System

Abstract

A system is provided.SOLUTION: A system, comprising: means for a user to upload a full-length photo; means for analyzing the full-length photo to obtain body shape information of the user; means for inputting data about fashion styles and preferences of the user; means for generating a plurality of wearing images based on the body shape information and fashion style data; means for generating a plurality of coordinate suggestions from the wearing images; means for displaying the coordinate suggestions to the user; and means for performing a purchase procedure based on a coordinate selected by the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In recent years, the spread of online shopping has made it easy for consumers to purchase clothes from the comfort of their own homes. However, because consumers cannot check the products in physical stores, problems such as the size not fitting or the color or material being different from what they expected often occur. This increases the hassle for consumers to return or exchange products after purchase, and increases dissatisfaction with online shopping. The present invention aims to solve these problems and provide a way for consumers to purchase clothes online with peace of mind. [Means for solving the problem]

[0005] The present invention is a system including means for a user to upload a full-body photo, means for analyzing the full-body photo to acquire the user's body shape information, means for inputting data related to the user's fashion style and preferences, means for generating a plurality of wearing images based on the body shape information and fashion style data, means for generating a plurality of outfit suggestions from the wearing images, means for displaying the outfit suggestions to the user, and means for completing a purchase based on the outfit selected by the user. This allows consumers to select clothes after checking how they will actually look when worn, reducing stress caused by mismatched sizes or differences in color or material and improving satisfaction with online shopping.

[0006] "User image upload means" refers to a means by which a user can send a full-body photo of themselves to the system.

[0007] The "image analysis means" is a means for analyzing an uploaded full-body photo and obtaining information about the user's body shape.

[0008] The "means for inputting fashion style and preferences" refers to a means by which a user inputs data such as his or her preferred fashion style, color, material, etc. into the system.

[0009] The "means for generating wearing images" is a means for generating a plurality of wearing images based on the user's body shape information and fashion style data.

[0010] The "coordination suggestion generating means" is a means for combining optimal coordination from the generated multiple wearing images and proposing it to the user.

[0011] The "coordination proposal display means" is a means for visually displaying the generated coordination proposal to the user.

[0012] The "purchase procedure means" is a means for the user to confirm the size and color based on the coordinated outfit selected and complete the final purchase procedure.

[0013] "Generative adversarial networks" is a type of machine learning technology that generates realistic synthetic images by training generative and discriminative models to compete with each other.

[0014] The "user profile database" is a database for storing information about a user's body shape and preferred fashion data.

[0015] A "fashion database" is a database that stores information on various types of clothing and allows users to search for the most suitable items based on their preferences. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention is an online shopping support system in which users upload full-body photos, analyze the images, and a generative AI proposes multiple outfits based on the user's body shape and preferences. This system is realized through the interaction between users, terminals, and a server.

[0038] Uploading and analyzing user images

[0039] First, the user takes a full-body photo and uploads it to the server from their device. The server then uses an AI image analysis engine to obtain the user's body shape information based on the received full-body photo. During this process, the server identifies feature points such as the face, shoulder width, waist, hips, and leg length, and extracts them as numerical data. If the user has entered height and weight data, this information is also supplemented. The results of this analysis are stored in a user profile database.

[0040] Enter your fashion style and preferences

[0041] Next, users enter data such as their preferred fashion style, color, material, and brand on the app or website, and the data is sent from the device to a server, which stores this data and combines it with the user's body shape information to create a user profile.

[0042] Wearing image generation and coordination suggestions

[0043] The server filters matching clothing from its internal fashion database based on the user profile data and fashion style data, extracting items that fit the user's body type and match their preferred fashion style.

[0044] Based on the extracted items, the server uses a generative AI model to generate synthetic images to apply to the user's photo. During this process, it utilizes a generative adversarial network (GAN) to ensure the clothing fits naturally based on the user's body shape. The server then generates multiple outfit patterns and saves them as images.

[0045] The server converts the generated coordinated image into a user interface for display to the user, and the terminal visually displays the received coordinated suggestion to the user.

[0046] Coordination selection and purchase process

[0047] The user selects their favorite outfit from the displayed outfits and sends the selection from their device to the server, which then reconfirms the details of the selected outfit (size, color, material) to ensure that the selection meets the user's expectations.

[0048] Finally, the user completes the purchase process based on the finalized outfit. The server then confirms the purchase information and places an order with the affiliated online shopping site. Once the order is complete, the server sends an order completion notification to the terminal.

[0049] Specific examples

[0050] For example, suppose User A sends a full-body photo of himself and inputs data that he likes casual-style blue denim jackets. The server analyzes User A's body shape information and generates multiple outfits that include a casual-style blue denim jacket. The generated outfits could be, for example, a combination of a white T-shirt, denim pants, and sneakers. User A then selects his favorite outfit and proceeds with the purchase. This entire process is seamless, reducing the stress of online shopping for User A.

[0051] The above is an embodiment of the present invention. This system allows users to select outfits that suit their body type and preferences with confidence, and enjoy online shopping in comfort.

[0052] The processing flow will be explained below.

[0053] Step 1: Upload a user image

[0054] The user takes a full-body photo.

[0055] Users use a dedicated app or website to upload a full-body photo from their device to the server.

[0056] Step 2: Receiving and analyzing images

[0057] The server sends the received full-body photo to an AI image analysis engine.

[0058] The server performs image analysis to obtain the user's body shape information (e.g., height, shoulder width, waist, hips, leg length, etc.).

[0059] Step 3: Enter user style information

[0060] Users enter their preferred fashion style, color, material, brand, etc. on the app or website.

[0061] The terminal transmits the input style information to the server.

[0062] Step 4: Integrating profile data

[0063] The server integrates the body shape information obtained through image analysis with the user's style information and stores it in a user profile database.

[0064] Step 5: Matching the clothing database

[0065] The server searches a fashion database for matching clothing items based on the user profile data.

[0066] The server filters the search results to extract items that match the user's size and preferences.

[0067] Step 6: Generative AI creates a wearing image

[0068] The server uses the generative AI model to apply the extracted clothing items to the user's full-body image.

[0069] The server uses a generative adversarial network (GAN) to generate a garment image that naturally fits the user's body shape.

[0070] Step 7: Generate outfit suggestions

[0071] The server creates multiple coordination suggestions based on the generated wearing images.

[0072] The server stores these coordination suggestions as image data and converts them into a user interface.

[0073] Step 8: View outfit suggestions

[0074] The server transmits image data of the coordinated outfit proposal to the terminal.

[0075] The device displays coordination suggestions to the user.

[0076] Step 9: Selecting Users

[0077] The user selects their favorite outfit from the displayed outfits.

[0078] The terminal transmits the selected coordinate information to the server.

[0079] Step 10: Check size and color

[0080] The server reconfirms the details of the selected garment (size, color, material).

[0081] Check whether the server matches your preferences and offer alternatives if necessary.

[0082] Step 11: Complete your purchase

[0083] The user confirms the final outfit and completes the purchase.

[0084] The server receives the purchase information and places an order with the affiliated online shopping site.

[0085] The server sends an order completion notification to the terminal and displays it to the user.

[0086] Through the above steps, this system provides an environment where users can purchase clothes online with peace of mind.

[0087] Example 1

[0088] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0089] Conventional online shopping systems have made it difficult for users to find the perfect outfit for their body type and preferences, leading to frequent returns due to incorrect sizes or colors after purchase. Another issue is that users cannot actually try on clothes, meaning they cannot see how they will look before purchasing.

[0090] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0091] In this invention, the server includes means for a user to upload a full-body photograph, means for analyzing the full-body photograph and acquiring the user's body shape information, means for inputting data related to the user's fashion style and preferences, means for generating a plurality of wearing images based on the body shape information and fashion style data, means for generating a plurality of coordination suggestions from the wearing images, means for displaying the coordination suggestions to the user, means for carrying out a purchase procedure based on the coordination selected by the user, and means for placing an order with an affiliated e-commerce site based on the user's selection. This allows users to shop online while checking coordinations that suit their body shape and preferences, and reduces the risk of returns due to mismatched sizes or colors.

[0092] "Means for uploading user images" refers to a function that allows a user to take a full-body photo and send that photo to the server.

[0093] "Means for analyzing full-body photos" refers to the function of obtaining the user's body shape information from full-body photos received by the server using an AI image analysis engine.

[0094] "Means for inputting data about the user's fashion style and preferences" refers to a function that allows the user to input their own fashion style and preferences through an app or website and send them to a server.

[0095] "Means for generating multiple wearing images" refers to the function of the server to create wearing images using a generative adversarial network (GAN) based on the user's body shape information and fashion style data.

[0096] "Means for generating multiple coordination suggestions" refers to a function that allows the server to suggest appropriate coordination to the user from the generated wearing images.

[0097] "Means for displaying coordination suggestions to the user" refers to a function that converts the coordination suggestions generated by the server into a user interface and visually displays them to the user via the terminal.

[0098] "Means for completing the purchase process based on the outfit selected by the user" refers to a function that allows the user to proceed with the purchase process based on the outfit they select from the outfits presented.

[0099] "Means for placing an order with an affiliated e-commerce site" refers to a function that enables the server to place an order with an affiliated e-commerce site based on the coordinates selected by the user.

[0100] A "generative adversarial network (GAN)" refers to a machine learning model used to generate new data based on existing data.

[0101] This invention is an online shopping support system in which users upload full-body photos, and a generative AI analyzes the images and suggests multiple outfits based on the user's body shape and preferences. This system is realized through the interaction between users, terminals, and a server.

[0102] First, the user takes a full-body photo using a device such as a smartphone and uploads it to a server. During this process, the photo is saved in the device's temporary storage and then sent to the server via the HTTPS protocol. Specifically, JPEG image files are often used.

[0103] Next, the server analyzes the received full-body photo using an AI image analysis engine (such as OpenCV or TensorFlow). Through this analysis, the server obtains body shape information such as face, shoulder width, waist, hips, and leg length as numerical data. If the user has entered their own height and weight data, this data is also used as a complement. The obtained data is stored in a user profile database on the server.

[0104] The user then enters data on the app or website, such as their preferred fashion style (e.g., casual, formal, sporty, etc.), color, material, brand, etc. This data is sent from the device to a server, which stores it and combines it with the user's body shape information to create a user profile.

[0105] The server filters matching clothing from its internal fashion database based on user profile data and fashion style data. Through filtering, items that fit the user's body shape and preferred fashion style are extracted. Based on this extracted list of items, the server uses a generative adversarial network (GAN) to generate a synthetic image that naturally fits the user's photo.

[0106] A specific example of a generative AI model is GAN (generative adversarial network) technology. This generates a synthetic image based on the user's body shape information so that the selected clothing naturally matches the user's photo. The generated coordination pattern is saved as an image and converted into a user interface. The device then visually displays the received coordination suggestions to the user.

[0107] The user selects their favorite outfit from the visually presented outfits and sends the selection to the server via their device. The server then reconfirms the details of the selected items (size, color, material, etc.) and makes a final confirmation that the selection meets the user's expectations. Finally, the user proceeds with the purchase based on the finalized outfit. After verifying the purchase information, the server places an order with the affiliated e-commerce site, and once the order is complete, the server sends an order completion notification to the device.

[0108] For example, if User A uploads a full-body photo and inputs that he or she likes casual blue denim jackets, the server analyzes User A's body shape information and generates multiple outfits that include a casual blue denim jacket. Examples of the outfits generated include a white T-shirt, denim pants, and sneakers. User A then selects his or her favorite outfit and proceeds with the purchase. This entire process is seamless, reducing the stress of online shopping for User A.

[0109] Example prompt sentence:

[0110] "Please suggest the best outfit for User A, who uploaded a full-body photo and entered that she likes casual blue denim jackets."

[0111] The above is an embodiment of the present invention. This system allows users to comfortably enjoy online shopping while checking outfits that suit their body type and preferences.

[0112] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0113] Step 1: Upload a user image

[0114] 1. A user takes a full-body photo using the smartphone camera app. The input is a full-body photo in JPEG format.

[0115] 2. The device takes a full-body photo and stores it in temporary storage.

[0116] 3. The device sends the saved JPEG image to the server using the HTTP protocol. The output is the image data sent to the server.

[0117] Step 2: Image analysis

[0118] 1. The server analyzes the received full-body photo using an AI image analysis engine (such as OpenCV or TensorFlow). The input is a JPEG full-body photo stored on the server.

[0119] 2. The server recognizes body shape information such as face, shoulder width, waist, hips, and leg length, and extracts it as numerical data. The output is numerical data of body shape information.

[0120] 3. The server completes the height and weight data previously entered by the user. The input is the user's height and weight data, and the output is the integrated profile data.

[0121] 4. The server stores the analysis results in the user profile database. The output is the user's body shape information stored in the database.

[0122] Step 3: Enter your fashion style and preferences

[0123] 1. A user inputs their preferred fashion style, color, material, brand, etc. on an app or website. The input is the user's preference data.

[0124] 2. The device sends the input fashion style and preference data to the server. The output is the user's preference data sent to the server.

[0125] 3. The server stores the received data and combines it with the body shape information to create a user profile. The output is the combined user profile.

[0126] Step 4: Creating outfit images and suggesting outfits

[0127] 1. The server filters matching clothes from the internal fashion database based on the user profile data and fashion style data. The input is the user profile data and fashion style data, and the output is a list of matching clothes.

[0128] 2. The server uses a generative adversarial network (GAN) to generate a synthetic image that naturally fits the user's photo based on the filtered items. The input is a list of matching clothing items and the user's full-body photo, and the output is the generated synthetic image.

[0129] 3. The server saves the generated coordinate pattern as an image. The output is the saved coordinate image.

[0130] 4. The server converts the generated coordinated image into a user interface. The output is the coordinated image converted into a user interface.

[0131] 5. The terminal retrieves the converted coordinated image and visually displays it to the user. The output is the coordinated image displayed to the user.

[0132] Step 5: Choose your outfit and checkout

[0133] 1. The user selects their favorite outfit from the presented outfits. The input is the user's choice.

[0134] 2. The terminal sends the selection to the server. The output is the selection sent to the server.

[0135] 3. The server reconfirms the details of the selected item (size, color, material). The input is the details of the selected item.

[0136] 4. The server performs a final check to ensure the selections meet the user's expectations. The output is the final, confirmed selections.

[0137] 5. The user proceeds with the purchase process based on the coordinated outfit they have decided on. The input is the coordinated outfit information they have decided on.

[0138] 6. After the server confirms the purchase information, it places an order with the affiliated e-commerce site. The output is the ordered item information.

[0139] 7. The server sends an order completion notification to the terminal. The output is the order completion notification sent to the user.

[0140] (Application example 1)

[0141] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0142] Currently, when shopping online, it takes a lot of time and effort for users to find the right fashion items to fit their body type. Furthermore, there are limited systems that suggest outfits that suit a user's preferences, which causes stress for users as they must choose from a large number of options. Since the online purchasing process does not allow users to try on items, users are also concerned about size and fit. It is necessary to solve these issues and improve the user shopping experience.

[0143] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0144] In this invention, the server includes means for a user to upload a full-body photo, means for analyzing the full-body photo and acquiring the user's body shape information, means for inputting data related to the user's fashion style and preferences, means for generating a plurality of wearing images based on the body shape information and fashion style data, means for generating a plurality of coordination suggestions from the wearing images, means for displaying the coordination suggestions to the user, means for completing a purchase based on the coordination selected by the user, and means for seamlessly completing the purchase using a generative AI model. This allows the user to visually check the coordination suggestions that suit their body shape and preferences, easily select appropriate items, and smoothly complete the purchase process.

[0145] A "user" is someone who uses the online shopping system to upload a full-body photo of themselves, suggest outfits, and complete the purchase process.

[0146] A "full-body photo" is an image that captures the user's face and entire body and is necessary to obtain body shape information.

[0147] "Analysis" is the process of extracting the user's body shape information from a full-body photo and quantifying each feature point.

[0148] "Body shape information" is numerical data that indicates the user's physical characteristics such as face, shoulder width, waist, hips, and leg length.

[0149] "Fashion style" refers to the type and design of clothing preferred by the user, and includes styles such as casual, formal, and sporty.

[0150] "Preferences" refer to individual fashion elements such as the user's preferred colors, materials, and brands.

[0151] "Data" is text and numeric information entered by the user, including information about fashion style and preferences.

[0152] A "wearing image" is a composite image of clothing generated based on the user's body shape information and preferences.

[0153] "Coordination suggestions" are clothing combinations that are generated based on multiple wearing images and suggested to the user.

[0154] "Display" refers to visually showing the generated coordination proposal on the user's terminal.

[0155] The "purchase procedure" refers to the series of processes required for a user to actually purchase items based on the outfit they have selected.

[0156] A "generative AI model" is an artificial intelligence model used to generate outfits based on a user's body shape information and fashion style.

[0157] "Seamless" refers to a state in which users have a consistent experience and there is a smooth transition between different processes.

[0158] This invention is an online shopping support system in which users upload full-body photos, analyze the images, and a generative AI proposes multiple outfits based on the user's body shape and preferences. This system is realized through the interaction between users, terminals, and a server.

[0159] Uploading and analyzing user images

[0160] First, the user takes a full-body photo and uploads it to the server from their device. The server then uses an AI image analysis engine to obtain the user's body shape information based on the received full-body photo. During this process, the server identifies feature points such as the face, shoulder width, waist, hips, and leg length, and extracts them as numerical data. If the user has entered height and weight data, this information is also supplemented. The results of this analysis are stored in a user profile database.

[0161] Enter your fashion style and preferences

[0162] Next, users enter data such as their preferred fashion style, color, material, and brand on the app or website, and the data is sent from the device to a server, which stores this data and combines it with the user's body shape information to create a user profile.

[0163] Wearing image generation and coordination suggestions

[0164] The server filters matching clothing from its internal fashion database based on user profile data and fashion style data. This filtering extracts items that fit the user's body shape and preferred fashion style. Based on the extracted items, the server uses a generative AI model to generate synthetic images to apply to the user's photo. This process utilizes a generative adversarial network (GAN) to ensure that the clothing fits naturally based on the user's body shape information. The server then generates multiple outfit patterns and saves them as images. The server converts the generated outfit images into a user interface for display to the user. The device visually displays the received outfit suggestions to the user.

[0165] Coordination selection and purchase process

[0166] The user selects their favorite outfit from the displayed outfits and sends the selection from their device to the server. The server then reconfirms the details of the selected outfit (size, color, material) to ensure that the selection meets the user's expectations. Finally, the user completes the purchase process based on the final outfit. The server uses a generative AI model to seamlessly complete the purchase process. This provides the user with a consistent experience and maintains smooth connections between different processes.

[0167] Specific examples

[0168] For example, suppose User A sends a full-body photo of himself and inputs data that he likes casual-style blue denim jackets. The server analyzes User A's body shape information and generates multiple outfits that include a casual-style blue denim jacket. The generated outfits might be, for example, a combination of a white T-shirt, denim pants, and sneakers. User A then selects his favorite outfit and proceeds with the purchase. This entire process is seamless, reducing the stress of online shopping for User A.

[0169] Prompt Sentence Examples

[0170] "Based on the user's body type and preferences, please suggest a casual outfit that includes a blue denim jacket."

[0171] This system allows users to confidently select outfits that suit their body type and preferences, and enjoy comfortable online shopping.

[0172] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0173] Step 1:

[0174] The user takes a full-body photo using a smartphone and uploads the image from the device to the server. The input of this step is the user's full-body photo, and the output is the completion of uploading the image to the server.

[0175] Step 2:

[0176] The server receives the uploaded full-body photo and uses an AI image analysis engine to analyze the user's body shape information. The analysis identifies feature points such as face, shoulder width, waist, hips, and leg length and extracts them as numerical data. The input in this step is the full-body photo, and the output is numerical data of body shape information.

[0177] Step 3:

[0178] Users input data such as their preferred fashion style, color, material, brand, etc. through an interface on the app or website. The input in this step is the user's fashion style and preference data, and the output is that this data is sent to a server and stored.

[0179] Step 4:

[0180] The server integrates the user's body shape information with fashion style and preference data to create a user profile, which includes the user's body shape information, preferred styles, colors, materials, brands, etc. The input in this step is the body shape information, fashion style and preference data, and the output is the creation of a user profile.

[0181] Step 5:

[0182] The server filters matching clothing items from its internal fashion database based on the generated user profile. This filtering extracts items that fit the user's body shape and preferred fashion style. The input of this step is the user profile, and the output is the extracted set of fashion items.

[0183] Step 6:

[0184] The server uses a generative AI model to generate a wearing image tailored to the user's body shape using the extracted fashion items. In this process, a generative adversarial network (GAN) is used to make the clothing fit naturally based on the user's body shape information. The input in this step is the extracted fashion items and the user's body shape information, and the output is a synthesized image of the wearing image.

[0185] Step 7:

[0186] The server generates multiple outfit suggestions from the generated outfit images and sends them to the user's device. The input in this step is a composite image of the outfit images, and the output is a set of outfit suggestion images.

[0187] Step 8:

[0188] The user selects their favorite outfit from multiple outfit suggestions displayed on the device. The input in this step is a group of outfit suggestion images, and the output is the data of the selected outfit.

[0189] Step 9:

[0190] The server checks the size and color again based on the coordinates selected by the user. The input in this step is the data of the selected coordinates, and the output is the result of the size and color check.

[0191] Step 10:

[0192] Based on the selected outfit, the server uses a generative AI model to seamlessly complete the purchase process. During this process, the order information is sent to the partner online shopping site and an order completion notification is sent to the terminal. The input in this step is the order information, and the output is a purchase completion notification.

[0193] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0194] This invention is an online shopping support system in which users upload full-body photos, analyze the images, and a generation AI proposes multiple outfits based on the user's body shape and preferences. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to propose even more appropriate outfits based on the user's emotional state. This system is realized through the interaction of the user, terminal, server, and emotion engine.

[0195] Uploading and analyzing user images

[0196] First, the user takes a full-body photo and uploads it to the server from their device. The server then uses an AI image analysis engine to obtain the user's body shape information based on the received full-body photo. During this process, the server identifies feature points such as the face, shoulder width, waist, hips, and leg length, and extracts them as numerical data. If the user has entered height and weight data, this information is also supplemented. The results of this analysis are stored in a user profile database.

[0197] Enter your fashion style and preferences

[0198] Next, users enter data such as their preferred fashion style, color, material, and brand on the app or website, and the data is sent from the device to a server, which stores this data and combines it with the user's body shape information to create a user profile.

[0199] Wearing image generation and coordination suggestions

[0200] The server filters matching clothing from its internal fashion database based on the user profile data and fashion style data, extracting items that fit the user's body type and match their preferred fashion style.

[0201] Based on the extracted items, the server uses a generative AI model to generate synthetic images to apply to the user's photo. During this process, it utilizes a generative adversarial network (GAN) to ensure the clothing fits naturally based on the user's body shape. The server then generates multiple outfit patterns and saves them as images.

[0202] Emotion engine recognizes user emotions

[0203] One of the features of this system is the addition of an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and voice to determine their emotional state. Specifically, it recognizes emotions such as joy, surprise, and sadness in real time from the user's facial expressions and tone of voice when selecting an outfit or looking at each suggestion.

[0204] Emotion-based coordination suggestions

[0205] Once the user's emotions are recognized, the server adjusts the outfit suggestions based on that information. For example, if the user expresses positive emotions toward the outfits displayed, the system records the user's emotional data and prioritizes suggestions for similar styles. On the other hand, if the user expresses negative emotions, the system adjusts to suggest different styles or colors.

[0206] Displaying outfit suggestions and final selection

[0207] The server converts the adjusted outfit suggestions into a user interface for display to the user. The device visually displays the received outfit suggestions to the user. The user selects their favorite outfit from the displayed outfits and sends the selection from the device to the server. The server reconfirms the details of the selected garment (size, color, material) to ensure that the selection meets the user's expectations.

[0208] Completing the purchase process

[0209] Finally, the user completes the purchase process based on the finalized outfit. The server then confirms the purchase information and places an order with the affiliated online shopping site. Once the order is complete, the server sends an order completion notification to the terminal.

[0210] Specific examples

[0211] For example, suppose User B sends a full-body photo of himself and inputs that he likes casual-style blue denim jackets. The server analyzes User B's body shape information and generates multiple outfits that include casual-style blue denim jackets. At this time, the emotion engine analyzes User B's facial expressions and voice and prioritizes suggestions of items that elicit a positive reaction. User B then selects his favorite outfit from the suggestions and proceeds with the purchase. This entire process is seamless, reducing the stress of online shopping for User B.

[0212] The above is an embodiment of the present invention. This system allows users to confidently select outfits that match their body type and preferences, and also receives suggestions based on their emotions, allowing them to enjoy a more satisfying online shopping experience.

[0213] The processing flow will be explained below.

[0214] Step 1: Upload a user image

[0215] The user takes a full-body photo.

[0216] Users use a dedicated app or website to upload a full-body photo from their device to the server.

[0217] Step 2: Receiving and analyzing images

[0218] The server sends the received full-body photo to an AI image analysis engine.

[0219] The server performs image analysis to obtain the user's body shape information (e.g., height, shoulder width, waist, hips, leg length, etc.).

[0220] Step 3: Enter user style information

[0221] Users enter their preferred fashion style, color, material, brand, etc. on the app or website.

[0222] The terminal transmits the input style information to the server.

[0223] Step 4: Integrating profile data

[0224] The server integrates the body shape information obtained through image analysis with the user's style information and stores it in a user profile database.

[0225] Step 5: Matching the clothing database

[0226] The server searches a fashion database for matching clothing items based on the user profile data.

[0227] The server filters the search results to extract items that match the user's size and preferences.

[0228] Step 6: Generative AI creates a wearing image

[0229] The server uses the generative AI model to apply the extracted clothing items to the user's full-body image.

[0230] The server uses a generative adversarial network (GAN) to generate a garment image that naturally fits the user's body shape.

[0231] Step 7: Generate outfit suggestions

[0232] The server creates multiple coordination suggestions based on the generated wearing images.

[0233] The server stores these coordination suggestions as image data and converts them into a user interface.

[0234] Step 8: Emotion recognition and regulation with the emotion engine

[0235] The server uses an emotion engine to analyze the user's facial expressions and voice and recognize their emotional state in real time.

[0236] The server adjusts the outfit suggestions based on the user's emotional data: if the user expresses positive emotions, it suggests a similar style, and if the user expresses negative emotions, it suggests a different style.

[0237] Step 9: View outfit suggestions

[0238] The server transmits image data of the coordinated outfit proposal to the terminal.

[0239] The device displays coordination suggestions to the user.

[0240] Step 10: Selecting Users

[0241] The user selects their favorite outfit from the displayed outfits.

[0242] The terminal transmits the selected coordinate information to the server.

[0243] Step 11: Check size and color

[0244] The server reconfirms the details of the selected garment (size, color, material).

[0245] Check whether the server matches your preferences and offer alternatives if necessary.

[0246] Step 12: Complete your purchase

[0247] The user confirms the final outfit and completes the purchase.

[0248] The server receives the purchase information and places an order with the affiliated online shopping site.

[0249] The server sends an order completion notification to the terminal and displays it to the user.

[0250] Through these successive processing steps, the system provides a safe and secure online clothing shopping environment for users. In addition to suggesting outfits based on the user's body shape and preferences, it also enhances user satisfaction through emotion recognition using an emotion engine.

[0251] Example 2

[0252] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0253] When shopping online, the process of selecting fashion items that suit a user's body type and preferences is time-consuming and laborious. Furthermore, the lack of a system that can provide more appropriate suggestions based on the user's emotional state makes it difficult to provide a satisfying shopping experience. Therefore, there is a need for technology that can recognize a user's emotions in real time and adjust outfit suggestions accordingly.

[0254] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0255] In this invention, the server includes means for a user to upload a full-body photograph, means for analyzing the full-body photograph and acquiring the user's body shape information, means for inputting data on the user's fashion style and preferences, means for generating a plurality of wearing images based on the body shape information and fashion style data, and means for recognizing the user's emotions and adjusting coordination suggestions based thereon. This not only makes it easier for the user to select fashion items that suit their body shape and preferences, but also enables a more satisfying online shopping experience by making suggestions based on the user's emotional state.

[0256] A "full-body photo" refers to a photo showing the user's entire body, and is used to obtain body shape information.

[0257] "Body shape information" refers to information about the shape of a user's biological body, including features such as the user's face, shoulder width, waist, hips, and leg length, as well as supplemental data (such as height and weight).

[0258] "Fashion style" refers to attributes such as design, color, material, and brand of clothing and accessories based on the user's preferences.

[0259] A "generative adversarial network (GAN)" is an algorithm that generates data by having two neural networks compete with each other, and is a model that demonstrates particularly strong performance in image generation.

[0260] An "emotion engine" refers to a system that analyzes a user's facial expressions and voice to recognize their emotional state in real time.

[0261] "Coordination suggestions" refer to multiple outfit images and sets generated by the server based on the user's body shape information, fashion style, and emotional data.

[0262] "Purchase Checkout" means the steps required to ultimately purchase the Products selected by the User, including the online payment process.

[0263] "User profile" refers to a database that integrates a user's body shape information, fashion style, emotional data, etc.

[0264] This invention is an online shopping support system in which users upload full-body photos, analyze the images, and a generation AI proposes multiple outfits based on the user's body shape and preferences. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to propose even more appropriate outfits based on the user's emotional state. This system is realized through the interaction of the user, terminal, server, and emotion engine.

[0265] Uploading and analyzing user images

[0266] First, the user takes a full-body photo and uploads it from their device to the server. The server then uses an AI image analysis engine (such as Google Cloud Vision API) to obtain the user's body shape information based on the received full-body photo. The server identifies feature points such as the face, shoulder width, waist, hips, and leg length, and extracts them as numerical data. If the user has entered height and weight data, this information is also supplemented. The results of this analysis are stored in a user profile database.

[0267] Enter your fashion style and preferences

[0268] Next, users enter data such as their preferred fashion style, color, material, and brand on the app or website, which is then sent from their device to a server, which stores this data and combines it with the user's body information to create a detailed user profile.

[0269] Wearing image generation and coordination suggestions

[0270] The server filters matching clothing from its internal fashion database based on user profile data and fashion style data. This filtering extracts items that fit the user's body shape and preferred fashion style. Based on the extracted items, the server uses a generative AI model (e.g., StyleGAN) to generate synthetic images. This process utilizes a generative adversarial network (GAN) to ensure that the clothing fits naturally based on the user's body shape information. The server generates multiple coordination patterns and saves them as images.

[0271] Emotion engine recognizes user emotions

[0272] One of the features of this system is that it incorporates an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and voice to determine their emotional state. When the user selects an outfit, the device's camera and microphone are used to collect emotional data in real time. Based on this data, the server recognizes the user's emotions, such as joy, surprise, and sadness, in real time.

[0273] Emotion-based coordination suggestions

[0274] The server dynamically adjusts outfit suggestions based on emotion data obtained from the emotion engine. If a user expresses positive emotions, the server records their emotion data and prioritizes suggestions for similar styles. On the other hand, if a user expresses negative emotions, the server suggests different styles and colors.

[0275] Displaying outfit suggestions and final selection

[0276] The server converts the adjusted outfit suggestions into a user interface. The device visually displays the received outfit suggestions to the user. The user selects their favorite outfit from the displayed outfits and sends the selection from the device to the server. The server then reconfirms the details of the selected garment (size, color, material) to ensure that the selection meets the user's expectations.

[0277] Completing the purchase process

[0278] Finally, the user completes the purchase process based on the finalized outfit. The server then confirms the purchase information and places an order with the affiliated online shopping site. Once the order is complete, the server sends an order completion notification to the terminal.

[0279] Specific examples

[0280] For example, suppose User B submits a full-body photo and inputs data indicating that he or she prefers casual-style blue denim jackets. The server analyzes User B's body shape information and generates multiple outfits that include casual-style blue denim jackets. At this time, the emotion engine analyzes User B's facial expressions and voice, and prioritizes suggestions of items that elicit a positive response. User B then selects his or her favorite outfit from the list and completes the purchase process.

[0281] Prompt Sentence Examples

[0282] Here are some example prompts to input to a generative AI model:

[0283] "Please explain the process of an online shopping support system that uploads a full-body photo of the user and obtains body shape information. It then generates outfit images based on the user's preferred styles and uses emotional data to suggest optimal outfits."

[0284] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0285] Step 1:

[0286] The user takes a full-body photo and uploads the image to the online shopping support system using a terminal.

[0287] The input is a full-body photo of the user, and the output is the image data being sent to the server. Specifically, the user takes a full-body photo with the camera on their smartphone or computer, and then uploads the image via the system's upload function.

[0288] Step 2:

[0289] The server analyzes the received full-body photo.

[0290] The input is an uploaded full-body photo, and the output is numerical data of the user's body shape information (face, shoulder width, waist, hips, leg length, etc.). Specifically, the server uses an AI image analysis engine (e.g., Google Cloud Vision API) to analyze the full-body photo and identify the body's feature points.

[0291] Step 3:

[0292] The server completes the user's height and weight data and stores the body shape information in a user profile database.

[0293] The input is the analyzed body shape information and supplementary data (height and weight), and the output is a detailed user profile. Specifically, the server further adjusts the body shape information based on the supplementary data and stores it in a database.

[0294] Step 4:

[0295] Users enter their preferences for fashion style, color, material, brand, etc. on the app or website.

[0296] The input is data about the user's fashion style, and the output is sending this data to the server. Specifically, the user selects and inputs their preferred fashion attributes through the interface.

[0297] Step 5:

[0298] The server combines the received fashion style data with the user's body shape information to create a detailed user profile.

[0299] The input is fashion style data and body shape information, and the output is an integrated user profile. Specifically, the server integrates these data and stores them in a database as individual profiles.

[0300] Step 6:

[0301] The server filters matching clothing items from an internal fashion database based on the user profile.

[0302] The input is a detailed user profile, and the output is a list of clothing items that match the user. Specifically, the server uses a filtering algorithm to select clothing items that match each attribute based on the profile.

[0303] Step 7:

[0304] The server uses a generative AI model (e.g., StyleGAN) to generate a synthetic image that applies the filtered clothing items to the user's photo.

[0305] The input is the filtered clothing items and the user's photo, and the output is a synthetic image. Specifically, the server uses a generative adversarial network (GAN) to generate a synthetic image in which the clothing fits naturally based on the user's body shape information.

[0306] Step 8:

[0307] The server generates a plurality of coordinate patterns and stores them as images.

[0308] The input is a composite image, and the output is images of multiple coordinate patterns. Specifically, the server creates different coordinate patterns based on the generated composite image and saves them as image data.

[0309] Step 9:

[0310] The device uses a camera and microphone to collect the user's facial expressions and voice in real time as they view the suggested outfits.

[0311] The input is the user's real-time facial expression and voice data, and the output is collected emotion data. Specifically, the device records the user's browsing behavior with a camera and microphone and sends the data to the emotion engine.

[0312] Step 10:

[0313] The server adjusts the coordination suggestions based on the emotional data obtained from the emotion engine.

[0314] The input is the user's emotional data, and the output is tailored outfit suggestions. Specifically, the server prioritizes items that show positive emotions and excludes items that show negative reactions.

[0315] Step 11:

[0316] The server converts the adjusted coordination proposal for a user interface, and the terminal visually displays the received proposal to the user.

[0317] The input is the adjusted coordinate proposal, and the output is the interface displayed to the user. Specifically, the server converts the adjusted information into an appropriate format and sends it to the terminal, which then displays it to the user.

[0318] Step 12:

[0319] The user selects their favorite outfit from the displayed outfits and transmits the selection to the server via their terminal.

[0320] The input is the user's selection, and the output is the selected outfit data. Specifically, the user operates the interface to select their favorite outfit.

[0321] Step 13:

[0322] The server then double-checks the selected garment details (size, color, material) to ensure they match the user's expectations.

[0323] The input is the details of the selected garment, and the output is the confirmation result. Specifically, the server checks the selection against the database again to check compatibility.

[0324] Step 14:

[0325] The user completes the purchase process based on the finalized outfit, and the server confirms the purchase information and then places an order with the affiliated online shopping site.

[0326] The input is the purchase procedure information, and the output is the order data. Specifically, the user completes the purchase procedure, and the server processes the order. Once the order is completed, the server sends an order completion notification to the terminal.

[0327] (Application example 2)

[0328] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0329] In today's online shopping environment, users face the challenge of finding the right fashion items. Furthermore, the lack of coordination suggestions tailored to the user's body type and individual preferences, as well as the lack of personalized suggestions that take into account the user's emotional state, reduces satisfaction with the shopping experience.

[0330] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to upload a full-body photograph, means for analyzing the full-body photograph and acquiring the user's body shape information, means for inputting data related to the user's fashion style and preferences, means for generating a plurality of wearing images based on the body shape information and fashion style data, means for generating a plurality of outfit suggestions, means for analyzing the user's emotional state and acquiring emotion data, means for adjusting the outfit suggestions based on the emotion data, means for displaying the outfit suggestions to the user, and means for completing a purchase based on the outfit selected by the user. This makes it easier for users to find outfits that suit their body shape and preferences, and by receiving suggestions based on their emotions, users can have a more satisfying online shopping experience.

[0331] "Full Body Photo" means a photograph of the User's entire body, which must include the User's entire body from face to toe.

[0332] "Analysis" refers to the process of analyzing image data using a computer and extracting specific information, such as identifying a user's body shape information from an image.

[0333] "Body Information" refers to data related to a user's physique and shape, including measurements and features such as face, shoulder width, waist, hips, and leg length.

[0334] "Fashion style" refers to the clothing designs and styles preferred by a user, including information about preferences for color, material, design, brand, etc.

[0335] "Wearing image" refers to an image of the user wearing the clothing, generated based on the user's body shape information. This visualizes how the user will look when actually wearing the clothing.

[0336] "Coordination suggestions" refer to multiple fashion combinations selected from wearing images generated based on the user's body shape information and fashion style.

[0337] "Emotional state" refers to data related to emotions analyzed from the user's facial expressions, tone of voice, etc. Specifically, it includes emotions such as joy, anger, sadness, and happiness.

[0338] "Emotional data" refers to data that quantifies or classifies an emotional state, which allows for suggestions and adjustments based on the user's emotions.

[0339] "Adjustment" refers to changing the suggestions based on specific criteria or conditions. In this case, it refers to changing the outfit suggestions based on the user's emotional data.

[0340] "Purchase procedure" refers to the series of actions required to actually purchase the product selected by the user, including product selection, payment, and delivery procedures.

[0341] "System" refers to the entire device or program that combines multiple means, including various functions necessary for users to comfortably shop online.

[0342] This invention is an online shopping support system that allows users to upload full-body photos and performs image analysis to suggest multiple outfits based on the user's body shape and preferences. By combining it with an emotion engine that recognizes the user's emotions, it is possible to suggest even more appropriate outfits based on the user's emotional state. This system is realized through the interaction of the user, terminal, server, and emotion engine.

[0343] Uploading and analyzing user images

[0344] First, the user takes a full-body photo and uploads it to the server from their device. The server then uses an AI image analysis engine to obtain the user's body shape information based on the received full-body photo. Specifically, image analysis technologies such as Google Cloud Vision API are used to identify feature points such as the face, shoulder width, waist, hips, and leg length, and extract them as numerical data. The results of this analysis are stored in a user profile database.

[0345] Enter your fashion style and preferences

[0346] Next, users enter data such as their preferred fashion style, color, material, and brand on the app or website, and the data is sent from the device to a server, which stores this data and combines it with the user's body shape information to create a user profile.

[0347] Wearing image generation and coordination suggestions

[0348] The server filters matching clothing from its internal fashion database based on user profile data and fashion style data. This filtering extracts items that fit the user's body shape and preferred fashion style. Based on the extracted items, the server uses a generative AI model to generate synthetic images to apply to the user's photo. This process utilizes a generative adversarial network (GAN) to ensure that the clothing fits naturally based on the user's body shape information. The server then generates multiple outfit patterns and saves them as images.

[0349] Emotion engine recognizes user emotions

[0350] One of the features of this system is the addition of an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and voice to determine their emotional state. Specifically, it uses Microsoft Azure's Emotion API to recognize emotions such as joy, surprise, and sadness in real time from the user's facial expressions and tone of voice when selecting an outfit or viewing each suggestion.

[0351] Emotion-based coordination suggestions

[0352] Once the user's emotions are recognized, the server adjusts the outfit suggestions based on that information. For example, if a user expresses positive emotions toward the outfits displayed, the server records the user's emotional data and prioritizes suggestions for similar styles. On the other hand, if the user expresses negative emotions, the system adjusts to suggest different styles or colors. The system also reviews the user interface, taking into account the results of the emotion analysis.

[0353] Displaying outfit suggestions and final selection

[0354] The server converts the adjusted outfit suggestions into a user interface for display to the user. The device visually displays the received outfit suggestions to the user. The user selects their favorite outfit from the displayed outfits and sends the selection from the device to the server. The server reconfirms the details of the selected garment (size, color, material) to ensure that the selection meets the user's expectations.

[0355] Completing the purchase process

[0356] Finally, the user completes the purchase process based on the finalized outfit. The server then confirms the purchase information and places an order with the affiliated online shopping site. Once the order is complete, the server sends an order completion notification to the terminal.

[0357] Specific examples

[0358] For example, suppose User B sends a full-body photo of himself and inputs that he likes casual-style blue denim jackets. The server analyzes User B's body shape information and generates multiple outfits that include casual-style blue denim jackets. At this time, the emotion engine analyzes User B's facial expressions and voice and prioritizes suggestions of items that elicit a positive reaction. User B then selects his favorite outfit from the suggestions and proceeds with the purchase. This entire process is seamless, reducing the stress of online shopping for User B.

[0359] Prompt Sentence Examples

[0360] You could provide a generative AI model with a prompt like this:

[0361] "The system identifies feature points such as face, shoulder width, waist, hips, and leg length from a user's full-body photo and extracts them as numerical data."

[0362] "The system uses facial expressions and voice data to determine the emotional state of the user regarding the outfit suggestions. If the emotion is positive, the suggestion is kept, and if it is negative, a new suggestion is generated."

[0363] This system allows users to confidently select outfits that suit their body type and preferences, and also receives suggestions that reflect their emotions, allowing them to enjoy a more satisfying online shopping experience.

[0364] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0365] Step 1:

[0366] The user takes a full-body photo and uploads it to the server from their device.

[0367] Input: A full-body photo of the user

[0368] Data processing: Uploading image files

[0369] Output: Image file saved on the server

[0370] Step 2:

[0371] Based on the full-body photo received by the server, the AI ​​image analysis engine is used to obtain the user's body shape information. Specifically, image analysis technology is used to identify feature points such as face, shoulder width, waist, hips, and leg length, and extract them as numerical data. The Google Cloud Vision API is used.

[0372] Input: A full-body photo stored on the server

[0373] Data processing: Identifying feature points such as face, shoulder width, waist, hips, and leg length and extracting numerical data

[0374] Output: Numerical data of body shape information

[0375] Step 3:

[0376] Users enter data such as their preferred fashion style, color, material, and brand on the app or website, and the data is sent from their device to a server.

[0377] Input: Data such as user's fashion style, color, material, brand, etc.

[0378] Data processing: Acquiring and saving user input data

[0379] Output: Fashion style data

[0380] Step 4:

[0381] The server filters matching garments from an internal fashion database based on the user profile data and fashion style data.

[0382] Input: Body shape information and fashion style data

[0383] Data Processing: Filtering Matching Clothing from a Fashion Database

[0384] Output: A list of matching clothing items

[0385] Step 5:

[0386] Based on the extracted items, the server uses a generative AI model to generate a synthetic image to apply to the user's photo, using a generative adversarial network (GAN).

[0387] Input: A list of matching clothing items, a full-body photo of the user

[0388] Data processing: Generating synthetic images using generative AI models (using GANs)

[0389] Output: Images of multiple outfit suggestions

[0390] Step 6:

[0391] The server analyzes the user's emotional state and acquires emotional data. Specifically, it analyzes the user's facial expressions and voice to recognize emotions such as joy, surprise, and sadness. It uses Microsoft Azure's Emotion API.

[0392] Input: User's facial expressions and voice data

[0393] Data processing: Emotion analysis of facial expressions and voice data

[0394] Output: Emotion data

[0395] Step 7:

[0396] The server adjusts coordination suggestions based on the emotion data, preferentially displaying suggestions based on positive emotion data and changing suggestions based on negative emotion data.

[0397] Input: Coordination proposal images, emotion data

[0398] Data manipulation: prioritizing or modifying suggestions based on sentiment data

[0399] Output: Image of the adjusted outfit suggestions

[0400] Step 8:

[0401] The server converts the adjusted coordination proposal into a user interface and transmits it to the terminal, which visually displays the received coordination proposal to the user.

[0402] Input: Image of the coordinated outfit suggestion

[0403] Data processing: conversion to user interface

[0404] Output: Coordination suggestion image displayed on the user's device

[0405] Step 9:

[0406] The user selects their favorite outfit from the displayed outfits and sends the selection from the terminal to the server.

[0407] Input: User's selected outfit

[0408] Data processing: Send selected data

[0409] Output: Selection data saved on the server

[0410] Step 10:

[0411] The server reviews the selected garment details (size, color, material) to ensure the selection meets the user's expectations.

[0412] Input: Detailed information about the selected outfit

[0413] Data processing: Rechecking detailed information

[0414] Output: Verification result

[0415] Step 11:

[0416] The user completes the purchase process based on the coordinated outfit they have decided on. After the server confirms the purchase information, it places an order with the affiliated online shopping site. Once the order is complete, the server sends an order completion notification to the terminal.

[0417] Input: Purchase information for the confirmed outfit

[0418] Data processing: verifying purchase information and processing orders

[0419] Output: Order completion notification

[0420] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0421] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0422] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0423] [Second embodiment]

[0424] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0425] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0426] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0427] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0428] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0429] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0430] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0431] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0432] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0433] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0434] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0435] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0436] This invention is an online shopping support system in which users upload full-body photos, analyze the images, and a generative AI proposes multiple outfits based on the user's body shape and preferences. This system is realized through the interaction between users, terminals, and a server.

[0437] Uploading and analyzing user images

[0438] First, the user takes a full-body photo and uploads it to the server from their device. The server then uses an AI image analysis engine to obtain the user's body shape information based on the received full-body photo. During this process, the server identifies feature points such as the face, shoulder width, waist, hips, and leg length, and extracts them as numerical data. If the user has entered height and weight data, this information is also supplemented. The results of this analysis are stored in a user profile database.

[0439] Enter your fashion style and preferences

[0440] Next, users enter data such as their preferred fashion style, color, material, and brand on the app or website, and the data is sent from the device to a server, which stores this data and combines it with the user's body shape information to create a user profile.

[0441] Wearing image generation and coordination suggestions

[0442] The server filters matching clothing from its internal fashion database based on the user profile data and fashion style data, extracting items that fit the user's body type and match their preferred fashion style.

[0443] Based on the extracted items, the server uses a generative AI model to generate synthetic images to apply to the user's photo. During this process, it utilizes a generative adversarial network (GAN) to ensure the clothing fits naturally based on the user's body shape. The server then generates multiple outfit patterns and saves them as images.

[0444] The server converts the generated coordinated image into a user interface for display to the user, and the terminal visually displays the received coordinated suggestion to the user.

[0445] Coordination selection and purchase process

[0446] The user selects their favorite outfit from the displayed outfits and sends the selection from their device to the server, which then reconfirms the details of the selected outfit (size, color, material) to ensure that the selection meets the user's expectations.

[0447] Finally, the user completes the purchase process based on the finalized outfit. The server then confirms the purchase information and places an order with the affiliated online shopping site. Once the order is complete, the server sends an order completion notification to the terminal.

[0448] Specific examples

[0449] For example, suppose User A sends a full-body photo of himself and inputs data that he likes casual-style blue denim jackets. The server analyzes User A's body shape information and generates multiple outfits that include a casual-style blue denim jacket. The generated outfits could be, for example, a combination of a white T-shirt, denim pants, and sneakers. User A then selects his favorite outfit and proceeds with the purchase. This entire process is seamless, reducing the stress of online shopping for User A.

[0450] The above is an embodiment of the present invention. This system allows users to select outfits that suit their body type and preferences with confidence, and enjoy online shopping in comfort.

[0451] The processing flow will be explained below.

[0452] Step 1: Upload a user image

[0453] The user takes a full-body photo.

[0454] Users use a dedicated app or website to upload a full-body photo from their device to the server.

[0455] Step 2: Receiving and analyzing images

[0456] The server sends the received full-body photo to an AI image analysis engine.

[0457] The server performs image analysis to obtain the user's body shape information (e.g., height, shoulder width, waist, hips, leg length, etc.).

[0458] Step 3: Enter user style information

[0459] Users enter their preferred fashion style, color, material, brand, etc. on the app or website.

[0460] The terminal transmits the input style information to the server.

[0461] Step 4: Integrating profile data

[0462] The server integrates the body shape information obtained through image analysis with the user's style information and stores it in a user profile database.

[0463] Step 5: Matching the clothing database

[0464] The server searches a fashion database for matching clothing items based on the user profile data.

[0465] The server filters the search results to extract items that match the user's size and preferences.

[0466] Step 6: Generative AI creates a wearing image

[0467] The server uses the generative AI model to apply the extracted clothing items to the user's full-body image.

[0468] The server uses a generative adversarial network (GAN) to generate a garment image that naturally fits the user's body shape.

[0469] Step 7: Generate outfit suggestions

[0470] The server creates multiple coordination suggestions based on the generated wearing images.

[0471] The server stores these coordination suggestions as image data and converts them into a user interface.

[0472] Step 8: View outfit suggestions

[0473] The server transmits image data of the coordinated outfit proposal to the terminal.

[0474] The device displays coordination suggestions to the user.

[0475] Step 9: Selecting Users

[0476] The user selects their favorite outfit from the displayed outfits.

[0477] The terminal transmits the selected coordinate information to the server.

[0478] Step 10: Check size and color

[0479] The server reconfirms the details of the selected garment (size, color, material).

[0480] Check whether the server matches your preferences and offer alternatives if necessary.

[0481] Step 11: Complete your purchase

[0482] The user confirms the final outfit and completes the purchase.

[0483] The server receives the purchase information and places an order with the affiliated online shopping site.

[0484] The server sends an order completion notification to the terminal and displays it to the user.

[0485] Through the above steps, this system provides an environment where users can purchase clothes online with peace of mind.

[0486] Example 1

[0487] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0488] Conventional online shopping systems have made it difficult for users to find the perfect outfit for their body type and preferences, leading to frequent returns due to incorrect sizes or colors after purchase. Another issue is that users cannot actually try on clothes, meaning they cannot see how they will look before purchasing.

[0489] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0490] In this invention, the server includes means for a user to upload a full-body photograph, means for analyzing the full-body photograph and acquiring the user's body shape information, means for inputting data related to the user's fashion style and preferences, means for generating a plurality of wearing images based on the body shape information and fashion style data, means for generating a plurality of coordination suggestions from the wearing images, means for displaying the coordination suggestions to the user, means for carrying out a purchase procedure based on the coordination selected by the user, and means for placing an order with an affiliated e-commerce site based on the user's selection. This allows users to shop online while checking coordinations that suit their body shape and preferences, and reduces the risk of returns due to mismatched sizes or colors.

[0491] "Means for uploading user images" refers to a function that allows a user to take a full-body photo and send that photo to the server.

[0492] "Means for analyzing full-body photos" refers to the function of obtaining the user's body shape information from full-body photos received by the server using an AI image analysis engine.

[0493] "Means for inputting data about the user's fashion style and preferences" refers to a function that allows the user to input their own fashion style and preferences through an app or website and send them to a server.

[0494] "Means for generating multiple wearing images" refers to the function of the server to create wearing images using a generative adversarial network (GAN) based on the user's body shape information and fashion style data.

[0495] "Means for generating multiple coordination suggestions" refers to a function that allows the server to suggest appropriate coordination to the user from the generated wearing images.

[0496] "Means for displaying coordination suggestions to the user" refers to a function that converts the coordination suggestions generated by the server into a user interface and visually displays them to the user via the terminal.

[0497] "Means for completing the purchase process based on the outfit selected by the user" refers to a function that allows the user to proceed with the purchase process based on the outfit they select from the outfits presented.

[0498] "Means for placing an order with an affiliated e-commerce site" refers to a function that enables the server to place an order with an affiliated e-commerce site based on the coordinates selected by the user.

[0499] A "generative adversarial network (GAN)" refers to a machine learning model used to generate new data based on existing data.

[0500] This invention is an online shopping support system in which users upload full-body photos, and a generative AI analyzes the images and suggests multiple outfits based on the user's body shape and preferences. This system is realized through the interaction between users, terminals, and a server.

[0501] First, the user takes a full-body photo using a device such as a smartphone and uploads it to a server. During this process, the photo is saved in the device's temporary storage and then sent to the server via the HTTPS protocol. Specifically, JPEG image files are often used.

[0502] Next, the server analyzes the received full-body photo using an AI image analysis engine (such as OpenCV or TensorFlow). Through this analysis, the server obtains body shape information such as face, shoulder width, waist, hips, and leg length as numerical data. If the user has entered their own height and weight data, this data is also used as a complement. The obtained data is stored in a user profile database on the server.

[0503] The user then enters data on the app or website, such as their preferred fashion style (e.g., casual, formal, sporty, etc.), color, material, brand, etc. This data is sent from the device to a server, which stores it and combines it with the user's body shape information to create a user profile.

[0504] The server filters matching clothing from its internal fashion database based on user profile data and fashion style data. Through filtering, items that fit the user's body shape and preferred fashion style are extracted. Based on this extracted list of items, the server uses a generative adversarial network (GAN) to generate a synthetic image that naturally fits the user's photo.

[0505] A specific example of a generative AI model is GAN (generative adversarial network) technology. This generates a synthetic image based on the user's body shape information so that the selected clothing naturally matches the user's photo. The generated coordination pattern is saved as an image and converted into a user interface. The device then visually displays the received coordination suggestions to the user.

[0506] The user selects their favorite outfit from the visually presented outfits and sends the selection to the server via their device. The server then reconfirms the details of the selected items (size, color, material, etc.) and makes a final confirmation that the selection meets the user's expectations. Finally, the user proceeds with the purchase based on the finalized outfit. After verifying the purchase information, the server places an order with the affiliated e-commerce site, and once the order is complete, the server sends an order completion notification to the device.

[0507] For example, if User A uploads a full-body photo and inputs that he or she likes casual blue denim jackets, the server analyzes User A's body shape information and generates multiple outfits that include a casual blue denim jacket. Examples of the outfits generated include a white T-shirt, denim pants, and sneakers. User A then selects his or her favorite outfit and proceeds with the purchase. This entire process is seamless, reducing the stress of online shopping for User A.

[0508] Example prompt sentence:

[0509] "Please suggest the best outfit for User A, who uploaded a full-body photo and entered that she likes casual blue denim jackets."

[0510] The above is an embodiment of the present invention. This system allows users to comfortably enjoy online shopping while checking outfits that suit their body type and preferences.

[0511] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0512] Step 1: Upload a user image

[0513] 1. A user takes a full-body photo using the smartphone camera app. The input is a full-body photo in JPEG format.

[0514] 2. The device takes a full-body photo and stores it in temporary storage.

[0515] 3. The device sends the saved JPEG image to the server using the HTTP protocol. The output is the image data sent to the server.

[0516] Step 2: Image analysis

[0517] 1. The server analyzes the received full-body photo using an AI image analysis engine (such as OpenCV or TensorFlow). The input is a JPEG full-body photo stored on the server.

[0518] 2. The server recognizes body shape information such as face, shoulder width, waist, hips, and leg length, and extracts it as numerical data. The output is numerical data of body shape information.

[0519] 3. The server completes the height and weight data previously entered by the user. The input is the user's height and weight data, and the output is the integrated profile data.

[0520] 4. The server stores the analysis results in the user profile database. The output is the user's body shape information stored in the database.

[0521] Step 3: Enter your fashion style and preferences

[0522] 1. A user inputs their preferred fashion style, color, material, brand, etc. on an app or website. The input is the user's preference data.

[0523] 2. The device sends the input fashion style and preference data to the server. The output is the user's preference data sent to the server.

[0524] 3. The server stores the received data and combines it with the body shape information to create a user profile. The output is the combined user profile.

[0525] Step 4: Creating outfit images and suggesting outfits

[0526] 1. The server filters matching clothes from the internal fashion database based on the user profile data and fashion style data. The input is the user profile data and fashion style data, and the output is a list of matching clothes.

[0527] 2. The server uses a generative adversarial network (GAN) to generate a synthetic image that naturally fits the user's photo based on the filtered items. The input is a list of matching clothing items and the user's full-body photo, and the output is the generated synthetic image.

[0528] 3. The server saves the generated coordinate pattern as an image. The output is the saved coordinate image.

[0529] 4. The server converts the generated coordinated image into a user interface. The output is the coordinated image converted into a user interface.

[0530] 5. The terminal retrieves the converted coordinated image and visually displays it to the user. The output is the coordinated image displayed to the user.

[0531] Step 5: Choose your outfit and checkout

[0532] 1. The user selects their favorite outfit from the presented outfits. The input is the user's choice.

[0533] 2. The terminal sends the selection to the server. The output is the selection sent to the server.

[0534] 3. The server reconfirms the details of the selected item (size, color, material). The input is the details of the selected item.

[0535] 4. The server performs a final check to ensure the selections meet the user's expectations. The output is the final, confirmed selections.

[0536] 5. The user proceeds with the purchase process based on the coordinated outfit they have decided on. The input is the coordinated outfit information they have decided on.

[0537] 6. After the server confirms the purchase information, it places an order with the affiliated e-commerce site. The output is the ordered item information.

[0538] 7. The server sends an order completion notification to the terminal. The output is the order completion notification sent to the user.

[0539] (Application example 1)

[0540] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0541] Currently, when shopping online, it takes a lot of time and effort for users to find the right fashion items to fit their body type. Furthermore, there are limited systems that suggest outfits that suit a user's preferences, which causes stress for users as they must choose from a large number of options. Since the online purchasing process does not allow users to try on items, users are also concerned about size and fit. It is necessary to solve these issues and improve the user shopping experience.

[0542] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0543] In this invention, the server includes means for a user to upload a full-body photo, means for analyzing the full-body photo and acquiring the user's body shape information, means for inputting data related to the user's fashion style and preferences, means for generating a plurality of wearing images based on the body shape information and fashion style data, means for generating a plurality of coordination suggestions from the wearing images, means for displaying the coordination suggestions to the user, means for completing a purchase based on the coordination selected by the user, and means for seamlessly completing the purchase using a generative AI model. This allows the user to visually check the coordination suggestions that suit their body shape and preferences, easily select appropriate items, and smoothly complete the purchase process.

[0544] A "user" is someone who uses the online shopping system to upload a full-body photo of themselves, suggest outfits, and complete the purchase process.

[0545] A "full-body photo" is an image that captures the user's face and entire body and is necessary to obtain body shape information.

[0546] "Analysis" is the process of extracting the user's body shape information from a full-body photo and quantifying each feature point.

[0547] "Body shape information" is numerical data that indicates the user's physical characteristics such as face, shoulder width, waist, hips, and leg length.

[0548] "Fashion style" refers to the type and design of clothing preferred by the user, and includes styles such as casual, formal, and sporty.

[0549] "Preferences" refer to individual fashion elements such as the user's preferred colors, materials, and brands.

[0550] "Data" is text and numeric information entered by the user, including information about fashion style and preferences.

[0551] A "wearing image" is a composite image of clothing generated based on the user's body shape information and preferences.

[0552] "Coordination suggestions" are clothing combinations that are generated based on multiple wearing images and suggested to the user.

[0553] "Display" refers to visually showing the generated coordination proposal on the user's terminal.

[0554] The "purchase procedure" refers to the series of processes required for a user to actually purchase items based on the outfit they have selected.

[0555] A "generative AI model" is an artificial intelligence model used to generate outfits based on a user's body shape information and fashion style.

[0556] "Seamless" refers to a state in which users have a consistent experience and there is a smooth transition between different processes.

[0557] This invention is an online shopping support system in which users upload full-body photos, analyze the images, and a generative AI proposes multiple outfits based on the user's body shape and preferences. This system is realized through the interaction between users, terminals, and a server.

[0558] Uploading and analyzing user images

[0559] First, the user takes a full-body photo and uploads it to the server from their device. The server then uses an AI image analysis engine to obtain the user's body shape information based on the received full-body photo. During this process, the server identifies feature points such as the face, shoulder width, waist, hips, and leg length, and extracts them as numerical data. If the user has entered height and weight data, this information is also supplemented. The results of this analysis are stored in a user profile database.

[0560] Enter your fashion style and preferences

[0561] Next, users enter data such as their preferred fashion style, color, material, and brand on the app or website, and the data is sent from the device to a server, which stores this data and combines it with the user's body shape information to create a user profile.

[0562] Wearing image generation and coordination suggestions

[0563] The server filters matching clothing from its internal fashion database based on user profile data and fashion style data. This filtering extracts items that fit the user's body shape and preferred fashion style. Based on the extracted items, the server uses a generative AI model to generate synthetic images to apply to the user's photo. This process utilizes a generative adversarial network (GAN) to ensure that the clothing fits naturally based on the user's body shape information. The server then generates multiple outfit patterns and saves them as images. The server converts the generated outfit images into a user interface for display to the user. The device visually displays the received outfit suggestions to the user.

[0564] Coordination selection and purchase process

[0565] The user selects their favorite outfit from the displayed outfits and sends the selection from their device to the server. The server then reconfirms the details of the selected outfit (size, color, material) to ensure that the selection meets the user's expectations. Finally, the user completes the purchase process based on the final outfit. The server uses a generative AI model to seamlessly complete the purchase process. This provides the user with a consistent experience and maintains smooth connections between different processes.

[0566] Specific examples

[0567] For example, suppose User A sends a full-body photo of himself and inputs data that he likes casual-style blue denim jackets. The server analyzes User A's body shape information and generates multiple outfits that include a casual-style blue denim jacket. The generated outfits might be, for example, a combination of a white T-shirt, denim pants, and sneakers. User A then selects his favorite outfit and proceeds with the purchase. This entire process is seamless, reducing the stress of online shopping for User A.

[0568] Prompt Sentence Examples

[0569] "Based on the user's body type and preferences, please suggest a casual outfit that includes a blue denim jacket."

[0570] This system allows users to confidently select outfits that suit their body type and preferences, and enjoy comfortable online shopping.

[0571] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0572] Step 1:

[0573] The user takes a full-body photo using a smartphone and uploads the image from the device to the server. The input of this step is the user's full-body photo, and the output is the completion of uploading the image to the server.

[0574] Step 2:

[0575] The server receives the uploaded full-body photo and uses an AI image analysis engine to analyze the user's body shape information. The analysis identifies feature points such as face, shoulder width, waist, hips, and leg length and extracts them as numerical data. The input in this step is the full-body photo, and the output is numerical data of body shape information.

[0576] Step 3:

[0577] Users input data such as their preferred fashion style, color, material, brand, etc. through an interface on the app or website. The input in this step is the user's fashion style and preference data, and the output is that this data is sent to a server and stored.

[0578] Step 4:

[0579] The server integrates the user's body shape information with fashion style and preference data to create a user profile, which includes the user's body shape information, preferred styles, colors, materials, brands, etc. The input in this step is the body shape information, fashion style and preference data, and the output is the creation of a user profile.

[0580] Step 5:

[0581] The server filters matching clothing items from its internal fashion database based on the generated user profile. This filtering extracts items that fit the user's body shape and preferred fashion style. The input of this step is the user profile, and the output is the extracted set of fashion items.

[0582] Step 6:

[0583] The server uses a generative AI model to generate a wearing image tailored to the user's body shape using the extracted fashion items. In this process, a generative adversarial network (GAN) is used to make the clothing fit naturally based on the user's body shape information. The input in this step is the extracted fashion items and the user's body shape information, and the output is a synthesized image of the wearing image.

[0584] Step 7:

[0585] The server generates multiple outfit suggestions from the generated outfit images and sends them to the user's device. The input in this step is a composite image of the outfit images, and the output is a set of outfit suggestion images.

[0586] Step 8:

[0587] The user selects their favorite outfit from multiple outfit suggestions displayed on the device. The input in this step is a group of outfit suggestion images, and the output is the data of the selected outfit.

[0588] Step 9:

[0589] The server checks the size and color again based on the coordinates selected by the user. The input in this step is the data of the selected coordinates, and the output is the result of the size and color check.

[0590] Step 10:

[0591] Based on the selected outfit, the server uses a generative AI model to seamlessly complete the purchase process. During this process, the order information is sent to the partner online shopping site and an order completion notification is sent to the terminal. The input in this step is the order information, and the output is a purchase completion notification.

[0592] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0593] This invention is an online shopping support system in which users upload full-body photos, analyze the images, and a generation AI proposes multiple outfits based on the user's body shape and preferences. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to propose even more appropriate outfits based on the user's emotional state. This system is realized through the interaction of the user, terminal, server, and emotion engine.

[0594] Uploading and analyzing user images

[0595] First, the user takes a full-body photo and uploads it to the server from their device. The server then uses an AI image analysis engine to obtain the user's body shape information based on the received full-body photo. During this process, the server identifies feature points such as the face, shoulder width, waist, hips, and leg length, and extracts them as numerical data. If the user has entered height and weight data, this information is also supplemented. The results of this analysis are stored in a user profile database.

[0596] Enter your fashion style and preferences

[0597] Next, users enter data such as their preferred fashion style, color, material, and brand on the app or website, and the data is sent from the device to a server, which stores this data and combines it with the user's body shape information to create a user profile.

[0598] Wearing image generation and coordination suggestions

[0599] The server filters matching clothing from its internal fashion database based on the user profile data and fashion style data, extracting items that fit the user's body type and match their preferred fashion style.

[0600] Based on the extracted items, the server uses a generative AI model to generate synthetic images to apply to the user's photo. During this process, it utilizes a generative adversarial network (GAN) to ensure the clothing fits naturally based on the user's body shape. The server then generates multiple outfit patterns and saves them as images.

[0601] Emotion engine recognizes user emotions

[0602] One of the features of this system is the addition of an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and voice to determine their emotional state. Specifically, it recognizes emotions such as joy, surprise, and sadness in real time from the user's facial expressions and tone of voice when selecting an outfit or looking at each suggestion.

[0603] Emotion-based coordination suggestions

[0604] Once the user's emotions are recognized, the server adjusts the outfit suggestions based on that information. For example, if the user expresses positive emotions toward the outfits displayed, the system records the user's emotional data and prioritizes suggestions for similar styles. On the other hand, if the user expresses negative emotions, the system adjusts to suggest different styles or colors.

[0605] Displaying outfit suggestions and final selection

[0606] The server converts the adjusted outfit suggestions into a user interface for display to the user. The device visually displays the received outfit suggestions to the user. The user selects their favorite outfit from the displayed outfits and sends the selection from the device to the server. The server reconfirms the details of the selected garment (size, color, material) to ensure that the selection meets the user's expectations.

[0607] Completing the purchase process

[0608] Finally, the user completes the purchase process based on the finalized outfit. The server then confirms the purchase information and places an order with the affiliated online shopping site. Once the order is complete, the server sends an order completion notification to the terminal.

[0609] Specific examples

[0610] For example, suppose User B sends a full-body photo of himself and inputs that he likes casual-style blue denim jackets. The server analyzes User B's body shape information and generates multiple outfits that include casual-style blue denim jackets. At this time, the emotion engine analyzes User B's facial expressions and voice and prioritizes suggestions of items that elicit a positive reaction. User B then selects his favorite outfit from the suggestions and proceeds with the purchase. This entire process is seamless, reducing the stress of online shopping for User B.

[0611] The above is an embodiment of the present invention. This system allows users to confidently select outfits that match their body type and preferences, and also receives suggestions based on their emotions, allowing them to enjoy a more satisfying online shopping experience.

[0612] The processing flow will be explained below.

[0613] Step 1: Upload a user image

[0614] The user takes a full-body photo.

[0615] Users use a dedicated app or website to upload a full-body photo from their device to the server.

[0616] Step 2: Receiving and analyzing images

[0617] The server sends the received full-body photo to an AI image analysis engine.

[0618] The server performs image analysis to obtain the user's body shape information (e.g., height, shoulder width, waist, hips, leg length, etc.).

[0619] Step 3: Enter user style information

[0620] Users enter their preferred fashion style, color, material, brand, etc. on the app or website.

[0621] The terminal transmits the input style information to the server.

[0622] Step 4: Integrating profile data

[0623] The server integrates the body shape information obtained through image analysis with the user's style information and stores it in a user profile database.

[0624] Step 5: Matching the clothing database

[0625] The server searches a fashion database for matching clothing items based on the user profile data.

[0626] The server filters the search results to extract items that match the user's size and preferences.

[0627] Step 6: Generative AI creates a wearing image

[0628] The server uses the generative AI model to apply the extracted clothing items to the user's full-body image.

[0629] The server uses a generative adversarial network (GAN) to generate a garment image that naturally fits the user's body shape.

[0630] Step 7: Generate outfit suggestions

[0631] The server creates multiple coordination suggestions based on the generated wearing images.

[0632] The server stores these coordination suggestions as image data and converts them into a user interface.

[0633] Step 8: Emotion recognition and regulation with the emotion engine

[0634] The server uses an emotion engine to analyze the user's facial expressions and voice and recognize their emotional state in real time.

[0635] The server adjusts the outfit suggestions based on the user's emotional data: if the user expresses positive emotions, it suggests a similar style, and if the user expresses negative emotions, it suggests a different style.

[0636] Step 9: View outfit suggestions

[0637] The server transmits image data of the coordinated outfit proposal to the terminal.

[0638] The device displays coordination suggestions to the user.

[0639] Step 10: Selecting Users

[0640] The user selects their favorite outfit from the displayed outfits.

[0641] The terminal transmits the selected coordinate information to the server.

[0642] Step 11: Check size and color

[0643] The server reconfirms the details of the selected garment (size, color, material).

[0644] Check whether the server matches your preferences and offer alternatives if necessary.

[0645] Step 12: Complete your purchase

[0646] The user confirms the final outfit and completes the purchase.

[0647] The server receives the purchase information and places an order with the affiliated online shopping site.

[0648] The server sends an order completion notification to the terminal and displays it to the user.

[0649] Through these successive processing steps, the system provides a safe and secure online clothing shopping environment for users. In addition to suggesting outfits based on the user's body shape and preferences, it also enhances user satisfaction through emotion recognition using an emotion engine.

[0650] Example 2

[0651] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0652] When shopping online, the process of selecting fashion items that suit a user's body type and preferences is time-consuming and laborious. Furthermore, the lack of a system that can provide more appropriate suggestions based on the user's emotional state makes it difficult to provide a satisfying shopping experience. Therefore, there is a need for technology that can recognize a user's emotions in real time and adjust outfit suggestions accordingly.

[0653] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0654] In this invention, the server includes means for a user to upload a full-body photograph, means for analyzing the full-body photograph and acquiring the user's body shape information, means for inputting data on the user's fashion style and preferences, means for generating a plurality of wearing images based on the body shape information and fashion style data, and means for recognizing the user's emotions and adjusting coordination suggestions based thereon. This not only makes it easier for the user to select fashion items that suit their body shape and preferences, but also enables a more satisfying online shopping experience by making suggestions based on the user's emotional state.

[0655] A "full-body photo" refers to a photo showing the user's entire body, and is used to obtain body shape information.

[0656] "Body shape information" refers to information about the shape of a user's biological body, including features such as the user's face, shoulder width, waist, hips, and leg length, as well as supplemental data (such as height and weight).

[0657] "Fashion style" refers to attributes such as design, color, material, and brand of clothing and accessories based on the user's preferences.

[0658] A "generative adversarial network (GAN)" is an algorithm that generates data by having two neural networks compete with each other, and is a model that demonstrates particularly strong performance in image generation.

[0659] An "emotion engine" refers to a system that analyzes a user's facial expressions and voice to recognize their emotional state in real time.

[0660] "Coordination suggestions" refer to multiple outfit images and sets generated by the server based on the user's body shape information, fashion style, and emotional data.

[0661] "Purchase Checkout" means the steps required to ultimately purchase the Products selected by the User, including the online payment process.

[0662] "User profile" refers to a database that integrates a user's body shape information, fashion style, emotional data, etc.

[0663] This invention is an online shopping support system in which users upload full-body photos, analyze the images, and a generation AI proposes multiple outfits based on the user's body shape and preferences. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to propose even more appropriate outfits based on the user's emotional state. This system is realized through the interaction of the user, terminal, server, and emotion engine.

[0664] Uploading and analyzing user images

[0665] First, the user takes a full-body photo and uploads it from their device to the server. The server then uses an AI image analysis engine (such as Google Cloud Vision API) to obtain the user's body shape information based on the received full-body photo. The server identifies feature points such as the face, shoulder width, waist, hips, and leg length, and extracts them as numerical data. If the user has entered height and weight data, this information is also supplemented. The results of this analysis are stored in a user profile database.

[0666] Enter your fashion style and preferences

[0667] Next, users enter data such as their preferred fashion style, color, material, and brand on the app or website, which is then sent from their device to a server, which stores this data and combines it with the user's body information to create a detailed user profile.

[0668] Wearing image generation and coordination suggestions

[0669] The server filters matching clothing from its internal fashion database based on user profile data and fashion style data. This filtering extracts items that fit the user's body shape and preferred fashion style. Based on the extracted items, the server uses a generative AI model (e.g., StyleGAN) to generate synthetic images. This process utilizes a generative adversarial network (GAN) to ensure that the clothing fits naturally based on the user's body shape information. The server generates multiple coordination patterns and saves them as images.

[0670] Emotion engine recognizes user emotions

[0671] One of the features of this system is that it incorporates an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and voice to determine their emotional state. When the user selects an outfit, the device's camera and microphone are used to collect emotional data in real time. Based on this data, the server recognizes the user's emotions, such as joy, surprise, and sadness, in real time.

[0672] Emotion-based coordination suggestions

[0673] The server dynamically adjusts outfit suggestions based on emotion data obtained from the emotion engine. If a user expresses positive emotions, the server records their emotion data and prioritizes suggestions for similar styles. On the other hand, if a user expresses negative emotions, the server suggests different styles and colors.

[0674] Displaying outfit suggestions and final selection

[0675] The server converts the adjusted outfit suggestions into a user interface. The device visually displays the received outfit suggestions to the user. The user selects their favorite outfit from the displayed outfits and sends the selection from the device to the server. The server then reconfirms the details of the selected garment (size, color, material) to ensure that the selection meets the user's expectations.

[0676] Completing the purchase process

[0677] Finally, the user completes the purchase process based on the finalized outfit. The server then confirms the purchase information and places an order with the affiliated online shopping site. Once the order is complete, the server sends an order completion notification to the terminal.

[0678] Specific examples

[0679] For example, suppose User B submits a full-body photo and inputs data indicating that he or she prefers casual-style blue denim jackets. The server analyzes User B's body shape information and generates multiple outfits that include casual-style blue denim jackets. At this time, the emotion engine analyzes User B's facial expressions and voice, and prioritizes suggestions of items that elicit a positive response. User B then selects his or her favorite outfit from the list and completes the purchase process.

[0680] Prompt Sentence Examples

[0681] Here are some example prompts to input to a generative AI model:

[0682] "Please explain the process of an online shopping support system that uploads a full-body photo of the user and obtains body shape information. It then generates outfit images based on the user's preferred styles and uses emotional data to suggest optimal outfits."

[0683] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0684] Step 1:

[0685] The user takes a full-body photo and uploads the image to the online shopping support system using a terminal.

[0686] The input is a full-body photo of the user, and the output is the image data being sent to the server. Specifically, the user takes a full-body photo with the camera on their smartphone or computer, and then uploads the image via the system's upload function.

[0687] Step 2:

[0688] The server analyzes the received full-body photo.

[0689] The input is an uploaded full-body photo, and the output is numerical data of the user's body shape information (face, shoulder width, waist, hips, leg length, etc.). Specifically, the server uses an AI image analysis engine (e.g., Google Cloud Vision API) to analyze the full-body photo and identify the body's feature points.

[0690] Step 3:

[0691] The server completes the user's height and weight data and stores the body shape information in a user profile database.

[0692] The input is the analyzed body shape information and supplementary data (height and weight), and the output is a detailed user profile. Specifically, the server further adjusts the body shape information based on the supplementary data and stores it in a database.

[0693] Step 4:

[0694] Users enter their preferences for fashion style, color, material, brand, etc. on the app or website.

[0695] The input is data about the user's fashion style, and the output is sending this data to the server. Specifically, the user selects and inputs their preferred fashion attributes through the interface.

[0696] Step 5:

[0697] The server combines the received fashion style data with the user's body shape information to create a detailed user profile.

[0698] The input is fashion style data and body shape information, and the output is an integrated user profile. Specifically, the server integrates these data and stores them in a database as individual profiles.

[0699] Step 6:

[0700] The server filters matching clothing items from an internal fashion database based on the user profile.

[0701] The input is a detailed user profile, and the output is a list of clothing items that match the user. Specifically, the server uses a filtering algorithm to select clothing items that match each attribute based on the profile.

[0702] Step 7:

[0703] The server uses a generative AI model (e.g., StyleGAN) to generate a synthetic image that applies the filtered clothing items to the user's photo.

[0704] The input is the filtered clothing items and the user's photo, and the output is a synthetic image. Specifically, the server uses a generative adversarial network (GAN) to generate a synthetic image in which the clothing fits naturally based on the user's body shape information.

[0705] Step 8:

[0706] The server generates a plurality of coordinate patterns and stores them as images.

[0707] The input is a composite image, and the output is images of multiple coordinate patterns. Specifically, the server creates different coordinate patterns based on the generated composite image and saves them as image data.

[0708] Step 9:

[0709] The device uses a camera and microphone to collect the user's facial expressions and voice in real time as they view the suggested outfits.

[0710] The input is the user's real-time facial expression and voice data, and the output is collected emotion data. Specifically, the device records the user's browsing behavior with a camera and microphone and sends the data to the emotion engine.

[0711] Step 10:

[0712] The server adjusts the coordination suggestions based on the emotional data obtained from the emotion engine.

[0713] The input is the user's emotional data, and the output is tailored outfit suggestions. Specifically, the server prioritizes items that show positive emotions and excludes items that show negative reactions.

[0714] Step 11:

[0715] The server converts the adjusted coordination proposal for a user interface, and the terminal visually displays the received proposal to the user.

[0716] The input is the adjusted coordinate proposal, and the output is the interface displayed to the user. Specifically, the server converts the adjusted information into an appropriate format and sends it to the terminal, which then displays it to the user.

[0717] Step 12:

[0718] The user selects their favorite outfit from the displayed outfits and transmits the selection to the server via their terminal.

[0719] The input is the user's selection, and the output is the selected outfit data. Specifically, the user operates the interface to select their favorite outfit.

[0720] Step 13:

[0721] The server then double-checks the selected garment details (size, color, material) to ensure they match the user's expectations.

[0722] The input is the details of the selected garment, and the output is the confirmation result. Specifically, the server checks the selection against the database again to check compatibility.

[0723] Step 14:

[0724] The user completes the purchase process based on the finalized outfit, and the server confirms the purchase information and then places an order with the affiliated online shopping site.

[0725] The input is the purchase procedure information, and the output is the order data. Specifically, the user completes the purchase procedure, and the server processes the order. Once the order is completed, the server sends an order completion notification to the terminal.

[0726] (Application example 2)

[0727] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0728] In today's online shopping environment, users face the challenge of finding the right fashion items. Furthermore, the lack of coordination suggestions tailored to the user's body type and individual preferences, as well as the lack of personalized suggestions that take into account the user's emotional state, reduces satisfaction with the shopping experience.

[0729] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to upload a full-body photograph, means for analyzing the full-body photograph and acquiring the user's body shape information, means for inputting data related to the user's fashion style and preferences, means for generating a plurality of wearing images based on the body shape information and fashion style data, means for generating a plurality of outfit suggestions, means for analyzing the user's emotional state and acquiring emotion data, means for adjusting the outfit suggestions based on the emotion data, means for displaying the outfit suggestions to the user, and means for completing a purchase based on the outfit selected by the user. This makes it easier for users to find outfits that suit their body shape and preferences, and by receiving suggestions based on their emotions, users can have a more satisfying online shopping experience.

[0730] "Full Body Photo" means a photograph of the User's entire body, which must include the User's entire body from face to toe.

[0731] "Analysis" refers to the process of analyzing image data using a computer and extracting specific information, such as identifying a user's body shape information from an image.

[0732] "Body Information" refers to data related to a user's physique and shape, including measurements and features such as face, shoulder width, waist, hips, and leg length.

[0733] "Fashion style" refers to the clothing designs and styles preferred by a user, including information about preferences for color, material, design, brand, etc.

[0734] "Wearing image" refers to an image of the user wearing the clothing, generated based on the user's body shape information. This visualizes how the user will look when actually wearing the clothing.

[0735] "Coordination suggestions" refer to multiple fashion combinations selected from wearing images generated based on the user's body shape information and fashion style.

[0736] "Emotional state" refers to data related to emotions analyzed from the user's facial expressions, tone of voice, etc. Specifically, it includes emotions such as joy, anger, sadness, and happiness.

[0737] "Emotional data" refers to data that quantifies or classifies an emotional state, which allows for suggestions and adjustments based on the user's emotions.

[0738] "Adjustment" refers to changing the suggestions based on specific criteria or conditions. In this case, it refers to changing the outfit suggestions based on the user's emotional data.

[0739] "Purchase procedure" refers to the series of actions required to actually purchase the product selected by the user, including product selection, payment, and delivery procedures.

[0740] "System" refers to the entire device or program that combines multiple means, including various functions necessary for users to comfortably shop online.

[0741] This invention is an online shopping support system that allows users to upload full-body photos and performs image analysis to suggest multiple outfits based on the user's body shape and preferences. By combining it with an emotion engine that recognizes the user's emotions, it is possible to suggest even more appropriate outfits based on the user's emotional state. This system is realized through the interaction of the user, terminal, server, and emotion engine.

[0742] Uploading and analyzing user images

[0743] First, the user takes a full-body photo and uploads it to the server from their device. The server then uses an AI image analysis engine to obtain the user's body shape information based on the received full-body photo. Specifically, image analysis technologies such as Google Cloud Vision API are used to identify feature points such as the face, shoulder width, waist, hips, and leg length, and extract them as numerical data. The results of this analysis are stored in a user profile database.

[0744] Enter your fashion style and preferences

[0745] Next, users enter data such as their preferred fashion style, color, material, and brand on the app or website, and the data is sent from the device to a server, which stores this data and combines it with the user's body shape information to create a user profile.

[0746] Wearing image generation and coordination suggestions

[0747] The server filters matching clothing from its internal fashion database based on user profile data and fashion style data. This filtering extracts items that fit the user's body shape and preferred fashion style. Based on the extracted items, the server uses a generative AI model to generate synthetic images to apply to the user's photo. This process utilizes a generative adversarial network (GAN) to ensure that the clothing fits naturally based on the user's body shape information. The server then generates multiple outfit patterns and saves them as images.

[0748] Emotion engine recognizes user emotions

[0749] One of the features of this system is the addition of an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and voice to determine their emotional state. Specifically, it uses Microsoft Azure's Emotion API to recognize emotions such as joy, surprise, and sadness in real time from the user's facial expressions and tone of voice when selecting an outfit or viewing each suggestion.

[0750] Emotion-based coordination suggestions

[0751] Once the user's emotions are recognized, the server adjusts the outfit suggestions based on that information. For example, if a user expresses positive emotions toward the outfits displayed, the server records the user's emotional data and prioritizes suggestions for similar styles. On the other hand, if the user expresses negative emotions, the system adjusts to suggest different styles or colors. The system also reviews the user interface, taking into account the results of the emotion analysis.

[0752] Displaying outfit suggestions and final selection

[0753] The server converts the adjusted outfit suggestions into a user interface for display to the user. The device visually displays the received outfit suggestions to the user. The user selects their favorite outfit from the displayed outfits and sends the selection from the device to the server. The server reconfirms the details of the selected garment (size, color, material) to ensure that the selection meets the user's expectations.

[0754] Completing the purchase process

[0755] Finally, the user completes the purchase process based on the finalized outfit. The server then confirms the purchase information and places an order with the affiliated online shopping site. Once the order is complete, the server sends an order completion notification to the terminal.

[0756] Specific examples

[0757] For example, suppose User B sends a full-body photo of himself and inputs that he likes casual-style blue denim jackets. The server analyzes User B's body shape information and generates multiple outfits that include casual-style blue denim jackets. At this time, the emotion engine analyzes User B's facial expressions and voice and prioritizes suggestions of items that elicit a positive reaction. User B then selects his favorite outfit from the suggestions and proceeds with the purchase. This entire process is seamless, reducing the stress of online shopping for User B.

[0758] Prompt Sentence Examples

[0759] You could provide a generative AI model with a prompt like this:

[0760] "The system identifies feature points such as face, shoulder width, waist, hips, and leg length from a user's full-body photo and extracts them as numerical data."

[0761] "The system uses facial expressions and voice data to determine the emotional state of the user regarding the outfit suggestions. If the emotion is positive, the suggestion is kept, and if it is negative, a new suggestion is generated."

[0762] This system allows users to confidently select outfits that suit their body type and preferences, and also receives suggestions that reflect their emotions, allowing them to enjoy a more satisfying online shopping experience.

[0763] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0764] Step 1:

[0765] The user takes a full-body photo and uploads it to the server from their device.

[0766] Input: A full-body photo of the user

[0767] Data processing: Uploading image files

[0768] Output: Image file saved on the server

[0769] Step 2:

[0770] Based on the full-body photo received by the server, the AI ​​image analysis engine is used to obtain the user's body shape information. Specifically, image analysis technology is used to identify feature points such as face, shoulder width, waist, hips, and leg length, and extract them as numerical data. The Google Cloud Vision API is used.

[0771] Input: A full-body photo stored on the server

[0772] Data processing: Identifying feature points such as face, shoulder width, waist, hips, and leg length and extracting numerical data

[0773] Output: Numerical data of body shape information

[0774] Step 3:

[0775] Users enter data such as their preferred fashion style, color, material, and brand on the app or website, and the data is sent from their device to a server.

[0776] Input: Data such as user's fashion style, color, material, brand, etc.

[0777] Data processing: Acquiring and saving user input data

[0778] Output: Fashion style data

[0779] Step 4:

[0780] The server filters matching garments from an internal fashion database based on the user profile data and fashion style data.

[0781] Input: Body shape information and fashion style data

[0782] Data Processing: Filtering Matching Clothing from a Fashion Database

[0783] Output: A list of matching clothing items

[0784] Step 5:

[0785] Based on the extracted items, the server uses a generative AI model to generate a synthetic image to apply to the user's photo, using a generative adversarial network (GAN).

[0786] Input: A list of matching clothing items, a full-body photo of the user

[0787] Data processing: Generating synthetic images using generative AI models (using GANs)

[0788] Output: Images of multiple outfit suggestions

[0789] Step 6:

[0790] The server analyzes the user's emotional state and acquires emotional data. Specifically, it analyzes the user's facial expressions and voice to recognize emotions such as joy, surprise, and sadness. It uses Microsoft Azure's Emotion API.

[0791] Input: User's facial expressions and voice data

[0792] Data processing: Emotion analysis of facial expressions and voice data

[0793] Output: Emotion data

[0794] Step 7:

[0795] The server adjusts coordination suggestions based on the emotion data, preferentially displaying suggestions based on positive emotion data and changing suggestions based on negative emotion data.

[0796] Input: Coordination proposal images, emotion data

[0797] Data manipulation: prioritizing or modifying suggestions based on sentiment data

[0798] Output: Image of the adjusted outfit suggestions

[0799] Step 8:

[0800] The server converts the adjusted coordination proposal into a user interface and transmits it to the terminal, which visually displays the received coordination proposal to the user.

[0801] Input: Image of the coordinated outfit suggestion

[0802] Data processing: conversion to user interface

[0803] Output: Coordination suggestion image displayed on the user's device

[0804] Step 9:

[0805] The user selects their favorite outfit from the displayed outfits and sends the selection from the terminal to the server.

[0806] Input: User's selected outfit

[0807] Data processing: Send selected data

[0808] Output: Selection data saved on the server

[0809] Step 10:

[0810] The server reviews the selected garment details (size, color, material) to ensure the selection meets the user's expectations.

[0811] Input: Detailed information about the selected outfit

[0812] Data processing: Rechecking detailed information

[0813] Output: Verification result

[0814] Step 11:

[0815] The user completes the purchase process based on the coordinated outfit they have decided on. After the server confirms the purchase information, it places an order with the affiliated online shopping site. Once the order is complete, the server sends an order completion notification to the terminal.

[0816] Input: Purchase information for the confirmed outfit

[0817] Data processing: verifying purchase information and processing orders

[0818] Output: Order completion notification

[0819] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0820] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0821] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0822] [Third embodiment]

[0823] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0824] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0825] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0826] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0827] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0828] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0829] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0830] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0831] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0832] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0833] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0834] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0835] This invention is an online shopping support system in which users upload full-body photos, analyze the images, and a generative AI proposes multiple outfits based on the user's body shape and preferences. This system is realized through the interaction between users, terminals, and a server.

[0836] Uploading and analyzing user images

[0837] First, the user takes a full-body photo and uploads it to the server from their device. The server then uses an AI image analysis engine to obtain the user's body shape information based on the received full-body photo. During this process, the server identifies feature points such as the face, shoulder width, waist, hips, and leg length, and extracts them as numerical data. If the user has entered height and weight data, this information is also supplemented. The results of this analysis are stored in a user profile database.

[0838] Enter your fashion style and preferences

[0839] Next, users enter data such as their preferred fashion style, color, material, and brand on the app or website, and the data is sent from the device to a server, which stores this data and combines it with the user's body shape information to create a user profile.

[0840] Wearing image generation and coordination suggestions

[0841] The server filters matching clothing from its internal fashion database based on the user profile data and fashion style data, extracting items that fit the user's body type and match their preferred fashion style.

[0842] Based on the extracted items, the server uses a generative AI model to generate synthetic images to apply to the user's photo. During this process, it utilizes a generative adversarial network (GAN) to ensure the clothing fits naturally based on the user's body shape. The server then generates multiple outfit patterns and saves them as images.

[0843] The server converts the generated coordinated image into a user interface for display to the user, and the terminal visually displays the received coordinated suggestion to the user.

[0844] Coordination selection and purchase process

[0845] The user selects their favorite outfit from the displayed outfits and sends the selection from their device to the server, which then reconfirms the details of the selected outfit (size, color, material) to ensure that the selection meets the user's expectations.

[0846] Finally, the user completes the purchase process based on the finalized outfit. The server then confirms the purchase information and places an order with the affiliated online shopping site. Once the order is complete, the server sends an order completion notification to the terminal.

[0847] Specific examples

[0848] For example, suppose User A sends a full-body photo of himself and inputs data that he likes casual-style blue denim jackets. The server analyzes User A's body shape information and generates multiple outfits that include a casual-style blue denim jacket. The generated outfits could be, for example, a combination of a white T-shirt, denim pants, and sneakers. User A then selects his favorite outfit and proceeds with the purchase. This entire process is seamless, reducing the stress of online shopping for User A.

[0849] The above is an embodiment of the present invention. This system allows users to select outfits that suit their body type and preferences with confidence, and enjoy online shopping in comfort.

[0850] The processing flow will be explained below.

[0851] Step 1: Upload a user image

[0852] The user takes a full-body photo.

[0853] Users use a dedicated app or website to upload a full-body photo from their device to the server.

[0854] Step 2: Receiving and analyzing images

[0855] The server sends the received full-body photo to an AI image analysis engine.

[0856] The server performs image analysis to obtain the user's body shape information (e.g., height, shoulder width, waist, hips, leg length, etc.).

[0857] Step 3: Enter user style information

[0858] Users enter their preferred fashion style, color, material, brand, etc. on the app or website.

[0859] The terminal transmits the input style information to the server.

[0860] Step 4: Integrating profile data

[0861] The server integrates the body shape information obtained through image analysis with the user's style information and stores it in a user profile database.

[0862] Step 5: Matching the clothing database

[0863] The server searches a fashion database for matching clothing items based on the user profile data.

[0864] The server filters the search results to extract items that match the user's size and preferences.

[0865] Step 6: Generative AI creates a wearing image

[0866] The server uses the generative AI model to apply the extracted clothing items to the user's full-body image.

[0867] The server uses a generative adversarial network (GAN) to generate a garment image that naturally fits the user's body shape.

[0868] Step 7: Generate outfit suggestions

[0869] The server creates multiple coordination suggestions based on the generated wearing images.

[0870] The server stores these coordination suggestions as image data and converts them into a user interface.

[0871] Step 8: View outfit suggestions

[0872] The server transmits image data of the coordinated outfit proposal to the terminal.

[0873] The device displays coordination suggestions to the user.

[0874] Step 9: Selecting Users

[0875] The user selects their favorite outfit from the displayed outfits.

[0876] The terminal transmits the selected coordinate information to the server.

[0877] Step 10: Check size and color

[0878] The server reconfirms the details of the selected garment (size, color, material).

[0879] Check whether the server matches your preferences and offer alternatives if necessary.

[0880] Step 11: Complete your purchase

[0881] The user confirms the final outfit and completes the purchase.

[0882] The server receives the purchase information and places an order with the affiliated online shopping site.

[0883] The server sends an order completion notification to the terminal and displays it to the user.

[0884] Through the above steps, this system provides an environment where users can purchase clothes online with peace of mind.

[0885] Example 1

[0886] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0887] Conventional online shopping systems have made it difficult for users to find the perfect outfit for their body type and preferences, leading to frequent returns due to incorrect sizes or colors after purchase. Another issue is that users cannot actually try on clothes, meaning they cannot see how they will look before purchasing.

[0888] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0889] In this invention, the server includes means for a user to upload a full-body photograph, means for analyzing the full-body photograph and acquiring the user's body shape information, means for inputting data related to the user's fashion style and preferences, means for generating a plurality of wearing images based on the body shape information and fashion style data, means for generating a plurality of coordination suggestions from the wearing images, means for displaying the coordination suggestions to the user, means for carrying out a purchase procedure based on the coordination selected by the user, and means for placing an order with an affiliated e-commerce site based on the user's selection. This allows users to shop online while checking coordinations that suit their body shape and preferences, and reduces the risk of returns due to mismatched sizes or colors.

[0890] "Means for uploading user images" refers to a function that allows a user to take a full-body photo and send that photo to the server.

[0891] "Means for analyzing full-body photos" refers to the function of obtaining the user's body shape information from full-body photos received by the server using an AI image analysis engine.

[0892] "Means for inputting data about the user's fashion style and preferences" refers to a function that allows the user to input their own fashion style and preferences through an app or website and send them to a server.

[0893] "Means for generating multiple wearing images" refers to the function of the server to create wearing images using a generative adversarial network (GAN) based on the user's body shape information and fashion style data.

[0894] "Means for generating multiple coordination suggestions" refers to a function that allows the server to suggest appropriate coordination to the user from the generated wearing images.

[0895] "Means for displaying coordination suggestions to the user" refers to a function that converts the coordination suggestions generated by the server into a user interface and visually displays them to the user via the terminal.

[0896] "Means for completing the purchase process based on the outfit selected by the user" refers to a function that allows the user to proceed with the purchase process based on the outfit they select from the outfits presented.

[0897] "Means for placing an order with an affiliated e-commerce site" refers to a function that enables the server to place an order with an affiliated e-commerce site based on the coordinates selected by the user.

[0898] A "generative adversarial network (GAN)" refers to a machine learning model used to generate new data based on existing data.

[0899] This invention is an online shopping support system in which users upload full-body photos, and a generative AI analyzes the images and suggests multiple outfits based on the user's body shape and preferences. This system is realized through the interaction between users, terminals, and a server.

[0900] First, the user takes a full-body photo using a device such as a smartphone and uploads it to a server. During this process, the photo is saved in the device's temporary storage and then sent to the server via the HTTPS protocol. Specifically, JPEG image files are often used.

[0901] Next, the server analyzes the received full-body photo using an AI image analysis engine (such as OpenCV or TensorFlow). Through this analysis, the server obtains body shape information such as face, shoulder width, waist, hips, and leg length as numerical data. If the user has entered their own height and weight data, this data is also used as a complement. The obtained data is stored in a user profile database on the server.

[0902] The user then enters data on the app or website, such as their preferred fashion style (e.g., casual, formal, sporty, etc.), color, material, brand, etc. This data is sent from the device to a server, which stores it and combines it with the user's body shape information to create a user profile.

[0903] The server filters matching clothing from its internal fashion database based on user profile data and fashion style data. Through filtering, items that fit the user's body shape and preferred fashion style are extracted. Based on this extracted list of items, the server uses a generative adversarial network (GAN) to generate a synthetic image that naturally fits the user's photo.

[0904] A specific example of a generative AI model is GAN (generative adversarial network) technology. This generates a synthetic image based on the user's body shape information so that the selected clothing naturally matches the user's photo. The generated coordination pattern is saved as an image and converted into a user interface. The device then visually displays the received coordination suggestions to the user.

[0905] The user selects their favorite outfit from the visually presented outfits and sends the selection to the server via their device. The server then reconfirms the details of the selected items (size, color, material, etc.) and makes a final confirmation that the selection meets the user's expectations. Finally, the user proceeds with the purchase based on the finalized outfit. After verifying the purchase information, the server places an order with the affiliated e-commerce site, and once the order is complete, the server sends an order completion notification to the device.

[0906] For example, if User A uploads a full-body photo and inputs that he or she likes casual blue denim jackets, the server analyzes User A's body shape information and generates multiple outfits that include a casual blue denim jacket. Examples of the outfits generated include a white T-shirt, denim pants, and sneakers. User A then selects his or her favorite outfit and proceeds with the purchase. This entire process is seamless, reducing the stress of online shopping for User A.

[0907] Example prompt sentence:

[0908] "Please suggest the best outfit for User A, who uploaded a full-body photo and entered that she likes casual blue denim jackets."

[0909] The above is an embodiment of the present invention. This system allows users to comfortably enjoy online shopping while checking outfits that suit their body type and preferences.

[0910] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0911] Step 1: Upload a user image

[0912] 1. A user takes a full-body photo using the smartphone camera app. The input is a full-body photo in JPEG format.

[0913] 2. The device takes a full-body photo and stores it in temporary storage.

[0914] 3. The device sends the saved JPEG image to the server using the HTTP protocol. The output is the image data sent to the server.

[0915] Step 2: Image analysis

[0916] 1. The server analyzes the received full-body photo using an AI image analysis engine (such as OpenCV or TensorFlow). The input is a JPEG full-body photo stored on the server.

[0917] 2. The server recognizes body shape information such as face, shoulder width, waist, hips, and leg length, and extracts it as numerical data. The output is numerical data of body shape information.

[0918] 3. The server completes the height and weight data previously entered by the user. The input is the user's height and weight data, and the output is the integrated profile data.

[0919] 4. The server stores the analysis results in the user profile database. The output is the user's body shape information stored in the database.

[0920] Step 3: Enter your fashion style and preferences

[0921] 1. A user inputs their preferred fashion style, color, material, brand, etc. on an app or website. The input is the user's preference data.

[0922] 2. The device sends the input fashion style and preference data to the server. The output is the user's preference data sent to the server.

[0923] 3. The server stores the received data and combines it with the body shape information to create a user profile. The output is the combined user profile.

[0924] Step 4: Creating outfit images and suggesting outfits

[0925] 1. The server filters matching clothes from the internal fashion database based on the user profile data and fashion style data. The input is the user profile data and fashion style data, and the output is a list of matching clothes.

[0926] 2. The server uses a generative adversarial network (GAN) to generate a synthetic image that naturally fits the user's photo based on the filtered items. The input is a list of matching clothing items and the user's full-body photo, and the output is the generated synthetic image.

[0927] 3. The server saves the generated coordinate pattern as an image. The output is the saved coordinate image.

[0928] 4. The server converts the generated coordinated image into a user interface. The output is the coordinated image converted into a user interface.

[0929] 5. The terminal retrieves the converted coordinated image and visually displays it to the user. The output is the coordinated image displayed to the user.

[0930] Step 5: Choose your outfit and checkout

[0931] 1. The user selects their favorite outfit from the presented outfits. The input is the user's choice.

[0932] 2. The terminal sends the selection to the server. The output is the selection sent to the server.

[0933] 3. The server reconfirms the details of the selected item (size, color, material). The input is the details of the selected item.

[0934] 4. The server performs a final check to ensure the selections meet the user's expectations. The output is the final, confirmed selections.

[0935] 5. The user proceeds with the purchase process based on the coordinated outfit they have decided on. The input is the coordinated outfit information they have decided on.

[0936] 6. After the server confirms the purchase information, it places an order with the affiliated e-commerce site. The output is the ordered item information.

[0937] 7. The server sends an order completion notification to the terminal. The output is the order completion notification sent to the user.

[0938] (Application example 1)

[0939] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0940] Currently, when shopping online, it takes a lot of time and effort for users to find the right fashion items to fit their body type. Furthermore, there are limited systems that suggest outfits that suit a user's preferences, which causes stress for users as they must choose from a large number of options. Since the online purchasing process does not allow users to try on items, users are also concerned about size and fit. It is necessary to solve these issues and improve the user shopping experience.

[0941] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0942] In this invention, the server includes means for a user to upload a full-body photo, means for analyzing the full-body photo and acquiring the user's body shape information, means for inputting data related to the user's fashion style and preferences, means for generating a plurality of wearing images based on the body shape information and fashion style data, means for generating a plurality of coordination suggestions from the wearing images, means for displaying the coordination suggestions to the user, means for completing a purchase based on the coordination selected by the user, and means for seamlessly completing the purchase using a generative AI model. This allows the user to visually check the coordination suggestions that suit their body shape and preferences, easily select appropriate items, and smoothly complete the purchase process.

[0943] A "user" is someone who uses the online shopping system to upload a full-body photo of themselves, suggest outfits, and complete the purchase process.

[0944] A "full-body photo" is an image that captures the user's face and entire body and is necessary to obtain body shape information.

[0945] "Analysis" is the process of extracting the user's body shape information from a full-body photo and quantifying each feature point.

[0946] "Body shape information" is numerical data that indicates the user's physical characteristics such as face, shoulder width, waist, hips, and leg length.

[0947] "Fashion style" refers to the type and design of clothing preferred by the user, and includes styles such as casual, formal, and sporty.

[0948] "Preferences" refer to individual fashion elements such as the user's preferred colors, materials, and brands.

[0949] "Data" is text and numeric information entered by the user, including information about fashion style and preferences.

[0950] A "wearing image" is a composite image of clothing generated based on the user's body shape information and preferences.

[0951] "Coordination suggestions" are clothing combinations that are generated based on multiple wearing images and suggested to the user.

[0952] "Display" refers to visually showing the generated coordination proposal on the user's terminal.

[0953] The "purchase procedure" refers to the series of processes required for a user to actually purchase items based on the outfit they have selected.

[0954] A "generative AI model" is an artificial intelligence model used to generate outfits based on a user's body shape information and fashion style.

[0955] "Seamless" refers to a state in which users have a consistent experience and there is a smooth transition between different processes.

[0956] This invention is an online shopping support system in which users upload full-body photos, analyze the images, and a generative AI proposes multiple outfits based on the user's body shape and preferences. This system is realized through the interaction between users, terminals, and a server.

[0957] Uploading and analyzing user images

[0958] First, the user takes a full-body photo and uploads it to the server from their device. The server then uses an AI image analysis engine to obtain the user's body shape information based on the received full-body photo. During this process, the server identifies feature points such as the face, shoulder width, waist, hips, and leg length, and extracts them as numerical data. If the user has entered height and weight data, this information is also supplemented. The results of this analysis are stored in a user profile database.

[0959] Enter your fashion style and preferences

[0960] Next, users enter data such as their preferred fashion style, color, material, and brand on the app or website, and the data is sent from the device to a server, which stores this data and combines it with the user's body shape information to create a user profile.

[0961] Wearing image generation and coordination suggestions

[0962] The server filters matching clothing from its internal fashion database based on user profile data and fashion style data. This filtering extracts items that fit the user's body shape and preferred fashion style. Based on the extracted items, the server uses a generative AI model to generate synthetic images to apply to the user's photo. This process utilizes a generative adversarial network (GAN) to ensure that the clothing fits naturally based on the user's body shape information. The server then generates multiple outfit patterns and saves them as images. The server converts the generated outfit images into a user interface for display to the user. The device visually displays the received outfit suggestions to the user.

[0963] Coordination selection and purchase process

[0964] The user selects their favorite outfit from the displayed outfits and sends the selection from their device to the server. The server then reconfirms the details of the selected outfit (size, color, material) to ensure that the selection meets the user's expectations. Finally, the user completes the purchase process based on the final outfit. The server uses a generative AI model to seamlessly complete the purchase process. This provides the user with a consistent experience and maintains smooth connections between different processes.

[0965] Specific examples

[0966] For example, suppose User A sends a full-body photo of himself and inputs data that he likes casual-style blue denim jackets. The server analyzes User A's body shape information and generates multiple outfits that include a casual-style blue denim jacket. The generated outfits might be, for example, a combination of a white T-shirt, denim pants, and sneakers. User A then selects his favorite outfit and proceeds with the purchase. This entire process is seamless, reducing the stress of online shopping for User A.

[0967] Prompt Sentence Examples

[0968] "Based on the user's body type and preferences, please suggest a casual outfit that includes a blue denim jacket."

[0969] This system allows users to confidently select outfits that suit their body type and preferences, and enjoy comfortable online shopping.

[0970] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0971] Step 1:

[0972] The user takes a full-body photo using a smartphone and uploads the image from the device to the server. The input of this step is the user's full-body photo, and the output is the completion of uploading the image to the server.

[0973] Step 2:

[0974] The server receives the uploaded full-body photo and uses an AI image analysis engine to analyze the user's body shape information. The analysis identifies feature points such as face, shoulder width, waist, hips, and leg length and extracts them as numerical data. The input in this step is the full-body photo, and the output is numerical data of body shape information.

[0975] Step 3:

[0976] Users input data such as their preferred fashion style, color, material, brand, etc. through an interface on the app or website. The input in this step is the user's fashion style and preference data, and the output is that this data is sent to a server and stored.

[0977] Step 4:

[0978] The server integrates the user's body shape information with fashion style and preference data to create a user profile, which includes the user's body shape information, preferred styles, colors, materials, brands, etc. The input in this step is the body shape information, fashion style and preference data, and the output is the creation of a user profile.

[0979] Step 5:

[0980] The server filters matching clothing items from its internal fashion database based on the generated user profile. This filtering extracts items that fit the user's body shape and preferred fashion style. The input of this step is the user profile, and the output is the extracted set of fashion items.

[0981] Step 6:

[0982] The server uses a generative AI model to generate a wearing image tailored to the user's body shape using the extracted fashion items. In this process, a generative adversarial network (GAN) is used to make the clothing fit naturally based on the user's body shape information. The input in this step is the extracted fashion items and the user's body shape information, and the output is a synthesized image of the wearing image.

[0983] Step 7:

[0984] The server generates multiple outfit suggestions from the generated outfit images and sends them to the user's device. The input in this step is a composite image of the outfit images, and the output is a set of outfit suggestion images.

[0985] Step 8:

[0986] The user selects their favorite outfit from multiple outfit suggestions displayed on the device. The input in this step is a group of outfit suggestion images, and the output is the data of the selected outfit.

[0987] Step 9:

[0988] The server checks the size and color again based on the coordinates selected by the user. The input in this step is the data of the selected coordinates, and the output is the result of the size and color check.

[0989] Step 10:

[0990] Based on the selected outfit, the server uses a generative AI model to seamlessly complete the purchase process. During this process, the order information is sent to the partner online shopping site and an order completion notification is sent to the terminal. The input in this step is the order information, and the output is a purchase completion notification.

[0991] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0992] This invention is an online shopping support system in which users upload full-body photos, analyze the images, and a generation AI proposes multiple outfits based on the user's body shape and preferences. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to propose even more appropriate outfits based on the user's emotional state. This system is realized through the interaction of the user, terminal, server, and emotion engine.

[0993] Uploading and analyzing user images

[0994] First, the user takes a full-body photo and uploads it to the server from their device. The server then uses an AI image analysis engine to obtain the user's body shape information based on the received full-body photo. During this process, the server identifies feature points such as the face, shoulder width, waist, hips, and leg length, and extracts them as numerical data. If the user has entered height and weight data, this information is also supplemented. The results of this analysis are stored in a user profile database.

[0995] Enter your fashion style and preferences

[0996] Next, users enter data such as their preferred fashion style, color, material, and brand on the app or website, and the data is sent from the device to a server, which stores this data and combines it with the user's body shape information to create a user profile.

[0997] Wearing image generation and coordination suggestions

[0998] The server filters matching clothing from its internal fashion database based on the user profile data and fashion style data, extracting items that fit the user's body type and match their preferred fashion style.

[0999] Based on the extracted items, the server uses a generative AI model to generate synthetic images to apply to the user's photo. During this process, it utilizes a generative adversarial network (GAN) to ensure the clothing fits naturally based on the user's body shape. The server then generates multiple outfit patterns and saves them as images.

[1000] Emotion engine recognizes user emotions

[1001] One of the features of this system is the addition of an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and voice to determine their emotional state. Specifically, it recognizes emotions such as joy, surprise, and sadness in real time from the user's facial expressions and tone of voice when selecting an outfit or looking at each suggestion.

[1002] Emotion-based coordination suggestions

[1003] Once the user's emotions are recognized, the server adjusts the outfit suggestions based on that information. For example, if the user expresses positive emotions toward the outfits displayed, the system records the user's emotional data and prioritizes suggestions for similar styles. On the other hand, if the user expresses negative emotions, the system adjusts to suggest different styles or colors.

[1004] Displaying outfit suggestions and final selection

[1005] The server converts the adjusted outfit suggestions into a user interface for display to the user. The device visually displays the received outfit suggestions to the user. The user selects their favorite outfit from the displayed outfits and sends the selection from the device to the server. The server reconfirms the details of the selected garment (size, color, material) to ensure that the selection meets the user's expectations.

[1006] Completing the purchase process

[1007] Finally, the user completes the purchase process based on the finalized outfit. The server then confirms the purchase information and places an order with the affiliated online shopping site. Once the order is complete, the server sends an order completion notification to the terminal.

[1008] Specific examples

[1009] For example, suppose User B sends a full-body photo of himself and inputs that he likes casual-style blue denim jackets. The server analyzes User B's body shape information and generates multiple outfits that include casual-style blue denim jackets. At this time, the emotion engine analyzes User B's facial expressions and voice and prioritizes suggestions of items that elicit a positive reaction. User B then selects his favorite outfit from the suggestions and proceeds with the purchase. This entire process is seamless, reducing the stress of online shopping for User B.

[1010] The above is an embodiment of the present invention. This system allows users to confidently select outfits that match their body type and preferences, and also receives suggestions based on their emotions, allowing them to enjoy a more satisfying online shopping experience.

[1011] The processing flow will be explained below.

[1012] Step 1: Upload a user image

[1013] The user takes a full-body photo.

[1014] Users use a dedicated app or website to upload a full-body photo from their device to the server.

[1015] Step 2: Receiving and analyzing images

[1016] The server sends the received full-body photo to an AI image analysis engine.

[1017] The server performs image analysis to obtain the user's body shape information (e.g., height, shoulder width, waist, hips, leg length, etc.).

[1018] Step 3: Enter user style information

[1019] Users enter their preferred fashion style, color, material, brand, etc. on the app or website.

[1020] The terminal transmits the input style information to the server.

[1021] Step 4: Integrating profile data

[1022] The server integrates the body shape information obtained through image analysis with the user's style information and stores it in a user profile database.

[1023] Step 5: Matching the clothing database

[1024] The server searches a fashion database for matching clothing items based on the user profile data.

[1025] The server filters the search results to extract items that match the user's size and preferences.

[1026] Step 6: Generative AI creates a wearing image

[1027] The server uses the generative AI model to apply the extracted clothing items to the user's full-body image.

[1028] The server uses a generative adversarial network (GAN) to generate a garment image that naturally fits the user's body shape.

[1029] Step 7: Generate outfit suggestions

[1030] The server creates multiple coordination suggestions based on the generated wearing images.

[1031] The server stores these coordination suggestions as image data and converts them into a user interface.

[1032] Step 8: Emotion recognition and regulation with the emotion engine

[1033] The server uses an emotion engine to analyze the user's facial expressions and voice and recognize their emotional state in real time.

[1034] The server adjusts the outfit suggestions based on the user's emotional data: if the user expresses positive emotions, it suggests a similar style, and if the user expresses negative emotions, it suggests a different style.

[1035] Step 9: View outfit suggestions

[1036] The server transmits image data of the coordinated outfit proposal to the terminal.

[1037] The device displays coordination suggestions to the user.

[1038] Step 10: Selecting Users

[1039] The user selects their favorite outfit from the displayed outfits.

[1040] The terminal transmits the selected coordinate information to the server.

[1041] Step 11: Check size and color

[1042] The server reconfirms the details of the selected garment (size, color, material).

[1043] Check whether the server matches your preferences and offer alternatives if necessary.

[1044] Step 12: Complete your purchase

[1045] The user confirms the final outfit and completes the purchase.

[1046] The server receives the purchase information and places an order with the affiliated online shopping site.

[1047] The server sends an order completion notification to the terminal and displays it to the user.

[1048] Through these successive processing steps, the system provides a safe and secure online clothing shopping environment for users. In addition to suggesting outfits based on the user's body shape and preferences, it also enhances user satisfaction through emotion recognition using an emotion engine.

[1049] Example 2

[1050] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1051] When shopping online, the process of selecting fashion items that suit a user's body type and preferences is time-consuming and laborious. Furthermore, the lack of a system that can provide more appropriate suggestions based on the user's emotional state makes it difficult to provide a satisfying shopping experience. Therefore, there is a need for technology that can recognize a user's emotions in real time and adjust outfit suggestions accordingly.

[1052] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1053] In this invention, the server includes means for a user to upload a full-body photograph, means for analyzing the full-body photograph and acquiring the user's body shape information, means for inputting data on the user's fashion style and preferences, means for generating a plurality of wearing images based on the body shape information and fashion style data, and means for recognizing the user's emotions and adjusting coordination suggestions based thereon. This not only makes it easier for the user to select fashion items that suit their body shape and preferences, but also enables a more satisfying online shopping experience by making suggestions based on the user's emotional state.

[1054] A "full-body photo" refers to a photo showing the user's entire body, and is used to obtain body shape information.

[1055] "Body shape information" refers to information about the shape of a user's biological body, including features such as the user's face, shoulder width, waist, hips, and leg length, as well as supplemental data (such as height and weight).

[1056] "Fashion style" refers to attributes such as design, color, material, and brand of clothing and accessories based on the user's preferences.

[1057] A "generative adversarial network (GAN)" is an algorithm that generates data by having two neural networks compete with each other, and is a model that demonstrates particularly strong performance in image generation.

[1058] An "emotion engine" refers to a system that analyzes a user's facial expressions and voice to recognize their emotional state in real time.

[1059] "Coordination suggestions" refer to multiple outfit images and sets generated by the server based on the user's body shape information, fashion style, and emotional data.

[1060] "Purchase Checkout" means the steps required to ultimately purchase the Products selected by the User, including the online payment process.

[1061] "User profile" refers to a database that integrates a user's body shape information, fashion style, emotional data, etc.

[1062] This invention is an online shopping support system in which users upload full-body photos, analyze the images, and a generation AI proposes multiple outfits based on the user's body shape and preferences. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to propose even more appropriate outfits based on the user's emotional state. This system is realized through the interaction of the user, terminal, server, and emotion engine.

[1063] Uploading and analyzing user images

[1064] First, the user takes a full-body photo and uploads it from their device to the server. The server then uses an AI image analysis engine (such as Google Cloud Vision API) to obtain the user's body shape information based on the received full-body photo. The server identifies feature points such as the face, shoulder width, waist, hips, and leg length, and extracts them as numerical data. If the user has entered height and weight data, this information is also supplemented. The results of this analysis are stored in a user profile database.

[1065] Enter your fashion style and preferences

[1066] Next, users enter data such as their preferred fashion style, color, material, and brand on the app or website, which is then sent from their device to a server, which stores this data and combines it with the user's body information to create a detailed user profile.

[1067] Wearing image generation and coordination suggestions

[1068] The server filters matching clothing from its internal fashion database based on user profile data and fashion style data. This filtering extracts items that fit the user's body shape and preferred fashion style. Based on the extracted items, the server uses a generative AI model (e.g., StyleGAN) to generate synthetic images. This process utilizes a generative adversarial network (GAN) to ensure that the clothing fits naturally based on the user's body shape information. The server generates multiple coordination patterns and saves them as images.

[1069] Emotion engine recognizes user emotions

[1070] One of the features of this system is that it incorporates an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and voice to determine their emotional state. When the user selects an outfit, the device's camera and microphone are used to collect emotional data in real time. Based on this data, the server recognizes the user's emotions, such as joy, surprise, and sadness, in real time.

[1071] Emotion-based coordination suggestions

[1072] The server dynamically adjusts outfit suggestions based on emotion data obtained from the emotion engine. If a user expresses positive emotions, the server records their emotion data and prioritizes suggestions for similar styles. On the other hand, if a user expresses negative emotions, the server suggests different styles and colors.

[1073] Displaying outfit suggestions and final selection

[1074] The server converts the adjusted outfit suggestions into a user interface. The device visually displays the received outfit suggestions to the user. The user selects their favorite outfit from the displayed outfits and sends the selection from the device to the server. The server then reconfirms the details of the selected garment (size, color, material) to ensure that the selection meets the user's expectations.

[1075] Completing the purchase process

[1076] Finally, the user completes the purchase process based on the finalized outfit. The server then confirms the purchase information and places an order with the affiliated online shopping site. Once the order is complete, the server sends an order completion notification to the terminal.

[1077] Specific examples

[1078] For example, suppose User B submits a full-body photo and inputs data indicating that he or she prefers casual-style blue denim jackets. The server analyzes User B's body shape information and generates multiple outfits that include casual-style blue denim jackets. At this time, the emotion engine analyzes User B's facial expressions and voice, and prioritizes suggestions of items that elicit a positive response. User B then selects his or her favorite outfit from the list and completes the purchase process.

[1079] Prompt Sentence Examples

[1080] Here are some example prompts to input to a generative AI model:

[1081] "Please explain the process of an online shopping support system that uploads a full-body photo of the user and obtains body shape information. It then generates outfit images based on the user's preferred styles and uses emotional data to suggest optimal outfits."

[1082] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1083] Step 1:

[1084] The user takes a full-body photo and uploads the image to the online shopping support system using a terminal.

[1085] The input is a full-body photo of the user, and the output is the image data being sent to the server. Specifically, the user takes a full-body photo with the camera on their smartphone or computer, and then uploads the image via the system's upload function.

[1086] Step 2:

[1087] The server analyzes the received full-body photo.

[1088] The input is an uploaded full-body photo, and the output is numerical data of the user's body shape information (face, shoulder width, waist, hips, leg length, etc.). Specifically, the server uses an AI image analysis engine (e.g., Google Cloud Vision API) to analyze the full-body photo and identify the body's feature points.

[1089] Step 3:

[1090] The server completes the user's height and weight data and stores the body shape information in a user profile database.

[1091] The input is the analyzed body shape information and supplementary data (height and weight), and the output is a detailed user profile. Specifically, the server further adjusts the body shape information based on the supplementary data and stores it in a database.

[1092] Step 4:

[1093] Users enter their preferences for fashion style, color, material, brand, etc. on the app or website.

[1094] The input is data about the user's fashion style, and the output is sending this data to the server. Specifically, the user selects and inputs their preferred fashion attributes through the interface.

[1095] Step 5:

[1096] The server combines the received fashion style data with the user's body shape information to create a detailed user profile.

[1097] The input is fashion style data and body shape information, and the output is an integrated user profile. Specifically, the server integrates these data and stores them in a database as individual profiles.

[1098] Step 6:

[1099] The server filters matching clothing items from an internal fashion database based on the user profile.

[1100] The input is a detailed user profile, and the output is a list of clothing items that match the user. Specifically, the server uses a filtering algorithm to select clothing items that match each attribute based on the profile.

[1101] Step 7:

[1102] The server uses a generative AI model (e.g., StyleGAN) to generate a synthetic image that applies the filtered clothing items to the user's photo.

[1103] The input is the filtered clothing items and the user's photo, and the output is a synthetic image. Specifically, the server uses a generative adversarial network (GAN) to generate a synthetic image in which the clothing fits naturally based on the user's body shape information.

[1104] Step 8:

[1105] The server generates a plurality of coordinate patterns and stores them as images.

[1106] The input is a composite image, and the output is images of multiple coordinate patterns. Specifically, the server creates different coordinate patterns based on the generated composite image and saves them as image data.

[1107] Step 9:

[1108] The device uses a camera and microphone to collect the user's facial expressions and voice in real time as they view the suggested outfits.

[1109] The input is the user's real-time facial expression and voice data, and the output is collected emotion data. Specifically, the device records the user's browsing behavior with a camera and microphone and sends the data to the emotion engine.

[1110] Step 10:

[1111] The server adjusts the coordination suggestions based on the emotional data obtained from the emotion engine.

[1112] The input is the user's emotional data, and the output is tailored outfit suggestions. Specifically, the server prioritizes items that show positive emotions and excludes items that show negative reactions.

[1113] Step 11:

[1114] The server converts the adjusted coordination proposal for a user interface, and the terminal visually displays the received proposal to the user.

[1115] The input is the adjusted coordinate proposal, and the output is the interface displayed to the user. Specifically, the server converts the adjusted information into an appropriate format and sends it to the terminal, which then displays it to the user.

[1116] Step 12:

[1117] The user selects their favorite outfit from the displayed outfits and transmits the selection to the server via their terminal.

[1118] The input is the user's selection, and the output is the selected outfit data. Specifically, the user operates the interface to select their favorite outfit.

[1119] Step 13:

[1120] The server then double-checks the selected garment details (size, color, material) to ensure they match the user's expectations.

[1121] The input is the details of the selected garment, and the output is the confirmation result. Specifically, the server checks the selection against the database again to check compatibility.

[1122] Step 14:

[1123] The user completes the purchase process based on the finalized outfit, and the server confirms the purchase information and then places an order with the affiliated online shopping site.

[1124] The input is the purchase procedure information, and the output is the order data. Specifically, the user completes the purchase procedure, and the server processes the order. Once the order is completed, the server sends an order completion notification to the terminal.

[1125] (Application example 2)

[1126] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1127] In today's online shopping environment, users face the challenge of finding the right fashion items. Furthermore, the lack of coordination suggestions tailored to the user's body type and individual preferences, as well as the lack of personalized suggestions that take into account the user's emotional state, reduces satisfaction with the shopping experience.

[1128] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to upload a full-body photograph, means for analyzing the full-body photograph and acquiring the user's body shape information, means for inputting data related to the user's fashion style and preferences, means for generating a plurality of wearing images based on the body shape information and fashion style data, means for generating a plurality of outfit suggestions, means for analyzing the user's emotional state and acquiring emotion data, means for adjusting the outfit suggestions based on the emotion data, means for displaying the outfit suggestions to the user, and means for completing a purchase based on the outfit selected by the user. This makes it easier for users to find outfits that suit their body shape and preferences, and by receiving suggestions based on their emotions, users can have a more satisfying online shopping experience.

[1129] "Full Body Photo" means a photograph of the User's entire body, which must include the User's entire body from face to toe.

[1130] "Analysis" refers to the process of analyzing image data using a computer and extracting specific information, such as identifying a user's body shape information from an image.

[1131] "Body Information" refers to data related to a user's physique and shape, including measurements and features such as face, shoulder width, waist, hips, and leg length.

[1132] "Fashion style" refers to the clothing designs and styles preferred by a user, including information about preferences for color, material, design, brand, etc.

[1133] "Wearing image" refers to an image of the user wearing the clothing, generated based on the user's body shape information. This visualizes how the user will look when actually wearing the clothing.

[1134] "Coordination suggestions" refer to multiple fashion combinations selected from wearing images generated based on the user's body shape information and fashion style.

[1135] "Emotional state" refers to data related to emotions analyzed from the user's facial expressions, tone of voice, etc. Specifically, it includes emotions such as joy, anger, sadness, and happiness.

[1136] "Emotional data" refers to data that quantifies or classifies an emotional state, which allows for suggestions and adjustments based on the user's emotions.

[1137] "Adjustment" refers to changing the suggestions based on specific criteria or conditions. In this case, it refers to changing the outfit suggestions based on the user's emotional data.

[1138] "Purchase procedure" refers to the series of actions required to actually purchase the product selected by the user, including product selection, payment, and delivery procedures.

[1139] "System" refers to the entire device or program that combines multiple means, including various functions necessary for users to comfortably shop online.

[1140] This invention is an online shopping support system that allows users to upload full-body photos and performs image analysis to suggest multiple outfits based on the user's body shape and preferences. By combining it with an emotion engine that recognizes the user's emotions, it is possible to suggest even more appropriate outfits based on the user's emotional state. This system is realized through the interaction of the user, terminal, server, and emotion engine.

[1141] Uploading and analyzing user images

[1142] First, the user takes a full-body photo and uploads it to the server from their device. The server then uses an AI image analysis engine to obtain the user's body shape information based on the received full-body photo. Specifically, image analysis technologies such as Google Cloud Vision API are used to identify feature points such as the face, shoulder width, waist, hips, and leg length, and extract them as numerical data. The results of this analysis are stored in a user profile database.

[1143] Enter your fashion style and preferences

[1144] Next, users enter data such as their preferred fashion style, color, material, and brand on the app or website, and the data is sent from the device to a server, which stores this data and combines it with the user's body shape information to create a user profile.

[1145] Wearing image generation and coordination suggestions

[1146] The server filters matching clothing from its internal fashion database based on user profile data and fashion style data. This filtering extracts items that fit the user's body shape and preferred fashion style. Based on the extracted items, the server uses a generative AI model to generate synthetic images to apply to the user's photo. This process utilizes a generative adversarial network (GAN) to ensure that the clothing fits naturally based on the user's body shape information. The server then generates multiple outfit patterns and saves them as images.

[1147] Emotion engine recognizes user emotions

[1148] One of the features of this system is the addition of an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and voice to determine their emotional state. Specifically, it uses Microsoft Azure's Emotion API to recognize emotions such as joy, surprise, and sadness in real time from the user's facial expressions and tone of voice when selecting an outfit or viewing each suggestion.

[1149] Emotion-based coordination suggestions

[1150] Once the user's emotions are recognized, the server adjusts the outfit suggestions based on that information. For example, if a user expresses positive emotions toward the outfits displayed, the server records the user's emotional data and prioritizes suggestions for similar styles. On the other hand, if the user expresses negative emotions, the system adjusts to suggest different styles or colors. The system also reviews the user interface, taking into account the results of the emotion analysis.

[1151] Displaying outfit suggestions and final selection

[1152] The server converts the adjusted outfit suggestions into a user interface for display to the user. The device visually displays the received outfit suggestions to the user. The user selects their favorite outfit from the displayed outfits and sends the selection from the device to the server. The server reconfirms the details of the selected garment (size, color, material) to ensure that the selection meets the user's expectations.

[1153] Completing the purchase process

[1154] Finally, the user completes the purchase process based on the finalized outfit. The server then confirms the purchase information and places an order with the affiliated online shopping site. Once the order is complete, the server sends an order completion notification to the terminal.

[1155] Specific examples

[1156] For example, suppose User B sends a full-body photo of himself and inputs that he likes casual-style blue denim jackets. The server analyzes User B's body shape information and generates multiple outfits that include casual-style blue denim jackets. At this time, the emotion engine analyzes User B's facial expressions and voice and prioritizes suggestions of items that elicit a positive reaction. User B then selects his favorite outfit from the suggestions and proceeds with the purchase. This entire process is seamless, reducing the stress of online shopping for User B.

[1157] Prompt Sentence Examples

[1158] You could provide a generative AI model with a prompt like this:

[1159] "The system identifies feature points such as face, shoulder width, waist, hips, and leg length from a user's full-body photo and extracts them as numerical data."

[1160] "The system uses facial expressions and voice data to determine the emotional state of the user regarding the outfit suggestions. If the emotion is positive, the suggestion is kept, and if it is negative, a new suggestion is generated."

[1161] This system allows users to confidently select outfits that suit their body type and preferences, and also receives suggestions that reflect their emotions, allowing them to enjoy a more satisfying online shopping experience.

[1162] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1163] Step 1:

[1164] The user takes a full-body photo and uploads it to the server from their device.

[1165] Input: A full-body photo of the user

[1166] Data processing: Uploading image files

[1167] Output: Image file saved on the server

[1168] Step 2:

[1169] Based on the full-body photo received by the server, the AI ​​image analysis engine is used to obtain the user's body shape information. Specifically, image analysis technology is used to identify feature points such as face, shoulder width, waist, hips, and leg length, and extract them as numerical data. The Google Cloud Vision API is used.

[1170] Input: A full-body photo stored on the server

[1171] Data processing: Identifying feature points such as face, shoulder width, waist, hips, and leg length and extracting numerical data

[1172] Output: Numerical data of body shape information

[1173] Step 3:

[1174] Users enter data such as their preferred fashion style, color, material, and brand on the app or website, and the data is sent from their device to a server.

[1175] Input: Data such as user's fashion style, color, material, brand, etc.

[1176] Data processing: Acquiring and saving user input data

[1177] Output: Fashion style data

[1178] Step 4:

[1179] The server filters matching garments from an internal fashion database based on the user profile data and fashion style data.

[1180] Input: Body shape information and fashion style data

[1181] Data Processing: Filtering Matching Clothing from a Fashion Database

[1182] Output: A list of matching clothing items

[1183] Step 5:

[1184] Based on the extracted items, the server uses a generative AI model to generate a synthetic image to apply to the user's photo, using a generative adversarial network (GAN).

[1185] Input: A list of matching clothing items, a full-body photo of the user

[1186] Data processing: Generating synthetic images using generative AI models (using GANs)

[1187] Output: Images of multiple outfit suggestions

[1188] Step 6:

[1189] The server analyzes the user's emotional state and acquires emotional data. Specifically, it analyzes the user's facial expressions and voice to recognize emotions such as joy, surprise, and sadness. It uses Microsoft Azure's Emotion API.

[1190] Input: User's facial expressions and voice data

[1191] Data processing: Emotion analysis of facial expressions and voice data

[1192] Output: Emotion data

[1193] Step 7:

[1194] The server adjusts coordination suggestions based on the emotion data, preferentially displaying suggestions based on positive emotion data and changing suggestions based on negative emotion data.

[1195] Input: Coordination proposal images, emotion data

[1196] Data manipulation: prioritizing or modifying suggestions based on sentiment data

[1197] Output: Image of the adjusted outfit suggestions

[1198] Step 8:

[1199] The server converts the adjusted coordination proposal into a user interface and transmits it to the terminal, which visually displays the received coordination proposal to the user.

[1200] Input: Image of the coordinated outfit suggestion

[1201] Data processing: conversion to user interface

[1202] Output: Coordination suggestion image displayed on the user's device

[1203] Step 9:

[1204] The user selects their favorite outfit from the displayed outfits and sends the selection from the terminal to the server.

[1205] Input: User's selected outfit

[1206] Data processing: Send selected data

[1207] Output: Selection data saved on the server

[1208] Step 10:

[1209] The server reviews the selected garment details (size, color, material) to ensure the selection meets the user's expectations.

[1210] Input: Detailed information about the selected outfit

[1211] Data processing: Rechecking detailed information

[1212] Output: Verification result

[1213] Step 11:

[1214] The user completes the purchase process based on the coordinated outfit they have decided on. After the server confirms the purchase information, it places an order with the affiliated online shopping site. Once the order is complete, the server sends an order completion notification to the terminal.

[1215] Input: Purchase information for the confirmed outfit

[1216] Data processing: verifying purchase information and processing orders

[1217] Output: Order completion notification

[1218] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1219] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1220] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1221] [Fourth embodiment]

[1222] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1223] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1224] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1225] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1226] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1227] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1228] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1229] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1230] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1231] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1232] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1233] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1234] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1235] This invention is an online shopping support system in which users upload full-body photos, analyze the images, and a generative AI proposes multiple outfits based on the user's body shape and preferences. This system is realized through the interaction between users, terminals, and a server.

[1236] Uploading and analyzing user images

[1237] First, the user takes a full-body photo and uploads it to the server from their device. The server then uses an AI image analysis engine to obtain the user's body shape information based on the received full-body photo. During this process, the server identifies feature points such as the face, shoulder width, waist, hips, and leg length, and extracts them as numerical data. If the user has entered height and weight data, this information is also supplemented. The results of this analysis are stored in a user profile database.

[1238] Enter your fashion style and preferences

[1239] Next, users enter data such as their preferred fashion style, color, material, and brand on the app or website, and the data is sent from the device to a server, which stores this data and combines it with the user's body shape information to create a user profile.

[1240] Wearing image generation and coordination suggestions

[1241] The server filters matching clothing from its internal fashion database based on the user profile data and fashion style data, extracting items that fit the user's body type and match their preferred fashion style.

[1242] Based on the extracted items, the server uses a generative AI model to generate synthetic images to apply to the user's photo. During this process, it utilizes a generative adversarial network (GAN) to ensure the clothing fits naturally based on the user's body shape. The server then generates multiple outfit patterns and saves them as images.

[1243] The server converts the generated coordinated image into a user interface for display to the user, and the terminal visually displays the received coordinated suggestion to the user.

[1244] Coordination selection and purchase process

[1245] The user selects their favorite outfit from the displayed outfits and sends the selection from their device to the server, which then reconfirms the details of the selected outfit (size, color, material) to ensure that the selection meets the user's expectations.

[1246] Finally, the user completes the purchase process based on the finalized outfit. The server then confirms the purchase information and places an order with the affiliated online shopping site. Once the order is complete, the server sends an order completion notification to the terminal.

[1247] Specific examples

[1248] For example, suppose User A sends a full-body photo of himself and inputs data that he likes casual-style blue denim jackets. The server analyzes User A's body shape information and generates multiple outfits that include a casual-style blue denim jacket. The generated outfits could be, for example, a combination of a white T-shirt, denim pants, and sneakers. User A then selects his favorite outfit and proceeds with the purchase. This entire process is seamless, reducing the stress of online shopping for User A.

[1249] The above is an embodiment of the present invention. This system allows users to select outfits that suit their body type and preferences with confidence, and enjoy online shopping in comfort.

[1250] The processing flow will be explained below.

[1251] Step 1: Upload a user image

[1252] The user takes a full-body photo.

[1253] Users use a dedicated app or website to upload a full-body photo from their device to the server.

[1254] Step 2: Receiving and analyzing images

[1255] The server sends the received full-body photo to an AI image analysis engine.

[1256] The server performs image analysis to obtain the user's body shape information (e.g., height, shoulder width, waist, hips, leg length, etc.).

[1257] Step 3: Enter user style information

[1258] Users enter their preferred fashion style, color, material, brand, etc. on the app or website.

[1259] The terminal transmits the input style information to the server.

[1260] Step 4: Integrating profile data

[1261] The server integrates the body shape information obtained through image analysis with the user's style information and stores it in a user profile database.

[1262] Step 5: Matching the clothing database

[1263] The server searches a fashion database for matching clothing items based on the user profile data.

[1264] The server filters the search results to extract items that match the user's size and preferences.

[1265] Step 6: Generative AI creates a wearing image

[1266] The server uses the generative AI model to apply the extracted clothing items to the user's full-body image.

[1267] The server uses a generative adversarial network (GAN) to generate a garment image that naturally fits the user's body shape.

[1268] Step 7: Generate outfit suggestions

[1269] The server creates multiple coordination suggestions based on the generated wearing images.

[1270] The server stores these coordination suggestions as image data and converts them into a user interface.

[1271] Step 8: View outfit suggestions

[1272] The server transmits image data of the coordinated outfit proposal to the terminal.

[1273] The device displays coordination suggestions to the user.

[1274] Step 9: Selecting Users

[1275] The user selects their favorite outfit from the displayed outfits.

[1276] The terminal transmits the selected coordinate information to the server.

[1277] Step 10: Check size and color

[1278] The server reconfirms the details of the selected garment (size, color, material).

[1279] Check whether the server matches your preferences and offer alternatives if necessary.

[1280] Step 11: Complete your purchase

[1281] The user confirms the final outfit and completes the purchase.

[1282] The server receives the purchase information and places an order with the affiliated online shopping site.

[1283] The server sends an order completion notification to the terminal and displays it to the user.

[1284] Through the above steps, this system provides an environment where users can purchase clothes online with peace of mind.

[1285] Example 1

[1286] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1287] Conventional online shopping systems have made it difficult for users to find the perfect outfit for their body type and preferences, leading to frequent returns due to incorrect sizes or colors after purchase. Another issue is that users cannot actually try on clothes, meaning they cannot see how they will look before purchasing.

[1288] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1289] In this invention, the server includes means for a user to upload a full-body photograph, means for analyzing the full-body photograph and acquiring the user's body shape information, means for inputting data related to the user's fashion style and preferences, means for generating a plurality of wearing images based on the body shape information and fashion style data, means for generating a plurality of coordination suggestions from the wearing images, means for displaying the coordination suggestions to the user, means for carrying out a purchase procedure based on the coordination selected by the user, and means for placing an order with an affiliated e-commerce site based on the user's selection. This allows users to shop online while checking coordinations that suit their body shape and preferences, and reduces the risk of returns due to mismatched sizes or colors.

[1290] "Means for uploading user images" refers to a function that allows a user to take a full-body photo and send that photo to the server.

[1291] "Means for analyzing full-body photos" refers to the function of obtaining the user's body shape information from full-body photos received by the server using an AI image analysis engine.

[1292] "Means for inputting data about the user's fashion style and preferences" refers to a function that allows the user to input their own fashion style and preferences through an app or website and send them to a server.

[1293] "Means for generating multiple wearing images" refers to the function of the server to create wearing images using a generative adversarial network (GAN) based on the user's body shape information and fashion style data.

[1294] "Means for generating multiple coordination suggestions" refers to a function that allows the server to suggest appropriate coordination to the user from the generated wearing images.

[1295] "Means for displaying coordination suggestions to the user" refers to a function that converts the coordination suggestions generated by the server into a user interface and visually displays them to the user via the terminal.

[1296] "Means for completing the purchase process based on the outfit selected by the user" refers to a function that allows the user to proceed with the purchase process based on the outfit they select from the outfits presented.

[1297] "Means for placing an order with an affiliated e-commerce site" refers to a function that enables the server to place an order with an affiliated e-commerce site based on the coordinates selected by the user.

[1298] A "generative adversarial network (GAN)" refers to a machine learning model used to generate new data based on existing data.

[1299] This invention is an online shopping support system in which users upload full-body photos, and a generative AI analyzes the images and suggests multiple outfits based on the user's body shape and preferences. This system is realized through the interaction between users, terminals, and a server.

[1300] First, the user takes a full-body photo using a device such as a smartphone and uploads it to a server. During this process, the photo is saved in the device's temporary storage and then sent to the server via the HTTPS protocol. Specifically, JPEG image files are often used.

[1301] Next, the server analyzes the received full-body photo using an AI image analysis engine (such as OpenCV or TensorFlow). Through this analysis, the server obtains body shape information such as face, shoulder width, waist, hips, and leg length as numerical data. If the user has entered their own height and weight data, this data is also used as a complement. The obtained data is stored in a user profile database on the server.

[1302] The user then enters data on the app or website, such as their preferred fashion style (e.g., casual, formal, sporty, etc.), color, material, brand, etc. This data is sent from the device to a server, which stores it and combines it with the user's body shape information to create a user profile.

[1303] The server filters matching clothing from its internal fashion database based on user profile data and fashion style data. Through filtering, items that fit the user's body shape and preferred fashion style are extracted. Based on this extracted list of items, the server uses a generative adversarial network (GAN) to generate a synthetic image that naturally fits the user's photo.

[1304] A specific example of a generative AI model is GAN (generative adversarial network) technology. This generates a synthetic image based on the user's body shape information so that the selected clothing naturally matches the user's photo. The generated coordination pattern is saved as an image and converted into a user interface. The device then visually displays the received coordination suggestions to the user.

[1305] The user selects their favorite outfit from the visually presented outfits and sends the selection to the server via their device. The server then reconfirms the details of the selected items (size, color, material, etc.) and makes a final confirmation that the selection meets the user's expectations. Finally, the user proceeds with the purchase based on the finalized outfit. After verifying the purchase information, the server places an order with the affiliated e-commerce site, and once the order is complete, the server sends an order completion notification to the device.

[1306] For example, if User A uploads a full-body photo and inputs that he or she likes casual blue denim jackets, the server analyzes User A's body shape information and generates multiple outfits that include a casual blue denim jacket. Examples of the outfits generated include a white T-shirt, denim pants, and sneakers. User A then selects his or her favorite outfit and proceeds with the purchase. This entire process is seamless, reducing the stress of online shopping for User A.

[1307] Example prompt sentence:

[1308] "Please suggest the best outfit for User A, who uploaded a full-body photo and entered that she likes casual blue denim jackets."

[1309] The above is an embodiment of the present invention. This system allows users to comfortably enjoy online shopping while checking outfits that suit their body type and preferences.

[1310] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1311] Step 1: Upload a user image

[1312] 1. A user takes a full-body photo using the smartphone camera app. The input is a full-body photo in JPEG format.

[1313] 2. The device takes a full-body photo and stores it in temporary storage.

[1314] 3. The device sends the saved JPEG image to the server using the HTTP protocol. The output is the image data sent to the server.

[1315] Step 2: Image analysis

[1316] 1. The server analyzes the received full-body photo using an AI image analysis engine (such as OpenCV or TensorFlow). The input is a JPEG full-body photo stored on the server.

[1317] 2. The server recognizes body shape information such as face, shoulder width, waist, hips, and leg length, and extracts it as numerical data. The output is numerical data of body shape information.

[1318] 3. The server completes the height and weight data previously entered by the user. The input is the user's height and weight data, and the output is the integrated profile data.

[1319] 4. The server stores the analysis results in the user profile database. The output is the user's body shape information stored in the database.

[1320] Step 3: Enter your fashion style and preferences

[1321] 1. A user inputs their preferred fashion style, color, material, brand, etc. on an app or website. The input is the user's preference data.

[1322] 2. The device sends the input fashion style and preference data to the server. The output is the user's preference data sent to the server.

[1323] 3. The server stores the received data and combines it with the body shape information to create a user profile. The output is the combined user profile.

[1324] Step 4: Creating outfit images and suggesting outfits

[1325] 1. The server filters matching clothes from the internal fashion database based on the user profile data and fashion style data. The input is the user profile data and fashion style data, and the output is a list of matching clothes.

[1326] 2. The server uses a generative adversarial network (GAN) to generate a synthetic image that naturally fits the user's photo based on the filtered items. The input is a list of matching clothing items and the user's full-body photo, and the output is the generated synthetic image.

[1327] 3. The server saves the generated coordinate pattern as an image. The output is the saved coordinate image.

[1328] 4. The server converts the generated coordinated image into a user interface. The output is the coordinated image converted into a user interface.

[1329] 5. The terminal retrieves the converted coordinated image and visually displays it to the user. The output is the coordinated image displayed to the user.

[1330] Step 5: Choose your outfit and checkout

[1331] 1. The user selects their favorite outfit from the presented outfits. The input is the user's choice.

[1332] 2. The terminal sends the selection to the server. The output is the selection sent to the server.

[1333] 3. The server reconfirms the details of the selected item (size, color, material). The input is the details of the selected item.

[1334] 4. The server performs a final check to ensure the selections meet the user's expectations. The output is the final, confirmed selections.

[1335] 5. The user proceeds with the purchase process based on the coordinated outfit they have decided on. The input is the coordinated outfit information they have decided on.

[1336] 6. After the server confirms the purchase information, it places an order with the affiliated e-commerce site. The output is the ordered item information.

[1337] 7. The server sends an order completion notification to the terminal. The output is the order completion notification sent to the user.

[1338] (Application example 1)

[1339] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1340] Currently, when shopping online, it takes a lot of time and effort for users to find the right fashion items to fit their body type. Furthermore, there are limited systems that suggest outfits that suit a user's preferences, which causes stress for users as they must choose from a large number of options. Since the online purchasing process does not allow users to try on items, users are also concerned about size and fit. It is necessary to solve these issues and improve the user shopping experience.

[1341] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1342] In this invention, the server includes means for a user to upload a full-body photo, means for analyzing the full-body photo and acquiring the user's body shape information, means for inputting data related to the user's fashion style and preferences, means for generating a plurality of wearing images based on the body shape information and fashion style data, means for generating a plurality of coordination suggestions from the wearing images, means for displaying the coordination suggestions to the user, means for completing a purchase based on the coordination selected by the user, and means for seamlessly completing the purchase using a generative AI model. This allows the user to visually check the coordination suggestions that suit their body shape and preferences, easily select appropriate items, and smoothly complete the purchase process.

[1343] A "user" is someone who uses the online shopping system to upload a full-body photo of themselves, suggest outfits, and complete the purchase process.

[1344] A "full-body photo" is an image that captures the user's face and entire body and is necessary to obtain body shape information.

[1345] "Analysis" is the process of extracting the user's body shape information from a full-body photo and quantifying each feature point.

[1346] "Body shape information" is numerical data that indicates the user's physical characteristics such as face, shoulder width, waist, hips, and leg length.

[1347] "Fashion style" refers to the type and design of clothing preferred by the user, and includes styles such as casual, formal, and sporty.

[1348] "Preferences" refer to individual fashion elements such as the user's preferred colors, materials, and brands.

[1349] "Data" is text and numeric information entered by the user, including information about fashion style and preferences.

[1350] A "wearing image" is a composite image of clothing generated based on the user's body shape information and preferences.

[1351] "Coordination suggestions" are clothing combinations that are generated based on multiple wearing images and suggested to the user.

[1352] "Display" refers to visually showing the generated coordination proposal on the user's terminal.

[1353] The "purchase procedure" refers to the series of processes required for a user to actually purchase items based on the outfit they have selected.

[1354] A "generative AI model" is an artificial intelligence model used to generate outfits based on a user's body shape information and fashion style.

[1355] "Seamless" refers to a state in which users have a consistent experience and there is a smooth transition between different processes.

[1356] This invention is an online shopping support system in which users upload full-body photos, analyze the images, and a generative AI proposes multiple outfits based on the user's body shape and preferences. This system is realized through the interaction between users, terminals, and a server.

[1357] Uploading and analyzing user images

[1358] First, the user takes a full-body photo and uploads it to the server from their device. The server then uses an AI image analysis engine to obtain the user's body shape information based on the received full-body photo. During this process, the server identifies feature points such as the face, shoulder width, waist, hips, and leg length, and extracts them as numerical data. If the user has entered height and weight data, this information is also supplemented. The results of this analysis are stored in a user profile database.

[1359] Enter your fashion style and preferences

[1360] Next, users enter data such as their preferred fashion style, color, material, and brand on the app or website, and the data is sent from the device to a server, which stores this data and combines it with the user's body shape information to create a user profile.

[1361] Wearing image generation and coordination suggestions

[1362] The server filters matching clothing from its internal fashion database based on user profile data and fashion style data. This filtering extracts items that fit the user's body shape and preferred fashion style. Based on the extracted items, the server uses a generative AI model to generate synthetic images to apply to the user's photo. This process utilizes a generative adversarial network (GAN) to ensure that the clothing fits naturally based on the user's body shape information. The server then generates multiple outfit patterns and saves them as images. The server converts the generated outfit images into a user interface for display to the user. The device visually displays the received outfit suggestions to the user.

[1363] Coordination selection and purchase process

[1364] The user selects their favorite outfit from the displayed outfits and sends the selection from their device to the server. The server then reconfirms the details of the selected outfit (size, color, material) to ensure that the selection meets the user's expectations. Finally, the user completes the purchase process based on the final outfit. The server uses a generative AI model to seamlessly complete the purchase process. This provides the user with a consistent experience and maintains smooth connections between different processes.

[1365] Specific examples

[1366] For example, suppose User A sends a full-body photo of himself and inputs data that he likes casual-style blue denim jackets. The server analyzes User A's body shape information and generates multiple outfits that include a casual-style blue denim jacket. The generated outfits might be, for example, a combination of a white T-shirt, denim pants, and sneakers. User A then selects his favorite outfit and proceeds with the purchase. This entire process is seamless, reducing the stress of online shopping for User A.

[1367] Prompt Sentence Examples

[1368] "Based on the user's body type and preferences, please suggest a casual outfit that includes a blue denim jacket."

[1369] This system allows users to confidently select outfits that suit their body type and preferences, and enjoy comfortable online shopping.

[1370] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1371] Step 1:

[1372] The user takes a full-body photo using a smartphone and uploads the image from the device to the server. The input of this step is the user's full-body photo, and the output is the completion of uploading the image to the server.

[1373] Step 2:

[1374] The server receives the uploaded full-body photo and uses an AI image analysis engine to analyze the user's body shape information. The analysis identifies feature points such as face, shoulder width, waist, hips, and leg length and extracts them as numerical data. The input in this step is the full-body photo, and the output is numerical data of body shape information.

[1375] Step 3:

[1376] Users input data such as their preferred fashion style, color, material, brand, etc. through an interface on the app or website. The input in this step is the user's fashion style and preference data, and the output is that this data is sent to a server and stored.

[1377] Step 4:

[1378] The server integrates the user's body shape information with fashion style and preference data to create a user profile, which includes the user's body shape information, preferred styles, colors, materials, brands, etc. The input in this step is the body shape information, fashion style and preference data, and the output is the creation of a user profile.

[1379] Step 5:

[1380] The server filters matching clothing items from its internal fashion database based on the generated user profile. This filtering extracts items that fit the user's body shape and preferred fashion style. The input of this step is the user profile, and the output is the extracted set of fashion items.

[1381] Step 6:

[1382] The server uses a generative AI model to generate a wearing image tailored to the user's body shape using the extracted fashion items. In this process, a generative adversarial network (GAN) is used to make the clothing fit naturally based on the user's body shape information. The input in this step is the extracted fashion items and the user's body shape information, and the output is a synthesized image of the wearing image.

[1383] Step 7:

[1384] The server generates multiple outfit suggestions from the generated outfit images and sends them to the user's device. The input in this step is a composite image of the outfit images, and the output is a set of outfit suggestion images.

[1385] Step 8:

[1386] The user selects their favorite outfit from multiple outfit suggestions displayed on the device. The input in this step is a group of outfit suggestion images, and the output is the data of the selected outfit.

[1387] Step 9:

[1388] The server checks the size and color again based on the coordinates selected by the user. The input in this step is the data of the selected coordinates, and the output is the result of the size and color check.

[1389] Step 10:

[1390] Based on the selected outfit, the server uses a generative AI model to seamlessly complete the purchase process. During this process, the order information is sent to the partner online shopping site and an order completion notification is sent to the terminal. The input in this step is the order information, and the output is a purchase completion notification.

[1391] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1392] This invention is an online shopping support system in which users upload full-body photos, analyze the images, and a generation AI proposes multiple outfits based on the user's body shape and preferences. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to propose even more appropriate outfits based on the user's emotional state. This system is realized through the interaction of the user, terminal, server, and emotion engine.

[1393] Uploading and analyzing user images

[1394] First, the user takes a full-body photo and uploads it to the server from their device. The server then uses an AI image analysis engine to obtain the user's body shape information based on the received full-body photo. During this process, the server identifies feature points such as the face, shoulder width, waist, hips, and leg length, and extracts them as numerical data. If the user has entered height and weight data, this information is also supplemented. The results of this analysis are stored in a user profile database.

[1395] Enter your fashion style and preferences

[1396] Next, users enter data such as their preferred fashion style, color, material, and brand on the app or website, and the data is sent from the device to a server, which stores this data and combines it with the user's body shape information to create a user profile.

[1397] Wearing image generation and coordination suggestions

[1398] The server filters matching clothing from its internal fashion database based on the user profile data and fashion style data, extracting items that fit the user's body type and match their preferred fashion style.

[1399] Based on the extracted items, the server uses a generative AI model to generate synthetic images to apply to the user's photo. During this process, it utilizes a generative adversarial network (GAN) to ensure the clothing fits naturally based on the user's body shape. The server then generates multiple outfit patterns and saves them as images.

[1400] Emotion engine recognizes user emotions

[1401] One of the features of this system is the addition of an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and voice to determine their emotional state. Specifically, it recognizes emotions such as joy, surprise, and sadness in real time from the user's facial expressions and tone of voice when selecting an outfit or looking at each suggestion.

[1402] Emotion-based coordination suggestions

[1403] Once the user's emotions are recognized, the server adjusts the outfit suggestions based on that information. For example, if the user expresses positive emotions toward the outfits displayed, the system records the user's emotional data and prioritizes suggestions for similar styles. On the other hand, if the user expresses negative emotions, the system adjusts to suggest different styles or colors.

[1404] Displaying outfit suggestions and final selection

[1405] The server converts the adjusted outfit suggestions into a user interface for display to the user. The device visually displays the received outfit suggestions to the user. The user selects their favorite outfit from the displayed outfits and sends the selection from the device to the server. The server reconfirms the details of the selected garment (size, color, material) to ensure that the selection meets the user's expectations.

[1406] Completing the purchase process

[1407] Finally, the user completes the purchase process based on the finalized outfit. The server then confirms the purchase information and places an order with the affiliated online shopping site. Once the order is complete, the server sends an order completion notification to the terminal.

[1408] Specific examples

[1409] For example, suppose User B sends a full-body photo of himself and inputs that he likes casual-style blue denim jackets. The server analyzes User B's body shape information and generates multiple outfits that include casual-style blue denim jackets. At this time, the emotion engine analyzes User B's facial expressions and voice and prioritizes suggestions of items that elicit a positive reaction. User B then selects his favorite outfit from the suggestions and proceeds with the purchase. This entire process is seamless, reducing the stress of online shopping for User B.

[1410] The above is an embodiment of the present invention. This system allows users to confidently select outfits that match their body type and preferences, and also receives suggestions based on their emotions, allowing them to enjoy a more satisfying online shopping experience.

[1411] The processing flow will be explained below.

[1412] Step 1: Upload a user image

[1413] The user takes a full-body photo.

[1414] Users use a dedicated app or website to upload a full-body photo from their device to the server.

[1415] Step 2: Receiving and analyzing images

[1416] The server sends the received full-body photo to an AI image analysis engine.

[1417] The server performs image analysis to obtain the user's body shape information (e.g., height, shoulder width, waist, hips, leg length, etc.).

[1418] Step 3: Enter user style information

[1419] Users enter their preferred fashion style, color, material, brand, etc. on the app or website.

[1420] The terminal transmits the input style information to the server.

[1421] Step 4: Integrating profile data

[1422] The server integrates the body shape information obtained through image analysis with the user's style information and stores it in a user profile database.

[1423] Step 5: Matching the clothing database

[1424] The server searches a fashion database for matching clothing items based on the user profile data.

[1425] The server filters the search results to extract items that match the user's size and preferences.

[1426] Step 6: Generative AI creates a wearing image

[1427] The server uses the generative AI model to apply the extracted clothing items to the user's full-body image.

[1428] The server uses a generative adversarial network (GAN) to generate a garment image that naturally fits the user's body shape.

[1429] Step 7: Generate outfit suggestions

[1430] The server creates multiple coordination suggestions based on the generated wearing images.

[1431] The server stores these coordination suggestions as image data and converts them into a user interface.

[1432] Step 8: Emotion recognition and regulation with the emotion engine

[1433] The server uses an emotion engine to analyze the user's facial expressions and voice and recognize their emotional state in real time.

[1434] The server adjusts the outfit suggestions based on the user's emotional data: if the user expresses positive emotions, it suggests a similar style, and if the user expresses negative emotions, it suggests a different style.

[1435] Step 9: View outfit suggestions

[1436] The server transmits image data of the coordinated outfit proposal to the terminal.

[1437] The device displays coordination suggestions to the user.

[1438] Step 10: Selecting Users

[1439] The user selects their favorite outfit from the displayed outfits.

[1440] The terminal transmits the selected coordinate information to the server.

[1441] Step 11: Check size and color

[1442] The server reconfirms the details of the selected garment (size, color, material).

[1443] Check whether the server matches your preferences and offer alternatives if necessary.

[1444] Step 12: Complete your purchase

[1445] The user confirms the final outfit and completes the purchase.

[1446] The server receives the purchase information and places an order with the affiliated online shopping site.

[1447] The server sends an order completion notification to the terminal and displays it to the user.

[1448] Through these successive processing steps, the system provides a safe and secure online clothing shopping environment for users. In addition to suggesting outfits based on the user's body shape and preferences, it also enhances user satisfaction through emotion recognition using an emotion engine.

[1449] Example 2

[1450] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1451] When shopping online, the process of selecting fashion items that suit a user's body type and preferences is time-consuming and laborious. Furthermore, the lack of a system that can provide more appropriate suggestions based on the user's emotional state makes it difficult to provide a satisfying shopping experience. Therefore, there is a need for technology that can recognize a user's emotions in real time and adjust outfit suggestions accordingly.

[1452] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1453] In this invention, the server includes means for a user to upload a full-body photograph, means for analyzing the full-body photograph and acquiring the user's body shape information, means for inputting data on the user's fashion style and preferences, means for generating a plurality of wearing images based on the body shape information and fashion style data, and means for recognizing the user's emotions and adjusting coordination suggestions based thereon. This not only makes it easier for the user to select fashion items that suit their body shape and preferences, but also enables a more satisfying online shopping experience by making suggestions based on the user's emotional state.

[1454] A "full-body photo" refers to a photo showing the user's entire body, and is used to obtain body shape information.

[1455] "Body shape information" refers to information about the shape of a user's biological body, including features such as the user's face, shoulder width, waist, hips, and leg length, as well as supplemental data (such as height and weight).

[1456] "Fashion style" refers to attributes such as design, color, material, and brand of clothing and accessories based on the user's preferences.

[1457] A "generative adversarial network (GAN)" is an algorithm that generates data by having two neural networks compete with each other, and is a model that demonstrates particularly strong performance in image generation.

[1458] An "emotion engine" refers to a system that analyzes a user's facial expressions and voice to recognize their emotional state in real time.

[1459] "Coordination suggestions" refer to multiple outfit images and sets generated by the server based on the user's body shape information, fashion style, and emotional data.

[1460] "Purchase Checkout" means the steps required to ultimately purchase the Products selected by the User, including the online payment process.

[1461] "User profile" refers to a database that integrates a user's body shape information, fashion style, emotional data, etc.

[1462] This invention is an online shopping support system in which users upload full-body photos, analyze the images, and a generation AI proposes multiple outfits based on the user's body shape and preferences. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to propose even more appropriate outfits based on the user's emotional state. This system is realized through the interaction of the user, terminal, server, and emotion engine.

[1463] Uploading and analyzing user images

[1464] First, the user takes a full-body photo and uploads it from their device to the server. The server then uses an AI image analysis engine (such as Google Cloud Vision API) to obtain the user's body shape information based on the received full-body photo. The server identifies feature points such as the face, shoulder width, waist, hips, and leg length, and extracts them as numerical data. If the user has entered height and weight data, this information is also supplemented. The results of this analysis are stored in a user profile database.

[1465] Enter your fashion style and preferences

[1466] Next, users enter data such as their preferred fashion style, color, material, and brand on the app or website, which is then sent from their device to a server, which stores this data and combines it with the user's body information to create a detailed user profile.

[1467] Wearing image generation and coordination suggestions

[1468] The server filters matching clothing from its internal fashion database based on user profile data and fashion style data. This filtering extracts items that fit the user's body shape and preferred fashion style. Based on the extracted items, the server uses a generative AI model (e.g., StyleGAN) to generate synthetic images. This process utilizes a generative adversarial network (GAN) to ensure that the clothing fits naturally based on the user's body shape information. The server generates multiple coordination patterns and saves them as images.

[1469] Emotion engine recognizes user emotions

[1470] One of the features of this system is that it incorporates an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and voice to determine their emotional state. When the user selects an outfit, the device's camera and microphone are used to collect emotional data in real time. Based on this data, the server recognizes the user's emotions, such as joy, surprise, and sadness, in real time.

[1471] Emotion-based coordination suggestions

[1472] The server dynamically adjusts outfit suggestions based on emotion data obtained from the emotion engine. If a user expresses positive emotions, the server records their emotion data and prioritizes suggestions for similar styles. On the other hand, if a user expresses negative emotions, the server suggests different styles and colors.

[1473] Displaying outfit suggestions and final selection

[1474] The server converts the adjusted outfit suggestions into a user interface. The device visually displays the received outfit suggestions to the user. The user selects their favorite outfit from the displayed outfits and sends the selection from the device to the server. The server then reconfirms the details of the selected garment (size, color, material) to ensure that the selection meets the user's expectations.

[1475] Completing the purchase process

[1476] Finally, the user completes the purchase process based on the finalized outfit. The server then confirms the purchase information and places an order with the affiliated online shopping site. Once the order is complete, the server sends an order completion notification to the terminal.

[1477] Specific examples

[1478] For example, suppose User B submits a full-body photo and inputs data indicating that he or she prefers casual-style blue denim jackets. The server analyzes User B's body shape information and generates multiple outfits that include casual-style blue denim jackets. At this time, the emotion engine analyzes User B's facial expressions and voice, and prioritizes suggestions of items that elicit a positive response. User B then selects his or her favorite outfit from the list and completes the purchase process.

[1479] Prompt Sentence Examples

[1480] Here are some example prompts to input to a generative AI model:

[1481] "Please explain the process of an online shopping support system that uploads a full-body photo of the user and obtains body shape information. It then generates outfit images based on the user's preferred styles and uses emotional data to suggest optimal outfits."

[1482] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1483] Step 1:

[1484] The user takes a full-body photo and uploads the image to the online shopping support system using a terminal.

[1485] The input is a full-body photo of the user, and the output is the image data being sent to the server. Specifically, the user takes a full-body photo with the camera on their smartphone or computer, and then uploads the image via the system's upload function.

[1486] Step 2:

[1487] The server analyzes the received full-body photo.

[1488] The input is an uploaded full-body photo, and the output is numerical data of the user's body shape information (face, shoulder width, waist, hips, leg length, etc.). Specifically, the server uses an AI image analysis engine (e.g., Google Cloud Vision API) to analyze the full-body photo and identify the body's feature points.

[1489] Step 3:

[1490] The server completes the user's height and weight data and stores the body shape information in a user profile database.

[1491] The input is the analyzed body shape information and supplementary data (height and weight), and the output is a detailed user profile. Specifically, the server further adjusts the body shape information based on the supplementary data and stores it in a database.

[1492] Step 4:

[1493] Users enter their preferences for fashion style, color, material, brand, etc. on the app or website.

[1494] The input is data about the user's fashion style, and the output is sending this data to the server. Specifically, the user selects and inputs their preferred fashion attributes through the interface.

[1495] Step 5:

[1496] The server combines the received fashion style data with the user's body shape information to create a detailed user profile.

[1497] The input is fashion style data and body shape information, and the output is an integrated user profile. Specifically, the server integrates these data and stores them in a database as individual profiles.

[1498] Step 6:

[1499] The server filters matching clothing items from an internal fashion database based on the user profile.

[1500] The input is a detailed user profile, and the output is a list of clothing items that match the user. Specifically, the server uses a filtering algorithm to select clothing items that match each attribute based on the profile.

[1501] Step 7:

[1502] The server uses a generative AI model (e.g., StyleGAN) to generate a synthetic image that applies the filtered clothing items to the user's photo.

[1503] The input is the filtered clothing items and the user's photo, and the output is a synthetic image. Specifically, the server uses a generative adversarial network (GAN) to generate a synthetic image in which the clothing fits naturally based on the user's body shape information.

[1504] Step 8:

[1505] The server generates a plurality of coordinate patterns and stores them as images.

[1506] The input is a composite image, and the output is images of multiple coordinate patterns. Specifically, the server creates different coordinate patterns based on the generated composite image and saves them as image data.

[1507] Step 9:

[1508] The device uses a camera and microphone to collect the user's facial expressions and voice in real time as they view the suggested outfits.

[1509] The input is the user's real-time facial expression and voice data, and the output is collected emotion data. Specifically, the device records the user's browsing behavior with a camera and microphone and sends the data to the emotion engine.

[1510] Step 10:

[1511] The server adjusts the coordination suggestions based on the emotional data obtained from the emotion engine.

[1512] The input is the user's emotional data, and the output is tailored outfit suggestions. Specifically, the server prioritizes items that show positive emotions and excludes items that show negative reactions.

[1513] Step 11:

[1514] The server converts the adjusted coordination proposal for a user interface, and the terminal visually displays the received proposal to the user.

[1515] The input is the adjusted coordinate proposal, and the output is the interface displayed to the user. Specifically, the server converts the adjusted information into an appropriate format and sends it to the terminal, which then displays it to the user.

[1516] Step 12:

[1517] The user selects their favorite outfit from the displayed outfits and transmits the selection to the server via their terminal.

[1518] The input is the user's selection, and the output is the selected outfit data. Specifically, the user operates the interface to select their favorite outfit.

[1519] Step 13:

[1520] The server then double-checks the selected garment details (size, color, material) to ensure they match the user's expectations.

[1521] The input is the details of the selected garment, and the output is the confirmation result. Specifically, the server checks the selection against the database again to check compatibility.

[1522] Step 14:

[1523] The user completes the purchase process based on the finalized outfit, and the server confirms the purchase information and then places an order with the affiliated online shopping site.

[1524] The input is the purchase procedure information, and the output is the order data. Specifically, the user completes the purchase procedure, and the server processes the order. Once the order is completed, the server sends an order completion notification to the terminal.

[1525] (Application example 2)

[1526] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1527] In today's online shopping environment, users face the challenge of finding the right fashion items. Furthermore, the lack of coordination suggestions tailored to the user's body type and individual preferences, as well as the lack of personalized suggestions that take into account the user's emotional state, reduces satisfaction with the shopping experience.

[1528] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to upload a full-body photograph, means for analyzing the full-body photograph and acquiring the user's body shape information, means for inputting data related to the user's fashion style and preferences, means for generating a plurality of wearing images based on the body shape information and fashion style data, means for generating a plurality of outfit suggestions, means for analyzing the user's emotional state and acquiring emotion data, means for adjusting the outfit suggestions based on the emotion data, means for displaying the outfit suggestions to the user, and means for completing a purchase based on the outfit selected by the user. This makes it easier for users to find outfits that suit their body shape and preferences, and by receiving suggestions based on their emotions, users can have a more satisfying online shopping experience.

[1529] "Full Body Photo" means a photograph of the User's entire body, which must include the User's entire body from face to toe.

[1530] "Analysis" refers to the process of analyzing image data using a computer and extracting specific information, such as identifying a user's body shape information from an image.

[1531] "Body Information" refers to data related to a user's physique and shape, including measurements and features such as face, shoulder width, waist, hips, and leg length.

[1532] "Fashion style" refers to the clothing designs and styles preferred by a user, including information about preferences for color, material, design, brand, etc.

[1533] "Wearing image" refers to an image of the user wearing the clothing, generated based on the user's body shape information. This visualizes how the user will look when actually wearing the clothing.

[1534] "Coordination suggestions" refer to multiple fashion combinations selected from wearing images generated based on the user's body shape information and fashion style.

[1535] "Emotional state" refers to data related to emotions analyzed from the user's facial expressions, tone of voice, etc. Specifically, it includes emotions such as joy, anger, sadness, and happiness.

[1536] "Emotional data" refers to data that quantifies or classifies an emotional state, which allows for suggestions and adjustments based on the user's emotions.

[1537] "Adjustment" refers to changing the suggestions based on specific criteria or conditions. In this case, it refers to changing the outfit suggestions based on the user's emotional data.

[1538] "Purchase procedure" refers to the series of actions required to actually purchase the product selected by the user, including product selection, payment, and delivery procedures.

[1539] "System" refers to the entire device or program that combines multiple means, including various functions necessary for users to comfortably shop online.

[1540] This invention is an online shopping support system that allows users to upload full-body photos and performs image analysis to suggest multiple outfits based on the user's body shape and preferences. By combining it with an emotion engine that recognizes the user's emotions, it is possible to suggest even more appropriate outfits based on the user's emotional state. This system is realized through the interaction of the user, terminal, server, and emotion engine.

[1541] Uploading and analyzing user images

[1542] First, the user takes a full-body photo and uploads it to the server from their device. The server then uses an AI image analysis engine to obtain the user's body shape information based on the received full-body photo. Specifically, image analysis technologies such as Google Cloud Vision API are used to identify feature points such as the face, shoulder width, waist, hips, and leg length, and extract them as numerical data. The results of this analysis are stored in a user profile database.

[1543] Enter your fashion style and preferences

[1544] Next, users enter data such as their preferred fashion style, color, material, and brand on the app or website, and the data is sent from the device to a server, which stores this data and combines it with the user's body shape information to create a user profile.

[1545] Wearing image generation and coordination suggestions

[1546] The server filters matching clothing from its internal fashion database based on user profile data and fashion style data. This filtering extracts items that fit the user's body shape and preferred fashion style. Based on the extracted items, the server uses a generative AI model to generate synthetic images to apply to the user's photo. This process utilizes a generative adversarial network (GAN) to ensure that the clothing fits naturally based on the user's body shape information. The server then generates multiple outfit patterns and saves them as images.

[1547] Emotion engine recognizes user emotions

[1548] One of the features of this system is the addition of an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and voice to determine their emotional state. Specifically, it uses Microsoft Azure's Emotion API to recognize emotions such as joy, surprise, and sadness in real time from the user's facial expressions and tone of voice when selecting an outfit or viewing each suggestion.

[1549] Emotion-based coordination suggestions

[1550] Once the user's emotions are recognized, the server adjusts the outfit suggestions based on that information. For example, if a user expresses positive emotions toward the outfits displayed, the server records the user's emotional data and prioritizes suggestions for similar styles. On the other hand, if the user expresses negative emotions, the system adjusts to suggest different styles or colors. The system also reviews the user interface, taking into account the results of the emotion analysis.

[1551] Displaying outfit suggestions and final selection

[1552] The server converts the adjusted outfit suggestions into a user interface for display to the user. The device visually displays the received outfit suggestions to the user. The user selects their favorite outfit from the displayed outfits and sends the selection from the device to the server. The server reconfirms the details of the selected garment (size, color, material) to ensure that the selection meets the user's expectations.

[1553] Completing the purchase process

[1554] Finally, the user completes the purchase process based on the finalized outfit. The server then confirms the purchase information and places an order with the affiliated online shopping site. Once the order is complete, the server sends an order completion notification to the terminal.

[1555] Specific examples

[1556] For example, suppose User B sends a full-body photo of himself and inputs that he likes casual-style blue denim jackets. The server analyzes User B's body shape information and generates multiple outfits that include casual-style blue denim jackets. At this time, the emotion engine analyzes User B's facial expressions and voice and prioritizes suggestions of items that elicit a positive reaction. User B then selects his favorite outfit from the suggestions and proceeds with the purchase. This entire process is seamless, reducing the stress of online shopping for User B.

[1557] Prompt Sentence Examples

[1558] You could provide a generative AI model with a prompt like this:

[1559] "The system identifies feature points such as face, shoulder width, waist, hips, and leg length from a user's full-body photo and extracts them as numerical data."

[1560] "The system uses facial expressions and voice data to determine the emotional state of the user regarding the outfit suggestions. If the emotion is positive, the suggestion is kept, and if it is negative, a new suggestion is generated."

[1561] This system allows users to confidently select outfits that suit their body type and preferences, and also receives suggestions that reflect their emotions, allowing them to enjoy a more satisfying online shopping experience.

[1562] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1563] Step 1:

[1564] The user takes a full-body photo and uploads it to the server from their device.

[1565] Input: A full-body photo of the user

[1566] Data processing: Uploading image files

[1567] Output: Image file saved on the server

[1568] Step 2:

[1569] Based on the full-body photo received by the server, the AI ​​image analysis engine is used to obtain the user's body shape information. Specifically, image analysis technology is used to identify feature points such as face, shoulder width, waist, hips, and leg length, and extract them as numerical data. The Google Cloud Vision API is used.

[1570] Input: A full-body photo stored on the server

[1571] Data processing: Identifying feature points such as face, shoulder width, waist, hips, and leg length and extracting numerical data

[1572] Output: Numerical data of body shape information

[1573] Step 3:

[1574] Users enter data such as their preferred fashion style, color, material, and brand on the app or website, and the data is sent from their device to a server.

[1575] Input: Data such as user's fashion style, color, material, brand, etc.

[1576] Data processing: Acquiring and saving user input data

[1577] Output: Fashion style data

[1578] Step 4:

[1579] The server filters matching garments from an internal fashion database based on the user profile data and fashion style data.

[1580] Input: Body shape information and fashion style data

[1581] Data Processing: Filtering Matching Clothing from a Fashion Database

[1582] Output: A list of matching clothing items

[1583] Step 5:

[1584] Based on the extracted items, the server uses a generative AI model to generate a synthetic image to apply to the user's photo, using a generative adversarial network (GAN).

[1585] Input: A list of matching clothing items, a full-body photo of the user

[1586] Data processing: Generating synthetic images using generative AI models (using GANs)

[1587] Output: Images of multiple outfit suggestions

[1588] Step 6:

[1589] The server analyzes the user's emotional state and acquires emotional data. Specifically, it analyzes the user's facial expressions and voice to recognize emotions such as joy, surprise, and sadness. It uses Microsoft Azure's Emotion API.

[1590] Input: User's facial expressions and voice data

[1591] Data processing: Emotion analysis of facial expressions and voice data

[1592] Output: Emotion data

[1593] Step 7:

[1594] The server adjusts coordination suggestions based on the emotion data, preferentially displaying suggestions based on positive emotion data and changing suggestions based on negative emotion data.

[1595] Input: Coordination proposal images, emotion data

[1596] Data manipulation: prioritizing or modifying suggestions based on sentiment data

[1597] Output: Image of the adjusted outfit suggestions

[1598] Step 8:

[1599] The server converts the adjusted coordination proposal into a user interface and transmits it to the terminal, which visually displays the received coordination proposal to the user.

[1600] Input: Image of the coordinated outfit suggestion

[1601] Data processing: conversion to user interface

[1602] Output: Coordination suggestion image displayed on the user's device

[1603] Step 9:

[1604] The user selects their favorite outfit from the displayed outfits and sends the selection from the terminal to the server.

[1605] Input: User's selected outfit

[1606] Data processing: Send selected data

[1607] Output: Selection data saved on the server

[1608] Step 10:

[1609] The server reviews the selected garment details (size, color, material) to ensure the selection meets the user's expectations.

[1610] Input: Detailed information about the selected outfit

[1611] Data processing: Rechecking detailed information

[1612] Output: Verification result

[1613] Step 11:

[1614] The user completes the purchase process based on the coordinated outfit they have decided on. After the server confirms the purchase information, it places an order with the affiliated online shopping site. Once the order is complete, the server sends an order completion notification to the terminal.

[1615] Input: Purchase information for the confirmed outfit

[1616] Data processing: verifying purchase information and processing orders

[1617] Output: Order completion notification

[1618] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1619] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1620] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1621] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1622] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1623] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1624] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1625] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1626] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1627] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1628] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1629] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1630] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1631] 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.

[1632] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1633] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for execu...

Claims

1. A means for users to upload full-body photos; means for analyzing the whole-body photograph and acquiring body shape information of the user; means for inputting data relating to the user's fashion style and preferences; means for generating a plurality of wearing images based on the body shape information and fashion style data; means for generating a plurality of coordination suggestions from the wearing images; means for displaying said coordination suggestions to a user; A means for completing a purchase based on the outfit selected by the user; A system including:

2. The system of claim 1 , wherein the means for generating the plurality of worn images uses a generative adversarial network to generate the synthetic images.

3. 2. The system according to claim 1, further comprising means for reconfirming the size and color based on the coordinates selected by the user.

Images