system

Abstract

We provide the system. [Solution] An information input means for inputting the user's physical characteristics and style preferences, A data transmission means that transmits the information entered by the information input means to a server, An analysis means on the server analyzes user information and generates clothing suggestions best suited to the user, A proposal presentation means that presents clothing suggestions generated by the analysis means to the user, A fitting video generation means that generates a virtual clothing try-on based on the user's selection, A means for presenting the fitting video generated by the fitting video generation means to the user, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] As a problem of fashion selection that many people face in modern times, there is a problem that it is difficult to efficiently select clothing items that suit one's body type and style preferences. Due to this problem, users cannot find the most suitable one for themselves among a large number of options, and as a result, they may make unsatisfactory choices. Also, in online shopping, since the products cannot be actually tried on, there are often concerns about size and appearance. For these problems, there is a need for means to provide personalized proposals for individual users and enable them to safely select clothing items through virtual fitting.

Means for Solving the Problems

[0005] The present invention includes a data transmission means that inputs the user's physical characteristics and style preferences through an information input means mounted on a user device and transmits this information to a server. The server includes an analysis means that analyzes the input user information and suggests clothing items best suited to the user, and a suggestion presentation means that presents the generated suggestions to the user. Furthermore, it provides a fitting video generation means that allows the user to virtually try on the clothing items they have selected, thereby providing a fitting experience close to that of trying on actual clothing. By integrating these means, the present invention provides a system that allows users to efficiently select clothing items that meet their preferences.

[0006] "Information input means" refers to a device or software that provides an interface for users to input their physical characteristics and style preferences.

[0007] "Data transmission means" refers to the process or system for sending entered user information to a server.

[0008] "Analysis means" refers to a system equipped with algorithms and programs for generating optimal clothing recommendations based on received user information.

[0009] "Suggestion presentation means" refers to a system or interface that presents clothing suggestions generated by the analysis means to the user visually or in other ways.

[0010] "Try-on video generation method" refers to a process or software that generates videos simulating a virtual try-on of clothing selected based on the user's photos and data.

[0011] A "purchase method" refers to a system that provides payment and ordering processes for users to use when purchasing suggested clothing items online.

[0012] The "reservation method" refers to a system for managing and processing reservations for trying on selected clothing items at a physical store. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0016] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] In an embodiment of the present invention, the user first installs a dedicated application on a device such as a smartphone. This application provides the user with an input interface, allowing the user to input their physical characteristics (e.g., height, weight, body type) and style preferences (e.g., casual, formal, etc.).

[0035] When a user enters information, the device sends it to the server as data. The server uses AI technology to analyze the received data and generate optimal clothing choices tailored to the user's profile. The analysis uses machine learning algorithms to understand the user's body type and style tendencies.

[0036] The generated clothing suggestions are sent from the server to the terminal, which then displays the suggested clothing items to the user. The user can then select the clothing items they wish to view in more detail from among the multiple suggestions.

[0037] After the selection is made, the device sends another request to the server to generate a try-on video based on the selected clothing items and the user's photo. In this try-on video, AI superimposes the clothing onto the user's image, providing a virtual try-on experience.

[0038] Users who watch the try-on videos can either purchase their favorite clothing items directly through the app or book a fitting appointment at a physical store. This allows users to effectively choose clothing that suits them best and enjoy online shopping with peace of mind.

[0039] For example, if a user is looking for a casual jacket, they can input information such as their height (175cm) and weight (70kg). Based on this information, the server will suggest jackets that match the user's color, size, and style. The selected jackets are displayed on the user's device as a try-on video, and the user can make a purchase decision after reviewing it. In this way, the present invention provides a system that enables personalized fashion suggestions that meet the individual needs of users.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user installs the application on their smartphone and launches it. The app displays a user registration screen and provides an interface for entering basic information such as gender, height, weight, and style preferences. The user enters their information accordingly and also uploads a full-body photo.

[0043] Step 2:

[0044] The device securely transmits user-entered information and photos to the server using encryption and other methods. Here, the data is strictly managed to protect user privacy.

[0045] Step 3:

[0046] The server analyzes the received user data using AI. Specifically, it applies machine learning algorithms to evaluate the user's body type and style characteristics, and based on that, generates suggestions for the most suitable clothing for the user. The analysis process includes searching and selecting relevant fashion item data from a database.

[0047] Step 4:

[0048] The server sends a list of suggested clothing items back to the terminal. Upon receiving it, the terminal displays the suggested list clearly in the user interface. The user can then select the clothing items from the displayed list for further details.

[0049] Step 5:

[0050] When a user selects a specific piece of clothing, the device sends a request to the server to generate a corresponding try-on video. The server uses AI-powered image processing technology to superimpose the selected clothing onto the user's photo, creating a virtual try-on video.

[0051] Step 6:

[0052] The server sends the generated try-on video to the device, which then provides the video to the user. The user can then virtually try on the clothing and, if satisfied, make a purchase decision via the app.

[0053] Step 7:

[0054] If a user wishes to make a purchase, the device opens a purchase screen and provides an interface for entering payment information and shipping address. Once the purchase is complete, the server verifies the order information and begins the process of shipping the product.

[0055] Thus, each step is a process designed to streamline the user experience and make online fashion selection easier.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] In modern times, consumers have a wide variety of clothing options, but it is not easy to quickly and accurately choose the best clothing. In particular, online shopping makes it difficult to find clothing that matches consumers' physical characteristics and style preferences, and the inability to try on clothes is a source of anxiety. This invention aims to solve these problems.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes information receiving means for inputting the user's physical attributes and style preferences, data transfer means for transferring the attributes input by the information receiving means to a computer, and analysis means in the computer for interpreting the user attributes and generating clothing suggestions optimized for the user. This allows consumers to easily select the clothing that best suits them and purchase it online with confidence through a virtual try-on experience.

[0061] An "information receiving mechanism" is an interface for users to input their own physical attributes and style preferences.

[0062] A "data transfer means" is a function for transferring attribute information entered by a user to a computer.

[0063] A "computer" is a device that analyzes user attributes and generates optimal clothing suggestions based on those attributes.

[0064] "Analysis means" refers to a process within a computer for generating optimized clothing suggestions using received user attribute information.

[0065] "Presentation means" refers to a function that displays clothing suggestions generated by the server to the user.

[0066] A "try-on video generation means" is a system that allows users to virtually try on selected clothing, enabling them to visually confirm the fit.

[0067] To implement this invention, the user must first install a dedicated application on a communication terminal connected to the internet, specifically a smartphone or tablet. This application includes an information receiving means that provides an interface for the user to input their physical attributes and style preferences. The user inputs their height, weight, and preferred style (e.g., casual, formal, etc.) and transmits this information to the server via data transfer means through the terminal.

[0068] The server uses a computer to execute the core analysis means of the present invention. This analysis means utilizes a generative AI model to analyze the received user information and generate clothing suggestions optimized for the user. The server uses a large amount of existing data and machine learning algorithms (e.g., neural networks) to perform data processing to select clothing that matches the user's characteristics.

[0069] The generated clothing suggestions are sent from the server to the terminal and visually displayed to the user through a display device on the terminal. The user can select clothing items of interest from this suggested list. Once the user selects clothing, the server uses a fitting video generation device to generate a video in which the selected clothing is virtually tried on over the user's photograph. This video is designed to allow the user to intuitively see what the clothing would look like when actually worn.

[0070] For example, if a user is looking for a "casual jacket," they would enter information such as their height (175cm) and weight (70kg), and select "casual" as a style option. Based on this information, the server would suggest jackets with the most suitable color, size, and design, and generate a virtual try-on video of the suggested jackets, which would then be displayed on the user's device.

[0071] An example of a prompt for a generative AI model would be: "Please suggest a casual style jacket for a man who is 175cm tall and weighs 70kg."

[0072] This allows users to intuitively select clothing online while also easily trying on and purchasing items at physical stores, making online shopping a more secure and enjoyable experience.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The user installs a dedicated application on their communication device, launches the app, and uses the means to receive information. The user inputs their physical attributes (e.g., height, weight) and style preferences (e.g., casual) into the interface. This input data forms the user's individual profile.

[0076] Step 2:

[0077] The terminal sends the user's entered physical attributes and style preferences to the server using a data transfer mechanism. The data is structured in JSON format and sent to the server via an HTTP request. The terminal's output is the transmitted data, which becomes the input for the next analysis step.

[0078] Step 3:

[0079] The server processes the received JSON data using parsing tools. A generative AI model is used to generate appropriate clothing suggestions based on the user's attribute information. Here, machine learning algorithms are employed to analyze the input data and existing fashion datasets, outputting the optimal clothing choices.

[0080] Step 4:

[0081] The server sends the analysis results to the terminal, and the terminal displays the suggestions to the user using a presentation tool. The suggestions are presented in a user-friendly list format. The terminal output is a list of clothing options presented to the user.

[0082] Step 5:

[0083] The user selects items of interest from the clothing suggestions displayed on the device. This selected information is then sent back to the server by the device, becoming the basic data for generating the try-on video. Specific item selection is performed through touch operations.

[0084] Step 6:

[0085] The server activates a virtual try-on video generation system based on the selected clothing items and composites the items onto the user's photograph. Image processing technology then creates a virtual try-on video in which the selected clothing appears to fit the user's figure naturally. The generated video is finally transmitted to the terminal.

[0086] Step 7:

[0087] The terminal displays a virtual try-on video received from the server to the user. The user reviews the video and decides whether to purchase the clothing or make an appointment to try it on at a physical store. The final output consists of the video and selection options to help the user make a decision through an enhanced try-on experience.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] Online shopping often causes anxiety for buyers when selecting clothing because they cannot try items on in person. Furthermore, it is difficult to efficiently choose clothing that perfectly suits one's body type and style preferences. Additionally, the inability to visualize how the garment would look when worn leads to a higher likelihood of returns after purchase.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes an information input means for inputting the user's physical characteristics and style preferences, a data transmission means for transmitting the information input by the information input means to a computer, and an analysis means for analyzing the user information in the computer and generating clothing suggestions that are best suited to the user. This allows the user to virtually try on clothing that is best suited to them and make a purchase decision with confidence.

[0093] An "information input device" is a device that provides an interface for users to input information about their physical characteristics and style preferences.

[0094] A "data transmission means" is a component that has the function of transferring information obtained from an information input means to a server.

[0095] "Analysis means" refers to software or a process used on a server to analyze user information and generate suggestions for clothing best suited to the user.

[0096] A "proposal presentation means" is a mechanism for visually presenting clothing suggestions generated by an analysis means to the user.

[0097] "Try-on video generation means" refers to technology for generating a virtual try-on experience based on the user's selection.

[0098] "Presentation means" refers to a component that has the function of displaying the virtual try-on experience generated by the try-on video generation means on the user's device.

[0099] A "purchase method" refers to an online platform that enables users to purchase selected clothing items through e-commerce.

[0100] The "reservation method" is a system that allows users to reserve a fitting appointment for their chosen clothing items at a physical store.

[0101] The system for implementing this invention uses a mobile device or personal computer to input the user's physical characteristics and preferences, and this information is entered through a dedicated application. First, the user inputs their height, weight, and style preferences using an information input means on the application.

[0102] The terminal sends this information to the server using a data transmission method. On the server, an AI model using machine learning algorithms as an analysis method processes the received user data and generates personalized clothing recommendations. This analysis typically uses software such as Python or TENSORFLOW®.

[0103] The suggestions generated by the server are sent back to the terminal via a data transmission means and presented to the user using a suggestion presentation means. The user can then choose the clothing items they would like to try from the displayed options.

[0104] For selected clothing items, a virtual try-on video is generated through a try-on video generation system. The generated video is visually displayed on the user's device using a presentation system. In this process, an AI model analyzes the user's photo and synthesizes a try-on image.

[0105] For example, if a user is looking for a casual jacket, they would input information such as their height (175cm) and weight (70kg) into the application. Based on this data, the server-side AI model would suggest suitable jackets and generate a virtual try-on video of the selected jacket. The user can then confidently purchase the jacket after viewing this virtual video.

[0106] Examples of prompt statements for a generative AI model are as follows:

[0107] "Design a machine learning model that receives user body data (e.g., height 175cm, weight 70kg) and style preferences, and suggests suitable clothing. Then, describe the steps to implement a process that generates a video of the user virtually trying on the selected clothing and presents it to the user."

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The user opens a dedicated application on their device and uses the input tools to enter their physical characteristics (e.g., height 175cm, weight 70kg) and style preferences (e.g., casual style). The entered data is temporarily stored within the application. Input is done via text boxes and selection menus.

[0111] Step 2:

[0112] The terminal uses a data transmission method to send user input data to the server. The HTTP protocol is used for transmission, and the data is converted to JSON format. The server prepares to parse the received JSON data.

[0113] Step 3:

[0114] The server applies analysis tools to generate optimal clothing suggestions based on the received user information. This analysis uses a machine learning algorithm implemented in Python to perform pattern matching on the data. Based on the user's body shape data and style preferences, it creates a list of recommended clothing items.

[0115] Step 4:

[0116] The server sends back a list of generated clothing suggestions to the terminal using a data transmission mechanism. The terminal's suggestion display mechanism displays the received data in a user interface. The user selects their preferred clothing items from the displayed list of suggestions.

[0117] Step 5:

[0118] After the user selects a specific piece of clothing, that information is sent back to the server. The server receives the data for the selected clothing and the user's image, and activates the virtual try-on video generation system.

[0119] Step 6:

[0120] A server-based virtual try-on video generation system analyzes the user's photos and synthesizes virtual try-on videos with selected clothing items. Using an AI model, it superimposes the clothing onto the user's body, generating videos that provide a realistic try-on experience.

[0121] Step 7:

[0122] The server sends the generated try-on video to the terminal, which then displays the video to the user through a presentation device. This allows the user to review the virtual try-on experience and supports their purchase decision.

[0123] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0124] In embodiments of the present invention, the user installs a dedicated application on a terminal such as a smartphone or personal computer. This application includes information input means for inputting the user's physical characteristics, style preferences, and emotional state. The user can use this input means to input their basic information and information about their current emotions.

[0125] The terminal transmits the input information to the server via a data transmission device. The server processes the received data using an analysis device and suggests the most suitable clothing items, taking into account the user's body type, style, and emotional state. The emotion recognition engine is used to analyze the emotional data entered by the user in real time and suggest clothing items that match the user's mood.

[0126] The suggested items are sent back from the server to the terminal, which then displays the suggestions to the user using a suggestion display device. The user selects items of interest from the suggested clothing, and based on this selection, a fitting video generation device operates to generate a virtual fitting video in which the selected clothing items are superimposed onto the user's photo. The terminal then presents this fitting video to the user, who can review it and, if necessary, make a purchase or book a fitting appointment at a store.

[0127] For example, when a user is feeling stressed, the emotion recognition engine can detect this state and recommend comfortable and relaxing clothing, such as loose-fitting casual wear. Furthermore, if a user responds positively to a particular suggestion, the learning function records that response and uses it to improve future suggestions. This allows the present invention to provide more personalized fashion suggestions that are tailored to the user's emotions.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] Users open a dedicated application on their smartphone or computer and enter their physical characteristics, style preferences, and current emotional state on an information input screen. Emotional states are recorded through a simple questionnaire or by taking photos of their facial expressions.

[0131] Step 2:

[0132] The device encrypts all information entered by the user and sends it to the server. This data includes the user's basic physical information, fashion preferences, and emotional data.

[0133] Step 3:

[0134] The server begins analyzing the received data. Using analysis tools, it selects the most suitable clothing items based on the user's body shape and style information, and evaluates the user's emotional state using an emotion recognition engine. The clothing recommendations are then adjusted based on these evaluation results.

[0135] Step 4:

[0136] The server generates a list of clothing suggestions that match the user's emotional state. This list changes depending on the emotions the user is currently feeling; for example, if the user is stressed, it will suggest relaxing clothing, and if they are excited, it will suggest colorful designs.

[0137] Step 5:

[0138] The server sends the generated list of suggestions back to the terminal. The terminal displays this list on its screen. The user reviews the details of the suggested clothing items and selects specific items that pique their interest.

[0139] Step 6:

[0140] Based on the clothing items selected by the user, the device requests a virtual try-on video generation method from the server. The server uses the user's photo data and information about the selected clothing items to create a virtual try-on video and sends it back to the device.

[0141] Step 7:

[0142] The device displays a generated try-on video to the user. The user reviews the video and visually confirms whether the selected clothing item suits them.

[0143] Step 8:

[0144] If a user wishes to purchase an item or schedule a fitting appointment at a store, the terminal provides access to these options. For a purchase, the user enters payment information to complete the purchase; for a fitting appointment, the user selects a preferred date and proceeds with the reservation.

[0145] This process allows users to efficiently and comfortably receive and select personalized fashion suggestions that are tailored to their emotional state.

[0146] (Example 2)

[0147] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0148] Modern consumers have diverse tastes and preferences, making it difficult to satisfy them with uniform fashion suggestions. Furthermore, emotional states influence purchasing decisions, requiring personalized suggestions tailored to the user's emotions. However, conventional systems struggle to provide optimal suggestions by comprehensively considering multiple pieces of user information, and a particular challenge exists in their ability to reflect emotional states in real time.

[0149] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0150] In this invention, the server includes an analysis means for analyzing user information, an emotion recognition means for generating emotion-based suggestions, and a fitting video generation means for generating virtual fitting videos. This makes it possible to suggest clothing that is individually optimized and reflect the user's emotional state in real time, taking into account the user's physical characteristics, style preferences, and emotional state.

[0151] "Information input means" refers to a device or software for inputting a user's physical characteristics, style preferences, and emotional information.

[0152] "Data transmission means" refers to a function or device that transmits user information entered by information input means to a computing device.

[0153] "Analysis means" refers to a function or program in a computing device that uses a generated AI model to analyze user information and generate clothing recommendations that are optimal for that user.

[0154] "Emotion recognition means" refers to a function or system that analyzes a user's emotional information and generates clothing suggestions that match the user's mood.

[0155] A "proposal presentation means" refers to a function or device that presents clothing suggestions generated by an analysis means to the user visually or audibly.

[0156] "Try-on video generation means" refers to a function or program that generates a virtual try-on video of clothing items based on the user's selection.

[0157] "Presentation means" refers to a function or device that visually displays the try-on video generated by the try-on video generation means to the user.

[0158] "Purchase method" refers to a function or platform that enables users to purchase selected clothing items online.

[0159] A "reservation method" refers to a function or system that allows users to reserve a fitting appointment for selected clothing items at a physical store.

[0160] In embodiments of the present invention, the user installs a dedicated application on a computer terminal such as a smartphone or personal computer. This application includes an interface for inputting the user's physical characteristics, style preferences, and emotional information.

[0161] The terminal collects information using input means and transmits it to the server via data transmission means. This information is encrypted and securely delivered to the server. Based on the received information, the server uses a generative AI model to analyze the user's data and suggests clothing that is most suitable for that user. By using emotion recognition means, the server generates suggestions that match the user's emotions and presents clothing that matches the user's current mood.

[0162] The suggested items are sent from the server to the terminal and displayed to the user by the suggestion presentation system. The user can view this display and select the clothing items they like. The selected clothing items are then superimposed onto the user's photo through the try-on video generation system. This superimposition allows the user to watch a virtual try-on video and have an experience similar to actually trying on the clothing.

[0163] For example, if a user is feeling stressed, the emotion recognition engine can detect this state and recommend relaxing casual wear. Based on this recommendation, the user can make a more appropriate choice.

[0164] An example of a prompt message is as follows:

[0165] User information: Height 170cm, weight 65kg, preferred style: casual, current emotional state: feeling stressed.

[0166] This configuration allows users to receive personalized clothing suggestions tailored to their individual emotions and preferences, resulting in a higher level of satisfaction compared to traditional shopping experiences.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The user installs a dedicated application on their device. This application includes a user interface for inputting physical characteristics, style preferences, and emotional state. Basic information such as the user's height, weight, style preferences, and emotional state is required as input. This information is then stored in a database as output.

[0170] Step 2:

[0171] The terminal transmits the entered user information to the server via a data transmission method. As input, it uses all the information entered by the user and packages it according to the data transmission protocol. As output, the server receives this information and prepares for analysis.

[0172] Step 3:

[0173] The server processes the received user information using analytical tools. The received information is passed as input to a generating AI model, which performs analysis based on the user's physical characteristics, style preferences, and emotional state. The output generates data suggesting the most suitable clothing for the user. This analysis includes database matching, algorithmic data processing, and calculations.

[0174] Step 4:

[0175] The emotion recognition system on the server analyzes the user's emotional data in real time. It takes the user's emotional state data as input and recognizes the appropriate emotional state based on this data. As output, it generates data for selecting the most suitable clothing for that emotion. An emotion recognition algorithm is used in this process.

[0176] Step 5:

[0177] The server sends the analysis results to the terminal. The server prepares the analyzed suggestion data and emotion recognition results as input and sends them to the terminal via a data transmission method. The terminal receives this output and prepares to present it to the user.

[0178] Step 6:

[0179] The terminal displays suggestions to the user using a suggestion presentation mechanism. It uses suggestion data received from the server as input and displays it to the user via a GUI (user interface). As output, the user can review the clothing suggestions and select the ones they like.

[0180] Step 7:

[0181] The user selects their preferred clothing items from the suggested options. This selection is processed by the device's virtual try-on video generation system. The input consists of data on the clothing items selected by the user and the user's photo information. The output is a video of the user virtually trying on the selected clothing items.

[0182] Step 8:

[0183] The terminal presents the generated try-on video to the user. As input, it reads the try-on video data and displays it using a playback tool. As output, the user can review the virtual try-on and make a decision to purchase or schedule a try-on if necessary.

[0184] (Application Example 2)

[0185] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0186] Traditional clothing recommendation systems rely solely on users' physical characteristics and style preferences, making it difficult to provide personalized recommendations that reflect the user's emotional state. Furthermore, virtual try-on features, designed to enhance the user experience, are limited, failing to adequately stimulate purchasing intent.

[0187] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0188] In this invention, the server includes an information input means for inputting the user's physical characteristics, style preferences, and emotional state information; a data transmission means for transmitting the information input by the information input means to a data storage device; and an analysis means for generating optimal clothing suggestions for the user using a generative artificial intelligence model. This makes it possible to provide optimal clothing suggestions that correspond to the user's emotions.

[0189] A "user" is an individual who utilizes the system and provides information about their physical characteristics, style preferences, and emotional state.

[0190] An "information input means" is an interface for users to input information about their physical characteristics, preferences, and emotional state.

[0191] A "data transmission means" is a function for transmitting information obtained through an information input means to a server.

[0192] A "generative artificial intelligence model" is an algorithm that analyzes information obtained from users and suggests clothing suitable for those users.

[0193] The "analysis means" refers to a function that processes user information received on the server and uses a generative artificial intelligence model to select the most suitable clothing.

[0194] The "proposal presentation means" is a function for displaying clothing suggestions generated by the analysis means to the user.

[0195] "Virtual display generation means" refers to a technology for generating virtual clothing try-on images or videos based on user selections and presenting them visually to the user.

[0196] "Visual representation" refers to an image or video that virtually shows the user what it looks like when trying on clothing.

[0197] The present invention is implemented as an application that runs on a user's device. This application provides an information input means for inputting the user's physical characteristics, style preferences, and emotional state. The user can input their body size, clothing preferences, and emotional information into a smartphone or personal computer. Once the information is entered, a data transmission means sends it to a server in the cloud.

[0198] The server operates using Amazon Web Services (AWS®). The server analyzes incoming data using a generative artificial intelligence model to generate optimal clothing suggestions to personalize the user experience. Azure® Cognitive Services acts as an emotion recognition engine, analyzing the user's emotional state to further personalize the suggestions.

[0199] The analyzed data is transmitted to the user's terminal via a suggestion presentation means, which the user can then view. For suggestions that interest the user, a virtual try-on image or video is generated using Unity or OpenCV via a virtual display generation means. The synthesized video is presented to the user as a visual display, allowing the user to review the content and either make a purchase or book a try-on appointment at a physical sales facility.

[0200] For example, if a user inputs "I'm feeling stressed today," the emotion recognition engine will suggest relaxing clothing, such as comfortable loungewear. The prompt to the generative AI model in this case would be as follows:

[0201] "User's emotional state: Emotional information entered by the user"

[0202] User style information: Style information entered by the user

[0203] Please select clothing items to propose and suggest up to 5 items that meet the following criteria:

[0204] Relax

[0205] User's preferred style

[0206] Virtual try-on images are available.

[0207] This configuration provides a highly customized purchasing experience based on emotionally resonant user preferences.

[0208] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0209] Step 1:

[0210] The user launches the application on their device and inputs information about their physical characteristics, style preferences, and emotional state through an input method. The data the user inputs includes their body size, clothing style preferences (e.g., casual, formal), and emotional information (e.g., want to relax, want to feel energized). This input data is stored in the device's local storage.

[0211] Step 2:

[0212] The terminal transmits data entered via an information input means to a server via a data transmission means. The terminal uses encryption technology to ensure data security while transmitting data to the cloud server. This protects the user's personal information.

[0213] Step 3:

[0214] The server processes the received data using analytical tools. The server utilizes AWS computing resources to run a generative AI model, generating real-time optimal clothing suggestions based on the user's body type, style, and emotional state. The input here is data submitted by the user, and the output is a list of suggested clothing items.

[0215] Step 4:

[0216] The server sends the generated clothing suggestions to the terminal via a suggestion presentation system. The server uses a high-speed and efficient data transfer protocol to send the suggestion list output by the generating AI model to the terminal. This allows the user to view the suggestions without delay.

[0217] Step 5:

[0218] The terminal uses a suggestion display mechanism to show the user clothing suggestions received from the server. The input here is the suggestion list sent from the server, and the output is the clothing list displayed in the terminal's GUI. The user reviews the suggested clothing and selects the items they like.

[0219] Step 6:

[0220] The user uses a virtual display generation means to virtually try on clothing based on their selection. In this step, the terminal uses Unity or OpenCV to composite the selected clothing onto the user's photo and generate a virtual try-on image or video according to the prompt messages generated by the server. The input is the user's selection data and photo, and the output is a virtual try-on image or video.

[0221] Step 7:

[0222] The terminal presents the generated virtual try-on image or video to the user as a visual display. The user can review it and, if they like it, proceed with the purchase or make a reservation to try it on at a store. The input here is the output of the virtual try-on, and the output is the user's purchase action.

[0223] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0224] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0225] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0226] [Second Embodiment]

[0227] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0228] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0229] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0230] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0231] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0232] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0233] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0234] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0235] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0236] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0237] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0238] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0239] In an embodiment of the present invention, the user first installs a dedicated application on a device such as a smartphone. This application provides the user with an input interface, allowing the user to input their physical characteristics (e.g., height, weight, body type) and style preferences (e.g., casual, formal, etc.).

[0240] When a user enters information, the device sends it to the server as data. The server uses AI technology to analyze the received data and generate optimal clothing choices tailored to the user's profile. The analysis uses machine learning algorithms to understand the user's body type and style tendencies.

[0241] The generated clothing suggestions are sent from the server to the terminal, which then displays the suggested clothing items to the user. The user can then select the clothing items they wish to view in more detail from among the multiple suggestions.

[0242] After the selection is made, the device sends another request to the server to generate a try-on video based on the selected clothing items and the user's photo. In this try-on video, AI superimposes the clothing onto the user's image, providing a virtual try-on experience.

[0243] Users who watch the try-on videos can either purchase their favorite clothing items directly through the app or book a fitting appointment at a physical store. This allows users to effectively choose clothing that suits them best and enjoy online shopping with peace of mind.

[0244] For example, if a user is looking for a casual jacket, they can input information such as their height (175cm) and weight (70kg). Based on this information, the server will suggest jackets that match the user's color, size, and style. The selected jackets are displayed on the user's device as a try-on video, and the user can make a purchase decision after reviewing it. In this way, the present invention provides a system that enables personalized fashion suggestions that meet the individual needs of users.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] The user installs the application on their smartphone and launches it. The app displays a user registration screen and provides an interface for entering basic information such as gender, height, weight, and style preferences. The user enters their information accordingly and also uploads a full-body photo.

[0248] Step 2:

[0249] The device securely transmits user-entered information and photos to the server using encryption and other methods. Here, the data is strictly managed to protect user privacy.

[0250] Step 3:

[0251] The server analyzes the received user data using AI. Specifically, it applies machine learning algorithms to evaluate the user's body type and style characteristics, and based on that, generates suggestions for the most suitable clothing for the user. The analysis process includes searching and selecting relevant fashion item data from a database.

[0252] Step 4:

[0253] The server sends a list of suggested clothing items back to the terminal. Upon receiving it, the terminal displays the suggested list clearly in the user interface. The user can then select the clothing items from the displayed list for further details.

[0254] Step 5:

[0255] When a user selects a specific piece of clothing, the device sends a request to the server to generate a corresponding try-on video. The server uses AI-powered image processing technology to superimpose the selected clothing onto the user's photo, creating a virtual try-on video.

[0256] Step 6:

[0257] The server sends the generated try-on video to the device, which then provides the video to the user. The user can then virtually try on the clothing and, if satisfied, make a purchase decision via the app.

[0258] Step 7:

[0259] If a user wishes to make a purchase, the device opens a purchase screen and provides an interface for entering payment information and shipping address. Once the purchase is complete, the server verifies the order information and begins the process of shipping the product.

[0260] Thus, each step is a process designed to streamline the user experience and make online fashion selection easier.

[0261] (Example 1)

[0262] Next, we will describe Example 1. 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."

[0263] In modern times, consumers have a wide variety of clothing options, but it is not easy to quickly and accurately choose the best clothing. In particular, online shopping makes it difficult to find clothing that matches consumers' physical characteristics and style preferences, and the inability to try on clothes is a source of anxiety. This invention aims to solve these problems.

[0264] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0265] In this invention, the server includes information receiving means for inputting the user's physical attributes and style preferences, data transfer means for transferring the attributes input by the information receiving means to a computer, and analysis means in the computer for interpreting the user attributes and generating clothing suggestions optimized for the user. This allows consumers to easily select the clothing that best suits them and purchase it online with confidence through a virtual try-on experience.

[0266] An "information receiving mechanism" is an interface for users to input their own physical attributes and style preferences.

[0267] A "data transfer means" is a function for transferring attribute information entered by a user to a computer.

[0268] A "computer" is a device that analyzes user attributes and generates optimal clothing suggestions based on those attributes.

[0269] "Analysis means" refers to a process within a computer for generating optimized clothing suggestions using received user attribute information.

[0270] "Presentation means" refers to a function that displays clothing suggestions generated by the server to the user.

[0271] A "try-on video generation means" is a system that allows users to virtually try on selected clothing, enabling them to visually confirm the fit.

[0272] To implement this invention, the user must first install a dedicated application on a communication terminal connected to the internet, specifically a smartphone or tablet. This application includes an information receiving means that provides an interface for the user to input their physical attributes and style preferences. The user inputs their height, weight, and preferred style (e.g., casual, formal, etc.) and transmits this information to the server via data transfer means through the terminal.

[0273] The server uses a computer to execute the core analysis means of the present invention. This analysis means utilizes a generative AI model to analyze the received user information and generate clothing suggestions optimized for the user. The server uses a large amount of existing data and machine learning algorithms (e.g., neural networks) to perform data processing to select clothing that matches the user's characteristics.

[0274] The generated clothing suggestions are sent from the server to the terminal and visually displayed to the user through a display device on the terminal. The user can select clothing items of interest from this suggested list. Once the user selects clothing, the server uses a fitting video generation device to generate a video in which the selected clothing is virtually tried on over the user's photograph. This video is designed to allow the user to intuitively see what the clothing would look like when actually worn.

[0275] For example, if a user is looking for a "casual jacket," they would enter information such as their height (175cm) and weight (70kg), and select "casual" as a style option. Based on this information, the server would suggest jackets with the most suitable color, size, and design, and generate a virtual try-on video of the suggested jackets, which would then be displayed on the user's device.

[0276] An example of a prompt for a generative AI model would be: "Please suggest a casual style jacket for a man who is 175cm tall and weighs 70kg."

[0277] This allows users to intuitively select clothing online while also easily trying on and purchasing items at physical stores, making online shopping a more secure and enjoyable experience.

[0278] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0279] Step 1:

[0280] The user installs a dedicated application on the communication terminal, launches the application, and uses the information receiving means. The user inputs their physical attributes (e.g., height, weight) and style preferences (e.g., casual) into the interface. These input data form the user's individual profile.

[0281] Step 2:

[0282] The terminal transmits the data of the physical attributes and style preferences input by the user to the server using the data transfer means. At this time, the data is structured in JSON format and sent to the server through an HTTP request. The output of the terminal is the transmitted data, which becomes the input for the next analysis step.

[0283] Step 3:

[0284] The server processes the received JSON data by the analysis means. Using the generated AI model, appropriate clothing proposals are generated based on the user's attribute information. Here, a machine learning algorithm is used to perform analysis based on the input data and the existing fashion dataset, and output the optimal clothing options.

[0285] Step 4:

[0286] The server sends the analysis result to the terminal and displays the proposal to the user using the presentation means on the terminal. Proposals listed in a user-friendly format are presented. The output of the terminal is the list of clothing options presented to the user.

[0287] Step 5:

[0288] The user selects an item of interest from the clothing proposals displayed on the terminal. The selected information is sent again by the terminal to the server and becomes the basic data for generating a try-on video. Here, specific items are selected through a touch operation.

[0289] Step 6:

[0290] The server activates a virtual try-on video generation system based on the selected clothing items and composites the items onto the user's photograph. Image processing technology then creates a virtual try-on video in which the selected clothing appears to fit the user's figure naturally. The generated video is finally transmitted to the terminal.

[0291] Step 7:

[0292] The terminal displays a virtual try-on video received from the server to the user. The user reviews the video and decides whether to purchase the clothing or make an appointment to try it on at a physical store. The final output consists of the video and selection options to help the user make a decision through an enhanced try-on experience.

[0293] (Application Example 1)

[0294] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0295] Online shopping often causes anxiety for buyers when selecting clothing because they cannot try items on in person. Furthermore, it is difficult to efficiently choose clothing that perfectly suits one's body type and style preferences. Additionally, the inability to visualize how the garment would look when worn leads to a higher likelihood of returns after purchase.

[0296] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0297] In this invention, the server includes an information input means for inputting the user's physical characteristics and style preferences, a data transmission means for transmitting the information input by the information input means to a computer, and an analysis means for analyzing the user information in the computer and generating clothing suggestions that are best suited to the user. This allows the user to virtually try on clothing that is best suited to them and make a purchase decision with confidence.

[0298] An "information input device" is a device that provides an interface for users to input information about their physical characteristics and style preferences.

[0299] A "data transmission means" is a component that has the function of transferring information obtained from an information input means to a server.

[0300] "Analysis means" refers to software or a process used on a server to analyze user information and generate suggestions for clothing best suited to the user.

[0301] A "proposal presentation means" is a mechanism for visually presenting clothing suggestions generated by an analysis means to the user.

[0302] "Try-on video generation means" refers to technology for generating a virtual try-on experience based on the user's selection.

[0303] "Presentation means" refers to a component that has the function of displaying the virtual try-on experience generated by the try-on video generation means on the user's device.

[0304] A "purchase method" refers to an online platform that enables users to purchase selected clothing items through e-commerce.

[0305] The "reservation method" is a system that allows users to reserve a fitting appointment for their chosen clothing items at a physical store.

[0306] The system for implementing this invention uses a mobile terminal or a personal computer to input the user's physical characteristics and preferences, and inputs this information through a dedicated application. First, the user uses the information input means on the application to input their height, weight, and style preferences, etc.

[0307] The terminal uses the data transmission means to send this information to the server. On the server, an AI model using a machine learning algorithm as the analysis means processes the received user data and generates personalized clothing proposals. It is common to use software such as Python or TensorFlow for this analysis.

[0308] The proposal generated by the server is sent to the terminal again via the data transmission means and presented to the user using the proposal presentation means. The user can select the clothing items they want to try from the displayed options.

[0309] For the selected clothing items, virtual try-on videos are generated through the try-on video generation means. The generated videos are visually displayed on the user's terminal using the presentation means. In this process, the AI model analyzes the user's photo and synthesizes the wearing image.

[0310] As a specific example, when the user is looking for a casual jacket, the user inputs information such as a height of 175 cm and a weight of 70 kg into the application. Based on this data, the AI model on the server side proposes a jacket suitable for the user and generates the selected one as a try-on video. The user who sees the virtual video can purchase the jacket with confidence.

[0311] Examples of prompt sentences for the generation AI model are as follows.

[0312] "Design a machine learning model that receives user body data (e.g., height 175cm, weight 70kg) and style preferences, and suggests suitable clothing. Then, describe the steps to implement a process that generates a video of the user virtually trying on the selected clothing and presents it to the user."

[0313] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0314] Step 1:

[0315] The user opens a dedicated application on their device and uses the input tools to enter their physical characteristics (e.g., height 175cm, weight 70kg) and style preferences (e.g., casual style). The entered data is temporarily stored within the application. Input is done via text boxes and selection menus.

[0316] Step 2:

[0317] The terminal uses a data transmission method to send user input data to the server. The HTTP protocol is used for transmission, and the data is converted to JSON format. The server prepares to parse the received JSON data.

[0318] Step 3:

[0319] The server applies analysis tools to generate optimal clothing suggestions based on the received user information. This analysis uses a machine learning algorithm implemented in Python to perform pattern matching on the data. Based on the user's body shape data and style preferences, it creates a list of recommended clothing items.

[0320] Step 4:

[0321] The server sends back a list of generated clothing suggestions to the terminal using a data transmission mechanism. The terminal's suggestion display mechanism displays the received data in a user interface. The user selects their preferred clothing items from the displayed list of suggestions.

[0322] Step 5:

[0323] After the user selects a specific piece of clothing, that information is sent back to the server. The server receives the data for the selected clothing and the user's image, and activates the virtual try-on video generation system.

[0324] Step 6:

[0325] A server-based virtual try-on video generation system analyzes the user's photos and synthesizes virtual try-on videos with selected clothing items. Using an AI model, it superimposes the clothing onto the user's body, generating videos that provide a realistic try-on experience.

[0326] Step 7:

[0327] The server sends the generated try-on video to the terminal, which then displays the video to the user through a presentation device. This allows the user to review the virtual try-on experience and supports their purchase decision.

[0328] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0329] In embodiments of the present invention, the user installs a dedicated application on a terminal such as a smartphone or personal computer. This application includes information input means for inputting the user's physical characteristics, style preferences, and emotional state. The user can use this input means to input their basic information and information about their current emotions.

[0330] The terminal transmits the input information to the server via a data transmission device. The server processes the received data using an analysis device and suggests the most suitable clothing items, taking into account the user's body type, style, and emotional state. The emotion recognition engine is used to analyze the emotional data entered by the user in real time and suggest clothing items that match the user's mood.

[0331] The suggested items are sent back from the server to the terminal, which then displays the suggestions to the user using a suggestion display device. The user selects items of interest from the suggested clothing, and based on this selection, a fitting video generation device operates to generate a virtual fitting video in which the selected clothing items are superimposed onto the user's photo. The terminal then presents this fitting video to the user, who can review it and, if necessary, make a purchase or book a fitting appointment at a store.

[0332] For example, when a user is feeling stressed, the emotion recognition engine can detect this state and recommend comfortable and relaxing clothing, such as loose-fitting casual wear. Furthermore, if a user responds positively to a particular suggestion, the learning function records that response and uses it to improve future suggestions. This allows the present invention to provide more personalized fashion suggestions that are tailored to the user's emotions.

[0333] The following describes the processing flow.

[0334] Step 1:

[0335] Users open a dedicated application on their smartphone or computer and enter their physical characteristics, style preferences, and current emotional state on an information input screen. Emotional states are recorded through a simple questionnaire or by taking photos of their facial expressions.

[0336] Step 2:

[0337] The device encrypts all information entered by the user and sends it to the server. This data includes the user's basic physical information, fashion preferences, and emotional data.

[0338] Step 3:

[0339] The server begins analyzing the received data. Using analysis tools, it selects the most suitable clothing items based on the user's body shape and style information, and evaluates the user's emotional state using an emotion recognition engine. The clothing recommendations are then adjusted based on these evaluation results.

[0340] Step 4:

[0341] The server generates a list of clothing suggestions that match the user's emotional state. This list changes depending on the emotions the user is currently feeling; for example, if the user is stressed, it will suggest relaxing clothing, and if they are excited, it will suggest colorful designs.

[0342] Step 5:

[0343] The server sends the generated list of suggestions back to the terminal. The terminal displays this list on its screen. The user reviews the details of the suggested clothing items and selects specific items that pique their interest.

[0344] Step 6:

[0345] Based on the clothing items selected by the user, the device requests a virtual try-on video generation method from the server. The server uses the user's photo data and information about the selected clothing items to create a virtual try-on video and sends it back to the device.

[0346] Step 7:

[0347] The device displays a generated try-on video to the user. The user reviews the video and visually confirms whether the selected clothing item suits them.

[0348] Step 8:

[0349] If a user wishes to purchase an item or schedule a fitting appointment at a store, the terminal provides access to these options. For a purchase, the user enters payment information to complete the purchase; for a fitting appointment, the user selects a preferred date and proceeds with the reservation.

[0350] This process allows users to efficiently and comfortably receive and select personalized fashion suggestions that are tailored to their emotional state.

[0351] (Example 2)

[0352] Next, we will describe Example 2. 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".

[0353] Modern consumers have diverse tastes and preferences, making it difficult to satisfy them with uniform fashion suggestions. Furthermore, emotional states influence purchasing decisions, requiring personalized suggestions tailored to the user's emotions. However, conventional systems struggle to provide optimal suggestions by comprehensively considering multiple pieces of user information, and a particular challenge exists in their ability to reflect emotional states in real time.

[0354] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0355] In this invention, the server includes an analysis means for analyzing user information, an emotion recognition means for generating emotion-based suggestions, and a fitting video generation means for generating virtual fitting videos. This makes it possible to suggest clothing that is individually optimized and reflect the user's emotional state in real time, taking into account the user's physical characteristics, style preferences, and emotional state.

[0356] "Information input means" refers to a device or software for inputting a user's physical characteristics, style preferences, and emotional information.

[0357] "Data transmission means" refers to a function or device that transmits user information entered by information input means to a computing device.

[0358] "Analysis means" refers to a function or program in a computing device that uses a generated AI model to analyze user information and generate clothing recommendations that are optimal for that user.

[0359] "Emotion recognition means" refers to a function or system that analyzes a user's emotional information and generates clothing suggestions that match the user's mood.

[0360] A "proposal presentation means" refers to a function or device that presents clothing suggestions generated by an analysis means to the user visually or audibly.

[0361] "Try-on video generation means" refers to a function or program that generates a virtual try-on video of clothing items based on the user's selection.

[0362] "Presentation means" refers to a function or device that visually displays the try-on video generated by the try-on video generation means to the user.

[0363] "Purchase method" refers to a function or platform that enables users to purchase selected clothing items online.

[0364] A "reservation method" refers to a function or system that allows users to reserve a fitting appointment for selected clothing items at a physical store.

[0365] In embodiments of the present invention, the user installs a dedicated application on a computer terminal such as a smartphone or personal computer. This application includes an interface for inputting the user's physical characteristics, style preferences, and emotional information.

[0366] The terminal collects information using input means and transmits it to the server via data transmission means. This information is encrypted and securely delivered to the server. Based on the received information, the server uses a generative AI model to analyze the user's data and suggests clothing that is most suitable for that user. By using emotion recognition means, the server generates suggestions that match the user's emotions and presents clothing that matches the user's current mood.

[0367] The suggested items are sent from the server to the terminal and displayed to the user by the suggestion presentation system. The user can view this display and select the clothing items they like. The selected clothing items are then superimposed onto the user's photo through the try-on video generation system. This superimposition allows the user to watch a virtual try-on video and have an experience similar to actually trying on the clothing.

[0368] For example, if a user is feeling stressed, the emotion recognition engine can detect this state and recommend relaxing casual wear. Based on this recommendation, the user can make a more appropriate choice.

[0369] An example of a prompt message is as follows:

[0370] User information: Height 170cm, weight 65kg, preferred style: casual, current emotional state: feeling stressed.

[0371] This configuration allows users to receive personalized clothing suggestions tailored to their individual emotions and preferences, resulting in a higher level of satisfaction compared to traditional shopping experiences.

[0372] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0373] Step 1:

[0374] The user installs a dedicated application on their device. This application includes a user interface for inputting physical characteristics, style preferences, and emotional state. Basic information such as the user's height, weight, style preferences, and emotional state is required as input. This information is then stored in a database as output.

[0375] Step 2:

[0376] The terminal transmits the entered user information to the server via a data transmission method. As input, it uses all the information entered by the user and packages it according to the data transmission protocol. As output, the server receives this information and prepares for analysis.

[0377] Step 3:

[0378] The server processes the received user information using analytical tools. The received information is passed as input to a generating AI model, which performs analysis based on the user's physical characteristics, style preferences, and emotional state. The output generates data suggesting the most suitable clothing for the user. This analysis includes database matching, algorithmic data processing, and calculations.

[0379] Step 4:

[0380] The emotion recognition system on the server analyzes the user's emotional data in real time. It takes the user's emotional state data as input and recognizes the appropriate emotional state based on this data. As output, it generates data for selecting the most suitable clothing for that emotion. An emotion recognition algorithm is used in this process.

[0381] Step 5:

[0382] The server sends the analysis results to the terminal. The server prepares the analyzed suggestion data and emotion recognition results as input and sends them to the terminal via a data transmission method. The terminal receives this output and prepares to present it to the user.

[0383] Step 6:

[0384] The terminal displays suggestions to the user using a suggestion presentation mechanism. It uses suggestion data received from the server as input and displays it to the user via a GUI (user interface). As output, the user can review the clothing suggestions and select the ones they like.

[0385] Step 7:

[0386] The user selects their preferred clothing items from the suggested options. This selection is processed by the device's virtual try-on video generation system. The input consists of data on the clothing items selected by the user and the user's photo information. The output is a video of the user virtually trying on the selected clothing items.

[0387] Step 8:

[0388] The terminal presents the generated try-on video to the user. As input, it reads the try-on video data and displays it using a playback tool. As output, the user can review the virtual try-on and make a decision to purchase or schedule a try-on if necessary.

[0389] (Application Example 2)

[0390] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0391] Traditional clothing recommendation systems rely solely on users' physical characteristics and style preferences, making it difficult to provide personalized recommendations that reflect the user's emotional state. Furthermore, virtual try-on features, designed to enhance the user experience, are limited, failing to adequately stimulate purchasing intent.

[0392] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0393] In this invention, the server includes an information input means for inputting the user's physical characteristics, style preferences, and emotional state information; a data transmission means for transmitting the information input by the information input means to a data storage device; and an analysis means for generating optimal clothing suggestions for the user using a generative artificial intelligence model. This makes it possible to provide optimal clothing suggestions that correspond to the user's emotions.

[0394] A "user" is an individual who utilizes the system and provides information about their physical characteristics, style preferences, and emotional state.

[0395] An "information input means" is an interface for users to input information about their physical characteristics, preferences, and emotional state.

[0396] A "data transmission means" is a function for transmitting information obtained through an information input means to a server.

[0397] A "generative artificial intelligence model" is an algorithm that analyzes information obtained from users and suggests clothing suitable for those users.

[0398] The "analysis means" refers to a function that processes user information received on the server and uses a generative artificial intelligence model to select the most suitable clothing.

[0399] The "proposal presentation means" is a function for displaying clothing suggestions generated by the analysis means to the user.

[0400] "Virtual display generation means" refers to a technology for generating virtual clothing try-on images or videos based on user selections and presenting them visually to the user.

[0401] "Visual representation" refers to an image or video that virtually shows the user what it looks like when trying on clothing.

[0402] The present invention is implemented as an application that runs on a user's device. This application provides an information input means for inputting the user's physical characteristics, style preferences, and emotional state. The user can input their body size, clothing preferences, and emotional information into a smartphone or personal computer. Once the information is entered, a data transmission means sends it to a server in the cloud.

[0403] The server operates using Amazon Web Services (AWS). The server analyzes incoming data using a generative artificial intelligence model to generate optimal clothing suggestions to personalize the user experience. Azure Cognitive Services acts as an emotion recognition engine, analyzing the user's emotional state to further personalize the suggestions.

[0404] The analyzed data is transmitted to the user's terminal via a suggestion presentation means, which the user can then view. For suggestions that interest the user, a virtual try-on image or video is generated using Unity or OpenCV via a virtual display generation means. The synthesized video is presented to the user as a visual display, allowing the user to review the content and either make a purchase or book a try-on appointment at a physical sales facility.

[0405] For example, if a user inputs "I'm feeling stressed today," the emotion recognition engine will suggest relaxing clothing, such as comfortable loungewear. The prompt to the generative AI model in this case would be as follows:

[0406] "User's emotional state: Emotional information entered by the user"

[0407] User style information: Style information entered by the user

[0408] Please select clothing items to propose and suggest up to 5 items that meet the following criteria:

[0409] Relax

[0410] User's preferred style

[0411] Virtual try-on images are available.

[0412] This configuration provides a highly customized purchasing experience based on emotionally resonant user preferences.

[0413] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0414] Step 1:

[0415] The user launches the application on their device and inputs information about their physical characteristics, style preferences, and emotional state through an input method. The data the user inputs includes their body size, clothing style preferences (e.g., casual, formal), and emotional information (e.g., want to relax, want to feel energized). This input data is stored in the device's local storage.

[0416] Step 2:

[0417] The terminal transmits data entered via an information input means to a server via a data transmission means. The terminal uses encryption technology to ensure data security while transmitting data to the cloud server. This protects the user's personal information.

[0418] Step 3:

[0419] The server processes the received data using analytical tools. The server utilizes AWS computing resources to run a generative AI model, generating real-time optimal clothing suggestions based on the user's body type, style, and emotional state. The input here is data submitted by the user, and the output is a list of suggested clothing items.

[0420] Step 4:

[0421] The server sends the generated clothing suggestions to the terminal via a suggestion presentation system. The server uses a high-speed and efficient data transfer protocol to send the suggestion list output by the generating AI model to the terminal. This allows the user to view the suggestions without delay.

[0422] Step 5:

[0423] The terminal uses a suggestion display mechanism to show the user clothing suggestions received from the server. The input here is the suggestion list sent from the server, and the output is the clothing list displayed in the terminal's GUI. The user reviews the suggested clothing and selects the items they like.

[0424] Step 6:

[0425] The user uses a virtual display generation means to virtually try on clothing based on their selection. In this step, the terminal uses Unity or OpenCV to composite the selected clothing onto the user's photo and generate a virtual try-on image or video according to the prompt messages generated by the server. The input is the user's selection data and photo, and the output is a virtual try-on image or video.

[0426] Step 7:

[0427] The terminal presents the generated virtual try-on image or video to the user as a visual display. The user can review it and, if they like it, proceed with the purchase or make a reservation to try it on at a store. The input here is the output of the virtual try-on, and the output is the user's purchase action.

[0428] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0429] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0430] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0431] [Third Embodiment]

[0432] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0433] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0434] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0435] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0436] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0437] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0438] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0439] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0440] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0441] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0442] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0443] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0444] In an embodiment of the present invention, the user first installs a dedicated application on a device such as a smartphone. This application provides the user with an input interface, allowing the user to input their physical characteristics (e.g., height, weight, body type) and style preferences (e.g., casual, formal, etc.).

[0445] When a user enters information, the device sends it to the server as data. The server uses AI technology to analyze the received data and generate optimal clothing choices tailored to the user's profile. The analysis uses machine learning algorithms to understand the user's body type and style tendencies.

[0446] The generated clothing suggestions are sent from the server to the terminal, which then displays the suggested clothing items to the user. The user can then select the clothing items they wish to view in more detail from among the multiple suggestions.

[0447] After the selection is made, the device sends another request to the server to generate a try-on video based on the selected clothing items and the user's photo. In this try-on video, AI superimposes the clothing onto the user's image, providing a virtual try-on experience.

[0448] Users who watch the try-on videos can either purchase their favorite clothing items directly through the app or book a fitting appointment at a physical store. This allows users to effectively choose clothing that suits them best and enjoy online shopping with peace of mind.

[0449] For example, if a user is looking for a casual jacket, they can input information such as their height (175cm) and weight (70kg). Based on this information, the server will suggest jackets that match the user's color, size, and style. The selected jackets are displayed on the user's device as a try-on video, and the user can make a purchase decision after reviewing it. In this way, the present invention provides a system that enables personalized fashion suggestions that meet the individual needs of users.

[0450] The following describes the processing flow.

[0451] Step 1:

[0452] The user installs the application on their smartphone and launches it. The app displays a user registration screen and provides an interface for entering basic information such as gender, height, weight, and style preferences. The user enters their information accordingly and also uploads a full-body photo.

[0453] Step 2:

[0454] The device securely transmits user-entered information and photos to the server using encryption and other methods. Here, the data is strictly managed to protect user privacy.

[0455] Step 3:

[0456] The server analyzes the received user data using AI. Specifically, it applies machine learning algorithms to evaluate the user's body type and style characteristics, and based on that, generates suggestions for the most suitable clothing for the user. The analysis process includes searching and selecting relevant fashion item data from a database.

[0457] Step 4:

[0458] The server sends a list of suggested clothing items back to the terminal. Upon receiving it, the terminal displays the suggested list clearly in the user interface. The user can then select the clothing items from the displayed list for further details.

[0459] Step 5:

[0460] When a user selects a specific piece of clothing, the device sends a request to the server to generate a corresponding try-on video. The server uses AI-powered image processing technology to superimpose the selected clothing onto the user's photo, creating a virtual try-on video.

[0461] Step 6:

[0462] The server sends the generated try-on video to the device, which then provides the video to the user. The user can then virtually try on the clothing and, if satisfied, make a purchase decision via the app.

[0463] Step 7:

[0464] If a user wishes to make a purchase, the device opens a purchase screen and provides an interface for entering payment information and shipping address. Once the purchase is complete, the server verifies the order information and begins the process of shipping the product.

[0465] Thus, each step is a process designed to streamline the user experience and make online fashion selection easier.

[0466] (Example 1)

[0467] Next, we will describe Example 1. 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."

[0468] In modern times, consumers have a wide variety of clothing options, but it is not easy to quickly and accurately choose the best clothing. In particular, online shopping makes it difficult to find clothing that matches consumers' physical characteristics and style preferences, and the inability to try on clothes is a source of anxiety. This invention aims to solve these problems.

[0469] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0470] In this invention, the server includes information receiving means for inputting the user's physical attributes and style preferences, data transfer means for transferring the attributes input by the information receiving means to a computer, and analysis means in the computer for interpreting the user attributes and generating clothing suggestions optimized for the user. This allows consumers to easily select the clothing that best suits them and purchase it online with confidence through a virtual try-on experience.

[0471] An "information receiving mechanism" is an interface for users to input their own physical attributes and style preferences.

[0472] A "data transfer means" is a function for transferring attribute information entered by a user to a computer.

[0473] A "computer" is a device that analyzes user attributes and generates optimal clothing suggestions based on those attributes.

[0474] "Analysis means" refers to a process within a computer for generating optimized clothing suggestions using received user attribute information.

[0475] "Presentation means" refers to a function that displays clothing suggestions generated by the server to the user.

[0476] A "try-on video generation means" is a system that allows users to virtually try on selected clothing, enabling them to visually confirm the fit.

[0477] To implement this invention, the user must first install a dedicated application on a communication terminal connected to the internet, specifically a smartphone or tablet. This application includes an information receiving means that provides an interface for the user to input their physical attributes and style preferences. The user inputs their height, weight, and preferred style (e.g., casual, formal, etc.) and transmits this information to the server via data transfer means through the terminal.

[0478] The server uses a computer to execute the core analysis means of the present invention. This analysis means utilizes a generative AI model to analyze the received user information and generate clothing suggestions optimized for the user. The server uses a large amount of existing data and machine learning algorithms (e.g., neural networks) to perform data processing to select clothing that matches the user's characteristics.

[0479] The generated clothing suggestions are sent from the server to the terminal and visually displayed to the user through a display device on the terminal. The user can select clothing items of interest from this suggested list. Once the user selects clothing, the server uses a fitting video generation device to generate a video in which the selected clothing is virtually tried on over the user's photograph. This video is designed to allow the user to intuitively see what the clothing would look like when actually worn.

[0480] For example, if a user is looking for a "casual jacket," they would enter information such as their height (175cm) and weight (70kg), and select "casual" as a style option. Based on this information, the server would suggest jackets with the most suitable color, size, and design, and generate a virtual try-on video of the suggested jackets, which would then be displayed on the user's device.

[0481] An example of a prompt for a generative AI model would be: "Please suggest a casual style jacket for a man who is 175cm tall and weighs 70kg."

[0482] This allows users to intuitively select clothing online while also easily trying on and purchasing items at physical stores, making online shopping a more secure and enjoyable experience.

[0483] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0484] Step 1:

[0485] The user installs a dedicated application on their communication device, launches the app, and uses the means to receive information. The user inputs their physical attributes (e.g., height, weight) and style preferences (e.g., casual) into the interface. This input data forms the user's individual profile.

[0486] Step 2:

[0487] The terminal sends the user's entered physical attributes and style preferences to the server using a data transfer mechanism. The data is structured in JSON format and sent to the server via an HTTP request. The terminal's output is the transmitted data, which becomes the input for the next analysis step.

[0488] Step 3:

[0489] The server processes the received JSON data using parsing tools. A generative AI model is used to generate appropriate clothing suggestions based on the user's attribute information. Here, machine learning algorithms are employed to analyze the input data and existing fashion datasets, outputting the optimal clothing choices.

[0490] Step 4:

[0491] The server sends the analysis results to the terminal, and the terminal displays the suggestions to the user using a presentation tool. The suggestions are presented in a user-friendly list format. The terminal output is a list of clothing options presented to the user.

[0492] Step 5:

[0493] The user selects items of interest from the clothing suggestions displayed on the device. This selected information is then sent back to the server by the device, becoming the basic data for generating the try-on video. Specific item selection is performed through touch operations.

[0494] Step 6:

[0495] The server activates a virtual try-on video generation system based on the selected clothing items and composites the items onto the user's photograph. Image processing technology then creates a virtual try-on video in which the selected clothing appears to fit the user's figure naturally. The generated video is finally transmitted to the terminal.

[0496] Step 7:

[0497] The terminal displays a virtual try-on video received from the server to the user. The user reviews the video and decides whether to purchase the clothing or make an appointment to try it on at a physical store. The final output consists of the video and selection options to help the user make a decision through an enhanced try-on experience.

[0498] (Application Example 1)

[0499] Next, we will explain Application Example 1. In the following explanation, 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."

[0500] Online shopping often causes anxiety for buyers when selecting clothing because they cannot try items on in person. Furthermore, it is difficult to efficiently choose clothing that perfectly suits one's body type and style preferences. Additionally, the inability to visualize how the garment would look when worn leads to a higher likelihood of returns after purchase.

[0501] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0502] In this invention, the server includes an information input means for inputting the user's physical characteristics and style preferences, a data transmission means for transmitting the information input by the information input means to a computer, and an analysis means for analyzing the user information in the computer and generating clothing suggestions that are best suited to the user. This allows the user to virtually try on clothing that is best suited to them and make a purchase decision with confidence.

[0503] An "information input device" is a device that provides an interface for users to input information about their physical characteristics and style preferences.

[0504] A "data transmission means" is a component that has the function of transferring information obtained from an information input means to a server.

[0505] "Analysis means" refers to software or a process used on a server to analyze user information and generate suggestions for clothing best suited to the user.

[0506] A "proposal presentation means" is a mechanism for visually presenting clothing suggestions generated by an analysis means to the user.

[0507] "Try-on video generation means" refers to technology for generating a virtual try-on experience based on the user's selection.

[0508] "Presentation means" refers to a component that has the function of displaying the virtual try-on experience generated by the try-on video generation means on the user's device.

[0509] A "purchase method" refers to an online platform that enables users to purchase selected clothing items through e-commerce.

[0510] The "reservation method" is a system that allows users to reserve a fitting appointment for their chosen clothing items at a physical store.

[0511] The system for implementing this invention uses a mobile device or personal computer to input the user's physical characteristics and preferences, and this information is entered through a dedicated application. First, the user inputs their height, weight, and style preferences using an information input means on the application.

[0512] The device sends this information to the server using a data transmission method. On the server, an AI model using machine learning algorithms as an analysis method processes the received user data and generates personalized clothing suggestions. Software such as Python or TensorFlow is commonly used for this analysis.

[0513] The suggestions generated by the server are sent back to the terminal via a data transmission means and presented to the user using a suggestion presentation means. The user can then choose the clothing items they would like to try from the displayed options.

[0514] For selected clothing items, a virtual try-on video is generated through a try-on video generation system. The generated video is visually displayed on the user's device using a presentation system. In this process, an AI model analyzes the user's photo and synthesizes a try-on image.

[0515] For example, if a user is looking for a casual jacket, they would input information such as their height (175cm) and weight (70kg) into the application. Based on this data, the server-side AI model would suggest suitable jackets and generate a virtual try-on video of the selected jacket. The user can then confidently purchase the jacket after viewing this virtual video.

[0516] Examples of prompt statements for a generative AI model are as follows:

[0517] "Design a machine learning model that receives user body data (e.g., height 175cm, weight 70kg) and style preferences, and suggests suitable clothing. Then, describe the steps to implement a process that generates a video of the user virtually trying on the selected clothing and presents it to the user."

[0518] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0519] Step 1:

[0520] The user opens a dedicated application on their device and uses the input tools to enter their physical characteristics (e.g., height 175cm, weight 70kg) and style preferences (e.g., casual style). The entered data is temporarily stored within the application. Input is done via text boxes and selection menus.

[0521] Step 2:

[0522] The terminal uses a data transmission method to send user input data to the server. The HTTP protocol is used for transmission, and the data is converted to JSON format. The server prepares to parse the received JSON data.

[0523] Step 3:

[0524] The server applies analysis tools to generate optimal clothing suggestions based on the received user information. This analysis uses a machine learning algorithm implemented in Python to perform pattern matching on the data. Based on the user's body shape data and style preferences, it creates a list of recommended clothing items.

[0525] Step 4:

[0526] The server sends back a list of generated clothing suggestions to the terminal using a data transmission mechanism. The terminal's suggestion display mechanism displays the received data in a user interface. The user selects their preferred clothing items from the displayed list of suggestions.

[0527] Step 5:

[0528] After the user selects a specific piece of clothing, that information is sent back to the server. The server receives the data for the selected clothing and the user's image, and activates the virtual try-on video generation system.

[0529] Step 6:

[0530] A server-based virtual try-on video generation system analyzes the user's photos and synthesizes virtual try-on videos with selected clothing items. Using an AI model, it superimposes the clothing onto the user's body, generating videos that provide a realistic try-on experience.

[0531] Step 7:

[0532] The server sends the generated try-on video to the terminal, which then displays the video to the user through a presentation device. This allows the user to review the virtual try-on experience and supports their purchase decision.

[0533] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0534] In embodiments of the present invention, the user installs a dedicated application on a terminal such as a smartphone or personal computer. This application includes information input means for inputting the user's physical characteristics, style preferences, and emotional state. The user can use this input means to input their basic information and information about their current emotions.

[0535] The terminal transmits the input information to the server via a data transmission device. The server processes the received data using an analysis device and suggests the most suitable clothing items, taking into account the user's body type, style, and emotional state. The emotion recognition engine is used to analyze the emotional data entered by the user in real time and suggest clothing items that match the user's mood.

[0536] The suggested items are sent back from the server to the terminal, which then displays the suggestions to the user using a suggestion display device. The user selects items of interest from the suggested clothing, and based on this selection, a fitting video generation device operates to generate a virtual fitting video in which the selected clothing items are superimposed onto the user's photo. The terminal then presents this fitting video to the user, who can review it and, if necessary, make a purchase or book a fitting appointment at a store.

[0537] For example, when a user is feeling stressed, the emotion recognition engine can detect this state and recommend comfortable and relaxing clothing, such as loose-fitting casual wear. Furthermore, if a user responds positively to a particular suggestion, the learning function records that response and uses it to improve future suggestions. This allows the present invention to provide more personalized fashion suggestions that are tailored to the user's emotions.

[0538] The following describes the processing flow.

[0539] Step 1:

[0540] Users open a dedicated application on their smartphone or computer and enter their physical characteristics, style preferences, and current emotional state on an information input screen. Emotional states are recorded through a simple questionnaire or by taking photos of their facial expressions.

[0541] Step 2:

[0542] The device encrypts all information entered by the user and sends it to the server. This data includes the user's basic physical information, fashion preferences, and emotional data.

[0543] Step 3:

[0544] The server begins analyzing the received data. Using analysis tools, it selects the most suitable clothing items based on the user's body shape and style information, and evaluates the user's emotional state using an emotion recognition engine. The clothing recommendations are then adjusted based on these evaluation results.

[0545] Step 4:

[0546] The server generates a list of clothing suggestions that match the user's emotional state. This list changes depending on the emotions the user is currently feeling; for example, if the user is stressed, it will suggest relaxing clothing, and if they are excited, it will suggest colorful designs.

[0547] Step 5:

[0548] The server sends the generated list of suggestions back to the terminal. The terminal displays this list on its screen. The user reviews the details of the suggested clothing items and selects specific items that pique their interest.

[0549] Step 6:

[0550] Based on the clothing items selected by the user, the device requests a virtual try-on video generation method from the server. The server uses the user's photo data and information about the selected clothing items to create a virtual try-on video and sends it back to the device.

[0551] Step 7:

[0552] The device displays a generated try-on video to the user. The user reviews the video and visually confirms whether the selected clothing item suits them.

[0553] Step 8:

[0554] If a user wishes to purchase an item or schedule a fitting appointment at a store, the terminal provides access to these options. For a purchase, the user enters payment information to complete the purchase; for a fitting appointment, the user selects a preferred date and proceeds with the reservation.

[0555] This process allows users to efficiently and comfortably receive and select personalized fashion suggestions that are tailored to their emotional state.

[0556] (Example 2)

[0557] Next, we will describe Example 2. 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."

[0558] Modern consumers have diverse tastes and preferences, making it difficult to satisfy them with uniform fashion suggestions. Furthermore, emotional states influence purchasing decisions, requiring personalized suggestions tailored to the user's emotions. However, conventional systems struggle to provide optimal suggestions by comprehensively considering multiple pieces of user information, and a particular challenge exists in their ability to reflect emotional states in real time.

[0559] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0560] In this invention, the server includes an analysis means for analyzing user information, an emotion recognition means for generating emotion-based suggestions, and a fitting video generation means for generating virtual fitting videos. This makes it possible to suggest clothing that is individually optimized and reflect the user's emotional state in real time, taking into account the user's physical characteristics, style preferences, and emotional state.

[0561] "Information input means" refers to a device or software for inputting a user's physical characteristics, style preferences, and emotional information.

[0562] "Data transmission means" refers to a function or device that transmits user information entered by information input means to a computing device.

[0563] "Analysis means" refers to a function or program in a computing device that uses a generated AI model to analyze user information and generate clothing recommendations that are optimal for that user.

[0564] "Emotion recognition means" refers to a function or system that analyzes a user's emotional information and generates clothing suggestions that match the user's mood.

[0565] A "proposal presentation means" refers to a function or device that presents clothing suggestions generated by an analysis means to the user visually or audibly.

[0566] "Try-on video generation means" refers to a function or program that generates a virtual try-on video of clothing items based on the user's selection.

[0567] "Presentation means" refers to a function or device that visually displays the try-on video generated by the try-on video generation means to the user.

[0568] "Purchase method" refers to a function or platform that enables users to purchase selected clothing items online.

[0569] A "reservation method" refers to a function or system that allows users to reserve a fitting appointment for selected clothing items at a physical store.

[0570] In embodiments of the present invention, the user installs a dedicated application on a computer terminal such as a smartphone or personal computer. This application includes an interface for inputting the user's physical characteristics, style preferences, and emotional information.

[0571] The terminal collects information using input means and transmits it to the server via data transmission means. This information is encrypted and securely delivered to the server. Based on the received information, the server uses a generative AI model to analyze the user's data and suggests clothing that is most suitable for that user. By using emotion recognition means, the server generates suggestions that match the user's emotions and presents clothing that matches the user's current mood.

[0572] The suggested items are sent from the server to the terminal and displayed to the user by the suggestion presentation system. The user can view this display and select the clothing items they like. The selected clothing items are then superimposed onto the user's photo through the try-on video generation system. This superimposition allows the user to watch a virtual try-on video and have an experience similar to actually trying on the clothing.

[0573] For example, if a user is feeling stressed, the emotion recognition engine can detect this state and recommend relaxing casual wear. Based on this recommendation, the user can make a more appropriate choice.

[0574] An example of a prompt message is as follows:

[0575] User information: Height 170cm, weight 65kg, preferred style: casual, current emotional state: feeling stressed.

[0576] This configuration allows users to receive personalized clothing suggestions tailored to their individual emotions and preferences, resulting in a higher level of satisfaction compared to traditional shopping experiences.

[0577] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0578] Step 1:

[0579] The user installs a dedicated application on their device. This application includes a user interface for inputting physical characteristics, style preferences, and emotional state. Basic information such as the user's height, weight, style preferences, and emotional state is required as input. This information is then stored in a database as output.

[0580] Step 2:

[0581] The terminal transmits the entered user information to the server via a data transmission method. As input, it uses all the information entered by the user and packages it according to the data transmission protocol. As output, the server receives this information and prepares for analysis.

[0582] Step 3:

[0583] The server processes the received user information using analytical tools. The received information is passed as input to a generating AI model, which performs analysis based on the user's physical characteristics, style preferences, and emotional state. The output generates data suggesting the most suitable clothing for the user. This analysis includes database matching, algorithmic data processing, and calculations.

[0584] Step 4:

[0585] The emotion recognition system on the server analyzes the user's emotional data in real time. It takes the user's emotional state data as input and recognizes the appropriate emotional state based on this data. As output, it generates data for selecting the most suitable clothing for that emotion. An emotion recognition algorithm is used in this process.

[0586] Step 5:

[0587] The server sends the analysis results to the terminal. The server prepares the analyzed suggestion data and emotion recognition results as input and sends them to the terminal via a data transmission method. The terminal receives this output and prepares to present it to the user.

[0588] Step 6:

[0589] The terminal displays suggestions to the user using a suggestion presentation mechanism. It uses suggestion data received from the server as input and displays it to the user via a GUI (user interface). As output, the user can review the clothing suggestions and select the ones they like.

[0590] Step 7:

[0591] The user selects their preferred clothing items from the suggested options. This selection is processed by the device's virtual try-on video generation system. The input consists of data on the clothing items selected by the user and the user's photo information. The output is a video of the user virtually trying on the selected clothing items.

[0592] Step 8:

[0593] The terminal presents the generated try-on video to the user. As input, it reads the try-on video data and displays it using a playback tool. As output, the user can review the virtual try-on and make a decision to purchase or schedule a try-on if necessary.

[0594] (Application Example 2)

[0595] Next, we will explain application example 2. In the following explanation, 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."

[0596] Traditional clothing recommendation systems rely solely on users' physical characteristics and style preferences, making it difficult to provide personalized recommendations that reflect the user's emotional state. Furthermore, virtual try-on features, designed to enhance the user experience, are limited, failing to adequately stimulate purchasing intent.

[0597] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0598] In this invention, the server includes an information input means for inputting the user's physical characteristics, style preferences, and emotional state information; a data transmission means for transmitting the information input by the information input means to a data storage device; and an analysis means for generating optimal clothing suggestions for the user using a generative artificial intelligence model. This makes it possible to provide optimal clothing suggestions that correspond to the user's emotions.

[0599] A "user" is an individual who utilizes the system and provides information about their physical characteristics, style preferences, and emotional state.

[0600] An "information input means" is an interface for users to input information about their physical characteristics, preferences, and emotional state.

[0601] A "data transmission means" is a function for transmitting information obtained through an information input means to a server.

[0602] A "generative artificial intelligence model" is an algorithm that analyzes information obtained from users and suggests clothing suitable for those users.

[0603] The "analysis means" refers to a function that processes user information received on the server and uses a generative artificial intelligence model to select the most suitable clothing.

[0604] The "proposal presentation means" is a function for displaying clothing suggestions generated by the analysis means to the user.

[0605] "Virtual display generation means" refers to a technology for generating virtual clothing try-on images or videos based on user selections and presenting them visually to the user.

[0606] "Visual representation" refers to an image or video that virtually shows the user what it looks like when trying on clothing.

[0607] The present invention is implemented as an application that runs on a user's device. This application provides an information input means for inputting the user's physical characteristics, style preferences, and emotional state. The user can input their body size, clothing preferences, and emotional information into a smartphone or personal computer. Once the information is entered, a data transmission means sends it to a server in the cloud.

[0608] The server operates using Amazon Web Services (AWS). The server analyzes incoming data using a generative artificial intelligence model to generate optimal clothing suggestions to personalize the user experience. Azure Cognitive Services acts as an emotion recognition engine, analyzing the user's emotional state to further personalize the suggestions.

[0609] The analyzed data is transmitted to the user's terminal via a suggestion presentation means, which the user can then view. For suggestions that interest the user, a virtual try-on image or video is generated using Unity or OpenCV via a virtual display generation means. The synthesized video is presented to the user as a visual display, allowing the user to review the content and either make a purchase or book a try-on appointment at a physical sales facility.

[0610] For example, if a user inputs "I'm feeling stressed today," the emotion recognition engine will suggest relaxing clothing, such as comfortable loungewear. The prompt to the generative AI model in this case would be as follows:

[0611] "User's emotional state: Emotional information entered by the user"

[0612] User style information: Style information entered by the user

[0613] Please select clothing items to propose and suggest up to 5 items that meet the following criteria:

[0614] Relax

[0615] User's preferred style

[0616] Virtual try-on images are available.

[0617] This configuration provides a highly customized purchasing experience based on emotionally resonant user preferences.

[0618] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0619] Step 1:

[0620] The user launches the application on their device and inputs information about their physical characteristics, style preferences, and emotional state through an input method. The data the user inputs includes their body size, clothing style preferences (e.g., casual, formal), and emotional information (e.g., want to relax, want to feel energized). This input data is stored in the device's local storage.

[0621] Step 2:

[0622] The terminal transmits data entered via an information input means to a server via a data transmission means. The terminal uses encryption technology to ensure data security while transmitting data to the cloud server. This protects the user's personal information.

[0623] Step 3:

[0624] The server processes the received data using analytical tools. The server utilizes AWS computing resources to run a generative AI model, generating real-time optimal clothing suggestions based on the user's body type, style, and emotional state. The input here is data submitted by the user, and the output is a list of suggested clothing items.

[0625] Step 4:

[0626] The server sends the generated clothing suggestions to the terminal via a suggestion presentation system. The server uses a high-speed and efficient data transfer protocol to send the suggestion list output by the generating AI model to the terminal. This allows the user to view the suggestions without delay.

[0627] Step 5:

[0628] The terminal uses a suggestion display mechanism to show the user clothing suggestions received from the server. The input here is the suggestion list sent from the server, and the output is the clothing list displayed in the terminal's GUI. The user reviews the suggested clothing and selects the items they like.

[0629] Step 6:

[0630] The user uses a virtual display generation means to virtually try on clothing based on their selection. In this step, the terminal uses Unity or OpenCV to composite the selected clothing onto the user's photo and generate a virtual try-on image or video according to the prompt messages generated by the server. The input is the user's selection data and photo, and the output is a virtual try-on image or video.

[0631] Step 7:

[0632] The terminal presents the generated virtual try-on image or video to the user as a visual display. The user can review it and, if they like it, proceed with the purchase or make a reservation to try it on at a store. The input here is the output of the virtual try-on, and the output is the user's purchase action.

[0633] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0634] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0635] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0636] [Fourth Embodiment]

[0637] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0638] As shown in Figure 7, the 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.

[0639] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0640] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0641] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0642] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0643] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0644] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0645] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0646] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0647] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0648] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0649] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0650] In an embodiment of the present invention, the user first installs a dedicated application on a device such as a smartphone. This application provides the user with an input interface, allowing the user to input their physical characteristics (e.g., height, weight, body type) and style preferences (e.g., casual, formal, etc.).

[0651] When a user enters information, the device sends it to the server as data. The server uses AI technology to analyze the received data and generate optimal clothing choices tailored to the user's profile. The analysis uses machine learning algorithms to understand the user's body type and style tendencies.

[0652] The generated clothing suggestions are sent from the server to the terminal, which then displays the suggested clothing items to the user. The user can then select the clothing items they wish to view in more detail from among the multiple suggestions.

[0653] After the selection is made, the device sends another request to the server to generate a try-on video based on the selected clothing items and the user's photo. In this try-on video, AI superimposes the clothing onto the user's image, providing a virtual try-on experience.

[0654] Users who watch the try-on videos can either purchase their favorite clothing items directly through the app or book a fitting appointment at a physical store. This allows users to effectively choose clothing that suits them best and enjoy online shopping with peace of mind.

[0655] For example, if a user is looking for a casual jacket, they can input information such as their height (175cm) and weight (70kg). Based on this information, the server will suggest jackets that match the user's color, size, and style. The selected jackets are displayed on the user's device as a try-on video, and the user can make a purchase decision after reviewing it. In this way, the present invention provides a system that enables personalized fashion suggestions that meet the individual needs of users.

[0656] The following describes the processing flow.

[0657] Step 1:

[0658] The user installs the application on their smartphone and launches it. The app displays a user registration screen and provides an interface for entering basic information such as gender, height, weight, and style preferences. The user enters their information accordingly and also uploads a full-body photo.

[0659] Step 2:

[0660] The device securely transmits user-entered information and photos to the server using encryption and other methods. Here, the data is strictly managed to protect user privacy.

[0661] Step 3:

[0662] The server analyzes the received user data using AI. Specifically, it applies machine learning algorithms to evaluate the user's body type and style characteristics, and based on that, generates suggestions for the most suitable clothing for the user. The analysis process includes searching and selecting relevant fashion item data from a database.

[0663] Step 4:

[0664] The server sends a list of suggested clothing items back to the terminal. Upon receiving it, the terminal displays the suggested list clearly in the user interface. The user can then select the clothing items from the displayed list for further details.

[0665] Step 5:

[0666] When a user selects a specific piece of clothing, the device sends a request to the server to generate a corresponding try-on video. The server uses AI-powered image processing technology to superimpose the selected clothing onto the user's photo, creating a virtual try-on video.

[0667] Step 6:

[0668] The server sends the generated try-on video to the device, which then provides the video to the user. The user can then virtually try on the clothing and, if satisfied, make a purchase decision via the app.

[0669] Step 7:

[0670] If a user wishes to make a purchase, the device opens a purchase screen and provides an interface for entering payment information and shipping address. Once the purchase is complete, the server verifies the order information and begins the process of shipping the product.

[0671] Thus, each step is a process designed to streamline the user experience and make online fashion selection easier.

[0672] (Example 1)

[0673] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0674] In modern times, consumers have a wide variety of clothing options, but it is not easy to quickly and accurately choose the best clothing. In particular, online shopping makes it difficult to find clothing that matches consumers' physical characteristics and style preferences, and the inability to try on clothes is a source of anxiety. This invention aims to solve these problems.

[0675] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0676] In this invention, the server includes information receiving means for inputting the user's physical attributes and style preferences, data transfer means for transferring the attributes input by the information receiving means to a computer, and analysis means in the computer for interpreting the user attributes and generating clothing suggestions optimized for the user. This allows consumers to easily select the clothing that best suits them and purchase it online with confidence through a virtual try-on experience.

[0677] An "information receiving mechanism" is an interface for users to input their own physical attributes and style preferences.

[0678] A "data transfer means" is a function for transferring attribute information entered by a user to a computer.

[0679] A "computer" is a device that analyzes user attributes and generates optimal clothing suggestions based on those attributes.

[0680] "Analysis means" refers to a process within a computer for generating optimized clothing suggestions using received user attribute information.

[0681] "Presentation means" refers to a function that displays clothing suggestions generated by the server to the user.

[0682] A "try-on video generation means" is a system that allows users to virtually try on selected clothing, enabling them to visually confirm the fit.

[0683] To implement this invention, the user must first install a dedicated application on a communication terminal connected to the internet, specifically a smartphone or tablet. This application includes an information receiving means that provides an interface for the user to input their physical attributes and style preferences. The user inputs their height, weight, and preferred style (e.g., casual, formal, etc.) and transmits this information to the server via data transfer means through the terminal.

[0684] The server uses a computer to execute the core analysis means of the present invention. This analysis means utilizes a generative AI model to analyze the received user information and generate clothing suggestions optimized for the user. The server uses a large amount of existing data and machine learning algorithms (e.g., neural networks) to perform data processing to select clothing that matches the user's characteristics.

[0685] The generated clothing suggestions are sent from the server to the terminal and visually displayed to the user through a display device on the terminal. The user can select clothing items of interest from this suggested list. Once the user selects clothing, the server uses a fitting video generation device to generate a video in which the selected clothing is virtually tried on over the user's photograph. This video is designed to allow the user to intuitively see what the clothing would look like when actually worn.

[0686] For example, if a user is looking for a "casual jacket," they would enter information such as their height (175cm) and weight (70kg), and select "casual" as a style option. Based on this information, the server would suggest jackets with the most suitable color, size, and design, and generate a virtual try-on video of the suggested jackets, which would then be displayed on the user's device.

[0687] An example of a prompt for a generative AI model would be: "Please suggest a casual style jacket for a man who is 175cm tall and weighs 70kg."

[0688] This allows users to intuitively select clothing online while also easily trying on and purchasing items at physical stores, making online shopping a more secure and enjoyable experience.

[0689] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0690] Step 1:

[0691] The user installs a dedicated application on their communication device, launches the app, and uses the means to receive information. The user inputs their physical attributes (e.g., height, weight) and style preferences (e.g., casual) into the interface. This input data forms the user's individual profile.

[0692] Step 2:

[0693] The terminal sends the user's entered physical attributes and style preferences to the server using a data transfer mechanism. The data is structured in JSON format and sent to the server via an HTTP request. The terminal's output is the transmitted data, which becomes the input for the next analysis step.

[0694] Step 3:

[0695] The server processes the received JSON data using parsing tools. A generative AI model is used to generate appropriate clothing suggestions based on the user's attribute information. Here, machine learning algorithms are employed to analyze the input data and existing fashion datasets, outputting the optimal clothing choices.

[0696] Step 4:

[0697] The server sends the analysis results to the terminal, and the terminal displays the suggestions to the user using a presentation tool. The suggestions are presented in a user-friendly list format. The terminal output is a list of clothing options presented to the user.

[0698] Step 5:

[0699] The user selects items of interest from the clothing suggestions displayed on the device. This selected information is then sent back to the server by the device, becoming the basic data for generating the try-on video. Specific item selection is performed through touch operations.

[0700] Step 6:

[0701] The server activates a virtual try-on video generation system based on the selected clothing items and composites the items onto the user's photograph. Image processing technology then creates a virtual try-on video in which the selected clothing appears to fit the user's figure naturally. The generated video is finally transmitted to the terminal.

[0702] Step 7:

[0703] The terminal displays a virtual try-on video received from the server to the user. The user reviews the video and decides whether to purchase the clothing or make an appointment to try it on at a physical store. The final output consists of the video and selection options to help the user make a decision through an enhanced try-on experience.

[0704] (Application Example 1)

[0705] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0706] Online shopping often causes anxiety for buyers when selecting clothing because they cannot try items on in person. Furthermore, it is difficult to efficiently choose clothing that perfectly suits one's body type and style preferences. Additionally, the inability to visualize how the garment would look when worn leads to a higher likelihood of returns after purchase.

[0707] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0708] In this invention, the server includes an information input means for inputting the user's physical characteristics and style preferences, a data transmission means for transmitting the information input by the information input means to a computer, and an analysis means for analyzing the user information in the computer and generating clothing suggestions that are best suited to the user. This allows the user to virtually try on clothing that is best suited to them and make a purchase decision with confidence.

[0709] An "information input device" is a device that provides an interface for users to input information about their physical characteristics and style preferences.

[0710] A "data transmission means" is a component that has the function of transferring information obtained from an information input means to a server.

[0711] "Analysis means" refers to software or a process used on a server to analyze user information and generate suggestions for clothing best suited to the user.

[0712] A "proposal presentation means" is a mechanism for visually presenting clothing suggestions generated by an analysis means to the user.

[0713] "Try-on video generation means" refers to technology for generating a virtual try-on experience based on the user's selection.

[0714] "Presentation means" refers to a component that has the function of displaying the virtual try-on experience generated by the try-on video generation means on the user's device.

[0715] A "purchase method" refers to an online platform that enables users to purchase selected clothing items through e-commerce.

[0716] The "reservation method" is a system that allows users to reserve a fitting appointment for their chosen clothing items at a physical store.

[0717] The system for implementing this invention uses a mobile device or personal computer to input the user's physical characteristics and preferences, and this information is entered through a dedicated application. First, the user inputs their height, weight, and style preferences using an information input means on the application.

[0718] The device sends this information to the server using a data transmission method. On the server, an AI model using machine learning algorithms as an analysis method processes the received user data and generates personalized clothing suggestions. Software such as Python or TensorFlow is commonly used for this analysis.

[0719] The suggestions generated by the server are sent back to the terminal via a data transmission means and presented to the user using a suggestion presentation means. The user can then choose the clothing items they would like to try from the displayed options.

[0720] For selected clothing items, a virtual try-on video is generated through a try-on video generation system. The generated video is visually displayed on the user's device using a presentation system. In this process, an AI model analyzes the user's photo and synthesizes a try-on image.

[0721] For example, if a user is looking for a casual jacket, they would input information such as their height (175cm) and weight (70kg) into the application. Based on this data, the server-side AI model would suggest suitable jackets and generate a virtual try-on video of the selected jacket. The user can then confidently purchase the jacket after viewing this virtual video.

[0722] Examples of prompt statements for a generative AI model are as follows:

[0723] "Design a machine learning model that receives user body data (e.g., height 175cm, weight 70kg) and style preferences, and suggests suitable clothing. Then, describe the steps to implement a process that generates a video of the user virtually trying on the selected clothing and presents it to the user."

[0724] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0725] Step 1:

[0726] The user opens a dedicated application on their device and uses the input tools to enter their physical characteristics (e.g., height 175cm, weight 70kg) and style preferences (e.g., casual style). The entered data is temporarily stored within the application. Input is done via text boxes and selection menus.

[0727] Step 2:

[0728] The terminal uses a data transmission method to send user input data to the server. The HTTP protocol is used for transmission, and the data is converted to JSON format. The server prepares to parse the received JSON data.

[0729] Step 3:

[0730] The server applies analysis tools to generate optimal clothing suggestions based on the received user information. This analysis uses a machine learning algorithm implemented in Python to perform pattern matching on the data. Based on the user's body shape data and style preferences, it creates a list of recommended clothing items.

[0731] Step 4:

[0732] The server sends back a list of generated clothing suggestions to the terminal using a data transmission mechanism. The terminal's suggestion display mechanism displays the received data in a user interface. The user selects their preferred clothing items from the displayed list of suggestions.

[0733] Step 5:

[0734] After the user selects a specific piece of clothing, that information is sent back to the server. The server receives the data for the selected clothing and the user's image, and activates the virtual try-on video generation system.

[0735] Step 6:

[0736] A server-based virtual try-on video generation system analyzes the user's photos and synthesizes virtual try-on videos with selected clothing items. Using an AI model, it superimposes the clothing onto the user's body, generating videos that provide a realistic try-on experience.

[0737] Step 7:

[0738] The server sends the generated try-on video to the terminal, which then displays the video to the user through a presentation device. This allows the user to review the virtual try-on experience and supports their purchase decision.

[0739] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0740] In embodiments of the present invention, the user installs a dedicated application on a terminal such as a smartphone or personal computer. This application includes information input means for inputting the user's physical characteristics, style preferences, and emotional state. The user can use this input means to input their basic information and information about their current emotions.

[0741] The terminal transmits the input information to the server via a data transmission device. The server processes the received data using an analysis device and suggests the most suitable clothing items, taking into account the user's body type, style, and emotional state. The emotion recognition engine is used to analyze the emotional data entered by the user in real time and suggest clothing items that match the user's mood.

[0742] The suggested items are sent back from the server to the terminal, which then displays the suggestions to the user using a suggestion display device. The user selects items of interest from the suggested clothing, and based on this selection, a fitting video generation device operates to generate a virtual fitting video in which the selected clothing items are superimposed onto the user's photo. The terminal then presents this fitting video to the user, who can review it and, if necessary, make a purchase or book a fitting appointment at a store.

[0743] For example, when a user is feeling stressed, the emotion recognition engine can detect this state and recommend comfortable and relaxing clothing, such as loose-fitting casual wear. Furthermore, if a user responds positively to a particular suggestion, the learning function records that response and uses it to improve future suggestions. This allows the present invention to provide more personalized fashion suggestions that are tailored to the user's emotions.

[0744] The following describes the processing flow.

[0745] Step 1:

[0746] Users open a dedicated application on their smartphone or computer and enter their physical characteristics, style preferences, and current emotional state on an information input screen. Emotional states are recorded through a simple questionnaire or by taking photos of their facial expressions.

[0747] Step 2:

[0748] The device encrypts all information entered by the user and sends it to the server. This data includes the user's basic physical information, fashion preferences, and emotional data.

[0749] Step 3:

[0750] The server begins analyzing the received data. Using analysis tools, it selects the most suitable clothing items based on the user's body shape and style information, and evaluates the user's emotional state using an emotion recognition engine. The clothing recommendations are then adjusted based on these evaluation results.

[0751] Step 4:

[0752] The server generates a list of clothing suggestions that match the user's emotional state. This list changes depending on the emotions the user is currently feeling; for example, if the user is stressed, it will suggest relaxing clothing, and if they are excited, it will suggest colorful designs.

[0753] Step 5:

[0754] The server sends the generated list of suggestions back to the terminal. The terminal displays this list on its screen. The user reviews the details of the suggested clothing items and selects specific items that pique their interest.

[0755] Step 6:

[0756] Based on the clothing items selected by the user, the device requests a virtual try-on video generation method from the server. The server uses the user's photo data and information about the selected clothing items to create a virtual try-on video and sends it back to the device.

[0757] Step 7:

[0758] The device displays a generated try-on video to the user. The user reviews the video and visually confirms whether the selected clothing item suits them.

[0759] Step 8:

[0760] If a user wishes to purchase an item or schedule a fitting appointment at a store, the terminal provides access to these options. For a purchase, the user enters payment information to complete the purchase; for a fitting appointment, the user selects a preferred date and proceeds with the reservation.

[0761] This process allows users to efficiently and comfortably receive and select personalized fashion suggestions that are tailored to their emotional state.

[0762] (Example 2)

[0763] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0764] Modern consumers have diverse tastes and preferences, making it difficult to satisfy them with uniform fashion suggestions. Furthermore, emotional states influence purchasing decisions, requiring personalized suggestions tailored to the user's emotions. However, conventional systems struggle to provide optimal suggestions by comprehensively considering multiple pieces of user information, and a particular challenge exists in their ability to reflect emotional states in real time.

[0765] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0766] In this invention, the server includes an analysis means for analyzing user information, an emotion recognition means for generating emotion-based suggestions, and a fitting video generation means for generating virtual fitting videos. This makes it possible to suggest clothing that is individually optimized and reflect the user's emotional state in real time, taking into account the user's physical characteristics, style preferences, and emotional state.

[0767] "Information input means" refers to a device or software for inputting a user's physical characteristics, style preferences, and emotional information.

[0768] "Data transmission means" refers to a function or device that transmits user information entered by information input means to a computing device.

[0769] "Analysis means" refers to a function or program in a computing device that uses a generated AI model to analyze user information and generate clothing recommendations that are optimal for that user.

[0770] "Emotion recognition means" refers to a function or system that analyzes a user's emotional information and generates clothing suggestions that match the user's mood.

[0771] A "proposal presentation means" refers to a function or device that presents clothing suggestions generated by an analysis means to the user visually or audibly.

[0772] "Try-on video generation means" refers to a function or program that generates a virtual try-on video of clothing items based on the user's selection.

[0773] "Presentation means" refers to a function or device that visually displays the try-on video generated by the try-on video generation means to the user.

[0774] "Purchase method" refers to a function or platform that enables users to purchase selected clothing items online.

[0775] A "reservation method" refers to a function or system that allows users to reserve a fitting appointment for selected clothing items at a physical store.

[0776] In embodiments of the present invention, the user installs a dedicated application on a computer terminal such as a smartphone or personal computer. This application includes an interface for inputting the user's physical characteristics, style preferences, and emotional information.

[0777] The terminal collects information using input means and transmits it to the server via data transmission means. This information is encrypted and securely delivered to the server. Based on the received information, the server uses a generative AI model to analyze the user's data and suggests clothing that is most suitable for that user. By using emotion recognition means, the server generates suggestions that match the user's emotions and presents clothing that matches the user's current mood.

[0778] The suggested items are sent from the server to the terminal and displayed to the user by the suggestion presentation system. The user can view this display and select the clothing items they like. The selected clothing items are then superimposed onto the user's photo through the try-on video generation system. This superimposition allows the user to watch a virtual try-on video and have an experience similar to actually trying on the clothing.

[0779] For example, if a user is feeling stressed, the emotion recognition engine can detect this state and recommend relaxing casual wear. Based on this recommendation, the user can make a more appropriate choice.

[0780] An example of a prompt message is as follows:

[0781] User information: Height 170cm, weight 65kg, preferred style: casual, current emotional state: feeling stressed.

[0782] This configuration allows users to receive personalized clothing suggestions tailored to their individual emotions and preferences, resulting in a higher level of satisfaction compared to traditional shopping experiences.

[0783] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0784] Step 1:

[0785] The user installs a dedicated application on their device. This application includes a user interface for inputting physical characteristics, style preferences, and emotional state. Basic information such as the user's height, weight, style preferences, and emotional state is required as input. This information is then stored in a database as output.

[0786] Step 2:

[0787] The terminal transmits the entered user information to the server via a data transmission method. As input, it uses all the information entered by the user and packages it according to the data transmission protocol. As output, the server receives this information and prepares for analysis.

[0788] Step 3:

[0789] The server processes the received user information using analytical tools. The received information is passed as input to a generating AI model, which performs analysis based on the user's physical characteristics, style preferences, and emotional state. The output generates data suggesting the most suitable clothing for the user. This analysis includes database matching, algorithmic data processing, and calculations.

[0790] Step 4:

[0791] The emotion recognition system on the server analyzes the user's emotional data in real time. It takes the user's emotional state data as input and recognizes the appropriate emotional state based on this data. As output, it generates data for selecting the most suitable clothing for that emotion. An emotion recognition algorithm is used in this process.

[0792] Step 5:

[0793] The server sends the analysis results to the terminal. The server prepares the analyzed suggestion data and emotion recognition results as input and sends them to the terminal via a data transmission method. The terminal receives this output and prepares to present it to the user.

[0794] Step 6:

[0795] The terminal displays suggestions to the user using a suggestion presentation mechanism. It uses suggestion data received from the server as input and displays it to the user via a GUI (user interface). As output, the user can review the clothing suggestions and select the ones they like.

[0796] Step 7:

[0797] The user selects their preferred clothing items from the suggested options. This selection is processed by the device's virtual try-on video generation system. The input consists of data on the clothing items selected by the user and the user's photo information. The output is a video of the user virtually trying on the selected clothing items.

[0798] Step 8:

[0799] The terminal presents the generated try-on video to the user. As input, it reads the try-on video data and displays it using a playback tool. As output, the user can review the virtual try-on and make a decision to purchase or schedule a try-on if necessary.

[0800] (Application Example 2)

[0801] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0802] Traditional clothing recommendation systems rely solely on users' physical characteristics and style preferences, making it difficult to provide personalized recommendations that reflect the user's emotional state. Furthermore, virtual try-on features, designed to enhance the user experience, are limited, failing to adequately stimulate purchasing intent.

[0803] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0804] In this invention, the server includes an information input means for inputting the user's physical characteristics, style preferences, and emotional state information; a data transmission means for transmitting the information input by the information input means to a data storage device; and an analysis means for generating optimal clothing suggestions for the user using a generative artificial intelligence model. This makes it possible to provide optimal clothing suggestions that correspond to the user's emotions.

[0805] A "user" is an individual who utilizes the system and provides information about their physical characteristics, style preferences, and emotional state.

[0806] An "information input means" is an interface for users to input information about their physical characteristics, preferences, and emotional state.

[0807] A "data transmission means" is a function for transmitting information obtained through an information input means to a server.

[0808] A "generative artificial intelligence model" is an algorithm that analyzes information obtained from users and suggests clothing suitable for those users.

[0809] The "analysis means" refers to a function that processes user information received on the server and uses a generative artificial intelligence model to select the most suitable clothing.

[0810] The "proposal presentation means" is a function for displaying clothing suggestions generated by the analysis means to the user.

[0811] "Virtual display generation means" refers to a technology for generating virtual clothing try-on images or videos based on user selections and presenting them visually to the user.

[0812] "Visual representation" refers to an image or video that virtually shows the user what it looks like when trying on clothing.

[0813] The present invention is implemented as an application that runs on a user's device. This application provides an information input means for inputting the user's physical characteristics, style preferences, and emotional state. The user can input their body size, clothing preferences, and emotional information into a smartphone or personal computer. Once the information is entered, a data transmission means sends it to a server in the cloud.

[0814] The server operates using Amazon Web Services (AWS). The server analyzes incoming data using a generative artificial intelligence model to generate optimal clothing suggestions to personalize the user experience. Azure Cognitive Services acts as an emotion recognition engine, analyzing the user's emotional state to further personalize the suggestions.

[0815] The analyzed data is transmitted to the user's terminal via a suggestion presentation means, which the user can then view. For suggestions that interest the user, a virtual try-on image or video is generated using Unity or OpenCV via a virtual display generation means. The synthesized video is presented to the user as a visual display, allowing the user to review the content and either make a purchase or book a try-on appointment at a physical sales facility.

[0816] For example, if a user inputs "I'm feeling stressed today," the emotion recognition engine will suggest relaxing clothing, such as comfortable loungewear. The prompt to the generative AI model in this case would be as follows:

[0817] "User's emotional state: Emotional information entered by the user"

[0818] User style information: Style information entered by the user

[0819] Please select clothing items to propose and suggest up to 5 items that meet the following criteria:

[0820] Relax

[0821] User's preferred style

[0822] Virtual try-on images are available.

[0823] This configuration provides a highly customized purchasing experience based on emotionally resonant user preferences.

[0824] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0825] Step 1:

[0826] The user launches the application on their device and inputs information about their physical characteristics, style preferences, and emotional state through an input method. The data the user inputs includes their body size, clothing style preferences (e.g., casual, formal), and emotional information (e.g., want to relax, want to feel energized). This input data is stored in the device's local storage.

[0827] Step 2:

[0828] The terminal transmits data entered via an information input means to a server via a data transmission means. The terminal uses encryption technology to ensure data security while transmitting data to the cloud server. This protects the user's personal information.

[0829] Step 3:

[0830] The server processes the received data using analytical tools. The server utilizes AWS computing resources to run a generative AI model, generating real-time optimal clothing suggestions based on the user's body type, style, and emotional state. The input here is data submitted by the user, and the output is a list of suggested clothing items.

[0831] Step 4:

[0832] The server sends the generated clothing suggestions to the terminal via a suggestion presentation system. The server uses a high-speed and efficient data transfer protocol to send the suggestion list output by the generating AI model to the terminal. This allows the user to view the suggestions without delay.

[0833] Step 5:

[0834] The terminal uses a suggestion display mechanism to show the user clothing suggestions received from the server. The input here is the suggestion list sent from the server, and the output is the clothing list displayed in the terminal's GUI. The user reviews the suggested clothing and selects the items they like.

[0835] Step 6:

[0836] The user uses a virtual display generation means to virtually try on clothing based on their selection. In this step, the terminal uses Unity or OpenCV to composite the selected clothing onto the user's photo and generate a virtual try-on image or video according to the prompt messages generated by the server. The input is the user's selection data and photo, and the output is a virtual try-on image or video.

[0837] Step 7:

[0838] The terminal presents the generated virtual try-on image or video to the user as a visual display. The user can review it and, if they like it, proceed with the purchase or make a reservation to try it on at a store. The input here is the output of the virtual try-on, and the output is the user's purchase action.

[0839] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0840] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0841] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0842] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0843] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0844] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0845] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0846] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0847] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0848] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0849] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0850] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0851] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0852] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0853] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0854] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0855] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0856] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0857] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0858] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0859] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0860] The following is further disclosed regarding the embodiments described above.

[0861] (Claim 1)

[0862] An information input means for inputting the user's physical characteristics and style preferences,

[0863] A data transmission means that transmits the information entered by the information input means to a server,

[0864] An analysis means on the server analyzes user information and generates clothing suggestions best suited to the user,

[0865] A proposal presentation means that presents clothing suggestions generated by the analysis means to the user,

[0866] A fitting video generation means that generates a virtual clothing try-on based on the user's selection,

[0867] A means for presenting the fitting video generated by the fitting video generation means to the user,

[0868] A system that includes this.

[0869] (Claim 2)

[0870] The system according to claim 1, comprising a means for enabling a user to purchase selected clothing items online.

[0871] (Claim 3)

[0872] The system according to claim 1, further comprising a means for a user to make a reservation to try on clothing selected by the user at a physical store.

[0873] "Example 1"

[0874] (Claim 1)

[0875] A means for receiving information to input the user's physical attributes and style preferences,

[0876] A data transfer means for transferring the attributes input by the information receiving means to a computer,

[0877] A computer includes an analysis means that interprets user attributes and generates clothing suggestions optimized for that user,

[0878] A presentation means for displaying clothing suggestions generated by the analysis means to the user,

[0879] A fitting video generation means that synthesizes a virtual clothing try-on based on user selection,

[0880] A means for displaying the video synthesized by the aforementioned fitting video generation means to the user,

[0881] A system that includes this.

[0882] (Claim 2)

[0883] The system according to claim 1, comprising a means for enabling users to purchase selected clothing over a network.

[0884] (Claim 3)

[0885] The system according to claim 1, comprising a reservation support means for a user to make a reservation to try on clothing selected by the user at a physical store.

[0886] "Application Example 1"

[0887] (Claim 1)

[0888] An information input means for inputting the user's physical characteristics and style preferences,

[0889] A data transmission means that transmits the information input by the information input means to a computer,

[0890] An analysis means that analyzes user information in a computer and generates clothing suggestions that are optimal for the user,

[0891] A proposal presentation means that presents clothing suggestions generated by the analysis means to the user,

[0892] A fitting video generation means that generates a virtual clothing try-on based on the user's selection,

[0893] A means for presenting the fitting video generated by the fitting video generation means to the user,

[0894] A purchasing method that allows users to purchase clothing they have selected through e-commerce,

[0895] A reservation method for users to make a reservation to try on clothing items they have selected at a sales facility,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, comprising a generation means for generating a try-on video based on clothing selected by the user and the user's image.

[0899] (Claim 3)

[0900] The system according to claim 1, comprising a presentation means for allowing a user to review a virtual try-on experience and support their purchase decision.

[0901] "Example 2 of combining an emotion engine"

[0902] (Claim 1)

[0903] An information input means for inputting the user's physical characteristics, style preferences, and emotional information,

[0904] A data transmission means that transmits the information input by the information input means to a computing device,

[0905] An analysis means that analyzes user information using a generated AI model in a computing device and generates clothing suggestions that are optimal for the user,

[0906] An emotion recognition means that generates clothing suggestions suitable for the user's mood based on emotion analysis,

[0907] A proposal presentation means that presents clothing suggestions generated by the analysis means to the user,

[0908] A fitting video generation means that generates a virtual clothing try-on based on the user's selection,

[0909] A presentation means for presenting the fitting video generated by the fitting video generation means to the user,

[0910] A system that includes this.

[0911] (Claim 2)

[0912] The system according to claim 1, comprising a means for enabling a user to purchase selected clothing items online.

[0913] (Claim 3)

[0914] The system according to claim 1, further comprising a reservation means for a user to make a reservation to try on clothing selected by the user at a physical store.

[0915] "Application example 2 when combining with an emotional engine"

[0916] (Claim 1)

[0917] Information input means for inputting the user's physical characteristics, style preferences, and emotional state information,

[0918] A data transmission means that transmits the information input by the information input means to a data storage device,

[0919] An analysis means that analyzes user information and emotional state on a server and generates optimal clothing suggestions for the user using a generative artificial intelligence model,

[0920] A suggestion presentation means that presents clothing suggestions generated by the analysis means to the user,

[0921] A virtual display generation means that generates and visually displays a virtual clothing try-on based on the user's selection,

[0922] A means for presenting a visual display generated by the virtual display generation means to the user,

[0923] A system that includes this.

[0924] (Claim 2)

[0925] The system according to claim 1, further comprising a purchase function that enables a user to purchase selected clothing via a communication means.

[0926] (Claim 3)

[0927] The system according to claim 1, further comprising a reservation function for users to make reservations to try on clothing selected by the user at a face-to-face sales facility. [Explanation of Symbols]

[0928] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An information input means for inputting the user's physical characteristics and style preferences, A data transmission means that transmits the information entered by the information input means to a server, An analysis means on the server analyzes user information and generates clothing suggestions best suited to the user, A proposal presentation means that presents clothing suggestions generated by the analysis means to the user, A fitting video generation means that generates a virtual clothing try-on based on the user's selection, A means for presenting the fitting video generated by the fitting video generation means to the user, A system that includes this.

2. The system according to claim 1, comprising a purchasing means that enables a user to purchase selected clothing items online.

3. The system according to claim 1, further comprising a means for making a reservation at a physical store for a user to try on clothing selected by the user.

Images