System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting personal information of a user; means for transmitting the personal information to a server; means for collecting recent fashion trend information from websites and application program interfaces; means comprising a generative artificial intelligence for analyzing the personal information and the trend information and generating personalized coordinates; means for transmitting and displaying the coordinates generated by the generative artificial intelligence to a user terminal; means for collecting feedback of the user and inputting the feedback to the generative artificial intelligence; and means for displaying the suitability of the coordinates to the user through a virtual fitting system.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's world, fashion trends change frequently, making it difficult for individual users to find clothing and styles that suit them. Furthermore, when shopping online, it is often impossible to check the actual size and fit of products, making it difficult to make appropriate purchasing decisions. This means that users spend time and effort finding the perfect outfit or outfit, resulting in an inability to make satisfying fashion choices. Furthermore, because personalized suggestions based on users' personal information and preferences currently do not exist, users are forced to rely on uniform suggestions, resulting in low satisfaction. [Means for solving the problem]

[0005] The present invention relates to a system that proposes personalized outfits based on a user's personal information. Specifically, the system includes a means for inputting information such as the user's gender, age, height, weight, personal color, and facial photo, and a means for transmitting this information to a server. The server also includes a generation AI that collects the latest fashion trend information via a website or application program interface, analyzes it, and generates outfits tailored to the user's preferences and body type. The generated outfits are then sent to the user's device and displayed. The user can then input feedback on the proposed outfits, which is then provided to the generation AI to further refine the suggestions. Furthermore, a virtual try-on system allows users to try on outfits on a three-dimensional avatar generated based on the user's body type information, allowing them to check the actual fit. In this way, users can receive personalized fashion advice and ensure the right size and fit, even when shopping online, allowing them to make satisfying purchases.

[0006] "User personal information" refers to information that identifies or characterizes an individual user, such as gender, age, height, weight, personal color, or facial photo, that the user provides when using a particular service or system.

[0007] "Server" means a computer on a network used to receive and process data sent by users and provide analytical results or generated information.

[0008] A "website" is a collection of web pages made available on the Internet via HTTP or HTTPS, and is an online platform for providing information.

[0009] An "Application Program Interface (API)" is an interface that allows different software applications to communicate with each other, and is made up of a set of definitions and protocols that support the exchange of data and the use of functions.

[0010] "Latest fashion trend information" refers to information about current and near-future fashion trends, popular styles, designs, and items.

[0011] "Generative AI" is AI that has the ability to generate new information and suggestions by learning and inferring from massive amounts of data.

[0012] "Generating a coordination" is the process of suggesting combinations of clothing and accessories based on the user's personal information and trend information.

[0013] A "user terminal" is a device used by a user (e.g., a smartphone, tablet, PC, etc.) that communicates data with a server.

[0014] "Feedback" means users providing ratings, comments, and reactions to information and services they receive, which is information that helps improve the system and the quality of individual suggestions.

[0015] The "virtual fitting system" is a system that generates a three-dimensional avatar based on the user's body type information and has the function of allowing the user to virtually try on clothes.

[0016] A "3D avatar" is a digital model created based on the user's physique information, which is a virtual reproduction of the user in a 3D space. [Brief explanation of the drawings]

[0017] [Figure 1]1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

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

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] This invention provides a system that proposes personalized outfits to users based on their personal information and the latest fashion trend information. This system mainly consists of a user terminal, a server, a generative artificial intelligence (AI), and a virtual try-on system.

[0039] Enter and submit user information

[0040] Terminal

[0041] The user starts the app and enters personal information such as gender, age, height, weight, personal color, and a photo of their face. They also select the desired styling scene (e.g., work, date, holiday). This information is sent from the device to the server.

[0042] Specific examples

[0043] The user fills in the app form with the following information: "Gender: Female, Age: 30, Height: 160cm, Weight: 55kg, Personal Color: Blue-based, Face Photo," presses the submit button, and then selects "Styling Scene: Work."

[0044] Acquisition and analysis of the latest trend information

[0045] server

[0046] The server periodically collects the latest trend information from fashion-related websites and APIs, which is then input into the AI ​​generator for analysis.

[0047] Specific examples

[0048] The server uses a web scraping tool to collect information such as "This season's work fashion trends are mid-length tight skirts and simple blouses."

[0049] Learning user information and suggesting outfits

[0050] Generation AI

[0051] The server inputs the user's personal information into the AI ​​generator, which learns the user's individual preferences and body type. The AI ​​then combines the collected trend information with the user's information to generate a personalized outfit. The generated outfit is then sent to the user's device via the server.

[0052] Specific examples

[0053] The AI ​​generates an outfit such as, "The user has a blue-based skin tone, so I suggest a refreshing blue blouse to go with a mid-length tight skirt that's on trend this season." The outfit is then sent from the server to the device.

[0054] Displaying outfits and collecting feedback

[0055] Terminal

[0056] The proposed coordinates are displayed on the user's device, and the user enters feedback (e.g., LIKE, DISLIKE, or comments) about the coordinates and sends it to the server.

[0057] Specific examples

[0058] Users can click the LIKE button to send feedback, saying "I like this outfit," and also enter comments such as "I like the ruffles on the blouse."

[0059] Virtual try-on feature

[0060] server

[0061] The server uses a virtual fitting system to generate a three-dimensional avatar based on the user's body type, then has the avatar try on the suggested outfits to simulate how they would actually fit.

[0062] Specific examples

[0063] Users can virtually try on a blouse to see how it fits, using their own avatar. The suggested outfit is applied to the avatar, and the user can check the fit from the front, back, left and right.

[0064] This system allows users to receive real-time fashion advice tailored to their individual personality and body type. The virtual try-on function also allows users to check the actual fit beforehand, reducing the chance of mistakes when shopping online. In this way, the present invention supports users in making more satisfying fashion choices.

[0065] The processing flow will be explained below.

[0066] Step 1:

[0067] The user launches the app.

[0068] The user launches the application on a device such as a smartphone or tablet, which displays a screen for entering personal information.

[0069] Step 2:

[0070] The user enters personal information.

[0071] Users enter personal information such as gender, age, height, weight, personal color, and a photo of their face into the app's form, and also select the desired styling occasion (e.g., work, date, holiday).

[0072] Step 3:

[0073] Users submit personal information.

[0074] When the user presses the submit button, this information is sent to the server as a JSON object via POST.

[0075] Step 4:

[0076] The server receives the user information.

[0077] The server receives the POST request and parses the JSON object to get the user information, which is then stored in a database.

[0078] Step 5:

[0079] The server collects the latest trend information.

[0080] The server periodically collects the latest trend information from fashion-related websites and APIs according to a schedule, and retrieves the data using web scraping tools and API clients.

[0081] Step 6:

[0082] The server analyzes the trend information.

[0083] Before inputting the collected trend information into the generation AI, the data is pre-processed (text analysis, data cleaning, etc.) to convert it into the format required for analysis.

[0084] Step 7:

[0085] The server inputs user information into the generation AI.

[0086] The server passes user information as a JSON object to the generation AI, which extracts features related to the user's preferences and body type.

[0087] Step 8:

[0088] Generative AI generates personalized outfits.

[0089] The AI ​​generates appropriate combinations of items based on user information and trend information, and the generated outfits are returned to the server in JSON format.

[0090] Step 9:

[0091] The server sends the coordinates to the user terminal.

[0092] The server sends the generated coordinates to the user device, which receives the data and displays it on the UI.

[0093] Step 10:

[0094] The user enters feedback on the outfit.

[0095] The user enters a rating (LIKE, DISLIKE) and a comment about the displayed outfit, and sends it to the server.

[0096] Step 11:

[0097] The server receives the feedback and reflects it in the generated AI.

[0098] The server receives feedback from users and provides it to the generative AI to use as training data to improve the accuracy of the model.

[0099] Step 12:

[0100] The server prepares the virtual try-on.

[0101] The server generates a three-dimensional avatar based on the user's body type information and prepares data for the avatar to try on suggested outfits.

[0102] Step 13:

[0103] The server sends the results of the virtual try-on to the device.

[0104] The server generates the fitting results in the form of images and videos and sends them to the user's device, where the user can view the fitting results and check the fit.

[0105] Through these steps, users can receive personalized fashion advice and take advantage of the virtual try-on feature to check the fit.

[0106] Example 1

[0107] 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."

[0108] Conventional online fashion coordination systems have difficulty proposing personalized outfits that fit the user's personality and body type, and it is also difficult to confirm the actual fit. This has led to problems such as lower satisfaction with online shopping and increased hassle with returns and exchanges.

[0109] 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.

[0110] In this invention, the server includes means for inputting a user's personal information, means for transmitting the personal information to the server, means for collecting the latest fashion trend information from an information providing system, means for providing a generative AI model that analyzes the personal information and the trend information and generates personalized outfits, means for transmitting and displaying the outfits generated by the generative AI model to a user terminal, means for collecting user feedback and inputting the feedback to the generative AI model, and means for displaying the suitability of the outfits to the user via a virtual try-on system. This allows users to receive fashion advice tailored to their individuality and body type in real time, and the virtual try-on function allows them to check the actual fit in advance, enabling them to make highly satisfying fashion choices.

[0111] "User personal information" refers to information that indicates individual attributes of the user, such as gender, age, height, weight, personal color, and facial photo.

[0112] A "server" is a computer system for collecting, analyzing, generating, and distributing data.

[0113] An "information providing system" is a system that provides the latest fashion trend information, such as a website or application program interface.

[0114] A "generative AI model" is an artificial intelligence system that analyzes a user's personal information and fashion trend information to generate personalized outfits.

[0115] The "virtual try-on system" is a system that generates a three-dimensional avatar based on the user's body type information and allows them to try on suggested outfits.

[0116] "Feedback" refers to the user's reaction to the proposed outfit, such as an evaluation or comment.

[0117] A "three-dimensional avatar" is a three-dimensional alter ego of a user that is virtually created based on the user's body shape information.

[0118] Basic configuration

[0119] This invention consists of a user terminal, a server, a generative AI model, and a virtual try-on system. The user terminal is basically a device used by the user, such as a smartphone or PC. The server is a computer system that collects, analyzes, generates, and distributes data. The generative AI model is an artificial intelligence that analyzes the user's personal information and fashion trend information to generate personalized fashion coordination. The virtual try-on system generates a three-dimensional avatar based on the user's body type information and allows the user to try on the suggested coordination.

[0120] Enter and submit user information

[0121] Terminal

[0122] The user starts the app and enters personal information such as gender, age, height, weight, personal color, and a photo of their face. In addition, they select the desired styling scene (e.g., work, date, holiday). Once the input is complete, they press the send button and this information is sent from the device to the server.

[0123] Specific examples

[0124] The user enters the following information into the app's input form: "Gender: Female, Age: 30, Height: 160cm, Weight: 55kg, Personal Color: Blue-based, Face Photo," selects "Styling Scene: Work," and presses the send button.

[0125] Collection and analysis of the latest trend information

[0126] server

[0127] The server periodically collects the latest fashion trend information from multiple fashion-related websites and application program interfaces (APIs). It uses a web scraping tool to obtain the trend information.

[0128] Specific examples

[0129] The server uses a web scraping tool to collect information such as "This season's work fashion trends are mid-length tight skirts and simple blouses," and inputs this information into a generative AI model for analysis.

[0130] Learning user information and suggesting outfits

[0131] Generation AI

[0132] The server inputs the user's collected personal information into a generative AI model, which learns the user's individual preferences and body type. The generative AI model then combines the collected trend information with the user's information to generate a personalized outfit. This generated outfit is then sent to the user's device via the server.

[0133] Specific examples

[0134] The generative AI model generates an outfit such as, "Since the user has blue-based skin, we suggest a refreshing blue blouse to go with a mid-length tight skirt that's trendy this season," and sends this information to the user's device via the server.

[0135] Displaying outfits and collecting feedback

[0136] Terminal

[0137] The proposed outfits are displayed on the user's device, and the user can provide feedback on the outfits by pressing the LIKE or DISLIKE button or by entering a comment. This feedback is then sent to the server.

[0138] Specific examples

[0139] The user checks the outfit displayed on their device, clicks the LIKE button to indicate that they like the outfit, enters a comment such as, "I like the ruffles on the blouse," and sends the feedback to the server.

[0140] Virtual try-on feature

[0141] server

[0142] The server uses a virtual fitting system to generate a three-dimensional avatar based on the user's body type information. The server then has the avatar try on the suggested outfits, simulating how they will actually fit. The simulation results are then displayed on the user's device.

[0143] Specific examples

[0144] The user selects "I want to check the fit of this blouse" on their device and begins the virtual try-on. The server applies the suggested outfit to a three-dimensional avatar, and the user can check the fit from the front, back, left, and right on their device.

[0145] In this way, the system can provide users with tailored fashion advice and a virtual try-on feature to check the actual fit beforehand, improving the satisfaction of online fashion shopping and reducing the risk of returns and exchanges.

[0146] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0147] The flow of this system's program processing

[0148] Step 1:

[0149] Enter and submit user information

[0150] The user launches the app on their device, enters personal information such as gender, age, height, weight, personal color, and a photo of their face, and selects the desired styling scene (e.g., work, date, holiday). This information is then sent from the device to the server.

[0151] Specific actions

[0152] The user enters the following information into the app's input form: "Gender: Female, Age: 30, Height: 160cm, Weight: 55kg, Personal Color: Blue-based, Face Photo," selects "Styling Scene: Work," and presses the send button.

[0153] input

[0154] User's personal information (gender, age, height, weight, personal color, face photo) and styling scene

[0155] output

[0156] User personal information and styling scenes transferred to the server

[0157] Step 2:

[0158] Collection and analysis of the latest trend information

[0159] The server periodically collects the latest fashion trend information from multiple fashion-related websites and application program interfaces (APIs), inputs the collected information into a generative AI model, and analyzes the trend information.

[0160] Specific actions

[0161] The server launches a web scraping tool, collects information such as "This season's work fashion trends are mid-length tight skirts and simple blouses," and inputs this information into a generative AI model.

[0162] input

[0163] Latest fashion trend information collected from fashion-related websites and APIs

[0164] output

[0165] Trend information fed into the generative AI model

[0166] Step 3:

[0167] Learning user information and suggesting outfits

[0168] The server inputs the user's personal information into a generative AI model, which learns the user's individual preferences and body type. The generative AI model combines the collected trend information with user information to generate a personalized outfit. The generated outfit is then sent to the user's device via the server.

[0169] Specific actions

[0170] The generative AI model generates an outfit such as, "Since the user has blue-based skin, we suggest a refreshing blue blouse to go with a mid-length tight skirt that's trendy this season," and sends it to the user's device via the server.

[0171] input

[0172] User's personal information (gender, age, height, weight, personal color, face photo), styling scenes, and the latest fashion trend information

[0173] output

[0174] Personalized coordination sent to user devices

[0175] Step 4:

[0176] Displaying outfits and collecting feedback

[0177] The proposed outfits are displayed on the user's device. The user can provide feedback by pressing the LIKE or DISLIKE button or by entering a comment. This feedback is sent to the server.

[0178] Specific actions

[0179] The user checks the outfit displayed on the device, clicks the LIKE button to indicate "I like this outfit," enters a comment such as "I like the ruffles on the blouse," and sends feedback.

[0180] input

[0181] Personalized outfits displayed on the user's device

[0182] output

[0183] User feedback sent to the server

[0184] Step 5:

[0185] Virtual try-on feature

[0186] The server uses a virtual fitting system to generate a three-dimensional avatar based on the user's body type information. The server then has the avatar try on the suggested outfits, and displays a simulation of how they fit on the user's device.

[0187] Specific actions

[0188] The user selects "I want to check the size of this blouse" on their device and begins the virtual try-on. The server applies the suggested outfit to a three-dimensional avatar, and the user can check the fit from the front, back, left and right on their device.

[0189] input

[0190] User's body type information and suggested outfits

[0191] output

[0192] Results of a fitting simulation using a 3D avatar displayed on a user's device

[0193] In this way, users can receive fashion advice tailored to their individual personalities and body types in real time, and the virtual try-on feature allows them to check the actual fit beforehand, enhancing the satisfaction of online shopping.

[0194] (Application example 1)

[0195] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0196] In recent years, with the diversification of fashion, there has been a growing need to provide fashion coordination that suits each user's unique personality and body type. In addition, the spread of online shopping has made it difficult to check the compatibility and fit of coordination in advance, as it is not possible to try on clothes in a physical store. Furthermore, it is difficult for users to efficiently find coordination that suits their preferences, which requires time and effort.

[0197] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0198] In this invention, the server includes means for inputting a user's personal information, means for transmitting the personal information to the server, means for collecting the latest fashion trend information from websites and application program interfaces, means for generating a personalized outfit by analyzing the personal information and the trend information, means for transmitting and displaying the outfit generated by the generating AI to a user terminal, means for collecting user feedback and inputting the feedback to the generating AI, means for displaying the suitability of the outfit to the user via a virtual try-on system, means for displaying the virtually tried-on outfit in the user's field of view using smart glasses, and means for acquiring the user's personal information and feedback using voice recognition. This allows users to easily find fashion outfits that suit their preferences and body type, and also enables the virtual try-on function to eliminate the hassle of trying on clothes when shopping online and to check the fit in real time.

[0199] "User's personal information" refers to information entered by the user, such as gender, age, height, weight, personal factors, and facial photo.

[0200] "Means for transmitting to the server" refers to the communication means used to transmit the user's personal information from the user terminal to the server.

[0201] "Website and Application Program Interface" refers to a program or API that periodically retrieves the latest fashion trend information via the Internet.

[0202] "Generative artificial intelligence" is an AI model that analyzes a user's personal information and the latest fashion trend information to generate personalized outfits for each individual user.

[0203] "User device" refers to an electronic device used by a user, such as a smartphone, smart glasses, or head-mounted display.

[0204] "Means for collecting feedback" is a function that allows users to input their evaluations and opinions on the proposed outfits and send them to the server.

[0205] A "virtual try-on system" is a system that generates a three-dimensional avatar based on the user's shape information and allows the user to virtually try on suggested outfits.

[0206] "Smart glasses" are eyeglass-type devices that have the function of projecting visual information in front of the user's eyes.

[0207] "Speech recognition" is a technology that converts a user's voice into text data and inputs it into a system.

[0208] "Means for obtaining personal information and feedback" refers to a function that uses voice recognition technology to collect feedback on personal information and coordination provided by the user and inputs it into the system.

[0209] This system proposes personalized outfits to users based on their personal information and the latest fashion trends, and checks their suitability through a virtual try-on function. The main components of this system are a user terminal, a server, a generative artificial intelligence, a virtual try-on system, and smart glasses.

[0210] The server includes the following means:

[0211] How users input their personal information: Users input their personal information (gender, age, height, weight, personal factors, and face photo) by voice through smart glasses or a smartphone.

[0212] Means of sending personal information to the server: The user's personal information is sent to the server using voice recognition technology or input forms.

[0213] Method for collecting the latest fashion trend information: The server periodically collects the latest trend information from fashion-related websites on the Internet using web scraping tools and APIs.

[0214] Coordination generation means using generative AI: Analyze the personal information and trend information and generate personalized coordination using a generative AI model (e.g., GPT-3).

[0215] Means for transmitting and displaying the generated coordination to the user terminal: The coordination generated by the server is transmitted to the user terminal and displayed on the smart glasses or smartphone.

[0216] The user terminal comprises the following means:

[0217] A means of obtaining personal information and feedback using voice recognition: Users can input feedback by voice through smart glasses or smartphones, which is converted into text data using voice recognition technology and sent to the server.

[0218] Virtual try-on display using smart glasses: Through the virtual try-on system, the user's three-dimensional avatar tries on the suggested outfits, and the suitability is displayed on the smart glasses display.

[0219] In this way, the user goes through the following process:

[0220] 1. Enter personal information by voice and send it to the server.

[0221] 2. The server collects the latest fashion trends from the internet and generates personalized outfits using a generative AI model.

[0222] 3. The generated coordinates are sent to the user's device and displayed on the smart glasses.

[0223] 4. Users can use the virtual try-on feature to check the suitability of the suggested outfits.

[0224] 5. Users provide voice feedback, which the server inputs into the generative AI for further improvements.

[0225] For example, a user can input a prompt such as, "Based on the latest fashion trends, please suggest a work outfit that suits my blue-toned skin tone." This process allows users to efficiently and easily find the perfect fashion outfit for themselves, eliminating the hassle of trying on clothes when shopping online and allowing them to check the fit in real time.

[0226] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0227] Step 1:

[0228] The user uses a smart device (e.g., smart glasses, smartphone) to input personal information by voice. Voice recognition technology converts this voice into text data, and personal information (e.g., gender, age, height, weight, personal factors, face photo) is obtained. This is then sent to the server as text data. The input is voice data, and the output is personal information in text format.

[0229] Step 2:

[0230] The server receives the personal information and stores it in a database. At the same time, the server uses web scraping tools and application program interfaces (APIs) to collect the latest fashion trend information from the Internet. The input is the user's personal information and online fashion trend information, and the output is the personal information and trend information stored in the database.

[0231] Step 3:

[0232] The server analyzes the user's personal information and the latest trend information and inputs this into a generative AI model (e.g., GPT-3). A prompt is generated, such as "Based on the latest fashion trends, please suggest a work outfit that suits blue-toned skin." The generative AI model analyzes the data and generates a personalized outfit. The input is the personal information and prompt in text format, and the output is the generated outfit.

[0233] Step 4:

[0234] The server sends the generated outfit to the user's device, which displays it on the display of their smart glasses or smartphone. The user can then view the proposed outfit and is ready to provide feedback. The input is the generated outfit, and the output is the outfit displayed on the user's device.

[0235] Step 5:

[0236] A user uses smart glasses or a smartphone to access the virtual try-on system. The system generates a three-dimensional avatar of the user and has the avatar try on suggested outfits. The user looks at the avatar displayed in their field of view and checks the suitability of the outfits. The input is the three-dimensional avatar and the suggested outfits, and the output is the virtual try-on result.

[0237] Step 6:

[0238] The user provides feedback on the suggested outfits via voice, which is again converted into text data using voice recognition technology and sent to the server. The server receives this feedback and inputs it into the generative AI to be reflected in future suggestions. The input is voice data and feedback text, and the output is feedback information. This feedback further improves the generative AI model, enabling more appropriate outfit suggestions.

[0239] The above is the process flow of the system that realizes the application example and the specific operations at each step.

[0240] 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.

[0241] This invention provides a system that proposes personalized outfits to users based on their personal information, emotional data, and the latest fashion trend information. This system mainly consists of a user terminal, a server, a generative artificial intelligence (AI), an emotion engine, and a virtual try-on system.

[0242] Enter and submit user information

[0243] Terminal

[0244] The user starts the app and enters personal information such as gender, age, height, weight, personal color, and a photo of their face. They also select the desired styling scene (e.g., work, date, holiday). This information is sent from the device to the server.

[0245] Specific examples

[0246] The user fills in the app form with the following information: "Gender: Female, Age: 30, Height: 160cm, Weight: 55kg, Personal Color: Blue-based, Face Photo," presses the submit button, and then selects "Styling Scene: Work."

[0247] Acquisition and analysis of the latest trend information

[0248] server

[0249] The server periodically collects the latest trend information from fashion-related websites and APIs, which is then input into the AI ​​generator for analysis.

[0250] Specific examples

[0251] The server uses a web scraping tool to collect information such as "This season's work fashion trends are mid-length tight skirts and simple blouses."

[0252] Emotion data collection and analysis

[0253] Emotion Engine

[0254] Using the camera and microphone on the user's device, emotion data is collected from the user's facial expressions and voice. The emotion engine analyzes this data to determine the user's current emotional state.

[0255] Specific examples

[0256] While a user is using the app, the emotion engine will recognize that "you look very happy today." If the user also says, "This outfit is lovely," the engine will analyze positive emotions from the voice.

[0257] Learning user and trend information

[0258] Generation AI

[0259] The server inputs the user's personal information and trend information into the AI ​​generator, which then learns the user's preferences and body type. Emotional data is also input into the AI ​​generator, which then generates outfits that take the user's emotions into consideration. The generated outfits are then sent to the user's device via the server.

[0260] Coordination generation and suggestions

[0261] Generation AI

[0262] The AI ​​generates personalized outfits based on the user's personal information, emotional data, and the latest trends, and the outfits are sent to the user's device and displayed to them.

[0263] Specific examples

[0264] The AI ​​generates an outfit such as, "The user has blue-based skin and looks very happy today, so I suggest a light-colored, medium-length tight skirt and a refreshing blue blouse." The outfit is then sent from the server to the device.

[0265] Displaying outfits and collecting feedback

[0266] Terminal

[0267] The proposed outfit is displayed on the user's device. The user enters feedback about the outfit (e.g., like, dislike, comment) and sends it to the server. The emotion engine also collects emotional data from the user's facial expressions and voice when providing feedback.

[0268] Specific examples

[0269] The user clicks the LIKE button to say, "I like this outfit," and enters a comment such as, "I especially like the design of the blouse." The emotion engine then recognizes that the user is expressing positive feelings toward the suggestion.

[0270] Learning from feedback and emotion data

[0271] Server and spawned AI

[0272] The server receives user feedback and emotion data from the emotion engine, and provides it to the generative AI to use as training data to improve the accuracy of the model.

[0273] Virtual try-on feature

[0274] Servers and Terminals

[0275] The server uses a virtual fitting system to generate a three-dimensional avatar based on the user's body type information. The generated avatar tries on suggested outfits to simulate a real fit. The fitting results are sent to the user's device, where the user can check the results.

[0276] Specific examples

[0277] Users can virtually try on outfits to see how they fit, and check them on their own avatar. The suggested outfit is applied to the avatar, and the user can check how it fits from the front, back, left, and right.

[0278] This system allows users to receive real-time fashion advice that takes into account their individuality, body type, and even their emotions. The virtual try-on feature also allows users to check the actual fit beforehand, reducing the chance of making mistakes when shopping online. In this way, the present invention supports users in making more satisfying fashion choices.

[0279] The processing flow will be explained below.

[0280] Step 1:

[0281] The user launches the app.

[0282] The user launches the application on a device such as a smartphone or tablet, which displays a screen for entering personal information.

[0283] Step 2:

[0284] The user enters personal information.

[0285] Users enter personal information such as gender, age, height, weight, personal color, and a photo of their face into the app's form, and also select the desired styling occasion (e.g., work, date, holiday).

[0286] Step 3:

[0287] The user sends personal information to the server.

[0288] When the user presses the submit button, this information is sent to the server as a JSON object via POST.

[0289] Step 4:

[0290] The server receives the user information.

[0291] The server receives the POST request and parses the JSON object to get the user information, which is then stored in a database.

[0292] Step 5:

[0293] The server collects the latest trend information.

[0294] The server periodically collects the latest trend information from fashion-related websites and APIs according to a schedule, and retrieves the data using web scraping tools and API clients.

[0295] Step 6:

[0296] The server inputs trend information into the generation AI.

[0297] The collected trend information is pre-processed (text analysis, data cleaning, etc.), converted into the format required for analysis, and input into the generation AI.

[0298] Step 7:

[0299] The user's device collects emotional data.

[0300] Using the camera and microphone on the user's device, emotion data is collected from the user's facial expressions and voice, and this data is provided to the emotion engine in real time.

[0301] Step 8:

[0302] The emotion engine analyzes the emotion data.

[0303] The emotion engine analyzes the user's emotional state from their facial expressions and voice, and provides the results to the generative AI.

[0304] Step 9:

[0305] The server inputs user information and emotional data into the generation AI.

[0306] The server inputs user information, trend information, and emotional data into the generation AI, which then generates outfits that take into account the user's preferences and emotions.

[0307] Step 10:

[0308] Generative AI generates personalized outfits.

[0309] The AI ​​generates appropriate combinations of items based on user information, emotional data, and trend information, and the generated outfits are returned to the server in JSON format.

[0310] Step 11:

[0311] The server sends the coordinates to the user terminal.

[0312] The server sends the generated coordinates to the user device, which receives the data and displays it on the UI.

[0313] Step 12:

[0314] The user enters feedback on the outfit.

[0315] The user enters their rating (LIKE, DISLIKE) and comments on the displayed outfits and sends them to the server. The emotion engine also collects emotional data from the user's facial expressions and voice when providing feedback.

[0316] Step 13:

[0317] The server receives the feedback and emotion data.

[0318] The server receives user feedback and emotion data from the emotion engine, and provides it to the generative AI to use as training data to improve the accuracy of the model.

[0319] Step 14:

[0320] The server prepares the virtual try-on.

[0321] The server generates a three-dimensional avatar based on the user's body type information and prepares data for the avatar to try on suggested outfits.

[0322] Step 15:

[0323] The server sends the results of the virtual try-on to the device.

[0324] The server generates the fitting results in the form of images and videos and sends them to the user's device, where the user can view the fitting results and check the fit.

[0325] Through these steps, users can receive fashion advice that takes into account their personality, body type, and even their emotions, and the virtual try-on function allows them to check the actual fit in advance.

[0326] Example 2

[0327] 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."

[0328] Conventional fashion recommendation systems often only offer generic clothing suggestions, lacking personalized suggestions that take into account each user's individuality, body shape, and emotions. Furthermore, when shopping online, users are unable to determine how the item will actually fit, resulting in problems such as the wrong size or design after purchase. Furthermore, few systems incorporate user feedback in real time, making it difficult to improve user satisfaction. There is a need for a system that can solve these issues and support users in making more satisfying fashion choices.

[0329] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting the latest fashion trend information from an information providing service or a program control interface, a means for analyzing the personal information and the trend information and providing a generation artificial intelligence for generating personalized outfit suggestions, and a means for collecting user opinions and inputting the opinions into the generation artificial intelligence. This enables personalized fashion suggestions that take into consideration the individuality, body shape, and emotions of each user.

[0330] "User's device" refers to a device used by a user to enter personal information, check suggested fashions, and enter feedback, and includes smartphones, tablets, and PCs.

[0331] "Central processing unit" refers to the server that processes and analyzes users' personal information, collected trend information, and feedback.

[0332] "Generative AI" is an AI model that generates personalized clothing suggestions based on a user's personal information, trend information, and emotional data.

[0333] "Information provision services" refers to websites and programmatic control interfaces (APIs) that provide the latest fashion-related trend information.

[0334] "Personal color tone" refers to the user's skin tone and personal color (blue-based, yellow-based, etc.), and is a factor used to make appropriate fashion suggestions.

[0335] A "face image" is a photo of the user's face, which is used to collect emotional data and make personalized suggestions through facial recognition.

[0336] The "virtual fitting system" is a system that generates a three-dimensional virtual image based on the user's body type information, allows the virtual image to try on suggested clothing, and simulates the fit.

[0337] A "three-dimensional virtual image" is a three-dimensional model generated based on the user's body type information, and is used to try on suggested clothing in the virtual try-on system.

[0338] This invention provides a system that proposes personalized outfits to users based on their personal information, emotional data, and the latest fashion trend information. This system mainly consists of a user terminal, a server, a generative artificial intelligence (AI), an emotion engine, and a virtual try-on system.

[0339] Enter and submit user information

[0340] The user starts the app and enters personal information such as gender, age, height, weight, personal color, and a photo of their face. They also select the desired styling occasion (e.g., work, date, holiday). This information is sent from the user's device to the server.

[0341] Specific examples

[0342] The user fills in the app form with the following information: "Gender: Female, Age: 30, Height: 160cm, Weight: 55kg, Personal Color: Blue-based, Face Photo," presses the submit button, and then selects "Styling Scene: Work."

[0343] Acquisition and analysis of the latest trend information

[0344] The server periodically collects the latest trend information from fashion-related websites and APIs. The data is obtained using web scraping tools and API requests. The collected trend information is then input into the generation AI for analysis.

[0345] Specific examples

[0346] The server uses a web scraping tool to collect information such as "This season's work fashion trends are mid-length tight skirts and simple blouses."

[0347] Emotion data collection and analysis

[0348] Using the camera and microphone installed on the user's device, emotional data is collected from the user's facial expressions and voice. Facial and voice recognition operates in real time to record the emotional data. The emotion engine analyzes the collected data and identifies the user's current emotional state.

[0349] Specific examples

[0350] While the user is using the app, the camera detects "smiles" and "surprised expressions," and the microphone records the user's "positive and negative voice tones." The emotion engine analyzes the facial recognition data and determines that the user has a "happy expression," and voice analysis detects that the user is "speaking in a positive tone."

[0351] Learning user and trend information

[0352] The server inputs the user's personal information and trend information into the AI ​​generator. Based on this information, the AI ​​learns the user's preferences, body type, and adapts to trends. Emotional data is also input into the AI ​​generator, which generates outfits that take the user's emotions into consideration.

[0353] Specific examples

[0354] The server combines the user information and trend information in JSON format and sends it to the generation AI. The generation AI updates its model to suggest, "The user has blue-based skin and looks very happy today, so a light-colored, medium-length tight skirt and a refreshing blue blouse."

[0355] Coordination generation and suggestions

[0356] The AI ​​generates personalized outfits based on the user's personal information, emotional data, and the latest trends, and the outfits are sent to the user's device and displayed to them.

[0357] Specific examples

[0358] The AI ​​generator suggests, "Since the user has blue-toned skin and looks happy today, we suggest a light-colored, medium-length tight skirt and a refreshing blue blouse."

[0359] Displaying outfits and collecting feedback

[0360] The proposed outfit is displayed on the user's device. The user can then enter feedback (like, dislike, or comment) about the outfit and send it to the server. The emotion engine also collects emotional data from the user's facial expressions and voice when providing feedback.

[0361] Specific examples

[0362] The user clicks the LIKE button to say, "I like this outfit," and enters a comment such as, "I especially like the design of the blouse." The emotion engine then recognizes that the user is expressing positive feelings toward the suggestion.

[0363] Learning from feedback and emotion data

[0364] The server receives user feedback and emotion data from the emotion engine, and provides it to the generative AI to use as training data to improve the accuracy of the model.

[0365] Virtual try-on feature

[0366] The server uses a virtual fitting system to generate a three-dimensional avatar based on the user's body type information. The generated avatar tries on suggested outfits to simulate a real fit. The fitting results are sent to the user's device, where the user can check the results.

[0367] Specific examples

[0368] Users can use the virtual try-on feature to check how an outfit fits on their own avatar. The suggested outfit is applied to the avatar, and the user can check how it fits from the front, back, left and right.

[0369] In this way, the present invention realizes a system that provides users with real-time fashion advice that takes into account their personality, body type, and emotions. The virtual try-on feature also allows users to check the actual fit beforehand, reducing mistakes when shopping online.

[0370] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0371] Step 1: Enter your user information

[0372] Terminal

[0373] Users launch the app and enter personal information such as gender, age, height, weight, personal color, and a photo of their face. They also select the desired styling occasion (e.g., work, date, holiday). This operation is performed by the user entering information into each input form in the app.

[0374] input

[0375] User's personal information (gender, age, height, weight, personal color, face photo) and styling scene selection.

[0376] output

[0377] Entered personal information and styling scene data.

[0378] Specific actions

[0379] The user enters "Gender: Female," "Age: 30," "Height: 160cm," "Personal color: Blue-based," and "Face photo," and then selects "Styling scene: Work."

[0380] Step 2: Submit user information

[0381] Terminal

[0382] When the user completes the input and presses the send button, this information is automatically sent from the terminal to the server.

[0383] input

[0384] Personal information and styling scene data entered by the user.

[0385] output

[0386] Data transmission result from the device to the server.

[0387] Specific actions

[0388] When the user presses the submit button, the user information is sent to the server in JSON format.

[0389] Step 3: Stay up to date on the latest trends

[0390] server

[0391] The server periodically collects the latest trend information from fashion-related websites and APIs, and retrieves the data using web scraping tools and API requests.

[0392] input

[0393] URLs for fashion-related websites and APIs.

[0394] output

[0395] Collected latest fashion trend information.

[0396] Specific actions

[0397] The server uses a web scraping tool to collect information such as, "This season's work fashion trends are mid-length tight skirts and simple blouses."

[0398] Step 4: Analyze the latest trend information

[0399] server

[0400] The collected trend information is input into the generative AI for analysis, which analyzes the trend data and updates the model that understands each attribute (color, design, material, etc.).

[0401] input

[0402] Collected latest trend information.

[0403] output

[0404] Analyzed trend data.

[0405] Specific actions

[0406] The trend data collected by the server is input into the generative AI using a Python script, and the model learns trend items such as "medium-length tight skirts" and "simple blouses."

[0407] Step 5: Collecting emotion data

[0408] Terminal

[0409] Using the camera and microphone installed on the user's device, emotional data is collected from the user's facial expressions and voice. Facial and voice recognition works in real time, and emotional data is recorded.

[0410] input

[0411] The user's facial expression and voice data.

[0412] output

[0413] Collected emotion data.

[0414] Specific actions

[0415] While the user is using the app, the camera detects "smiles" and "surprised expressions," and the microphone records the user's "positive tone of voice" and "negative tone of voice."

[0416] Step 6: Analyze the sentiment data

[0417] Emotion Engine

[0418] The emotion engine analyzes the collected data to determine the user's current emotional state, using emotion recognition algorithms (e.g., OpenCV or TensorFlow).

[0419] input

[0420] Collected user sentiment data.

[0421] output

[0422] The analyzed emotional state of the user.

[0423] Specific actions

[0424] The emotion engine analyzes facial recognition data to determine if the user has a happy facial expression, and voice analysis detects if the user is speaking in a positive tone.

[0425] Step 7: Learn about users and trends

[0426] server

[0427] The server inputs the user's personal information and trend information into the AI ​​generator. Based on this information, the AI ​​learns the user's preferences, body type, and adapts to trends. Emotional data is also input into the AI ​​generator, which generates outfits that take the user's emotions into consideration.

[0428] input

[0429] User personal information, latest trend information, and sentiment data.

[0430] output

[0431] Trained model data.

[0432] Specific actions

[0433] The server combines user information and trend information in JSON format and sends it to the AI ​​generator, which then suggests a light-colored, medium-length tight skirt and a refreshing blue blouse, since the user has blue-based skin and looks happy today.

[0434] Step 8: Generate coordinates

[0435] Generation AI

[0436] Generative AI generates personalized outfits based on the user's personal information, emotional data, and the latest trends.

[0437] input

[0438] User personal information, sentiment data, and analyzed trend information.

[0439] output

[0440] The generated coordinates.

[0441] Specific actions

[0442] The AI ​​generator suggests, "Since the user has blue-toned skin and a happy expression, a light-colored, medium-length tight skirt and a refreshing blue blouse."

[0443] Step 9: Coordination suggestions

[0444] server

[0445] The generated coordinates are sent to the user's terminal via the server and displayed to the user.

[0446] input

[0447] Generated coordinate data.

[0448] output

[0449] Coordinate display on user device.

[0450] Specific actions

[0451] The server sends the generated coordinates in JSON format to the user's device, where they are displayed in the device app.

[0452] Step 10: Show your outfit and get feedback

[0453] Terminal

[0454] The proposed outfit is displayed on the user's device. The user can then enter feedback (like, dislike, or comment) about the outfit and send it to the server. The emotion engine also collects emotional data from the user's facial expressions and voice when providing feedback.

[0455] input

[0456] User feedback data (likes, dislikes, comments), facial expressions and voice data.

[0457] output

[0458] Results of sending feedback data and emotion data to the server.

[0459] Specific actions

[0460] The user clicks the LIKE button to say, "I like this outfit," and enters a comment such as, "I especially like the design of the blouse." The emotion engine then recognizes that the user is expressing positive feelings toward the suggestion.

[0461] Step 11: Learning from feedback and sentiment data

[0462] Server and spawned AI

[0463] The server receives user feedback and emotion data from the emotion engine, and provides it to the generative AI to use as training data to improve the accuracy of the model.

[0464] input

[0465] User feedback and sentiment data.

[0466] output

[0467] Updated generative AI models.

[0468] Specific actions

[0469] The generative AI incorporates new feedback data and updates its models to more accurately reflect the user's preferences and emotional state.

[0470] Step 12: Run the Virtual Try-On Feature

[0471] Servers and Terminals

[0472] The server uses a virtual fitting system to generate a three-dimensional avatar based on the user's body type information. The generated avatar tries on suggested outfits to simulate a real fit. The fitting results are sent to the user's device, where the user can check the results.

[0473] input

[0474] User's body type information and suggested outfits.

[0475] output

[0476] Results of trying on a 3D avatar.

[0477] Specific actions

[0478] Users can use the virtual try-on feature to check how an outfit fits on their own avatar. The suggested outfit is applied to the avatar, and the user can check how it fits from the front, back, left and right.

[0479] (Application example 2)

[0480] 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."

[0481] Conventional fashion suggestion systems only use the user's personal information and the latest fashion trend information, but are unable to suggest personalized outfits that reflect the user's emotions. In addition, the virtual try-on function is limited, making it difficult to provide a satisfying fitting based on the user's emotions.

[0482] The specific processing by the specific 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 means for inputting the user's personal information, means for transmitting the personal information to the server, means for collecting the latest fashion trend information from websites and application program interfaces, means for providing a generation AI that analyzes the personal information and the trend information and generates a personalized outfit, means for collecting and analyzing emotional data from the user's facial expressions and voice, means for transmitting and displaying the outfit generated by the generation AI to the user terminal, means for displaying the suitability of the outfit to the user via a virtual try-on system, and means for collecting user feedback and inputting the feedback to the generation AI. This makes it possible to provide a personalized outfit that reflects the user's personal information and emotional data, and to confirm the actual fit in advance through virtual try-on.

[0483] "User information" refers to information including personal information, attribute information, and emotional data of a user.

[0484] "Personal information" refers to various data about the user, such as the user's gender, age, height, weight, personal color, and facial photo.

[0485] "Emotional data" is information about the emotional state of a user that is analyzed from their facial expressions and voice.

[0486] A "server" is a centralized computing resource for collecting and analyzing user information and trend information.

[0487] "Generative AI" is an AI algorithm that generates personalized outfits based on collected data.

[0488] "Latest Fashion Trend Information" refers to information about current fashion industry trends collected from websites and application program interfaces.

[0489] An "emotion analysis engine" is a technology that collects and analyzes emotional data from a user's facial expressions and voice.

[0490] A "user device" is a display device used by a user, such as a smartphone or head-mounted display.

[0491] "Coordination" is a suggested clothing combination generated based on the user's personal information, the latest trends, and emotional data.

[0492] The "virtual fitting system" is a system that displays the fit of suggested outfits to users through a three-dimensional avatar.

[0493] "Feedback" refers to the user's reactions to the proposed outfits, such as ratings and comments.

[0494] A "3D avatar" is a virtual 3D character generated based on the user's body type information.

[0495] The system for implementing this invention involves a series of processes that collects a user's personal information, emotional data, and the latest fashion trend information, analyzes them, proposes personalized outfits, and allows the user to virtually try them on. The system is mainly composed of the following elements: a server, a user terminal, a generative artificial intelligence (generative AI), an emotion analysis engine, and a virtual try-on system.

[0496] Specific examples of hardware and software used

[0497] Hardware

[0498] Smartphone or Head-Mounted Display (HMD): Used as the user device. Example: Oculus Quest.

[0499] Camera and microphone: A device used to capture a user's facial photograph and voice data.

[0500] software

[0501] Web scraping tools: Tools for gathering trend information from fashion-related websites. Examples: Beautiful Soup, Scrapy.

[0502] Generative artificial intelligence (generative AI) models: AI algorithms for generating personalized outfits. Example: OpenAI's GPT-based models.

[0503] Emotion analysis engine: Technology for analyzing emotional data from a user's facial expressions and voice. Example: Microsoft Azure's Emotion API.

[0504] Virtual fitting system: A system that generates a three-dimensional avatar based on the user's body type information and lets them try on outfits. Example: Clo Virtual Fashion.

[0505] System action

[0506] Enter and submit user information

[0507] The user starts the application on their device and enters personal information such as gender, age, height, weight, personal color, and a photo of their face. They also select the desired styling scene (e.g., work, date, or holiday). This information is sent to the server.

[0508] As a specific example, a user enters "gender: female, age: 30, height: 160cm, weight: 55kg, personal color: blue-based, face photo" and selects "styling scene: work."

[0509] Acquisition and analysis of the latest trend information

[0510] The server periodically collects the latest trend information from fashion-related websites and APIs, which is then input into the generative AI and analyzed using natural language processing.

[0511] For example, collect information such as, "This season's work fashion trends are mid-length tight skirts and simple blouses."

[0512] Emotion data collection and analysis

[0513] The camera and microphone on the user's device are used to collect emotional data from facial expressions and voice, and the emotion analysis engine analyzes the data to determine the user's emotional state.

[0514] For example, if a user says, "You look very happy today," or "This outfit is lovely," the system will analyze the positive emotion from the voice.

[0515] Personalized outfit creation

[0516] The server inputs the user's personal information, emotional data, and trend information into the AI ​​generator, which then generates outfits that take into account the user's preferences, body type, and emotions. The generated outfits are then sent to the user's device and displayed.

[0517] Example prompt

[0518] Here are some example prompts to input to the AI ​​generator:

[0519] User Information:

[0520] Gender: Female

[0521] Age: 30

[0522] Height: 160cm

[0523] Weight: 55kg

[0524] Personal color: Blue-based

[0525] Preferred styling occasion: Work

[0526] Latest Trends:

[0527] This season's work fashion trends are mid-length tight skirts and simple blouses

[0528] Emotional state:

[0529] A very happy look

[0530] A positive comment that this outfit is lovely

[0531] Generate personalized work outfits for blue-toned skin based on your preferences and the latest trends.

[0532] Virtual try-on

[0533] The virtual fitting system generates a three-dimensional avatar based on the user's body type and emotional data, and then allows the user to try on suggested outfits, allowing them to check how the outfits actually fit.

[0534] The system allows users to receive personalized fashion suggestions in real time that take into account their personality, body type, and emotions, and the virtual try-on feature reduces mistakes when shopping online.

[0535] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0536] Step 1:

[0537] Enter and submit user information

[0538] The user starts the application on their smartphone or head-mounted display (HMD) and inputs their gender, age, height, weight, personal color, a photo of their face, and the desired styling scene. This information is then sent from the device to the server and saved.

[0539] Input: Gender, age, height, weight, personal color, face photo, styling scene

[0540] Output: User information stored on the server

[0541] Step 2:

[0542] Collection and analysis of the latest trend information

[0543] The server periodically collects the latest fashion trend information from fashion-related websites and APIs using a web scraping tool, which is then input into a generative AI model for analysis.

[0544] Input: Fashion trend information obtained from websites and APIs

[0545] Output: Latest analyzed trend information

[0546] Step 3:

[0547] Emotion data collection and analysis

[0548] The camera and microphone on the user's device collect the user's facial expressions and voice. This data is input into an emotion analysis engine to analyze the user's current emotional state. The analysis results are then sent to the server.

[0549] Input: User facial and voice data

[0550] Output: Parsed emotion data

[0551] Step 4:

[0552] Personalized outfit creation

[0553] The server inputs user information, trend information, and emotional data into the generative AI model to generate personalized outfits tailored to the user's preferences, body type, and emotions. The generated outfits are sent to the user's device and displayed.

[0554] Input: User information, latest trend information, sentiment data

[0555] Output: Personalized outfit ideas

[0556] Step 5:

[0557] Virtual try-on

[0558] The virtual fitting system generates a three-dimensional avatar based on the user's body type information, has them try on suggested outfits, and sends the results to the user's device, where the user can check the fitting results.

[0559] Input: User's body type information, suggested outfits

[0560] Output: 3D avatar fitting results

[0561] Step 6:

[0562] Collecting and analyzing feedback

[0563] Users provide feedback (likes, dislikes, comments) on outfits, which is analyzed by a sentiment analysis engine and the results are sent to the server and used as training data for the generative AI model.

[0564] Input: User feedback and sentiment data

[0565] Output: Updated training data by generative AI model

[0566] Through this process, users can easily find outfits that are optimized to suit their individuality, body type, and emotions, and can check the actual fit in advance through virtual try-on.

[0567] 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.

[0568] 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.

[0569] 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.

[0570] [Second embodiment]

[0571] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0572] 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.

[0573] 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).

[0574] 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.

[0575] 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.

[0576] 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).

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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."

[0583] This invention provides a system that proposes personalized outfits to users based on their personal information and the latest fashion trend information. This system mainly consists of a user terminal, a server, a generative artificial intelligence (AI), and a virtual try-on system.

[0584] Enter and submit user information

[0585] Terminal

[0586] The user starts the app and enters personal information such as gender, age, height, weight, personal color, and a photo of their face. They also select the desired styling scene (e.g., work, date, holiday). This information is sent from the device to the server.

[0587] Specific examples

[0588] The user fills in the app form with the following information: "Gender: Female, Age: 30, Height: 160cm, Weight: 55kg, Personal Color: Blue-based, Face Photo," presses the submit button, and then selects "Styling Scene: Work."

[0589] Acquisition and analysis of the latest trend information

[0590] server

[0591] The server periodically collects the latest trend information from fashion-related websites and APIs, which is then input into the AI ​​generator for analysis.

[0592] Specific examples

[0593] The server uses a web scraping tool to collect information such as "This season's work fashion trends are mid-length tight skirts and simple blouses."

[0594] Learning user information and suggesting outfits

[0595] Generation AI

[0596] The server inputs the user's personal information into the AI ​​generator, which learns the user's individual preferences and body type. The AI ​​then combines the collected trend information with the user's information to generate a personalized outfit. The generated outfit is then sent to the user's device via the server.

[0597] Specific examples

[0598] The AI ​​generates an outfit such as, "The user has a blue-based skin tone, so I suggest a refreshing blue blouse to go with a mid-length tight skirt that's on trend this season." The outfit is then sent from the server to the device.

[0599] Displaying outfits and collecting feedback

[0600] Terminal

[0601] The proposed coordinates are displayed on the user's device, and the user enters feedback (e.g., LIKE, DISLIKE, or comments) about the coordinates and sends it to the server.

[0602] Specific examples

[0603] Users can click the LIKE button to send feedback, saying "I like this outfit," and also enter comments such as "I like the ruffles on the blouse."

[0604] Virtual try-on feature

[0605] server

[0606] The server uses a virtual fitting system to generate a three-dimensional avatar based on the user's body type, then has the avatar try on the suggested outfits to simulate how they would actually fit.

[0607] Specific examples

[0608] Users can virtually try on a blouse to see how it fits, using their own avatar. The suggested outfit is applied to the avatar, and the user can check the fit from the front, back, left and right.

[0609] This system allows users to receive real-time fashion advice tailored to their individual personality and body type. The virtual try-on function also allows users to check the actual fit beforehand, reducing the chance of mistakes when shopping online. In this way, the present invention supports users in making more satisfying fashion choices.

[0610] The processing flow will be explained below.

[0611] Step 1:

[0612] The user launches the app.

[0613] The user launches the application on a device such as a smartphone or tablet, which displays a screen for entering personal information.

[0614] Step 2:

[0615] The user enters personal information.

[0616] Users enter personal information such as gender, age, height, weight, personal color, and a photo of their face into the app's form, and also select the desired styling occasion (e.g., work, date, holiday).

[0617] Step 3:

[0618] Users submit personal information.

[0619] When the user presses the submit button, this information is sent to the server as a JSON object via POST.

[0620] Step 4:

[0621] The server receives the user information.

[0622] The server receives the POST request and parses the JSON object to get the user information, which is then stored in a database.

[0623] Step 5:

[0624] The server collects the latest trend information.

[0625] The server periodically collects the latest trend information from fashion-related websites and APIs according to a schedule, and retrieves the data using web scraping tools and API clients.

[0626] Step 6:

[0627] The server analyzes the trend information.

[0628] Before inputting the collected trend information into the generation AI, the data is pre-processed (text analysis, data cleaning, etc.) to convert it into the format required for analysis.

[0629] Step 7:

[0630] The server inputs user information into the generation AI.

[0631] The server passes user information as a JSON object to the generation AI, which extracts features related to the user's preferences and body type.

[0632] Step 8:

[0633] Generative AI generates personalized outfits.

[0634] The AI ​​generates appropriate combinations of items based on user information and trend information, and the generated outfits are returned to the server in JSON format.

[0635] Step 9:

[0636] The server sends the coordinates to the user terminal.

[0637] The server sends the generated coordinates to the user device, which receives the data and displays it on the UI.

[0638] Step 10:

[0639] The user enters feedback on the outfit.

[0640] The user enters a rating (LIKE, DISLIKE) and a comment about the displayed outfit, and sends it to the server.

[0641] Step 11:

[0642] The server receives the feedback and reflects it in the generated AI.

[0643] The server receives feedback from users and provides it to the generative AI to use as training data to improve the accuracy of the model.

[0644] Step 12:

[0645] The server prepares the virtual try-on.

[0646] The server generates a three-dimensional avatar based on the user's body type information and prepares data for the avatar to try on suggested outfits.

[0647] Step 13:

[0648] The server sends the results of the virtual try-on to the device.

[0649] The server generates the fitting results in the form of images and videos and sends them to the user's device, where the user can view the fitting results and check the fit.

[0650] Through these steps, users can receive personalized fashion advice and take advantage of the virtual try-on feature to check the fit.

[0651] Example 1

[0652] 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."

[0653] Conventional online fashion coordination systems have difficulty proposing personalized outfits that fit the user's personality and body type, and it is also difficult to confirm the actual fit. This has led to problems such as lower satisfaction with online shopping and increased hassle with returns and exchanges.

[0654] 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.

[0655] In this invention, the server includes means for inputting a user's personal information, means for transmitting the personal information to the server, means for collecting the latest fashion trend information from an information providing system, means for providing a generative AI model that analyzes the personal information and the trend information and generates personalized outfits, means for transmitting and displaying the outfits generated by the generative AI model to a user terminal, means for collecting user feedback and inputting the feedback to the generative AI model, and means for displaying the suitability of the outfits to the user via a virtual try-on system. This allows users to receive fashion advice tailored to their individuality and body type in real time, and the virtual try-on function allows them to check the actual fit in advance, enabling them to make highly satisfying fashion choices.

[0656] "User personal information" refers to information that indicates individual attributes of the user, such as gender, age, height, weight, personal color, and facial photo.

[0657] A "server" is a computer system for collecting, analyzing, generating, and distributing data.

[0658] An "information providing system" is a system that provides the latest fashion trend information, such as a website or application program interface.

[0659] A "generative AI model" is an artificial intelligence system that analyzes a user's personal information and fashion trend information to generate personalized outfits.

[0660] The "virtual try-on system" is a system that generates a three-dimensional avatar based on the user's body type information and allows them to try on suggested outfits.

[0661] "Feedback" refers to the user's reaction to the proposed outfit, such as an evaluation or comment.

[0662] A "three-dimensional avatar" is a three-dimensional alter ego of a user that is virtually created based on the user's body shape information.

[0663] Basic configuration

[0664] This invention consists of a user terminal, a server, a generative AI model, and a virtual try-on system. The user terminal is basically a device used by the user, such as a smartphone or PC. The server is a computer system that collects, analyzes, generates, and distributes data. The generative AI model is an artificial intelligence that analyzes the user's personal information and fashion trend information to generate personalized fashion coordination. The virtual try-on system generates a three-dimensional avatar based on the user's body type information and allows the user to try on the suggested coordination.

[0665] Enter and submit user information

[0666] Terminal

[0667] The user starts the app and enters personal information such as gender, age, height, weight, personal color, and a photo of their face. In addition, they select the desired styling scene (e.g., work, date, holiday). Once the input is complete, they press the send button and this information is sent from the device to the server.

[0668] Specific examples

[0669] The user enters the following information into the app's input form: "Gender: Female, Age: 30, Height: 160cm, Weight: 55kg, Personal Color: Blue-based, Face Photo," selects "Styling Scene: Work," and presses the send button.

[0670] Collection and analysis of the latest trend information

[0671] server

[0672] The server periodically collects the latest fashion trend information from multiple fashion-related websites and application program interfaces (APIs). It uses a web scraping tool to obtain the trend information.

[0673] Specific examples

[0674] The server uses a web scraping tool to collect information such as "This season's work fashion trends are mid-length tight skirts and simple blouses," and inputs this information into a generative AI model for analysis.

[0675] Learning user information and suggesting outfits

[0676] Generation AI

[0677] The server inputs the user's collected personal information into a generative AI model, which learns the user's individual preferences and body type. The generative AI model then combines the collected trend information with the user's information to generate a personalized outfit. This generated outfit is then sent to the user's device via the server.

[0678] Specific examples

[0679] The generative AI model generates an outfit such as, "Since the user has blue-based skin, we suggest a refreshing blue blouse to go with a mid-length tight skirt that's trendy this season," and sends this information to the user's device via the server.

[0680] Displaying outfits and collecting feedback

[0681] Terminal

[0682] The proposed outfits are displayed on the user's device, and the user can provide feedback on the outfits by pressing the LIKE or DISLIKE button or by entering a comment. This feedback is then sent to the server.

[0683] Specific examples

[0684] The user checks the outfit displayed on their device, clicks the LIKE button to indicate that they like the outfit, enters a comment such as, "I like the ruffles on the blouse," and sends the feedback to the server.

[0685] Virtual try-on feature

[0686] server

[0687] The server uses a virtual fitting system to generate a three-dimensional avatar based on the user's body type information. The server then has the avatar try on the suggested outfits, simulating how they will actually fit. The simulation results are then displayed on the user's device.

[0688] Specific examples

[0689] The user selects "I want to check the fit of this blouse" on their device and begins the virtual try-on. The server applies the suggested outfit to a three-dimensional avatar, and the user can check the fit from the front, back, left, and right on their device.

[0690] In this way, the system can provide users with tailored fashion advice and a virtual try-on feature to check the actual fit beforehand, improving the satisfaction of online fashion shopping and reducing the risk of returns and exchanges.

[0691] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0692] The flow of this system's program processing

[0693] Step 1:

[0694] Enter and submit user information

[0695] The user launches the app on their device, enters personal information such as gender, age, height, weight, personal color, and a photo of their face, and selects the desired styling scene (e.g., work, date, holiday). This information is then sent from the device to the server.

[0696] Specific actions

[0697] The user enters the following information into the app's input form: "Gender: Female, Age: 30, Height: 160cm, Weight: 55kg, Personal Color: Blue-based, Face Photo," selects "Styling Scene: Work," and presses the send button.

[0698] input

[0699] User's personal information (gender, age, height, weight, personal color, face photo) and styling scene

[0700] output

[0701] User personal information and styling scenes transferred to the server

[0702] Step 2:

[0703] Collection and analysis of the latest trend information

[0704] The server periodically collects the latest fashion trend information from multiple fashion-related websites and application program interfaces (APIs), inputs the collected information into a generative AI model, and analyzes the trend information.

[0705] Specific actions

[0706] The server launches a web scraping tool, collects information such as "This season's work fashion trends are mid-length tight skirts and simple blouses," and inputs this information into a generative AI model.

[0707] input

[0708] Latest fashion trend information collected from fashion-related websites and APIs

[0709] output

[0710] Trend information fed into the generative AI model

[0711] Step 3:

[0712] Learning user information and suggesting outfits

[0713] The server inputs the user's personal information into a generative AI model, which learns the user's individual preferences and body type. The generative AI model combines the collected trend information with user information to generate a personalized outfit. The generated outfit is then sent to the user's device via the server.

[0714] Specific actions

[0715] The generative AI model generates an outfit such as, "Since the user has blue-based skin, we suggest a refreshing blue blouse to go with a mid-length tight skirt that's trendy this season," and sends it to the user's device via the server.

[0716] input

[0717] User's personal information (gender, age, height, weight, personal color, face photo), styling scenes, and the latest fashion trend information

[0718] output

[0719] Personalized coordination sent to user devices

[0720] Step 4:

[0721] Displaying outfits and collecting feedback

[0722] The proposed outfits are displayed on the user's device. The user can provide feedback by pressing the LIKE or DISLIKE button or by entering a comment. This feedback is sent to the server.

[0723] Specific actions

[0724] The user checks the outfit displayed on the device, clicks the LIKE button to indicate "I like this outfit," enters a comment such as "I like the ruffles on the blouse," and sends feedback.

[0725] input

[0726] Personalized outfits displayed on the user's device

[0727] output

[0728] User feedback sent to the server

[0729] Step 5:

[0730] Virtual try-on feature

[0731] The server uses a virtual fitting system to generate a three-dimensional avatar based on the user's body type information. The server then has the avatar try on the suggested outfits, and displays a simulation of how they fit on the user's device.

[0732] Specific actions

[0733] The user selects "I want to check the size of this blouse" on their device and begins the virtual try-on. The server applies the suggested outfit to a three-dimensional avatar, and the user can check the fit from the front, back, left and right on their device.

[0734] input

[0735] User's body type information and suggested outfits

[0736] output

[0737] Results of a fitting simulation using a 3D avatar displayed on a user's device

[0738] In this way, users can receive fashion advice tailored to their individual personalities and body types in real time, and the virtual try-on feature allows them to check the actual fit beforehand, enhancing the satisfaction of online shopping.

[0739] (Application example 1)

[0740] 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."

[0741] In recent years, with the diversification of fashion, there has been a growing need to provide fashion coordination that suits each user's unique personality and body type. In addition, the spread of online shopping has made it difficult to check the compatibility and fit of coordination in advance, as it is not possible to try on clothes in a physical store. Furthermore, it is difficult for users to efficiently find coordination that suits their preferences, which requires time and effort.

[0742] 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.

[0743] In this invention, the server includes means for inputting a user's personal information, means for transmitting the personal information to the server, means for collecting the latest fashion trend information from websites and application program interfaces, means for generating a personalized outfit by analyzing the personal information and the trend information, means for transmitting and displaying the outfit generated by the generating AI to a user terminal, means for collecting user feedback and inputting the feedback to the generating AI, means for displaying the suitability of the outfit to the user via a virtual try-on system, means for displaying the virtually tried-on outfit in the user's field of view using smart glasses, and means for acquiring the user's personal information and feedback using voice recognition. This allows users to easily find fashion outfits that suit their preferences and body type, and also enables the virtual try-on function to eliminate the hassle of trying on clothes when shopping online and to check the fit in real time.

[0744] "User's personal information" refers to information entered by the user, such as gender, age, height, weight, personal factors, and facial photo.

[0745] "Means for transmitting to the server" refers to the communication means used to transmit the user's personal information from the user terminal to the server.

[0746] "Website and Application Program Interface" refers to a program or API that periodically retrieves the latest fashion trend information via the Internet.

[0747] "Generative artificial intelligence" is an AI model that analyzes a user's personal information and the latest fashion trend information to generate personalized outfits for each individual user.

[0748] "User device" refers to an electronic device used by a user, such as a smartphone, smart glasses, or head-mounted display.

[0749] "Means for collecting feedback" is a function that allows users to input their evaluations and opinions on the proposed outfits and send them to the server.

[0750] A "virtual try-on system" is a system that generates a three-dimensional avatar based on the user's shape information and allows the user to virtually try on suggested outfits.

[0751] "Smart glasses" are eyeglass-type devices that have the function of projecting visual information in front of the user's eyes.

[0752] "Speech recognition" is a technology that converts a user's voice into text data and inputs it into a system.

[0753] "Means for obtaining personal information and feedback" refers to a function that uses voice recognition technology to collect feedback on personal information and coordination provided by the user and inputs it into the system.

[0754] This system proposes personalized outfits to users based on their personal information and the latest fashion trends, and checks their suitability through a virtual try-on function. The main components of this system are a user terminal, a server, a generative artificial intelligence, a virtual try-on system, and smart glasses.

[0755] The server includes the following means:

[0756] How users input their personal information: Users input their personal information (gender, age, height, weight, personal factors, and face photo) by voice through smart glasses or a smartphone.

[0757] Means of sending personal information to the server: The user's personal information is sent to the server using voice recognition technology or input forms.

[0758] Method for collecting the latest fashion trend information: The server periodically collects the latest trend information from fashion-related websites on the Internet using web scraping tools and APIs.

[0759] Coordination generation means using generative AI: Analyze the personal information and trend information and generate personalized coordination using a generative AI model (e.g., GPT-3).

[0760] Means for transmitting and displaying the generated coordination to the user terminal: The coordination generated by the server is transmitted to the user terminal and displayed on the smart glasses or smartphone.

[0761] The user terminal comprises the following means:

[0762] A means of obtaining personal information and feedback using voice recognition: Users can input feedback by voice through smart glasses or smartphones, which is converted into text data using voice recognition technology and sent to the server.

[0763] Virtual try-on display using smart glasses: Through the virtual try-on system, the user's three-dimensional avatar tries on the suggested outfits, and the suitability is displayed on the smart glasses display.

[0764] In this way, the user goes through the following process:

[0765] 1. Enter personal information by voice and send it to the server.

[0766] 2. The server collects the latest fashion trends from the internet and generates personalized outfits using a generative AI model.

[0767] 3. The generated coordinates are sent to the user's device and displayed on the smart glasses.

[0768] 4. Users can use the virtual try-on feature to check the suitability of the suggested outfits.

[0769] 5. Users provide voice feedback, which the server inputs into the generative AI for further improvements.

[0770] For example, a user can input a prompt such as, "Based on the latest fashion trends, please suggest a work outfit that suits my blue-toned skin tone." This process allows users to efficiently and easily find the perfect fashion outfit for themselves, eliminating the hassle of trying on clothes when shopping online and allowing them to check the fit in real time.

[0771] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0772] Step 1:

[0773] The user uses a smart device (e.g., smart glasses, smartphone) to input personal information by voice. Voice recognition technology converts this voice into text data, and personal information (e.g., gender, age, height, weight, personal factors, face photo) is obtained. This is then sent to the server as text data. The input is voice data, and the output is personal information in text format.

[0774] Step 2:

[0775] The server receives the personal information and stores it in a database. At the same time, the server uses web scraping tools and application program interfaces (APIs) to collect the latest fashion trend information from the Internet. The input is the user's personal information and online fashion trend information, and the output is the personal information and trend information stored in the database.

[0776] Step 3:

[0777] The server analyzes the user's personal information and the latest trend information and inputs this into a generative AI model (e.g., GPT-3). A prompt is generated, such as "Based on the latest fashion trends, please suggest a work outfit that suits blue-toned skin." The generative AI model analyzes the data and generates a personalized outfit. The input is the personal information and prompt in text format, and the output is the generated outfit.

[0778] Step 4:

[0779] The server sends the generated outfit to the user's device, which displays it on the display of their smart glasses or smartphone. The user can then view the proposed outfit and is ready to provide feedback. The input is the generated outfit, and the output is the outfit displayed on the user's device.

[0780] Step 5:

[0781] A user uses smart glasses or a smartphone to access the virtual try-on system. The system generates a three-dimensional avatar of the user and has the avatar try on suggested outfits. The user looks at the avatar displayed in their field of view and checks the suitability of the outfits. The input is the three-dimensional avatar and the suggested outfits, and the output is the virtual try-on result.

[0782] Step 6:

[0783] The user provides feedback on the suggested outfits via voice, which is again converted into text data using voice recognition technology and sent to the server. The server receives this feedback and inputs it into the generative AI to be reflected in future suggestions. The input is voice data and feedback text, and the output is feedback information. This feedback further improves the generative AI model, enabling more appropriate outfit suggestions.

[0784] The above is the process flow of the system that realizes the application example and the specific operations at each step.

[0785] 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.

[0786] This invention provides a system that proposes personalized outfits to users based on their personal information, emotional data, and the latest fashion trend information. This system mainly consists of a user terminal, a server, a generative artificial intelligence (AI), an emotion engine, and a virtual try-on system.

[0787] Enter and submit user information

[0788] Terminal

[0789] The user starts the app and enters personal information such as gender, age, height, weight, personal color, and a photo of their face. They also select the desired styling scene (e.g., work, date, holiday). This information is sent from the device to the server.

[0790] Specific examples

[0791] The user fills in the app form with the following information: "Gender: Female, Age: 30, Height: 160cm, Weight: 55kg, Personal Color: Blue-based, Face Photo," presses the submit button, and then selects "Styling Scene: Work."

[0792] Acquisition and analysis of the latest trend information

[0793] server

[0794] The server periodically collects the latest trend information from fashion-related websites and APIs, which is then input into the AI ​​generator for analysis.

[0795] Specific examples

[0796] The server uses a web scraping tool to collect information such as "This season's work fashion trends are mid-length tight skirts and simple blouses."

[0797] Emotion data collection and analysis

[0798] Emotion Engine

[0799] Using the camera and microphone on the user's device, emotion data is collected from the user's facial expressions and voice. The emotion engine analyzes this data to determine the user's current emotional state.

[0800] Specific examples

[0801] While a user is using the app, the emotion engine will recognize that "you look very happy today." If the user also says, "This outfit is lovely," the engine will analyze positive emotions from the voice.

[0802] Learning user and trend information

[0803] Generation AI

[0804] The server inputs the user's personal information and trend information into the AI ​​generator, which then learns the user's preferences and body type. Emotional data is also input into the AI ​​generator, which then generates outfits that take the user's emotions into consideration. The generated outfits are then sent to the user's device via the server.

[0805] Coordination generation and suggestions

[0806] Generation AI

[0807] The AI ​​generates personalized outfits based on the user's personal information, emotional data, and the latest trends, and the outfits are sent to the user's device and displayed to them.

[0808] Specific examples

[0809] The AI ​​generates an outfit such as, "The user has blue-based skin and looks very happy today, so I suggest a light-colored, medium-length tight skirt and a refreshing blue blouse." The outfit is then sent from the server to the device.

[0810] Displaying outfits and collecting feedback

[0811] Terminal

[0812] The proposed outfit is displayed on the user's device. The user enters feedback about the outfit (e.g., like, dislike, comment) and sends it to the server. The emotion engine also collects emotional data from the user's facial expressions and voice when providing feedback.

[0813] Specific examples

[0814] The user clicks the LIKE button to say, "I like this outfit," and enters a comment such as, "I especially like the design of the blouse." The emotion engine then recognizes that the user is expressing positive feelings toward the suggestion.

[0815] Learning from feedback and emotion data

[0816] Server and spawned AI

[0817] The server receives user feedback and emotion data from the emotion engine, and provides it to the generative AI to use as training data to improve the accuracy of the model.

[0818] Virtual try-on feature

[0819] Servers and Terminals

[0820] The server uses a virtual fitting system to generate a three-dimensional avatar based on the user's body type information. The generated avatar tries on suggested outfits to simulate a real fit. The fitting results are sent to the user's device, where the user can check the results.

[0821] Specific examples

[0822] Users can virtually try on outfits to see how they fit, and check them on their own avatar. The suggested outfit is applied to the avatar, and the user can check how it fits from the front, back, left, and right.

[0823] This system allows users to receive real-time fashion advice that takes into account their individuality, body type, and even their emotions. The virtual try-on feature also allows users to check the actual fit beforehand, reducing the chance of making mistakes when shopping online. In this way, the present invention supports users in making more satisfying fashion choices.

[0824] The processing flow will be explained below.

[0825] Step 1:

[0826] The user launches the app.

[0827] The user launches the application on a device such as a smartphone or tablet, which displays a screen for entering personal information.

[0828] Step 2:

[0829] The user enters personal information.

[0830] Users enter personal information such as gender, age, height, weight, personal color, and a photo of their face into the app's form, and also select the desired styling occasion (e.g., work, date, holiday).

[0831] Step 3:

[0832] The user sends personal information to the server.

[0833] When the user presses the submit button, this information is sent to the server as a JSON object via POST.

[0834] Step 4:

[0835] The server receives the user information.

[0836] The server receives the POST request and parses the JSON object to get the user information, which is then stored in a database.

[0837] Step 5:

[0838] The server collects the latest trend information.

[0839] The server periodically collects the latest trend information from fashion-related websites and APIs according to a schedule, and retrieves the data using web scraping tools and API clients.

[0840] Step 6:

[0841] The server inputs trend information into the generation AI.

[0842] The collected trend information is pre-processed (text analysis, data cleaning, etc.), converted into the format required for analysis, and input into the generation AI.

[0843] Step 7:

[0844] The user's device collects emotional data.

[0845] Using the camera and microphone on the user's device, emotion data is collected from the user's facial expressions and voice, and this data is provided to the emotion engine in real time.

[0846] Step 8:

[0847] The emotion engine analyzes the emotion data.

[0848] The emotion engine analyzes the user's emotional state from their facial expressions and voice, and provides the results to the generative AI.

[0849] Step 9:

[0850] The server inputs user information and emotional data into the generation AI.

[0851] The server inputs user information, trend information, and emotional data into the generation AI, which then generates outfits that take into account the user's preferences and emotions.

[0852] Step 10:

[0853] Generative AI generates personalized outfits.

[0854] The AI ​​generates appropriate combinations of items based on user information, emotional data, and trend information, and the generated outfits are returned to the server in JSON format.

[0855] Step 11:

[0856] The server sends the coordinates to the user terminal.

[0857] The server sends the generated coordinates to the user device, which receives the data and displays it on the UI.

[0858] Step 12:

[0859] The user enters feedback on the outfit.

[0860] The user enters their rating (LIKE, DISLIKE) and comments on the displayed outfits and sends them to the server. The emotion engine also collects emotional data from the user's facial expressions and voice when providing feedback.

[0861] Step 13:

[0862] The server receives the feedback and emotion data.

[0863] The server receives user feedback and emotion data from the emotion engine, and provides it to the generative AI to use as training data to improve the accuracy of the model.

[0864] Step 14:

[0865] The server prepares the virtual try-on.

[0866] The server generates a three-dimensional avatar based on the user's body type information and prepares data for the avatar to try on suggested outfits.

[0867] Step 15:

[0868] The server sends the results of the virtual try-on to the device.

[0869] The server generates the fitting results in the form of images and videos and sends them to the user's device, where the user can view the fitting results and check the fit.

[0870] Through these steps, users can receive fashion advice that takes into account their personality, body type, and even their emotions, and the virtual try-on function allows them to check the actual fit in advance.

[0871] Example 2

[0872] 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."

[0873] Conventional fashion recommendation systems often only offer generic clothing suggestions, lacking personalized suggestions that take into account each user's individuality, body shape, and emotions. Furthermore, when shopping online, users are unable to determine how the item will actually fit, resulting in problems such as the wrong size or design after purchase. Furthermore, few systems incorporate user feedback in real time, making it difficult to improve user satisfaction. There is a need for a system that can solve these issues and support users in making more satisfying fashion choices.

[0874] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting the latest fashion trend information from an information providing service or a program control interface, a means for analyzing the personal information and the trend information and providing a generation artificial intelligence for generating personalized outfit suggestions, and a means for collecting user opinions and inputting the opinions into the generation artificial intelligence. This enables personalized fashion suggestions that take into consideration the individuality, body shape, and emotions of each user.

[0875] "User's device" refers to a device used by a user to enter personal information, check suggested fashions, and enter feedback, and includes smartphones, tablets, and PCs.

[0876] "Central processing unit" refers to the server that processes and analyzes users' personal information, collected trend information, and feedback.

[0877] "Generative AI" is an AI model that generates personalized clothing suggestions based on a user's personal information, trend information, and emotional data.

[0878] "Information provision services" refers to websites and programmatic control interfaces (APIs) that provide the latest fashion-related trend information.

[0879] "Personal color tone" refers to the user's skin tone and personal color (blue-based, yellow-based, etc.), and is a factor used to make appropriate fashion suggestions.

[0880] A "face image" is a photo of the user's face, which is used to collect emotional data and make personalized suggestions through facial recognition.

[0881] The "virtual fitting system" is a system that generates a three-dimensional virtual image based on the user's body type information, allows the virtual image to try on suggested clothing, and simulates the fit.

[0882] A "three-dimensional virtual image" is a three-dimensional model generated based on the user's body type information, and is used to try on suggested clothing in the virtual try-on system.

[0883] This invention provides a system that proposes personalized outfits to users based on their personal information, emotional data, and the latest fashion trend information. This system mainly consists of a user terminal, a server, a generative artificial intelligence (AI), an emotion engine, and a virtual try-on system.

[0884] Enter and submit user information

[0885] The user starts the app and enters personal information such as gender, age, height, weight, personal color, and a photo of their face. They also select the desired styling occasion (e.g., work, date, holiday). This information is sent from the user's device to the server.

[0886] Specific examples

[0887] The user fills in the app form with the following information: "Gender: Female, Age: 30, Height: 160cm, Weight: 55kg, Personal Color: Blue-based, Face Photo," presses the submit button, and then selects "Styling Scene: Work."

[0888] Acquisition and analysis of the latest trend information

[0889] The server periodically collects the latest trend information from fashion-related websites and APIs. The data is obtained using web scraping tools and API requests. The collected trend information is then input into the generation AI for analysis.

[0890] Specific examples

[0891] The server uses a web scraping tool to collect information such as "This season's work fashion trends are mid-length tight skirts and simple blouses."

[0892] Emotion data collection and analysis

[0893] Using the camera and microphone installed on the user's device, emotional data is collected from the user's facial expressions and voice. Facial and voice recognition operates in real time to record the emotional data. The emotion engine analyzes the collected data and identifies the user's current emotional state.

[0894] Specific examples

[0895] While the user is using the app, the camera detects "smiles" and "surprised expressions," and the microphone records the user's "positive and negative voice tones." The emotion engine analyzes the facial recognition data and determines that the user has a "happy expression," and voice analysis detects that the user is "speaking in a positive tone."

[0896] Learning user and trend information

[0897] The server inputs the user's personal information and trend information into the AI ​​generator. Based on this information, the AI ​​learns the user's preferences, body type, and adapts to trends. Emotional data is also input into the AI ​​generator, which generates outfits that take the user's emotions into consideration.

[0898] Specific examples

[0899] The server combines the user information and trend information in JSON format and sends it to the generation AI. The generation AI updates its model to suggest, "The user has blue-based skin and looks very happy today, so a light-colored, medium-length tight skirt and a refreshing blue blouse."

[0900] Coordination generation and suggestions

[0901] The AI ​​generates personalized outfits based on the user's personal information, emotional data, and the latest trends, and the outfits are sent to the user's device and displayed to them.

[0902] Specific examples

[0903] The AI ​​generator suggests, "Since the user has blue-toned skin and looks happy today, we suggest a light-colored, medium-length tight skirt and a refreshing blue blouse."

[0904] Displaying outfits and collecting feedback

[0905] The proposed outfit is displayed on the user's device. The user can then enter feedback (like, dislike, or comment) about the outfit and send it to the server. The emotion engine also collects emotional data from the user's facial expressions and voice when providing feedback.

[0906] Specific examples

[0907] The user clicks the LIKE button to say, "I like this outfit," and enters a comment such as, "I especially like the design of the blouse." The emotion engine then recognizes that the user is expressing positive feelings toward the suggestion.

[0908] Learning from feedback and emotion data

[0909] The server receives user feedback and emotion data from the emotion engine, and provides it to the generative AI to use as training data to improve the accuracy of the model.

[0910] Virtual try-on feature

[0911] The server uses a virtual fitting system to generate a three-dimensional avatar based on the user's body type information. The generated avatar tries on suggested outfits to simulate a real fit. The fitting results are sent to the user's device, where the user can check the results.

[0912] Specific examples

[0913] Users can use the virtual try-on feature to check how an outfit fits on their own avatar. The suggested outfit is applied to the avatar, and the user can check how it fits from the front, back, left and right.

[0914] In this way, the present invention realizes a system that provides users with real-time fashion advice that takes into account their personality, body type, and emotions. The virtual try-on feature also allows users to check the actual fit beforehand, reducing mistakes when shopping online.

[0915] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0916] Step 1: Enter your user information

[0917] Terminal

[0918] Users launch the app and enter personal information such as gender, age, height, weight, personal color, and a photo of their face. They also select the desired styling occasion (e.g., work, date, holiday). This operation is performed by the user entering information into each input form in the app.

[0919] input

[0920] User's personal information (gender, age, height, weight, personal color, face photo) and styling scene selection.

[0921] output

[0922] Entered personal information and styling scene data.

[0923] Specific actions

[0924] The user enters "Gender: Female," "Age: 30," "Height: 160cm," "Personal color: Blue-based," and "Face photo," and then selects "Styling scene: Work."

[0925] Step 2: Submit user information

[0926] Terminal

[0927] When the user completes the input and presses the send button, this information is automatically sent from the terminal to the server.

[0928] input

[0929] Personal information and styling scene data entered by the user.

[0930] output

[0931] Data transmission result from the device to the server.

[0932] Specific actions

[0933] When the user presses the submit button, the user information is sent to the server in JSON format.

[0934] Step 3: Stay up to date on the latest trends

[0935] server

[0936] The server periodically collects the latest trend information from fashion-related websites and APIs, and retrieves the data using web scraping tools and API requests.

[0937] input

[0938] URLs for fashion-related websites and APIs.

[0939] output

[0940] Collected latest fashion trend information.

[0941] Specific actions

[0942] The server uses a web scraping tool to collect information such as, "This season's work fashion trends are mid-length tight skirts and simple blouses."

[0943] Step 4: Analyze the latest trend information

[0944] server

[0945] The collected trend information is input into the generative AI for analysis, which analyzes the trend data and updates the model that understands each attribute (color, design, material, etc.).

[0946] input

[0947] Collected latest trend information.

[0948] output

[0949] Analyzed trend data.

[0950] Specific actions

[0951] The trend data collected by the server is input into the generative AI using a Python script, and the model learns trend items such as "medium-length tight skirts" and "simple blouses."

[0952] Step 5: Collecting emotion data

[0953] Terminal

[0954] Using the camera and microphone installed on the user's device, emotional data is collected from the user's facial expressions and voice. Facial and voice recognition works in real time, and emotional data is recorded.

[0955] input

[0956] The user's facial expression and voice data.

[0957] output

[0958] Collected emotion data.

[0959] Specific actions

[0960] While the user is using the app, the camera detects "smiles" and "surprised expressions," and the microphone records the user's "positive tone of voice" and "negative tone of voice."

[0961] Step 6: Analyze the sentiment data

[0962] Emotion Engine

[0963] The emotion engine analyzes the collected data to determine the user's current emotional state, using emotion recognition algorithms (e.g., OpenCV or TensorFlow).

[0964] input

[0965] Collected user sentiment data.

[0966] output

[0967] The analyzed emotional state of the user.

[0968] Specific actions

[0969] The emotion engine analyzes facial recognition data to determine if the user has a happy facial expression, and voice analysis detects if the user is speaking in a positive tone.

[0970] Step 7: Learn about users and trends

[0971] server

[0972] The server inputs the user's personal information and trend information into the AI ​​generator. Based on this information, the AI ​​learns the user's preferences, body type, and adapts to trends. Emotional data is also input into the AI ​​generator, which generates outfits that take the user's emotions into consideration.

[0973] input

[0974] User personal information, latest trend information, and sentiment data.

[0975] output

[0976] Trained model data.

[0977] Specific actions

[0978] The server combines user information and trend information in JSON format and sends it to the AI ​​generator, which then suggests a light-colored, medium-length tight skirt and a refreshing blue blouse, since the user has blue-based skin and looks happy today.

[0979] Step 8: Generate coordinates

[0980] Generation AI

[0981] Generative AI generates personalized outfits based on the user's personal information, emotional data, and the latest trends.

[0982] input

[0983] User personal information, sentiment data, and analyzed trend information.

[0984] output

[0985] The generated coordinates.

[0986] Specific actions

[0987] The AI ​​generator suggests, "Since the user has blue-toned skin and a happy expression, a light-colored, medium-length tight skirt and a refreshing blue blouse."

[0988] Step 9: Coordination suggestions

[0989] server

[0990] The generated coordinates are sent to the user's terminal via the server and displayed to the user.

[0991] input

[0992] Generated coordinate data.

[0993] output

[0994] Coordinate display on user device.

[0995] Specific actions

[0996] The server sends the generated coordinates in JSON format to the user's device, where they are displayed in the device app.

[0997] Step 10: Show your outfit and get feedback

[0998] Terminal

[0999] The proposed outfit is displayed on the user's device. The user can then enter feedback (like, dislike, or comment) about the outfit and send it to the server. The emotion engine also collects emotional data from the user's facial expressions and voice when providing feedback.

[1000] input

[1001] User feedback data (likes, dislikes, comments), facial expressions and voice data.

[1002] output

[1003] Results of sending feedback data and emotion data to the server.

[1004] Specific actions

[1005] The user clicks the LIKE button to say, "I like this outfit," and enters a comment such as, "I especially like the design of the blouse." The emotion engine then recognizes that the user is expressing positive feelings toward the suggestion.

[1006] Step 11: Learning from feedback and sentiment data

[1007] Server and spawned AI

[1008] The server receives user feedback and emotion data from the emotion engine, and provides it to the generative AI to use as training data to improve the accuracy of the model.

[1009] input

[1010] User feedback and sentiment data.

[1011] output

[1012] Updated generative AI models.

[1013] Specific actions

[1014] The generative AI incorporates new feedback data and updates its models to more accurately reflect the user's preferences and emotional state.

[1015] Step 12: Run the Virtual Try-On Feature

[1016] Servers and Terminals

[1017] The server uses a virtual fitting system to generate a three-dimensional avatar based on the user's body type information. The generated avatar tries on suggested outfits to simulate a real fit. The fitting results are sent to the user's device, where the user can check the results.

[1018] input

[1019] User's body type information and suggested outfits.

[1020] output

[1021] Results of trying on a 3D avatar.

[1022] Specific actions

[1023] Users can use the virtual try-on feature to check how an outfit fits on their own avatar. The suggested outfit is applied to the avatar, and the user can check how it fits from the front, back, left and right.

[1024] (Application example 2)

[1025] 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."

[1026] Conventional fashion suggestion systems only use the user's personal information and the latest fashion trend information, but are unable to suggest personalized outfits that reflect the user's emotions. In addition, the virtual try-on function is limited, making it difficult to provide a satisfying fitting based on the user's emotions.

[1027] The specific processing by the specific 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 means for inputting the user's personal information, means for transmitting the personal information to the server, means for collecting the latest fashion trend information from websites and application program interfaces, means for providing a generation AI that analyzes the personal information and the trend information and generates a personalized outfit, means for collecting and analyzing emotional data from the user's facial expressions and voice, means for transmitting and displaying the outfit generated by the generation AI to the user terminal, means for displaying the suitability of the outfit to the user via a virtual try-on system, and means for collecting user feedback and inputting the feedback to the generation AI. This makes it possible to provide a personalized outfit that reflects the user's personal information and emotional data, and to confirm the actual fit in advance through virtual try-on.

[1028] "User information" refers to information including personal information, attribute information, and emotional data of a user.

[1029] "Personal information" refers to various data about the user, such as the user's gender, age, height, weight, personal color, and facial photo.

[1030] "Emotional data" is information about the emotional state of a user that is analyzed from their facial expressions and voice.

[1031] A "server" is a centralized computing resource for collecting and analyzing user information and trend information.

[1032] "Generative AI" is an AI algorithm that generates personalized outfits based on collected data.

[1033] "Latest Fashion Trend Information" refers to information about current fashion industry trends collected from websites and application program interfaces.

[1034] An "emotion analysis engine" is a technology that collects and analyzes emotional data from a user's facial expressions and voice.

[1035] A "user device" is a display device used by a user, such as a smartphone or head-mounted display.

[1036] "Coordination" is a suggested clothing combination generated based on the user's personal information, the latest trends, and emotional data.

[1037] The "virtual fitting system" is a system that displays the fit of suggested outfits to users through a three-dimensional avatar.

[1038] "Feedback" refers to the user's reactions to the proposed outfits, such as ratings and comments.

[1039] A "3D avatar" is a virtual 3D character generated based on the user's body type information.

[1040] The system for implementing this invention involves a series of processes that collects a user's personal information, emotional data, and the latest fashion trend information, analyzes them, proposes personalized outfits, and allows the user to virtually try them on. The system is mainly composed of the following elements: a server, a user terminal, a generative artificial intelligence (generative AI), an emotion analysis engine, and a virtual try-on system.

[1041] Specific examples of hardware and software used

[1042] Hardware

[1043] Smartphone or Head-Mounted Display (HMD): Used as the user device. Example: Oculus Quest.

[1044] Camera and microphone: A device used to capture a user's facial photograph and voice data.

[1045] software

[1046] Web scraping tools: Tools for gathering trend information from fashion-related websites. Examples: Beautiful Soup, Scrapy.

[1047] Generative artificial intelligence (generative AI) models: AI algorithms for generating personalized outfits. Example: OpenAI's GPT-based models.

[1048] Emotion analysis engine: Technology for analyzing emotional data from a user's facial expressions and voice. Example: Microsoft Azure's Emotion API.

[1049] Virtual fitting system: A system that generates a three-dimensional avatar based on the user's body type information and lets them try on outfits. Example: Clo Virtual Fashion.

[1050] System action

[1051] Enter and submit user information

[1052] The user starts the application on their device and enters personal information such as gender, age, height, weight, personal color, and a photo of their face. They also select the desired styling scene (e.g., work, date, or holiday). This information is sent to the server.

[1053] As a specific example, a user enters "gender: female, age: 30, height: 160cm, weight: 55kg, personal color: blue-based, face photo" and selects "styling scene: work."

[1054] Acquisition and analysis of the latest trend information

[1055] The server periodically collects the latest trend information from fashion-related websites and APIs, which is then input into the generative AI and analyzed using natural language processing.

[1056] For example, collect information such as, "This season's work fashion trends are mid-length tight skirts and simple blouses."

[1057] Emotion data collection and analysis

[1058] The camera and microphone on the user's device are used to collect emotional data from facial expressions and voice, and the emotion analysis engine analyzes the data to determine the user's emotional state.

[1059] For example, if a user says, "You look very happy today," or "This outfit is lovely," the system will analyze the positive emotion from the voice.

[1060] Personalized outfit creation

[1061] The server inputs the user's personal information, emotional data, and trend information into the AI ​​generator, which then generates outfits that take into account the user's preferences, body type, and emotions. The generated outfits are then sent to the user's device and displayed.

[1062] Example prompt

[1063] Here are some example prompts to input to the AI ​​generator:

[1064] User Information:

[1065] Gender: Female

[1066] Age: 30

[1067] Height: 160cm

[1068] Weight: 55kg

[1069] Personal color: Blue-based

[1070] Preferred styling occasion: Work

[1071] Latest Trends:

[1072] This season's work fashion trends are mid-length tight skirts and simple blouses

[1073] Emotional state:

[1074] A very happy look

[1075] A positive comment that this outfit is lovely

[1076] Generate personalized work outfits for blue-toned skin based on your preferences and the latest trends.

[1077] Virtual try-on

[1078] The virtual fitting system generates a three-dimensional avatar based on the user's body type and emotional data, and then allows the user to try on suggested outfits, allowing them to check how the outfits actually fit.

[1079] The system allows users to receive personalized fashion suggestions in real time that take into account their personality, body type, and emotions, and the virtual try-on feature reduces mistakes when shopping online.

[1080] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1081] Step 1:

[1082] Enter and submit user information

[1083] The user starts the application on their smartphone or head-mounted display (HMD) and inputs their gender, age, height, weight, personal color, a photo of their face, and the desired styling scene. This information is then sent from the device to the server and saved.

[1084] Input: Gender, age, height, weight, personal color, face photo, styling scene

[1085] Output: User information stored on the server

[1086] Step 2:

[1087] Collection and analysis of the latest trend information

[1088] The server periodically collects the latest fashion trend information from fashion-related websites and APIs using a web scraping tool, which is then input into a generative AI model for analysis.

[1089] Input: Fashion trend information obtained from websites and APIs

[1090] Output: Latest analyzed trend information

[1091] Step 3:

[1092] Emotion data collection and analysis

[1093] The camera and microphone on the user's device collect the user's facial expressions and voice. This data is input into an emotion analysis engine to analyze the user's current emotional state. The analysis results are then sent to the server.

[1094] Input: User facial and voice data

[1095] Output: Parsed emotion data

[1096] Step 4:

[1097] Personalized outfit creation

[1098] The server inputs user information, trend information, and emotional data into the generative AI model to generate personalized outfits tailored to the user's preferences, body type, and emotions. The generated outfits are sent to the user's device and displayed.

[1099] Input: User information, latest trend information, sentiment data

[1100] Output: Personalized outfit ideas

[1101] Step 5:

[1102] Virtual try-on

[1103] The virtual fitting system generates a three-dimensional avatar based on the user's body type information, has them try on suggested outfits, and sends the results to the user's device, where the user can check the fitting results.

[1104] Input: User's body type information, suggested outfits

[1105] Output: 3D avatar fitting results

[1106] Step 6:

[1107] Collecting and analyzing feedback

[1108] Users provide feedback (likes, dislikes, comments) on outfits, which is analyzed by a sentiment analysis engine and the results are sent to the server and used as training data for the generative AI model.

[1109] Input: User feedback and sentiment data

[1110] Output: Updated training data by generative AI model

[1111] Through this process, users can easily find outfits that are optimized to suit their individuality, body type, and emotions, and can check the actual fit in advance through virtual try-on.

[1112] 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.

[1113] 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.

[1114] 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.

[1115] [Third embodiment]

[1116] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1117] 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.

[1118] 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).

[1119] 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.

[1120] 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.

[1121] 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).

[1122] 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.

[1123] 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.

[1124] 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.

[1125] 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.

[1126] 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.

[1127] 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."

[1128] This invention provides a system that proposes personalized outfits to users based on their personal information and the latest fashion trend information. This system mainly consists of a user terminal, a server, a generative artificial intelligence (AI), and a virtual try-on system.

[1129] Enter and submit user information

[1130] Terminal

[1131] The user starts the app and enters personal information such as gender, age, height, weight, personal color, and a photo of their face. They also select the desired styling scene (e.g., work, date, holiday). This information is sent from the device to the server.

[1132] Specific examples

[1133] The user fills in the app form with the following information: "Gender: Female, Age: 30, Height: 160cm, Weight: 55kg, Personal Color: Blue-based, Face Photo," presses the submit button, and then selects "Styling Scene: Work."

[1134] Acquisition and analysis of the latest trend information

[1135] server

[1136] The server periodically collects the latest trend information from fashion-related websites and APIs, which is then input into the AI ​​generator for analysis.

[1137] Specific examples

[1138] The server uses a web scraping tool to collect information such as "This season's work fashion trends are mid-length tight skirts and simple blouses."

[1139] Learning user information and suggesting outfits

[1140] Generation AI

[1141] The server inputs the user's personal information into the AI ​​generator, which learns the user's individual preferences and body type. The AI ​​then combines the collected trend information with the user's information to generate a personalized outfit. The generated outfit is then sent to the user's device via the server.

[1142] Specific examples

[1143] The AI ​​generates an outfit such as, "The user has a blue-based skin tone, so I suggest a refreshing blue blouse to go with a mid-length tight skirt that's on trend this season." The outfit is then sent from the server to the device.

[1144] Displaying outfits and collecting feedback

[1145] Terminal

[1146] The proposed coordinates are displayed on the user's device, and the user enters feedback (e.g., LIKE, DISLIKE, or comments) about the coordinates and sends it to the server.

[1147] Specific examples

[1148] Users can click the LIKE button to send feedback, saying "I like this outfit," and also enter comments such as "I like the ruffles on the blouse."

[1149] Virtual try-on feature

[1150] server

[1151] The server uses a virtual fitting system to generate a three-dimensional avatar based on the user's body type, then has the avatar try on the suggested outfits to simulate how they would actually fit.

[1152] Specific examples

[1153] Users can virtually try on a blouse to see how it fits, using their own avatar. The suggested outfit is applied to the avatar, and the user can check the fit from the front, back, left and right.

[1154] This system allows users to receive real-time fashion advice tailored to their individual personality and body type. The virtual try-on function also allows users to check the actual fit beforehand, reducing the chance of mistakes when shopping online. In this way, the present invention supports users in making more satisfying fashion choices.

[1155] The processing flow will be explained below.

[1156] Step 1:

[1157] The user launches the app.

[1158] The user launches the application on a device such as a smartphone or tablet, which displays a screen for entering personal information.

[1159] Step 2:

[1160] The user enters personal information.

[1161] Users enter personal information such as gender, age, height, weight, personal color, and a photo of their face into the app's form, and also select the desired styling occasion (e.g., work, date, holiday).

[1162] Step 3:

[1163] Users submit personal information.

[1164] When the user presses the submit button, this information is sent to the server as a JSON object via POST.

[1165] Step 4:

[1166] The server receives the user information.

[1167] The server receives the POST request and parses the JSON object to get the user information, which is then stored in a database.

[1168] Step 5:

[1169] The server collects the latest trend information.

[1170] The server periodically collects the latest trend information from fashion-related websites and APIs according to a schedule, and retrieves the data using web scraping tools and API clients.

[1171] Step 6:

[1172] The server analyzes the trend information.

[1173] Before inputting the collected trend information into the generation AI, the data is pre-processed (text analysis, data cleaning, etc.) to convert it into the format required for analysis.

[1174] Step 7:

[1175] The server inputs user information into the generation AI.

[1176] The server passes user information as a JSON object to the generation AI, which extracts features related to the user's preferences and body type.

[1177] Step 8:

[1178] Generative AI generates personalized outfits.

[1179] The AI ​​generates appropriate combinations of items based on user information and trend information, and the generated outfits are returned to the server in JSON format.

[1180] Step 9:

[1181] The server sends the coordinates to the user terminal.

[1182] The server sends the generated coordinates to the user device, which receives the data and displays it on the UI.

[1183] Step 10:

[1184] The user enters feedback on the outfit.

[1185] The user enters a rating (LIKE, DISLIKE) and a comment about the displayed outfit, and sends it to the server.

[1186] Step 11:

[1187] The server receives the feedback and reflects it in the generated AI.

[1188] The server receives feedback from users and provides it to the generative AI to use as training data to improve the accuracy of the model.

[1189] Step 12:

[1190] The server prepares the virtual try-on.

[1191] The server generates a three-dimensional avatar based on the user's body type information and prepares data for the avatar to try on suggested outfits.

[1192] Step 13:

[1193] The server sends the results of the virtual try-on to the device.

[1194] The server generates the fitting results in the form of images and videos and sends them to the user's device, where the user can view the fitting results and check the fit.

[1195] Through these steps, users can receive personalized fashion advice and take advantage of the virtual try-on feature to check the fit.

[1196] Example 1

[1197] 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."

[1198] Conventional online fashion coordination systems have difficulty proposing personalized outfits that fit the user's personality and body type, and it is also difficult to confirm the actual fit. This has led to problems such as lower satisfaction with online shopping and increased hassle with returns and exchanges.

[1199] 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.

[1200] In this invention, the server includes means for inputting a user's personal information, means for transmitting the personal information to the server, means for collecting the latest fashion trend information from an information providing system, means for providing a generative AI model that analyzes the personal information and the trend information and generates personalized outfits, means for transmitting and displaying the outfits generated by the generative AI model to a user terminal, means for collecting user feedback and inputting the feedback to the generative AI model, and means for displaying the suitability of the outfits to the user via a virtual try-on system. This allows users to receive fashion advice tailored to their individuality and body type in real time, and the virtual try-on function allows them to check the actual fit in advance, enabling them to make highly satisfying fashion choices.

[1201] "User personal information" refers to information that indicates individual attributes of the user, such as gender, age, height, weight, personal color, and facial photo.

[1202] A "server" is a computer system for collecting, analyzing, generating, and distributing data.

[1203] An "information providing system" is a system that provides the latest fashion trend information, such as a website or application program interface.

[1204] A "generative AI model" is an artificial intelligence system that analyzes a user's personal information and fashion trend information to generate personalized outfits.

[1205] The "virtual try-on system" is a system that generates a three-dimensional avatar based on the user's body type information and allows them to try on suggested outfits.

[1206] "Feedback" refers to the user's reaction to the proposed outfit, such as an evaluation or comment.

[1207] A "three-dimensional avatar" is a three-dimensional alter ego of a user that is virtually created based on the user's body shape information.

[1208] Basic configuration

[1209] This invention consists of a user terminal, a server, a generative AI model, and a virtual try-on system. The user terminal is basically a device used by the user, such as a smartphone or PC. The server is a computer system that collects, analyzes, generates, and distributes data. The generative AI model is an artificial intelligence that analyzes the user's personal information and fashion trend information to generate personalized fashion coordination. The virtual try-on system generates a three-dimensional avatar based on the user's body type information and allows the user to try on the suggested coordination.

[1210] Enter and submit user information

[1211] Terminal

[1212] The user starts the app and enters personal information such as gender, age, height, weight, personal color, and a photo of their face. In addition, they select the desired styling scene (e.g., work, date, holiday). Once the input is complete, they press the send button and this information is sent from the device to the server.

[1213] Specific examples

[1214] The user enters the following information into the app's input form: "Gender: Female, Age: 30, Height: 160cm, Weight: 55kg, Personal Color: Blue-based, Face Photo," selects "Styling Scene: Work," and presses the send button.

[1215] Collection and analysis of the latest trend information

[1216] server

[1217] The server periodically collects the latest fashion trend information from multiple fashion-related websites and application program interfaces (APIs). It uses a web scraping tool to obtain the trend information.

[1218] Specific examples

[1219] The server uses a web scraping tool to collect information such as "This season's work fashion trends are mid-length tight skirts and simple blouses," and inputs this information into a generative AI model for analysis.

[1220] Learning user information and suggesting outfits

[1221] Generation AI

[1222] The server inputs the user's collected personal information into a generative AI model, which learns the user's individual preferences and body type. The generative AI model then combines the collected trend information with the user's information to generate a personalized outfit. This generated outfit is then sent to the user's device via the server.

[1223] Specific examples

[1224] The generative AI model generates an outfit such as, "Since the user has blue-based skin, we suggest a refreshing blue blouse to go with a mid-length tight skirt that's trendy this season," and sends this information to the user's device via the server.

[1225] Displaying outfits and collecting feedback

[1226] Terminal

[1227] The proposed outfits are displayed on the user's device, and the user can provide feedback on the outfits by pressing the LIKE or DISLIKE button or by entering a comment. This feedback is then sent to the server.

[1228] Specific examples

[1229] The user checks the outfit displayed on their device, clicks the LIKE button to indicate that they like the outfit, enters a comment such as, "I like the ruffles on the blouse," and sends the feedback to the server.

[1230] Virtual try-on feature

[1231] server

[1232] The server uses a virtual fitting system to generate a three-dimensional avatar based on the user's body type information. The server then has the avatar try on the suggested outfits, simulating how they will actually fit. The simulation results are then displayed on the user's device.

[1233] Specific examples

[1234] The user selects "I want to check the fit of this blouse" on their device and begins the virtual try-on. The server applies the suggested outfit to a three-dimensional avatar, and the user can check the fit from the front, back, left, and right on their device.

[1235] In this way, the system can provide users with tailored fashion advice and a virtual try-on feature to check the actual fit beforehand, improving the satisfaction of online fashion shopping and reducing the risk of returns and exchanges.

[1236] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1237] The flow of this system's program processing

[1238] Step 1:

[1239] Enter and submit user information

[1240] The user launches the app on their device, enters personal information such as gender, age, height, weight, personal color, and a photo of their face, and selects the desired styling scene (e.g., work, date, holiday). This information is then sent from the device to the server.

[1241] Specific actions

[1242] The user enters the following information into the app's input form: "Gender: Female, Age: 30, Height: 160cm, Weight: 55kg, Personal Color: Blue-based, Face Photo," selects "Styling Scene: Work," and presses the send button.

[1243] input

[1244] User's personal information (gender, age, height, weight, personal color, face photo) and styling scene

[1245] output

[1246] User personal information and styling scenes transferred to the server

[1247] Step 2:

[1248] Collection and analysis of the latest trend information

[1249] The server periodically collects the latest fashion trend information from multiple fashion-related websites and application program interfaces (APIs), inputs the collected information into a generative AI model, and analyzes the trend information.

[1250] Specific actions

[1251] The server launches a web scraping tool, collects information such as "This season's work fashion trends are mid-length tight skirts and simple blouses," and inputs this information into a generative AI model.

[1252] input

[1253] Latest fashion trend information collected from fashion-related websites and APIs

[1254] output

[1255] Trend information fed into the generative AI model

[1256] Step 3:

[1257] Learning user information and suggesting outfits

[1258] The server inputs the user's personal information into a generative AI model, which learns the user's individual preferences and body type. The generative AI model combines the collected trend information with user information to generate a personalized outfit. The generated outfit is then sent to the user's device via the server.

[1259] Specific actions

[1260] The generative AI model generates an outfit such as, "Since the user has blue-based skin, we suggest a refreshing blue blouse to go with a mid-length tight skirt that's trendy this season," and sends it to the user's device via the server.

[1261] input

[1262] User's personal information (gender, age, height, weight, personal color, face photo), styling scenes, and the latest fashion trend information

[1263] output

[1264] Personalized coordination sent to user devices

[1265] Step 4:

[1266] Displaying outfits and collecting feedback

[1267] The proposed outfits are displayed on the user's device. The user can provide feedback by pressing the LIKE or DISLIKE button or by entering a comment. This feedback is sent to the server.

[1268] Specific actions

[1269] The user checks the outfit displayed on the device, clicks the LIKE button to indicate "I like this outfit," enters a comment such as "I like the ruffles on the blouse," and sends feedback.

[1270] input

[1271] Personalized outfits displayed on the user's device

[1272] output

[1273] User feedback sent to the server

[1274] Step 5:

[1275] Virtual try-on feature

[1276] The server uses a virtual fitting system to generate a three-dimensional avatar based on the user's body type information. The server then has the avatar try on the suggested outfits, and displays a simulation of how they fit on the user's device.

[1277] Specific actions

[1278] The user selects "I want to check the size of this blouse" on their device and begins the virtual try-on. The server applies the suggested outfit to a three-dimensional avatar, and the user can check the fit from the front, back, left and right on their device.

[1279] input

[1280] User's body type information and suggested outfits

[1281] output

[1282] Results of a fitting simulation using a 3D avatar displayed on a user's device

[1283] In this way, users can receive fashion advice tailored to their individual personalities and body types in real time, and the virtual try-on feature allows them to check the actual fit beforehand, enhancing the satisfaction of online shopping.

[1284] (Application example 1)

[1285] 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."

[1286] In recent years, with the diversification of fashion, there has been a growing need to provide fashion coordination that suits each user's unique personality and body type. In addition, the spread of online shopping has made it difficult to check the compatibility and fit of coordination in advance, as it is not possible to try on clothes in a physical store. Furthermore, it is difficult for users to efficiently find coordination that suits their preferences, which requires time and effort.

[1287] 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.

[1288] In this invention, the server includes means for inputting a user's personal information, means for transmitting the personal information to the server, means for collecting the latest fashion trend information from websites and application program interfaces, means for generating a personalized outfit by analyzing the personal information and the trend information, means for transmitting and displaying the outfit generated by the generating AI to a user terminal, means for collecting user feedback and inputting the feedback to the generating AI, means for displaying the suitability of the outfit to the user via a virtual try-on system, means for displaying the virtually tried-on outfit in the user's field of view using smart glasses, and means for acquiring the user's personal information and feedback using voice recognition. This allows users to easily find fashion outfits that suit their preferences and body type, and also enables the virtual try-on function to eliminate the hassle of trying on clothes when shopping online and to check the fit in real time.

[1289] "User's personal information" refers to information entered by the user, such as gender, age, height, weight, personal factors, and facial photo.

[1290] "Means for transmitting to the server" refers to the communication means used to transmit the user's personal information from the user terminal to the server.

[1291] "Website and Application Program Interface" refers to a program or API that periodically retrieves the latest fashion trend information via the Internet.

[1292] "Generative artificial intelligence" is an AI model that analyzes a user's personal information and the latest fashion trend information to generate personalized outfits for each individual user.

[1293] "User device" refers to an electronic device used by a user, such as a smartphone, smart glasses, or head-mounted display.

[1294] "Means for collecting feedback" is a function that allows users to input their evaluations and opinions on the proposed outfits and send them to the server.

[1295] A "virtual try-on system" is a system that generates a three-dimensional avatar based on the user's shape information and allows the user to virtually try on suggested outfits.

[1296] "Smart glasses" are eyeglass-type devices that have the function of projecting visual information in front of the user's eyes.

[1297] "Speech recognition" is a technology that converts a user's voice into text data and inputs it into a system.

[1298] "Means for obtaining personal information and feedback" refers to a function that uses voice recognition technology to collect feedback on personal information and coordination provided by the user and inputs it into the system.

[1299] This system proposes personalized outfits to users based on their personal information and the latest fashion trends, and checks their suitability through a virtual try-on function. The main components of this system are a user terminal, a server, a generative artificial intelligence, a virtual try-on system, and smart glasses.

[1300] The server includes the following means:

[1301] How users input their personal information: Users input their personal information (gender, age, height, weight, personal factors, and face photo) by voice through smart glasses or a smartphone.

[1302] Means of sending personal information to the server: The user's personal information is sent to the server using voice recognition technology or input forms.

[1303] Method for collecting the latest fashion trend information: The server periodically collects the latest trend information from fashion-related websites on the Internet using web scraping tools and APIs.

[1304] Coordination generation means using generative AI: Analyze the personal information and trend information and generate personalized coordination using a generative AI model (e.g., GPT-3).

[1305] Means for transmitting and displaying the generated coordination to the user terminal: The coordination generated by the server is transmitted to the user terminal and displayed on the smart glasses or smartphone.

[1306] The user terminal comprises the following means:

[1307] A means of obtaining personal information and feedback using voice recognition: Users can input feedback by voice through smart glasses or smartphones, which is converted into text data using voice recognition technology and sent to the server.

[1308] Virtual try-on display using smart glasses: Through the virtual try-on system, the user's three-dimensional avatar tries on the suggested outfits, and the suitability is displayed on the smart glasses display.

[1309] In this way, the user goes through the following process:

[1310] 1. Enter personal information by voice and send it to the server.

[1311] 2. The server collects the latest fashion trends from the internet and generates personalized outfits using a generative AI model.

[1312] 3. The generated coordinates are sent to the user's device and displayed on the smart glasses.

[1313] 4. Users can use the virtual try-on feature to check the suitability of the suggested outfits.

[1314] 5. Users provide voice feedback, which the server inputs into the generative AI for further improvements.

[1315] For example, a user can input a prompt such as, "Based on the latest fashion trends, please suggest a work outfit that suits my blue-toned skin tone." This process allows users to efficiently and easily find the perfect fashion outfit for themselves, eliminating the hassle of trying on clothes when shopping online and allowing them to check the fit in real time.

[1316] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1317] Step 1:

[1318] The user uses a smart device (e.g., smart glasses, smartphone) to input personal information by voice. Voice recognition technology converts this voice into text data, and personal information (e.g., gender, age, height, weight, personal factors, face photo) is obtained. This is then sent to the server as text data. The input is voice data, and the output is personal information in text format.

[1319] Step 2:

[1320] The server receives the personal information and stores it in a database. At the same time, the server uses web scraping tools and application program interfaces (APIs) to collect the latest fashion trend information from the Internet. The input is the user's personal information and online fashion trend information, and the output is the personal information and trend information stored in the database.

[1321] Step 3:

[1322] The server analyzes the user's personal information and the latest trend information and inputs this into a generative AI model (e.g., GPT-3). A prompt is generated, such as "Based on the latest fashion trends, please suggest a work outfit that suits blue-toned skin." The generative AI model analyzes the data and generates a personalized outfit. The input is the personal information and prompt in text format, and the output is the generated outfit.

[1323] Step 4:

[1324] The server sends the generated outfit to the user's device, which displays it on the display of their smart glasses or smartphone. The user can then view the proposed outfit and is ready to provide feedback. The input is the generated outfit, and the output is the outfit displayed on the user's device.

[1325] Step 5:

[1326] A user uses smart glasses or a smartphone to access the virtual try-on system. The system generates a three-dimensional avatar of the user and has the avatar try on suggested outfits. The user looks at the avatar displayed in their field of view and checks the suitability of the outfits. The input is the three-dimensional avatar and the suggested outfits, and the output is the virtual try-on result.

[1327] Step 6:

[1328] The user provides feedback on the suggested outfits via voice, which is again converted into text data using voice recognition technology and sent to the server. The server receives this feedback and inputs it into the generative AI to be reflected in future suggestions. The input is voice data and feedback text, and the output is feedback information. This feedback further improves the generative AI model, enabling more appropriate outfit suggestions.

[1329] The above is the process flow of the system that realizes the application example and the specific operations at each step.

[1330] 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.

[1331] This invention provides a system that proposes personalized outfits to users based on their personal information, emotional data, and the latest fashion trend information. This system mainly consists of a user terminal, a server, a generative artificial intelligence (AI), an emotion engine, and a virtual try-on system.

[1332] Enter and submit user information

[1333] Terminal

[1334] The user starts the app and enters personal information such as gender, age, height, weight, personal color, and a photo of their face. They also select the desired styling scene (e.g., work, date, holiday). This information is sent from the device to the server.

[1335] Specific examples

[1336] The user fills in the app form with the following information: "Gender: Female, Age: 30, Height: 160cm, Weight: 55kg, Personal Color: Blue-based, Face Photo," presses the submit button, and then selects "Styling Scene: Work."

[1337] Acquisition and analysis of the latest trend information

[1338] server

[1339] The server periodically collects the latest trend information from fashion-related websites and APIs, which is then input into the AI ​​generator for analysis.

[1340] Specific examples

[1341] The server uses a web scraping tool to collect information such as "This season's work fashion trends are mid-length tight skirts and simple blouses."

[1342] Emotion data collection and analysis

[1343] Emotion Engine

[1344] Using the camera and microphone on the user's device, emotion data is collected from the user's facial expressions and voice. The emotion engine analyzes this data to determine the user's current emotional state.

[1345] Specific examples

[1346] While a user is using the app, the emotion engine will recognize that "you look very happy today." If the user also says, "This outfit is lovely," the engine will analyze positive emotions from the voice.

[1347] Learning user and trend information

[1348] Generation AI

[1349] The server inputs the user's personal information and trend information into the AI ​​generator, which then learns the user's preferences and body type. Emotional data is also input into the AI ​​generator, which then generates outfits that take the user's emotions into consideration. The generated outfits are then sent to the user's device via the server.

[1350] Coordination generation and suggestions

[1351] Generation AI

[1352] The AI ​​generates personalized outfits based on the user's personal information, emotional data, and the latest trends, and the outfits are sent to the user's device and displayed to them.

[1353] Specific examples

[1354] The AI ​​generates an outfit such as, "The user has blue-based skin and looks very happy today, so I suggest a light-colored, medium-length tight skirt and a refreshing blue blouse." The outfit is then sent from the server to the device.

[1355] Displaying outfits and collecting feedback

[1356] Terminal

[1357] The proposed outfit is displayed on the user's device. The user enters feedback about the outfit (e.g., like, dislike, comment) and sends it to the server. The emotion engine also collects emotional data from the user's facial expressions and voice when providing feedback.

[1358] Specific examples

[1359] The user clicks the LIKE button to say, "I like this outfit," and enters a comment such as, "I especially like the design of the blouse." The emotion engine then recognizes that the user is expressing positive feelings toward the suggestion.

[1360] Learning from feedback and emotion data

[1361] Server and spawned AI

[1362] The server receives user feedback and emotion data from the emotion engine, and provides it to the generative AI to use as training data to improve the accuracy of the model.

[1363] Virtual try-on feature

[1364] Servers and Terminals

[1365] The server uses a virtual fitting system to generate a three-dimensional avatar based on the user's body type information. The generated avatar tries on suggested outfits to simulate a real fit. The fitting results are sent to the user's device, where the user can check the results.

[1366] Specific examples

[1367] Users can virtually try on outfits to see how they fit, and check them on their own avatar. The suggested outfit is applied to the avatar, and the user can check how it fits from the front, back, left, and right.

[1368] This system allows users to receive real-time fashion advice that takes into account their individuality, body type, and even their emotions. The virtual try-on feature also allows users to check the actual fit beforehand, reducing the chance of making mistakes when shopping online. In this way, the present invention supports users in making more satisfying fashion choices.

[1369] The processing flow will be explained below.

[1370] Step 1:

[1371] The user launches the app.

[1372] The user launches the application on a device such as a smartphone or tablet, which displays a screen for entering personal information.

[1373] Step 2:

[1374] The user enters personal information.

[1375] Users enter personal information such as gender, age, height, weight, personal color, and a photo of their face into the app's form, and also select the desired styling occasion (e.g., work, date, holiday).

[1376] Step 3:

[1377] The user sends personal information to the server.

[1378] When the user presses the submit button, this information is sent to the server as a JSON object via POST.

[1379] Step 4:

[1380] The server receives the user information.

[1381] The server receives the POST request and parses the JSON object to get the user information, which is then stored in a database.

[1382] Step 5:

[1383] The server collects the latest trend information.

[1384] The server periodically collects the latest trend information from fashion-related websites and APIs according to a schedule, and retrieves the data using web scraping tools and API clients.

[1385] Step 6:

[1386] The server inputs trend information into the generation AI.

[1387] The collected trend information is pre-processed (text analysis, data cleaning, etc.), converted into the format required for analysis, and input into the generation AI.

[1388] Step 7:

[1389] The user's device collects emotional data.

[1390] Using the camera and microphone on the user's device, emotion data is collected from the user's facial expressions and voice, and this data is provided to the emotion engine in real time.

[1391] Step 8:

[1392] The emotion engine analyzes the emotion data.

[1393] The emotion engine analyzes the user's emotional state from their facial expressions and voice, and provides the results to the generative AI.

[1394] Step 9:

[1395] The server inputs user information and emotional data into the generation AI.

[1396] The server inputs user information, trend information, and emotional data into the generation AI, which then generates outfits that take into account the user's preferences and emotions.

[1397] Step 10:

[1398] Generative AI generates personalized outfits.

[1399] The AI ​​generates appropriate combinations of items based on user information, emotional data, and trend information, and the generated outfits are returned to the server in JSON format.

[1400] Step 11:

[1401] The server sends the coordinates to the user terminal.

[1402] The server sends the generated coordinates to the user device, which receives the data and displays it on the UI.

[1403] Step 12:

[1404] The user enters feedback on the outfit.

[1405] The user enters their rating (LIKE, DISLIKE) and comments on the displayed outfits and sends them to the server. The emotion engine also collects emotional data from the user's facial expressions and voice when providing feedback.

[1406] Step 13:

[1407] The server receives the feedback and emotion data.

[1408] The server receives user feedback and emotion data from the emotion engine, and provides it to the generative AI to use as training data to improve the accuracy of the model.

[1409] Step 14:

[1410] The server prepares the virtual try-on.

[1411] The server generates a three-dimensional avatar based on the user's body type information and prepares data for the avatar to try on suggested outfits.

[1412] Step 15:

[1413] The server sends the results of the virtual try-on to the device.

[1414] The server generates the fitting results in the form of images and videos and sends them to the user's device, where the user can view the fitting results and check the fit.

[1415] Through these steps, users can receive fashion advice that takes into account their personality, body type, and even their emotions, and the virtual try-on function allows them to check the actual fit in advance.

[1416] Example 2

[1417] 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."

[1418] Conventional fashion recommendation systems often only offer generic clothing suggestions, lacking personalized suggestions that take into account each user's individuality, body shape, and emotions. Furthermore, when shopping online, users are unable to determine how the item will actually fit, resulting in problems such as the wrong size or design after purchase. Furthermore, few systems incorporate user feedback in real time, making it difficult to improve user satisfaction. There is a need for a system that can solve these issues and support users in making more satisfying fashion choices.

[1419] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting the latest fashion trend information from an information providing service or a program control interface, a means for analyzing the personal information and the trend information and providing a generation artificial intelligence for generating personalized outfit suggestions, and a means for collecting user opinions and inputting the opinions into the generation artificial intelligence. This enables personalized fashion suggestions that take into consideration the individuality, body shape, and emotions of each user.

[1420] "User's device" refers to a device used by a user to enter personal information, check suggested fashions, and enter feedback, and includes smartphones, tablets, and PCs.

[1421] "Central processing unit" refers to the server that processes and analyzes users' personal information, collected trend information, and feedback.

[1422] "Generative AI" is an AI model that generates personalized clothing suggestions based on a user's personal information, trend information, and emotional data.

[1423] "Information provision services" refers to websites and programmatic control interfaces (APIs) that provide the latest fashion-related trend information.

[1424] "Personal color tone" refers to the user's skin tone and personal color (blue-based, yellow-based, etc.), and is a factor used to make appropriate fashion suggestions.

[1425] A "face image" is a photo of the user's face, which is used to collect emotional data and make personalized suggestions through facial recognition.

[1426] The "virtual fitting system" is a system that generates a three-dimensional virtual image based on the user's body type information, allows the virtual image to try on suggested clothing, and simulates the fit.

[1427] A "three-dimensional virtual image" is a three-dimensional model generated based on the user's body type information, and is used to try on suggested clothing in the virtual try-on system.

[1428] This invention provides a system that proposes personalized outfits to users based on their personal information, emotional data, and the latest fashion trend information. This system mainly consists of a user terminal, a server, a generative artificial intelligence (AI), an emotion engine, and a virtual try-on system.

[1429] Enter and submit user information

[1430] The user starts the app and enters personal information such as gender, age, height, weight, personal color, and a photo of their face. They also select the desired styling occasion (e.g., work, date, holiday). This information is sent from the user's device to the server.

[1431] Specific examples

[1432] The user fills in the app form with the following information: "Gender: Female, Age: 30, Height: 160cm, Weight: 55kg, Personal Color: Blue-based, Face Photo," presses the submit button, and then selects "Styling Scene: Work."

[1433] Acquisition and analysis of the latest trend information

[1434] The server periodically collects the latest trend information from fashion-related websites and APIs. The data is obtained using web scraping tools and API requests. The collected trend information is then input into the generation AI for analysis.

[1435] Specific examples

[1436] The server uses a web scraping tool to collect information such as "This season's work fashion trends are mid-length tight skirts and simple blouses."

[1437] Emotion data collection and analysis

[1438] Using the camera and microphone installed on the user's device, emotional data is collected from the user's facial expressions and voice. Facial and voice recognition operates in real time to record the emotional data. The emotion engine analyzes the collected data and identifies the user's current emotional state.

[1439] Specific examples

[1440] While the user is using the app, the camera detects "smiles" and "surprised expressions," and the microphone records the user's "positive and negative voice tones." The emotion engine analyzes the facial recognition data and determines that the user has a "happy expression," and voice analysis detects that the user is "speaking in a positive tone."

[1441] Learning user and trend information

[1442] The server inputs the user's personal information and trend information into the AI ​​generator. Based on this information, the AI ​​learns the user's preferences, body type, and adapts to trends. Emotional data is also input into the AI ​​generator, which generates outfits that take the user's emotions into consideration.

[1443] Specific examples

[1444] The server combines the user information and trend information in JSON format and sends it to the generation AI. The generation AI updates its model to suggest, "The user has blue-based skin and looks very happy today, so a light-colored, medium-length tight skirt and a refreshing blue blouse."

[1445] Coordination generation and suggestions

[1446] The AI ​​generates personalized outfits based on the user's personal information, emotional data, and the latest trends, and the outfits are sent to the user's device and displayed to them.

[1447] Specific examples

[1448] The AI ​​generator suggests, "Since the user has blue-toned skin and looks happy today, we suggest a light-colored, medium-length tight skirt and a refreshing blue blouse."

[1449] Displaying outfits and collecting feedback

[1450] The proposed outfit is displayed on the user's device. The user can then enter feedback (like, dislike, or comment) about the outfit and send it to the server. The emotion engine also collects emotional data from the user's facial expressions and voice when providing feedback.

[1451] Specific examples

[1452] The user clicks the LIKE button to say, "I like this outfit," and enters a comment such as, "I especially like the design of the blouse." The emotion engine then recognizes that the user is expressing positive feelings toward the suggestion.

[1453] Learning from feedback and emotion data

[1454] The server receives user feedback and emotion data from the emotion engine, and provides it to the generative AI to use as training data to improve the accuracy of the model.

[1455] Virtual try-on feature

[1456] The server uses a virtual fitting system to generate a three-dimensional avatar based on the user's body type information. The generated avatar tries on suggested outfits to simulate a real fit. The fitting results are sent to the user's device, where the user can check the results.

[1457] Specific examples

[1458] Users can use the virtual try-on feature to check how an outfit fits on their own avatar. The suggested outfit is applied to the avatar, and the user can check how it fits from the front, back, left and right.

[1459] In this way, the present invention realizes a system that provides users with real-time fashion advice that takes into account their personality, body type, and emotions. The virtual try-on feature also allows users to check the actual fit beforehand, reducing mistakes when shopping online.

[1460] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1461] Step 1: Enter your user information

[1462] Terminal

[1463] Users launch the app and enter personal information such as gender, age, height, weight, personal color, and a photo of their face. They also select the desired styling occasion (e.g., work, date, holiday). This operation is performed by the user entering information into each input form in the app.

[1464] input

[1465] User's personal information (gender, age, height, weight, personal color, face photo) and styling scene selection.

[1466] output

[1467] Entered personal information and styling scene data.

[1468] Specific actions

[1469] The user enters "Gender: Female," "Age: 30," "Height: 160cm," "Personal color: Blue-based," and "Face photo," and then selects "Styling scene: Work."

[1470] Step 2: Submit user information

[1471] Terminal

[1472] When the user completes the input and presses the send button, this information is automatically sent from the terminal to the server.

[1473] input

[1474] Personal information and styling scene data entered by the user.

[1475] output

[1476] Data transmission result from the device to the server.

[1477] Specific actions

[1478] When the user presses the submit button, the user information is sent to the server in JSON format.

[1479] Step 3: Stay up to date on the latest trends

[1480] server

[1481] The server periodically collects the latest trend information from fashion-related websites and APIs, and retrieves the data using web scraping tools and API requests.

[1482] input

[1483] URLs for fashion-related websites and APIs.

[1484] output

[1485] Collected latest fashion trend information.

[1486] Specific actions

[1487] The server uses a web scraping tool to collect information such as, "This season's work fashion trends are mid-length tight skirts and simple blouses."

[1488] Step 4: Analyze the latest trend information

[1489] server

[1490] The collected trend information is input into the generative AI for analysis, which analyzes the trend data and updates the model that understands each attribute (color, design, material, etc.).

[1491] input

[1492] Collected latest trend information.

[1493] output

[1494] Analyzed trend data.

[1495] Specific actions

[1496] The trend data collected by the server is input into the generative AI using a Python script, and the model learns trend items such as "medium-length tight skirts" and "simple blouses."

[1497] Step 5: Collecting emotion data

[1498] Terminal

[1499] Using the camera and microphone installed on the user's device, emotional data is collected from the user's facial expressions and voice. Facial and voice recognition works in real time, and emotional data is recorded.

[1500] input

[1501] The user's facial expression and voice data.

[1502] output

[1503] Collected emotion data.

[1504] Specific actions

[1505] While the user is using the app, the camera detects "smiles" and "surprised expressions," and the microphone records the user's "positive tone of voice" and "negative tone of voice."

[1506] Step 6: Analyze the sentiment data

[1507] Emotion Engine

[1508] The emotion engine analyzes the collected data to determine the user's current emotional state, using emotion recognition algorithms (e.g., OpenCV or TensorFlow).

[1509] input

[1510] Collected user sentiment data.

[1511] output

[1512] The analyzed emotional state of the user.

[1513] Specific actions

[1514] The emotion engine analyzes facial recognition data to determine if the user has a happy facial expression, and voice analysis detects if the user is speaking in a positive tone.

[1515] Step 7: Learn about users and trends

[1516] server

[1517] The server inputs the user's personal information and trend information into the AI ​​generator. Based on this information, the AI ​​learns the user's preferences, body type, and adapts to trends. Emotional data is also input into the AI ​​generator, which generates outfits that take the user's emotions into consideration.

[1518] input

[1519] User personal information, latest trend information, and sentiment data.

[1520] output

[1521] Trained model data.

[1522] Specific actions

[1523] The server combines user information and trend information in JSON format and sends it to the AI ​​generator, which then suggests a light-colored, medium-length tight skirt and a refreshing blue blouse, since the user has blue-based skin and looks happy today.

[1524] Step 8: Generate coordinates

[1525] Generation AI

[1526] Generative AI generates personalized outfits based on the user's personal information, emotional data, and the latest trends.

[1527] input

[1528] User personal information, sentiment data, and analyzed trend information.

[1529] output

[1530] The generated coordinates.

[1531] Specific actions

[1532] The AI ​​generator suggests, "Since the user has blue-toned skin and a happy expression, a light-colored, medium-length tight skirt and a refreshing blue blouse."

[1533] Step 9: Coordination suggestions

[1534] server

[1535] The generated coordinates are sent to the user's terminal via the server and displayed to the user.

[1536] input

[1537] Generated coordinate data.

[1538] output

[1539] Coordinate display on user device.

[1540] Specific actions

[1541] The server sends the generated coordinates in JSON format to the user's device, where they are displayed in the device app.

[1542] Step 10: Show your outfit and get feedback

[1543] Terminal

[1544] The proposed outfit is displayed on the user's device. The user can then enter feedback (like, dislike, or comment) about the outfit and send it to the server. The emotion engine also collects emotional data from the user's facial expressions and voice when providing feedback.

[1545] input

[1546] User feedback data (likes, dislikes, comments), facial expressions and voice data.

[1547] output

[1548] Results of sending feedback data and emotion data to the server.

[1549] Specific actions

[1550] The user clicks the LIKE button to say, "I like this outfit," and enters a comment such as, "I especially like the design of the blouse." The emotion engine then recognizes that the user is expressing positive feelings toward the suggestion.

[1551] Step 11: Learning from feedback and sentiment data

[1552] Server and spawned AI

[1553] The server receives user feedback and emotion data from the emotion engine, and provides it to the generative AI to use as training data to improve the accuracy of the model.

[1554] input

[1555] User feedback and sentiment data.

[1556] output

[1557] Updated generative AI models.

[1558] Specific actions

[1559] The generative AI incorporates new feedback data and updates its models to more accurately reflect the user's preferences and emotional state.

[1560] Step 12: Run the Virtual Try-On Feature

[1561] Servers and Terminals

[1562] The server uses a virtual fitting system to generate a three-dimensional avatar based on the user's body type information. The generated avatar tries on suggested outfits to simulate a real fit. The fitting results are sent to the user's device, where the user can check the results.

[1563] input

[1564] User's body type information and suggested outfits.

[1565] output

[1566] Results of trying on a 3D avatar.

[1567] Specific actions

[1568] Users can use the virtual try-on feature to check how an outfit fits on their own avatar. The suggested outfit is applied to the avatar, and the user can check how it fits from the front, back, left and right.

[1569] (Application example 2)

[1570] 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."

[1571] Conventional fashion suggestion systems only use the user's personal information and the latest fashion trend information, but are unable to suggest personalized outfits that reflect the user's emotions. In addition, the virtual try-on function is limited, making it difficult to provide a satisfying fitting based on the user's emotions.

[1572] The specific processing by the specific 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 means for inputting the user's personal information, means for transmitting the personal information to the server, means for collecting the latest fashion trend information from websites and application program interfaces, means for providing a generation AI that analyzes the personal information and the trend information and generates a personalized outfit, means for collecting and analyzing emotional data from the user's facial expressions and voice, means for transmitting and displaying the outfit generated by the generation AI to the user terminal, means for displaying the suitability of the outfit to the user via a virtual try-on system, and means for collecting user feedback and inputting the feedback to the generation AI. This makes it possible to provide a personalized outfit that reflects the user's personal information and emotional data, and to confirm the actual fit in advance through virtual try-on.

[1573] "User information" refers to information including personal information, attribute information, and emotional data of a user.

[1574] "Personal information" refers to various data about the user, such as the user's gender, age, height, weight, personal color, and facial photo.

[1575] "Emotional data" is information about the emotional state of a user that is analyzed from their facial expressions and voice.

[1576] A "server" is a centralized computing resource for collecting and analyzing user information and trend information.

[1577] "Generative AI" is an AI algorithm that generates personalized outfits based on collected data.

[1578] "Latest Fashion Trend Information" refers to information about current fashion industry trends collected from websites and application program interfaces.

[1579] An "emotion analysis engine" is a technology that collects and analyzes emotional data from a user's facial expressions and voice.

[1580] A "user device" is a display device used by a user, such as a smartphone or head-mounted display.

[1581] "Coordination" is a suggested clothing combination generated based on the user's personal information, the latest trends, and emotional data.

[1582] The "virtual fitting system" is a system that displays the fit of suggested outfits to users through a three-dimensional avatar.

[1583] "Feedback" refers to the user's reactions to the proposed outfits, such as ratings and comments.

[1584] A "3D avatar" is a virtual 3D character generated based on the user's body type information.

[1585] The system for implementing this invention involves a series of processes that collects a user's personal information, emotional data, and the latest fashion trend information, analyzes them, proposes personalized outfits, and allows the user to virtually try them on. The system is mainly composed of the following elements: a server, a user terminal, a generative artificial intelligence (generative AI), an emotion analysis engine, and a virtual try-on system.

[1586] Specific examples of hardware and software used

[1587] Hardware

[1588] Smartphone or Head-Mounted Display (HMD): Used as the user device. Example: Oculus Quest.

[1589] Camera and microphone: A device used to capture a user's facial photograph and voice data.

[1590] software

[1591] Web scraping tools: Tools for gathering trend information from fashion-related websites. Examples: Beautiful Soup, Scrapy.

[1592] Generative artificial intelligence (generative AI) models: AI algorithms for generating personalized outfits. Example: OpenAI's GPT-based models.

[1593] Emotion analysis engine: Technology for analyzing emotional data from a user's facial expressions and voice. Example: Microsoft Azure's Emotion API.

[1594] Virtual fitting system: A system that generates a three-dimensional avatar based on the user's body type information and lets them try on outfits. Example: Clo Virtual Fashion.

[1595] System action

[1596] Enter and submit user information

[1597] The user starts the application on their device and enters personal information such as gender, age, height, weight, personal color, and a photo of their face. They also select the desired styling scene (e.g., work, date, or holiday). This information is sent to the server.

[1598] As a specific example, a user enters "gender: female, age: 30, height: 160cm, weight: 55kg, personal color: blue-based, face photo" and selects "styling scene: work."

[1599] Acquisition and analysis of the latest trend information

[1600] The server periodically collects the latest trend information from fashion-related websites and APIs, which is then input into the generative AI and analyzed using natural language processing.

[1601] For example, collect information such as, "This season's work fashion trends are mid-length tight skirts and simple blouses."

[1602] Emotion data collection and analysis

[1603] The camera and microphone on the user's device are used to collect emotional data from facial expressions and voice, and the emotion analysis engine analyzes the data to determine the user's emotional state.

[1604] For example, if a user says, "You look very happy today," or "This outfit is lovely," the system will analyze the positive emotion from the voice.

[1605] Personalized outfit creation

[1606] The server inputs the user's personal information, emotional data, and trend information into the AI ​​generator, which then generates outfits that take into account the user's preferences, body type, and emotions. The generated outfits are then sent to the user's device and displayed.

[1607] Example prompt

[1608] Here are some example prompts to input to the AI ​​generator:

[1609] User Information:

[1610] Gender: Female

[1611] Age: 30

[1612] Height: 160cm

[1613] Weight: 55kg

[1614] Personal color: Blue-based

[1615] Preferred styling occasion: Work

[1616] Latest Trends:

[1617] This season's work fashion trends are mid-length tight skirts and simple blouses

[1618] Emotional state:

[1619] A very happy look

[1620] A positive comment that this outfit is lovely

[1621] Generate personalized work outfits for blue-toned skin based on your preferences and the latest trends.

[1622] Virtual try-on

[1623] The virtual fitting system generates a three-dimensional avatar based on the user's body type and emotional data, and then allows the user to try on suggested outfits, allowing them to check how the outfits actually fit.

[1624] The system allows users to receive personalized fashion suggestions in real time that take into account their personality, body type, and emotions, and the virtual try-on feature reduces mistakes when shopping online.

[1625] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1626] Step 1:

[1627] Enter and submit user information

[1628] The user starts the application on their smartphone or head-mounted display (HMD) and inputs their gender, age, height, weight, personal color, a photo of their face, and the desired styling scene. This information is then sent from the device to the server and saved.

[1629] Input: Gender, age, height, weight, personal color, face photo, styling scene

[1630] Output: User information stored on the server

[1631] Step 2:

[1632] Collection and analysis of the latest trend information

[1633] The server periodically collects the latest fashion trend information from fashion-related websites and APIs using a web scraping tool, which is then input into a generative AI model for analysis.

[1634] Input: Fashion trend information obtained from websites and APIs

[1635] Output: Latest analyzed trend information

[1636] Step 3:

[1637] Emotion data collection and analysis

[1638] The camera and microphone on the user's device collect the user's facial expressions and voice. This data is input into an emotion analysis engine to analyze the user's current emotional state. The analysis results are then sent to the server.

[1639] Input: User facial and voice data

[1640] Output: Parsed emotion data

[1641] Step 4:

[1642] Personalized outfit creation

[1643] The server inputs user information, trend information, and emotional data into the generative AI model to generate personalized outfits tailored to the user's preferences, body type, and emotions. The generated outfits are sent to the user's device and displayed.

[1644] Input: User information, latest trend information, sentiment data

[1645] Output: Personalized outfit ideas

[1646] Step 5:

[1647] Virtual try-on

[1648] The virtual fitting system generates a three-dimensional avatar based on the user's body type information, has them try on suggested outfits, and sends the results to the user's device, where the user can check the fitting results.

[1649] Input: User's body type information, suggested outfits

[1650] Output: 3D avatar fitting results

[1651] Step 6:

[1652] Collecting and analyzing feedback

[1653] Users provide feedback (likes, dislikes, comments) on outfits, which is analyzed by a sentiment analysis engine and the results are sent to the server and used as training data for the generative AI model.

[1654] Input: User feedback and sentiment data

[1655] Output: Updated training data by generative AI model

[1656] Through this process, users can easily find outfits that are optimized to suit their individuality, body type, and emotions, and can check the actual fit in advance through virtual try-on.

[1657] 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.

[1658] 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.

[1659] 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.

[1660] [Fourth embodiment]

[1661] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1662] 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.

[1663] 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).

[1664] 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.

[1665] 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.

[1666] 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).

[1667] 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.

[1668] 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.

[1669] 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.

[1670] 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.

[1671] 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.

[1672] 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.

[1673] 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."

[1674] This invention provides a system that proposes personalized outfits to users based on their personal information and the latest fashion trend information. This system mainly consists of a user terminal, a server, a generative artificial intelligence (AI), and a virtual try-on system.

[1675] Enter and submit user information

[1676] Terminal

[1677] The user starts the app and enters personal information such as gender, age, height, weight, personal color, and a photo of their face. They also select the desired styling scene (e.g., work, date, holiday). This information is sent from the device to the server.

[1678] Specific examples

[1679] The user fills in the app form with the following information: "Gender: Female, Age: 30, Height: 160cm, Weight: 55kg, Personal Color: Blue-based, Face Photo," presses the submit button, and then selects "Styling Scene: Work."

[1680] Acquisition and analysis of the latest trend information

[1681] server

[1682] The server periodically collects the latest trend information from fashion-related websites and APIs, which is then input into the AI ​​generator for analysis.

[1683] Specific examples

[1684] The server uses a web scraping tool to collect information such as "This season's work fashion trends are mid-length tight skirts and simple blouses."

[1685] Learning user information and suggesting outfits

[1686] Generation AI

[1687] The server inputs the user's personal information into the AI ​​generator, which learns the user's individual preferences and body type. The AI ​​then combines the collected trend information with the user's information to generate a personalized outfit. The generated outfit is then sent to the user's device via the server.

[1688] Specific examples

[1689] The AI ​​generates an outfit such as, "The user has a blue-based skin tone, so I suggest a refreshing blue blouse to go with a mid-length tight skirt that's on trend this season." The outfit is then sent from the server to the device.

[1690] Displaying outfits and collecting feedback

[1691] Terminal

[1692] The proposed coordinates are displayed on the user's device, and the user enters feedback (e.g., LIKE, DISLIKE, or comments) about the coordinates and sends it to the server.

[1693] Specific examples

[1694] Users can click the LIKE button to send feedback, saying "I like this outfit," and also enter comments such as "I like the ruffles on the blouse."

[1695] Virtual try-on feature

[1696] server

[1697] The server uses a virtual fitting system to generate a three-dimensional avatar based on the user's body type, then has the avatar try on the suggested outfits to simulate how they would actually fit.

[1698] Specific examples

[1699] Users can virtually try on a blouse to see how it fits, using their own avatar. The suggested outfit is applied to the avatar, and the user can check the fit from the front, back, left and right.

[1700] This system allows users to receive real-time fashion advice tailored to their individual personality and body type. The virtual try-on function also allows users to check the actual fit beforehand, reducing the chance of mistakes when shopping online. In this way, the present invention supports users in making more satisfying fashion choices.

[1701] The processing flow will be explained below.

[1702] Step 1:

[1703] The user launches the app.

[1704] The user launches the application on a device such as a smartphone or tablet, which displays a screen for entering personal information.

[1705] Step 2:

[1706] The user enters personal information.

[1707] Users enter personal information such as gender, age, height, weight, personal color, and a photo of their face into the app's form, and also select the desired styling occasion (e.g., work, date, holiday).

[1708] Step 3:

[1709] Users submit personal information.

[1710] When the user presses the submit button, this information is sent to the server as a JSON object via POST.

[1711] Step 4:

[1712] The server receives the user information.

[1713] The server receives the POST request and parses the JSON object to get the user information, which is then stored in a database.

[1714] Step 5:

[1715] The server collects the latest trend information.

[1716] The server periodically collects the latest trend information from fashion-related websites and APIs according to a schedule, and retrieves the data using web scraping tools and API clients.

[1717] Step 6:

[1718] The server analyzes the trend information.

[1719] Before inputting the collected trend information into the generation AI, the data is pre-processed (text analysis, data cleaning, etc.) to convert it into the format required for analysis.

[1720] Step 7:

[1721] The server inputs user information into the generation AI.

[1722] The server passes user information as a JSON object to the generation AI, which extracts features related to the user's preferences and body type.

[1723] Step 8:

[1724] Generative AI generates personalized outfits.

[1725] The AI ​​generates appropriate combinations of items based on user information and trend information, and the generated outfits are returned to the server in JSON format.

[1726] Step 9:

[1727] The server sends the coordinates to the user terminal.

[1728] The server sends the generated coordinates to the user device, which receives the data and displays it on the UI.

[1729] Step 10:

[1730] The user enters feedback on the outfit.

[1731] The user enters a rating (LIKE, DISLIKE) and a comment about the displayed outfit, and sends it to the server.

[1732] Step 11:

[1733] The server receives the feedback and reflects it in the generated AI.

[1734] The server receives feedback from users and provides it to the generative AI to use as training data to improve the accuracy of the model.

[1735] Step 12:

[1736] The server prepares the virtual try-on.

[1737] The server generates a three-dimensional avatar based on the user's body type information and prepares data for the avatar to try on suggested outfits.

[1738] Step 13:

[1739] The server sends the results of the virtual try-on to the device.

[1740] The server generates the fitting results in the form of images and videos and sends them to the user's device, where the user can view the fitting results and check the fit.

[1741] Through these steps, users can receive personalized fashion advice and take advantage of the virtual try-on feature to check the fit.

[1742] Example 1

[1743] 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."

[1744] Conventional online fashion coordination systems have difficulty proposing personalized outfits that fit the user's personality and body type, and it is also difficult to confirm the actual fit. This has led to problems such as lower satisfaction with online shopping and increased hassle with returns and exchanges.

[1745] 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.

[1746] In this invention, the server includes means for inputting a user's personal information, means for transmitting the personal information to the server, means for collecting the latest fashion trend information from an information providing system, means for providing a generative AI model that analyzes the personal information and the trend information and generates personalized outfits, means for transmitting and displaying the outfits generated by the generative AI model to a user terminal, means for collecting user feedback and inputting the feedback to the generative AI model, and means for displaying the suitability of the outfits to the user via a virtual try-on system. This allows users to receive fashion advice tailored to their individuality and body type in real time, and the virtual try-on function allows them to check the actual fit in advance, enabling them to make highly satisfying fashion choices.

[1747] "User personal information" refers to information that indicates individual attributes of the user, such as gender, age, height, weight, personal color, and facial photo.

[1748] A "server" is a computer system for collecting, analyzing, generating, and distributing data.

[1749] An "information providing system" is a system that provides the latest fashion trend information, such as a website or application program interface.

[1750] A "generative AI model" is an artificial intelligence system that analyzes a user's personal information and fashion trend information to generate personalized outfits.

[1751] The "virtual try-on system" is a system that generates a three-dimensional avatar based on the user's body type information and allows them to try on suggested outfits.

[1752] "Feedback" refers to the user's reaction to the proposed outfit, such as an evaluation or comment.

[1753] A "three-dimensional avatar" is a three-dimensional alter ego of a user that is virtually created based on the user's body shape information.

[1754] Basic configuration

[1755] This invention consists of a user terminal, a server, a generative AI model, and a virtual try-on system. The user terminal is basically a device used by the user, such as a smartphone or PC. The server is a computer system that collects, analyzes, generates, and distributes data. The generative AI model is an artificial intelligence that analyzes the user's personal information and fashion trend information to generate personalized fashion coordination. The virtual try-on system generates a three-dimensional avatar based on the user's body type information and allows the user to try on the suggested coordination.

[1756] Enter and submit user information

[1757] Terminal

[1758] The user starts the app and enters personal information such as gender, age, height, weight, personal color, and a photo of their face. In addition, they select the desired styling scene (e.g., work, date, holiday). Once the input is complete, they press the send button and this information is sent from the device to the server.

[1759] Specific examples

[1760] The user enters the following information into the app's input form: "Gender: Female, Age: 30, Height: 160cm, Weight: 55kg, Personal Color: Blue-based, Face Photo," selects "Styling Scene: Work," and presses the send button.

[1761] Collection and analysis of the latest trend information

[1762] server

[1763] The server periodically collects the latest fashion trend information from multiple fashion-related websites and application program interfaces (APIs). It uses a web scraping tool to obtain the trend information.

[1764] Specific examples

[1765] The server uses a web scraping tool to collect information such as "This season's work fashion trends are mid-length tight skirts and simple blouses," and inputs this information into a generative AI model for analysis.

[1766] Learning user information and suggesting outfits

[1767] Generation AI

[1768] The server inputs the user's collected personal information into a generative AI model, which learns the user's individual preferences and body type. The generative AI model then combines the collected trend information with the user's information to generate a personalized outfit. This generated outfit is then sent to the user's device via the server.

[1769] Specific examples

[1770] The generative AI model generates an outfit such as, "Since the user has blue-based skin, we suggest a refreshing blue blouse to go with a mid-length tight skirt that's trendy this season," and sends this information to the user's device via the server.

[1771] Displaying outfits and collecting feedback

[1772] Terminal

[1773] The proposed outfits are displayed on the user's device, and the user can provide feedback on the outfits by pressing the LIKE or DISLIKE button or by entering a comment. This feedback is then sent to the server.

[1774] Specific examples

[1775] The user checks the outfit displayed on their device, clicks the LIKE button to indicate that they like the outfit, enters a comment such as, "I like the ruffles on the blouse," and sends the feedback to the server.

[1776] Virtual try-on feature

[1777] server

[1778] The server uses a virtual fitting system to generate a three-dimensional avatar based on the user's body type information. The server then has the avatar try on the suggested outfits, simulating how they will actually fit. The simulation results are then displayed on the user's device.

[1779] Specific examples

[1780] The user selects "I want to check the fit of this blouse" on their device and begins the virtual try-on. The server applies the suggested outfit to a three-dimensional avatar, and the user can check the fit from the front, back, left, and right on their device.

[1781] In this way, the system can provide users with tailored fashion advice and a virtual try-on feature to check the actual fit beforehand, improving the satisfaction of online fashion shopping and reducing the risk of returns and exchanges.

[1782] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1783] The flow of this system's program processing

[1784] Step 1:

[1785] Enter and submit user information

[1786] The user launches the app on their device, enters personal information such as gender, age, height, weight, personal color, and a photo of their face, and selects the desired styling scene (e.g., work, date, holiday). This information is then sent from the device to the server.

[1787] Specific actions

[1788] The user enters the following information into the app's input form: "Gender: Female, Age: 30, Height: 160cm, Weight: 55kg, Personal Color: Blue-based, Face Photo," selects "Styling Scene: Work," and presses the send button.

[1789] input

[1790] User's personal information (gender, age, height, weight, personal color, face photo) and styling scene

[1791] output

[1792] User personal information and styling scenes transferred to the server

[1793] Step 2:

[1794] Collection and analysis of the latest trend information

[1795] The server periodically collects the latest fashion trend information from multiple fashion-related websites and application program interfaces (APIs), inputs the collected information into a generative AI model, and analyzes the trend information.

[1796] Specific actions

[1797] The server launches a web scraping tool, collects information such as "This season's work fashion trends are mid-length tight skirts and simple blouses," and inputs this information into a generative AI model.

[1798] input

[1799] Latest fashion trend information collected from fashion-related websites and APIs

[1800] output

[1801] Trend information fed into the generative AI model

[1802] Step 3:

[1803] Learning user information and suggesting outfits

[1804] The server inputs the user's personal information into a generative AI model, which learns the user's individual preferences and body type. The generative AI model combines the collected trend information with user information to generate a personalized outfit. The generated outfit is then sent to the user's device via the server.

[1805] Specific actions

[1806] The generative AI model generates an outfit such as, "Since the user has blue-based skin, we suggest a refreshing blue blouse to go with a mid-length tight skirt that's trendy this season," and sends it to the user's device via the server.

[1807] input

[1808] User's personal information (gender, age, height, weight, personal color, face photo), styling scenes, and the latest fashion trend information

[1809] output

[1810] Personalized coordination sent to user devices

[1811] Step 4:

[1812] Displaying outfits and collecting feedback

[1813] The proposed outfits are displayed on the user's device. The user can provide feedback by pressing the LIKE or DISLIKE button or by entering a comment. This feedback is sent to the server.

[1814] Specific actions

[1815] The user checks the outfit displayed on the device, clicks the LIKE button to indicate "I like this outfit," enters a comment such as "I like the ruffles on the blouse," and sends feedback.

[1816] input

[1817] Personalized outfits displayed on the user's device

[1818] output

[1819] User feedback sent to the server

[1820] Step 5:

[1821] Virtual try-on feature

[1822] The server uses a virtual fitting system to generate a three-dimensional avatar based on the user's body type information. The server then has the avatar try on the suggested outfits, and displays a simulation of how they fit on the user's device.

[1823] Specific actions

[1824] The user selects "I want to check the size of this blouse" on their device and begins the virtual try-on. The server applies the suggested outfit to a three-dimensional avatar, and the user can check the fit from the front, back, left and right on their device.

[1825] input

[1826] User's body type information and suggested outfits

[1827] output

[1828] Results of a fitting simulation using a 3D avatar displayed on a user's device

[1829] In this way, users can receive fashion advice tailored to their individual personalities and body types in real time, and the virtual try-on feature allows them to check the actual fit beforehand, enhancing the satisfaction of online shopping.

[1830] (Application example 1)

[1831] 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."

[1832] In recent years, with the diversification of fashion, there has been a growing need to provide fashion coordination that suits each user's unique personality and body type. In addition, the spread of online shopping has made it difficult to check the compatibility and fit of coordination in advance, as it is not possible to try on clothes in a physical store. Furthermore, it is difficult for users to efficiently find coordination that suits their preferences, which requires time and effort.

[1833] 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.

[1834] In this invention, the server includes means for inputting a user's personal information, means for transmitting the personal information to the server, means for collecting the latest fashion trend information from websites and application program interfaces, means for generating a personalized outfit by analyzing the personal information and the trend information, means for transmitting and displaying the outfit generated by the generating AI to a user terminal, means for collecting user feedback and inputting the feedback to the generating AI, means for displaying the suitability of the outfit to the user via a virtual try-on system, means for displaying the virtually tried-on outfit in the user's field of view using smart glasses, and means for acquiring the user's personal information and feedback using voice recognition. This allows users to easily find fashion outfits that suit their preferences and body type, and also enables the virtual try-on function to eliminate the hassle of trying on clothes when shopping online and to check the fit in real time.

[1835] "User's personal information" refers to information entered by the user, such as gender, age, height, weight, personal factors, and facial photo.

[1836] "Means for transmitting to the server" refers to the communication means used to transmit the user's personal information from the user terminal to the server.

[1837] "Website and Application Program Interface" refers to a program or API that periodically retrieves the latest fashion trend information via the Internet.

[1838] "Generative artificial intelligence" is an AI model that analyzes a user's personal information and the latest fashion trend information to generate personalized outfits for each individual user.

[1839] "User device" refers to an electronic device used by a user, such as a smartphone, smart glasses, or head-mounted display.

[1840] "Means for collecting feedback" is a function that allows users to input their evaluations and opinions on the proposed outfits and send them to the server.

[1841] A "virtual try-on system" is a system that generates a three-dimensional avatar based on the user's shape information and allows the user to virtually try on suggested outfits.

[1842] "Smart glasses" are eyeglass-type devices that have the function of projecting visual information in front of the user's eyes.

[1843] "Speech recognition" is a technology that converts a user's voice into text data and inputs it into a system.

[1844] "Means for obtaining personal information and feedback" refers to a function that uses voice recognition technology to collect feedback on personal information and coordination provided by the user and inputs it into the system.

[1845] This system proposes personalized outfits to users based on their personal information and the latest fashion trends, and checks their suitability through a virtual try-on function. The main components of this system are a user terminal, a server, a generative artificial intelligence, a virtual try-on system, and smart glasses.

[1846] The server includes the following means:

[1847] How users input their personal information: Users input their personal information (gender, age, height, weight, personal factors, and face photo) by voice through smart glasses or a smartphone.

[1848] Means of sending personal information to the server: The user's personal information is sent to the server using voice recognition technology or input forms.

[1849] Method for collecting the latest fashion trend information: The server periodically collects the latest trend information from fashion-related websites on the Internet using web scraping tools and APIs.

[1850] Coordination generation means using generative AI: Analyze the personal information and trend information and generate personalized coordination using a generative AI model (e.g., GPT-3).

[1851] Means for transmitting and displaying the generated coordination to the user terminal: The coordination generated by the server is transmitted to the user terminal and displayed on the smart glasses or smartphone.

[1852] The user terminal comprises the following means:

[1853] A means of obtaining personal information and feedback using voice recognition: Users can input feedback by voice through smart glasses or smartphones, which is converted into text data using voice recognition technology and sent to the server.

[1854] Virtual try-on display using smart glasses: Through the virtual try-on system, the user's three-dimensional avatar tries on the suggested outfits, and the suitability is displayed on the smart glasses display.

[1855] In this way, the user goes through the following process:

[1856] 1. Enter personal information by voice and send it to the server.

[1857] 2. The server collects the latest fashion trends from the internet and generates personalized outfits using a generative AI model.

[1858] 3. The generated coordinates are sent to the user's device and displayed on the smart glasses.

[1859] 4. Users can use the virtual try-on feature to check the suitability of the suggested outfits.

[1860] 5. Users provide voice feedback, which the server inputs into the generative AI for further improvements.

[1861] For example, a user can input a prompt such as, "Based on the latest fashion trends, please suggest a work outfit that suits my blue-toned skin tone." This process allows users to efficiently and easily find the perfect fashion outfit for themselves, eliminating the hassle of trying on clothes when shopping online and allowing them to check the fit in real time.

[1862] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1863] Step 1:

[1864] The user uses a smart device (e.g., smart glasses, smartphone) to input personal information by voice. Voice recognition technology converts this voice into text data, and personal information (e.g., gender, age, height, weight, personal factors, face photo) is obtained. This is then sent to the server as text data. The input is voice data, and the output is personal information in text format.

[1865] Step 2:

[1866] The server receives the personal information and stores it in a database. At the same time, the server uses web scraping tools and application program interfaces (APIs) to collect the latest fashion trend information from the Internet. The input is the user's personal information and online fashion trend information, and the output is the personal information and trend information stored in the database.

[1867] Step 3:

[1868] The server analyzes the user's personal information and the latest trend information and inputs this into a generative AI model (e.g., GPT-3). A prompt is generated, such as "Based on the latest fashion trends, please suggest a work outfit that suits blue-toned skin." The generative AI model analyzes the data and generates a personalized outfit. The input is the personal information and prompt in text format, and the output is the generated outfit.

[1869] Step 4:

[1870] The server sends the generated outfit to the user's device, which displays it on the display of their smart glasses or smartphone. The user can then view the proposed outfit and is ready to provide feedback. The input is the generated outfit, and the output is the outfit displayed on the user's device.

[1871] Step 5:

[1872] A user uses smart glasses or a smartphone to access the virtual try-on system. The system generates a three-dimensional avatar of the user and has the avatar try on suggested outfits. The user looks at the avatar displayed in their field of view and checks the suitability of the outfits. The input is the three-dimensional avatar and the suggested outfits, and the output is the virtual try-on result.

[1873] Step 6:

[1874] The user provides feedback on the suggested outfits via voice, which is again converted into text data using voice recognition technology and sent to the server. The server receives this feedback and inputs it into the generative AI to be reflected in future suggestions. The input is voice data and feedback text, and the output is feedback information. This feedback further improves the generative AI model, enabling more appropriate outfit suggestions.

[1875] The above is the process flow of the system that realizes the application example and the specific operations at each step.

[1876] 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.

[1877] This invention provides a system that proposes personalized outfits to users based on their personal information, emotional data, and the latest fashion trend information. This system mainly consists of a user terminal, a server, a generative artificial intelligence (AI), an emotion engine, and a virtual try-on system.

[1878] Enter and submit user information

[1879] Terminal

[1880] The user starts the app and enters personal information such as gender, age, height, weight, personal color, and a photo of their face. They also select the desired styling scene (e.g., work, date, holiday). This information is sent from the device to the server.

[1881] Specific examples

[1882] The user fills in the app form with the following information: "Gender: Female, Age: 30, Height: 160cm, Weight: 55kg, Personal Color: Blue-based, Face Photo," presses the submit button, and then selects "Styling Scene: Work."

[1883] Acquisition and analysis of the latest trend information

[1884] server

[1885] The server periodically collects the latest trend information from fashion-related websites and APIs, which is then input into the AI ​​generator for analysis.

[1886] Specific examples

[1887] The server uses a web scraping tool to collect information such as "This season's work fashion trends are mid-length tight skirts and simple blouses."

[1888] Emotion data collection and analysis

[1889] Emotion Engine

[1890] Using the camera and microphone on the user's device, emotion data is collected from the user's facial expressions and voice. The emotion engine analyzes this data to determine the user's current emotional state.

[1891] Specific examples

[1892] While a user is using the app, the emotion engine will recognize that "you look very happy today." If the user also says, "This outfit is lovely," the engine will analyze positive emotions from the voice.

[1893] Learning user and trend information

[1894] Generation AI

[1895] The server inputs the user's personal information and trend information into the AI ​​generator, which then learns the user's preferences and body type. Emotional data is also input into the AI ​​generator, which then generates outfits that take the user's emotions into consideration. The generated outfits are then sent to the user's device via the server.

[1896] Coordination generation and suggestions

[1897] Generation AI

[1898] The AI ​​generates personalized outfits based on the user's personal information, emotional data, and the latest trends, and the outfits are sent to the user's device and displayed to them.

[1899] Specific examples

[1900] The AI ​​generates an outfit such as, "The user has blue-based skin and looks very happy today, so I suggest a light-colored, medium-length tight skirt and a refreshing blue blouse." The outfit is then sent from the server to the device.

[1901] Displaying outfits and collecting feedback

[1902] Terminal

[1903] The proposed outfit is displayed on the user's device. The user enters feedback about the outfit (e.g., like, dislike, comment) and sends it to the server. The emotion engine also collects emotional data from the user's facial expressions and voice when providing feedback.

[1904] Specific examples

[1905] The user clicks the LIKE button to say, "I like this outfit," and enters a comment such as, "I especially like the design of the blouse." The emotion engine then recognizes that the user is expressing positive feelings toward the suggestion.

[1906] Learning from feedback and emotion data

[1907] Server and spawned AI

[1908] The server receives user feedback and emotion data from the emotion engine, and provides it to the generative AI to use as training data to improve the accuracy of the model.

[1909] Virtual try-on feature

[1910] Servers and Terminals

[1911] The server uses a virtual fitting system to generate a three-dimensional avatar based on the user's body type information. The generated avatar tries on suggested outfits to simulate a real fit. The fitting results are sent to the user's device, where the user can check the results.

[1912] Specific examples

[1913] Users can virtually try on outfits to see how they fit, and check them on their own avatar. The suggested outfit is applied to the avatar, and the user can check how it fits from the front, back, left, and right.

[1914] This system allows users to receive real-time fashion advice that takes into account their individuality, body type, and even their emotions. The virtual try-on feature also allows users to check the actual fit beforehand, reducing the chance of making mistakes when shopping online. In this way, the present invention supports users in making more satisfying fashion choices.

[1915] The processing flow will be explained below.

[1916] Step 1:

[1917] The user launches the app.

[1918] The user launches the application on a device such as a smartphone or tablet, which displays a screen for entering personal information.

[1919] Step 2:

[1920] The user enters personal information.

[1921] Users enter personal information such as gender, age, height, weight, personal color, and a photo of their face into the app's form, and also select the desired styling occasion (e.g., work, date, holiday).

[1922] Step 3:

[1923] The user sends personal information to the server.

[1924] When the user presses the submit button, this information is sent to the server as a JSON object via POST.

[1925] Step 4:

[1926] The server receives the user information.

[1927] The server receives the POST request and parses the JSON object to get the user information, which is then stored in a database.

[1928] Step 5:

[1929] The server collects the latest trend information.

[1930] The server periodically collects the latest trend information from fashion-related websites and APIs according to a schedule, and retrieves the data using web scraping tools and API clients.

[1931] Step 6:

[1932] The server inputs trend information into the generation AI.

[1933] The collected trend information is pre-processed (text analysis, data cleaning, etc.), converted into the format required for analysis, and input into the generation AI.

[1934] Step 7:

[1935] The user's device collects emotional data.

[1936] Using the camera and microphone on the user's device, emotion data is collected from the user's facial expressions and voice, and this data is provided to the emotion engine in real time.

[1937] Step 8:

[1938] The emotion engine analyzes the emotion data.

[1939] The emotion engine analyzes the user's emotional state from their facial expressions and voice, and provides the results to the generative AI.

[1940] Step 9:

[1941] The server inputs user information and emotional data into the generation AI.

[1942] The server inputs user information, trend information, and emotional data into the generation AI, which then generates outfits that take into account the user's preferences and emotions.

[1943] Step 10:

[1944] Generative AI generates personalized outfits.

[1945] The AI ​​generates appropriate combinations of items based on user information, emotional data, and trend information, and the generated outfits are returned to the server in JSON format.

[1946] Step 11:

[1947] The server sends the coordinates to the user terminal.

[1948] The server sends the generated coordinates to the user device, which receives the data and displays it on the UI.

[1949] Step 12:

[1950] The user enters feedback on the outfit.

[1951] The user enters their rating (LIKE, DISLIKE) and comments on the displayed outfits and sends them to the server. The emotion engine also collects emotional data from the user's facial expressions and voice when providing feedback.

[1952] Step 13:

[1953] The server receives the feedback and emotion data.

[1954] The server receives user feedback and emotion data from the emotion engine, and provides it to the generative AI to use as training data to improve the accuracy of the model.

[1955] Step 14:

[1956] The server prepares the virtual try-on.

[1957] The server generates a three-dimensional avatar based on the user's body type information and prepares data for the avatar to try on suggested outfits.

[1958] Step 15:

[1959] The server sends the results of the virtual try-on to the device.

[1960] The server generates the fitting results in the form of images and videos and sends them to the user's device, where the user can view the fitting results and check the fit.

[1961] Through these steps, users can receive fashion advice that takes into account their personality, body type, and even their emotions, and the virtual try-on function allows them to check the actual fit in advance.

[1962] Example 2

[1963] 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."

[1964] Conventional fashion recommendation systems often only offer generic clothing suggestions, lacking personalized suggestions that take into account each user's individuality, body shape, and emotions. Furthermore, when shopping online, users are unable to determine how the item will actually fit, resulting in problems such as the wrong size or design after purchase. Furthermore, few systems incorporate user feedback in real time, making it difficult to improve user satisfaction. There is a need for a system that can solve these issues and support users in making more satisfying fashion choices.

[1965] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting the latest fashion trend information from an information providing service or a program control interface, a means for analyzing the personal information and the trend information and providing a generation artificial intelligence for generating personalized outfit suggestions, and a means for collecting user opinions and inputting the opinions into the generation artificial intelligence. This enables personalized fashion suggestions that take into consideration the individuality, body shape, and emotions of each user.

[1966] "User's device" refers to a device used by a user to enter personal information, check suggested fashions, and enter feedback, and includes smartphones, tablets, and PCs.

[1967] "Central processing unit" refers to the server that processes and analyzes users' personal information, collected trend information, and feedback.

[1968] "Generative AI" is an AI model that generates personalized clothing suggestions based on a user's personal information, trend information, and emotional data.

[1969] "Information provision services" refers to websites and programmatic control interfaces (APIs) that provide the latest fashion-related trend information.

[1970] "Personal color tone" refers to the user's skin tone and personal color (blue-based, yellow-based, etc.), and is a factor used to make appropriate fashion suggestions.

[1971] A "face image" is a photo of the user's face, which is used to collect emotional data and make personalized suggestions through facial recognition.

[1972] The "virtual fitting system" is a system that generates a three-dimensional virtual image based on the user's body type information, allows the virtual image to try on suggested clothing, and simulates the fit.

[1973] A "three-dimensional virtual image" is a three-dimensional model generated based on the user's body type information, and is used to try on suggested clothing in the virtual try-on system.

[1974] This invention provides a system that proposes personalized outfits to users based on their personal information, emotional data, and the latest fashion trend information. This system mainly consists of a user terminal, a server, a generative artificial intelligence (AI), an emotion engine, and a virtual try-on system.

[1975] Enter and submit user information

[1976] The user starts the app and enters personal information such as gender, age, height, weight, personal color, and a photo of their face. They also select the desired styling occasion (e.g., work, date, holiday). This information is sent from the user's device to the server.

[1977] Specific examples

[1978] The user fills in the app form with the following information: "Gender: Female, Age: 30, Height: 160cm, Weight: 55kg, Personal Color: Blue-based, Face Photo," presses the submit button, and then selects "Styling Scene: Work."

[1979] Acquisition and analysis of the latest trend information

[1980] The server periodically collects the latest trend information from fashion-related websites and APIs. The data is obtained using web scraping tools and API requests. The collected trend information is then input into the generation AI for analysis.

[1981] Specific examples

[1982] The server uses a web scraping tool to collect information such as "This season's work fashion trends are mid-length tight skirts and simple blouses."

[1983] Emotion data collection and analysis

[1984] Using the camera and microphone installed on the user's device, emotional data is collected from the user's facial expressions and voice. Facial and voice recognition operates in real time to record the emotional data. The emotion engine analyzes the collected data and identifies the user's current emotional state.

[1985] Specific examples

[1986] While the user is using the app, the camera detects "smiles" and "surprised expressions," and the microphone records the user's "positive and negative voice tones." The emotion engine analyzes the facial recognition data and determines that the user has a "happy expression," and voice analysis detects that the user is "speaking in a positive tone."

[1987] Learning user and trend information

[1988] The server inputs the user's personal information and trend information into the AI ​​generator. Based on this information, the AI ​​learns the user's preferences, body type, and adapts to trends. Emotional data is also input into the AI ​​generator, which generates outfits that take the user's emotions into consideration.

[1989] Specific examples

[1990] The server combines the user information and trend information in JSON format and sends it to the generation AI. The generation AI updates its model to suggest, "The user has blue-based skin and looks very happy today, so a light-colored, medium-length tight skirt and a refreshing blue blouse."

[1991] Coordination generation and suggestions

[1992] The AI ​​generates personalized outfits based on the user's personal information, emotional data, and the latest trends, and the outfits are sent to the user's device and displayed to them.

[1993] Specific examples

[1994] The AI ​​generator suggests, "Since the user has blue-toned skin and looks happy today, we suggest a light-colored, medium-length tight skirt and a refreshing blue blouse."

[1995] Displaying outfits and collecting feedback

[1996] The proposed outfit is displayed on the user's device. The user can then enter feedback (like, dislike, or comment) about the outfit and send it to the server. The emotion engine also collects emotional data from the user's facial expressions and voice when providing feedback.

[1997] Specific examples

[1998] The user clicks the LIKE button to say, "I like this outfit," and enters a comment such as, "I especially like the design of the blouse." The emotion engine then recognizes that the user is expressing positive feelings toward the suggestion.

[1999] Learning from feedback and emotion data

[2000] The server receives user feedback and emotion data from the emotion engine, and provides it to the generative AI to use as training data to improve the accuracy of the model.

[2001] Virtual try-on feature

[2002] The server uses a virtual fitting system to generate a three-dimensional avatar based on the user's body type information. The generated avatar tries on suggested outfits to simulate a real fit. The fitting results are sent to the user's device, where the user can check the results.

[2003] Specific examples

[2004] Users can use the virtual try-on feature to check how an outfit fits on their own avatar. The suggested outfit is applied to the avatar, and the user can check how it fits from the front, back, left and right.

[2005] In this way, the present invention realizes a system that provides users with real-time fashion advice that takes into account their personality, body type, and emotions. The virtual try-on feature also allows users to check the actual fit beforehand, reducing mistakes when shopping online.

[2006] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2007] Step 1: Enter your user information

[2008] Terminal

[2009] Users launch the app and enter personal information such as gender, age, height, weight, personal color, and a photo of their face. They also select the desired styling occasion (e.g., work, date, holiday). This operation is performed by the user entering information into each input form in the app.

[2010] input

[2011] User's personal information (gender, age, height, weight, personal color, face photo) and styling scene selection.

[2012] output

[2013] Entered personal information and styling scene data.

[2014] Specific actions

[2015] The user enters "Gender: Female," "Age: 30," "Height: 160cm," "Personal color: Blue-based," and "Face photo," and then selects "Styling scene: Work."

[2016] Step 2: Submit user information

[2017] Terminal

[2018] When the user completes the input and presses the send button, this information is automatically sent from the terminal to the server.

[2019] input

[2020] Personal information and styling scene data entered by the user.

[2021] output

[2022] Data transmission result from the device to the server.

[2023] Specific actions

[2024] When the user presses the submit button, the user information is sent to the server in JSON format.

[2025] Step 3: Stay up to date on the latest trends

[2026] server

[2027] The server periodically collects the latest trend information from fashion-related websites and APIs, and retrieves the data using web scraping tools and API requests.

[2028] input

[2029] URLs for fashion-related websites and APIs.

[2030] output

[2031] Collected latest fashion trend information.

[2032] Specific actions

[2033] The server uses a web scraping tool to collect information such as, "This season's work fashion trends are mid-length tight skirts and simple blouses."

[2034] Step 4: Analyze the latest trend information

[2035] server

[2036] The collected trend information is input into the generative AI for analysis, which analyzes the trend data and updates the model that understands each attribute (color, design, material, etc.).

[2037] input

[2038] Collected latest trend information.

[2039] output

[2040] Analyzed trend data.

[2041] Specific actions

[2042] The trend data collected by the server is input into the generative AI using a Python script, and the model learns trend items such as "medium-length tight skirts" and "simple blouses."

[2043] Step 5: Collecting emotion data

[2044] Terminal

[2045] Using the camera and microphone installed on the user's device, emotional data is collected from the user's facial expressions and voice. Facial and voice recognition works in real time, and emotional data is recorded.

[2046] input

[2047] The user's facial expression and voice data.

[2048] output

[2049] Collected emotion data.

[2050] Specific actions

[2051] While the user is using the app, the camera detects "smiles" and "surprised expressions," and the microphone records the user's "positive tone of voice" and "negative tone of voice."

[2052] Step 6: Analyze the sentiment data

[2053] Emotion Engine

[2054] The emotion engine analyzes the collected data to determine the user's current emotional state, using emotion recognition algorithms (e.g., OpenCV or TensorFlow).

[2055] input

[2056] Collected user sentiment data.

[2057] output

[2058] The analyzed emotional state of the user.

[2059] Specific actions

[2060] The emotion engine analyzes facial recognition data to determine if the user has a happy facial expression, and voice analysis detects if the user is speaking in a positive tone.

[2061] Step 7: Learn about users and trends

[2062] server

[2063] The server inputs the user's personal information and trend information into the AI ​​generator. Based on this information, the AI ​​learns the user's preferences, body type, and adapts to trends. Emotional data is also input into the AI ​​generator, which generates outfits that take the user's emotions into consideration.

[2064] input

[2065] User personal information, latest trend information, and sentiment data.

[2066] output

[2067] Trained model data.

[2068] Specific actions

[2069] The server combines user information and trend information in JSON format and sends it to the AI ​​generator, which then suggests a light-colored, medium-length tight skirt and a refreshing blue blouse, since the user has blue-based skin and looks happy today.

[2070] Step 8: Generate coordinates

[2071] Generation AI

[2072] Generative AI generates personalized outfits based on the user's personal information, emotional data, and the latest trends.

[2073] input

[2074] User personal information, sentiment data, and analyzed trend information.

[2075] output

[2076] The generated coordinates.

[2077] Specific actions

[2078] The AI ​​generator suggests, "Since the user has blue-toned skin and a happy expression, a light-colored, medium-length tight skirt and a refreshing blue blouse."

[2079] Step 9: Coordination suggestions

[2080] server

[2081] The generated coordinates are sent to the user's terminal via the server and displayed to the user.

[2082] input

[2083] Generated coordinate data.

[2084] output

[2085] Coordinate display on user device.

[2086] Specific actions

[2087] The server sends the generated coordinates in JSON format to the user's device, where they are displayed in the device app.

[2088] Step 10: Show your outfit and get feedback

[2089] Terminal

[2090] The proposed outfit is displayed on the user's device. The user can then enter feedback (like, dislike, or comment) about the outfit and send it to the server. The emotion engine also collects emotional data from the user's facial expressions and voice when providing feedback.

[2091] input

[2092] User feedback data (likes, dislikes, comments), facial expressions and voice data.

[2093] output

[2094] Results of sending feedback data and emotion data to the server.

[2095] Specific actions

[2096] The user clicks the LIKE button to say, "I like this outfit," and enters a comment such as, "I especially like the design of the blouse." The emotion engine then recognizes that the user is expressing positive feelings toward the suggestion.

[2097] Step 11: Learning from feedback and sentiment data

[2098] Server and spawned AI

[2099] The server receives user feedback and emotion data from the emotion engine, and provides it to the generative AI to use as training data to improve the accuracy of the model.

[2100] input

[2101] User feedback and sentiment data.

[2102] output

[2103] Updated generative AI models.

[2104] Specific actions

[2105] The generative AI incorporates new feedback data and updates its models to more accurately reflect the user's preferences and emotional state.

[2106] Step 12: Run the Virtual Try-On Feature

[2107] Servers and Terminals

[2108] The server uses a virtual fitting system to generate a three-dimensional avatar based on the user's body type information. The generated avatar tries on suggested outfits to simulate a real fit. The fitting results are sent to the user's device, where the user can check the results.

[2109] input

[2110] User's body type information and suggested outfits.

[2111] output

[2112] Results of trying on a 3D avatar.

[2113] Specific actions

[2114] Users can use the virtual try-on feature to check how an outfit fits on their own avatar. The suggested outfit is applied to the avatar, and the user can check how it fits from the front, back, left and right.

[2115] (Application example 2)

[2116] 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."

[2117] Conventional fashion suggestion systems only use the user's personal information and the latest fashion trend information, but are unable to suggest personalized outfits that reflect the user's emotions. In addition, the virtual try-on function is limited, making it difficult to provide a satisfying fitting based on the user's emotions.

[2118] The specific processing by the specific 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 means for inputting the user's personal information, means for transmitting the personal information to the server, means for collecting the latest fashion trend information from websites and application program interfaces, means for providing a generation AI that analyzes the personal information and the trend information and generates a personalized outfit, means for collecting and analyzing emotional data from the user's facial expressions and voice, means for transmitting and displaying the outfit generated by the generation AI to the user terminal, means for displaying the suitability of the outfit to the user via a virtual try-on system, and means for collecting user feedback and inputting the feedback to the generation AI. This makes it possible to provide a personalized outfit that reflects the user's personal information and emotional data, and to confirm the actual fit in advance through virtual try-on.

[2119] "User information" refers to information including personal information, attribute information, and emotional data of a user.

[2120] "Personal information" refers to various data about the user, such as the user's gender, age, height, weight, personal color, and facial photo.

[2121] "Emotional data" is information about the emotional state of a user that is analyzed from their facial expressions and voice.

[2122] A "server" is a centralized computing resource for collecting and analyzing user information and trend information.

[2123] "Generative AI" is an AI algorithm that generates personalized outfits based on collected data.

[2124] "Latest Fashion Trend Information" refers to information about current fashion industry trends collected from websites and application program interfaces.

[2125] An "emotion analysis engine" is a technology that collects and analyzes emotional data from a user's facial expressions and voice.

[2126] A "user device" is a display device used by a user, such as a smartphone or head-mounted display.

[2127] "Coordination" is a suggested clothing combination generated based on the user's personal information, the latest trends, and emotional data.

[2128] The "virtual fitting system" is a system that displays the fit of suggested outfits to users through a three-dimensional avatar.

[2129] "Feedback" refers to the user's reactions to the proposed outfits, such as ratings and comments.

[2130] A "3D avatar" is a virtual 3D character generated based on the user's body type information.

[2131] The system for implementing this invention involves a series of processes that collects a user's personal information, emotional data, and the latest fashion trend information, analyzes them, proposes personalized outfits, and allows the user to virtually try them on. The system is mainly composed of the following elements: a server, a user terminal, a generative artificial intelligence (generative AI), an emotion analysis engine, and a virtual try-on system.

[2132] Specific examples of hardware and software used

[2133] Hardware

[2134] Smartphone or Head-Mounted Display (HMD): Used as the user device. Example: Oculus Quest.

[2135] Camera and microphone: A device used to capture a user's facial photograph and voice data.

[2136] software

[2137] Web scraping tools: Tools for gathering trend information from fashion-related websites. Examples: Beautiful Soup, Scrapy.

[2138] Generative artificial intelligence (generative AI) models: AI algorithms for generating personalized outfits. Example: OpenAI's GPT-based models.

[2139] Emotion analysis engine: Technology for analyzing emotional data from a user's facial expressions and voice. Example: Microsoft Azure's Emotion API.

[2140] Virtual fitting system: A system that generates a three-dimensional avatar based on the user's body type information and lets them try on outfits. Example: Clo Virtual Fashion.

[2141] System action

[2142] Enter and submit user information

[2143] The user starts the application on their device and enters personal information such as gender, age, height, weight, personal color, and a photo of their face. They also select the desired styling scene (e.g., work, date, or holiday). This information is sent to the server.

[2144] As a specific example, a user enters "gender: female, age: 30, height: 160cm, weight: 55kg, personal color: blue-based, face photo" and selects "styling scene: work."

[2145] Acquisition and analysis of the latest trend information

[2146] The server periodically collects the latest trend information from fashion-related websites and APIs, which is then input into the generative AI and analyzed using natural language processing.

[2147] For example, collect information such as, "This season's work fashion trends are mid-length tight skirts and simple blouses."

[2148] Emotion data collection and analysis

[2149] The camera and microphone on the user's device are used to collect emotional data from facial expressions and voice, and the emotion analysis engine analyzes the data to determine the user's emotional state.

[2150] For example, if a user says, "You look very happy today," or "This outfit is lovely," the system will analyze the positive emotion from the voice.

[2151] Personalized outfit creation

[2152] The server inputs the user's personal information, emotional data, and trend information into the AI ​​generator, which then generates outfits that take into account the user's preferences, body type, and emotions. The generated outfits are then sent to the user's device and displayed.

[2153] Example prompt

[2154] Here are some example prompts to input to the AI ​​generator:

[2155] User Information:

[2156] Gender: Female

[2157] Age: 30

[2158] Height: 160cm

[2159] Weight: 55kg

[2160] Personal color: Blue-based

[2161] Preferred styling occasion: Work

[2162] Latest Trends:

[2163] This season's work fashion trends are mid-length tight skirts and simple blouses

[2164] Emotional state:

[2165] A very happy look

[2166] A positive comment that this outfit is lovely

[2167] Generate personalized work outfits for blue-toned skin based on your preferences and the latest trends.

[2168] Virtual try-on

[2169] The virtual fitting system generates a three-dimensional avatar based on the user's body type and emotional data, and then allows the user to try on suggested outfits, allowing them to check how the outfits actually fit.

[2170] The system allows users to receive personalized fashion suggestions in real time that take into account their personality, body type, and emotions, and the virtual try-on feature reduces mistakes when shopping online.

[2171] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2172] Step 1:

[2173] Enter and submit user information

[2174] The user starts the application on their smartphone or head-mounted display (HMD) and inputs their gender, age, height, weight, personal color, a photo of their face, and the desired styling scene. This information is then sent from the device to the server and saved.

[2175] Input: Gender, age, height, weight, personal color, face photo, styling scene

[2176] Output: User information stored on the server

[2177] Step 2:

[2178] Collection and analysis of the latest trend information

[2179] The server periodically collects the latest fashion trend information from fashion-related websites and APIs using a web scraping tool, which is then input into a generative AI model for analysis.

[2180] Input: Fashion trend information obtained from websites and APIs

[2181] Output: Latest analyzed trend information

[2182] Step 3:

[2183] Emotion data collection and analysis

[2184] The camera and microphone on the user's device collect the user's facial expressions and voice. This data is input into an emotion analysis engine to analyze the user's current emotional state. The analysis results are then sent to the server.

[2185] Input: User facial and voice data

[2186] Output: Parsed emotion data

[2187] Step 4:

[2188] Personalized outfit creation

[2189] The server inputs user information, trend information, and emotional data into the generative AI model to generate personalized outfits tailored to the user's preferences, body type, and emotions. The generated outfits are sent to the user's device and displayed.

[2190] Input: User information, latest trend information, sentiment data

[2191] Output: Personalized outfit ideas

[2192] Step 5:

[2193] Virtual try-on

[2194] The virtual fitting system generates a three-dimensional avatar based on the user's body type information, has them try on suggested outfits, and sends the results to the user's device, where the user can check the fitting results.

[2195] Input: User's body type information, suggested outfits

[2196] Output: 3D avatar fitting results

[2197] Step 6:

[2198] Collecting and analyzing feedback

[2199] Users provide feedback (likes, dislikes, comments) on outfits, which is analyzed by a sentiment analysis engine and the results are sent to the server and used as training data for the generative AI model.

[2200] Input: User feedback and sentiment data

[2201] Output: Updated training data by generative AI model

[2202] Through this process, users can easily find outfits that are optimized to suit their individuality, body type, and emotions, and can check the actual fit in advance through virtual try-on.

[2203] 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.

[2204] 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.

[2205] 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.

[2206] 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.

[2207] 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.

[2208] 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.

[2209] 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).

[2210] 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.

[2211] 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."

[2212] 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.

[2213] 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).

[2214] 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.

[2215] 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 2...

Claims

1. a means for inputting the user's personal information; means for transmitting the personal information to a server; means for collecting latest fashion trend information from websites and application program interfaces; A generating artificial intelligence means for analyzing the personal information and the trend information and generating a personalized coordinate; A means for transmitting and displaying the coordinates generated by the generating artificial intelligence to a user terminal; A means for collecting user feedback and inputting the feedback into the generating artificial intelligence; A means for displaying the suitability of the coordinated outfit to a user through a virtual try-on system. A system including:

2. The system according to claim 1 , wherein the personal information of the user includes gender, age, height, weight, personal color, and a facial photograph.

3. 2. The system according to claim 1, wherein the virtual try-on system includes means for generating a three-dimensional avatar based on the user's body type information and allowing the user to try on suggested outfits.

Images