system

Abstract

A system is provided.SOLUTION: A system comprising: means for uploading an image of an item owned by a user; means for a AI to recognize the image, identify attributes of the item, and store the attributes in a database; means for collecting location information and social network information of the user; means for analyzing the collected information and generating optimal coordination for the user; means for recommending an item missing in the generated coordination from a partner EC site; and means for the user to purchase the recommended item.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] The purpose of this invention is to solve the current problem of users finding products that match their existing fashion items. It also aims to improve the user's shopping experience by efficiently suggesting outfits that suit the climate, culture, and trends. Furthermore, there is a need for a system that allows users to centrally manage their online closets while encouraging communication that inspires each other. [Means for solving the problem]

[0005] The present invention solves the above problems by the following means. First, it provides a means for users to upload images of items they own, and includes a means for AI to recognize the images, identify the attributes of the items, and store them in a database. Next, it provides a means for collecting the user's location information and social network data, and includes a means for analyzing this data to generate an optimal outfit for the user. Furthermore, it provides a means for recommending items missing from partner e-commerce sites in the generated outfit. Finally, it provides a system that includes a means for users to purchase recommended items. This system allows users to efficiently manage their own items, easily obtain optimal fashion outfits, and easily purchase the items they need.

[0006] "User" refers to an individual who uses the system to manage fashion items and receive coordination suggestions.

[0007] "Image" refers to visual information that a user photographs or selects of fashion items that they own and uploads to the system.

[0008] "AI" is an abbreviation for artificial intelligence and refers to technology that performs advanced data processing such as image recognition, data analysis, and recommendation systems.

[0009] "Location information" refers to data about a user's current location and range of movement. This data is obtained using technologies such as GPS.

[0010] "Social network data" refers to information obtained from social media with which a user is connected, including information about friendships and accounts followed.

[0011] "Coordination" refers to styling suggestions that are proposed by combining the user's fashion items.

[0012] "Partner E-commerce Site" refers to an online e-commerce platform that is linked to the AI ​​Fashion Remix system.

[0013] "Recommendation" refers to the act of presenting items recommended for purchase to a user.

[0014] An "online closet" is a system that allows users to centrally manage all of their fashion items on a database.

[0015] "Database" refers to a collection of information used to store and manage users' fashion items and related data.

[0016] "Analysis" refers to the process of extracting and understanding specific information and trends from collected data.

[0017] "Recognition" refers to the act of AI analyzing an image and understanding its content (e.g., the attributes of an item).

[0018] "Checkout" refers to the series of online steps a user must take to purchase a recommended item.

[0019] "Feedback" refers to the opinions and evaluations that users give about the generated coordination. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] The following describes an embodiment of the present invention. This invention is a system that efficiently manages fashion items owned by a user and suggests optimal outfits based on location and social data. This system consists of the following steps, and how each step works will be described below.

[0042] User Registration and Login

[0043] When a user uses the app for the first time, they must launch the app and create an account. They enter the required information (email address, password, etc.) and this information is sent from the device to the server. The server stores this information in a database and creates an account for the user. When logging in, the server also verifies the information entered and performs authentication.

[0044] Uploading fashion items

[0045] Users take or select images of their fashion items via their device and send them to the server, which uses an image recognition algorithm to identify the item's attributes (category, color, material, brand, etc.) and store them in a database, creating the user's online closet.

[0046] Location and Social Data Collection

[0047] The device will obtain location information with the user's permission, and also collect social network data if the user allows it to be linked, and this data will be sent to a server.

[0048] Analyzing data and generating coordinates

[0049] The server analyzes the received location and social data, taking into account the climate and cultural characteristics of the current location, and combines items from the user's online closet to generate an appropriate outfit. Data on the style preferences of the user's friends and followers is also used in the analysis.

[0050] Coordinate presentation and purchase process

[0051] The generated outfit is presented to the user via the device. The user can check the outfit and provide feedback if necessary. The server then retrieves any missing items from the partner e-commerce site and displays them on the device. The user can select the recommended items displayed and complete the purchase process. The purchase process is completed via the device, with the server coordinating with the e-commerce site to complete the process.

[0052] Specific examples

[0053] For example, if a user wants to create a winter outfit, they can upload an image of their winter clothing: a coat, scarf, and boots. The server recognizes these items, stores them in a database, and adds them to their online closet. The device then verifies that the user is in a cold climate. The server then uses this information to generate an outfit that includes the coat, scarf, and boots, as well as a sweater and gloves made from warm materials.

[0054] In this way, the present invention provides a system that enables users to efficiently manage items they own, create optimal fashion coordination based on location and trends, and easily purchase the items they need.

[0055] The processing flow will be explained below.

[0056] Step 1:

[0057] The user launches the app for the first time and enters the required information (email address, password, etc.) on the account creation screen.

[0058] Step 2:

[0059] The terminal transmits the input information to the server.

[0060] Step 3:

[0061] The server stores the received information in a database and sends a response to the device to create an account.

[0062] Step 4:

[0063] The user enters their email address and password on the login screen, and the device again sends this information to the server.

[0064] Step 5:

[0065] The server verifies the entered information by comparing it with information in a database, and if the authentication is successful, it sends a success message to the terminal.

[0066] Step 6:

[0067] The user navigates to the closet management screen, presses the button to add a new item, and activates the camera.

[0068] Step 7:

[0069] The user takes a photo of a fashion item or selects an existing image, and the device sends the image to the server.

[0070] Step 8:

[0071] The server passes the received images to an image recognition engine to identify attributes such as item category, color, material, and brand.

[0072] Step 9:

[0073] The server stores the identified attribute information in a database and generates an online closet.

[0074] Step 10:

[0075] The device will present the user with a confirmation screen of their online closet, allowing them to edit or delete items as needed.

[0076] Step 11:

[0077] If the user grants permission to obtain location information, the device will collect GPS data.

[0078] Step 12:

[0079] When a user gives permission to connect with a social network, the device collects social data.

[0080] Step 13:

[0081] The device sends the location and social data it collects to a server.

[0082] Step 14:

[0083] The server analyzes the location and social data it receives and extracts data such as the user's current climate and cultural background, as well as their friends' style information.

[0084] Step 15:

[0085] Based on the analysis results, the server combines items from the user's online closet to generate the optimal outfit.

[0086] Step 16:

[0087] The terminal presents the generated coordinates to the user and receives feedback from the user.

[0088] Step 17:

[0089] The server requests the partner EC site for items that are missing from the generated coordinated outfit, and acquires the corresponding items.

[0090] Step 18:

[0091] The device displays the recommended items to the user, and when the user selects one, they are taken to the purchase process screen.

[0092] Step 19:

[0093] The user selects the recommended item on the checkout screen and enters their shipping address, payment information, etc. to complete the purchase.

[0094] Step 20:

[0095] The server sends the entered purchase procedure information to the partner EC site and completes the purchase procedure.

[0096] Example 1

[0097] 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."

[0098] Conventional fashion coordination support systems often require a lot of effort to manage the items a user owns and suggest appropriate outfits, resulting in ineffective functionality. A particular challenge is generating outfits that take into account multiple factors, such as the weather in the user's current location and the fashion trends of their friends. Furthermore, recommending new items and completing the purchasing process are time-consuming and inconvenient for users.

[0099] 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.

[0100] In this invention, the server includes means for uploading images of items owned by the user, means for a recognition device to recognize the images and identify attributes of the items and store them in data storage, means for collecting location information and social media data of the user, means for analyzing the collected data and generating a coordinated outfit suitable for the user, means for recommending items missing from an external sales site for the generated coordinated outfit, and means for the user to purchase the recommended items. This allows users to efficiently manage their own fashion items, easily receive optimal coordinated outfits based on location and trends, and easily purchase new items they need.

[0101] "User" refers to an individual who uses the system to manage their own fashion items and receive coordination suggestions.

[0102] "Items" refers to all fashion items owned by a User and uploaded to the System.

[0103] "Image upload means" refers to a function that allows a user to upload images of items owned by the user into the system.

[0104] "Recognition device" refers to a device that analyzes uploaded images and implements algorithms to identify attributes of items.

[0105] "Attributes" refer to specific characteristics of an item, such as its category, color, material, or brand.

[0106] "Data storage" refers to a database for storing attribute information identified by a recognition device.

[0107] "Location information" refers to information that identifies a user's current location.

[0108] "Social Media Data" refers to information about a user's social networks that is collected with the user's permission.

[0109] "Recommendation means" refers to a function that identifies items that are missing from the generated outfit and suggests them to the user from an external sales site.

[0110] "Purchase method" refers to the functionality that allows users to purchase recommended items.

[0111] "Online data storage" refers to storage within a system that automatically organizes and stores items uploaded by users.

[0112] "Feedback means" refers to a function that allows users to provide opinions and evaluations of the presented coordination.

[0113] "Correction measures" refer to functions for improving the content of coordination based on collected feedback.

[0114] "Analysis means" refers to the functionality for generating coordination based on collected location information and social media data.

[0115] The following describes in detail the mode for carrying out the present invention. This invention is a system that efficiently manages a user's fashion items and suggests optimal outfits based on location and social media data. This system is based on cooperation between a server, a terminal, and a user.

[0116] User Registration and Login

[0117] When a user uses the app for the first time, they must create an account. At this time, they enter an email address and password. The device sends the entered information to the server. The server stores the received information in a MySQL database and creates a user account. When logging in, the device similarly sends the email address and password, and the server performs authentication.

[0118] Uploading fashion items

[0119] 1. The user takes or selects an image of a fashion item they own.

[0120] 2. The device sends this image to the server.

[0121] 3. The server uses a TensorFlow-based image recognition algorithm to identify the item's attribute information (category, color, material, brand, etc.) from the image.

[0122] 4. The server stores the identification results in a database and updates the user's online closet.

[0123] Location and Social Data Collection

[0124] When a user allows location information and social media integration in the app, the device will use the GPS module to obtain current location information. The device will also collect user posts and friend information from social media via API. All of this data is sent to the server and stored in data storage.

[0125] Analyzing data and generating coordinates

[0126] 1. The server obtains weather data from the weather API based on the collected location information.

[0127] 2. The server analyzes social media data to understand the fashion trends of the user's friends and followers.

[0128] 3. The server combines this information and inputs a prompt into a generative AI model (e.g., OpenAI GPT). For example, a prompt like, "The user is currently in a cold climate. Please suggest the best outfit using winter items from the user's online closet."

[0129] 4. Based on the prompts, the generative AI model generates outfits that take into account the season, location, and the user's social data.

[0130] Coordinate presentation and purchase process

[0131] 1. The server sends the generated coordinates to the terminal and presents them to the user.

[0132] 2. Users can review the coordinates and provide feedback if necessary.

[0133] 3. The server determines the missing items and retrieves information about related products using the API of an external sales site (e.g., Amazon).

[0134] 4. The device displays product information obtained from the external sales site to the user.

[0135] 5. Once the user decides to purchase, the device proceeds with the purchase process, and the server connects with the external sales site to complete the purchase.

[0136] Specific examples

[0137] If a user wants to create a winter outfit, they upload images of a coat, scarf, and boots. The server recognizes these items, saves them in data storage, and adds them to their online closet. If the device determines that the user's current location is in a cold climate, the server inputs the following prompt into the generative AI model: "You are currently in a cold climate, and your online closet contains a coat, scarf, and boots. Based on this, please suggest the perfect winter outfit for you." The generated outfit may include a warm sweater or gloves.

[0138] In this way, the present invention allows users to efficiently manage their fashion items, provide optimal coordination based on location and trends, and easily purchase necessary items.

[0139] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0140] Step 1: User registration and login

[0141] Input: The user enters their email address and password.

[0142] How it works: When a user first uses the app, they enter their email address and password on the account creation screen. The device then sends this information to the server.

[0143] Data processing: The server stores the received information in a MySQL database and creates a user account.

[0144] Output: The user account is saved in the database. Similarly, when you log in again, the server will authenticate you by checking the email address and password you entered.

[0145] Step 2: Upload your fashion items

[0146] Input: The user takes or selects an image of a fashion item they own.

[0147] How it works: A user opens the app's fashion item upload screen, takes a photo of an item, or selects an existing image. The device then sends this image to the server.

[0148] Data processing: The server uses a TensorFlow-based image recognition algorithm to identify item attribute information (category, color, material, brand, etc.) from the image.

[0149] Output: The server stores the identification results in a database and updates the user's online closet.

[0150] Step 3: Collect location and social data

[0151] Input: Location and social media data with user permission.

[0152] How it works: When a user allows the app to access location information and social media, the device will use the GPS module to obtain current location information. The device will also collect user data from social media via APIs.

[0153] Data processing: The device sends location and social data to the server.

[0154] Output: The server stores these data in a database.

[0155] Step 4: Analyze the data and generate coordinates

[0156] Inputs: Collected location information, weather data, social data, and online closet information.

[0157] How it works: The server receives location information, retrieves weather data from a weather API, and analyzes social media data to understand the fashion trends of the user's friends and followers.

[0158] Data calculation: The server integrates this information and inputs a prompt into the generative AI model (e.g., OpenAI GPT). An example prompt might be, "The current location is in a cold climate. Please suggest the best outfit using winter items from the user's online closet."

[0159] Output: The generative AI model generates the optimal coordination based on the prompts and returns it to the server.

[0160] Step 5: Present your outfit and complete the purchase

[0161] Input: The generated coordinates.

[0162] Operation: The server sends the generated coordinates to the device and presents them to the user, who can review the coordinates and provide feedback if necessary.

[0163] Data processing: The server determines the missing items and retrieves information about related products using the API of an external sales site.

[0164] Output: The terminal displays the acquired product information to the user, who selects and completes the purchase procedure. The server connects with the external sales site to complete the purchase procedure.

[0165] Through these steps, a system is realized that manages the user's fashion items, creates optimal coordinations, and assists in purchasing necessary items.

[0166] (Application example 1)

[0167] 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."

[0168] Today's consumers own many fashion items, but lack a system that can efficiently manage them and automatically suggest the optimal outfit for each location and situation. Furthermore, when users need an item they don't have on hand, there is no system that allows them to easily check inventory information at nearby brick-and-mortar stores and quickly purchase and receive it. Therefore, it is necessary to build a system that can properly manage the fashion items owned by users, provide optimal outfits, and instantly check inventory information for missing items, allowing them to purchase and receive them.

[0169] 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.

[0170] In this invention, the server includes means for uploading images of items owned by the user, means for AI to recognize the images and identify item attributes and store them in a database, means for collecting the user's location information and social network data, means for analyzing the collected data and generating an optimal outfit for the user, means for recommending items missing from partner e-commerce sites that are missing from the generated outfit, means for the user to purchase the recommended items, means for checking whether the generated outfit is in stock at a nearby physical store, and means for selecting a method for receiving the items at the physical store. This allows the user to efficiently manage their own items, obtain optimal outfits, and quickly obtain the items they need from a nearby physical store.

[0171] "Uploading an image of an item" is the process by which a user takes a photo of a fashion item they own and sends it to the server through the application.

[0172] "Image recognition" is a technology that uses AI technology to analyze the content of uploaded images and identify attributes such as item category, color, material, and brand.

[0173] "Storing in database" refers to the process of recording the identified attribute information in a database in structured data format so that it can be efficiently searched and used later.

[0174] "Location collection" is the process of obtaining a user's current geographic location using technologies such as GPS and transmitting it to a server.

[0175] "Social Network Data" is data about the styles and trends of your friends and followers obtained from social media platforms that you have authorized to connect.

[0176] "Data analysis" refers to the computational process of generating optimal fashion coordination based on collected location information and social network data, taking into account the user's preferences, trends, weather, etc.

[0177] "Coordination generation" is a process that suggests recommended fashion styles by combining items owned by the user based on analyzed data.

[0178] "Recommendation" refers to the act of selecting items that are missing from a generated outfit from a partner e-commerce site and recommending them to the user.

[0179] "Purchasing recommended items" is the process in which a user actually orders the suggested missing items and completes the purchase process.

[0180] "Check physical store inventory" is a function that checks whether the items in the generated outfit are in stock at a nearby physical store.

[0181] "In-store pickup" is the process by which a user selects how to collect an item they ordered online from a physical store.

[0182] The following describes an embodiment of the present invention. This system efficiently manages the fashion items owned by the user, suggests optimal outfits based on location and social data, and allows the user to quickly obtain the necessary items from nearby physical stores.

[0183] User Registration and Login

[0184] When a user first uses the service, they launch the smartphone app and create an account. They enter the necessary information (email address, password, etc.), and this information is sent from the device to the server. The server stores this information in a database and creates an account for the user. When the user logs in, the server also verifies the information entered and performs authentication.

[0185] Uploading fashion items

[0186] Users take pictures of their fashion items with their smartphones and send them to the server via the app. The server then uses an image recognition algorithm to identify the item's attributes (category, color, material, brand, etc.) and stores them in a database, creating the user's online closet.

[0187] Location and Social Data Collection

[0188] The device will obtain location information with the user's permission, and also collect social network data if the user allows it to be linked, and this data will be sent to a server.

[0189] Analyzing data and generating coordinates

[0190] The server analyzes the received location and social data, taking into account the climate and cultural characteristics of the current location, and combines items from the user's online closet to generate an appropriate outfit. Data on the style preferences of the user's friends and followers is also used in the analysis.

[0191] Coordination suggestions and stock confirmation

[0192] The generated outfits are presented to the user via their device. The user can check the outfit details and see if the items are in stock at a nearby physical store. The physical store's inventory information is linked to the server and updated in real time.

[0193] Purchase procedure and delivery method selection

[0194] Users can select the recommended items displayed and proceed with the purchase. The purchase process is carried out via the terminal, and the server completes it by connecting with the designated physical store. Users can also choose how to receive the ordered items. For example, they can choose to collect them directly at the store or use a delivery service.

[0195] Specific examples

[0196] For example, if a user wants to create a winter outfit, they can upload an image of their winter clothing: a coat, scarf, and boots. The server recognizes these items, stores them in a database, and adds them to their online closet. The device then verifies that the user's current location is in a cold climate. The server then uses this information to generate an outfit that includes a coat, scarf, and boots, as well as warmer materials like sweaters and gloves. The server then checks whether these items are in stock at nearby physical stores and displays them to the user.

[0197] Prompt Sentence Examples

[0198] "Taking into account the red coat uploaded by the user and the current location's cold climate, suggest an appropriate fashion coordination. Also, check whether related items are in stock at nearby physical stores."

[0199] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0200] Step 1:

[0201] When a user uses the app for the first time, they launch the smartphone app and create an account. The user enters their email address and password, and sends this information from their device to the server. The server stores the received information in a database and creates an account for the user. It also performs a comparison to authenticate the entered information, and if authentication is successful, the login is complete. The input is user information, and the output is the registered user account.

[0202] Step 2:

[0203] Users take pictures of their fashion items with their smartphones and send them to the server via the app. The server uses an image recognition algorithm (e.g., TensorFlow or PyTorch) to identify the item's attributes (category, color, material, brand, etc.) and stores the identification results in a database. The input is an image of the fashion item, and the output is item data with attribute information added.

[0204] Step 3:

[0205] With the user's permission, the device obtains location information and also collects social network data that the user has authorized to be linked. This data is sent from the device to a server. The server analyzes the received data and generates analysis results based on the climate and cultural characteristics of the current location and the social network data. The input is location information and social network data, and the output is the analysis results.

[0206] Step 4:

[0207] The server generates the optimal outfit for the user based on the items in the online closet. This takes into account the location information and social data received. For example, cold weather items are selected taking into account the cold climate. The generated outfit is saved in a database and processed to be presented to the user. The input is the analysis results and the online closet data, and the output is the generated outfit.

[0208] Step 5:

[0209] The generated outfit is displayed on the user's device. The server checks whether the items in the outfit presented to the user are in stock at nearby physical stores. The physical store's inventory information is updated in real time and presented to the user. The input is the generated outfit and the physical store's inventory information, and the output is the outfit whose inventory has been confirmed.

[0210] Step 6:

[0211] The user selects the recommended items displayed and proceeds with the purchase if necessary. The purchase process is carried out via the terminal, and the server completes the order by connecting with partner e-commerce sites and physical stores. The user can select a method of collection at the physical store. For example, they can choose to collect the item directly at the store or use a delivery service. The input is the item and collection method selected by the user, and the output is a notification that the purchase process has been completed.

[0212] Prompt Sentence Examples

[0213] "Taking into account the red coat uploaded by the user and the current location's cold climate, suggest an appropriate fashion coordination. Also, check whether related items are in stock at nearby physical stores."

[0214] 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.

[0215] The following describes an embodiment of the present invention. The present invention is a system that efficiently manages a user's owned fashion items and suggests optimal outfits based on location information, social data, and the user's emotions. By combining this system with an emotion engine that analyzes the user's emotions, it is possible to suggest more personalized outfits.

[0216] User Registration and Login

[0217] When a user first starts the app and enters the required information (email address, password, etc.) on the account creation screen, the device sends the entered information to the server. The server stores this information in a database and creates an account. When the user logs in, the server also verifies the entered information and performs authentication.

[0218] Uploading fashion items

[0219] Users take or select images of their fashion items via their device and send them to the server, which uses an image recognition algorithm to identify the item's attributes (category, color, material, brand, etc.) and store them in a database, creating the user's online closet.

[0220] Location and Social Data Collection

[0221] The device will obtain location information with the user's permission, and also collect social network data if the user allows it to be linked, and this data will be sent to a server.

[0222] Analyzing data and generating coordinates

[0223] The server analyzes the received location and social data, taking into account the climate and cultural characteristics of the current location, and combines items from the user's online closet to generate an appropriate outfit. Data on the style preferences of the user's friends and followers is also used in the analysis.

[0224] Use of emotion engine

[0225] The emotion engine has the ability to analyze emotions from the user's facial expressions, voice, text input, etc. The device collects the user's emotional data and sends it to the server. The server analyzes this emotional data to identify the user's current emotional state. It then generates outfit suggestions based on the user's emotional state, thereby recommending more appropriate fashion to match the user's mood.

[0226] Coordinate presentation and purchase process

[0227] The generated outfit is presented to the user via the device. The user can check the outfit and provide feedback if necessary. The server then retrieves any missing items from the partner e-commerce site and displays them on the device. The user can select the recommended items displayed and complete the purchase process. The purchase process is completed via the device, with the server coordinating with the e-commerce site to complete the process.

[0228] Specific examples

[0229] For example, if a user is feeling stressed, the emotion engine will detect this and suggest a casual, comfortable outfit that will have a relaxing effect. On the other hand, if a user is excited about a particular event, the emotion engine will suggest a more glamorous, appropriate outfit. In this way, the outfits generated by the emotion engine are more deeply adapted to the user's emotional state.

[0230] In this way, the present invention provides a system that allows users to efficiently manage the items they own, suggests optimal fashion coordination based on location, trends, and even emotions, and allows them to easily purchase the items they need.

[0231] The processing flow will be explained below.

[0232] Step 1:

[0233] The user launches the app for the first time and enters the required information (email address, password, etc.) on the account creation screen.

[0234] Step 2:

[0235] The terminal transmits the input information to the server.

[0236] Step 3:

[0237] The server stores the received information in a database and sends a response to the device to create an account.

[0238] Step 4:

[0239] The user enters their email address and password on the login screen, and the device again sends this information to the server.

[0240] Step 5:

[0241] The server verifies the entered information by comparing it with information in a database, and if the authentication is successful, it sends a success message to the terminal.

[0242] Step 6:

[0243] The user navigates to the closet management screen, presses the button to add a new item, and activates the camera.

[0244] Step 7:

[0245] The user takes a photo of a fashion item or selects an existing image, and the device sends the image to the server.

[0246] Step 8:

[0247] The server passes the received images to an image recognition engine to identify attributes such as item category, color, material, and brand.

[0248] Step 9:

[0249] The server stores the identified attribute information in a database and generates an online closet.

[0250] Step 10:

[0251] The device will present the user with a confirmation screen of their online closet, allowing them to edit or delete items as needed.

[0252] Step 11:

[0253] If the user grants permission to obtain location information, the device will collect GPS data.

[0254] Step 12:

[0255] When a user gives permission to connect with a social network, the device collects social data.

[0256] Step 13:

[0257] The device sends the location and social data it collects to a server.

[0258] Step 14:

[0259] The server analyzes the location and social data it receives and extracts data such as the user's current climate and cultural background, as well as their friends' style information.

[0260] Step 15:

[0261] Based on the analysis results, the server combines items from the user's online closet to generate the optimal outfit.

[0262] Step 16:

[0263] The emotion engine has the ability to analyze emotions from the user's facial expressions, voice, text input, etc. The device collects the user's emotional data and sends it to the server.

[0264] Step 17:

[0265] The server analyzes the emotion data received from the emotion engine to determine the user's current emotional state.

[0266] Step 18:

[0267] The server generates coordination suggestions based on the emotional state, thereby providing coordination that matches the user's mood.

[0268] Step 19:

[0269] The terminal presents the generated coordinates to the user and receives feedback from the user.

[0270] Step 20:

[0271] The server requests the partner EC site for items that are missing from the generated coordinated outfit, and acquires the corresponding items.

[0272] Step 21:

[0273] The device displays the recommended items to the user, and when the user selects one, they are taken to the purchase process screen.

[0274] Step 22:

[0275] The user selects the recommended item on the checkout screen and enters their shipping address, payment information, etc. to complete the purchase.

[0276] Step 23:

[0277] The server sends the entered purchase procedure information to the partner EC site and completes the purchase procedure.

[0278] Example 2

[0279] 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."

[0280] Conventional fashion management systems require users to manage their personal items in a cumbersome manner, and lack sufficient integration with location information and social data. Furthermore, they lack personalized suggestions for individual users, and do not take into account the user's emotional state. This makes it difficult to provide optimal outfits for users. Furthermore, the generated outfits do not integrate recommendations for missing items or a purchasing process.

[0281] 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.

[0282] In this invention, the server includes a means for uploading images of items owned by the user, a means for a generative AI model to recognize the images, identify the attributes of the items, and store them in a database, a means for collecting the user's location information and social network data, a means for analyzing the collected data and the user's emotional data to generate an optimal outfit for the user, a means for recommending items missing from a partner e-commerce site, and a means for the user to purchase the recommended items. This allows the user to efficiently manage the items they own and receive suggestions for optimal fashion outfits based on their location information, social data, and emotional state. Furthermore, the system also facilitates the recommendation and purchase process for missing items.

[0283] "User" refers to an individual who uses this system.

[0284] "Owned Items" refers to fashion-related items, including apparel and accessories, owned by a User.

[0285] "Means for uploading images" refers to the functionality that allows a user to submit photos of items they own to the system.

[0286] A "generative AI model" refers to an artificial intelligence technology that uses algorithms to generate specific results based on input data.

[0287] "Item attributes" refer to characteristics of a fashion item such as category, color, material, brand, etc.

[0288] "Database" refers to a system for organizing and storing identified data.

[0289] "Location information" refers to data that indicates a user's current location.

[0290] "Social Network Data" refers to information obtained from a user's social media.

[0291] "Means of analysis" refers to the process of analyzing collected data to extract useful information.

[0292] "Emotional data" refers to information about a user's emotional state obtained from facial expressions, voice, text input, etc.

[0293] "Means for generating coordination" refers to the function that suggests suitable fashion combinations for users based on collected data.

[0294] "Partner e-commerce site" refers to the online shopping platform with which the System is integrated.

[0295] "Means of recommendation" refers to the function of recommending specific products to users.

[0296] "Means to purchase" refers to the functionality that allows users to purchase recommended items.

[0297] The present invention relates to a system that efficiently manages a user's fashion items and suggests optimal outfits based on location information, social network data, and emotional data. Specific embodiments of this system are described below.

[0298] User Registration and Login

[0299] When a user uses the application for the first time, they start it and enter the necessary information, such as their email address and password, on the account creation screen. The device then sends the entered information to the server, which then stores this information in a database and creates an account. When the user enters their email address and password on the login screen and taps the "Login" button, the server compares this information with the information in the database and performs authentication.

[0300] Uploading fashion items

[0301] The user takes a photo of a fashion item they own on their device or selects it from their photo library. The device then sends the selected or captured image to the server. The server then uses an image recognition algorithm (e.g., TensorFlow or OpenCV) to identify the attribute information (category, color, material, brand, etc.) of the item in the image. This identified attribute information is stored in a database, forming the user's online closet.

[0302] Location and Social Data Collection

[0303] The device obtains location information with the user's permission. It measures the current location using GPS, and the device also collects data from social networks (e.g., Facebook, Instagram) with the user's permission. This data is sent to the server.

[0304] Analyzing data and generating coordinates

[0305] The server analyzes the received location and social data. It uses machine learning algorithms (e.g., Scikit-learn and TensorFlow) to take into account the climate and cultural background of the current location. Based on the analysis results, the server combines items from the user's online closet to generate an appropriate outfit. The server also uses data on the style preferences of the user's friends and followers.

[0306] Use of emotion engine

[0307] The emotion engine has the ability to analyze emotional data from the user's facial expressions, voice, text input, etc. The device sends the collected emotional data to the server, which analyzes this emotional data to identify the user's current emotional state. Based on this emotional state, personalized outfit suggestions are generated.

[0308] Coordinate presentation and purchase process

[0309] The server sends the generated coordinated outfit to the device, which then displays it to the user. The user can review the coordinated outfit and provide feedback. The server also retrieves any missing items from partner e-commerce sites and displays them on the device. The user selects the displayed items and completes the purchase. The purchase is completed via the device, and the server works with the e-commerce site to complete the purchase.

[0310] Specific examples

[0311] For example, if a user is feeling stressed, the emotion engine will detect this and suggest a casual, comfortable outfit that will have a relaxing effect. On the other hand, if a user is excited about a particular event, it will suggest a more glamorous, event-appropriate outfit. In this way, the outfits generated by the emotion engine are deeply adapted to the user's emotional state.

[0312] Example prompts for generative AI models

[0313] We developed a system that smoothly manages the fashion items owned by users and suggests optimal outfits based on location, social, and emotional data. We analyze various data such as users' photos, voice, location, and SNS data to provide guidance on how to make personalized fashion suggestions.

[0314] In this way, the present invention provides a system that allows users to efficiently manage the items they own, suggests optimal fashion coordination based on location information, social data, and emotional state, and allows them to easily purchase the items they need.

[0315] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0316] Step 1:

[0317] The user launches an application.

[0318] Input: Tap the application icon

[0319] Output: Display of the application's welcome screen

[0320] What happens: A user launches an app by tapping on the application icon on their smartphone or tablet, and is presented with the option to create an account and log in.

[0321] Step 2:

[0322] The user enters the required information on the account creation screen.

[0323] Input: Email address, password

[0324] Output: Sending input data to the server

[0325] Specific operation: The user enters an email address and password and taps the "Send" button. The device receives this and sends it to the server.

[0326] Step 3:

[0327] The server stores the user information and creates an account.

[0328] Input: Email address, password

[0329] Output: Account information stored in the database

[0330] Specific operations: The server saves the received user information in the database and returns a success message to the user.

[0331] Step 4:

[0332] The user enters their credentials on the login screen.

[0333] Input: Email address, password

[0334] Output: Authentication result

[0335] Specific behavior: The user enters their email address and password on the login screen and taps the "Login" button.

[0336] Step 5:

[0337] The server checks the authentication information against a database and performs authentication.

[0338] Input: Email address, password

[0339] Output: Authentication result (success / failure)

[0340] Specific operation: The server compares the user information in the database with the login information and returns the authentication result to the device. If successful, the home screen is displayed.

[0341] Step 6:

[0342] The user takes or selects an image of a fashion item they own.

[0343] Input: Fashion item image

[0344] Output: Sending image data to the server

[0345] What happens: The user takes a photo of an item using the camera or selects one from their photo library. The image is sent from the device to the server.

[0346] Step 7:

[0347] The server identifies the image and stores the item's attributes in a database.

[0348] Input: Fashion item image

[0349] Output: Attribute information (category, color, material, brand, etc.)

[0350] Specific operation: The server uses an image recognition algorithm (e.g., TensorFlow or OpenCV) to identify the attributes of the item and store that information in a database.

[0351] Step 8:

[0352] The device collects the user's location and social network data.

[0353] Input: Location permission, Social network data permission

[0354] Output: Sending location and social network data to a server

[0355] What it does: The device uses GPS to obtain location information and, with the user's permission, collects social network data, which is then sent to a server.

[0356] Step 9:

[0357] The server analyzes the location and social data.

[0358] Input: Location information, social network data

[0359] Output: Analysis results

[0360] Specific operation: The server uses machine learning algorithms (e.g., Scikit-learn or TensorFlow) to perform analysis that takes into account the climate and cultural background of the current location.

[0361] Step 10:

[0362] The server generates the optimal coordination based on the analysis results.

[0363] Input: Analysis results, online closet data

[0364] Output: Coordination suggestions

[0365] Specific operation: The server uses the analysis results to combine items in the online closet and generate the optimal outfit.

[0366] Step 11:

[0367] The device collects the user's emotional data and sends it to the server.

[0368] Input: User facial expressions, voice, and text input

[0369] Output: Sending emotion data to the server

[0370] Specific operation: The device uses the camera and microphone to collect the user's emotional data and sends it to the server.

[0371] Step 12:

[0372] The server analyzes the emotional data and identifies the user's emotional state.

[0373] Input: Emotion data

[0374] Output: Emotional state identification result

[0375] What it does: The server uses an emotion analysis algorithm to determine the user's current emotional state.

[0376] Step 13:

[0377] The server generates coordination suggestions based on the emotional state.

[0378] Input: Emotional state, analysis results

[0379] Output: Personalized outfit suggestions

[0380] Specific operation: The server generates coordinates that match the user's mood based on their emotional state.

[0381] Step 14:

[0382] The server sends the generated coordinates to the terminal and presents them to the user.

[0383] Input: Coordination suggestion

[0384] Output: Coordinates displayed on the terminal

[0385] Specific operation: The server sends a coordination proposal to the terminal, and the terminal displays it to the user.

[0386] Step 15:

[0387] The user provides feedback on the presented outfit.

[0388] Input: Feedback information

[0389] Output: Sending feedback information to the server

[0390] Specific operation: The user inputs feedback on the coordination, and the device sends the information to the server.

[0391] Step 16:

[0392] The server retrieves the missing items from the partner e-commerce site and displays them on the device.

[0393] Input: Coordination suggestions, missing item information

[0394] Output: Display of recommended items

[0395] Specific operation: The server retrieves information about missing items from partner e-commerce sites and displays it on the device.

[0396] Step 17:

[0397] The user selects a recommended item and completes the purchase process.

[0398] Input: Recommended item selection, purchase information

[0399] Output: Information to complete the purchase

[0400] Specific operation: The user selects a recommended item and completes the purchase process. The purchase process is completed through the terminal, and the server connects with the e-commerce site to complete the purchase.

[0401] (Application example 2)

[0402] 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."

[0403] Previous fashion coordination suggestion systems did not take into account the user's shopping experience in a physical store, nor did they optimize coordination through emotion analysis. This meant that users could not receive personalized suggestions based on their current mood or the items they were actually looking at. Furthermore, they lacked technology to visually present coordination suggestions in real time using smart glasses. To solve these issues, a new system that takes user emotions into account and improves the shopping experience in a physical store is needed.

[0404] 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.

[0405] In this invention, the server includes means for uploading images of items owned by the user, means for AI to recognize the images and identify item attributes and store them in a database, means for collecting user location information and social network data, means for analyzing the collected data and generating an optimal outfit for the user, means for analyzing the user's emotions and optimizing the outfit based on the emotions, means for visually displaying real-time coordination suggestions of fashion items in a physical store using smart glasses, means for recommending items missing from a partner e-commerce site that are missing from the generated outfit, and means for the user to purchase the recommended items. This makes it possible to propose personalized fashion coordination that takes into account the user's emotions, location information, and purchasing behavior in a physical store.

[0406] "Means for uploading images of items owned by the user" refers to means for the user to take or select photos of fashion items they own and send them to the system.

[0407] "Means for AI to recognize images, identify item attributes, and store them in a database" refers to a means of using artificial intelligence technology to extract information such as the type, color, material, and brand of a fashion item from uploaded images and record that information in a database.

[0408] "Means for collecting user location information and social network data" refers to means for obtaining a user's current geographic location and activity data on social networking sites.

[0409] "Means for analyzing collected data and generating optimal outfits for users" refers to a means for automatically creating fashion outfits that suit users based on acquired location information and social network data.

[0410] "Means for analyzing user emotions and optimizing coordination based on emotions" refers to a means for detecting emotions from the user's facial expressions, voice, text input, etc., and suggesting fashion coordination that matches that emotional state.

[0411] "A means for visually displaying real-time coordination suggestions for fashion items in a physical store using smart glasses" refers to a means for displaying, in real time, coordination suggestions that combine the fashion items that a user is looking at in a physical store with items in their online closet, using smart glasses.

[0412] "Means for recommending missing items from partner e-commerce sites in the created outfit" refers to a means for searching for missing fashion items in the created outfit from an online shopping site and recommending them to the user.

[0413] "Means for users to purchase recommended items" refers to the procedures for users to actually purchase items recommended by the system, and the means to provide functions to support this.

[0414] This system efficiently manages a user's fashion items and suggests optimal outfits based on location information, social data, and the user's emotions. This system can also suggest outfits in real time in physical stores using smart glasses.

[0415] System configuration

[0416] 1. Image upload method

[0417] The user captures an image of the item they own via their smartphone or other device and sends it to the server. This requires a camera function and communication with the server. Specifically, the smartphone's camera app and internet connection are used.

[0418] 2. Image Recognition Methods

[0419] The server uses AI (artificial intelligence) to analyze the received images and identify the item's attributes. Here, the image is processed using OpenCV, and machine learning models (such as TensorFlow or PyTorch) are used to obtain information such as the item's category, color, material, and brand.

[0420] 3. Location and Social Data Collection Methods

[0421] With the user's permission, the device obtains location information from GPS and collects data from the user's social media accounts, which is then sent over the internet to a server.

[0422] 4. Data analysis and coordinate generation methods

[0423] The server analyzes the collected location and social data to generate optimal fashion coordination for the user, taking into account weather information and cultural characteristics. Data analysis is performed using database software (e.g., MySQL or PostgreSQL) and analysis tools (e.g., Pandas or NumPy).

[0424] 5. Emotion analysis method

[0425] The device collects emotional data based on the user's facial expressions, voice, and text input, and sends it to a server. The server then uses AI to analyze this emotional data and determine the user's current emotional state. Emotion analysis tools such as EmotionEngine are used for this analysis.

[0426] 6. Real-time Coordination Proposal Method Using Smart Glasses

[0427] The smart glasses have the ability to recognize the fashion items the user is looking at in a physical store in real time and visually display coordination suggestions that combine them with the user's online closet. Here, OpenCV is used to capture camera images and generate suggestions through communication with a cloud server.

[0428] 7. Recommendation methods

[0429] The server recommends missing items from partner e-commerce sites, allowing users to easily purchase the missing items. The recommendation algorithm uses collaborative filtering and content-based recommendation systems.

[0430] Example

[0431] For example, if a user visits a shopping mall on a holiday and picks up a red dress, the smart glasses will recognize the dress in real time and, if it determines that the user's emotion is "happy," it will suggest a glamorous outfit, providing users with a more personalized shopping experience.

[0432] Prompt Sentence Examples

[0433] "Design an application that recognizes the fashion items a user is looking at and suggests the best fashion coordination based on the user's emotions and location. This application will be installed on smart glasses."

[0434] This invention makes it possible to propose personalized fashion coordination that takes into account the user's emotions and location information, significantly improving the shopping experience in physical stores.

[0435] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0436] Step 1:

[0437] Image capture and upload

[0438] A user uses a smartphone or other device to take a picture of a fashion item they own and send it to the server. The input is the image of the fashion item, and the output is the image data sent to the server. Specific operations include the user launching a camera app, capturing an image, and then pressing the image upload button.

[0439] Step 2:

[0440] Image Recognition and Attribute Identification

[0441] The server analyzes the received image data using AI (image recognition algorithms) to identify attributes such as the type, color, material, and brand of the fashion item. The input is the uploaded image data, and the output is the item's attribute information. Specifically, the server processes the image using OpenCV, identifies the attributes using TensorFlow or PyTorch models, and stores them in a database.

[0442] Step 3:

[0443] Location and social data collection

[0444] With the user's permission, the device obtains the user's current location from GPS and also collects data from the user's social media accounts and sends it to a server. The input is location information and social data, and the output is these data sent to the server. Specifically, the device periodically obtains GPS data and uses social media APIs to collect and send the necessary data.

[0445] Step 4:

[0446] Data analysis and coordinate generation

[0447] The server analyzes the acquired location information and social data and generates the optimal fashion coordination for the user based on that information. The input is location information, social data, and item attribute information, and the output is the generated coordination proposal. Specifically, the server queries the necessary data from the database, processes the data using analysis tools (Pandas or NumPy), and generates the optimal coordination.

[0448] Step 5:

[0449] Emotion analysis

[0450] The device collects the user's facial expressions and voice and sends them to the server. The server then uses EmotionEngine to analyze and identify the user's emotional state. The input is the user's facial expression data and voice data, and the output is information about the user's emotional state. The specific operation is a process in which the device uses a camera and microphone to capture the user's data and send it to the server.

[0451] Step 6:

[0452] Real-time outfit suggestions using smart glasses

[0453] The device (smart glasses) captures real-time video footage from the physical store and sends the image data to a server. The server analyzes the images, generates real-time outfit suggestions that combine them with items from the online closet, and displays them on the smart glasses. The input is real-time video data, and the output is the outfit suggestions presented to the user. The specific operation is a process in which the smart glasses activate their camera and constantly capture the items the user is looking at and send them to the server.

[0454] Step 7:

[0455] Recommendations and checkout

[0456] The server searches partner e-commerce sites for items missing from the generated outfit and recommends them to the user. The user purchases the recommended items through their device. The input is the outfit plan, and the output is the recommended items and the purchase procedure based on them. Specifically, the server recommends products using collaborative filtering or a content-based recommendation system, and the purchase procedure is carried out through the e-commerce site's API.

[0457] This system allows users to receive real-time fashion coordination suggestions based on their emotions and location information, improving their shopping experience.

[0458] 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.

[0459] 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.

[0460] 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.

[0461] [Second embodiment]

[0462] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0463] 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.

[0464] 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).

[0465] 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.

[0466] 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.

[0467] 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).

[0468] 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.

[0469] 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.

[0470] 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.

[0471] 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.

[0472] 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.

[0473] 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."

[0474] The following describes an embodiment of the present invention. This invention is a system that efficiently manages fashion items owned by a user and suggests optimal outfits based on location and social data. This system consists of the following steps, and how each step works will be described below.

[0475] User Registration and Login

[0476] When a user uses the app for the first time, they must launch the app and create an account. They enter the required information (email address, password, etc.) and this information is sent from the device to the server. The server stores this information in a database and creates an account for the user. When logging in, the server also verifies the information entered and performs authentication.

[0477] Uploading fashion items

[0478] Users take or select images of their fashion items via their device and send them to the server, which uses an image recognition algorithm to identify the item's attributes (category, color, material, brand, etc.) and store them in a database, creating the user's online closet.

[0479] Location and Social Data Collection

[0480] The device will obtain location information with the user's permission, and also collect social network data if the user allows it to be linked, and this data will be sent to a server.

[0481] Analyzing data and generating coordinates

[0482] The server analyzes the received location and social data, taking into account the climate and cultural characteristics of the current location, and combines items from the user's online closet to generate an appropriate outfit. Data on the style preferences of the user's friends and followers is also used in the analysis.

[0483] Coordinate presentation and purchase process

[0484] The generated outfit is presented to the user via the device. The user can check the outfit and provide feedback if necessary. The server then retrieves any missing items from the partner e-commerce site and displays them on the device. The user can select the recommended items displayed and complete the purchase process. The purchase process is completed via the device, with the server coordinating with the e-commerce site to complete the process.

[0485] Specific examples

[0486] For example, if a user wants to create a winter outfit, they can upload an image of their winter clothing: a coat, scarf, and boots. The server recognizes these items, stores them in a database, and adds them to their online closet. The device then verifies that the user is in a cold climate. The server then uses this information to generate an outfit that includes the coat, scarf, and boots, as well as a sweater and gloves made from warm materials.

[0487] In this way, the present invention provides a system that enables users to efficiently manage items they own, generate optimal fashion coordination based on location and trends, and easily purchase the items they need.

[0488] The processing flow will be explained below.

[0489] Step 1:

[0490] The user launches the app for the first time and enters the required information (email address, password, etc.) on the account creation screen.

[0491] Step 2:

[0492] The terminal transmits the input information to the server.

[0493] Step 3:

[0494] The server stores the received information in a database and sends a response to the device to create an account.

[0495] Step 4:

[0496] The user enters their email address and password on the login screen, and the device again sends this information to the server.

[0497] Step 5:

[0498] The server verifies the entered information by comparing it with information in a database, and if the authentication is successful, it sends a success message to the terminal.

[0499] Step 6:

[0500] The user navigates to the closet management screen, presses the button to add a new item, and activates the camera.

[0501] Step 7:

[0502] The user takes a photo of a fashion item or selects an existing image, and the device sends the image to the server.

[0503] Step 8:

[0504] The server passes the received images to an image recognition engine to identify attributes such as item category, color, material, and brand.

[0505] Step 9:

[0506] The server stores the identified attribute information in a database and generates an online closet.

[0507] Step 10:

[0508] The device will present the user with a confirmation screen of their online closet, allowing them to edit or delete items as needed.

[0509] Step 11:

[0510] If the user grants permission to obtain location information, the device will collect GPS data.

[0511] Step 12:

[0512] When a user gives permission to connect with a social network, the device collects social data.

[0513] Step 13:

[0514] The device sends the location and social data it collects to a server.

[0515] Step 14:

[0516] The server analyzes the location and social data it receives and extracts data such as the user's current climate and cultural background, as well as their friends' style information.

[0517] Step 15:

[0518] Based on the analysis results, the server combines items from the user's online closet to generate the optimal outfit.

[0519] Step 16:

[0520] The terminal presents the generated coordinates to the user and receives feedback from the user.

[0521] Step 17:

[0522] The server requests the partner EC site for items that are missing from the generated coordinated outfit, and acquires the corresponding items.

[0523] Step 18:

[0524] The device displays the recommended items to the user, and when the user selects one, they are taken to the purchase process screen.

[0525] Step 19:

[0526] The user selects the recommended item on the checkout screen and enters their shipping address, payment information, etc. to complete the purchase.

[0527] Step 20:

[0528] The server sends the entered purchase procedure information to the partner EC site and completes the purchase procedure.

[0529] Example 1

[0530] 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."

[0531] Conventional fashion coordination support systems often require a lot of effort to manage the items a user owns and suggest appropriate outfits, resulting in ineffective functionality. A particular challenge is generating outfits that take into account multiple factors, such as the weather in the user's current location and the fashion trends of their friends. Furthermore, recommending new items and completing the purchasing process are time-consuming and inconvenient for users.

[0532] 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.

[0533] In this invention, the server includes means for uploading images of items owned by the user, means for a recognition device to recognize the images and identify attributes of the items and store them in data storage, means for collecting location information and social media data of the user, means for analyzing the collected data and generating a coordinated outfit suitable for the user, means for recommending items missing from an external sales site for the generated coordinated outfit, and means for the user to purchase the recommended items. This allows users to efficiently manage their own fashion items, easily receive optimal coordinated outfits based on location and trends, and easily purchase new items they need.

[0534] "User" refers to an individual who uses the system to manage their own fashion items and receive coordination suggestions.

[0535] "Items" refers to all fashion items owned by a User and uploaded to the System.

[0536] "Image upload means" refers to a function that allows a user to upload images of items owned by the user into the system.

[0537] "Recognition device" refers to a device that analyzes uploaded images and implements algorithms to identify attributes of items.

[0538] "Attributes" refer to specific characteristics of an item, such as its category, color, material, or brand.

[0539] "Data storage" refers to a database for storing attribute information identified by a recognition device.

[0540] "Location information" refers to information that identifies a user's current location.

[0541] "Social Media Data" refers to information about a user's social networks that is collected with the user's permission.

[0542] "Recommendation means" refers to a function that identifies items that are missing from the generated outfit and suggests them to the user from an external sales site.

[0543] "Purchase method" refers to the functionality that allows users to purchase recommended items.

[0544] "Online data storage" refers to storage within a system that automatically organizes and stores items uploaded by users.

[0545] "Feedback means" refers to a function that allows users to provide opinions and evaluations of the presented coordination.

[0546] "Correction measures" refer to functions for improving the content of coordination based on collected feedback.

[0547] "Analysis means" refers to the functionality for generating coordination based on collected location information and social media data.

[0548] The following describes in detail the mode for carrying out the present invention. This invention is a system that efficiently manages a user's fashion items and suggests optimal outfits based on location and social media data. This system is based on cooperation between a server, a terminal, and a user.

[0549] User Registration and Login

[0550] When a user uses the app for the first time, they must create an account. At this time, they enter an email address and password. The device sends the entered information to the server. The server stores the received information in a MySQL database and creates a user account. When logging in, the device similarly sends the email address and password, and the server performs authentication.

[0551] Uploading fashion items

[0552] 1. The user takes or selects an image of a fashion item they own.

[0553] 2. The device sends this image to the server.

[0554] 3. The server uses a TensorFlow-based image recognition algorithm to identify the item's attribute information (category, color, material, brand, etc.) from the image.

[0555] 4. The server stores the identification results in a database and updates the user's online closet.

[0556] Location and Social Data Collection

[0557] When a user allows location information and social media integration in the app, the device will use the GPS module to obtain current location information. The device will also collect user posts and friend information from social media via API. All of this data is sent to the server and stored in data storage.

[0558] Analyzing data and generating coordinates

[0559] 1. The server obtains weather data from the weather API based on the collected location information.

[0560] 2. The server analyzes social media data to understand the fashion trends of the user's friends and followers.

[0561] 3. The server combines this information and inputs a prompt into a generative AI model (e.g., OpenAI GPT). For example, a prompt like, "The user is currently in a cold climate. Please suggest the best outfit using winter items from the user's online closet."

[0562] 4. Based on the prompts, the generative AI model generates outfits that take into account the season, location, and the user's social data.

[0563] Coordinate presentation and purchase process

[0564] 1. The server sends the generated coordinates to the terminal and presents them to the user.

[0565] 2. Users can review the coordinates and provide feedback if necessary.

[0566] 3. The server determines the missing items and retrieves information about related products using the API of an external sales site (e.g., Amazon).

[0567] 4. The device displays product information obtained from the external sales site to the user.

[0568] 5. Once the user decides to purchase, the device proceeds with the purchase process, and the server connects with the external sales site to complete the purchase.

[0569] Specific examples

[0570] If a user wants to create a winter outfit, they upload images of a coat, scarf, and boots. The server recognizes these items, saves them in data storage, and adds them to their online closet. If the device determines that the user's current location is in a cold climate, the server inputs the following prompt into the generative AI model: "You are currently in a cold climate, and your online closet contains a coat, scarf, and boots. Based on this, please suggest the perfect winter outfit for you." The generated outfit may include a warm sweater or gloves.

[0571] In this way, the present invention allows users to efficiently manage their fashion items, provide optimal coordination based on location and trends, and easily purchase necessary items.

[0572] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0573] Step 1: User registration and login

[0574] Input: The user enters their email address and password.

[0575] How it works: When a user first uses the app, they enter their email address and password on the account creation screen. The device then sends this information to the server.

[0576] Data processing: The server stores the received information in a MySQL database and creates a user account.

[0577] Output: The user account is saved in the database. Similarly, when you log in again, the server will authenticate you by checking the email address and password you entered.

[0578] Step 2: Upload your fashion items

[0579] Input: The user takes or selects an image of a fashion item they own.

[0580] How it works: The user opens the app's fashion item upload screen, takes a photo of the item, or selects an existing image. The device then sends this image to the server.

[0581] Data processing: The server uses a TensorFlow-based image recognition algorithm to identify item attribute information (category, color, material, brand, etc.) from the image.

[0582] Output: The server stores the identification results in a database and updates the user's online closet.

[0583] Step 3: Collect location and social data

[0584] Input: Location and social media data with user permission.

[0585] How it works: When a user allows the app to access location information and social media, the device will use the GPS module to obtain current location information. The device will also collect user data from social media via APIs.

[0586] Data processing: The device sends location and social data to the server.

[0587] Output: The server stores these data in a database.

[0588] Step 4: Analyze the data and generate coordinates

[0589] Inputs: Collected location information, weather data, social data, and online closet information.

[0590] How it works: The server receives location information, retrieves weather data from a weather API, and analyzes social media data to understand the fashion trends of the user's friends and followers.

[0591] Data calculation: The server integrates this information and inputs a prompt into the generative AI model (e.g., OpenAI GPT). An example prompt might be, "The current location is in a cold climate. Please suggest the best outfit using winter items from the user's online closet."

[0592] Output: The generative AI model generates the optimal coordination based on the prompts and returns it to the server.

[0593] Step 5: Present your outfit and complete the purchase

[0594] Input: The generated coordinates.

[0595] Operation: The server sends the generated coordinates to the device and presents them to the user, who can review the coordinates and provide feedback if necessary.

[0596] Data processing: The server determines the missing items and retrieves information about related products using the API of an external sales site.

[0597] Output: The terminal displays the acquired product information to the user, who selects and completes the purchase procedure. The server connects with the external sales site to complete the purchase procedure.

[0598] Through these steps, a system is realized that manages the user's fashion items, generates optimal coordinations, and assists in purchasing necessary items.

[0599] (Application example 1)

[0600] 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."

[0601] Today's consumers own many fashion items, but lack a system that can efficiently manage them and automatically suggest the optimal outfit for each location and situation. Furthermore, when users need an item they don't have on hand, there is no system that allows them to easily check inventory information at nearby brick-and-mortar stores and quickly purchase and receive it. Therefore, it is necessary to build a system that can properly manage the fashion items owned by users, provide optimal outfits, and instantly check inventory information for missing items, allowing them to purchase and receive them.

[0602] 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.

[0603] In this invention, the server includes means for uploading images of items owned by the user, means for AI to recognize the images and identify item attributes and store them in a database, means for collecting the user's location information and social network data, means for analyzing the collected data and generating an optimal outfit for the user, means for recommending items missing from partner e-commerce sites that are missing from the generated outfit, means for the user to purchase the recommended items, means for checking whether the generated outfit is in stock at a nearby physical store, and means for selecting a method for receiving the items at the physical store. This allows the user to efficiently manage their own items, obtain optimal outfits, and quickly obtain the items they need from a nearby physical store.

[0604] "Uploading an item image" is the process by which a user takes a photo of a fashion item they own and sends it to the server through the application.

[0605] "Image recognition" is a technology that uses AI technology to analyze the content of uploaded images and identify attributes such as item category, color, material, and brand.

[0606] "Storing in database" refers to the process of recording the identified attribute information in a database in structured data format so that it can be efficiently searched and used later.

[0607] "Location collection" is the process of obtaining a user's current geographic location using technologies such as GPS and transmitting it to a server.

[0608] "Social Network Data" is data about the styles and trends of your friends and followers obtained from social media platforms to which you have given permission.

[0609] "Data analysis" refers to the computational process of generating optimal fashion coordination based on collected location information and social network data, taking into account the user's preferences, trends, weather, etc.

[0610] "Coordination generation" is a process that suggests recommended fashion styles by combining items owned by the user based on analyzed data.

[0611] "Recommendation" refers to the act of selecting items that are missing from a generated outfit from a partner e-commerce site and recommending them to the user.

[0612] "Purchasing recommended items" is the process in which a user actually orders the suggested missing items and completes the purchase process.

[0613] "Check physical store inventory" is a function that checks whether the items in the generated outfit are in stock at a nearby physical store.

[0614] "In-store pickup" is the process by which a user selects how they would like to collect their online order from a physical store.

[0615] The following describes an embodiment of the present invention. This system efficiently manages the fashion items owned by the user, suggests optimal outfits based on location and social data, and allows the user to quickly obtain the necessary items from nearby physical stores.

[0616] User Registration and Login

[0617] When a user first uses the service, they launch the smartphone app and create an account. They enter the necessary information (email address, password, etc.), and this information is sent from the device to the server. The server stores this information in a database and creates an account for the user. When the user logs in, the server also verifies the information entered and performs authentication.

[0618] Uploading fashion items

[0619] Users take pictures of their fashion items with their smartphones and send them to the server via the app. The server then uses an image recognition algorithm to identify the item's attributes (category, color, material, brand, etc.) and stores them in a database, creating the user's online closet.

[0620] Location and Social Data Collection

[0621] The device will obtain location information with the user's permission, and also collect social network data if the user allows it to be linked, and this data will be sent to a server.

[0622] Analyzing data and generating coordinates

[0623] The server analyzes the received location and social data, taking into account the climate and cultural characteristics of the current location, and combines items from the user's online closet to generate an appropriate outfit. Data on the style preferences of the user's friends and followers is also used in the analysis.

[0624] Coordination suggestions and stock confirmation

[0625] The generated outfits are presented to the user via their device. The user can check the outfit details and see if the items are in stock at a nearby physical store. The physical store's inventory information is linked to the server and updated in real time.

[0626] Purchase procedure and delivery method selection

[0627] Users can select the recommended items displayed and proceed with the purchase. The purchase process is carried out via the terminal, and the server completes it by connecting with the designated physical store. Users can also choose how to receive the ordered items. For example, they can choose to collect them directly at the store or use a delivery service.

[0628] Specific examples

[0629] For example, if a user wants to create a winter outfit, they can upload an image of their winter clothing: a coat, scarf, and boots. The server recognizes these items, stores them in a database, and adds them to their online closet. The device then verifies that the user's current location is in a cold climate. The server then uses this information to generate an outfit that includes a coat, scarf, and boots, as well as warmer materials like sweaters and gloves. The server then checks whether these items are in stock at nearby physical stores and displays them to the user.

[0630] Prompt Sentence Examples

[0631] "Taking into account the red coat uploaded by the user and the current location's cold climate, suggest an appropriate fashion coordination. Also, check whether related items are in stock at nearby physical stores."

[0632] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0633] Step 1:

[0634] When a user uses the app for the first time, they launch the smartphone app and create an account. The user enters their email address and password, and sends this information from their device to the server. The server stores the received information in a database and creates an account for the user. It also performs a comparison to authenticate the entered information, and if authentication is successful, the login is complete. The input is user information, and the output is the registered user account.

[0635] Step 2:

[0636] Users take pictures of their fashion items with their smartphones and send them to the server via the app. The server uses an image recognition algorithm (e.g., TensorFlow or PyTorch) to identify the item's attributes (category, color, material, brand, etc.) and stores the identification results in a database. The input is an image of the fashion item, and the output is item data with attribute information added.

[0637] Step 3:

[0638] With the user's permission, the device obtains location information and also collects social network data that the user has authorized to be linked. This data is sent from the device to a server. The server analyzes the received data and generates analysis results based on the climate and cultural characteristics of the current location and the social network data. The input is location information and social network data, and the output is the analysis results.

[0639] Step 4:

[0640] The server generates the optimal outfit for the user based on the items in the online closet. This takes into account the location information and social data received. For example, cold weather items are selected taking into account the cold climate. The generated outfit is saved in a database and processed to be presented to the user. The input is the analysis results and the online closet data, and the output is the generated outfit.

[0641] Step 5:

[0642] The generated outfit is displayed on the user's device. The server checks whether the items in the outfit presented to the user are in stock at nearby physical stores. The physical store's inventory information is updated in real time and presented to the user. The input is the generated outfit and the physical store's inventory information, and the output is the outfit whose inventory has been confirmed.

[0643] Step 6:

[0644] The user selects the recommended items displayed and proceeds with the purchase if necessary. The purchase process is carried out via the terminal, and the server completes the order by connecting with partner e-commerce sites and physical stores. The user can select a method of collection at the physical store. For example, they can choose to collect the item directly at the store or use a delivery service. The input is the item and collection method selected by the user, and the output is a notification that the purchase process has been completed.

[0645] Prompt Sentence Examples

[0646] "Taking into account the red coat uploaded by the user and the current location's cold climate, suggest an appropriate fashion coordination. Also, check whether related items are in stock at nearby physical stores."

[0647] 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.

[0648] The following describes an embodiment of the present invention. The present invention is a system that efficiently manages a user's owned fashion items and suggests optimal outfits based on location information, social data, and the user's emotions. By combining this system with an emotion engine that analyzes the user's emotions, it is possible to suggest more personalized outfits.

[0649] User Registration and Login

[0650] When a user first starts the app and enters the required information (email address, password, etc.) on the account creation screen, the device sends the entered information to the server. The server stores this information in a database and creates an account. When the user logs in, the server also verifies the entered information and performs authentication.

[0651] Uploading fashion items

[0652] Users take or select images of their fashion items via their device and send them to the server, which uses an image recognition algorithm to identify the item's attributes (category, color, material, brand, etc.) and store them in a database, creating the user's online closet.

[0653] Location and Social Data Collection

[0654] The device will obtain location information with the user's permission, and also collect social network data if the user allows it to be linked, and this data will be sent to a server.

[0655] Analyzing data and generating coordinates

[0656] The server analyzes the received location and social data, taking into account the climate and cultural characteristics of the current location, and combines items from the user's online closet to generate an appropriate outfit. Data on the style preferences of the user's friends and followers is also used in the analysis.

[0657] Use of emotion engine

[0658] The emotion engine has the ability to analyze emotions from the user's facial expressions, voice, text input, etc. The device collects the user's emotional data and sends it to the server. The server analyzes this emotional data to identify the user's current emotional state. It then generates outfit suggestions based on the user's emotional state, thereby recommending more appropriate fashion to match the user's mood.

[0659] Coordinate presentation and purchase process

[0660] The generated outfit is presented to the user via the device. The user can check the outfit and provide feedback if necessary. The server then retrieves any missing items from the partner e-commerce site and displays them on the device. The user can select the recommended items displayed and complete the purchase process. The purchase process is completed via the device, with the server coordinating with the e-commerce site to complete the process.

[0661] Specific examples

[0662] For example, if a user is feeling stressed, the emotion engine will detect this and suggest a casual, comfortable outfit that will have a relaxing effect. On the other hand, if a user is excited about a particular event, the emotion engine will suggest a more glamorous, appropriate outfit. In this way, the outfits generated by the emotion engine are more deeply adapted to the user's emotional state.

[0663] In this way, the present invention provides a system that allows users to efficiently manage the items they own, suggests optimal fashion coordination based on location, trends, and even emotions, and allows them to easily purchase the items they need.

[0664] The processing flow will be explained below.

[0665] Step 1:

[0666] The user launches the app for the first time and enters the required information (email address, password, etc.) on the account creation screen.

[0667] Step 2:

[0668] The terminal transmits the input information to the server.

[0669] Step 3:

[0670] The server stores the received information in a database and sends a response to the device to create an account.

[0671] Step 4:

[0672] The user enters their email address and password on the login screen, and the device again sends this information to the server.

[0673] Step 5:

[0674] The server verifies the entered information by comparing it with information in a database, and if the authentication is successful, it sends a success message to the terminal.

[0675] Step 6:

[0676] The user navigates to the closet management screen, presses the button to add a new item, and activates the camera.

[0677] Step 7:

[0678] The user takes a photo of a fashion item or selects an existing image, and the device sends the image to the server.

[0679] Step 8:

[0680] The server passes the received images to an image recognition engine to identify attributes such as item category, color, material, and brand.

[0681] Step 9:

[0682] The server stores the identified attribute information in a database and generates an online closet.

[0683] Step 10:

[0684] The device will present the user with a confirmation screen of their online closet, allowing them to edit or delete items as needed.

[0685] Step 11:

[0686] If the user grants permission to obtain location information, the device will collect GPS data.

[0687] Step 12:

[0688] When a user gives permission to connect with a social network, the device collects social data.

[0689] Step 13:

[0690] The device sends the location and social data it collects to a server.

[0691] Step 14:

[0692] The server analyzes the location and social data it receives and extracts data such as the user's current climate and cultural background, as well as their friends' style information.

[0693] Step 15:

[0694] Based on the analysis results, the server combines items from the user's online closet to generate the optimal outfit.

[0695] Step 16:

[0696] The emotion engine has the ability to analyze emotions from the user's facial expressions, voice, text input, etc. The device collects the user's emotional data and sends it to the server.

[0697] Step 17:

[0698] The server analyzes the emotion data received from the emotion engine to determine the user's current emotional state.

[0699] Step 18:

[0700] The server generates coordination suggestions based on the emotional state, thereby providing coordination that matches the user's mood.

[0701] Step 19:

[0702] The terminal presents the generated coordinates to the user and receives feedback from the user.

[0703] Step 20:

[0704] The server requests the partner EC site for items that are missing from the generated coordinated outfit, and acquires the corresponding items.

[0705] Step 21:

[0706] The device displays the recommended items to the user, and when the user selects one, they are taken to the purchase process screen.

[0707] Step 22:

[0708] The user selects the recommended item on the checkout screen and enters their shipping address, payment information, etc. to complete the purchase.

[0709] Step 23:

[0710] The server sends the entered purchase procedure information to the partner EC site and completes the purchase procedure.

[0711] Example 2

[0712] 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."

[0713] Conventional fashion management systems require users to manage their personal items in a cumbersome manner, and lack sufficient integration with location information and social data. Furthermore, they lack personalized suggestions for individual users, and do not take into account the user's emotional state. This makes it difficult to provide optimal outfits for users. Furthermore, the generated outfits do not integrate recommendations for missing items or a purchasing process.

[0714] 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.

[0715] In this invention, the server includes a means for uploading images of items owned by the user, a means for a generative AI model to recognize the images, identify the attributes of the items, and store them in a database, a means for collecting the user's location information and social network data, a means for analyzing the collected data and the user's emotional data to generate an optimal outfit for the user, a means for recommending items missing from a partner e-commerce site, and a means for the user to purchase the recommended items. This allows the user to efficiently manage the items they own and receive suggestions for optimal fashion outfits based on their location information, social data, and emotional state. Furthermore, the system also facilitates the recommendation and purchase process for missing items.

[0716] "User" refers to an individual who uses this system.

[0717] "Owned Items" refers to fashion-related items, including apparel and accessories, owned by a User.

[0718] "Means for uploading images" refers to the functionality that allows a user to submit photos of items they own to the system.

[0719] A "generative AI model" refers to an artificial intelligence technology that uses algorithms to generate specific results based on input data.

[0720] "Item attributes" refer to characteristics of a fashion item such as category, color, material, brand, etc.

[0721] "Database" refers to a system for organizing and storing identified data.

[0722] "Location information" refers to data that indicates a user's current location.

[0723] "Social Network Data" refers to information obtained from a user's social media.

[0724] "Means of analysis" refers to the process of analyzing collected data to extract useful information.

[0725] "Emotional data" refers to information about a user's emotional state obtained from facial expressions, voice, text input, etc.

[0726] "Means for generating coordination" refers to the function that suggests suitable fashion combinations for users based on collected data.

[0727] "Partner e-commerce site" refers to the online shopping platform with which the System is integrated.

[0728] "Means of recommendation" refers to the function of recommending specific products to users.

[0729] "Means to purchase" refers to the functionality that allows users to purchase recommended items.

[0730] The present invention relates to a system that efficiently manages fashion items owned by a user and suggests optimal outfits based on location information, social network data, and emotional data. Specific embodiments of this system are described below.

[0731] User Registration and Login

[0732] When a user uses the application for the first time, they start it and enter the necessary information, such as their email address and password, on the account creation screen. The device then sends the entered information to the server, which then stores this information in a database and creates an account. When the user enters their email address and password on the login screen and taps the "Login" button, the server compares this information with the information in the database and performs authentication.

[0733] Uploading fashion items

[0734] The user takes a photo of a fashion item they own on their device or selects it from their photo library. The device then sends the selected or captured image to the server. The server then uses an image recognition algorithm (e.g., TensorFlow or OpenCV) to identify the attribute information (category, color, material, brand, etc.) of the item in the image. This identified attribute information is stored in a database, forming the user's online closet.

[0735] Location and Social Data Collection

[0736] The device obtains location information with the user's permission. It measures the current location using GPS, and the device also collects data from social networks (e.g., Facebook, Instagram) with the user's permission. This data is sent to the server.

[0737] Analyzing data and generating coordinates

[0738] The server analyzes the received location and social data. It uses machine learning algorithms (e.g., Scikit-learn and TensorFlow) to take into account the climate and cultural background of the current location. Based on the analysis results, the server combines items from the user's online closet to generate an appropriate outfit. The server also uses data on the style preferences of the user's friends and followers.

[0739] Use of emotion engine

[0740] The emotion engine has the ability to analyze emotional data from the user's facial expressions, voice, text input, etc. The device sends the collected emotional data to the server, which analyzes this emotional data to identify the user's current emotional state. Based on this emotional state, personalized outfit suggestions are generated.

[0741] Coordinate presentation and purchase process

[0742] The server sends the generated coordinated outfit to the device, which then displays it to the user. The user can review the coordinated outfit and provide feedback. The server also retrieves any missing items from partner e-commerce sites and displays them on the device. The user selects the displayed items and completes the purchase. The purchase is completed via the device, and the server works with the e-commerce site to complete the purchase.

[0743] Specific examples

[0744] For example, if a user is feeling stressed, the emotion engine will detect this and suggest a casual, comfortable outfit that will have a relaxing effect. On the other hand, if a user is excited about a particular event, it will suggest a more glamorous, event-appropriate outfit. In this way, the outfits generated by the emotion engine are deeply adapted to the user's emotional state.

[0745] Example prompts for generative AI models

[0746] We developed a system that smoothly manages the fashion items owned by users and suggests optimal outfits based on location, social, and emotional data. We analyze various data such as users' photos, voice, location, and SNS data to provide guidance on how to make personalized fashion suggestions.

[0747] In this way, the present invention provides a system that allows users to efficiently manage the items they own, suggests optimal fashion coordination based on location information, social data, and emotional state, and allows them to easily purchase the items they need.

[0748] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0749] Step 1:

[0750] The user launches an application.

[0751] Input: Tap the application icon

[0752] Output: Display of the application's welcome screen

[0753] What happens: A user launches an app by tapping on the application icon on their smartphone or tablet, and is presented with the option to create an account and log in.

[0754] Step 2:

[0755] The user enters the required information on the account creation screen.

[0756] Input: Email address, password

[0757] Output: Sending input data to the server

[0758] Specific operation: The user enters an email address and password and taps the "Send" button. The device receives this and sends it to the server.

[0759] Step 3:

[0760] The server stores the user information and creates an account.

[0761] Input: Email address, password

[0762] Output: Account information stored in the database

[0763] Specific operations: The server saves the received user information in the database and returns a success message to the user.

[0764] Step 4:

[0765] The user enters their credentials on the login screen.

[0766] Input: Email address, password

[0767] Output: Authentication result

[0768] Specific behavior: The user enters their email address and password on the login screen and taps the "Login" button.

[0769] Step 5:

[0770] The server checks the authentication information against a database and performs authentication.

[0771] Input: Email address, password

[0772] Output: Authentication result (success / failure)

[0773] Specific operation: The server compares the user information in the database with the login information and returns the authentication result to the device. If successful, the home screen is displayed.

[0774] Step 6:

[0775] The user takes or selects an image of a fashion item they own.

[0776] Input: Fashion item image

[0777] Output: Sending image data to the server

[0778] What happens: The user takes a photo of an item using the camera or selects one from their photo library. The image is sent from the device to the server.

[0779] Step 7:

[0780] The server identifies the image and stores the item's attributes in a database.

[0781] Input: Fashion item image

[0782] Output: Attribute information (category, color, material, brand, etc.)

[0783] Specific operation: The server uses an image recognition algorithm (e.g., TensorFlow or OpenCV) to identify the attributes of the item and store that information in a database.

[0784] Step 8:

[0785] The device collects the user's location and social network data.

[0786] Input: Location permission, Social network data permission

[0787] Output: Sending location and social network data to a server

[0788] What it does: The device uses GPS to obtain location information and, with the user's permission, collects social network data, which is then sent to a server.

[0789] Step 9:

[0790] The server analyzes the location and social data.

[0791] Input: Location information, social network data

[0792] Output: Analysis results

[0793] Specific operation: The server uses machine learning algorithms (e.g., Scikit-learn or TensorFlow) to perform analysis that takes into account the climate and cultural background of the current location.

[0794] Step 10:

[0795] The server generates the optimal coordination based on the analysis results.

[0796] Input: Analysis results, online closet data

[0797] Output: Coordination suggestions

[0798] Specific operation: The server uses the analysis results to combine items in the online closet and generate the optimal outfit.

[0799] Step 11:

[0800] The device collects the user's emotional data and sends it to the server.

[0801] Input: User facial expressions, voice, and text input

[0802] Output: Sending emotion data to the server

[0803] Specific operation: The device uses the camera and microphone to collect the user's emotional data and sends it to the server.

[0804] Step 12:

[0805] The server analyzes the emotional data and identifies the user's emotional state.

[0806] Input: Emotion data

[0807] Output: Emotional state identification result

[0808] What it does: The server uses an emotion analysis algorithm to determine the user's current emotional state.

[0809] Step 13:

[0810] The server generates coordination suggestions based on the emotional state.

[0811] Input: Emotional state, analysis results

[0812] Output: Personalized outfit suggestions

[0813] Specific operation: The server generates coordinates that match the user's mood based on their emotional state.

[0814] Step 14:

[0815] The server sends the generated coordinates to the terminal and presents them to the user.

[0816] Input: Coordination suggestion

[0817] Output: Coordinates displayed on the terminal

[0818] Specific operation: The server sends a coordination proposal to the terminal, and the terminal displays it to the user.

[0819] Step 15:

[0820] The user provides feedback on the presented outfit.

[0821] Input: Feedback information

[0822] Output: Sending feedback information to the server

[0823] Specific operation: The user inputs feedback on the coordination, and the device sends the information to the server.

[0824] Step 16:

[0825] The server retrieves the missing items from the partner e-commerce site and displays them on the device.

[0826] Input: Coordination suggestions, missing item information

[0827] Output: Display of recommended items

[0828] Specific operation: The server retrieves information about missing items from partner e-commerce sites and displays it on the device.

[0829] Step 17:

[0830] The user selects a recommended item and completes the purchase process.

[0831] Input: Recommended item selection, purchase information

[0832] Output: Information to complete the purchase

[0833] Specific operation: The user selects a recommended item and completes the purchase process. The purchase process is completed through the terminal, and the server connects with the e-commerce site to complete the purchase.

[0834] (Application example 2)

[0835] 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."

[0836] Previous fashion coordination suggestion systems did not take into account the user's shopping experience in a physical store, nor did they optimize coordination through emotion analysis. This meant that users could not receive personalized suggestions based on their current mood or the items they were actually looking at. Furthermore, they lacked technology to visually present coordination suggestions in real time using smart glasses. To solve these issues, a new system that takes user emotions into account and improves the shopping experience in a physical store is needed.

[0837] 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.

[0838] In this invention, the server includes means for uploading images of items owned by the user, means for AI to recognize the images and identify item attributes and store them in a database, means for collecting user location information and social network data, means for analyzing the collected data and generating an optimal outfit for the user, means for analyzing the user's emotions and optimizing the outfit based on the emotions, means for visually displaying real-time coordination suggestions of fashion items in a physical store using smart glasses, means for recommending items missing from a partner e-commerce site that are missing from the generated outfit, and means for the user to purchase the recommended items. This makes it possible to propose personalized fashion coordination that takes into account the user's emotions, location information, and purchasing behavior in a physical store.

[0839] "Means for uploading images of items owned by the user" refers to means for the user to take or select photos of fashion items they own and send them to the system.

[0840] "Means for AI to recognize images, identify item attributes, and store them in a database" refers to a means of using artificial intelligence technology to extract information such as the type, color, material, and brand of a fashion item from uploaded images and record that information in a database.

[0841] "Means for collecting user location information and social network data" refers to means for obtaining a user's current geographic location and activity data on social networking sites.

[0842] "Means for analyzing collected data and generating optimal outfits for users" refers to a means for automatically creating fashion outfits that suit users based on acquired location information and social network data.

[0843] "Means for analyzing user emotions and optimizing coordination based on emotions" refers to a means for detecting emotions from the user's facial expressions, voice, text input, etc., and suggesting fashion coordination that matches that emotional state.

[0844] "A means for visually displaying real-time coordination suggestions for fashion items in a physical store using smart glasses" refers to a means for displaying, in real time, coordination suggestions that combine the fashion items that a user is looking at in a physical store with items in their online closet, using smart glasses.

[0845] "Means for recommending missing items from partner e-commerce sites in the created outfit" refers to a means for searching for missing fashion items in the created outfit from an online shopping site and recommending them to the user.

[0846] "Means for users to purchase recommended items" refers to the procedures for users to actually purchase items recommended by the system, and the means to provide functions to support this.

[0847] This system efficiently manages a user's fashion items and suggests optimal outfits based on location information, social data, and the user's emotions. This system can also suggest outfits in real time in physical stores using smart glasses.

[0848] System configuration

[0849] 1. Image upload method

[0850] The user captures an image of the item they own via their smartphone or other device and sends it to the server. This requires a camera function and communication with the server. Specifically, the smartphone's camera app and internet connection are used.

[0851] 2. Image Recognition Methods

[0852] The server uses AI (artificial intelligence) to analyze the received images and identify the item's attributes. Here, the image is processed using OpenCV, and machine learning models (such as TensorFlow or PyTorch) are used to obtain information such as the item's category, color, material, and brand.

[0853] 3. Location and Social Data Collection Methods

[0854] With the user's permission, the device obtains location information from GPS and collects data from the user's social media accounts, which is then sent over the internet to a server.

[0855] 4. Data analysis and coordinate generation methods

[0856] The server analyzes the collected location and social data to generate optimal fashion coordination for the user, taking into account weather information and cultural characteristics. Data analysis is performed using database software (e.g., MySQL or PostgreSQL) and analysis tools (e.g., Pandas or NumPy).

[0857] 5. Emotion analysis method

[0858] The device collects emotional data based on the user's facial expressions, voice, and text input, and sends it to a server. The server then uses AI to analyze this emotional data and determine the user's current emotional state. Emotion analysis tools such as EmotionEngine are used for this analysis.

[0859] 6. Real-time Coordination Proposal Method Using Smart Glasses

[0860] The smart glasses have the ability to recognize the fashion items the user is looking at in a physical store in real time and visually display coordination suggestions that combine them with the user's online closet. Here, OpenCV is used to capture camera images and generate suggestions through communication with a cloud server.

[0861] 7. Recommendation methods

[0862] The server recommends missing items from partner e-commerce sites, allowing users to easily purchase the missing items. The recommendation algorithm uses collaborative filtering and content-based recommendation systems.

[0863] Example

[0864] For example, if a user visits a shopping mall on a holiday and picks up a red dress, the smart glasses will recognize the dress in real time and, if it determines that the user's emotion is "happy," it will suggest a glamorous outfit, providing users with a more personalized shopping experience.

[0865] Prompt Sentence Examples

[0866] "Design an application that recognizes the fashion items a user is looking at and suggests the best fashion coordination based on the user's emotions and location. This application will be installed on smart glasses."

[0867] This invention makes it possible to propose personalized fashion coordination that takes into account the user's emotions and location information, significantly improving the shopping experience in physical stores.

[0868] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0869] Step 1:

[0870] Image capture and upload

[0871] A user uses a smartphone or other device to take a picture of a fashion item they own and send it to the server. The input is the image of the fashion item, and the output is the image data sent to the server. Specific operations include the user launching a camera app, capturing an image, and then pressing the image upload button.

[0872] Step 2:

[0873] Image Recognition and Attribute Identification

[0874] The server analyzes the received image data using AI (image recognition algorithms) to identify attributes such as the type, color, material, and brand of the fashion item. The input is the uploaded image data, and the output is the item's attribute information. Specifically, the server processes the image using OpenCV, identifies the attributes using TensorFlow or PyTorch models, and stores them in a database.

[0875] Step 3:

[0876] Location and social data collection

[0877] With the user's permission, the device obtains the user's current location from GPS and also collects data from the user's social media accounts and sends it to a server. The input is location information and social data, and the output is these data sent to the server. Specifically, the device periodically obtains GPS data and uses social media APIs to collect and send the necessary data.

[0878] Step 4:

[0879] Data analysis and coordinate generation

[0880] The server analyzes the acquired location information and social data and generates the optimal fashion coordination for the user based on that information. The input is location information, social data, and item attribute information, and the output is the generated coordination proposal. Specifically, the server queries the necessary data from the database, processes the data using analysis tools (Pandas or NumPy), and generates the optimal coordination.

[0881] Step 5:

[0882] Emotion analysis

[0883] The device collects the user's facial expressions and voice and sends them to the server. The server then uses EmotionEngine to analyze and identify the user's emotional state. The input is the user's facial expression data and voice data, and the output is information about the user's emotional state. The specific operation is a process in which the device uses a camera and microphone to capture the user's data and send it to the server.

[0884] Step 6:

[0885] Real-time outfit suggestions using smart glasses

[0886] The device (smart glasses) captures real-time video footage from the physical store and sends the image data to a server. The server analyzes the images, generates real-time outfit suggestions that combine them with items from the online closet, and displays them on the smart glasses. The input is real-time video data, and the output is the outfit suggestions presented to the user. The specific operation is a process in which the smart glasses activate their camera and constantly capture the items the user is looking at and send them to the server.

[0887] Step 7:

[0888] Recommendations and checkout

[0889] The server searches partner e-commerce sites for items missing from the generated outfit and recommends them to the user. The user purchases the recommended items through their device. The input is the outfit plan, and the output is the recommended items and the purchase procedure based on them. Specifically, the server recommends products using collaborative filtering or a content-based recommendation system, and the purchase procedure is carried out through the e-commerce site's API.

[0890] This system allows users to receive real-time fashion coordination suggestions based on their emotions and location information, improving their shopping experience.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] [Third embodiment]

[0895] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0896] 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.

[0897] 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).

[0898] 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.

[0899] 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.

[0900] 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).

[0901] 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.

[0902] 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.

[0903] 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.

[0904] 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.

[0905] 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.

[0906] 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."

[0907] The following describes an embodiment of the present invention. This invention is a system that efficiently manages fashion items owned by a user and suggests optimal outfits based on location and social data. This system consists of the following steps, and how each step works will be described below.

[0908] User Registration and Login

[0909] When a user uses the app for the first time, they must launch the app and create an account. They enter the required information (email address, password, etc.) and this information is sent from the device to the server. The server stores this information in a database and creates an account for the user. When logging in, the server also verifies the information entered and performs authentication.

[0910] Uploading fashion items

[0911] Users take or select images of their fashion items via their device and send them to the server, which uses an image recognition algorithm to identify the item's attributes (category, color, material, brand, etc.) and store them in a database, creating the user's online closet.

[0912] Location and Social Data Collection

[0913] The device will obtain location information with the user's permission, and also collect social network data if the user allows it to be linked, and this data will be sent to a server.

[0914] Analyzing data and generating coordinates

[0915] The server analyzes the received location and social data, taking into account the climate and cultural characteristics of the current location, and combines items from the user's online closet to generate an appropriate outfit. Data on the style preferences of the user's friends and followers is also used in the analysis.

[0916] Coordinate presentation and purchase process

[0917] The generated outfit is presented to the user via the device. The user can check the outfit and provide feedback if necessary. The server then retrieves any missing items from the partner e-commerce site and displays them on the device. The user can select the recommended items displayed and complete the purchase process. The purchase process is completed via the device, with the server coordinating with the e-commerce site to complete the process.

[0918] Specific examples

[0919] For example, if a user wants to create a winter outfit, they can upload an image of their winter clothing: a coat, scarf, and boots. The server recognizes these items, stores them in a database, and adds them to their online closet. The device then verifies that the user is in a cold climate. The server then uses this information to generate an outfit that includes the coat, scarf, and boots, as well as a sweater and gloves made from warm materials.

[0920] In this way, the present invention provides a system that enables users to efficiently manage items they own, generate optimal fashion coordination based on location and trends, and easily purchase the items they need.

[0921] The processing flow will be explained below.

[0922] Step 1:

[0923] The user launches the app for the first time and enters the required information (email address, password, etc.) on the account creation screen.

[0924] Step 2:

[0925] The terminal transmits the input information to the server.

[0926] Step 3:

[0927] The server stores the received information in a database and sends a response to the device to create an account.

[0928] Step 4:

[0929] The user enters their email address and password on the login screen, and the device again sends this information to the server.

[0930] Step 5:

[0931] The server verifies the entered information by comparing it with information in a database, and if the authentication is successful, it sends a success message to the terminal.

[0932] Step 6:

[0933] The user navigates to the closet management screen, presses the button to add a new item, and activates the camera.

[0934] Step 7:

[0935] The user takes a photo of a fashion item or selects an existing image, and the device sends the image to the server.

[0936] Step 8:

[0937] The server passes the received images to an image recognition engine to identify attributes such as item category, color, material, and brand.

[0938] Step 9:

[0939] The server stores the identified attribute information in a database and generates an online closet.

[0940] Step 10:

[0941] The device will present the user with a confirmation screen of their online closet, allowing them to edit or delete items as needed.

[0942] Step 11:

[0943] If the user grants permission to obtain location information, the device will collect GPS data.

[0944] Step 12:

[0945] When a user gives permission to connect with a social network, the device collects social data.

[0946] Step 13:

[0947] The device sends the location and social data it collects to a server.

[0948] Step 14:

[0949] The server analyzes the location and social data it receives and extracts data such as the user's current climate and cultural background, as well as their friends' style information.

[0950] Step 15:

[0951] Based on the analysis results, the server combines items from the user's online closet to generate the optimal outfit.

[0952] Step 16:

[0953] The terminal presents the generated coordinates to the user and receives feedback from the user.

[0954] Step 17:

[0955] The server requests the partner EC site for items that are missing from the generated coordinated outfit, and acquires the corresponding items.

[0956] Step 18:

[0957] The device displays the recommended items to the user, and when the user selects one, they are taken to the purchase process screen.

[0958] Step 19:

[0959] The user selects the recommended item on the checkout screen and enters their shipping address, payment information, etc. to complete the purchase.

[0960] Step 20:

[0961] The server sends the entered purchase procedure information to the partner EC site and completes the purchase procedure.

[0962] Example 1

[0963] 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."

[0964] Conventional fashion coordination support systems often require a lot of effort to manage the items a user owns and suggest appropriate outfits, resulting in ineffective functionality. A particular challenge is generating outfits that take into account multiple factors, such as the weather in the user's current location and the fashion trends of their friends. Furthermore, recommending new items and completing the purchasing process are time-consuming and inconvenient for users.

[0965] 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.

[0966] In this invention, the server includes means for uploading images of items owned by the user, means for a recognition device to recognize the images and identify attributes of the items and store them in data storage, means for collecting location information and social media data of the user, means for analyzing the collected data and generating a coordinated outfit suitable for the user, means for recommending items missing from an external sales site for the generated coordinated outfit, and means for the user to purchase the recommended items. This allows users to efficiently manage their own fashion items, easily receive optimal coordinated outfits based on location and trends, and easily purchase new items they need.

[0967] "User" refers to an individual who uses the system to manage their own fashion items and receive coordination suggestions.

[0968] "Items" refers to all fashion items owned by a User and uploaded to the System.

[0969] "Image upload means" refers to a function that allows a user to upload images of items owned by the user into the system.

[0970] "Recognition device" refers to a device that analyzes uploaded images and implements algorithms to identify attributes of items.

[0971] "Attributes" refer to specific characteristics of an item, such as its category, color, material, or brand.

[0972] "Data storage" refers to a database for storing attribute information identified by a recognition device.

[0973] "Location information" refers to information that identifies a user's current location.

[0974] "Social Media Data" refers to information about a user's social networks that is collected with the user's permission.

[0975] "Recommendation means" refers to a function that identifies items that are missing from the generated outfit and suggests them to the user from an external sales site.

[0976] "Purchase method" refers to the functionality that allows users to purchase recommended items.

[0977] "Online data storage" refers to storage within a system that automatically organizes and stores items uploaded by users.

[0978] "Feedback means" refers to a function that allows users to provide opinions and evaluations of the presented coordination.

[0979] "Correction measures" refer to functions for improving the content of coordination based on collected feedback.

[0980] "Analysis means" refers to the functionality for generating coordination based on collected location information and social media data.

[0981] The following describes in detail the mode for carrying out the present invention. This invention is a system that efficiently manages a user's fashion items and suggests optimal outfits based on location and social media data. This system is based on cooperation between a server, a terminal, and a user.

[0982] User Registration and Login

[0983] When a user uses the app for the first time, they must create an account. At this time, they enter an email address and password. The device sends the entered information to the server. The server stores the received information in a MySQL database and creates a user account. When logging in, the device similarly sends the email address and password, and the server performs authentication.

[0984] Uploading fashion items

[0985] 1. The user takes or selects an image of a fashion item they own.

[0986] 2. The device sends this image to the server.

[0987] 3. The server uses a TensorFlow-based image recognition algorithm to identify the item's attribute information (category, color, material, brand, etc.) from the image.

[0988] 4. The server stores the identification results in a database and updates the user's online closet.

[0989] Location and Social Data Collection

[0990] When a user allows location information and social media integration in the app, the device will use the GPS module to obtain current location information. The device will also collect user posts and friend information from social media via API. All of this data is sent to the server and stored in data storage.

[0991] Analyzing data and generating coordinates

[0992] 1. The server obtains weather data from the weather API based on the collected location information.

[0993] 2. The server analyzes social media data to understand the fashion trends of the user's friends and followers.

[0994] 3. The server combines this information and inputs a prompt into a generative AI model (e.g., OpenAI GPT). For example, a prompt like, "The user is currently in a cold climate. Please suggest the best outfit using winter items from the user's online closet."

[0995] 4. Based on the prompts, the generative AI model generates outfits that take into account the season, location, and the user's social data.

[0996] Coordinate presentation and purchase process

[0997] 1. The server sends the generated coordinates to the terminal and presents them to the user.

[0998] 2. Users can review the coordinates and provide feedback if necessary.

[0999] 3. The server determines the missing items and retrieves information about related products using the API of an external sales site (e.g., Amazon).

[1000] 4. The device displays product information obtained from the external sales site to the user.

[1001] 5. Once the user decides to purchase, the device proceeds with the purchase process, and the server connects with the external sales site to complete the purchase.

[1002] Specific examples

[1003] If a user wants to create a winter outfit, they upload images of a coat, scarf, and boots. The server recognizes these items, saves them in data storage, and adds them to their online closet. If the device determines that the user's current location is in a cold climate, the server inputs the following prompt into the generative AI model: "You are currently in a cold climate, and your online closet contains a coat, scarf, and boots. Based on this, please suggest the perfect winter outfit for you." The generated outfit may include a warm sweater or gloves.

[1004] In this way, the present invention allows users to efficiently manage their fashion items, provide optimal coordination based on location and trends, and easily purchase necessary items.

[1005] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1006] Step 1: User registration and login

[1007] Input: The user enters their email address and password.

[1008] How it works: When a user first uses the app, they enter their email address and password on the account creation screen. The device then sends this information to the server.

[1009] Data processing: The server stores the received information in a MySQL database and creates a user account.

[1010] Output: The user account is saved in the database. Similarly, when you log in again, the server will authenticate you by checking the email address and password you entered.

[1011] Step 2: Upload your fashion items

[1012] Input: The user takes or selects an image of a fashion item they own.

[1013] How it works: The user opens the app's fashion item upload screen, takes a photo of the item, or selects an existing image. The device then sends this image to the server.

[1014] Data processing: The server uses a TensorFlow-based image recognition algorithm to identify item attribute information (category, color, material, brand, etc.) from the image.

[1015] Output: The server stores the identification results in a database and updates the user's online closet.

[1016] Step 3: Collect location and social data

[1017] Input: Location and social media data with user permission.

[1018] How it works: When a user allows the app to access location information and social media, the device will use the GPS module to obtain current location information. The device will also collect user data from social media via APIs.

[1019] Data processing: The device sends location and social data to the server.

[1020] Output: The server stores these data in a database.

[1021] Step 4: Analyze the data and generate coordinates

[1022] Inputs: Collected location information, weather data, social data, and online closet information.

[1023] How it works: The server receives location information, retrieves weather data from a weather API, and analyzes social media data to understand the fashion trends of the user's friends and followers.

[1024] Data calculation: The server integrates this information and inputs a prompt into the generative AI model (e.g., OpenAI GPT). An example prompt might be, "The current location is in a cold climate. Please suggest the best outfit using winter items from the user's online closet."

[1025] Output: The generative AI model generates the optimal coordination based on the prompts and returns it to the server.

[1026] Step 5: Present your outfit and complete the purchase

[1027] Input: The generated coordinates.

[1028] Operation: The server sends the generated coordinates to the device and presents them to the user, who can review the coordinates and provide feedback if necessary.

[1029] Data processing: The server determines the missing items and retrieves information about related products using the API of an external sales site.

[1030] Output: The terminal displays the acquired product information to the user, who selects and completes the purchase procedure. The server connects with the external sales site to complete the purchase procedure.

[1031] Through these steps, a system is realized that manages the user's fashion items, generates optimal coordinations, and assists in purchasing necessary items.

[1032] (Application example 1)

[1033] 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."

[1034] Today's consumers own many fashion items, but lack a system that can efficiently manage them and automatically suggest the optimal outfit for each location and situation. Furthermore, when users need an item they don't have on hand, there is no system that allows them to easily check inventory information at nearby brick-and-mortar stores and quickly purchase and receive it. Therefore, it is necessary to build a system that can properly manage the fashion items owned by users, provide optimal outfits, and instantly check inventory information for missing items, allowing them to purchase and receive them.

[1035] 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.

[1036] In this invention, the server includes means for uploading images of items owned by the user, means for AI to recognize the images and identify item attributes and store them in a database, means for collecting the user's location information and social network data, means for analyzing the collected data and generating an optimal outfit for the user, means for recommending items missing from partner e-commerce sites that are missing from the generated outfit, means for the user to purchase the recommended items, means for checking whether the generated outfit is in stock at a nearby physical store, and means for selecting a method for receiving the items at the physical store. This allows the user to efficiently manage their own items, obtain optimal outfits, and quickly obtain the items they need from a nearby physical store.

[1037] "Uploading an item image" is the process by which a user takes a photo of a fashion item they own and sends it to the server through the application.

[1038] "Image recognition" is a technology that uses AI technology to analyze the content of uploaded images and identify attributes such as item category, color, material, and brand.

[1039] "Storing in database" refers to the process of recording the identified attribute information in a database in structured data format so that it can be efficiently searched and used later.

[1040] "Location collection" is the process of obtaining a user's current geographic location using technologies such as GPS and transmitting it to a server.

[1041] "Social Network Data" is data about the styles and trends of your friends and followers obtained from social media platforms to which you have given permission.

[1042] "Data analysis" refers to the computational process of generating optimal fashion coordination based on collected location information and social network data, taking into account the user's preferences, trends, weather, etc.

[1043] "Coordination generation" is a process that suggests recommended fashion styles by combining items owned by the user based on analyzed data.

[1044] "Recommendation" refers to the act of selecting items that are missing from a generated outfit from a partner e-commerce site and recommending them to the user.

[1045] "Purchasing recommended items" is the process in which a user actually orders the suggested missing items and completes the purchase process.

[1046] "Check physical store inventory" is a function that checks whether the items in the generated outfit are in stock at a nearby physical store.

[1047] "In-store pickup" is the process by which a user selects how they would like to collect their online order from a physical store.

[1048] The following describes an embodiment of the present invention. This system efficiently manages the fashion items owned by the user, suggests optimal outfits based on location and social data, and allows the user to quickly obtain the necessary items from nearby physical stores.

[1049] User Registration and Login

[1050] When a user first uses the service, they launch the smartphone app and create an account. They enter the necessary information (email address, password, etc.), and this information is sent from the device to the server. The server stores this information in a database and creates an account for the user. When the user logs in, the server also verifies the information entered and performs authentication.

[1051] Uploading fashion items

[1052] Users take pictures of their fashion items with their smartphones and send them to the server via the app. The server then uses an image recognition algorithm to identify the item's attributes (category, color, material, brand, etc.) and stores them in a database, creating the user's online closet.

[1053] Location and Social Data Collection

[1054] The device will obtain location information with the user's permission, and also collect social network data if the user allows it to be linked, and this data will be sent to a server.

[1055] Analyzing data and generating coordinates

[1056] The server analyzes the received location and social data, taking into account the climate and cultural characteristics of the current location, and combines items from the user's online closet to generate an appropriate outfit. Data on the style preferences of the user's friends and followers is also used in the analysis.

[1057] Coordination suggestions and stock confirmation

[1058] The generated outfits are presented to the user via their device. The user can check the outfit details and see if the items are in stock at a nearby physical store. The physical store's inventory information is linked to the server and updated in real time.

[1059] Purchase procedure and delivery method selection

[1060] Users can select the recommended items displayed and proceed with the purchase. The purchase process is carried out via the terminal, and the server completes it by connecting with the designated physical store. Users can also choose how to receive the ordered items. For example, they can choose to collect them directly at the store or use a delivery service.

[1061] Specific examples

[1062] For example, if a user wants to create a winter outfit, they can upload an image of their winter clothing: a coat, scarf, and boots. The server recognizes these items, stores them in a database, and adds them to their online closet. The device then verifies that the user's current location is in a cold climate. The server then uses this information to generate an outfit that includes a coat, scarf, and boots, as well as warmer materials like sweaters and gloves. The server then checks whether these items are in stock at nearby physical stores and displays them to the user.

[1063] Prompt Sentence Examples

[1064] "Taking into account the red coat uploaded by the user and the current location's cold climate, suggest an appropriate fashion coordination. Also, check whether related items are in stock at nearby physical stores."

[1065] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1066] Step 1:

[1067] When a user uses the app for the first time, they launch the smartphone app and create an account. The user enters their email address and password, and sends this information from their device to the server. The server stores the received information in a database and creates an account for the user. It also performs a comparison to authenticate the entered information, and if authentication is successful, the login is complete. The input is user information, and the output is the registered user account.

[1068] Step 2:

[1069] Users take pictures of their fashion items with their smartphones and send them to the server via the app. The server uses an image recognition algorithm (e.g., TensorFlow or PyTorch) to identify the item's attributes (category, color, material, brand, etc.) and stores the identification results in a database. The input is an image of the fashion item, and the output is item data with attribute information added.

[1070] Step 3:

[1071] With the user's permission, the device obtains location information and also collects social network data that the user has authorized to be linked. This data is sent from the device to a server. The server analyzes the received data and generates analysis results based on the climate and cultural characteristics of the current location and the social network data. The input is location information and social network data, and the output is the analysis results.

[1072] Step 4:

[1073] The server generates the optimal outfit for the user based on the items in the online closet. This takes into account the location information and social data received. For example, cold weather items are selected taking into account the cold climate. The generated outfit is saved in a database and processed to be presented to the user. The input is the analysis results and the online closet data, and the output is the generated outfit.

[1074] Step 5:

[1075] The generated outfit is displayed on the user's device. The server checks whether the items in the outfit presented to the user are in stock at nearby physical stores. The physical store's inventory information is updated in real time and presented to the user. The input is the generated outfit and the physical store's inventory information, and the output is the outfit whose inventory has been confirmed.

[1076] Step 6:

[1077] The user selects the recommended items displayed and proceeds with the purchase if necessary. The purchase process is carried out via the terminal, and the server completes the order by connecting with partner e-commerce sites and physical stores. The user can select a method of collection at the physical store. For example, they can choose to collect the item directly at the store or use a delivery service. The input is the item and collection method selected by the user, and the output is a notification that the purchase process has been completed.

[1078] Prompt Sentence Examples

[1079] "Taking into account the red coat uploaded by the user and the current location's cold climate, suggest an appropriate fashion coordination. Also, check whether related items are in stock at nearby physical stores."

[1080] 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.

[1081] The following describes an embodiment of the present invention. The present invention is a system that efficiently manages a user's owned fashion items and suggests optimal outfits based on location information, social data, and the user's emotions. By combining this system with an emotion engine that analyzes the user's emotions, it is possible to suggest more personalized outfits.

[1082] User Registration and Login

[1083] When a user first starts the app and enters the required information (email address, password, etc.) on the account creation screen, the device sends the entered information to the server. The server stores this information in a database and creates an account. When the user logs in, the server also verifies the entered information and performs authentication.

[1084] Uploading fashion items

[1085] Users take or select images of their fashion items via their device and send them to the server, which uses an image recognition algorithm to identify the item's attributes (category, color, material, brand, etc.) and store them in a database, creating the user's online closet.

[1086] Location and Social Data Collection

[1087] The device will obtain location information with the user's permission, and also collect social network data if the user allows it to be linked, and this data will be sent to a server.

[1088] Analyzing data and generating coordinates

[1089] The server analyzes the received location and social data, taking into account the climate and cultural characteristics of the current location, and combines items from the user's online closet to generate an appropriate outfit. Data on the style preferences of the user's friends and followers is also used in the analysis.

[1090] Use of emotion engine

[1091] The emotion engine has the ability to analyze emotions from the user's facial expressions, voice, text input, etc. The device collects the user's emotional data and sends it to the server. The server analyzes this emotional data to identify the user's current emotional state. It then generates outfit suggestions based on the user's emotional state, thereby recommending more appropriate fashion to match the user's mood.

[1092] Coordinate presentation and purchase process

[1093] The generated outfit is presented to the user via the device. The user can check the outfit and provide feedback if necessary. The server then retrieves any missing items from the partner e-commerce site and displays them on the device. The user can select the recommended items displayed and complete the purchase process. The purchase process is completed via the device, with the server coordinating with the e-commerce site to complete the process.

[1094] Specific examples

[1095] For example, if a user is feeling stressed, the emotion engine will detect this and suggest a casual, comfortable outfit that will have a relaxing effect. On the other hand, if a user is excited about a particular event, the emotion engine will suggest a more glamorous, appropriate outfit. In this way, the outfits generated by the emotion engine are more deeply adapted to the user's emotional state.

[1096] In this way, the present invention provides a system that allows users to efficiently manage the items they own, suggests optimal fashion coordination based on location, trends, and even emotions, and allows them to easily purchase the items they need.

[1097] The processing flow will be explained below.

[1098] Step 1:

[1099] The user launches the app for the first time and enters the required information (email address, password, etc.) on the account creation screen.

[1100] Step 2:

[1101] The terminal transmits the input information to the server.

[1102] Step 3:

[1103] The server stores the received information in a database and sends a response to the device to create an account.

[1104] Step 4:

[1105] The user enters their email address and password on the login screen, and the device again sends this information to the server.

[1106] Step 5:

[1107] The server verifies the entered information by comparing it with information in a database, and if the authentication is successful, it sends a success message to the terminal.

[1108] Step 6:

[1109] The user navigates to the closet management screen, presses the button to add a new item, and activates the camera.

[1110] Step 7:

[1111] The user takes a photo of a fashion item or selects an existing image, and the device sends the image to the server.

[1112] Step 8:

[1113] The server passes the received images to an image recognition engine to identify attributes such as item category, color, material, and brand.

[1114] Step 9:

[1115] The server stores the identified attribute information in a database and generates an online closet.

[1116] Step 10:

[1117] The device will present the user with a confirmation screen of their online closet, allowing them to edit or delete items as needed.

[1118] Step 11:

[1119] If the user grants permission to obtain location information, the device will collect GPS data.

[1120] Step 12:

[1121] When a user gives permission to connect with a social network, the device collects social data.

[1122] Step 13:

[1123] The device sends the location and social data it collects to a server.

[1124] Step 14:

[1125] The server analyzes the location and social data it receives and extracts data such as the user's current climate and cultural background, as well as their friends' style information.

[1126] Step 15:

[1127] Based on the analysis results, the server combines items from the user's online closet to generate the optimal outfit.

[1128] Step 16:

[1129] The emotion engine has the ability to analyze emotions from the user's facial expressions, voice, text input, etc. The device collects the user's emotional data and sends it to the server.

[1130] Step 17:

[1131] The server analyzes the emotion data received from the emotion engine to determine the user's current emotional state.

[1132] Step 18:

[1133] The server generates coordination suggestions based on the emotional state, thereby providing coordination that matches the user's mood.

[1134] Step 19:

[1135] The terminal presents the generated coordinates to the user and receives feedback from the user.

[1136] Step 20:

[1137] The server requests the partner EC site for items that are missing from the generated coordinated outfit, and acquires the corresponding items.

[1138] Step 21:

[1139] The device displays the recommended items to the user, and when the user selects one, they are taken to the purchase process screen.

[1140] Step 22:

[1141] The user selects the recommended item on the checkout screen and enters their shipping address, payment information, etc. to complete the purchase.

[1142] Step 23:

[1143] The server sends the entered purchase procedure information to the partner EC site and completes the purchase procedure.

[1144] Example 2

[1145] 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."

[1146] Conventional fashion management systems require users to manage their personal items in a cumbersome manner, and lack sufficient integration with location information and social data. Furthermore, they lack personalized suggestions for individual users, and do not take into account the user's emotional state. This makes it difficult to provide optimal outfits for users. Furthermore, the generated outfits do not integrate recommendations for missing items or a purchasing process.

[1147] 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.

[1148] In this invention, the server includes a means for uploading images of items owned by the user, a means for a generative AI model to recognize the images, identify the attributes of the items, and store them in a database, a means for collecting the user's location information and social network data, a means for analyzing the collected data and the user's emotional data to generate an optimal outfit for the user, a means for recommending items missing from a partner e-commerce site, and a means for the user to purchase the recommended items. This allows the user to efficiently manage the items they own and receive suggestions for optimal fashion outfits based on their location information, social data, and emotional state. Furthermore, the system also facilitates the recommendation and purchase process for missing items.

[1149] "User" refers to an individual who uses this system.

[1150] "Owned Items" refers to fashion-related items, including apparel and accessories, owned by a User.

[1151] "Means for uploading images" refers to the functionality that allows a user to submit photos of items they own to the system.

[1152] A "generative AI model" refers to an artificial intelligence technology that uses algorithms to generate specific results based on input data.

[1153] "Item attributes" refer to characteristics of a fashion item such as category, color, material, brand, etc.

[1154] "Database" refers to a system for organizing and storing identified data.

[1155] "Location information" refers to data that indicates a user's current location.

[1156] "Social Network Data" refers to information obtained from a user's social media.

[1157] "Means of analysis" refers to the process of analyzing collected data to extract useful information.

[1158] "Emotional data" refers to information about a user's emotional state obtained from facial expressions, voice, text input, etc.

[1159] "Means for generating coordination" refers to the function that suggests suitable fashion combinations for users based on collected data.

[1160] "Partner e-commerce site" refers to the online shopping platform with which the System is integrated.

[1161] "Means of recommendation" refers to the function of recommending specific products to users.

[1162] "Means to purchase" refers to the functionality that allows users to purchase recommended items.

[1163] The present invention relates to a system that efficiently manages fashion items owned by a user and suggests optimal outfits based on location information, social network data, and emotional data. Specific embodiments of this system are described below.

[1164] User Registration and Login

[1165] When a user uses the application for the first time, they start it and enter the necessary information, such as their email address and password, on the account creation screen. The device then sends the entered information to the server, which then stores this information in a database and creates an account. When the user enters their email address and password on the login screen and taps the "Login" button, the server compares this information with the information in the database and performs authentication.

[1166] Uploading fashion items

[1167] The user takes a photo of a fashion item they own on their device or selects it from their photo library. The device then sends the selected or captured image to the server. The server then uses an image recognition algorithm (e.g., TensorFlow or OpenCV) to identify the attribute information (category, color, material, brand, etc.) of the item in the image. This identified attribute information is stored in a database, forming the user's online closet.

[1168] Location and Social Data Collection

[1169] The device obtains location information with the user's permission. It measures the current location using GPS, and the device also collects data from social networks (e.g., Facebook, Instagram) with the user's permission. This data is sent to the server.

[1170] Analyzing data and generating coordinates

[1171] The server analyzes the received location and social data. It uses machine learning algorithms (e.g., Scikit-learn and TensorFlow) to take into account the climate and cultural background of the current location. Based on the analysis results, the server combines items from the user's online closet to generate an appropriate outfit. The server also uses data on the style preferences of the user's friends and followers.

[1172] Use of emotion engine

[1173] The emotion engine has the ability to analyze emotional data from the user's facial expressions, voice, text input, etc. The device sends the collected emotional data to the server, which analyzes this emotional data to identify the user's current emotional state. Based on this emotional state, personalized outfit suggestions are generated.

[1174] Coordinate presentation and purchase process

[1175] The server sends the generated coordinated outfit to the device, which then displays it to the user. The user can review the coordinated outfit and provide feedback. The server also retrieves any missing items from partner e-commerce sites and displays them on the device. The user selects the displayed items and completes the purchase. The purchase is completed via the device, and the server works with the e-commerce site to complete the purchase.

[1176] Specific examples

[1177] For example, if a user is feeling stressed, the emotion engine will detect this and suggest a casual, comfortable outfit that will have a relaxing effect. On the other hand, if a user is excited about a particular event, it will suggest a more glamorous, event-appropriate outfit. In this way, the outfits generated by the emotion engine are deeply adapted to the user's emotional state.

[1178] Example prompts for generative AI models

[1179] We developed a system that smoothly manages the fashion items owned by users and suggests optimal outfits based on location, social, and emotional data. We analyze various data such as users' photos, voice, location, and SNS data to provide guidance on how to make personalized fashion suggestions.

[1180] In this way, the present invention provides a system that allows users to efficiently manage the items they own, suggests optimal fashion coordination based on location information, social data, and emotional state, and allows them to easily purchase the items they need.

[1181] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1182] Step 1:

[1183] The user launches an application.

[1184] Input: Tap the application icon

[1185] Output: Display of the application's welcome screen

[1186] What happens: A user launches an app by tapping on the application icon on their smartphone or tablet, and is presented with the option to create an account and log in.

[1187] Step 2:

[1188] The user enters the required information on the account creation screen.

[1189] Input: Email address, password

[1190] Output: Sending input data to the server

[1191] Specific operation: The user enters an email address and password and taps the "Send" button. The device receives this and sends it to the server.

[1192] Step 3:

[1193] The server stores the user information and creates an account.

[1194] Input: Email address, password

[1195] Output: Account information stored in the database

[1196] Specific operations: The server saves the received user information in the database and returns a success message to the user.

[1197] Step 4:

[1198] The user enters their credentials on the login screen.

[1199] Input: Email address, password

[1200] Output: Authentication result

[1201] Specific behavior: The user enters their email address and password on the login screen and taps the "Login" button.

[1202] Step 5:

[1203] The server checks the authentication information against a database and performs authentication.

[1204] Input: Email address, password

[1205] Output: Authentication result (success / failure)

[1206] Specific operation: The server compares the user information in the database with the login information and returns the authentication result to the device. If successful, the home screen is displayed.

[1207] Step 6:

[1208] The user takes or selects an image of a fashion item they own.

[1209] Input: Fashion item image

[1210] Output: Sending image data to the server

[1211] What happens: The user takes a photo of an item using the camera or selects one from their photo library. The image is sent from the device to the server.

[1212] Step 7:

[1213] The server identifies the image and stores the item's attributes in a database.

[1214] Input: Fashion item image

[1215] Output: Attribute information (category, color, material, brand, etc.)

[1216] Specific operation: The server uses an image recognition algorithm (e.g., TensorFlow or OpenCV) to identify the attributes of the item and store that information in a database.

[1217] Step 8:

[1218] The device collects the user's location and social network data.

[1219] Input: Location permission, Social network data permission

[1220] Output: Sending location and social network data to a server

[1221] What it does: The device uses GPS to obtain location information and, with the user's permission, collects social network data, which is then sent to a server.

[1222] Step 9:

[1223] The server analyzes the location and social data.

[1224] Input: Location information, social network data

[1225] Output: Analysis results

[1226] Specific operation: The server uses machine learning algorithms (e.g., Scikit-learn or TensorFlow) to perform analysis that takes into account the climate and cultural background of the current location.

[1227] Step 10:

[1228] The server generates the optimal coordination based on the analysis results.

[1229] Input: Analysis results, online closet data

[1230] Output: Coordination suggestions

[1231] Specific operation: The server uses the analysis results to combine items in the online closet and generate the optimal outfit.

[1232] Step 11:

[1233] The device collects the user's emotional data and sends it to the server.

[1234] Input: User facial expressions, voice, and text input

[1235] Output: Sending emotion data to the server

[1236] Specific operation: The device uses the camera and microphone to collect the user's emotional data and sends it to the server.

[1237] Step 12:

[1238] The server analyzes the emotional data and identifies the user's emotional state.

[1239] Input: Emotion data

[1240] Output: Emotional state identification result

[1241] What it does: The server uses an emotion analysis algorithm to determine the user's current emotional state.

[1242] Step 13:

[1243] The server generates coordination suggestions based on the emotional state.

[1244] Input: Emotional state, analysis results

[1245] Output: Personalized outfit suggestions

[1246] Specific operation: The server generates coordinates that match the user's mood based on their emotional state.

[1247] Step 14:

[1248] The server sends the generated coordinates to the terminal and presents them to the user.

[1249] Input: Coordination suggestion

[1250] Output: Coordinates displayed on the terminal

[1251] Specific operation: The server sends a coordination proposal to the terminal, and the terminal displays it to the user.

[1252] Step 15:

[1253] The user provides feedback on the presented outfit.

[1254] Input: Feedback information

[1255] Output: Sending feedback information to the server

[1256] Specific operation: The user inputs feedback on the coordination, and the device sends the information to the server.

[1257] Step 16:

[1258] The server retrieves the missing items from the partner e-commerce site and displays them on the device.

[1259] Input: Coordination suggestions, missing item information

[1260] Output: Display of recommended items

[1261] Specific operation: The server retrieves information about missing items from partner e-commerce sites and displays it on the device.

[1262] Step 17:

[1263] The user selects a recommended item and completes the purchase process.

[1264] Input: Recommended item selection, purchase information

[1265] Output: Information to complete the purchase

[1266] Specific operation: The user selects a recommended item and completes the purchase process. The purchase process is completed through the terminal, and the server connects with the e-commerce site to complete the purchase.

[1267] (Application example 2)

[1268] 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."

[1269] Previous fashion coordination suggestion systems did not take into account the user's shopping experience in a physical store, nor did they optimize coordination through emotion analysis. This meant that users could not receive personalized suggestions based on their current mood or the items they were actually looking at. Furthermore, they lacked technology to visually present coordination suggestions in real time using smart glasses. To solve these issues, a new system that takes user emotions into account and improves the shopping experience in a physical store is needed.

[1270] 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.

[1271] In this invention, the server includes means for uploading images of items owned by the user, means for AI to recognize the images and identify item attributes and store them in a database, means for collecting the user's location information and social network data, means for analyzing the collected data and generating an optimal outfit for the user, means for analyzing the user's emotions and optimizing the outfit based on the emotions, means for visually displaying real-time coordination suggestions of fashion items in a physical store using smart glasses, means for recommending items missing from a partner e-commerce site that are missing from the generated outfit, and means for the user to purchase the recommended items. This makes it possible to propose personalized fashion coordination that takes into account the user's emotions, location information, and purchasing behavior in a physical store.

[1272] "Means for uploading images of items owned by the user" refers to means for the user to take or select photos of fashion items they own and send them to the system.

[1273] "Means for AI to recognize images, identify item attributes, and store them in a database" refers to a means of using artificial intelligence technology to extract information such as the type, color, material, and brand of a fashion item from uploaded images and record that information in a database.

[1274] "Means for collecting user location information and social network data" refers to means for obtaining a user's current geographic location and activity data on social networking sites.

[1275] "Means for analyzing collected data and generating optimal outfits for users" refers to a means for automatically creating fashion outfits that suit users based on acquired location information and social network data.

[1276] "Means for analyzing user emotions and optimizing coordination based on emotions" refers to a means for detecting emotions from the user's facial expressions, voice, text input, etc., and suggesting fashion coordination that matches that emotional state.

[1277] "A means for visually displaying real-time coordination suggestions for fashion items in a physical store using smart glasses" refers to a means for displaying, in real time, coordination suggestions that combine the fashion items that a user is looking at in a physical store with items in their online closet, using smart glasses.

[1278] "Means for recommending missing items from partner e-commerce sites in the created outfit" refers to a means for searching for missing fashion items in the created outfit from an online shopping site and recommending them to the user.

[1279] "Means for users to purchase recommended items" refers to the procedures for users to actually purchase items recommended by the system, and the means to provide functions to support this.

[1280] This system efficiently manages a user's fashion items and suggests optimal outfits based on location information, social data, and the user's emotions. This system can also suggest outfits in real time in physical stores using smart glasses.

[1281] System configuration

[1282] 1. Image upload method

[1283] The user captures an image of the item they own via their smartphone or other device and sends it to the server. This requires a camera function and communication with the server. Specifically, the smartphone's camera app and internet connection are used.

[1284] 2. Image Recognition Methods

[1285] The server uses AI (artificial intelligence) to analyze the received images and identify the item's attributes. Here, the image is processed using OpenCV, and machine learning models (such as TensorFlow or PyTorch) are used to obtain information such as the item's category, color, material, and brand.

[1286] 3. Location and Social Data Collection Methods

[1287] With the user's permission, the device obtains location information from GPS and collects data from the user's social media accounts, which is then sent over the internet to a server.

[1288] 4. Data analysis and coordinate generation methods

[1289] The server analyzes the collected location and social data to generate optimal fashion coordination for the user, taking into account weather information and cultural characteristics. Data analysis is performed using database software (e.g., MySQL or PostgreSQL) and analysis tools (e.g., Pandas or NumPy).

[1290] 5. Emotion analysis method

[1291] The device collects emotional data based on the user's facial expressions, voice, and text input, and sends it to a server. The server then uses AI to analyze this emotional data and determine the user's current emotional state. Emotion analysis tools such as EmotionEngine are used for this analysis.

[1292] 6. Real-time Coordination Proposal Method Using Smart Glasses

[1293] The smart glasses have the ability to recognize the fashion items the user is looking at in a physical store in real time and visually display coordination suggestions that combine them with the user's online closet. Here, OpenCV is used to capture camera images and generate suggestions through communication with a cloud server.

[1294] 7. Recommendation methods

[1295] The server recommends missing items from partner e-commerce sites, allowing users to easily purchase the missing items. The recommendation algorithm uses collaborative filtering and content-based recommendation systems.

[1296] Example

[1297] For example, if a user visits a shopping mall on a holiday and picks up a red dress, the smart glasses will recognize the dress in real time and, if it determines that the user's emotion is "happy," it will suggest a glamorous outfit. In this way, users can enjoy a more personalized shopping experience.

[1298] Prompt Sentence Examples

[1299] "Design an application that recognizes the fashion items a user is looking at and suggests the best fashion coordination based on the user's emotions and location. This application will be installed on smart glasses."

[1300] This invention makes it possible to propose personalized fashion coordination that takes into account the user's emotions and location information, significantly improving the shopping experience in physical stores.

[1301] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1302] Step 1:

[1303] Image capture and upload

[1304] A user uses a smartphone or other device to take a picture of a fashion item they own and send it to the server. The input is the image of the fashion item, and the output is the image data sent to the server. Specific operations include the user launching a camera app, capturing an image, and then pressing the image upload button.

[1305] Step 2:

[1306] Image Recognition and Attribute Identification

[1307] The server analyzes the received image data using AI (image recognition algorithms) to identify attributes such as the type, color, material, and brand of the fashion item. The input is the uploaded image data, and the output is the item's attribute information. Specifically, the server processes the image using OpenCV, identifies the attributes using TensorFlow or PyTorch models, and stores them in a database.

[1308] Step 3:

[1309] Location and social data collection

[1310] With the user's permission, the device obtains the user's current location from GPS and also collects data from the user's social media accounts and sends it to a server. The input is location information and social data, and the output is these data sent to the server. Specifically, the device periodically obtains GPS data and uses social media APIs to collect and send the necessary data.

[1311] Step 4:

[1312] Data analysis and coordinate generation

[1313] The server analyzes the acquired location information and social data and generates the optimal fashion coordination for the user based on that information. The input is location information, social data, and item attribute information, and the output is the generated coordination proposal. Specifically, the server queries the necessary data from the database, processes the data using analysis tools (Pandas or NumPy), and generates the optimal coordination.

[1314] Step 5:

[1315] Emotion analysis

[1316] The device collects the user's facial expressions and voice and sends them to the server. The server then uses EmotionEngine to analyze and identify the user's emotional state. The input is the user's facial expression data and voice data, and the output is information about the user's emotional state. The specific operation is a process in which the device uses a camera and microphone to capture the user's data and send it to the server.

[1317] Step 6:

[1318] Real-time coordination suggestions using smart glasses

[1319] The device (smart glasses) captures real-time video footage from the physical store and sends the image data to a server. The server analyzes the images, generates real-time outfit suggestions that combine them with items from the online closet, and displays them on the smart glasses. The input is real-time video data, and the output is the outfit suggestions presented to the user. The specific operation is a process in which the smart glasses activate their camera and constantly capture the items the user is looking at and send them to the server.

[1320] Step 7:

[1321] Recommendations and checkout

[1322] The server searches partner e-commerce sites for items missing from the generated outfit and recommends them to the user. The user purchases the recommended items through their device. The input is the outfit plan, and the output is the recommended items and the purchase procedure based on them. Specifically, the server recommends products using collaborative filtering or a content-based recommendation system, and the purchase procedure is carried out through the e-commerce site's API.

[1323] This system allows users to receive real-time fashion coordination suggestions based on their emotions and location information, improving their shopping experience.

[1324] 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.

[1325] 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.

[1326] 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.

[1327] [Fourth embodiment]

[1328] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1329] 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.

[1330] 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).

[1331] 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.

[1332] 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.

[1333] 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).

[1334] 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.

[1335] 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.

[1336] 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.

[1337] 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.

[1338] 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.

[1339] 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.

[1340] 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."

[1341] The following describes an embodiment of the present invention. This invention is a system that efficiently manages fashion items owned by a user and suggests optimal outfits based on location and social data. This system consists of the following steps, and how each step works will be described below.

[1342] User Registration and Login

[1343] When a user uses the app for the first time, they must launch the app and create an account. They enter the required information (email address, password, etc.) and this information is sent from the device to the server. The server stores this information in a database and creates an account for the user. When logging in, the server also verifies the information entered and performs authentication.

[1344] Uploading fashion items

[1345] Users take or select images of their fashion items via their device and send them to the server, which uses an image recognition algorithm to identify the item's attributes (category, color, material, brand, etc.) and store them in a database, creating the user's online closet.

[1346] Location and Social Data Collection

[1347] The device will obtain location information with the user's permission, and also collect social network data if the user allows it to be linked, and this data will be sent to a server.

[1348] Analyzing data and generating coordinates

[1349] The server analyzes the received location and social data, taking into account the climate and cultural characteristics of the current location, and combines items from the user's online closet to generate an appropriate outfit. Data on the style preferences of the user's friends and followers is also used in the analysis.

[1350] Coordinate presentation and purchase process

[1351] The generated outfit is presented to the user via the device. The user can check the outfit and provide feedback if necessary. The server then retrieves any missing items from the partner e-commerce site and displays them on the device. The user can select the recommended items displayed and complete the purchase process. The purchase process is completed via the device, with the server coordinating with the e-commerce site to complete the process.

[1352] Specific examples

[1353] For example, if a user wants to create a winter outfit, they can upload an image of their winter clothing: a coat, scarf, and boots. The server recognizes these items, stores them in a database, and adds them to their online closet. The device then verifies that the user is in a cold climate. The server then uses this information to generate an outfit that includes the coat, scarf, and boots, as well as a sweater and gloves made from warm materials.

[1354] In this way, the present invention provides a system that enables users to efficiently manage items they own, generate optimal fashion coordination based on location and trends, and easily purchase the items they need.

[1355] The processing flow will be explained below.

[1356] Step 1:

[1357] The user launches the app for the first time and enters the required information (email address, password, etc.) on the account creation screen.

[1358] Step 2:

[1359] The terminal transmits the input information to the server.

[1360] Step 3:

[1361] The server stores the received information in a database and sends a response to the device to create an account.

[1362] Step 4:

[1363] The user enters their email address and password on the login screen, and the device again sends this information to the server.

[1364] Step 5:

[1365] The server verifies the entered information by comparing it with information in a database, and if the authentication is successful, it sends a success message to the terminal.

[1366] Step 6:

[1367] The user navigates to the closet management screen, presses the button to add a new item, and activates the camera.

[1368] Step 7:

[1369] The user takes a photo of a fashion item or selects an existing image, and the device sends the image to the server.

[1370] Step 8:

[1371] The server passes the received images to an image recognition engine to identify attributes such as item category, color, material, and brand.

[1372] Step 9:

[1373] The server stores the identified attribute information in a database and generates an online closet.

[1374] Step 10:

[1375] The device will present the user with a confirmation screen of their online closet, allowing them to edit or delete items as needed.

[1376] Step 11:

[1377] If the user grants permission to obtain location information, the device will collect GPS data.

[1378] Step 12:

[1379] When a user gives permission to connect with a social network, the device collects social data.

[1380] Step 13:

[1381] The device sends the location and social data it collects to a server.

[1382] Step 14:

[1383] The server analyzes the location and social data it receives and extracts data such as the user's current climate and cultural background, as well as their friends' style information.

[1384] Step 15:

[1385] Based on the analysis results, the server combines items from the user's online closet to generate the optimal outfit.

[1386] Step 16:

[1387] The terminal presents the generated coordinates to the user and receives feedback from the user.

[1388] Step 17:

[1389] The server requests the partner EC site for items that are missing from the generated coordinated outfit, and acquires the corresponding items.

[1390] Step 18:

[1391] The device displays the recommended items to the user, and when the user selects one, they are taken to the purchase process screen.

[1392] Step 19:

[1393] The user selects the recommended item on the checkout screen and enters their shipping address, payment information, etc. to complete the purchase.

[1394] Step 20:

[1395] The server sends the entered purchase procedure information to the partner EC site and completes the purchase procedure.

[1396] Example 1

[1397] 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."

[1398] Conventional fashion coordination support systems often require a lot of effort to manage the items a user owns and suggest appropriate outfits, resulting in ineffective functionality. A particular challenge is generating outfits that take into account multiple factors, such as the weather in the user's current location and the fashion trends of their friends. Furthermore, recommending new items and completing the purchasing process are time-consuming and inconvenient for users.

[1399] 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.

[1400] In this invention, the server includes means for uploading images of items owned by the user, means for a recognition device to recognize the images and identify attributes of the items and store them in data storage, means for collecting location information and social media data of the user, means for analyzing the collected data and generating a coordinated outfit suitable for the user, means for recommending items missing from an external sales site for the generated coordinated outfit, and means for the user to purchase the recommended items. This allows users to efficiently manage their own fashion items, easily receive optimal coordinated outfits based on location and trends, and easily purchase new items they need.

[1401] "User" refers to an individual who uses the system to manage their own fashion items and receive coordination suggestions.

[1402] "Items" refers to all fashion items owned by a User and uploaded to the System.

[1403] "Image upload means" refers to a function that allows a user to upload images of items owned by the user into the system.

[1404] "Recognition device" refers to a device that analyzes uploaded images and implements algorithms to identify attributes of items.

[1405] "Attributes" refer to specific characteristics of an item, such as its category, color, material, or brand.

[1406] "Data storage" refers to a database for storing attribute information identified by a recognition device.

[1407] "Location information" refers to information that identifies a user's current location.

[1408] "Social Media Data" refers to information about a user's social networks that is collected with the user's permission.

[1409] "Recommendation means" refers to a function that identifies items that are missing from the generated outfit and suggests them to the user from an external sales site.

[1410] "Purchase method" refers to the functionality that allows users to purchase recommended items.

[1411] "Online data storage" refers to storage within a system that automatically organizes and stores items uploaded by users.

[1412] "Feedback means" refers to a function that allows users to provide opinions and evaluations of the presented coordination.

[1413] "Correction measures" refer to functions for improving the content of coordination based on collected feedback.

[1414] "Analysis means" refers to the functionality for generating coordination based on collected location information and social media data.

[1415] The following describes in detail the mode for carrying out the present invention. This invention is a system that efficiently manages a user's fashion items and suggests optimal outfits based on location and social media data. This system is based on cooperation between a server, a terminal, and a user.

[1416] User Registration and Login

[1417] When a user uses the app for the first time, they must create an account. At this time, they enter an email address and password. The device sends the entered information to the server. The server stores the received information in a MySQL database and creates a user account. When logging in, the device similarly sends the email address and password, and the server performs authentication.

[1418] Uploading fashion items

[1419] 1. The user takes or selects an image of a fashion item they own.

[1420] 2. The device sends this image to the server.

[1421] 3. The server uses a TensorFlow-based image recognition algorithm to identify the item's attribute information (category, color, material, brand, etc.) from the image.

[1422] 4. The server stores the identification results in a database and updates the user's online closet.

[1423] Location and Social Data Collection

[1424] When a user allows location information and social media integration in the app, the device will use the GPS module to obtain current location information. The device will also collect user posts and friend information from social media via API. All of this data is sent to the server and stored in data storage.

[1425] Analyzing data and generating coordinates

[1426] 1. The server obtains weather data from the weather API based on the collected location information.

[1427] 2. The server analyzes social media data to understand the fashion trends of the user's friends and followers.

[1428] 3. The server combines this information and inputs a prompt into a generative AI model (e.g., OpenAI GPT). For example, a prompt like, "The user is currently in a cold climate. Please suggest the best outfit using winter items from the user's online closet."

[1429] 4. Based on the prompts, the generative AI model generates outfits that take into account the season, location, and the user's social data.

[1430] Coordinate presentation and purchase process

[1431] 1. The server sends the generated coordinates to the terminal and presents them to the user.

[1432] 2. Users can review the coordinates and provide feedback if necessary.

[1433] 3. The server determines the missing items and retrieves information about related products using the API of an external sales site (e.g., Amazon).

[1434] 4. The device displays product information obtained from the external sales site to the user.

[1435] 5. Once the user decides to purchase, the device proceeds with the purchase process, and the server connects with the external sales site to complete the purchase.

[1436] Specific examples

[1437] If a user wants to create a winter outfit, they upload images of a coat, scarf, and boots. The server recognizes these items, saves them in data storage, and adds them to their online closet. If the device determines that the user's current location is in a cold climate, the server inputs the following prompt into the generative AI model: "You are currently in a cold climate, and your online closet contains a coat, scarf, and boots. Based on this, please suggest the perfect winter outfit for you." The generated outfit may include a warm sweater or gloves.

[1438] In this way, the present invention allows users to efficiently manage their fashion items, provide optimal coordination based on location and trends, and easily purchase necessary items.

[1439] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1440] Step 1: User registration and login

[1441] Input: The user enters their email address and password.

[1442] How it works: When a user first uses the app, they enter their email address and password on the account creation screen. The device then sends this information to the server.

[1443] Data processing: The server stores the received information in a MySQL database and creates a user account.

[1444] Output: The user account is saved in the database. Similarly, when you log in again, the server will authenticate you by checking the email address and password you entered.

[1445] Step 2: Upload your fashion items

[1446] Input: The user takes or selects an image of a fashion item they own.

[1447] How it works: The user opens the app's fashion item upload screen, takes a photo of the item, or selects an existing image. The device then sends this image to the server.

[1448] Data processing: The server uses a TensorFlow-based image recognition algorithm to identify item attribute information (category, color, material, brand, etc.) from the image.

[1449] Output: The server stores the identification results in a database and updates the user's online closet.

[1450] Step 3: Collect location and social data

[1451] Input: Location and social media data with user permission.

[1452] How it works: When a user allows the app to access location information and social media, the device will use the GPS module to obtain current location information. The device will also collect user data from social media via APIs.

[1453] Data processing: The device sends location and social data to the server.

[1454] Output: The server stores these data in a database.

[1455] Step 4: Analyze the data and generate coordinates

[1456] Inputs: Collected location information, weather data, social data, and online closet information.

[1457] How it works: The server receives location information, retrieves weather data from a weather API, and analyzes social media data to understand the fashion trends of the user's friends and followers.

[1458] Data calculation: The server integrates this information and inputs a prompt into the generative AI model (e.g., OpenAI GPT). An example prompt might be, "The current location is in a cold climate. Please suggest the best outfit using winter items from the user's online closet."

[1459] Output: The generative AI model generates the optimal coordination based on the prompts and returns it to the server.

[1460] Step 5: Present your outfit and complete the purchase

[1461] Input: The generated coordinates.

[1462] Operation: The server sends the generated coordinates to the device and presents them to the user, who can review the coordinates and provide feedback if necessary.

[1463] Data processing: The server determines the missing items and retrieves information about related products using the API of an external sales site.

[1464] Output: The terminal displays the acquired product information to the user, who selects and completes the purchase procedure. The server connects with the external sales site to complete the purchase procedure.

[1465] Through these steps, a system is realized that manages the user's fashion items, generates optimal coordinations, and assists in purchasing necessary items.

[1466] (Application example 1)

[1467] 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."

[1468] Today's consumers own many fashion items, but lack a system that can efficiently manage them and automatically suggest the optimal outfit for each location and situation. Furthermore, when users need an item they don't have on hand, there is no system that allows them to easily check inventory information at nearby brick-and-mortar stores and quickly purchase and receive it. Therefore, it is necessary to build a system that can properly manage the fashion items owned by users, provide optimal outfits, and instantly check inventory information for missing items, allowing them to purchase and receive them.

[1469] 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.

[1470] In this invention, the server includes means for uploading images of items owned by the user, means for AI to recognize the images and identify item attributes and store them in a database, means for collecting the user's location information and social network data, means for analyzing the collected data and generating an optimal outfit for the user, means for recommending items missing from partner e-commerce sites that are missing from the generated outfit, means for the user to purchase the recommended items, means for checking whether the generated outfit is in stock at a nearby physical store, and means for selecting a method for receiving the items at the physical store. This allows the user to efficiently manage their own items, obtain optimal outfits, and quickly obtain the items they need from a nearby physical store.

[1471] "Uploading an item image" is the process by which a user takes a photo of a fashion item they own and sends it to the server through the application.

[1472] "Image recognition" is a technology that uses AI technology to analyze the content of uploaded images and identify attributes such as item category, color, material, and brand.

[1473] "Storing in database" refers to the process of recording the identified attribute information in a database in structured data format so that it can be efficiently searched and used later.

[1474] "Location collection" is the process of obtaining a user's current geographic location using technologies such as GPS and transmitting it to a server.

[1475] "Social Network Data" is data about the styles and trends of your friends and followers obtained from social media platforms to which you have given permission.

[1476] "Data analysis" refers to the computational process of generating optimal fashion coordination based on collected location information and social network data, taking into account the user's preferences, trends, weather, etc.

[1477] "Coordination generation" is a process that suggests recommended fashion styles by combining items owned by the user based on analyzed data.

[1478] "Recommendation" refers to the act of selecting items that are missing from a generated outfit from a partner e-commerce site and recommending them to the user.

[1479] "Purchasing recommended items" is the process in which a user actually orders the suggested missing items and completes the purchase process.

[1480] "Check physical store inventory" is a function that checks whether the items in the generated outfit are in stock at a nearby physical store.

[1481] "In-store pickup" is the process by which a user selects how to collect an item they ordered online from a physical store.

[1482] The following describes an embodiment of the present invention. This system efficiently manages the fashion items owned by the user, suggests optimal outfits based on location and social data, and allows the user to quickly obtain the necessary items from nearby physical stores.

[1483] User Registration and Login

[1484] When a user first uses the service, they launch the smartphone app and create an account. They enter the necessary information (email address, password, etc.), and this information is sent from the device to the server. The server stores this information in a database and creates an account for the user. When the user logs in, the server also verifies the information entered and performs authentication.

[1485] Uploading fashion items

[1486] Users take pictures of their fashion items with their smartphones and send them to the server via the app. The server then uses an image recognition algorithm to identify the item's attributes (category, color, material, brand, etc.) and stores them in a database, creating the user's online closet.

[1487] Location and Social Data Collection

[1488] The device will obtain location information with the user's permission, and also collect social network data if the user allows it to be linked, and this data will be sent to a server.

[1489] Analyzing data and generating coordinates

[1490] The server analyzes the received location and social data, taking into account the climate and cultural characteristics of the current location, and combines items from the user's online closet to generate an appropriate outfit. Data on the style preferences of the user's friends and followers is also used in the analysis.

[1491] Coordination suggestions and stock confirmation

[1492] The generated outfits are presented to the user via their device. The user can check the outfit details and see if the items are in stock at a nearby physical store. The physical store's inventory information is linked to the server and updated in real time.

[1493] Purchase procedure and delivery method selection

[1494] Users can select the recommended items displayed and proceed with the purchase. The purchase process is carried out via the terminal, and the server completes it by connecting with the designated physical store. Users can also choose how to receive the ordered items. For example, they can choose to collect them directly at the store or use a delivery service.

[1495] Specific examples

[1496] For example, if a user wants to create a winter outfit, they can upload an image of their winter clothing: a coat, scarf, and boots. The server recognizes these items, stores them in a database, and adds them to their online closet. The device then verifies that the user's current location is in a cold climate. The server then uses this information to generate an outfit that includes a coat, scarf, and boots, as well as warmer materials like sweaters and gloves. The server then checks whether these items are in stock at nearby physical stores and displays them to the user.

[1497] Prompt Sentence Examples

[1498] "Taking into account the red coat uploaded by the user and the current location's cold climate, suggest an appropriate fashion coordination. Also, check whether related items are in stock at nearby physical stores."

[1499] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1500] Step 1:

[1501] When a user uses the app for the first time, they launch the smartphone app and create an account. The user enters their email address and password, and sends this information from their device to the server. The server stores the received information in a database and creates an account for the user. It also performs a comparison to authenticate the entered information, and if authentication is successful, the login is complete. The input is user information, and the output is the registered user account.

[1502] Step 2:

[1503] Users take pictures of their fashion items with their smartphones and send them to the server via the app. The server uses an image recognition algorithm (e.g., TensorFlow or PyTorch) to identify the item's attributes (category, color, material, brand, etc.) and stores the identification results in a database. The input is an image of the fashion item, and the output is item data with attribute information added.

[1504] Step 3:

[1505] With the user's permission, the device obtains location information and also collects social network data that the user has authorized to be linked. This data is sent from the device to a server. The server analyzes the received data and generates analysis results based on the climate and cultural characteristics of the current location and the social network data. The input is location information and social network data, and the output is the analysis results.

[1506] Step 4:

[1507] The server generates the optimal outfit for the user based on the items in the online closet. This takes into account the location information and social data received. For example, cold weather items are selected taking into account the cold climate. The generated outfit is saved in a database and processed to be presented to the user. The input is the analysis results and the online closet data, and the output is the generated outfit.

[1508] Step 5:

[1509] The generated outfit is displayed on the user's device. The server checks whether the items in the outfit presented to the user are in stock at nearby physical stores. The physical store's inventory information is updated in real time and presented to the user. The input is the generated outfit and the physical store's inventory information, and the output is the outfit whose inventory has been confirmed.

[1510] Step 6:

[1511] The user selects the recommended items displayed and proceeds with the purchase if necessary. The purchase process is carried out via the terminal, and the server completes the order by connecting with partner e-commerce sites and physical stores. The user can select a method of collection at the physical store. For example, they can choose to collect the item directly at the store or use a delivery service. The input is the item and collection method selected by the user, and the output is a notification that the purchase process has been completed.

[1512] Prompt Sentence Examples

[1513] "Taking into account the red coat uploaded by the user and the current location's cold climate, suggest an appropriate fashion coordination. Also, check whether related items are in stock at nearby physical stores."

[1514] 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.

[1515] The following describes an embodiment of the present invention. The present invention is a system that efficiently manages a user's owned fashion items and suggests optimal outfits based on location information, social data, and the user's emotions. By combining this system with an emotion engine that analyzes the user's emotions, it is possible to suggest more personalized outfits.

[1516] User Registration and Login

[1517] When a user first starts the app and enters the required information (email address, password, etc.) on the account creation screen, the device sends the entered information to the server. The server stores this information in a database and creates an account. When the user logs in, the server also verifies the entered information and performs authentication.

[1518] Uploading fashion items

[1519] Users take or select images of their fashion items via their device and send them to the server, which uses an image recognition algorithm to identify the item's attributes (category, color, material, brand, etc.) and store them in a database, creating the user's online closet.

[1520] Location and Social Data Collection

[1521] The device will obtain location information with the user's permission, and also collect social network data if the user allows it to be linked, and this data will be sent to a server.

[1522] Analyzing data and generating coordinates

[1523] The server analyzes the received location and social data, taking into account the climate and cultural characteristics of the current location, and combines items from the user's online closet to generate an appropriate outfit. Data on the style preferences of the user's friends and followers is also used in the analysis.

[1524] Use of emotion engine

[1525] The emotion engine has the ability to analyze emotions from the user's facial expressions, voice, text input, etc. The device collects the user's emotional data and sends it to the server. The server analyzes this emotional data to identify the user's current emotional state. It then generates outfit suggestions based on the user's emotional state, thereby recommending more appropriate fashion to match the user's mood.

[1526] Coordinate presentation and purchase process

[1527] The generated outfit is presented to the user via the device. The user can check the outfit and provide feedback if necessary. The server then retrieves any missing items from the partner e-commerce site and displays them on the device. The user can select the recommended items displayed and complete the purchase process. The purchase process is completed via the device, with the server coordinating with the e-commerce site to complete the process.

[1528] Specific examples

[1529] For example, if a user is feeling stressed, the emotion engine will detect this and suggest a casual, comfortable outfit that will have a relaxing effect. On the other hand, if a user is excited about a particular event, the emotion engine will suggest a more glamorous, appropriate outfit. In this way, the outfits generated by the emotion engine are more deeply adapted to the user's emotional state.

[1530] In this way, the present invention provides a system that allows users to efficiently manage the items they own, suggests optimal fashion coordination based on location, trends, and even emotions, and allows them to easily purchase the items they need.

[1531] The processing flow will be explained below.

[1532] Step 1:

[1533] The user launches the app for the first time and enters the required information (email address, password, etc.) on the account creation screen.

[1534] Step 2:

[1535] The terminal transmits the input information to the server.

[1536] Step 3:

[1537] The server stores the received information in a database and sends a response to the device to create an account.

[1538] Step 4:

[1539] The user enters their email address and password on the login screen, and the device again sends this information to the server.

[1540] Step 5:

[1541] The server verifies the entered information by comparing it with information in a database, and if the authentication is successful, it sends a success message to the terminal.

[1542] Step 6:

[1543] The user navigates to the closet management screen, presses the button to add a new item, and activates the camera.

[1544] Step 7:

[1545] The user takes a photo of a fashion item or selects an existing image, and the device sends the image to the server.

[1546] Step 8:

[1547] The server passes the received images to an image recognition engine to identify attributes such as item category, color, material, and brand.

[1548] Step 9:

[1549] The server stores the identified attribute information in a database and generates an online closet.

[1550] Step 10:

[1551] The device will present the user with a confirmation screen of their online closet, allowing them to edit or delete items as needed.

[1552] Step 11:

[1553] If the user grants permission to obtain location information, the device will collect GPS data.

[1554] Step 12:

[1555] When a user gives permission to connect with a social network, the device collects social data.

[1556] Step 13:

[1557] The device sends the location and social data it collects to a server.

[1558] Step 14:

[1559] The server analyzes the location and social data it receives and extracts data such as the user's current climate and cultural background, as well as their friends' style information.

[1560] Step 15:

[1561] Based on the analysis results, the server combines items from the user's online closet to generate the optimal outfit.

[1562] Step 16:

[1563] The emotion engine has the ability to analyze emotions from the user's facial expressions, voice, text input, etc. The device collects the user's emotional data and sends it to the server.

[1564] Step 17:

[1565] The server analyzes the emotion data received from the emotion engine to determine the user's current emotional state.

[1566] Step 18:

[1567] The server generates coordination suggestions based on the emotional state, thereby providing coordination that matches the user's mood.

[1568] Step 19:

[1569] The terminal presents the generated coordinates to the user and receives feedback from the user.

[1570] Step 20:

[1571] The server requests the partner EC site for items that are missing from the generated coordinated outfit, and acquires the corresponding items.

[1572] Step 21:

[1573] The device displays the recommended items to the user, and when the user selects one, they are taken to the purchase process screen.

[1574] Step 22:

[1575] The user selects the recommended item on the checkout screen and enters their shipping address, payment information, etc. to complete the purchase.

[1576] Step 23:

[1577] The server sends the entered purchase procedure information to the partner EC site and completes the purchase procedure.

[1578] Example 2

[1579] 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."

[1580] Conventional fashion management systems require users to manage their personal items in a cumbersome manner, and lack sufficient integration with location information and social data. Furthermore, they lack personalized suggestions for individual users, and do not take into account the user's emotional state. This makes it difficult to provide optimal outfits for users. Furthermore, the generated outfits do not integrate recommendations for missing items or a purchasing process.

[1581] 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.

[1582] In this invention, the server includes a means for uploading images of items owned by the user, a means for a generative AI model to recognize the images, identify the attributes of the items, and store them in a database, a means for collecting the user's location information and social network data, a means for analyzing the collected data and the user's emotional data to generate an optimal outfit for the user, a means for recommending items missing from a partner e-commerce site, and a means for the user to purchase the recommended items. This allows the user to efficiently manage the items they own and receive suggestions for optimal fashion outfits based on their location information, social data, and emotional state. Furthermore, the system also facilitates the recommendation and purchase process for missing items.

[1583] "User" refers to an individual who uses this system.

[1584] "Owned Items" refers to fashion-related items, including apparel and accessories, owned by a User.

[1585] "Means for uploading images" refers to the functionality that allows a user to submit photos of items they own to the system.

[1586] A "generative AI model" refers to an artificial intelligence technology that uses algorithms to generate specific results based on input data.

[1587] "Item attributes" refer to characteristics of a fashion item such as category, color, material, brand, etc.

[1588] "Database" refers to a system for organizing and storing identified data.

[1589] "Location information" refers to data that indicates a user's current location.

[1590] "Social Network Data" refers to information obtained from a user's social media.

[1591] "Means of analysis" refers to the process of analyzing collected data to extract useful information.

[1592] "Emotional data" refers to information about a user's emotional state obtained from facial expressions, voice, text input, etc.

[1593] "Means for generating coordination" refers to the function that suggests suitable fashion combinations for users based on collected data.

[1594] "Partner e-commerce site" refers to the online shopping platform with which the System is integrated.

[1595] "Means of recommendation" refers to the function of recommending specific products to users.

[1596] "Means to purchase" refers to the functionality that allows users to purchase recommended items.

[1597] The present invention relates to a system that efficiently manages fashion items owned by a user and suggests optimal outfits based on location information, social network data, and emotional data. Specific embodiments of this system are described below.

[1598] User Registration and Login

[1599] When a user uses the application for the first time, they start it and enter the necessary information, such as their email address and password, on the account creation screen. The device then sends the entered information to the server, which then stores this information in a database and creates an account. When the user enters their email address and password on the login screen and taps the "Login" button, the server compares this information with the information in the database and performs authentication.

[1600] Uploading fashion items

[1601] The user takes a photo of a fashion item they own on their device or selects it from their photo library. The device then sends the selected or captured image to the server. The server then uses an image recognition algorithm (e.g., TensorFlow or OpenCV) to identify the attribute information (category, color, material, brand, etc.) of the item in the image. This identified attribute information is stored in a database, forming the user's online closet.

[1602] Location and Social Data Collection

[1603] The device obtains location information with the user's permission. It measures the current location using GPS, and the device also collects data from social networks (e.g., Facebook, Instagram) with the user's permission. This data is sent to the server.

[1604] Analyzing data and generating coordinates

[1605] The server analyzes the received location and social data. It uses machine learning algorithms (e.g., Scikit-learn and TensorFlow) to take into account the climate and cultural background of the current location. Based on the analysis results, the server combines items from the user's online closet to generate an appropriate outfit. The server also uses data on the style preferences of the user's friends and followers.

[1606] Use of emotion engine

[1607] The emotion engine has the ability to analyze emotional data from the user's facial expressions, voice, text input, etc. The device sends the collected emotional data to the server, which analyzes this emotional data to identify the user's current emotional state. Based on this emotional state, personalized outfit suggestions are generated.

[1608] Coordinate presentation and purchase process

[1609] The server sends the generated coordinated outfit to the device, which then displays it to the user. The user can review the coordinated outfit and provide feedback. The server also retrieves any missing items from partner e-commerce sites and displays them on the device. The user selects the displayed items and completes the purchase. The purchase is completed via the device, and the server works with the e-commerce site to complete the purchase.

[1610] Specific examples

[1611] For example, if a user is feeling stressed, the emotion engine will detect this and suggest a casual, comfortable outfit that will have a relaxing effect. On the other hand, if a user is excited about a particular event, it will suggest a more glamorous, event-appropriate outfit. In this way, the outfits generated by the emotion engine are deeply adapted to the user's emotional state.

[1612] Example prompts for generative AI models

[1613] We developed a system that smoothly manages the fashion items owned by users and suggests optimal outfits based on location, social, and emotional data. We analyze various data such as users' photos, voice, location, and SNS data to provide guidance on how to make personalized fashion suggestions.

[1614] In this way, the present invention provides a system that allows users to efficiently manage the items they own, suggests optimal fashion coordination based on location information, social data, and emotional state, and allows them to easily purchase the items they need.

[1615] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1616] Step 1:

[1617] The user launches an application.

[1618] Input: Tap the application icon

[1619] Output: Display of the application's welcome screen

[1620] What happens: A user launches an app by tapping on the application icon on their smartphone or tablet, and is presented with the option to create an account and log in.

[1621] Step 2:

[1622] The user enters the required information on the account creation screen.

[1623] Input: Email address, password

[1624] Output: Sending input data to the server

[1625] Specific operation: The user enters an email address and password and taps the "Send" button. The device receives this and sends it to the server.

[1626] Step 3:

[1627] The server stores the user information and creates an account.

[1628] Input: Email address, password

[1629] Output: Account information stored in the database

[1630] Specific operations: The server saves the received user information in the database and returns a success message to the user.

[1631] Step 4:

[1632] The user enters their credentials on the login screen.

[1633] Input: Email address, password

[1634] Output: Authentication result

[1635] Specific behavior: The user enters their email address and password on the login screen and taps the "Login" button.

[1636] Step 5:

[1637] The server checks the authentication information against a database and performs authentication.

[1638] Input: Email address, password

[1639] Output: Authentication result (success / failure)

[1640] Specific operation: The server compares the user information in the database with the login information and returns the authentication result to the device. If successful, the home screen is displayed.

[1641] Step 6:

[1642] The user takes or selects an image of a fashion item they own.

[1643] Input: Fashion item image

[1644] Output: Sending image data to the server

[1645] What happens: The user takes a photo of an item using the camera or selects one from their photo library. The image is sent from the device to the server.

[1646] Step 7:

[1647] The server identifies the image and stores the item's attributes in a database.

[1648] Input: Fashion item image

[1649] Output: Attribute information (category, color, material, brand, etc.)

[1650] Specific operation: The server uses an image recognition algorithm (e.g., TensorFlow or OpenCV) to identify the attributes of the item and store that information in a database.

[1651] Step 8:

[1652] The device collects the user's location and social network data.

[1653] Input: Location permission, Social network data permission

[1654] Output: Sending location and social network data to a server

[1655] What it does: The device uses GPS to obtain location information and, with the user's permission, collects social network data, which is then sent to a server.

[1656] Step 9:

[1657] The server analyzes the location and social data.

[1658] Input: Location information, social network data

[1659] Output: Analysis results

[1660] Specific operation: The server uses machine learning algorithms (e.g., Scikit-learn or TensorFlow) to perform analysis that takes into account the climate and cultural background of the current location.

[1661] Step 10:

[1662] The server generates the optimal coordination based on the analysis results.

[1663] Input: Analysis results, online closet data

[1664] Output: Coordination suggestions

[1665] Specific operation: The server uses the analysis results to combine items in the online closet and generate the optimal outfit.

[1666] Step 11:

[1667] The device collects the user's emotional data and sends it to the server.

[1668] Input: User facial expressions, voice, and text input

[1669] Output: Sending emotion data to the server

[1670] Specific operation: The device uses the camera and microphone to collect the user's emotional data and sends it to the server.

[1671] Step 12:

[1672] The server analyzes the emotional data and identifies the user's emotional state.

[1673] Input: Emotion data

[1674] Output: Emotional state identification result

[1675] What it does: The server uses an emotion analysis algorithm to determine the user's current emotional state.

[1676] Step 13:

[1677] The server generates coordination suggestions based on the emotional state.

[1678] Input: Emotional state, analysis results

[1679] Output: Personalized outfit suggestions

[1680] Specific operation: The server generates coordinates that match the user's mood based on their emotional state.

[1681] Step 14:

[1682] The server sends the generated coordinates to the terminal and presents them to the user.

[1683] Input: Coordination suggestion

[1684] Output: Coordinates displayed on the terminal

[1685] Specific operation: The server sends a coordination proposal to the terminal, and the terminal displays it to the user.

[1686] Step 15:

[1687] The user provides feedback on the presented outfit.

[1688] Input: Feedback information

[1689] Output: Sending feedback information to the server

[1690] Specific operation: The user inputs feedback on the coordination, and the device sends the information to the server.

[1691] Step 16:

[1692] The server retrieves the missing items from the partner e-commerce site and displays them on the device.

[1693] Input: Coordination suggestions, missing item information

[1694] Output: Display of recommended items

[1695] Specific operation: The server retrieves information about missing items from partner e-commerce sites and displays it on the device.

[1696] Step 17:

[1697] The user selects a recommended item and completes the purchase process.

[1698] Input: Recommended item selection, purchase information

[1699] Output: Information to complete the purchase

[1700] Specific operation: The user selects a recommended item and completes the purchase process. The purchase process is completed through the terminal, and the server connects with the e-commerce site to complete the purchase.

[1701] (Application example 2)

[1702] 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."

[1703] Previous fashion coordination suggestion systems did not take into account the user's shopping experience in a physical store, nor did they optimize coordination through emotion analysis. This meant that users could not receive personalized suggestions based on their current mood or the items they were actually looking at. Furthermore, they lacked technology to visually present coordination suggestions in real time using smart glasses. To solve these issues, a new system that takes user emotions into account and improves the shopping experience in a physical store is needed.

[1704] 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.

[1705] In this invention, the server includes means for uploading images of items owned by the user, means for AI to recognize the images and identify item attributes and store them in a database, means for collecting the user's location information and social network data, means for analyzing the collected data and generating an optimal outfit for the user, means for analyzing the user's emotions and optimizing the outfit based on the emotions, means for visually displaying real-time coordination suggestions of fashion items in a physical store using smart glasses, means for recommending items missing from a partner e-commerce site that are missing from the generated outfit, and means for the user to purchase the recommended items. This makes it possible to propose personalized fashion coordination that takes into account the user's emotions, location information, and purchasing behavior in a physical store.

[1706] "Means for uploading images of items owned by the user" refers to means for the user to take or select photos of fashion items they own and send them to the system.

[1707] "Means for AI to recognize images, identify item attributes, and store them in a database" refers to a means of using artificial intelligence technology to extract information such as the type, color, material, and brand of a fashion item from uploaded images and record that information in a database.

[1708] "Means for collecting user location information and social network data" refers to means for obtaining a user's current geographic location and activity data on social networking sites.

[1709] "Means for analyzing collected data and generating optimal outfits for users" refers to a means for automatically creating fashion outfits that suit users based on acquired location information and social network data.

[1710] "Means for analyzing user emotions and optimizing coordination based on emotions" refers to a means for detecting emotions from the user's facial expressions, voice, text input, etc., and suggesting fashion coordination that matches that emotional state.

[1711] "A means for visually displaying real-time coordination suggestions for fashion items in a physical store using smart glasses" refers to a means for displaying, in real time, coordination suggestions that combine the fashion items that a user is looking at in a physical store with items in their online closet, using smart glasses.

[1712] "Means for recommending missing items from partner e-commerce sites in the created outfit" refers to a means for searching for missing fashion items in the created outfit from an online shopping site and recommending them to the user.

[1713] "Means for users to purchase recommended items" refers to the procedures for users to actually purchase items recommended by the system, and the means to provide functions to support this.

[1714] This system efficiently manages a user's fashion items and suggests optimal outfits based on location information, social data, and the user's emotions. This system can also suggest outfits in real time in physical stores using smart glasses.

[1715] System configuration

[1716] 1. Image upload method

[1717] The user captures an image of the item they own via their smartphone or other device and sends it to the server. This requires a camera function and communication with the server. Specifically, the smartphone's camera app and internet connection are used.

[1718] 2. Image Recognition Methods

[1719] The server uses AI (artificial intelligence) to analyze the received images and identify the item's attributes. Here, the image is processed using OpenCV, and machine learning models (such as TensorFlow or PyTorch) are used to obtain information such as the item's category, color, material, and brand.

[1720] 3. Location and Social Data Collection Methods

[1721] With the user's permission, the device obtains location information from GPS and collects data from the user's social media accounts, which is then sent over the internet to a server.

[1722] 4. Data analysis and coordinate generation methods

[1723] The server analyzes the collected location and social data to generate optimal fashion coordination for the user, taking into account weather information and cultural characteristics. Data analysis is performed using database software (e.g., MySQL or PostgreSQL) and analysis tools (e.g., Pandas or NumPy).

[1724] 5. Emotion analysis method

[1725] The device collects emotional data based on the user's facial expressions, voice, and text input, and sends it to a server. The server then uses AI to analyze this emotional data and determine the user's current emotional state. Emotion analysis tools such as EmotionEngine are used for this analysis.

[1726] 6. Real-time Coordination Proposal Method Using Smart Glasses

[1727] The smart glasses have the ability to recognize the fashion items the user is looking at in a physical store in real time and visually display coordination suggestions that combine them with the user's online closet. Here, OpenCV is used to capture camera images and generate suggestions through communication with a cloud server.

[1728] 7. Recommendation methods

[1729] The server recommends missing items from partner e-commerce sites, allowing users to easily purchase the missing items. The recommendation algorithm uses collaborative filtering and content-based recommendation systems.

[1730] Example

[1731] For example, if a user visits a shopping mall on a holiday and picks up a red dress, the smart glasses will recognize the dress in real time and, if it determines that the user's emotion is "happy," it will suggest a glamorous outfit. In this way, users can enjoy a more personalized shopping experience.

[1732] Prompt Sentence Examples

[1733] "Design an application that recognizes the fashion items a user is looking at and suggests the best fashion coordination based on the user's emotions and location. This application will be installed on smart glasses."

[1734] This invention makes it possible to propose personalized fashion coordination that takes into account the user's emotions and location information, significantly improving the shopping experience in physical stores.

[1735] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1736] Step 1:

[1737] Image capture and upload

[1738] A user uses a smartphone or other device to take a picture of a fashion item they own and send it to the server. The input is the image of the fashion item, and the output is the image data sent to the server. Specific operations include the user launching a camera app, capturing an image, and then pressing the image upload button.

[1739] Step 2:

[1740] Image Recognition and Attribute Identification

[1741] The server analyzes the received image data using AI (image recognition algorithms) to identify attributes such as the type, color, material, and brand of the fashion item. The input is the uploaded image data, and the output is the item's attribute information. Specifically, the server processes the image using OpenCV, identifies the attributes using TensorFlow or PyTorch models, and stores them in a database.

[1742] Step 3:

[1743] Location and social data collection

[1744] With the user's permission, the device obtains the user's current location from GPS and also collects data from the user's social media accounts and sends it to a server. The input is location information and social data, and the output is these data sent to the server. Specifically, the device periodically obtains GPS data and uses social media APIs to collect and send the necessary data.

[1745] Step 4:

[1746] Data analysis and coordinate generation

[1747] The server analyzes the acquired location information and social data and generates the optimal fashion coordination for the user based on that information. The input is location information, social data, and item attribute information, and the output is the generated coordination proposal. Specifically, the server queries the necessary data from the database, processes the data using analysis tools (Pandas or NumPy), and generates the optimal coordination.

[1748] Step 5:

[1749] Emotion analysis

[1750] The device collects the user's facial expressions and voice and sends them to the server. The server then uses EmotionEngine to analyze and identify the user's emotional state. The input is the user's facial expression data and voice data, and the output is information about the user's emotional state. The specific operation is a process in which the device uses a camera and microphone to capture the user's data and send it to the server.

[1751] Step 6:

[1752] Real-time coordination suggestions using smart glasses

[1753] The device (smart glasses) captures real-time video footage from the physical store and sends the image data to a server. The server analyzes the images, generates real-time outfit suggestions that combine them with items from the online closet, and displays them on the smart glasses. The input is real-time video data, and the output is the outfit suggestions presented to the user. The specific operation is a process in which the smart glasses activate their camera and constantly capture the items the user is looking at and send them to the server.

[1754] Step 7:

[1755] Recommendations and checkout

[1756] The server searches partner e-commerce sites for items missing from the generated outfit and recommends them to the user. The user purchases the recommended items through their device. The input is the outfit plan, and the output is the recommended items and the purchase procedure based on them. Specifically, the server recommends products using collaborative filtering or a content-based recommendation system, and the purchase procedure is carried out through the e-commerce site's API.

[1757] This system allows users to receive real-time fashion coordination suggestions based on their emotions and location information, improving their shopping experience.

[1758] 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.

[1759] 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.

[1760] 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.

[1761] 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.

[1762] 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.

[1763] 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.

[1764] 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).

[1765] 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.

[1766] 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."

[1767] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1768] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1769] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1770] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1771] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1772] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1773] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1774] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1775] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1776] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1777] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1778] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1779] The following is further disclosed regarding the above embodiment.

[1780] (Claim 1)

[1781] A means for users to upload images of items they own;

[1782] A means for AI to recognize images, identify item attributes, and store them in a database;

[1783] A means for collecting user location information and social network data;

[1784] A means of analyzing the collected data and generating the optimal outfit for the user;

[1785] A means to recommend items missing from partner e-commerce sites in the generated outfits,

[1786] A way for users to purchase recommended items,

[1787] A system including:

[1788] (Claim 2)

[1789] 10. The system of claim 1, further comprising means for automatically generating and organizing an online closet through user image uploads.

[1790] (Claim 3)

[1791] 10. The system according to claim 1, further comprising means for collecting feedback on the coordination presented to the user and improving the coordination based on the feedback.

[1792] "Example 1"

[1793] (Claim 1)

[1794] a means for users to upload images of items they own;

[1795] a recognition device for recognizing the image, identifying attributes of the item, and storing the identified attributes in a data storage;

[1796] a means of collecting user location and social media data;

[1797] A means for analyzing the collected data and generating a coordinated outfit suitable for the user;

[1798] A means to recommend items missing from an external sales site for the generated outfit;

[1799] a means for users to purchase the recommended items;

[1800] A system including:

[1801] (Claim 2)

[1802] 10. The system of claim 1, further comprising means for automatically generating and organizing online data storage through user image uploads.

[1803] (Claim 3)

[1804] 2. The system according to claim 1, further comprising means for collecting feedback on the outfits presented to the user and modifying the outfits based on the feedback.

[1805] "Application Example 1"

[1806] (Claim 1)

[1807] A means for users to upload images of items they own;

[1808] A means for AI to recognize images, identify item attributes, and store them in a database;

[1809] A means for collecting user location information and social network data;

[1810] A means of analyzing the collected data and generating the optimal outfit for the user;

[1811] A means to recommend items missing from partner e-commerce sites in the generated outfits,

[1812] A way for users to purchase recommended items,

[1813] A way to check whether the generated outfit is in stock at a nearby physical store,

[1814] A means to choose in-store pickup options,

[1815] A system including:

[1816] (Claim 2)

[1817] 10. The system of claim 1, further comprising means for automatically generating and organizing an online closet through user image uploads.

[1818] (Claim 3)

[1819] 10. The system according to claim 1, further comprising means for collecting feedback on the coordination presented to the user and improving the coordination based on the feedback.

[1820] "Example 2: Combining Emotion Engines"

[1821] (Claim 1)

[1822] A means for users to upload images of items they own;

[1823] A means for the generative AI model to recognize images, identify item attributes, and store them in a database;

[1824] means for collecting user location and social network data;

[1825] A means for analyzing the collected data and the user's emotional data to generate an optimal outfit for the user;

[1826] a means for recommending missing items from a partner e-commerce site for the generated outfit;

[1827] A way for users to purchase recommended items,

[1828] A system including:

[1829] (Claim 2)

[1830] 10. The system of claim 1, further comprising means for automatically generating and organizing an online closet through user image uploads.

[1831] (Claim 3)

[1832] 10. The system of claim 1, further comprising means for collecting user emotion data and adjusting the outfit based thereon.

[1833] "Application example 2 when combining emotion engines"

[1834] (Claim 1)

[1835] A means for users to upload images of items they own;

[1836] A means for AI to recognize images, identify item attributes, and store them in a database;

[1837] A means for collecting user location information and social network data;

[1838] A means of analyzing the collected data and generating the optimal outfit for the user;

[1839] A means for analyzing user emotions and optimizing outfits based on those emotions;

[1840] A means for visually displaying real-time coordination suggestions for fashion items in a physical store using smart glasses;

[1841] A means to recommend items missing from partner e-commerce sites in the generated outfits,

[1842] A way for users to purchase recommended items,

[1843] A system including:

[1844] (Claim 2)

[1845] 10. The system of claim 1, further comprising means for automatically generating and organizing an online closet through user image uploads.

[1846] (Claim 3)

[1847] 10. The system according to claim 1, further comprising means for collecting feedback on the coordination presented to the user and improving the coordination based on the feedback. [Explanation of symbols]

[1848] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for users to upload images of items they own; A means for AI to recognize images, identify item attributes, and store them in a database; A means for collecting user location information and social network data; A means of analyzing the collected data and generating the optimal outfit for the user; A means to recommend items missing from partner e-commerce sites in the generated outfits, A way for users to purchase recommended items, A system including:

2. The system of claim 1 further comprising means for automatically generating and organizing an online closet through user image uploads.

3. The system according to claim 1, further comprising means for collecting feedback on the coordinated outfits presented to the user and improving the coordinated outfits based on the feedback.

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