System

Abstract

A system is provided.SOLUTION: The system includes a means for inputting information of clothes owned by a user and storing the information in a database, a means for executing an algorithm for proposing coordination based on the user's feeling, and a means for identifying an insufficient item based on the proposed coordination and acquiring a link of a corresponding commodity from an external electronic commerce site.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] Modern consumers often own a wide variety of clothing and find it difficult to combine them appropriately. They also face challenges such as not having time to think about appropriate outfits or finding outfits that match a particular mood. Furthermore, when new items are needed, there are often few ways to smoothly purchase them. The present invention aims to solve these challenges by providing a system that allows users to effectively manage their clothing, provide appropriate outfits, and smoothly purchase missing items. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for inputting information about clothing owned by a user and storing the information in a database, a means for executing an algorithm for suggesting outfits based on the user's mood, and a means for identifying missing items based on the suggested outfits and obtaining links to corresponding products from external e-commerce sites. The system also uses a machine learning model to suggest outfits, and includes a means for inputting detailed information about the user's clothing, including category, color, and season, to achieve more accurate suggestions. This allows users to easily manage their clothing, receive outfits that match their mood, and smoothly purchase missing items.

[0006] "User" means an individual or organization that uses this system.

[0007] "Clothing" refers to clothing items and accessories owned by the user and registered in the system.

[0008] "Information" refers to detailed data about the clothing, such as name, category, color, and season.

[0009] A "database" is a piece of storage within a system that organizes information so that it can be efficiently stored, retrieved, and managed.

[0010] An "algorithm" refers to a set of computational steps or rules for performing a specific task.

[0011] "Coordination" refers to the overall look of an outfit created by combining multiple pieces of clothing.

[0012] "Mood" refers to the emotions and feelings that a user feels at that time, and affects the coordination suggestions.

[0013] A "machine learning model" refers to a group of mathematical algorithms that learn data patterns to make predictions or classifications.

[0014] "Third-Party E-Commerce Site" means a third-party online marketplace that sells products over the Internet.

[0015] "Product Link" means the URL to a specific product page on an e-commerce site. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0024] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0037] The present invention is a system that manages information about clothing owned by a user, suggests outfits based on that information, and provides links to acquire missing items from e-commerce sites. This system operates through mutual communication between a server, terminals, and users.

[0038] When a user wants to purchase new clothing and register it in the system, they first log in to the system using a device such as a smartphone or computer. Next, the user enters the information for "Winter Jacket" (category "Outerwear", color "Black", season "Winter"). The device sends this information to the server. The server parses the received information and saves it as a new record in the database.

[0039] When a user requests suggestions for outfits that match their current mood, the device sends the request to the server. For example, if the user specifies that they are "feeling casual," the device sends that information to the server. The server then queries the database and retrieves a list of the clothes the user owns.

[0040] The server uses an AI algorithm (e.g., a machine learning model) to generate an optimal outfit based on the retrieved clothing list and the user's specified mood. This suggested outfit (e.g., blue jeans + white T-shirt + black jacket) is sent from the server to the device, which then displays it to the user.

[0041] Furthermore, if the proposed outfit includes items that the user does not own, the server identifies the missing items. For example, if the user needs a white T-shirt to complete their outfit, the server searches an e-commerce site and retrieves a link to the relevant item. The server sends this link to the device, which then displays it to the user, allowing the user to easily purchase the missing items.

[0042] To explain with a concrete example, the process is as follows:

[0043] 1. Clothing registration: A user purchases a new jacket, "Winter Jacket," and enters it into the system. The device sends this information to the server, which stores it in a database.

[0044] 2. Coordination suggestions: When a user requests a "casual mood," the server uses an AI algorithm to suggest a coordinated outfit of "blue jeans + white T-shirt + black jacket" based on the user's clothing data. This information is sent to the device and displayed to the user.

[0045] 3. Item Purchase Suggestion: If the user does not own a white T-shirt in the suggested outfit, the server will search an e-commerce site and retrieve a link to purchase the white T-shirt. This link will be sent to the device and displayed to the user.

[0046] This system allows users to manage their clothing more efficiently, enjoy coordinating outfits to suit their mood, and smoothly purchase items they are running low on.

[0047] The processing flow will be explained below.

[0048] Step 1:

[0049] When a user purchases new clothes, they log in to the system using a device such as a smartphone or computer.

[0050] Step 2:

[0051] The user enters information about the clothes they purchased (e.g., "Winter Jacket," category "Outerwear," color "Black," season "Winter," etc.) into the terminal.

[0052] Step 3:

[0053] The device checks the clothing information entered and checks for any missing items.

[0054] Step 4:

[0055] The terminal transmits the confirmed information to the server.

[0056] Step 5:

[0057] The server parses the received information and stores it as a new record in the database.

[0058] Step 6:

[0059] The user requests suggestions for outfits that match their current mood, for example, specifying "I'm feeling casual."

[0060] Step 7:

[0061] The terminal receives a request from the user and transmits the specified mood information to the server.

[0062] Step 8:

[0063] The server retrieves the user's clothing list from the database.

[0064] Step 9:

[0065] Based on the list of clothes retrieved by the server and the specified mood, an AI algorithm (e.g., machine learning model) is used to generate the optimal outfit.

[0066] Step 10:

[0067] The server sends the generated coordinates to the terminal.

[0068] Step 11:

[0069] The terminal displays the suggested coordination to the user.

[0070] Step 12:

[0071] The server identifies missing items in the proposed outfit, for example, if the user does not have a "white T-shirt."

[0072] Step 13:

[0073] The server searches the e-commerce site based on the missing item and obtains the link for the appropriate product.

[0074] Step 14:

[0075] The server sends the acquired product link to the terminal.

[0076] Step 15:

[0077] The device displays product links to the user and encourages them to make a purchase.

[0078] The above-described processing steps enable the user to easily manage clothing, receive coordination suggestions that match their mood, and smoothly purchase any items they are missing.

[0079] Example 1

[0080] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0081] Existing clothing management systems not only efficiently manage users' clothing, but also automatically suggest outfits that match their mood and lack support for easily purchasing missing items. As a result, users have to spend time and effort selecting their daily outfits, and the process of finding and purchasing missing items is cumbersome.

[0082] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0083] In this invention, the server includes means for inputting information about clothing owned by a user and storing the information in a database, means for executing an algorithm for suggesting outfits based on the user's mood, means for identifying missing items based on the suggested outfits and obtaining links to corresponding products from external e-commerce sites, means for displaying the outfit suggestions to the user, and means for the user to check the missing items in the suggestions and purchase them via the links. This allows the user to efficiently manage their clothing, easily enjoy coordinating outfits that suit their mood, and quickly purchase missing items.

[0084] "User" refers to an individual who uses the system to manage clothing information and receive coordination suggestions.

[0085] "Clothing information" refers to attribute information such as category, color, and season of each piece of clothing owned by the user.

[0086] "Database" means an electronic data repository for storing and managing clothing information entered by Users in the System.

[0087] "Mood" refers to the user's current orientation regarding a particular style or taste.

[0088] "Coordination" refers to a fashion style suggested by combining clothing owned by the user.

[0089] An "algorithm" refers to a sequence of computational steps designed to accomplish a particular purpose.

[0090] A "generative AI model" refers to a machine learning model that uses artificial intelligence to generate new outputs from data.

[0091] "External E-Commerce Site" means a third-party website that sells products over the Internet.

[0092] "Link" refers to a Uniform Resource Locator (URL) for accessing a specific web page on the Internet.

[0093] "Means for displaying suggestions to the user" refers to a method for displaying the results of coordination and information about missing items on the screen of the user's terminal.

[0094] "Means to purchase" refers to the method by which a user can purchase the missing item using the suggested link.

[0095] The present invention is a system that manages information about clothing owned by a user, suggests outfits based on that information, and provides links to acquire missing items from e-commerce sites. This system operates through mutual communication between a server, terminals, and users.

[0096] First, when a user purchases new clothes and wants to register them in the system, they log in to the system using a device such as a smartphone or computer. The user opens an application (e.g., a closet management app) and enters their username and password on the login screen to access the system.

[0097] Next, the user enters information about the "Winter Jacket" (category "Outerwear", color "Black", season "Winter"). The user enters details such as category, color, and season on the "Register Clothing Information" page and clicks the "Register" button. The device sends this input information to the server. The device converts the user's input into JSON format and sends an HTTP request to the server.

[0098] The server parses the received information and saves it as a new record in the database. The server receives the HTTP request, parses the JSON data, and adds the parsed data to the database (e.g., MySQL (registered trademark), PostgreSQL) as a new record.

[0099] When a user requests suggestions for outfits that match their current mood, they select a mood, such as "casual," from the menu on the "Outfit Suggestions" page within the application and click the "Request" button. The device then sends the request to the server. The device then converts the user's selections into JSON format and sends an HTTP request to the server.

[0100] The server queries the database and retrieves a list of clothes owned by the user. The server executes the database query and retrieves the user's clothing list. It then converts the retrieved results into JSON format. The server then uses an AI algorithm (e.g., a machine learning model) to generate an optimal outfit based on the retrieved clothing list and the specified mood. The server invokes an AI model (e.g., the TENSORFLOW (registered trademark) model) and generates an optimal outfit (e.g., "blue jeans + white T-shirt + black jacket") using the clothing list and mood information as input.

[0101] The server sends the proposed coordination to the device. The server converts the proposed coordination into JSON format and sends it to the device as an HTTP response. The device displays the coordination information to the user. The device analyzes the received data and displays the coordination on the application's UI.

[0102] Additionally, if the proposed outfit includes items that the user does not own, the server identifies the missing items. The server compares the user's clothing list with the proposed outfit and identifies the missing items (e.g., a white T-shirt). The server searches e-commerce sites and obtains links to the relevant products. The server uses an API to search e-commerce sites (e.g., online marketplaces) and obtains the URLs for the relevant products (e.g., a white T-shirt).

[0103] The server sends this link to the device, which then displays it to the user. The device then parses the link and displays it in the application's UI to notify the user, allowing the user to easily purchase the missing item.

[0104] As a concrete example, the following flow can be considered:

[0105] 1. Clothing registration: A user purchases a new jacket, "Winter Jacket," and enters it into the system. The device sends this information to the server, which stores it in a database.

[0106] 2. Coordination suggestions: When a user requests a "casual mood," the server uses an AI algorithm to suggest a coordinated outfit of "blue jeans + white T-shirt + black jacket" based on the user's clothing data. This information is sent to the device and displayed to the user.

[0107] 3. Item Purchase Suggestion: If the user does not own a white T-shirt in the suggested outfit, the server will search the e-commerce site and get a link to purchase the white T-shirt. This link will be sent to the device and displayed to the user.

[0108] An example of a prompt to input to a generative AI model is as follows:

[0109] Here is a list of clothes the user owns:

[0110] 1. Blue jeans

[0111] 2. White T-shirt

[0112] 3. Black jacket

[0113] ...

[0114] The user is in a "casual mood." Please suggest the best outfit based on this.

[0115] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0116] Step 1:

[0117] A user logs in to the system to register a new piece of clothing, a "Winter Jacket." The input is a username and password, and the output is login status information upon success. Specifically, the user opens the application on their smartphone or computer, enters their username and password on the login screen, and accesses the system.

[0118] Step 2:

[0119] The user enters information about the "Winter Jacket" (category "Outerwear", color "Black", season "Winter"). The input is detailed information about the clothing, and the output is the transmission of the input information from the user to the terminal. The user enters details such as category, color, and season on the "Register Clothing Information" page, and clicks the "Register" button. The terminal sends the input information to the server.

[0120] Step 3:

[0121] The terminal converts the user's input into JSON format and sends an HTTP request to the server. The input is the user's input data and the output is an HTTP request to the server. The terminal converts the user's input into the appropriate format and sends it over the Internet.

[0122] Step 4:

[0123] The server parses the received information and stores it as a new record in the database. The input is the data from the HTTP request, and the output is a new record stored in the database. Specifically, the server receives the HTTP request, parses the JSON data, and stores the parsed data as a new record in the database (e.g., MySQL, PostgreSQL).

[0124] Step 5:

[0125] The user requests outfit suggestions. The input is the request, such as "feeling casual," and the output is confirmation of the request. The user selects a mood from the menu on the "Outfit Suggestions" page within the application and clicks the "Request" button.

[0126] Step 6:

[0127] The terminal converts the user's request information into JSON format and sends an HTTP request to the server. The input is the user's request content and the output is an HTTP request to the server. The terminal converts the request data into the appropriate format and sends it over the Internet.

[0128] Step 7:

[0129] The server queries the database to retrieve a list of the user's clothes. The input is user identification information, such as a user ID, and the output is the user's clothes list. The server executes the database query and retrieves the user's clothes list.

[0130] Step 8:

[0131] The server uses an AI algorithm to generate the optimal outfit based on the acquired clothing list and the specified mood. The input is the user's clothing list and mood information, and the output is a suggested outfit. Specifically, the server calls an AI model (e.g., TensorFlow model) and generates the optimal outfit based on the clothing list and mood information.

[0132] Step 9:

[0133] The server sends the proposed outfits to the device. The input is the outfit information generated by the AI, and the output is an HTTP response to the device. The server converts the outfit information into JSON format and sends it to the device as an HTTP response.

[0134] Step 10:

[0135] The terminal displays the coordinate information to the user. The input is the coordinate information in JSON format received from the server, and the output is a visual display to the user. The terminal parses the received data and displays the coordinates in the application UI.

[0136] Step 11:

[0137] The server identifies items in the proposed outfit that the user does not own. The input is the user's clothing list and the proposed outfit, and the output is a list of missing items. The server matches all items in the proposed outfit against the user's clothing list to identify the missing items.

[0138] Step 12:

[0139] The server searches an external e-commerce site and retrieves links to the relevant products. The input is a list of missing items, and the output is a purchase link on an external site. The server uses an API to search an e-commerce site and retrieve links to the relevant products.

[0140] Step 13:

[0141] The server sends the acquired link to the terminal. The input is the acquired purchase link, and the output is the HTTP response to the terminal. The server converts the link to JSON format and sends it to the terminal as an HTTP response.

[0142] Step 14:

[0143] The terminal displays the link to the user. The input is the purchase link information received from the server, and the output is a visual display to the user. The terminal analyzes the received link and displays it in the application's UI to notify the user.

[0144] (Application example 1)

[0145] 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."

[0146] The objective of this invention is to efficiently manage information about clothing that users regularly own, provide optimal outfits that match their mood and situation, and enable users to easily purchase missing items. It also aims to improve the user's purchasing experience by introducing technology that allows users to virtually try on suggested outfits.

[0147] 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.

[0148] In this invention, the server includes means for inputting information about clothing owned by a user and storing the information in a database, means for executing an algorithm for suggesting outfits based on the user's mood, means for identifying missing items based on the suggested outfits and obtaining links to corresponding products from external e-commerce sites, and means for allowing the user to virtually try on the suggested outfits using augmented reality technology, thereby enabling the user to efficiently manage their clothing, enjoy coordinating outfits that match their mood, virtually try on the suggested outfits, and easily purchase missing items.

[0149] A "user" is an individual who uses the system to manage clothing information and receive coordination suggestions.

[0150] "Clothing information" is detailed information such as the category, color, and season of clothing owned by the user.

[0151] A "database" is an information storage system for saving and managing clothing information entered by users.

[0152] An "outfit" is a set of clothes that is proposed by combining multiple pieces of clothing that the user owns.

[0153] An "algorithm" is a procedure for calculating and proposing optimal outfits based on the user's mood.

[0154] A "machine learning model" is a type of artificial intelligence used to suggest optimal outfits based on a user's preferences and past data.

[0155] An "e-commerce site" is a website that sells products over the Internet.

[0156] "Link" means a URL that provides access to a particular product on an e-commerce site.

[0157] "Augmented reality technology" is a technology that displays virtual information overlaid on the real environment.

[0158] "Virtual try-on" is a process that uses augmented reality technology to make suggested outfits appear as if they are actually worn by the user.

[0159] The present invention is a system that manages information about clothing owned by a user, suggests optimal outfits using an AI algorithm, and provides links to easily purchase missing items on an e-commerce site. In a specific embodiment, the system operates in cooperation with a server, a terminal, and a user.

[0160] First, a user accesses the system using a device such as a smartphone or computer. When a user purchases new clothing and wants to register this information, they enter information such as the clothing category, color, and season. This input information is sent from the device to the server, which parses the information and stores it in a database. The database used here is, for example, a relational database such as MySQL.

[0161] Next, when the user requests outfit suggestions, the device sends the request to the server. For example, if the user specifies "I'm in a casual mood," that information is sent to the server. The server queries a database to obtain a list of the user's clothing. The server then uses an AI algorithm (for example, a machine learning model using TensorFlow) to generate the optimal outfit. The server then sends this suggested outfit to the device, which then displays it to the user.

[0162] Furthermore, if the proposed outfit includes an item that the user does not own, the server identifies the missing item and obtains a link to the corresponding product from an external e-commerce site. This link information is sent to the terminal, and the terminal displays the link to the user, allowing the user to easily purchase the missing item.

[0163] In addition, the present invention uses augmented reality (AR) technology to allow users to virtually try on the suggested outfits. This technology allows users to visually see how the suggested outfits look on their body through their smartphones or smart glasses. Examples of AR technology used here include Apple's ARKit and Google's ARCore.

[0164] Let's add a concrete example. A user purchases a new jacket, a "Winter Jacket," and enters the information into the system. The information is stored in a database. Next, if the user requests a "casual look," the AI ​​algorithm suggests a combination of "blue jeans + white T-shirt + black jacket." The user receives this suggestion and virtually tries on the combination using smart glasses. If the user does not have the required item, a white T-shirt, the server retrieves a link to the corresponding product from an e-commerce site and displays it to the user.

[0165] An example of a prompt is:

[0166] prompt:

[0167] Consider how to achieve clothing management, AI coordination, AR try-on, and item purchase in a single application. Specifically, describe how the AI ​​model and AR technology are used when a user uses smart glasses to receive outfit suggestions based on their mood. Also, describe the back-end process for providing purchase links for missing items.

[0168] Reference output:

[0169] The application, which helps users decide what they want to wear, first stores the user's clothing data in a database via their smartphone. It then uses an AI model (using TensorFlow) to generate outfits that best fit the user's mood. The generated outfits are visually presented to the user in real time through AR technology (e.g., ARKit or ARCore) in the smart glasses. Missing items are searched for using the e-commerce site's API, and a link to purchase them is provided.

[0170] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0171] Step 1:

[0172] Users log in to the system using devices such as smartphones or computers. After successful login, users can access an interface to input new clothing information, including details such as clothing category, color, and season.

[0173] Input: User-entered details such as clothing category, color, season, etc.

[0174] Output: The entered information is sent from the device to the server.

[0175] Step 2:

[0176] The server receives the clothing information sent from the device, parses it, and stores it in a database, while also checking the data for consistency.

[0177] Input: Clothing information sent from the device

[0178] Data processing / data calculation: Parse the data, check its integrity, and save it to the database

[0179] Output: New clothing information is saved in the database

[0180] Step 3:

[0181] The user requests a coordination suggestion, and the device sends information such as the user's specified "mood" and "situation" to the server.

[0182] Input: "Mood" or "Situation" specified by the user

[0183] Output: The specified information is sent from the terminal to the server.

[0184] Step 4:

[0185] The server queries the database to retrieve a list of clothing owned by the user, including all clothing information that the user has previously registered.

[0186] Input: A database containing information about clothing owned by the user.

[0187] Data processing / data calculation: Obtain all user clothing information from the database

[0188] Output: The user's clothing list is retrieved by the server.

[0189] Step 5:

[0190] The server runs an AI algorithm that uses a machine learning model (using TensorFlow, for example) to generate the optimal outfit based on the clothing list and the user's specified "mood."

[0191] Input: User's clothing list, user-specified "mood"

[0192] Data processing / data calculation: Generate optimal outfits using machine learning models

[0193] Output: The generated coordinates are sent to the server

[0194] Step 6:

[0195] The proposed coordinates are sent to the terminal, which displays this information to the user.

[0196] Input: Generated coordinates

[0197] Output: The suggested outfits are displayed on the device.

[0198] Step 7:

[0199] To allow users to virtually try on suggested outfits, the device uses augmented reality technology (such as ARKit or ARCore) to overlay the suggested items onto the user's camera image.

[0200] Input: User's camera footage, suggested coordinates

[0201] Output: The virtual try-on video is displayed on the device.

[0202] Step 8:

[0203] The server identifies items in the proposed outfit that the user does not own and uses the e-commerce site's API to obtain links to those products.

[0204] Input: Suggested outfits, information on items the user does not own

[0205] Data processing / data calculation: Call the API of an e-commerce site to obtain product links

[0206] Output: The link of the retrieved product is saved on the server.

[0207] Step 9:

[0208] The server sends the link of the acquired product to the terminal, and the terminal displays the link to the user, who can click the link to purchase the missing item.

[0209] Input: Link of the retrieved product

[0210] Output: A link to purchase the product will be displayed on the device.

[0211] 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.

[0212] This system manages information about clothing owned by a user, suggests outfits based on that information, and provides links to acquire missing items from e-commerce sites. By combining this system with an emotion engine that recognizes the user's emotions, the system makes more personalized suggestions. This system is realized through the interaction of a server, terminals, and users.

[0213] First, when a user purchases new clothing, they log in to the system using a device such as a smartphone or computer. The user enters information about the "Winter Jacket" (category "Outerwear," color "Black," season "Winter," etc.) into the device. The device sends this information to the server, which saves it as a new record in the database.

[0214] Next, when the user requests suggestions for outfits that match their current mood, the emotion engine recognizes the user's emotion from their facial expression, voice, or text input. For example, by analyzing the user's facial expression through a camera, the emotion engine recognizes that the user is in a "happy" mood. This emotion information is sent from the device to the server.

[0215] The server queries the database to obtain a list of the clothes owned by the user. Based on the obtained list of clothes, the recognized emotion, and the specified mood, an AI algorithm (e.g., a machine learning model) is used to generate an optimal outfit. This outfit suggestion (e.g., blue jeans + white T-shirt + black jacket) is sent from the server to the device and displayed to the user.

[0216] Furthermore, the server identifies missing items in the proposed outfit. For example, if the user does not own a white T-shirt, the server searches an e-commerce site to obtain a link to the corresponding item. This link is sent to the device and displayed to the user. The emotion engine can also suggest more suitable items based on the user's emotions when suggesting outfits or purchasing products.

[0217] To explain with a concrete example, the process is as follows:

[0218] 1. Clothing registration: A user purchases a new jacket, "Winter Jacket," and enters it into the system. The device sends this information to the server, which stores it in a database.

[0219] 2. Emotion recognition and outfit suggestions: When a user logs in to the system, the emotion engine analyzes the user's facial expressions via the device's camera. The emotion engine recognizes that the user is in a "happy" mood and sends this information to the server. The server then uses an AI algorithm to suggest an outfit combination of "blue jeans + white T-shirt + black jacket" based on the user's clothing data and emotional information. This suggestion is sent to the device and displayed to the user.

[0220] 3. Item Purchase Suggestion: If the user does not own a white T-shirt in the suggested outfit, the server will search an e-commerce site and retrieve a link to purchase the white T-shirt. This link will be sent to the device and displayed to the user. The server can also check whether the purchase suggestion is appropriate based on the user's emotions recognized by the emotion engine.

[0221] The system allows users to easily manage their clothing, receive more personalized outfit suggestions through emotion recognition, and smoothly purchase missing items.

[0222] The processing flow will be explained below.

[0223] Step 1:

[0224] When a user purchases new clothes, they log in to the system using a device such as a smartphone or computer.

[0225] Step 2:

[0226] The user enters information about the clothes they purchased (e.g., "Winter Jacket," category "Outerwear," color "Black," season "Winter," etc.) into the terminal.

[0227] Step 3:

[0228] The device checks the clothing information entered and checks for any missing items.

[0229] Step 4:

[0230] The terminal transmits the confirmed information to the server.

[0231] Step 5:

[0232] The server parses the received information and stores it as a new record in the database.

[0233] Step 6:

[0234] The user uses the terminal to request a coordination suggestion that matches the user's current mood.

[0235] Step 7:

[0236] The device's camera captures the user's facial expressions, and the emotion engine analyzes the user's emotions.

[0237] Step 8:

[0238] The emotion engine identifies the user's emotion and sends the result to the server via the device. For example, the user may recognize that they are "having fun."

[0239] Step 9:

[0240] The terminal sends the user's mood request (e.g., "casual mood") together with the emotion information to the server.

[0241] Step 10:

[0242] The server queries the database and retrieves a list of the clothes owned by the user.

[0243] Step 11:

[0244] Based on the list of clothes obtained by the server and the user's emotional and mood information, an AI algorithm is used to generate the optimal outfit.

[0245] Step 12:

[0246] The server sends the generated outfit (e.g., blue jeans + white T-shirt + black jacket) to the device.

[0247] Step 13:

[0248] The terminal displays the suggested coordination to the user.

[0249] Step 14:

[0250] The server identifies missing items in the proposed outfit, for example, if the user does not have a "white T-shirt."

[0251] Step 15:

[0252] The server searches the e-commerce site based on the missing item and obtains the link for the appropriate product.

[0253] Step 16:

[0254] The server sends the acquired product link to the terminal.

[0255] Step 17:

[0256] The device displays product links to the user and encourages them to make a purchase.

[0257] This specific processing flow allows users to easily manage their clothing, receive personalized outfit suggestions based on their emotions and moods, and smoothly purchase missing items.

[0258] Example 2

[0259] 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."

[0260] Conventional outfit suggestion systems manage a user's clothing information and can suggest outfits based on the user's mood, but these suggestions are not optimized based on the user's real-time emotional state. Furthermore, many systems offer uniform suggestions for purchasing missing items without considering the user's emotions, resulting in a poor user experience. Therefore, there is a need for a system that recognizes the user's emotions, provides more personalized outfit suggestions based on those emotions, and consistently suggests the purchase of missing items.

[0261] 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.

[0262] In this invention, the server includes means for inputting information about clothing owned by a user and storing the information in a database, means for recognizing emotions from the user's facial expressions, voice, or text input, means for executing an algorithm for proposing outfits that match the user's mood based on the emotion recognition, means for identifying missing items based on the proposed outfit and obtaining links to corresponding products from external e-commerce sites, and means for suggesting more suitable items based on the user's emotions when suggesting the proposed outfit or purchasing a product. This allows the user to receive optimal outfit suggestions based on their real-time emotional state, and furthermore, purchasing suggestions for missing items are personalized based on emotions, improving the user experience.

[0263] "User" refers to an individual or organization that uses the System.

[0264] "Clothing" refers to clothing that a user owns and that is registered in the system.

[0265] "Database" refers to a collection of information stored on a server for managing a user's clothing information and other necessary data.

[0266] "Emotion recognition" refers to the technology of analyzing and recognizing a user's emotional state from facial expressions, voice, text input, etc.

[0267] "Coordination suggestions" refers to generating and suggesting optimal outfit combinations based on the user's clothing information and emotional information.

[0268] A "machine learning model" is a mathematical model that learns patterns and rules from large amounts of data and makes predictions and classifications.

[0269] An "algorithm" refers to a set of steps or a computational method for solving a problem.

[0270] "E-commerce site" refers to a website that sells and purchases goods over the Internet.

[0271] A "link" refers to a reference that connects to other content within a web page or application.

[0272] "Missing items" refer to clothing, accessories, etc. that the user does not own and needs to purchase in addition to the items in the proposed outfit.

[0273] The present invention is a system that manages information about clothing owned by a user and suggests outfits based on that information. This system improves the user experience by recognizing the user's emotions and suggesting more personalized outfits based on those emotions. The system is implemented primarily through the interaction between a server, a terminal, and the user.

[0274] First, when a user purchases new clothing, they log in to the system using a device such as a smartphone or computer. After logging in, the user enters information about the purchased clothing (e.g., category "outerwear," color "black," season "winter," etc.) into the device. The device sends this input information to the server, which then stores the received information in the database as a new record. The database stores information about all clothing owned by the user.

[0275] Next, when a user requests suggestions for outfits that match their current mood, they log in to the device using the device's camera and microphone. At this time, the emotion engine recognizes the user's emotions from their facial expressions, voice, or text input. For example, by analyzing the user's facial expressions through the camera, the emotion engine recognizes that the user is in a "happy" mood. The device analyzes this information and sends it to the server.

[0276] The server receives the emotion information and queries a database to obtain a list of all the clothes owned by the user. An AI algorithm (e.g., a machine learning model) within the server generates an optimal outfit based on the obtained list of clothes and the recognized emotion. The generated outfit suggestion (e.g., "blue jeans + white T-shirt + black jacket") is sent from the server to the device and displayed to the user.

[0277] Furthermore, the server identifies missing items based on the suggested outfit. For example, if the user does not own a white T-shirt, the server searches an e-commerce site and retrieves a link to the relevant item. This link is sent to the user's device and displayed. By clicking the link, the user is immediately redirected to the e-commerce site and can proceed with the purchase. The emotion engine can also suggest more suitable items based on the user's emotions when suggesting outfits or purchasing products.

[0278] As a concrete example, a user purchases a new "Winter Jacket" and enters the information into the system. This information is sent from the device to the server and stored in a database. The user then logs into the system, and the emotion engine analyzes their facial expressions and recognizes that they are in a "happy" mood. Based on this information, a coordination of "blue jeans + white T-shirt + black jacket" is suggested. If the user does not own a white T-shirt, the server searches an e-commerce site and sends a link to a suitable product to the device.

[0279] Prompt Sentence Examples

[0280] 1. Emotion Recognition Prompts

[0281] Which model should be used to analyze the user's facial expressions and recognize the "happy" emotion?

[0282] 2. Prompt for outfit suggestions

[0283] Please tell us the specific algorithm for how the most suitable outfit is generated based on the user's clothing data and emotional information.

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

[0285] Step 1:

[0286] The user inputs information about the newly purchased clothing.

[0287] Specific actions

[0288] A user logs into the system using a device such as a smartphone or computer and enters information about a new garment (e.g., "Winter Jacket"). This information includes the category (outerwear), color (black), season (winter), etc. Based on this user input, the device sends the data to the server.

[0289] Input: Clothing information entered by the user into the device (category, color, season)

[0290] Output: The sent clothing information is stored in a database by the server.

[0291] Step 2:

[0292] The server stores the clothing information in a database.

[0293] Specific actions

[0294] The server receives the clothing information sent from the device and stores it in the database as a new record, allowing information about the user's clothing to be managed centrally.

[0295] Input: Clothing information sent from the device

[0296] Output: New clothing record saved in the database

[0297] Step 3:

[0298] The user requests outfit suggestions and recognizes emotions.

[0299] Specific actions

[0300] The user requests "coordinate suggestions" using the device. At this time, the device's camera and microphone capture the user's facial expressions and voice. The emotion engine analyzes this captured data and recognizes the user's emotion (e.g., "fun").

[0301] Input: Coordination suggestions requested by the user and captured emotional data (facial expressions, voice)

[0302] Output: Analyzed user emotion information

[0303] Step 4:

[0304] The device transmits the emotion information to the server.

[0305] Specific actions

[0306] The emotion information analyzed by the emotion engine is sent from the device to the server, allowing the server to know the user's current emotional state.

[0307] Input: Analyzed user emotion information

[0308] Output: Emotion information is sent to the server

[0309] Step 5:

[0310] The server generates a coordinate based on the user's emotional information and clothing information.

[0311] Specific actions

[0312] Based on the received emotion information, the server queries a database to obtain a list of the user's clothing, and then uses an AI algorithm (e.g., a machine learning model) to generate an optimal outfit that combines emotion information and clothing information.

[0313] Input: User's emotional information, list of clothing owned by the user

[0314] Output: Generated outfit suggestions (e.g., "blue jeans + white T-shirt + black jacket")

[0315] Step 6:

[0316] The server sends the generated coordinates to the terminal and displays them to the user.

[0317] Specific actions

[0318] The server sends the generated coordination proposal to the terminal, which displays this information to the user.

[0319] Input: Generated outfit suggestions

[0320] Output: Coordinate information displayed to the user

[0321] Step 7:

[0322] The server detects missing items and makes purchasing suggestions.

[0323] Specific actions

[0324] The server examines the proposed outfit and identifies any missing items that the user does not own. If the missing items exist, the server searches the e-commerce site to obtain a link to purchase the product.

[0325] Input: Suggested outfits, list of clothing owned by the user

[0326] Output: Detected missing items and corresponding purchase links

[0327] Step 8:

[0328] The server sends the purchase link to the terminal and displays it to the user.

[0329] Specific actions

[0330] The server sends the acquired purchase link to the terminal, which displays this information to the user. When the user clicks the link, they are immediately redirected to the e-commerce site and can complete the product purchase procedure.

[0331] Input: Purchase link from e-commerce site

[0332] Output: The purchase link that is displayed to the user

[0333] As an example, the following prompt sentence can be used:

[0334] Prompt Sentence Examples

[0335] 1. Emotion Recognition Prompts

[0336] Which model should be used to analyze the user's facial expressions and recognize the "happy" emotion?

[0337] 2. Prompt for outfit suggestions

[0338] Please tell us the specific algorithm for how the most suitable outfit is generated based on the user's clothing data and emotional information.

[0339] (Application example 2)

[0340] 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."

[0341] While conventional clothing management systems can record information about the clothing owned by a user and suggest appropriate outfits, they are unable to provide personalized suggestions that take into account the user's emotional state. Furthermore, only some systems offer a function to provide links to purchase missing items, preventing a consistent user shopping experience.

[0342] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting information about clothing owned by the user and saving the information in a database, emotion analysis means for recognizing the user's mood, means for executing an algorithm for suggesting outfits based on the user's mood, means for identifying missing items based on the suggested outfits and obtaining links to corresponding products from external e-commerce sites, and means for managing the user's clothing information, making personalized outfit suggestions through emotion recognition, and supporting the purchase of missing items. This enables personalized outfit suggestions tailored to the user's mood and smooth purchase of missing items.

[0343] "User" refers to an individual who uses this system to manage clothing information and receive coordination suggestions.

[0344] "Clothing information" refers to detailed information about each piece of clothing owned by the user, including data such as category, color, and season.

[0345] "Database" refers to a system that systematically stores clothing information entered by users.

[0346] "Emotion analysis means" refers to technology that analyzes facial expressions, voice, or text input to recognize a user's mood or emotion.

[0347] "Algorithm" refers to the processing procedure for suggesting optimal outfits based on the user's clothing information and emotional information.

[0348] "E-commerce Site" means a website that enables the purchase of goods and services over the Internet.

[0349] "Purchase Link" refers to a URL for purchasing a particular item online.

[0350] A "machine learning model" refers to a technology that learns from large amounts of data and performs data analysis and predictions.

[0351] "Server" refers to a central computer system for processing and storing data submitted by users.

[0352] "Personalized outfits" refer to outfit combinations that are specifically suggested based on the user's personal information and emotions.

[0353] System program generation

[0354] The system program for realizing this application includes the following main functions:

[0355] 1. Clothing information management function

[0356] The user inputs information about the newly purchased clothing (category, color, season, etc.) into the system using a terminal. This information is sent from the terminal to the server, which then stores it in a database.

[0357] 2. Emotion recognition function

[0358] When a user uses the system, the emotion engine analyzes the user's facial expressions through the device's camera, recognizes the user's emotions based on facial expressions, voice, or text input, and sends that information to the server.

[0359] 3. Personalized outfit suggestions

[0360] The server uses a machine learning model to generate optimal outfits based on the acquired clothing and emotion information. These outfit suggestions are sent from the server to the device and displayed to the user.

[0361] 4. Purchasing support function

[0362] The server identifies missing items in the proposed outfit and searches e-commerce sites to obtain purchase links for the appropriate items, which are then sent from the server to the terminal and displayed to the user.

[0363] Detailed explanation of the process

[0364] The detailed procedure for implementing this system is as follows:

[0365] 1. Clothing information management

[0366] A user uses a smartphone or computer to enter information about a newly purchased garment into the system. For example, when entering information about a "Winter Jacket," the user enters details such as the category (outerwear), color (black), and season (winter). This information is sent from the device to the server, which stores it as a new record in the database.

[0367] 2. Emotion recognition

[0368] When a user logs in to the system, the emotion engine analyzes the user's facial expression through the device's camera. For example, the emotion engine captures the user's facial expression through the camera and recognizes that the user is in a "happy" mood. This emotional information is sent from the device to the server.

[0369] 3. Coordination suggestions

[0370] The server uses a machine learning model to generate optimal outfits based on the user's clothing information and emotional information stored in the database. For example, if a user is in a "happy" mood, it will suggest an outfit of "blue jeans + white T-shirt + black jacket." This suggestion is sent from the server to the user's device and displayed to them.

[0371] 4. Providing a purchasing link

[0372] The server identifies missing items in the proposed outfit and retrieves links to relevant products from e-commerce sites. For example, if the user does not own a white T-shirt, the server retrieves a link to purchase a white T-shirt from the e-commerce site. This link is then sent from the server to the device and displayed to the user.

[0373] Specific examples

[0374] If a user purchases a new jacket, "Winter Jacket," the following process occurs:

[0375] The user uses a smartphone to enter information about the "Winter Jacket" (category "Outerwear," color "Black," season "Winter") into the system. This information is sent to the server via the device and stored in the database.

[0376] When a user logs in, the emotion engine analyzes the user's facial expressions through the device's camera and recognizes that the user is in a "happy" mood. This information is sent to the server.

[0377] The server uses machine learning models to generate optimal outfits based on the user's clothing and emotional information, suggesting, for example, a combination of blue jeans, a white T-shirt, and a black jacket.

[0378] If the user does not have the white T-shirt required for the suggested outfit, the server retrieves a link to purchase the white T-shirt from an e-commerce site and provides it to the user.

[0379] Prompt Sentence Examples

[0380] "I just bought a new jacket, 'Winter Jacket'. The category is 'Outerwear', the color is 'Black', and the season is 'Winter'. Please register this in the app."

[0381] "Please recognize my facial expression on camera. I'm in a fun mood right now."

[0382] "Please suggest an outfit that matches my current mood."

[0383] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0384] Step 1:

[0385] A user uses a terminal to input information about newly purchased clothing into the system, including details such as the clothing category (e.g., outerwear), color (e.g., black), and season (e.g., winter). The terminal then sends this information to the server.

[0386] Input: Details such as clothing category, color, season, etc.

[0387] Output: Clothing information sent to the server

[0388] Step 2:

[0389] The server receives the clothing information sent from the terminal and stores it as a new record in the database, allowing for centralized management of all clothing information owned by the user.

[0390] Input: Clothing information sent from the device

[0391] Output: New clothing record saved in the database

[0392] Step 3:

[0393] The user logs in to the system, and the emotion engine analyzes the user's facial expressions through the device's camera. The emotion engine recognizes the user's emotions based on facial expressions, voice, or text input.

[0394] Input: User facial, voice, or text input data

[0395] Output: User's emotional information (e.g., "fun")

[0396] Step 4:

[0397] The device transmits the recognized emotion information to the server, which then processes the data based on the user's mood.

[0398] Input: Emotion information recognized by the device

[0399] Output: Emotion information sent to the server

[0400] Step 5:

[0401] The server acquires the user's clothing information stored in a database and emotion information sent from the terminal.

[0402] Input: Clothing information stored in the database, emotion information sent from the device

[0403] Output: Clothing information and emotion information to be processed by the server

[0404] Step 6:

[0405] The server uses a machine learning model to generate optimal outfits based on the acquired clothing and emotional information, suggesting, for example, a combination of blue jeans, a white T-shirt, and a black jacket.

[0406] Input: Clothing information, emotion information

[0407] Output: Proposed coordinates

[0408] Step 7:

[0409] The server transmits the generated coordinates to the terminal and displays them to the user.

[0410] Input: Proposed outfit information

[0411] Output: The coordinates displayed to the user

[0412] Step 8:

[0413] The server identifies the missing items based on the proposed coordinates.

[0414] Input: Proposed coordinates

[0415] Output: Missing items list

[0416] Step 9:

[0417] The server identifies the missing items and searches e-commerce sites to obtain purchasing links for the appropriate products.

[0418] Input: Missing items list

[0419] Output: Product purchase link

[0420] Step 10:

[0421] The server sends the obtained purchase link to the terminal and displays it to the user, who can use this link to purchase the missing item.

[0422] Enter: Purchasing Link

[0423] Output: The purchase link that is displayed to the user

[0424] 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.

[0425] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0426] 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.

[0427] [Second embodiment]

[0428] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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).

[0434] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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."

[0440] The present invention is a system that manages information about clothing owned by a user, suggests outfits based on that information, and provides links to acquire missing items from e-commerce sites. This system operates through mutual communication between a server, terminals, and users.

[0441] When a user wants to purchase new clothing and register it in the system, they first log in to the system using a device such as a smartphone or computer. Next, the user enters the information for "Winter Jacket" (category "Outerwear", color "Black", season "Winter"). The device sends this information to the server. The server parses the received information and saves it as a new record in the database.

[0442] When a user requests suggestions for outfits that match their current mood, the device sends the request to the server. For example, if the user specifies that they are "feeling casual," the device sends that information to the server. The server then queries the database and retrieves a list of the clothes the user owns.

[0443] The server uses an AI algorithm (e.g., a machine learning model) to generate an optimal outfit based on the retrieved clothing list and the user's specified mood. This suggested outfit (e.g., blue jeans + white T-shirt + black jacket) is sent from the server to the device, which then displays it to the user.

[0444] Furthermore, if the proposed outfit includes items that the user does not own, the server identifies the missing items. For example, if the user needs a white T-shirt to complete their outfit, the server searches an e-commerce site and retrieves a link to the relevant item. The server sends this link to the device, which then displays it to the user, allowing the user to easily purchase the missing items.

[0445] To explain with a concrete example, the process is as follows:

[0446] 1. Clothing registration: A user purchases a new jacket, "Winter Jacket," and enters it into the system. The device sends this information to the server, which stores it in a database.

[0447] 2. Coordination suggestions: When a user requests a "casual mood," the server uses an AI algorithm to suggest a coordinated outfit of "blue jeans + white T-shirt + black jacket" based on the user's clothing data. This information is sent to the device and displayed to the user.

[0448] 3. Item Purchase Suggestion: If the user does not own a white T-shirt in the suggested outfit, the server will search an e-commerce site and retrieve a link to purchase the white T-shirt. This link will be sent to the device and displayed to the user.

[0449] This system allows users to manage their clothing more efficiently, enjoy coordinating outfits to suit their mood, and smoothly purchase items they are running low on.

[0450] The processing flow will be explained below.

[0451] Step 1:

[0452] When a user purchases new clothes, they log in to the system using a device such as a smartphone or computer.

[0453] Step 2:

[0454] The user enters information about the clothes they purchased (e.g., "Winter Jacket," category "Outerwear," color "Black," season "Winter," etc.) into the terminal.

[0455] Step 3:

[0456] The device checks the clothing information entered and checks for any missing items.

[0457] Step 4:

[0458] The terminal transmits the confirmed information to the server.

[0459] Step 5:

[0460] The server parses the received information and stores it as a new record in the database.

[0461] Step 6:

[0462] The user requests suggestions for outfits that match their current mood, for example, specifying "I'm feeling casual."

[0463] Step 7:

[0464] The terminal receives a request from the user and transmits the specified mood information to the server.

[0465] Step 8:

[0466] The server retrieves the user's clothing list from the database.

[0467] Step 9:

[0468] Based on the list of clothes retrieved by the server and the specified mood, an AI algorithm (e.g., machine learning model) is used to generate the optimal outfit.

[0469] Step 10:

[0470] The server sends the generated coordinates to the terminal.

[0471] Step 11:

[0472] The terminal displays the suggested coordination to the user.

[0473] Step 12:

[0474] The server identifies missing items in the proposed outfit, for example, if the user does not have a "white T-shirt."

[0475] Step 13:

[0476] The server searches the e-commerce site based on the missing item and obtains the link for the appropriate product.

[0477] Step 14:

[0478] The server sends the acquired product link to the terminal.

[0479] Step 15:

[0480] The device displays product links to the user and encourages them to make a purchase.

[0481] The above-described processing steps enable the user to easily manage clothing, receive coordination suggestions that match their mood, and smoothly purchase any items they are missing.

[0482] Example 1

[0483] 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."

[0484] Existing clothing management systems not only efficiently manage users' clothing, but also automatically suggest outfits that match their mood and lack support for easily purchasing missing items. As a result, users have to spend time and effort selecting their daily outfits, and the process of finding and purchasing missing items is cumbersome.

[0485] 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.

[0486] In this invention, the server includes means for inputting information about clothing owned by a user and storing the information in a database, means for executing an algorithm for suggesting outfits based on the user's mood, means for identifying missing items based on the suggested outfits and obtaining links to corresponding products from external e-commerce sites, means for displaying the outfit suggestions to the user, and means for the user to check the missing items in the suggestions and purchase them via the links. This allows the user to efficiently manage their clothing, easily enjoy coordinating outfits that suit their mood, and quickly purchase missing items.

[0487] "User" refers to an individual who uses the system to manage clothing information and receive coordination suggestions.

[0488] "Clothing information" refers to attribute information such as category, color, and season of each piece of clothing owned by the user.

[0489] "Database" means an electronic data repository for storing and managing clothing information entered by Users in the System.

[0490] "Mood" refers to the user's current orientation regarding a particular style or taste.

[0491] "Coordination" refers to a fashion style suggested by combining clothing owned by the user.

[0492] An "algorithm" refers to a sequence of computational steps designed to accomplish a particular purpose.

[0493] A "generative AI model" refers to a machine learning model that uses artificial intelligence to generate new outputs from data.

[0494] "External E-Commerce Site" means a third-party website that sells products over the Internet.

[0495] "Link" refers to a Uniform Resource Locator (URL) for accessing a specific web page on the Internet.

[0496] "Means for displaying suggestions to the user" refers to a method for displaying the results of coordination and information about missing items on the screen of the user's terminal.

[0497] "Means to purchase" refers to the method by which a user can purchase the missing item using the suggested link.

[0498] The present invention is a system that manages information about clothing owned by a user, suggests outfits based on that information, and provides links to acquire missing items from e-commerce sites. This system operates through mutual communication between a server, terminals, and users.

[0499] First, when a user purchases new clothes and wants to register them in the system, they log in to the system using a device such as a smartphone or computer. The user opens an application (e.g., a closet management app) and enters their username and password on the login screen to access the system.

[0500] Next, the user enters information about the "Winter Jacket" (category "Outerwear", color "Black", season "Winter"). The user enters details such as category, color, and season on the "Register Clothing Information" page and clicks the "Register" button. The device sends this input information to the server. The device converts the user's input into JSON format and sends an HTTP request to the server.

[0501] The server parses the received information and saves it as a new record in the database. The server receives the HTTP request, parses the JSON data, and adds the parsed data to the database (e.g. MySQL, PostgreSQL) as a new record.

[0502] When a user requests suggestions for outfits that match their current mood, they select a mood, such as "casual," from the menu on the "Outfit Suggestions" page within the application and click the "Request" button. The device then sends the request to the server. The device then converts the user's selections into JSON format and sends an HTTP request to the server.

[0503] The server queries the database and retrieves a list of clothes owned by the user. The server executes the database query and retrieves the user's clothing list. It then converts the retrieved results into JSON format. The server then uses an AI algorithm (e.g., a machine learning model) to generate an optimal outfit based on the retrieved clothing list and the specified mood. The server invokes an AI model (e.g., a TensorFlow model) and generates an optimal outfit (e.g., "blue jeans + white T-shirt + black jacket") using the clothing list and mood information as input.

[0504] The server sends the proposed coordination to the device. The server converts the proposed coordination into JSON format and sends it to the device as an HTTP response. The device displays the coordination information to the user. The device analyzes the received data and displays the coordination on the application's UI.

[0505] Additionally, if the proposed outfit includes items that the user does not own, the server identifies the missing items. The server compares the user's clothing list with the proposed outfit and identifies the missing items (e.g., a white T-shirt). The server searches e-commerce sites and obtains links to the relevant products. The server uses an API to search e-commerce sites (e.g., online marketplaces) and obtains the URLs for the relevant products (e.g., a white T-shirt).

[0506] The server sends this link to the device, which then displays it to the user. The device then parses the link and displays it in the application's UI to notify the user, allowing the user to easily purchase the missing item.

[0507] As a concrete example, the following flow can be considered:

[0508] 1. Clothing registration: A user purchases a new jacket, "Winter Jacket," and enters it into the system. The device sends this information to the server, which stores it in a database.

[0509] 2. Coordination suggestions: When a user requests a "casual mood," the server uses an AI algorithm to suggest a coordinated outfit of "blue jeans + white T-shirt + black jacket" based on the user's clothing data. This information is sent to the device and displayed to the user.

[0510] 3. Item Purchase Suggestion: If the user does not own a white T-shirt in the suggested outfit, the server will search the e-commerce site and get a link to purchase the white T-shirt. This link will be sent to the device and displayed to the user.

[0511] An example of a prompt to input to a generative AI model is as follows:

[0512] Here is a list of clothes the user owns:

[0513] 1. Blue jeans

[0514] 2. White T-shirt

[0515] 3. Black jacket

[0516] ...

[0517] The user is in a "casual mood." Please suggest the best outfit based on this.

[0518] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0519] Step 1:

[0520] A user logs in to the system to register a new piece of clothing, a "Winter Jacket." The input is a username and password, and the output is login status information upon success. Specifically, the user opens the application on their smartphone or computer, enters their username and password on the login screen, and accesses the system.

[0521] Step 2:

[0522] The user enters information about the "Winter Jacket" (category "Outerwear", color "Black", season "Winter"). The input is detailed information about the clothing, and the output is the transmission of the input information from the user to the terminal. The user enters details such as category, color, and season on the "Register Clothing Information" page, and clicks the "Register" button. The terminal sends the input information to the server.

[0523] Step 3:

[0524] The terminal converts the user's input into JSON format and sends an HTTP request to the server. The input is the user's input data and the output is an HTTP request to the server. The terminal converts the user's input into the appropriate format and sends it over the Internet.

[0525] Step 4:

[0526] The server parses the received information and stores it as a new record in the database. The input is the data from the HTTP request, and the output is a new record stored in the database. Specifically, the server receives the HTTP request, parses the JSON data, and stores the parsed data as a new record in the database (e.g., MySQL, PostgreSQL).

[0527] Step 5:

[0528] The user requests outfit suggestions. The input is the request, such as "feeling casual," and the output is confirmation of the request. The user selects a mood from the menu on the "Outfit Suggestions" page within the application and clicks the "Request" button.

[0529] Step 6:

[0530] The terminal converts the user's request information into JSON format and sends an HTTP request to the server. The input is the user's request content and the output is an HTTP request to the server. The terminal converts the request data into the appropriate format and sends it over the Internet.

[0531] Step 7:

[0532] The server queries the database to retrieve a list of the user's clothes. The input is user identification information, such as a user ID, and the output is the user's clothes list. The server executes the database query and retrieves the user's clothes list.

[0533] Step 8:

[0534] The server uses an AI algorithm to generate the optimal outfit based on the acquired clothing list and the specified mood. The input is the user's clothing list and mood information, and the output is a suggested outfit. Specifically, the server calls an AI model (e.g., TensorFlow model) and generates the optimal outfit based on the clothing list and mood information.

[0535] Step 9:

[0536] The server sends the proposed outfits to the device. The input is the outfit information generated by the AI, and the output is an HTTP response to the device. The server converts the outfit information into JSON format and sends it to the device as an HTTP response.

[0537] Step 10:

[0538] The terminal displays the coordinate information to the user. The input is the coordinate information in JSON format received from the server, and the output is a visual display to the user. The terminal parses the received data and displays the coordinates in the application UI.

[0539] Step 11:

[0540] The server identifies items in the proposed outfit that the user does not own. The input is the user's clothing list and the proposed outfit, and the output is a list of missing items. The server matches all items in the proposed outfit against the user's clothing list to identify the missing items.

[0541] Step 12:

[0542] The server searches an external e-commerce site and retrieves links to the relevant products. The input is a list of missing items, and the output is a purchase link on an external site. The server uses an API to search an e-commerce site and retrieve links to the relevant products.

[0543] Step 13:

[0544] The server sends the acquired link to the terminal. The input is the acquired purchase link, and the output is the HTTP response to the terminal. The server converts the link to JSON format and sends it to the terminal as an HTTP response.

[0545] Step 14:

[0546] The terminal displays the link to the user. The input is the purchase link information received from the server, and the output is a visual display to the user. The terminal analyzes the received link and displays it in the application's UI to notify the user.

[0547] (Application example 1)

[0548] 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."

[0549] The objective of this invention is to efficiently manage information about clothing that users regularly own, provide optimal outfits that match their mood and situation, and enable users to easily purchase missing items. It also aims to improve the user's purchasing experience by introducing technology that allows users to virtually try on suggested outfits.

[0550] 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.

[0551] In this invention, the server includes means for inputting information about clothing owned by a user and storing the information in a database, means for executing an algorithm for suggesting outfits based on the user's mood, means for identifying missing items based on the suggested outfits and obtaining links to corresponding products from external e-commerce sites, and means for allowing the user to virtually try on the suggested outfits using augmented reality technology, thereby enabling the user to efficiently manage their clothing, enjoy coordinating outfits that match their mood, virtually try on the suggested outfits, and easily purchase missing items.

[0552] A "user" is an individual who uses the system to manage clothing information and receive coordination suggestions.

[0553] "Clothing information" is detailed information such as the category, color, and season of clothing owned by the user.

[0554] A "database" is an information storage system for saving and managing clothing information entered by users.

[0555] An "outfit" is a set of clothes that is proposed by combining multiple pieces of clothing that the user owns.

[0556] An "algorithm" is a procedure for calculating and proposing optimal outfits based on the user's mood.

[0557] A "machine learning model" is a type of artificial intelligence used to suggest optimal outfits based on a user's preferences and past data.

[0558] An "e-commerce site" is a website that sells products over the Internet.

[0559] "Link" means a URL that provides access to a particular product on an e-commerce site.

[0560] "Augmented reality technology" is a technology that displays virtual information overlaid on the real environment.

[0561] "Virtual try-on" is a process that uses augmented reality technology to make suggested outfits appear as if they are actually worn by the user.

[0562] The present invention is a system that manages information about clothing owned by a user, suggests optimal outfits using an AI algorithm, and provides links to easily purchase missing items on an e-commerce site. In a specific embodiment, the system operates in cooperation with a server, a terminal, and a user.

[0563] First, a user accesses the system using a device such as a smartphone or computer. When a user purchases new clothing and wants to register this information, they enter information such as the clothing category, color, and season. This input information is sent from the device to the server, which parses the information and stores it in a database. The database used here is, for example, a relational database such as MySQL.

[0564] Next, when the user requests outfit suggestions, the device sends the request to the server. For example, if the user specifies "I'm in a casual mood," that information is sent to the server. The server queries a database to obtain a list of the user's clothing. The server then uses an AI algorithm (for example, a machine learning model using TensorFlow) to generate the optimal outfit. The server then sends this suggested outfit to the device, which then displays it to the user.

[0565] Furthermore, if the proposed outfit includes an item that the user does not own, the server identifies the missing item and obtains a link to the corresponding product from an external e-commerce site. This link information is sent to the terminal, and the terminal displays the link to the user, allowing the user to easily purchase the missing item.

[0566] In addition, the present invention uses augmented reality (AR) technology to allow users to virtually try on the suggested outfits. This technology allows users to visually see how the suggested outfits look on their body through their smartphones or smart glasses. Examples of AR technologies used here include Apple's ARKit and Google's ARCore.

[0567] Let's add a concrete example. A user purchases a new jacket, a "Winter Jacket," and enters the information into the system. The information is stored in a database. Next, if the user requests a "casual look," the AI ​​algorithm suggests a combination of "blue jeans + white T-shirt + black jacket." The user receives this suggestion and virtually tries on the combination using smart glasses. If the user does not have the required item, a white T-shirt, the server retrieves a link to the corresponding product from an e-commerce site and displays it to the user.

[0568] An example of a prompt is:

[0569] prompt:

[0570] Consider how to achieve clothing management, AI coordination, AR try-on, and item purchase in a single application. Specifically, describe how the AI ​​model and AR technology are used when a user uses smart glasses to receive outfit suggestions based on their mood. Also, describe the back-end process for providing purchase links for missing items.

[0571] Reference output:

[0572] The application, which helps users decide what they want to wear, first stores the user's clothing data in a database via their smartphone. It then uses an AI model (using TensorFlow) to generate outfits that best fit the user's mood. The generated outfits are visually presented to the user in real time through AR technology (e.g., ARKit or ARCore) in the smart glasses. Missing items are searched for using the e-commerce site's API, and a link to purchase them is provided.

[0573] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0574] Step 1:

[0575] Users log in to the system using devices such as smartphones or computers. After successful login, users can access an interface to input new clothing information, including details such as clothing category, color, and season.

[0576] Input: User-entered details such as clothing category, color, season, etc.

[0577] Output: The entered information is sent from the device to the server.

[0578] Step 2:

[0579] The server receives the clothing information sent from the device, parses it, and stores it in a database, while also checking the data for consistency.

[0580] Input: Clothing information sent from the device

[0581] Data processing / data calculation: Parse the data, check its integrity, and save it to the database

[0582] Output: New clothing information is saved in the database

[0583] Step 3:

[0584] The user requests a coordination suggestion, and the device sends information such as the user's specified "mood" and "situation" to the server.

[0585] Input: "Mood" or "Situation" specified by the user

[0586] Output: The specified information is sent from the terminal to the server.

[0587] Step 4:

[0588] The server queries the database to retrieve a list of clothing owned by the user, including all clothing information that the user has previously registered.

[0589] Input: A database containing information about clothing owned by the user.

[0590] Data processing / data calculation: Obtain all user clothing information from the database

[0591] Output: The user's clothing list is retrieved by the server.

[0592] Step 5:

[0593] The server runs an AI algorithm that uses a machine learning model (using TensorFlow, for example) to generate the optimal outfit based on the clothing list and the user's specified "mood."

[0594] Input: User's clothing list, user-specified "mood"

[0595] Data processing / data calculation: Generate optimal outfits using machine learning models

[0596] Output: The generated coordinates are sent to the server

[0597] Step 6:

[0598] The proposed coordinates are sent to the terminal, which displays this information to the user.

[0599] Input: Generated coordinates

[0600] Output: The suggested outfits are displayed on the device.

[0601] Step 7:

[0602] To allow users to virtually try on suggested outfits, the device uses augmented reality technology (such as ARKit or ARCore) to overlay the suggested items onto the user's camera image.

[0603] Input: User's camera footage, suggested coordinates

[0604] Output: The virtual try-on video is displayed on the device.

[0605] Step 8:

[0606] The server identifies items in the proposed outfit that the user does not own and uses the e-commerce site's API to obtain links to those products.

[0607] Input: Suggested outfits, information on items the user does not own

[0608] Data processing / data calculation: Call the API of an e-commerce site to obtain product links

[0609] Output: The link of the retrieved product is saved on the server.

[0610] Step 9:

[0611] The server sends the link of the acquired product to the terminal, and the terminal displays the link to the user, who can click the link to purchase the missing item.

[0612] Input: Link of the retrieved product

[0613] Output: A link to purchase the product will be displayed on the device.

[0614] 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.

[0615] This system manages information about clothing owned by a user, suggests outfits based on that information, and provides links to acquire missing items from e-commerce sites. By combining this system with an emotion engine that recognizes the user's emotions, the system makes more personalized suggestions. This system is realized through the interaction of a server, terminals, and users.

[0616] First, when a user purchases new clothing, they log in to the system using a device such as a smartphone or computer. The user enters information about the "Winter Jacket" (category "Outerwear," color "Black," season "Winter," etc.) into the device. The device sends this information to the server, which saves it as a new record in the database.

[0617] Next, when the user requests suggestions for outfits that match their current mood, the emotion engine recognizes the user's emotion from their facial expression, voice, or text input. For example, by analyzing the user's facial expression through a camera, the emotion engine recognizes that the user is in a "happy" mood. This emotion information is sent from the device to the server.

[0618] The server queries the database to obtain a list of the clothes owned by the user. Based on the obtained list of clothes, the recognized emotion, and the specified mood, an AI algorithm (e.g., a machine learning model) is used to generate an optimal outfit. This outfit suggestion (e.g., blue jeans + white T-shirt + black jacket) is sent from the server to the device and displayed to the user.

[0619] Furthermore, the server identifies missing items in the proposed outfit. For example, if the user does not own a white T-shirt, the server searches an e-commerce site to obtain a link to the corresponding item. This link is sent to the device and displayed to the user. The emotion engine can also suggest more suitable items based on the user's emotions when suggesting outfits or purchasing products.

[0620] To explain with a concrete example, the process is as follows:

[0621] 1. Clothing registration: A user purchases a new jacket, "Winter Jacket," and enters it into the system. The device sends this information to the server, which stores it in a database.

[0622] 2. Emotion recognition and outfit suggestions: When a user logs in to the system, the emotion engine analyzes the user's facial expressions via the device's camera. The emotion engine recognizes that the user is in a "happy" mood and sends this information to the server. The server then uses an AI algorithm to suggest an outfit combination of "blue jeans + white T-shirt + black jacket" based on the user's clothing data and emotional information. This suggestion is sent to the device and displayed to the user.

[0623] 3. Item Purchase Suggestion: If the user does not own a white T-shirt in the suggested outfit, the server will search an e-commerce site and retrieve a link to purchase the white T-shirt. This link will be sent to the device and displayed to the user. The server can also check whether the purchase suggestion is appropriate based on the user's emotions recognized by the emotion engine.

[0624] The system allows users to easily manage their clothing, receive more personalized outfit suggestions through emotion recognition, and smoothly purchase missing items.

[0625] The processing flow will be explained below.

[0626] Step 1:

[0627] When a user purchases new clothes, they log in to the system using a device such as a smartphone or computer.

[0628] Step 2:

[0629] The user enters information about the clothes they purchased (e.g., "Winter Jacket," category "Outerwear," color "Black," season "Winter," etc.) into the terminal.

[0630] Step 3:

[0631] The device checks the clothing information entered and checks for any missing items.

[0632] Step 4:

[0633] The terminal transmits the confirmed information to the server.

[0634] Step 5:

[0635] The server parses the received information and stores it as a new record in the database.

[0636] Step 6:

[0637] The user uses the terminal to request a coordination suggestion that matches the user's current mood.

[0638] Step 7:

[0639] The device's camera captures the user's facial expressions, and the emotion engine analyzes the user's emotions.

[0640] Step 8:

[0641] The emotion engine identifies the user's emotion and sends the result to the server via the device. For example, the user may recognize that they are "having fun."

[0642] Step 9:

[0643] The terminal sends the user's mood request (e.g., "casual mood") together with the emotion information to the server.

[0644] Step 10:

[0645] The server queries the database and retrieves a list of the clothes owned by the user.

[0646] Step 11:

[0647] Based on the list of clothes obtained by the server and the user's emotional and mood information, an AI algorithm is used to generate the optimal outfit.

[0648] Step 12:

[0649] The server sends the generated outfit (e.g., blue jeans + white T-shirt + black jacket) to the device.

[0650] Step 13:

[0651] The terminal displays the suggested coordination to the user.

[0652] Step 14:

[0653] The server identifies missing items in the proposed outfit, for example, if the user does not have a "white T-shirt."

[0654] Step 15:

[0655] The server searches the e-commerce site based on the missing item and obtains the link for the appropriate product.

[0656] Step 16:

[0657] The server sends the acquired product link to the terminal.

[0658] Step 17:

[0659] The device displays product links to the user and encourages them to make a purchase.

[0660] This specific processing flow allows users to easily manage their clothing, receive personalized outfit suggestions based on their emotions and moods, and smoothly purchase missing items.

[0661] Example 2

[0662] 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."

[0663] Conventional outfit suggestion systems manage a user's clothing information and can suggest outfits based on the user's mood, but these suggestions are not optimized based on the user's real-time emotional state. Furthermore, many systems offer uniform suggestions for purchasing missing items without considering the user's emotions, resulting in a poor user experience. Therefore, there is a need for a system that recognizes the user's emotions, provides more personalized outfit suggestions based on those emotions, and consistently suggests the purchase of missing items.

[0664] 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.

[0665] In this invention, the server includes means for inputting information about clothing owned by a user and storing the information in a database, means for recognizing emotions from the user's facial expressions, voice, or text input, means for executing an algorithm for proposing outfits that match the user's mood based on the emotion recognition, means for identifying missing items based on the proposed outfit and obtaining links to corresponding products from external e-commerce sites, and means for suggesting more suitable items based on the user's emotions when suggesting the proposed outfit or purchasing a product. This allows the user to receive optimal outfit suggestions based on their real-time emotional state, and furthermore, purchasing suggestions for missing items are personalized based on emotions, improving the user experience.

[0666] "User" refers to an individual or organization that uses the System.

[0667] "Clothing" refers to clothing that a user owns and that is registered in the system.

[0668] "Database" refers to a collection of information stored on a server for managing a user's clothing information and other necessary data.

[0669] "Emotion recognition" refers to the technology of analyzing and recognizing a user's emotional state from facial expressions, voice, text input, etc.

[0670] "Coordination suggestions" refers to generating and suggesting optimal outfit combinations based on the user's clothing information and emotional information.

[0671] A "machine learning model" is a mathematical model that learns patterns and rules from large amounts of data and makes predictions and classifications.

[0672] An "algorithm" refers to a set of steps or a computational method for solving a problem.

[0673] "E-commerce site" refers to a website that sells and purchases goods over the Internet.

[0674] A "link" refers to a reference that connects to other content within a web page or application.

[0675] "Missing items" refer to clothing, accessories, etc. that the user does not own and needs to purchase in addition to the items in the proposed outfit.

[0676] The present invention is a system that manages information about clothing owned by a user and suggests outfits based on that information. This system improves the user experience by recognizing the user's emotions and suggesting more personalized outfits based on those emotions. The system is implemented primarily through the interaction between a server, a terminal, and the user.

[0677] First, when a user purchases new clothing, they log in to the system using a device such as a smartphone or computer. After logging in, the user enters information about the purchased clothing (e.g., category "outerwear," color "black," season "winter," etc.) into the device. The device sends this input information to the server, which then stores the received information in the database as a new record. The database stores information about all clothing owned by the user.

[0678] Next, when a user requests suggestions for outfits that match their current mood, they log in to the device using the device's camera and microphone. At this time, the emotion engine recognizes the user's emotions from their facial expressions, voice, or text input. For example, by analyzing the user's facial expressions through the camera, the emotion engine recognizes that the user is in a "happy" mood. The device analyzes this information and sends it to the server.

[0679] The server receives the emotion information and queries a database to obtain a list of all the clothes owned by the user. An AI algorithm (e.g., a machine learning model) within the server generates an optimal outfit based on the obtained list of clothes and the recognized emotion. The generated outfit suggestion (e.g., "blue jeans + white T-shirt + black jacket") is sent from the server to the device and displayed to the user.

[0680] Furthermore, the server identifies missing items based on the suggested outfit. For example, if the user does not own a white T-shirt, the server searches an e-commerce site and retrieves a link to the relevant item. This link is sent to the user's device and displayed. By clicking the link, the user is immediately redirected to the e-commerce site and can proceed with the purchase. The emotion engine can also suggest more suitable items based on the user's emotions when suggesting outfits or purchasing products.

[0681] As a concrete example, a user purchases a new "Winter Jacket" and enters the information into the system. This information is sent from the device to the server and stored in a database. The user then logs into the system, and the emotion engine analyzes their facial expressions and recognizes that they are in a "happy" mood. Based on this information, a coordination of "blue jeans + white T-shirt + black jacket" is suggested. If the user does not own a white T-shirt, the server searches an e-commerce site and sends a link to a suitable product to the device.

[0682] Prompt Sentence Examples

[0683] 1. Emotion Recognition Prompts

[0684] Which model should be used to analyze the user's facial expressions and recognize the "happy" emotion?

[0685] 2. Prompt for outfit suggestions

[0686] Please tell us the specific algorithm for how the most suitable outfit is generated based on the user's clothing data and emotional information.

[0687] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0688] Step 1:

[0689] The user inputs information about the newly purchased clothing.

[0690] Specific actions

[0691] A user logs into the system using a device such as a smartphone or computer and enters information about a new garment (e.g., "Winter Jacket"). This information includes the category (outerwear), color (black), season (winter), etc. Based on this user input, the device sends the data to the server.

[0692] Input: Clothing information entered by the user into the device (category, color, season)

[0693] Output: The sent clothing information is stored in a database by the server.

[0694] Step 2:

[0695] The server stores the clothing information in a database.

[0696] Specific actions

[0697] The server receives the clothing information sent from the device and stores it in the database as a new record, allowing information about the user's clothing to be managed centrally.

[0698] Input: Clothing information sent from the device

[0699] Output: New clothing record saved in the database

[0700] Step 3:

[0701] The user requests outfit suggestions and recognizes emotions.

[0702] Specific actions

[0703] The user requests "coordinate suggestions" using the device. At this time, the device's camera and microphone capture the user's facial expressions and voice. The emotion engine analyzes this captured data and recognizes the user's emotion (e.g., "fun").

[0704] Input: Coordination suggestions requested by the user and captured emotional data (facial expressions, voice)

[0705] Output: Analyzed user emotion information

[0706] Step 4:

[0707] The device transmits the emotion information to the server.

[0708] Specific actions

[0709] The emotion information analyzed by the emotion engine is sent from the device to the server, allowing the server to know the user's current emotional state.

[0710] Input: Analyzed user emotion information

[0711] Output: Emotion information is sent to the server

[0712] Step 5:

[0713] The server generates a coordinate based on the user's emotional information and clothing information.

[0714] Specific actions

[0715] Based on the received emotion information, the server queries a database to obtain a list of the user's clothing, and then uses an AI algorithm (e.g., a machine learning model) to generate an optimal outfit that combines emotion information and clothing information.

[0716] Input: User's emotional information, list of clothing owned by the user

[0717] Output: Generated outfit suggestions (e.g., "blue jeans + white T-shirt + black jacket")

[0718] Step 6:

[0719] The server sends the generated coordinates to the terminal and displays them to the user.

[0720] Specific actions

[0721] The server sends the generated coordination proposal to the terminal, which displays this information to the user.

[0722] Input: Generated outfit suggestions

[0723] Output: Coordinate information displayed to the user

[0724] Step 7:

[0725] The server detects missing items and makes purchasing suggestions.

[0726] Specific actions

[0727] The server examines the proposed outfit and identifies any missing items that the user does not own. If the missing items exist, the server searches the e-commerce site to obtain a link to purchase the product.

[0728] Input: Suggested outfits, list of clothing owned by the user

[0729] Output: Detected missing items and corresponding purchase links

[0730] Step 8:

[0731] The server sends the purchase link to the terminal and displays it to the user.

[0732] Specific actions

[0733] The server sends the acquired purchase link to the terminal, which displays this information to the user. When the user clicks the link, they are immediately redirected to the e-commerce site and can complete the product purchase procedure.

[0734] Input: Purchase link from e-commerce site

[0735] Output: The purchase link that is displayed to the user

[0736] As an example, the following prompt sentence can be used:

[0737] Prompt Sentence Examples

[0738] 1. Emotion Recognition Prompts

[0739] Which model should be used to analyze the user's facial expressions and recognize the "happy" emotion?

[0740] 2. Prompt for outfit suggestions

[0741] Please tell us the specific algorithm for how the most suitable outfit is generated based on the user's clothing data and emotional information.

[0742] (Application example 2)

[0743] 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."

[0744] While conventional clothing management systems can record information about the clothing owned by a user and suggest appropriate outfits, they are unable to provide personalized suggestions that take into account the user's emotional state. Furthermore, only some systems offer a function to provide links to purchase missing items, preventing a consistent user shopping experience.

[0745] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting information about clothing owned by the user and saving the information in a database, emotion analysis means for recognizing the user's mood, means for executing an algorithm for suggesting outfits based on the user's mood, means for identifying missing items based on the suggested outfits and obtaining links to corresponding products from external e-commerce sites, and means for managing the user's clothing information, making personalized outfit suggestions through emotion recognition, and supporting the purchase of missing items. This enables personalized outfit suggestions tailored to the user's mood and smooth purchase of missing items.

[0746] "User" refers to an individual who uses this system to manage clothing information and receive coordination suggestions.

[0747] "Clothing information" refers to detailed information about each piece of clothing owned by the user, including data such as category, color, and season.

[0748] "Database" refers to a system that systematically stores clothing information entered by users.

[0749] "Emotion analysis means" refers to technology that analyzes facial expressions, voice, or text input to recognize a user's mood or emotion.

[0750] "Algorithm" refers to the processing procedure for suggesting optimal outfits based on the user's clothing information and emotional information.

[0751] "E-commerce Site" means a website that enables the purchase of goods and services over the Internet.

[0752] "Purchase Link" refers to a URL for purchasing a particular item online.

[0753] A "machine learning model" refers to a technology that learns from large amounts of data and performs data analysis and predictions.

[0754] "Server" refers to a central computer system for processing and storing data submitted by users.

[0755] "Personalized outfits" refer to outfit combinations that are specifically suggested based on the user's personal information and emotions.

[0756] System program generation

[0757] The system program for realizing this application includes the following main functions:

[0758] 1. Clothing information management function

[0759] The user inputs information about the newly purchased clothing (category, color, season, etc.) into the system using a terminal. This information is sent from the terminal to the server, which then stores it in a database.

[0760] 2. Emotion recognition function

[0761] When a user uses the system, the emotion engine analyzes the user's facial expressions through the device's camera, recognizes the user's emotions based on facial expressions, voice, or text input, and sends that information to the server.

[0762] 3. Personalized outfit suggestions

[0763] The server uses a machine learning model to generate optimal outfits based on the acquired clothing and emotion information. These outfit suggestions are sent from the server to the device and displayed to the user.

[0764] 4. Purchasing support function

[0765] The server identifies missing items in the proposed outfit and searches e-commerce sites to obtain purchase links for the appropriate items, which are then sent from the server to the terminal and displayed to the user.

[0766] Detailed explanation of the process

[0767] The detailed procedure for implementing this system is as follows:

[0768] 1. Clothing information management

[0769] A user uses a smartphone or computer to enter information about a newly purchased garment into the system. For example, when entering information about a "Winter Jacket," the user enters details such as the category (outerwear), color (black), and season (winter). This information is sent from the device to the server, which stores it as a new record in the database.

[0770] 2. Emotion recognition

[0771] When a user logs in to the system, the emotion engine analyzes the user's facial expression through the device's camera. For example, the emotion engine captures the user's facial expression through the camera and recognizes that the user is in a "happy" mood. This emotional information is sent from the device to the server.

[0772] 3. Coordination suggestions

[0773] The server uses a machine learning model to generate optimal outfits based on the user's clothing information and emotional information stored in the database. For example, if a user is in a "happy" mood, it will suggest an outfit of "blue jeans + white T-shirt + black jacket." This suggestion is sent from the server to the user's device and displayed to them.

[0774] 4. Providing a purchasing link

[0775] The server identifies missing items in the proposed outfit and retrieves links to relevant products from e-commerce sites. For example, if the user does not own a white T-shirt, the server retrieves a link to purchase a white T-shirt from the e-commerce site. This link is then sent from the server to the device and displayed to the user.

[0776] Specific examples

[0777] If a user purchases a new jacket, "Winter Jacket," the following process occurs:

[0778] The user uses a smartphone to enter information about the "Winter Jacket" (category "Outerwear," color "Black," season "Winter") into the system. This information is sent to the server via the device and stored in the database.

[0779] When a user logs in, the emotion engine analyzes the user's facial expressions through the device's camera and recognizes that the user is in a "happy" mood. This information is sent to the server.

[0780] The server uses machine learning models to generate optimal outfits based on the user's clothing and emotional information, suggesting, for example, a combination of blue jeans, a white T-shirt, and a black jacket.

[0781] If the user does not have the white T-shirt required for the suggested outfit, the server retrieves a link to purchase the white T-shirt from an e-commerce site and provides it to the user.

[0782] Prompt Sentence Examples

[0783] "I just bought a new jacket, 'Winter Jacket'. The category is 'Outerwear', the color is 'Black', and the season is 'Winter'. Please register this in the app."

[0784] "Please recognize my facial expression on camera. I'm in a fun mood right now."

[0785] "Please suggest an outfit that matches my current mood."

[0786] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0787] Step 1:

[0788] A user uses a terminal to input information about newly purchased clothing into the system, including details such as the clothing category (e.g., outerwear), color (e.g., black), and season (e.g., winter). The terminal then sends this information to the server.

[0789] Input: Details such as clothing category, color, season, etc.

[0790] Output: Clothing information sent to the server

[0791] Step 2:

[0792] The server receives the clothing information sent from the terminal and stores it as a new record in the database, allowing for centralized management of all clothing information owned by the user.

[0793] Input: Clothing information sent from the device

[0794] Output: New clothing record saved in the database

[0795] Step 3:

[0796] The user logs in to the system, and the emotion engine analyzes the user's facial expressions through the device's camera. The emotion engine recognizes the user's emotions based on facial expressions, voice, or text input.

[0797] Input: User facial, voice, or text input data

[0798] Output: User's emotional information (e.g., "fun")

[0799] Step 4:

[0800] The device transmits the recognized emotion information to the server, which then processes the data based on the user's mood.

[0801] Input: Emotion information recognized by the device

[0802] Output: Emotion information sent to the server

[0803] Step 5:

[0804] The server acquires the user's clothing information stored in a database and emotion information sent from the terminal.

[0805] Input: Clothing information stored in the database, emotion information sent from the device

[0806] Output: Clothing information and emotion information to be processed by the server

[0807] Step 6:

[0808] The server uses a machine learning model to generate optimal outfits based on the acquired clothing and emotional information, suggesting, for example, a combination of blue jeans, a white T-shirt, and a black jacket.

[0809] Input: Clothing information, emotion information

[0810] Output: Proposed coordinates

[0811] Step 7:

[0812] The server transmits the generated coordinates to the terminal and displays them to the user.

[0813] Input: Proposed outfit information

[0814] Output: The coordinates displayed to the user

[0815] Step 8:

[0816] The server identifies the missing items based on the proposed coordinates.

[0817] Input: Proposed coordinates

[0818] Output: Missing items list

[0819] Step 9:

[0820] The server identifies the missing items and searches e-commerce sites to obtain purchasing links for the appropriate products.

[0821] Input: Missing items list

[0822] Output: Product purchase link

[0823] Step 10:

[0824] The server sends the obtained purchase link to the terminal and displays it to the user, who can use this link to purchase the missing item.

[0825] Enter: Purchasing Link

[0826] Output: The purchase link that is displayed to the user

[0827] 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.

[0828] 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.

[0829] 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.

[0830] [Third embodiment]

[0831] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0832] 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.

[0833] 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).

[0834] 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.

[0835] 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.

[0836] 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).

[0837] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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."

[0843] The present invention is a system that manages information about clothing owned by a user, suggests outfits based on that information, and provides links to acquire missing items from e-commerce sites. This system operates through mutual communication between a server, terminals, and users.

[0844] When a user wants to purchase new clothing and register it in the system, they first log in to the system using a device such as a smartphone or computer. Next, the user enters the information for "Winter Jacket" (category "Outerwear", color "Black", season "Winter"). The device sends this information to the server. The server parses the received information and saves it as a new record in the database.

[0845] When a user requests suggestions for outfits that match their current mood, the device sends the request to the server. For example, if the user specifies that they are "feeling casual," the device sends that information to the server. The server then queries the database and retrieves a list of the clothes the user owns.

[0846] The server uses an AI algorithm (e.g., a machine learning model) to generate an optimal outfit based on the retrieved clothing list and the user's specified mood. This suggested outfit (e.g., blue jeans + white T-shirt + black jacket) is sent from the server to the device, which then displays it to the user.

[0847] Furthermore, if the proposed outfit includes items that the user does not own, the server identifies the missing items. For example, if the user needs a white T-shirt to complete their outfit, the server searches an e-commerce site and retrieves a link to the relevant item. The server sends this link to the device, which then displays it to the user, allowing the user to easily purchase the missing items.

[0848] To explain with a concrete example, the process is as follows:

[0849] 1. Clothing registration: A user purchases a new jacket, "Winter Jacket," and enters it into the system. The device sends this information to the server, which stores it in a database.

[0850] 2. Coordination suggestions: When a user requests a "casual mood," the server uses an AI algorithm to suggest a coordinated outfit of "blue jeans + white T-shirt + black jacket" based on the user's clothing data. This information is sent to the device and displayed to the user.

[0851] 3. Item Purchase Suggestion: If the user does not own a white T-shirt in the suggested outfit, the server will search an e-commerce site and retrieve a link to purchase the white T-shirt. This link will be sent to the device and displayed to the user.

[0852] This system allows users to manage their clothing more efficiently, enjoy coordinating outfits to suit their mood, and smoothly purchase items they are running low on.

[0853] The processing flow will be explained below.

[0854] Step 1:

[0855] When a user purchases new clothes, they log in to the system using a device such as a smartphone or computer.

[0856] Step 2:

[0857] The user enters information about the clothes they purchased (e.g., "Winter Jacket," category "Outerwear," color "Black," season "Winter," etc.) into the terminal.

[0858] Step 3:

[0859] The device checks the clothing information entered and checks for any missing items.

[0860] Step 4:

[0861] The terminal transmits the confirmed information to the server.

[0862] Step 5:

[0863] The server parses the received information and stores it as a new record in the database.

[0864] Step 6:

[0865] The user requests suggestions for outfits that match their current mood, for example, specifying "I'm feeling casual."

[0866] Step 7:

[0867] The terminal receives a request from the user and transmits the specified mood information to the server.

[0868] Step 8:

[0869] The server retrieves the user's clothing list from the database.

[0870] Step 9:

[0871] Based on the list of clothes retrieved by the server and the specified mood, an AI algorithm (e.g., machine learning model) is used to generate the optimal outfit.

[0872] Step 10:

[0873] The server sends the generated coordinates to the terminal.

[0874] Step 11:

[0875] The terminal displays the suggested coordination to the user.

[0876] Step 12:

[0877] The server identifies missing items in the proposed outfit, for example, if the user does not have a "white T-shirt."

[0878] Step 13:

[0879] The server searches the e-commerce site based on the missing item and obtains the link for the appropriate product.

[0880] Step 14:

[0881] The server sends the acquired product link to the terminal.

[0882] Step 15:

[0883] The device displays product links to the user and encourages them to make a purchase.

[0884] The above-described processing steps enable the user to easily manage clothing, receive coordination suggestions that match their mood, and smoothly purchase any items they are missing.

[0885] Example 1

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

[0887] Existing clothing management systems not only efficiently manage users' clothing, but also automatically suggest outfits that match their mood and lack support for easily purchasing missing items. As a result, users have to spend time and effort selecting their daily outfits, and the process of finding and purchasing missing items is cumbersome.

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

[0889] In this invention, the server includes means for inputting information about clothing owned by a user and storing the information in a database, means for executing an algorithm for suggesting outfits based on the user's mood, means for identifying missing items based on the suggested outfits and obtaining links to corresponding products from external e-commerce sites, means for displaying the outfit suggestions to the user, and means for the user to check the missing items in the suggestions and purchase them via the links. This allows the user to efficiently manage their clothing, easily enjoy coordinating outfits that suit their mood, and quickly purchase missing items.

[0890] "User" refers to an individual who uses the system to manage clothing information and receive coordination suggestions.

[0891] "Clothing information" refers to attribute information such as category, color, and season of each piece of clothing owned by the user.

[0892] "Database" means an electronic data repository for storing and managing clothing information entered by Users in the System.

[0893] "Mood" refers to the user's current orientation regarding a particular style or taste.

[0894] "Coordination" refers to a fashion style suggested by combining clothing owned by the user.

[0895] An "algorithm" refers to a sequence of computational steps designed to accomplish a particular purpose.

[0896] A "generative AI model" refers to a machine learning model that uses artificial intelligence to generate new outputs from data.

[0897] "External E-Commerce Site" means a third-party website that sells products over the Internet.

[0898] "Link" refers to a Uniform Resource Locator (URL) for accessing a specific web page on the Internet.

[0899] "Means for displaying suggestions to the user" refers to a method for displaying the results of coordination and information about missing items on the screen of the user's terminal.

[0900] "Means to purchase" refers to the method by which a user can purchase the missing item using the suggested link.

[0901] The present invention is a system that manages information about clothing owned by a user, suggests outfits based on that information, and provides links to acquire missing items from e-commerce sites. This system operates through mutual communication between a server, terminals, and users.

[0902] First, when a user purchases new clothes and wants to register them in the system, they log in to the system using a device such as a smartphone or computer. The user opens an application (e.g., a closet management app) and enters their username and password on the login screen to access the system.

[0903] Next, the user enters information about the "Winter Jacket" (category "Outerwear", color "Black", season "Winter"). The user enters details such as category, color, and season on the "Register Clothing Information" page and clicks the "Register" button. The device sends this input information to the server. The device converts the user's input into JSON format and sends an HTTP request to the server.

[0904] The server parses the received information and saves it as a new record in the database. The server receives the HTTP request, parses the JSON data, and adds the parsed data to the database (e.g. MySQL, PostgreSQL) as a new record.

[0905] When a user requests suggestions for outfits that match their current mood, they select a mood, such as "casual," from the menu on the "Outfit Suggestions" page within the application and click the "Request" button. The device then sends the request to the server. The device then converts the user's selections into JSON format and sends an HTTP request to the server.

[0906] The server queries the database and retrieves a list of clothes owned by the user. The server executes the database query and retrieves the user's clothing list. It then converts the retrieved results into JSON format. The server then uses an AI algorithm (e.g., a machine learning model) to generate an optimal outfit based on the retrieved clothing list and the specified mood. The server invokes an AI model (e.g., a TensorFlow model) and generates an optimal outfit (e.g., "blue jeans + white T-shirt + black jacket") using the clothing list and mood information as input.

[0907] The server sends the proposed coordination to the device. The server converts the proposed coordination into JSON format and sends it to the device as an HTTP response. The device displays the coordination information to the user. The device analyzes the received data and displays the coordination on the application's UI.

[0908] Additionally, if the proposed outfit includes items that the user does not own, the server identifies the missing items. The server compares the user's clothing list with the proposed outfit and identifies the missing items (e.g., a white T-shirt). The server searches e-commerce sites and obtains links to the relevant products. The server uses an API to search e-commerce sites (e.g., online marketplaces) and obtains the URLs for the relevant products (e.g., a white T-shirt).

[0909] The server sends this link to the device, which then displays it to the user. The device then parses the link and displays it in the application's UI to notify the user, allowing the user to easily purchase the missing item.

[0910] As a concrete example, the following flow can be considered:

[0911] 1. Clothing registration: A user purchases a new jacket, "Winter Jacket," and enters it into the system. The device sends this information to the server, which stores it in a database.

[0912] 2. Coordination suggestions: When a user requests a "casual mood," the server uses an AI algorithm to suggest a coordinated outfit of "blue jeans + white T-shirt + black jacket" based on the user's clothing data. This information is sent to the device and displayed to the user.

[0913] 3. Item Purchase Suggestion: If the user does not own a white T-shirt in the suggested outfit, the server will search the e-commerce site and get a link to purchase the white T-shirt. This link will be sent to the device and displayed to the user.

[0914] An example of a prompt to input to a generative AI model is as follows:

[0915] Here is a list of clothes the user owns:

[0916] 1. Blue jeans

[0917] 2. White T-shirt

[0918] 3. Black jacket

[0919] ...

[0920] The user is in a "casual mood." Please suggest the best outfit based on this.

[0921] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0922] Step 1:

[0923] A user logs in to the system to register a new piece of clothing, a "Winter Jacket." The input is a username and password, and the output is login status information upon success. Specifically, the user opens the application on their smartphone or computer, enters their username and password on the login screen, and accesses the system.

[0924] Step 2:

[0925] The user enters information about the "Winter Jacket" (category "Outerwear", color "Black", season "Winter"). The input is detailed information about the clothing, and the output is the transmission of the input information from the user to the terminal. The user enters details such as category, color, and season on the "Register Clothing Information" page, and clicks the "Register" button. The terminal sends the input information to the server.

[0926] Step 3:

[0927] The terminal converts the user's input into JSON format and sends an HTTP request to the server. The input is the user's input data and the output is an HTTP request to the server. The terminal converts the user's input into the appropriate format and sends it over the Internet.

[0928] Step 4:

[0929] The server parses the received information and stores it as a new record in the database. The input is the data from the HTTP request, and the output is a new record stored in the database. Specifically, the server receives the HTTP request, parses the JSON data, and stores the parsed data as a new record in the database (e.g., MySQL, PostgreSQL).

[0930] Step 5:

[0931] The user requests outfit suggestions. The input is the request, such as "feeling casual," and the output is confirmation of the request. The user selects a mood from the menu on the "Outfit Suggestions" page within the application and clicks the "Request" button.

[0932] Step 6:

[0933] The terminal converts the user's request information into JSON format and sends an HTTP request to the server. The input is the user's request content and the output is an HTTP request to the server. The terminal converts the request data into the appropriate format and sends it over the Internet.

[0934] Step 7:

[0935] The server queries the database to retrieve a list of the user's clothes. The input is user identification information, such as a user ID, and the output is the user's clothes list. The server executes the database query and retrieves the user's clothes list.

[0936] Step 8:

[0937] The server uses an AI algorithm to generate the optimal outfit based on the acquired clothing list and the specified mood. The input is the user's clothing list and mood information, and the output is a suggested outfit. Specifically, the server calls an AI model (e.g., TensorFlow model) and generates the optimal outfit based on the clothing list and mood information.

[0938] Step 9:

[0939] The server sends the proposed outfits to the device. The input is the outfit information generated by the AI, and the output is an HTTP response to the device. The server converts the outfit information into JSON format and sends it to the device as an HTTP response.

[0940] Step 10:

[0941] The terminal displays the coordinate information to the user. The input is the coordinate information in JSON format received from the server, and the output is a visual display to the user. The terminal parses the received data and displays the coordinates in the application UI.

[0942] Step 11:

[0943] The server identifies items in the proposed outfit that the user does not own. The input is the user's clothing list and the proposed outfit, and the output is a list of missing items. The server matches all items in the proposed outfit against the user's clothing list to identify the missing items.

[0944] Step 12:

[0945] The server searches an external e-commerce site and retrieves links to the relevant products. The input is a list of missing items, and the output is a purchase link on an external site. The server uses an API to search an e-commerce site and retrieve links to the relevant products.

[0946] Step 13:

[0947] The server sends the acquired link to the terminal. The input is the acquired purchase link, and the output is the HTTP response to the terminal. The server converts the link to JSON format and sends it to the terminal as an HTTP response.

[0948] Step 14:

[0949] The terminal displays the link to the user. The input is the purchase link information received from the server, and the output is a visual display to the user. The terminal analyzes the received link and displays it in the application's UI to notify the user.

[0950] (Application example 1)

[0951] 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."

[0952] The objective of this invention is to efficiently manage information about clothing that users regularly own, provide optimal outfits that match their mood and situation, and enable users to easily purchase missing items. It also aims to improve the user's purchasing experience by introducing technology that allows users to virtually try on suggested outfits.

[0953] 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.

[0954] In this invention, the server includes means for inputting information about clothing owned by a user and storing the information in a database, means for executing an algorithm for suggesting outfits based on the user's mood, means for identifying missing items based on the suggested outfits and obtaining links to corresponding products from external e-commerce sites, and means for allowing the user to virtually try on the suggested outfits using augmented reality technology, thereby enabling the user to efficiently manage their clothing, enjoy coordinating outfits that match their mood, virtually try on the suggested outfits, and easily purchase missing items.

[0955] A "user" is an individual who uses the system to manage clothing information and receive coordination suggestions.

[0956] "Clothing information" is detailed information such as the category, color, and season of clothing owned by the user.

[0957] A "database" is an information storage system for saving and managing clothing information entered by users.

[0958] An "outfit" is a set of clothes that is proposed by combining multiple pieces of clothing that the user owns.

[0959] An "algorithm" is a procedure for calculating and proposing optimal outfits based on the user's mood.

[0960] A "machine learning model" is a type of artificial intelligence used to suggest optimal outfits based on a user's preferences and past data.

[0961] An "e-commerce site" is a website that sells products over the Internet.

[0962] "Link" means a URL that provides access to a particular product on an e-commerce site.

[0963] "Augmented reality technology" is a technology that displays virtual information overlaid on the real environment.

[0964] "Virtual try-on" is a process that uses augmented reality technology to make suggested outfits appear as if they are actually worn by the user.

[0965] The present invention is a system that manages information about clothing owned by a user, suggests optimal outfits using an AI algorithm, and provides links to easily purchase missing items on an e-commerce site. In a specific embodiment, the system operates in cooperation with a server, a terminal, and a user.

[0966] First, a user accesses the system using a device such as a smartphone or computer. When a user purchases new clothing and wants to register this information, they enter information such as the clothing category, color, and season. This input information is sent from the device to the server, which parses the information and stores it in a database. The database used here is, for example, a relational database such as MySQL.

[0967] Next, when the user requests outfit suggestions, the device sends the request to the server. For example, if the user specifies "I'm in a casual mood," that information is sent to the server. The server queries a database to obtain a list of the user's clothing. The server then uses an AI algorithm (for example, a machine learning model using TensorFlow) to generate the optimal outfit. The server then sends this suggested outfit to the device, which then displays it to the user.

[0968] Furthermore, if the proposed outfit includes an item that the user does not own, the server identifies the missing item and obtains a link to the corresponding product from an external e-commerce site. This link information is sent to the terminal, and the terminal displays the link to the user, allowing the user to easily purchase the missing item.

[0969] In addition, the present invention uses augmented reality (AR) technology to allow users to virtually try on the suggested outfits. This technology allows users to visually see how the suggested outfits look on their body through their smartphones or smart glasses. Examples of AR technologies used here include Apple's ARKit and Google's ARCore.

[0970] Let's add a concrete example. A user purchases a new jacket, a "Winter Jacket," and enters the information into the system. The information is stored in a database. Next, if the user requests a "casual look," the AI ​​algorithm suggests a combination of "blue jeans + white T-shirt + black jacket." The user receives this suggestion and virtually tries on the combination using smart glasses. If the user does not have the required item, a white T-shirt, the server retrieves a link to the corresponding product from an e-commerce site and displays it to the user.

[0971] An example of a prompt is:

[0972] prompt:

[0973] Consider how to achieve clothing management, AI coordination, AR try-on, and item purchase in a single application. Specifically, describe how the AI ​​model and AR technology are used when a user uses smart glasses to receive outfit suggestions based on their mood. Also, describe the back-end process for providing purchase links for missing items.

[0974] Reference output:

[0975] The application, which helps users decide what they want to wear, first stores the user's clothing data in a database via their smartphone. It then uses an AI model (using TensorFlow) to generate outfits that best fit the user's mood. The generated outfits are visually presented to the user in real time through AR technology (e.g., ARKit or ARCore) in the smart glasses. Missing items are searched for using the e-commerce site's API, and a link to purchase them is provided.

[0976] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0977] Step 1:

[0978] Users log in to the system using devices such as smartphones or computers. After successful login, users can access an interface to input new clothing information, including details such as clothing category, color, and season.

[0979] Input: User-entered details such as clothing category, color, season, etc.

[0980] Output: The entered information is sent from the device to the server.

[0981] Step 2:

[0982] The server receives the clothing information sent from the device, parses it, and stores it in a database, while also checking the data for consistency.

[0983] Input: Clothing information sent from the device

[0984] Data processing / data calculation: Parse the data, check its integrity, and save it to the database

[0985] Output: New clothing information is saved in the database

[0986] Step 3:

[0987] The user requests a coordination suggestion, and the device sends information such as the user's specified "mood" and "situation" to the server.

[0988] Input: "Mood" or "Situation" specified by the user

[0989] Output: The specified information is sent from the terminal to the server.

[0990] Step 4:

[0991] The server queries the database to retrieve a list of clothing owned by the user, including all clothing information that the user has previously registered.

[0992] Input: A database containing information about clothing owned by the user.

[0993] Data processing / data calculation: Obtain all user clothing information from the database

[0994] Output: The user's clothing list is retrieved by the server.

[0995] Step 5:

[0996] The server runs an AI algorithm that uses a machine learning model (using TensorFlow, for example) to generate the optimal outfit based on the clothing list and the user's specified "mood."

[0997] Input: User's clothing list, user-specified "mood"

[0998] Data processing / data calculation: Generate optimal outfits using machine learning models

[0999] Output: The generated coordinates are sent to the server

[1000] Step 6:

[1001] The proposed coordinates are sent to the terminal, which displays this information to the user.

[1002] Input: Generated coordinates

[1003] Output: The suggested outfits are displayed on the device.

[1004] Step 7:

[1005] To allow users to virtually try on suggested outfits, the device uses augmented reality technology (such as ARKit or ARCore) to overlay the suggested items onto the user's camera image.

[1006] Input: User's camera footage, suggested coordinates

[1007] Output: The virtual try-on video is displayed on the device.

[1008] Step 8:

[1009] The server identifies items in the proposed outfit that the user does not own and uses the e-commerce site's API to obtain links to those products.

[1010] Input: Suggested outfits, information on items the user does not own

[1011] Data processing / data calculation: Call the API of an e-commerce site to obtain product links

[1012] Output: The link of the retrieved product is saved on the server.

[1013] Step 9:

[1014] The server sends the link of the acquired product to the terminal, and the terminal displays the link to the user, who can click the link to purchase the missing item.

[1015] Input: Link of the retrieved product

[1016] Output: A link to purchase the product will be displayed on the device.

[1017] 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.

[1018] This system manages information about clothing owned by a user, suggests outfits based on that information, and provides links to acquire missing items from e-commerce sites. By combining this system with an emotion engine that recognizes the user's emotions, the system makes more personalized suggestions. This system is realized through the interaction of a server, terminals, and users.

[1019] First, when a user purchases new clothing, they log in to the system using a device such as a smartphone or computer. The user enters information about the "Winter Jacket" (category "Outerwear," color "Black," season "Winter," etc.) into the device. The device sends this information to the server, which saves it as a new record in the database.

[1020] Next, when the user requests suggestions for outfits that match their current mood, the emotion engine recognizes the user's emotion from their facial expression, voice, or text input. For example, by analyzing the user's facial expression through a camera, the emotion engine recognizes that the user is in a "happy" mood. This emotion information is sent from the device to the server.

[1021] The server queries the database to obtain a list of the clothes owned by the user. Based on the obtained list of clothes, the recognized emotion, and the specified mood, an AI algorithm (e.g., a machine learning model) is used to generate an optimal outfit. This outfit suggestion (e.g., blue jeans + white T-shirt + black jacket) is sent from the server to the device and displayed to the user.

[1022] Furthermore, the server identifies missing items in the proposed outfit. For example, if the user does not own a white T-shirt, the server searches an e-commerce site to obtain a link to the corresponding item. This link is sent to the device and displayed to the user. The emotion engine can also suggest more suitable items based on the user's emotions when suggesting outfits or purchasing products.

[1023] To explain with a concrete example, the process is as follows:

[1024] 1. Clothing registration: A user purchases a new jacket, "Winter Jacket," and enters it into the system. The device sends this information to the server, which stores it in a database.

[1025] 2. Emotion recognition and outfit suggestions: When a user logs in to the system, the emotion engine analyzes the user's facial expressions via the device's camera. The emotion engine recognizes that the user is in a "happy" mood and sends this information to the server. The server then uses an AI algorithm to suggest an outfit combination of "blue jeans + white T-shirt + black jacket" based on the user's clothing data and emotional information. This suggestion is sent to the device and displayed to the user.

[1026] 3. Item Purchase Suggestion: If the user does not own a white T-shirt in the suggested outfit, the server will search an e-commerce site and retrieve a link to purchase the white T-shirt. This link will be sent to the device and displayed to the user. The server can also check whether the purchase suggestion is appropriate based on the user's emotions recognized by the emotion engine.

[1027] The system allows users to easily manage their clothing, receive more personalized outfit suggestions through emotion recognition, and smoothly purchase missing items.

[1028] The processing flow will be explained below.

[1029] Step 1:

[1030] When a user purchases new clothes, they log in to the system using a device such as a smartphone or computer.

[1031] Step 2:

[1032] The user enters information about the clothes they purchased (e.g., "Winter Jacket," category "Outerwear," color "Black," season "Winter," etc.) into the terminal.

[1033] Step 3:

[1034] The device checks the clothing information entered and checks for any missing items.

[1035] Step 4:

[1036] The terminal transmits the confirmed information to the server.

[1037] Step 5:

[1038] The server parses the received information and stores it as a new record in the database.

[1039] Step 6:

[1040] The user uses the terminal to request a coordination suggestion that matches the user's current mood.

[1041] Step 7:

[1042] The device's camera captures the user's facial expressions, and the emotion engine analyzes the user's emotions.

[1043] Step 8:

[1044] The emotion engine identifies the user's emotion and sends the result to the server via the device. For example, the user may recognize that they are "having fun."

[1045] Step 9:

[1046] The terminal sends the user's mood request (e.g., "casual mood") together with the emotion information to the server.

[1047] Step 10:

[1048] The server queries the database and retrieves a list of the clothes owned by the user.

[1049] Step 11:

[1050] Based on the list of clothes obtained by the server and the user's emotional and mood information, an AI algorithm is used to generate the optimal outfit.

[1051] Step 12:

[1052] The server sends the generated outfit (e.g., blue jeans + white T-shirt + black jacket) to the device.

[1053] Step 13:

[1054] The terminal displays the suggested coordination to the user.

[1055] Step 14:

[1056] The server identifies missing items in the proposed outfit, for example, if the user does not have a "white T-shirt."

[1057] Step 15:

[1058] The server searches the e-commerce site based on the missing item and obtains the link for the appropriate product.

[1059] Step 16:

[1060] The server sends the acquired product link to the terminal.

[1061] Step 17:

[1062] The device displays product links to the user and encourages them to make a purchase.

[1063] This specific processing flow allows users to easily manage their clothing, receive personalized outfit suggestions based on their emotions and moods, and smoothly purchase missing items.

[1064] Example 2

[1065] 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."

[1066] Conventional outfit suggestion systems manage a user's clothing information and can suggest outfits based on the user's mood, but these suggestions are not optimized based on the user's real-time emotional state. Furthermore, many systems offer uniform suggestions for purchasing missing items without considering the user's emotions, resulting in a poor user experience. Therefore, there is a need for a system that recognizes the user's emotions, provides more personalized outfit suggestions based on those emotions, and consistently suggests the purchase of missing items.

[1067] 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.

[1068] In this invention, the server includes means for inputting information about clothing owned by a user and storing the information in a database, means for recognizing emotions from the user's facial expressions, voice, or text input, means for executing an algorithm for proposing outfits that match the user's mood based on the emotion recognition, means for identifying missing items based on the proposed outfit and obtaining links to corresponding products from external e-commerce sites, and means for suggesting more suitable items based on the user's emotions when suggesting the proposed outfit or purchasing a product. This allows the user to receive optimal outfit suggestions based on their real-time emotional state, and furthermore, purchasing suggestions for missing items are personalized based on emotions, improving the user experience.

[1069] "User" refers to an individual or organization that uses the System.

[1070] "Clothing" refers to clothing that a user owns and that is registered in the system.

[1071] "Database" refers to a collection of information stored on a server for managing a user's clothing information and other necessary data.

[1072] "Emotion recognition" refers to the technology of analyzing and recognizing a user's emotional state from facial expressions, voice, text input, etc.

[1073] "Coordination suggestions" refers to generating and suggesting optimal outfit combinations based on the user's clothing information and emotional information.

[1074] A "machine learning model" is a mathematical model that learns patterns and rules from large amounts of data and makes predictions and classifications.

[1075] An "algorithm" refers to a set of steps or a computational method for solving a problem.

[1076] "E-commerce site" refers to a website that sells and purchases goods over the Internet.

[1077] A "link" refers to a reference that connects to other content within a web page or application.

[1078] "Missing items" refer to clothing, accessories, etc. that the user does not own and needs to purchase in addition to the items in the proposed outfit.

[1079] The present invention is a system that manages information about clothing owned by a user and suggests outfits based on that information. This system improves the user experience by recognizing the user's emotions and suggesting more personalized outfits based on those emotions. The system is implemented primarily through the interaction between a server, a terminal, and the user.

[1080] First, when a user purchases new clothing, they log in to the system using a device such as a smartphone or computer. After logging in, the user enters information about the purchased clothing (e.g., category "outerwear," color "black," season "winter," etc.) into the device. The device sends this input information to the server, which then stores the received information in the database as a new record. The database stores information about all clothing owned by the user.

[1081] Next, when a user requests suggestions for outfits that match their current mood, they log in to the device using the device's camera and microphone. At this time, the emotion engine recognizes the user's emotions from their facial expressions, voice, or text input. For example, by analyzing the user's facial expressions through the camera, the emotion engine recognizes that the user is in a "happy" mood. The device analyzes this information and sends it to the server.

[1082] The server receives the emotion information and queries a database to obtain a list of all the clothes owned by the user. An AI algorithm (e.g., a machine learning model) within the server generates an optimal outfit based on the obtained list of clothes and the recognized emotion. The generated outfit suggestion (e.g., "blue jeans + white T-shirt + black jacket") is sent from the server to the device and displayed to the user.

[1083] Furthermore, the server identifies missing items based on the suggested outfit. For example, if the user does not own a white T-shirt, the server searches an e-commerce site and retrieves a link to the relevant item. This link is sent to the user's device and displayed. By clicking the link, the user is immediately redirected to the e-commerce site and can proceed with the purchase. The emotion engine can also suggest more suitable items based on the user's emotions when suggesting outfits or purchasing products.

[1084] As a concrete example, a user purchases a new "Winter Jacket" and enters the information into the system. This information is sent from the device to the server and stored in a database. The user then logs into the system, and the emotion engine analyzes their facial expressions and recognizes that they are in a "happy" mood. Based on this information, a coordination of "blue jeans + white T-shirt + black jacket" is suggested. If the user does not own a white T-shirt, the server searches an e-commerce site and sends a link to a suitable product to the device.

[1085] Prompt Sentence Examples

[1086] 1. Emotion Recognition Prompts

[1087] Which model should be used to analyze the user's facial expressions and recognize the "happy" emotion?

[1088] 2. Prompt for outfit suggestions

[1089] Please tell us the specific algorithm for how the most suitable outfit is generated based on the user's clothing data and emotional information.

[1090] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1091] Step 1:

[1092] The user inputs information about the newly purchased clothing.

[1093] Specific actions

[1094] A user logs into the system using a device such as a smartphone or computer and enters information about a new garment (e.g., "Winter Jacket"). This information includes the category (outerwear), color (black), season (winter), etc. Based on this user input, the device sends the data to the server.

[1095] Input: Clothing information entered by the user into the device (category, color, season)

[1096] Output: The sent clothing information is stored in a database by the server.

[1097] Step 2:

[1098] The server stores the clothing information in a database.

[1099] Specific actions

[1100] The server receives the clothing information sent from the device and stores it in the database as a new record, allowing information about the user's clothing to be managed centrally.

[1101] Input: Clothing information sent from the device

[1102] Output: New clothing record saved in the database

[1103] Step 3:

[1104] The user requests outfit suggestions and recognizes emotions.

[1105] Specific actions

[1106] The user requests "coordinate suggestions" using the device. At this time, the device's camera and microphone capture the user's facial expressions and voice. The emotion engine analyzes this captured data and recognizes the user's emotion (e.g., "fun").

[1107] Input: Coordination suggestions requested by the user and captured emotional data (facial expressions, voice)

[1108] Output: Analyzed user emotion information

[1109] Step 4:

[1110] The device transmits the emotion information to the server.

[1111] Specific actions

[1112] The emotion information analyzed by the emotion engine is sent from the device to the server, allowing the server to know the user's current emotional state.

[1113] Input: Analyzed user emotion information

[1114] Output: Emotion information is sent to the server

[1115] Step 5:

[1116] The server generates a coordinate based on the user's emotional information and clothing information.

[1117] Specific actions

[1118] Based on the received emotion information, the server queries a database to obtain a list of the user's clothing, and then uses an AI algorithm (e.g., a machine learning model) to generate an optimal outfit that combines emotion information and clothing information.

[1119] Input: User's emotional information, list of clothing owned by the user

[1120] Output: Generated outfit suggestions (e.g., "blue jeans + white T-shirt + black jacket")

[1121] Step 6:

[1122] The server sends the generated coordinates to the terminal and displays them to the user.

[1123] Specific actions

[1124] The server sends the generated coordination proposal to the terminal, which displays this information to the user.

[1125] Input: Generated outfit suggestions

[1126] Output: Coordinate information displayed to the user

[1127] Step 7:

[1128] The server detects missing items and makes purchasing suggestions.

[1129] Specific actions

[1130] The server examines the proposed outfit and identifies any missing items that the user does not own. If the missing items exist, the server searches the e-commerce site to obtain a link to purchase the product.

[1131] Input: Suggested outfits, list of clothing owned by the user

[1132] Output: Detected missing items and corresponding purchase links

[1133] Step 8:

[1134] The server sends the purchase link to the terminal and displays it to the user.

[1135] Specific actions

[1136] The server sends the acquired purchase link to the terminal, which displays this information to the user. When the user clicks the link, they are immediately redirected to the e-commerce site and can complete the product purchase procedure.

[1137] Input: Purchase link from e-commerce site

[1138] Output: The purchase link that is displayed to the user

[1139] As an example, the following prompt sentence can be used:

[1140] Prompt Sentence Examples

[1141] 1. Emotion Recognition Prompts

[1142] Which model should be used to analyze the user's facial expressions and recognize the "happy" emotion?

[1143] 2. Prompt for outfit suggestions

[1144] Please tell us the specific algorithm for how the most suitable outfit is generated based on the user's clothing data and emotional information.

[1145] (Application example 2)

[1146] 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."

[1147] While conventional clothing management systems can record information about the clothing owned by a user and suggest appropriate outfits, they are unable to provide personalized suggestions that take into account the user's emotional state. Furthermore, only some systems offer a function to provide links to purchase missing items, preventing a consistent user shopping experience.

[1148] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting information about clothing owned by the user and saving the information in a database, emotion analysis means for recognizing the user's mood, means for executing an algorithm for suggesting outfits based on the user's mood, means for identifying missing items based on the suggested outfits and obtaining links to corresponding products from external e-commerce sites, and means for managing the user's clothing information, making personalized outfit suggestions through emotion recognition, and supporting the purchase of missing items. This enables personalized outfit suggestions tailored to the user's mood and smooth purchase of missing items.

[1149] "User" refers to an individual who uses this system to manage clothing information and receive coordination suggestions.

[1150] "Clothing information" refers to detailed information about each piece of clothing owned by the user, including data such as category, color, and season.

[1151] "Database" refers to a system that systematically stores clothing information entered by users.

[1152] "Emotion analysis means" refers to technology that analyzes facial expressions, voice, or text input to recognize a user's mood or emotion.

[1153] "Algorithm" refers to the processing procedure for suggesting optimal outfits based on the user's clothing information and emotional information.

[1154] "E-commerce Site" means a website that enables the purchase of goods and services over the Internet.

[1155] "Purchase Link" refers to a URL for purchasing a particular item online.

[1156] A "machine learning model" refers to a technology that learns from large amounts of data and performs data analysis and predictions.

[1157] "Server" refers to a central computer system for processing and storing data submitted by users.

[1158] "Personalized outfits" refer to outfit combinations that are specifically suggested based on the user's personal information and emotions.

[1159] System program generation

[1160] The system program for realizing this application includes the following main functions:

[1161] 1. Clothing information management function

[1162] The user inputs information about the newly purchased clothing (category, color, season, etc.) into the system using a terminal. This information is sent from the terminal to the server, which then stores it in a database.

[1163] 2. Emotion recognition function

[1164] When a user uses the system, the emotion engine analyzes the user's facial expressions through the device's camera, recognizes the user's emotions based on facial expressions, voice, or text input, and sends that information to the server.

[1165] 3. Personalized outfit suggestions

[1166] The server uses a machine learning model to generate optimal outfits based on the acquired clothing and emotion information. These outfit suggestions are sent from the server to the device and displayed to the user.

[1167] 4. Purchasing support function

[1168] The server identifies missing items in the proposed outfit and searches e-commerce sites to obtain purchase links for the appropriate items, which are then sent from the server to the terminal and displayed to the user.

[1169] Detailed explanation of the process

[1170] The detailed procedure for implementing this system is as follows:

[1171] 1. Clothing information management

[1172] A user uses a smartphone or computer to enter information about a newly purchased garment into the system. For example, when entering information about a "Winter Jacket," the user enters details such as the category (outerwear), color (black), and season (winter). This information is sent from the device to the server, which stores it as a new record in the database.

[1173] 2. Emotion recognition

[1174] When a user logs in to the system, the emotion engine analyzes the user's facial expression through the device's camera. For example, the emotion engine captures the user's facial expression through the camera and recognizes that the user is in a "happy" mood. This emotional information is sent from the device to the server.

[1175] 3. Coordination suggestions

[1176] The server uses a machine learning model to generate optimal outfits based on the user's clothing information and emotional information stored in the database. For example, if a user is in a "happy" mood, it will suggest an outfit of "blue jeans + white T-shirt + black jacket." This suggestion is sent from the server to the user's device and displayed to them.

[1177] 4. Providing a purchasing link

[1178] The server identifies missing items in the proposed outfit and retrieves links to relevant products from e-commerce sites. For example, if the user does not own a white T-shirt, the server retrieves a link to purchase a white T-shirt from the e-commerce site. This link is then sent from the server to the device and displayed to the user.

[1179] Specific examples

[1180] If a user purchases a new jacket, "Winter Jacket," the following process occurs:

[1181] The user uses a smartphone to enter information about the "Winter Jacket" (category "Outerwear," color "Black," season "Winter") into the system. This information is sent to the server via the device and stored in the database.

[1182] When a user logs in, the emotion engine analyzes the user's facial expressions through the device's camera and recognizes that the user is in a "happy" mood. This information is sent to the server.

[1183] The server uses machine learning models to generate optimal outfits based on the user's clothing and emotional information, suggesting, for example, a combination of blue jeans, a white T-shirt, and a black jacket.

[1184] If the user does not have the white T-shirt required for the suggested outfit, the server retrieves a link to purchase the white T-shirt from an e-commerce site and provides it to the user.

[1185] Prompt Sentence Examples

[1186] "I just bought a new jacket, 'Winter Jacket'. The category is 'Outerwear', the color is 'Black', and the season is 'Winter'. Please register this in the app."

[1187] "Please recognize my facial expression on camera. I'm in a fun mood right now."

[1188] "Please suggest an outfit that matches my current mood."

[1189] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1190] Step 1:

[1191] A user uses a terminal to input information about newly purchased clothing into the system, including details such as the clothing category (e.g., outerwear), color (e.g., black), and season (e.g., winter). The terminal then sends this information to the server.

[1192] Input: Details such as clothing category, color, season, etc.

[1193] Output: Clothing information sent to the server

[1194] Step 2:

[1195] The server receives the clothing information sent from the terminal and stores it as a new record in the database, allowing for centralized management of all clothing information owned by the user.

[1196] Input: Clothing information sent from the device

[1197] Output: New clothing record saved in the database

[1198] Step 3:

[1199] The user logs in to the system, and the emotion engine analyzes the user's facial expressions through the device's camera. The emotion engine recognizes the user's emotions based on facial expressions, voice, or text input.

[1200] Input: User facial, voice, or text input data

[1201] Output: User's emotional information (e.g., "fun")

[1202] Step 4:

[1203] The device transmits the recognized emotion information to the server, which then processes the data based on the user's mood.

[1204] Input: Emotion information recognized by the device

[1205] Output: Emotion information sent to the server

[1206] Step 5:

[1207] The server acquires the user's clothing information stored in a database and emotion information sent from the terminal.

[1208] Input: Clothing information stored in the database, emotion information sent from the device

[1209] Output: Clothing information and emotion information to be processed by the server

[1210] Step 6:

[1211] The server uses a machine learning model to generate optimal outfits based on the acquired clothing and emotional information, suggesting, for example, a combination of blue jeans, a white T-shirt, and a black jacket.

[1212] Input: Clothing information, emotion information

[1213] Output: Proposed coordinates

[1214] Step 7:

[1215] The server transmits the generated coordinates to the terminal and displays them to the user.

[1216] Input: Proposed outfit information

[1217] Output: The coordinates displayed to the user

[1218] Step 8:

[1219] The server identifies the missing items based on the proposed coordinates.

[1220] Input: Proposed coordinates

[1221] Output: Missing items list

[1222] Step 9:

[1223] The server identifies the missing items and searches e-commerce sites to obtain purchasing links for the appropriate products.

[1224] Input: Missing items list

[1225] Output: Product purchase link

[1226] Step 10:

[1227] The server sends the obtained purchase link to the terminal and displays it to the user, who can use this link to purchase the missing item.

[1228] Enter: Purchasing Link

[1229] Output: The purchase link that is displayed to the user

[1230] 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.

[1231] 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.

[1232] 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.

[1233] [Fourth embodiment]

[1234] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1235] 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.

[1236] 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).

[1237] 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.

[1238] 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.

[1239] 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).

[1240] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1241] 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.

[1242] 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.

[1243] 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.

[1244] 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.

[1245] 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.

[1246] 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."

[1247] The present invention is a system that manages information about clothing owned by a user, suggests outfits based on that information, and provides links to acquire missing items from e-commerce sites. This system operates through mutual communication between a server, terminals, and users.

[1248] When a user wants to purchase new clothing and register it in the system, they first log in to the system using a device such as a smartphone or computer. Next, the user enters the information for "Winter Jacket" (category "Outerwear", color "Black", season "Winter"). The device sends this information to the server. The server parses the received information and saves it as a new record in the database.

[1249] When a user requests suggestions for outfits that match their current mood, the device sends the request to the server. For example, if the user specifies that they are "feeling casual," the device sends that information to the server. The server then queries the database and retrieves a list of the clothes the user owns.

[1250] The server uses an AI algorithm (e.g., a machine learning model) to generate an optimal outfit based on the retrieved clothing list and the user's specified mood. This suggested outfit (e.g., blue jeans + white T-shirt + black jacket) is sent from the server to the device, which then displays it to the user.

[1251] Furthermore, if the proposed outfit includes items that the user does not own, the server identifies the missing items. For example, if the user needs a white T-shirt to complete their outfit, the server searches an e-commerce site and retrieves a link to the relevant item. The server sends this link to the device, which then displays it to the user, allowing the user to easily purchase the missing items.

[1252] To explain with a concrete example, the process is as follows:

[1253] 1. Clothing registration: A user purchases a new jacket, "Winter Jacket," and enters it into the system. The device sends this information to the server, which stores it in a database.

[1254] 2. Coordination suggestions: When a user requests a "casual mood," the server uses an AI algorithm to suggest a coordinated outfit of "blue jeans + white T-shirt + black jacket" based on the user's clothing data. This information is sent to the device and displayed to the user.

[1255] 3. Item Purchase Suggestion: If the user does not own a white T-shirt in the suggested outfit, the server will search an e-commerce site and retrieve a link to purchase the white T-shirt. This link will be sent to the device and displayed to the user.

[1256] This system allows users to manage their clothing more efficiently, enjoy coordinating outfits to suit their mood, and smoothly purchase items they are running low on.

[1257] The processing flow will be explained below.

[1258] Step 1:

[1259] When a user purchases new clothes, they log in to the system using a device such as a smartphone or computer.

[1260] Step 2:

[1261] The user enters information about the clothes they purchased (e.g., "Winter Jacket," category "Outerwear," color "Black," season "Winter," etc.) into the terminal.

[1262] Step 3:

[1263] The device checks the clothing information entered and checks for any missing items.

[1264] Step 4:

[1265] The terminal transmits the confirmed information to the server.

[1266] Step 5:

[1267] The server parses the received information and stores it as a new record in the database.

[1268] Step 6:

[1269] The user requests suggestions for outfits that match their current mood, for example, specifying "I'm feeling casual."

[1270] Step 7:

[1271] The terminal receives a request from the user and transmits the specified mood information to the server.

[1272] Step 8:

[1273] The server retrieves the user's clothing list from the database.

[1274] Step 9:

[1275] Based on the list of clothes retrieved by the server and the specified mood, an AI algorithm (e.g., machine learning model) is used to generate the optimal outfit.

[1276] Step 10:

[1277] The server sends the generated coordinates to the terminal.

[1278] Step 11:

[1279] The terminal displays the suggested coordination to the user.

[1280] Step 12:

[1281] The server identifies missing items in the proposed outfit, for example, if the user does not have a "white T-shirt."

[1282] Step 13:

[1283] The server searches the e-commerce site based on the missing item and obtains the link for the appropriate product.

[1284] Step 14:

[1285] The server sends the acquired product link to the terminal.

[1286] Step 15:

[1287] The device displays product links to the user and encourages them to make a purchase.

[1288] The above-described processing steps enable the user to easily manage clothing, receive coordination suggestions that match their mood, and smoothly purchase any items they are missing.

[1289] Example 1

[1290] 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."

[1291] Existing clothing management systems not only efficiently manage users' clothing, but also automatically suggest outfits that match their mood and lack support for easily purchasing missing items. As a result, users have to spend time and effort selecting their daily outfits, and the process of finding and purchasing missing items is cumbersome.

[1292] 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.

[1293] In this invention, the server includes means for inputting information about clothing owned by a user and storing the information in a database, means for executing an algorithm for suggesting outfits based on the user's mood, means for identifying missing items based on the suggested outfits and obtaining links to corresponding products from external e-commerce sites, means for displaying the outfit suggestions to the user, and means for the user to check the missing items in the suggestions and purchase them via the links. This allows the user to efficiently manage their clothing, easily enjoy coordinating outfits that suit their mood, and quickly purchase missing items.

[1294] "User" refers to an individual who uses the system to manage clothing information and receive coordination suggestions.

[1295] "Clothing information" refers to attribute information such as category, color, and season of each piece of clothing owned by the user.

[1296] "Database" means an electronic data repository for storing and managing clothing information entered by Users in the System.

[1297] "Mood" refers to the user's current orientation regarding a particular style or taste.

[1298] "Coordination" refers to a fashion style suggested by combining clothing owned by the user.

[1299] An "algorithm" refers to a sequence of computational steps designed to accomplish a particular purpose.

[1300] A "generative AI model" refers to a machine learning model that uses artificial intelligence to generate new outputs from data.

[1301] "External E-Commerce Site" means a third-party website that sells products over the Internet.

[1302] "Link" refers to a Uniform Resource Locator (URL) for accessing a specific web page on the Internet.

[1303] "Means for displaying suggestions to the user" refers to a method for displaying the results of coordination and information about missing items on the screen of the user's terminal.

[1304] "Means to purchase" refers to the method by which a user can purchase the missing item using the suggested link.

[1305] The present invention is a system that manages information about clothing owned by a user, suggests outfits based on that information, and provides links to acquire missing items from e-commerce sites. This system operates through mutual communication between a server, terminals, and users.

[1306] First, when a user purchases new clothes and wants to register them in the system, they log in to the system using a device such as a smartphone or computer. The user opens an application (e.g., a closet management app) and enters their username and password on the login screen to access the system.

[1307] Next, the user enters information about the "Winter Jacket" (category "Outerwear", color "Black", season "Winter"). The user enters details such as category, color, and season on the "Register Clothing Information" page and clicks the "Register" button. The device sends this input information to the server. The device converts the user's input into JSON format and sends an HTTP request to the server.

[1308] The server parses the received information and saves it as a new record in the database. The server receives the HTTP request, parses the JSON data, and adds the parsed data to the database (e.g. MySQL, PostgreSQL) as a new record.

[1309] When a user requests suggestions for outfits that match their current mood, they select a mood, such as "casual," from the menu on the "Outfit Suggestions" page within the application and click the "Request" button. The device then sends the request to the server. The device then converts the user's selections into JSON format and sends an HTTP request to the server.

[1310] The server queries the database and retrieves a list of clothes owned by the user. The server executes the database query and retrieves the user's clothing list. It then converts the retrieved results into JSON format. The server then uses an AI algorithm (e.g., a machine learning model) to generate an optimal outfit based on the retrieved clothing list and the specified mood. The server invokes an AI model (e.g., a TensorFlow model) and generates an optimal outfit (e.g., "blue jeans + white T-shirt + black jacket") using the clothing list and mood information as input.

[1311] The server sends the proposed coordination to the device. The server converts the proposed coordination into JSON format and sends it to the device as an HTTP response. The device displays the coordination information to the user. The device analyzes the received data and displays the coordination on the application's UI.

[1312] Additionally, if the proposed outfit includes items that the user does not own, the server identifies the missing items. The server compares the user's clothing list with the proposed outfit and identifies the missing items (e.g., a white T-shirt). The server searches e-commerce sites and obtains links to the relevant products. The server uses an API to search e-commerce sites (e.g., online marketplaces) and obtains the URLs for the relevant products (e.g., a white T-shirt).

[1313] The server sends this link to the device, which then displays it to the user. The device then parses the link and displays it in the application's UI to notify the user, allowing the user to easily purchase the missing item.

[1314] As a concrete example, the following flow can be considered:

[1315] 1. Clothing registration: A user purchases a new jacket, "Winter Jacket," and enters it into the system. The device sends this information to the server, which stores it in a database.

[1316] 2. Coordination suggestions: When a user requests a "casual mood," the server uses an AI algorithm to suggest a coordinated outfit of "blue jeans + white T-shirt + black jacket" based on the user's clothing data. This information is sent to the device and displayed to the user.

[1317] 3. Item Purchase Suggestion: If the user does not own a white T-shirt in the suggested outfit, the server will search the e-commerce site and get a link to purchase the white T-shirt. This link will be sent to the device and displayed to the user.

[1318] An example of a prompt to input to a generative AI model is as follows:

[1319] Here is a list of clothes the user owns:

[1320] 1. Blue jeans

[1321] 2. White T-shirt

[1322] 3. Black jacket

[1323] ...

[1324] The user is in a "casual mood." Please suggest the best outfit based on this.

[1325] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1326] Step 1:

[1327] A user logs in to the system to register a new piece of clothing, a "Winter Jacket." The input is a username and password, and the output is login status information upon success. Specifically, the user opens the application on their smartphone or computer, enters their username and password on the login screen, and accesses the system.

[1328] Step 2:

[1329] The user enters information about the "Winter Jacket" (category "Outerwear", color "Black", season "Winter"). The input is detailed information about the clothing, and the output is the transmission of the input information from the user to the terminal. The user enters details such as category, color, and season on the "Register Clothing Information" page, and clicks the "Register" button. The terminal sends the input information to the server.

[1330] Step 3:

[1331] The terminal converts the user's input into JSON format and sends an HTTP request to the server. The input is the user's input data and the output is an HTTP request to the server. The terminal converts the user's input into the appropriate format and sends it over the Internet.

[1332] Step 4:

[1333] The server parses the received information and stores it as a new record in the database. The input is the data from the HTTP request, and the output is a new record stored in the database. Specifically, the server receives the HTTP request, parses the JSON data, and stores the parsed data as a new record in the database (e.g., MySQL, PostgreSQL).

[1334] Step 5:

[1335] The user requests outfit suggestions. The input is the request, such as "feeling casual," and the output is confirmation of the request. The user selects a mood from the menu on the "Outfit Suggestions" page within the application and clicks the "Request" button.

[1336] Step 6:

[1337] The terminal converts the user's request information into JSON format and sends an HTTP request to the server. The input is the user's request content and the output is an HTTP request to the server. The terminal converts the request data into the appropriate format and sends it over the Internet.

[1338] Step 7:

[1339] The server queries the database to retrieve a list of the user's clothes. The input is user identification information, such as a user ID, and the output is the user's clothes list. The server executes the database query and retrieves the user's clothes list.

[1340] Step 8:

[1341] The server uses an AI algorithm to generate the optimal outfit based on the acquired clothing list and the specified mood. The input is the user's clothing list and mood information, and the output is a suggested outfit. Specifically, the server calls an AI model (e.g., TensorFlow model) and generates the optimal outfit based on the clothing list and mood information.

[1342] Step 9:

[1343] The server sends the proposed outfits to the device. The input is the outfit information generated by the AI, and the output is an HTTP response to the device. The server converts the outfit information into JSON format and sends it to the device as an HTTP response.

[1344] Step 10:

[1345] The terminal displays the coordinate information to the user. The input is the coordinate information in JSON format received from the server, and the output is a visual display to the user. The terminal parses the received data and displays the coordinates in the application UI.

[1346] Step 11:

[1347] The server identifies items in the proposed outfit that the user does not own. The input is the user's clothing list and the proposed outfit, and the output is a list of missing items. The server matches all items in the proposed outfit against the user's clothing list to identify the missing items.

[1348] Step 12:

[1349] The server searches an external e-commerce site and retrieves links to the relevant products. The input is a list of missing items, and the output is a purchase link on an external site. The server uses an API to search an e-commerce site and retrieve links to the relevant products.

[1350] Step 13:

[1351] The server sends the acquired link to the terminal. The input is the acquired purchase link, and the output is the HTTP response to the terminal. The server converts the link to JSON format and sends it to the terminal as an HTTP response.

[1352] Step 14:

[1353] The terminal displays the link to the user. The input is the purchase link information received from the server, and the output is a visual display to the user. The terminal analyzes the received link and displays it in the application's UI to notify the user.

[1354] (Application example 1)

[1355] 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."

[1356] The objective of this invention is to efficiently manage information about clothing that users regularly own, provide optimal outfits that match their mood and situation, and enable users to easily purchase missing items. It also aims to improve the user's purchasing experience by introducing technology that allows users to virtually try on suggested outfits.

[1357] 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.

[1358] In this invention, the server includes means for inputting information about clothing owned by a user and storing the information in a database, means for executing an algorithm for suggesting outfits based on the user's mood, means for identifying missing items based on the suggested outfits and obtaining links to corresponding products from external e-commerce sites, and means for allowing the user to virtually try on the suggested outfits using augmented reality technology, thereby enabling the user to efficiently manage their clothing, enjoy coordinating outfits that match their mood, virtually try on the suggested outfits, and easily purchase missing items.

[1359] A "user" is an individual who uses the system to manage clothing information and receive coordination suggestions.

[1360] "Clothing information" is detailed information such as the category, color, and season of clothing owned by the user.

[1361] A "database" is an information storage system for saving and managing clothing information entered by users.

[1362] An "outfit" is a set of clothes that is proposed by combining multiple pieces of clothing that the user owns.

[1363] An "algorithm" is a procedure for calculating and proposing optimal outfits based on the user's mood.

[1364] A "machine learning model" is a type of artificial intelligence used to suggest optimal outfits based on a user's preferences and past data.

[1365] An "e-commerce site" is a website that sells products over the Internet.

[1366] "Link" means a URL that provides access to a particular product on an e-commerce site.

[1367] "Augmented reality technology" is a technology that displays virtual information overlaid on the real environment.

[1368] "Virtual try-on" is a process that uses augmented reality technology to make suggested outfits appear as if they are actually worn by the user.

[1369] The present invention is a system that manages information about clothing owned by a user, suggests optimal outfits using an AI algorithm, and provides links to easily purchase missing items on an e-commerce site. In a specific embodiment, the system operates in cooperation with a server, a terminal, and a user.

[1370] First, a user accesses the system using a device such as a smartphone or computer. When a user purchases new clothing and wants to register this information, they enter information such as the clothing category, color, and season. This input information is sent from the device to the server, which parses the information and stores it in a database. The database used here is, for example, a relational database such as MySQL.

[1371] Next, when the user requests outfit suggestions, the device sends the request to the server. For example, if the user specifies "I'm in a casual mood," that information is sent to the server. The server queries a database to obtain a list of the user's clothing. The server then uses an AI algorithm (for example, a machine learning model using TensorFlow) to generate the optimal outfit. The server then sends this suggested outfit to the device, which then displays it to the user.

[1372] Furthermore, if the proposed outfit includes an item that the user does not own, the server identifies the missing item and obtains a link to the corresponding product from an external e-commerce site. This link information is sent to the terminal, and the terminal displays the link to the user, allowing the user to easily purchase the missing item.

[1373] In addition, the present invention uses augmented reality (AR) technology to allow users to virtually try on the suggested outfits. This technology allows users to visually see how the suggested outfits look on their body through their smartphones or smart glasses. Examples of AR technologies used here include Apple's ARKit and Google's ARCore.

[1374] Let's add a concrete example. A user purchases a new jacket, a "Winter Jacket," and enters the information into the system. The information is stored in a database. Next, if the user requests a "casual look," the AI ​​algorithm suggests a combination of "blue jeans + white T-shirt + black jacket." The user receives this suggestion and virtually tries on the combination using smart glasses. If the user does not have the required item, a white T-shirt, the server retrieves a link to the corresponding product from an e-commerce site and displays it to the user.

[1375] An example of a prompt is:

[1376] prompt:

[1377] Consider how to achieve clothing management, AI coordination, AR try-on, and item purchase in a single application. Specifically, describe how the AI ​​model and AR technology are used when a user uses smart glasses to receive outfit suggestions based on their mood. Also, describe the back-end process for providing purchase links for missing items.

[1378] Reference output:

[1379] The application, which helps users decide what they want to wear, first stores the user's clothing data in a database via their smartphone. It then uses an AI model (using TensorFlow) to generate outfits that best fit the user's mood. The generated outfits are visually presented to the user in real time through AR technology (e.g., ARKit or ARCore) in the smart glasses. Missing items are searched for using the e-commerce site's API, and a link to purchase them is provided.

[1380] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1381] Step 1:

[1382] Users log in to the system using devices such as smartphones or computers. After successful login, users can access an interface to input new clothing information, including details such as clothing category, color, and season.

[1383] Input: User-entered details such as clothing category, color, season, etc.

[1384] Output: The entered information is sent from the device to the server.

[1385] Step 2:

[1386] The server receives the clothing information sent from the device, parses it, and stores it in a database, while also checking the data for consistency.

[1387] Input: Clothing information sent from the device

[1388] Data processing / data calculation: Parse the data, check its integrity, and save it to the database

[1389] Output: New clothing information is saved in the database

[1390] Step 3:

[1391] The user requests a coordination suggestion, and the device sends information such as the user's specified "mood" and "situation" to the server.

[1392] Input: "Mood" or "Situation" specified by the user

[1393] Output: The specified information is sent from the terminal to the server.

[1394] Step 4:

[1395] The server queries the database to retrieve a list of clothing owned by the user, including all clothing information that the user has previously registered.

[1396] Input: A database containing information about clothing owned by the user.

[1397] Data processing / data calculation: Obtain all user clothing information from the database

[1398] Output: The user's clothing list is retrieved by the server.

[1399] Step 5:

[1400] The server runs an AI algorithm that uses a machine learning model (using TensorFlow, for example) to generate the optimal outfit based on the clothing list and the user's specified "mood."

[1401] Input: User's clothing list, user-specified "mood"

[1402] Data processing / data calculation: Generate optimal outfits using machine learning models

[1403] Output: The generated coordinates are sent to the server

[1404] Step 6:

[1405] The proposed coordinates are sent to the terminal, which displays this information to the user.

[1406] Input: Generated coordinates

[1407] Output: The suggested outfits are displayed on the device.

[1408] Step 7:

[1409] To allow users to virtually try on suggested outfits, the device uses augmented reality technology (such as ARKit or ARCore) to overlay the suggested items onto the user's camera image.

[1410] Input: User's camera footage, suggested coordinates

[1411] Output: The virtual try-on video is displayed on the device.

[1412] Step 8:

[1413] The server identifies items in the proposed outfit that the user does not own and uses the e-commerce site's API to obtain links to those products.

[1414] Input: Suggested outfits, information on items the user does not own

[1415] Data processing / data calculation: Call the API of an e-commerce site to obtain product links

[1416] Output: The link of the retrieved product is saved on the server.

[1417] Step 9:

[1418] The server sends the link of the acquired product to the terminal, and the terminal displays the link to the user, who can click the link to purchase the missing item.

[1419] Input: Link of the retrieved product

[1420] Output: A link to purchase the product will be displayed on the device.

[1421] 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.

[1422] This system manages information about clothing owned by a user, suggests outfits based on that information, and provides links to acquire missing items from e-commerce sites. By combining this system with an emotion engine that recognizes the user's emotions, the system makes more personalized suggestions. This system is realized through the interaction of a server, terminals, and users.

[1423] First, when a user purchases new clothing, they log in to the system using a device such as a smartphone or computer. The user enters information about the "Winter Jacket" (category "Outerwear," color "Black," season "Winter," etc.) into the device. The device sends this information to the server, which saves it as a new record in the database.

[1424] Next, when the user requests suggestions for outfits that match their current mood, the emotion engine recognizes the user's emotion from their facial expression, voice, or text input. For example, by analyzing the user's facial expression through a camera, the emotion engine recognizes that the user is in a "happy" mood. This emotion information is sent from the device to the server.

[1425] The server queries the database to obtain a list of the clothes owned by the user. Based on the obtained list of clothes, the recognized emotion, and the specified mood, an AI algorithm (e.g., a machine learning model) is used to generate an optimal outfit. This outfit suggestion (e.g., blue jeans + white T-shirt + black jacket) is sent from the server to the device and displayed to the user.

[1426] Furthermore, the server identifies missing items in the proposed outfit. For example, if the user does not own a white T-shirt, the server searches an e-commerce site to obtain a link to the corresponding item. This link is sent to the device and displayed to the user. The emotion engine can also suggest more suitable items based on the user's emotions when suggesting outfits or purchasing products.

[1427] To explain with a concrete example, the process is as follows:

[1428] 1. Clothing registration: A user purchases a new jacket, "Winter Jacket," and enters it into the system. The device sends this information to the server, which stores it in a database.

[1429] 2. Emotion recognition and outfit suggestions: When a user logs in to the system, the emotion engine analyzes the user's facial expressions via the device's camera. The emotion engine recognizes that the user is in a "happy" mood and sends this information to the server. The server then uses an AI algorithm to suggest an outfit combination of "blue jeans + white T-shirt + black jacket" based on the user's clothing data and emotional information. This suggestion is sent to the device and displayed to the user.

[1430] 3. Item Purchase Suggestion: If the user does not own a white T-shirt in the suggested outfit, the server will search an e-commerce site and retrieve a link to purchase the white T-shirt. This link will be sent to the device and displayed to the user. The server can also check whether the purchase suggestion is appropriate based on the user's emotions recognized by the emotion engine.

[1431] The system allows users to easily manage their clothing, receive more personalized outfit suggestions through emotion recognition, and smoothly purchase missing items.

[1432] The processing flow will be explained below.

[1433] Step 1:

[1434] When a user purchases new clothes, they log in to the system using a device such as a smartphone or computer.

[1435] Step 2:

[1436] The user enters information about the clothes they purchased (e.g., "Winter Jacket," category "Outerwear," color "Black," season "Winter," etc.) into the terminal.

[1437] Step 3:

[1438] The device checks the clothing information entered and checks for any missing items.

[1439] Step 4:

[1440] The terminal transmits the confirmed information to the server.

[1441] Step 5:

[1442] The server parses the received information and stores it as a new record in the database.

[1443] Step 6:

[1444] The user uses the terminal to request a coordination suggestion that matches the user's current mood.

[1445] Step 7:

[1446] The device's camera captures the user's facial expressions, and the emotion engine analyzes the user's emotions.

[1447] Step 8:

[1448] The emotion engine identifies the user's emotion and sends the result to the server via the device. For example, the user may recognize that they are "having fun."

[1449] Step 9:

[1450] The terminal sends the user's mood request (e.g., "casual mood") together with the emotion information to the server.

[1451] Step 10:

[1452] The server queries the database and retrieves a list of the clothes owned by the user.

[1453] Step 11:

[1454] Based on the list of clothes obtained by the server and the user's emotional and mood information, an AI algorithm is used to generate the optimal outfit.

[1455] Step 12:

[1456] The server sends the generated outfit (e.g., blue jeans + white T-shirt + black jacket) to the device.

[1457] Step 13:

[1458] The terminal displays the suggested coordination to the user.

[1459] Step 14:

[1460] The server identifies missing items in the proposed outfit, for example, if the user does not have a "white T-shirt."

[1461] Step 15:

[1462] The server searches the e-commerce site based on the missing item and obtains the link for the appropriate product.

[1463] Step 16:

[1464] The server sends the acquired product link to the terminal.

[1465] Step 17:

[1466] The device displays product links to the user and encourages them to make a purchase.

[1467] This specific processing flow allows users to easily manage their clothing, receive personalized outfit suggestions based on their emotions and moods, and smoothly purchase missing items.

[1468] Example 2

[1469] 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."

[1470] Conventional outfit suggestion systems manage a user's clothing information and can suggest outfits based on the user's mood, but these suggestions are not optimized based on the user's real-time emotional state. Furthermore, many systems offer uniform suggestions for purchasing missing items without considering the user's emotions, resulting in a poor user experience. Therefore, there is a need for a system that recognizes the user's emotions, provides more personalized outfit suggestions based on those emotions, and consistently suggests the purchase of missing items.

[1471] 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.

[1472] In this invention, the server includes means for inputting information about clothing owned by a user and storing the information in a database, means for recognizing emotions from the user's facial expressions, voice, or text input, means for executing an algorithm for proposing outfits that match the user's mood based on the emotion recognition, means for identifying missing items based on the proposed outfit and obtaining links to corresponding products from external e-commerce sites, and means for suggesting more suitable items based on the user's emotions when suggesting the proposed outfit or purchasing a product. This allows the user to receive optimal outfit suggestions based on their real-time emotional state, and furthermore, purchasing suggestions for missing items are personalized based on emotions, improving the user experience.

[1473] "User" refers to an individual or organization that uses the System.

[1474] "Clothing" refers to clothing that a user owns and that is registered in the system.

[1475] "Database" refers to a collection of information stored on a server for managing a user's clothing information and other necessary data.

[1476] "Emotion recognition" refers to the technology of analyzing and recognizing a user's emotional state from facial expressions, voice, text input, etc.

[1477] "Coordination suggestions" refers to generating and suggesting optimal outfit combinations based on the user's clothing information and emotional information.

[1478] A "machine learning model" is a mathematical model that learns patterns and rules from large amounts of data and makes predictions and classifications.

[1479] An "algorithm" refers to a set of steps or a computational method for solving a problem.

[1480] "E-commerce site" refers to a website that sells and purchases goods over the Internet.

[1481] A "link" refers to a reference that connects to other content within a web page or application.

[1482] "Missing items" refer to clothing, accessories, etc. that the user does not own and needs to purchase in addition to the items in the proposed outfit.

[1483] The present invention is a system that manages information about clothing owned by a user and suggests outfits based on that information. This system improves the user experience by recognizing the user's emotions and suggesting more personalized outfits based on those emotions. The system is implemented primarily through the interaction between a server, a terminal, and the user.

[1484] First, when a user purchases new clothing, they log in to the system using a device such as a smartphone or computer. After logging in, the user enters information about the purchased clothing (e.g., category "outerwear," color "black," season "winter," etc.) into the device. The device sends this input information to the server, which then stores the received information in the database as a new record. The database stores information about all clothing owned by the user.

[1485] Next, when a user requests suggestions for outfits that match their current mood, they log in to the device using the device's camera and microphone. At this time, the emotion engine recognizes the user's emotions from their facial expressions, voice, or text input. For example, by analyzing the user's facial expressions through the camera, the emotion engine recognizes that the user is in a "happy" mood. The device analyzes this information and sends it to the server.

[1486] The server receives the emotion information and queries a database to obtain a list of all the clothes owned by the user. An AI algorithm (e.g., a machine learning model) within the server generates an optimal outfit based on the obtained list of clothes and the recognized emotion. The generated outfit suggestion (e.g., "blue jeans + white T-shirt + black jacket") is sent from the server to the device and displayed to the user.

[1487] Furthermore, the server identifies missing items based on the suggested outfit. For example, if the user does not own a white T-shirt, the server searches an e-commerce site and retrieves a link to the relevant item. This link is sent to the user's device and displayed. By clicking the link, the user is immediately redirected to the e-commerce site and can proceed with the purchase. The emotion engine can also suggest more suitable items based on the user's emotions when suggesting outfits or purchasing products.

[1488] As a concrete example, a user purchases a new "Winter Jacket" and enters the information into the system. This information is sent from the device to the server and stored in a database. The user then logs into the system, and the emotion engine analyzes their facial expressions and recognizes that they are in a "happy" mood. Based on this information, a coordination of "blue jeans + white T-shirt + black jacket" is suggested. If the user does not own a white T-shirt, the server searches an e-commerce site and sends a link to a suitable product to the device.

[1489] Prompt Sentence Examples

[1490] 1. Emotion Recognition Prompts

[1491] Which model should be used to analyze the user's facial expressions and recognize the "happy" emotion?

[1492] 2. Prompt for outfit suggestions

[1493] Please tell us the specific algorithm for how the most suitable outfit is generated based on the user's clothing data and emotional information.

[1494] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1495] Step 1:

[1496] The user inputs information about the newly purchased clothing.

[1497] Specific actions

[1498] A user logs into the system using a device such as a smartphone or computer and enters information about a new garment (e.g., "Winter Jacket"). This information includes the category (outerwear), color (black), season (winter), etc. Based on this user input, the device sends the data to the server.

[1499] Input: Clothing information entered by the user into the device (category, color, season)

[1500] Output: The sent clothing information is stored in a database by the server.

[1501] Step 2:

[1502] The server stores the clothing information in a database.

[1503] Specific actions

[1504] The server receives the clothing information sent from the device and stores it in the database as a new record, allowing information about the user's clothing to be managed centrally.

[1505] Input: Clothing information sent from the device

[1506] Output: New clothing record saved in the database

[1507] Step 3:

[1508] The user requests outfit suggestions and recognizes emotions.

[1509] Specific actions

[1510] The user requests "coordinate suggestions" using the device. At this time, the device's camera and microphone capture the user's facial expressions and voice. The emotion engine analyzes this captured data and recognizes the user's emotion (e.g., "fun").

[1511] Input: Coordination suggestions requested by the user and captured emotional data (facial expressions, voice)

[1512] Output: Analyzed user emotion information

[1513] Step 4:

[1514] The device transmits the emotion information to the server.

[1515] Specific actions

[1516] The emotion information analyzed by the emotion engine is sent from the device to the server, allowing the server to know the user's current emotional state.

[1517] Input: Analyzed user emotion information

[1518] Output: Emotion information is sent to the server

[1519] Step 5:

[1520] The server generates a coordinate based on the user's emotional information and clothing information.

[1521] Specific actions

[1522] Based on the received emotion information, the server queries a database to obtain a list of the user's clothing, and then uses an AI algorithm (e.g., a machine learning model) to generate an optimal outfit that combines emotion information and clothing information.

[1523] Input: User's emotional information, list of clothing owned by the user

[1524] Output: Generated outfit suggestions (e.g., "blue jeans + white T-shirt + black jacket")

[1525] Step 6:

[1526] The server sends the generated coordinates to the terminal and displays them to the user.

[1527] Specific actions

[1528] The server sends the generated coordination proposal to the terminal, which displays this information to the user.

[1529] Input: Generated outfit suggestions

[1530] Output: Coordinate information displayed to the user

[1531] Step 7:

[1532] The server detects missing items and makes purchasing suggestions.

[1533] Specific actions

[1534] The server examines the proposed outfit and identifies any missing items that the user does not own. If the missing items exist, the server searches the e-commerce site to obtain a link to purchase the product.

[1535] Input: Suggested outfits, list of clothing owned by the user

[1536] Output: Detected missing items and corresponding purchase links

[1537] Step 8:

[1538] The server sends the purchase link to the terminal and displays it to the user.

[1539] Specific actions

[1540] The server sends the acquired purchase link to the terminal, which displays this information to the user. When the user clicks the link, they are immediately redirected to the e-commerce site and can complete the product purchase procedure.

[1541] Input: Purchase link from e-commerce site

[1542] Output: The purchase link that is displayed to the user

[1543] As an example, the following prompt sentence can be used:

[1544] Prompt Sentence Examples

[1545] 1. Emotion Recognition Prompts

[1546] Which model should be used to analyze the user's facial expressions and recognize the "happy" emotion?

[1547] 2. Prompt for outfit suggestions

[1548] Please tell us the specific algorithm for how the most suitable outfit is generated based on the user's clothing data and emotional information.

[1549] (Application example 2)

[1550] 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."

[1551] While conventional clothing management systems can record information about the clothing owned by a user and suggest appropriate outfits, they are unable to provide personalized suggestions that take into account the user's emotional state. Furthermore, only some systems offer a function to provide links to purchase missing items, preventing a consistent user shopping experience.

[1552] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting information about clothing owned by the user and saving the information in a database, emotion analysis means for recognizing the user's mood, means for executing an algorithm for suggesting outfits based on the user's mood, means for identifying missing items based on the suggested outfits and obtaining links to corresponding products from external e-commerce sites, and means for managing the user's clothing information, making personalized outfit suggestions through emotion recognition, and supporting the purchase of missing items. This enables personalized outfit suggestions tailored to the user's mood and smooth purchase of missing items.

[1553] "User" refers to an individual who uses this system to manage clothing information and receive coordination suggestions.

[1554] "Clothing information" refers to detailed information about each piece of clothing owned by the user, including data such as category, color, and season.

[1555] "Database" refers to a system that systematically stores clothing information entered by users.

[1556] "Emotion analysis means" refers to technology that analyzes facial expressions, voice, or text input to recognize a user's mood or emotion.

[1557] "Algorithm" refers to the processing procedure for suggesting optimal outfits based on the user's clothing information and emotional information.

[1558] "E-commerce Site" means a website that enables the purchase of goods and services over the Internet.

[1559] "Purchase Link" refers to a URL for purchasing a particular item online.

[1560] A "machine learning model" refers to a technology that learns from large amounts of data and performs data analysis and predictions.

[1561] "Server" refers to a central computer system for processing and storing data submitted by users.

[1562] "Personalized outfits" refer to outfit combinations that are specifically suggested based on the user's personal information and emotions.

[1563] System program generation

[1564] The system program for realizing this application includes the following main functions:

[1565] 1. Clothing information management function

[1566] The user inputs information about the newly purchased clothing (category, color, season, etc.) into the system using a terminal. This information is sent from the terminal to the server, which then stores it in a database.

[1567] 2. Emotion recognition function

[1568] When a user uses the system, the emotion engine analyzes the user's facial expressions through the device's camera, recognizes the user's emotions based on facial expressions, voice, or text input, and sends that information to the server.

[1569] 3. Personalized outfit suggestions

[1570] The server uses a machine learning model to generate optimal outfits based on the acquired clothing and emotion information. These outfit suggestions are sent from the server to the device and displayed to the user.

[1571] 4. Purchasing support function

[1572] The server identifies missing items in the proposed outfit and searches e-commerce sites to obtain purchase links for the appropriate items, which are then sent from the server to the terminal and displayed to the user.

[1573] Detailed explanation of the process

[1574] The detailed procedure for implementing this system is as follows:

[1575] 1. Clothing information management

[1576] A user uses a smartphone or computer to enter information about a newly purchased garment into the system. For example, when entering information about a "Winter Jacket," the user enters details such as the category (outerwear), color (black), and season (winter). This information is sent from the device to the server, which stores it as a new record in the database.

[1577] 2. Emotion recognition

[1578] When a user logs in to the system, the emotion engine analyzes the user's facial expression through the device's camera. For example, the emotion engine captures the user's facial expression through the camera and recognizes that the user is in a "happy" mood. This emotional information is sent from the device to the server.

[1579] 3. Coordination suggestions

[1580] The server uses a machine learning model to generate optimal outfits based on the user's clothing information and emotional information stored in the database. For example, if a user is in a "happy" mood, it will suggest an outfit of "blue jeans + white T-shirt + black jacket." This suggestion is sent from the server to the user's device and displayed to them.

[1581] 4. Providing a purchasing link

[1582] The server identifies missing items in the proposed outfit and retrieves links to relevant products from e-commerce sites. For example, if the user does not own a white T-shirt, the server retrieves a link to purchase a white T-shirt from the e-commerce site. This link is then sent from the server to the device and displayed to the user.

[1583] Specific examples

[1584] If a user purchases a new jacket, "Winter Jacket," the following process occurs:

[1585] The user uses a smartphone to enter information about the "Winter Jacket" (category "Outerwear," color "Black," season "Winter") into the system. This information is sent to the server via the device and stored in the database.

[1586] When a user logs in, the emotion engine analyzes the user's facial expressions through the device's camera and recognizes that the user is in a "happy" mood. This information is sent to the server.

[1587] The server uses machine learning models to generate optimal outfits based on the user's clothing and emotional information, suggesting, for example, a combination of blue jeans, a white T-shirt, and a black jacket.

[1588] If the user does not have the white T-shirt required for the suggested outfit, the server retrieves a link to purchase the white T-shirt from an e-commerce site and provides it to the user.

[1589] Prompt Sentence Examples

[1590] "I just bought a new jacket, 'Winter Jacket'. The category is 'Outerwear', the color is 'Black', and the season is 'Winter'. Please register this in the app."

[1591] "Please recognize my facial expression on camera. I'm in a fun mood right now."

[1592] "Please suggest an outfit that matches my current mood."

[1593] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1594] Step 1:

[1595] A user uses a terminal to input information about newly purchased clothing into the system, including details such as the clothing category (e.g., outerwear), color (e.g., black), and season (e.g., winter). The terminal then sends this information to the server.

[1596] Input: Details such as clothing category, color, season, etc.

[1597] Output: Clothing information sent to the server

[1598] Step 2:

[1599] The server receives the clothing information sent from the terminal and stores it as a new record in the database, allowing for centralized management of all clothing information owned by the user.

[1600] Input: Clothing information sent from the device

[1601] Output: New clothing record saved in the database

[1602] Step 3:

[1603] The user logs in to the system, and the emotion engine analyzes the user's facial expressions through the device's camera. The emotion engine recognizes the user's emotions based on facial expressions, voice, or text input.

[1604] Input: User facial, voice, or text input data

[1605] Output: User's emotional information (e.g., "fun")

[1606] Step 4:

[1607] The device transmits the recognized emotion information to the server, which then processes the data based on the user's mood.

[1608] Input: Emotion information recognized by the device

[1609] Output: Emotion information sent to the server

[1610] Step 5:

[1611] The server acquires the user's clothing information stored in a database and emotion information sent from the terminal.

[1612] Input: Clothing information stored in the database, emotion information sent from the device

[1613] Output: Clothing information and emotion information to be processed by the server

[1614] Step 6:

[1615] The server uses a machine learning model to generate optimal outfits based on the acquired clothing and emotional information, suggesting, for example, a combination of blue jeans, a white T-shirt, and a black jacket.

[1616] Input: Clothing information, emotion information

[1617] Output: Proposed coordinates

[1618] Step 7:

[1619] The server transmits the generated coordinates to the terminal and displays them to the user.

[1620] Input: Proposed outfit information

[1621] Output: The coordinates displayed to the user

[1622] Step 8:

[1623] The server identifies the missing items based on the proposed coordinates.

[1624] Input: Proposed coordinates

[1625] Output: Missing items list

[1626] Step 9:

[1627] The server identifies the missing items and searches e-commerce sites to obtain purchasing links for the appropriate products.

[1628] Input: Missing items list

[1629] Output: Product purchase link

[1630] Step 10:

[1631] The server sends the obtained purchase link to the terminal and displays it to the user, who can use this link to purchase the missing item.

[1632] Enter: Purchasing Link

[1633] Output: The purchase link that is displayed to the user

[1634] 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.

[1635] 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.

[1636] 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.

[1637] 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.

[1638] 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.

[1639] 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.

[1640] 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).

[1641] 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.

[1642] 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."

[1643] 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.

[1644] 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).

[1645] 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.

[1646] 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.

[1647] 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.

[1648] 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.

[1649] 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.

[1650] 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.

[1651] 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.

[1652] 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.

[1653] 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.

[1654] 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.

[1655] The following is further disclosed regarding the above embodiment.

[1656] (Claim 1)

[1657] A means for a user to input information about clothing owned by the user and store the information in a database;

[1658] means for executing an algorithm for suggesting outfits based on the user's mood;

[1659] The system includes means for identifying missing items based on the suggested coordination and obtaining links to the corresponding products from external e-commerce sites.

[1660] (Claim 2)

[1661] The system of claim 1 uses a machine learning model to suggest outfits.

[1662] (Claim 3)

[1663] The system of claim 1, further comprising means for inputting detailed information about clothing owned by a user, including category, color, and season.

[1664] "Example 1"

[1665] (Claim 1)

[1666] A means for a user to input information about clothing owned by the user and store the information in a database;

[1667] means for executing an algorithm for suggesting outfits based on the user's mood;

[1668] means for identifying missing items based on the suggested outfit and obtaining links to the corresponding items from external e-commerce sites;

[1669] a means for displaying coordination suggestions to a user;

[1670] a means for the user to review the missing items in the offer and purchase them through a link;

[1671] A system including:

[1672] (Claim 2)

[1673] The system of claim 1 uses a generative AI model to suggest outfits.

[1674] (Claim 3)

[1675] The system of claim 1, further comprising means for inputting detailed information about clothing owned by a user, including category, color, and season.

[1676] "Application Example 1"

[1677] (Claim 1)

[1678] A means for a user to input information about clothing owned by the user and store the information in a database;

[1679] means for executing an algorithm for suggesting outfits based on the user's mood;

[1680] means for identifying missing items based on the suggested outfit and obtaining links to the corresponding items from external e-commerce sites;

[1681] A means for allowing a user to virtually try on the suggested outfits using augmented reality technology;

[1682] A system including:

[1683] (Claim 2)

[1684] The system of claim 1 uses a machine learning model to suggest outfits.

[1685] (Claim 3)

[1686] The system of claim 1, further comprising means for inputting detailed information about clothing owned by a user, including category, color, and season.

[1687] "Example 2: Combining Emotion Engines"

[1688] (Claim 1)

[1689] A means for a user to input information about clothing owned by the user and store the information in a database;

[1690] means for recognizing emotions from a user's facial expression, voice, or text input;

[1691] means for executing an algorithm for suggesting outfits that match the user's mood based on emotion recognition;

[1692] means for identifying missing items based on the suggested outfit and obtaining links to the corresponding items from external e-commerce sites;

[1693] A means to suggest more suitable items based on the user's emotions when suggesting coordination or purchasing products, and

[1694] A system including:

[1695] (Claim 2)

[1696] The system of claim 1 uses a machine learning model to suggest outfits.

[1697] (Claim 3)

[1698] The system of claim 1, further comprising means for inputting detailed information about clothing owned by a user, including category, color, and season.

[1699] "Application example 2 when combining emotion engines"

[1700] (Claim 1)

[1701] A means for a user to input information about clothing owned by the user and store the information in a database;

[1702] emotion analysis means for recognizing a user's mood;

[1703] means for executing an algorithm for suggesting outfits based on the user's mood;

[1704] means for identifying missing items based on the suggested outfit and obtaining links to the corresponding items from external e-commerce sites;

[1705] A means to manage user clothing information, provide personalized outfit suggestions through emotion recognition, and assist with purchasing missing items.

[1706] A system including:

[1707] (Claim 2)

[1708] The system of claim 1 uses a machine learning model to suggest outfits.

[1709] (Claim 3)

[1710] The system of claim 1, further comprising means for inputting detailed information about clothing owned by a user, including category, color, and season. [Explanation of symbols]

[1711] 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 a user to input information about clothing owned by the user and store the information in a database; means for executing an algorithm for suggesting outfits based on the user's mood; The system includes means for identifying missing items based on the proposed coordination and obtaining links to the corresponding products from external e-commerce sites.

2. The system according to claim 1, wherein a machine learning model is used to suggest coordination.

3. The system according to claim 1, further comprising means for inputting detailed information about clothing owned by a user, including category, color, and season.

Images