the user's entire body, and includes multiple photos taken from the front, back, and side.

[0654] "Purchasable Item Data" means information about products such as clothing and accessories sold through affiliated online stores and physical stores, including attributes such as images, sizes, materials, and colors.

[0655] "3D Model" refers to a high-resolution three-dimensional digital model generated based on a user's body shape information and a full-body photograph.

[0656] "Means for synthesizing items" refers to the process of virtually applying selected clothing and accessories to the generated 3D model of the user to generate a realistic try-on image.

[0657] "Virtual try-on image" refers to an image that combines selected clothing and accessories with a user's 3D model, and simulates trying on the clothes from multiple angles.

[0658] "API of affiliated online stores" refers to an application programming interface for automatically obtaining data on products sold by affiliated online stores.

[0659] "Web scraping technology" refers to technology that automatically extracts data from websites.

[0660] "Means for generating a high-resolution 3D model" refers to the process of analyzing a user's body shape information and full-body photograph to create a highly accurate three-dimensional digital model.

[0661] The "means for requesting coordination suggestions" refers to a function that allows a user to request a salesperson to suggest a coordination based on the user's preferences.

[0662] "Performing a virtual try-on" refers to the act of allowing a user to try on an item on a digital model instead of actually trying it on.

[0663] This invention is a system that enables virtual try-on using a user's body type information and full-body photo to improve the fitting experience in physical stores. This system is built using the user's device (smartphone), a series of servers, and APIs or web scraping technologies of affiliated online stores.

[0664] Generating a Program

[0665] The system uses the following hardware and software:

[0666] User device (smartphone): Android or iOS device with camera function

[0667] Server: Data processing and execution of generative AI models (using TensorFlow or PyTorch)

[0668] Partner online store APIs or web scraping technologies (Beautiful Soup, Scrapy, etc.)

[0669] Database: MySQL or PostgreSQL

[0670] Program processing overview

[0671] The server first receives the user's body type information and full-body photo from their device, and then uses image analysis technology to generate a high-resolution 3D model of the user. This 3D model is precisely created using deep learning, recreating a realistic fit to the user's body type.

[0672] The server then uses clothing data collected from partner online stores to synthesize the selected clothing onto the user's 3D model. This synthesis process utilizes generative AI models to accurately reproduce the texture and fit of the clothing. Try-on images taken from multiple angles are generated and sent to the user's device.

[0673] The user can check the virtual try-on images on the device and, if they like them, proceed to the purchase process. If the user would like a coordination suggestion, they can send a request to the salesperson. The coordination suggested by the salesperson is again generated as a virtual try-on image and presented to the user.

[0674] Examples and prompts

[0675] To use the API of a partner online store to collect clothing data, run the following command:

[0676] curl -X GET "https: / / netstore.com / api / v1 / products" -H "Authorization: Bearer YOUR_API_KEY"

[0677] Alternatively, if you use web scraping technology, here's a script that uses Python's Beautiful Soup to extract product data from a specified URL:

[0678] from bs4 import BeautifulSoup

[0679] import requests

[0680] url = "https: / / netstore.com / products"

[0681] response = requests.get(url)

[0682] soup = BeautifulSoup(response.content, 'html.parser')

[0683] for product in soup.find_all('div', class_='product'):

[0684] product_name = product.find('h2').get_text()

[0685] product_price = product.find('span', class_='price').get_text()

[0686] print(product_name, product_price)

[0687] This system allows users to seamlessly try on clothes virtually in a physical store, shortening the time spent trying them on in person and combining the convenience of online with the experience of a physical store.

[0688] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0689] Step 1:

[0690] The user inputs their own body type information into the terminal. The user inputs physical attributes such as height, weight, and three sizes into the terminal, and the input data is sent to the server. At this point, the input is the user's physical numerical data, and the output is raw data sent to the server.

[0691] Step 2:

[0692] The user takes full-body photos from multiple angles and uploads them via the device. The user takes photos from the front, back, and side using the device's camera and sends the image data to the server. At this point, the input is multiple image files, and the output is image data stored on the server.

[0693] Step 3:

[0694] The server uses the API of a partner online store or web scraping technology to collect data on the items being sold. Specifically, the server sends a request to an API endpoint and receives product information in response. Alternatively, it parses a web page to obtain the required product data. At this point, the input is the API request or HTML content, and the output is a set of product data.

[0695] Step 4:

[0696] The server analyzes the user's body shape information and full-body photo to generate a high-resolution 3D model. The collected image data and body shape information are analyzed using a deep learning model (using TensorFlow and PyTorch) to create a highly accurate 3D model of the user. At this point, the input is body shape information and image data, and the output is a 3D model.

[0697] Step 5:

[0698] The server uses a generative AI model to synthesize the selected item onto a 3D model. The collected product data is then synthesized onto the 3D model, creating a virtual try-on image. The generative AI model accurately reproduces the texture and fit of the clothing. At this point, the input is the 3D model and product data, and the output is a try-on image.

[0699] Step 6:

[0700] The server sends the generated try-on images, photographed from multiple angles, to the user's device. These images are displayed on the user's device, allowing the user to check the virtual try-on image. At this point, the input is the try-on image data, and the output is the virtual try-on image displayed on the user's device.

[0701] Step 7:

[0702] When a user requests a coordination suggestion, they send a request from their terminal to the server. The server receives this request and notifies the salesperson in charge. At this point, the input is the request from the user, and the output is a notification to the salesperson.

[0703] Step 8:

[0704] The salesperson creates a coordination suggestion and sends feedback to the server, which then generates a virtual try-on image of the suggested outfit based on the user's 3D model. At this point, the input is the coordination suggestion from the salesperson, and the output is the new try-on image.

[0705] Step 9:

[0706] The server sends the generated coordinated try-on image to the user's device, where the user confirms it. If the user decides to purchase, they click the "Purchase" button on their device, and the purchase information is sent to the server. The input at this point is the virtual try-on image and the user's purchase decision, and the output is the progress of the purchase procedure.

[0707] 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.

[0708] This invention combines a virtual fitting system that generates a 3D model based on the user's body type information and full-body photographs from multiple angles, and then synthesizes clothing onto it, with an emotion engine that recognizes the user's emotions. This system can suggest clothing and outfits that correspond to the user's emotions, providing a more personalized shopping experience.

[0709] 1. Enter your body type information and upload a full-body photo

[0710] Users start the application, input their body information (height, weight, three sizes, etc.), and upload full-body photos taken from multiple angles.

[0711] The terminal transmits this body type information and a full-body photo to the server.

[0712] 2. Collection of clothing data

[0713] The server uses API or web scraping technology to collect data on the clothes being sold (images, sizes, materials, colors, etc.) from partner online stores and stores it in a database.

[0714] 3. 3D model generation and clothing synthesis

[0715] The server analyzes the user's body shape information and full-body photo data to generate a high-resolution 3D model of the user, using the latest image recognition algorithms.

[0716] The server then synthesizes the selected garment onto the 3D model using a generative AI model, a process that leverages deep learning techniques to realistically recreate the garment's fit and texture.

[0717] 4. Showing images of the clothes when they are tried on

[0718] The server transmits the generated try-on images to the user's terminal as images taken from multiple angles.

[0719] The terminal displays the received try-on images on a user interface.

[0720] The user checks the displayed try-on images and requests try-on images from different angles or requests to try on other clothes, if necessary.

[0721] 5. Emotion Recognition by Emotion Engine

[0722] The device uses an emotion engine to analyze the user's facial expressions and voice to identify their emotional state, using a camera and microphone to capture data in real time.

[0723] The server evaluates the user's reaction to the try-on images based on the emotion data from the emotion engine.

[0724] 6. Coordination proposals and adjustments

[0725] If the user desires a coordination suggestion, the user clicks the "Request coordination suggestion" button.

[0726] The server sends a request to a salesperson who is responsible for suggesting a coordination.

[0727] The salesperson proposes an appropriate outfit based on the user's body type information, preferences, and emotional data, and sends feedback to the server.

[0728] The server generates images of the suggested outfits to try on and displays them on the user's device, and adjusts the suggested outfits in real time based on the user's emotional data.

[0729] 7. Purchase Procedure

[0730] The user checks the suggested coordination and, if he / she decides to purchase, clicks the "Purchase" button.

[0731] The terminal sends the purchase information to the server and connects to the online store's payment system.

[0732] The server records the purchase information and notifies the user of the order confirmation and shipping information.

[0733] The terminal displays the order confirmation and shipping information to the user.

[0734] The system of the present invention allows users to easily select clothes that fit their body type and experience the virtual fitting process as if they were actually trying them on. Furthermore, by utilizing an emotion engine to make suggestions based on the user's emotional state, a more personalized shopping experience is realized. This series of processes increases user satisfaction and improves the online shopping experience.

[0735] The processing flow will be explained below.

[0736] Step 1:

[0737] Users start the application and enter their body information, such as height, weight, and measurements, and then upload full-body photos taken from the front, back, and side to the application.

[0738] Step 2:

[0739] The terminal transmits the body type information and full-body photograph input by the user to the server.

[0740] Step 3:

[0741] The server analyzes the received body shape information and full-body photo data and uses image recognition algorithms to generate a high-resolution 3D model of the user.

[0742] Step 4:

[0743] The server uses API or web scraping technology to collect data on the clothes being sold (images, sizes, materials, colors, etc.) from partner online stores and stores it in a database.

[0744] Step 5:

[0745] The server then synthesizes the clothing selected by the user onto the generated 3D model of the user, using a generative AI model to realistically recreate the fit and texture of the clothing.

[0746] Step 6:

[0747] The server transmits the generated try-on images to the user's terminal as images taken from multiple angles.

[0748] Step 7:

[0749] The terminal displays the received try-on images on a user interface.

[0750] Step 8:

[0751] The user checks the displayed try-on images and requests images from different angles or requests to try on other clothes as needed.

[0752] Step 9:

[0753] The device captures the user's facial expressions with a camera or collects their voice with a microphone, and sends this data to the emotion engine.

[0754] Step 10:

[0755] The server uses an emotion engine to analyze the user's facial expressions and voice to identify the user's emotional state, for example, a smiling user is identified as "satisfied" and a frowning user is identified as "anxious."

[0756] Step 11:

[0757] The server uses the emotional state data from the emotion engine to determine the next action, for example, continuing with the proposal if the user is satisfied, or presenting a different option if the user is anxious.

[0758] Step 12:

[0759] If the user desires a coordination suggestion, the user clicks the "Request coordination suggestion" button.

[0760] Step 13:

[0761] The server sends a request to a salesperson who is responsible for suggesting a coordination.

[0762] Step 14:

[0763] The salesperson proposes an appropriate outfit based on the user's body type information, preferences, and emotional data, and sends feedback to the server.

[0764] Step 15:

[0765] The server generates images of the suggested outfits to try on and sends them to the user's device, and also adjusts the suggested outfits in real time based on the user's emotional data.

[0766] Step 16:

[0767] The terminal displays the received try-on image of the coordinated outfit on a user interface.

[0768] Step 17:

[0769] The user checks the suggested coordination and, if he / she decides to purchase, clicks the "Purchase" button.

[0770] Step 18:

[0771] The terminal transmits the purchase information to the server.

[0772] Step 19:

[0773] The server records the purchase information and notifies the user of the order confirmation and shipping information.

[0774] Step 20:

[0775] The terminal displays the order confirmation and shipping information to the user.

[0776] Example 2

[0777] 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."

[0778] In recent years, the spread of online shopping has led to an increasing demand for remote try-on and personalized shopping experiences. However, current systems have difficulty fully reproducing the fit and texture of clothing when trying on in person, and are unable to make recommendations based on the user's emotions. Therefore, there is a need for a more realistic virtual try-on system to improve the user experience.

[0779] 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.

[0780] In this invention, the server includes means for inputting the user's body type information, means for taking and uploading full-body images of the user from multiple angles, means for collecting data on clothing available for sale, means for combining clothing items with the generated three-dimensional model of the user, means for presenting images of the items to be tried on at the user's terminal, means for analyzing the user's facial expressions and voice to recognize emotions, and means for adjusting suggestions based on emotions. This allows the user to virtually try on clothes that fit their body type and receive personalized suggestions based on their emotional state.

[0781] "Means for inputting user's body type information" refers to an interface and system for inputting data related to the user's body type, such as height, weight, and measurements.

[0782] "Means for taking and uploading full-body images of the user from multiple angles" refers to a camera and upload function for taking images of the user's entire body from different angles and uploading the image data to the system.

[0783] "Means of collecting data on clothing for sale" refers to technology used to collect and store data such as images, sizes, materials, and colors of clothing for sale from online stores and other sources.

[0784] "Means for synthesizing clothing onto a generated three-dimensional model of a user" refers to an algorithm and system for realistically synthesizing selected clothing onto a three-dimensional model generated based on the user's body shape information and full-body image.

[0785] "Means for presenting try-on images on a user's device" refers to an interface and system for displaying the generated try-on images on the screen of a user's device (smartphone, tablet, PC, etc.).

[0786] "Means for analyzing a user's facial expressions and voice to recognize emotions" refers to algorithms and systems for analyzing a user's facial expressions and voice data acquired using devices such as cameras and microphones, and identifying the user's emotional state.

[0787] "Means for adjusting suggestions based on emotions" refers to technologies and systems that allow suggested clothing and outfits to be changed or adjusted in real time based on the user's emotional data.

[0788] This virtual fitting system generates a high-resolution 3D model based on the user's physique information and full-body image, and then realistically combines clothing onto it. It also analyzes the user's emotions from their facial expressions and voice, and adjusts clothing suggestions accordingly.

[0789] The system uses the following hardware and software:

[0790] An interface for inputting the user's body shape information (such as a smartphone or PC)

[0791] A camera that takes a full-body image of the user (such as a camera built into a smartphone)

[0792] API or web scraping tool to collect data on the clothes being sold

[0793] Generative AI models for generating 3D models (e.g., deep learning models built in Python)

[0794] A user interface that displays synthesized images of clothes to try on

[0795] Emotion recognition engine for analyzing the user's facial expressions and voice (e.g., emotion analysis API)

[0796] The system performs the following operations:

[0797] The user enters their body type information through the application and takes and uploads full-body images from multiple angles. The device then sends this data to the server. The server then collects data on clothing sold by online stores using APIs or web scraping technology and stores it in a database. The server then analyzes the user's body type information and full-body images and uses a generative AI model to generate a high-resolution 3D model of the user. The server then combines the clothing selected by the user into the 3D model to generate a realistic try-on image. This try-on image is then sent to the user's device and displayed.

[0798] The device also uses a camera and microphone to capture the user's facial expressions and voice in real time, and an emotion recognition engine to analyze their emotions. The server then uses this emotional data to adjust the clothing and outfit suggestions in real time.

[0799] As a concrete example, suppose a user inputs their height of 170cm, weight of 65kg, and measurements (B:95cm, W:75cm, H:95cm), and uploads full-body images from the front and side. Based on this, the server generates a 3D model of the user and synthesizes a navy jacket. An example of a prompt at this stage is as follows: "Based on the user's height of 170cm, weight of 65kg, and measurements (B:95, W:75, H:95), generate a 3D model based on the front and side full-body photos and synthesize a high-resolution navy jacket. Also, recognize positive emotions from the user's smiling face and suggest further outfit recommendations."

[0800] The system allows users to virtually try on clothes that fit their body type and receive personalized suggestions based on their emotional state.

[0801] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0802] Program processing flow

[0803] Step 1:

[0804] The user starts the application and inputs body type information.

[0805] Input: User's body information (height, weight, three sizes, etc.)

[0806] Output: Body shape information is saved in the system.

[0807] Specific operation: The user enters body type information into the application's input fields and clicks the "Submit" button. This information is sent to the server via the terminal.

[0808] Step 2:

[0809] The user takes full-body images from multiple angles and uploads them.

[0810] Input: Full-body images of the user taken from the front and side

[0811] Output: The whole body image is saved in the system.

[0812] Specific operation: The user takes a full-body image of themselves from multiple angles using the smartphone camera, selects the image in the application, and clicks the "Upload" button. The image data is sent to the server via the device.

[0813] Step 3:

[0814] The server collects clothing data from affiliated online stores.

[0815] Input: Online store API information or web page URL

[0816] Output: Garment data (image, size, material, color, etc.) is saved in a database.

[0817] How it works: The server automatically collects clothing data from online stores using APIs and web scraping tools. During data collection, it also checks for duplicates and missing data.

[0818] Step 4:

[0819] The server analyzes the user's body shape information and full-body image, and generates a three-dimensional model using a generative AI model.

[0820] Input: User's body shape information and full-body image

[0821] Output: A high-resolution 3D model of the user

[0822] How it works: The server uses deep learning algorithms to analyze the user's body shape information and full-body image to generate a 3D model, utilizing GPUs to accelerate the calculations in this process.

[0823] Step 5:

[0824] The server synthesizes the user's selected clothing onto the three-dimensional model.

[0825] Input: 3D model of the user and selected clothing data

[0826] Output: Composite fitting image

[0827] How it works: The server uses a generative AI model to synthesize the selected clothing onto the user's 3D model in a way that realistically reproduces the fit and texture, resulting in a try-on image.

[0828] Step 6:

[0829] The server transmits the generated try-on images to the user's terminal.

[0830] Input: Synthesized try-on image

[0831] Output: Try-on image displayed on the user's device

[0832] Specific operation: The try-on images are encoded as images taken from multiple angles and sent from the server to the device, which then displays them on the user interface.

[0833] Step 7:

[0834] The device captures the user's facial expressions and voice and analyzes them using an emotion recognition engine.

[0835] Input: User's facial and voice data

[0836] Output: User emotion data

[0837] Specific operation: The device uses a camera and microphone to capture the user's facial expressions and voice in real time, and then analyzes this data with an emotion recognition engine to identify the user's emotional state.

[0838] Step 8:

[0839] The server analyzes the emotional data and adjusts the suggestions in real time.

[0840] Input: User emotion data

[0841] Output: Adjusted proposal

[0842] Specific operation: Based on the emotional data, the server changes and adjusts the fitting images and coordination suggestions in real time to make optimal suggestions.

[0843] Step 9:

[0844] The user selects the suggested outfit and completes the purchase procedure.

[0845] Input: User's purchasing decision information

[0846] Output: Purchase confirmation and shipping information

[0847] Specific operation: When the user clicks the "Purchase" button, the device sends this information to the server. The server then links the purchase information to the online store's payment system and completes the purchase process. The user is then notified of order confirmation and delivery information.

[0848] This allows users to virtually try on clothes that fit their body type, receive personalized suggestions based on their emotions, and enjoy an optimal shopping experience.

[0849] (Application example 2)

[0850] 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."

[0851] Conventional virtual try-on systems can generate fitting images based on the user's body shape information, but they cannot make suggestions that take the user's emotions into account. This makes it difficult to improve the user's satisfaction with the clothing and outfits they select. Furthermore, the lack of coordination adjustments based on real-time emotional feedback makes it difficult to fully meet the user's individual needs.

[0852] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting the user's physique information, a means for taking and uploading full-body photos of the user from multiple angles, a means for collecting data on clothing for sale, a means for combining clothing with the generated high-resolution 3D model of the user, a means for analyzing the user's emotions and suggesting clothing and outfits based on the emotion data, and a means for displaying try-on images on the user's device. This enables personalized clothing and outfit suggestions based on the user's emotions, improving user satisfaction. Furthermore, by adjusting the suggestions based on real-time emotion feedback, the system can more appropriately meet the user's individual needs.

[0853] "User's body type information" is data related to the user's body dimensions such as height, weight, and three sizes.

[0854] A "full-body photo" is an image of the user taken from multiple angles (front, back, side, etc.).

[0855] "Data on clothing for sale" refers to detailed information such as images, sizes, materials, and colors of clothing collected from affiliated online stores.

[0856] A "high-resolution 3D model" is a 3D model that is generated based on the user's body shape information and a full-body photograph and is rendered in great detail.

[0857] "Means for synthesizing clothing" refers to a technology that uses data on clothing for sale to realistically display clothing on the generated high-resolution 3D model.

[0858] "Means for analyzing emotions and suggesting clothing and outfits based on emotional data" refers to technology that analyzes a user's facial expressions and voice, and then suggests the most suitable clothing and outfits for the user based on the emotional data obtained from that.

[0859] The "means for presenting a try-on image" is a technology for displaying the generated virtual try-on image on the user's terminal.

[0860] "Emotion data" is data that represents an emotional state such as joy, surprise, or dissatisfaction obtained from the analysis results of the user's facial expressions and voice.

[0861] "Coordination" refers to the user's clothing combination and includes the selection process.

[0862] "Feedback" is the process of adjusting and re-proposing suggestions based on the user's emotional data.

[0863] This invention is a virtual fitting system that combines an emotion engine that recognizes the user's emotions with a 3D model that is generated based on the user's body type information and full-body photographs taken from multiple angles, and then synthesizes clothing onto the 3D model. This system can suggest clothing and outfits that correspond to the user's emotions, providing a more personalized shopping experience.

[0864] The server realizes this system by integrating the following technologies. First, the server provides a means for inputting the user's body type information. This allows the user to enter information such as their height, weight, and three sizes. In addition, the server provides a means for uploading full-body photos taken from multiple angles. This allows the user to upload photos of their front, side, and back.

[0865] The server collects data on clothing sold from online stores and other data sources. This data includes detailed information such as clothing images, sizes, materials, and colors. The server then uses state-of-the-art image recognition algorithms (e.g., TensorFlow) to analyze the user's body shape and full-body photo data to generate a high-resolution 3D model. Deep learning techniques are then used to synthesize clothing onto this 3D model, generating realistic try-on images.

[0866] Furthermore, it has a means to analyze the user's facial expressions and voice using an emotion engine (e.g., Affectiva API) to obtain emotional data. It also incorporates a means to suggest optimal clothing and outfits for the user based on the emotional data. This makes it possible to make appropriate suggestions in real time based on the user's emotions.

[0867] A means of displaying try-on images on the user's device is also in place, allowing the user to view the generated virtual try-on image from multiple angles. After the try-on image is displayed, the user can provide feedback on their emotions on the spot and adjust their outfit as needed.

[0868] For example, a user enters their height and weight into the app and uploads three full-body photos (front, back, and side). After the photo shoot is complete, if the user is happy based on emotion analysis, the system will suggest outfits with bright colors and the latest fashions. On the other hand, if the user is dissatisfied, the system will adjust to suggest new outfits.

[0869] An example of a prompt is as follows:

[0870] The user is 170cm tall and weighs 65kg. They have uploaded photos of the front, back, and side. Sentiment analysis indicates that the user is currently feeling "joy." Based on this, suggest multiple outfits in bright colors and the latest fashions.

[0871] In this way, users can choose the best clothing for their body type and get the same feeling of trying them on in real life through a virtual try-on experience. Real-time emotional feedback provides a more personalized shopping experience.

[0872] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0873] Step 1:

[0874] The user launches the application, inputs their body type information (height, weight, measurements, etc.), and uploads full-body photos taken from multiple angles (front, back, and side). The input data consists of body type information and photo data, which are sent from the device to the server. As a result, data related to the user's body type and full-body photos is stored on the server.

[0875] Step 2:

[0876] The server collects data about clothing sold (images, sizes, materials, colors, etc.) from online stores and other data sources. This collection is done using APIs or web scraping technology. The acquired data is stored in a database on the server. The input is clothing data, and the output is clothing data stored in the database.

[0877] Step 3:

[0878] The server uses the latest image recognition algorithms (e.g., TensorFlow) to analyze the user's body shape information and full-body photo data and generate a high-resolution 3D model. During this process, each user's photo is pixel-analyzed to create the shape of the 3D model. The input for this step is the user's body shape information and photo data, and the output is a high-resolution 3D model.

[0879] Step 4:

[0880] The server uses a generative AI model to synthesize commercially available clothing onto the generated 3D model. Using deep learning technology, the server processes the data to realistically reproduce the fit and texture of the clothing. The input is a high-resolution 3D model and clothing data, and the output is a try-on image.

[0881] Step 5:

[0882] The device uses an emotion engine (e.g., Affectiva API) to analyze the user's facial expressions and voice in real time and acquire emotional data. The captured facial and voice data are input, and analyzed emotional data is output. In this step, the device acquires data using the camera and microphone.

[0883] Step 6:

[0884] The server then proposes optimal clothing and outfits to the user based on the emotional data. The input is the emotional data, and the output is outfit suggestions tailored to the user's preferences. These suggestions are made dynamically and adjusted in real time.

[0885] Step 7:

[0886] The user's terminal displays the try-on images sent from the server. The input is the try-on image, and the output is the try-on image displayed on the user interface. The user can check the displayed try-on image and request images from different angles or to try on other clothes as needed.

[0887] Step 8:

[0888] If the user likes the suggested outfit, they proceed with the purchase. The user clicks the "Purchase" button, and the terminal sends the purchase information to the server. The input is the purchase information, and the output is a purchase confirmation and delivery information. The server connects with the online store's payment system, records the order, and sends a confirmation to the user.

[0889] This allows users to virtually try on clothes that fit their body type, receive personalized coordination suggestions based on emotional data, and finally make a purchase.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] [Third embodiment]

[0894] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0895] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0896] 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).

[0897] 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.

[0898] 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.

[0899] 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).

[0900] 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.

[0901] 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.

[0902] 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.

[0903] 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.

[0904] 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.

[0905] 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."

[0906] This invention is a virtual try-on system that uses a user's body type information and full-body photos taken from multiple angles to generate a fitting image tailored to the user. The system begins by the user entering body type information and uploading a full-body photo using their own device. The system then utilizes a generative AI model based on the collected clothing data to synthesize the clothing onto the user's 3D model and present a virtual fitting image. Furthermore, the user can receive coordination suggestions from sales staff, making the process of selecting and purchasing clothing even more enjoyable.

[0907] 1. Enter your body type information and upload a full-body photo

[0908] Users start the application and enter their body information, such as height, weight, and measurements, and also upload full-body photos taken from the front, back, and side.

[0909] The terminal transmits the body type information entered by the user and the whole-body photograph taken to the server.

[0910] 2. Collection of clothing data

[0911] The server collects data on clothing sold online (images, sizes, materials, colors, etc.) using APIs or web scraping technology from partner online stores and stores it in a database.

[0912] 3. 3D Model Generation and Synthesis

[0913] The server analyzes the user's body shape information and uploaded full-body photos to generate a high-resolution 3D model of the user.

[0914] The server uses a generative AI model to synthesize the user's chosen clothing onto the 3D model, using deep learning techniques to realistically recreate the fit and texture of the clothing.

[0915] 4. Showing images of the clothes when they are tried on

[0916] The server transmits the generated try-on images to the user's terminal as images taken from multiple angles.

[0917] The terminal displays the received try-on images on a user interface.

[0918] The user can check the displayed try-on images and, if necessary, request images from different angles or images of other clothes being tried on.

[0919] 5. Coordination suggestions and purchase procedures

[0920] If the user desires a coordination suggestion, the user clicks the "Request coordination suggestion" button.

[0921] The server sends a request to a salesperson who is responsible for suggesting a coordination.

[0922] The salesperson proposes appropriate outfits based on the user's body type and preferences, and sends feedback to the server.

[0923] The server generates a try-on image of the proposed outfit and presents it to the user's terminal.

[0924] The user checks the suggested coordination and, if he / she decides to purchase, clicks the "Purchase" button.

[0925] The terminal sends the purchase information to the server and connects to the online store's payment system.

[0926] This invention allows users to easily select clothes that fit their body type and experience the virtual fitting process as if they were actually trying them on. Furthermore, they can smoothly complete the online purchase process while checking suggested outfits.

[0927] The processing flow will be explained below.

[0928] Step 1:

[0929] Users start the application and enter their body information, such as height, weight, and measurements, and also upload full-body photos taken from the front, back, and side.

[0930] Step 2:

[0931] The terminal transmits the body type information and full-body photograph input by the user to the server.

[0932] Step 3:

[0933] The server analyzes the received body shape information and full-body photo data to generate a high-resolution 3D model of the user, using image recognition algorithms.

[0934] Step 4:

[0935] The server uses APIs or web scraping technology from affiliated online stores to collect data on the clothes being sold (images, sizes, materials, colors, etc.) and stores it in a database.

[0936] Step 5:

[0937] The server then synthesizes the clothing selected by the user onto the generated 3D model of the user, using a generative AI model to realistically recreate the fit and texture of the clothing.

[0938] Step 6:

[0939] The server transmits the generated try-on images to the user's terminal as images taken from multiple angles.

[0940] Step 7:

[0941] The terminal displays the received try-on images on a user interface.

[0942] Step 8:

[0943] The user checks the displayed try-on images and requests try-on images from different angles or try-on images of other clothes as needed.

[0944] Step 9:

[0945] If the user desires a coordination suggestion, the user clicks the "Request coordination suggestion" button.

[0946] Step 10:

[0947] The server sends a request to a salesperson who is responsible for suggesting a coordination.

[0948] Step 11:

[0949] The salesperson proposes appropriate outfits based on the user's body type and preferences, and sends feedback to the server.

[0950] Step 12:

[0951] The server generates a try-on image of the proposed outfit and sends it to the user's terminal.

[0952] Step 13:

[0953] The terminal displays the received try-on image of the coordinated outfit on a user interface.

[0954] Step 14:

[0955] The user checks the suggested coordination and, if he / she decides to purchase, clicks the "Purchase" button.

[0956] Step 15:

[0957] The terminal transmits the purchase information to the server.

[0958] Step 16:

[0959] The server records the purchase information and connects to the online store's payment system.

[0960] Step 17:

[0961] The server notifies the user of the order confirmation and delivery information.

[0962] Step 18:

[0963] The terminal displays the order confirmation and shipping information to the user.

[0964] Example 1

[0965] 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."

[0966] Conventional online shopping has the problem that users cannot accurately understand how clothes fit or how the design will look on them unless they actually try them on. Furthermore, coordination suggestions tailored to the user's preferences and the ability to check how the clothes will look when they are tried on are insufficient, which discourages users from making a purchase. This has led to a decline in user satisfaction with online shopping.

[0967] 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.

[0968] In this invention, the server includes a means for inputting the user's body type information, a means for taking and uploading full-body photos of the user from multiple angles, a means for collecting data on clothing sold online, a means for combining clothing with the generated high-resolution 3D model of the user, a means for transmitting the generated try-on images to the user's device, and a means for displaying the try-on images on the user's device. This allows the user to easily select clothing that fits their body type and experience the virtual try-on experience as if they were actually trying on the clothes. Furthermore, the user can request coordination suggestions from a salesperson and check the suggested try-on images, which increases the user's motivation to purchase.

[0969] "User" refers to an individual who utilizes the System to input body type information and take and upload a full-body photograph.

[0970] "Body type information" refers to detailed data about the user's body, such as height, weight, and measurements.

[0971] A "full-body photo" refers to an image of the user's body taken from multiple angles, including the front, back, and side.

[0972] "Online clothing data" refers to information such as clothing images, sizes, materials, and colors collected from online sales sites and stores.

[0973] "High-resolution 3D model" refers to a three-dimensional computer graphics model that reproduces the user's body in detail, generated based on the user's body shape information and a full-body photograph.

[0974] "Generative AI model" refers to an artificial intelligence model trained using deep learning techniques to realistically synthesize clothing onto a user's 3D model.

[0975] "Try-on image" refers to an image created by using a generative AI model to synthesize selected clothing onto a user's 3D model.

[0976] "User's device" refers to an electronic device used by a user, such as a smartphone, tablet, or PC.

[0977] "Coordination suggestions" refer to combinations of multiple clothes recommended by a salesperson based on the user's body type information and preferences.

[0978] A "salesperson" refers to a person in charge of making coordination suggestions to users through the system.

[0979] The present invention is a virtual try-on system that uses a user's body type information and full-body photographs taken from multiple angles to generate a fitting image tailored to the user. In this system, a program is executed in the following procedure.

[0980] First, the user launches a dedicated application on their smartphone or PC and enters their body information, including height, weight, and measurements. Then, the user takes full-body photos of the front, back, and side and uploads them to the application. This uploaded data is then sent to the server via the device.

[0981] The device securely transmits input and uploaded data to the server using the HTTPS protocol. The server receives this data and uses OpenCV, an image analysis technology, to analyze the user's body shape with high accuracy. Based on the analyzed data, it uses deep learning frameworks such as TensorFlow and PyTorch to generate a high-resolution 3D model.

[0982] After generating a 3D model for the user, the server uses APIs or web scraping technology from partner online stores to collect data on clothing sold online, including images of the clothing, sizes, materials, colors, etc. This data is stored in a database and used to synthesize clothing onto the user's 3D model.

[0983] The server then synthesizes the clothing selected by the user onto the generated 3D model using a generative AI model. The generative AI model is trained using deep learning technology to realistically reproduce the fit and texture of the clothing. Deep learning algorithms using TensorFlow and PyTorch are used in this process.

[0984] The generated try-on images are taken from multiple angles and sent to the user's device as JPEG or PNG image files. The device displays the received try-on images on the application's user interface. The user can review the displayed try-on images and request images from different angles or try-on images of other clothes as needed.

[0985] Furthermore, if the user wishes to receive a coordination suggestion, they click the "Request Coordination Suggestion" button. The server sends a request to the salesperson in charge of coordination suggestions, who then suggests an appropriate coordination based on the user's body type information and preferences, and sends feedback to the server. Based on this feedback, the server generates a new fitting image and displays it on the user's device. Finally, the user checks the suggested coordination and clicks the "Purchase" button if they decide to purchase. This purchase information is sent to the server via the device, and the purchase procedure is carried out in conjunction with the online store's payment system.

[0986] Specific examples

[0987] For example, if a user wants to try on "blue jeans" and a "white T-shirt,"

[0988] 1. The user enters their height as 170cm, weight as 65kg, measurements as 90-75-90, and uploads full-body photos of the front, back, and side.

[0989] 2. The device sends this information to the server.

[0990] 3. The server uses web scraping technology to collect data on "blue jeans" and "white T-shirts" and stores it in a database.

[0991] 4. The server uses image analysis technology to generate a high-resolution 3D model of the user, and then uses a generative AI model to synthesize jeans and a T-shirt onto the 3D model.

[0992] 5. The server generates images of the clothes being tried on from multiple angles and sends them to the user's device.

[0993] 6. The user can check the newly generated try-on image and then decide to "purchase this outfit."

[0994] Prompt Sentence Examples

[0995] "Enter your height, weight, and measurements, then upload full-body photos taken from the front, back, and side."

[0996] This system allows users to easily find clothes that fit their body type, and experience the virtual fitting process as if they were actually trying them on. Users can also request outfit suggestions and check the suggested try-on images, making online clothing shopping smoother and more satisfying.

[0997] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0998] Step 1:

[0999] The user launches an application.

[1000] Specific operation: The user taps or clicks on the dedicated application on their smartphone or PC to launch it and proceeds to the first screen.

[1001] Input: None

[1002] Output: The application launches and displays a screen where the user can enter their body information.

[1003] Step 2:

[1004] The user inputs body type information.

[1005] Specific operation: The user enters body information such as height, weight, and measurements into the application form. The user also prepares full-body photos taken from the front, back, and side.

[1006] Input: Body information such as height, weight, and measurements

[1007] Output: Display of input body shape information and full-body photo

[1008] Step 3:

[1009] The user uploads a full-body photo.

[1010] Specific operation: The user uploads full-body photos of the front, back, and side using the upload function within the application.

[1011] Input: Full-body photos of the front, back, and side

[1012] Output: Full body photo upload complete message

[1013] Step 4:

[1014] The terminal sends the input data to the server.

[1015] Specific operation: The device sends the user's body shape information and a full-body photo to the server via the HTTPS protocol.

[1016] Input: User's body type information and full-body photo

[1017] Output: The data is sent to the server and awaits processing.

[1018] Step 5:

[1019] The server analyzes body shape information and full-body photos.

[1020] Specific operation: The server performs image analysis using OpenCV and extracts the features necessary to create a 3D model based on the user's body shape information.

[1021] Input: Body type information and full-body photo

[1022] Output: Feature-extracted data

[1023] Step 6:

[1024] The server generates a high-resolution 3D model.

[1025] Specific operation: The server uses TensorFlow and PyTorch for deep learning to generate a high-resolution 3D model based on the user's body shape.

[1026] Input: Feature-extracted data

[1027] Output: High-resolution 3D model

[1028] Step 7:

[1029] A server collects data about clothing sold online.

[1030] Specific operation: The server calls the API of partner online stores or uses web scraping to collect the latest clothing data (images, sizes, materials, colors, etc.).

[1031] Input: API endpoint of partner online store or scraping target URL

[1032] Output: Latest clothing data stored in the clothing database

[1033] Step 8:

[1034] The server uses a generative AI model to synthesize the clothing onto a 3D model.

[1035] Specific operation: The server uses a generative AI model to synthesize clothing onto the user's 3D model based on the collected clothing data.

[1036] Input: 3D model and clothing data

[1037] Output: Try-on images from multiple angles

[1038] Step 9:

[1039] The server transmits the generated try-on images to the terminal.

[1040] How it works: The server generates try-on images in JPEG or PNG format and sends them to the user's device. High-speed data transfer via a CDN may also be used.

[1041] Input: Try-on image

[1042] Output: Try-on image sent to the device

[1043] Step 10:

[1044] The terminal displays the try-on image on the user interface.

[1045] Specific operation: The device displays the received try-on images on the application's user interface, allowing the user to operate them.

[1046] Input: Try-on image

[1047] Output: Try-on image displayed on the user interface

[1048] Step 11:

[1049] The user checks the try-on images and sends a request if necessary.

[1050] Specific operation: The user checks the try-on images, and if they want to request images from different angles or images of other clothes being tried on, they click the corresponding button in the application.

[1051] Input: User request

[1052] Output: The request is sent to the server.

[1053] Step 12:

[1054] The user requests outfit suggestions.

[1055] Specific operation: If the user wants outfit suggestions, he / she clicks the "Request outfit suggestions" button.

[1056] Input: User's outfit suggestion request

[1057] Output: The request is sent to the server.

[1058] Step 13:

[1059] The server sends the request to the salesperson.

[1060] Specific operation: The server sends a coordination proposal request to the salesperson, along with the necessary user information.

[1061] Input: User request

[1062] Output: The request is sent to the salesperson

[1063] Step 14:

[1064] Salespeople provide feedback on suggestions.

[1065] Specific operation: The salesperson considers the appropriate outfit based on the user's body type and preferences, and sends feedback to the server.

[1066] Input: User's body type information and preferences

[1067] Output: Feedback of outfit suggestions

[1068] Step 15:

[1069] The server generates an image of a coordinated outfit to try on.

[1070] Specific operation: The server generates new try-on images based on feedback from the salesperson and sends them to the user's device.

[1071] Input: Feedback on outfit suggestions

[1072] Output: Coordinate try-on image sent to the device

[1073] Step 16:

[1074] The user reviews the offer and decides to purchase.

[1075] Specific operation: The user checks the newly generated outfit try-on image and clicks the "Purchase" button if they like it.

[1076] Input: Coordination try-on image

[1077] Output: Purchase request sent to server

[1078] Step 17:

[1079] The device sends purchase information to the server and connects with the online store.

[1080] Specific operation: The terminal sends purchase information to the server, and the server works with the online store's payment system to complete the purchase process.

[1081] Input: Purchase information

[1082] Output: The purchase is completed and a confirmation is sent to the user.

[1083] (Application example 1)

[1084] 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."

[1085] Traditionally, trying on clothes in physical stores was time-consuming and labor-intensive. Furthermore, the types of clothing that could be tried on were limited, making it difficult to try out many options. Furthermore, online shopping presents the problem of frequent returns and exchanges after purchase, since users are unable to see how the actual product will look. To solve these problems, a system is needed that allows users to easily try on clothes and select the best fit for their body type.

[1086] 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.

[1087] In this invention, the server includes: means for inputting a user's body type information; means for taking and uploading full-body photos of the user from multiple angles; means for collecting data on available items; means for combining items with the generated 3D model of the user; means for displaying virtual try-on images on the user's device; means for the user to input body type information and upload a full-body photo using their own device; means for collecting and storing data on items sold online using APIs or web scraping technology of affiliated online stores; means for analyzing the user's body type information and the uploaded full-body photo to generate a high-resolution 3D model of the user; and means for generating try-on images from multiple angles and sending them to the user's device. This allows users to smoothly try on clothes virtually in a physical store, shortening the time it takes to actually try on clothes and combining the convenience of online shopping with the experience of a physical store.

[1088] "User's body type information" refers to the user's physical measurements and characteristics, such as height, weight, and three sizes.

[1089] A "full-body photo" is a photo that shows the user's entire body, and includes multiple photos taken from the front, back, and side.

[1090] "Purchasable Item Data" means information about products such as clothing and accessories sold through affiliated online stores and physical stores, including attributes such as images, sizes, materials, and colors.

[1091] "3D Model" refers to a high-resolution three-dimensional digital model generated based on a user's body shape information and a full-body photograph.

[1092] "Means for synthesizing items" refers to the process of virtually applying selected clothing and accessories to the generated 3D model of the user to generate a realistic try-on image.

[1093] "Virtual try-on image" refers to an image that combines selected clothing and accessories with a user's 3D model, and simulates trying on the clothes from multiple angles.

[1094] "API of affiliated online stores" refers to an application programming interface for automatically obtaining data on products sold by affiliated online stores.

[1095] "Web scraping technology" refers to technology that automatically extracts data from websites.

[1096] "Means for generating a high-resolution 3D model" refers to the process of analyzing a user's body shape information and full-body photograph to create a highly accurate three-dimensional digital model.

[1097] The "means for requesting coordination suggestions" refers to a function that allows a user to request a salesperson to suggest a coordination based on the user's preferences.

[1098] "Performing a virtual try-on" refers to the act of allowing a user to try on an item on a digital model instead of actually trying it on.

[1099] This invention is a system that enables virtual try-on using a user's body type information and full-body photo to improve the fitting experience in physical stores. This system is built using the user's device (smartphone), a series of servers, and APIs or web scraping technologies of affiliated online stores.

[1100] Generating a Program

[1101] The system uses the following hardware and software:

[1102] User device (smartphone): Android or iOS device with camera function

[1103] Server: Data processing and execution of generative AI models (using TensorFlow or PyTorch)

[1104] Partner online store APIs or web scraping technologies (Beautiful Soup, Scrapy, etc.)

[1105] Database: MySQL or PostgreSQL

[1106] Program processing overview

[1107] The server first receives the user's body type information and full-body photo from their device, and then uses image analysis technology to generate a high-resolution 3D model of the user. This 3D model is precisely created using deep learning, recreating a realistic fit to the user's body type.

[1108] The server then uses clothing data collected from partner online stores to synthesize the selected clothing onto the user's 3D model. This synthesis process utilizes generative AI models to accurately reproduce the texture and fit of the clothing. Try-on images taken from multiple angles are generated and sent to the user's device.

[1109] The user can check the virtual try-on images on the device and, if they like them, proceed to the purchase process. If the user would like a coordination suggestion, they can send a request to the salesperson. The coordination suggested by the salesperson is again generated as a virtual try-on image and presented to the user.

[1110] Examples and prompts

[1111] To use the API of a partner online store to collect clothing data, run the following command:

[1112] curl -X GET "https: / / netstore.com / api / v1 / products" -H "Authorization: Bearer YOUR_API_KEY"

[1113] Alternatively, if you use web scraping technology, here's a script that uses Python's Beautiful Soup to extract product data from a specified URL:

[1114] from bs4 import BeautifulSoup

[1115] import requests

[1116] url = "https: / / netstore.com / products"

[1117] response = requests.get(url)

[1118] soup = BeautifulSoup(response.content, 'html.parser')

[1119] for product in soup.find_all('div', class_='product'):

[1120] product_name = product.find('h2').get_text()

[1121] product_price = product.find('span', class_='price').get_text()

[1122] print(product_name, product_price)

[1123] This system allows users to seamlessly try on clothes virtually in a physical store, shortening the time spent trying them on in person and combining the convenience of online with the experience of a physical store.

[1124] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1125] Step 1:

[1126] The user inputs their own body type information into the terminal. The user inputs physical attributes such as height, weight, and three sizes into the terminal, and the input data is sent to the server. At this point, the input is the user's physical numerical data, and the output is raw data sent to the server.

[1127] Step 2:

[1128] The user takes full-body photos from multiple angles and uploads them via the device. The user takes photos from the front, back, and side using the device's camera and sends the image data to the server. At this point, the input is multiple image files, and the output is image data stored on the server.

[1129] Step 3:

[1130] The server uses the API of a partner online store or web scraping technology to collect data on the items being sold. Specifically, the server sends a request to an API endpoint and receives product information in response. Alternatively, it parses a web page to obtain the required product data. At this point, the input is the API request or HTML content, and the output is a set of product data.

[1131] Step 4:

[1132] The server analyzes the user's body shape information and full-body photo to generate a high-resolution 3D model. The collected image data and body shape information are analyzed using a deep learning model (using TensorFlow and PyTorch) to create a highly accurate 3D model of the user. At this point, the input is body shape information and image data, and the output is a 3D model.

[1133] Step 5:

[1134] The server uses a generative AI model to synthesize the selected item onto a 3D model. The collected product data is then synthesized onto the 3D model, creating a virtual try-on image. The generative AI model accurately reproduces the texture and fit of the clothing. At this point, the input is the 3D model and product data, and the output is a try-on image.

[1135] Step 6:

[1136] The server sends the generated try-on images, photographed from multiple angles, to the user's device. These images are displayed on the user's device, allowing the user to check the virtual try-on image. At this point, the input is the try-on image data, and the output is the virtual try-on image displayed on the user's device.

[1137] Step 7:

[1138] When a user requests a coordination suggestion, they send a request from their terminal to the server. The server receives this request and notifies the salesperson in charge. At this point, the input is the request from the user, and the output is a notification to the salesperson.

[1139] Step 8:

[1140] The salesperson creates a coordination suggestion and sends feedback to the server, which then generates a virtual try-on image of the suggested outfit based on the user's 3D model. At this point, the input is the coordination suggestion from the salesperson, and the output is the new try-on image.

[1141] Step 9:

[1142] The server sends the generated coordinated try-on image to the user's device, where the user confirms it. If the user decides to purchase, they click the "Purchase" button on their device, and the purchase information is sent to the server. The input at this point is the virtual try-on image and the user's purchase decision, and the output is the progress of the purchase procedure.

[1143] 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.

[1144] This invention combines a virtual fitting system that generates a 3D model based on the user's body type information and full-body photographs from multiple angles, and then synthesizes clothing onto it, with an emotion engine that recognizes the user's emotions. This system can suggest clothing and outfits that correspond to the user's emotions, providing a more personalized shopping experience.

[1145] 1. Enter your body type information and upload a full-body photo

[1146] Users start the application, input their body information (height, weight, three sizes, etc.), and upload full-body photos taken from multiple angles.

[1147] The terminal transmits this body type information and a full-body photo to the server.

[1148] 2. Collection of clothing data

[1149] The server uses API or web scraping technology to collect data on the clothes being sold (images, sizes, materials, colors, etc.) from partner online stores and stores it in a database.

[1150] 3. 3D model generation and clothing synthesis

[1151] The server analyzes the user's body shape information and full-body photo data to generate a high-resolution 3D model of the user, using the latest image recognition algorithms.

[1152] The server then synthesizes the selected garment onto the 3D model using a generative AI model, a process that leverages deep learning techniques to realistically recreate the garment's fit and texture.

[1153] 4. Showing images of the clothes when they are tried on

[1154] The server transmits the generated try-on images to the user's terminal as images taken from multiple angles.

[1155] The terminal displays the received try-on images on a user interface.

[1156] The user checks the displayed try-on images and requests try-on images from different angles or requests to try on other clothes, if necessary.

[1157] 5. Emotion Recognition by Emotion Engine

[1158] The device uses an emotion engine to analyze the user's facial expressions and voice to identify their emotional state, using a camera and microphone to capture data in real time.

[1159] The server evaluates the user's reaction to the try-on images based on the emotion data from the emotion engine.

[1160] 6. Coordination proposals and adjustments

[1161] If the user desires a coordination suggestion, the user clicks the "Request coordination suggestion" button.

[1162] The server sends a request to a salesperson who is responsible for suggesting a coordination.

[1163] The salesperson proposes an appropriate outfit based on the user's body type information, preferences, and emotional data, and sends feedback to the server.

[1164] The server generates images of the suggested outfits to try on and displays them on the user's device, and adjusts the suggested outfits in real time based on the user's emotional data.

[1165] 7. Purchase Procedure

[1166] The user checks the suggested coordination and, if he / she decides to purchase, clicks the "Purchase" button.

[1167] The terminal sends the purchase information to the server and connects to the online store's payment system.

[1168] The server records the purchase information and notifies the user of the order confirmation and shipping information.

[1169] The terminal displays the order confirmation and shipping information to the user.

[1170] The system of the present invention allows users to easily select clothes that fit their body type and experience the virtual fitting process as if they were actually trying them on. Furthermore, by utilizing an emotion engine to make suggestions based on the user's emotional state, a more personalized shopping experience is realized. This series of processes increases user satisfaction and improves the online shopping experience.

[1171] The processing flow will be explained below.

[1172] Step 1:

[1173] Users start the application and enter their body information, such as height, weight, and measurements, and then upload full-body photos taken from the front, back, and side to the application.

[1174] Step 2:

[1175] The terminal transmits the body type information and full-body photograph input by the user to the server.

[1176] Step 3:

[1177] The server analyzes the received body shape information and full-body photo data and uses image recognition algorithms to generate a high-resolution 3D model of the user.

[1178] Step 4:

[1179] The server uses API or web scraping technology to collect data on the clothes being sold (images, sizes, materials, colors, etc.) from partner online stores and stores it in a database.

[1180] Step 5:

[1181] The server then synthesizes the clothing selected by the user onto the generated 3D model of the user, using a generative AI model to realistically recreate the fit and texture of the clothing.

[1182] Step 6:

[1183] The server transmits the generated try-on images to the user's terminal as images taken from multiple angles.

[1184] Step 7:

[1185] The terminal displays the received try-on images on a user interface.

[1186] Step 8:

[1187] The user checks the displayed try-on images and requests images from different angles or requests to try on other clothes as needed.

[1188] Step 9:

[1189] The device captures the user's facial expressions with a camera or collects their voice with a microphone, and sends this data to the emotion engine.

[1190] Step 10:

[1191] The server uses an emotion engine to analyze the user's facial expressions and voice to identify the user's emotional state, for example, a smiling user is identified as "satisfied" and a frowning user is identified as "anxious."

[1192] Step 11:

[1193] The server uses the emotional state data from the emotion engine to determine the next action, for example, continuing with the proposal if the user is satisfied, or presenting a different option if the user is anxious.

[1194] Step 12:

[1195] If the user desires a coordination suggestion, the user clicks the "Request coordination suggestion" button.

[1196] Step 13:

[1197] The server sends a request to a salesperson who is responsible for suggesting a coordination.

[1198] Step 14:

[1199] The salesperson proposes an appropriate outfit based on the user's body type information, preferences, and emotional data, and sends feedback to the server.

[1200] Step 15:

[1201] The server generates images of the suggested outfits to try on and sends them to the user's device, and also adjusts the suggested outfits in real time based on the user's emotional data.

[1202] Step 16:

[1203] The terminal displays the received try-on image of the coordinated outfit on a user interface.

[1204] Step 17:

[1205] The user checks the suggested coordination and, if he / she decides to purchase, clicks the "Purchase" button.

[1206] Step 18:

[1207] The terminal transmits the purchase information to the server.

[1208] Step 19:

[1209] The server records the purchase information and notifies the user of the order confirmation and shipping information.

[1210] Step 20:

[1211] The terminal displays the order confirmation and shipping information to the user.

[1212] Example 2

[1213] 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."

[1214] In recent years, the spread of online shopping has led to an increasing demand for remote try-on and personalized shopping experiences. However, current systems have difficulty fully reproducing the fit and texture of clothing when trying on in person, and are unable to make recommendations based on the user's emotions. Therefore, there is a need for a more realistic virtual try-on system to improve the user experience.

[1215] 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.

[1216] In this invention, the server includes means for inputting the user's body type information, means for taking and uploading full-body images of the user from multiple angles, means for collecting data on clothing available for sale, means for combining clothing items with the generated three-dimensional model of the user, means for presenting images of the items to be tried on at the user's terminal, means for analyzing the user's facial expressions and voice to recognize emotions, and means for adjusting suggestions based on emotions. This allows the user to virtually try on clothes that fit their body type and receive personalized suggestions based on their emotional state.

[1217] "Means for inputting user's body type information" refers to an interface and system for inputting data related to the user's body type, such as height, weight, and measurements.

[1218] "Means for taking and uploading full-body images of the user from multiple angles" refers to a camera and upload function for taking images of the user's entire body from different angles and uploading the image data to the system.

[1219] "Means of collecting data on clothing for sale" refers to technology used to collect and store data such as images, sizes, materials, and colors of clothing for sale from online stores and other sources.

[1220] "Means for synthesizing clothing onto a generated three-dimensional model of a user" refers to an algorithm and system for realistically synthesizing selected clothing onto a three-dimensional model generated based on the user's body shape information and full-body image.

[1221] "Means for presenting try-on images on a user's device" refers to an interface and system for displaying the generated try-on images on the screen of a user's device (smartphone, tablet, PC, etc.).

[1222] "Means for analyzing a user's facial expressions and voice to recognize emotions" refers to algorithms and systems for analyzing a user's facial expressions and voice data acquired using devices such as cameras and microphones, and identifying the user's emotional state.

[1223] "Means for adjusting suggestions based on emotions" refers to technologies and systems that allow suggested clothing and outfits to be changed or adjusted in real time based on the user's emotional data.

[1224] This virtual fitting system generates a high-resolution 3D model based on the user's physique information and full-body image, and then realistically combines clothing onto it. It also analyzes the user's emotions from their facial expressions and voice, and adjusts clothing suggestions accordingly.

[1225] The system uses the following hardware and software:

[1226] An interface for inputting the user's body shape information (such as a smartphone or PC)

[1227] A camera that takes a full-body image of the user (such as a camera built into a smartphone)

[1228] API or web scraping tool to collect data on the clothes being sold

[1229] Generative AI models for generating 3D models (e.g., deep learning models built in Python)

[1230] A user interface that displays synthesized images of clothes to try on

[1231] Emotion recognition engine for analyzing the user's facial expressions and voice (e.g., emotion analysis API)

[1232] The system performs the following operations:

[1233] The user enters their body type information through the application and takes and uploads full-body images from multiple angles. The device then sends this data to the server. The server then collects data on clothing sold by online stores using APIs or web scraping technology and stores it in a database. The server then analyzes the user's body type information and full-body images and uses a generative AI model to generate a high-resolution 3D model of the user. The server then combines the clothing selected by the user into the 3D model to generate a realistic try-on image. This try-on image is then sent to the user's device and displayed.

[1234] The device also uses a camera and microphone to capture the user's facial expressions and voice in real time, and an emotion recognition engine to analyze their emotions. The server then uses this emotional data to adjust the clothing and outfit suggestions in real time.

[1235] As a concrete example, suppose a user inputs their height of 170cm, weight of 65kg, and measurements (B:95cm, W:75cm, H:95cm), and uploads full-body images from the front and side. Based on this, the server generates a 3D model of the user and synthesizes a navy jacket. An example of a prompt at this stage is as follows: "Based on the user's height of 170cm, weight of 65kg, and measurements (B:95, W:75, H:95), generate a 3D model based on the front and side full-body photos and synthesize a high-resolution navy jacket. Also, recognize positive emotions from the user's smiling face and suggest further outfit recommendations."

[1236] The system allows users to virtually try on clothes that fit their body type and receive personalized suggestions based on their emotional state.

[1237] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1238] Program processing flow

[1239] Step 1:

[1240] The user starts the application and inputs body type information.

[1241] Input: User's body information (height, weight, three sizes, etc.)

[1242] Output: Body shape information is saved in the system.

[1243] Specific operation: The user enters body type information into the application's input fields and clicks the "Submit" button. This information is sent to the server via the terminal.

[1244] Step 2:

[1245] The user takes full-body images from multiple angles and uploads them.

[1246] Input: Full-body images of the user taken from the front and side

[1247] Output: The whole body image is saved in the system.

[1248] Specific operation: The user takes a full-body image of themselves from multiple angles using the smartphone camera, selects the image in the application, and clicks the "Upload" button. The image data is sent to the server via the device.

[1249] Step 3:

[1250] The server collects clothing data from affiliated online stores.

[1251] Input: Online store API information or web page URL

[1252] Output: Garment data (image, size, material, color, etc.) is saved in a database.

[1253] How it works: The server automatically collects clothing data from online stores using APIs and web scraping tools. During data collection, it also checks for duplicates and missing data.

[1254] Step 4:

[1255] The server analyzes the user's body shape information and full-body image, and generates a three-dimensional model using a generative AI model.

[1256] Input: User's body shape information and full-body image

[1257] Output: A high-resolution 3D model of the user

[1258] How it works: The server uses deep learning algorithms to analyze the user's body shape information and full-body image to generate a 3D model, utilizing GPUs to accelerate the calculations in this process.

[1259] Step 5:

[1260] The server synthesizes the user's selected clothing onto the three-dimensional model.

[1261] Input: 3D model of the user and selected clothing data

[1262] Output: Composite fitting image

[1263] How it works: The server uses a generative AI model to synthesize the selected clothing onto the user's 3D model in a way that realistically reproduces the fit and texture, resulting in a try-on image.

[1264] Step 6:

[1265] The server transmits the generated try-on images to the user's terminal.

[1266] Input: Synthesized try-on image

[1267] Output: Try-on image displayed on the user's device

[1268] Specific operation: The try-on images are encoded as images taken from multiple angles and sent from the server to the device, which then displays them on the user interface.

[1269] Step 7:

[1270] The device captures the user's facial expressions and voice and analyzes them using an emotion recognition engine.

[1271] Input: User's facial and voice data

[1272] Output: User emotion data

[1273] Specific operation: The device uses a camera and microphone to capture the user's facial expressions and voice in real time, and then analyzes this data with an emotion recognition engine to identify the user's emotional state.

[1274] Step 8:

[1275] The server analyzes the emotional data and adjusts the suggestions in real time.

[1276] Input: User emotion data

[1277] Output: Adjusted proposal

[1278] Specific operation: Based on the emotional data, the server changes and adjusts the fitting images and coordination suggestions in real time to make optimal suggestions.

[1279] Step 9:

[1280] The user selects the suggested outfit and completes the purchase procedure.

[1281] Input: User's purchasing decision information

[1282] Output: Purchase confirmation and shipping information

[1283] Specific operation: When the user clicks the "Purchase" button, the device sends this information to the server. The server then links the purchase information to the online store's payment system and completes the purchase process. The user is then notified of order confirmation and delivery information.

[1284] This allows users to virtually try on clothes that fit their body type, receive personalized suggestions based on their emotions, and enjoy an optimal shopping experience.

[1285] (Application example 2)

[1286] 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."

[1287] Conventional virtual try-on systems can generate fitting images based on the user's body shape information, but they cannot make suggestions that take the user's emotions into account. This makes it difficult to improve the user's satisfaction with the clothing and outfits they select. Furthermore, the lack of coordination adjustments based on real-time emotional feedback makes it difficult to fully meet the user's individual needs.

[1288] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting the user's physique information, a means for taking and uploading full-body photos of the user from multiple angles, a means for collecting data on clothing for sale, a means for combining clothing with the generated high-resolution 3D model of the user, a means for analyzing the user's emotions and suggesting clothing and outfits based on the emotion data, and a means for displaying try-on images on the user's device. This enables personalized clothing and outfit suggestions based on the user's emotions, improving user satisfaction. Furthermore, by adjusting the suggestions based on real-time emotion feedback, the system can more appropriately meet the user's individual needs.

[1289] "User's body type information" is data related to the user's body dimensions such as height, weight, and three sizes.

[1290] A "full-body photo" is an image of the user taken from multiple angles (front, back, side, etc.).

[1291] "Data on clothing for sale" refers to detailed information such as images, sizes, materials, and colors of clothing collected from affiliated online stores.

[1292] A "high-resolution 3D model" is a 3D model that is generated based on the user's body shape information and a full-body photograph and is rendered in great detail.

[1293] "Means for synthesizing clothing" refers to a technology that uses data on clothing for sale to realistically display clothing on the generated high-resolution 3D model.

[1294] "Means for analyzing emotions and suggesting clothing and outfits based on emotional data" refers to technology that analyzes a user's facial expressions and voice, and then suggests the most suitable clothing and outfits for the user based on the emotional data obtained from that.

[1295] The "means for presenting a try-on image" is a technology for displaying the generated virtual try-on image on the user's terminal.

[1296] "Emotion data" is data that represents an emotional state such as joy, surprise, or dissatisfaction obtained from the analysis results of the user's facial expressions and voice.

[1297] "Coordination" refers to the user's clothing combination and includes the selection process.

[1298] "Feedback" is the process of adjusting and re-proposing suggestions based on the user's emotional data.

[1299] This invention is a virtual fitting system that combines an emotion engine that recognizes the user's emotions with a 3D model that is generated based on the user's body type information and full-body photographs taken from multiple angles, and then synthesizes clothing onto the 3D model. This system can suggest clothing and outfits that correspond to the user's emotions, providing a more personalized shopping experience.

[1300] The server realizes this system by integrating the following technologies. First, the server provides a means for inputting the user's body type information. This allows the user to enter information such as their height, weight, and three sizes. In addition, the server provides a means for uploading full-body photos taken from multiple angles. This allows the user to upload photos of their front, side, and back.

[1301] The server collects data on clothing sold from online stores and other data sources. This data includes detailed information such as clothing images, sizes, materials, and colors. The server then uses state-of-the-art image recognition algorithms (e.g., TensorFlow) to analyze the user's body shape and full-body photo data to generate a high-resolution 3D model. Deep learning techniques are then used to synthesize clothing onto this 3D model, generating realistic try-on images.

[1302] Furthermore, it has a means to analyze the user's facial expressions and voice using an emotion engine (e.g., Affectiva API) to obtain emotional data. It also incorporates a means to suggest optimal clothing and outfits for the user based on the emotional data. This makes it possible to make appropriate suggestions in real time based on the user's emotions.

[1303] A means of displaying try-on images on the user's device is also in place, allowing the user to view the generated virtual try-on image from multiple angles. After the try-on image is displayed, the user can provide feedback on their emotions on the spot and adjust their outfit as needed.

[1304] For example, a user enters their height and weight into the app and uploads three full-body photos (front, back, and side). After the photo shoot is complete, if the user is happy based on emotion analysis, the system will suggest outfits with bright colors and the latest fashions. On the other hand, if the user is dissatisfied, the system will adjust to suggest new outfits.

[1305] An example of a prompt is as follows:

[1306] The user is 170cm tall and weighs 65kg. They have uploaded photos of the front, back, and side. Sentiment analysis indicates that the user is currently feeling "joy." Based on this, suggest multiple outfits in bright colors and the latest fashions.

[1307] In this way, users can choose the best clothing for their body type and get the same feeling of trying them on in real life through a virtual try-on experience. Real-time emotional feedback provides a more personalized shopping experience.

[1308] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1309] Step 1:

[1310] The user launches the application, inputs their body type information (height, weight, measurements, etc.), and uploads full-body photos taken from multiple angles (front, back, and side). The input data consists of body type information and photo data, which are sent from the device to the server. As a result, data related to the user's body type and full-body photos is stored on the server.

[1311] Step 2:

[1312] The server collects data about clothing sold (images, sizes, materials, colors, etc.) from online stores and other data sources. This collection is done using APIs or web scraping technology. The acquired data is stored in a database on the server. The input is clothing data, and the output is clothing data stored in the database.

[1313] Step 3:

[1314] The server uses the latest image recognition algorithms (e.g., TensorFlow) to analyze the user's body shape information and full-body photo data and generate a high-resolution 3D model. During this process, each user's photo is pixel-analyzed to create the shape of the 3D model. The input for this step is the user's body shape information and photo data, and the output is a high-resolution 3D model.

[1315] Step 4:

[1316] The server uses a generative AI model to synthesize commercially available clothing onto the generated 3D model. Using deep learning technology, the server processes the data to realistically reproduce the fit and texture of the clothing. The input is a high-resolution 3D model and clothing data, and the output is a try-on image.

[1317] Step 5:

[1318] The device uses an emotion engine (e.g., Affectiva API) to analyze the user's facial expressions and voice in real time and acquire emotional data. The captured facial and voice data are input, and analyzed emotional data is output. In this step, the device acquires data using the camera and microphone.

[1319] Step 6:

[1320] The server then proposes optimal clothing and outfits to the user based on the emotional data. The input is the emotional data, and the output is outfit suggestions tailored to the user's preferences. These suggestions are made dynamically and adjusted in real time.

[1321] Step 7:

[1322] The user's terminal displays the try-on images sent from the server. The input is the try-on image, and the output is the try-on image displayed on the user interface. The user can check the displayed try-on image and request images from different angles or to try on other clothes as needed.

[1323] Step 8:

[1324] If the user likes the suggested outfit, they proceed with the purchase. The user clicks the "Purchase" button, and the terminal sends the purchase information to the server. The input is the purchase information, and the output is a purchase confirmation and delivery information. The server connects with the online store's payment system, records the order, and sends a confirmation to the user.

[1325] This allows users to virtually try on clothes that fit their body type, receive personalized coordination suggestions based on emotional data, and finally make a purchase.

[1326] 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.

[1327] 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.

[1328] 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.

[1329] [Fourth embodiment]

[1330] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1331] 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.

[1332] 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).

[1333] 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.

[1334] 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.

[1335] 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).

[1336] 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.

[1337] 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.

[1338] 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.

[1339] 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.

[1340] 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.

[1341] 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.

[1342] 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."

[1343] This invention is a virtual try-on system that uses a user's body type information and full-body photos taken from multiple angles to generate a fitting image tailored to the user. The system begins by the user entering body type information and uploading a full-body photo using their own device. The system then utilizes a generative AI model based on the collected clothing data to synthesize the clothing onto the user's 3D model and present a virtual fitting image. Furthermore, the user can receive coordination suggestions from sales staff, making the process of selecting and purchasing clothing even more enjoyable.

[1344] 1. Enter your body type information and upload a full-body photo

[1345] Users start the application and enter their body information, such as height, weight, and measurements, and also upload full-body photos taken from the front, back, and side.

[1346] The terminal transmits the body type information entered by the user and the whole-body photograph taken to the server.

[1347] 2. Collection of clothing data

[1348] The server collects data on clothing sold online (images, sizes, materials, colors, etc.) using APIs or web scraping technology from partner online stores and stores it in a database.

[1349] 3. 3D Model Generation and Synthesis

[1350] The server analyzes the user's body shape information and uploaded full-body photos to generate a high-resolution 3D model of the user.

[1351] The server uses a generative AI model to synthesize the user's chosen clothing onto the 3D model, using deep learning techniques to realistically recreate the fit and texture of the clothing.

[1352] 4. Showing images of the clothes when they are tried on

[1353] The server transmits the generated try-on images to the user's terminal as images taken from multiple angles.

[1354] The terminal displays the received try-on images on a user interface.

[1355] The user can check the displayed try-on images and, if necessary, request images from different angles or images of other clothes being tried on.

[1356] 5. Coordination suggestions and purchase procedures

[1357] If the user desires a coordination suggestion, the user clicks the "Request coordination suggestion" button.

[1358] The server sends a request to a salesperson who is responsible for suggesting a coordination.

[1359] The salesperson proposes appropriate outfits based on the user's body type and preferences, and sends feedback to the server.

[1360] The server generates a try-on image of the proposed outfit and presents it to the user's terminal.

[1361] The user checks the suggested coordination and, if he / she decides to purchase, clicks the "Purchase" button.

[1362] The terminal sends the purchase information to the server and connects to the online store's payment system.

[1363] This invention allows users to easily select clothes that fit their body type and experience the virtual fitting process as if they were actually trying them on. Furthermore, they can smoothly complete the online purchase process while checking suggested outfits.

[1364] The processing flow will be explained below.

[1365] Step 1:

[1366] Users start the application and enter their body information, such as height, weight, and measurements, and also upload full-body photos taken from the front, back, and side.

[1367] Step 2:

[1368] The terminal transmits the body type information and full-body photograph input by the user to the server.

[1369] Step 3:

[1370] The server analyzes the received body shape information and full-body photo data to generate a high-resolution 3D model of the user, using image recognition algorithms.

[1371] Step 4:

[1372] The server uses APIs or web scraping technology from affiliated online stores to collect data on the clothes being sold (images, sizes, materials, colors, etc.) and stores it in a database.

[1373] Step 5:

[1374] The server then synthesizes the clothing selected by the user onto the generated 3D model of the user, using a generative AI model to realistically recreate the fit and texture of the clothing.

[1375] Step 6:

[1376] The server transmits the generated try-on images to the user's terminal as images taken from multiple angles.

[1377] Step 7:

[1378] The terminal displays the received try-on images on a user interface.

[1379] Step 8:

[1380] The user checks the displayed try-on images and requests try-on images from different angles or try-on images of other clothes as needed.

[1381] Step 9:

[1382] If the user desires a coordination suggestion, the user clicks the "Request coordination suggestion" button.

[1383] Step 10:

[1384] The server sends a request to a salesperson who is responsible for suggesting a coordination.

[1385] Step 11:

[1386] The salesperson proposes appropriate outfits based on the user's body type and preferences, and sends feedback to the server.

[1387] Step 12:

[1388] The server generates a try-on image of the proposed outfit and sends it to the user's terminal.

[1389] Step 13:

[1390] The terminal displays the received try-on image of the coordinated outfit on a user interface.

[1391] Step 14:

[1392] The user checks the suggested coordination and, if he / she decides to purchase, clicks the "Purchase" button.

[1393] Step 15:

[1394] The terminal transmits the purchase information to the server.

[1395] Step 16:

[1396] The server records the purchase information and connects to the online store's payment system.

[1397] Step 17:

[1398] The server notifies the user of the order confirmation and delivery information.

[1399] Step 18:

[1400] The terminal displays the order confirmation and shipping information to the user.

[1401] Example 1

[1402] 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."

[1403] Conventional online shopping has the problem that users cannot accurately understand how clothes fit or how the design will look on them unless they actually try them on. Furthermore, coordination suggestions tailored to the user's preferences and the ability to check how the clothes will look when they are tried on are insufficient, which discourages users from making a purchase. This has led to a decline in user satisfaction with online shopping.

[1404] 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.

[1405] In this invention, the server includes a means for inputting the user's body type information, a means for taking and uploading full-body photos of the user from multiple angles, a means for collecting data on clothing sold online, a means for combining clothing with the generated high-resolution 3D model of the user, a means for transmitting the generated try-on images to the user's device, and a means for displaying the try-on images on the user's device. This allows the user to easily select clothing that fits their body type and experience the virtual try-on experience as if they were actually trying on the clothes. Furthermore, the user can request coordination suggestions from a salesperson and check the suggested try-on images, which increases the user's motivation to purchase.

[1406] "User" refers to an individual who utilizes the System to input body type information and take and upload a full-body photograph.

[1407] "Body type information" refers to detailed data about the user's body, such as height, weight, and measurements.

[1408] A "full-body photo" refers to an image of the user's body taken from multiple angles, including the front, back, and side.

[1409] "Online clothing data" refers to information such as clothing images, sizes, materials, and colors collected from online sales sites and stores.

[1410] "High-resolution 3D model" refers to a three-dimensional computer graphics model that reproduces the user's body in detail, generated based on the user's body shape information and a full-body photograph.

[1411] "Generative AI model" refers to an artificial intelligence model trained using deep learning techniques to realistically synthesize clothing onto a user's 3D model.

[1412] "Try-on image" refers to an image created by using a generative AI model to synthesize selected clothing onto a user's 3D model.

[1413] "User's device" refers to an electronic device used by a user, such as a smartphone, tablet, or PC.

[1414] "Coordination suggestions" refer to combinations of multiple clothes recommended by a salesperson based on the user's body type information and preferences.

[1415] A "salesperson" refers to a person in charge of making coordination suggestions to users through the system.

[1416] The present invention is a virtual try-on system that uses a user's body type information and full-body photographs taken from multiple angles to generate a fitting image tailored to the user. In this system, a program is executed in the following procedure.

[1417] First, the user launches a dedicated application on their smartphone or PC and enters their body information, including height, weight, and measurements. Then, the user takes full-body photos of the front, back, and side and uploads them to the application. This uploaded data is then sent to the server via the device.

[1418] The device securely transmits input and uploaded data to the server using the HTTPS protocol. The server receives this data and uses OpenCV, an image analysis technology, to analyze the user's body shape with high accuracy. Based on the analyzed data, it uses deep learning frameworks such as TensorFlow and PyTorch to generate a high-resolution 3D model.

[1419] After generating a 3D model for the user, the server uses APIs or web scraping technology from partner online stores to collect data on clothing sold online, including images of the clothing, sizes, materials, colors, etc. This data is stored in a database and used to synthesize clothing onto the user's 3D model.

[1420] The server then synthesizes the clothing selected by the user onto the generated 3D model using a generative AI model. The generative AI model is trained using deep learning technology to realistically reproduce the fit and texture of the clothing. Deep learning algorithms using TensorFlow and PyTorch are used in this process.

[1421] The generated try-on images are taken from multiple angles and sent to the user's device as JPEG or PNG image files. The device displays the received try-on images on the application's user interface. The user can review the displayed try-on images and request images from different angles or try-on images of other clothes as needed.

[1422] Furthermore, if the user wishes to receive a coordination suggestion, they click the "Request Coordination Suggestion" button. The server sends a request to the salesperson in charge of coordination suggestions, who then suggests an appropriate coordination based on the user's body type information and preferences, and sends feedback to the server. Based on this feedback, the server generates a new fitting image and displays it on the user's device. Finally, the user checks the suggested coordination and clicks the "Purchase" button if they decide to purchase. This purchase information is sent to the server via the device, and the purchase procedure is carried out in conjunction with the online store's payment system.

[1423] Specific examples

[1424] For example, if a user wants to try on "blue jeans" and a "white T-shirt,"

[1425] 1. The user enters their height as 170cm, weight as 65kg, measurements as 90-75-90, and uploads full-body photos of the front, back, and side.

[1426] 2. The device sends this information to the server.

[1427] 3. The server uses web scraping technology to collect data on "blue jeans" and "white T-shirts" and stores it in a database.

[1428] 4. The server uses image analysis technology to generate a high-resolution 3D model of the user, and then uses a generative AI model to synthesize jeans and a T-shirt onto the 3D model.

[1429] 5. The server generates images of the clothes being tried on from multiple angles and sends them to the user's device.

[1430] 6. The user can check the newly generated try-on image and then decide to "purchase this outfit."

[1431] Prompt Sentence Examples

[1432] "Enter your height, weight, and measurements, then upload full-body photos taken from the front, back, and side."

[1433] This system allows users to easily find clothes that fit their body type, and experience the virtual fitting process as if they were actually trying them on. Users can also request outfit suggestions and check the suggested try-on images, making online clothing shopping smoother and more satisfying.

[1434] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1435] Step 1:

[1436] The user launches an application.

[1437] Specific operation: The user taps or clicks on the dedicated application on their smartphone or PC to launch it and proceeds to the first screen.

[1438] Input: None

[1439] Output: The application launches and displays a screen where the user can enter their body information.

[1440] Step 2:

[1441] The user inputs body type information.

[1442] Specific operation: The user enters body information such as height, weight, and measurements into the application form. The user also prepares full-body photos taken from the front, back, and side.

[1443] Input: Body information such as height, weight, and measurements

[1444] Output: Display of input body shape information and full-body photo

[1445] Step 3:

[1446] The user uploads a full-body photo.

[1447] Specific operation: The user uploads full-body photos of the front, back, and side using the upload function within the application.

[1448] Input: Full-body photos of the front, back, and side

[1449] Output: Full body photo upload complete message

[1450] Step 4:

[1451] The terminal sends the input data to the server.

[1452] Specific operation: The device sends the user's body shape information and a full-body photo to the server via the HTTPS protocol.

[1453] Input: User's body type information and full-body photo

[1454] Output: The data is sent to the server and awaits processing.

[1455] Step 5:

[1456] The server analyzes body shape information and full-body photos.

[1457] Specific operation: The server performs image analysis using OpenCV and extracts the features necessary to create a 3D model based on the user's body shape information.

[1458] Input: Body type information and full-body photo

[1459] Output: Feature-extracted data

[1460] Step 6:

[1461] The server generates a high-resolution 3D model.

[1462] Specific operation: The server uses TensorFlow and PyTorch for deep learning to generate a high-resolution 3D model based on the user's body shape.

[1463] Input: Feature-extracted data

[1464] Output: High-resolution 3D model

[1465] Step 7:

[1466] A server collects data about clothing sold online.

[1467] Specific operation: The server calls the API of partner online stores or uses web scraping to collect the latest clothing data (images, sizes, materials, colors, etc.).

[1468] Input: API endpoint of partner online store or scraping target URL

[1469] Output: Latest clothing data stored in the clothing database

[1470] Step 8:

[1471] The server uses a generative AI model to synthesize the clothing onto a 3D model.

[1472] Specific operation: The server uses a generative AI model to synthesize clothing onto the user's 3D model based on the collected clothing data.

[1473] Input: 3D model and clothing data

[1474] Output: Try-on images from multiple angles

[1475] Step 9:

[1476] The server transmits the generated try-on images to the terminal.

[1477] How it works: The server generates try-on images in JPEG or PNG format and sends them to the user's device. High-speed data transfer via a CDN may also be used.

[1478] Input: Try-on image

[1479] Output: Try-on image sent to the device

[1480] Step 10:

[1481] The terminal displays the try-on image on the user interface.

[1482] Specific operation: The device displays the received try-on images on the application's user interface, allowing the user to operate them.

[1483] Input: Try-on image

[1484] Output: Try-on image displayed on the user interface

[1485] Step 11:

[1486] The user checks the try-on images and sends a request if necessary.

[1487] Specific operation: The user checks the try-on images, and if they want to request images from different angles or images of other clothes being tried on, they click the corresponding button in the application.

[1488] Input: User request

[1489] Output: The request is sent to the server.

[1490] Step 12:

[1491] The user requests outfit suggestions.

[1492] Specific operation: If the user wants outfit suggestions, he / she clicks the "Request outfit suggestions" button.

[1493] Input: User's outfit suggestion request

[1494] Output: The request is sent to the server.

[1495] Step 13:

[1496] The server sends the request to the salesperson.

[1497] Specific operation: The server sends a coordination proposal request to the salesperson, along with the necessary user information.

[1498] Input: User request

[1499] Output: The request is sent to the salesperson

[1500] Step 14:

[1501] Salespeople provide feedback on suggestions.

[1502] Specific operation: The salesperson considers the appropriate outfit based on the user's body type and preferences, and sends feedback to the server.

[1503] Input: User's body type information and preferences

[1504] Output: Feedback of outfit suggestions

[1505] Step 15:

[1506] The server generates an image of a coordinated outfit to try on.

[1507] Specific operation: The server generates new try-on images based on feedback from the salesperson and sends them to the user's device.

[1508] Input: Feedback on outfit suggestions

[1509] Output: Coordinate try-on image sent to the device

[1510] Step 16:

[1511] The user reviews the offer and decides to purchase.

[1512] Specific operation: The user checks the newly generated outfit try-on image and clicks the "Purchase" button if they like it.

[1513] Input: Coordination try-on image

[1514] Output: Purchase request sent to server

[1515] Step 17:

[1516] The device sends purchase information to the server and connects with the online store.

[1517] Specific operation: The terminal sends purchase information to the server, and the server works with the online store's payment system to complete the purchase process.

[1518] Input: Purchase information

[1519] Output: The purchase is completed and a confirmation is sent to the user.

[1520] (Application example 1)

[1521] 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."

[1522] Traditionally, trying on clothes in physical stores was time-consuming and labor-intensive. Furthermore, the types of clothing that could be tried on were limited, making it difficult to try out many options. Furthermore, online shopping presents the problem of frequent returns and exchanges after purchase, since users are unable to see how the actual product will look. To solve these problems, a system is needed that allows users to easily try on clothes and select the best fit for their body type.

[1523] 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.

[1524] In this invention, the server includes: means for inputting a user's body type information; means for taking and uploading full-body photos of the user from multiple angles; means for collecting data on available items; means for combining items with the generated 3D model of the user; means for displaying virtual try-on images on the user's device; means for the user to input body type information and upload a full-body photo using their own device; means for collecting and storing data on items sold online using APIs or web scraping technology of affiliated online stores; means for analyzing the user's body type information and the uploaded full-body photo to generate a high-resolution 3D model of the user; and means for generating try-on images from multiple angles and sending them to the user's device. This allows users to smoothly try on clothes virtually in a physical store, shortening the time it takes to actually try on clothes and combining the convenience of online shopping with the experience of a physical store.

[1525] "User's body type information" refers to the user's physical measurements and characteristics, such as height, weight, and three sizes.

[1526] A "full-body photo" is a photo that shows the user's entire body, and includes multiple photos taken from the front, back, and side.

[1527] "Purchasable Item Data" means information about products such as clothing and accessories sold through affiliated online stores and physical stores, including attributes such as images, sizes, materials, and colors.

[1528] "3D Model" refers to a high-resolution three-dimensional digital model generated based on a user's body shape information and a full-body photograph.

[1529] "Means for synthesizing items" refers to the process of virtually applying selected clothing and accessories to the generated 3D model of the user to generate a realistic try-on image.

[1530] "Virtual try-on image" refers to an image that combines selected clothing and accessories with a user's 3D model, and simulates trying on the clothes from multiple angles.

[1531] "API of affiliated online stores" refers to an application programming interface for automatically obtaining data on products sold by affiliated online stores.

[1532] "Web scraping technology" refers to technology that automatically extracts data from websites.

[1533] "Means for generating a high-resolution 3D model" refers to the process of analyzing a user's body shape information and full-body photograph to create a highly accurate three-dimensional digital model.

[1534] The "means for requesting coordination suggestions" refers to a function that allows a user to request a salesperson to suggest a coordination based on the user's preferences.

[1535] "Performing a virtual try-on" refers to the act of allowing a user to try on an item on a digital model instead of actually trying it on.

[1536] This invention is a system that enables virtual try-on using a user's body type information and full-body photo to improve the fitting experience in physical stores. This system is built using the user's device (smartphone), a series of servers, and APIs or web scraping technologies of affiliated online stores.

[1537] Generating a Program

[1538] The system uses the following hardware and software:

[1539] User device (smartphone): Android or iOS device with camera function

[1540] Server: Data processing and execution of generative AI models (using TensorFlow or PyTorch)

[1541] Partner online store APIs or web scraping technologies (Beautiful Soup, Scrapy, etc.)

[1542] Database: MySQL or PostgreSQL

[1543] Program processing overview

[1544] The server first receives the user's body type information and full-body photo from their device, and then uses image analysis technology to generate a high-resolution 3D model of the user. This 3D model is precisely created using deep learning, recreating a realistic fit to the user's body type.

[1545] The server then uses clothing data collected from partner online stores to synthesize the selected clothing onto the user's 3D model. This synthesis process utilizes generative AI models to accurately reproduce the texture and fit of the clothing. Try-on images taken from multiple angles are generated and sent to the user's device.

[1546] The user can check the virtual try-on images on the device and, if they like them, proceed to the purchase process. If the user would like a coordination suggestion, they can send a request to the salesperson. The coordination suggested by the salesperson is again generated as a virtual try-on image and presented to the user.

[1547] Examples and prompts

[1548] To use the API of a partner online store to collect clothing data, run the following command:

[1549] curl -X GET "https: / / netstore.com / api / v1 / products" -H "Authorization: Bearer YOUR_API_KEY"

[1550] Alternatively, if you use web scraping technology, here's a script that uses Python's Beautiful Soup to extract product data from a specified URL:

[1551] from bs4 import BeautifulSoup

[1552] import requests

[1553] url = "https: / / netstore.com / products"

[1554] response = requests.get(url)

[1555] soup = BeautifulSoup(response.content, 'html.parser')

[1556] for product in soup.find_all('div', class_='product'):

[1557] product_name = product.find('h2').get_text()

[1558] product_price = product.find('span', class_='price').get_text()

[1559] print(product_name, product_price)

[1560] This system allows users to seamlessly try on clothes virtually in a physical store, shortening the time spent trying them on in person and combining the convenience of online with the experience of a physical store.

[1561] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1562] Step 1:

[1563] The user inputs their own body type information into the terminal. The user inputs physical attributes such as height, weight, and three sizes into the terminal, and the input data is sent to the server. At this point, the input is the user's physical numerical data, and the output is raw data sent to the server.

[1564] Step 2:

[1565] The user takes full-body photos from multiple angles and uploads them via the device. The user takes photos from the front, back, and side using the device's camera and sends the image data to the server. At this point, the input is multiple image files, and the output is image data stored on the server.

[1566] Step 3:

[1567] The server uses the API of a partner online store or web scraping technology to collect data on the items being sold. Specifically, the server sends a request to an API endpoint and receives product information in response. Alternatively, it parses a web page to obtain the required product data. At this point, the input is the API request or HTML content, and the output is a set of product data.

[1568] Step 4:

[1569] The server analyzes the user's body shape information and full-body photo to generate a high-resolution 3D model. The collected image data and body shape information are analyzed using a deep learning model (using TensorFlow and PyTorch) to create a highly accurate 3D model of the user. At this point, the input is body shape information and image data, and the output is a 3D model.

[1570] Step 5:

[1571] The server uses a generative AI model to synthesize the selected item onto a 3D model. The collected product data is then synthesized onto the 3D model, creating a virtual try-on image. The generative AI model accurately reproduces the texture and fit of the clothing. At this point, the input is the 3D model and product data, and the output is a try-on image.

[1572] Step 6:

[1573] The server sends the generated try-on images, photographed from multiple angles, to the user's device. These images are displayed on the user's device, allowing the user to check the virtual try-on image. At this point, the input is the try-on image data, and the output is the virtual try-on image displayed on the user's device.

[1574] Step 7:

[1575] When a user requests a coordination suggestion, they send a request from their terminal to the server. The server receives this request and notifies the salesperson in charge. At this point, the input is the request from the user, and the output is a notification to the salesperson.

[1576] Step 8:

[1577] The salesperson creates a coordination suggestion and sends feedback to the server, which then generates a virtual try-on image of the suggested outfit based on the user's 3D model. At this point, the input is the coordination suggestion from the salesperson, and the output is the new try-on image.

[1578] Step 9:

[1579] The server sends the generated coordinated try-on image to the user's device, where the user confirms it. If the user decides to purchase, they click the "Purchase" button on their device, and the purchase information is sent to the server. The input at this point is the virtual try-on image and the user's purchase decision, and the output is the progress of the purchase procedure.

[1580] 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.

[1581] This invention combines a virtual fitting system that generates a 3D model based on the user's body type information and full-body photographs from multiple angles, and then synthesizes clothing onto it, with an emotion engine that recognizes the user's emotions. This system can suggest clothing and outfits that correspond to the user's emotions, providing a more personalized shopping experience.

[1582] 1. Enter your body type information and upload a full-body photo

[1583] Users start the application, input their body information (height, weight, three sizes, etc.), and upload full-body photos taken from multiple angles.

[1584] The terminal transmits this body type information and a full-body photo to the server.

[1585] 2. Collection of clothing data

[1586] The server uses API or web scraping technology to collect data on the clothes being sold (images, sizes, materials, colors, etc.) from partner online stores and stores it in a database.

[1587] 3. 3D model generation and clothing synthesis

[1588] The server analyzes the user's body shape information and full-body photo data to generate a high-resolution 3D model of the user, using the latest image recognition algorithms.

[1589] The server then synthesizes the selected garment onto the 3D model using a generative AI model, a process that leverages deep learning techniques to realistically recreate the garment's fit and texture.

[1590] 4. Showing images of the clothes when they are tried on

[1591] The server transmits the generated try-on images to the user's terminal as images taken from multiple angles.

[1592] The terminal displays the received try-on images on a user interface.

[1593] The user checks the displayed try-on images and requests try-on images from different angles or requests to try on other clothes, if necessary.

[1594] 5. Emotion Recognition by Emotion Engine

[1595] The device uses an emotion engine to analyze the user's facial expressions and voice to identify their emotional state, using a camera and microphone to capture data in real time.

[1596] The server evaluates the user's reaction to the try-on images based on the emotion data from the emotion engine.

[1597] 6. Coordination proposals and adjustments

[1598] If the user desires a coordination suggestion, the user clicks the "Request coordination suggestion" button.

[1599] The server sends a request to a salesperson who is responsible for suggesting a coordination.

[1600] The salesperson proposes an appropriate outfit based on the user's body type information, preferences, and emotional data, and sends feedback to the server.

[1601] The server generates images of the suggested outfits to try on and displays them on the user's device, and adjusts the suggested outfits in real time based on the user's emotional data.

[1602] 7. Purchase Procedure

[1603] The user checks the suggested coordination and, if he / she decides to purchase, clicks the "Purchase" button.

[1604] The terminal sends the purchase information to the server and connects to the online store's payment system.

[1605] The server records the purchase information and notifies the user of the order confirmation and shipping information.

[1606] The terminal displays the order confirmation and shipping information to the user.

[1607] The system of the present invention allows users to easily select clothes that fit their body type and experience the virtual fitting process as if they were actually trying them on. Furthermore, by utilizing an emotion engine to make suggestions based on the user's emotional state, a more personalized shopping experience is realized. This series of processes increases user satisfaction and improves the online shopping experience.

[1608] The processing flow will be explained below.

[1609] Step 1:

[1610] Users start the application and enter their body information, such as height, weight, and measurements, and then upload full-body photos taken from the front, back, and side to the application.

[1611] Step 2:

[1612] The terminal transmits the body type information and full-body photograph input by the user to the server.

[1613] Step 3:

[1614] The server analyzes the received body shape information and full-body photo data and uses image recognition algorithms to generate a high-resolution 3D model of the user.

[1615] Step 4:

[1616] The server uses API or web scraping technology to collect data on the clothes being sold (images, sizes, materials, colors, etc.) from partner online stores and stores it in a database.

[1617] Step 5:

[1618] The server then synthesizes the clothing selected by the user onto the generated 3D model of the user, using a generative AI model to realistically recreate the fit and texture of the clothing.

[1619] Step 6:

[1620] The server transmits the generated try-on images to the user's terminal as images taken from multiple angles.

[1621] Step 7:

[1622] The terminal displays the received try-on images on a user interface.

[1623] Step 8:

[1624] The user checks the displayed try-on images and requests images from different angles or requests to try on other clothes as needed.

[1625] Step 9:

[1626] The device captures the user's facial expressions with a camera or collects their voice with a microphone, and sends this data to the emotion engine.

[1627] Step 10:

[1628] The server uses an emotion engine to analyze the user's facial expressions and voice to identify the user's emotional state, for example, a smiling user is identified as "satisfied" and a frowning user is identified as "anxious."

[1629] Step 11:

[1630] The server uses the emotional state data from the emotion engine to determine the next action, for example, continuing with the proposal if the user is satisfied, or presenting a different option if the user is anxious.

[1631] Step 12:

[1632] If the user desires a coordination suggestion, the user clicks the "Request coordination suggestion" button.

[1633] Step 13:

[1634] The server sends a request to a salesperson who is responsible for suggesting a coordination.

[1635] Step 14:

[1636] The salesperson proposes an appropriate outfit based on the user's body type information, preferences, and emotional data, and sends feedback to the server.

[1637] Step 15:

[1638] The server generates images of the suggested outfits to try on and sends them to the user's device, and also adjusts the suggested outfits in real time based on the user's emotional data.

[1639] Step 16:

[1640] The terminal displays the received try-on image of the coordinated outfit on a user interface.

[1641] Step 17:

[1642] The user checks the suggested coordination and, if he / she decides to purchase, clicks the "Purchase" button.

[1643] Step 18:

[1644] The terminal transmits the purchase information to the server.

[1645] Step 19:

[1646] The server records the purchase information and notifies the user of the order confirmation and shipping information.

[1647] Step 20:

[1648] The terminal displays the order confirmation and shipping information to the user.

[1649] Example 2

[1650] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1651] In recent years, the spread of online shopping has led to an increasing demand for remote try-on and personalized shopping experiences. However, current systems have difficulty fully reproducing the fit and texture of clothing when trying on in person, and are unable to make recommendations based on the user's emotions. Therefore, there is a need for a more realistic virtual try-on system to improve the user experience.

[1652] 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.

[1653] In this invention, the server includes means for inputting the user's body type information, means for taking and uploading full-body images of the user from multiple angles, means for collecting data on clothing available for sale, means for combining clothing items with the generated three-dimensional model of the user, means for presenting images of the items to be tried on at the user's terminal, means for analyzing the user's facial expressions and voice to recognize emotions, and means for adjusting suggestions based on emotions. This allows the user to virtually try on clothes that fit their body type and receive personalized suggestions based on their emotional state.

[1654] "Means for inputting user's body type information" refers to an interface and system for inputting data related to the user's body type, such as height, weight, and measurements.

[1655] "Means for taking and uploading full-body images of the user from multiple angles" refers to a camera and upload function for taking images of the user's entire body from different angles and uploading the image data to the system.

[1656] "Means of collecting data on clothing for sale" refers to technology used to collect and store data such as images, sizes, materials, and colors of clothing for sale from online stores and other sources.

[1657] "Means for synthesizing clothing onto a generated three-dimensional model of a user" refers to an algorithm and system for realistically synthesizing selected clothing onto a three-dimensional model generated based on the user's body shape information and full-body image.

[1658] "Means for presenting try-on images on a user's device" refers to an interface and system for displaying the generated try-on images on the screen of a user's device (smartphone, tablet, PC, etc.).

[1659] "Means for analyzing a user's facial expressions and voice to recognize emotions" refers to algorithms and systems for analyzing a user's facial expressions and voice data acquired using devices such as cameras and microphones, and identifying the user's emotional state.

[1660] "Means for adjusting suggestions based on emotions" refers to technologies and systems that allow suggested clothing and outfits to be changed or adjusted in real time based on the user's emotional data.

[1661] This virtual fitting system generates a high-resolution 3D model based on the user's physique information and full-body image, and then realistically combines clothing onto it. It also analyzes the user's emotions from their facial expressions and voice, and adjusts clothing suggestions accordingly.

[1662] The system uses the following hardware and software:

[1663] An interface for inputting the user's body shape information (such as a smartphone or PC)

[1664] A camera that takes a full-body image of the user (such as a camera built into a smartphone)

[1665] API or web scraping tool to collect data on the clothes being sold

[1666] Generative AI models for generating 3D models (e.g., deep learning models built in Python)

[1667] A user interface that displays synthesized images of clothes to try on

[1668] Emotion recognition engine for analyzing the user's facial expressions and voice (e.g., emotion analysis API)

[1669] The system performs the following operations:

[1670] The user enters their body type information through the application and takes and uploads full-body images from multiple angles. The device then sends this data to the server. The server then collects data on clothing sold by online stores using APIs or web scraping technology and stores it in a database. The server then analyzes the user's body type information and full-body images and uses a generative AI model to generate a high-resolution 3D model of the user. The server then combines the clothing selected by the user into the 3D model to generate a realistic try-on image. This try-on image is then sent to the user's device and displayed.

[1671] The device also uses a camera and microphone to capture the user's facial expressions and voice in real time, and an emotion recognition engine to analyze their emotions. The server then uses this emotional data to adjust the clothing and outfit suggestions in real time.

[1672] As a concrete example, suppose a user inputs their height of 170cm, weight of 65kg, and measurements (B:95cm, W:75cm, H:95cm), and uploads full-body images from the front and side. Based on this, the server generates a 3D model of the user and synthesizes a navy jacket. An example of a prompt at this stage is as follows: "Based on the user's height of 170cm, weight of 65kg, and measurements (B:95, W:75, H:95), generate a 3D model based on the front and side full-body photos and synthesize a high-resolution navy jacket. Also, recognize positive emotions from the user's smiling face and suggest further outfit recommendations."

[1673] The system allows users to virtually try on clothes that fit their body type and receive personalized suggestions based on their emotional state.

[1674] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1675] Program processing flow

[1676] Step 1:

[1677] The user starts the application and inputs body type information.

[1678] Input: User's body information (height, weight, three sizes, etc.)

[1679] Output: Body shape information is saved in the system.

[1680] Specific operation: The user enters body type information into the application's input fields and clicks the "Submit" button. This information is sent to the server via the terminal.

[1681] Step 2:

[1682] The user takes full-body images from multiple angles and uploads them.

[1683] Input: Full-body images of the user taken from the front and side

[1684] Output: The whole body image is saved in the system.

[1685] Specific operation: The user takes a full-body image of themselves from multiple angles using the smartphone camera, selects the image in the application, and clicks the "Upload" button. The image data is sent to the server via the device.

[1686] Step 3:

[1687] The server collects clothing data from affiliated online stores.

[1688] Input: Online store API information or web page URL

[1689] Output: Garment data (image, size, material, color, etc.) is saved in a database.

[1690] How it works: The server automatically collects clothing data from online stores using APIs and web scraping tools. During data collection, it also checks for duplicates and missing data.

[1691] Step 4:

[1692] The server analyzes the user's body shape information and full-body image, and generates a three-dimensional model using a generative AI model.

[1693] Input: User's body shape information and full-body image

[1694] Output: A high-resolution 3D model of the user

[1695] How it works: The server uses deep learning algorithms to analyze the user's body shape information and full-body image to generate a 3D model, utilizing GPUs to accelerate the calculations in this process.

[1696] Step 5:

[1697] The server synthesizes the user's selected clothing onto the three-dimensional model.

[1698] Input: 3D model of the user and selected clothing data

[1699] Output: Composite fitting image

[1700] How it works: The server uses a generative AI model to synthesize the selected clothing onto the user's 3D model in a way that realistically reproduces the fit and texture, resulting in a try-on image.

[1701] Step 6:

[1702] The server transmits the generated try-on images to the user's terminal.

[1703] Input: Synthesized try-on image

[1704] Output: Try-on image displayed on the user's device

[1705] Specific operation: The try-on images are encoded as images taken from multiple angles and sent from the server to the device, which then displays them on the user interface.

[1706] Step 7:

[1707] The device captures the user's facial expressions and voice and analyzes them using an emotion recognition engine.

[1708] Input: User's facial and voice data

[1709] Output: User emotion data

[1710] Specific operation: The device uses a camera and microphone to capture the user's facial expressions and voice in real time, and then analyzes this data with an emotion recognition engine to identify the user's emotional state.

[1711] Step 8:

[1712] The server analyzes the emotional data and adjusts the suggestions in real time.

[1713] Input: User emotion data

[1714] Output: Adjusted proposal

[1715] Specific operation: Based on the emotional data, the server changes and adjusts the fitting images and coordination suggestions in real time to make optimal suggestions.

[1716] Step 9:

[1717] The user selects the suggested outfit and completes the purchase procedure.

[1718] Input: User's purchasing decision information

[1719] Output: Purchase confirmation and shipping information

[1720] Specific operation: When the user clicks the "Purchase" button, the device sends this information to the server. The server then links the purchase information to the online store's payment system and completes the purchase process. The user is then notified of order confirmation and delivery information.

[1721] This allows users to virtually try on clothes that fit their body type, receive personalized suggestions based on their emotions, and enjoy an optimal shopping experience.

[1722] (Application example 2)

[1723] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1724] Conventional virtual try-on systems can generate fitting images based on the user's body shape information, but they cannot make suggestions that take the user's emotions into account. This makes it difficult to improve the user's satisfaction with the clothing and outfits they select. Furthermore, the lack of coordination adjustments based on real-time emotional feedback makes it difficult to fully meet the user's individual needs.

[1725] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting the user's physique information, a means for taking and uploading full-body photos of the user from multiple angles, a means for collecting data on clothing for sale, a means for combining clothing with the generated high-resolution 3D model of the user, a means for analyzing the user's emotions and suggesting clothing and outfits based on the emotion data, and a means for displaying try-on images on the user's device. This enables personalized clothing and outfit suggestions based on the user's emotions, improving user satisfaction. Furthermore, by adjusting the suggestions based on real-time emotion feedback, the system can more appropriately meet the user's individual needs.

[1726] "User's body type information" is data related to the user's body dimensions such as height, weight, and three sizes.

[1727] A "full-body photo" is an image of the user taken from multiple angles (front, back, side, etc.).

[1728] "Data on clothing for sale" refers to detailed information such as images, sizes, materials, and colors of clothing collected from affiliated online stores.

[1729] A "high-resolution 3D model" is a 3D model that is generated based on the user's body shape information and a full-body photograph and is rendered in great detail.

[1730] "Means for synthesizing clothing" refers to a technology that uses data on clothing for sale to realistically display clothing on the generated high-resolution 3D model.

[1731] "Means for analyzing emotions and suggesting clothing and outfits based on emotional data" refers to technology that analyzes a user's facial expressions and voice, and then suggests the most suitable clothing and outfits for the user based on the emotional data obtained from that.

[1732] The "means for presenting a try-on image" is a technology for displaying the generated virtual try-on image on the user's terminal.

[1733] "Emotion data" is data that represents an emotional state such as joy, surprise, or dissatisfaction obtained from the analysis results of the user's facial expressions and voice.

[1734] "Coordination" refers to the user's clothing combination and includes the selection process.

[1735] "Feedback" is the process of adjusting and re-proposing suggestions based on the user's emotional data.

[1736] This invention is a virtual fitting system that combines an emotion engine that recognizes the user's emotions with a 3D model that is generated based on the user's body type information and full-body photographs taken from multiple angles, and then synthesizes clothing onto the 3D model. This system can suggest clothing and outfits that correspond to the user's emotions, providing a more personalized shopping experience.

[1737] The server realizes this system by integrating the following technologies. First, the server provides a means for inputting the user's body type information. This allows the user to enter information such as their height, weight, and three sizes. In addition, the server provides a means for uploading full-body photos taken from multiple angles. This allows the user to upload photos of their front, side, and back.

[1738] The server collects data on clothing sold from online stores and other data sources. This data includes detailed information such as clothing images, sizes, materials, and colors. The server then uses state-of-the-art image recognition algorithms (e.g., TensorFlow) to analyze the user's body shape and full-body photo data to generate a high-resolution 3D model. Deep learning techniques are then used to synthesize clothing onto this 3D model, generating realistic try-on images.

[1739] Furthermore, it has a means to analyze the user's facial expressions and voice using an emotion engine (e.g., Affectiva API) to obtain emotional data. It also incorporates a means to suggest optimal clothing and outfits for the user based on the emotional data. This makes it possible to make appropriate suggestions in real time based on the user's emotions.

[1740] A means of displaying try-on images on the user's device is also in place, allowing the user to view the generated virtual try-on image from multiple angles. After the try-on image is displayed, the user can provide feedback on their emotions on the spot and adjust their outfit as needed.

[1741] For example, a user enters their height and weight into the app and uploads three full-body photos (front, back, and side). After the photo shoot is complete, if the user is happy based on emotion analysis, the system will suggest outfits with bright colors and the latest fashions. On the other hand, if the user is dissatisfied, the system will adjust to suggest new outfits.

[1742] An example of a prompt is as follows:

[1743] The user is 170cm tall and weighs 65kg. They have uploaded photos of the front, back, and side. Sentiment analysis indicates that the user is currently feeling "joy." Based on this, suggest multiple outfits in bright colors and the latest fashions.

[1744] In this way, users can choose the best clothing for their body type and get the same feeling of trying them on in real life through a virtual try-on experience. Real-time emotional feedback provides a more personalized shopping experience.

[1745] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1746] Step 1:

[1747] The user launches the application, inputs their body type information (height, weight, measurements, etc.), and uploads full-body photos taken from multiple angles (front, back, and side). The input data consists of body type information and photo data, which are sent from the device to the server. As a result, data related to the user's body type and full-body photos is stored on the server.

[1748] Step 2:

[1749] The server collects data about clothing sold (images, sizes, materials, colors, etc.) from online stores and other data sources. This collection is done using APIs or web scraping technology. The acquired data is stored in a database on the server. The input is clothing data, and the output is clothing data stored in the database.

[1750] Step 3:

[1751] The server uses the latest image recognition algorithms (e.g., TensorFlow) to analyze the user's body shape information and full-body photo data and generate a high-resolution 3D model. During this process, each user's photo is pixel-analyzed to create the shape of the 3D model. The input for this step is the user's body shape information and photo data, and the output is a high-resolution 3D model.

[1752] Step 4:

[1753] The server uses a generative AI model to synthesize commercially available clothing onto the generated 3D model. Using deep learning technology, the server processes the data to realistically reproduce the fit and texture of the clothing. The input is a high-resolution 3D model and clothing data, and the output is a try-on image.

[1754] Step 5:

[1755] The device uses an emotion engine (e.g., Affectiva API) to analyze the user's facial expressions and voice in real time and acquire emotional data. The captured facial and voice data are input, and analyzed emotional data is output. In this step, the device acquires data using the camera and microphone.

[1756] Step 6:

[1757] The server then proposes optimal clothing and outfits to the user based on the emotional data. The input is the emotional data, and the output is outfit suggestions tailored to the user's preferences. These suggestions are made dynamically and adjusted in real time.

[1758] Step 7:

[1759] The user's terminal displays the try-on images sent from the server. The input is the try-on image, and the output is the try-on image displayed on the user interface. The user can check the displayed try-on image and request images from different angles or to try on other clothes as needed.

[1760] Step 8:

[1761] If the user likes the suggested outfit, they proceed with the purchase. The user clicks the "Purchase" button, and the terminal sends the purchase information to the server. The input is the purchase information, and the output is a purchase confirmation and delivery information. The server connects with the online store's payment system, records the order, and sends a confirmation to the user.

[1762] This allows users to virtually try on clothes that fit their body type, receive personalized coordination suggestions based on emotional data, and finally make a purchase.

[1763] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1764] 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.

[1765] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1766] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1767] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1768] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1769] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1770] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1771] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1772] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1773] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1774] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1775] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1776] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1777] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1778] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1779] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1780] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1781] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1782] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1783] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1784] The following is further disclosed regarding the above embodiment.

[1785] (Claim 1)

[1786] A means for inputting user body type information;

[1787] A means for taking and uploading full-body photographs of the user from multiple angles;

[1788] a means of collecting data on the clothing being sold;

[1789] means for synthesizing clothing onto the generated 3D model of the user;

[1790] means for displaying try-on images on a user's device;

[1791] A system including:

[1792] (Claim 2)

[1793] The system according to claim 1, further comprising means for requesting a salesperson to suggest an outfit based on the user's preferences.

[1794] (Claim 3)

[1795] 10. The system according to claim 1, further comprising means for generating an image of the proposed outfit to be tried on and presenting it on the user's terminal.

[1796] "Example 1"

[1797] (Claim 1)

[1798] A means for inputting user body type information;

[1799] A means for taking and uploading full-body photographs of the user from multiple angles;

[1800] A means of collecting data on clothing sold online;

[1801] means for synthesizing clothing onto the generated high-resolution 3D model of the user;

[1802] means for transmitting the generated try-on image to a user's terminal;

[1803] means for displaying try-on images on a user's terminal;

[1804] A system including:

[1805] (Claim 2)

[1806] 10. The system according to claim 1, further comprising means for requesting a salesperson to suggest a coordination based on the user's preferences.

[1807] (Claim 3)

[1808] 2. The system according to claim 1, further comprising means for generating an image of the proposed outfit to be tried on and presenting it on the user's terminal.

[1809] "Application Example 1"

[1810] (Claim 1)

[1811] A means for inputting user body type information;

[1812] A means for taking and uploading full-body photographs of the user from multiple angles;

[1813] a means for collecting data on items available for purchase;

[1814] means for compositing an article onto the generated 3D model of the user;

[1815] means for displaying a virtual try-on image on a user's device;

[1816] A means for a user to input body type information and upload a full-body photo using their own device;

[1817] A means of collecting and storing data on items sold online using APIs of affiliated online stores or web scraping technology;

[1818] A means for analyzing the user's body shape information and uploaded full-body photograph to generate a high-resolution 3D model of the user;

[1819] means for generating try-on images from multiple angles and transmitting them to a user's device;

[1820] A system including:

[1821] (Claim 2)

[1822] a means for requesting a salesperson to suggest a coordination based on the user's preferences;

[1823] 10. The system according to claim 1, further comprising means for sending a request for coordinated outfit suggestions to the server, generating an image of the suggested coordinated outfits to try on, and presenting the image on the user's terminal.

[1824] (Claim 3)

[1825] The system according to claim 1, further comprising means for generating an image of the proposed outfit to be tried on and presenting it on the user's terminal.

[1826] "Example 2: Combining Emotion Engines"

[1827] (Claim 1)

[1828] A means for inputting user body type information;

[1829] A means for taking and uploading full-body images of the user from multiple angles;

[1830] a means of collecting data on the clothing being sold;

[1831] means for synthesizing clothing items onto the generated three-dimensional model of the user;

[1832] means for displaying try-on images on a user's device;

[1833] A means for recognizing emotions by analyzing the user's facial expressions and voice;

[1834] a means of tailoring suggestions based on emotion;

[1835] A system including:

[1836] (Claim 2)

[1837] The system according to claim 1, further comprising means for requesting a salesperson to suggest an outfit based on the user's preferences.

[1838] (Claim 3)

[1839] 10. The system according to claim 1, further comprising means for generating an image of the proposed outfit to be tried on and presenting it on the user's terminal.

[1840] "Application example 2 when combining emotion engines"

[1841] (Claim 1)

[1842] A means for inputting user body type information;

[1843] A means for taking and uploading full-body photographs of the user from multiple angles;

[1844] a means of collecting data on the clothing being sold;

[1845] means for synthesizing clothing onto the generated high-resolution 3D model of the user;

[1846] A means for analyzing the user's emotions and suggesting clothing and outfits based on the emotion data;

[1847] means for displaying try-on images on a user's device;

[1848] A system including:

[1849] (Claim 2)

[1850] 10. The system according to claim 1, further comprising means for requesting a salesperson to suggest an outfit based on the user's preferences, and adjusting the suggestion in real time using emotion data.

[1851] (Claim 3)

[1852] 10. The system according to claim 1, further comprising means for generating an image of the proposed outfit to be tried on, presenting the image on the user's terminal, and providing feedback on the outfit based on emotion data. [Explanation of symbols]

[1853] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for inputting user body type information; A means for taking and uploading full-body photographs of the user from multiple angles; a means of collecting data on the clothing being sold; means for synthesizing clothing onto the generated 3D model of the user; means for displaying try-on images on a user's device; A system including:

2. The system according to claim 1 , further comprising means for requesting a salesperson to suggest a coordination based on the user's preferences.

3. The system according to claim 1 , further comprising means for generating an image of the proposed outfit to be tried on and presenting it on the user's terminal.

Images