System

Abstract

A system is provided.SOLUTION: A system, comprising: means for obtaining data of clothing to be discarded; means for obtaining profile information of a consumer; means for suggesting matching clothing from the clothing data based on the profile information of the consumer using a generative artificial intelligence model; means for displaying information of the suggested clothing to the consumer; and means for confirming and notifying purchase of the selected clothing by the consumer.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] Currently, many apparel stores simply discard clothing that is scheduled for disposal, resulting in a large amount of waste and environmental impact. To solve this problem, there is a need to efficiently provide these clothing to consumers and reuse them. However, traditional sales methods make it difficult to offer suggestions that match consumer preferences, and are unable to stimulate purchasing motivation. Many apparel stores are also concerned that the brand image of clothing scheduled for disposal will be damaged. Furthermore, one of the concerns about online purchases is that the product may be "different from what was expected," which is a barrier to online purchases. [Means for solving the problem]

[0005] The present invention provides a system that efficiently matches consumers with clothing to be discarded and makes appropriate recommendations to them. The system acquires data on clothing to be discarded and acquires consumer profile information. Then, it uses a generative artificial intelligence model to recommend suitable clothing based on the consumer's profile information. It also includes a means for displaying information on the recommended clothing to the consumer, confirming the purchase of the clothing selected by the consumer, and notifying the apparel store. Furthermore, by receiving data from apparel stores, storing it in a database, and providing a try-on simulation using virtual reality and augmented reality technology, it is possible to stimulate consumers' desire to purchase and alleviate their anxiety about online purchases. This reduces the waste of discarded clothing and reduces the environmental impact.

[0006] "Clothing scheduled for disposal" refers to clothing that is not scheduled to be sold in apparel stores and is likely to be disposed of.

[0007] "Consumer profile information" refers to personal information necessary for suggesting clothing to purchase, such as the consumer's body type, clothing preferences, and budget.

[0008] A "generative artificial intelligence model" is a model that uses artificial intelligence technology to make suggestions that match consumer preferences based on acquired data.

[0009] A "database" is an information system for effectively managing, storing, and retrieving multiple data.

[0010] "Virtual reality (VR)" and "augmented reality (AR)" are technologies that overlay computer-generated information onto a real-time environment, providing users with a real-time interactive experience.

[0011] "Try-on simulation" is a method that uses computer technology to visually allow consumers to experience the look and fit of clothing when trying it on, without actually trying it on.

[0012] An "apparel store" is a retailer that sells fashion items such as clothing.

[0013] "Confirming purchase" is the process by which a consumer formally confirms their intention to purchase a product they have selected.

[0014] "Notification" is the act of informing others of a fact or information. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] This invention provides an online platform that provides consumers with discarded clothing, and is composed of three main components: a server, a terminal, and a user. The platform is equipped with a clothing recommendation function using a generative AI model to recommend the most suitable clothing based on the consumer's profile information. It also utilizes virtual reality and augmented reality technology to provide an online try-on experience, increasing consumer motivation and satisfaction.

[0037] Acquiring and saving clothing data

[0038] server

[0039] The server receives data on discarded clothing from apparel stores and stores it in a database. This data includes photos of the clothing, size, color, condition, etc. To prevent damage to the brand's image, apparel stores are encouraged to provide the data without tags or logos.

[0040] Examples:

[0041] The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and stores it in the database.

[0042] Consumer profile setting

[0043] User

[0044] Consumers enter their body type information (e.g., height, weight), clothing preferences (e.g., style, color), and budget on the device. Based on this profile information, the server will suggest the most suitable clothing for the consumer.

[0045] Terminal

[0046] The terminal sends the profile information entered by the user to the server, which then stores the profile information in a database.

[0047] Examples:

[0048] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[0049] Clothing suggestions using generative AI

[0050] server

[0051] The server uses a generative AI model to suggest optimal clothing based on the consumer's profile information, and selects clothing that matches the consumer's preferences and body shape from clothing data stored in a database.

[0052] Examples:

[0053] The server's generation AI filters for "casual style, blue, budget under 2,000 yen" and includes "T-shirt (size M, blue)" in the suggestion list.

[0054] View and select suggestions

[0055] Terminal

[0056] The terminal displays clothing suggestions sent from the server to the consumer and provides detailed information (photos, sizes, prices, etc.).

[0057] User

[0058] The user selects the clothing item they like from the suggested items and indicates their intention to purchase it (by pressing the purchase button).

[0059] Examples:

[0060] The device displays detailed information about the "T-shirt (size M, blue)" and the user presses the purchase button.

[0061] Purchase confirmation and shipping procedures

[0062] User

[0063] When the user presses the purchase button, they confirm the purchase and enter their payment information.

[0064] Terminal

[0065] The terminal transmits the purchase and payment information to the server.

[0066] server

[0067] The server verifies the payment information and confirms the purchase. The server then notifies the apparel store of the purchase information and proceeds with the purchase of the clothing. The server also pays a portion of the purchase price to the apparel store.

[0068] Apparel store

[0069] The apparel store will receive a confirmation of purchase and ship the garment to the specified address.

[0070] Examples:

[0071] After the purchase is confirmed, the server notifies the apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price. The apparel store then ships the clothing to the consumer's address.

[0072] Online try-on experience (optional)

[0073] server

[0074] The server generates a try-on simulation using virtual reality (VR) or augmented reality (AR) technology based on the consumer's profile information, and sends the simulated images and videos to the device.

[0075] Terminal

[0076] The device receives simulated images and videos and displays them to consumers, giving them a feeling similar to a real try-on experience.

[0077] Examples:

[0078] The server uses the consumer's profile information to generate a simulation image of what it would look like to try on a "T-shirt (size M, blue)" and sends it to the terminal. The terminal then displays the simulation image to the consumer, providing the feeling of trying it on.

[0079] By implementing the present invention in the above-described manner, an efficient system is realized for providing clothes that are scheduled to be discarded to consumers, thereby reducing the amount of discarded clothes and the burden on the environment.

[0080] The processing flow will be explained below.

[0081] Step 1:

[0082] server

[0083] Data on clothing to be discarded is received from apparel stores. The received data includes photos, size, color, condition, whether or not the item has tags, etc. The data is format-checked, and if there are no problems, it is stored in a database.

[0084] Examples:

[0085] The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and stores it in a database.

[0086] Step 2:

[0087] User

[0088] Enter your profile information on a dedicated screen on your device, including your height, weight, clothing preferences (style and color), budget, etc.

[0089] Terminal

[0090] The entered profile information is sent to the server.

[0091] Step 3:

[0092] server

[0093] The received profile information is stored in a database.

[0094] Examples:

[0095] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[0096] Step 4:

[0097] server

[0098] The system retrieves consumer profile information from the database, analyzes matching clothing using a generative artificial intelligence model, extracts matching clothing data, and generates a list.

[0099] Step 5:

[0100] server

[0101] Send detailed information about the generated clothing list (e.g., photos, sizes, prices, etc.) to the device.

[0102] Examples:

[0103] The server's generation AI filters items by "casual style, blue, budget within 2000 yen" and adds "T-shirt (size M, blue)" to the list of suggestions. This information is then sent to the device.

[0104] Step 6:

[0105] Terminal

[0106] Receives the clothing list sent from the server and displays it to the consumer. Displays detailed information.

[0107] User

[0108] Choose the clothing you like from the suggested items and indicate your intention to purchase (press the purchase button).

[0109] Examples:

[0110] The device displays detailed information about the "T-shirt (size M, blue)" and the user presses the purchase button.

[0111] Step 7:

[0112] User

[0113] By pressing the purchase button, you confirm your purchase and enter your payment information.

[0114] Step 8:

[0115] Terminal

[0116] Send purchase and payment information to the server.

[0117] Step 9:

[0118] server

[0119] The payment information is verified and the purchase is confirmed. The purchase information is notified to the apparel store, and the procedure for purchasing the clothing is carried out. A portion of the purchase price is also paid to the apparel store.

[0120] Examples:

[0121] After the server confirms the purchase, it notifies the apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price.

[0122] Step 10:

[0123] Apparel store

[0124] After receiving confirmation of purchase, the clothing will be shipped to the specified address.

[0125] Examples:

[0126] The apparel store will ship a "T-shirt (size M, blue)" to the specified address.

[0127] Step 11 (Optional):

[0128] server

[0129] Based on the consumer's profile information, a virtual reality (VR) or augmented reality (AR) try-on simulation is generated, and simulated images and videos are sent to the device.

[0130] Terminal

[0131] Receives simulated images and videos and displays them to consumers.

[0132] Examples:

[0133] The server generates a simulation of trying on a "T-shirt (size M, blue)" and sends it to the terminal. The terminal displays the simulation image, allowing the consumer to experience the sensation of trying on the item.

[0134] The above are the processing steps of the program in carrying out the present invention.

[0135] Example 1

[0136] 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."

[0137] While supporting modern comfortable lifestyles, the fashion industry faces the social problem of discarding large amounts of clothing. Discarded clothing places a burden on the environment, and a system is needed to effectively reduce this volume. Furthermore, to support consumers in choosing clothing, it is necessary to provide customized suggestions and realistic try-on experiences. To address these issues, it is necessary to realize a system that efficiently provides discarded clothing to consumers and improves consumer satisfaction.

[0138] 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.

[0139] In this invention, the server includes means for acquiring product data to be discarded, means for acquiring consumer profile information, means for using a generative artificial intelligence model to suggest suitable products from the product data based on the consumer profile information, means for displaying information on the suggested products to the consumer, means for confirming and notifying the purchase of the product selected by the consumer, means for creating a try-on simulation based on the consumer profile information and the suggested product information, means for generating simulation images and videos and sending them to a terminal, and means for displaying the try-on simulation data to the consumer. This makes it possible to efficiently provide clothes to be discarded, to make it easier for consumers to select appropriate clothes based on their own preferences, and to increase their desire to purchase through the try-on experience.

[0140] "Product data scheduled for disposal" refers to information about products that manufacturers, apparel stores, etc. had not planned to offer to consumers, but which they have discovered new value for and are now offering.

[0141] "Consumer profile information" refers to data provided by a consumer relating to personal characteristics and preferences, such as height, weight, preferred style, and budget.

[0142] "Generative artificial intelligence model" refers to a machine learning algorithm and model that selects and suggests optimal products from data on products scheduled for disposal based on consumer profile information.

[0143] "Means for suggesting suitable products" refers to a method and system that uses a generative artificial intelligence model to select and provide information on optimal products based on consumer profile information.

[0144] "Means for displaying product information" refers to the technology and devices that allow consumers to check detailed information such as photos, sizes, and prices of the proposed products through a user interface.

[0145] "Means for confirming and notifying purchase" refers to the methods and technologies that confirm a consumer's intention to purchase a selected product, process payment, and notify related systems and manufacturers of that information.

[0146] "Means for creating a try-on simulation" refers to methods and technologies that use virtual reality or augmented reality technology to recreate the state of a consumer trying on an item based on the consumer's profile information and proposed product information.

[0147] "Means for generating simulation images and videos and transmitting them to a terminal" refers to methods and technologies for generating images and videos for performing a fitting simulation and transmitting them to a consumer's terminal.

[0148] "Means for displaying try-on simulation data to consumers" refers to methods and technologies for displaying the received simulation images and videos on a terminal so that consumers can check them.

[0149] This invention provides an online platform that provides consumers with discarded products, and is composed of three main components: a server, a terminal, and a user. This platform is equipped with a product recommendation function that uses a generative artificial intelligence model to recommend optimal products based on the consumer's profile information. It also utilizes virtual reality and augmented reality technology to provide an online try-on experience, increasing consumer motivation and satisfaction.

[0150] Acquisition and storage of data on products scheduled for disposal

[0151] server

[0152] The server receives data on products to be discarded from manufacturers and apparel stores and stores that data in a database. This data includes product photos, sizes, colors, and conditions. Manufacturers and apparel stores are encouraged to provide the data without tags or logos to prevent damage to their brand image.

[0153] Examples:

[0154] The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and stores it in the database.

[0155] Consumer profile setting

[0156] User

[0157] Consumers enter their body type information (height, weight), clothing preferences (style, color), and budget on the device. Based on this profile information, the server will suggest the most suitable products for the consumer.

[0158] Terminal

[0159] The terminal transmits the profile information entered by the user to the server, and the server stores the profile information in a database.

[0160] Examples:

[0161] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[0162] Product suggestions by generative AI

[0163] server

[0164] The server uses a generative AI model to suggest optimal products based on the consumer's profile information. From product data stored in a database, it selects products that match the consumer's preferences and body shape. This generative AI model can use TensorFlow, for example.

[0165] Examples:

[0166] The server's generation AI filters for "casual style, blue, budget under 2,000 yen" and includes "T-shirt (size M, blue)" in the suggestion list.

[0167] View and select suggestions

[0168] Terminal

[0169] The terminal displays the product suggestions sent from the server to the consumer and provides detailed information (photos, sizes, prices, etc.).

[0170] User

[0171] The user selects the product they like from the suggested products and indicates their intention to purchase it (by pressing the purchase button).

[0172] Examples:

[0173] The device displays detailed information about the "T-shirt (size M, blue)" and the user presses the purchase button.

[0174] Purchase confirmation and shipping procedures

[0175] User

[0176] When the user presses the purchase button, they confirm the purchase and enter their payment information.

[0177] Terminal

[0178] The terminal transmits the purchase and payment information to the server.

[0179] server

[0180] The server verifies the payment information and confirms the purchase. The server then notifies the manufacturer or apparel store of the purchase information, processes the purchase of the product, and pays a portion of the purchase price to the manufacturer or apparel store.

[0181] Manufacturers and apparel stores

[0182] The manufacturer or apparel store will receive a confirmation of purchase and ship the product to the specified address.

[0183] Examples:

[0184] After the purchase is confirmed, the server notifies the manufacturer or apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price. The manufacturer or apparel store then ships the product to the consumer's address.

[0185] Online try-on experience (optional)

[0186] server

[0187] The server generates a try-on simulation using virtual reality (VR) or augmented reality (AR) technology based on the consumer's profile information, and sends the simulated images and videos to the device.

[0188] Terminal

[0189] The device receives simulated images and videos and displays them to consumers, giving them a feeling similar to a real try-on experience.

[0190] Examples:

[0191] The server uses the consumer's profile information to generate a simulation image of what it would look like to try on a "T-shirt (size M, blue)" and sends it to the terminal. The terminal then displays the simulation image to the consumer, providing the feeling of trying it on.

[0192] Prompt Sentence Examples

[0193] Examples of input prompts for a generative AI model include:

[0194] Examples:

[0195] User profile:

[0196] Height: 170cm

[0197] Weight: 60kg

[0198] Preference: Casual

[0199] Color: Blue

[0200] Budget: Under 2000 yen

[0201] Prompt the generative AI model:

[0202] Please suggest a casual style blue product for under 2000 yen.

[0203] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0204] Step 1: Acquire and store data on products to be disposed of

[0205] server

[0206] Input: Data on discarded products provided by manufacturers and apparel stores (e.g., product photos, size, color, condition, etc.)

[0207] Processing: The server receives product data from manufacturers and apparel stores via a data transmission API. The received data is stored in a database.

[0208] Output: Product data stored in a database

[0209] What happens: The server connects to the manufacturer's data system, receives data about "T-shirt (size M, blue, condition: like new)" via API, and stores it in a database.

[0210] Step 2: Setting up a consumer profile

[0211] User

[0212] Input: Your body shape information (height, weight), clothing preferences (style, color), budget (entered via web form, etc.)

[0213] Action: The user enters this information and clicks the submit button.

[0214] Output: The terminal stores the input and prepares it to be sent.

[0215] Specific actions: A user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: under 2000 yen" into a web form and presses the submit button.

[0216] Terminal

[0217] Input: Profile information entered by the user

[0218] Processing: The device checks and organizes the input and sends it to the server.

[0219] Output: Profile information sent to the server

[0220] Specific operation: The terminal sends the user input to the server via SSL communication.

[0221] Step 3: Product suggestions by generative AI

[0222] server

[0223] Input: Consumer profile information, product data stored in a database

[0224] Processing: Extract candidate product data from a database based on consumer profile information. Use a generative AI model (e.g., using TensorFlow) to select the best product from the extracted data.

[0225] Output: A list of best product suggestions

[0226] Specific operation: The server executes a database query based on the profile information, extracts products that match the criteria of "casual style, blue, under 2,000 yen," evaluates them using a generative AI model, and creates a list of suggestions.

[0227] Step 4: View and select suggestions

[0228] Terminal

[0229] Input: A list of suggestions sent by the server

[0230] Action: Display the suggested product list to the user and provide details (photos, sizes, prices, etc.).

[0231] Output: Product information displayed in the user interface

[0232] Specific operation: The device receives data from the server and displays detailed information about the "T-shirt (size M, blue)" on the screen.

[0233] User

[0234] Input: Select from the displayed product list

[0235] Action: Choose the product you like and indicate your intention to purchase (press the purchase button).

[0236] Output: Selected product information and purchase intention

[0237] What happens: The user reviews the details and clicks the "Purchase" button.

[0238] Step 5: Confirm your purchase and process your shipment

[0239] User

[0240] Input: Clicking the purchase button, entering payment information

[0241] Processing: After pressing the purchase button, enter the required payment information and submit.

[0242] Output: Confirmed purchase and payment information

[0243] Specific actions: Enter payment information (such as credit card information) on the terminal.

[0244] Terminal

[0245] Input: User purchase and payment information

[0246] Process: Send the entered information to the server.

[0247] Output: Purchase and payment information sent to the server

[0248] What it does: The device securely sends payment information to the server using SSL.

[0249] server

[0250] Input: Purchase and payment information

[0251] Processing: Verify the payment information, confirm the purchase, notify the manufacturer or apparel store of the purchase information, process the purchase of the relevant products, and pay a portion of the purchase price to the manufacturer or apparel store.

[0252] Output: Purchase confirmation, payment confirmation, product purchase

[0253] What it does: Confirms payment information through the payment gateway, confirms the purchase, and notifies the manufacturer or apparel store to arrange for the product to be shipped.

[0254] Manufacturers and apparel stores

[0255] Input: Purchase confirmation notice

[0256] Processing: receiving the purchase confirmation and shipping the product to the consumer's designated address.

[0257] Output: Shipped products

[0258] Specific operation: Upon receiving a purchase confirmation notification, a "T-shirt (size M, blue)" is shipped to the specified consumer address.

[0259] Step 6: Online try-on experience (optional)

[0260] server

[0261] Input: Consumer profile information, suggested product information

[0262] Processing: Using virtual reality (VR) or augmented reality (AR) technology, we generate try-on simulations and create images and videos.

[0263] Output: Simulation images and videos

[0264] Specific operation: The server uses 3D modeling software (e.g., Unity or Blender) to create a simulation of trying on a "T-shirt (size M, blue)" and generates images and videos.

[0265] Terminal

[0266] Input: Simulation images and videos from the server

[0267] Processing: Receives the simulation data and displays it on the user interface.

[0268] Output: Displayed simulation data

[0269] Specific operation: The device displays simulated images and videos received from the server, giving the user the feeling of trying on clothes.

[0270] (Application example 1)

[0271] 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."

[0272] In modern society, the amount of clothing discarded in the apparel industry is enormous, posing a major problem from the perspective of environmental impact. Furthermore, the inability of consumers to try on clothes when purchasing online has led to an increase in returns due to mismatched sizes and styles. Furthermore, for consumers, the effort required to select the right clothing can lead to reduced purchase satisfaction. There is a need to develop a system that can solve these problems and provide a more efficient and satisfying clothing shopping experience.

[0273] 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.

[0274] In this invention, the server includes: means for acquiring data on clothing to be discarded; means for acquiring consumer profile information; means for using a generative artificial intelligence model to suggest suitable clothing from the clothing data based on the consumer profile information; means for providing the consumer with a simulation of trying on the suggested clothing using virtual reality or augmented reality technology; means for displaying information on the suggested clothing to the consumer; and means for confirming and notifying the consumer of the purchase of the clothing selected by the consumer. This makes it possible to recommend clothing that is most suitable for the consumer, and to reduce the risk of returns and improve purchase satisfaction through a try-on experience using virtual reality or augmented reality. This also promotes the effective use of clothing that would otherwise be discarded at apparel stores, contributing to reducing environmental impact.

[0275] "Clothing destined for disposal" refers to clothing that apparel stores or manufacturers plan to dispose of rather than sell.

[0276] "Means for obtaining data" refers to the functionality that allows the server to collect and store information from external resources.

[0277] "Profile information" refers to personal information about a consumer, such as their height, weight, preferred style, and budget.

[0278] A "generative artificial intelligence model" refers to an algorithm that predicts and generates information based on input data.

[0279] "Means of suggestion" refers to the function of presenting appropriate options to consumers based on the generated data.

[0280] "Means for displaying" refers to the functionality of the device to visually present information to the consumer.

[0281] "Means for confirming and notifying purchase" refers to the function for a consumer to purchase the selected product and transmit that information to the server and other relevant parties.

[0282] A "system" refers to a set of functions or mechanisms in which multiple elements function together.

[0283] "Means of storing in a database" refers to the function of systematically organizing and storing acquired information.

[0284] "Virtual reality technology" refers to a virtual space created using computer technology that allows users to experience a sense of reality.

[0285] "Augmented reality technology" refers to technology that overlays digital information onto a real-world environment, allowing users to experience the real world and digital information simultaneously.

[0286] "Means for providing a try-on simulation" refers to a function for providing a consumer with the sensation of virtually trying on clothing.

[0287] A "head-mounted display" is a device worn by the user on the head that projects images directly in front of the eyes.

[0288] "Consumer" refers to the general consumer who purchases and uses clothing.

[0289] This invention is an online platform that provides consumers with discarded clothing, and is composed of three main elements: a server, a terminal, and a user. The platform is equipped with a clothing recommendation function using a generative AI model, and suggests optimal clothing based on the consumer's profile information. It also utilizes virtual reality (VR) and augmented reality (AR) technologies to provide an online try-on experience, which increases consumer motivation to purchase and improves satisfaction.

[0290] Acquiring and saving clothing data

[0291] server

[0292] The server uses a high-performance server (e.g., Amazon Web Services, AWS) to receive data on discarded clothing from apparel stores and store the data in a database (e.g., MySQL). This data includes photos of the clothing, sizes, colors, and conditions. It is recommended that apparel stores provide the data with tags and logos removed to prevent damage to the brand's image.

[0293] Examples:

[0294] The server receives data on "T-shirt (size M, blue, condition: like new)" from an apparel store and stores it in a database.

[0295] Consumer profile setting

[0296] User

[0297] Consumers enter their body type information (e.g., height, weight), clothing preferences (e.g., style, color), and budget on the device. Based on this profile information, the server will suggest the most suitable clothing for the consumer.

[0298] Terminal

[0299] The device, whether it's an iOS or Android device, sends the profile information entered by the user to the server, which then stores the profile information in a database.

[0300] Examples:

[0301] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[0302] Clothing suggestions using generative AI

[0303] server

[0304] The server uses a generative AI model (e.g., GPT-4) to suggest the most suitable clothing based on the consumer's profile information. It selects clothing that matches the consumer's preferences and body shape from clothing data stored in a database.

[0305] Examples:

[0306] The server's generation AI filters for "casual style, blue, budget under 2,000 yen" and includes "T-shirt (size M, blue)" in the suggestion list.

[0307] View and select suggestions

[0308] Terminal

[0309] The terminal displays the clothing suggestions sent from the server to the consumer and provides detailed information (photos, sizes, prices, etc.).

[0310] User

[0311] The user selects the clothing item they like from the suggested items and indicates their intention to purchase it (by pressing the purchase button).

[0312] Examples:

[0313] The device displays detailed information about the "T-shirt (size M, blue)" and the user presses the purchase button.

[0314] Purchase confirmation and shipping procedures

[0315] User

[0316] When the user presses the purchase button, they confirm the purchase and enter their payment information.

[0317] Terminal

[0318] The terminal transmits the purchase and payment information to the server.

[0319] server

[0320] The server verifies the payment information, confirms the purchase, and notifies the apparel store of the purchase information and completes the procedure for purchasing the clothing.

[0321] Examples:

[0322] After the purchase is confirmed, the server notifies the apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price. The apparel store then ships the clothing to the consumer's address.

[0323] Online try-on experience

[0324] server

[0325] Based on the consumer's profile information, the server generates a try-on simulation using virtual reality (VR) or augmented reality (AR) technology and sends the simulated images and videos to the device.

[0326] Terminal

[0327] The device receives simulated images and videos and displays them to consumers using a smartphone or head-mounted display (HMD), giving consumers a feeling similar to a real fitting experience.

[0328] Examples:

[0329] The server uses the consumer's profile information to generate a simulation image of what it would look like to try on a "T-shirt (size M, blue)" and sends it to the terminal. The terminal then displays the simulation image to the consumer, providing the feeling of trying it on.

[0330] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0331] Step 1:

[0332] The server receives data on discarded clothing from apparel stores. The server uses a database to store this data. Input includes photos of the clothing, size, color, condition, etc. The server receives this data and stores it in the database.

[0333] Examples:

[0334] The data for "size M, blue, like new T-shirt" sent from an apparel store is received and saved in a database.

[0335] Step 2:

[0336] The user uses the device to enter their profile information, such as height, weight, preferred style, color, budget, etc. The device then sends this data to the server.

[0337] Examples:

[0338] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: under 2000 yen," and the device sends this to the server.

[0339] Step 3:

[0340] The server receives the user's profile information and stores it in a database. It then uses a generative artificial intelligence model (e.g., GPT-4) to suggest the most suitable clothing based on the profile information. In this case, it selects clothing that matches the consumer's preferences and body shape from the clothing data stored in the database.

[0341] Examples:

[0342] The server's generation AI filters for "casual style, blue, budget under 2,000 yen" and includes "a blue T-shirt in size M" in the list of suggestions.

[0343] Step 4:

[0344] The server sends the suggested clothing information to the terminal. As input, the result of combining the user's profile information and the clothing data in the database is used. As output, detailed information of the suggested clothing is generated and sent to the terminal.

[0345] Examples:

[0346] Detailed information about the "T-shirt (size M, blue)" included in the suggestion list is sent to the terminal.

[0347] Step 5:

[0348] The terminal displays the clothing suggestions received from the server to the consumer, along with detailed information (e.g., photos, sizes, prices, etc.).

[0349] Examples:

[0350] The device will display details of a "size medium blue T-shirt" to the consumer.

[0351] Step 6:

[0352] The user selects the clothing item they like from the suggested items and indicates their intention to purchase it by pressing the purchase button to proceed with the purchase process.

[0353] Examples:

[0354] The user selects "Blue T-shirt (size M)" and presses the purchase button.

[0355] Step 7:

[0356] The terminal sends the purchase and payment information to the server. As input, the user's clothing selection and payment information are used. As output, a request for purchase confirmation is sent to the server.

[0357] Examples:

[0358] The user enters payment information and the terminal sends a request to the server to confirm the purchase.

[0359] Step 8:

[0360] The server verifies the payment information and confirms the purchase. If the purchase is successful, it notifies the apparel store of the purchase information and proceeds to purchase the clothing. The inputs are the user's payment information and the selected clothing information. The output is a purchase notification to the apparel store and a payment request for the purchase amount.

[0361] Examples:

[0362] The server receives the purchase information, notifies the apparel store of the purchase details of the "blue T-shirt (size M)," and pays the apparel store a portion of the purchase price.

[0363] Step 9:

[0364] The apparel store receives the notification from the server and ships the clothes to the specified address. The purchase notification from the server is used as input. The clothes are shipped to the consumer's address as output.

[0365] Examples:

[0366] An apparel store ships a blue T-shirt (size medium) to a consumer's address.

[0367] Step 10:

[0368] The server generates a try-on simulation using virtual reality (VR) or augmented reality (AR) technology based on the consumer's profile information and sends the simulation images and videos to the device. The user's profile information and suggested clothing information are used as input. As output, data from the try-on simulation is generated and sent to the device.

[0369] Examples:

[0370] The server uses the consumer's profile information to generate a simulated image of what it would look like when trying on a "T-shirt (size M, blue)" and sends it to the terminal.

[0371] 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.

[0372] This invention provides an online platform that provides consumers with clothing that would otherwise be discarded, and is composed of three main elements: a server, a terminal, and a user. The platform has a clothing recommendation function that uses a generative AI model to recommend the most suitable clothing based on the consumer's profile information. Furthermore, an emotion engine is used to obtain user emotion data and optimize the recommendations. Furthermore, by utilizing virtual reality and augmented reality technology to provide an online try-on experience, it increases consumer motivation to purchase and improves satisfaction.

[0373] Acquiring and saving clothing data

[0374] server

[0375] The server receives data on discarded clothing from apparel stores and stores it in a database. This data includes photos of the clothing, size, color, condition, and whether or not tags are present. To prevent damage to the brand image, apparel stores are encouraged to provide the data without tags or logos.

[0376] Examples:

[0377] The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and stores it in a database.

[0378] Consumer profile setting

[0379] User

[0380] Consumers enter their body type information (e.g., height, weight), clothing preferences (e.g., style, color), and budget on the device. Based on this profile information, the server will suggest the most suitable clothing for the consumer.

[0381] Terminal

[0382] The terminal transmits the profile information entered by the user to the server.

[0383] Examples:

[0384] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[0385] Clothing recommendations using generative AI and emotion engine

[0386] server

[0387] The server uses a generative AI model to suggest optimal clothing based on the consumer's profile information and emotional data obtained from the emotion engine. It selects clothing that matches the consumer's preferences, body shape, and emotions from clothing data stored in a database.

[0388] Examples:

[0389] The server's generation AI filters the results by "casual style, blue, budget under 2000 yen" and adds "T-shirt (size M, blue)" to the list of suggestions. Using data from the emotion engine, it prioritizes suggestions that will satisfy the user.

[0390] View and select suggestions

[0391] Terminal

[0392] The device displays clothing suggestions sent from the server to the consumer and provides detailed information (photos, sizes, prices, etc.) It also analyzes consumer reactions in real time through an emotion engine and sends the results to the server.

[0393] User

[0394] The user selects the clothing item they like from the suggested items and indicates their intention to purchase it (by pressing the purchase button).

[0395] Examples:

[0396] The device displays detailed information about the "T-shirt (size M, blue)" and the user presses the purchase button.

[0397] Purchase confirmation and shipping procedures

[0398] User

[0399] When the user presses the purchase button, they confirm the purchase and enter their payment information.

[0400] Terminal

[0401] The terminal transmits the purchase and payment information to the server.

[0402] server

[0403] The server verifies the payment information and confirms the purchase. The server then notifies the apparel store of the purchase information and proceeds with the purchase of the clothing. The server also pays a portion of the purchase price to the apparel store.

[0404] Apparel store

[0405] The apparel store will receive a confirmation of purchase and ship the garment to the specified address.

[0406] Examples:

[0407] After the purchase is confirmed, the server notifies the apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price. The apparel store then ships the clothing to the consumer's address.

[0408] Online try-on experience

[0409] server

[0410] The server generates a try-on simulation using virtual reality (VR) or augmented reality (AR) technology based on the consumer's profile information and emotional data, and sends simulated images and videos to the device.

[0411] Terminal

[0412] The device receives simulated images and videos and displays them to the consumer, giving them a feeling similar to a real fitting experience. Emotional data during the fitting process is also collected and sent to the server.

[0413] Examples:

[0414] The server uses the consumer's profile information and emotional data to generate a simulated image of what it would be like to try on a "T-shirt (size M, blue)" and sends it to the device. The device displays the simulated image, collects the consumer's emotional data in real time, and sends it to the server.

[0415] By implementing the present invention in the above manner, an efficient system is realized that provides consumers with clothes that would otherwise be discarded. This reduces the amount of discarded clothes and the burden on the environment. Furthermore, by combining it with an emotion engine, it is possible to improve consumer satisfaction and stimulate purchasing motivation.

[0416] The processing flow will be explained below.

[0417] Step 1:

[0418] server

[0419] Receives data on clothing to be discarded from apparel stores. The received data includes photos, size, color, condition, whether or not it has tags, etc. The format of this data is checked, and if there are no problems, it is stored in a database.

[0420] Examples:

[0421] The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and stores it in a database.

[0422] Step 2:

[0423] User

[0424] Enter your profile information on a dedicated screen on your device, including your height, weight, clothing preferences (style and color), budget, etc.

[0425] Terminal

[0426] The entered profile information is sent to the server.

[0427] Step 3:

[0428] server

[0429] The received profile information is stored in a database.

[0430] Examples:

[0431] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[0432] Step 4:

[0433] server

[0434] The system retrieves consumer profile information from the database, analyzes suitable clothing using a generative AI model, extracts suitable clothing data, and generates a list. It also uses an emotion engine to analyze the user's past responses and current emotional data to optimize the recommendations.

[0435] Step 5:

[0436] server

[0437] The generated clothing list and the emotion data associated with it are transmitted to the terminal.

[0438] Examples:

[0439] The server's generation AI filters items by "casual style, blue, budget within 2,000 yen" and adds "T-shirt (size M, blue)" to the list of suggestions. The emotion engine prioritizes items with high satisfaction based on past consumer responses.

[0440] Step 6:

[0441] Terminal

[0442] It receives the clothing list sent from the server and displays it to the consumer, displaying detailed information and analyzing the consumer's real-time reaction through an emotion engine.

[0443] User

[0444] Choose the clothing you like from the suggested items and indicate your intention to purchase (press the purchase button).

[0445] Examples:

[0446] The device displays detailed information about a "T-shirt (size M, blue)" and the user presses the purchase button. The device then analyzes the user's facial expressions and tone of voice using an emotion engine and sends real-time reaction data to the server.

[0447] Step 7:

[0448] User

[0449] By pressing the purchase button, you confirm your purchase and enter your payment information.

[0450] Step 8:

[0451] Terminal

[0452] Send purchase and payment information to the server.

[0453] Step 9:

[0454] server

[0455] The payment information is verified and the purchase is confirmed. The purchase information is notified to the apparel store, and the procedure for purchasing the clothing is carried out. A portion of the purchase price is also paid to the apparel store.

[0456] Examples:

[0457] After the server confirms the purchase, it notifies the apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price.

[0458] Step 10:

[0459] Apparel store

[0460] You will receive a confirmation of your purchase and your garment will be shipped to the address you specified.

[0461] Examples:

[0462] The apparel store will ship a "T-shirt (size M, blue)" to the specified address.

[0463] Step 11 (Optional):

[0464] server

[0465] Based on the consumer's profile information and emotional data, a try-on simulation is generated using virtual reality (VR) or augmented reality (AR) technology, and the simulated images and videos are sent to the device.

[0466] Terminal

[0467] Simulation images and videos are received and displayed to the consumer, and emotional data of the consumer while trying on the clothes is also collected and sent to the server.

[0468] Examples:

[0469] The server uses the consumer's profile information and emotional data to generate a simulated image of what it would be like to try on a "T-shirt (size M, blue)" and sends it to the device. The device displays the simulated image, collects the consumer's emotional data in real time, and sends it to the server.

[0470] The above are the processing steps of the program for implementing the present invention. By combining emotion engines in this way, it is possible to improve consumer satisfaction and stimulate purchasing motivation.

[0471] Example 2

[0472] 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."

[0473] Nowadays, many apparel stores dispose of clothing that is scheduled to be discarded, resulting in an increased burden on the environment. An efficient and user-friendly system is needed to solve this problem and promote the reuse of clothing. Also, a means is needed to suggest the most suitable clothing to consumers and motivate them to purchase.

[0474] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for acquiring data on clothes to be discarded, a means for acquiring consumer profile information, and a means for proposing suitable clothes from the clothing data based on the consumer profile information and emotion data using a generative AI model. This makes it possible to efficiently reuse clothes to be discarded and to propose optimal clothes to consumers.

[0475] "Clothing scheduled for disposal" refers to clothing that is not sold at apparel stores or the like and is scheduled to be disposed of.

[0476] "Consumer profile information" is personal information provided by a consumer, such as height, weight, preferred style, color, budget, etc.

[0477] A "generative artificial intelligence model" is an artificial intelligence program that uses machine learning and deep learning to make clothing suggestions.

[0478] "Emotional data" is data that indicates the emotional state of consumers, and is obtained by analyzing consumers' reactions and emotions in real time.

[0479] A "database" is a system for storing data such as clothing to be disposed of and consumer profile information.

[0480] "Virtual reality" is a technology that uses computer technology to provide a realistic visual and auditory experience.

[0481] "Augmented reality" is a technology that overlays computer-generated information onto the real world.

[0482] A "try-on simulation" is a simulation that uses virtual reality or augmented reality technology to allow consumers to experience the experience of trying on clothing.

[0483] "Consumer response" refers to the emotions and behaviors that consumers exhibit toward the proposed garment.

[0484] "Payment Information" means credit card or other payment method information provided by a consumer when making a purchase.

[0485] This invention provides an online platform that provides consumers with clothing that would otherwise be discarded, and includes a clothing recommendation function using a generative AI model, an optimization function using an emotion engine, and an online try-on experience using virtual reality and augmented reality technology. This system is mainly composed of three elements: a server, a terminal, and a user.

[0486] Acquiring and saving clothing data

[0487] server

[0488] The server receives data on clothing to be discarded from apparel stores and stores the data in a database. Specifically, the server receives information such as photos of the clothing to be discarded, size, color, condition, and whether or not it has tags. In order to maintain the brand image, apparel stores provide data with tags and logos removed. For example, the server receives data on a "T-shirt (size M, blue, condition: like new)" from an apparel store and stores the data in a database.

[0489] Consumer profile setting

[0490] User

[0491] The user enters their profile information on the device. This information includes height, weight, preferred style, color, budget, etc. The device sends this information to the server. For example, the user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: under 2000 yen," and the device sends it to the server. The server stores this information in a database.

[0492] Clothing recommendations using generative AI and emotion engine

[0493] server

[0494] The server retrieves the user's profile information from the database and emotion data from the emotion engine, and then uses a generative AI model to suggest the most suitable clothing based on this information. The generative AI model selects clothing that matches the user's preferences, body shape, and emotions, and generates a list of suggestions. For example, the server's generative AI inputs the following data: "Height: 170 cm, Weight: 60 kg, Casual style, Blue, Budget: 2000 yen or less, Emotion: Positive," and generates "T-shirt (Size M, Blue)" as a suggestion list.

[0495] View and select suggestions

[0496] Terminal

[0497] The device displays the list of suggestions sent from the server to the user and provides detailed information (photos, sizes, prices, etc.). It also analyzes the user's reactions in real time through an emotion engine and sends the results to the server. The user selects the item they wish to purchase from the suggested clothing and presses the purchase button. For example, the device displays detailed information about a "T-shirt (size M, blue)" and the user presses the purchase button.

[0498] Purchase confirmation and shipping procedures

[0499] User

[0500] When the user presses the purchase button, they confirm the purchase and enter their payment information.

[0501] Terminal

[0502] The terminal transmits the purchase and payment information to the server.

[0503] server

[0504] The server verifies the payment information and confirms the purchase. The server notifies the apparel store of the purchase information and proceeds with the purchase of the clothing. The server also pays a portion of the purchase price to the apparel store. The apparel store then ships the clothing after receiving the notification. For example, after the purchase is confirmed, the server notifies the apparel store of the purchase information for a "T-shirt (size M, blue)," and the apparel store ships the clothing to the consumer's address.

[0505] Online try-on experience

[0506] server

[0507] Based on the consumer's profile information and emotional data, the server uses virtual reality (VR) or augmented reality (AR) technology to generate a try-on simulation and send it to the device.

[0508] Terminal

[0509] The device receives the simulated images and videos and displays them to the user, allowing the user to experience a sensation similar to that of a real try-on. Emotional data from the user trying on clothes is also collected and sent to the server. For example, the server uses the consumer's profile information and emotional data to generate a simulated image of the user trying on a "T-shirt (size M, blue)" and sends it to the device. The device displays the simulated image and collects the user's emotional data in real time, which is then sent to the server.

[0510] Prompt Sentence Examples

[0511] Example prompts for users to enter profile information:

[0512] Enter your height, weight, clothing preferences (e.g. style, color), and budget.

[0513] Example: Height: 170cm, Weight: 60kg, Preference: Casual, Color: Blue, Budget: Under 2000 yen

[0514] Example prompts when the server makes suggestions using AI generation:

[0515] Considering the consumer's profile information (height 170cm, weight 60kg, preference: casual, color: blue, budget: under 2000 yen) and emotional data, please suggest the most suitable clothing from the database.

[0516] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0517] Program processing flow and specific explanation

[0518] Step 1:

[0519] Acquiring and saving clothing data

[0520] Server: Receives data on clothing to be discarded from apparel stores and stores the data in a database. Specifically, the server receives information such as photos of clothing, size, color, condition, and whether or not tags are attached, sent from apparel stores. The server stores the received data in a database and updates it in real time.

[0521] Input: Clothing data (e.g. T-shirt (size M, blue, condition: like new))

[0522] Output: Clothing data stored in a database

[0523] Specific operation: The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and registers it in the database.

[0524] Step 2:

[0525] Consumer profile setting

[0526] User: Through an application on their device or through a website, the user enters their profile information, including height, weight, preferred style, color, budget, etc.

[0527] Terminal: The terminal sends the profile information entered by the user to the server, which receives it and stores it in a database.

[0528] Input: Profile information (e.g., height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen)

[0529] Output: User profile information stored in the database

[0530] Specific operation: The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: under 2000 yen," and the device sends this to the server. The server stores the received information in a database.

[0531] Step 3:

[0532] Clothing recommendations using generative AI and emotion engine

[0533] Server: The server retrieves the user's profile information from the database and also retrieves the user's emotional data from the emotion engine. Based on this, the server uses a generative AI model to suggest the most suitable clothing. The server then sends the generated suggestion list to the device.

[0534] Input: User profile information, emotion data

[0535] Output: Suggestion list (e.g. T-shirt (size M, blue))

[0536] Specific operation: The server filters the generated AI model with "casual style, blue, budget under 2,000 yen," and combines the user's profile information (height: 170 cm, weight: 60 kg) with emotional data to add "T-shirt (size M, blue)" to the suggestion list.

[0537] Step 4:

[0538] View and select suggestions

[0539] Terminal: The terminal displays the list of suggestions sent from the server to the user and provides detailed information (photos, size, price, etc.). It also analyzes the user's reactions in real time through an emotion engine and sends the results to the server.

[0540] User: The user selects the clothing item they wish to purchase from the suggested items and presses the purchase button.

[0541] Input: Suggestion list (e.g. T-shirt (size M, blue))

[0542] Output: Intention to purchase (pressing the purchase button)

[0543] Specific operation: The device displays detailed information about a "T-shirt (size M, blue)" including a photo, size, and price, and the user presses the purchase button.

[0544] Step 5:

[0545] Purchase confirmation and shipping procedures

[0546] User: After the user presses the purchase button, they confirm the purchase and enter their payment information.

[0547] Terminal: The terminal sends purchase and payment information to the server.

[0548] Server: The server verifies the payment information and confirms the purchase. The server also notifies the apparel store of the purchase information and processes the purchase of the clothing. At the same time, the server pays a portion of the purchase price to the apparel store. The apparel store then ships the clothing upon receiving the notification.

[0549] Input: Purchase information, payment information

[0550] Output: Purchase confirmation, notification to apparel store

[0551] Specific operation: The server verifies the payment information, and after the purchase is confirmed, it notifies the apparel store of the purchase information for the "T-shirt (size M, blue)", and the apparel store ships the clothing to the consumer's address.

[0552] Step 6:

[0553] Online try-on experience

[0554] Server: Based on the user's profile information and emotional data, the server generates a fitting simulation using VR or AR technology and sends the simulation images and videos to the device.

[0555] Device: The device receives simulated images and videos and displays them to the user, giving the user a feeling similar to a real fitting experience. It also collects emotional data during the fitting process and sends it to the server.

[0556] Input: User profile information, emotion data

[0557] Output: Simulation images, emotion data

[0558] Specific operation: The server uses the consumer's profile information and emotional data to generate a simulated image of a "T-shirt (size M, blue)" being tried on and sends it to the device. The device displays the simulated image and collects the user's emotional data in real time and sends it to the server.

[0559] (Application example 2)

[0560] 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."

[0561] Conventional methods for utilizing discarded clothing do not adequately approach consumers and do not provide proposals that reflect their preferences and emotions. Furthermore, the lack of a try-on experience reduces consumer satisfaction and motivation to purchase. This has hindered progress in effectively utilizing discarded clothing, and the environmental impact problem has not been resolved.

[0562] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring data on clothing to be discarded, means for acquiring consumer profile information, means for using a generative artificial intelligence model to suggest suitable clothing from the clothing data based on the consumer profile information, means for acquiring consumer emotion data using an emotion engine and optimizing the suggestion content, and means for providing the consumer with a simulation of trying on the suggested clothing using virtual reality or augmented reality technology. This makes it possible to suggest optimal clothing that reflects the consumer's preferences and emotions, improving consumer satisfaction and promoting the effective use of clothing to be discarded.

[0563] "Clothing scheduled for disposal" refers to clothing that is scheduled for disposal by apparel stores or clothing manufacturers, but is still usable and can be offered to consumers.

[0564] "Means for acquiring data" refers to the means for receiving detailed information such as photographs, size, color, and condition of clothing to be discarded from apparel stores and clothing manufacturers via servers, terminals, etc.

[0565] "Consumer profile information" refers to information that indicates a user's personal preferences and physical characteristics, such as height, weight, clothing preferences, and budget.

[0566] A "generative artificial intelligence model" is an AI algorithm or machine learning model that suggests appropriate clothing based on a consumer's profile information.

[0567] The "means of suggestion" refers to a system that selects the most suitable clothing and suggests it to users based on consumer profile information and clothing data.

[0568] The "emotion engine" is a technology that acquires real-time emotional data from consumers and optimizes the content of proposals based on that data.

[0569] "Virtual reality technology" and "augmented reality technology" refer to technologies that allow consumers to try on clothes in a virtual space or try on virtual clothes superimposed on real space.

[0570] A "means for providing simulated try-on clothing" is a system or application that uses virtual reality or augmented reality technology to provide a consumer with a visual and sensory experience of trying on clothing.

[0571] "Means of notification" refers to the means by which a consumer confirms the purchase of the selected clothing item and notifies the apparel store or clothing manufacturer of that information.

[0572] The present invention is a system that functions as an online platform that provides consumers with clothes that are scheduled to be discarded, and is mainly composed of three elements: a server, a terminal, and a user. How each element in this system works together to realize the invention is explained in detail below.

[0573] Acquiring and saving clothing data

[0574] The server receives data on discarded clothing from apparel stores and stores it in a database. This data includes photos of the clothing, size, color, condition, and whether or not tags are attached. To prevent damage to the brand image, it is recommended that apparel stores provide the data without tags or logos.

[0575] Examples:

[0576] The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and stores it in a database.

[0577] Consumer profile setting

[0578] Users enter their body type information (e.g., height, weight), clothing preferences (e.g., style, color), and budget on their device. Based on this profile information, the server will suggest the most suitable clothing for the consumer.

[0579] The terminal transmits the profile information entered by the user to the server.

[0580] Examples:

[0581] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[0582] Clothing recommendations using generative AI and emotion engine

[0583] The server uses a generative AI model to suggest optimal clothing based on the consumer's profile information and emotional data obtained from the emotion engine. It selects clothing that matches the consumer's preferences, body shape, and emotions from clothing data stored in a database.

[0584] Examples:

[0585] Using the prompt "The user is 170cm tall, weighs 60kg, prefers a casual style, and wants blue clothing within a budget of 2000 yen. Please generate the best clothing suggestions you can offer," the generative AI model filters "casual style, blue, budget within 2000 yen" and adds "T-shirt (size M, blue)" to the suggestion list. Using data from the emotion engine, it prioritizes suggestions that provide the highest user satisfaction.

[0586] View and select suggestions

[0587] The device displays clothing suggestions sent from the server to the consumer and provides detailed information (photos, sizes, prices, etc.) It also analyzes consumer reactions in real time through an emotion engine and sends the results to the server.

[0588] The user selects the clothing item they like from the suggested items and indicates their intention to purchase it (by pressing the purchase button).

[0589] Examples:

[0590] The device displays detailed information about the "T-shirt (size M, blue)" and the user presses the purchase button.

[0591] Purchase confirmation and shipping procedures

[0592] When the user presses the purchase button, they confirm the purchase and enter their payment information.

[0593] The terminal transmits the purchase and payment information to the server.

[0594] The server verifies the payment information and confirms the purchase. The server then notifies the apparel store of the purchase information and proceeds with the purchase of the clothing. The server also pays a portion of the purchase price to the apparel store.

[0595] The apparel store will receive a confirmation of purchase and ship the garment to the specified address.

[0596] Examples:

[0597] After the purchase is confirmed, the server notifies the apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price. The apparel store then ships the clothing to the consumer's address.

[0598] Online try-on experience

[0599] The server generates a try-on simulation using virtual reality (VR) or augmented reality (AR) technology based on the consumer's profile information and emotional data, and sends simulated images and videos to the device.

[0600] The device receives simulated images and videos and displays them to the consumer, giving them a feeling similar to a real fitting experience. Emotional data during the fitting process is also collected and sent to the server.

[0601] Examples:

[0602] The server uses the consumer's profile information and emotional data to generate a simulated image of what it would be like to try on a "T-shirt (size M, blue)" and sends it to the device. The device displays the simulated image, collects the consumer's emotional data in real time, and sends it to the server.

[0603] As described above, the present invention realizes a system that proposes optimal clothing that reflects the consumer's preferences and feelings and provides a realistic try-on experience, thereby improving consumer satisfaction and promoting the effective use of clothing that would otherwise be discarded.

[0604] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0605] Step 1: Acquire and save clothing data

[0606] The server receives data on the clothes to be discarded (e.g., photos, size, color, condition, presence or absence of tags) from the apparel store and stores that information in a database. The input is the clothing data provided by the apparel store, and the output is the clothing data stored in the database.

[0607] Specifically, the data provided by the apparel store is obtained through an API, and each item (photo, size, color, condition, presence or absence of tags) is stored in a database in JSON format.

[0608] Step 2: Fill out your consumer profile

[0609] The terminal prompts the user to enter information about their physical shape (e.g., height, weight), clothing preferences (e.g., style, color), and budget, and then transmits this information to the server.

[0610] The input is the profile information that the user enters into the terminal, and the output is the profile information that is sent to the server.

[0611] Specifically, the user enters profile information through the device's UI, and the input data is sent to the server via an HTTPS request.

[0612] Step 3: Clothing suggestions by generative AI

[0613] The server uses a generative AI model to suggest optimal clothing based on the consumer's profile information and clothing data stored in the database. The input is the consumer's profile information and the clothing data in the database, and the output is a list of suggested clothing items.

[0614] Specifically, the generative AI model is fed a prompt such as, "The user is 170 cm tall, weighs 60 kg, prefers a casual style, desires blue clothing, and has a budget of 2,000 yen or less," and the model outputs a list of the most suitable clothing items.

[0615] Step 4: Acquiring emotion data using the emotion engine

[0616] When the user browses the proposed clothing or tries on the clothing in a simulated fashion, the device captures emotional data using a camera and microphone and sends it to the server. The input is the user's real-time emotional data, and the output is the emotional data sent to the server.

[0617] Specifically, the device uses a camera and microphone to capture the user's facial expressions and voice in real time, analyzes the data through the Affectiva SDK, and sends the emotional data to the server.

[0618] Step 5: View and select suggestions

[0619] The device displays the optimized clothing suggestions sent from the server, along with detailed information (photos, sizes, prices, etc.) The user selects the clothing they like and presses the purchase button.

[0620] The input is a list of clothing suggestions sent from the server, and the output is detailed information displayed on the terminal.

[0621] Specifically, the system obtains a list of suggestions from the server and displays detailed information about the clothing to the user through the device's UI.

[0622] Step 6: Purchase confirmation and notification

[0623] When the user presses the purchase button, the terminal sends the purchase information and payment information to the server. The server verifies the information, confirms the purchase, and notifies the apparel store. The input is the user's purchase information and payment information, and the output is a notification of the purchase information to the apparel store.

[0624] Specifically, the user presses the purchase button, the server receives the purchase information sent from the terminal, processes the payment, and notifies the apparel store of the purchase information.

[0625] Step 7: Online try-on experience

[0626] The server generates a try-on simulation using virtual reality or augmented reality technology based on the consumer's profile information and emotional data, and sends it to the device. The input is the profile information and emotional data, and the output is images or videos of the try-on simulation.

[0627] Specifically, the server uses VR / AR technologies such as Unity or Unreal Engine to create a clothing simulation based on the user's profile information, and sends the simulation data to the device. The user experiences the clothing simulation, and emotional data from the simulation is collected and retransmitted in real time.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] [Second embodiment]

[0632] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0633] 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.

[0634] 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).

[0635] 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.

[0636] 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.

[0637] 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).

[0638] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0639] 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.

[0640] 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.

[0641] 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.

[0642] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0643] 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."

[0644] This invention provides an online platform that provides consumers with discarded clothing, and is composed of three main components: a server, a terminal, and a user. The platform is equipped with a clothing recommendation function using a generative AI model to recommend the most suitable clothing based on the consumer's profile information. It also utilizes virtual reality and augmented reality technology to provide an online try-on experience, increasing consumer motivation and satisfaction.

[0645] Acquiring and saving clothing data

[0646] server

[0647] The server receives data on discarded clothing from apparel stores and stores it in a database. This data includes photos of the clothing, size, color, condition, etc. To prevent damage to the brand's image, apparel stores are encouraged to provide the data without tags or logos.

[0648] Examples:

[0649] The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and stores it in the database.

[0650] Consumer profile setting

[0651] User

[0652] Consumers enter their body type information (e.g., height, weight), clothing preferences (e.g., style, color), and budget on the device. Based on this profile information, the server will suggest the most suitable clothing for the consumer.

[0653] Terminal

[0654] The terminal sends the profile information entered by the user to the server, which then stores the profile information in a database.

[0655] Examples:

[0656] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[0657] Clothing suggestions using generative AI

[0658] server

[0659] The server uses a generative AI model to suggest optimal clothing based on the consumer's profile information, and selects clothing that matches the consumer's preferences and body shape from clothing data stored in a database.

[0660] Examples:

[0661] The server's generation AI filters for "casual style, blue, budget under 2,000 yen" and includes "T-shirt (size M, blue)" in the suggestion list.

[0662] View and select suggestions

[0663] Terminal

[0664] The terminal displays clothing suggestions sent from the server to the consumer and provides detailed information (photos, sizes, prices, etc.).

[0665] User

[0666] The user selects the clothing item they like from the suggested items and indicates their intention to purchase it (by pressing the purchase button).

[0667] Examples:

[0668] The device displays detailed information about the "T-shirt (size M, blue)" and the user presses the purchase button.

[0669] Purchase confirmation and shipping procedures

[0670] User

[0671] When the user presses the purchase button, they confirm the purchase and enter their payment information.

[0672] Terminal

[0673] The terminal transmits the purchase and payment information to the server.

[0674] server

[0675] The server verifies the payment information and confirms the purchase. The server then notifies the apparel store of the purchase information and proceeds with the purchase of the clothing. The server also pays a portion of the purchase price to the apparel store.

[0676] Apparel store

[0677] The apparel store will receive a confirmation of purchase and ship the garment to the specified address.

[0678] Examples:

[0679] After the purchase is confirmed, the server notifies the apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price. The apparel store then ships the clothing to the consumer's address.

[0680] Online try-on experience (optional)

[0681] server

[0682] The server generates a try-on simulation using virtual reality (VR) or augmented reality (AR) technology based on the consumer's profile information, and sends the simulated images and videos to the device.

[0683] Terminal

[0684] The device receives simulated images and videos and displays them to consumers, giving them a feeling similar to a real try-on experience.

[0685] Examples:

[0686] The server uses the consumer's profile information to generate a simulation image of what it would look like to try on a "T-shirt (size M, blue)" and sends it to the terminal. The terminal then displays the simulation image to the consumer, providing the feeling of trying it on.

[0687] By implementing the present invention in the above-described manner, an efficient system is realized for providing clothes that are scheduled to be discarded to consumers, thereby reducing the amount of discarded clothes and the burden on the environment.

[0688] The processing flow will be explained below.

[0689] Step 1:

[0690] server

[0691] Data on clothing to be discarded is received from apparel stores. The received data includes photos, size, color, condition, whether or not the item has tags, etc. The data is format-checked, and if there are no problems, it is stored in a database.

[0692] Examples:

[0693] The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and stores it in a database.

[0694] Step 2:

[0695] User

[0696] Enter your profile information on a dedicated screen on your device, including your height, weight, clothing preferences (style and color), budget, etc.

[0697] Terminal

[0698] The entered profile information is sent to the server.

[0699] Step 3:

[0700] server

[0701] The received profile information is stored in a database.

[0702] Examples:

[0703] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[0704] Step 4:

[0705] server

[0706] The system retrieves consumer profile information from the database, analyzes matching clothing using a generative artificial intelligence model, extracts matching clothing data, and generates a list.

[0707] Step 5:

[0708] server

[0709] Send detailed information about the generated clothing list (e.g., photos, sizes, prices, etc.) to the device.

[0710] Examples:

[0711] The server's generation AI filters items by "casual style, blue, budget within 2000 yen" and adds "T-shirt (size M, blue)" to the list of suggestions. This information is then sent to the device.

[0712] Step 6:

[0713] Terminal

[0714] Receives the clothing list sent from the server and displays it to the consumer. Displays detailed information.

[0715] User

[0716] Choose the clothing you like from the suggested items and indicate your intention to purchase (press the purchase button).

[0717] Examples:

[0718] The device displays detailed information about the "T-shirt (size M, blue)" and the user presses the purchase button.

[0719] Step 7:

[0720] User

[0721] By pressing the purchase button, you confirm your purchase and enter your payment information.

[0722] Step 8:

[0723] Terminal

[0724] Send purchase and payment information to the server.

[0725] Step 9:

[0726] server

[0727] The payment information is verified and the purchase is confirmed. The purchase information is notified to the apparel store, and the procedure for purchasing the clothing is carried out. A portion of the purchase price is also paid to the apparel store.

[0728] Examples:

[0729] After the server confirms the purchase, it notifies the apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price.

[0730] Step 10:

[0731] Apparel store

[0732] After receiving confirmation of purchase, the clothing will be shipped to the specified address.

[0733] Examples:

[0734] The apparel store will ship a "T-shirt (size M, blue)" to the specified address.

[0735] Step 11 (Optional):

[0736] server

[0737] Based on the consumer's profile information, a virtual reality (VR) or augmented reality (AR) try-on simulation is generated, and simulated images and videos are sent to the device.

[0738] Terminal

[0739] Receives simulated images and videos and displays them to consumers.

[0740] Examples:

[0741] The server generates a simulation of trying on a "T-shirt (size M, blue)" and sends it to the terminal. The terminal displays the simulation image, allowing the consumer to experience the sensation of trying on the item.

[0742] The above are the processing steps of the program in carrying out the present invention.

[0743] Example 1

[0744] 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."

[0745] While supporting modern comfortable lifestyles, the fashion industry faces the social problem of discarding large amounts of clothing. Discarded clothing places a burden on the environment, and a system is needed to effectively reduce this volume. Furthermore, to support consumers in choosing clothing, it is necessary to provide customized suggestions and realistic try-on experiences. To address these issues, it is necessary to realize a system that efficiently provides discarded clothing to consumers and improves consumer satisfaction.

[0746] 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.

[0747] In this invention, the server includes means for acquiring product data to be discarded, means for acquiring consumer profile information, means for using a generative artificial intelligence model to suggest suitable products from the product data based on the consumer profile information, means for displaying information on the suggested products to the consumer, means for confirming and notifying the purchase of the product selected by the consumer, means for creating a try-on simulation based on the consumer profile information and the suggested product information, means for generating simulation images and videos and sending them to a terminal, and means for displaying the try-on simulation data to the consumer. This makes it possible to efficiently provide clothes to be discarded, to make it easier for consumers to select appropriate clothes based on their own preferences, and to increase their desire to purchase through the try-on experience.

[0748] "Product data scheduled for disposal" refers to information about products that manufacturers, apparel stores, etc. had not planned to offer to consumers, but which they have discovered new value for and are now offering.

[0749] "Consumer profile information" refers to data provided by a consumer relating to personal characteristics and preferences, such as height, weight, preferred style, and budget.

[0750] "Generative artificial intelligence model" refers to a machine learning algorithm and model that selects and suggests optimal products from data on products scheduled for disposal based on consumer profile information.

[0751] "Means for suggesting suitable products" refers to a method and system that uses a generative artificial intelligence model to select and provide information on optimal products based on consumer profile information.

[0752] "Means for displaying product information" refers to the technology and devices that allow consumers to check detailed information such as photos, sizes, and prices of the proposed products through a user interface.

[0753] "Means for confirming and notifying purchase" refers to the methods and technologies that confirm a consumer's intention to purchase a selected product, process payment, and notify related systems and manufacturers of that information.

[0754] "Means for creating a try-on simulation" refers to methods and technologies that use virtual reality or augmented reality technology to recreate the state of a consumer trying on an item based on the consumer's profile information and proposed product information.

[0755] "Means for generating simulation images and videos and transmitting them to a terminal" refers to methods and technologies for generating images and videos for performing a fitting simulation and transmitting them to a consumer's terminal.

[0756] "Means for displaying try-on simulation data to consumers" refers to methods and technologies for displaying the received simulation images and videos on a terminal so that consumers can check them.

[0757] This invention provides an online platform that provides consumers with discarded products, and is composed of three main components: a server, a terminal, and a user. This platform is equipped with a product recommendation function that uses a generative artificial intelligence model to recommend optimal products based on the consumer's profile information. It also utilizes virtual reality and augmented reality technology to provide an online try-on experience, increasing consumer motivation and satisfaction.

[0758] Acquisition and storage of data on products scheduled for disposal

[0759] server

[0760] The server receives data on products to be discarded from manufacturers and apparel stores and stores that data in a database. This data includes product photos, sizes, colors, and conditions. Manufacturers and apparel stores are encouraged to provide the data without tags or logos to prevent damage to their brand image.

[0761] Examples:

[0762] The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and stores it in the database.

[0763] Consumer profile setting

[0764] User

[0765] Consumers enter their body type information (height, weight), clothing preferences (style, color), and budget on the device. Based on this profile information, the server will suggest the most suitable products for the consumer.

[0766] Terminal

[0767] The terminal transmits the profile information entered by the user to the server, and the server stores the profile information in a database.

[0768] Examples:

[0769] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[0770] Product suggestions by generative AI

[0771] server

[0772] The server uses a generative AI model to suggest optimal products based on the consumer's profile information. From product data stored in a database, it selects products that match the consumer's preferences and body shape. This generative AI model can use TensorFlow, for example.

[0773] Examples:

[0774] The server's generation AI filters for "casual style, blue, budget under 2,000 yen" and includes "T-shirt (size M, blue)" in the suggestion list.

[0775] View and select suggestions

[0776] Terminal

[0777] The terminal displays the product suggestions sent from the server to the consumer and provides detailed information (photos, sizes, prices, etc.).

[0778] User

[0779] The user selects the product they like from the suggested products and indicates their intention to purchase it (by pressing the purchase button).

[0780] Examples:

[0781] The device displays detailed information about the "T-shirt (size M, blue)" and the user presses the purchase button.

[0782] Purchase confirmation and shipping procedures

[0783] User

[0784] When the user presses the purchase button, they confirm the purchase and enter their payment information.

[0785] Terminal

[0786] The terminal transmits the purchase and payment information to the server.

[0787] server

[0788] The server verifies the payment information and confirms the purchase. The server then notifies the manufacturer or apparel store of the purchase information, processes the purchase of the product, and pays a portion of the purchase price to the manufacturer or apparel store.

[0789] Manufacturers and apparel stores

[0790] The manufacturer or apparel store will receive a confirmation of purchase and ship the product to the specified address.

[0791] Examples:

[0792] After the purchase is confirmed, the server notifies the manufacturer or apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price. The manufacturer or apparel store then ships the product to the consumer's address.

[0793] Online try-on experience (optional)

[0794] server

[0795] The server generates a try-on simulation using virtual reality (VR) or augmented reality (AR) technology based on the consumer's profile information, and sends the simulated images and videos to the device.

[0796] Terminal

[0797] The device receives simulated images and videos and displays them to consumers, giving them a feeling similar to a real try-on experience.

[0798] Examples:

[0799] The server uses the consumer's profile information to generate a simulation image of what it would look like to try on a "T-shirt (size M, blue)" and sends it to the terminal. The terminal then displays the simulation image to the consumer, providing the feeling of trying it on.

[0800] Prompt Sentence Examples

[0801] Examples of input prompts for a generative AI model include:

[0802] Examples:

[0803] User profile:

[0804] Height: 170cm

[0805] Weight: 60kg

[0806] Preference: Casual

[0807] Color: Blue

[0808] Budget: Under 2000 yen

[0809] Prompt the generative AI model:

[0810] Please suggest a casual style blue product for under 2000 yen.

[0811] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0812] Step 1: Acquire and store data on products to be disposed of

[0813] server

[0814] Input: Data on discarded products provided by manufacturers and apparel stores (e.g., product photos, size, color, condition, etc.)

[0815] Processing: The server receives product data from manufacturers and apparel stores via a data transmission API. The received data is stored in a database.

[0816] Output: Product data stored in a database

[0817] What happens: The server connects to the manufacturer's data system, receives data about "T-shirt (size M, blue, condition: like new)" via API, and stores it in a database.

[0818] Step 2: Setting up a consumer profile

[0819] User

[0820] Input: Your body shape information (height, weight), clothing preferences (style, color), budget (entered via web form, etc.)

[0821] Action: The user enters this information and clicks the submit button.

[0822] Output: The terminal stores the input and prepares it to be sent.

[0823] Specific actions: A user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: under 2000 yen" into a web form and presses the submit button.

[0824] Terminal

[0825] Input: Profile information entered by the user

[0826] Processing: The device checks and organizes the input and sends it to the server.

[0827] Output: Profile information sent to the server

[0828] Specific operation: The terminal sends the user input to the server via SSL communication.

[0829] Step 3: Product suggestions by generative AI

[0830] server

[0831] Input: Consumer profile information, product data stored in a database

[0832] Processing: Extract candidate product data from a database based on consumer profile information. Use a generative AI model (e.g., using TensorFlow) to select the best product from the extracted data.

[0833] Output: A list of best product suggestions

[0834] Specific operation: The server executes a database query based on the profile information, extracts products that match the criteria of "casual style, blue, under 2,000 yen," evaluates them using a generative AI model, and creates a list of suggestions.

[0835] Step 4: View and select suggestions

[0836] Terminal

[0837] Input: A list of suggestions sent by the server

[0838] Action: Display the suggested product list to the user and provide details (photos, sizes, prices, etc.).

[0839] Output: Product information displayed in the user interface

[0840] Specific operation: The device receives data from the server and displays detailed information about the "T-shirt (size M, blue)" on the screen.

[0841] User

[0842] Input: Select from the displayed product list

[0843] Action: Choose the product you like and indicate your intention to purchase (press the purchase button).

[0844] Output: Selected product information and purchase intention

[0845] What happens: The user reviews the details and clicks the "Purchase" button.

[0846] Step 5: Confirm your purchase and process your shipment

[0847] User

[0848] Input: Clicking the purchase button, entering payment information

[0849] Processing: After pressing the purchase button, enter the required payment information and submit.

[0850] Output: Confirmed purchase and payment information

[0851] Specific actions: Enter payment information (such as credit card information) on the terminal.

[0852] Terminal

[0853] Input: User purchase and payment information

[0854] Process: Send the entered information to the server.

[0855] Output: Purchase and payment information sent to the server

[0856] What it does: The device securely sends payment information to the server using SSL.

[0857] server

[0858] Input: Purchase and payment information

[0859] Processing: Verify the payment information, confirm the purchase, notify the manufacturer or apparel store of the purchase information, process the purchase of the relevant products, and pay a portion of the purchase price to the manufacturer or apparel store.

[0860] Output: Purchase confirmation, payment confirmation, product purchase

[0861] What it does: Confirms payment information through the payment gateway, confirms the purchase, and notifies the manufacturer or apparel store to arrange for the product to be shipped.

[0862] Manufacturers and apparel stores

[0863] Input: Purchase confirmation notice

[0864] Processing: receiving the purchase confirmation and shipping the product to the consumer's designated address.

[0865] Output: Shipped products

[0866] Specific operation: Upon receiving a purchase confirmation notification, a "T-shirt (size M, blue)" is shipped to the specified consumer address.

[0867] Step 6: Online try-on experience (optional)

[0868] server

[0869] Input: Consumer profile information, suggested product information

[0870] Processing: Using virtual reality (VR) or augmented reality (AR) technology, we generate try-on simulations and create images and videos.

[0871] Output: Simulation images and videos

[0872] Specific operation: The server uses 3D modeling software (e.g., Unity or Blender) to create a simulation of trying on a "T-shirt (size M, blue)" and generates images and videos.

[0873] Terminal

[0874] Input: Simulation images and videos from the server

[0875] Processing: Receives the simulation data and displays it on the user interface.

[0876] Output: Displayed simulation data

[0877] Specific operation: The device displays simulated images and videos received from the server, giving the user the feeling of trying on clothes.

[0878] (Application example 1)

[0879] 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."

[0880] In modern society, the amount of clothing discarded in the apparel industry is enormous, posing a major problem from the perspective of environmental impact. Furthermore, the inability of consumers to try on clothes when purchasing online has led to an increase in returns due to mismatched sizes and styles. Furthermore, for consumers, the effort required to select the right clothing can lead to reduced purchase satisfaction. There is a need to develop a system that can solve these problems and provide a more efficient and satisfying clothing shopping experience.

[0881] 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.

[0882] In this invention, the server includes: means for acquiring data on clothing to be discarded; means for acquiring consumer profile information; means for using a generative artificial intelligence model to suggest suitable clothing from the clothing data based on the consumer profile information; means for providing the consumer with a simulation of trying on the suggested clothing using virtual reality or augmented reality technology; means for displaying information on the suggested clothing to the consumer; and means for confirming and notifying the consumer of the purchase of the clothing selected by the consumer. This makes it possible to recommend clothing that is most suitable for the consumer, and to reduce the risk of returns and improve purchase satisfaction through a try-on experience using virtual reality or augmented reality. This also promotes the effective use of clothing that would otherwise be discarded at apparel stores, contributing to reducing environmental impact.

[0883] "Clothing destined for disposal" refers to clothing that apparel stores or manufacturers plan to dispose of rather than sell.

[0884] "Means for obtaining data" refers to the functionality that allows the server to collect and store information from external resources.

[0885] "Profile information" refers to personal information about a consumer, such as their height, weight, preferred style, and budget.

[0886] A "generative artificial intelligence model" refers to an algorithm that predicts and generates information based on input data.

[0887] "Means of suggestion" refers to the function of presenting appropriate options to consumers based on the generated data.

[0888] "Means for displaying" refers to the functionality of the device to visually present information to the consumer.

[0889] "Means for confirming and notifying purchase" refers to the function for a consumer to purchase the selected product and transmit that information to the server and other relevant parties.

[0890] A "system" refers to a set of functions or mechanisms in which multiple elements function together.

[0891] "Means of storing in a database" refers to the function of systematically organizing and storing acquired information.

[0892] "Virtual reality technology" refers to a virtual space created using computer technology that allows users to experience a sense of reality.

[0893] "Augmented reality technology" refers to technology that overlays digital information onto a real-world environment, allowing users to experience the real world and digital information simultaneously.

[0894] "Means for providing a try-on simulation" refers to a function for providing a consumer with the sensation of virtually trying on clothing.

[0895] A "head-mounted display" is a device worn by the user on the head that projects images directly in front of the eyes.

[0896] "Consumer" refers to the general consumer who purchases and uses clothing.

[0897] This invention is an online platform that provides consumers with discarded clothing, and is composed of three main elements: a server, a terminal, and a user. The platform is equipped with a clothing recommendation function using a generative AI model, and suggests optimal clothing based on the consumer's profile information. It also utilizes virtual reality (VR) and augmented reality (AR) technologies to provide an online try-on experience, which increases consumer motivation to purchase and improves satisfaction.

[0898] Acquiring and saving clothing data

[0899] server

[0900] The server uses a high-performance server (e.g., Amazon Web Services, AWS) to receive data on discarded clothing from apparel stores and store the data in a database (e.g., MySQL). This data includes photos of the clothing, sizes, colors, and conditions. It is recommended that apparel stores provide the data with tags and logos removed to prevent damage to the brand's image.

[0901] Examples:

[0902] The server receives data on "T-shirt (size M, blue, condition: like new)" from an apparel store and stores it in a database.

[0903] Consumer profile setting

[0904] User

[0905] Consumers enter their body type information (e.g., height, weight), clothing preferences (e.g., style, color), and budget on the device. Based on this profile information, the server will suggest the most suitable clothing for the consumer.

[0906] Terminal

[0907] The device, whether it's an iOS or Android device, sends the profile information entered by the user to the server, which then stores the profile information in a database.

[0908] Examples:

[0909] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[0910] Clothing suggestions using generative AI

[0911] server

[0912] The server uses a generative AI model (e.g., GPT-4) to suggest the most suitable clothing based on the consumer's profile information. It selects clothing that matches the consumer's preferences and body shape from clothing data stored in a database.

[0913] Examples:

[0914] The server's generation AI filters for "casual style, blue, budget under 2,000 yen" and includes "T-shirt (size M, blue)" in the suggestion list.

[0915] View and select suggestions

[0916] Terminal

[0917] The terminal displays the clothing suggestions sent from the server to the consumer and provides detailed information (photos, sizes, prices, etc.).

[0918] User

[0919] The user selects the clothing item they like from the suggested items and indicates their intention to purchase it (by pressing the purchase button).

[0920] Examples:

[0921] The device displays detailed information about the "T-shirt (size M, blue)" and the user presses the purchase button.

[0922] Purchase confirmation and shipping procedures

[0923] User

[0924] When the user presses the purchase button, they confirm the purchase and enter their payment information.

[0925] Terminal

[0926] The terminal transmits the purchase and payment information to the server.

[0927] server

[0928] The server verifies the payment information, confirms the purchase, and notifies the apparel store of the purchase information and completes the procedure for purchasing the clothing.

[0929] Examples:

[0930] After the purchase is confirmed, the server notifies the apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price. The apparel store then ships the clothing to the consumer's address.

[0931] Online try-on experience

[0932] server

[0933] Based on the consumer's profile information, the server generates a try-on simulation using virtual reality (VR) or augmented reality (AR) technology and sends the simulated images and videos to the device.

[0934] Terminal

[0935] The device receives simulated images and videos and displays them to consumers using a smartphone or head-mounted display (HMD), giving consumers a feeling similar to a real fitting experience.

[0936] Examples:

[0937] The server uses the consumer's profile information to generate a simulation image of what it would look like to try on a "T-shirt (size M, blue)" and sends it to the terminal. The terminal then displays the simulation image to the consumer, providing the feeling of trying it on.

[0938] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0939] Step 1:

[0940] The server receives data on discarded clothing from apparel stores. The server uses a database to store this data. Input includes photos of the clothing, size, color, condition, etc. The server receives this data and stores it in the database.

[0941] Examples:

[0942] The data for "size M, blue, like new T-shirt" sent from an apparel store is received and saved in a database.

[0943] Step 2:

[0944] The user uses the device to enter their profile information, such as height, weight, preferred style, color, budget, etc. The device then sends this data to the server.

[0945] Examples:

[0946] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: under 2000 yen," and the device sends this to the server.

[0947] Step 3:

[0948] The server receives the user's profile information and stores it in a database. It then uses a generative artificial intelligence model (e.g., GPT-4) to suggest the most suitable clothing based on the profile information. In this case, it selects clothing that matches the consumer's preferences and body shape from the clothing data stored in the database.

[0949] Examples:

[0950] The server's generation AI filters for "casual style, blue, budget under 2,000 yen" and includes "a blue T-shirt in size M" in the list of suggestions.

[0951] Step 4:

[0952] The server sends the suggested clothing information to the terminal. As input, the result of combining the user's profile information and the clothing data in the database is used. As output, detailed information of the suggested clothing is generated and sent to the terminal.

[0953] Examples:

[0954] Detailed information about the "T-shirt (size M, blue)" included in the suggestion list is sent to the terminal.

[0955] Step 5:

[0956] The terminal displays the clothing suggestions received from the server to the consumer, along with detailed information (e.g., photos, sizes, prices, etc.).

[0957] Examples:

[0958] The device will display details of a "size medium blue T-shirt" to the consumer.

[0959] Step 6:

[0960] The user selects the clothing item they like from the suggested items and indicates their intention to purchase it by pressing the purchase button to proceed with the purchase process.

[0961] Examples:

[0962] The user selects "Blue T-shirt (size M)" and presses the purchase button.

[0963] Step 7:

[0964] The terminal sends the purchase and payment information to the server. As input, the user's clothing selection and payment information are used. As output, a request for purchase confirmation is sent to the server.

[0965] Examples:

[0966] The user enters payment information and the terminal sends a request to the server to confirm the purchase.

[0967] Step 8:

[0968] The server verifies the payment information and confirms the purchase. If the purchase is successful, it notifies the apparel store of the purchase information and proceeds to purchase the clothing. The inputs are the user's payment information and the selected clothing information. The output is a purchase notification to the apparel store and a payment request for the purchase amount.

[0969] Examples:

[0970] The server receives the purchase information, notifies the apparel store of the purchase details of the "blue T-shirt (size M)," and pays the apparel store a portion of the purchase price.

[0971] Step 9:

[0972] The apparel store receives the notification from the server and ships the clothes to the specified address. The purchase notification from the server is used as input. The clothes are shipped to the consumer's address as output.

[0973] Examples:

[0974] An apparel store ships a blue T-shirt (size medium) to a consumer's address.

[0975] Step 10:

[0976] The server generates a try-on simulation using virtual reality (VR) or augmented reality (AR) technology based on the consumer's profile information and sends the simulation images and videos to the device. The user's profile information and suggested clothing information are used as input. As output, data from the try-on simulation is generated and sent to the device.

[0977] Examples:

[0978] The server uses the consumer's profile information to generate a simulated image of what it would look like when trying on a "T-shirt (size M, blue)" and sends it to the terminal.

[0979] 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.

[0980] This invention provides an online platform that provides consumers with clothing that would otherwise be discarded, and is composed of three main elements: a server, a terminal, and a user. The platform has a clothing recommendation function that uses a generative AI model to recommend the most suitable clothing based on the consumer's profile information. Furthermore, an emotion engine is used to obtain user emotion data and optimize the recommendations. Furthermore, by utilizing virtual reality and augmented reality technology to provide an online try-on experience, it increases consumer motivation to purchase and improves satisfaction.

[0981] Acquiring and saving clothing data

[0982] server

[0983] The server receives data on discarded clothing from apparel stores and stores it in a database. This data includes photos of the clothing, size, color, condition, and whether or not tags are present. To prevent damage to the brand image, apparel stores are encouraged to provide the data without tags or logos.

[0984] Examples:

[0985] The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and stores it in a database.

[0986] Consumer profile setting

[0987] User

[0988] Consumers enter their body type information (e.g., height, weight), clothing preferences (e.g., style, color), and budget on the device. Based on this profile information, the server will suggest the most suitable clothing for the consumer.

[0989] Terminal

[0990] The terminal transmits the profile information entered by the user to the server.

[0991] Examples:

[0992] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[0993] Clothing recommendations using generative AI and emotion engine

[0994] server

[0995] The server uses a generative AI model to suggest optimal clothing based on the consumer's profile information and emotional data obtained from the emotion engine. It selects clothing that matches the consumer's preferences, body shape, and emotions from clothing data stored in a database.

[0996] Examples:

[0997] The server's generation AI filters the results by "casual style, blue, budget under 2000 yen" and adds "T-shirt (size M, blue)" to the list of suggestions. Using data from the emotion engine, it prioritizes suggestions that will satisfy the user.

[0998] View and select suggestions

[0999] Terminal

[1000] The device displays clothing suggestions sent from the server to the consumer and provides detailed information (photos, sizes, prices, etc.) It also analyzes consumer reactions in real time through an emotion engine and sends the results to the server.

[1001] User

[1002] The user selects the clothing item they like from the suggested items and indicates their intention to purchase it (by pressing the purchase button).

[1003] Examples:

[1004] The device displays detailed information about the "T-shirt (size M, blue)" and the user presses the purchase button.

[1005] Purchase confirmation and shipping procedures

[1006] User

[1007] When the user presses the purchase button, they confirm the purchase and enter their payment information.

[1008] Terminal

[1009] The terminal transmits the purchase and payment information to the server.

[1010] server

[1011] The server verifies the payment information and confirms the purchase. The server then notifies the apparel store of the purchase information and proceeds with the purchase of the clothing. The server also pays a portion of the purchase price to the apparel store.

[1012] Apparel store

[1013] The apparel store will receive a confirmation of purchase and ship the garment to the specified address.

[1014] Examples:

[1015] After the purchase is confirmed, the server notifies the apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price. The apparel store then ships the clothing to the consumer's address.

[1016] Online try-on experience

[1017] server

[1018] The server generates a try-on simulation using virtual reality (VR) or augmented reality (AR) technology based on the consumer's profile information and emotional data, and sends simulated images and videos to the device.

[1019] Terminal

[1020] The device receives simulated images and videos and displays them to the consumer, giving them a feeling similar to a real fitting experience. Emotional data during the fitting process is also collected and sent to the server.

[1021] Examples:

[1022] The server uses the consumer's profile information and emotional data to generate a simulated image of what it would be like to try on a "T-shirt (size M, blue)" and sends it to the device. The device displays the simulated image, collects the consumer's emotional data in real time, and sends it to the server.

[1023] By implementing the present invention in the above manner, an efficient system is realized that provides consumers with clothes that would otherwise be discarded. This reduces the amount of discarded clothes and the burden on the environment. Furthermore, by combining it with an emotion engine, it is possible to improve consumer satisfaction and stimulate purchasing motivation.

[1024] The processing flow will be explained below.

[1025] Step 1:

[1026] server

[1027] Receives data on clothing to be discarded from apparel stores. The received data includes photos, size, color, condition, whether or not it has tags, etc. The format of this data is checked, and if there are no problems, it is stored in a database.

[1028] Examples:

[1029] The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and stores it in a database.

[1030] Step 2:

[1031] User

[1032] Enter your profile information on a dedicated screen on your device, including your height, weight, clothing preferences (style and color), budget, etc.

[1033] Terminal

[1034] The entered profile information is sent to the server.

[1035] Step 3:

[1036] server

[1037] The received profile information is stored in a database.

[1038] Examples:

[1039] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[1040] Step 4:

[1041] server

[1042] The system retrieves consumer profile information from the database, analyzes suitable clothing using a generative AI model, extracts suitable clothing data, and generates a list. It also uses an emotion engine to analyze the user's past responses and current emotional data to optimize the recommendations.

[1043] Step 5:

[1044] server

[1045] The generated clothing list and the emotion data associated with it are transmitted to the terminal.

[1046] Examples:

[1047] The server's generation AI filters items by "casual style, blue, budget within 2,000 yen" and adds "T-shirt (size M, blue)" to the list of suggestions. The emotion engine prioritizes items with high satisfaction based on past consumer responses.

[1048] Step 6:

[1049] Terminal

[1050] It receives the clothing list sent from the server and displays it to the consumer, displaying detailed information and analyzing the consumer's real-time reaction through an emotion engine.

[1051] User

[1052] Choose the clothing you like from the suggested items and indicate your intention to purchase (press the purchase button).

[1053] Examples:

[1054] The device displays detailed information about a "T-shirt (size M, blue)" and the user presses the purchase button. The device then analyzes the user's facial expressions and tone of voice using an emotion engine and sends real-time reaction data to the server.

[1055] Step 7:

[1056] User

[1057] By pressing the purchase button, you confirm your purchase and enter your payment information.

[1058] Step 8:

[1059] Terminal

[1060] Send purchase and payment information to the server.

[1061] Step 9:

[1062] server

[1063] The payment information is verified and the purchase is confirmed. The purchase information is notified to the apparel store, and the procedure for purchasing the clothing is carried out. A portion of the purchase price is also paid to the apparel store.

[1064] Examples:

[1065] After the server confirms the purchase, it notifies the apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price.

[1066] Step 10:

[1067] Apparel store

[1068] You will receive a confirmation of your purchase and your garment will be shipped to the address you specified.

[1069] Examples:

[1070] The apparel store will ship a "T-shirt (size M, blue)" to the specified address.

[1071] Step 11 (Optional):

[1072] server

[1073] Based on the consumer's profile information and emotional data, a try-on simulation is generated using virtual reality (VR) or augmented reality (AR) technology, and the simulated images and videos are sent to the device.

[1074] Terminal

[1075] Simulation images and videos are received and displayed to the consumer, and emotional data of the consumer while trying on the clothes is also collected and sent to the server.

[1076] Examples:

[1077] The server uses the consumer's profile information and emotional data to generate a simulated image of what it would be like to try on a "T-shirt (size M, blue)" and sends it to the device. The device displays the simulated image, collects the consumer's emotional data in real time, and sends it to the server.

[1078] The above are the processing steps of the program for implementing the present invention. By combining emotion engines in this way, it is possible to improve consumer satisfaction and stimulate purchasing motivation.

[1079] Example 2

[1080] 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."

[1081] Nowadays, many apparel stores dispose of clothing that is scheduled to be discarded, resulting in an increased burden on the environment. An efficient and user-friendly system is needed to solve this problem and promote the reuse of clothing. Also, a means is needed to suggest the most suitable clothing to consumers and motivate them to purchase.

[1082] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for acquiring data on clothes to be discarded, a means for acquiring consumer profile information, and a means for proposing suitable clothes from the clothing data based on the consumer profile information and emotion data using a generative AI model. This makes it possible to efficiently reuse clothes to be discarded and to propose optimal clothes to consumers.

[1083] "Clothing scheduled for disposal" refers to clothing that is not sold at apparel stores or the like and is scheduled to be disposed of.

[1084] "Consumer profile information" is personal information provided by a consumer, such as height, weight, preferred style, color, budget, etc.

[1085] A "generative artificial intelligence model" is an artificial intelligence program that uses machine learning and deep learning to make clothing suggestions.

[1086] "Emotional data" is data that indicates the emotional state of consumers, and is obtained by analyzing consumers' reactions and emotions in real time.

[1087] A "database" is a system for storing data such as clothing to be disposed of and consumer profile information.

[1088] "Virtual reality" is a technology that uses computer technology to provide a realistic visual and auditory experience.

[1089] "Augmented reality" is a technology that overlays computer-generated information onto the real world.

[1090] A "try-on simulation" is a simulation that uses virtual reality or augmented reality technology to allow consumers to experience the experience of trying on clothing.

[1091] "Consumer response" refers to the emotions and behaviors that consumers exhibit toward the proposed garment.

[1092] "Payment Information" means credit card or other payment method information provided by a consumer when making a purchase.

[1093] This invention provides an online platform that provides consumers with clothing that would otherwise be discarded, and includes a clothing recommendation function using a generative AI model, an optimization function using an emotion engine, and an online try-on experience using virtual reality and augmented reality technology. This system is mainly composed of three elements: a server, a terminal, and a user.

[1094] Acquiring and saving clothing data

[1095] server

[1096] The server receives data on clothing to be discarded from apparel stores and stores the data in a database. Specifically, the server receives information such as photos of the clothing to be discarded, size, color, condition, and whether or not it has tags. In order to maintain the brand image, apparel stores provide data with tags and logos removed. For example, the server receives data on a "T-shirt (size M, blue, condition: like new)" from an apparel store and stores the data in a database.

[1097] Consumer profile setting

[1098] User

[1099] The user enters their profile information on the device. This information includes height, weight, preferred style, color, budget, etc. The device sends this information to the server. For example, the user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: under 2000 yen," and the device sends it to the server. The server stores this information in a database.

[1100] Clothing recommendations using generative AI and emotion engine

[1101] server

[1102] The server retrieves the user's profile information from the database and emotion data from the emotion engine, and then uses a generative AI model to suggest the most suitable clothing based on this information. The generative AI model selects clothing that matches the user's preferences, body shape, and emotions, and generates a list of suggestions. For example, the server's generative AI inputs the following data: "Height: 170 cm, Weight: 60 kg, Casual style, Blue, Budget: 2000 yen or less, Emotion: Positive," and generates "T-shirt (Size M, Blue)" as a suggestion list.

[1103] View and select suggestions

[1104] Terminal

[1105] The device displays the list of suggestions sent from the server to the user and provides detailed information (photos, sizes, prices, etc.). It also analyzes the user's reactions in real time through an emotion engine and sends the results to the server. The user selects the item they wish to purchase from the suggested clothing and presses the purchase button. For example, the device displays detailed information about a "T-shirt (size M, blue)" and the user presses the purchase button.

[1106] Purchase confirmation and shipping procedures

[1107] User

[1108] When the user presses the purchase button, they confirm the purchase and enter their payment information.

[1109] Terminal

[1110] The terminal transmits the purchase and payment information to the server.

[1111] server

[1112] The server verifies the payment information and confirms the purchase. The server notifies the apparel store of the purchase information and proceeds with the purchase of the clothing. The server also pays a portion of the purchase price to the apparel store. The apparel store then ships the clothing after receiving the notification. For example, after the purchase is confirmed, the server notifies the apparel store of the purchase information for a "T-shirt (size M, blue)," and the apparel store ships the clothing to the consumer's address.

[1113] Online try-on experience

[1114] server

[1115] Based on the consumer's profile information and emotional data, the server uses virtual reality (VR) or augmented reality (AR) technology to generate a try-on simulation and send it to the device.

[1116] Terminal

[1117] The device receives the simulated images and videos and displays them to the user, allowing the user to experience a sensation similar to that of a real try-on. Emotional data from the user trying on clothes is also collected and sent to the server. For example, the server uses the consumer's profile information and emotional data to generate a simulated image of the user trying on a "T-shirt (size M, blue)" and sends it to the device. The device displays the simulated image and collects the user's emotional data in real time, which is then sent to the server.

[1118] Prompt Sentence Examples

[1119] Example prompts for users to enter profile information:

[1120] Enter your height, weight, clothing preferences (e.g. style, color), and budget.

[1121] Example: Height: 170cm, Weight: 60kg, Preference: Casual, Color: Blue, Budget: Under 2000 yen

[1122] Example prompts when the server makes suggestions using AI generation:

[1123] Considering the consumer's profile information (height 170cm, weight 60kg, preference: casual, color: blue, budget: under 2000 yen) and emotional data, please suggest the most suitable clothing from the database.

[1124] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1125] Program processing flow and specific explanation

[1126] Step 1:

[1127] Acquiring and saving clothing data

[1128] Server: Receives data on clothing to be discarded from apparel stores and stores the data in a database. Specifically, the server receives information such as photos of clothing, size, color, condition, and whether or not tags are attached, sent from apparel stores. The server stores the received data in a database and updates it in real time.

[1129] Input: Clothing data (e.g. T-shirt (size M, blue, condition: like new))

[1130] Output: Clothing data stored in a database

[1131] Specific operation: The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and registers it in the database.

[1132] Step 2:

[1133] Consumer profile setting

[1134] User: Through an application on their device or through a website, the user enters their profile information, including height, weight, preferred style, color, budget, etc.

[1135] Terminal: The terminal sends the profile information entered by the user to the server, which receives it and stores it in a database.

[1136] Input: Profile information (e.g., height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen)

[1137] Output: User profile information stored in the database

[1138] Specific operation: The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: under 2000 yen," and the device sends this to the server. The server stores the received information in a database.

[1139] Step 3:

[1140] Clothing recommendations using generative AI and emotion engine

[1141] Server: The server retrieves the user's profile information from the database and also retrieves the user's emotional data from the emotion engine. Based on this, the server uses a generative AI model to suggest the most suitable clothing. The server then sends the generated suggestion list to the device.

[1142] Input: User profile information, emotion data

[1143] Output: Suggestion list (e.g. T-shirt (size M, blue))

[1144] Specific operation: The server filters the generated AI model with "casual style, blue, budget under 2,000 yen," and combines the user's profile information (height: 170 cm, weight: 60 kg) with emotional data to add "T-shirt (size M, blue)" to the suggestion list.

[1145] Step 4:

[1146] View and select suggestions

[1147] Terminal: The terminal displays the list of suggestions sent from the server to the user and provides detailed information (photos, size, price, etc.). It also analyzes the user's reactions in real time through an emotion engine and sends the results to the server.

[1148] User: The user selects the clothing item they wish to purchase from the suggested items and presses the purchase button.

[1149] Input: Suggestion list (e.g. T-shirt (size M, blue))

[1150] Output: Intention to purchase (pressing the purchase button)

[1151] Specific operation: The device displays detailed information about a "T-shirt (size M, blue)" including a photo, size, and price, and the user presses the purchase button.

[1152] Step 5:

[1153] Purchase confirmation and shipping procedures

[1154] User: After the user presses the purchase button, they confirm the purchase and enter their payment information.

[1155] Terminal: The terminal sends purchase and payment information to the server.

[1156] Server: The server verifies the payment information and confirms the purchase. The server also notifies the apparel store of the purchase information and processes the purchase of the clothing. At the same time, the server pays a portion of the purchase price to the apparel store. The apparel store then ships the clothing upon receiving the notification.

[1157] Input: Purchase information, payment information

[1158] Output: Purchase confirmation, notification to apparel store

[1159] Specific operation: The server verifies the payment information, and after the purchase is confirmed, it notifies the apparel store of the purchase information for the "T-shirt (size M, blue)", and the apparel store ships the clothing to the consumer's address.

[1160] Step 6:

[1161] Online try-on experience

[1162] Server: Based on the user's profile information and emotional data, the server generates a fitting simulation using VR or AR technology and sends the simulation images and videos to the device.

[1163] Device: The device receives simulated images and videos and displays them to the user, giving the user a feeling similar to a real fitting experience. It also collects emotional data during the fitting process and sends it to the server.

[1164] Input: User profile information, emotion data

[1165] Output: Simulation images, emotion data

[1166] Specific operation: The server uses the consumer's profile information and emotional data to generate a simulated image of a "T-shirt (size M, blue)" being tried on and sends it to the device. The device displays the simulated image and collects the user's emotional data in real time and sends it to the server.

[1167] (Application example 2)

[1168] 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."

[1169] Conventional methods for utilizing discarded clothing do not adequately approach consumers and do not provide proposals that reflect their preferences and emotions. Furthermore, the lack of a try-on experience reduces consumer satisfaction and motivation to purchase. This has hindered progress in effectively utilizing discarded clothing, and the environmental impact problem has not been resolved.

[1170] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring data on clothing to be discarded, means for acquiring consumer profile information, means for using a generative artificial intelligence model to suggest suitable clothing from the clothing data based on the consumer profile information, means for acquiring consumer emotion data using an emotion engine and optimizing the suggestion content, and means for providing the consumer with a simulation of trying on the suggested clothing using virtual reality or augmented reality technology. This makes it possible to suggest optimal clothing that reflects the consumer's preferences and emotions, improving consumer satisfaction and promoting the effective use of clothing to be discarded.

[1171] "Clothing scheduled for disposal" refers to clothing that is scheduled for disposal by apparel stores or clothing manufacturers, but is still usable and can be offered to consumers.

[1172] "Means for acquiring data" refers to the means for receiving detailed information such as photographs, size, color, and condition of clothing to be discarded from apparel stores and clothing manufacturers via servers, terminals, etc.

[1173] "Consumer profile information" refers to information that indicates a user's personal preferences and physical characteristics, such as height, weight, clothing preferences, and budget.

[1174] A "generative artificial intelligence model" is an AI algorithm or machine learning model that suggests appropriate clothing based on a consumer's profile information.

[1175] The "means of suggestion" refers to a system that selects the most suitable clothing and suggests it to users based on consumer profile information and clothing data.

[1176] The "emotion engine" is a technology that acquires real-time emotional data from consumers and optimizes the content of proposals based on that data.

[1177] "Virtual reality technology" and "augmented reality technology" refer to technologies that allow consumers to try on clothes in a virtual space or try on virtual clothes superimposed on real space.

[1178] A "means for providing simulated try-on clothing" is a system or application that uses virtual reality or augmented reality technology to provide a consumer with a visual and sensory experience of trying on clothing.

[1179] "Means of notification" refers to the means by which a consumer confirms the purchase of the selected clothing item and notifies the apparel store or clothing manufacturer of that information.

[1180] The present invention is a system that functions as an online platform that provides consumers with clothes that are scheduled to be discarded, and is mainly composed of three elements: a server, a terminal, and a user. How each element in this system works together to realize the invention is explained in detail below.

[1181] Acquiring and saving clothing data

[1182] The server receives data on discarded clothing from apparel stores and stores it in a database. This data includes photos of the clothing, size, color, condition, and whether or not tags are attached. To prevent damage to the brand image, it is recommended that apparel stores provide the data without tags or logos.

[1183] Examples:

[1184] The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and stores it in a database.

[1185] Consumer profile setting

[1186] Users enter their body type information (e.g., height, weight), clothing preferences (e.g., style, color), and budget on their device. Based on this profile information, the server will suggest the most suitable clothing for the consumer.

[1187] The terminal transmits the profile information entered by the user to the server.

[1188] Examples:

[1189] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[1190] Clothing recommendations using generative AI and emotion engine

[1191] The server uses a generative AI model to suggest optimal clothing based on the consumer's profile information and emotional data obtained from the emotion engine. It selects clothing that matches the consumer's preferences, body shape, and emotions from clothing data stored in a database.

[1192] Examples:

[1193] Using the prompt "The user is 170cm tall, weighs 60kg, prefers a casual style, and wants blue clothing within a budget of 2000 yen. Please generate the best clothing suggestions you can offer," the generative AI model filters "casual style, blue, budget within 2000 yen" and adds "T-shirt (size M, blue)" to the suggestion list. Using data from the emotion engine, it prioritizes suggestions that provide the highest user satisfaction.

[1194] View and select suggestions

[1195] The device displays clothing suggestions sent from the server to the consumer and provides detailed information (photos, sizes, prices, etc.) It also analyzes consumer reactions in real time through an emotion engine and sends the results to the server.

[1196] The user selects the clothing item they like from the suggested items and indicates their intention to purchase it (by pressing the purchase button).

[1197] Examples:

[1198] The device displays detailed information about the "T-shirt (size M, blue)" and the user presses the purchase button.

[1199] Purchase confirmation and shipping procedures

[1200] When the user presses the purchase button, they confirm the purchase and enter their payment information.

[1201] The terminal transmits the purchase and payment information to the server.

[1202] The server verifies the payment information and confirms the purchase. The server then notifies the apparel store of the purchase information and proceeds with the purchase of the clothing. The server also pays a portion of the purchase price to the apparel store.

[1203] The apparel store will receive a confirmation of purchase and ship the garment to the specified address.

[1204] Examples:

[1205] After the purchase is confirmed, the server notifies the apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price. The apparel store then ships the clothing to the consumer's address.

[1206] Online try-on experience

[1207] The server generates a try-on simulation using virtual reality (VR) or augmented reality (AR) technology based on the consumer's profile information and emotional data, and sends simulated images and videos to the device.

[1208] The device receives simulated images and videos and displays them to the consumer, giving them a feeling similar to a real fitting experience. Emotional data during the fitting process is also collected and sent to the server.

[1209] Examples:

[1210] The server uses the consumer's profile information and emotional data to generate a simulated image of what it would be like to try on a "T-shirt (size M, blue)" and sends it to the device. The device displays the simulated image, collects the consumer's emotional data in real time, and sends it to the server.

[1211] As described above, the present invention realizes a system that proposes optimal clothing that reflects the consumer's preferences and feelings and provides a realistic try-on experience, thereby improving consumer satisfaction and promoting the effective use of clothing that would otherwise be discarded.

[1212] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1213] Step 1: Acquire and save clothing data

[1214] The server receives data on the clothes to be discarded (e.g., photos, size, color, condition, presence or absence of tags) from the apparel store and stores that information in a database. The input is the clothing data provided by the apparel store, and the output is the clothing data stored in the database.

[1215] Specifically, the data provided by the apparel store is obtained through an API, and each item (photo, size, color, condition, presence or absence of tags) is stored in a database in JSON format.

[1216] Step 2: Fill out your consumer profile

[1217] The terminal prompts the user to enter information about their physical shape (e.g., height, weight), clothing preferences (e.g., style, color), and budget, and then transmits this information to the server.

[1218] The input is the profile information that the user enters into the terminal, and the output is the profile information that is sent to the server.

[1219] Specifically, the user enters profile information through the device's UI, and the input data is sent to the server via an HTTPS request.

[1220] Step 3: Clothing suggestions by generative AI

[1221] The server uses a generative AI model to suggest optimal clothing based on the consumer's profile information and clothing data stored in the database. The input is the consumer's profile information and the clothing data in the database, and the output is a list of suggested clothing items.

[1222] Specifically, the generative AI model is fed a prompt such as, "The user is 170 cm tall, weighs 60 kg, prefers a casual style, desires blue clothing, and has a budget of 2,000 yen or less," and the model outputs a list of the most suitable clothing items.

[1223] Step 4: Acquiring emotion data using the emotion engine

[1224] When the user browses the proposed clothing or tries on the clothing in a simulated fashion, the device captures emotional data using a camera and microphone and sends it to the server. The input is the user's real-time emotional data, and the output is the emotional data sent to the server.

[1225] Specifically, the device uses a camera and microphone to capture the user's facial expressions and voice in real time, analyzes the data through the Affectiva SDK, and sends the emotional data to the server.

[1226] Step 5: View and select suggestions

[1227] The device displays the optimized clothing suggestions sent from the server, along with detailed information (photos, sizes, prices, etc.) The user selects the clothing they like and presses the purchase button.

[1228] The input is a list of clothing suggestions sent from the server, and the output is detailed information displayed on the terminal.

[1229] Specifically, the system obtains a list of suggestions from the server and displays detailed information about the clothing to the user through the device's UI.

[1230] Step 6: Purchase confirmation and notification

[1231] When the user presses the purchase button, the terminal sends the purchase information and payment information to the server. The server verifies the information, confirms the purchase, and notifies the apparel store. The input is the user's purchase information and payment information, and the output is a notification of the purchase information to the apparel store.

[1232] Specifically, the user presses the purchase button, the server receives the purchase information sent from the terminal, processes the payment, and notifies the apparel store of the purchase information.

[1233] Step 7: Online try-on experience

[1234] The server generates a try-on simulation using virtual reality or augmented reality technology based on the consumer's profile information and emotional data, and sends it to the device. The input is the profile information and emotional data, and the output is images or videos of the try-on simulation.

[1235] Specifically, the server uses VR / AR technologies such as Unity or Unreal Engine to create a clothing simulation based on the user's profile information, and sends the simulation data to the device. The user experiences the clothing simulation, and emotional data from the simulation is collected and retransmitted in real time.

[1236] 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.

[1237] 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.

[1238] 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.

[1239] [Third embodiment]

[1240] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1241] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1242] 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).

[1243] 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.

[1244] 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.

[1245] 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).

[1246] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1247] 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.

[1248] 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.

[1249] 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.

[1250] 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.

[1251] 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."

[1252] This invention provides an online platform that provides consumers with discarded clothing, and is composed of three main components: a server, a terminal, and a user. The platform is equipped with a clothing recommendation function using a generative AI model to recommend the most suitable clothing based on the consumer's profile information. It also utilizes virtual reality and augmented reality technology to provide an online try-on experience, increasing consumer motivation and satisfaction.

[1253] Acquiring and saving clothing data

[1254] server

[1255] The server receives data on discarded clothing from apparel stores and stores it in a database. This data includes photos of the clothing, size, color, condition, etc. To prevent damage to the brand's image, apparel stores are encouraged to provide the data without tags or logos.

[1256] Examples:

[1257] The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and stores it in the database.

[1258] Consumer profile setting

[1259] User

[1260] Consumers enter their body type information (e.g., height, weight), clothing preferences (e.g., style, color), and budget on the device. Based on this profile information, the server will suggest the most suitable clothing for the consumer.

[1261] Terminal

[1262] The terminal sends the profile information entered by the user to the server, which then stores the profile information in a database.

[1263] Examples:

[1264] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[1265] Clothing suggestions using generative AI

[1266] server

[1267] The server uses a generative AI model to suggest optimal clothing based on the consumer's profile information, and selects clothing that matches the consumer's preferences and body shape from clothing data stored in a database.

[1268] Examples:

[1269] The server's generation AI filters for "casual style, blue, budget under 2,000 yen" and includes "T-shirt (size M, blue)" in the suggestion list.

[1270] View and select suggestions

[1271] Terminal

[1272] The terminal displays clothing suggestions sent from the server to the consumer and provides detailed information (photos, sizes, prices, etc.).

[1273] User

[1274] The user selects the clothing item they like from the suggested items and indicates their intention to purchase it (by pressing the purchase button).

[1275] Examples:

[1276] The device displays detailed information about the "T-shirt (size M, blue)" and the user presses the purchase button.

[1277] Purchase confirmation and shipping procedures

[1278] User

[1279] When the user presses the purchase button, they confirm the purchase and enter their payment information.

[1280] Terminal

[1281] The terminal transmits the purchase and payment information to the server.

[1282] server

[1283] The server verifies the payment information and confirms the purchase. The server then notifies the apparel store of the purchase information and proceeds with the purchase of the clothing. The server also pays a portion of the purchase price to the apparel store.

[1284] Apparel store

[1285] The apparel store will receive a confirmation of purchase and ship the garment to the specified address.

[1286] Examples:

[1287] After the purchase is confirmed, the server notifies the apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price. The apparel store then ships the clothing to the consumer's address.

[1288] Online try-on experience (optional)

[1289] server

[1290] The server generates a try-on simulation using virtual reality (VR) or augmented reality (AR) technology based on the consumer's profile information, and sends the simulated images and videos to the device.

[1291] Terminal

[1292] The device receives simulated images and videos and displays them to consumers, giving them a feeling similar to a real try-on experience.

[1293] Examples:

[1294] The server uses the consumer's profile information to generate a simulation image of what it would look like to try on a "T-shirt (size M, blue)" and sends it to the terminal. The terminal then displays the simulation image to the consumer, providing the feeling of trying it on.

[1295] By implementing the present invention in the above-described manner, an efficient system is realized for providing clothes that are scheduled to be discarded to consumers, thereby reducing the amount of discarded clothes and the burden on the environment.

[1296] The processing flow will be explained below.

[1297] Step 1:

[1298] server

[1299] Data on clothing to be discarded is received from apparel stores. The received data includes photos, size, color, condition, whether or not the item has tags, etc. The data is format-checked, and if there are no problems, it is stored in a database.

[1300] Examples:

[1301] The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and stores it in a database.

[1302] Step 2:

[1303] User

[1304] Enter your profile information on a dedicated screen on your device, including your height, weight, clothing preferences (style and color), budget, etc.

[1305] Terminal

[1306] The entered profile information is sent to the server.

[1307] Step 3:

[1308] server

[1309] The received profile information is stored in a database.

[1310] Examples:

[1311] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[1312] Step 4:

[1313] server

[1314] The system retrieves consumer profile information from the database, analyzes matching clothing using a generative artificial intelligence model, extracts matching clothing data, and generates a list.

[1315] Step 5:

[1316] server

[1317] Send detailed information about the generated clothing list (e.g., photos, sizes, prices, etc.) to the device.

[1318] Examples:

[1319] The server's generation AI filters items by "casual style, blue, budget within 2000 yen" and adds "T-shirt (size M, blue)" to the list of suggestions. This information is then sent to the device.

[1320] Step 6:

[1321] Terminal

[1322] Receives the clothing list sent from the server and displays it to the consumer. Displays detailed information.

[1323] User

[1324] Choose the clothing you like from the suggested items and indicate your intention to purchase (press the purchase button).

[1325] Examples:

[1326] The device displays detailed information about the "T-shirt (size M, blue)" and the user presses the purchase button.

[1327] Step 7:

[1328] User

[1329] By pressing the purchase button, you confirm your purchase and enter your payment information.

[1330] Step 8:

[1331] Terminal

[1332] Send purchase and payment information to the server.

[1333] Step 9:

[1334] server

[1335] The payment information is verified and the purchase is confirmed. The purchase information is notified to the apparel store, and the procedure for purchasing the clothing is carried out. A portion of the purchase price is also paid to the apparel store.

[1336] Examples:

[1337] After the server confirms the purchase, it notifies the apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price.

[1338] Step 10:

[1339] Apparel store

[1340] After receiving confirmation of purchase, the clothing will be shipped to the specified address.

[1341] Examples:

[1342] The apparel store will ship a "T-shirt (size M, blue)" to the specified address.

[1343] Step 11 (Optional):

[1344] server

[1345] Based on the consumer's profile information, a virtual reality (VR) or augmented reality (AR) try-on simulation is generated, and simulated images and videos are sent to the device.

[1346] Terminal

[1347] Receives simulated images and videos and displays them to consumers.

[1348] Examples:

[1349] The server generates a simulation of trying on a "T-shirt (size M, blue)" and sends it to the terminal. The terminal displays the simulation image, allowing the consumer to experience the sensation of trying on the item.

[1350] The above are the processing steps of the program in carrying out the present invention.

[1351] Example 1

[1352] 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."

[1353] While supporting modern comfortable lifestyles, the fashion industry faces the social problem of discarding large amounts of clothing. Discarded clothing places a burden on the environment, and a system is needed to effectively reduce this volume. Furthermore, to support consumers in choosing clothing, it is necessary to provide customized suggestions and realistic try-on experiences. To address these issues, it is necessary to realize a system that efficiently provides discarded clothing to consumers and improves consumer satisfaction.

[1354] 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.

[1355] In this invention, the server includes means for acquiring product data to be discarded, means for acquiring consumer profile information, means for using a generative artificial intelligence model to suggest suitable products from the product data based on the consumer profile information, means for displaying information on the suggested products to the consumer, means for confirming and notifying the purchase of the product selected by the consumer, means for creating a try-on simulation based on the consumer profile information and the suggested product information, means for generating simulation images and videos and sending them to a terminal, and means for displaying the try-on simulation data to the consumer. This makes it possible to efficiently provide clothes to be discarded, to make it easier for consumers to select appropriate clothes based on their own preferences, and to increase their desire to purchase through the try-on experience.

[1356] "Product data scheduled for disposal" refers to information about products that manufacturers, apparel stores, etc. had not planned to offer to consumers, but which they have discovered new value for and are now offering.

[1357] "Consumer profile information" refers to data provided by a consumer relating to personal characteristics and preferences, such as height, weight, preferred style, and budget.

[1358] "Generative artificial intelligence model" refers to a machine learning algorithm and model that selects and suggests optimal products from data on products scheduled for disposal based on consumer profile information.

[1359] "Means for suggesting suitable products" refers to a method and system that uses a generative artificial intelligence model to select and provide information on optimal products based on consumer profile information.

[1360] "Means for displaying product information" refers to the technology and devices that allow consumers to check detailed information such as photos, sizes, and prices of the proposed products through a user interface.

[1361] "Means for confirming and notifying purchase" refers to the methods and technologies that confirm a consumer's intention to purchase a selected product, process payment, and notify related systems and manufacturers of that information.

[1362] "Means for creating a try-on simulation" refers to methods and technologies that use virtual reality or augmented reality technology to recreate the state of a consumer trying on an item based on the consumer's profile information and proposed product information.

[1363] "Means for generating simulation images and videos and transmitting them to a terminal" refers to methods and technologies for generating images and videos for performing a fitting simulation and transmitting them to a consumer's terminal.

[1364] "Means for displaying try-on simulation data to consumers" refers to methods and technologies for displaying the received simulation images and videos on a terminal so that consumers can check them.

[1365] This invention provides an online platform that provides consumers with discarded products, and is composed of three main components: a server, a terminal, and a user. This platform is equipped with a product recommendation function that uses a generative artificial intelligence model to recommend optimal products based on the consumer's profile information. It also utilizes virtual reality and augmented reality technology to provide an online try-on experience, increasing consumer motivation and satisfaction.

[1366] Acquisition and storage of data on products scheduled for disposal

[1367] server

[1368] The server receives data on products to be discarded from manufacturers and apparel stores and stores that data in a database. This data includes product photos, sizes, colors, and conditions. Manufacturers and apparel stores are encouraged to provide the data without tags or logos to prevent damage to their brand image.

[1369] Examples:

[1370] The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and stores it in the database.

[1371] Consumer profile setting

[1372] User

[1373] Consumers enter their body type information (height, weight), clothing preferences (style, color), and budget on the device. Based on this profile information, the server will suggest the most suitable products for the consumer.

[1374] Terminal

[1375] The terminal transmits the profile information entered by the user to the server, and the server stores the profile information in a database.

[1376] Examples:

[1377] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[1378] Product suggestions by generative AI

[1379] server

[1380] The server uses a generative AI model to suggest optimal products based on the consumer's profile information. From product data stored in a database, it selects products that match the consumer's preferences and body shape. This generative AI model can use TensorFlow, for example.

[1381] Examples:

[1382] The server's generation AI filters for "casual style, blue, budget under 2,000 yen" and includes "T-shirt (size M, blue)" in the suggestion list.

[1383] View and select suggestions

[1384] Terminal

[1385] The terminal displays the product suggestions sent from the server to the consumer and provides detailed information (photos, sizes, prices, etc.).

[1386] User

[1387] The user selects the product they like from the suggested products and indicates their intention to purchase it (by pressing the purchase button).

[1388] Examples:

[1389] The device displays detailed information about the "T-shirt (size M, blue)" and the user presses the purchase button.

[1390] Purchase confirmation and shipping procedures

[1391] User

[1392] When the user presses the purchase button, they confirm the purchase and enter their payment information.

[1393] Terminal

[1394] The terminal transmits the purchase and payment information to the server.

[1395] server

[1396] The server verifies the payment information and confirms the purchase. The server then notifies the manufacturer or apparel store of the purchase information, processes the purchase of the product, and pays a portion of the purchase price to the manufacturer or apparel store.

[1397] Manufacturers and apparel stores

[1398] The manufacturer or apparel store will receive a confirmation of purchase and ship the product to the specified address.

[1399] Examples:

[1400] After the purchase is confirmed, the server notifies the manufacturer or apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price. The manufacturer or apparel store then ships the product to the consumer's address.

[1401] Online try-on experience (optional)

[1402] server

[1403] The server generates a try-on simulation using virtual reality (VR) or augmented reality (AR) technology based on the consumer's profile information, and sends the simulated images and videos to the device.

[1404] Terminal

[1405] The device receives simulated images and videos and displays them to consumers, giving them a feeling similar to a real try-on experience.

[1406] Examples:

[1407] The server uses the consumer's profile information to generate a simulation image of what it would look like to try on a "T-shirt (size M, blue)" and sends it to the terminal. The terminal then displays the simulation image to the consumer, providing the feeling of trying it on.

[1408] Prompt Sentence Examples

[1409] Examples of input prompts for a generative AI model include:

[1410] Examples:

[1411] User profile:

[1412] Height: 170cm

[1413] Weight: 60kg

[1414] Preference: Casual

[1415] Color: Blue

[1416] Budget: Under 2000 yen

[1417] Prompt the generative AI model:

[1418] Please suggest a casual style blue product for under 2000 yen.

[1419] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1420] Step 1: Acquire and store data on products to be disposed of

[1421] server

[1422] Input: Data on discarded products provided by manufacturers and apparel stores (e.g., product photos, size, color, condition, etc.)

[1423] Processing: The server receives product data from manufacturers and apparel stores via a data transmission API. The received data is stored in a database.

[1424] Output: Product data stored in a database

[1425] What happens: The server connects to the manufacturer's data system, receives data about "T-shirt (size M, blue, condition: like new)" via API, and stores it in a database.

[1426] Step 2: Setting up a consumer profile

[1427] User

[1428] Input: Your body shape information (height, weight), clothing preferences (style, color), budget (entered via web form, etc.)

[1429] Action: The user enters this information and clicks the submit button.

[1430] Output: The terminal stores the input and prepares it to be sent.

[1431] Specific actions: A user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: under 2000 yen" into a web form and presses the submit button.

[1432] Terminal

[1433] Input: Profile information entered by the user

[1434] Processing: The device checks and organizes the input and sends it to the server.

[1435] Output: Profile information sent to the server

[1436] Specific operation: The terminal sends the user input to the server via SSL communication.

[1437] Step 3: Product suggestions by generative AI

[1438] server

[1439] Input: Consumer profile information, product data stored in a database

[1440] Processing: Extract candidate product data from a database based on consumer profile information. Use a generative AI model (e.g., using TensorFlow) to select the best product from the extracted data.

[1441] Output: A list of best product suggestions

[1442] Specific operation: The server executes a database query based on the profile information, extracts products that match the criteria of "casual style, blue, under 2,000 yen," evaluates them using a generative AI model, and creates a list of suggestions.

[1443] Step 4: View and select suggestions

[1444] Terminal

[1445] Input: A list of suggestions sent by the server

[1446] Action: Display the suggested product list to the user and provide details (photos, sizes, prices, etc.).

[1447] Output: Product information displayed in the user interface

[1448] Specific operation: The device receives data from the server and displays detailed information about the "T-shirt (size M, blue)" on the screen.

[1449] User

[1450] Input: Select from the displayed product list

[1451] Action: Choose the product you like and indicate your intention to purchase (press the purchase button).

[1452] Output: Selected product information and purchase intention

[1453] What happens: The user reviews the details and clicks the "Purchase" button.

[1454] Step 5: Confirm your purchase and process your shipment

[1455] User

[1456] Input: Clicking the purchase button, entering payment information

[1457] Processing: After pressing the purchase button, enter the required payment information and submit.

[1458] Output: Confirmed purchase and payment information

[1459] Specific actions: Enter payment information (such as credit card information) on the terminal.

[1460] Terminal

[1461] Input: User purchase and payment information

[1462] Process: Send the entered information to the server.

[1463] Output: Purchase and payment information sent to the server

[1464] What it does: The device securely sends payment information to the server using SSL.

[1465] server

[1466] Input: Purchase and payment information

[1467] Processing: Verify the payment information, confirm the purchase, notify the manufacturer or apparel store of the purchase information, process the purchase of the relevant products, and pay a portion of the purchase price to the manufacturer or apparel store.

[1468] Output: Purchase confirmation, payment confirmation, product purchase

[1469] What it does: Confirms payment information through the payment gateway, confirms the purchase, and notifies the manufacturer or apparel store to arrange for the product to be shipped.

[1470] Manufacturers and apparel stores

[1471] Input: Purchase confirmation notice

[1472] Processing: receiving the purchase confirmation and shipping the product to the consumer's designated address.

[1473] Output: Shipped products

[1474] Specific operation: Upon receiving a purchase confirmation notification, a "T-shirt (size M, blue)" is shipped to the specified consumer address.

[1475] Step 6: Online try-on experience (optional)

[1476] server

[1477] Input: Consumer profile information, suggested product information

[1478] Processing: Using virtual reality (VR) or augmented reality (AR) technology, we generate try-on simulations and create images and videos.

[1479] Output: Simulation images and videos

[1480] Specific operation: The server uses 3D modeling software (e.g., Unity or Blender) to create a simulation of trying on a "T-shirt (size M, blue)" and generates images and videos.

[1481] Terminal

[1482] Input: Simulation images and videos from the server

[1483] Processing: Receives the simulation data and displays it on the user interface.

[1484] Output: Displayed simulation data

[1485] Specific operation: The device displays simulated images and videos received from the server, giving the user the feeling of trying on clothes.

[1486] (Application example 1)

[1487] 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."

[1488] In modern society, the amount of clothing discarded in the apparel industry is enormous, posing a major problem from the perspective of environmental impact. Furthermore, the inability of consumers to try on clothes when purchasing online has led to an increase in returns due to mismatched sizes and styles. Furthermore, for consumers, the effort required to select the right clothing can lead to reduced purchase satisfaction. There is a need to develop a system that can solve these problems and provide a more efficient and satisfying clothing shopping experience.

[1489] 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.

[1490] In this invention, the server includes: means for acquiring data on clothing to be discarded; means for acquiring consumer profile information; means for using a generative artificial intelligence model to suggest suitable clothing from the clothing data based on the consumer profile information; means for providing the consumer with a simulation of trying on the suggested clothing using virtual reality or augmented reality technology; means for displaying information on the suggested clothing to the consumer; and means for confirming and notifying the consumer of the purchase of the clothing selected by the consumer. This makes it possible to recommend clothing that is most suitable for the consumer, and to reduce the risk of returns and improve purchase satisfaction through a try-on experience using virtual reality or augmented reality. This also promotes the effective use of clothing that would otherwise be discarded at apparel stores, contributing to reducing environmental impact.

[1491] "Clothing destined for disposal" refers to clothing that apparel stores or manufacturers plan to dispose of rather than sell.

[1492] "Means for obtaining data" refers to the functionality that allows the server to collect and store information from external resources.

[1493] "Profile information" refers to personal information about a consumer, such as their height, weight, preferred style, and budget.

[1494] A "generative artificial intelligence model" refers to an algorithm that predicts and generates information based on input data.

[1495] "Means of suggestion" refers to the function of presenting appropriate options to consumers based on the generated data.

[1496] "Means for displaying" refers to the functionality of the device to visually present information to the consumer.

[1497] "Means for confirming and notifying purchase" refers to the function for a consumer to purchase the selected product and transmit that information to the server and other relevant parties.

[1498] A "system" refers to a set of functions or mechanisms in which multiple elements function together.

[1499] "Means of storing in a database" refers to the function of systematically organizing and storing acquired information.

[1500] "Virtual reality technology" refers to a virtual space created using computer technology that allows users to experience a sense of reality.

[1501] "Augmented reality technology" refers to technology that overlays digital information onto a real-world environment, allowing users to experience the real world and digital information simultaneously.

[1502] "Means for providing a try-on simulation" refers to a function for providing a consumer with the sensation of virtually trying on clothing.

[1503] A "head-mounted display" is a device worn by the user on the head that projects images directly in front of the eyes.

[1504] "Consumer" refers to the general consumer who purchases and uses clothing.

[1505] This invention is an online platform that provides consumers with discarded clothing, and is composed of three main elements: a server, a terminal, and a user. The platform is equipped with a clothing recommendation function using a generative AI model, and suggests optimal clothing based on the consumer's profile information. It also utilizes virtual reality (VR) and augmented reality (AR) technologies to provide an online try-on experience, which increases consumer motivation to purchase and improves satisfaction.

[1506] Acquiring and saving clothing data

[1507] server

[1508] The server uses a high-performance server (e.g., Amazon Web Services, AWS) to receive data on discarded clothing from apparel stores and store the data in a database (e.g., MySQL). This data includes photos of the clothing, sizes, colors, and conditions. It is recommended that apparel stores provide the data with tags and logos removed to prevent damage to the brand's image.

[1509] Examples:

[1510] The server receives data on "T-shirt (size M, blue, condition: like new)" from an apparel store and stores it in a database.

[1511] Consumer profile setting

[1512] User

[1513] Consumers enter their body type information (e.g., height, weight), clothing preferences (e.g., style, color), and budget on the device. Based on this profile information, the server will suggest the most suitable clothing for the consumer.

[1514] Terminal

[1515] The device, whether it's an iOS or Android device, sends the profile information entered by the user to the server, which then stores the profile information in a database.

[1516] Examples:

[1517] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[1518] Clothing suggestions using generative AI

[1519] server

[1520] The server uses a generative AI model (e.g., GPT-4) to suggest the most suitable clothing based on the consumer's profile information. It selects clothing that matches the consumer's preferences and body shape from clothing data stored in a database.

[1521] Examples:

[1522] The server's generation AI filters for "casual style, blue, budget under 2,000 yen" and includes "T-shirt (size M, blue)" in the suggestion list.

[1523] View and select suggestions

[1524] Terminal

[1525] The terminal displays the clothing suggestions sent from the server to the consumer and provides detailed information (photos, sizes, prices, etc.).

[1526] User

[1527] The user selects the clothing item they like from the suggested items and indicates their intention to purchase it (by pressing the purchase button).

[1528] Examples:

[1529] The device displays detailed information about the "T-shirt (size M, blue)" and the user presses the purchase button.

[1530] Purchase confirmation and shipping procedures

[1531] User

[1532] When the user presses the purchase button, they confirm the purchase and enter their payment information.

[1533] Terminal

[1534] The terminal transmits the purchase and payment information to the server.

[1535] server

[1536] The server verifies the payment information, confirms the purchase, and notifies the apparel store of the purchase information and completes the procedure for purchasing the clothing.

[1537] Examples:

[1538] After the purchase is confirmed, the server notifies the apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price. The apparel store then ships the clothing to the consumer's address.

[1539] Online try-on experience

[1540] server

[1541] Based on the consumer's profile information, the server generates a try-on simulation using virtual reality (VR) or augmented reality (AR) technology and sends the simulated images and videos to the device.

[1542] Terminal

[1543] The device receives simulated images and videos and displays them to consumers using a smartphone or head-mounted display (HMD), giving consumers a feeling similar to a real fitting experience.

[1544] Examples:

[1545] The server uses the consumer's profile information to generate a simulation image of what it would look like to try on a "T-shirt (size M, blue)" and sends it to the terminal. The terminal then displays the simulation image to the consumer, providing the feeling of trying it on.

[1546] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1547] Step 1:

[1548] The server receives data on discarded clothing from apparel stores. The server uses a database to store this data. Input includes photos of the clothing, size, color, condition, etc. The server receives this data and stores it in the database.

[1549] Examples:

[1550] The data for "size M, blue, like new T-shirt" sent from an apparel store is received and saved in a database.

[1551] Step 2:

[1552] The user uses the device to enter their profile information, such as height, weight, preferred style, color, budget, etc. The device then sends this data to the server.

[1553] Examples:

[1554] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: under 2000 yen," and the device sends this to the server.

[1555] Step 3:

[1556] The server receives the user's profile information and stores it in a database. It then uses a generative artificial intelligence model (e.g., GPT-4) to suggest the most suitable clothing based on the profile information. In this case, it selects clothing that matches the consumer's preferences and body shape from the clothing data stored in the database.

[1557] Examples:

[1558] The server's generation AI filters for "casual style, blue, budget under 2,000 yen" and includes "a blue T-shirt in size M" in the list of suggestions.

[1559] Step 4:

[1560] The server sends the suggested clothing information to the terminal. As input, the result of combining the user's profile information and the clothing data in the database is used. As output, detailed information of the suggested clothing is generated and sent to the terminal.

[1561] Examples:

[1562] Detailed information about the "T-shirt (size M, blue)" included in the suggestion list is sent to the terminal.

[1563] Step 5:

[1564] The terminal displays the clothing suggestions received from the server to the consumer, along with detailed information (e.g., photos, sizes, prices, etc.).

[1565] Examples:

[1566] The device will display details of a "size medium blue T-shirt" to the consumer.

[1567] Step 6:

[1568] The user selects the clothing item they like from the suggested items and indicates their intention to purchase it by pressing the purchase button to proceed with the purchase process.

[1569] Examples:

[1570] The user selects "Blue T-shirt (size M)" and presses the purchase button.

[1571] Step 7:

[1572] The terminal sends the purchase and payment information to the server. As input, the user's clothing selection and payment information are used. As output, a request for purchase confirmation is sent to the server.

[1573] Examples:

[1574] The user enters payment information and the terminal sends a request to the server to confirm the purchase.

[1575] Step 8:

[1576] The server verifies the payment information and confirms the purchase. If the purchase is successful, it notifies the apparel store of the purchase information and proceeds to purchase the clothing. The inputs are the user's payment information and the selected clothing information. The output is a purchase notification to the apparel store and a payment request for the purchase amount.

[1577] Examples:

[1578] The server receives the purchase information, notifies the apparel store of the purchase details of the "blue T-shirt (size M)," and pays the apparel store a portion of the purchase price.

[1579] Step 9:

[1580] The apparel store receives the notification from the server and ships the clothes to the specified address. The purchase notification from the server is used as input. The clothes are shipped to the consumer's address as output.

[1581] Examples:

[1582] An apparel store ships a blue T-shirt (size medium) to a consumer's address.

[1583] Step 10:

[1584] The server generates a try-on simulation using virtual reality (VR) or augmented reality (AR) technology based on the consumer's profile information and sends the simulation images and videos to the device. The user's profile information and suggested clothing information are used as input. As output, data from the try-on simulation is generated and sent to the device.

[1585] Examples:

[1586] The server uses the consumer's profile information to generate a simulated image of what it would look like when trying on a "T-shirt (size M, blue)" and sends it to the terminal.

[1587] 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.

[1588] This invention provides an online platform that provides consumers with clothing that would otherwise be discarded, and is composed of three main elements: a server, a terminal, and a user. The platform has a clothing recommendation function that uses a generative AI model to recommend the most suitable clothing based on the consumer's profile information. Furthermore, an emotion engine is used to obtain user emotion data and optimize the recommendations. Furthermore, by utilizing virtual reality and augmented reality technology to provide an online try-on experience, it increases consumer motivation to purchase and improves satisfaction.

[1589] Acquiring and saving clothing data

[1590] server

[1591] The server receives data on discarded clothing from apparel stores and stores it in a database. This data includes photos of the clothing, size, color, condition, and whether or not tags are present. To prevent damage to the brand image, apparel stores are encouraged to provide the data without tags or logos.

[1592] Examples:

[1593] The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and stores it in a database.

[1594] Consumer profile setting

[1595] User

[1596] Consumers enter their body type information (e.g., height, weight), clothing preferences (e.g., style, color), and budget on the device. Based on this profile information, the server will suggest the most suitable clothing for the consumer.

[1597] Terminal

[1598] The terminal transmits the profile information entered by the user to the server.

[1599] Examples:

[1600] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[1601] Clothing recommendations using generative AI and emotion engine

[1602] server

[1603] The server uses a generative AI model to suggest optimal clothing based on the consumer's profile information and emotional data obtained from the emotion engine. It selects clothing that matches the consumer's preferences, body shape, and emotions from clothing data stored in a database.

[1604] Examples:

[1605] The server's generation AI filters the results by "casual style, blue, budget under 2000 yen" and adds "T-shirt (size M, blue)" to the list of suggestions. Using data from the emotion engine, it prioritizes suggestions that will satisfy the user.

[1606] View and select suggestions

[1607] Terminal

[1608] The device displays clothing suggestions sent from the server to the consumer and provides detailed information (photos, sizes, prices, etc.) It also analyzes consumer reactions in real time through an emotion engine and sends the results to the server.

[1609] User

[1610] The user selects the clothing item they like from the suggested items and indicates their intention to purchase it (by pressing the purchase button).

[1611] Examples:

[1612] The device displays detailed information about the "T-shirt (size M, blue)" and the user presses the purchase button.

[1613] Purchase confirmation and shipping procedures

[1614] User

[1615] When the user presses the purchase button, they confirm the purchase and enter their payment information.

[1616] Terminal

[1617] The terminal transmits the purchase and payment information to the server.

[1618] server

[1619] The server verifies the payment information and confirms the purchase. The server then notifies the apparel store of the purchase information and proceeds with the purchase of the clothing. The server also pays a portion of the purchase price to the apparel store.

[1620] Apparel store

[1621] The apparel store will receive a confirmation of purchase and ship the garment to the specified address.

[1622] Examples:

[1623] After the purchase is confirmed, the server notifies the apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price. The apparel store then ships the clothing to the consumer's address.

[1624] Online try-on experience

[1625] server

[1626] The server generates a try-on simulation using virtual reality (VR) or augmented reality (AR) technology based on the consumer's profile information and emotional data, and sends simulated images and videos to the device.

[1627] Terminal

[1628] The device receives simulated images and videos and displays them to the consumer, giving them a feeling similar to a real fitting experience. Emotional data during the fitting process is also collected and sent to the server.

[1629] Examples:

[1630] The server uses the consumer's profile information and emotional data to generate a simulated image of what it would be like to try on a "T-shirt (size M, blue)" and sends it to the device. The device displays the simulated image, collects the consumer's emotional data in real time, and sends it to the server.

[1631] By implementing the present invention in the above manner, an efficient system is realized that provides consumers with clothes that would otherwise be discarded. This reduces the amount of discarded clothes and the burden on the environment. Furthermore, by combining it with an emotion engine, it is possible to improve consumer satisfaction and stimulate purchasing motivation.

[1632] The processing flow will be explained below.

[1633] Step 1:

[1634] server

[1635] Receives data on clothing to be discarded from apparel stores. The received data includes photos, size, color, condition, whether or not it has tags, etc. The format of this data is checked, and if there are no problems, it is stored in a database.

[1636] Examples:

[1637] The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and stores it in a database.

[1638] Step 2:

[1639] User

[1640] Enter your profile information on a dedicated screen on your device, including your height, weight, clothing preferences (style and color), budget, etc.

[1641] Terminal

[1642] The entered profile information is sent to the server.

[1643] Step 3:

[1644] server

[1645] The received profile information is stored in a database.

[1646] Examples:

[1647] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[1648] Step 4:

[1649] server

[1650] The system retrieves consumer profile information from the database, analyzes suitable clothing using a generative AI model, extracts suitable clothing data, and generates a list. It also uses an emotion engine to analyze the user's past responses and current emotional data to optimize the recommendations.

[1651] Step 5:

[1652] server

[1653] The generated clothing list and the emotion data associated with it are transmitted to the terminal.

[1654] Examples:

[1655] The server's generation AI filters items by "casual style, blue, budget within 2,000 yen" and adds "T-shirt (size M, blue)" to the list of suggestions. The emotion engine prioritizes items with high satisfaction based on past consumer responses.

[1656] Step 6:

[1657] Terminal

[1658] It receives the clothing list sent from the server and displays it to the consumer, displaying detailed information and analyzing the consumer's real-time reaction through an emotion engine.

[1659] User

[1660] Choose the clothing you like from the suggested items and indicate your intention to purchase (press the purchase button).

[1661] Examples:

[1662] The device displays detailed information about a "T-shirt (size M, blue)" and the user presses the purchase button. The device then analyzes the user's facial expressions and tone of voice using an emotion engine and sends real-time reaction data to the server.

[1663] Step 7:

[1664] User

[1665] By pressing the purchase button, you confirm your purchase and enter your payment information.

[1666] Step 8:

[1667] Terminal

[1668] Send purchase and payment information to the server.

[1669] Step 9:

[1670] server

[1671] The payment information is verified and the purchase is confirmed. The purchase information is notified to the apparel store, and the procedure for purchasing the clothing is carried out. A portion of the purchase price is also paid to the apparel store.

[1672] Examples:

[1673] After the server confirms the purchase, it notifies the apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price.

[1674] Step 10:

[1675] Apparel store

[1676] You will receive a confirmation of your purchase and your garment will be shipped to the address you specified.

[1677] Examples:

[1678] The apparel store will ship a "T-shirt (size M, blue)" to the specified address.

[1679] Step 11 (Optional):

[1680] server

[1681] Based on the consumer's profile information and emotional data, a try-on simulation is generated using virtual reality (VR) or augmented reality (AR) technology, and the simulated images and videos are sent to the device.

[1682] Terminal

[1683] Simulation images and videos are received and displayed to the consumer, and emotional data of the consumer while trying on the clothes is also collected and sent to the server.

[1684] Examples:

[1685] The server uses the consumer's profile information and emotional data to generate a simulated image of what it would be like to try on a "T-shirt (size M, blue)" and sends it to the device. The device displays the simulated image, collects the consumer's emotional data in real time, and sends it to the server.

[1686] The above are the processing steps of the program for implementing the present invention. By combining emotion engines in this way, it is possible to improve consumer satisfaction and stimulate purchasing motivation.

[1687] Example 2

[1688] 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."

[1689] Nowadays, many apparel stores dispose of clothing that is scheduled to be discarded, resulting in an increased burden on the environment. An efficient and user-friendly system is needed to solve this problem and promote the reuse of clothing. Also, a means is needed to suggest the most suitable clothing to consumers and motivate them to purchase.

[1690] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for acquiring data on clothes to be discarded, a means for acquiring consumer profile information, and a means for proposing suitable clothes from the clothing data based on the consumer profile information and emotion data using a generative AI model. This makes it possible to efficiently reuse clothes to be discarded and to propose optimal clothes to consumers.

[1691] "Clothing scheduled for disposal" refers to clothing that is not sold at apparel stores or the like and is scheduled to be disposed of.

[1692] "Consumer profile information" is personal information provided by a consumer, such as height, weight, preferred style, color, budget, etc.

[1693] A "generative artificial intelligence model" is an artificial intelligence program that uses machine learning and deep learning to make clothing suggestions.

[1694] "Emotional data" is data that indicates the emotional state of consumers, and is obtained by analyzing consumers' reactions and emotions in real time.

[1695] A "database" is a system for storing data such as clothing to be disposed of and consumer profile information.

[1696] "Virtual reality" is a technology that uses computer technology to provide a realistic visual and auditory experience.

[1697] "Augmented reality" is a technology that overlays computer-generated information onto the real world.

[1698] A "try-on simulation" is a simulation that uses virtual reality or augmented reality technology to allow consumers to experience the experience of trying on clothing.

[1699] "Consumer response" refers to the emotions and behaviors that consumers exhibit toward the proposed garment.

[1700] "Payment Information" means credit card or other payment method information provided by a consumer when making a purchase.

[1701] This invention provides an online platform that provides consumers with clothing that would otherwise be discarded, and includes a clothing recommendation function using a generative AI model, an optimization function using an emotion engine, and an online try-on experience using virtual reality and augmented reality technology. This system is mainly composed of three elements: a server, a terminal, and a user.

[1702] Acquiring and saving clothing data

[1703] server

[1704] The server receives data on clothing to be discarded from apparel stores and stores the data in a database. Specifically, the server receives information such as photos of the clothing to be discarded, size, color, condition, and whether or not it has tags. In order to maintain the brand image, apparel stores provide data with tags and logos removed. For example, the server receives data on a "T-shirt (size M, blue, condition: like new)" from an apparel store and stores the data in a database.

[1705] Consumer profile setting

[1706] User

[1707] The user enters their profile information on the device. This information includes height, weight, preferred style, color, budget, etc. The device sends this information to the server. For example, the user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: under 2000 yen," and the device sends it to the server. The server stores this information in a database.

[1708] Clothing recommendations using generative AI and emotion engine

[1709] server

[1710] The server retrieves the user's profile information from the database and emotion data from the emotion engine, and then uses a generative AI model to suggest the most suitable clothing based on this information. The generative AI model selects clothing that matches the user's preferences, body shape, and emotions, and generates a list of suggestions. For example, the server's generative AI inputs the following data: "Height: 170 cm, Weight: 60 kg, Casual style, Blue, Budget: 2000 yen or less, Emotion: Positive," and generates "T-shirt (Size M, Blue)" as a suggestion list.

[1711] View and select suggestions

[1712] Terminal

[1713] The device displays the list of suggestions sent from the server to the user and provides detailed information (photos, sizes, prices, etc.). It also analyzes the user's reactions in real time through an emotion engine and sends the results to the server. The user selects the item they wish to purchase from the suggested clothing and presses the purchase button. For example, the device displays detailed information about a "T-shirt (size M, blue)" and the user presses the purchase button.

[1714] Purchase confirmation and shipping procedures

[1715] User

[1716] When the user presses the purchase button, they confirm the purchase and enter their payment information.

[1717] Terminal

[1718] The terminal transmits the purchase and payment information to the server.

[1719] server

[1720] The server verifies the payment information and confirms the purchase. The server notifies the apparel store of the purchase information and proceeds with the purchase of the clothing. The server also pays a portion of the purchase price to the apparel store. The apparel store then ships the clothing after receiving the notification. For example, after the purchase is confirmed, the server notifies the apparel store of the purchase information for a "T-shirt (size M, blue)," and the apparel store ships the clothing to the consumer's address.

[1721] Online try-on experience

[1722] server

[1723] Based on the consumer's profile information and emotional data, the server uses virtual reality (VR) or augmented reality (AR) technology to generate a try-on simulation and send it to the device.

[1724] Terminal

[1725] The device receives the simulated images and videos and displays them to the user, allowing the user to experience a sensation similar to that of a real try-on. Emotional data from the user trying on clothes is also collected and sent to the server. For example, the server uses the consumer's profile information and emotional data to generate a simulated image of the user trying on a "T-shirt (size M, blue)" and sends it to the device. The device displays the simulated image and collects the user's emotional data in real time, which is then sent to the server.

[1726] Prompt Sentence Examples

[1727] Example prompts for users to enter profile information:

[1728] Enter your height, weight, clothing preferences (e.g. style, color), and budget.

[1729] Example: Height: 170cm, Weight: 60kg, Preference: Casual, Color: Blue, Budget: Under 2000 yen

[1730] Example prompts when the server makes suggestions using AI generation:

[1731] Considering the consumer's profile information (height 170cm, weight 60kg, preference: casual, color: blue, budget: under 2000 yen) and emotional data, please suggest the most suitable clothing from the database.

[1732] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1733] Program processing flow and specific explanation

[1734] Step 1:

[1735] Acquiring and saving clothing data

[1736] Server: Receives data on clothing to be discarded from apparel stores and stores the data in a database. Specifically, the server receives information such as photos of clothing, size, color, condition, and whether or not tags are attached, sent from apparel stores. The server stores the received data in a database and updates it in real time.

[1737] Input: Clothing data (e.g. T-shirt (size M, blue, condition: like new))

[1738] Output: Clothing data stored in a database

[1739] Specific operation: The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and registers it in the database.

[1740] Step 2:

[1741] Consumer profile setting

[1742] User: Through an application on their device or through a website, the user enters their profile information, including height, weight, preferred style, color, budget, etc.

[1743] Terminal: The terminal sends the profile information entered by the user to the server, which receives it and stores it in a database.

[1744] Input: Profile information (e.g., height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen)

[1745] Output: User profile information stored in the database

[1746] Specific operation: The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: under 2000 yen," and the device sends this to the server. The server stores the received information in a database.

[1747] Step 3:

[1748] Clothing recommendations using generative AI and emotion engine

[1749] Server: The server retrieves the user's profile information from the database and also retrieves the user's emotional data from the emotion engine. Based on this, the server uses a generative AI model to suggest the most suitable clothing. The server then sends the generated suggestion list to the device.

[1750] Input: User profile information, emotion data

[1751] Output: Suggestion list (e.g. T-shirt (size M, blue))

[1752] Specific operation: The server filters the generated AI model with "casual style, blue, budget under 2,000 yen," and combines the user's profile information (height: 170 cm, weight: 60 kg) with emotional data to add "T-shirt (size M, blue)" to the suggestion list.

[1753] Step 4:

[1754] View and select suggestions

[1755] Terminal: The terminal displays the list of suggestions sent from the server to the user and provides detailed information (photos, size, price, etc.). It also analyzes the user's reactions in real time through an emotion engine and sends the results to the server.

[1756] User: The user selects the clothing item they wish to purchase from the suggested items and presses the purchase button.

[1757] Input: Suggestion list (e.g. T-shirt (size M, blue))

[1758] Output: Intention to purchase (pressing the purchase button)

[1759] Specific operation: The device displays detailed information about a "T-shirt (size M, blue)" including a photo, size, and price, and the user presses the purchase button.

[1760] Step 5:

[1761] Purchase confirmation and shipping procedures

[1762] User: After the user presses the purchase button, they confirm the purchase and enter their payment information.

[1763] Terminal: The terminal sends purchase and payment information to the server.

[1764] Server: The server verifies the payment information and confirms the purchase. The server also notifies the apparel store of the purchase information and processes the purchase of the clothing. At the same time, the server pays a portion of the purchase price to the apparel store. The apparel store then ships the clothing upon receiving the notification.

[1765] Input: Purchase information, payment information

[1766] Output: Purchase confirmation, notification to apparel store

[1767] Specific operation: The server verifies the payment information, and after the purchase is confirmed, it notifies the apparel store of the purchase information for the "T-shirt (size M, blue)", and the apparel store ships the clothing to the consumer's address.

[1768] Step 6:

[1769] Online try-on experience

[1770] Server: Based on the user's profile information and emotional data, the server generates a fitting simulation using VR or AR technology and sends the simulation images and videos to the device.

[1771] Device: The device receives simulated images and videos and displays them to the user, giving the user a feeling similar to a real fitting experience. It also collects emotional data during the fitting process and sends it to the server.

[1772] Input: User profile information, emotion data

[1773] Output: Simulation images, emotion data

[1774] Specific operation: The server uses the consumer's profile information and emotional data to generate a simulated image of a "T-shirt (size M, blue)" being tried on and sends it to the device. The device displays the simulated image and collects the user's emotional data in real time and sends it to the server.

[1775] (Application example 2)

[1776] 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."

[1777] Conventional methods for utilizing discarded clothing do not adequately approach consumers and do not provide proposals that reflect their preferences and emotions. Furthermore, the lack of a try-on experience reduces consumer satisfaction and motivation to purchase. This has hindered progress in effectively utilizing discarded clothing, and the environmental impact problem has not been resolved.

[1778] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring data on clothing to be discarded, means for acquiring consumer profile information, means for using a generative artificial intelligence model to suggest suitable clothing from the clothing data based on the consumer profile information, means for acquiring consumer emotion data using an emotion engine and optimizing the suggestion content, and means for providing the consumer with a simulation of trying on the suggested clothing using virtual reality or augmented reality technology. This makes it possible to suggest optimal clothing that reflects the consumer's preferences and emotions, improving consumer satisfaction and promoting the effective use of clothing to be discarded.

[1779] "Clothing scheduled for disposal" refers to clothing that is scheduled for disposal by apparel stores or clothing manufacturers, but is still usable and can be offered to consumers.

[1780] "Means for acquiring data" refers to the means for receiving detailed information such as photographs, size, color, and condition of clothing to be discarded from apparel stores and clothing manufacturers via servers, terminals, etc.

[1781] "Consumer profile information" refers to information that indicates a user's personal preferences and physical characteristics, such as height, weight, clothing preferences, and budget.

[1782] A "generative artificial intelligence model" is an AI algorithm or machine learning model that suggests appropriate clothing based on a consumer's profile information.

[1783] The "means of suggestion" refers to a system that selects the most suitable clothing and suggests it to users based on consumer profile information and clothing data.

[1784] The "emotion engine" is a technology that acquires real-time emotional data from consumers and optimizes the content of proposals based on that data.

[1785] "Virtual reality technology" and "augmented reality technology" refer to technologies that allow consumers to try on clothes in a virtual space or try on virtual clothes superimposed on real space.

[1786] A "means for providing simulated try-on clothing" is a system or application that uses virtual reality or augmented reality technology to provide a consumer with a visual and sensory experience of trying on clothing.

[1787] "Means of notification" refers to the means by which a consumer confirms the purchase of the selected clothing item and notifies the apparel store or clothing manufacturer of that information.

[1788] The present invention is a system that functions as an online platform that provides consumers with clothes that are scheduled to be discarded, and is mainly composed of three elements: a server, a terminal, and a user. How each element in this system works together to realize the invention is explained in detail below.

[1789] Acquiring and saving clothing data

[1790] The server receives data on discarded clothing from apparel stores and stores it in a database. This data includes photos of the clothing, size, color, condition, and whether or not tags are attached. To prevent damage to the brand image, it is recommended that apparel stores provide the data without tags or logos.

[1791] Examples:

[1792] The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and stores it in a database.

[1793] Consumer profile setting

[1794] Users enter their body type information (e.g., height, weight), clothing preferences (e.g., style, color), and budget on their device. Based on this profile information, the server will suggest the most suitable clothing for the consumer.

[1795] The terminal transmits the profile information entered by the user to the server.

[1796] Examples:

[1797] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[1798] Clothing recommendations using generative AI and emotion engine

[1799] The server uses a generative AI model to suggest optimal clothing based on the consumer's profile information and emotional data obtained from the emotion engine. It selects clothing that matches the consumer's preferences, body shape, and emotions from clothing data stored in a database.

[1800] Examples:

[1801] Using the prompt "The user is 170cm tall, weighs 60kg, prefers a casual style, and wants blue clothing within a budget of 2000 yen. Please generate the best clothing suggestions you can offer," the generative AI model filters "casual style, blue, budget within 2000 yen" and adds "T-shirt (size M, blue)" to the suggestion list. Using data from the emotion engine, it prioritizes suggestions that provide the highest user satisfaction.

[1802] View and select suggestions

[1803] The device displays clothing suggestions sent from the server to the consumer and provides detailed information (photos, sizes, prices, etc.) It also analyzes consumer reactions in real time through an emotion engine and sends the results to the server.

[1804] The user selects the clothing item they like from the suggested items and indicates their intention to purchase it (by pressing the purchase button).

[1805] Examples:

[1806] The device displays detailed information about the "T-shirt (size M, blue)" and the user presses the purchase button.

[1807] Purchase confirmation and shipping procedures

[1808] When the user presses the purchase button, they confirm the purchase and enter their payment information.

[1809] The terminal transmits the purchase and payment information to the server.

[1810] The server verifies the payment information and confirms the purchase. The server then notifies the apparel store of the purchase information and proceeds with the purchase of the clothing. The server also pays a portion of the purchase price to the apparel store.

[1811] The apparel store will receive a confirmation of purchase and ship the garment to the specified address.

[1812] Examples:

[1813] After the purchase is confirmed, the server notifies the apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price. The apparel store then ships the clothing to the consumer's address.

[1814] Online try-on experience

[1815] The server generates a try-on simulation using virtual reality (VR) or augmented reality (AR) technology based on the consumer's profile information and emotional data, and sends simulated images and videos to the device.

[1816] The device receives simulated images and videos and displays them to the consumer, giving them a feeling similar to a real fitting experience. Emotional data during the fitting process is also collected and sent to the server.

[1817] Examples:

[1818] The server uses the consumer's profile information and emotional data to generate a simulated image of what it would be like to try on a "T-shirt (size M, blue)" and sends it to the device. The device displays the simulated image, collects the consumer's emotional data in real time, and sends it to the server.

[1819] As described above, the present invention realizes a system that proposes optimal clothing that reflects the consumer's preferences and feelings and provides a realistic try-on experience, thereby improving consumer satisfaction and promoting the effective use of clothing that would otherwise be discarded.

[1820] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1821] Step 1: Acquire and save clothing data

[1822] The server receives data on the clothes to be discarded (e.g., photos, size, color, condition, presence or absence of tags) from the apparel store and stores that information in a database. The input is the clothing data provided by the apparel store, and the output is the clothing data stored in the database.

[1823] Specifically, the data provided by the apparel store is obtained through an API, and each item (photo, size, color, condition, presence or absence of tags) is stored in a database in JSON format.

[1824] Step 2: Fill out your consumer profile

[1825] The terminal prompts the user to enter information about their physical shape (e.g., height, weight), clothing preferences (e.g., style, color), and budget, and then transmits this information to the server.

[1826] The input is the profile information that the user enters into the terminal, and the output is the profile information that is sent to the server.

[1827] Specifically, the user enters profile information through the device's UI, and the input data is sent to the server via an HTTPS request.

[1828] Step 3: Clothing suggestions by generative AI

[1829] The server uses a generative AI model to suggest optimal clothing based on the consumer's profile information and clothing data stored in the database. The input is the consumer's profile information and the clothing data in the database, and the output is a list of suggested clothing items.

[1830] Specifically, the generative AI model is fed a prompt such as, "The user is 170 cm tall, weighs 60 kg, prefers a casual style, desires blue clothing, and has a budget of 2,000 yen or less," and the model outputs a list of the most suitable clothing items.

[1831] Step 4: Acquiring emotion data using the emotion engine

[1832] When the user browses the proposed clothing or tries on the clothing in a simulated fashion, the device captures emotional data using a camera and microphone and sends it to the server. The input is the user's real-time emotional data, and the output is the emotional data sent to the server.

[1833] Specifically, the device uses a camera and microphone to capture the user's facial expressions and voice in real time, analyzes the data through the Affectiva SDK, and sends the emotional data to the server.

[1834] Step 5: View and select suggestions

[1835] The device displays the optimized clothing suggestions sent from the server, along with detailed information (photos, sizes, prices, etc.) The user selects the clothing they like and presses the purchase button.

[1836] The input is a list of clothing suggestions sent from the server, and the output is detailed information displayed on the terminal.

[1837] Specifically, the system obtains a list of suggestions from the server and displays detailed information about the clothing to the user through the device's UI.

[1838] Step 6: Purchase confirmation and notification

[1839] When the user presses the purchase button, the terminal sends the purchase information and payment information to the server. The server verifies the information, confirms the purchase, and notifies the apparel store. The input is the user's purchase information and payment information, and the output is a notification of the purchase information to the apparel store.

[1840] Specifically, the user presses the purchase button, the server receives the purchase information sent from the terminal, processes the payment, and notifies the apparel store of the purchase information.

[1841] Step 7: Online try-on experience

[1842] The server generates a try-on simulation using virtual reality or augmented reality technology based on the consumer's profile information and emotional data, and sends it to the device. The input is the profile information and emotional data, and the output is images or videos of the try-on simulation.

[1843] Specifically, the server uses VR / AR technologies such as Unity or Unreal Engine to create a clothing simulation based on the user's profile information, and sends the simulation data to the device. The user experiences the clothing simulation, and emotional data from the simulation is collected and retransmitted in real time.

[1844] 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.

[1845] 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.

[1846] 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.

[1847] [Fourth embodiment]

[1848] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1849] 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.

[1850] 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).

[1851] 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.

[1852] 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.

[1853] 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).

[1854] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1855] 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.

[1856] 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.

[1857] 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.

[1858] 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.

[1859] 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.

[1860] 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."

[1861] This invention provides an online platform that provides consumers with discarded clothing, and is composed of three main components: a server, a terminal, and a user. The platform is equipped with a clothing recommendation function using a generative AI model to recommend the most suitable clothing based on the consumer's profile information. It also utilizes virtual reality and augmented reality technology to provide an online try-on experience, increasing consumer motivation and satisfaction.

[1862] Acquiring and saving clothing data

[1863] server

[1864] The server receives data on discarded clothing from apparel stores and stores it in a database. This data includes photos of the clothing, size, color, condition, etc. To prevent damage to the brand's image, apparel stores are encouraged to provide the data without tags or logos.

[1865] Examples:

[1866] The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and stores it in the database.

[1867] Consumer profile setting

[1868] User

[1869] Consumers enter their body type information (e.g., height, weight), clothing preferences (e.g., style, color), and budget on the device. Based on this profile information, the server will suggest the most suitable clothing for the consumer.

[1870] Terminal

[1871] The terminal sends the profile information entered by the user to the server, which then stores the profile information in a database.

[1872] Examples:

[1873] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[1874] Clothing suggestions using generative AI

[1875] server

[1876] The server uses a generative AI model to suggest optimal clothing based on the consumer's profile information, and selects clothing that matches the consumer's preferences and body shape from clothing data stored in a database.

[1877] Examples:

[1878] The server's generation AI filters for "casual style, blue, budget under 2,000 yen" and includes "T-shirt (size M, blue)" in the suggestion list.

[1879] View and select suggestions

[1880] Terminal

[1881] The terminal displays clothing suggestions sent from the server to the consumer and provides detailed information (photos, sizes, prices, etc.).

[1882] User

[1883] The user selects the clothing item they like from the suggested items and indicates their intention to purchase it (by pressing the purchase button).

[1884] Examples:

[1885] The device displays detailed information about the "T-shirt (size M, blue)" and the user presses the purchase button.

[1886] Purchase confirmation and shipping procedures

[1887] User

[1888] When the user presses the purchase button, they confirm the purchase and enter their payment information.

[1889] Terminal

[1890] The terminal transmits the purchase and payment information to the server.

[1891] server

[1892] The server verifies the payment information and confirms the purchase. The server then notifies the apparel store of the purchase information and proceeds with the purchase of the clothing. The server also pays a portion of the purchase price to the apparel store.

[1893] Apparel store

[1894] The apparel store will receive a confirmation of purchase and ship the garment to the specified address.

[1895] Examples:

[1896] After the purchase is confirmed, the server notifies the apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price. The apparel store then ships the clothing to the consumer's address.

[1897] Online try-on experience (optional)

[1898] server

[1899] The server generates a try-on simulation using virtual reality (VR) or augmented reality (AR) technology based on the consumer's profile information, and sends the simulated images and videos to the device.

[1900] Terminal

[1901] The device receives simulated images and videos and displays them to consumers, giving them a feeling similar to a real try-on experience.

[1902] Examples:

[1903] The server uses the consumer's profile information to generate a simulation image of what it would look like to try on a "T-shirt (size M, blue)" and sends it to the terminal. The terminal then displays the simulation image to the consumer, providing the feeling of trying it on.

[1904] By implementing the present invention in the above-described manner, an efficient system is realized for providing clothes that are scheduled to be discarded to consumers, thereby reducing the amount of discarded clothes and the burden on the environment.

[1905] The processing flow will be explained below.

[1906] Step 1:

[1907] server

[1908] Data on clothing to be discarded is received from apparel stores. The received data includes photos, size, color, condition, whether or not the item has tags, etc. The data is format-checked, and if there are no problems, it is stored in a database.

[1909] Examples:

[1910] The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and stores it in a database.

[1911] Step 2:

[1912] User

[1913] Enter your profile information on a dedicated screen on your device, including your height, weight, clothing preferences (style and color), budget, etc.

[1914] Terminal

[1915] The entered profile information is sent to the server.

[1916] Step 3:

[1917] server

[1918] The received profile information is stored in a database.

[1919] Examples:

[1920] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[1921] Step 4:

[1922] server

[1923] The system retrieves consumer profile information from the database, analyzes matching clothing using a generative artificial intelligence model, extracts matching clothing data, and generates a list.

[1924] Step 5:

[1925] server

[1926] Send detailed information about the generated clothing list (e.g., photos, sizes, prices, etc.) to the device.

[1927] Examples:

[1928] The server's generation AI filters items by "casual style, blue, budget within 2000 yen" and adds "T-shirt (size M, blue)" to the list of suggestions. This information is then sent to the device.

[1929] Step 6:

[1930] Terminal

[1931] Receives the clothing list sent from the server and displays it to the consumer. Displays detailed information.

[1932] User

[1933] Choose the clothing you like from the suggested items and indicate your intention to purchase (press the purchase button).

[1934] Examples:

[1935] The device displays detailed information about the "T-shirt (size M, blue)" and the user presses the purchase button.

[1936] Step 7:

[1937] User

[1938] By pressing the purchase button, you confirm your purchase and enter your payment information.

[1939] Step 8:

[1940] Terminal

[1941] Send purchase and payment information to the server.

[1942] Step 9:

[1943] server

[1944] The payment information is verified and the purchase is confirmed. The purchase information is notified to the apparel store, and the procedure for purchasing the clothing is carried out. A portion of the purchase price is also paid to the apparel store.

[1945] Examples:

[1946] After the server confirms the purchase, it notifies the apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price.

[1947] Step 10:

[1948] Apparel store

[1949] After receiving confirmation of purchase, the clothing will be shipped to the specified address.

[1950] Examples:

[1951] The apparel store will ship a "T-shirt (size M, blue)" to the specified address.

[1952] Step 11 (Optional):

[1953] server

[1954] Based on the consumer's profile information, a virtual reality (VR) or augmented reality (AR) try-on simulation is generated, and simulated images and videos are sent to the device.

[1955] Terminal

[1956] Receives simulated images and videos and displays them to consumers.

[1957] Examples:

[1958] The server generates a simulation of trying on a "T-shirt (size M, blue)" and sends it to the terminal. The terminal displays the simulation image, allowing the consumer to experience the sensation of trying on the item.

[1959] The above are the processing steps of the program in carrying out the present invention.

[1960] Example 1

[1961] 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."

[1962] While supporting modern comfortable lifestyles, the fashion industry faces the social problem of discarding large amounts of clothing. Discarded clothing places a burden on the environment, and a system is needed to effectively reduce this volume. Furthermore, to support consumers in choosing clothing, it is necessary to provide customized suggestions and realistic try-on experiences. To address these issues, it is necessary to realize a system that efficiently provides discarded clothing to consumers and improves consumer satisfaction.

[1963] 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.

[1964] In this invention, the server includes means for acquiring product data to be discarded, means for acquiring consumer profile information, means for using a generative artificial intelligence model to suggest suitable products from the product data based on the consumer profile information, means for displaying information on the suggested products to the consumer, means for confirming and notifying the purchase of the product selected by the consumer, means for creating a try-on simulation based on the consumer profile information and the suggested product information, means for generating simulation images and videos and sending them to a terminal, and means for displaying the try-on simulation data to the consumer. This makes it possible to efficiently provide clothes to be discarded, to make it easier for consumers to select appropriate clothes based on their own preferences, and to increase their desire to purchase through the try-on experience.

[1965] "Product data scheduled for disposal" refers to information about products that manufacturers, apparel stores, etc. had not planned to offer to consumers, but which they have discovered new value for and are now offering.

[1966] "Consumer profile information" refers to data provided by a consumer relating to personal characteristics and preferences, such as height, weight, preferred style, and budget.

[1967] "Generative artificial intelligence model" refers to a machine learning algorithm and model that selects and suggests optimal products from data on products scheduled for disposal based on consumer profile information.

[1968] "Means for suggesting suitable products" refers to a method and system that uses a generative artificial intelligence model to select and provide information on optimal products based on consumer profile information.

[1969] "Means for displaying product information" refers to the technology and devices that allow consumers to check detailed information such as photos, sizes, and prices of the proposed products through a user interface.

[1970] "Means for confirming and notifying purchase" refers to the methods and technologies that confirm a consumer's intention to purchase a selected product, process payment, and notify related systems and manufacturers of that information.

[1971] "Means for creating a try-on simulation" refers to methods and technologies that use virtual reality or augmented reality technology to recreate the state of a consumer trying on an item based on the consumer's profile information and proposed product information.

[1972] "Means for generating simulation images and videos and transmitting them to a terminal" refers to methods and technologies for generating images and videos for performing a fitting simulation and transmitting them to a consumer's terminal.

[1973] "Means for displaying try-on simulation data to consumers" refers to methods and technologies for displaying the received simulation images and videos on a terminal so that consumers can check them.

[1974] This invention provides an online platform that provides consumers with discarded products, and is composed of three main components: a server, a terminal, and a user. This platform is equipped with a product recommendation function that uses a generative artificial intelligence model to recommend optimal products based on the consumer's profile information. It also utilizes virtual reality and augmented reality technology to provide an online try-on experience, increasing consumer motivation and satisfaction.

[1975] Acquisition and storage of data on products scheduled for disposal

[1976] server

[1977] The server receives data on products to be discarded from manufacturers and apparel stores and stores that data in a database. This data includes product photos, sizes, colors, and conditions. Manufacturers and apparel stores are encouraged to provide the data without tags or logos to prevent damage to their brand image.

[1978] Examples:

[1979] The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and stores it in the database.

[1980] Consumer profile setting

[1981] User

[1982] Consumers enter their body type information (height, weight), clothing preferences (style, color), and budget on the device. Based on this profile information, the server will suggest the most suitable products for the consumer.

[1983] Terminal

[1984] The terminal transmits the profile information entered by the user to the server, and the server stores the profile information in a database.

[1985] Examples:

[1986] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[1987] Product suggestions by generative AI

[1988] server

[1989] The server uses a generative AI model to suggest optimal products based on the consumer's profile information. From product data stored in a database, it selects products that match the consumer's preferences and body shape. This generative AI model can use TensorFlow, for example.

[1990] Examples:

[1991] The server's generation AI filters for "casual style, blue, budget under 2,000 yen" and includes "T-shirt (size M, blue)" in the suggestion list.

[1992] View and select suggestions

[1993] Terminal

[1994] The terminal displays the product suggestions sent from the server to the consumer and provides detailed information (photos, sizes, prices, etc.).

[1995] User

[1996] The user selects the product they like from the suggested products and indicates their intention to purchase it (by pressing the purchase button).

[1997] Examples:

[1998] The device displays detailed information about the "T-shirt (size M, blue)" and the user presses the purchase button.

[1999] Purchase confirmation and shipping procedures

[2000] User

[2001] When the user presses the purchase button, they confirm the purchase and enter their payment information.

[2002] Terminal

[2003] The terminal transmits the purchase and payment information to the server.

[2004] server

[2005] The server verifies the payment information and confirms the purchase. The server then notifies the manufacturer or apparel store of the purchase information, processes the purchase of the product, and pays a portion of the purchase price to the manufacturer or apparel store.

[2006] Manufacturers and apparel stores

[2007] The manufacturer or apparel store will receive a confirmation of purchase and ship the product to the specified address.

[2008] Examples:

[2009] After the purchase is confirmed, the server notifies the manufacturer or apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price. The manufacturer or apparel store then ships the product to the consumer's address.

[2010] Online try-on experience (optional)

[2011] server

[2012] The server generates a try-on simulation using virtual reality (VR) or augmented reality (AR) technology based on the consumer's profile information, and sends the simulated images and videos to the device.

[2013] Terminal

[2014] The device receives simulated images and videos and displays them to consumers, giving them a feeling similar to a real try-on experience.

[2015] Examples:

[2016] The server uses the consumer's profile information to generate a simulation image of what it would look like to try on a "T-shirt (size M, blue)" and sends it to the terminal. The terminal then displays the simulation image to the consumer, providing the feeling of trying it on.

[2017] Prompt Sentence Examples

[2018] Examples of input prompts for a generative AI model include:

[2019] Examples:

[2020] User profile:

[2021] Height: 170cm

[2022] Weight: 60kg

[2023] Preference: Casual

[2024] Color: Blue

[2025] Budget: Under 2000 yen

[2026] Prompt the generative AI model:

[2027] Please suggest a casual style blue product for under 2000 yen.

[2028] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2029] Step 1: Acquire and store data on products to be disposed of

[2030] server

[2031] Input: Data on discarded products provided by manufacturers and apparel stores (e.g., product photos, size, color, condition, etc.)

[2032] Processing: The server receives product data from manufacturers and apparel stores via a data transmission API. The received data is stored in a database.

[2033] Output: Product data stored in a database

[2034] What happens: The server connects to the manufacturer's data system, receives data about "T-shirt (size M, blue, condition: like new)" via API, and stores it in a database.

[2035] Step 2: Setting up a consumer profile

[2036] User

[2037] Input: Your body shape information (height, weight), clothing preferences (style, color), budget (entered via web form, etc.)

[2038] Action: The user enters this information and clicks the submit button.

[2039] Output: The terminal stores the input and prepares it to be sent.

[2040] Specific actions: A user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: under 2000 yen" into a web form and presses the submit button.

[2041] Terminal

[2042] Input: Profile information entered by the user

[2043] Processing: The device checks and organizes the input and sends it to the server.

[2044] Output: Profile information sent to the server

[2045] Specific operation: The terminal sends the user input to the server via SSL communication.

[2046] Step 3: Product suggestions by generative AI

[2047] server

[2048] Input: Consumer profile information, product data stored in a database

[2049] Processing: Extract candidate product data from a database based on consumer profile information. Use a generative AI model (e.g., using TensorFlow) to select the best product from the extracted data.

[2050] Output: A list of best product suggestions

[2051] Specific operation: The server executes a database query based on the profile information, extracts products that match the criteria of "casual style, blue, under 2,000 yen," evaluates them using a generative AI model, and creates a list of suggestions.

[2052] Step 4: View and select suggestions

[2053] Terminal

[2054] Input: A list of suggestions sent by the server

[2055] Action: Display the suggested product list to the user and provide details (photos, sizes, prices, etc.).

[2056] Output: Product information displayed in the user interface

[2057] Specific operation: The device receives data from the server and displays detailed information about the "T-shirt (size M, blue)" on the screen.

[2058] User

[2059] Input: Select from the displayed product list

[2060] Action: Choose the product you like and indicate your intention to purchase (press the purchase button).

[2061] Output: Selected product information and purchase intention

[2062] What happens: The user reviews the details and clicks the "Purchase" button.

[2063] Step 5: Confirm your purchase and process your shipment

[2064] User

[2065] Input: Clicking the purchase button, entering payment information

[2066] Processing: After pressing the purchase button, enter the required payment information and submit.

[2067] Output: Confirmed purchase and payment information

[2068] Specific actions: Enter payment information (such as credit card information) on the terminal.

[2069] Terminal

[2070] Input: User purchase and payment information

[2071] Process: Send the entered information to the server.

[2072] Output: Purchase and payment information sent to the server

[2073] What it does: The device securely sends payment information to the server using SSL.

[2074] server

[2075] Input: Purchase and payment information

[2076] Processing: Verify the payment information, confirm the purchase, notify the manufacturer or apparel store of the purchase information, process the purchase of the relevant products, and pay a portion of the purchase price to the manufacturer or apparel store.

[2077] Output: Purchase confirmation, payment confirmation, product purchase

[2078] What it does: Confirms payment information through the payment gateway, confirms the purchase, and notifies the manufacturer or apparel store to arrange for the product to be shipped.

[2079] Manufacturers and apparel stores

[2080] Input: Purchase confirmation notice

[2081] Processing: receiving the purchase confirmation and shipping the product to the consumer's designated address.

[2082] Output: Shipped products

[2083] Specific operation: Upon receiving a purchase confirmation notification, a "T-shirt (size M, blue)" is shipped to the specified consumer address.

[2084] Step 6: Online try-on experience (optional)

[2085] server

[2086] Input: Consumer profile information, suggested product information

[2087] Processing: Using virtual reality (VR) or augmented reality (AR) technology, we generate try-on simulations and create images and videos.

[2088] Output: Simulation images and videos

[2089] Specific operation: The server uses 3D modeling software (e.g., Unity or Blender) to create a simulation of trying on a "T-shirt (size M, blue)" and generates images and videos.

[2090] Terminal

[2091] Input: Simulation images and videos from the server

[2092] Processing: Receives the simulation data and displays it on the user interface.

[2093] Output: Displayed simulation data

[2094] Specific operation: The device displays simulated images and videos received from the server, giving the user the feeling of trying on clothes.

[2095] (Application example 1)

[2096] 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."

[2097] In modern society, the amount of clothing discarded in the apparel industry is enormous, posing a major problem from the perspective of environmental impact. Furthermore, the inability of consumers to try on clothes when purchasing online has led to an increase in returns due to mismatched sizes and styles. Furthermore, for consumers, the effort required to select the right clothing can lead to reduced purchase satisfaction. There is a need to develop a system that can solve these problems and provide a more efficient and satisfying clothing shopping experience.

[2098] 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.

[2099] In this invention, the server includes: means for acquiring data on clothing to be discarded; means for acquiring consumer profile information; means for using a generative artificial intelligence model to suggest suitable clothing from the clothing data based on the consumer profile information; means for providing the consumer with a simulation of trying on the suggested clothing using virtual reality or augmented reality technology; means for displaying information on the suggested clothing to the consumer; and means for confirming and notifying the consumer of the purchase of the clothing selected by the consumer. This makes it possible to recommend clothing that is most suitable for the consumer, and to reduce the risk of returns and improve purchase satisfaction through a try-on experience using virtual reality or augmented reality. This also promotes the effective use of clothing that would otherwise be discarded at apparel stores, contributing to reducing environmental impact.

[2100] "Clothing destined for disposal" refers to clothing that apparel stores or manufacturers plan to dispose of rather than sell.

[2101] "Means for obtaining data" refers to the functionality that allows the server to collect and store information from external resources.

[2102] "Profile information" refers to personal information about a consumer, such as their height, weight, preferred style, and budget.

[2103] A "generative artificial intelligence model" refers to an algorithm that predicts and generates information based on input data.

[2104] "Means of suggestion" refers to the function of presenting appropriate options to consumers based on the generated data.

[2105] "Means for displaying" refers to the functionality of the device to visually present information to the consumer.

[2106] "Means for confirming and notifying purchase" refers to the function for a consumer to purchase the selected product and transmit that information to the server and other relevant parties.

[2107] A "system" refers to a set of functions or mechanisms in which multiple elements function together.

[2108] "Means of storing in a database" refers to the function of systematically organizing and storing acquired information.

[2109] "Virtual reality technology" refers to a virtual space created using computer technology that allows users to experience a sense of reality.

[2110] "Augmented reality technology" refers to technology that overlays digital information onto a real-world environment, allowing users to experience the real world and digital information simultaneously.

[2111] "Means for providing a try-on simulation" refers to a function for providing a consumer with the sensation of virtually trying on clothing.

[2112] A "head-mounted display" is a device worn by the user on the head that projects images directly in front of the eyes.

[2113] "Consumer" refers to the general consumer who purchases and uses clothing.

[2114] This invention is an online platform that provides consumers with discarded clothing, and is composed of three main elements: a server, a terminal, and a user. The platform is equipped with a clothing recommendation function using a generative AI model, and suggests optimal clothing based on the consumer's profile information. It also utilizes virtual reality (VR) and augmented reality (AR) technologies to provide an online try-on experience, which increases consumer motivation to purchase and improves satisfaction.

[2115] Acquiring and saving clothing data

[2116] server

[2117] The server uses a high-performance server (e.g., Amazon Web Services, AWS) to receive data on discarded clothing from apparel stores and store the data in a database (e.g., MySQL). This data includes photos of the clothing, sizes, colors, and conditions. It is recommended that apparel stores provide the data with tags and logos removed to prevent damage to the brand's image.

[2118] Examples:

[2119] The server receives data on "T-shirt (size M, blue, condition: like new)" from an apparel store and stores it in a database.

[2120] Consumer profile setting

[2121] User

[2122] Consumers enter their body type information (e.g., height, weight), clothing preferences (e.g., style, color), and budget on the device. Based on this profile information, the server will suggest the most suitable clothing for the consumer.

[2123] Terminal

[2124] The device, whether it's an iOS or Android device, sends the profile information entered by the user to the server, which then stores the profile information in a database.

[2125] Examples:

[2126] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[2127] Clothing suggestions using generative AI

[2128] server

[2129] The server uses a generative AI model (e.g., GPT-4) to suggest the most suitable clothing based on the consumer's profile information. It selects clothing that matches the consumer's preferences and body shape from clothing data stored in a database.

[2130] Examples:

[2131] The server's generation AI filters for "casual style, blue, budget under 2,000 yen" and includes "T-shirt (size M, blue)" in the suggestion list.

[2132] View and select suggestions

[2133] Terminal

[2134] The terminal displays the clothing suggestions sent from the server to the consumer and provides detailed information (photos, sizes, prices, etc.).

[2135] User

[2136] The user selects the clothing item they like from the suggested items and indicates their intention to purchase it (by pressing the purchase button).

[2137] Examples:

[2138] The device displays detailed information about the "T-shirt (size M, blue)" and the user presses the purchase button.

[2139] Purchase confirmation and shipping procedures

[2140] User

[2141] When the user presses the purchase button, they confirm the purchase and enter their payment information.

[2142] Terminal

[2143] The terminal transmits the purchase and payment information to the server.

[2144] server

[2145] The server verifies the payment information, confirms the purchase, and notifies the apparel store of the purchase information and completes the procedure for purchasing the clothing.

[2146] Examples:

[2147] After the purchase is confirmed, the server notifies the apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price. The apparel store then ships the clothing to the consumer's address.

[2148] Online try-on experience

[2149] server

[2150] Based on the consumer's profile information, the server generates a try-on simulation using virtual reality (VR) or augmented reality (AR) technology and sends the simulated images and videos to the device.

[2151] Terminal

[2152] The device receives simulated images and videos and displays them to consumers using a smartphone or head-mounted display (HMD), giving consumers a feeling similar to a real fitting experience.

[2153] Examples:

[2154] The server uses the consumer's profile information to generate a simulation image of what it would look like to try on a "T-shirt (size M, blue)" and sends it to the terminal. The terminal then displays the simulation image to the consumer, providing the feeling of trying it on.

[2155] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2156] Step 1:

[2157] The server receives data on discarded clothing from apparel stores. The server uses a database to store this data. Input includes photos of the clothing, size, color, condition, etc. The server receives this data and stores it in the database.

[2158] Examples:

[2159] The data for "size M, blue, like new T-shirt" sent from an apparel store is received and saved in a database.

[2160] Step 2:

[2161] The user uses the device to enter their profile information, such as height, weight, preferred style, color, budget, etc. The device then sends this data to the server.

[2162] Examples:

[2163] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: under 2000 yen," and the device sends this to the server.

[2164] Step 3:

[2165] The server receives the user's profile information and stores it in a database. It then uses a generative artificial intelligence model (e.g., GPT-4) to suggest the most suitable clothing based on the profile information. In this case, it selects clothing that matches the consumer's preferences and body shape from the clothing data stored in the database.

[2166] Examples:

[2167] The server's generation AI filters for "casual style, blue, budget under 2,000 yen" and includes "a blue T-shirt in size M" in the list of suggestions.

[2168] Step 4:

[2169] The server sends the suggested clothing information to the terminal. As input, the result of combining the user's profile information and the clothing data in the database is used. As output, detailed information of the suggested clothing is generated and sent to the terminal.

[2170] Examples:

[2171] Detailed information about the "T-shirt (size M, blue)" included in the suggestion list is sent to the terminal.

[2172] Step 5:

[2173] The terminal displays the clothing suggestions received from the server to the consumer, along with detailed information (e.g., photos, sizes, prices, etc.).

[2174] Examples:

[2175] The device will display details of a "size medium blue T-shirt" to the consumer.

[2176] Step 6:

[2177] The user selects the clothing item they like from the suggested items and indicates their intention to purchase it by pressing the purchase button to proceed with the purchase process.

[2178] Examples:

[2179] The user selects "Blue T-shirt (size M)" and presses the purchase button.

[2180] Step 7:

[2181] The terminal sends the purchase and payment information to the server. As input, the user's clothing selection and payment information are used. As output, a request for purchase confirmation is sent to the server.

[2182] Examples:

[2183] The user enters payment information and the terminal sends a request to the server to confirm the purchase.

[2184] Step 8:

[2185] The server verifies the payment information and confirms the purchase. If the purchase is successful, it notifies the apparel store of the purchase information and proceeds to purchase the clothing. The inputs are the user's payment information and the selected clothing information. The output is a purchase notification to the apparel store and a payment request for the purchase amount.

[2186] Examples:

[2187] The server receives the purchase information, notifies the apparel store of the purchase details of the "blue T-shirt (size M)," and pays the apparel store a portion of the purchase price.

[2188] Step 9:

[2189] The apparel store receives the notification from the server and ships the clothes to the specified address. The purchase notification from the server is used as input. The clothes are shipped to the consumer's address as output.

[2190] Examples:

[2191] An apparel store ships a blue T-shirt (size medium) to a consumer's address.

[2192] Step 10:

[2193] The server generates a try-on simulation using virtual reality (VR) or augmented reality (AR) technology based on the consumer's profile information and sends the simulation images and videos to the device. The user's profile information and suggested clothing information are used as input. As output, data from the try-on simulation is generated and sent to the device.

[2194] Examples:

[2195] The server uses the consumer's profile information to generate a simulated image of what it would look like when trying on a "T-shirt (size M, blue)" and sends it to the terminal.

[2196] 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.

[2197] This invention provides an online platform that provides consumers with clothing that would otherwise be discarded, and is composed of three main elements: a server, a terminal, and a user. The platform has a clothing recommendation function that uses a generative AI model to recommend the most suitable clothing based on the consumer's profile information. Furthermore, an emotion engine is used to obtain user emotion data and optimize the recommendations. Furthermore, by utilizing virtual reality and augmented reality technology to provide an online try-on experience, it increases consumer motivation to purchase and improves satisfaction.

[2198] Acquiring and saving clothing data

[2199] server

[2200] The server receives data on discarded clothing from apparel stores and stores it in a database. This data includes photos of the clothing, size, color, condition, and whether or not tags are present. To prevent damage to the brand image, apparel stores are encouraged to provide the data without tags or logos.

[2201] Examples:

[2202] The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and stores it in a database.

[2203] Consumer profile setting

[2204] User

[2205] Consumers enter their body type information (e.g., height, weight), clothing preferences (e.g., style, color), and budget on the device. Based on this profile information, the server will suggest the most suitable clothing for the consumer.

[2206] Terminal

[2207] The terminal transmits the profile information entered by the user to the server.

[2208] Examples:

[2209] The user enters "height: 170cm, weight: 60kg, preference: casual, color: blue, budget: within 2000 yen," and the terminal sends this to the server, which stores it in a database.

[2210] Clothing recommendations using generative AI and emotion engine

[2211] server

[2212] The server uses a generative AI model to suggest optimal clothing based on the consumer's profile information and emotional data obtained from the emotion engine. It selects clothing that matches the consumer's preferences, body shape, and emotions from clothing data stored in a database.

[2213] Examples:

[2214] The server's generation AI filters the results by "casual style, blue, budget under 2000 yen" and adds "T-shirt (size M, blue)" to the list of suggestions. Using data from the emotion engine, it prioritizes suggestions that will satisfy the user.

[2215] View and select suggestions

[2216] Terminal

[2217] The device displays clothing suggestions sent from the server to the consumer and provides detailed information (photos, sizes, prices, etc.) It also analyzes consumer reactions in real time through an emotion engine and sends the results to the server.

[2218] User

[2219] The user selects the clothing item they like from the suggested items and indicates their intention to purchase it (by pressing the purchase button).

[2220] Examples:

[2221] The device displays detailed information about the "T-shirt (size M, blue)" and the user presses the purchase button.

[2222] Purchase confirmation and shipping procedures

[2223] User

[2224] When the user presses the purchase button, they confirm the purchase and enter their payment information.

[2225] Terminal

[2226] The terminal transmits the purchase and payment information to the server.

[2227] server

[2228] The server verifies the payment information and confirms the purchase. The server then notifies the apparel store of the purchase information and proceeds with the purchase of the clothing. The server also pays a portion of the purchase price to the apparel store.

[2229] Apparel store

[2230] The apparel store will receive a confirmation of purchase and ship the garment to the specified address.

[2231] Examples:

[2232] After the purchase is confirmed, the server notifies the apparel store of the purchase information for the "T-shirt (size M, blue)" and pays a portion of the purchase price. The apparel store then ships the clothing to the consumer's address.

[2233] Online try-on experience

[2234] server

[2235] The server generates a try-on simulation using virtual reality (VR) or augmented reality (AR) technology based on the consumer's profile information and emotional data, and sends simulated images and videos to the device.

[2236] Terminal

[2237] The device receives simulated images and videos and displays them to the consumer, giving them a feeling similar to a real fitting experience. Emotional data during the fitting process is also collected and sent to the server.

[2238] Examples:

[2239] The server uses the consumer's profile information and emotional data to generate a simulated image of what it would be like to try on a "T-shirt (size M, blue)" and sends it to the device. The device displays the simulated image, collects the consumer's emotional data in real time, and sends it to the server.

[2240] By implementing the present invention in the above manner, an efficient system is realized that provides consumers with clothes that would otherwise be discarded. This reduces the amount of discarded clothes and the burden on the environment. Furthermore, by combining it with an emotion engine, it is possible to improve consumer satisfaction and stimulate purchasing motivation.

[2241] The processing flow will be explained below.

[2242] Step 1:

[2243] server

[2244] Receives data on clothing to be discarded from apparel stores. The received data includes photos, size, color, condition, whether or not it has tags, etc. The format of this data is checked, and if there are no problems, it is stored in a database.

[2245] Examples:

[2246] The server receives data on "T-shirt (size M, blue, condition: like new)" from the apparel store and stores it in a database.

[2247] Step 2:

[2248] User

[2249] Enter your profile information on a dedicated screen on your device, including your height, weight, clothing preferences (style and color), budget, etc.

[2250] Terminal

[2251] The...

Claims

1. A means for obtaining data on clothing to be disposed of; a means for obtaining consumer profile information; means for suggesting suitable clothing from the clothing data based on the consumer's profile information using a generative artificial intelligence model; a means for displaying information about the suggested garment to the consumer; a means for confirming and notifying the consumer of the purchase of the selected garment; A system including:

2. 2. The system according to claim 1, further comprising means for receiving data on the clothes to be disposed of from the apparel store and storing the data in a database.

3. 10. The system of claim 1, further comprising means for providing a consumer with a simulated try-on of the proposed garment using virtual reality or augmented reality technology.

Images