System

Abstract

A system is provided.SOLUTION: A system, comprising: means for inputting height data; means for uploading a whole body image; means for selecting product data; means for transmitting the height data and the whole body image to a server; means for generating a list of available products and transmitting the list to a terminal; means for transmitting data of a selected product to the server; means for synthesizing an image of the product with the whole body image at an appropriate size and position to generate a fitting image; and means for transmitting the generated fitting image to the terminal.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] A common problem with online shopping is that users cannot actually try on products, making it difficult to determine whether the size and style of a product suit them. This leads to an increase in returns and exchanges of purchased products, resulting in a waste of time and money. Furthermore, the lack of an actual try-on experience can discourage users from making a purchase. There is a need to address these issues and improve online shopping satisfaction. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for a user to input height data and upload a full-body image, and transmits the data to a server. The server generates a list of available products and transmits it to a terminal. The user selects product data, and the terminal transmits the data to the server. The server includes a means for overlaying product images on the full-body image at an appropriate size and position to generate try-on images. The generated try-on images are transmitted to the terminal, which displays the try-on images and provides an option to display try-on images for each body part selected by the user. An interface is also provided for sharing the try-on images with family and friends. This allows users to have a visual experience as if they were trying on products online, allowing them to confirm the suitability of their product selection and improving their online shopping satisfaction.

[0006] "Height data" refers to numerical information about a user's body height, and is used by the system to determine the size and suitability of products.

[0007] A "full-body image" is an image that captures the user's entire body in one photograph, and is the image that serves as the basis for the system to generate a try-on image.

[0008] "Product data" is information about products such as clothing and accessories that a user wants to try on, and includes image data and attributes such as dimensions and style.

[0009] A "terminal" is a device operated by a user, such as a smartphone, tablet, or PC, and is a means for uploading full-body images, selecting products, displaying images of items being tried on, and so on.

[0010] "Server" means the central computing system that processes and stores data provided by users, generates product listings, and synthesizes try-on images.

[0011] A "product list" is a collection of product information generated by a server and sent to a terminal, providing a list of items that the user can select.

[0012] "Display options" is a function that allows the user to view try-on images from various perspectives and ranges, including full-body display, upper-body display, lower-body display, and bust-up display.

[0013] The "sharing interface" refers to the operation screen and functions for sharing try-on images with family and friends, and is a means for sending images via applications such as LINE or email.

[0014] A "try-on image" is an image generated by the server in which product data is combined with a full-body image of the user to make it appear as if the user is actually trying on the item.

[0015] "Synthesis" is the process of placing a product image in an appropriate size and position on a full-body image of the user, blending them seamlessly together. [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] The present invention is a system that provides users with a visual experience of trying on products when shopping online. In this system, users input their height data, upload a full-body image, and select products they are interested in. The server then generates a try-on image tailored to the user's body type and displays it on the terminal.

[0038] System operation explanation

[0039] User Preferences

[0040] First, the user enters their height data into the system. This height data is used to obtain a fitting image of the correct size. Next, the user follows a specified guide to take a full-body image and uploads it to the server via their device. This full-body image becomes the base data for generating fitting images.

[0041] Processing on the server

[0042] The server stores the received height data and full-body image, then sends a list of available products to the device, including a wide range of items such as clothes, hats, shoes, bags, and accessories.

[0043] Product selection and data transmission

[0044] The user selects the product of interest from the product list on the terminal. The selected product data is then sent back to the server via the terminal. The selected product data includes product ID, category information, etc., and is used to generate try-on images on the server side.

[0045] Generation of try-on images

[0046] The server composites the selected product image onto the user's full-body image at the appropriate size and position. To do this, it first adjusts the scale of the product image based on the user's height data. Then it positions the product appropriately to fit the user's body shape. The composition algorithm adjusts the size and shape to recreate a natural fit of the clothing. It also handles multiple selected products simultaneously, adjusting the position and size of each.

[0047] Displaying try-on images

[0048] Once the try-on images are generated, the server sends them to the device, which then displays them. The user can view the images from various perspectives, including full-body, upper-body, lower-body, and bust-up views.

[0049] Sharing try-on images

[0050] The device also provides an interface for sharing try-on images with family and friends, allowing users to easily send try-on images via applications such as LINE and email.

[0051] Specific examples

[0052] For example, let us consider the case where a female user who is 160 cm tall uploads a full-body image of herself and tries on a red dress, black heels, and gold earrings.

[0053] First, the user inputs and uploads their height data (160cm) and a full-body image to the device. The device then sends this data to the server. The server receives and stores the data. The server then sends a list of available products to the device.

[0054] The list includes a red dress, black heels, and gold earrings. The user selects these items and the selection is sent to the server.

[0055] The server overlays the selected item onto the user's full-body image. The dress size is adjusted to fit the user's height of 160cm, and the black heels and gold earrings are also placed in the appropriate positions. The synthesized try-on image is sent to the terminal and displayed to the user. The user can check the try-on image using options such as full-body view or upper-body view.

[0056] Finally, users can share the try-on images with friends via LINE and get their opinions. Through this process, users can get the same experience as actually trying on the items. This allows them to check the suitability of the items they are purchasing, making online shopping more satisfying.

[0057] In this way, the present invention provides a concrete solution to solve the problems of online shopping and improve user satisfaction.

[0058] The processing flow will be explained below.

[0059] Step 1:

[0060] The user inputs their height and follows the guide to take a full-body image, which is then uploaded to the device.

[0061] Step 2:

[0062] The device sends the height data entered by the user and the uploaded full-body image to the server.

[0063] Step 3:

[0064] The server stores the received height data and full-body image, generates a list of products that can be offered to the user, and sends the list to the terminal.

[0065] Step 4:

[0066] The terminal displays the product list received from the server to the user, who then selects the product they wish to try on from the product list.

[0067] Step 5:

[0068] The terminal transmits data on the product selected by the user, such as the product ID and category information, to the server.

[0069] Step 6:

[0070] The server starts the process of combining the selected product image with the user's full-body image in an appropriate size and position.

[0071] First, the product image is scaled based on the height data, and then appropriately positioned relative to the full-body image.

[0072] When multiple products are selected, the images are composited while adjusting the position and size of each product appropriately.

[0073] Step 7:

[0074] The try-on image generated by the server is sent to the terminal.

[0075] Step 8:

[0076] The terminal displays the try-on images received from the server to the user, who can check the images from various perspectives, such as full-body view, upper-body view, lower-body view, and bust-up view.

[0077] Step 9:

[0078] The device provides an interface for users to share try-on images with family and friends via applications such as LINE or email.

[0079] Example 1

[0080] 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."

[0081] When selecting products online, there is a demand for a visual experience equivalent to the try-on experience in a physical store. However, conventional systems have difficulty generating try-on images that fit the user's body type, resulting in low user satisfaction with product selection. Furthermore, functions such as sharing try-on images or partial display are not adequately provided, which prevents users from increasing their motivation to purchase. This leaves the challenge of improving the user experience and product purchase rates.

[0082] 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.

[0083] In this invention, the server includes means for a user to input height data, means for a user to upload a full-body image, means for a user to select product data, means for a terminal to send the height data and full-body image to the server, means for the server to generate a list of available products and send it to the terminal, means for the terminal to send data on the selected products to the server, means for the server to combine product images with the full-body image at an appropriate size and position and generate try-on images using a generative artificial intelligence model, means for sending the generated try-on images to the terminal, and means for the terminal to display and share the try-on images. This allows users to easily obtain try-on images that suit their body type, and further increases their motivation to purchase by sharing try-on images and using the partial display function.

[0084] "Height data" is data that the user inputs as a numerical value representing his or her height, and is information that serves as a reference for generating try-on images.

[0085] The "full-body image" is image data of the user's entire body, and serves as the basis for the try-on image.

[0086] "Product data" refers to information about products that can be selected in online shopping, and includes product IDs, categories, image URLs, etc.

[0087] A "terminal" is an electronic device operated by a user, which has the functions of inputting and displaying data and communicating with a server.

[0088] The "server" is a remote computer system that receives and stores user input data, generates product lists, and synthesizes try-on images.

[0089] A "product list" is a list of detailed information about available products that is generated by the server and sent to the terminal.

[0090] A "try-on image" is an image generated by combining a full-body image of the user with a product image, and provides a realistic try-on experience.

[0091] A "generative artificial intelligence model" is an algorithm used to synthesize a user's full-body image with a product image, and has the ability to position the product in an appropriate size and position.

[0092] The "sharing means" is an interface for sharing the generated try-on images with other people, and has the function of easily sending images via social networking sites, email, etc.

[0093] The present invention is a system that provides users with a visual experience of trying on products when shopping online. In this system, users input their height data, upload a full-body image, and select products they are interested in. The server then generates a try-on image tailored to the user's body type and displays it on the terminal.

[0094] First, the user enters their height data into the system. This data is used to obtain fitting images of the correct size. Next, the user follows a specified guide to take a full-body image and uploads it to the server via their device. This full-body image becomes the base data for generating fitting images.

[0095] The server stores the received height data and full-body image and sends a list of available products to the device. The product list includes information on a wide range of products, including clothing, hats, shoes, bags, and accessories. The user selects products of interest from the product list on the device. The selected product data is sent back to the server via the device and used to generate try-on images.

[0096] The server then composites the selected product image onto the user's full-body image at the appropriate size and position. This involves first scaling the product image based on height data and then positioning the product appropriately to fit the user's body shape. Using generative AI models, the server adjusts the size and shape of clothing and accessories to recreate a natural fit. It also simultaneously accommodates multiple selected products, adjusting the position and size of each.

[0097] The generated try-on images are sent from the server to the device. The device displays the received try-on images, allowing the user to view them from various perspectives, such as full-body view, upper body view, lower body view, and bust-up view. The device also provides an interface for sharing the try-on images with family and friends. Using this interface, users can easily send the try-on images via applications such as social networking sites and email.

[0098] As a specific example, let's consider the case where a female user who is 160 cm tall uploads a full-body image of herself and tries on a red dress, black heels, and gold earrings. The user inputs and uploads her height data (160 cm) and a full-body image she has taken to her device, and the device sends this data to the server. The server receives and stores the data, and sends a list of available products to the device. The list includes a red dress, black heels, and gold earrings, and the user selects these products, and the selection data is sent to the server.

[0099] The server overlays the selected product onto the user's full-body image, adjusts the dress size to fit the user's height of 160cm, and places the black heels and gold earrings in the appropriate positions. The try-on image generated through the synthesis is sent to the user's device and displayed to the user. The user can check the try-on image using options such as full-body or upper-body view. Finally, the user can share the try-on image with friends via LINE and get their opinions. Through this process, the user can experience the feeling of actually trying on the clothes.

[0100] An example of a prompt is as follows:

[0101] "I uploaded a full-body photo of me, 160cm tall. Try on a red dress, black heels, and gold earrings."

[0102] "A female user who is 160cm tall has selected a red dress, black heels, and gold earrings. Please composite these items into a full-body image."

[0103] In this way, the present invention provides a concrete solution to solve the problems of online shopping and improve user satisfaction.

[0104] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0105] Step 1:

[0106] The user inputs height data.

[0107] Input: The user inputs their height (e.g., 160 cm) on the terminal screen.

[0108] Output: The entered height data is saved on the device.

[0109] Specific operation: When the user enters their height in the input field and presses the confirmation button, the entered data is saved in a variable within the device.

[0110] Step 2:

[0111] The user takes a full-body image and uploads it to the server via the terminal.

[0112] Input: The user takes a full-body image using a smartphone or camera and uploads the image to the device.

[0113] Output: The uploaded whole-body image data is saved on the device.

[0114] Specific operation: The user takes a full-body image using the camera app and presses the "upload" button, which saves the image file to the device.

[0115] Step 3:

[0116] The terminal transmits height data and a full-body image to the server.

[0117] Input: Height data and whole-body image data stored on the device.

[0118] Output: Height data and whole body image data sent to the server.

[0119] Specific operation: The device detects the "Send" button and sends the saved height data and full-body image to the server using the HTTPS protocol. The data is encrypted and received by the server.

[0120] Step 4:

[0121] The server stores the received data and generates a list of available products.

[0122] Input: Height data and whole body image data sent to the server.

[0123] Output: The generated product list.

[0124] Specific operation: The server stores the received data in a database, identifies the user, and generates a list of available products from the associated product database.

[0125] Step 5:

[0126] The server sends the product list to the terminal.

[0127] Input: Generated product list.

[0128] Output: The product list sent to the terminal.

[0129] Specific operation: The server sends a product list to the terminal, and the terminal uses middleware to receive the data necessary to display the list.

[0130] Step 6:

[0131] The user uses the terminal to select the product they want to try on from the product list.

[0132] Input: Product list.

[0133] Output: Selected product data.

[0134] Specific operation: The user selects a product of interest from the product list displayed on the device screen and presses the "Select" button. The selected product data is temporarily saved on the device.

[0135] Step 7:

[0136] The terminal transmits the selected product data to the server.

[0137] Input: Selected product data.

[0138] Output: The product data sent to the server.

[0139] Specific operation: The terminal sends product data to the server via an HTTP request, and the server receives and processes it.

[0140] Step 8:

[0141] The server synthesizes the product image with the full-body image at an appropriate size and position to generate a try-on image.

[0142] Input: whole body image data, height data, product data.

[0143] Output: The generated try-on image.

[0144] How it works: The server uses the generated AI model to analyze the full-body image and product image, adjust the scale of the product image based on the customer's height, and generate a try-on image by naturally overlaying the scaled product image on the full-body image.

[0145] Step 9:

[0146] The server transmits the generated try-on image to the terminal.

[0147] Input: Generated try-on images.

[0148] Output: Try-on image sent to the device.

[0149] Specific operation: The server encodes the try-on image and sends it to the device as an HTTP response. The device stores the received image in temporary memory.

[0150] Step 10:

[0151] The device displays the try-on image.

[0152] Input: Try-on images sent from the server.

[0153] Output: Try-on image shown to the user.

[0154] Specific behavior: The device displays try-on images within the application and loads a UI that gives the user viewing options such as full body, upper body, lower body, and bust-up.

[0155] Step 11:

[0156] The device provides an interface for sharing try-on images.

[0157] Input: Try-on image.

[0158] Output: Shared try-on images.

[0159] Specific operation: The device detects that the share button has been pressed and displays an interface for transferring images to social media or email applications. The user selects a destination and the image is shared.

[0160] (Application example 1)

[0161] 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."

[0162] When shopping online, it is difficult to check the fit and appearance of products before purchasing them because customers cannot actually try them on. This often leads to dissatisfaction with product size or design after purchase. Furthermore, virtual stores, in particular, are required to provide a level of realism that makes users feel as if they are actually trying on products. Furthermore, generating and displaying try-on images in real time is technically challenging, making it difficult for existing systems to address this issue. New technologies are needed to address this issue and improve users' online shopping experiences.

[0163] 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.

[0164] In this invention, the server includes means for inputting height data, means for uploading a full-body image, means for selecting product data, means for transmitting the height data and the full-body image to the server, means for the server to generate a list of available products and transmit it to the terminal, means for the terminal to transmit data on the selected products to the server, means for the server to synthesize product images with the full-body image at appropriate sizes and positions to generate try-on images, means for transmitting the generated try-on images to the terminal, means for the terminal to display and share the try-on images, means for using a deep learning model to generate and display try-on images in real time, and means for synthesizing the full-body image and product images using the deep learning model. This allows users to experience trying on products in real time in a virtual store.

[0165] "Height data" is information relating to the height of the user, and is data used to appropriately adjust the scale of the product when generating a try-on image.

[0166] A "full-body image" is a photograph including the user's entire body, and serves as basic data for generating try-on images.

[0167] "Product Data" means data containing detailed information about products available for purchase online, including images, sizes, prices, etc.

[0168] The "server" is a computer system that stores and processes data received from users, and is a device that generates try-on images and provides product lists.

[0169] A "deep learning model" is an artificial intelligence model that uses deep learning algorithms to analyze data and generate try-on images.

[0170] A "product list" is data that lists products that can be selected by the user, and is sent from the server to the terminal.

[0171] The "try-on image" is an image showing a virtual try-on state, generated by combining a selected product image with a full-body image of the user.

[0172] A "terminal" is a device used by a user to input data and check try-on images, and includes smartphones, personal computers, etc.

[0173] "Synthesis" is the process of combining a full-body image of the user with an image of the product in the appropriate size and position to recreate a natural fitting look.

[0174] "Real-time" refers to immediate response to user operations. In the case of try-on image generation, this means that try-on images are generated and displayed immediately every time a user selects an item.

[0175] The system of the present invention generates try-on images in real time using height data and a full-body image to provide a visual experience that makes users feel as if they are trying on products during online shopping. To achieve this, the system uses a deep learning model to generate try-on images.

[0176] System configuration

[0177] Hardware

[0178] Device: A smartphone or computer used by the user to input height data, upload full-body images, and view try-on images.

[0179] Server: A computer system that stores data sent by users, generates try-on images using a deep learning model, and sends them to the device.

[0180] software

[0181] Programming language: Python

[0182] Image processing library: OpenCV

[0183] Deep Learning Framework: TensorFlow

[0184] Image manipulation library: PIL (Pillow)

[0185] System Operation

[0186] User operations

[0187] 1. The user enters their height data on the terminal.

[0188] 2. The user takes a full-body image using a camera and uploads it to the server via the device.

[0189] 3. The user selects a product from the list of available products sent by the server.

[0190] Server Processing

[0191] 1. The server stores the received height data and full-body image.

[0192] 2. Based on the data of the selected product, the server uses a deep learning model to synthesize a full-body image of the user and an image of the product, generating a try-on image that reproduces a natural fit.

[0193] 3. The generated try-on images are sent to the device in real time.

[0194] Terminal display

[0195] 1. The device immediately displays the received try-on images. The user can view the images using options such as full-body view, upper-body view, and lower-body view.

[0196] 2. Users can share try-on images with family and friends via LINE, email, etc.

[0197] Specific examples

[0198] For example, consider a case where a user who is 160 cm tall wants to try on a red dress. The user first enters their height data and uploads a full-body image to their device. Next, they select a red dress from a list of available products. The server receives this data and uses a deep learning model to synthesize the red dress onto the user's full-body image. This synthesized try-on image is sent to the user's device in real time and displayed. The user can review this image and, if necessary, share it with friends to get their opinions.

[0199] Prompt Sentence Examples

[0200] "I want to see in real time how I look trying on a red dress at 160cm height."

[0201] This system can significantly improve the user's shopping experience in virtual stores. Real-time generation of try-on images can solve the problem of product selection and increase online shopping satisfaction.

[0202] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0203] Step 1:

[0204] The user inputs their height data into the terminal. The input data is saved as "user height" and used to adjust the scale of product images in subsequent processing.

[0205] Step 2:

[0206] The user takes a full-body image using a camera and uploads it to the server via their device. This full-body image ("user image") becomes the basic data for generating try-on images.

[0207] Step 3:

[0208] The server stores the received user height data and user image, which will be used in the next processing step.

[0209] Step 4:

[0210] The server generates a list of available products and sends it to the terminal, from which the user can select the products they wish to try on.

[0211] Step 5:

[0212] The product data selected by the user ("selected product") is sent to the server via the terminal again. The product data includes product images, sizes, category information, etc.

[0213] Step 6:

[0214] The server receives the selected product data and uses a deep learning model to synthesize the user image with the product image. First, the scale of the product image is adjusted based on the user's height data, and then the deep learning model is used to adjust the position to recreate a natural fit.

[0215] Step 7:

[0216] The server then sends the generated try-on images to the device, which are generated in real time and displayed instantly on the device.

[0217] Step 8:

[0218] The device displays the received fitting images. The user can check the fitting images using options such as full-body display, upper body display, and lower body display. The fitting images can also be shared with family and friends via LINE or email.

[0219] Step 9:

[0220] Users can view try-on images and make a final purchase decision, and can also get feedback from friends and family using the sharing feature.

[0221] These steps allow users to have a real-time visual experience that makes them feel as if they are actually trying on products in a virtual store.

[0222] 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.

[0223] This invention combines an emotion engine with a system that provides a visual experience that makes users feel as if they are trying on products online, recognizing the user's emotions and personalizing the try-on experience based on that emotion data. In this system, users input their height data, upload a full-body image, and select the products they want to try on. The server then generates try-on images tailored to the user's body type and displays them on the device. The emotion engine also recognizes the user's emotions, and that data is used within the system.

[0224] System operation explanation

[0225] User Preferences

[0226] First, the user enters their height data into the system. This height data is used to generate fitting images of the appropriate size for the user. Next, the user follows a specified guide to take a full-body image and uploads it to the server via their device. This full-body image becomes the basic data for generating fitting images.

[0227] Processing on the server

[0228] The server stores the received height data and full-body image, then sends a list of available products to the device, including a wide range of items such as clothes, hats, shoes, bags, and accessories.

[0229] Product selection and emotional data collection

[0230] The user selects a product they are interested in from a product list on their device. During this process, the emotion engine collects emotional data from the user's facial expressions, voice, operation patterns, etc., and sends this data to the server, where it is stored.

[0231] Generation of try-on images

[0232] The server composites the selected product image with the user's full-body image at the appropriate size and position. The composite process first scales the product image based on the user's height data, then applies an algorithm to position it appropriately relative to the full-body image. Even if multiple products are selected, the composite process adjusts the position and size of each product.

[0233] Emotion data analysis and image display

[0234] The server analyzes the emotional data provided by the emotion engine and adjusts the display content to match the user's emotions. For example, if the user is happy, it can add effects to the try-on images to emphasize that emotion. It also recommends other products that the user might be interested in based on the emotional data.

[0235] Displaying try-on images

[0236] The generated try-on images are sent from the server to the device, where they are displayed, allowing the user to check the details using options such as full-body view, upper-body view, lower-body view, and bust-up view.

[0237] Sharing try-on images

[0238] The device also provides an interface for sharing try-on images with family and friends, allowing users to easily share images via applications such as LINE and email.

[0239] Specific examples

[0240] For example, consider the case where a female user who is 160 cm tall uploads a full-body image of herself and tries on a red dress, black heels, and gold earrings.

[0241] First, the user inputs and uploads their height data (160cm) and a full-body photo to the device. The device then sends this data to the server. The server receives and stores the data, then sends a list of available items to the device. This list includes a red dress, black heels, and gold earrings.

[0242] The user selects one of these products, and the emotion engine collects emotional data from the user's facial expressions and voice during the selection process. The selection and emotional data are sent to the server, which then overlays the selected product onto the user's full-body image. The server adjusts the dress size to fit the user's height of 160cm, and also positions the black heels and gold earrings appropriately. The synthesized try-on image is sent from the server to the user's device and displayed to the user. The user can then review the try-on image using options such as full-body or upper-body display.

[0243] Finally, users share the try-on images with friends via LINE and receive their opinions. Through this process, the invention combines an experience that feels like trying on clothes with a personalized try-on service based on emotions.

[0244] In this way, the present invention provides a concrete solution to solve the problems of online shopping and improve user satisfaction.

[0245] The processing flow will be explained below.

[0246] Step 1:

[0247] The user inputs their height data and follows the guide to take a full-body image, which is then uploaded to the device.

[0248] Step 2:

[0249] The device sends the height data entered by the user and the uploaded full-body image to the server.

[0250] Step 3:

[0251] The server stores the received height data and full-body image, generates a list of products that can be offered to the user, and sends the list to the terminal.

[0252] Step 4:

[0253] The terminal displays the product list received from the server to the user, who then selects the product they wish to try on from the product list.

[0254] Step 5:

[0255] As the user selects a product, the emotion engine collects emotion data from the user's facial expressions, voice, and operation patterns. The device then transmits the selected product data and emotion data to the server.

[0256] Step 6:

[0257] The server composites the selected product image with the user's full-body image at the appropriate size and position. First, it scales the product image based on the height data, and then positions it appropriately relative to the full-body image. If multiple products are selected, the server composites them while adjusting the position and size of each product.

[0258] Step 7:

[0259] The server analyzes the emotion data provided by the emotion engine and adjusts the display content of the try-on images to match the user's emotions. For example, if the user is happy, an effect that matches that emotion is added to the try-on images.

[0260] Step 8:

[0261] The try-on image generated by the server is sent to the terminal.

[0262] Step 9:

[0263] The terminal displays the try-on images received from the server to the user, who can check the images from various perspectives, such as full-body view, upper-body view, lower-body view, and bust-up view.

[0264] Step 10:

[0265] The device provides an interface for sharing try-on images with family and friends. Users can share try-on images via applications such as LINE or email.

[0266] Step 11:

[0267] Based on the emotion data, the server recommends other products that the user may be interested in. The recommendation information is sent to the terminal and displayed to the user.

[0268] Example 2

[0269] 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."

[0270] While online shopping is very popular these days, the inability to actually touch and try on products poses a major challenge for customers. Furthermore, the try-on experience is uniform and not personalized based on individual customer preferences and emotions, which can discourage purchasing. This invention aims to solve these challenges.

[0271] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting height data, a means for uploading a full-body image, a means for selecting product data, a means for collecting user emotion data using an emotion engine and adjusting the display content, and a means for sending the generated try-on images to the terminal. This allows the online try-on experience to be personalized based on the emotions of each individual user, making for a more realistic try-on experience.

[0272] "Height data" is numerical information about the user's height, and is used for scaling and compositing product images.

[0273] A "full-body image" is an image of the user's entire body, and is used as basic data for generating try-on images.

[0274] "Product data" refers to data including images of products to be tried on and related information.

[0275] The "emotion engine" is a software component that collects and analyzes emotional data in real time from the user's facial expressions, voice, operation patterns, etc.

[0276] "Emotion data" is data that represents the user's emotional state collected by the emotion engine.

[0277] The "server" is a computer system that processes data received from a user and generates and transmits try-on images.

[0278] A "terminal" is a device operated by a user that provides an interface for communicating with a server and sending and receiving data.

[0279] A "try-on image" is a composite image in which a product image is superimposed on a full-body image of the user, and is intended to provide a virtual try-on experience.

[0280] A "product list" is a server-generated list of available products from which a user can select.

[0281] A "user" is an individual who uses the system to try on products online.

[0282] The present invention relates to a system that provides a visual experience that makes users feel as if they are actually wearing the products when trying on products online. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotions and personalize the try-on experience based on the emotion data.

[0283] System Overview

[0284] The system includes the following components:

[0285] A means of inputting user height data

[0286] A means to upload a full-body image of the user

[0287] How to select product data

[0288] A means of sending height data and a full-body image to the server

[0289] A means to collect user emotion data using an emotion engine and send it to a server

[0290] The server synthesizes the product image with the full-body image at the appropriate size and position to generate a try-on image.

[0291] A means for the server to analyze emotional data and adjust the content displayed

[0292] A means for sending the generated try-on image to the terminal

[0293] A means for the device to display and share try-on images

[0294] User Interface

[0295] The user inputs their height data, takes a full-body image using the device's camera function, and uploads this data to the server. This allows the user to obtain accurate images of the clothes they will be trying on. The device uses an emotion engine to collect emotional data from the user's facial expressions, voice, operation patterns, etc. This emotional data is sent to the server and used to generate and personalize the try-on images.

[0296] Server Processing

[0297] The server stores the received height data and full-body image, generates a list of available products, and sends it to the terminal. It acquires the product data and emotion data selected by the user, and generates a try-on image by combining the product image with the full-body image at an appropriate size and position. The generated try-on image is personalized based on the emotion data; for example, if the user is happy, an effect that emphasizes that emotion is added. Finally, the server sends the generated try-on image to the terminal and displays it for the user.

[0298] Specific examples

[0299] For example, imagine a female user who is 160 cm tall uploads a full-body image of herself and tries on a red dress, black heels, and gold earrings. The user first enters her height data (160 cm) and a full-body image into the device and uploads it. The device then sends this data to the server, which receives and stores it. The server then generates a list of available products and sends it to the device. As the user tries on the selected products, the emotion engine collects emotional data from the user's facial expressions and voice. This data is sent to the server, and the try-on image is personalized based on the emotional data. For example, if the user is happy with the red dress, an effect that emphasizes that emotion is added to the try-on image. Finally, the generated try-on image is sent to the device so that the user can view it.

[0300] Example prompts for generative AI models

[0301] Here are some example prompts to input to a generative AI model:

[0302] "Please describe the process for a 160cm tall user to upload a full-body image and try on a red dress, black heels, and gold earrings. Also, please detail how the user's emotional data is used to personalize the try-on image."

[0303] Using these prompts, the generative AI model can generate answers on specific ways to make the user's try-on experience more realistic and personalized.

[0304] By using the above-described techniques, the present invention improves the try-on experience in online shopping and increases user satisfaction.

[0305] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0306] Step 1: Initial User Setup

[0307] 1.1. A user launches an application and creates an account by entering personal information. Input data includes username, email address, password, etc. This information is sent from the device to the server, which stores it in a database. The output is a message confirming the account creation.

[0308] 1.2. The user inputs their height data. The input data includes the user's height (e.g., 160 cm). This data is sent from the terminal to the server, which saves it in the database. The output is a message confirming that the height data has been saved.

[0309] 1.3. A user takes a full-body image of themselves on their device and uploads it to the server. The input data includes the captured full-body image file. This image file is uploaded from the device to the server, and the server stores it in a database. The output is a message confirming the image upload.

[0310] Step 2: Provide a product list

[0311] 2.1. The server generates a list of available products based on the stored data. The input data includes the product catalog stored on the server. This data is processed to filter and generate a list of products based on the user's height and body type. The output is the generated list of products.

[0312] 2.2. Sending the available product list to the terminal. The input data includes the generated product list. This list is sent to the terminal and displayed for the user to view. The output is the product list displayed on the terminal.

[0313] 2.3. The user selects the product of interest on the device. The input data includes the product ID selected by the user. This information is sent from the device to the server, which retrieves the selected product data. The output is the data of the selected product.

[0314] Step 3: Collecting emotion data

[0315] 3.1. The emotion engine monitors the user's facial expressions, voice, and operation patterns. The input data includes the user's real-time facial expressions and voice data. The emotion engine analyzes these data to identify the user's emotional state. The output is the analyzed emotion data.

[0316] 3.2. Collect emotion data in real time and send it to the server. The input data includes emotion data generated by the emotion engine. This data is sent from the device to the server, which stores it in a database. The output is the stored emotion data.

[0317] Step 4: Generate try-on images

[0318] 4.1. The server acquires the user's full-body image and the selected product image. The input data includes the full-body image and the selected product image stored on the server. Processing begins based on this data. The output is a message confirming the acquisition of both data.

[0319] 4.2. The server adjusts the scale of the product image based on the user's height data. The input data includes the user's height data and the selected product image. The server adjusts the size of the product image based on this data. The output is the scaled product image.

[0320] The server composites the scaled product image into a full-body image. The input data includes the scaled product image and a full-body image. The algorithm is applied to composite the images. The output is a composite try-on image.

[0321] Step 5: Analyze emotion data and display images

[0322] 5.1. The server analyzes the emotion data provided by the emotion engine. The input data includes the collected emotion data. This data is analyzed to determine the appropriate effect based on the user's emotion. The output is effect information.

[0323] 5.2. Adjust the display content of the try-on image based on the user's emotions. The input data includes the synthesized try-on image and the determined effect information. Effects are added to the try-on image based on this data. The output is the adjusted try-on image.

[0324] Step 6: Viewing try-on images

[0325] 6.1. The server sends the generated try-on image to the terminal. The input data includes the adjusted try-on image. This image is sent to the terminal. The output is the try-on image sent to the terminal.

[0326] 6.2. The terminal displays the try-on image to the user. The input data includes the try-on image sent to the terminal. This image is displayed to the user. The output is the displayed try-on image.

[0327] Step 7: Share your try-on photos

[0328] 7.1. Provide an interface for a device to share try-on images. The input data includes try-on images displayed on the device. The interface for sharing these images is displayed. The output is the sharing interface.

[0329] 7.2. The user shares try-on images with family and friends via LINE or email. The input data includes a sharing interface and try-on images. This data is used to share try-on images. The output is the shared try-on images.

[0330] (Application example 2)

[0331] 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."

[0332] When selecting products online, it is difficult for users to check the fit and appearance of products without actually trying them on. Furthermore, the inability to recommend or tailor products based on the user's individual emotions and preferences can lead to reduced user satisfaction. A particular challenge is the lack of a personalized try-on experience that utilizes emotion recognition.

[0333] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0334] In this invention, the server includes means for inputting height data, means for uploading a full-body image, means for selecting product data, means for transmitting the height data and the full-body image to the server, means for the server to generate a list of available products and transmit it to the terminal, means for the terminal to transmit data on the selected products to the server, means for the server to combine the product images with the full-body image at appropriate sizes and positions to generate try-on images, means for transmitting the generated try-on images to the terminal, means for the terminal to display and share the try-on images, means for collecting user emotion data using an emotion engine and transmitting it to the server, and means for personalizing the try-on images based on the emotion data, thereby enabling the user to have a personalized try-on experience based on their own emotions.

[0335] "Height data" is information about the height of the user, and is basic data for generating try-on images.

[0336] The "full-body image" is an image of the user's entire body, and is basic data for generating try-on images.

[0337] "Product data" is information about the product that the customer wishes to try on, and is data that is sent from the terminal to the server.

[0338] A "server" is a computer system that stores and processes data received from users.

[0339] A "product list" is a list of products that can be tried on, provided by the server.

[0340] The "emotion engine" is a system that recognizes emotions from the user's facial expressions, voice, etc. and collects that data.

[0341] "Synthesis" is a process of combining a full-body image of the user with a selected product image to generate a try-on image.

[0342] "Personalization" means customizing the try-on experience and display content to suit the user's individual feelings and preferences.

[0343] A "try-on image" is an image generated by combining a product selected by the user with a full-body image.

[0344] A "terminal" is a device that a user operates to input and display data.

[0345] "Sharing" means sending and receiving the generated try-on image with other people via communication means.

[0346] "Effects" are visual effects or filters added to try-on images.

[0347] The present invention is a system for enabling users to try on products online and receive a personalized experience based on emotional data. The system is implemented using the following hardware and software:

[0348] Hardware

[0349] Smartphone or tablet: A device with a camera and internet connection.

[0350] Server: A computer system that performs processes such as facial expression analysis and try-on image generation.

[0351] software

[0352] OpenCV: A library for image processing.

[0353] dlib: A library for detecting facial feature points, etc.

[0354] EmotionRecognizer: A custom library for recognizing emotions from facial expressions.

[0355] VirtualTryOn: A custom library for generating virtual try-on images.

[0356] Recommendation Engine: A custom library that adjusts and recommends try-on images based on emotions.

[0357] System operation explanation

[0358] User Preferences

[0359] Users input their height data, take a full-body image using a smartphone or tablet, and upload it to the system. The server receives and stores this data.

[0360] Get product list

[0361] The server generates a list of available products and sends it to the device. The user selects the products they want to try on from the list. During this process, the emotion engine collects emotion data from the user's facial expressions and voice and sends it to the server.

[0362] Generation of try-on images

[0363] The server generates a fitting image by combining the image of the selected product with the user's full-body image at the appropriate size and position. Based on the emotion data provided by the emotion engine, the server can add effects to the fitting image and adjust the display content.

[0364] Viewing and sharing images

[0365] The generated try-on images are sent from the server to the device, where users can check the details using options such as full-body or partial view. Furthermore, users can share the try-on images with family and friends.

[0366] Specific examples

[0367] Let's take a specific example of a 160cm tall female user uploading a full-body image of herself and trying on a red dress, black heels, and gold earrings. The user first enters her height as 160cm and uploads a full-body image. The server receives the data, generates a list of available products, and sends it to the device. The user selects a product, and the emotion engine collects the user's emotions during the selection process. The server then scales the selected product to an appropriate size and combines it with the full-body image to generate a try-on image. The try-on image, with effects added based on the emotion data, is displayed on the device, and the user can view and share the image.

[0368] Prompt Sentence Examples

[0369] Generate Python code for a virtual try-on app that meets the following requirements:

[0370] It takes the user's height and a full-body image as input.

[0371] Collect user emotion data using an emotion recognition engine.

[0372] Select the item you want to try on from the product list.

[0373] The selected items are combined with a full-body image to generate a try-on image.

[0374] To personalize the display of try-on images based on user emotion data.

[0375] In this way, the present invention provides users with an emotionally-based and personalized try-on experience.

[0376] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0377] Step 1:

[0378] User Preferences

[0379] The user inputs their height data, which is later used to generate try-on images.

[0380] The user takes a full-body image using a smartphone or tablet and uploads this image to the system. The input is "height data" and "full-body image." The output is that "height data" and "full-body image" are sent to the server. This gives the server basic data for generating try-on images.

[0381] Step 2:

[0382] Get product list

[0383] The server stores the received height data and full-body image.

[0384] The server generates a list of available products and sends this list to the terminal. The input is "height data" and "full-body image." The output is the "product list" displayed on the terminal. This allows the user to check the products that can be tried on.

[0385] Step 3:

[0386] Product selection and emotional data collection

[0387] The user selects the product they wish to try on from the product list on the terminal.

[0388] The emotion engine monitors the user's facial expressions and operation patterns to collect emotion data. This emotion data is sent to the server. The input is "product selection" and "facial expression data." The output is "selected product data" and "emotion data" sent to the server. This allows the server to provide a personalized experience based on the user's emotions.

[0389] Step 4:

[0390] Generation of try-on images

[0391] The server synthesizes the selected product image with the user's full-body image at the appropriate size and position. The input is the "full-body image," "selected product data," and "height data."

[0392] The system scales and positions product images on a full-body image, and applies effects to the image based on emotion data. The output is a "try-on image," allowing users to visually confirm the results of trying on the product.

[0393] Step 5:

[0394] Viewing and sharing images

[0395] The generated try-on images are sent from the server to the device. The input is the "try-on image."

[0396] The device displays the generated try-on image, allowing the user to check it. The user can also share the try-on image with family and friends. The output is "display of try-on image" and "image sharing function." This allows the user to share the try-on image with other people to get their opinions.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] [Second embodiment]

[0401] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0402] 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.

[0403] 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).

[0404] 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.

[0405] 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.

[0406] 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).

[0407] 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. 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] In the smart glasses 214, 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.

[0412] 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."

[0413] The present invention is a system that provides users with a visual experience of trying on products when shopping online. In this system, users input their height data, upload a full-body image, and select products they are interested in. The server then generates a try-on image tailored to the user's body type and displays it on the terminal.

[0414] System operation explanation

[0415] User Preferences

[0416] First, the user enters their height data into the system. This height data is used to obtain a fitting image of the correct size. Next, the user follows a specified guide to take a full-body image and uploads it to the server via their device. This full-body image becomes the base data for generating fitting images.

[0417] Processing on the server

[0418] The server stores the received height data and full-body image, then sends a list of available products to the device, including a wide range of items such as clothes, hats, shoes, bags, and accessories.

[0419] Product selection and data transmission

[0420] The user selects the product of interest from the product list on the terminal. The selected product data is then sent back to the server via the terminal. The selected product data includes product ID, category information, etc., and is used to generate try-on images on the server side.

[0421] Generation of try-on images

[0422] The server composites the selected product image onto the user's full-body image at the appropriate size and position. To do this, it first adjusts the scale of the product image based on the user's height data. Then it positions the product appropriately to fit the user's body shape. The composition algorithm adjusts the size and shape to recreate a natural fit of the clothing. It also handles multiple selected products simultaneously, adjusting the position and size of each.

[0423] Displaying try-on images

[0424] Once the try-on images are generated, the server sends them to the device, which then displays them. The user can view the images from various perspectives, including full-body, upper-body, lower-body, and bust-up views.

[0425] Sharing try-on images

[0426] The device also provides an interface for sharing try-on images with family and friends, allowing users to easily send try-on images via applications such as LINE and email.

[0427] Specific examples

[0428] For example, let us consider the case where a female user who is 160 cm tall uploads a full-body image of herself and tries on a red dress, black heels, and gold earrings.

[0429] First, the user inputs and uploads their height data (160cm) and a full-body image to the device. The device then sends this data to the server. The server receives and stores the data. The server then sends a list of available products to the device.

[0430] The list includes a red dress, black heels, and gold earrings. The user selects these items and the selection is sent to the server.

[0431] The server overlays the selected item onto the user's full-body image. The dress size is adjusted to fit the user's height of 160cm, and the black heels and gold earrings are also placed in the appropriate positions. The synthesized try-on image is sent to the terminal and displayed to the user. The user can check the try-on image using options such as full-body view or upper-body view.

[0432] Finally, users can share the try-on images with friends via LINE and get their opinions. Through this process, users can get the same experience as actually trying on the items. This allows them to check the suitability of the items they are purchasing, making online shopping more satisfying.

[0433] In this way, the present invention provides a concrete solution to solve the problems of online shopping and improve user satisfaction.

[0434] The processing flow will be explained below.

[0435] Step 1:

[0436] The user inputs their height and follows the guide to take a full-body image, which is then uploaded to the device.

[0437] Step 2:

[0438] The device sends the height data entered by the user and the uploaded full-body image to the server.

[0439] Step 3:

[0440] The server stores the received height data and full-body image, generates a list of products that can be offered to the user, and sends the list to the terminal.

[0441] Step 4:

[0442] The terminal displays the product list received from the server to the user, who then selects the product they wish to try on from the product list.

[0443] Step 5:

[0444] The terminal transmits data on the product selected by the user, such as the product ID and category information, to the server.

[0445] Step 6:

[0446] The server starts the process of combining the selected product image with the user's full-body image in an appropriate size and position.

[0447] First, the product image is scaled based on the height data, and then appropriately positioned relative to the full-body image.

[0448] When multiple products are selected, the images are composited while adjusting the position and size of each product appropriately.

[0449] Step 7:

[0450] The try-on image generated by the server is sent to the terminal.

[0451] Step 8:

[0452] The terminal displays the try-on images received from the server to the user, who can check the images from various perspectives, such as full-body view, upper-body view, lower-body view, and bust-up view.

[0453] Step 9:

[0454] The device provides an interface for sharing try-on images with family and friends. Users can share try-on images via applications such as LINE or email.

[0455] Example 1

[0456] 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."

[0457] When selecting products online, there is a demand for a visual experience equivalent to the try-on experience in a physical store. However, conventional systems have difficulty generating try-on images that fit the user's body type, resulting in low user satisfaction with product selection. Furthermore, functions such as sharing try-on images or partial display are not adequately provided, which prevents users from increasing their motivation to purchase. This leaves the challenge of improving the user experience and product purchase rates.

[0458] 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.

[0459] In this invention, the server includes means for a user to input height data, means for a user to upload a full-body image, means for a user to select product data, means for a terminal to send the height data and full-body image to the server, means for the server to generate a list of available products and send it to the terminal, means for the terminal to send data on the selected products to the server, means for the server to combine product images with the full-body image at an appropriate size and position and generate try-on images using a generative artificial intelligence model, means for sending the generated try-on images to the terminal, and means for the terminal to display and share the try-on images. This allows users to easily obtain try-on images that suit their body type, and further increases their motivation to purchase by sharing try-on images and using the partial display function.

[0460] "Height data" is data that the user inputs as a numerical value representing his or her height, and is information that serves as a reference for generating try-on images.

[0461] The "full-body image" is image data of the user's entire body, and serves as the basis for the try-on image.

[0462] "Product data" refers to information about products that can be selected in online shopping, and includes product IDs, categories, image URLs, etc.

[0463] A "terminal" is an electronic device operated by a user, which has the functions of inputting and displaying data and communicating with a server.

[0464] The "server" is a remote computer system that receives and stores user input data, generates product lists, and synthesizes try-on images.

[0465] A "product list" is a list of detailed information about available products that is generated by the server and sent to the terminal.

[0466] A "try-on image" is an image generated by combining a full-body image of the user with a product image, and provides a realistic try-on experience.

[0467] A "generative artificial intelligence model" is an algorithm used to synthesize a user's full-body image with a product image, and has the ability to position the product in an appropriate size and position.

[0468] The "sharing means" is an interface for sharing the generated try-on images with other people, and has the function of easily sending images via social networking sites, email, etc.

[0469] The present invention is a system that provides users with a visual experience of trying on products when shopping online. In this system, users input their height data, upload a full-body image, and select products they are interested in. The server then generates a try-on image tailored to the user's body type and displays it on the terminal.

[0470] First, the user enters their height data into the system. This data is used to obtain fitting images of the correct size. Next, the user follows a specified guide to take a full-body image and uploads it to the server via their device. This full-body image becomes the base data for generating fitting images.

[0471] The server stores the received height data and full-body image and sends a list of available products to the device. The product list includes information on a wide range of products, including clothing, hats, shoes, bags, and accessories. The user selects products of interest from the product list on the device. The selected product data is sent back to the server via the device and used to generate try-on images.

[0472] The server then composites the selected product image onto the user's full-body image at the appropriate size and position. This involves first scaling the product image based on height data and then positioning the product appropriately to fit the user's body shape. Using generative AI models, the server adjusts the size and shape of clothing and accessories to recreate a natural fit. It also simultaneously accommodates multiple selected products, adjusting the position and size of each.

[0473] The generated try-on images are sent from the server to the device. The device displays the received try-on images, allowing the user to view them from various perspectives, such as full-body view, upper body view, lower body view, and bust-up view. The device also provides an interface for sharing the try-on images with family and friends. Using this interface, users can easily send the try-on images via applications such as social networking sites and email.

[0474] As a specific example, let's consider the case where a female user who is 160 cm tall uploads a full-body image of herself and tries on a red dress, black heels, and gold earrings. The user inputs and uploads her height data (160 cm) and a full-body image she has taken to her device, and the device sends this data to the server. The server receives and stores the data, and sends a list of available products to the device. The list includes a red dress, black heels, and gold earrings, and the user selects these products, and the selection data is sent to the server.

[0475] The server overlays the selected product onto the user's full-body image, adjusts the dress size to fit the user's height of 160cm, and places the black heels and gold earrings in the appropriate positions. The try-on image generated through the synthesis is sent to the user's device and displayed to the user. The user can check the try-on image using options such as full-body or upper-body view. Finally, the user can share the try-on image with friends via LINE and get their opinions. Through this process, the user can experience the feeling of actually trying on the clothes.

[0476] An example of a prompt is as follows:

[0477] "I uploaded a full-body photo of me, 160cm tall. Try on a red dress, black heels, and gold earrings."

[0478] "A female user who is 160cm tall has selected a red dress, black heels, and gold earrings. Please composite these items into a full-body image."

[0479] In this way, the present invention provides a concrete solution to solve the problems of online shopping and improve user satisfaction.

[0480] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0481] Step 1:

[0482] The user inputs height data.

[0483] Input: The user inputs their height (e.g., 160 cm) on the terminal screen.

[0484] Output: The entered height data is saved on the device.

[0485] Specific operation: When the user enters their height in the input field and presses the confirmation button, the entered data is saved in a variable within the device.

[0486] Step 2:

[0487] The user takes a full-body image and uploads it to the server via the terminal.

[0488] Input: The user takes a full-body image using a smartphone or camera and uploads the image to the device.

[0489] Output: The uploaded whole-body image data is saved on the device.

[0490] Specific operation: The user takes a full-body image using the camera app and presses the "upload" button, which saves the image file to the device.

[0491] Step 3:

[0492] The terminal transmits height data and a full-body image to the server.

[0493] Input: Height data and whole-body image data stored on the device.

[0494] Output: Height data and whole body image data sent to the server.

[0495] Specific operation: The device detects the "Send" button and sends the saved height data and full-body image to the server using the HTTPS protocol. The data is encrypted and received by the server.

[0496] Step 4:

[0497] The server stores the received data and generates a list of available products.

[0498] Input: Height data and whole body image data sent to the server.

[0499] Output: The generated product list.

[0500] Specific operation: The server stores the received data in a database, identifies the user, and generates a list of available products from the associated product database.

[0501] Step 5:

[0502] The server sends the product list to the terminal.

[0503] Input: Generated product list.

[0504] Output: The product list sent to the terminal.

[0505] Specific operation: The server sends a product list to the terminal, and the terminal uses middleware to receive the data necessary to display the list.

[0506] Step 6:

[0507] The user uses the terminal to select the product they want to try on from the product list.

[0508] Input: Product list.

[0509] Output: Selected product data.

[0510] Specific operation: The user selects a product of interest from the product list displayed on the device screen and presses the "Select" button. The selected product data is temporarily saved on the device.

[0511] Step 7:

[0512] The terminal transmits the selected product data to the server.

[0513] Input: Selected product data.

[0514] Output: The product data sent to the server.

[0515] Specific operation: The terminal sends product data to the server via an HTTP request, and the server receives and processes it.

[0516] Step 8:

[0517] The server synthesizes the product image with the full-body image at an appropriate size and position to generate a try-on image.

[0518] Input: whole body image data, height data, product data.

[0519] Output: The generated try-on image.

[0520] How it works: The server uses the generated AI model to analyze the full-body image and product image, adjust the scale of the product image based on the customer's height, and generate a try-on image by naturally overlaying the scaled product image on the full-body image.

[0521] Step 9:

[0522] The server transmits the generated try-on image to the terminal.

[0523] Input: Generated try-on images.

[0524] Output: Try-on image sent to the device.

[0525] Specific operation: The server encodes the try-on image and sends it to the device as an HTTP response. The device stores the received image in temporary memory.

[0526] Step 10:

[0527] The device displays the try-on image.

[0528] Input: Try-on images sent from the server.

[0529] Output: Try-on image shown to the user.

[0530] Specific behavior: The device displays try-on images within the application and loads a UI that gives the user viewing options such as full body, upper body, lower body, and bust-up.

[0531] Step 11:

[0532] The device provides an interface for sharing try-on images.

[0533] Input: Try-on image.

[0534] Output: Shared try-on images.

[0535] Specific operation: The device detects that the share button has been pressed and displays an interface for transferring images to social media or email applications. The user selects a destination and the image is shared.

[0536] (Application example 1)

[0537] 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."

[0538] When shopping online, it is difficult to check the fit and appearance of products before purchasing them because customers cannot actually try them on. This often leads to dissatisfaction with product size or design after purchase. Furthermore, virtual stores, in particular, are required to provide a level of realism that makes users feel as if they are actually trying on products. Furthermore, generating and displaying try-on images in real time is technically challenging, making it difficult for existing systems to address this issue. New technologies are needed to address this issue and improve users' online shopping experiences.

[0539] 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.

[0540] In this invention, the server includes means for inputting height data, means for uploading a full-body image, means for selecting product data, means for transmitting the height data and the full-body image to the server, means for the server to generate a list of available products and transmit it to the terminal, means for the terminal to transmit data on the selected products to the server, means for the server to synthesize product images with the full-body image at appropriate sizes and positions to generate try-on images, means for transmitting the generated try-on images to the terminal, means for the terminal to display and share the try-on images, means for using a deep learning model to generate and display try-on images in real time, and means for synthesizing the full-body image and product images using the deep learning model. This allows users to experience trying on products in real time in a virtual store.

[0541] "Height data" is information relating to the height of the user, and is data used to appropriately adjust the scale of the product when generating a try-on image.

[0542] A "full-body image" is a photograph including the user's entire body, and serves as basic data for generating try-on images.

[0543] "Product Data" means data containing detailed information about products available for purchase online, including images, sizes, prices, etc.

[0544] The "server" is a computer system that stores and processes data received from users, and is a device that generates try-on images and provides product lists.

[0545] A "deep learning model" is an artificial intelligence model that uses deep learning algorithms to analyze data and generate try-on images.

[0546] A "product list" is data that lists products that can be selected by the user, and is sent from the server to the terminal.

[0547] The "try-on image" is an image showing a virtual try-on state, generated by combining a selected product image with a full-body image of the user.

[0548] A "terminal" is a device used by a user to input data and check try-on images, and includes smartphones, personal computers, etc.

[0549] "Synthesis" is the process of combining a full-body image of the user with an image of the product in the appropriate size and position to recreate a natural fitting look.

[0550] "Real-time" refers to immediate response to user operations. In the case of try-on image generation, this means that try-on images are generated and displayed immediately every time a user selects an item.

[0551] The system of the present invention generates try-on images in real time using height data and a full-body image to provide a visual experience that makes users feel as if they are trying on products during online shopping. To achieve this, the system uses a deep learning model to generate try-on images.

[0552] System configuration

[0553] Hardware

[0554] Device: A smartphone or computer used by the user to input height data, upload full-body images, and view try-on images.

[0555] Server: A computer system that stores data sent by users, generates try-on images using a deep learning model, and sends them to the device.

[0556] software

[0557] Programming language: Python

[0558] Image processing library: OpenCV

[0559] Deep Learning Framework: TensorFlow

[0560] Image manipulation library: PIL (Pillow)

[0561] System Operation

[0562] User operations

[0563] 1. The user enters their height data on the terminal.

[0564] 2. The user takes a full-body image using a camera and uploads it to the server via the device.

[0565] 3. The user selects a product from the list of available products sent by the server.

[0566] Server Processing

[0567] 1. The server stores the received height data and full-body image.

[0568] 2. Based on the data of the selected product, the server uses a deep learning model to synthesize a full-body image of the user and an image of the product, generating a try-on image that reproduces a natural fit.

[0569] 3. The generated try-on images are sent to the device in real time.

[0570] Terminal display

[0571] 1. The device immediately displays the received try-on images. The user can view the images using options such as full-body view, upper-body view, and lower-body view.

[0572] 2. Users can share try-on images with family and friends via LINE, email, etc.

[0573] Specific examples

[0574] For example, consider a case where a user who is 160 cm tall wants to try on a red dress. The user first enters their height data and uploads a full-body image to their device. Next, they select a red dress from a list of available products. The server receives this data and uses a deep learning model to synthesize the red dress onto the user's full-body image. This synthesized try-on image is sent to the user's device in real time and displayed. The user can review this image and, if necessary, share it with friends to get their opinions.

[0575] Prompt Sentence Examples

[0576] "I want to see in real time how I look trying on a red dress at 160cm height."

[0577] This system can significantly improve the user's shopping experience in virtual stores. Real-time generation of try-on images can solve the problem of product selection and increase online shopping satisfaction.

[0578] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0579] Step 1:

[0580] The user inputs their height data into the terminal. The input data is saved as "user height" and used to adjust the scale of product images in subsequent processing.

[0581] Step 2:

[0582] The user takes a full-body image using a camera and uploads it to the server via their device. This full-body image ("user image") becomes the basic data for generating try-on images.

[0583] Step 3:

[0584] The server stores the received user height data and user image, which will be used in the next processing step.

[0585] Step 4:

[0586] The server generates a list of available products and sends it to the terminal, from which the user can select the products they wish to try on.

[0587] Step 5:

[0588] The product data selected by the user ("selected product") is sent to the server via the terminal again. The product data includes product images, sizes, category information, etc.

[0589] Step 6:

[0590] The server receives the selected product data and uses a deep learning model to synthesize the user image with the product image. First, the scale of the product image is adjusted based on the user's height data, and then the deep learning model is used to adjust the position to recreate a natural fit.

[0591] Step 7:

[0592] The server then sends the generated try-on images to the device, which are generated in real time and displayed instantly on the device.

[0593] Step 8:

[0594] The device displays the received fitting images. The user can check the fitting images using options such as full-body display, upper body display, and lower body display. The fitting images can also be shared with family and friends via LINE or email.

[0595] Step 9:

[0596] Users can view try-on images and make a final purchase decision, and can also get feedback from friends and family using the sharing feature.

[0597] These steps allow users to have a real-time visual experience that makes them feel as if they are actually trying on products in a virtual store.

[0598] 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.

[0599] This invention combines an emotion engine with a system that provides a visual experience that makes users feel as if they are trying on products online, recognizing the user's emotions and personalizing the try-on experience based on that emotion data. In this system, users input their height data, upload a full-body image, and select the products they want to try on. The server then generates try-on images tailored to the user's body type and displays them on the device. The emotion engine also recognizes the user's emotions, and that data is used within the system.

[0600] System operation explanation

[0601] User Preferences

[0602] First, the user enters their height data into the system. This height data is used to generate fitting images of the appropriate size for the user. Next, the user follows a specified guide to take a full-body image and uploads it to the server via their device. This full-body image becomes the basic data for generating fitting images.

[0603] Processing on the server

[0604] The server stores the received height data and full-body image, then sends a list of available products to the device, including a wide range of items such as clothes, hats, shoes, bags, and accessories.

[0605] Product selection and emotional data collection

[0606] The user selects a product they are interested in from a product list on their device. During this process, the emotion engine collects emotional data from the user's facial expressions, voice, operation patterns, etc., and sends this data to the server, where it is stored.

[0607] Generation of try-on images

[0608] The server composites the selected product image with the user's full-body image at the appropriate size and position. The composite process first scales the product image based on the user's height data, then applies an algorithm to position it appropriately relative to the full-body image. Even if multiple products are selected, the composite process adjusts the position and size of each product.

[0609] Emotion data analysis and image display

[0610] The server analyzes the emotional data provided by the emotion engine and adjusts the display content to match the user's emotions. For example, if the user is happy, it can add effects to the try-on images to emphasize that emotion. It also recommends other products that the user might be interested in based on the emotional data.

[0611] Displaying try-on images

[0612] The generated try-on images are sent from the server to the device, where they are displayed, allowing the user to check the details using options such as full-body view, upper-body view, lower-body view, and bust-up view.

[0613] Sharing try-on images

[0614] The device also provides an interface for sharing try-on images with family and friends, allowing users to easily share images via applications such as LINE and email.

[0615] Specific examples

[0616] For example, consider the case where a female user who is 160 cm tall uploads a full-body image of herself and tries on a red dress, black heels, and gold earrings.

[0617] First, the user inputs and uploads their height data (160cm) and a full-body photo to the device. The device then sends this data to the server. The server receives and stores the data, then sends a list of available items to the device. This list includes a red dress, black heels, and gold earrings.

[0618] The user selects one of these products, and the emotion engine collects emotional data from the user's facial expressions and voice during the selection process. The selection and emotional data are sent to the server, which then overlays the selected product onto the user's full-body image. The server adjusts the dress size to fit the user's height of 160cm, and also positions the black heels and gold earrings appropriately. The synthesized try-on image is sent from the server to the user's device and displayed to the user. The user can then review the try-on image using options such as full-body or upper-body display.

[0619] Finally, users share the try-on images with friends via LINE and receive their opinions. Through this process, the invention combines an experience that feels like trying on clothes with a personalized try-on service based on emotions.

[0620] In this way, the present invention provides a concrete solution to solve the problems of online shopping and improve user satisfaction.

[0621] The processing flow will be explained below.

[0622] Step 1:

[0623] The user inputs their height data and follows the guide to take a full-body image, which is then uploaded to the device.

[0624] Step 2:

[0625] The device sends the height data entered by the user and the uploaded full-body image to the server.

[0626] Step 3:

[0627] The server stores the received height data and full-body image, generates a list of products that can be offered to the user, and sends the list to the terminal.

[0628] Step 4:

[0629] The terminal displays the product list received from the server to the user, who then selects the product they wish to try on from the product list.

[0630] Step 5:

[0631] As the user selects a product, the emotion engine collects emotion data from the user's facial expressions, voice, and operation patterns. The device then transmits the selected product data and emotion data to the server.

[0632] Step 6:

[0633] The server composites the selected product image with the user's full-body image at the appropriate size and position. First, it scales the product image based on the height data, and then positions it appropriately relative to the full-body image. If multiple products are selected, the server composites them while adjusting the position and size of each product.

[0634] Step 7:

[0635] The server analyzes the emotion data provided by the emotion engine and adjusts the display content of the try-on images to match the user's emotions. For example, if the user is happy, an effect that matches that emotion is added to the try-on images.

[0636] Step 8:

[0637] The try-on image generated by the server is sent to the terminal.

[0638] Step 9:

[0639] The terminal displays the try-on images received from the server to the user, who can check the images from various perspectives, such as full-body view, upper-body view, lower-body view, and bust-up view.

[0640] Step 10:

[0641] The device provides an interface for sharing try-on images with family and friends. Users can share try-on images via applications such as LINE or email.

[0642] Step 11:

[0643] Based on the emotion data, the server recommends other products that the user may be interested in. The recommendation information is sent to the terminal and displayed to the user.

[0644] Example 2

[0645] 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."

[0646] While online shopping is very popular these days, the inability to actually touch and try on products poses a major challenge for customers. Furthermore, the try-on experience is uniform and not personalized based on individual customer preferences and emotions, which can discourage purchasing. This invention aims to solve these challenges.

[0647] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting height data, a means for uploading a full-body image, a means for selecting product data, a means for collecting user emotion data using an emotion engine and adjusting the display content, and a means for sending the generated try-on images to the terminal. This allows the online try-on experience to be personalized based on the emotions of each individual user, making for a more realistic try-on experience.

[0648] "Height data" is numerical information about the user's height, and is used for scaling and compositing product images.

[0649] A "full-body image" is an image of the user's entire body, and is used as basic data for generating try-on images.

[0650] "Product data" refers to data including images of products to be tried on and related information.

[0651] The "emotion engine" is a software component that collects and analyzes emotional data in real time from the user's facial expressions, voice, operation patterns, etc.

[0652] "Emotion data" is data that represents the user's emotional state collected by the emotion engine.

[0653] The "server" is a computer system that processes data received from a user and generates and transmits try-on images.

[0654] A "terminal" is a device operated by a user that provides an interface for communicating with a server and sending and receiving data.

[0655] A "try-on image" is a composite image in which a product image is superimposed on a full-body image of the user, and is intended to provide a virtual try-on experience.

[0656] A "product list" is a server-generated list of available products from which a user can select.

[0657] A "user" is an individual who uses the system to try on products online.

[0658] The present invention relates to a system that provides a visual experience that makes users feel as if they are actually wearing the products when trying on products online. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotions and personalize the try-on experience based on the emotion data.

[0659] System Overview

[0660] The system includes the following components:

[0661] A means of inputting user height data

[0662] A means to upload a full-body image of the user

[0663] How to select product data

[0664] A means of sending height data and a full-body image to the server

[0665] A means to collect user emotion data using an emotion engine and send it to a server

[0666] The server synthesizes the product image with the full-body image at the appropriate size and position to generate a try-on image.

[0667] A means for the server to analyze emotional data and adjust the content displayed

[0668] A means for sending the generated try-on image to the terminal

[0669] A means for the device to display and share try-on images

[0670] User Interface

[0671] The user inputs their height data, takes a full-body image using the device's camera function, and uploads this data to the server. This allows the user to obtain accurate images of the clothes they will be trying on. The device uses an emotion engine to collect emotional data from the user's facial expressions, voice, operation patterns, etc. This emotional data is sent to the server and used to generate and personalize the try-on images.

[0672] Server Processing

[0673] The server stores the received height data and full-body image, generates a list of available products, and sends it to the terminal. It acquires the product data and emotion data selected by the user, and generates a try-on image by combining the product image with the full-body image at an appropriate size and position. The generated try-on image is personalized based on the emotion data; for example, if the user is happy, an effect that emphasizes that emotion is added. Finally, the server sends the generated try-on image to the terminal and displays it for the user.

[0674] Specific examples

[0675] For example, imagine a female user who is 160 cm tall uploads a full-body image of herself and tries on a red dress, black heels, and gold earrings. The user first enters her height data (160 cm) and a full-body image into the device and uploads it. The device then sends this data to the server, which receives and stores it. The server then generates a list of available products and sends it to the device. As the user tries on the selected products, the emotion engine collects emotional data from the user's facial expressions and voice. This data is sent to the server, and the try-on image is personalized based on the emotional data. For example, if the user is happy with the red dress, an effect that emphasizes that emotion is added to the try-on image. Finally, the generated try-on image is sent to the device so that the user can view it.

[0676] Example prompts for generative AI models

[0677] Here are some example prompts to input to a generative AI model:

[0678] "Please describe the process for a 160cm tall user to upload a full-body image and try on a red dress, black heels, and gold earrings. Also, please detail how the user's emotional data is used to personalize the try-on image."

[0679] Using these prompts, the generative AI model can generate answers on specific ways to make the user's try-on experience more realistic and personalized.

[0680] By using the above-described techniques, the present invention improves the try-on experience in online shopping and increases user satisfaction.

[0681] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0682] Step 1: Initial User Setup

[0683] 1.1. A user launches an application and creates an account by entering personal information. Input data includes username, email address, password, etc. This information is sent from the device to the server, which stores it in a database. The output is a message confirming the account creation.

[0684] 1.2. The user inputs their height data. The input data includes the user's height (e.g., 160 cm). This data is sent from the terminal to the server, which saves it in the database. The output is a message confirming that the height data has been saved.

[0685] 1.3. A user takes a full-body image of themselves on their device and uploads it to the server. The input data includes the captured full-body image file. This image file is uploaded from the device to the server, and the server stores it in a database. The output is a message confirming the image upload.

[0686] Step 2: Provide a product list

[0687] 2.1. The server generates a list of available products based on the stored data. The input data includes the product catalog stored on the server. This data is processed to filter and generate a list of products based on the user's height and body type. The output is the generated list of products.

[0688] 2.2. Sending the available product list to the terminal. The input data includes the generated product list. This list is sent to the terminal and displayed for the user to view. The output is the product list displayed on the terminal.

[0689] 2.3. The user selects the product of interest on the device. The input data includes the product ID selected by the user. This information is sent from the device to the server, which retrieves the selected product data. The output is the data of the selected product.

[0690] Step 3: Collecting emotion data

[0691] 3.1. The emotion engine monitors the user's facial expressions, voice, and operation patterns. The input data includes the user's real-time facial expressions and voice data. The emotion engine analyzes these data to identify the user's emotional state. The output is the analyzed emotion data.

[0692] 3.2. Collect emotion data in real time and send it to the server. The input data includes emotion data generated by the emotion engine. This data is sent from the device to the server, which stores it in a database. The output is the stored emotion data.

[0693] Step 4: Generate try-on images

[0694] 4.1. The server acquires the user's full-body image and the selected product image. The input data includes the full-body image and the selected product image stored on the server. Processing begins based on this data. The output is a message confirming the acquisition of both data.

[0695] 4.2. The server adjusts the scale of the product image based on the user's height data. The input data includes the user's height data and the selected product image. The server adjusts the size of the product image based on this data. The output is the scaled product image.

[0696] The server composites the scaled product image into a full-body image. The input data includes the scaled product image and a full-body image. The algorithm is applied to composite the images. The output is a composite try-on image.

[0697] Step 5: Analyze emotion data and display images

[0698] 5.1. The server analyzes the emotion data provided by the emotion engine. The input data includes the collected emotion data. This data is analyzed to determine the appropriate effect based on the user's emotion. The output is effect information.

[0699] 5.2. Adjust the display content of the try-on image based on the user's emotions. The input data includes the synthesized try-on image and the determined effect information. Effects are added to the try-on image based on this data. The output is the adjusted try-on image.

[0700] Step 6: Viewing try-on images

[0701] 6.1. The server sends the generated try-on image to the terminal. The input data includes the adjusted try-on image. This image is sent to the terminal. The output is the try-on image sent to the terminal.

[0702] 6.2. The terminal displays the try-on image to the user. The input data includes the try-on image sent to the terminal. This image is displayed to the user. The output is the displayed try-on image.

[0703] Step 7: Share your try-on photos

[0704] 7.1. Provide an interface for a device to share try-on images. The input data includes try-on images displayed on the device. The interface for sharing these images is displayed. The output is the sharing interface.

[0705] 7.2. The user shares try-on images with family and friends via LINE or email. The input data includes a sharing interface and try-on images. This data is used to share try-on images. The output is the shared try-on images.

[0706] (Application example 2)

[0707] 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."

[0708] When selecting products online, it is difficult for users to check the fit and appearance of products without actually trying them on. Furthermore, the inability to recommend or tailor products based on the user's individual emotions and preferences can lead to reduced user satisfaction. A particular challenge is the lack of a personalized try-on experience that utilizes emotion recognition.

[0709] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0710] In this invention, the server includes means for inputting height data, means for uploading a full-body image, means for selecting product data, means for transmitting the height data and the full-body image to the server, means for the server to generate a list of available products and transmit it to the terminal, means for the terminal to transmit data on the selected products to the server, means for the server to combine the product images with the full-body image at appropriate sizes and positions to generate try-on images, means for transmitting the generated try-on images to the terminal, means for the terminal to display and share the try-on images, means for collecting user emotion data using an emotion engine and transmitting it to the server, and means for personalizing the try-on images based on the emotion data, thereby enabling the user to have a personalized try-on experience based on their own emotions.

[0711] "Height data" is information about the height of the user, and is basic data for generating try-on images.

[0712] The "full-body image" is an image of the user's entire body, and is basic data for generating try-on images.

[0713] "Product data" is information about the product that the customer wishes to try on, and is data that is sent from the terminal to the server.

[0714] A "server" is a computer system that stores and processes data received from users.

[0715] A "product list" is a list of products that can be tried on, provided by the server.

[0716] The "emotion engine" is a system that recognizes emotions from the user's facial expressions, voice, etc. and collects that data.

[0717] "Synthesis" is a process of combining a full-body image of the user with a selected product image to generate a try-on image.

[0718] "Personalization" means customizing the try-on experience and display content to suit the user's individual feelings and preferences.

[0719] A "try-on image" is an image generated by combining a product selected by the user with a full-body image.

[0720] A "terminal" is a device that a user operates to input and display data.

[0721] "Sharing" means sending and receiving the generated try-on image with other people via communication means.

[0722] "Effects" are visual effects or filters added to try-on images.

[0723] The present invention is a system for enabling users to try on products online and receive a personalized experience based on emotional data. The system is implemented using the following hardware and software:

[0724] Hardware

[0725] Smartphone or tablet: A device with a camera and internet connection.

[0726] Server: A computer system that performs processes such as facial expression analysis and try-on image generation.

[0727] software

[0728] OpenCV: A library for image processing.

[0729] dlib: A library for detecting facial feature points, etc.

[0730] EmotionRecognizer: A custom library for recognizing emotions from facial expressions.

[0731] VirtualTryOn: A custom library for generating virtual try-on images.

[0732] Recommendation Engine: A custom library that adjusts and recommends try-on images based on emotions.

[0733] System operation explanation

[0734] User Preferences

[0735] Users input their height data, take a full-body image using a smartphone or tablet, and upload it to the system. The server receives and stores this data.

[0736] Get product list

[0737] The server generates a list of available products and sends it to the device. The user selects the products they want to try on from the list. During this process, the emotion engine collects emotion data from the user's facial expressions and voice and sends it to the server.

[0738] Generation of try-on images

[0739] The server generates a fitting image by combining the image of the selected product with the user's full-body image at the appropriate size and position. Based on the emotion data provided by the emotion engine, the server can add effects to the fitting image and adjust the display content.

[0740] Viewing and sharing images

[0741] The generated try-on images are sent from the server to the device, where users can check the details using options such as full-body or partial view. Furthermore, users can share the try-on images with family and friends.

[0742] Specific examples

[0743] Let's take a specific example of a 160cm tall female user uploading a full-body image of herself and trying on a red dress, black heels, and gold earrings. The user first enters her height as 160cm and uploads a full-body image. The server receives the data, generates a list of available products, and sends it to the device. The user selects a product, and the emotion engine collects the user's emotions during the selection process. The server then scales the selected product to an appropriate size and combines it with the full-body image to generate a try-on image. The try-on image, with effects added based on the emotion data, is displayed on the device, and the user can view and share the image.

[0744] Prompt Sentence Examples

[0745] Generate Python code for a virtual try-on app that meets the following requirements:

[0746] It takes the user's height and a full-body image as input.

[0747] Collect user emotion data using an emotion recognition engine.

[0748] Select the item you want to try on from the product list.

[0749] The selected items are combined with a full-body image to generate a try-on image.

[0750] To personalize the display of try-on images based on user emotion data.

[0751] In this way, the present invention provides users with an emotionally-based and personalized try-on experience.

[0752] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0753] Step 1:

[0754] User Preferences

[0755] The user inputs their height data, which is later used to generate try-on images.

[0756] The user takes a full-body image using a smartphone or tablet and uploads this image to the system. The input is "height data" and "full-body image." The output is that "height data" and "full-body image" are sent to the server. This gives the server basic data for generating try-on images.

[0757] Step 2:

[0758] Get product list

[0759] The server stores the received height data and full-body image.

[0760] The server generates a list of available products and sends this list to the terminal. The input is "height data" and "full-body image." The output is the "product list" displayed on the terminal. This allows the user to check the products that can be tried on.

[0761] Step 3:

[0762] Product selection and emotional data collection

[0763] The user selects the product they wish to try on from the product list on the terminal.

[0764] The emotion engine monitors the user's facial expressions and operation patterns to collect emotion data. This emotion data is sent to the server. The input is "product selection" and "facial expression data." The output is "selected product data" and "emotion data" sent to the server. This allows the server to provide a personalized experience based on the user's emotions.

[0765] Step 4:

[0766] Generation of try-on images

[0767] The server synthesizes the selected product image with the user's full-body image at the appropriate size and position. The input is the "full-body image," "selected product data," and "height data."

[0768] The system scales and positions product images on a full-body image, and applies effects to the image based on emotion data. The output is a "try-on image," allowing users to visually confirm the results of trying on the product.

[0769] Step 5:

[0770] Viewing and sharing images

[0771] The generated try-on images are sent from the server to the device. The input is the "try-on image."

[0772] The device displays the generated try-on image, allowing the user to check it. The user can also share the try-on image with family and friends. The output is "display of try-on image" and "image sharing function." This allows the user to share the try-on image with other people to get their opinions.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] [Third embodiment]

[0777] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0778] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0779] 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).

[0780] 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.

[0781] 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.

[0782] 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).

[0783] 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. 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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."

[0789] The present invention is a system that provides users with a visual experience of trying on products when shopping online. In this system, users input their height data, upload a full-body image, and select products they are interested in. The server then generates a try-on image tailored to the user's body type and displays it on the terminal.

[0790] System operation explanation

[0791] User Preferences

[0792] First, the user enters their height data into the system. This height data is used to obtain a fitting image of the correct size. Next, the user follows a specified guide to take a full-body image and uploads it to the server via their device. This full-body image becomes the base data for generating fitting images.

[0793] Processing on the server

[0794] The server stores the received height data and full-body image, then sends a list of available products to the device, including a wide range of items such as clothes, hats, shoes, bags, and accessories.

[0795] Product selection and data transmission

[0796] The user selects the product of interest from the product list on the terminal. The selected product data is then sent back to the server via the terminal. The selected product data includes product ID, category information, etc., and is used to generate try-on images on the server side.

[0797] Generation of try-on images

[0798] The server composites the selected product image onto the user's full-body image at the appropriate size and position. To do this, it first adjusts the scale of the product image based on the user's height data. Then it positions the product appropriately to fit the user's body shape. The composition algorithm adjusts the size and shape to recreate a natural fit of the clothing. It also handles multiple selected products simultaneously, adjusting the position and size of each.

[0799] Displaying try-on images

[0800] Once the try-on images are generated, the server sends them to the device, which then displays them. The user can view the images from various perspectives, including full-body, upper-body, lower-body, and bust-up views.

[0801] Sharing try-on images

[0802] The device also provides an interface for sharing try-on images with family and friends, allowing users to easily send try-on images via applications such as LINE and email.

[0803] Specific examples

[0804] For example, let us consider the case where a female user who is 160 cm tall uploads a full-body image of herself and tries on a red dress, black heels, and gold earrings.

[0805] First, the user inputs and uploads their height data (160cm) and a full-body image to the device. The device then sends this data to the server. The server receives and stores the data. The server then sends a list of available products to the device.

[0806] The list includes a red dress, black heels, and gold earrings. The user selects these items and the selection is sent to the server.

[0807] The server overlays the selected item onto the user's full-body image. The dress size is adjusted to fit the user's height of 160cm, and the black heels and gold earrings are also placed in the appropriate positions. The synthesized try-on image is sent to the terminal and displayed to the user. The user can check the try-on image using options such as full-body view or upper-body view.

[0808] Finally, users can share the try-on images with friends via LINE and get their opinions. Through this process, users can get the same experience as actually trying on the items. This allows them to check the suitability of the items they are purchasing, making online shopping more satisfying.

[0809] In this way, the present invention provides a concrete solution to solve the problems of online shopping and improve user satisfaction.

[0810] The processing flow will be explained below.

[0811] Step 1:

[0812] The user inputs their height and follows the guide to take a full-body image, which is then uploaded to the device.

[0813] Step 2:

[0814] The device sends the height data entered by the user and the uploaded full-body image to the server.

[0815] Step 3:

[0816] The server stores the received height data and full-body image, generates a list of products that can be offered to the user, and sends the list to the terminal.

[0817] Step 4:

[0818] The terminal displays the product list received from the server to the user, who then selects the product they wish to try on from the product list.

[0819] Step 5:

[0820] The terminal transmits data on the product selected by the user, such as the product ID and category information, to the server.

[0821] Step 6:

[0822] The server starts the process of combining the selected product image with the user's full-body image in an appropriate size and position.

[0823] First, the product image is scaled based on the height data, and then appropriately positioned relative to the full-body image.

[0824] When multiple products are selected, the images are composited while adjusting the position and size of each product appropriately.

[0825] Step 7:

[0826] The try-on image generated by the server is sent to the terminal.

[0827] Step 8:

[0828] The terminal displays the try-on images received from the server to the user, who can check the images from various perspectives, such as full-body view, upper-body view, lower-body view, and bust-up view.

[0829] Step 9:

[0830] The device provides an interface for sharing try-on images with family and friends. Users can share try-on images via applications such as LINE or email.

[0831] Example 1

[0832] 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."

[0833] When selecting products online, there is a demand for a visual experience equivalent to the try-on experience in a physical store. However, conventional systems have difficulty generating try-on images that fit the user's body type, resulting in low user satisfaction with product selection. Furthermore, functions such as sharing try-on images or partial display are not adequately provided, which prevents users from increasing their motivation to purchase. This leaves the challenge of improving the user experience and product purchase rates.

[0834] 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.

[0835] In this invention, the server includes means for a user to input height data, means for a user to upload a full-body image, means for a user to select product data, means for a terminal to send the height data and full-body image to the server, means for the server to generate a list of available products and send it to the terminal, means for the terminal to send data on the selected products to the server, means for the server to combine product images with the full-body image at an appropriate size and position and generate try-on images using a generative artificial intelligence model, means for sending the generated try-on images to the terminal, and means for the terminal to display and share the try-on images. This allows users to easily obtain try-on images that suit their body type, and further increases their motivation to purchase by sharing try-on images and using the partial display function.

[0836] "Height data" is data that the user inputs as a numerical value representing his or her height, and is information that serves as a reference for generating try-on images.

[0837] The "full-body image" is image data of the user's entire body, and serves as the basis for the try-on image.

[0838] "Product data" refers to information about products that can be selected in online shopping, and includes product IDs, categories, image URLs, etc.

[0839] A "terminal" is an electronic device operated by a user, which has the functions of inputting and displaying data and communicating with a server.

[0840] The "server" is a remote computer system that receives and stores user input data, generates product lists, and synthesizes try-on images.

[0841] A "product list" is a list of detailed information about available products that is generated by the server and sent to the terminal.

[0842] A "try-on image" is an image generated by combining a full-body image of the user with a product image, and provides a realistic try-on experience.

[0843] A "generative artificial intelligence model" is an algorithm used to synthesize a user's full-body image with a product image, and has the ability to position the product in an appropriate size and position.

[0844] The "sharing means" is an interface for sharing the generated try-on images with other people, and has the function of easily sending images via social networking sites, email, etc.

[0845] The present invention is a system that provides users with a visual experience of trying on products when shopping online. In this system, users input their height data, upload a full-body image, and select products they are interested in. The server then generates a try-on image tailored to the user's body type and displays it on the terminal.

[0846] First, the user enters their height data into the system. This data is used to obtain fitting images of the correct size. Next, the user follows a specified guide to take a full-body image and uploads it to the server via their device. This full-body image becomes the base data for generating fitting images.

[0847] The server stores the received height data and full-body image and sends a list of available products to the device. The product list includes information on a wide range of products, including clothing, hats, shoes, bags, and accessories. The user selects products of interest from the product list on the device. The selected product data is sent back to the server via the device and used to generate try-on images.

[0848] The server then composites the selected product image onto the user's full-body image at the appropriate size and position. This involves first scaling the product image based on height data and then positioning the product appropriately to fit the user's body shape. Using generative AI models, the server adjusts the size and shape of clothing and accessories to recreate a natural fit. It also simultaneously accommodates multiple selected products, adjusting the position and size of each.

[0849] The generated try-on images are sent from the server to the device. The device displays the received try-on images, allowing the user to view them from various perspectives, such as full-body view, upper body view, lower body view, and bust-up view. The device also provides an interface for sharing the try-on images with family and friends. Using this interface, users can easily send the try-on images via applications such as social networking sites and email.

[0850] As a specific example, let's consider the case where a female user who is 160 cm tall uploads a full-body image of herself and tries on a red dress, black heels, and gold earrings. The user inputs and uploads her height data (160 cm) and a full-body image she has taken to her device, and the device sends this data to the server. The server receives and stores the data, and sends a list of available products to the device. The list includes a red dress, black heels, and gold earrings, and the user selects these products, and the selection data is sent to the server.

[0851] The server overlays the selected product onto the user's full-body image, adjusts the dress size to fit the user's height of 160cm, and places the black heels and gold earrings in the appropriate positions. The try-on image generated through the synthesis is sent to the user's device and displayed to the user. The user can check the try-on image using options such as full-body or upper-body view. Finally, the user can share the try-on image with friends via LINE and get their opinions. Through this process, the user can experience the feeling of actually trying on the clothes.

[0852] An example of a prompt is as follows:

[0853] "I uploaded a full-body photo of me, 160cm tall. Try on a red dress, black heels, and gold earrings."

[0854] "A female user who is 160cm tall has selected a red dress, black heels, and gold earrings. Please composite these items into a full-body image."

[0855] In this way, the present invention provides a concrete solution to solve the problems of online shopping and improve user satisfaction.

[0856] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0857] Step 1:

[0858] The user inputs height data.

[0859] Input: The user inputs their height (e.g., 160 cm) on the terminal screen.

[0860] Output: The entered height data is saved on the device.

[0861] Specific operation: When the user enters their height in the input field and presses the confirmation button, the entered data is saved in a variable within the device.

[0862] Step 2:

[0863] The user takes a full-body image and uploads it to the server via the terminal.

[0864] Input: The user takes a full-body image using a smartphone or camera and uploads the image to the device.

[0865] Output: The uploaded whole-body image data is saved on the device.

[0866] Specific operation: The user takes a full-body image using the camera app and presses the "upload" button, which saves the image file to the device.

[0867] Step 3:

[0868] The terminal transmits height data and a full-body image to the server.

[0869] Input: Height data and whole-body image data stored on the device.

[0870] Output: Height data and whole body image data sent to the server.

[0871] Specific operation: The device detects the "Send" button and sends the saved height data and full-body image to the server using the HTTPS protocol. The data is encrypted and received by the server.

[0872] Step 4:

[0873] The server stores the received data and generates a list of available products.

[0874] Input: Height data and whole body image data sent to the server.

[0875] Output: The generated product list.

[0876] Specific operation: The server stores the received data in a database, identifies the user, and generates a list of available products from the associated product database.

[0877] Step 5:

[0878] The server sends the product list to the terminal.

[0879] Input: Generated product list.

[0880] Output: The product list sent to the terminal.

[0881] Specific operation: The server sends a product list to the terminal, and the terminal uses middleware to receive the data necessary to display the list.

[0882] Step 6:

[0883] The user uses the terminal to select the product they want to try on from the product list.

[0884] Input: Product list.

[0885] Output: Selected product data.

[0886] Specific operation: The user selects a product of interest from the product list displayed on the device screen and presses the "Select" button. The selected product data is temporarily saved on the device.

[0887] Step 7:

[0888] The terminal transmits the selected product data to the server.

[0889] Input: Selected product data.

[0890] Output: The product data sent to the server.

[0891] Specific operation: The terminal sends product data to the server via an HTTP request, and the server receives and processes it.

[0892] Step 8:

[0893] The server synthesizes the product image with the full-body image at an appropriate size and position to generate a try-on image.

[0894] Input: whole body image data, height data, product data.

[0895] Output: The generated try-on image.

[0896] How it works: The server uses the generated AI model to analyze the full-body image and product image, adjust the scale of the product image based on the customer's height, and generate a try-on image by naturally overlaying the scaled product image on the full-body image.

[0897] Step 9:

[0898] The server transmits the generated try-on image to the terminal.

[0899] Input: Generated try-on images.

[0900] Output: Try-on image sent to the device.

[0901] Specific operation: The server encodes the try-on image and sends it to the device as an HTTP response. The device stores the received image in temporary memory.

[0902] Step 10:

[0903] The device displays the try-on image.

[0904] Input: Try-on images sent from the server.

[0905] Output: Try-on image shown to the user.

[0906] Specific behavior: The device displays try-on images within the application and loads a UI that gives the user viewing options such as full body, upper body, lower body, and bust-up.

[0907] Step 11:

[0908] The device provides an interface for sharing try-on images.

[0909] Input: Try-on image.

[0910] Output: Shared try-on images.

[0911] Specific operation: The device detects that the share button has been pressed and displays an interface for transferring images to social media or email applications. The user selects a destination and the image is shared.

[0912] (Application example 1)

[0913] 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."

[0914] When shopping online, it is difficult to check the fit and appearance of products before purchasing them because customers cannot actually try them on. This often leads to dissatisfaction with product size or design after purchase. Furthermore, virtual stores, in particular, are required to provide a level of realism that makes users feel as if they are actually trying on products. Furthermore, generating and displaying try-on images in real time is technically challenging, making it difficult for existing systems to address this issue. New technologies are needed to address this issue and improve users' online shopping experiences.

[0915] 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.

[0916] In this invention, the server includes means for inputting height data, means for uploading a full-body image, means for selecting product data, means for transmitting the height data and the full-body image to the server, means for the server to generate a list of available products and transmit it to the terminal, means for the terminal to transmit data on the selected products to the server, means for the server to synthesize product images with the full-body image at appropriate sizes and positions to generate try-on images, means for transmitting the generated try-on images to the terminal, means for the terminal to display and share the try-on images, means for using a deep learning model to generate and display try-on images in real time, and means for synthesizing the full-body image and product images using the deep learning model. This allows users to experience trying on products in real time in a virtual store.

[0917] "Height data" is information relating to the height of the user, and is data used to appropriately adjust the scale of the product when generating a try-on image.

[0918] A "full-body image" is a photograph including the user's entire body, and serves as basic data for generating try-on images.

[0919] "Product Data" means data containing detailed information about products available for purchase online, including images, sizes, prices, etc.

[0920] The "server" is a computer system that stores and processes data received from users, and is a device that generates try-on images and provides product lists.

[0921] A "deep learning model" is an artificial intelligence model that uses deep learning algorithms to analyze data and generate try-on images.

[0922] A "product list" is data that lists products that can be selected by the user, and is sent from the server to the terminal.

[0923] The "try-on image" is an image showing a virtual try-on state, generated by combining a selected product image with a full-body image of the user.

[0924] A "terminal" is a device used by a user to input data and check try-on images, and includes smartphones, personal computers, etc.

[0925] "Synthesis" is the process of combining a full-body image of the user with an image of the product in the appropriate size and position to recreate a natural fitting look.

[0926] "Real-time" refers to immediate response to user operations. In the case of try-on image generation, this means that try-on images are generated and displayed immediately every time a user selects an item.

[0927] The system of the present invention generates try-on images in real time using height data and a full-body image to provide a visual experience that makes users feel as if they are trying on products during online shopping. To achieve this, the system uses a deep learning model to generate try-on images.

[0928] System configuration

[0929] Hardware

[0930] Device: A smartphone or computer used by the user to input height data, upload full-body images, and view try-on images.

[0931] Server: A computer system that stores data sent by users, generates try-on images using a deep learning model, and sends them to the device.

[0932] software

[0933] Programming language: Python

[0934] Image processing library: OpenCV

[0935] Deep Learning Framework: TensorFlow

[0936] Image manipulation library: PIL (Pillow)

[0937] System Operation

[0938] User operations

[0939] 1. The user enters their height data on the terminal.

[0940] 2. The user takes a full-body image using a camera and uploads it to the server via the device.

[0941] 3. The user selects a product from the list of available products sent by the server.

[0942] Server Processing

[0943] 1. The server stores the received height data and full-body image.

[0944] 2. Based on the data of the selected product, the server uses a deep learning model to synthesize a full-body image of the user and an image of the product, generating a try-on image that reproduces a natural fit.

[0945] 3. The generated try-on images are sent to the device in real time.

[0946] Terminal display

[0947] 1. The device immediately displays the received try-on images. The user can view the images using options such as full-body view, upper-body view, and lower-body view.

[0948] 2. Users can share try-on images with family and friends via LINE, email, etc.

[0949] Specific examples

[0950] For example, consider a case where a user who is 160 cm tall wants to try on a red dress. The user first enters their height data and uploads a full-body image to their device. Next, they select a red dress from a list of available products. The server receives this data and uses a deep learning model to synthesize the red dress onto the user's full-body image. This synthesized try-on image is sent to the user's device in real time and displayed. The user can review this image and, if necessary, share it with friends to get their opinions.

[0951] Prompt Sentence Examples

[0952] "I want to see in real time how I look trying on a red dress at 160cm height."

[0953] This system can significantly improve the user's shopping experience in virtual stores. Real-time generation of try-on images can solve the problem of product selection and increase online shopping satisfaction.

[0954] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0955] Step 1:

[0956] The user inputs their height data into the terminal. The input data is saved as "user height" and used to adjust the scale of product images in subsequent processing.

[0957] Step 2:

[0958] The user takes a full-body image using a camera and uploads it to the server via their device. This full-body image ("user image") becomes the basic data for generating try-on images.

[0959] Step 3:

[0960] The server stores the received user height data and user image, which will be used in the next processing step.

[0961] Step 4:

[0962] The server generates a list of available products and sends it to the terminal, from which the user can select the products they wish to try on.

[0963] Step 5:

[0964] The product data selected by the user ("selected product") is sent to the server via the terminal again. The product data includes product images, sizes, category information, etc.

[0965] Step 6:

[0966] The server receives the selected product data and uses a deep learning model to synthesize the user image with the product image. First, the scale of the product image is adjusted based on the user's height data, and then the deep learning model is used to adjust the position to recreate a natural fit.

[0967] Step 7:

[0968] The server then sends the generated try-on images to the device, which are generated in real time and displayed instantly on the device.

[0969] Step 8:

[0970] The device displays the received fitting images. The user can check the fitting images using options such as full-body display, upper body display, and lower body display. The fitting images can also be shared with family and friends via LINE or email.

[0971] Step 9:

[0972] Users can view try-on images and make a final purchase decision, and can also get feedback from friends and family using the sharing feature.

[0973] These steps allow users to have a real-time visual experience that makes them feel as if they are actually trying on products in a virtual store.

[0974] 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.

[0975] This invention combines an emotion engine with a system that provides a visual experience that makes users feel as if they are trying on products online, recognizing the user's emotions and personalizing the try-on experience based on that emotion data. In this system, users input their height data, upload a full-body image, and select the products they want to try on. The server then generates try-on images tailored to the user's body type and displays them on the device. The emotion engine also recognizes the user's emotions, and that data is used within the system.

[0976] System operation explanation

[0977] User Preferences

[0978] First, the user enters their height data into the system. This height data is used to generate fitting images of the appropriate size for the user. Next, the user follows a specified guide to take a full-body image and uploads it to the server via their device. This full-body image becomes the basic data for generating fitting images.

[0979] Processing on the server

[0980] The server stores the received height data and full-body image, then sends a list of available products to the device, including a wide range of items such as clothes, hats, shoes, bags, and accessories.

[0981] Product selection and emotional data collection

[0982] The user selects a product they are interested in from a product list on their device. During this process, the emotion engine collects emotional data from the user's facial expressions, voice, operation patterns, etc., and sends this data to the server, where it is stored.

[0983] Generation of try-on images

[0984] The server composites the selected product image with the user's full-body image at the appropriate size and position. The composite process first scales the product image based on the user's height data, then applies an algorithm to position it appropriately relative to the full-body image. Even if multiple products are selected, the composite process adjusts the position and size of each product.

[0985] Emotion data analysis and image display

[0986] The server analyzes the emotional data provided by the emotion engine and adjusts the display content to match the user's emotions. For example, if the user is happy, it can add effects to the try-on images to emphasize that emotion. It also recommends other products that the user might be interested in based on the emotional data.

[0987] Displaying try-on images

[0988] The generated try-on images are sent from the server to the device, where they are displayed, allowing the user to check the details using options such as full-body view, upper-body view, lower-body view, and bust-up view.

[0989] Sharing try-on images

[0990] The device also provides an interface for sharing try-on images with family and friends, allowing users to easily share images via applications such as LINE and email.

[0991] Specific examples

[0992] For example, consider the case where a female user who is 160 cm tall uploads a full-body image of herself and tries on a red dress, black heels, and gold earrings.

[0993] First, the user inputs and uploads their height data (160cm) and a full-body photo to the device. The device then sends this data to the server. The server receives and stores the data, then sends a list of available items to the device. This list includes a red dress, black heels, and gold earrings.

[0994] The user selects one of these products, and the emotion engine collects emotional data from the user's facial expressions and voice during the selection process. The selection and emotional data are sent to the server, which then overlays the selected product onto the user's full-body image. The server adjusts the dress size to fit the user's height of 160cm, and also positions the black heels and gold earrings appropriately. The synthesized try-on image is sent from the server to the user's device and displayed to the user. The user can then review the try-on image using options such as full-body or upper-body display.

[0995] Finally, users share the try-on images with friends via LINE and receive their opinions. Through this process, the invention combines an experience that feels like trying on clothes with a personalized try-on service based on emotions.

[0996] In this way, the present invention provides a concrete solution to solve the problems of online shopping and improve user satisfaction.

[0997] The processing flow will be explained below.

[0998] Step 1:

[0999] The user inputs their height data and follows the guide to take a full-body image, which is then uploaded to the device.

[1000] Step 2:

[1001] The device sends the height data entered by the user and the uploaded full-body image to the server.

[1002] Step 3:

[1003] The server stores the received height data and full-body image, generates a list of products that can be offered to the user, and sends the list to the terminal.

[1004] Step 4:

[1005] The terminal displays the product list received from the server to the user, who then selects the product they wish to try on from the product list.

[1006] Step 5:

[1007] As the user selects a product, the emotion engine collects emotion data from the user's facial expressions, voice, and operation patterns. The device then transmits the selected product data and emotion data to the server.

[1008] Step 6:

[1009] The server composites the selected product image with the user's full-body image at the appropriate size and position. First, it scales the product image based on the height data, and then positions it appropriately relative to the full-body image. If multiple products are selected, the server composites them while adjusting the position and size of each product.

[1010] Step 7:

[1011] The server analyzes the emotion data provided by the emotion engine and adjusts the display content of the try-on images to match the user's emotions. For example, if the user is happy, an effect that matches that emotion is added to the try-on images.

[1012] Step 8:

[1013] The try-on image generated by the server is sent to the terminal.

[1014] Step 9:

[1015] The terminal displays the try-on images received from the server to the user, who can check the images from various perspectives, such as full-body view, upper-body view, lower-body view, and bust-up view.

[1016] Step 10:

[1017] The device provides an interface for sharing try-on images with family and friends. Users can share try-on images via applications such as LINE or email.

[1018] Step 11:

[1019] Based on the emotion data, the server recommends other products that the user may be interested in. The recommendation information is sent to the terminal and displayed to the user.

[1020] Example 2

[1021] 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."

[1022] While online shopping is very popular these days, the inability to actually touch and try on products poses a major challenge for customers. Furthermore, the try-on experience is uniform and not personalized based on individual customer preferences and emotions, which can discourage purchasing. This invention aims to solve these challenges.

[1023] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting height data, a means for uploading a full-body image, a means for selecting product data, a means for collecting user emotion data using an emotion engine and adjusting the display content, and a means for sending the generated try-on images to the terminal. This allows the online try-on experience to be personalized based on the emotions of each individual user, making for a more realistic try-on experience.

[1024] "Height data" is numerical information about the user's height, and is used for scaling and compositing product images.

[1025] A "full-body image" is an image of the user's entire body, and is used as basic data for generating try-on images.

[1026] "Product data" refers to data including images of products to be tried on and related information.

[1027] The "emotion engine" is a software component that collects and analyzes emotional data in real time from the user's facial expressions, voice, operation patterns, etc.

[1028] "Emotion data" is data that represents the user's emotional state collected by the emotion engine.

[1029] The "server" is a computer system that processes data received from a user and generates and transmits try-on images.

[1030] A "terminal" is a device operated by a user that provides an interface for communicating with a server and sending and receiving data.

[1031] A "try-on image" is a composite image in which a product image is superimposed on a full-body image of the user, and is intended to provide a virtual try-on experience.

[1032] A "product list" is a server-generated list of available products from which a user can select.

[1033] A "user" is an individual who uses the system to try on products online.

[1034] The present invention relates to a system that provides a visual experience that makes users feel as if they are actually wearing the products when trying on products online. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotions and personalize the try-on experience based on the emotion data.

[1035] System Overview

[1036] The system includes the following components:

[1037] A means of inputting user height data

[1038] A means to upload a full-body image of the user

[1039] How to select product data

[1040] A means of sending height data and a full-body image to the server

[1041] A means to collect user emotion data using an emotion engine and send it to a server

[1042] The server synthesizes the product image with the full-body image at the appropriate size and position to generate a try-on image.

[1043] A means for the server to analyze emotional data and adjust the content displayed

[1044] A means for sending the generated try-on image to the terminal

[1045] A means for the device to display and share try-on images

[1046] User Interface

[1047] The user inputs their height data, takes a full-body image using the device's camera function, and uploads this data to the server. This allows the user to obtain accurate images of the clothes they will be trying on. The device uses an emotion engine to collect emotional data from the user's facial expressions, voice, operation patterns, etc. This emotional data is sent to the server and used to generate and personalize the try-on images.

[1048] Server Processing

[1049] The server stores the received height data and full-body image, generates a list of available products, and sends it to the terminal. It acquires the product data and emotion data selected by the user, and generates a try-on image by combining the product image with the full-body image at an appropriate size and position. The generated try-on image is personalized based on the emotion data; for example, if the user is happy, an effect that emphasizes that emotion is added. Finally, the server sends the generated try-on image to the terminal and displays it for the user.

[1050] Specific examples

[1051] For example, imagine a female user who is 160 cm tall uploads a full-body image of herself and tries on a red dress, black heels, and gold earrings. The user first enters her height data (160 cm) and a full-body image into the device and uploads it. The device then sends this data to the server, which receives and stores it. The server then generates a list of available products and sends it to the device. As the user tries on the selected products, the emotion engine collects emotional data from the user's facial expressions and voice. This data is sent to the server, and the try-on image is personalized based on the emotional data. For example, if the user is happy with the red dress, an effect that emphasizes that emotion is added to the try-on image. Finally, the generated try-on image is sent to the device so that the user can view it.

[1052] Example prompts for generative AI models

[1053] Here are some example prompts to input to a generative AI model:

[1054] "Please describe the process for a 160cm tall user to upload a full-body image and try on a red dress, black heels, and gold earrings. Also, please detail how the user's emotional data is used to personalize the try-on image."

[1055] Using these prompts, the generative AI model can generate answers on specific ways to make the user's try-on experience more realistic and personalized.

[1056] By using the above-described techniques, the present invention improves the try-on experience in online shopping and increases user satisfaction.

[1057] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1058] Step 1: Initial User Setup

[1059] 1.1. A user launches an application and creates an account by entering personal information. Input data includes username, email address, password, etc. This information is sent from the device to the server, which stores it in a database. The output is a message confirming the account creation.

[1060] 1.2. The user inputs their height data. The input data includes the user's height (e.g., 160 cm). This data is sent from the terminal to the server, which saves it in the database. The output is a message confirming that the height data has been saved.

[1061] 1.3. A user takes a full-body image of themselves on their device and uploads it to the server. The input data includes the captured full-body image file. This image file is uploaded from the device to the server, and the server stores it in a database. The output is a message confirming the image upload.

[1062] Step 2: Provide a product list

[1063] 2.1. The server generates a list of available products based on the stored data. The input data includes the product catalog stored on the server. This data is processed to filter and generate a list of products based on the user's height and body type. The output is the generated list of products.

[1064] 2.2. Sending the available product list to the terminal. The input data includes the generated product list. This list is sent to the terminal and displayed for the user to view. The output is the product list displayed on the terminal.

[1065] 2.3. The user selects the product of interest on the device. The input data includes the product ID selected by the user. This information is sent from the device to the server, which retrieves the selected product data. The output is the data of the selected product.

[1066] Step 3: Collecting emotion data

[1067] 3.1. The emotion engine monitors the user's facial expressions, voice, and operation patterns. The input data includes the user's real-time facial expressions and voice data. The emotion engine analyzes these data to identify the user's emotional state. The output is the analyzed emotion data.

[1068] 3.2. Collect emotion data in real time and send it to the server. The input data includes emotion data generated by the emotion engine. This data is sent from the device to the server, which stores it in a database. The output is the stored emotion data.

[1069] Step 4: Generate try-on images

[1070] 4.1. The server acquires the user's full-body image and the selected product image. The input data includes the full-body image and the selected product image stored on the server. Processing begins based on this data. The output is a message confirming the acquisition of both data.

[1071] 4.2. The server adjusts the scale of the product image based on the user's height data. The input data includes the user's height data and the selected product image. The server adjusts the size of the product image based on this data. The output is the scaled product image.

[1072] The server composites the scaled product image into a full-body image. The input data includes the scaled product image and a full-body image. The algorithm is applied to composite the images. The output is a composite try-on image.

[1073] Step 5: Analyze emotion data and display images

[1074] 5.1. The server analyzes the emotion data provided by the emotion engine. The input data includes the collected emotion data. This data is analyzed to determine the appropriate effect based on the user's emotion. The output is the effect information.

[1075] 5.2. Adjust the display content of the try-on image based on the user's emotions. The input data includes the synthesized try-on image and the determined effect information. Effects are added to the try-on image based on this data. The output is the adjusted try-on image.

[1076] Step 6: Viewing try-on images

[1077] 6.1. The server sends the generated try-on image to the terminal. The input data includes the adjusted try-on image. This image is sent to the terminal. The output is the try-on image sent to the terminal.

[1078] 6.2. The terminal displays the try-on image to the user. The input data includes the try-on image sent to the terminal. This image is displayed to the user. The output is the displayed try-on image.

[1079] Step 7: Share your try-on photos

[1080] 7.1. Provide an interface for a device to share try-on images. The input data includes try-on images displayed on the device. The interface for sharing these images is displayed. The output is the sharing interface.

[1081] 7.2. The user shares try-on images with family and friends via LINE or email. The input data includes a sharing interface and try-on images. This data is used to share try-on images. The output is the shared try-on images.

[1082] (Application example 2)

[1083] 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."

[1084] When selecting products online, it is difficult for users to check the fit and appearance of products without actually trying them on. Furthermore, the inability to recommend or tailor products based on the user's individual emotions and preferences can lead to reduced user satisfaction. A particular challenge is the lack of a personalized try-on experience that utilizes emotion recognition.

[1085] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1086] In this invention, the server includes means for inputting height data, means for uploading a full-body image, means for selecting product data, means for transmitting the height data and the full-body image to the server, means for the server to generate a list of available products and transmit it to the terminal, means for the terminal to transmit data on the selected products to the server, means for the server to combine the product images with the full-body image at appropriate sizes and positions to generate try-on images, means for transmitting the generated try-on images to the terminal, means for the terminal to display and share the try-on images, means for collecting user emotion data using an emotion engine and transmitting it to the server, and means for personalizing the try-on images based on the emotion data, thereby enabling the user to have a personalized try-on experience based on their own emotions.

[1087] "Height data" is information about the height of the user, and is basic data for generating try-on images.

[1088] The "full-body image" is an image of the user's entire body, and is basic data for generating try-on images.

[1089] "Product data" is information about the product that the customer wishes to try on, and is data that is sent from the terminal to the server.

[1090] A "server" is a computer system that stores and processes data received from users.

[1091] A "product list" is a list of products that can be tried on, provided by the server.

[1092] The "emotion engine" is a system that recognizes emotions from the user's facial expressions, voice, etc. and collects that data.

[1093] "Synthesis" is a process of combining a full-body image of the user with a selected product image to generate a try-on image.

[1094] "Personalization" means customizing the try-on experience and display content to suit the user's individual feelings and preferences.

[1095] A "try-on image" is an image generated by combining a product selected by the user with a full-body image.

[1096] A "terminal" is a device that a user operates to input and display data.

[1097] "Sharing" means sending and receiving the generated try-on image with other people via communication means.

[1098] "Effects" are visual effects or filters added to try-on images.

[1099] The present invention is a system for enabling users to try on products online and receive a personalized experience based on emotional data. The system is implemented using the following hardware and software:

[1100] Hardware

[1101] Smartphone or tablet: A device with a camera and internet connection.

[1102] Server: A computer system that performs processes such as facial expression analysis and try-on image generation.

[1103] software

[1104] OpenCV: A library for image processing.

[1105] dlib: A library for detecting facial feature points, etc.

[1106] EmotionRecognizer: A custom library for recognizing emotions from facial expressions.

[1107] VirtualTryOn: A custom library for generating virtual try-on images.

[1108] Recommendation Engine: A custom library that adjusts and recommends try-on images based on emotions.

[1109] System operation explanation

[1110] User Preferences

[1111] Users input their height data, take a full-body image using a smartphone or tablet, and upload it to the system. The server receives and stores this data.

[1112] Get product list

[1113] The server generates a list of available products and sends it to the device. The user selects the products they want to try on from the list. During this process, the emotion engine collects emotion data from the user's facial expressions and voice and sends it to the server.

[1114] Generation of try-on images

[1115] The server generates a fitting image by combining the image of the selected product with the user's full-body image at the appropriate size and position. Based on the emotion data provided by the emotion engine, the server can add effects to the fitting image and adjust the display content.

[1116] Viewing and sharing images

[1117] The generated try-on images are sent from the server to the device, where users can check the details using options such as full-body or partial view. Furthermore, users can share the try-on images with family and friends.

[1118] Specific examples

[1119] Let's take a specific example of a 160cm tall female user uploading a full-body image of herself and trying on a red dress, black heels, and gold earrings. The user first enters her height as 160cm and uploads a full-body image. The server receives the data, generates a list of available products, and sends it to the device. The user selects a product, and the emotion engine collects the user's emotions during the selection process. The server then scales the selected product to an appropriate size and combines it with the full-body image to generate a try-on image. The try-on image, with effects added based on the emotion data, is displayed on the device, and the user can view and share the image.

[1120] Prompt Sentence Examples

[1121] Generate Python code for a virtual try-on app that meets the following requirements:

[1122] It takes the user's height and a full-body image as input.

[1123] Collect user emotion data using an emotion recognition engine.

[1124] Select the item you want to try on from the product list.

[1125] The selected items are combined with a full-body image to generate a try-on image.

[1126] To personalize the display of try-on images based on user emotion data.

[1127] In this way, the present invention provides users with an emotionally-based and personalized try-on experience.

[1128] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1129] Step 1:

[1130] User Preferences

[1131] The user inputs their height data, which is later used to generate try-on images.

[1132] The user takes a full-body image using a smartphone or tablet and uploads this image to the system. The input is "height data" and "full-body image." The output is that "height data" and "full-body image" are sent to the server. This gives the server basic data for generating try-on images.

[1133] Step 2:

[1134] Get product list

[1135] The server stores the received height data and full-body image.

[1136] The server generates a list of available products and sends this list to the terminal. The input is "height data" and "full-body image." The output is the "product list" displayed on the terminal. This allows the user to check the products that can be tried on.

[1137] Step 3:

[1138] Product selection and emotional data collection

[1139] The user selects the product they wish to try on from the product list on the terminal.

[1140] The emotion engine monitors the user's facial expressions and operation patterns to collect emotion data. This emotion data is sent to the server. The input is "product selection" and "facial expression data." The output is "selected product data" and "emotion data" sent to the server. This allows the server to provide a personalized experience based on the user's emotions.

[1141] Step 4:

[1142] Generation of try-on images

[1143] The server synthesizes the selected product image with the user's full-body image at the appropriate size and position. The input is the "full-body image," "selected product data," and "height data."

[1144] The system scales and positions product images on a full-body image, and applies effects to the image based on emotion data. The output is a "try-on image," allowing users to visually confirm the results of trying on the product.

[1145] Step 5:

[1146] Viewing and sharing images

[1147] The generated try-on images are sent from the server to the device. The input is the "try-on image."

[1148] The device displays the generated try-on image, allowing the user to check it. The user can also share the try-on image with family and friends. The output is "display of try-on image" and "image sharing function." This allows the user to share the try-on image with other people to get their opinions.

[1149] 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.

[1150] 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.

[1151] 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.

[1152] [Fourth embodiment]

[1153] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1154] 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.

[1155] 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).

[1156] 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.

[1157] 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.

[1158] 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).

[1159] 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. 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.

[1160] 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.

[1161] 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.

[1162] 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.

[1163] 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.

[1164] 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.

[1165] 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."

[1166] The present invention is a system that provides users with a visual experience of trying on products when shopping online. In this system, users input their height data, upload a full-body image, and select products they are interested in. The server then generates a try-on image tailored to the user's body type and displays it on the terminal.

[1167] System operation explanation

[1168] User Preferences

[1169] First, the user enters their height data into the system. This height data is used to obtain a fitting image of the correct size. Next, the user follows a specified guide to take a full-body image and uploads it to the server via their device. This full-body image becomes the base data for generating fitting images.

[1170] Processing on the server

[1171] The server stores the received height data and full-body image, then sends a list of available products to the device, including a wide range of items such as clothes, hats, shoes, bags, and accessories.

[1172] Product selection and data transmission

[1173] The user selects the product of interest from the product list on the terminal. The selected product data is then sent back to the server via the terminal. The selected product data includes product ID, category information, etc., and is used to generate try-on images on the server side.

[1174] Generation of try-on images

[1175] The server composites the selected product image onto the user's full-body image at the appropriate size and position. To do this, it first adjusts the scale of the product image based on the user's height data. Then it positions the product appropriately to fit the user's body shape. The composition algorithm adjusts the size and shape to recreate a natural fit of the clothing. It also handles multiple selected products simultaneously, adjusting the position and size of each.

[1176] Displaying try-on images

[1177] Once the try-on images are generated, the server sends them to the device, which then displays them. The user can view the images from various perspectives, including full-body, upper-body, lower-body, and bust-up views.

[1178] Sharing try-on images

[1179] The device also provides an interface for sharing try-on images with family and friends, allowing users to easily send try-on images via applications such as LINE and email.

[1180] Specific examples

[1181] For example, let us consider the case where a female user who is 160 cm tall uploads a full-body image of herself and tries on a red dress, black heels, and gold earrings.

[1182] First, the user inputs and uploads their height data (160cm) and a full-body image to the device. The device then sends this data to the server. The server receives and stores the data. The server then sends a list of available products to the device.

[1183] The list includes a red dress, black heels, and gold earrings. The user selects these items and the selection is sent to the server.

[1184] The server overlays the selected item onto the user's full-body image. The dress size is adjusted to fit the user's height of 160cm, and the black heels and gold earrings are also placed in the appropriate positions. The synthesized try-on image is sent to the terminal and displayed to the user. The user can check the try-on image using options such as full-body view or upper-body view.

[1185] Finally, users can share the try-on images with friends via LINE and get their opinions. Through this process, users can get the same experience as actually trying on the items. This allows them to check the suitability of the items they are purchasing, making online shopping more satisfying.

[1186] In this way, the present invention provides a concrete solution to solve the problems of online shopping and improve user satisfaction.

[1187] The processing flow will be explained below.

[1188] Step 1:

[1189] The user inputs their height and follows the guide to take a full-body image, which is then uploaded to the device.

[1190] Step 2:

[1191] The device sends the height data entered by the user and the uploaded full-body image to the server.

[1192] Step 3:

[1193] The server stores the received height data and full-body image, generates a list of products that can be offered to the user, and sends the list to the terminal.

[1194] Step 4:

[1195] The terminal displays the product list received from the server to the user, who then selects the product they wish to try on from the product list.

[1196] Step 5:

[1197] The terminal transmits data on the product selected by the user, such as the product ID and category information, to the server.

[1198] Step 6:

[1199] The server starts the process of combining the selected product image with the user's full-body image in an appropriate size and position.

[1200] First, the product image is scaled based on the height data, and then appropriately positioned relative to the full-body image.

[1201] When multiple products are selected, the images are composited while adjusting the position and size of each product appropriately.

[1202] Step 7:

[1203] The try-on image generated by the server is sent to the terminal.

[1204] Step 8:

[1205] The terminal displays the try-on images received from the server to the user, who can check the images from various perspectives, such as full-body view, upper-body view, lower-body view, and bust-up view.

[1206] Step 9:

[1207] The device provides an interface for sharing try-on images with family and friends. Users can share try-on images via applications such as LINE or email.

[1208] Example 1

[1209] 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."

[1210] When selecting products online, there is a demand for a visual experience equivalent to the try-on experience in a physical store. However, conventional systems have difficulty generating try-on images that fit the user's body type, resulting in low user satisfaction with product selection. Furthermore, functions such as sharing try-on images or partial display are not adequately provided, which prevents users from increasing their motivation to purchase. This leaves the challenge of improving the user experience and product purchase rates.

[1211] 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.

[1212] In this invention, the server includes means for a user to input height data, means for a user to upload a full-body image, means for a user to select product data, means for a terminal to send the height data and full-body image to the server, means for the server to generate a list of available products and send it to the terminal, means for the terminal to send data on the selected products to the server, means for the server to combine product images with the full-body image at an appropriate size and position and generate try-on images using a generative artificial intelligence model, means for sending the generated try-on images to the terminal, and means for the terminal to display and share the try-on images. This allows users to easily obtain try-on images that suit their body type, and further increases their motivation to purchase by sharing try-on images and using the partial display function.

[1213] "Height data" is data that the user inputs as a numerical value representing his or her height, and is information that serves as a reference for generating try-on images.

[1214] The "full-body image" is image data of the user's entire body, and serves as the basis for the try-on image.

[1215] "Product data" refers to information about products that can be selected in online shopping, and includes product IDs, categories, image URLs, etc.

[1216] A "terminal" is an electronic device operated by a user, which has the functions of inputting and displaying data and communicating with a server.

[1217] The "server" is a remote computer system that receives and stores user input data, generates product lists, and synthesizes try-on images.

[1218] A "product list" is a list of detailed information about available products that is generated by the server and sent to the terminal.

[1219] A "try-on image" is an image generated by combining a full-body image of the user with a product image, and provides a realistic try-on experience.

[1220] A "generative artificial intelligence model" is an algorithm used to synthesize a user's full-body image with a product image, and has the ability to position the product in an appropriate size and position.

[1221] The "sharing means" is an interface for sharing the generated try-on images with other people, and has the function of easily sending images via social networking sites, email, etc.

[1222] The present invention is a system that provides users with a visual experience of trying on products when shopping online. In this system, users input their height data, upload a full-body image, and select products they are interested in. The server then generates a try-on image tailored to the user's body type and displays it on the terminal.

[1223] First, the user enters their height data into the system. This data is used to obtain fitting images of the correct size. Next, the user follows a specified guide to take a full-body image and uploads it to the server via their device. This full-body image becomes the base data for generating fitting images.

[1224] The server stores the received height data and full-body image and sends a list of available products to the device. The product list includes information on a wide range of products, including clothing, hats, shoes, bags, and accessories. The user selects products of interest from the product list on the device. The selected product data is sent back to the server via the device and used to generate try-on images.

[1225] The server then composites the selected product image onto the user's full-body image at the appropriate size and position. This involves first scaling the product image based on height data and then positioning the product appropriately to fit the user's body shape. Using generative AI models, the server adjusts the size and shape of clothing and accessories to recreate a natural fit. It also simultaneously accommodates multiple selected products, adjusting the position and size of each.

[1226] The generated try-on images are sent from the server to the device. The device displays the received try-on images, allowing the user to view them from various perspectives, such as full-body view, upper body view, lower body view, and bust-up view. The device also provides an interface for sharing the try-on images with family and friends. Using this interface, users can easily send the try-on images via applications such as social networking sites and email.

[1227] As a specific example, let's consider the case where a female user who is 160 cm tall uploads a full-body image of herself and tries on a red dress, black heels, and gold earrings. The user inputs and uploads her height data (160 cm) and a full-body image she has taken to her device, and the device sends this data to the server. The server receives and stores the data, and sends a list of available products to the device. The list includes a red dress, black heels, and gold earrings, and the user selects these products, and the selection data is sent to the server.

[1228] The server overlays the selected product onto the user's full-body image, adjusts the dress size to fit the user's height of 160cm, and places the black heels and gold earrings in the appropriate positions. The try-on image generated through the synthesis is sent to the user's device and displayed to the user. The user can check the try-on image using options such as full-body or upper-body view. Finally, the user can share the try-on image with friends via LINE and get their opinions. Through this process, the user can experience the feeling of actually trying on the clothes.

[1229] An example of a prompt is as follows:

[1230] "I uploaded a full-body photo of me, 160cm tall. Try on a red dress, black heels, and gold earrings."

[1231] "A female user who is 160cm tall has selected a red dress, black heels, and gold earrings. Please composite these items into a full-body image."

[1232] In this way, the present invention provides a concrete solution to solve the problems of online shopping and improve user satisfaction.

[1233] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1234] Step 1:

[1235] The user inputs height data.

[1236] Input: The user inputs their height (e.g., 160 cm) on the terminal screen.

[1237] Output: The entered height data is saved on the device.

[1238] Specific operation: When the user enters their height in the input field and presses the confirmation button, the entered data is saved in a variable within the device.

[1239] Step 2:

[1240] The user takes a full-body image and uploads it to the server via the terminal.

[1241] Input: The user takes a full-body image using a smartphone or camera and uploads the image to the device.

[1242] Output: The uploaded whole-body image data is saved on the device.

[1243] Specific operation: The user takes a full-body image using the camera app and presses the "upload" button, which saves the image file to the device.

[1244] Step 3:

[1245] The terminal transmits height data and a full-body image to the server.

[1246] Input: Height data and whole-body image data stored on the device.

[1247] Output: Height data and whole body image data sent to the server.

[1248] Specific operation: The device detects the "Send" button and sends the saved height data and full-body image to the server using the HTTPS protocol. The data is encrypted and received by the server.

[1249] Step 4:

[1250] The server stores the received data and generates a list of available products.

[1251] Input: Height data and whole body image data sent to the server.

[1252] Output: The generated product list.

[1253] Specific operation: The server stores the received data in a database, identifies the user, and generates a list of available products from the associated product database.

[1254] Step 5:

[1255] The server sends the product list to the terminal.

[1256] Input: Generated product list.

[1257] Output: The product list sent to the terminal.

[1258] Specific operation: The server sends a product list to the terminal, and the terminal uses middleware to receive the data necessary to display the list.

[1259] Step 6:

[1260] The user uses the terminal to select the product they want to try on from the product list.

[1261] Input: Product list.

[1262] Output: Selected product data.

[1263] Specific operation: The user selects a product of interest from the product list displayed on the device screen and presses the "Select" button. The selected product data is temporarily saved on the device.

[1264] Step 7:

[1265] The terminal transmits the selected product data to the server.

[1266] Input: Selected product data.

[1267] Output: The product data sent to the server.

[1268] Specific operation: The terminal sends product data to the server via an HTTP request, and the server receives and processes it.

[1269] Step 8:

[1270] The server synthesizes the product image with the full-body image at an appropriate size and position to generate a try-on image.

[1271] Input: whole body image data, height data, product data.

[1272] Output: The generated try-on image.

[1273] How it works: The server uses the generated AI model to analyze the full-body image and product image, adjust the scale of the product image based on the customer's height, and generate a try-on image by naturally overlaying the scaled product image on the full-body image.

[1274] Step 9:

[1275] The server transmits the generated try-on image to the terminal.

[1276] Input: Generated try-on images.

[1277] Output: Try-on image sent to the device.

[1278] Specific operation: The server encodes the try-on image and sends it to the device as an HTTP response. The device stores the received image in temporary memory.

[1279] Step 10:

[1280] The device displays the try-on image.

[1281] Input: Try-on images sent from the server.

[1282] Output: Try-on image shown to the user.

[1283] Specific behavior: The device displays try-on images within the application and loads a UI that gives the user viewing options such as full body, upper body, lower body, and bust-up.

[1284] Step 11:

[1285] The device provides an interface for sharing try-on images.

[1286] Input: Try-on image.

[1287] Output: Shared try-on images.

[1288] Specific operation: The device detects that the share button has been pressed and displays an interface for transferring images to social media or email applications. The user selects a destination and the image is shared.

[1289] (Application example 1)

[1290] 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."

[1291] When shopping online, it is difficult to check the fit and appearance of products before purchasing them because customers cannot actually try them on. This often leads to dissatisfaction with product size or design after purchase. Furthermore, virtual stores, in particular, are required to provide a level of realism that makes users feel as if they are actually trying on products. Furthermore, generating and displaying try-on images in real time is technically challenging, making it difficult for existing systems to address this issue. New technologies are needed to address this issue and improve users' online shopping experiences.

[1292] 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.

[1293] In this invention, the server includes means for inputting height data, means for uploading a full-body image, means for selecting product data, means for transmitting the height data and the full-body image to the server, means for the server to generate a list of available products and transmit it to the terminal, means for the terminal to transmit data on the selected products to the server, means for the server to synthesize product images with the full-body image at appropriate sizes and positions to generate try-on images, means for transmitting the generated try-on images to the terminal, means for the terminal to display and share the try-on images, means for using a deep learning model to generate and display try-on images in real time, and means for synthesizing the full-body image and product images using the deep learning model. This allows users to experience trying on products in real time in a virtual store.

[1294] "Height data" is information relating to the height of the user, and is data used to appropriately adjust the scale of the product when generating a try-on image.

[1295] A "full-body image" is a photograph including the user's entire body, and serves as basic data for generating try-on images.

[1296] "Product Data" means data containing detailed information about products available for purchase online, including images, sizes, prices, etc.

[1297] The "server" is a computer system that stores and processes data received from users, and is a device that generates try-on images and provides product lists.

[1298] A "deep learning model" is an artificial intelligence model that uses deep learning algorithms to analyze data and generate try-on images.

[1299] A "product list" is data that lists products that can be selected by the user, and is sent from the server to the terminal.

[1300] The "try-on image" is an image showing a virtual try-on state, generated by combining a selected product image with a full-body image of the user.

[1301] A "terminal" is a device used by a user to input data and check try-on images, and includes smartphones, personal computers, etc.

[1302] "Synthesis" is the process of combining a full-body image of the user with an image of the product in the appropriate size and position to recreate a natural fitting look.

[1303] "Real-time" refers to immediate response to user operations. In the case of try-on image generation, this means that try-on images are generated and displayed immediately every time a user selects an item.

[1304] The system of the present invention generates try-on images in real time using height data and a full-body image to provide a visual experience that makes users feel as if they are trying on products during online shopping. To achieve this, the system uses a deep learning model to generate try-on images.

[1305] System configuration

[1306] Hardware

[1307] Device: A smartphone or computer used by the user to input height data, upload full-body images, and view try-on images.

[1308] Server: A computer system that stores data sent by users, generates try-on images using a deep learning model, and sends them to the device.

[1309] software

[1310] Programming language: Python

[1311] Image processing library: OpenCV

[1312] Deep Learning Framework: TensorFlow

[1313] Image manipulation library: PIL (Pillow)

[1314] System Operation

[1315] User operations

[1316] 1. The user enters their height data on the terminal.

[1317] 2. The user takes a full-body image using a camera and uploads it to the server via the device.

[1318] 3. The user selects a product from the list of available products sent by the server.

[1319] Server Processing

[1320] 1. The server stores the received height data and full-body image.

[1321] 2. Based on the data of the selected product, the server uses a deep learning model to synthesize a full-body image of the user and an image of the product, generating a try-on image that reproduces a natural fit.

[1322] 3. The generated try-on images are sent to the device in real time.

[1323] Terminal display

[1324] 1. The device immediately displays the received try-on images. The user can view the images using options such as full-body view, upper-body view, and lower-body view.

[1325] 2. Users can share try-on images with family and friends via LINE, email, etc.

[1326] Specific examples

[1327] For example, consider a case where a user who is 160 cm tall wants to try on a red dress. The user first enters their height data and uploads a full-body image to their device. Next, they select a red dress from a list of available products. The server receives this data and uses a deep learning model to synthesize the red dress onto the user's full-body image. This synthesized try-on image is sent to the user's device in real time and displayed. The user can review this image and, if necessary, share it with friends to get their opinions.

[1328] Prompt Sentence Examples

[1329] "I want to see in real time how I look trying on a red dress at 160cm height."

[1330] This system can significantly improve the user's shopping experience in virtual stores. Real-time generation of try-on images can solve the problem of product selection and increase online shopping satisfaction.

[1331] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1332] Step 1:

[1333] The user inputs their height data into the terminal. The input data is saved as "user height" and used to adjust the scale of product images in subsequent processing.

[1334] Step 2:

[1335] The user takes a full-body image using a camera and uploads it to the server via their device. This full-body image ("user image") becomes the basic data for generating try-on images.

[1336] Step 3:

[1337] The server stores the received user height data and user image, which will be used in the next processing step.

[1338] Step 4:

[1339] The server generates a list of available products and sends it to the terminal, from which the user can select the products they wish to try on.

[1340] Step 5:

[1341] The product data selected by the user ("selected product") is sent to the server via the terminal again. The product data includes product images, sizes, category information, etc.

[1342] Step 6:

[1343] The server receives the selected product data and uses a deep learning model to synthesize the user image with the product image. First, the scale of the product image is adjusted based on the user's height data, and then the deep learning model is used to adjust the position to recreate a natural fit.

[1344] Step 7:

[1345] The server then sends the generated try-on images to the device, which are generated in real time and displayed instantly on the device.

[1346] Step 8:

[1347] The device displays the received fitting images. The user can check the fitting images using options such as full-body display, upper body display, and lower body display. The fitting images can also be shared with family and friends via LINE or email.

[1348] Step 9:

[1349] Users can view try-on images and make a final purchase decision, and can also get feedback from friends and family using the sharing feature.

[1350] These steps allow users to have a real-time visual experience that makes them feel as if they are actually trying on products in a virtual store.

[1351] 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.

[1352] This invention combines an emotion engine with a system that provides a visual experience that makes users feel as if they are trying on products online, recognizing the user's emotions and personalizing the try-on experience based on that emotion data. In this system, users input their height data, upload a full-body image, and select the products they want to try on. The server then generates try-on images tailored to the user's body type and displays them on the device. The emotion engine also recognizes the user's emotions, and that data is used within the system.

[1353] System operation explanation

[1354] User Preferences

[1355] First, the user enters their height data into the system. This height data is used to generate fitting images of the appropriate size for the user. Next, the user follows a specified guide to take a full-body image and uploads it to the server via their device. This full-body image becomes the basic data for generating fitting images.

[1356] Processing on the server

[1357] The server stores the received height data and full-body image, then sends a list of available products to the device, including a wide range of items such as clothes, hats, shoes, bags, and accessories.

[1358] Product selection and emotional data collection

[1359] The user selects a product they are interested in from a product list on their device. During this process, the emotion engine collects emotional data from the user's facial expressions, voice, operation patterns, etc., and sends this data to the server, where it is stored.

[1360] Generation of try-on images

[1361] The server composites the selected product image with the user's full-body image at the appropriate size and position. The composite process first scales the product image based on the user's height data, then applies an algorithm to position it appropriately relative to the full-body image. Even if multiple products are selected, the composite process adjusts the position and size of each product.

[1362] Emotion data analysis and image display

[1363] The server analyzes the emotional data provided by the emotion engine and adjusts the display content to match the user's emotions. For example, if the user is happy, it can add effects to the try-on images to emphasize that emotion. It also recommends other products that the user might be interested in based on the emotional data.

[1364] Displaying try-on images

[1365] The generated try-on images are sent from the server to the device, where they are displayed, allowing the user to check the details using options such as full-body view, upper-body view, lower-body view, and bust-up view.

[1366] Sharing try-on images

[1367] The device also provides an interface for sharing try-on images with family and friends, allowing users to easily share images via applications such as LINE and email.

[1368] Specific examples

[1369] For example, consider the case where a female user who is 160 cm tall uploads a full-body image of herself and tries on a red dress, black heels, and gold earrings.

[1370] First, the user inputs and uploads their height data (160cm) and a full-body photo to the device. The device then sends this data to the server. The server receives and stores the data, then sends a list of available items to the device. This list includes a red dress, black heels, and gold earrings.

[1371] The user selects one of these products, and the emotion engine collects emotional data from the user's facial expressions and voice during the selection process. The selection and emotional data are sent to the server, which then overlays the selected product onto the user's full-body image. The server adjusts the dress size to fit the user's height of 160cm, and also positions the black heels and gold earrings appropriately. The synthesized try-on image is sent from the server to the user's device and displayed to the user. The user can further review the try-on image using options such as full-body view or upper-body view.

[1372] Finally, users share the try-on images with friends via LINE and receive their opinions. Through this process, the invention combines an experience that feels like trying on clothes with a personalized try-on service based on emotions.

[1373] In this way, the present invention provides a concrete solution to solve the problems of online shopping and improve user satisfaction.

[1374] The processing flow will be explained below.

[1375] Step 1:

[1376] The user inputs their height data and follows the guide to take a full-body image, which is then uploaded to the device.

[1377] Step 2:

[1378] The device sends the height data entered by the user and the uploaded full-body image to the server.

[1379] Step 3:

[1380] The server stores the received height data and full-body image, generates a list of products that can be offered to the user, and sends the list to the terminal.

[1381] Step 4:

[1382] The terminal displays the product list received from the server to the user, who then selects the product they wish to try on from the product list.

[1383] Step 5:

[1384] As the user selects a product, the emotion engine collects emotion data from the user's facial expressions, voice, and operation patterns. The device then transmits the selected product data and emotion data to the server.

[1385] Step 6:

[1386] The server composites the selected product image with the user's full-body image at the appropriate size and position. First, it scales the product image based on the height data, and then positions it appropriately relative to the full-body image. If multiple products are selected, the server composites them while adjusting the position and size of each product.

[1387] Step 7:

[1388] The server analyzes the emotion data provided by the emotion engine and adjusts the display content of the try-on images to match the user's emotions. For example, if the user is happy, an effect that matches that emotion is added to the try-on images.

[1389] Step 8:

[1390] The try-on image generated by the server is sent to the terminal.

[1391] Step 9:

[1392] The terminal displays the try-on images received from the server to the user, who can check the images from various perspectives, such as full-body view, upper-body view, lower-body view, and bust-up view.

[1393] Step 10:

[1394] The device provides an interface for sharing try-on images with family and friends. Users can share try-on images via applications such as LINE or email.

[1395] Step 11:

[1396] Based on the emotion data, the server recommends other products that the user may be interested in. The recommendation information is sent to the terminal and displayed to the user.

[1397] Example 2

[1398] 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."

[1399] While online shopping is very popular these days, the inability to actually touch and try on products poses a major challenge for customers. Furthermore, the try-on experience is uniform and not personalized based on individual customer preferences and emotions, which can discourage purchasing. This invention aims to solve these challenges.

[1400] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting height data, a means for uploading a full-body image, a means for selecting product data, a means for collecting user emotion data using an emotion engine and adjusting the display content, and a means for sending the generated try-on images to the terminal. This allows the online try-on experience to be personalized based on the emotions of each individual user, making for a more realistic try-on experience.

[1401] "Height data" is numerical information about the user's height, and is used for scaling and compositing product images.

[1402] A "full-body image" is an image of the user's entire body, and is used as basic data for generating try-on images.

[1403] "Product data" refers to data including images of products to be tried on and related information.

[1404] The "emotion engine" is a software component that collects and analyzes emotional data in real time from the user's facial expressions, voice, operation patterns, etc.

[1405] "Emotion data" is data that represents the user's emotional state collected by the emotion engine.

[1406] The "server" is a computer system that processes data received from a user and generates and transmits try-on images.

[1407] A "terminal" is a device operated by a user that provides an interface for communicating with a server and sending and receiving data.

[1408] A "try-on image" is a composite image in which a product image is superimposed on a full-body image of the user, and is intended to provide a virtual try-on experience.

[1409] A "product list" is a server-generated list of available products from which a user can select.

[1410] A "user" is an individual who uses the system to try on products online.

[1411] The present invention relates to a system that provides a visual experience that makes users feel as if they are actually wearing the products when trying on products online. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotions and personalize the try-on experience based on the emotion data.

[1412] System Overview

[1413] The system includes the following components:

[1414] A means of inputting user height data

[1415] A means to upload a full-body image of the user

[1416] How to select product data

[1417] A means of sending height data and a full-body image to the server

[1418] A means to collect user emotion data using an emotion engine and send it to a server

[1419] The server synthesizes the product image with the full-body image at the appropriate size and position to generate a try-on image.

[1420] A means for the server to analyze emotional data and adjust the content displayed

[1421] A means for sending the generated try-on image to the terminal

[1422] A means for the device to display and share try-on images

[1423] User Interface

[1424] The user inputs their height data, takes a full-body image using the device's camera function, and uploads this data to the server. This allows the user to obtain accurate images of the clothes they will be trying on. The device uses an emotion engine to collect emotional data from the user's facial expressions, voice, operation patterns, etc. This emotional data is sent to the server and used to generate and personalize the try-on images.

[1425] Server Processing

[1426] The server stores the received height data and full-body image, generates a list of available products, and sends it to the terminal. It acquires the product data and emotion data selected by the user, and generates a try-on image by combining the product image with the full-body image at an appropriate size and position. The generated try-on image is personalized based on the emotion data; for example, if the user is happy, an effect that emphasizes that emotion is added. Finally, the server sends the generated try-on image to the terminal and displays it for the user.

[1427] Specific examples

[1428] For example, imagine a female user who is 160 cm tall uploads a full-body image of herself and tries on a red dress, black heels, and gold earrings. The user first enters her height data (160 cm) and a full-body image into the device and uploads it. The device then sends this data to the server, which receives and stores it. The server then generates a list of available products and sends it to the device. As the user tries on the selected products, the emotion engine collects emotional data from the user's facial expressions and voice. This data is sent to the server, and the try-on image is personalized based on the emotional data. For example, if the user is happy with the red dress, an effect that emphasizes that emotion is added to the try-on image. Finally, the generated try-on image is sent to the device so that the user can view it.

[1429] Example prompts for generative AI models

[1430] Here are some example prompts to input to a generative AI model:

[1431] "Please describe the process for a 160cm tall user to upload a full-body image and try on a red dress, black heels, and gold earrings. Also, please detail how the user's emotional data is used to personalize the try-on image."

[1432] Using these prompts, the generative AI model can generate answers on specific ways to make the user's try-on experience more realistic and personalized.

[1433] By using the above-described techniques, the present invention improves the try-on experience in online shopping and increases user satisfaction.

[1434] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1435] Step 1: Initial User Setup

[1436] 1.1. A user launches an application and creates an account by entering personal information. Input data includes username, email address, password, etc. This information is sent from the device to the server, which stores it in a database. The output is a message confirming the account creation.

[1437] 1.2. The user inputs their height data. The input data includes the user's height (e.g., 160 cm). This data is sent from the terminal to the server, which saves it in the database. The output is a message confirming that the height data has been saved.

[1438] 1.3. A user takes a full-body image of themselves on their device and uploads it to the server. The input data includes the captured full-body image file. This image file is uploaded from the device to the server, and the server stores it in a database. The output is a message confirming the image upload.

[1439] Step 2: Provide a product list

[1440] 2.1. The server generates a list of available products based on the stored data. The input data includes the product catalog stored on the server. This data is processed to filter and generate a list of products based on the user's height and body type. The output is the generated list of products.

[1441] 2.2. Sending the available product list to the terminal. The input data includes the generated product list. This list is sent to the terminal and displayed for the user to view. The output is the product list displayed on the terminal.

[1442] 2.3. The user selects the product of interest on the device. The input data includes the product ID selected by the user. This information is sent from the device to the server, which retrieves the selected product data. The output is the data of the selected product.

[1443] Step 3: Collecting emotion data

[1444] 3.1. The emotion engine monitors the user's facial expressions, voice, and operation patterns. The input data includes the user's real-time facial expressions and voice data. The emotion engine analyzes these data to identify the user's emotional state. The output is the analyzed emotion data.

[1445] 3.2. Collect emotion data in real time and send it to the server. The input data includes emotion data generated by the emotion engine. This data is sent from the device to the server, which stores it in a database. The output is the stored emotion data.

[1446] Step 4: Generate try-on images

[1447] 4.1. The server acquires the user's full-body image and the selected product image. The input data includes the full-body image and the selected product image stored on the server. Processing begins based on this data. The output is a message confirming the acquisition of both data.

[1448] 4.2. The server adjusts the scale of the product image based on the user's height data. The input data includes the user's height data and the selected product image. The server adjusts the size of the product image based on this data. The output is the scaled product image.

[1449] The server composites the scaled product image into a full-body image. The input data includes the scaled product image and a full-body image. The algorithm is applied to composite the images. The output is a composite try-on image.

[1450] Step 5: Analyze emotion data and display images

[1451] 5.1. The server analyzes the emotion data provided by the emotion engine. The input data includes the collected emotion data. This data is analyzed to determine the appropriate effect based on the user's emotion. The output is the effect information.

[1452] 5.2. Adjust the display content of the try-on image based on the user's emotions. The input data includes the synthesized try-on image and the determined effect information. Effects are added to the try-on image based on this data. The output is the adjusted try-on image.

[1453] Step 6: Viewing try-on images

[1454] 6.1. The server sends the generated try-on image to the terminal. The input data includes the adjusted try-on image. This image is sent to the terminal. The output is the try-on image sent to the terminal.

[1455] 6.2. The terminal displays the try-on image to the user. The input data includes the try-on image sent to the terminal. This image is displayed to the user. The output is the displayed try-on image.

[1456] Step 7: Share your try-on photos

[1457] 7.1. Provide an interface for a device to share try-on images. The input data includes try-on images displayed on the device. The interface for sharing these images is displayed. The output is the sharing interface.

[1458] 7.2. The user shares try-on images with family and friends via LINE or email. The input data includes a sharing interface and try-on images. This data is used to share try-on images. The output is the shared try-on images.

[1459] (Application example 2)

[1460] 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."

[1461] When selecting products online, it is difficult for users to check the fit and appearance of products without actually trying them on. Furthermore, the inability to recommend or tailor products based on the user's individual emotions and preferences can lead to reduced user satisfaction. A particular challenge is the lack of a personalized try-on experience that utilizes emotion recognition.

[1462] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1463] In this invention, the server includes means for inputting height data, means for uploading a full-body image, means for selecting product data, means for transmitting the height data and the full-body image to the server, means for the server to generate a list of available products and transmit it to the terminal, means for the terminal to transmit data on the selected products to the server, means for the server to combine the product images with the full-body image at appropriate sizes and positions to generate try-on images, means for transmitting the generated try-on images to the terminal, means for the terminal to display and share the try-on images, means for collecting user emotion data using an emotion engine and transmitting it to the server, and means for personalizing the try-on images based on the emotion data, thereby enabling the user to have a personalized try-on experience based on their own emotions.

[1464] "Height data" is information about the height of the user, and is basic data for generating try-on images.

[1465] The "full-body image" is an image of the user's entire body, and is basic data for generating try-on images.

[1466] "Product data" is information about the product that the customer wishes to try on, and is data that is sent from the terminal to the server.

[1467] A "server" is a computer system that stores and processes data received from users.

[1468] A "product list" is a list of products that can be tried on, provided by the server.

[1469] The "emotion engine" is a system that recognizes emotions from the user's facial expressions, voice, etc. and collects that data.

[1470] "Synthesis" is a process of combining a full-body image of the user with a selected product image to generate a try-on image.

[1471] "Personalization" means customizing the try-on experience and display content to suit the user's individual feelings and preferences.

[1472] A "try-on image" is an image generated by combining a product selected by the user with a full-body image.

[1473] A "terminal" is a device that a user operates to input and display data.

[1474] "Sharing" means sending and receiving the generated try-on image with other people via communication means.

[1475] "Effects" are visual effects or filters added to try-on images.

[1476] The present invention is a system for enabling users to try on products online and receive a personalized experience based on emotional data. The system is implemented using the following hardware and software:

[1477] Hardware

[1478] Smartphone or tablet: A device with a camera and internet connection.

[1479] Server: A computer system that performs processes such as facial expression analysis and try-on image generation.

[1480] software

[1481] OpenCV: A library for image processing.

[1482] dlib: A library for detecting facial feature points, etc.

[1483] EmotionRecognizer: A custom library for recognizing emotions from facial expressions.

[1484] VirtualTryOn: A custom library for generating virtual try-on images.

[1485] Recommendation Engine: A custom library that adjusts and recommends try-on images based on emotions.

[1486] System operation explanation

[1487] User Preferences

[1488] Users input their height data, take a full-body image using a smartphone or tablet, and upload it to the system. The server receives and stores this data.

[1489] Get product list

[1490] The server generates a list of available products and sends it to the device. The user selects the products they want to try on from the list. During this process, the emotion engine collects emotion data from the user's facial expressions and voice and sends it to the server.

[1491] Generation of try-on images

[1492] The server generates a fitting image by combining the image of the selected product with the user's full-body image at the appropriate size and position. Based on the emotion data provided by the emotion engine, the server can add effects to the fitting image and adjust the display content.

[1493] Viewing and sharing images

[1494] The generated try-on images are sent from the server to the device, where users can check the details using options such as full-body or partial view. Furthermore, users can share the try-on images with family and friends.

[1495] Specific examples

[1496] Let's take a specific example of a 160cm tall female user uploading a full-body image of herself and trying on a red dress, black heels, and gold earrings. The user first enters her height as 160cm and uploads a full-body image. The server receives the data, generates a list of available products, and sends it to the device. The user selects a product, and the emotion engine collects the user's emotions during the selection process. The server then scales the selected product to an appropriate size and combines it with the full-body image to generate a try-on image. The try-on image, with effects added based on the emotion data, is displayed on the device, and the user can view and share the image.

[1497] Prompt Sentence Examples

[1498] Generate Python code for a virtual try-on app that meets the following requirements:

[1499] It takes the user's height and a full-body image as input.

[1500] Collect user emotion data using an emotion recognition engine.

[1501] Select the item you want to try on from the product list.

[1502] The selected items are combined with a full-body image to generate a try-on image.

[1503] To personalize the display of try-on images based on user emotion data.

[1504] In this way, the present invention provides users with an emotionally-based and personalized try-on experience.

[1505] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1506] Step 1:

[1507] User Preferences

[1508] The user inputs their height data, which is later used to generate try-on images.

[1509] The user takes a full-body image using a smartphone or tablet and uploads this image to the system. The input is "height data" and "full-body image." The output is that "height data" and "full-body image" are sent to the server. This gives the server basic data for generating try-on images.

[1510] Step 2:

[1511] Get product list

[1512] The server stores the received height data and full-body image.

[1513] The server generates a list of available products and sends this list to the terminal. The input is "height data" and "full-body image." The output is the "product list" displayed on the terminal. This allows the user to check the products that can be tried on.

[1514] Step 3:

[1515] Product selection and emotional data collection

[1516] The user selects the product they wish to try on from the product list on the terminal.

[1517] The emotion engine monitors the user's facial expressions and operation patterns to collect emotion data. This emotion data is sent to the server. The input is "product selection" and "facial expression data." The output is "selected product data" and "emotion data" sent to the server. This allows the server to provide a personalized experience based on the user's emotions.

[1518] Step 4:

[1519] Generation of try-on images

[1520] The server synthesizes the selected product image with the user's full-body image at the appropriate size and position. The input is the "full-body image," "selected product data," and "height data."

[1521] The system scales and positions product images on a full-body image, and applies effects to the image based on emotion data. The output is a "try-on image," allowing users to visually confirm the results of trying on the product.

[1522] Step 5:

[1523] Viewing and sharing images

[1524] The generated try-on images are sent from the server to the device. The input is the "try-on image."

[1525] The device displays the generated try-on image, allowing the user to check it. The user can also share the try-on image with family and friends. The output is "display of try-on image" and "image sharing function." This allows the user to share the try-on image with other people to get their opinions.

[1526] 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.

[1527] 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.

[1528] 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.

[1529] 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.

[1530] 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.

[1531] 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.

[1532] 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).

[1533] 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.

[1534] 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."

[1535] 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.

[1536] 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).

[1537] 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.

[1538] 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.

[1539] 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.

[1540] 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.

[1541] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1542] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1543] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1544] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1545] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1546] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1547] The following is further disclosed regarding the above embodiment.

[1548] (Claim 1)

[1549] a means for inputting height data;

[1550] A means to upload a full-body image,

[1551] a means for selecting product data;

[1552] means for transmitting height data and a whole-body image to a server;

[1553] A means for the server to generate a list of available products and transmit the list to the terminal;

[1554] A means for transmitting data of the selected product from the terminal to a server;

[1555] A means for the server to synthesize an image of a product with a full-body image at an appropriate size and position to generate a try-on image;

[1556] means for transmitting the generated try-on image to a terminal;

[1557] A means for the device to display and share try-on images;

[1558] A system including:

[1559] (Claim 2)

[1560] A means for providing an option to display try-on images of each body part of a full-body image;

[1561] Further, a means for adjusting the product size based on the height data is provided.

[1562] 10. The system of claim 1.

[1563] (Claim 3)

[1564] The device further comprises means for providing an interface for sharing the try-on image with family and friends.

[1565] 10. The system of claim 1.

[1566] "Example 1"

[1567] (Claim 1)

[1568] a means for a user to input height data;

[1569] a means for a user to upload a full-body image;

[1570] A means for a user to select product data;

[1571] A means for the terminal to transmit height data and a whole-body image to a server;

[1572] A means for the server to generate a list of available products and transmit the list to the terminal;

[1573] A means for transmitting data of the selected product from the terminal to a server;

[1574] A means for the server to synthesize an image of a product with a full-body image at an appropriate size and position, and generate a try-on image using a generative artificial intelligence model;

[1575] means for transmitting the generated try-on image to a terminal;

[1576] A means for the device to display and share try-on images;

[1577] A system including:

[1578] (Claim 2)

[1579] A means for the terminal to provide an option to display try-on images of each body part of the whole body image;

[1580] The server further comprises means for adjusting product size based on height data.

[1581] 10. The system of claim 1.

[1582] (Claim 3)

[1583] The terminal further comprises means for providing an interface for sharing the try-on images with family and friends.

[1584] 10. The system of claim 1.

[1585] "Application Example 1"

[1586] (Claim 1)

[1587] a means for inputting height data;

[1588] A means to upload a full-body image,

[1589] a means for selecting product data;

[1590] means for transmitting height data and a whole-body image to a server;

[1591] A means for the server to generate a list of available products and transmit the list to the terminal;

[1592] A means for transmitting data of the selected product from the terminal to a server;

[1593] A means for the server to synthesize an image of a product with a full-body image at an appropriate size and position to generate a try-on image;

[1594] means for transmitting the generated try-on image to a terminal;

[1595] A means for the device to display and share try-on images;

[1596] A means for utilizing a deep learning model to generate and display try-on images in real time;

[1597] A method for synthesizing a full-body image and a product image using a deep learning model;

[1598] A system including:

[1599] (Claim 2)

[1600] A means for providing an option to display try-on images of each body part of a full-body image;

[1601] A means for adjusting product size based on height data;

[1602] The device further includes a fitting image generation means using a deep learning model.

[1603] 10. The system of claim 1.

[1604] (Claim 3)

[1605] A means for providing an interface for sharing try-on images with family and friends;

[1606] and means for using a deep learning model to generate try-on images in real time.

[1607] 10. The system of claim 1.

[1608] "Example 2: Combining Emotion Engines"

[1609] (Claim 1)

[1610] a means for inputting height data;

[1611] A means to upload a full-body image,

[1612] a means for selecting product data;

[1613] means for transmitting height data and a whole-body image to a server;

[1614] A means for the server to generate a list of available products and transmit the list to the terminal;

[1615] A means for transmitting data of the selected product from the terminal to a server;

[1616] means for collecting user emotion data by an emotion engine and transmitting the data to a server;

[1617] A means for the server to synthesize an image of a product with a full-body image at an appropriate size and position to generate a try-on image;

[1618] A means for the server to analyze the emotion data and adjust the display content;

[1619] means for transmitting the generated try-on image to a terminal;

[1620] A means for the device to display and share try-on images;

[1621] A system including:

[1622] (Claim 2)

[1623] A means for providing an option to display try-on images of each body part of a full-body image;

[1624] Further, a means for adjusting the product size based on the height data is provided.

[1625] 10. The system of claim 1.

[1626] (Claim 3)

[1627] The device further comprises means for providing an interface for sharing the try-on image with family and friends.

[1628] 10. The system of claim 1.

[1629] "Application example 2 when combining emotion engines"

[1630] (Claim 1)

[1631] a means for inputting height data;

[1632] A means to upload a full-body image,

[1633] a means for selecting product data;

[1634] means for transmitting height data and a whole-body image to a server;

[1635] A means for the server to generate a list of available products and transmit the list to the terminal;

[1636] A means for transmitting data of the selected product from the terminal to a server;

[1637] A means for the server to synthesize an image of a product with a full-body image at an appropriate size and position to generate a try-on image;

[1638] means for transmitting the generated try-on image to a terminal;

[1639] A means for the device to display and share try-on images;

[1640] means for collecting user emotion data using an emotion engine and transmitting the data to a server;

[1641] means for personalizing try-on images based on emotion data;

[1642] A system including:

[1643] (Claim 2)

[1644] A means for providing an option to display try-on images of each body part of a full-body image;

[1645] A means for adjusting product size based on height data;

[1646] The device further comprises means for adjusting the effects and display contents of the try-on images based on the emotion data.

[1647] 10. The system of claim 1.

[1648] (Claim 3)

[1649] The device further comprises means for providing an interface for sharing the try-on image with family and friends.

[1650] 10. The system of claim 1. [Explanation of symbols]

[1651] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for inputting height data; A means to upload a full-body image, a means for selecting product data; means for transmitting height data and a whole-body image to a server; A means for the server to generate a list of available products and transmit the list to the terminal; A means for transmitting data of the selected product from the terminal to a server; A means for the server to synthesize an image of a product with a full-body image at an appropriate size and position to generate a try-on image; means for transmitting the generated try-on image to a terminal; A means for the device to display and share try-on images; A system including:

2. A means for providing an option to display try-on images of each body part of a full-body image; Further, a means for adjusting the product size based on the height data is provided. The system of claim 1 .

3. The device further comprises means for providing an interface for sharing the try-on image with family and friends. The system of claim 1 .

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