System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving personal data input by a user; means for storing the input personal data in a database; means for receiving an image of clothing of the user; means for analyzing the received image data and extracting clothing information; means for storing the extracted clothing information in the database; means for acquiring weather information and fashion information from outside; means for generating a fashion proposal based on the personal data, the clothing information, the weather information, and the fashion information; and means for providing the generated fashion proposal to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In the modern fashion industry, mass production and mass consumption have led to the problem of a large amount of unnecessary clothing lying dormant. This wastes resources and increases the environmental burden. Furthermore, there is a lack of easy ways for individual users to receive optimal fashion suggestions, which means they are unable to make the most of their own clothing. To solve these problems, a system is needed that can suggest optimal fashion styles for individuals based on their tastes, preferences, and the clothing they own. [Means for solving the problem]

[0005] This invention solves this problem by providing a means for receiving personal data entered by a user and storing it in a database, and a means for receiving and analyzing images of the user's clothing. Specifically, the received image data is analyzed, clothing information is extracted, and the extracted information is stored in a database. Weather and trend information is then acquired from external sources, and fashion suggestions are generated based on the personal data, clothing information, weather information, and trend information. Furthermore, by providing the generated fashion suggestions to the user, the user can easily reuse their own clothing and encourage new purchasing behavior. Furthermore, a means is provided for the user to select the generated fashion suggestions, and outfit images are generated and displayed based on the selected suggestions. This allows the user to list items at flea markets, post on social media, and receive points based on the outfit images selected. These means enable a sustainable fashion lifestyle and provide optimal fashion suggestions to each individual user.

[0006] 1. "Personal Data" is a general term for information about an individual, such as a user's hobbies, preferences, physical characteristics, personality traits, and lifestyle characteristics.

[0007] 2. "Database" means a data storage system for efficiently storing and managing collected personal data and clothing information.

[0008] 3. "Clothing image" refers to the photographic data of clothing held by the user, which is used to extract clothing information from the image.

[0009] 4. "Image analysis" is a technology that automatically extracts information such as clothing color, material, and brand based on received image data.

[0010] 5. "Weather information" refers to weather-related data such as weekly weather forecasts and current weather conditions.

[0011] 6. "Trend information" refers to data about current fashion trends and popular styles.

[0012] 7. "Fashion suggestions" are specific fashion style suggestions to users that are generated based on personal data, clothing information, weather information, and trend information.

[0013] 8. "Outfit image" is a visual image of a specific outfit that is generated based on the fashion suggestions selected by the user.

[0014] 9. "Flea market listing" refers to the act of a user listing unwanted clothing on an online flea market.

[0015] 10. "SNS posting" refers to the act of users sharing clothing images and fashion suggestions they have generated through social networking services.

[0016] 11. "Point Award" is an incentive system that awards points to users for certain actions. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram 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

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

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

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

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

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

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

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

[0025] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0038] MODE FOR CARRYING OUT THE INVENTION

[0039] This invention is an AI platform that proposes sustainable fashion based on the user's individual information. This platform efficiently collects and analyzes personal data and clothing information entered by the user, and proposes optimal fashion styles. A specific embodiment of the system is shown below.

[0040] System configuration

[0041] The system consists of the following main components:

[0042] 1. User terminal: A device used to input and send personal data and clothing image data. Typically, a smartphone or tablet is used.

[0043] 2. Server: A central processing unit that analyzes personal data and clothing information and generates fashion suggestions.

[0044] 3. Database: A data storage system that stores and manages all collected data.

[0045] Program processing flow

[0046] 1. Collection of personal data

[0047] The user inputs personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics from the terminal.

[0048] The terminal transmits the input data to the server.

[0049] The server analyzes the received personal data and stores it in a database.

[0050] 2. Collection of clothing data

[0051] The user takes a picture of the clothes they are holding with the device.

[0052] The device sends the captured image to the server.

[0053] The server uses image analysis algorithms to extract information such as the color, material, and brand of the clothing.

[0054] The extracted information is stored in a database.

[0055] 3. Obtaining weather and trend information

[0056] The server obtains weekly weather forecasts and trend information through an external API.

[0057] The information obtained is also stored in a database.

[0058] 4. Fashion Proposal Generation

[0059] The server generates optimal fashion suggestions for the user based on personal data, clothing data, weather information, and trend information.

[0060] The generated proposals are sent to the user's terminal as multiple options.

[0061] 5. Selection and display of proposals

[0062] The user selects one of the fashion suggestions provided by the terminal.

[0063] The terminal generates an image of the outfit in the selected fashion style and displays it to the user.

[0064] 6. Provision of Additional Features

[0065] Based on the generated outfit image, users can take actions such as listing the outfit at a flea market, sharing it on social media, and receiving points.

[0066] The server tracks these actions and awards incentives such as points as appropriate.

[0067] Specific examples of processing

[0068] Example 1: Entering and saving personal data

[0069] User A opens the smartphone app and enters his / her hobbies and preferences (e.g., he / she likes casual style), physical characteristics (e.g., he / she is 165 cm tall and weighs 55 kg), and lifestyle characteristics (e.g., he / she commutes to the office five days a week). The device sends this data to the server, which then stores the received data in a database.

[0070] Example 2: Uploading and analyzing clothing images

[0071] User A takes a photo of his casual shirt and jeans with his smartphone. The device sends the image to the server, which analyzes it and extracts the shirt's color, material, and brand information. This information is then stored in a database.

[0072] Example 3: Generating and selecting fashion suggestions

[0073] The server generates multiple fashion styles based on user A's personal data, clothing data, acquired weather information (e.g. sunny, temperature 20 degrees) and trend information. Suggested styles are displayed to user A, and user A selects one. Based on the selected style, an outfit image is generated and displayed on the smartphone.

[0074] Example 4: Using additional features

[0075] User A shares the generated outfit image on a social networking site. The server tracks this action and awards points to User A. User A can use the points to purchase new clothes at an online flea market.

[0076] In this way, the present invention makes it possible to provide optimal fashion suggestions based on the user's individual information, thereby realizing a sustainable fashion lifestyle.

[0077] The processing flow will be explained below.

[0078] Program processing flow and specific explanation

[0079] 1. Collection of personal data

[0080] Step 1:

[0081] The user inputs personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics from the terminal.

[0082] Specific operation: The user enters information such as "height 165cm," "weight 55kg," and "likes casual style" into the app's input form.

[0083] Step 2:

[0084] The terminal transmits the entered personal data to the server.

[0085] What it does: The app sends the collected data to the server as an HTTP request.

[0086] Step 3:

[0087] The server stores the received personal data in a database.

[0088] What happens: The server receives the data, formats it appropriately, and stores it in the user profile table in the database.

[0089] 2. Collection of clothing data

[0090] Step 1:

[0091] The user takes a picture of the clothes they are holding with the device.

[0092] What happens: A user uses the app to take a photo of an item of clothing.

[0093] Step 2:

[0094] The terminal transmits the captured image data to the server.

[0095] What happens: The app encodes the image data and sends it to the server.

[0096] Step 3:

[0097] The server uses image analysis algorithms to extract information about the clothing (color, material, brand, etc.).

[0098] Specific operation: The server inputs the received image into the machine learning model and obtains the analysis result (e.g., "red cotton shirt" or "blue denim jeans").

[0099] Step 4:

[0100] The server stores the extracted clothing information in a database.

[0101] Specific operation: The server saves the analysis results in the clothing information table of the database.

[0102] 3. Obtaining weather and trend information

[0103] Step 1:

[0104] The server retrieves the weekly weather forecast through an external API.

[0105] Specific operation: The server sends a request to the weather API to obtain weekly weather data (e.g., "sunny, temperature 20 degrees").

[0106] Step 2:

[0107] The server obtains trend information through an external API.

[0108] Specific operation: The server sends a request to the trend information API to obtain the latest fashion trend information (for example, "casual style is in fashion").

[0109] 4. Fashion Proposal Generation

[0110] Step 1:

[0111] The server generates fashion suggestions based on personal data, clothing data, weather information, and trend information.

[0112] How it works: The server aggregates all the data and uses machine learning algorithms to generate the best fashion style for the user (e.g., "a casual shirt and jeans combination").

[0113] Step 2:

[0114] The server transmits the generated fashion suggestions to the terminal.

[0115] What it does: The server sends the suggestions in JSON format to the device, and the app reads and displays them.

[0116] 5. Selection and display of proposals

[0117] Step 1:

[0118] The user selects one of the fashion suggestions provided by the terminal.

[0119] Specific behavior: The user taps to select one of the suggestions displayed in the app.

[0120] Step 2:

[0121] The terminal generates an image of the outfit in the selected fashion style and displays it to the user.

[0122] What it does: The app generates a visual image of the outfit based on the selected style and displays it to the user.

[0123] 6. Provision of Additional Features

[0124] Step 1:

[0125] Based on the generated outfit image, users can take actions such as listing the outfit at a flea market, sharing it on social media, and receiving points.

[0126] Specific operation: The user presses the "Share to social media" button within the app to post their outfit.

[0127] Step 2:

[0128] The server tracks these actions and awards incentives such as points as appropriate.

[0129] Specific operation: The server analyzes the user's action log and processes the points to add to the user's account.

[0130] The above is a specific processing flow for implementing the present invention. We have explained in detail how the user, terminal, and server operate in each step. This allows us to build an optimal system for realizing a sustainable fashion life.

[0131] Example 1

[0132] 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."

[0133] The present invention aims to efficiently and effectively suggest sustainable fashion based on individual user information. It also aims to provide a system that provides optimal fashion suggestions by analyzing the user's clothing information in detail and combining it with current weather and fashion trends. Furthermore, the system allows users to easily use the suggested fashion styles to access additional features such as posting on social media, listing at flea markets, and earning points.

[0134] 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.

[0135] In this invention, the server includes a means for transmitting data and images input by the user to the server, a means for the server to extract clothing information using an image analysis algorithm, and a means for generating suggestions using a generative AI model. This allows for rapid analysis of personal data and clothing information, enabling optimal fashion suggestions to be made to the user. Furthermore, by obtaining weather and fashion trends via an external API, suggestions based on real-time information are possible. Furthermore, based on the outfit image selected by the user, the item can be listed at a flea market, posted on social media, and awarded points, and these actions are tracked to provide incentives, improving user convenience.

[0136] "User" means an individual or organization that uses the system.

[0137] A "terminal" is a device used by a user, such as a smartphone or tablet.

[0138] A "server" is a computer device that performs central processing such as analyzing and storing data and generating fashion suggestions.

[0139] A "data storage device" is a system that stores and manages personal data, clothing information, weather information, fashion information, etc.

[0140] An "image analysis algorithm" is a software program that analyzes received image data and extracts information.

[0141] A "generative AI model" is an algorithmic model that uses artificial intelligence to generate fashion suggestions for users.

[0142] A "prompt" refers to the input information or question given to a generative AI model.

[0143] "Fashion suggestions" are recommendations for fashion styles that suit the user.

[0144] "Outfit images" are images or graphics that visually represent the proposed fashion style.

[0145] An "external API" is an interface for obtaining information in collaboration with external services.

[0146] "Weather information" refers to meteorological data such as the weekly weather forecast and temperature.

[0147] "Trend information" is data about current fashion trends.

[0148] "Flea market listing" refers to the act of a user listing unwanted clothing or other items on an online marketplace.

[0149] "SNS posting" refers to the act of a user sharing a proposed fashion style or outfit image on a social networking service.

[0150] "Point awarding" is a system that provides points as an incentive for user actions.

[0151] "Action tracking" means tracking and recording user behavior.

[0152] The present invention is an AI platform that proposes sustainable fashion based on individual user information. A specific embodiment of the system is described below.

[0153] The system mainly consists of three main components: a user terminal, a server, and a data storage device. The user terminal is typically a smartphone or tablet, and is used to input and transmit personal data and clothing image data. The server is a central processing unit that analyzes personal data and clothing information and generates fashion suggestions. The data storage device is a system that stores and manages all collected data.

[0154] First, a user uses a smartphone or tablet to input personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics. For example, the user inputs information such as that they like casual clothing, are 165 cm tall, weigh 55 kg, and commute to the office five days a week. The device sends this data to a server, which then analyzes the received data and stores it in a data storage device.

[0155] Next, the user takes a picture of the clothes they are wearing with their device. For example, they can take a picture of a casual shirt and jeans. The device sends the image to the server, which then uses an image analysis algorithm (e.g., Amazon Rekognition, Google Cloud Vision) to extract information about the clothes, such as color, material, and brand. The extracted information is then stored on a data storage device.

[0156] In addition, the server obtains weekly weather forecasts and trend information through external APIs (e.g., OpenWeather, FashionAPI). The obtained information is also stored in the data storage device. For example, the weather forecast for next week is sunny with a temperature of 20 degrees.

[0157] This system uses a generative AI model (e.g., GPT-3) to generate fashion suggestions. The server inputs a prompt to generate the optimal fashion style based on collected personal data, clothing data, weather information, and trend information, and the generative AI model outputs the suggestion. The prompt uses the following text: "Provide fashion suggestions based on the personal data entered by User A (hobbies and preferences: casual style, height 165 cm, weight 55 kg, commute 5 days a week), an image of a casual shirt and jeans, weather information (sunny, temperature 20 degrees), and trend information."

[0158] The user's device receives the fashion suggestions sent from the server and displays them to the user. The user selects one of the suggestions provided, and an outfit image based on the selected style is generated and displayed on the smartphone. The user can also take actions based on the generated outfit image, such as listing it at a flea market, sharing it on social media, or receiving points. The server tracks these actions and awards incentives such as points as appropriate.

[0159] In this way, the present invention can make optimal fashion suggestions based on the user's individual information, and can comprehensively utilize the user's clothing, the latest weather information, trend information, etc. In addition, the user can further enhance their fashion life by using additional functions.

[0160] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0161] Step 1:

[0162] The user uses a smartphone or tablet to enter personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics. Specifically, the user enters information such as "I like casual style," "I'm 165cm tall," "I weigh 55kg," and "I commute to the office five days a week" into the app's input form.

[0163] Input data: User's hobbies, preferences, physical characteristics, lifestyle characteristics, etc.

[0164] Output data: Personal data sent from the device to the server

[0165] Step 2:

[0166] The device sends the entered personal data to the server, which analyzes the received personal data, converts it into an appropriate format, and stores it in a data storage device, where the personal data is stored in a structured format.

[0167] Input data: Personal data sent from the device

[0168] Output data: Personal data stored in the data storage device

[0169] Step 3:

[0170] The user takes a picture of their clothing with their smartphone, or uses the app's camera function to take a picture of their belongings, such as a casual shirt or jeans.

[0171] Input data: Images of clothing taken by the user

[0172] Output data: Image file of the clothing saved on the device

[0173] Step 4:

[0174] The device sends the captured image to a server, which uses an image analysis algorithm (e.g., Amazon Rekognition, Google Cloud Vision) to extract information about the clothing, such as its color, material, and brand. The extracted information is then stored on a data storage device.

[0175] Input data: Clothing images sent from the device

[0176] Output data: Clothing information (color, material, brand, etc.) stored in the data storage device

[0177] Step 5:

[0178] The server uses external APIs (e.g., OpenWeather, FashionAPI) to obtain weekly weather forecasts and trend information. The obtained information is also stored in the data storage device.

[0179] Input data: Weather and trend information requests obtained from external APIs

[0180] Output data: Weather information and trend information stored in a data storage device

[0181] Step 6:

[0182] The server generates fashion suggestions using a generative AI model (e.g., GPT-3) based on personal data, clothing data, weather information, and trend information. The generated suggestions are sent to the device as multiple options. The server inputs the following prompt into the generative AI model: "Provide fashion suggestions based on the personal data entered by User A (hobbies and preferences: casual style, height 165 cm, weight 55 kg, commutes 5 days a week), images of casual shirts and jeans, weather information (sunny, temperature 20 degrees), and trend information."

[0183] Input data: personal data, clothing data, weather information, fashion information

[0184] Output data: Fashion suggestions output by the generative AI model

[0185] Step 7:

[0186] The terminal displays the fashion suggestions received from the server to the user, who then selects one of the suggestions.

[0187] Input data: Fashion suggestions received from the server

[0188] Output data: The fashion style selected by the user

[0189] Step 8:

[0190] The device generates an outfit image based on the selected style and displays it to the user. The generated outfit image is displayed on the user's smartphone screen.

[0191] Input data: Fashion style selected by the user

[0192] Output data: Image of the garment displayed on the device

[0193] Step 9:

[0194] Users can share the created outfit images on social media or put them up for sale at a flea market. The server tracks these actions and awards points to users, which can be used to make purchases at the online flea market.

[0195] Input data: User actions (SNS sharing, flea market listing, etc.)

[0196] Output data: Actions recorded by the server and points awarded

[0197] (Application example 1)

[0198] 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."

[0199] Conventional fashion suggestion systems suggest appropriate fashion styles based on a user's individual information, but the suggestions lack practicality and a sense of actually trying on the clothes. Furthermore, the fashion suggestions are often unsustainable. Furthermore, the functionality for tracking user behavior and providing incentives is insufficient. Therefore, users do not get the feeling of actually trying on the suggested clothes, making it difficult to increase their motivation to purchase.

[0200] 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.

[0201] In this invention, the server includes means for receiving personal data entered by the user, means for saving the entered personal data in a database, means for receiving images of clothing owned by the user, means for analyzing the received image data and extracting clothing information, means for saving the extracted clothing information in a database, means for externally acquiring weather information and trend information, means for generating fashion suggestions based on the personal data, clothing information, weather information, and trend information, means for providing the generated fashion suggestions to the user, means for using augmented reality technology to display the generated fashion suggestions as a virtual try-on, and means for tracking the user's shared behavior and awarding points. This allows the user to feel as if they are actually trying on the suggested clothing, increasing their desire to purchase and making it possible to award incentives according to the user's behavior.

[0202] "Personal data" refers to information entered by the user regarding hobbies, preferences, physical characteristics, and lifestyle characteristics.

[0203] A "database" is a system that stores and manages collected personal data, clothing information, weather information, fashion information, etc.

[0204] "Clothing image" is photographic data of clothing owned by the user.

[0205] "Means for analyzing image data" refers to technology, such as image recognition algorithms, for extracting information such as color, material, and brand from images of clothing.

[0206] "Weather information" is weekly weather forecast data obtained from an external API.

[0207] "Trend information" is data on the latest fashion trends obtained from external APIs.

[0208] "Fashion suggestions" refers to styling recommendations generated based on personal data, clothing information, weather information, and trend information.

[0209] "Means for providing" refers to a medium for notifying or displaying the generated fashion suggestions to the user, such as a smartphone app or a web interface.

[0210] "Augmented reality technology" is a technology that enables virtual try-on, and refers to the technology that displays virtual elements overlaid on real-world images.

[0211] "Sharing behavior" refers to actions such as users sharing fashion suggestions on social media.

[0212] The "point awarding means" is a system that awards points as a reward according to the user's actions.

[0213] MODE FOR CARRYING OUT THE INVENTION

[0214] System Overview

[0215] The system of the present invention is mainly composed of a server, a user terminal, and a database. This system collects and analyzes personal data and clothing images entered by the user, and then proposes optimal fashion. Specific embodiments for each step are described below.

[0216] Hardware and software configuration

[0217] 1. User Device:

[0218] It consists of mobile devices such as smartphones and tablets.

[0219] Enter and send personal data and clothing images.

[0220] 2. Server:

[0221] It functions as a central processing unit and analyzes data sent by users.

[0222] It also acquires necessary external data (weather information and trend information).

[0223] 3. Database:

[0224] Store and manage collected personal data, image data, weather information, and trend information.

[0225] Technology used

[0226] Image analysis algorithm: Using libraries such as OpenCV and TensorFlow, color information, material, brand, etc. are extracted from clothing images uploaded by users.

[0227] Weather Information API: Use WeatherAPI etc. to get the weekly weather forecast.

[0228] Trend information API: Obtain the latest fashion trends using FashionAPI etc.

[0229] Augmented reality technology: Using Apple's ARKit and Google's ARCore, the company offers a feature that allows users to virtually try on suggested fashion items.

[0230] Detailed System Operation

[0231] 1. Entering and saving personal data

[0232] The user opens the smartphone app and enters their hobbies, preferences, physical characteristics, lifestyle characteristics, etc.

[0233] The terminal transmits the input data to the server, and the server stores the received data in a database.

[0234] 2. Upload and analyze clothing images

[0235] Users take photos of their clothing and upload the images through a smartphone app.

[0236] The server uses image analysis algorithms (e.g., OpenCV) to extract information such as color, material, and brand from the image and store it in a database.

[0237] 3. Obtaining weather and trend information

[0238] The server uses external APIs (such as WeatherAPI and FashionAPI) to obtain weekly weather forecasts and the latest fashion trends, and also stores this information in the database.

[0239] 4. Fashion Proposal Generation

[0240] The server generates optimal fashion suggestions for the user based on personal data, clothing information, weather information, and trend information.

[0241] The suggestions are sent to the user terminal as multiple styles.

[0242] 5. Viewing proposals and virtual try-on

[0243] Users use their smartphones to browse suggested fashion styles.

[0244] Augmented reality technology is used to display the user's chosen style as a virtual try-on.

[0245] 6. Sharing actions and reward points

[0246] When a user takes an action such as sharing suggested fashion on social media, this is tracked and points are awarded.

[0247] Points can be used at online flea markets and other places.

[0248] Specific examples

[0249] 1. Examples of inputting and saving personal data

[0250] "Generate fashion suggestions for a user who likes casual style, is 165cm tall, weighs 55kg, and commutes to the office five days a week."

[0251] 2. Example of uploading and analyzing clothing images

[0252] "Users take a photo of their casual shirt and jeans with their smartphone and upload it through the app."

[0253] 3. Example of fashion proposal generation and selection

[0254] "Suggested fashion styles are displayed based on the user's personal data, clothing data, and acquired weather information (e.g., sunny, temperature 20 degrees)."

[0255] In this way, the present invention makes optimal fashion suggestions to users and provides them with the experience of virtually trying on clothes, thereby increasing user satisfaction and willingness to purchase.

[0256] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0257] Step 1:

[0258] The two steps are to receive personal data entered by the user and to store the entered personal data in a database. Specifically, the user opens a smartphone app and enters personal data such as their hobbies, preferences, physical characteristics, and lifestyle characteristics. The entered data is sent from the smartphone to a server, which receives it and stores it in a database. The input of this step is the user's personal data, and the output is the stored data.

[0259] Step 2:

[0260] The system has two steps: a means for receiving images of the clothes the user owns, and a means for analyzing the received image data and extracting information about the clothes. Specifically, the user takes an image of the clothes they own with their smartphone and uploads the image to the server via the app. The server uses an image analysis algorithm (e.g., OpenCV) to extract information about the clothes, such as color, material, and brand, and the extracted information is stored in a database. The input of this step is the image of the clothes, and the output is the extracted clothing information.

[0261] Step 3:

[0262] This is a means of obtaining weather information and trend information from an external source. Specifically, the server uses an external API (e.g., WeatherAPI, FashionAPI) to obtain the weekly weather forecast and the latest fashion trends. The obtained information is also stored in the database. The input to this step is a request to the external API, and the output is the obtained weather information and trend information.

[0263] Step 4:

[0264] This is a means of generating fashion suggestions based on personal data, clothing information, weather information, and trend information. Specifically, the server uses an AI model to generate optimal fashion suggestions for the user based on the personal data, clothing information, weather information, and trend information stored in the database. The generated suggestions are sent to the user's smartphone as multiple options. The input for this step is various information from the database, and the output is suggested fashion styles.

[0265] Step 5:

[0266] The two steps are to provide the generated fashion suggestions to the user and to use augmented reality technology to display the generated fashion suggestions as a virtual try-on. Specifically, the user browses the suggested fashion styles using a smartphone app. To display the selected style as a virtual try-on, the smartphone camera and AR technology (e.g., ARKit) are used to display the user wearing the outfit on the screen. The input of this step is the fashion suggestions, and the output is a video of the virtual try-on.

[0267] Step 6:

[0268] This is a means of tracking users' sharing behavior and awarding points. Specifically, when a user takes an action such as sharing a suggested fashion style on social media, the server tracks this and awards points appropriately. The points are registered in a database and can be used by the user at online flea markets, etc. The input to this step is data on the user's sharing behavior, and the output is the awarded point information.

[0269] 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.

[0270] MODE FOR CARRYING OUT THE INVENTION

[0271] This invention is an AI platform that makes sustainable fashion recommendations based on a user's individual information and emotions. This platform efficiently collects and analyzes personal data entered by the user and information about the clothing they own, and then recommends optimal fashion styles. In addition, by combining it with an emotion engine, it provides even more personalized fashion recommendations based on the user's emotions. A specific embodiment of the system is shown below.

[0272] System configuration

[0273] The system consists of the following main components:

[0274] 1. User terminal: A device used to input and transmit personal data, emotional information, and clothing image data. Typically, a smartphone or tablet is used.

[0275] 2. Server: A central processing unit that analyzes personal data, clothing information, and emotional information to generate fashion suggestions.

[0276] 3. Database: A data storage system that stores and manages all collected data.

[0277] 4. Emotion Engine: It has the ability to recognize the user's emotions and adapt fashion suggestions based on those emotions.

[0278] Program processing flow

[0279] 1. Collection of personal data

[0280] The user inputs personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics from the terminal.

[0281] The terminal transmits the input data to the server.

[0282] The server analyzes the received personal data and stores it in a database.

[0283] 2. Collection of clothing data

[0284] The user takes a picture of the clothes they are holding with the device.

[0285] The device sends the captured image to the server.

[0286] The server uses image analysis algorithms to extract information about the clothing (color, material, brand, etc.).

[0287] The extracted information is stored in a database.

[0288] 3. Collecting emotional information

[0289] The user takes a photo of their facial expression, records audio, or enters text on their device.

[0290] The device transmits the collected emotion information to the server.

[0291] The emotion engine analyzes this data and recognizes the user's emotions.

[0292] The recognized emotion information is stored in a database.

[0293] 4. Obtaining weather and trend information

[0294] The server obtains weekly weather forecasts and trend information through an external API.

[0295] The information obtained is also stored in a database.

[0296] 5. Fashion Proposal Generation

[0297] The server generates optimal fashion suggestions for the user based on personal data, clothing data, emotional information, weather information, and trend information.

[0298] The generated proposals are sent to the user's terminal as multiple options.

[0299] 6. Selection and Display of Proposals

[0300] The user selects one of the fashion suggestions provided by the terminal.

[0301] The terminal generates an image of the outfit in the selected fashion style and displays it to the user.

[0302] 7. Provision of Additional Features

[0303] Based on the generated outfit image, users can take actions such as listing the outfit at a flea market, sharing it on social media, and receiving points.

[0304] The server tracks these actions and awards incentives such as points as appropriate.

[0305] Specific examples of processing

[0306] Example 1: Entering and saving personal data

[0307] User A opens the smartphone app and enters his / her hobbies and preferences (e.g., he / she likes casual style), physical characteristics (e.g., he / she is 165 cm tall and weighs 55 kg), and lifestyle characteristics (e.g., he / she commutes to the office five days a week). The device sends this data to the server, which then stores the received data in a database.

[0308] Example 2: Uploading and analyzing clothing images

[0309] User A takes a photo of his casual shirt and jeans with his smartphone. The device sends the image to the server, which analyzes it and extracts the shirt's color, material, and brand information. This information is then stored in a database.

[0310] Example 3: Collecting and analyzing emotional information

[0311] User A takes a photo of their facial expression with their smartphone. The device sends the image data to the server, where the emotion engine analyzes the data and recognizes User A's emotions, such as "happy" or "sad." The recognized emotional information is stored in a database.

[0312] Example 4: Generating and selecting fashion suggestions

[0313] The server generates multiple fashion styles based on user A's personal data, clothing data, acquired weather information (e.g., sunny, temperature 20 degrees), trend information, and recognized emotional information. Suggested styles are displayed to user A, and user A selects one. Based on the selected style, an outfit image is generated and displayed on the smartphone.

[0314] Example 5: Using additional features

[0315] User A shares the generated outfit image on a social networking site. The server tracks this action and awards points to User A. User A can use the points to purchase new clothes at an online flea market.

[0316] In this way, the present invention makes it possible to propose optimal fashion based on the user's individual information and emotional information, thereby realizing a sustainable fashion lifestyle.

[0317] The processing flow will be explained below.

[0318] Program processing flow and specific explanation

[0319] 1. Collection of personal data

[0320] Step 1:

[0321] The user inputs personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics from the terminal.

[0322] Specific operation: The user enters information such as "height 165cm," "weight 55kg," and "likes casual style" into the app's input form.

[0323] Step 2:

[0324] The terminal transmits the entered personal data to the server.

[0325] What it does: The app sends the collected data to the server as an HTTP request.

[0326] Step 3:

[0327] The server stores the received personal data in a database.

[0328] What happens: The server receives the data, formats it appropriately, and stores it in the user profile table in the database.

[0329] 2. Collection of clothing data

[0330] Step 1:

[0331] The user takes a picture of the clothes they are holding with the device.

[0332] What happens: A user uses the app to take a photo of an item of clothing.

[0333] Step 2:

[0334] The terminal transmits the captured image data to the server.

[0335] What happens: The app encodes the image data and sends it to the server.

[0336] Step 3:

[0337] The server uses image analysis algorithms to extract information about the clothing (color, material, brand, etc.).

[0338] Specific operation: The server inputs the received image into the machine learning model and obtains the analysis result (e.g., "red cotton shirt" or "blue denim jeans").

[0339] Step 4:

[0340] The server stores the extracted clothing information in a database.

[0341] Specific operation: The server saves the analysis results in the clothing information table of the database.

[0342] 3. Collecting emotional information

[0343] Step 1:

[0344] The user takes a photo of their facial expression, records audio, or enters text on their device.

[0345] Specific actions: The user takes a photo of their facial expression with their smartphone camera, records audio using the microphone, or enters text about their mood.

[0346] Step 2:

[0347] The device transmits the collected emotional information to a server.

[0348] Specific operation: Send captured image data, recorded audio data, or text data to the server.

[0349] Step 3:

[0350] The emotion engine analyzes this data and recognizes the user's emotions.

[0351] What it does: The emotion engine uses image analysis, audio analysis, or natural language processing to determine the user's emotion (e.g., "happiness," "sadness," "surprise," etc.).

[0352] Step 4:

[0353] The server stores the recognized emotion information in a database.

[0354] Specific operation: The recognized emotion information is added or updated to the user profile in the database.

[0355] 4. Obtaining weather and trend information

[0356] Step 1:

[0357] The server retrieves the weekly weather forecast through an external API.

[0358] Specific operation: The server sends a request to the weather API to obtain weekly weather data (e.g., "sunny, temperature 20 degrees").

[0359] Step 2:

[0360] The server obtains trend information through an external API.

[0361] Specific operation: The server sends a request to the trend information API to obtain the latest fashion trend information (for example, "casual style is in fashion").

[0362] 5. Fashion Proposal Generation

[0363] Step 1:

[0364] The server generates fashion suggestions based on personal data, clothing data, emotional information, weather information, and trend information.

[0365] How it works: The server aggregates all the data and uses machine learning algorithms to generate the best fashion style for the user (e.g., "a casual shirt and jeans combination").

[0366] Step 2:

[0367] The server transmits the generated fashion suggestions to the terminal.

[0368] What it does: The server sends the suggestions in JSON format to the device, and the app reads and displays them.

[0369] 6. Selection and Display of Proposals

[0370] Step 1:

[0371] The user selects one of the fashion suggestions provided by the terminal.

[0372] Specific behavior: The user taps to select one of the suggestions displayed in the app.

[0373] Step 2:

[0374] The terminal generates an image of the outfit in the selected fashion style and displays it to the user.

[0375] What it does: The app generates a visual image of the outfit based on the selected style and displays it to the user.

[0376] 7. Provision of Additional Features

[0377] Step 1:

[0378] Based on the generated outfit image, users can take actions such as listing the outfit at a flea market, sharing it on social media, and receiving points.

[0379] Specific operation: The user presses the "Share to social media" button within the app to post their outfit.

[0380] Step 2:

[0381] The server tracks these actions and awards incentives such as points as appropriate.

[0382] Specific operation: The server analyzes the user's action log and processes the points to add to the user's account.

[0383] The above is a specific processing flow that combines the emotion engine of the present invention. We have explained in detail how the user, terminal, and server operate at each step. This makes it possible to propose more personalized fashion based on the user's emotional information, helping to realize a sustainable fashion lifestyle.

[0384] Example 2

[0385] 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."

[0386] Conventional fashion suggestion systems make suggestions based on a user's personal data and clothing information, but they have not yet realized personalized suggestions based on the user's emotional information. It is also difficult to efficiently incorporate weather and trend information and reflect it in suggestions. This makes it difficult to make optimal fashion suggestions for users. Therefore, the objective of this invention is to provide more accurate and sustainable fashion suggestions by comprehensively considering a user's personal data, clothing information, emotional information, weather information, and trend information.

[0387] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0388] In this invention, the server includes means for receiving personal data entered by the user, means for saving the entered personal data in a database, means for receiving images of clothes owned by the user, means for analyzing the received image data and extracting clothing information, means for saving the extracted clothing information in the database, means for collecting user emotion information, means for analyzing the collected emotion information and recognizing emotions, means for saving the recognized emotion information in the database, means for acquiring weather information and trend information from outside, means for generating fashion suggestions based on the personal data, clothing information, emotion information, weather information, and trend information, and means for providing the generated fashion suggestions to the user. This makes it possible to provide personalized fashion suggestions that comprehensively incorporate the user's emotion information and external information.

[0389] "Personal data" refers to individual information about a user, such as the user's hobbies, preferences, physical characteristics, and lifestyle characteristics.

[0390] A "database" is an information system for storing and managing collected data.

[0391] "Clothing image" is image data of a photograph of clothing owned by the user.

[0392] "Image data" is data that represents a photographed image of clothing in digital format.

[0393] "Image analysis" is the process of extracting information such as clothing color, material, and brand from the received image data.

[0394] "Emotion information" is data related to emotions obtained from the user's facial expressions, voice, text input, etc.

[0395] The "emotion engine" is a system that analyzes collected emotional information and recognizes the user's emotions.

[0396] "Weather information" is information about the weather obtained from external weather data.

[0397] "Trend information" refers to information about trends obtained from the fashion industry, social media, etc.

[0398] "Fashion suggestions" are suggestions for fashion styles generated based on personal data, clothing information, emotional information, weather information, and trend information.

[0399] A "wearing image" is a visual image of what the user will look like wearing the clothes, generated based on the selected fashion suggestion.

[0400] "Listing at a flea market" refers to listing an item on an online marketplace based on a user-selected outfit image.

[0401] "SNS posting" means sharing the selected outfit image on a social networking service.

[0402] "Points awarded" are reward points given to users when they perform specific actions within the system.

[0403] MODE FOR CARRYING OUT THE INVENTION

[0404] This invention is a system for proposing sustainable fashion based on a user's individual information and emotions. This system efficiently collects and analyzes personal data and clothing information entered by the user, and proposes optimal fashion styles. In addition, by combining it with an emotion engine, it provides even more personalized fashion suggestions based on the user's emotions.

[0405] System configuration

[0406] The system consists of the following main components:

[0407] 1. User terminal: A device used to input and transmit personal data, emotional information, and clothing image data. Typically, a smartphone or tablet is used.

[0408] 2. Server: A central processing unit that analyzes personal data, clothing information, and emotional information to generate fashion suggestions.

[0409] 3. Database: An information system that stores and manages all collected data.

[0410] 4. Emotion Engine: A system that has the ability to recognize the user's emotions and adapt fashion suggestions based on those emotions.

[0411] Hardware and Software Examples

[0412] 1. User device: Smartphone (e.g., iPhone), tablet (e.g., iPad)

[0413] 2. Server: High-performance server (e.g. AWS EC2)

[0414] 3. Database: Relational database (e.g. MySQL)

[0415] 4. Emotion engine: Emotion analysis software (e.g., IBM Watson Tone Analyzer)

[0416] 5. Image analysis algorithm: Image analysis API (e.g. Azure Computer Vision API)

[0417] 6. External API: Weather forecast API (e.g. OpenWeatherMap API)

[0418] Program processing

[0419] When a user launches the dedicated app on their device, they can enter personal data such as their hobbies, preferences, physical characteristics such as height and weight, and lifestyle characteristics. For example, a user might enter data such as "I like casual style, I'm 165cm tall, I weigh 55kg, and I work five days a week."

[0420] The device sends this data to the server's API endpoint. The server validates the received data, confirms that it is in the correct format, and then stores it in a database. The user can also take a photo of their clothing with the device and send the image data to the server. The server uses image analysis algorithms to extract information about the clothing's color, material, and brand, and stores it in a database.

[0421] Users can use their devices to capture facial expressions, record voice, and input text to collect emotional information. The devices then send this emotional data to a server, which then uses an emotion engine to analyze it and recognize the user's emotions. This information is also stored in a database.

[0422] In addition, the server obtains weather forecasts and trend information through external APIs. The obtained information is also stored in a database, and fashion suggestions are generated based on personal data, clothing information, emotional information, weather information, and trend information. Using a generative AI model (e.g., GPT-4), optimal fashion suggestions are generated for the user by inputting a prompt such as, "Please suggest autumn casual fashion that suits the user's height and weight."

[0423] The generated fashion suggestions are sent to the user's device as multiple options, and the user selects one of these suggestions. Based on the selected fashion style, the device generates an outfit image and displays it to the user. For example, a realistic outfit image can be generated using 3D modeling technology.

[0424] Specific examples

[0425] An example of a prompt sentence is, "Please suggest a casual style suitable for sunny days for the user, who is 165 cm tall and weighs 55 kg." Based on the input data and acquired information, the system will make optimal fashion suggestions for the user.

[0426] Users can share the generated outfit images on social media, and the server tracks this action and awards points, which users can use to purchase new clothes at online flea markets.

[0427] In this way, the present invention makes it possible to propose optimal fashion based on the user's individual information and emotional information, thereby realizing a sustainable fashion lifestyle.

[0428] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0429] The flow of this system's program processing

[0430] Step 1: Collecting personal data

[0431] Input: The user inputs personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics from the device.

[0432] Processing: The device validates the entered personal data and ensures that it is in the correct format. For example, a user enters data such as "I like casual style," "I'm 165cm tall," "I weigh 55kg," and "I commute to the office five days a week."

[0433] Output: The device sends this personal data to the server, which analyzes it and stores it in a database.

[0434] Step 2: Collecting clothing data

[0435] Input: The user takes a picture of the clothes they are holding on their device, for example, a casual shirt and jeans.

[0436] Processing: The device sends the captured image data to a server. For example, the device sends the image data to an image analysis API to extract color, material, and brand information.

[0437] Output: The server stores the extracted clothing information in a database.

[0438] Step 3: Collecting emotional information

[0439] Input: The user uses the device to capture facial expressions, record audio, or input text.

[0440] Processing: The device sends these emotion data to the server. For example, a user takes a picture of themselves smiling with their smartphone.

[0441] Output: The server analyzes the user's emotion using the emotion engine and recognizes the user's emotion as "happy." This information is stored in the database.

[0442] Step 4: Get weather and trend information

[0443] Input: The server periodically calls an external API to obtain weather and trend information.

[0444] Processing: The server uses a weather forecast API to retrieve, for example, the "weekly weather forecast." It also scrapes trend information from fashion-related websites and social media.

[0445] Output: Save the obtained weather and trend information in a database.

[0446] Step 5: Generate fashion suggestions

[0447] Input: Personal data, clothing data, emotional information, weather information, and fashion information are stored in the database.

[0448] Processing: The server inputs a prompt into a generative AI model (e.g., GPT-4) to generate optimal fashion suggestions for the user. For example, the prompt might be, "Please suggest casual autumn fashion that suits the user's height and weight."

[0449] Output: The generated fashion suggestions are sent to the user's device as multiple options.

[0450] Step 6: Select and view suggestions

[0451] Input: The user selects one of the suggestions provided by the device.

[0452] Processing: The device generates a realistic image of the selected fashion style, using 3D modeling technology to create a realistic image of the outfit.

[0453] Output: The generated outfit image is displayed on the device.

[0454] Step 7: Providing additional functionality

[0455] Input: User shares outfit image on social media.

[0456] Processing: The server tracks the sharing action and gives points to the user. For example, when a user clicks the post button on a social networking site, the server records that information.

[0457] Output: The points are added to the user's account, and the user can use them to purchase new clothes at the online flea market.

[0458] (Application example 2)

[0459] 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."

[0460] The shopping experience in physical stores is often not personalized enough for the user because it is often conducted without taking into account information such as the user's clothing, personal preferences, weather, and emotions. This leads to a problem of reduced satisfaction when making purchasing decisions. Furthermore, trying on clothes in physical stores is often time-consuming and inefficient. Therefore, new methods are needed to ensure users have an effective shopping experience.

[0461] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0462] In this invention, the server includes means for receiving personal data and emotional information entered by the user, means for analyzing the data and saving the data in a database, means for receiving images of the user's clothing, analyzing the images to extract and save information about the clothing, means for acquiring weather information and trend information, means for generating fashion suggestions based on the personal data, clothing information, emotional information, weather information, and trend information, means for providing the generated fashion suggestions to the user, means for the user to have a smart try-on experience in a physical store, means for providing virtual try-on experiences in cooperation with a smart mirror installed in the physical store, means for collecting the user's emotional information in real time and reflecting it in fashion suggestions, and means for suggesting optimal fashion items in real time, thereby enabling the user to have a personalized and efficient shopping experience in a physical store.

[0463] "Personal data" refers to data provided by a user, including hobbies, preferences, physical characteristics, lifestyle characteristics, emotional information, and the like.

[0464] "Clothing image" is image data of clothing owned by the user.

[0465] "Weather information" is weekly weather forecast information obtained from an external weather database.

[0466] "Trend information" refers to information about current fashion trends.

[0467] "Emotion information" is data related to emotions acquired from the user's facial expressions, voice, text, etc.

[0468] "Fashion suggestions" are suggestions for fashion styles generated based on personal data, clothing information, weather information, trend information, and emotional information.

[0469] The "smart try-on experience" is a virtual try-on process that users go through in a physical store using a smart mirror.

[0470] A "smart mirror" is a mirror-shaped device that is installed in a physical store and helps users try on clothes virtually.

[0471] "Real-time suggestions" is a system function that instantly suggests fashion items based on the user's current information.

[0472] "Point awarding" refers to providing points as an incentive for a user's specific behavior.

[0473] "SNS posting" refers to the act of a user sharing the created outfit image on a social networking service.

[0474] "Listing at a flea market" refers to the act of a user listing unwanted clothing on a flea market application.

[0475] The present invention is a system for proposing sustainable fashion based on individual data and emotional information of a user. This system is configured using the following hardware and software.

[0476] Hardware Configuration

[0477] User device: A smartphone or tablet through which the user inputs and transmits personal data, emotional information, and clothing image data.

[0478] Server: A central processing unit used to analyze received data and generate fashion suggestions.

[0479] Smart mirror: A device installed in a physical store that allows virtual try-on of clothes.

[0480] Software Configuration

[0481] Frontend: A mobile application based on React Native that provides the user interface and manages data entry and display.

[0482] Backend: A server-side application built with Node.js and Express that analyzes data and generates fashion suggestions.

[0483] Image analysis engine: Uses OpenCV to analyze images of clothing and extract information such as color, material, and brand.

[0484] Sentiment analysis engine: Analyzes user sentiment using Google Cloud Vision AI.

[0485] Database: MongoDB is used to store and manage various data.

[0486] External API: Used to get weather and trend information from OpenWeatherMap and Trendyol.

[0487] System processing overview

[0488] User data entry and collection

[0489] The user enters personal data (hobbies, preferences, physical characteristics, lifestyle characteristics) from the device and sends it to the server. The server receives this data and stores it in a database. For example, a user enters data such as "I like casual style," "I'm 165 cm tall and weigh 55 kg," and "I commute to the office five days a week."

[0490] Clothing data capture and analysis

[0491] The user takes a photo of the clothes they are wearing with their device and sends it to the server. The image data is analyzed by the server, and the color, material, and brand information of the clothes are extracted. For example, the user can take a photo of a "casual shirt" or "jeans."

[0492] Collecting emotional information

[0493] The user takes a photo of their facial expression on the device and sends it to the server, where the emotion analysis engine analyzes the user's emotions (e.g., "happy," "sad," etc.).

[0494] Retrieving External Data

[0495] The server uses an external API to obtain weekly weather and trend information, which is then stored in a database.

[0496] Fashion proposal generation

[0497] Based on the collected personal data, clothing data, emotional information, weather information, and trend information, the AI ​​model generates optimal fashion suggestions, which are then sent to the user's device as multiple options.

[0498] Virtual try-on experience

[0499] When users visit a physical store, they can virtually try on clothes by connecting their smart mirror to their device. The mirror reflects the user's individual data and provides a real-time virtual try-on experience.

[0500] Examples of prompt statements

[0501] Enter your personal data: "Please enter your physical characteristics, hobbies, preferences, and lifestyle characteristics."

[0502] Collecting emotional information: "Facial expressions are captured on camera to analyze current emotions."

[0503] Upload clothing images: "Upload images of your clothing here."

[0504] In this way, the present invention generates optimal fashion suggestions based on the user's personal and emotional information, providing a sustainable shopping experience.

[0505] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0506] Step 1:

[0507] The user enters personal data (hobbies, preferences, physical characteristics, lifestyle characteristics) from their device and sends it to the server. At this time, the data entered may include "I like casual style," "I'm 165cm tall," "I weigh 55kg," and "I commute to the office five days a week." The server analyzes the received data and stores it in a MongoDB database. To store the input data in key-value format, the data is converted to JSON format as part of data processing.

[0508] Step 2:

[0509] A user takes a photo of their clothing on their device and sends it to the server. For example, they upload an image of a casual shirt or jeans. The server uses OpenCV to analyze the received image data and extract information about the clothing (color, material, brand, etc.). The extracted information is stored in a database. The input is image data, and the output is analyzed clothing information.

[0510] Step 3:

[0511] The user takes a photo of their facial expression on their device and sends the image data to the server. The server uses Google Cloud Vision AI to analyze the image and recognize the user's emotional information (e.g., "happy" or "sad"). The recognized emotional information is stored in a database. The input is image data of the facial expression, and the output is text information about the recognized emotion.

[0512] Step 4:

[0513] The server uses external APIs (OpenWeatherMap, Trendyol) to obtain the weekly weather forecast and trend information. This data is also stored in the database. The input is the API request, and the output is the obtained weather forecast and trend information. The weather information includes temperature and weather conditions, and the trend information shows current fashion trends.

[0514] Step 5:

[0515] The server uses a generative AI model to generate fashion suggestions based on personal data, clothing information, emotional information, weather information, and trend information. For example, it may suggest a casual style suitable for a sunny 20-degree day. The generated suggestions are sent to the user's device as multiple options. The input is the aforementioned multiple datasets, and the output is multiple fashion suggestions.

[0516] Step 6:

[0517] The user selects one of the fashion suggestions provided by the terminal. Based on the selected suggestion, the terminal generates an outfit image. This outfit image is displayed to the user in real time. The input is the selected fashion suggestion, and the output is the generated outfit image.

[0518] Step 7:

[0519] When a user visits a physical store, the smart mirror and device are connected. Personal data stored on the device is sent to the mirror, which then provides a virtual try-on experience that reflects that data. For example, the user can check in real time on the smart mirror whether a particular shirt looks good on them. The input is personal data and clothing data, and the output is a real-time virtual try-on image.

[0520] Step 8:

[0521] When a user shares the generated outfit image on SNS, the action is sent to the server and points are awarded. The server tracks this and manages the points system. The points can be used for the next purchase, providing an incentive to the user. The input is the SNS sharing information and the output is the awarded points.

[0522] 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.

[0523] 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.

[0524] 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.

[0525] [Second embodiment]

[0526] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0527] 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.

[0528] 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).

[0529] 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.

[0530] 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.

[0531] 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).

[0532] 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.

[0533] 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.

[0534] 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.

[0535] 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.

[0536] 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.

[0537] 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."

[0538] MODE FOR CARRYING OUT THE INVENTION

[0539] This invention is an AI platform that proposes sustainable fashion based on the user's individual information. This platform efficiently collects and analyzes personal data and clothing information entered by the user, and proposes optimal fashion styles. A specific embodiment of the system is shown below.

[0540] System configuration

[0541] The system consists of the following main components:

[0542] 1. User terminal: A device used to input and send personal data and clothing image data. Typically, a smartphone or tablet is used.

[0543] 2. Server: A central processing unit that analyzes personal data and clothing information and generates fashion suggestions.

[0544] 3. Database: A data storage system that stores and manages all collected data.

[0545] Program processing flow

[0546] 1. Collection of personal data

[0547] The user inputs personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics from the terminal.

[0548] The terminal transmits the input data to the server.

[0549] The server analyzes the received personal data and stores it in a database.

[0550] 2. Collection of clothing data

[0551] The user takes a picture of the clothes they are holding with the device.

[0552] The device sends the captured image to the server.

[0553] The server uses image analysis algorithms to extract information such as the color, material, and brand of the clothing.

[0554] The extracted information is stored in a database.

[0555] 3. Obtaining weather and trend information

[0556] The server obtains weekly weather forecasts and trend information through an external API.

[0557] The information obtained is also stored in a database.

[0558] 4. Fashion Proposal Generation

[0559] The server generates optimal fashion suggestions for the user based on personal data, clothing data, weather information, and trend information.

[0560] The generated suggestions are sent to the user's terminal as multiple options.

[0561] 5. Selection and display of proposals

[0562] The user selects one of the fashion suggestions provided by the terminal.

[0563] The terminal generates an image of the outfit in the selected fashion style and displays it to the user.

[0564] 6. Provision of Additional Features

[0565] Based on the generated outfit image, users can take actions such as listing the item at a flea market, sharing it on social media, and earning points.

[0566] The server tracks these actions and awards incentives such as points as appropriate.

[0567] Specific examples of processing

[0568] Example 1: Entering and saving personal data

[0569] User A opens the smartphone app and enters his / her hobbies and preferences (e.g., he / she likes casual style), physical characteristics (e.g., he / she is 165 cm tall and weighs 55 kg), and lifestyle characteristics (e.g., he / she commutes to the office five days a week). The device sends this data to the server, which then stores the received data in a database.

[0570] Example 2: Uploading and analyzing clothing images

[0571] User A takes a photo of his casual shirt and jeans with his smartphone. The device sends the image to the server, which analyzes it and extracts the shirt's color, material, and brand information. This information is then stored in a database.

[0572] Example 3: Generating and selecting fashion suggestions

[0573] The server generates multiple fashion styles based on user A's personal data, clothing data, acquired weather information (e.g. sunny, temperature 20 degrees) and trend information. Suggested styles are displayed to user A, and user A selects one. Based on the selected style, an outfit image is generated and displayed on the smartphone.

[0574] Example 4: Using additional features

[0575] User A shares the created outfit image on a social networking site. The server tracks this action and awards points to User A. User A can use the points to purchase new clothes at an online flea market.

[0576] In this way, the present invention makes it possible to provide optimal fashion suggestions based on the user's individual information, thereby realizing a sustainable fashion lifestyle.

[0577] The processing flow will be explained below.

[0578] Program processing flow and specific explanation

[0579] 1. Collection of personal data

[0580] Step 1:

[0581] The user inputs personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics from the terminal.

[0582] Specific operation: The user enters information such as "height 165cm," "weight 55kg," and "likes casual style" into the app's input form.

[0583] Step 2:

[0584] The terminal transmits the entered personal data to the server.

[0585] What it does: The app sends the collected data to the server as an HTTP request.

[0586] Step 3:

[0587] The server stores the received personal data in a database.

[0588] What happens: The server receives the data, formats it appropriately, and stores it in the user profile table in the database.

[0589] 2. Collection of clothing data

[0590] Step 1:

[0591] The user takes a picture of the clothes they are holding with the device.

[0592] What happens: A user uses the app to take a photo of an item of clothing.

[0593] Step 2:

[0594] The terminal transmits the captured image data to the server.

[0595] What happens: The app encodes the image data and sends it to the server.

[0596] Step 3:

[0597] The server uses image analysis algorithms to extract information about the clothing (color, material, brand, etc.).

[0598] Specific operation: The server inputs the received image into the machine learning model and obtains the analysis result (e.g., "red cotton shirt" or "blue denim jeans").

[0599] Step 4:

[0600] The server stores the extracted clothing information in a database.

[0601] Specific operation: The server saves the analysis results in the clothing information table of the database.

[0602] 3. Obtaining weather and trend information

[0603] Step 1:

[0604] The server retrieves the weekly weather forecast through an external API.

[0605] Specific operation: The server sends a request to the weather API to obtain weekly weather data (e.g., "sunny, temperature 20 degrees").

[0606] Step 2:

[0607] The server obtains trend information through an external API.

[0608] Specific operation: The server sends a request to the trend information API to obtain the latest fashion trend information (for example, "casual style is in fashion").

[0609] 4. Fashion Proposal Generation

[0610] Step 1:

[0611] The server generates fashion suggestions based on personal data, clothing data, weather information, and trend information.

[0612] How it works: The server aggregates all the data and uses machine learning algorithms to generate the best fashion style for the user (e.g., "a casual shirt and jeans combination").

[0613] Step 2:

[0614] The server transmits the generated fashion suggestions to the terminal.

[0615] What it does: The server sends the suggestions in JSON format to the device, and the app reads and displays them.

[0616] 5. Selection and display of proposals

[0617] Step 1:

[0618] The user selects one of the fashion suggestions provided by the terminal.

[0619] Specific behavior: The user taps to select one of the suggestions displayed in the app.

[0620] Step 2:

[0621] The terminal generates an image of the outfit in the selected fashion style and displays it to the user.

[0622] What it does: The app generates a visual image of the outfit based on the selected style and displays it to the user.

[0623] 6. Provision of Additional Features

[0624] Step 1:

[0625] Based on the generated outfit image, users can take actions such as listing the outfit at a flea market, sharing it on social media, and receiving points.

[0626] Specific operation: The user presses the "Share to social media" button within the app to post their outfit.

[0627] Step 2:

[0628] The server tracks these actions and awards incentives such as points as appropriate.

[0629] Specific operation: The server analyzes the user's action log and processes the points to add to the user's account.

[0630] The above is a specific processing flow for implementing the present invention. We have explained in detail how the user, terminal, and server operate in each step. This allows us to build an optimal system for realizing a sustainable fashion life.

[0631] Example 1

[0632] 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."

[0633] The present invention aims to efficiently and effectively suggest sustainable fashion based on individual user information. It also aims to provide a system that provides optimal fashion suggestions by analyzing the user's clothing information in detail and combining it with current weather and fashion trends. Furthermore, the system allows users to easily use the suggested fashion styles to access additional features such as posting on social media, listing at flea markets, and earning points.

[0634] 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.

[0635] In this invention, the server includes a means for transmitting data and images input by the user to the server, a means for the server to extract clothing information using an image analysis algorithm, and a means for generating suggestions using a generative AI model. This allows for rapid analysis of personal data and clothing information, enabling optimal fashion suggestions to be made to the user. Furthermore, by obtaining weather and fashion trends via an external API, suggestions based on real-time information are possible. Furthermore, based on the outfit image selected by the user, the system can list the item at a flea market, post on social media, and award points, and these actions are tracked to provide incentives, improving user convenience.

[0636] "User" means an individual or organization that uses the system.

[0637] A "terminal" is a device used by a user, such as a smartphone or tablet.

[0638] A "server" is a computer device that performs central processing such as analyzing and storing data and generating fashion suggestions.

[0639] A "data storage device" is a system that stores and manages personal data, clothing information, weather information, fashion information, etc.

[0640] An "image analysis algorithm" is a software program that analyzes received image data to extract information.

[0641] A "generative AI model" is an algorithmic model that uses artificial intelligence to generate fashion suggestions for users.

[0642] A "prompt" refers to the input information or question given to a generative AI model.

[0643] "Fashion suggestions" are recommendations for fashion styles that suit the user.

[0644] "Outfit images" are images or graphics that visually represent the proposed fashion style.

[0645] An "external API" is an interface for obtaining information in collaboration with external services.

[0646] "Weather information" refers to meteorological data such as the weekly weather forecast and temperature.

[0647] "Trend information" is data about current fashion trends.

[0648] "Flea market listing" refers to the act of a user listing unwanted clothing or other items on an online marketplace.

[0649] "SNS posting" refers to the act of a user sharing a proposed fashion style or outfit image on a social networking service.

[0650] "Point awarding" is a system that provides points as an incentive for user actions.

[0651] "Action tracking" means tracking and recording user behavior.

[0652] The present invention is an AI platform that proposes sustainable fashion based on individual user information. A specific embodiment of the system is described below.

[0653] The system mainly consists of three main components: a user terminal, a server, and a data storage device. The user terminal is typically a smartphone or tablet, and is used to input and transmit personal data and clothing image data. The server is a central processing unit that analyzes personal data and clothing information and generates fashion suggestions. The data storage device is a system that stores and manages all collected data.

[0654] First, a user uses a smartphone or tablet to input personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics. For example, the user inputs information such as that they like casual clothing, are 165 cm tall, weigh 55 kg, and commute to the office five days a week. The device sends this data to a server, which then analyzes the received data and stores it in a data storage device.

[0655] Next, the user takes a picture of the clothes they are wearing with their device. For example, they can take a picture of a casual shirt and jeans. The device sends the image to the server, which then uses an image analysis algorithm (e.g., Amazon Rekognition, Google Cloud Vision) to extract information about the clothes, such as color, material, and brand. The extracted information is then stored on a data storage device.

[0656] In addition, the server obtains weekly weather forecasts and trend information through external APIs (e.g., OpenWeather, FashionAPI). The obtained information is also stored in the data storage device. For example, the weather forecast for next week is sunny with a temperature of 20 degrees.

[0657] This system uses a generative AI model (e.g., GPT-3) to generate fashion suggestions. The server inputs a prompt to generate the optimal fashion style based on collected personal data, clothing data, weather information, and trend information, and the generative AI model outputs the suggestion. The prompt uses the following text: "Provide fashion suggestions based on the personal data entered by User A (hobbies and preferences: casual style, height 165 cm, weight 55 kg, commute 5 days a week), an image of a casual shirt and jeans, weather information (sunny, temperature 20 degrees), and trend information."

[0658] The user's device receives the fashion suggestions sent from the server and displays them to the user. The user selects one of the suggestions provided, and an outfit image based on the selected style is generated and displayed on the smartphone. The user can also take actions based on the generated outfit image, such as listing it at a flea market, sharing it on social media, or receiving points. The server tracks these actions and awards incentives such as points as appropriate.

[0659] In this way, the present invention can make optimal fashion suggestions based on the user's individual information, and can comprehensively utilize the user's clothing, the latest weather information, trend information, etc. In addition, the user can further enhance their fashion life by using additional functions.

[0660] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0661] Step 1:

[0662] The user uses a smartphone or tablet to enter personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics. Specifically, the user enters information such as "I like casual style," "I'm 165cm tall," "I weigh 55kg," and "I commute to the office five days a week" into the app's input form.

[0663] Input data: User's hobbies, preferences, physical characteristics, lifestyle characteristics, etc.

[0664] Output data: Personal data sent from the device to the server

[0665] Step 2:

[0666] The device sends the entered personal data to the server, which analyzes the received personal data, converts it into an appropriate format, and stores it in a data storage device, where the personal data is stored in a structured format.

[0667] Input data: Personal data sent from the device

[0668] Output data: Personal data stored in the data storage device

[0669] Step 3:

[0670] The user takes a picture of their clothing with their smartphone, or uses the app's camera function to take a picture of their belongings, such as a casual shirt or jeans.

[0671] Input data: Images of clothing taken by the user

[0672] Output data: Image file of the clothing saved on the device

[0673] Step 4:

[0674] The device sends the captured image to a server, which uses an image analysis algorithm (e.g., Amazon Rekognition, Google Cloud Vision) to extract information about the clothing, such as its color, material, and brand. The extracted information is then stored on a data storage device.

[0675] Input data: Clothing images sent from the device

[0676] Output data: Clothing information (color, material, brand, etc.) stored in the data storage device

[0677] Step 5:

[0678] The server uses external APIs (e.g., OpenWeather, FashionAPI) to obtain weekly weather forecasts and trend information. The obtained information is also stored in the data storage device.

[0679] Input data: Weather and trend information requests obtained from external APIs

[0680] Output data: Weather information and trend information stored in a data storage device

[0681] Step 6:

[0682] The server generates fashion suggestions using a generative AI model (e.g., GPT-3) based on personal data, clothing data, weather information, and trend information. The generated suggestions are sent to the device as multiple options. The server inputs the following prompt into the generative AI model: "Provide fashion suggestions based on the personal data entered by User A (hobbies and preferences: casual style, height 165 cm, weight 55 kg, commutes 5 days a week), images of casual shirts and jeans, weather information (sunny, temperature 20 degrees), and trend information."

[0683] Input data: personal data, clothing data, weather information, fashion information

[0684] Output data: Fashion suggestions output by the generative AI model

[0685] Step 7:

[0686] The terminal displays the fashion suggestions received from the server to the user, who then selects one of the suggestions.

[0687] Input data: Fashion suggestions received from the server

[0688] Output data: The fashion style selected by the user

[0689] Step 8:

[0690] The device generates an outfit image based on the selected style and displays it to the user. The generated outfit image is displayed on the user's smartphone screen.

[0691] Input data: Fashion style selected by the user

[0692] Output data: Image of the garment displayed on the device

[0693] Step 9:

[0694] Users can share the created outfit images on social media or put them up for sale at a flea market. The server tracks these actions and awards points to users, which can be used to make purchases at the online flea market.

[0695] Input data: User actions (SNS sharing, flea market listing, etc.)

[0696] Output data: Actions recorded by the server and points awarded

[0697] (Application example 1)

[0698] 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."

[0699] Conventional fashion suggestion systems suggest appropriate fashion styles based on a user's individual information, but the suggestions lack practicality and a sense of actually trying on the clothes. Furthermore, the fashion suggestions are often unsustainable. Furthermore, the functionality for tracking user behavior and providing incentives is insufficient. Therefore, users do not get the feeling of actually trying on the suggested clothes, making it difficult to increase their motivation to purchase.

[0700] 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.

[0701] In this invention, the server includes means for receiving personal data entered by the user, means for saving the entered personal data in a database, means for receiving images of clothing owned by the user, means for analyzing the received image data and extracting clothing information, means for saving the extracted clothing information in a database, means for externally acquiring weather information and trend information, means for generating fashion suggestions based on the personal data, clothing information, weather information, and trend information, means for providing the generated fashion suggestions to the user, means for using augmented reality technology to display the generated fashion suggestions as a virtual try-on, and means for tracking the user's shared behavior and awarding points. This allows the user to feel as if they are actually trying on the suggested clothing, increasing their desire to purchase and making it possible to award incentives according to the user's behavior.

[0702] "Personal data" refers to information entered by the user regarding hobbies, preferences, physical characteristics, and lifestyle characteristics.

[0703] A "database" is a system that stores and manages collected personal data, clothing information, weather information, fashion information, etc.

[0704] "Clothing image" is photographic data of clothing owned by the user.

[0705] "Means for analyzing image data" refers to technology, such as image recognition algorithms, for extracting information such as color, material, and brand from images of clothing.

[0706] "Weather information" is weekly weather forecast data obtained from an external API.

[0707] "Trend information" is data on the latest fashion trends obtained from external APIs.

[0708] "Fashion suggestions" refers to styling recommendations generated based on personal data, clothing information, weather information, and trend information.

[0709] "Means for providing" refers to a medium for notifying or displaying the generated fashion suggestions to the user, such as a smartphone app or a web interface.

[0710] "Augmented reality technology" is a technology that enables virtual try-on, and refers to the technology that displays virtual elements overlaid on real-world images.

[0711] "Sharing behavior" refers to actions such as users sharing fashion suggestions on social media.

[0712] The "point awarding means" is a system that awards points as a reward according to the user's actions.

[0713] MODE FOR CARRYING OUT THE INVENTION

[0714] System Overview

[0715] The system of the present invention is mainly composed of a server, a user terminal, and a database. This system collects and analyzes personal data and clothing images entered by the user, and then proposes optimal fashion. Specific embodiments for each step are described below.

[0716] Hardware and software configuration

[0717] 1. User Device:

[0718] It consists of mobile devices such as smartphones and tablets.

[0719] Enter and send personal data and clothing images.

[0720] 2. Server:

[0721] It functions as a central processing unit and analyzes data sent by users.

[0722] It also acquires necessary external data (weather information and trend information).

[0723] 3. Database:

[0724] Store and manage collected personal data, image data, weather information, and trend information.

[0725] Technology used

[0726] Image analysis algorithm: Using libraries such as OpenCV and TensorFlow, color information, material, brand, etc. are extracted from clothing images uploaded by users.

[0727] Weather Information API: Use WeatherAPI etc. to get the weekly weather forecast.

[0728] Trend information API: Obtain the latest fashion trends using FashionAPI etc.

[0729] Augmented reality technology: Using Apple's ARKit and Google's ARCore, the company offers a feature that allows users to virtually try on suggested fashion items.

[0730] Detailed System Operation

[0731] 1. Entering and saving personal data

[0732] The user opens the smartphone app and enters their hobbies, preferences, physical characteristics, lifestyle characteristics, etc.

[0733] The terminal transmits the input data to the server, and the server stores the received data in a database.

[0734] 2. Upload and analyze clothing images

[0735] Users take photos of their clothing and upload the images through a smartphone app.

[0736] The server uses image analysis algorithms (e.g., OpenCV) to extract information such as color, material, and brand from the image and store it in a database.

[0737] 3. Obtaining weather and trend information

[0738] The server uses external APIs (such as WeatherAPI and FashionAPI) to obtain weekly weather forecasts and the latest fashion trends, and also stores this information in the database.

[0739] 4. Fashion Proposal Generation

[0740] The server generates optimal fashion suggestions for the user based on personal data, clothing information, weather information, and trend information.

[0741] The suggestions are sent to the user terminal as multiple styles.

[0742] 5. Viewing proposals and virtual try-on

[0743] Users use their smartphones to browse suggested fashion styles.

[0744] Augmented reality technology is used to display the user's chosen style as a virtual try-on.

[0745] 6. Sharing actions and reward points

[0746] When a user takes an action such as sharing suggested fashion on social media, this is tracked and points are awarded.

[0747] Points can be used at online flea markets and other places.

[0748] Specific examples

[0749] 1. Examples of inputting and saving personal data

[0750] "Generate fashion suggestions for a user who likes casual style, is 165cm tall, weighs 55kg, and commutes to the office five days a week."

[0751] 2. Example of uploading and analyzing clothing images

[0752] "Users take a photo of their casual shirt and jeans with their smartphone and upload it through the app."

[0753] 3. Example of fashion proposal generation and selection

[0754] "Suggested fashion styles are displayed based on the user's personal data, clothing data, and acquired weather information (e.g., sunny, temperature 20 degrees)."

[0755] In this way, the present invention makes optimal fashion suggestions to users and provides them with the experience of virtually trying on clothes, thereby increasing user satisfaction and willingness to purchase.

[0756] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0757] Step 1:

[0758] The two steps are to receive personal data entered by the user and to store the entered personal data in a database. Specifically, the user opens a smartphone app and enters personal data such as their hobbies, preferences, physical characteristics, and lifestyle characteristics. The entered data is sent from the smartphone to a server, which receives it and stores it in a database. The input of this step is the user's personal data, and the output is the stored data.

[0759] Step 2:

[0760] The system has two steps: a means for receiving images of the clothes the user owns, and a means for analyzing the received image data and extracting information about the clothes. Specifically, the user takes an image of the clothes they own with their smartphone and uploads the image to the server via the app. The server uses an image analysis algorithm (e.g., OpenCV) to extract information about the clothes, such as color, material, and brand, and the extracted information is stored in a database. The input of this step is the image of the clothes, and the output is the extracted clothing information.

[0761] Step 3:

[0762] This is a means of obtaining weather information and trend information from an external source. Specifically, the server uses an external API (e.g., WeatherAPI, FashionAPI) to obtain the weekly weather forecast and the latest fashion trends. The obtained information is also stored in the database. The input to this step is a request to the external API, and the output is the obtained weather information and trend information.

[0763] Step 4:

[0764] This is a means of generating fashion suggestions based on personal data, clothing information, weather information, and trend information. Specifically, the server uses an AI model to generate optimal fashion suggestions for the user based on the personal data, clothing information, weather information, and trend information stored in the database. The generated suggestions are sent to the user's smartphone as multiple options. The input for this step is various information from the database, and the output is suggested fashion styles.

[0765] Step 5:

[0766] The two steps are to provide the generated fashion suggestions to the user and to use augmented reality technology to display the generated fashion suggestions as a virtual try-on. Specifically, the user browses the suggested fashion styles using a smartphone app. To display the selected style as a virtual try-on, the smartphone camera and AR technology (e.g., ARKit) are used to display the user wearing the outfit on the screen. The input of this step is the fashion suggestions, and the output is a video of the virtual try-on.

[0767] Step 6:

[0768] This is a means of tracking users' sharing behavior and awarding points. Specifically, when a user takes an action such as sharing a suggested fashion style on social media, the server tracks this and awards points appropriately. The points are registered in a database and can be used by the user at online flea markets, etc. The input to this step is data on the user's sharing behavior, and the output is the awarded point information.

[0769] 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.

[0770] MODE FOR CARRYING OUT THE INVENTION

[0771] This invention is an AI platform that makes sustainable fashion recommendations based on a user's individual information and emotions. This platform efficiently collects and analyzes personal data entered by the user and information about the clothing they own, and then recommends optimal fashion styles. In addition, by combining it with an emotion engine, it provides even more personalized fashion recommendations based on the user's emotions. A specific embodiment of the system is shown below.

[0772] System configuration

[0773] The system consists of the following main components:

[0774] 1. User terminal: A device used to input and transmit personal data, emotional information, and clothing image data. Typically, a smartphone or tablet is used.

[0775] 2. Server: A central processing unit that analyzes personal data, clothing information, and emotional information to generate fashion suggestions.

[0776] 3. Database: A data storage system that stores and manages all collected data.

[0777] 4. Emotion Engine: It has the ability to recognize the user's emotions and adapt fashion suggestions based on those emotions.

[0778] Program processing flow

[0779] 1. Collection of personal data

[0780] The user inputs personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics from the terminal.

[0781] The terminal transmits the input data to the server.

[0782] The server analyzes the received personal data and stores it in a database.

[0783] 2. Collection of clothing data

[0784] The user takes a picture of the clothes they are holding with the device.

[0785] The device sends the captured image to the server.

[0786] The server uses image analysis algorithms to extract information about the clothing (color, material, brand, etc.).

[0787] The extracted information is stored in a database.

[0788] 3. Collecting emotional information

[0789] The user takes a photo of their facial expression, records audio, or enters text on their device.

[0790] The device transmits the collected emotion information to the server.

[0791] The emotion engine analyzes this data and recognizes the user's emotions.

[0792] The recognized emotion information is stored in a database.

[0793] 4. Obtaining weather and trend information

[0794] The server obtains weekly weather forecasts and trend information through an external API.

[0795] The information obtained is also stored in a database.

[0796] 5. Fashion Proposal Generation

[0797] The server generates optimal fashion suggestions for the user based on personal data, clothing data, emotional information, weather information, and trend information.

[0798] The generated proposals are sent to the user's terminal as multiple options.

[0799] 6. Selection and Display of Proposals

[0800] The user selects one of the fashion suggestions provided by the terminal.

[0801] The terminal generates an image of the outfit in the selected fashion style and displays it to the user.

[0802] 7. Provision of Additional Features

[0803] Based on the generated outfit image, users can take actions such as listing the outfit at a flea market, sharing it on social media, and receiving points.

[0804] The server tracks these actions and awards incentives such as points as appropriate.

[0805] Specific examples of processing

[0806] Example 1: Entering and saving personal data

[0807] User A opens the smartphone app and enters his / her hobbies and preferences (e.g., he / she likes casual style), physical characteristics (e.g., he / she is 165 cm tall and weighs 55 kg), and lifestyle characteristics (e.g., he / she commutes to the office five days a week). The device sends this data to the server, which then stores the received data in a database.

[0808] Example 2: Uploading and analyzing clothing images

[0809] User A takes a photo of his casual shirt and jeans with his smartphone. The device sends the image to the server, which analyzes it and extracts the shirt's color, material, and brand information. This information is then stored in a database.

[0810] Example 3: Collecting and analyzing emotional information

[0811] User A takes a photo of their facial expression with their smartphone. The device sends the image data to the server, where the emotion engine analyzes the data and recognizes User A's emotions, such as "happy" or "sad." The recognized emotional information is stored in a database.

[0812] Example 4: Generating and selecting fashion suggestions

[0813] The server generates multiple fashion styles based on user A's personal data, clothing data, acquired weather information (e.g., sunny, temperature 20 degrees), trend information, and recognized emotional information. Suggested styles are displayed to user A, and user A selects one. Based on the selected style, an outfit image is generated and displayed on the smartphone.

[0814] Example 5: Using additional features

[0815] User A shares the generated outfit image on a social networking site. The server tracks this action and awards points to User A. User A can use the points to purchase new clothes at an online flea market.

[0816] In this way, the present invention makes it possible to propose optimal fashion based on the user's individual information and emotional information, thereby realizing a sustainable fashion lifestyle.

[0817] The processing flow will be explained below.

[0818] Program processing flow and specific explanation

[0819] 1. Collection of personal data

[0820] Step 1:

[0821] The user inputs personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics from the terminal.

[0822] Specific operation: The user enters information such as "height 165cm," "weight 55kg," and "likes casual style" into the app's input form.

[0823] Step 2:

[0824] The terminal transmits the entered personal data to the server.

[0825] What it does: The app sends the collected data to the server as an HTTP request.

[0826] Step 3:

[0827] The server stores the received personal data in a database.

[0828] What happens: The server receives the data, formats it appropriately, and stores it in the user profile table in the database.

[0829] 2. Collection of clothing data

[0830] Step 1:

[0831] The user takes a picture of the clothes they are holding with the device.

[0832] What happens: A user uses the app to take a photo of an item of clothing.

[0833] Step 2:

[0834] The terminal transmits the captured image data to the server.

[0835] What happens: The app encodes the image data and sends it to the server.

[0836] Step 3:

[0837] The server uses image analysis algorithms to extract information about the clothing (color, material, brand, etc.).

[0838] Specific operation: The server inputs the received image into the machine learning model and obtains the analysis result (e.g., "red cotton shirt" or "blue denim jeans").

[0839] Step 4:

[0840] The server stores the extracted clothing information in a database.

[0841] Specific operation: The server saves the analysis results in the clothing information table of the database.

[0842] 3. Collecting emotional information

[0843] Step 1:

[0844] The user takes a photo of their facial expression, records audio, or enters text on their device.

[0845] Specific actions: The user takes a photo of their facial expression with their smartphone camera, records audio using the microphone, or enters text about their mood.

[0846] Step 2:

[0847] The device transmits the collected emotional information to a server.

[0848] Specific operation: Send captured image data, recorded audio data, or text data to the server.

[0849] Step 3:

[0850] The emotion engine analyzes this data and recognizes the user's emotions.

[0851] What it does: The emotion engine uses image analysis, audio analysis, or natural language processing to determine the user's emotion (e.g., "happiness," "sadness," "surprise," etc.).

[0852] Step 4:

[0853] The server stores the recognized emotion information in a database.

[0854] Specific operation: The recognized emotion information is added or updated to the user profile in the database.

[0855] 4. Obtaining weather and trend information

[0856] Step 1:

[0857] The server retrieves the weekly weather forecast through an external API.

[0858] Specific operation: The server sends a request to the weather API to obtain weekly weather data (e.g., "sunny, temperature 20 degrees").

[0859] Step 2:

[0860] The server obtains trend information through an external API.

[0861] Specific operation: The server sends a request to the trend information API to obtain the latest fashion trend information (for example, "casual style is in fashion").

[0862] 5. Fashion Proposal Generation

[0863] Step 1:

[0864] The server generates fashion suggestions based on personal data, clothing data, emotional information, weather information, and trend information.

[0865] How it works: The server aggregates all the data and uses machine learning algorithms to generate the best fashion style for the user (e.g., "a casual shirt and jeans combination").

[0866] Step 2:

[0867] The server transmits the generated fashion suggestions to the terminal.

[0868] What it does: The server sends the suggestions in JSON format to the device, and the app reads and displays them.

[0869] 6. Selection and Display of Proposals

[0870] Step 1:

[0871] The user selects one of the fashion suggestions provided by the terminal.

[0872] Specific behavior: The user taps to select one of the suggestions displayed in the app.

[0873] Step 2:

[0874] The terminal generates an image of the outfit in the selected fashion style and displays it to the user.

[0875] What it does: The app generates a visual image of the outfit based on the selected style and displays it to the user.

[0876] 7. Provision of Additional Features

[0877] Step 1:

[0878] Based on the generated outfit image, users can take actions such as listing the outfit at a flea market, sharing it on social media, and receiving points.

[0879] Specific operation: The user presses the "Share to social media" button within the app to post their outfit.

[0880] Step 2:

[0881] The server tracks these actions and awards incentives such as points as appropriate.

[0882] Specific operation: The server analyzes the user's action log and processes the points to add to the user's account.

[0883] The above is a specific processing flow that combines the emotion engine of the present invention. We have explained in detail how the user, terminal, and server operate at each step. This makes it possible to propose more personalized fashion based on the user's emotional information, helping to realize a sustainable fashion lifestyle.

[0884] Example 2

[0885] 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."

[0886] Conventional fashion suggestion systems make suggestions based on a user's personal data and clothing information, but they have not yet realized personalized suggestions based on the user's emotional information. It is also difficult to efficiently incorporate weather and trend information and reflect it in suggestions. This makes it difficult to make optimal fashion suggestions for users. Therefore, the objective of this invention is to provide more accurate and sustainable fashion suggestions by comprehensively considering a user's personal data, clothing information, emotional information, weather information, and trend information.

[0887] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0888] In this invention, the server includes means for receiving personal data entered by the user, means for saving the entered personal data in a database, means for receiving images of clothes owned by the user, means for analyzing the received image data and extracting clothing information, means for saving the extracted clothing information in the database, means for collecting user emotion information, means for analyzing the collected emotion information and recognizing emotions, means for saving the recognized emotion information in the database, means for acquiring weather information and trend information from outside, means for generating fashion suggestions based on the personal data, clothing information, emotion information, weather information, and trend information, and means for providing the generated fashion suggestions to the user. This makes it possible to provide personalized fashion suggestions that comprehensively incorporate the user's emotion information and external information.

[0889] "Personal data" refers to individual information about a user, such as the user's hobbies, preferences, physical characteristics, and lifestyle characteristics.

[0890] A "database" is an information system for storing and managing collected data.

[0891] "Clothing image" is image data of a photograph of clothing owned by the user.

[0892] "Image data" is data that represents a photographed image of clothing in digital format.

[0893] "Image analysis" is the process of extracting information such as clothing color, material, and brand from the received image data.

[0894] "Emotion information" is data related to emotions obtained from the user's facial expressions, voice, text input, etc.

[0895] The "emotion engine" is a system that analyzes collected emotional information and recognizes the user's emotions.

[0896] "Weather information" is information about the weather obtained from external weather data.

[0897] "Trend information" refers to information about trends obtained from the fashion industry, social media, etc.

[0898] "Fashion suggestions" are suggestions for fashion styles generated based on personal data, clothing information, emotional information, weather information, and trend information.

[0899] A "wearing image" is a visual image of what the user will look like wearing the clothes, generated based on the selected fashion suggestion.

[0900] "Listing at a flea market" refers to listing an item on an online marketplace based on a user-selected outfit image.

[0901] "SNS posting" means sharing the selected outfit image on a social networking service.

[0902] "Points awarded" are reward points given to users when they perform specific actions within the system.

[0903] MODE FOR CARRYING OUT THE INVENTION

[0904] This invention is a system for proposing sustainable fashion based on a user's individual information and emotions. This system efficiently collects and analyzes personal data and clothing information entered by the user, and proposes optimal fashion styles. In addition, by combining it with an emotion engine, it provides even more personalized fashion suggestions based on the user's emotions.

[0905] System configuration

[0906] The system consists of the following main components:

[0907] 1. User terminal: A device used to input and transmit personal data, emotional information, and clothing image data. Typically, a smartphone or tablet is used.

[0908] 2. Server: A central processing unit that analyzes personal data, clothing information, and emotional information to generate fashion suggestions.

[0909] 3. Database: An information system that stores and manages all collected data.

[0910] 4. Emotion Engine: A system that has the ability to recognize the user's emotions and adapt fashion suggestions based on those emotions.

[0911] Hardware and Software Examples

[0912] 1. User device: Smartphone (e.g., iPhone), tablet (e.g., iPad)

[0913] 2. Server: High-performance server (e.g. AWS EC2)

[0914] 3. Database: Relational database (e.g. MySQL)

[0915] 4. Emotion engine: Emotion analysis software (e.g., IBM Watson Tone Analyzer)

[0916] 5. Image analysis algorithm: Image analysis API (e.g. Azure Computer Vision API)

[0917] 6. External API: Weather forecast API (e.g. OpenWeatherMap API)

[0918] Program processing

[0919] When a user launches the dedicated app on their device, they can enter personal data such as their hobbies, preferences, physical characteristics such as height and weight, and lifestyle characteristics. For example, a user might enter data such as "I like casual style, I'm 165cm tall, I weigh 55kg, and I work five days a week."

[0920] The device sends this data to the server's API endpoint. The server validates the received data, confirms that it is in the correct format, and then stores it in a database. The user can also take a photo of their clothing with the device and send the image data to the server. The server uses image analysis algorithms to extract information about the clothing's color, material, and brand, and stores it in a database.

[0921] Users can use their devices to capture facial expressions, record voice, and input text to collect emotional information. The devices then send this emotional data to a server, which then uses an emotion engine to analyze it and recognize the user's emotions. This information is also stored in a database.

[0922] In addition, the server obtains weather forecasts and trend information through external APIs. The obtained information is also stored in a database, and fashion suggestions are generated based on personal data, clothing information, emotional information, weather information, and trend information. Using a generative AI model (e.g., GPT-4), optimal fashion suggestions are generated for the user by inputting a prompt such as, "Please suggest autumn casual fashion that suits the user's height and weight."

[0923] The generated fashion suggestions are sent to the user's device as multiple options, and the user selects one of these suggestions. Based on the selected fashion style, the device generates an outfit image and displays it to the user. For example, a realistic outfit image can be generated using 3D modeling technology.

[0924] Specific examples

[0925] An example of a prompt sentence is, "Please suggest a casual style suitable for sunny days for the user, who is 165 cm tall and weighs 55 kg." Based on the input data and acquired information, the system will make optimal fashion suggestions for the user.

[0926] Users can share the generated outfit images on social media, and the server tracks this action and awards points, which users can use to purchase new clothes at online flea markets.

[0927] In this way, the present invention makes it possible to propose optimal fashion based on the user's individual information and emotional information, thereby realizing a sustainable fashion lifestyle.

[0928] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0929] The flow of this system's program processing

[0930] Step 1: Collecting personal data

[0931] Input: The user inputs personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics from the device.

[0932] Processing: The device validates the entered personal data and ensures that it is in the correct format. For example, a user enters data such as "I like casual style," "I'm 165cm tall," "I weigh 55kg," and "I commute to the office five days a week."

[0933] Output: The device sends this personal data to the server, which analyzes it and stores it in a database.

[0934] Step 2: Collecting clothing data

[0935] Input: The user takes a picture of the clothes they are holding on their device, for example, a casual shirt and jeans.

[0936] Processing: The device sends the captured image data to a server. For example, the device sends the image data to an image analysis API to extract color, material, and brand information.

[0937] Output: The server stores the extracted clothing information in a database.

[0938] Step 3: Collecting emotional information

[0939] Input: The user uses the device to capture facial expressions, record audio, or input text.

[0940] Processing: The device sends these emotion data to the server. For example, a user takes a picture of themselves smiling with their smartphone.

[0941] Output: The server analyzes the user's emotion using the emotion engine and recognizes the user's emotion as "happy." This information is stored in the database.

[0942] Step 4: Get weather and trend information

[0943] Input: The server periodically calls an external API to obtain weather and trend information.

[0944] Processing: The server uses a weather forecast API to retrieve, for example, the "weekly weather forecast." It also scrapes trend information from fashion-related websites and social media.

[0945] Output: Save the obtained weather and trend information in a database.

[0946] Step 5: Generate fashion suggestions

[0947] Input: Personal data, clothing data, emotional information, weather information, and fashion information are stored in the database.

[0948] Processing: The server inputs a prompt into a generative AI model (e.g., GPT-4) to generate optimal fashion suggestions for the user. For example, the prompt might be, "Please suggest casual autumn fashion that suits the user's height and weight."

[0949] Output: The generated fashion suggestions are sent to the user's device as multiple options.

[0950] Step 6: Select and view suggestions

[0951] Input: The user selects one of the suggestions provided by the device.

[0952] Processing: The device generates a realistic image of the selected fashion style, using 3D modeling technology to create a realistic image of the outfit.

[0953] Output: The generated outfit image is displayed on the device.

[0954] Step 7: Providing additional functionality

[0955] Input: User shares outfit image on social media.

[0956] Processing: The server tracks the sharing action and gives points to the user. For example, when a user clicks the post button on a social networking site, the server records that information.

[0957] Output: The points are added to the user's account, and the user can use them to purchase new clothes at the online flea market.

[0958] (Application example 2)

[0959] 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."

[0960] The shopping experience in physical stores is often not personalized enough for the user because it is often conducted without taking into account information such as the user's clothing, personal preferences, weather, and emotions. This leads to a problem of reduced satisfaction when making purchasing decisions. Furthermore, trying on clothes in physical stores is often time-consuming and inefficient. Therefore, new methods are needed to ensure users have an effective shopping experience.

[0961] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0962] In this invention, the server includes means for receiving personal data and emotional information entered by the user, means for analyzing the data and saving the data in a database, means for receiving images of the user's clothing, analyzing the images to extract and save information about the clothing, means for acquiring weather information and trend information, means for generating fashion suggestions based on the personal data, clothing information, emotional information, weather information, and trend information, means for providing the generated fashion suggestions to the user, means for the user to have a smart try-on experience in a physical store, means for providing virtual try-on experiences in cooperation with a smart mirror installed in the physical store, means for collecting the user's emotional information in real time and reflecting it in fashion suggestions, and means for suggesting optimal fashion items in real time, thereby enabling the user to have a personalized and efficient shopping experience in a physical store.

[0963] "Personal data" refers to data provided by a user, including hobbies, preferences, physical characteristics, lifestyle characteristics, emotional information, and the like.

[0964] "Clothing image" is image data of clothing owned by the user.

[0965] "Weather information" is weekly weather forecast information obtained from an external weather database.

[0966] "Trend information" refers to information about current fashion trends.

[0967] "Emotion information" is data related to emotions acquired from the user's facial expressions, voice, text, etc.

[0968] "Fashion suggestions" are suggestions for fashion styles generated based on personal data, clothing information, weather information, trend information, and emotional information.

[0969] The "smart try-on experience" is a virtual try-on process that users go through in a physical store using a smart mirror.

[0970] A "smart mirror" is a mirror-shaped device that is installed in a physical store and helps users try on clothes virtually.

[0971] "Real-time suggestions" is a system function that instantly suggests fashion items based on the user's current information.

[0972] "Point awarding" refers to providing points as an incentive for a user's specific behavior.

[0973] "SNS posting" refers to the act of a user sharing the created outfit image on a social networking service.

[0974] "Listing at a flea market" refers to the act of a user listing unwanted clothing on a flea market application.

[0975] The present invention is a system for proposing sustainable fashion based on individual data and emotional information of a user. This system is configured using the following hardware and software.

[0976] Hardware Configuration

[0977] User device: A smartphone or tablet through which the user inputs and transmits personal data, emotional information, and clothing image data.

[0978] Server: A central processing unit used to analyze received data and generate fashion suggestions.

[0979] Smart mirror: A device installed in a physical store that allows virtual try-on of clothes.

[0980] Software Configuration

[0981] Frontend: A mobile application based on React Native that provides the user interface and manages data entry and display.

[0982] Backend: A server-side application built with Node.js and Express that analyzes data and generates fashion suggestions.

[0983] Image analysis engine: Uses OpenCV to analyze images of clothing and extract information such as color, material, and brand.

[0984] Sentiment analysis engine: Analyzes user sentiment using Google Cloud Vision AI.

[0985] Database: MongoDB is used to store and manage various data.

[0986] External API: Used to get weather and trend information from OpenWeatherMap and Trendyol.

[0987] System processing overview

[0988] User data entry and collection

[0989] The user enters personal data (hobbies, preferences, physical characteristics, lifestyle characteristics) from the device and sends it to the server. The server receives this data and stores it in a database. For example, a user enters data such as "I like casual style," "I'm 165 cm tall and weigh 55 kg," and "I commute to the office five days a week."

[0990] Clothing data capture and analysis

[0991] The user takes a photo of the clothes they are wearing with their device and sends it to the server. The image data is analyzed by the server, and the color, material, and brand information of the clothes are extracted. For example, the user can take a photo of a "casual shirt" or "jeans."

[0992] Collecting emotional information

[0993] The user takes a photo of their facial expression on the device and sends it to the server, where the emotion analysis engine analyzes the user's emotions (e.g., "happy," "sad," etc.).

[0994] Retrieving External Data

[0995] The server uses an external API to obtain weekly weather and trend information, which is then stored in a database.

[0996] Fashion proposal generation

[0997] Based on the collected personal data, clothing data, emotional information, weather information, and trend information, the AI ​​model generates optimal fashion suggestions, which are then sent to the user's device as multiple options.

[0998] Virtual try-on experience

[0999] When users visit a physical store, they can virtually try on clothes by connecting their smart mirror to their device. The mirror reflects the user's individual data and provides a real-time virtual try-on experience.

[1000] Examples of prompt statements

[1001] Enter your personal data: "Please enter your physical characteristics, hobbies, preferences, and lifestyle characteristics."

[1002] Collecting emotional information: "Facial expressions are captured on camera to analyze current emotions."

[1003] Upload clothing images: "Upload images of your clothing here."

[1004] In this way, the present invention generates optimal fashion suggestions based on the user's personal and emotional information, providing a sustainable shopping experience.

[1005] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1006] Step 1:

[1007] The user enters personal data (hobbies, preferences, physical characteristics, lifestyle characteristics) from their device and sends it to the server. At this time, the data entered may include "I like casual style," "I'm 165cm tall," "I weigh 55kg," and "I commute to the office five days a week." The server analyzes the received data and stores it in a MongoDB database. To store the input data in key-value format, the data is converted to JSON format as part of data processing.

[1008] Step 2:

[1009] A user takes a photo of their clothing on their device and sends it to the server. For example, they upload an image of a casual shirt or jeans. The server uses OpenCV to analyze the received image data and extract information about the clothing (color, material, brand, etc.). The extracted information is stored in a database. The input is image data, and the output is analyzed clothing information.

[1010] Step 3:

[1011] The user takes a photo of their facial expression on their device and sends the image data to the server. The server uses Google Cloud Vision AI to analyze the image and recognize the user's emotional information (e.g., "happy" or "sad"). The recognized emotional information is stored in a database. The input is image data of the facial expression, and the output is text information about the recognized emotion.

[1012] Step 4:

[1013] The server uses external APIs (OpenWeatherMap, Trendyol) to obtain the weekly weather forecast and trend information. This data is also stored in the database. The input is the API request, and the output is the obtained weather forecast and trend information. The weather information includes temperature and weather conditions, and the trend information shows current fashion trends.

[1014] Step 5:

[1015] The server uses a generative AI model to generate fashion suggestions based on personal data, clothing information, emotional information, weather information, and trend information. For example, it may suggest a casual style suitable for a sunny 20-degree day. The generated suggestions are sent to the user's device as multiple options. The input is the aforementioned multiple datasets, and the output is multiple fashion suggestions.

[1016] Step 6:

[1017] The user selects one of the fashion suggestions provided by the terminal. Based on the selected suggestion, the terminal generates an outfit image. This outfit image is displayed to the user in real time. The input is the selected fashion suggestion, and the output is the generated outfit image.

[1018] Step 7:

[1019] When a user visits a physical store, the smart mirror and device are connected. Personal data stored on the device is sent to the mirror, which then provides a virtual try-on experience that reflects that data. For example, the user can check in real time on the smart mirror whether a particular shirt looks good on them. The input is personal data and clothing data, and the output is a real-time virtual try-on image.

[1020] Step 8:

[1021] When a user shares the generated outfit image on SNS, the action is sent to the server and points are awarded. The server tracks this and manages the points system. The points can be used for the next purchase, providing an incentive to the user. The input is the SNS sharing information and the output is the awarded points.

[1022] 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.

[1023] 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.

[1024] 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.

[1025] [Third embodiment]

[1026] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1027] 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.

[1028] 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).

[1029] 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.

[1030] 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.

[1031] 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).

[1032] 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.

[1033] 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.

[1034] 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.

[1035] 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.

[1036] 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.

[1037] 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."

[1038] MODE FOR CARRYING OUT THE INVENTION

[1039] This invention is an AI platform that proposes sustainable fashion based on the user's individual information. This platform efficiently collects and analyzes personal data and clothing information entered by the user, and proposes optimal fashion styles. A specific embodiment of the system is shown below.

[1040] System configuration

[1041] The system consists of the following main components:

[1042] 1. User terminal: A device used to input and send personal data and clothing image data. Typically, a smartphone or tablet is used.

[1043] 2. Server: A central processing unit that analyzes personal data and clothing information and generates fashion suggestions.

[1044] 3. Database: A data storage system that stores and manages all collected data.

[1045] Program processing flow

[1046] 1. Collection of personal data

[1047] The user inputs personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics from the terminal.

[1048] The terminal transmits the input data to the server.

[1049] The server analyzes the received personal data and stores it in a database.

[1050] 2. Collection of clothing data

[1051] The user takes a picture of the clothes they are holding with the device.

[1052] The device sends the captured image to the server.

[1053] The server uses image analysis algorithms to extract information such as the color, material, and brand of the clothing.

[1054] The extracted information is stored in a database.

[1055] 3. Obtaining weather and trend information

[1056] The server obtains weekly weather forecasts and trend information through an external API.

[1057] The information obtained is also stored in a database.

[1058] 4. Fashion Proposal Generation

[1059] The server generates optimal fashion suggestions for the user based on personal data, clothing data, weather information, and trend information.

[1060] The generated suggestions are sent to the user's terminal as multiple options.

[1061] 5. Selection and display of proposals

[1062] The user selects one of the fashion suggestions provided by the terminal.

[1063] The terminal generates an image of the outfit in the selected fashion style and displays it to the user.

[1064] 6. Provision of Additional Features

[1065] Based on the generated outfit image, users can take actions such as listing the item at a flea market, sharing it on social media, and earning points.

[1066] The server tracks these actions and awards incentives such as points as appropriate.

[1067] Specific examples of processing

[1068] Example 1: Entering and saving personal data

[1069] User A opens the smartphone app and enters his / her hobbies and preferences (e.g., he / she likes casual style), physical characteristics (e.g., he / she is 165 cm tall and weighs 55 kg), and lifestyle characteristics (e.g., he / she commutes to the office five days a week). The device sends this data to the server, which then stores the received data in a database.

[1070] Example 2: Uploading and analyzing clothing images

[1071] User A takes a photo of his casual shirt and jeans with his smartphone. The device sends the image to the server, which analyzes it and extracts the shirt's color, material, and brand information. This information is then stored in a database.

[1072] Example 3: Generating and selecting fashion suggestions

[1073] The server generates multiple fashion styles based on user A's personal data, clothing data, acquired weather information (e.g. sunny, temperature 20 degrees) and trend information. Suggested styles are displayed to user A, and user A selects one. Based on the selected style, an outfit image is generated and displayed on the smartphone.

[1074] Example 4: Using additional features

[1075] User A shares the created outfit image on a social networking site. The server tracks this action and awards points to User A. User A can use the points to purchase new clothes at an online flea market.

[1076] In this way, the present invention makes it possible to provide optimal fashion suggestions based on the user's individual information, thereby realizing a sustainable fashion lifestyle.

[1077] The processing flow will be explained below.

[1078] Program processing flow and specific explanation

[1079] 1. Collection of personal data

[1080] Step 1:

[1081] The user inputs personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics from the terminal.

[1082] Specific operation: The user enters information such as "height 165cm," "weight 55kg," and "likes casual style" into the app's input form.

[1083] Step 2:

[1084] The terminal transmits the entered personal data to the server.

[1085] What it does: The app sends the collected data to the server as an HTTP request.

[1086] Step 3:

[1087] The server stores the received personal data in a database.

[1088] What happens: The server receives the data, formats it appropriately, and stores it in the user profile table in the database.

[1089] 2. Collection of clothing data

[1090] Step 1:

[1091] The user takes a picture of the clothes they are holding with the device.

[1092] What happens: A user uses the app to take a photo of an item of clothing.

[1093] Step 2:

[1094] The terminal transmits the captured image data to the server.

[1095] What happens: The app encodes the image data and sends it to the server.

[1096] Step 3:

[1097] The server uses image analysis algorithms to extract information about the clothing (color, material, brand, etc.).

[1098] Specific operation: The server inputs the received image into the machine learning model and obtains the analysis result (e.g., "red cotton shirt" or "blue denim jeans").

[1099] Step 4:

[1100] The server stores the extracted clothing information in a database.

[1101] Specific operation: The server saves the analysis results in the clothing information table of the database.

[1102] 3. Obtaining weather and trend information

[1103] Step 1:

[1104] The server retrieves the weekly weather forecast through an external API.

[1105] Specific operation: The server sends a request to the weather API to obtain weekly weather data (e.g., "sunny, temperature 20 degrees").

[1106] Step 2:

[1107] The server obtains trend information through an external API.

[1108] Specific operation: The server sends a request to the trend information API to obtain the latest fashion trend information (for example, "casual style is in fashion").

[1109] 4. Fashion Proposal Generation

[1110] Step 1:

[1111] The server generates fashion suggestions based on personal data, clothing data, weather information, and trend information.

[1112] How it works: The server aggregates all the data and uses machine learning algorithms to generate the best fashion style for the user (e.g., "a casual shirt and jeans combination").

[1113] Step 2:

[1114] The server transmits the generated fashion suggestions to the terminal.

[1115] What it does: The server sends the suggestions in JSON format to the device, and the app reads and displays them.

[1116] 5. Selection and display of proposals

[1117] Step 1:

[1118] The user selects one of the fashion suggestions provided by the terminal.

[1119] Specific behavior: The user taps to select one of the suggestions displayed in the app.

[1120] Step 2:

[1121] The terminal generates an image of the outfit in the selected fashion style and displays it to the user.

[1122] What it does: The app generates a visual image of the outfit based on the selected style and displays it to the user.

[1123] 6. Provision of Additional Features

[1124] Step 1:

[1125] Based on the generated outfit image, users can take actions such as listing the outfit at a flea market, sharing it on social media, and receiving points.

[1126] Specific operation: The user presses the "Share to social media" button within the app to post their outfit.

[1127] Step 2:

[1128] The server tracks these actions and awards incentives such as points as appropriate.

[1129] Specific operation: The server analyzes the user's action log and processes the points to add to the user's account.

[1130] The above is a specific processing flow for implementing the present invention. We have explained in detail how the user, terminal, and server operate in each step. This allows us to build an optimal system for realizing a sustainable fashion life.

[1131] Example 1

[1132] 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."

[1133] The present invention aims to efficiently and effectively suggest sustainable fashion based on individual user information. It also aims to provide a system that provides optimal fashion suggestions by analyzing the user's clothing information in detail and combining it with current weather and fashion trends. Furthermore, the system allows users to easily use the suggested fashion styles to access additional features such as posting on social media, listing at flea markets, and earning points.

[1134] 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.

[1135] In this invention, the server includes a means for transmitting data and images input by the user to the server, a means for the server to extract clothing information using an image analysis algorithm, and a means for generating suggestions using a generative AI model. This allows for rapid analysis of personal data and clothing information, enabling optimal fashion suggestions to be made to the user. Furthermore, by obtaining weather and fashion trends via an external API, suggestions based on real-time information are possible. Furthermore, based on the outfit image selected by the user, the system can list the item at a flea market, post on social media, and award points, and these actions are tracked to provide incentives, improving user convenience.

[1136] "User" means an individual or organization that uses the system.

[1137] A "terminal" is a device used by a user, such as a smartphone or tablet.

[1138] A "server" is a computer device that performs central processing such as analyzing and storing data and generating fashion suggestions.

[1139] A "data storage device" is a system that stores and manages personal data, clothing information, weather information, fashion information, etc.

[1140] An "image analysis algorithm" is a software program that analyzes received image data to extract information.

[1141] A "generative AI model" is an algorithmic model that uses artificial intelligence to generate fashion suggestions for users.

[1142] A "prompt" refers to the input information or question given to a generative AI model.

[1143] "Fashion suggestions" are recommendations for fashion styles that suit the user.

[1144] "Outfit images" are images or graphics that visually represent the proposed fashion style.

[1145] An "external API" is an interface for obtaining information in collaboration with external services.

[1146] "Weather information" refers to meteorological data such as the weekly weather forecast and temperature.

[1147] "Trend information" is data about current fashion trends.

[1148] "Flea market listing" refers to the act of a user listing unwanted clothing or other items on an online marketplace.

[1149] "SNS posting" refers to the act of a user sharing a proposed fashion style or outfit image on a social networking service.

[1150] "Point awarding" is a system that provides points as an incentive for user actions.

[1151] "Action tracking" means tracking and recording user behavior.

[1152] The present invention is an AI platform that proposes sustainable fashion based on individual user information. A specific embodiment of the system is described below.

[1153] The system mainly consists of three main components: a user terminal, a server, and a data storage device. The user terminal is typically a smartphone or tablet, and is used to input and transmit personal data and clothing image data. The server is a central processing unit that analyzes personal data and clothing information and generates fashion suggestions. The data storage device is a system that stores and manages all collected data.

[1154] First, a user uses a smartphone or tablet to input personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics. For example, the user inputs information such as that they like casual clothing, are 165 cm tall, weigh 55 kg, and commute to the office five days a week. The device sends this data to a server, which then analyzes the received data and stores it in a data storage device.

[1155] Next, the user takes a picture of the clothes they are wearing with their device. For example, they can take a picture of a casual shirt and jeans. The device sends the image to the server, which then uses an image analysis algorithm (e.g., Amazon Rekognition, Google Cloud Vision) to extract information about the clothes, such as color, material, and brand. The extracted information is then stored on a data storage device.

[1156] In addition, the server obtains weekly weather forecasts and trend information through external APIs (e.g., OpenWeather, FashionAPI). The obtained information is also stored in the data storage device. For example, the weather forecast for next week is sunny with a temperature of 20 degrees.

[1157] This system uses a generative AI model (e.g., GPT-3) to generate fashion suggestions. The server inputs a prompt to generate the optimal fashion style based on collected personal data, clothing data, weather information, and trend information, and the generative AI model outputs the suggestion. The prompt uses the following text: "Provide fashion suggestions based on the personal data entered by User A (hobbies and preferences: casual style, height 165 cm, weight 55 kg, commute 5 days a week), an image of a casual shirt and jeans, weather information (sunny, temperature 20 degrees), and trend information."

[1158] The user's device receives the fashion suggestions sent from the server and displays them to the user. The user selects one of the suggestions provided, and an outfit image based on the selected style is generated and displayed on the smartphone. The user can also take actions based on the generated outfit image, such as listing it at a flea market, sharing it on social media, or receiving points. The server tracks these actions and awards incentives such as points as appropriate.

[1159] In this way, the present invention can make optimal fashion suggestions based on the user's individual information, and can comprehensively utilize the user's clothing, the latest weather information, trend information, etc. In addition, the user can further enhance their fashion life by using additional functions.

[1160] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1161] Step 1:

[1162] The user uses a smartphone or tablet to enter personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics. Specifically, the user enters information such as "I like casual style," "I'm 165cm tall," "I weigh 55kg," and "I commute to the office five days a week" into the app's input form.

[1163] Input data: User's hobbies, preferences, physical characteristics, lifestyle characteristics, etc.

[1164] Output data: Personal data sent from the device to the server

[1165] Step 2:

[1166] The device sends the entered personal data to the server, which analyzes the received personal data, converts it into an appropriate format, and stores it in a data storage device, where the personal data is stored in a structured format.

[1167] Input data: Personal data sent from the device

[1168] Output data: Personal data stored in the data storage device

[1169] Step 3:

[1170] The user takes a picture of their clothing with their smartphone, or uses the app's camera function to take a picture of their belongings, such as a casual shirt or jeans.

[1171] Input data: Images of clothing taken by the user

[1172] Output data: Image file of the clothing saved on the device

[1173] Step 4:

[1174] The device sends the captured image to a server, which uses image analysis algorithms (e.g., Amazon Rekognition, Google Cloud Vision) to extract information such as the color, material, and brand of the clothing. The extracted information is then stored on a data storage device.

[1175] Input data: Clothing images sent from the device

[1176] Output data: Clothing information (color, material, brand, etc.) stored in the data storage device

[1177] Step 5:

[1178] The server uses external APIs (e.g., OpenWeather, FashionAPI) to obtain weekly weather forecasts and trend information. The obtained information is also stored in the data storage device.

[1179] Input data: Weather and trend information requests obtained from external APIs

[1180] Output data: Weather information and trend information stored in a data storage device

[1181] Step 6:

[1182] The server generates fashion suggestions using a generative AI model (e.g., GPT-3) based on personal data, clothing data, weather information, and trend information. The generated suggestions are sent to the device as multiple options. The server inputs the following prompt into the generative AI model: "Provide fashion suggestions based on the personal data entered by User A (hobbies and preferences: casual style, height 165 cm, weight 55 kg, commutes 5 days a week), images of casual shirts and jeans, weather information (sunny, temperature 20 degrees), and trend information."

[1183] Input data: personal data, clothing data, weather information, fashion information

[1184] Output data: Fashion suggestions output by the generative AI model

[1185] Step 7:

[1186] The terminal displays the fashion suggestions received from the server to the user, who then selects one of the suggestions.

[1187] Input data: Fashion suggestions received from the server

[1188] Output data: The fashion style selected by the user

[1189] Step 8:

[1190] The device generates an outfit image based on the selected style and displays it to the user. The generated outfit image is displayed on the user's smartphone screen.

[1191] Input data: Fashion style selected by the user

[1192] Output data: Image of the garment displayed on the device

[1193] Step 9:

[1194] Users can share the created outfit images on social media or put them up for sale at a flea market. The server tracks these actions and awards points to users, which can be used to make purchases at the online flea market.

[1195] Input data: User actions (SNS sharing, flea market listing, etc.)

[1196] Output data: Actions recorded by the server and points awarded

[1197] (Application example 1)

[1198] 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."

[1199] Conventional fashion suggestion systems suggest appropriate fashion styles based on a user's individual information, but the suggestions lack practicality and a sense of actually trying on the clothes. Furthermore, the fashion suggestions are often unsustainable. Furthermore, the functionality for tracking user behavior and providing incentives is insufficient. Therefore, users do not get the feeling of actually trying on the suggested clothes, making it difficult to increase their motivation to purchase.

[1200] 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.

[1201] In this invention, the server includes means for receiving personal data entered by the user, means for saving the entered personal data in a database, means for receiving images of clothing owned by the user, means for analyzing the received image data and extracting clothing information, means for saving the extracted clothing information in a database, means for externally acquiring weather information and trend information, means for generating fashion suggestions based on the personal data, clothing information, weather information, and trend information, means for providing the generated fashion suggestions to the user, means for using augmented reality technology to display the generated fashion suggestions as a virtual try-on, and means for tracking the user's shared behavior and awarding points. This allows the user to feel as if they are actually trying on the suggested clothing, increasing their desire to purchase and making it possible to award incentives according to the user's behavior.

[1202] "Personal data" refers to information entered by the user regarding hobbies, preferences, physical characteristics, and lifestyle characteristics.

[1203] A "database" is a system that stores and manages collected personal data, clothing information, weather information, fashion information, etc.

[1204] "Clothing image" is photographic data of clothing owned by the user.

[1205] "Means for analyzing image data" refers to technology, such as image recognition algorithms, for extracting information such as color, material, and brand from images of clothing.

[1206] "Weather information" is weekly weather forecast data obtained from an external API.

[1207] "Trend information" is data on the latest fashion trends obtained from external APIs.

[1208] "Fashion suggestions" refers to styling recommendations generated based on personal data, clothing information, weather information, and trend information.

[1209] "Means for providing" refers to a medium for notifying or displaying the generated fashion suggestions to the user, such as a smartphone app or a web interface.

[1210] "Augmented reality technology" is a technology that enables virtual try-on, and refers to the technology that displays virtual elements overlaid on real-world images.

[1211] "Sharing behavior" refers to actions such as users sharing fashion suggestions on social media.

[1212] The "point awarding means" is a system that awards points as a reward according to the user's actions.

[1213] MODE FOR CARRYING OUT THE INVENTION

[1214] System Overview

[1215] The system of the present invention is mainly composed of a server, a user terminal, and a database. This system collects and analyzes personal data and clothing images entered by the user, and then proposes optimal fashion. Specific embodiments for each step are described below.

[1216] Hardware and software configuration

[1217] 1. User Device:

[1218] It consists of mobile devices such as smartphones and tablets.

[1219] Enter and send personal data and clothing images.

[1220] 2. Server:

[1221] It functions as a central processing unit and analyzes data sent by users.

[1222] It also acquires necessary external data (weather information and trend information).

[1223] 3. Database:

[1224] Store and manage collected personal data, image data, weather information, and trend information.

[1225] Technology used

[1226] Image analysis algorithm: Using libraries such as OpenCV and TensorFlow, color information, material, brand, etc. are extracted from clothing images uploaded by users.

[1227] Weather Information API: Use WeatherAPI etc. to get the weekly weather forecast.

[1228] Trend information API: Obtain the latest fashion trends using FashionAPI etc.

[1229] Augmented reality technology: Using Apple's ARKit and Google's ARCore, the company offers a feature that allows users to virtually try on suggested fashion items.

[1230] Detailed System Operation

[1231] 1. Entering and saving personal data

[1232] The user opens the smartphone app and enters their hobbies, preferences, physical characteristics, lifestyle characteristics, etc.

[1233] The terminal transmits the input data to the server, and the server stores the received data in a database.

[1234] 2. Upload and analyze clothing images

[1235] Users take photos of their clothing and upload the images through a smartphone app.

[1236] The server uses image analysis algorithms (e.g., OpenCV) to extract information such as color, material, and brand from the image and store it in a database.

[1237] 3. Obtaining weather and trend information

[1238] The server uses external APIs (such as WeatherAPI and FashionAPI) to obtain weekly weather forecasts and the latest fashion trends, and also stores this information in the database.

[1239] 4. Fashion Proposal Generation

[1240] The server generates optimal fashion suggestions for the user based on personal data, clothing information, weather information, and trend information.

[1241] The suggestions are sent to the user terminal as multiple styles.

[1242] 5. Viewing proposals and virtual try-on

[1243] Users use their smartphones to browse suggested fashion styles.

[1244] Augmented reality technology is used to display the user's chosen style as a virtual try-on.

[1245] 6. Sharing actions and reward points

[1246] When a user takes an action such as sharing suggested fashion on social media, this is tracked and points are awarded.

[1247] Points can be used at online flea markets and other places.

[1248] Specific examples

[1249] 1. Examples of inputting and saving personal data

[1250] "Generate fashion suggestions for a user who likes casual style, is 165cm tall, weighs 55kg, and commutes to the office five days a week."

[1251] 2. Example of uploading and analyzing clothing images

[1252] "Users take a photo of their casual shirt and jeans with their smartphone and upload it through the app."

[1253] 3. Example of fashion proposal generation and selection

[1254] "Suggested fashion styles are displayed based on the user's personal data, clothing data, and acquired weather information (e.g., sunny, temperature 20 degrees)."

[1255] In this way, the present invention makes optimal fashion suggestions to users and provides them with the experience of virtually trying on clothes, thereby increasing user satisfaction and willingness to purchase.

[1256] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1257] Step 1:

[1258] The two steps are to receive personal data entered by the user and to store the entered personal data in a database. Specifically, the user opens a smartphone app and enters personal data such as their hobbies, preferences, physical characteristics, and lifestyle characteristics. The entered data is sent from the smartphone to a server, which receives it and stores it in a database. The input of this step is the user's personal data, and the output is the stored data.

[1259] Step 2:

[1260] The system has two steps: a means for receiving images of the clothes the user owns, and a means for analyzing the received image data and extracting information about the clothes. Specifically, the user takes an image of the clothes they own with their smartphone and uploads the image to the server via the app. The server uses an image analysis algorithm (e.g., OpenCV) to extract information about the clothes, such as color, material, and brand, and the extracted information is stored in a database. The input of this step is the image of the clothes, and the output is the extracted clothing information.

[1261] Step 3:

[1262] This is a means of obtaining weather information and trend information from an external source. Specifically, the server uses an external API (e.g., WeatherAPI, FashionAPI) to obtain the weekly weather forecast and the latest fashion trends. The obtained information is also stored in the database. The input to this step is a request to the external API, and the output is the obtained weather information and trend information.

[1263] Step 4:

[1264] This is a means of generating fashion suggestions based on personal data, clothing information, weather information, and trend information. Specifically, the server uses an AI model to generate optimal fashion suggestions for the user based on the personal data, clothing information, weather information, and trend information stored in the database. The generated suggestions are sent to the user's smartphone as multiple options. The input for this step is various information from the database, and the output is suggested fashion styles.

[1265] Step 5:

[1266] The two steps are to provide the generated fashion suggestions to the user and to use augmented reality technology to display the generated fashion suggestions as a virtual try-on. Specifically, the user browses the suggested fashion styles using a smartphone app. To display the selected style as a virtual try-on, the smartphone camera and AR technology (e.g., ARKit) are used to display the user wearing the outfit on the screen. The input of this step is the fashion suggestions, and the output is a video of the virtual try-on.

[1267] Step 6:

[1268] This is a means of tracking users' sharing behavior and awarding points. Specifically, when a user takes an action such as sharing a suggested fashion style on social media, the server tracks this and awards points appropriately. The points are registered in a database and can be used by the user at online flea markets, etc. The input to this step is data on the user's sharing behavior, and the output is the awarded point information.

[1269] 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.

[1270] MODE FOR CARRYING OUT THE INVENTION

[1271] This invention is an AI platform that makes sustainable fashion recommendations based on a user's individual information and emotions. This platform efficiently collects and analyzes personal data entered by the user and information about the clothing they own, and then recommends optimal fashion styles. In addition, by combining it with an emotion engine, it provides even more personalized fashion recommendations based on the user's emotions. A specific embodiment of the system is shown below.

[1272] System configuration

[1273] The system consists of the following main components:

[1274] 1. User terminal: A device used to input and transmit personal data, emotional information, and clothing image data. Typically, a smartphone or tablet is used.

[1275] 2. Server: A central processing unit that analyzes personal data, clothing information, and emotional information to generate fashion suggestions.

[1276] 3. Database: A data storage system that stores and manages all collected data.

[1277] 4. Emotion Engine: It has the ability to recognize the user's emotions and adapt fashion suggestions based on those emotions.

[1278] Program processing flow

[1279] 1. Collection of personal data

[1280] The user inputs personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics from the terminal.

[1281] The terminal transmits the input data to the server.

[1282] The server analyzes the received personal data and stores it in a database.

[1283] 2. Collection of clothing data

[1284] The user takes a picture of the clothes they are holding with the device.

[1285] The device sends the captured image to the server.

[1286] The server uses image analysis algorithms to extract information about the clothing (color, material, brand, etc.).

[1287] The extracted information is stored in a database.

[1288] 3. Collecting emotional information

[1289] The user takes a photo of their facial expression, records audio, or enters text on their device.

[1290] The device transmits the collected emotion information to the server.

[1291] The emotion engine analyzes this data and recognizes the user's emotions.

[1292] The recognized emotion information is stored in a database.

[1293] 4. Obtaining weather and trend information

[1294] The server obtains weekly weather forecasts and trend information through an external API.

[1295] The information obtained is also stored in a database.

[1296] 5. Fashion Proposal Generation

[1297] The server generates optimal fashion suggestions for the user based on personal data, clothing data, emotional information, weather information, and trend information.

[1298] The generated suggestions are sent to the user's terminal as multiple options.

[1299] 6. Selection and Display of Proposals

[1300] The user selects one of the fashion suggestions provided by the terminal.

[1301] The terminal generates an image of the outfit in the selected fashion style and displays it to the user.

[1302] 7. Provision of Additional Features

[1303] Based on the generated outfit image, users can take actions such as listing the item at a flea market, sharing it on social media, and earning points.

[1304] The server tracks these actions and awards incentives such as points as appropriate.

[1305] Specific examples of processing

[1306] Example 1: Entering and saving personal data

[1307] User A opens the smartphone app and enters his / her hobbies and preferences (e.g., he / she likes casual style), physical characteristics (e.g., he / she is 165 cm tall and weighs 55 kg), and lifestyle characteristics (e.g., he / she commutes to the office five days a week). The device sends this data to the server, which then stores the received data in a database.

[1308] Example 2: Uploading and analyzing clothing images

[1309] User A takes a photo of his casual shirt and jeans with his smartphone. The device sends the image to the server, which analyzes it and extracts the shirt's color, material, and brand information. This information is then stored in a database.

[1310] Example 3: Collecting and analyzing emotional information

[1311] User A takes a photo of their facial expression with their smartphone. The device sends the image data to the server, where the emotion engine analyzes the data and recognizes User A's emotions, such as "happy" or "sad." The recognized emotional information is stored in a database.

[1312] Example 4: Generating and selecting fashion suggestions

[1313] The server generates multiple fashion styles based on user A's personal data, clothing data, acquired weather information (e.g., sunny, temperature 20 degrees), trend information, and recognized emotional information. Suggested styles are displayed to user A, and user A selects one. Based on the selected style, an outfit image is generated and displayed on the smartphone.

[1314] Example 5: Using additional features

[1315] User A shares the created outfit image on a social networking site. The server tracks this action and awards points to User A. User A can use the points to purchase new clothes at an online flea market.

[1316] In this way, the present invention makes it possible to propose optimal fashion based on the user's individual information and emotional information, thereby realizing a sustainable fashion lifestyle.

[1317] The processing flow will be explained below.

[1318] Program processing flow and specific explanation

[1319] 1. Collection of personal data

[1320] Step 1:

[1321] The user inputs personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics from the terminal.

[1322] Specific operation: The user enters information such as "height 165cm," "weight 55kg," and "likes casual style" into the app's input form.

[1323] Step 2:

[1324] The terminal transmits the entered personal data to the server.

[1325] What it does: The app sends the collected data to the server as an HTTP request.

[1326] Step 3:

[1327] The server stores the received personal data in a database.

[1328] What happens: The server receives the data, formats it appropriately, and stores it in the user profile table in the database.

[1329] 2. Collection of clothing data

[1330] Step 1:

[1331] The user takes a picture of the clothes they are holding with the device.

[1332] What happens: A user uses the app to take a photo of an item of clothing.

[1333] Step 2:

[1334] The terminal transmits the captured image data to the server.

[1335] What happens: The app encodes the image data and sends it to the server.

[1336] Step 3:

[1337] The server uses image analysis algorithms to extract information about the clothing (color, material, brand, etc.).

[1338] Specific operation: The server inputs the received image into the machine learning model and obtains the analysis result (e.g., "red cotton shirt" or "blue denim jeans").

[1339] Step 4:

[1340] The server stores the extracted clothing information in a database.

[1341] Specific operation: The server saves the analysis results in the clothing information table of the database.

[1342] 3. Collecting emotional information

[1343] Step 1:

[1344] The user takes a photo of their facial expression, records audio, or enters text on their device.

[1345] Specific actions: The user takes a photo of their facial expression with their smartphone camera, records audio using the microphone, or enters text about their mood.

[1346] Step 2:

[1347] The device transmits the collected emotional information to a server.

[1348] Specific operation: Send captured image data, recorded audio data, or text data to the server.

[1349] Step 3:

[1350] The emotion engine analyzes this data and recognizes the user's emotions.

[1351] What it does: The emotion engine uses image analysis, audio analysis, or natural language processing to determine the user's emotion (e.g., "happiness," "sadness," "surprise," etc.).

[1352] Step 4:

[1353] The server stores the recognized emotion information in a database.

[1354] Specific operation: The recognized emotion information is added or updated to the user profile in the database.

[1355] 4. Obtaining weather and trend information

[1356] Step 1:

[1357] The server retrieves the weekly weather forecast through an external API.

[1358] Specific operation: The server sends a request to the weather API to obtain weekly weather data (e.g., "sunny, temperature 20 degrees").

[1359] Step 2:

[1360] The server obtains trend information through an external API.

[1361] Specific operation: The server sends a request to the trend information API to obtain the latest fashion trend information (for example, "casual style is in fashion").

[1362] 5. Fashion Proposal Generation

[1363] Step 1:

[1364] The server generates fashion suggestions based on personal data, clothing data, emotional information, weather information, and trend information.

[1365] How it works: The server aggregates all the data and uses machine learning algorithms to generate the best fashion style for the user (e.g., "a casual shirt and jeans combination").

[1366] Step 2:

[1367] The server transmits the generated fashion suggestions to the terminal.

[1368] What it does: The server sends the suggestions in JSON format to the device, and the app reads and displays them.

[1369] 6. Selection and Display of Proposals

[1370] Step 1:

[1371] The user selects one of the fashion suggestions provided by the terminal.

[1372] Specific behavior: The user taps to select one of the suggestions displayed in the app.

[1373] Step 2:

[1374] The terminal generates an image of the outfit in the selected fashion style and displays it to the user.

[1375] What it does: The app generates a visual image of the outfit based on the selected style and displays it to the user.

[1376] 7. Provision of Additional Features

[1377] Step 1:

[1378] Based on the generated outfit image, users can take actions such as listing the outfit at a flea market, sharing it on social media, and receiving points.

[1379] Specific operation: The user presses the "Share to social media" button within the app to post their outfit.

[1380] Step 2:

[1381] The server tracks these actions and awards incentives such as points as appropriate.

[1382] Specific operation: The server analyzes the user's action log and processes the points to add to the user's account.

[1383] The above is a specific processing flow that combines the emotion engine of the present invention. We have explained in detail how the user, terminal, and server operate at each step. This makes it possible to propose more personalized fashion based on the user's emotional information, helping to realize a sustainable fashion lifestyle.

[1384] Example 2

[1385] 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."

[1386] Conventional fashion suggestion systems make suggestions based on a user's personal data and clothing information, but they have not yet realized personalized suggestions based on the user's emotional information. It is also difficult to efficiently incorporate weather and trend information and reflect it in suggestions. This makes it difficult to make optimal fashion suggestions for users. Therefore, the objective of this invention is to provide more accurate and sustainable fashion suggestions by comprehensively considering a user's personal data, clothing information, emotional information, weather information, and trend information.

[1387] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1388] In this invention, the server includes means for receiving personal data entered by the user, means for saving the entered personal data in a database, means for receiving images of clothes owned by the user, means for analyzing the received image data and extracting clothing information, means for saving the extracted clothing information in the database, means for collecting user emotion information, means for analyzing the collected emotion information and recognizing emotions, means for saving the recognized emotion information in the database, means for acquiring weather information and trend information from outside, means for generating fashion suggestions based on the personal data, clothing information, emotion information, weather information, and trend information, and means for providing the generated fashion suggestions to the user. This makes it possible to provide personalized fashion suggestions that comprehensively incorporate the user's emotion information and external information.

[1389] "Personal data" refers to individual information about a user, such as the user's hobbies, preferences, physical characteristics, and lifestyle characteristics.

[1390] A "database" is an information system for storing and managing collected data.

[1391] "Clothing image" is image data of a photograph of clothing owned by the user.

[1392] "Image data" is data that represents a photographed image of clothing in digital format.

[1393] "Image analysis" is the process of extracting information such as clothing color, material, and brand from the received image data.

[1394] "Emotion information" is data related to emotions obtained from the user's facial expressions, voice, text input, etc.

[1395] The "emotion engine" is a system that analyzes collected emotional information and recognizes the user's emotions.

[1396] "Weather information" is information about the weather obtained from external weather data.

[1397] "Trend information" refers to information about trends obtained from the fashion industry, social media, etc.

[1398] "Fashion suggestions" are suggestions for fashion styles generated based on personal data, clothing information, emotional information, weather information, and trend information.

[1399] A "wearing image" is a visual image of what the user will look like wearing the clothes, generated based on the selected fashion suggestion.

[1400] "Listing at a flea market" refers to listing an item on an online marketplace based on a user-selected outfit image.

[1401] "SNS posting" means sharing the selected outfit image on a social networking service.

[1402] "Points awarded" are reward points given to users when they perform specific actions within the system.

[1403] MODE FOR CARRYING OUT THE INVENTION

[1404] This invention is a system for proposing sustainable fashion based on a user's individual information and emotions. This system efficiently collects and analyzes personal data and clothing information entered by the user, and proposes optimal fashion styles. In addition, by combining it with an emotion engine, it provides even more personalized fashion suggestions based on the user's emotions.

[1405] System configuration

[1406] The system consists of the following main components:

[1407] 1. User terminal: A device used to input and transmit personal data, emotional information, and clothing image data. Typically, a smartphone or tablet is used.

[1408] 2. Server: A central processing unit that analyzes personal data, clothing information, and emotional information to generate fashion suggestions.

[1409] 3. Database: An information system that stores and manages all collected data.

[1410] 4. Emotion Engine: A system that has the ability to recognize the user's emotions and adapt fashion suggestions based on those emotions.

[1411] Hardware and Software Examples

[1412] 1. User device: Smartphone (e.g., iPhone), tablet (e.g., iPad)

[1413] 2. Server: High-performance server (e.g. AWS EC2)

[1414] 3. Database: Relational database (e.g. MySQL)

[1415] 4. Emotion engine: Emotion analysis software (e.g., IBM Watson Tone Analyzer)

[1416] 5. Image analysis algorithm: Image analysis API (e.g. Azure Computer Vision API)

[1417] 6. External API: Weather forecast API (e.g. OpenWeatherMap API)

[1418] Program processing

[1419] When a user launches the dedicated app on their device, they can enter personal data such as their hobbies, preferences, physical characteristics such as height and weight, and lifestyle characteristics. For example, a user might enter data such as "I like casual style, I'm 165cm tall, I weigh 55kg, and I work five days a week."

[1420] The device sends this data to the server's API endpoint. The server validates the received data, confirms that it is in the correct format, and then stores it in a database. The user can also take a photo of their clothing with the device and send the image data to the server. The server uses image analysis algorithms to extract information about the clothing's color, material, and brand, and stores it in a database.

[1421] Users can use their devices to capture facial expressions, record voice, and input text to collect emotional information. The devices then send this emotional data to a server, which then uses an emotion engine to analyze it and recognize the user's emotions. This information is also stored in a database.

[1422] In addition, the server obtains weather forecasts and trend information through external APIs. The obtained information is also stored in a database, and fashion suggestions are generated based on personal data, clothing information, emotional information, weather information, and trend information. Using a generative AI model (e.g., GPT-4), optimal fashion suggestions are generated for the user by inputting a prompt such as, "Please suggest autumn casual fashion that suits the user's height and weight."

[1423] The generated fashion suggestions are sent to the user's device as multiple options, and the user selects one of these suggestions. Based on the selected fashion style, the device generates an outfit image and displays it to the user. For example, a realistic outfit image can be generated using 3D modeling technology.

[1424] Specific examples

[1425] An example of a prompt sentence is, "Please suggest a casual style suitable for sunny days for the user, who is 165 cm tall and weighs 55 kg." Based on the input data and acquired information, the system will make optimal fashion suggestions for the user.

[1426] Users can share the generated outfit images on social media, and the server tracks this action and awards points, which users can use to purchase new clothes at online flea markets.

[1427] In this way, the present invention makes it possible to propose optimal fashion based on the user's individual information and emotional information, thereby realizing a sustainable fashion lifestyle.

[1428] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1429] The flow of this system's program processing

[1430] Step 1: Collecting personal data

[1431] Input: The user inputs personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics from the device.

[1432] Processing: The device validates the personal data entered and ensures that it is in the correct format. For example, the user enters data such as "I like casual style," "I'm 165cm tall," "I weigh 55kg," and "I commute to the office five days a week."

[1433] Output: The device sends this personal data to the server, which analyzes it and stores it in a database.

[1434] Step 2: Collecting clothing data

[1435] Input: The user takes a picture of the clothing they are holding on their device, for example, a casual shirt and jeans.

[1436] Processing: The device sends the captured image data to a server. For example, the device sends the image data to an image analysis API to extract color, material, and brand information.

[1437] Output: The server stores the extracted clothing information in a database.

[1438] Step 3: Collecting emotional information

[1439] Input: The user uses the device to capture facial expressions, record audio, or input text.

[1440] Processing: The device sends these emotion data to the server. For example, a user takes a picture of themselves smiling with their smartphone.

[1441] Output: The server analyzes the user's emotion using the emotion engine and recognizes the user's emotion as "happy." This information is stored in the database.

[1442] Step 4: Get weather and trend information

[1443] Input: The server periodically calls an external API to obtain weather and trend information.

[1444] Processing: The server uses a weather forecast API to retrieve, for example, the "weekly weather forecast." It also scrapes trend information from fashion-related websites and social media.

[1445] Output: Save the obtained weather and trend information in a database.

[1446] Step 5: Generate fashion suggestions

[1447] Input: Personal data, clothing data, emotional information, weather information, and fashion information are stored in the database.

[1448] Processing: The server inputs a prompt into a generative AI model (e.g., GPT-4) to generate optimal fashion suggestions for the user. For example, the prompt might be, "Please suggest casual autumn fashion that suits the user's height and weight."

[1449] Output: The generated fashion suggestions are sent to the user's device as multiple options.

[1450] Step 6: Select and view suggestions

[1451] Input: The user selects one of the suggestions provided by the device.

[1452] Processing: The device generates a realistic outfit image for the selected fashion style, for example by using 3D modeling technology.

[1453] Output: The generated outfit image is displayed on the device.

[1454] Step 7: Providing additional functionality

[1455] Input: User shares outfit image on social media.

[1456] Processing: The server tracks the sharing action and gives points to the user. For example, when a user clicks the post button on a social networking site, the server records that information.

[1457] Output: The points are added to the user's account, and the user can use them to purchase new clothes at the online flea market.

[1458] (Application example 2)

[1459] 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."

[1460] The shopping experience in physical stores is often not personalized enough for the user because it is often conducted without taking into account information such as the user's clothing, personal preferences, weather, and emotions. This leads to a problem of reduced satisfaction when making purchasing decisions. Furthermore, trying on clothes in physical stores is often time-consuming and inefficient. Therefore, new methods are needed to ensure users have an effective shopping experience.

[1461] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1462] In this invention, the server includes means for receiving personal data and emotional information entered by the user, means for analyzing the data and saving the data in a database, means for receiving images of the user's clothing, analyzing the images to extract and save information about the clothing, means for acquiring weather information and trend information, means for generating fashion suggestions based on the personal data, clothing information, emotional information, weather information, and trend information, means for providing the generated fashion suggestions to the user, means for the user to have a smart try-on experience in a physical store, means for providing virtual try-on experiences in cooperation with a smart mirror installed in the physical store, means for collecting the user's emotional information in real time and reflecting it in fashion suggestions, and means for suggesting optimal fashion items in real time, thereby enabling the user to have a personalized and efficient shopping experience in a physical store.

[1463] "Personal data" refers to data provided by a user, including hobbies, preferences, physical characteristics, lifestyle characteristics, emotional information, and the like.

[1464] "Clothing image" is image data of clothing owned by the user.

[1465] "Weather information" is weekly weather forecast information obtained from an external weather database.

[1466] "Trend information" refers to information about current fashion trends.

[1467] "Emotion information" is data related to emotions acquired from the user's facial expressions, voice, text, etc.

[1468] "Fashion suggestions" are suggestions for fashion styles generated based on personal data, clothing information, weather information, trend information, and emotional information.

[1469] The "smart try-on experience" is a virtual try-on process that users go through in a physical store using a smart mirror.

[1470] A "smart mirror" is a mirror-shaped device that is installed in a physical store and helps users try on clothes virtually.

[1471] "Real-time suggestions" is a system function that instantly suggests fashion items based on the user's current information.

[1472] "Point awarding" refers to providing points as an incentive for a user's specific behavior.

[1473] "SNS posting" refers to the act of a user sharing the created outfit image on a social networking service.

[1474] "Listing at a flea market" refers to the act of a user listing unwanted clothing on a flea market application.

[1475] The present invention is a system for proposing sustainable fashion based on individual data and emotional information of a user. This system is configured using the following hardware and software.

[1476] Hardware Configuration

[1477] User device: A smartphone or tablet through which the user inputs and transmits personal data, emotional information, and clothing image data.

[1478] Server: A central processing unit used to analyze received data and generate fashion suggestions.

[1479] Smart mirror: A device installed in a physical store that allows virtual try-on of clothes.

[1480] Software Configuration

[1481] Frontend: A mobile application based on React Native that provides the user interface and manages data entry and display.

[1482] Backend: A server-side application built with Node.js and Express that analyzes data and generates fashion suggestions.

[1483] Image analysis engine: Uses OpenCV to analyze images of clothing and extract information such as color, material, and brand.

[1484] Sentiment analysis engine: Analyzes user sentiment using Google Cloud Vision AI.

[1485] Database: MongoDB is used to store and manage various data.

[1486] External API: Used to get weather and trend information from OpenWeatherMap and Trendyol.

[1487] System processing overview

[1488] User data entry and collection

[1489] The user enters personal data (hobbies, preferences, physical characteristics, lifestyle characteristics) from the device and sends it to the server. The server receives this data and stores it in a database. For example, a user enters data such as "I like casual style," "I'm 165 cm tall and weigh 55 kg," and "I commute to the office five days a week."

[1490] Clothing data capture and analysis

[1491] The user takes a photo of the clothes they are wearing with their device and sends it to the server. The image data is analyzed by the server, and the color, material, and brand information of the clothes are extracted. For example, the user can take a photo of a "casual shirt" or "jeans."

[1492] Collecting emotional information

[1493] The user takes a photo of their facial expression on the device and sends it to the server, where the emotion analysis engine analyzes the user's emotions (e.g., "happy," "sad," etc.).

[1494] Retrieving External Data

[1495] The server uses an external API to obtain weekly weather and trend information, which is then stored in a database.

[1496] Fashion proposal generation

[1497] Based on the collected personal data, clothing data, emotional information, weather information, and trend information, the AI ​​model generates optimal fashion suggestions, which are then sent to the user's device as multiple options.

[1498] Virtual try-on experience

[1499] When users visit a physical store, they can virtually try on clothes by connecting their smart mirror to their device. The mirror reflects the user's individual data and provides a real-time virtual try-on experience.

[1500] Examples of prompt statements

[1501] Enter your personal data: "Please enter your physical characteristics, hobbies, preferences, and lifestyle characteristics."

[1502] Collecting emotional information: "Facial expressions are captured on camera to analyze current emotions."

[1503] Upload clothing images: "Upload images of your clothing here."

[1504] In this way, the present invention generates optimal fashion suggestions based on the user's personal and emotional information, providing a sustainable shopping experience.

[1505] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1506] Step 1:

[1507] The user enters personal data (hobbies, preferences, physical characteristics, lifestyle characteristics) from their device and sends it to the server. At this time, the data entered may include "I like casual style," "I'm 165cm tall," "I weigh 55kg," and "I commute to the office five days a week." The server analyzes the received data and stores it in a MongoDB database. To store the input data in key-value format, the data is converted to JSON format as part of data processing.

[1508] Step 2:

[1509] A user takes a photo of their clothing on their device and sends it to the server. For example, they upload an image of a casual shirt or jeans. The server uses OpenCV to analyze the received image data and extract information about the clothing (color, material, brand, etc.). The extracted information is stored in a database. The input is image data, and the output is analyzed clothing information.

[1510] Step 3:

[1511] The user takes a photo of their facial expression on their device and sends the image data to the server. The server uses Google Cloud Vision AI to analyze the image and recognize the user's emotional information (e.g., "happy" or "sad"). The recognized emotional information is stored in a database. The input is image data of the facial expression, and the output is text information about the recognized emotion.

[1512] Step 4:

[1513] The server uses external APIs (OpenWeatherMap, Trendyol) to obtain the weekly weather forecast and trend information. This data is also stored in the database. The input is the API request, and the output is the obtained weather forecast and trend information. The weather information includes temperature and weather conditions, and the trend information shows current fashion trends.

[1514] Step 5:

[1515] The server uses a generative AI model to generate fashion suggestions based on personal data, clothing information, emotional information, weather information, and trend information. For example, it may suggest a casual style suitable for a sunny 20-degree day. The generated suggestions are sent to the user's device as multiple options. The input is the aforementioned multiple datasets, and the output is multiple fashion suggestions.

[1516] Step 6:

[1517] The user selects one of the fashion suggestions provided by the terminal. Based on the selected suggestion, the terminal generates an outfit image. This outfit image is displayed to the user in real time. The input is the selected fashion suggestion, and the output is the generated outfit image.

[1518] Step 7:

[1519] When a user visits a physical store, the smart mirror and device are connected. Personal data stored on the device is sent to the mirror, which then provides a virtual try-on experience that reflects that data. For example, the user can check in real time on the smart mirror whether a particular shirt looks good on them. The input is personal data and clothing data, and the output is a real-time virtual try-on image.

[1520] Step 8:

[1521] When a user shares the generated outfit image on SNS, the action is sent to the server and points are awarded. The server tracks this and manages the points system. The points can be used for the next purchase, providing an incentive to the user. The input is the SNS sharing information and the output is the awarded points.

[1522] 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.

[1523] 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.

[1524] 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.

[1525] [Fourth embodiment]

[1526] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1527] 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.

[1528] 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).

[1529] 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.

[1530] 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.

[1531] 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).

[1532] 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.

[1533] 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.

[1534] 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.

[1535] 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.

[1536] 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.

[1537] 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.

[1538] 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."

[1539] MODE FOR CARRYING OUT THE INVENTION

[1540] This invention is an AI platform that proposes sustainable fashion based on the user's individual information. This platform efficiently collects and analyzes personal data and clothing information entered by the user, and proposes optimal fashion styles. A specific embodiment of the system is shown below.

[1541] System configuration

[1542] The system consists of the following main components:

[1543] 1. User terminal: A device used to input and send personal data and clothing image data. Typically, a smartphone or tablet is used.

[1544] 2. Server: A central processing unit that analyzes personal data and clothing information and generates fashion suggestions.

[1545] 3. Database: A data storage system that stores and manages all collected data.

[1546] Program processing flow

[1547] 1. Collection of personal data

[1548] The user inputs personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics from the terminal.

[1549] The terminal transmits the input data to the server.

[1550] The server analyzes the received personal data and stores it in a database.

[1551] 2. Collection of clothing data

[1552] The user takes a picture of the clothes they are holding with the device.

[1553] The device sends the captured image to the server.

[1554] The server uses image analysis algorithms to extract information such as the color, material, and brand of the clothing.

[1555] The extracted information is stored in a database.

[1556] 3. Obtaining weather and trend information

[1557] The server obtains weekly weather forecasts and trend information through an external API.

[1558] The information obtained is also stored in a database.

[1559] 4. Fashion Proposal Generation

[1560] The server generates optimal fashion suggestions for the user based on personal data, clothing data, weather information, and trend information.

[1561] The generated suggestions are sent to the user's terminal as multiple options.

[1562] 5. Selection and display of proposals

[1563] The user selects one of the fashion suggestions provided by the terminal.

[1564] The terminal generates an image of the outfit in the selected fashion style and displays it to the user.

[1565] 6. Provision of Additional Features

[1566] Based on the generated outfit image, users can take actions such as listing the item at a flea market, sharing it on social media, and earning points.

[1567] The server tracks these actions and awards incentives such as points as appropriate.

[1568] Specific examples of processing

[1569] Example 1: Entering and saving personal data

[1570] User A opens the smartphone app and enters his / her hobbies and preferences (e.g., he / she likes casual style), physical characteristics (e.g., he / she is 165 cm tall and weighs 55 kg), and lifestyle characteristics (e.g., he / she commutes to the office five days a week). The device sends this data to the server, which then stores the received data in a database.

[1571] Example 2: Uploading and analyzing clothing images

[1572] User A takes a photo of his casual shirt and jeans with his smartphone. The device sends the image to the server, which analyzes it and extracts the shirt's color, material, and brand information. This information is then stored in a database.

[1573] Example 3: Generating and selecting fashion suggestions

[1574] The server generates multiple fashion styles based on user A's personal data, clothing data, acquired weather information (e.g. sunny, temperature 20 degrees) and trend information. Suggested styles are displayed to user A, and user A selects one. Based on the selected style, an outfit image is generated and displayed on the smartphone.

[1575] Example 4: Using additional features

[1576] User A shares the created outfit image on a social networking site. The server tracks this action and awards points to User A. User A can use the points to purchase new clothes at an online flea market.

[1577] In this way, the present invention makes it possible to provide optimal fashion suggestions based on the user's individual information, thereby realizing a sustainable fashion lifestyle.

[1578] The processing flow will be explained below.

[1579] Program processing flow and specific explanation

[1580] 1. Collection of personal data

[1581] Step 1:

[1582] The user inputs personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics from the terminal.

[1583] Specific operation: The user enters information such as "height 165cm," "weight 55kg," and "likes casual style" into the app's input form.

[1584] Step 2:

[1585] The terminal transmits the entered personal data to the server.

[1586] What it does: The app sends the collected data to the server as an HTTP request.

[1587] Step 3:

[1588] The server stores the received personal data in a database.

[1589] What happens: The server receives the data, formats it appropriately, and stores it in the user profile table in the database.

[1590] 2. Collection of clothing data

[1591] Step 1:

[1592] The user takes a picture of the clothes they are holding with the device.

[1593] What happens: A user uses the app to take a photo of an item of clothing.

[1594] Step 2:

[1595] The terminal transmits the captured image data to the server.

[1596] What happens: The app encodes the image data and sends it to the server.

[1597] Step 3:

[1598] The server uses image analysis algorithms to extract information about the clothing (color, material, brand, etc.).

[1599] Specific operation: The server inputs the received image into the machine learning model and obtains the analysis result (e.g., "red cotton shirt" or "blue denim jeans").

[1600] Step 4:

[1601] The server stores the extracted clothing information in a database.

[1602] Specific operation: The server saves the analysis results in the clothing information table of the database.

[1603] 3. Obtaining weather and trend information

[1604] Step 1:

[1605] The server retrieves the weekly weather forecast through an external API.

[1606] Specific operation: The server sends a request to the weather API to obtain weekly weather data (e.g., "sunny, temperature 20 degrees").

[1607] Step 2:

[1608] The server obtains trend information through an external API.

[1609] Specific operation: The server sends a request to the trend information API to obtain the latest fashion trend information (for example, "casual style is in fashion").

[1610] 4. Fashion Proposal Generation

[1611] Step 1:

[1612] The server generates fashion suggestions based on personal data, clothing data, weather information, and trend information.

[1613] How it works: The server aggregates all the data and uses machine learning algorithms to generate the best fashion style for the user (e.g., "a casual shirt and jeans combination").

[1614] Step 2:

[1615] The server transmits the generated fashion suggestions to the terminal.

[1616] What it does: The server sends the suggestions in JSON format to the device, and the app reads and displays them.

[1617] 5. Selection and display of proposals

[1618] Step 1:

[1619] The user selects one of the fashion suggestions provided by the terminal.

[1620] Specific behavior: The user taps to select one of the suggestions displayed in the app.

[1621] Step 2:

[1622] The terminal generates an image of the outfit in the selected fashion style and displays it to the user.

[1623] What it does: The app generates a visual image of the outfit based on the selected style and displays it to the user.

[1624] 6. Provision of Additional Features

[1625] Step 1:

[1626] Based on the generated outfit image, users can take actions such as listing the outfit at a flea market, sharing it on social media, and receiving points.

[1627] Specific operation: The user presses the "Share to social media" button within the app to post their outfit.

[1628] Step 2:

[1629] The server tracks these actions and awards incentives such as points as appropriate.

[1630] Specific operation: The server analyzes the user's action log and processes the points to add to the user's account.

[1631] The above is a specific processing flow for implementing the present invention. We have explained in detail how the user, terminal, and server operate in each step. This allows us to build an optimal system for realizing a sustainable fashion life.

[1632] Example 1

[1633] 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."

[1634] The present invention aims to efficiently and effectively suggest sustainable fashion based on individual user information. It also aims to provide a system that provides optimal fashion suggestions by analyzing the user's clothing information in detail and combining it with current weather and fashion trends. Furthermore, the system allows users to easily use the suggested fashion styles to access additional features such as posting on social media, listing at flea markets, and earning points.

[1635] 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.

[1636] In this invention, the server includes a means for transmitting data and images input by the user to the server, a means for the server to extract clothing information using an image analysis algorithm, and a means for generating suggestions using a generative AI model. This allows for rapid analysis of personal data and clothing information, enabling optimal fashion suggestions to be made to the user. Furthermore, by obtaining weather and fashion trends via an external API, suggestions based on real-time information are possible. Furthermore, based on the outfit image selected by the user, the system can list the item at a flea market, post on social media, and award points, and these actions are tracked to provide incentives, improving user convenience.

[1637] "User" means an individual or organization that uses the system.

[1638] A "terminal" is a device used by a user, such as a smartphone or tablet.

[1639] A "server" is a computer device that performs central processing such as analyzing and storing data and generating fashion suggestions.

[1640] A "data storage device" is a system that stores and manages personal data, clothing information, weather information, fashion information, etc.

[1641] An "image analysis algorithm" is a software program that analyzes received image data to extract information.

[1642] A "generative AI model" is an algorithmic model that uses artificial intelligence to generate fashion suggestions for users.

[1643] A "prompt" refers to the input information or question given to a generative AI model.

[1644] "Fashion suggestions" are recommendations for fashion styles that suit the user.

[1645] "Outfit images" are images or graphics that visually represent the proposed fashion style.

[1646] An "external API" is an interface for obtaining information in collaboration with external services.

[1647] "Weather information" refers to meteorological data such as the weekly weather forecast and temperature.

[1648] "Trend information" is data about current fashion trends.

[1649] "Flea market listing" refers to the act of a user listing unwanted clothing or other items on an online marketplace.

[1650] "SNS posting" refers to the act of a user sharing a proposed fashion style or outfit image on a social networking service.

[1651] "Point awarding" is a system that provides points as an incentive for user actions.

[1652] "Action tracking" means tracking and recording user behavior.

[1653] The present invention is an AI platform that proposes sustainable fashion based on individual user information. A specific embodiment of the system is described below.

[1654] The system mainly consists of three main components: a user terminal, a server, and a data storage device. The user terminal is typically a smartphone or tablet, and is used to input and transmit personal data and clothing image data. The server is a central processing unit that analyzes personal data and clothing information and generates fashion suggestions. The data storage device is a system that stores and manages all collected data.

[1655] First, a user uses a smartphone or tablet to input personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics. For example, the user inputs information such as that they like casual clothing, are 165 cm tall, weigh 55 kg, and commute to the office five days a week. The device sends this data to a server, which then analyzes the received data and stores it in a data storage device.

[1656] Next, the user takes a picture of the clothes they are wearing with their device. For example, they can take a picture of a casual shirt and jeans. The device sends the image to the server, which then uses an image analysis algorithm (e.g., Amazon Rekognition, Google Cloud Vision) to extract information about the clothes, such as color, material, and brand. The extracted information is then stored on a data storage device.

[1657] In addition, the server obtains weekly weather forecasts and trend information through external APIs (e.g., OpenWeather, FashionAPI). The obtained information is also stored in the data storage device. For example, the weather forecast for next week is sunny with a temperature of 20 degrees.

[1658] This system uses a generative AI model (e.g., GPT-3) to generate fashion suggestions. The server inputs a prompt to generate the optimal fashion style based on collected personal data, clothing data, weather information, and trend information, and the generative AI model outputs the suggestion. The prompt uses the following text: "Provide fashion suggestions based on the personal data entered by User A (hobbies and preferences: casual style, height 165 cm, weight 55 kg, commute 5 days a week), an image of a casual shirt and jeans, weather information (sunny, temperature 20 degrees), and trend information."

[1659] The user's device receives the fashion suggestions sent from the server and displays them to the user. The user selects one of the suggestions provided, and an outfit image based on the selected style is generated and displayed on the smartphone. The user can also take actions based on the generated outfit image, such as listing it at a flea market, sharing it on social media, or receiving points. The server tracks these actions and awards incentives such as points as appropriate.

[1660] In this way, the present invention can make optimal fashion suggestions based on the user's individual information, and can comprehensively utilize the user's clothing, the latest weather information, trend information, etc. In addition, the user can further enhance their fashion life by using additional functions.

[1661] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1662] Step 1:

[1663] The user uses a smartphone or tablet to enter personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics. Specifically, the user enters information such as "I like casual style," "I'm 165cm tall," "I weigh 55kg," and "I commute to the office five days a week" into the app's input form.

[1664] Input data: User's hobbies, preferences, physical characteristics, lifestyle characteristics, etc.

[1665] Output data: Personal data sent from the device to the server

[1666] Step 2:

[1667] The device sends the entered personal data to the server, which analyzes the received personal data, converts it into an appropriate format, and stores it in a data storage device, where the personal data is stored in a structured format.

[1668] Input data: Personal data sent from the device

[1669] Output data: Personal data stored in the data storage device

[1670] Step 3:

[1671] The user takes a picture of their clothing with their smartphone, or uses the app's camera function to take a picture of their belongings, such as a casual shirt or jeans.

[1672] Input data: Images of clothing taken by the user

[1673] Output data: Image file of the clothing saved on the device

[1674] Step 4:

[1675] The device sends the captured image to a server, which uses image analysis algorithms (e.g., Amazon Rekognition, Google Cloud Vision) to extract information such as the color, material, and brand of the clothing. The extracted information is then stored on a data storage device.

[1676] Input data: Clothing images sent from the device

[1677] Output data: Clothing information (color, material, brand, etc.) stored in the data storage device

[1678] Step 5:

[1679] The server uses external APIs (e.g., OpenWeather, FashionAPI) to obtain weekly weather forecasts and trend information. The obtained information is also stored in the data storage device.

[1680] Input data: Weather and trend information requests obtained from external APIs

[1681] Output data: Weather information and trend information stored in a data storage device

[1682] Step 6:

[1683] The server generates fashion suggestions using a generative AI model (e.g., GPT-3) based on personal data, clothing data, weather information, and trend information. The generated suggestions are sent to the device as multiple options. The server inputs the following prompt into the generative AI model: "Provide fashion suggestions based on the personal data entered by User A (hobbies and preferences: casual style, height 165 cm, weight 55 kg, commutes 5 days a week), images of casual shirts and jeans, weather information (sunny, temperature 20 degrees), and trend information."

[1684] Input data: personal data, clothing data, weather information, fashion information

[1685] Output data: Fashion suggestions output by the generative AI model

[1686] Step 7:

[1687] The terminal displays the fashion suggestions received from the server to the user, who then selects one of the suggestions.

[1688] Input data: Fashion suggestions received from the server

[1689] Output data: The fashion style selected by the user

[1690] Step 8:

[1691] The device generates an outfit image based on the selected style and displays it to the user. The generated outfit image is displayed on the user's smartphone screen.

[1692] Input data: Fashion style selected by the user

[1693] Output data: Image of the garment displayed on the device

[1694] Step 9:

[1695] Users can share the created outfit images on social media or put them up for sale at a flea market. The server tracks these actions and awards points to users, which can be used to make purchases at the online flea market.

[1696] Input data: User actions (SNS sharing, flea market listing, etc.)

[1697] Output data: Actions recorded by the server and points awarded

[1698] (Application example 1)

[1699] 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."

[1700] Conventional fashion suggestion systems suggest appropriate fashion styles based on a user's individual information, but the suggestions lack practicality and a sense of actually trying on the clothes. Furthermore, the fashion suggestions are often unsustainable. Furthermore, the functionality for tracking user behavior and providing incentives is insufficient. Therefore, users do not get the feeling of actually trying on the suggested clothes, making it difficult to increase their motivation to purchase.

[1701] 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.

[1702] In this invention, the server includes means for receiving personal data entered by the user, means for saving the entered personal data in a database, means for receiving images of clothing owned by the user, means for analyzing the received image data and extracting clothing information, means for saving the extracted clothing information in a database, means for externally acquiring weather information and trend information, means for generating fashion suggestions based on the personal data, clothing information, weather information, and trend information, means for providing the generated fashion suggestions to the user, means for using augmented reality technology to display the generated fashion suggestions as a virtual try-on, and means for tracking the user's shared behavior and awarding points. This allows the user to feel as if they are actually trying on the suggested clothing, increasing their desire to purchase and making it possible to award incentives according to the user's behavior.

[1703] "Personal data" refers to information entered by the user regarding hobbies, preferences, physical characteristics, and lifestyle characteristics.

[1704] A "database" is a system that stores and manages collected personal data, clothing information, weather information, fashion information, etc.

[1705] "Clothing image" is photographic data of clothing owned by the user.

[1706] "Means for analyzing image data" refers to technology, such as image recognition algorithms, for extracting information such as color, material, and brand from images of clothing.

[1707] "Weather information" is weekly weather forecast data obtained from an external API.

[1708] "Trend information" is data on the latest fashion trends obtained from external APIs.

[1709] "Fashion suggestions" refers to styling recommendations generated based on personal data, clothing information, weather information, and trend information.

[1710] "Means for providing" refers to a medium for notifying or displaying the generated fashion suggestions to the user, such as a smartphone app or a web interface.

[1711] "Augmented reality technology" is a technology that enables virtual try-on, and refers to the technology that displays virtual elements overlaid on real-world images.

[1712] "Sharing behavior" refers to actions such as users sharing fashion suggestions on social media.

[1713] The "point awarding means" is a system that awards points as a reward according to the user's actions.

[1714] MODE FOR CARRYING OUT THE INVENTION

[1715] System Overview

[1716] The system of the present invention is mainly composed of a server, a user terminal, and a database. This system collects and analyzes personal data and clothing images entered by the user, and then proposes optimal fashion. Specific embodiments for each step are described below.

[1717] Hardware and software configuration

[1718] 1. User Device:

[1719] It consists of mobile devices such as smartphones and tablets.

[1720] Enter and send personal data and clothing images.

[1721] 2. Server:

[1722] It functions as a central processing unit and analyzes data sent by users.

[1723] It also acquires necessary external data (weather information and trend information).

[1724] 3. Database:

[1725] Store and manage collected personal data, image data, weather information, and trend information.

[1726] Technology used

[1727] Image analysis algorithm: Using libraries such as OpenCV and TensorFlow, color information, material, brand, etc. are extracted from clothing images uploaded by users.

[1728] Weather Information API: Use WeatherAPI etc. to get the weekly weather forecast.

[1729] Trend information API: Obtain the latest fashion trends using FashionAPI etc.

[1730] Augmented reality technology: Using Apple's ARKit and Google's ARCore, the company offers a feature that allows users to virtually try on suggested fashion items.

[1731] Detailed System Operation

[1732] 1. Entering and saving personal data

[1733] The user opens the smartphone app and enters their hobbies, preferences, physical characteristics, lifestyle characteristics, etc.

[1734] The terminal transmits the input data to the server, and the server stores the received data in a database.

[1735] 2. Upload and analyze clothing images

[1736] Users take photos of their clothing and upload the images through a smartphone app.

[1737] The server uses image analysis algorithms (e.g., OpenCV) to extract information such as color, material, and brand from the image and store it in a database.

[1738] 3. Obtaining weather and trend information

[1739] The server uses external APIs (such as WeatherAPI and FashionAPI) to obtain weekly weather forecasts and the latest fashion trends, and also stores this information in the database.

[1740] 4. Fashion Proposal Generation

[1741] The server generates optimal fashion suggestions for the user based on personal data, clothing information, weather information, and trend information.

[1742] The suggestions are sent to the user terminal as multiple styles.

[1743] 5. Viewing proposals and virtual try-on

[1744] Users use their smartphones to browse suggested fashion styles.

[1745] Augmented reality technology is used to display the user's chosen style as a virtual try-on.

[1746] 6. Sharing actions and reward points

[1747] When a user takes an action such as sharing suggested fashion on social media, this is tracked and points are awarded.

[1748] Points can be used at online flea markets and other places.

[1749] Specific examples

[1750] 1. Examples of inputting and saving personal data

[1751] "Generate fashion suggestions for a user who likes casual style, is 165cm tall, weighs 55kg, and commutes to the office five days a week."

[1752] 2. Example of uploading and analyzing clothing images

[1753] "Users take a photo of their casual shirt and jeans with their smartphone and upload it through the app."

[1754] 3. Example of fashion proposal generation and selection

[1755] "Suggested fashion styles are displayed based on the user's personal data, clothing data, and acquired weather information (e.g., sunny, temperature 20 degrees)."

[1756] In this way, the present invention makes optimal fashion suggestions to users and provides them with the experience of virtually trying on clothes, thereby increasing user satisfaction and willingness to purchase.

[1757] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1758] Step 1:

[1759] The two steps are to receive personal data entered by the user and to store the entered personal data in a database. Specifically, the user opens a smartphone app and enters personal data such as their hobbies, preferences, physical characteristics, and lifestyle characteristics. The entered data is sent from the smartphone to a server, which receives it and stores it in a database. The input of this step is the user's personal data, and the output is the stored data.

[1760] Step 2:

[1761] The system has two steps: a means for receiving images of the clothes the user owns, and a means for analyzing the received image data and extracting information about the clothes. Specifically, the user takes an image of the clothes they own with their smartphone and uploads the image to the server via the app. The server uses an image analysis algorithm (e.g., OpenCV) to extract information about the clothes, such as color, material, and brand, and the extracted information is stored in a database. The input of this step is the image of the clothes, and the output is the extracted clothing information.

[1762] Step 3:

[1763] This is a means of obtaining weather information and trend information from an external source. Specifically, the server uses an external API (e.g., WeatherAPI, FashionAPI) to obtain the weekly weather forecast and the latest fashion trends. The obtained information is also stored in the database. The input to this step is a request to the external API, and the output is the obtained weather information and trend information.

[1764] Step 4:

[1765] This is a means of generating fashion suggestions based on personal data, clothing information, weather information, and trend information. Specifically, the server uses an AI model to generate optimal fashion suggestions for the user based on the personal data, clothing information, weather information, and trend information stored in the database. The generated suggestions are sent to the user's smartphone as multiple options. The input for this step is various information from the database, and the output is suggested fashion styles.

[1766] Step 5:

[1767] The two steps are to provide the generated fashion suggestions to the user and to use augmented reality technology to display the generated fashion suggestions as a virtual try-on. Specifically, the user browses the suggested fashion styles using a smartphone app. To display the selected style as a virtual try-on, the smartphone camera and AR technology (e.g., ARKit) are used to display the user wearing the outfit on the screen. The input of this step is the fashion suggestions, and the output is a video of the virtual try-on.

[1768] Step 6:

[1769] This is a means of tracking users' sharing behavior and awarding points. Specifically, when a user takes an action such as sharing a suggested fashion style on social media, the server tracks this and awards points appropriately. The points are registered in a database and can be used by the user at online flea markets, etc. The input to this step is data on the user's sharing behavior, and the output is the awarded point information.

[1770] 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.

[1771] MODE FOR CARRYING OUT THE INVENTION

[1772] This invention is an AI platform that makes sustainable fashion recommendations based on a user's individual information and emotions. This platform efficiently collects and analyzes personal data entered by the user and information about the clothing they own, and then recommends optimal fashion styles. In addition, by combining it with an emotion engine, it provides even more personalized fashion recommendations based on the user's emotions. A specific embodiment of the system is shown below.

[1773] System configuration

[1774] The system consists of the following main components:

[1775] 1. User terminal: A device used to input and transmit personal data, emotional information, and clothing image data. Typically, a smartphone or tablet is used.

[1776] 2. Server: A central processing unit that analyzes personal data, clothing information, and emotional information to generate fashion suggestions.

[1777] 3. Database: A data storage system that stores and manages all collected data.

[1778] 4. Emotion Engine: It has the ability to recognize the user's emotions and adapt fashion suggestions based on those emotions.

[1779] Program processing flow

[1780] 1. Collection of personal data

[1781] The user inputs personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics from the terminal.

[1782] The terminal transmits the input data to the server.

[1783] The server analyzes the received personal data and stores it in a database.

[1784] 2. Collection of clothing data

[1785] The user takes a picture of the clothes they are holding with the device.

[1786] The device sends the captured image to the server.

[1787] The server uses image analysis algorithms to extract information about the clothing (color, material, brand, etc.).

[1788] The extracted information is stored in a database.

[1789] 3. Collecting emotional information

[1790] The user takes a photo of their facial expression, records audio, or enters text on their device.

[1791] The device transmits the collected emotion information to the server.

[1792] The emotion engine analyzes this data and recognizes the user's emotions.

[1793] The recognized emotion information is stored in a database.

[1794] 4. Obtaining weather and trend information

[1795] The server obtains weekly weather forecasts and trend information through an external API.

[1796] The information obtained is also stored in a database.

[1797] 5. Fashion Proposal Generation

[1798] The server generates optimal fashion suggestions for the user based on personal data, clothing data, emotional information, weather information, and trend information.

[1799] The generated suggestions are sent to the user's terminal as multiple options.

[1800] 6. Selection and Display of Proposals

[1801] The user selects one of the fashion suggestions provided by the terminal.

[1802] The terminal generates an image of the outfit in the selected fashion style and displays it to the user.

[1803] 7. Provision of Additional Features

[1804] Based on the generated outfit image, users can take actions such as listing the item at a flea market, sharing it on social media, and earning points.

[1805] The server tracks these actions and awards incentives such as points as appropriate.

[1806] Specific examples of processing

[1807] Example 1: Entering and saving personal data

[1808] User A opens the smartphone app and enters his / her hobbies and preferences (e.g., he / she likes casual style), physical characteristics (e.g., he / she is 165 cm tall and weighs 55 kg), and lifestyle characteristics (e.g., he / she commutes to the office five days a week). The device sends this data to the server, which then stores the received data in a database.

[1809] Example 2: Uploading and analyzing clothing images

[1810] User A takes a photo of his casual shirt and jeans with his smartphone. The device sends the image to the server, which analyzes it and extracts the shirt's color, material, and brand information. This information is then stored in a database.

[1811] Example 3: Collecting and analyzing emotional information

[1812] User A takes a photo of their facial expression with their smartphone. The device sends the image data to the server, where the emotion engine analyzes the data and recognizes User A's emotions, such as "happy" or "sad." The recognized emotional information is stored in a database.

[1813] Example 4: Generating and selecting fashion suggestions

[1814] The server generates multiple fashion styles based on user A's personal data, clothing data, acquired weather information (e.g., sunny, temperature 20 degrees), trend information, and recognized emotional information. Suggested styles are displayed to user A, and user A selects one. Based on the selected style, an outfit image is generated and displayed on the smartphone.

[1815] Example 5: Using additional features

[1816] User A shares the created outfit image on a social networking site. The server tracks this action and awards points to User A. User A can use the points to purchase new clothes at an online flea market.

[1817] In this way, the present invention makes it possible to propose optimal fashion based on the user's individual information and emotional information, thereby realizing a sustainable fashion lifestyle.

[1818] The processing flow will be explained below.

[1819] Program processing flow and specific explanation

[1820] 1. Collection of personal data

[1821] Step 1:

[1822] The user inputs personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics from the terminal.

[1823] Specific operation: The user enters information such as "height 165cm," "weight 55kg," and "likes casual style" into the app's input form.

[1824] Step 2:

[1825] The terminal transmits the entered personal data to the server.

[1826] What it does: The app sends the collected data to the server as an HTTP request.

[1827] Step 3:

[1828] The server stores the received personal data in a database.

[1829] What happens: The server receives the data, formats it appropriately, and stores it in the user profile table in the database.

[1830] 2. Collection of clothing data

[1831] Step 1:

[1832] The user takes a picture of the clothes they are holding with the device.

[1833] What happens: A user uses the app to take a photo of an item of clothing.

[1834] Step 2:

[1835] The terminal transmits the captured image data to the server.

[1836] What happens: The app encodes the image data and sends it to the server.

[1837] Step 3:

[1838] The server uses image analysis algorithms to extract information about the clothing (color, material, brand, etc.).

[1839] Specific operation: The server inputs the received image into the machine learning model and obtains the analysis result (e.g., "red cotton shirt" or "blue denim jeans").

[1840] Step 4:

[1841] The server stores the extracted clothing information in a database.

[1842] Specific operation: The server saves the analysis results in the clothing information table of the database.

[1843] 3. Collecting emotional information

[1844] Step 1:

[1845] The user takes a photo of their facial expression, records audio, or enters text on their device.

[1846] Specific actions: The user takes a photo of their facial expression with their smartphone camera, records audio using the microphone, or enters text about their mood.

[1847] Step 2:

[1848] The device transmits the collected emotional information to a server.

[1849] Specific operation: Send captured image data, recorded audio data, or text data to the server.

[1850] Step 3:

[1851] The emotion engine analyzes this data and recognizes the user's emotions.

[1852] What it does: The emotion engine uses image analysis, audio analysis, or natural language processing to determine the user's emotion (e.g., "happiness," "sadness," "surprise," etc.).

[1853] Step 4:

[1854] The server stores the recognized emotion information in a database.

[1855] Specific operation: The recognized emotion information is added or updated to the user profile in the database.

[1856] 4. Obtaining weather and trend information

[1857] Step 1:

[1858] The server retrieves the weekly weather forecast through an external API.

[1859] Specific operation: The server sends a request to the weather API to obtain weekly weather data (e.g., "sunny, temperature 20 degrees").

[1860] Step 2:

[1861] The server obtains trend information through an external API.

[1862] Specific operation: The server sends a request to the trend information API to obtain the latest fashion trend information (for example, "casual style is in fashion").

[1863] 5. Fashion Proposal Generation

[1864] Step 1:

[1865] The server generates fashion suggestions based on personal data, clothing data, emotional information, weather information, and trend information.

[1866] How it works: The server aggregates all the data and uses machine learning algorithms to generate the best fashion style for the user (e.g., "a casual shirt and jeans combination").

[1867] Step 2:

[1868] The server transmits the generated fashion suggestions to the terminal.

[1869] What it does: The server sends the suggestions in JSON format to the device, and the app reads and displays them.

[1870] 6. Selection and Display of Proposals

[1871] Step 1:

[1872] The user selects one of the fashion suggestions provided by the terminal.

[1873] Specific behavior: The user taps to select one of the suggestions displayed in the app.

[1874] Step 2:

[1875] The terminal generates an image of the outfit in the selected fashion style and displays it to the user.

[1876] What it does: The app generates a visual image of the outfit based on the selected style and displays it to the user.

[1877] 7. Provision of Additional Features

[1878] Step 1:

[1879] Based on the generated outfit image, users can take actions such as listing the outfit at a flea market, sharing it on social media, and receiving points.

[1880] Specific operation: The user presses the "Share to social media" button within the app to post their outfit.

[1881] Step 2:

[1882] The server tracks these actions and awards incentives such as points as appropriate.

[1883] Specific operation: The server analyzes the user's action log and processes the points to add to the user's account.

[1884] The above is a specific processing flow that combines the emotion engine of the present invention. We have explained in detail how the user, terminal, and server operate at each step. This makes it possible to propose more personalized fashion based on the user's emotional information, helping to realize a sustainable fashion lifestyle.

[1885] Example 2

[1886] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1887] Conventional fashion suggestion systems make suggestions based on a user's personal data and clothing information, but they have not yet realized personalized suggestions based on the user's emotional information. It is also difficult to efficiently incorporate weather and trend information and reflect it in suggestions. This makes it difficult to make optimal fashion suggestions for users. Therefore, the objective of this invention is to provide more accurate and sustainable fashion suggestions by comprehensively considering a user's personal data, clothing information, emotional information, weather information, and trend information.

[1888] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1889] In this invention, the server includes means for receiving personal data entered by the user, means for saving the entered personal data in a database, means for receiving images of clothes owned by the user, means for analyzing the received image data and extracting clothing information, means for saving the extracted clothing information in the database, means for collecting user emotion information, means for analyzing the collected emotion information and recognizing emotions, means for saving the recognized emotion information in the database, means for acquiring weather information and trend information from outside, means for generating fashion suggestions based on the personal data, clothing information, emotion information, weather information, and trend information, and means for providing the generated fashion suggestions to the user. This makes it possible to provide personalized fashion suggestions that comprehensively incorporate the user's emotion information and external information.

[1890] "Personal data" refers to individual information about a user, such as the user's hobbies, preferences, physical characteristics, and lifestyle characteristics.

[1891] A "database" is an information system for storing and managing collected data.

[1892] "Clothing image" is image data of a photograph of clothing owned by the user.

[1893] "Image data" is data that represents a photographed image of clothing in digital format.

[1894] "Image analysis" is the process of extracting information such as clothing color, material, and brand from the received image data.

[1895] "Emotion information" is data related to emotions obtained from the user's facial expressions, voice, text input, etc.

[1896] The "emotion engine" is a system that analyzes collected emotional information and recognizes the user's emotions.

[1897] "Weather information" is information about the weather obtained from external weather data.

[1898] "Trend information" refers to information about trends obtained from the fashion industry, social media, etc.

[1899] "Fashion suggestions" are suggestions for fashion styles generated based on personal data, clothing information, emotional information, weather information, and trend information.

[1900] A "wearing image" is a visual image of what the user will look like wearing the clothes, generated based on the selected fashion suggestion.

[1901] "Listing at a flea market" refers to listing an item on an online marketplace based on a user-selected outfit image.

[1902] "SNS posting" means sharing the selected outfit image on a social networking service.

[1903] "Points awarded" are reward points given to users when they perform specific actions within the system.

[1904] MODE FOR CARRYING OUT THE INVENTION

[1905] This invention is a system for proposing sustainable fashion based on a user's individual information and emotions. This system efficiently collects and analyzes personal data and clothing information entered by the user, and proposes optimal fashion styles. In addition, by combining it with an emotion engine, it provides even more personalized fashion suggestions based on the user's emotions.

[1906] System configuration

[1907] The system consists of the following main components:

[1908] 1. User terminal: A device used to input and transmit personal data, emotional information, and clothing image data. Typically, a smartphone or tablet is used.

[1909] 2. Server: A central processing unit that analyzes personal data, clothing information, and emotional information to generate fashion suggestions.

[1910] 3. Database: An information system that stores and manages all collected data.

[1911] 4. Emotion Engine: A system that has the ability to recognize the user's emotions and adapt fashion suggestions based on those emotions.

[1912] Hardware and Software Examples

[1913] 1. User device: Smartphone (e.g., iPhone), tablet (e.g., iPad)

[1914] 2. Server: High-performance server (e.g. AWS EC2)

[1915] 3. Database: Relational database (e.g. MySQL)

[1916] 4. Emotion engine: Emotion analysis software (e.g., IBM Watson Tone Analyzer)

[1917] 5. Image analysis algorithm: Image analysis API (e.g. Azure Computer Vision API)

[1918] 6. External API: Weather forecast API (e.g. OpenWeatherMap API)

[1919] Program processing

[1920] When a user launches the dedicated app on their device, they can enter personal data such as their hobbies, preferences, physical characteristics such as height and weight, and lifestyle characteristics. For example, a user might enter data such as "I like casual style, I'm 165cm tall, I weigh 55kg, and I work five days a week."

[1921] The device sends this data to the server's API endpoint. The server validates the received data, confirms that it is in the correct format, and then stores it in a database. The user can also take a photo of their clothing with the device and send the image data to the server. The server uses image analysis algorithms to extract information about the clothing's color, material, and brand, and stores it in a database.

[1922] Users can use their devices to capture facial expressions, record voice, and input text to collect emotional information. The devices then send this emotional data to a server, which then uses an emotion engine to analyze it and recognize the user's emotions. This information is also stored in a database.

[1923] In addition, the server obtains weather forecasts and trend information through external APIs. The obtained information is also stored in a database, and fashion suggestions are generated based on personal data, clothing information, emotional information, weather information, and trend information. Using a generative AI model (e.g., GPT-4), optimal fashion suggestions are generated for the user by inputting a prompt such as, "Please suggest autumn casual fashion that suits the user's height and weight."

[1924] The generated fashion suggestions are sent to the user's device as multiple options, and the user selects one of these suggestions. Based on the selected fashion style, the device generates an outfit image and displays it to the user. For example, a realistic outfit image can be generated using 3D modeling technology.

[1925] Specific examples

[1926] An example of a prompt sentence is, "Please suggest a casual style suitable for sunny days for the user, who is 165 cm tall and weighs 55 kg." Based on the input data and acquired information, the system will make optimal fashion suggestions for the user.

[1927] Users can share the generated outfit images on social media, and the server tracks this action and awards points, which users can use to purchase new clothes at online flea markets.

[1928] In this way, the present invention makes it possible to propose optimal fashion based on the user's individual information and emotional information, thereby realizing a sustainable fashion lifestyle.

[1929] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1930] The flow of this system's program processing

[1931] Step 1: Collecting personal data

[1932] Input: The user inputs personal data such as hobbies, preferences, physical characteristics, and lifestyle characteristics from the device.

[1933] Processing: The device validates the personal data entered and ensures that it is in the correct format. For example, the user enters data such as "I like casual style," "I'm 165cm tall," "I weigh 55kg," and "I commute to the office five days a week."

[1934] Output: The device sends this personal data to the server, which analyzes it and stores it in a database.

[1935] Step 2: Collecting clothing data

[1936] Input: The user takes a picture of the clothing they are holding on their device, for example, a casual shirt and jeans.

[1937] Processing: The device sends the captured image data to a server. For example, the device sends the image data to an image analysis API to extract color, material, and brand information.

[1938] Output: The server stores the extracted clothing information in a database.

[1939] Step 3: Collecting emotional information

[1940] Input: The user uses the device to capture facial expressions, record audio, or input text.

[1941] Processing: The device sends these emotion data to the server. For example, a user takes a picture of themselves smiling with their smartphone.

[1942] Output: The server analyzes the user's emotion using the emotion engine and recognizes the user's emotion as "happy." This information is stored in the database.

[1943] Step 4: Get weather and trend information

[1944] Input: The server periodically calls an external API to obtain weather and trend information.

[1945] Processing: The server uses a weather forecast API to retrieve, for example, the "weekly weather forecast." It also scrapes trend information from fashion-related websites and social media.

[1946] Output: Save the obtained weather and trend information in a database.

[1947] Step 5: Generate fashion suggestions

[1948] Input: Personal data, clothing data, emotional information, weather information, and fashion information are stored in the database.

[1949] Processing: The server inputs a prompt into a generative AI model (e.g., GPT-4) to generate optimal fashion suggestions for the user. For example, the prompt might be, "Please suggest casual autumn fashion that suits the user's height and weight."

[1950] Output: The generated fashion suggestions are sent to the user's device as multiple options.

[1951] Step 6: Select and view suggestions

[1952] Input: The user selects one of the suggestions provided by the device.

[1953] Processing: The device generates a realistic outfit image for the selected fashion style, for example by using 3D modeling technology.

[1954] Output: The generated outfit image is displayed on the device.

[1955] Step 7: Providing additional functionality

[1956] Input: User shares outfit image on social media.

[1957] Processing: The server tracks the sharing action and gives points to the user. For example, when a user clicks the post button on a social networking site, the server records that information.

[1958] Output: The points are added to the user's account, and the user can use them to purchase new clothes at the online flea market.

[1959] (Application example 2)

[1960] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1961] The shopping experience in physical stores is often not personalized enough for the user because it is often conducted without taking into account information such as the user's clothing, personal preferences, weather, and emotions. This leads to a problem of reduced satisfaction when making purchasing decisions. Furthermore, trying on clothes in physical stores is often time-consuming and inefficient. Therefore, new methods are needed to ensure users have an effective shopping experience.

[1962] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1963] In this invention, the server includes means for receiving personal data and emotional information entered by the user, means for analyzing the data and saving the data in a database, means for receiving images of the user's clothing, analyzing the images to extract and save information about the clothing, means for acquiring weather information and trend information, means for generating fashion suggestions based on the personal data, clothing information, emotional information, weather information, and trend information, means for providing the generated fashion suggestions to the user, means for the user to have a smart try-on experience in a physical store, means for providing virtual try-on experiences in cooperation with a smart mirror installed in the physical store, means for collecting the user's emotional information in real time and reflecting it in fashion suggestions, and means for suggesting optimal fashion items in real time, thereby enabling the user to have a personalized and efficient shopping experience in a physical store.

[1964] "Personal data" refers to data provided by a user, including hobbies, preferences, physical characteristics, lifestyle characteristics, emotional information, and the like.

[1965] "Clothing image" is image data of clothing owned by the user.

[1966] "Weather information" is weekly weather forecast information obtained from an external weather database.

[1967] "Trend information" refers to information about current fashion trends.

[1968] "Emotion information" is data related to emotions acquired from the user's facial expressions, voice, text, etc.

[1969] "Fashion suggestions" are suggestions for fashion styles generated based on personal data, clothing information, weather information, trend information, and emotional information.

[1970] The "smart try-on experience" is a virtual try-on process that users go through in a physical store using a smart mirror.

[1971] A "smart mirror" is a mirror-shaped device that is installed in a physical store and helps users try on clothes virtually.

[1972] "Real-time suggestions" is a system function that instantly suggests fashion items based on the user's current information.

[1973] "Point awarding" refers to providing points as an incentive for a user's specific behavior.

[1974] "SNS posting" refers to the act of a user sharing the created outfit image on a social networking service.

[1975] "Listing at a flea market" refers to the act of a user listing unwanted clothing on a flea market application.

[1976] The present invention is a system for proposing sustainable fashion based on individual data and emotional information of a user. This system is configured using the following hardware and software.

[1977] Hardware Configuration

[1978] User device: A smartphone or tablet through which the user inputs and transmits personal data, emotional information, and clothing image data.

[1979] Server: A central processing unit used to analyze received data and generate fashion suggestions.

[1980] Smart mirror: A device installed in a physical store that allows virtual try-on of clothes.

[1981] Software Configuration

[1982] Frontend: A mobile application based on React Native that provides the user interface and manages data entry and display.

[1983] Backend: A server-side application built with Node.js and Express that analyzes data and generates fashion suggestions.

[1984] Image analysis engine: Uses OpenCV to analyze images of clothing and extract information such as color, material, and brand.

[1985] Sentiment analysis engine: Analyzes user sentiment using Google Cloud Vision AI.

[1986] Database: MongoDB is used to store and manage various data.

[1987] External API: Used to get weather and trend information from OpenWeatherMap and Trendyol.

[1988] System processing overview

[1989] User data entry and collection

[1990] The user enters personal data (hobbies, preferences, physical characteristics, lifestyle characteristics) from the device and sends it to the server. The server receives this data and stores it in a database. For example, a user enters data such as "I like casual style," "I'm 165 cm tall and weigh 55 kg," and "I commute to the office five days a week."

[1991] Clothing data capture and analysis

[1992] The user takes a photo of the clothes they are wearing with their device and sends it to the server. The image data is analyzed by the server, and the color, material, and brand information of the clothes are extracted. For example, the user can take a photo of a "casual shirt" or "jeans."

[1993] Collecting emotional information

[1994] The user takes a photo of their facial expression on the device and sends it to the server, where the emotion analysis engine analyzes the user's emotions (e.g., "happy," "sad," etc.).

[1995] Retrieving External Data

[1996] The server uses an external API to obtain weekly weather and trend information, which is then stored in a database.

[1997] Fashion proposal generation

[1998] Based on the collected personal data, clothing data, emotional information, weather information, and trend information, the AI ​​model generates optimal fashion suggestions, which are then sent to the user's device as multiple options.

[1999] Virtual try-on experience

[2000] When users visit a physical store, they can virtually try on clothes by connecting their smart mirror to their device. The mirror reflects the user's individual data and provides a real-time virtual try-on experience.

[2001] Examples of prompt statements

[2002] Enter your personal data: "Please enter your physical characteristics, hobbies, preferences, and lifestyle characteristics."

[2003] Collecting emotional information: "Facial expressions are captured on camera to analyze current emotions."

[2004] Upload clothing images: "Upload images of your clothing here."

[2005] In this way, the present invention generates optimal fashion suggestions based on the user's personal and emotional information, providing a sustainable shopping experience.

[2006] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2007] Step 1:

[2008] The user enters personal data (hobbies, preferences, physical characteristics, lifestyle characteristics) from their device and sends it to the server. At this time, the data entered may include "I like casual style," "I'm 165cm tall," "I weigh 55kg," and "I commute to the office five days a week." The server analyzes the received data and stores it in a MongoDB database. To store the input data in key-value format, the data is converted to JSON format as part of data processing.

[2009] Step 2:

[2010] A user takes a photo of their clothing on their device and sends it to the server. For example, they upload an image of a casual shirt or jeans. The server uses OpenCV to analyze the received image data and extract information about the clothing (color, material, brand, etc.). The extracted information is stored in a database. The input is image data, and the output is analyzed clothing information.

[2011] Step 3:

[2012] The user takes a photo of their facial expression on their device and sends the image data to the server. The server uses Google Cloud Vision AI to analyze the image and recognize the user's emotional information (e.g., "happy" or "sad"). The recognized emotional information is stored in a database. The input is image data of the facial expression, and the output is text information about the recognized emotion.

[2013] Step 4:

[2014] The server uses external APIs (OpenWeatherMap, Trendyol) to obtain the weekly weather forecast and trend information. This data is also stored in the database. The input is the API request, and the output is the obtained weather forecast and trend information. The weather information includes temperature and weather conditions, and the trend information shows current fashion trends.

[2015] Step 5:

[2016] The server uses a generative AI model to generate fashion suggestions based on personal data, clothing information, emotional information, weather information, and trend information. For example, it may suggest a casual style suitable for a sunny 20-degree day. The generated suggestions are sent to the user's device as multiple options. The input is the aforementioned multiple datasets, and the output is multiple fashion suggestions.

[2017] Step 6:

[2018] The user selects one of the fashion suggestions provided by the terminal. Based on the selected suggestion, the terminal generates an outfit image. This outfit image is displayed to the user in real time. The input is the selected fashion suggestion, and the output is the generated outfit image.

[2019] Step 7:

[2020] When a user visits a physical store, the smart mirror and device are connected. Personal data stored on the device is sent to the mirror, which then provides a virtual try-on experience that reflects that data. For example, the user can check in real time on the smart mirror whether a particular shirt looks good on them. The input is personal data and clothing data, and the output is a real-time virtual try-on image.

[2021] Step 8:

[2022] When a user shares the generated outfit image on SNS, the action is sent to the server and points are awarded. The server tracks this and manages the points system. The points can be used for the next purchase, providing an incentive to the user. The input is the SNS sharing information and the output is the awarded points.

[2023] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2024] 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.

[2025] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2026] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2027] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2028] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2029] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2030] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2031] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2032] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which mul...

Claims

1. means for receiving personal data entered by a user; A means for storing the entered personal data in a database; means for receiving an image of clothing carried by the user; means for analyzing the received image data and extracting information about the clothing; a means for storing the extracted clothing information in a database; A means of obtaining weather information and trend information from an external source; A means for generating fashion suggestions based on personal data, clothing information, weather information, and trend information; The system includes a means for providing the generated fashion suggestions to a user.

2. means for a user to select generated fashion suggestions; A means for generating a wearing image based on the selected suggestion; 2. The system according to claim 1, further comprising means for displaying the generated outfit image to the user.

3. The system according to claim 1, further comprising means for listing at a flea market, posting on social media, and awarding points based on the outfit image selected by the user.

Images