System

Abstract

A system is provided.SOLUTION: A system comprising: means for maintaining personal information entered by a user; means for entering and maintaining fashion style and lifestyle information of the user; means for generating a coordinate suggestion customized to the user based on the entered information and current fashion information; and means for identifying needed items and listing potential purchases based on the suggested coordinates.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] Traditionally, fashion-conscious consumers have had to spend a lot of time and effort figuring out appropriate outfits for different work and personal situations. In particular, there were limited ways to suggest combinations between items they already had and new items to purchase, which often led to stress and confusion. Furthermore, there were few ways to find out about the latest trend-driven outfits in real time, placing a significant burden on busy users. The present invention aims to solve these problems related to fashion coordination and enable users to easily achieve their own unique style. [Means for solving the problem]

[0005] The present invention provides the following means: By providing a means for storing personal information entered by the user, the user's basic attribute information can be reliably managed. Furthermore, by providing a means for inputting and storing the user's fashion style and lifestyle information, a foundation for providing individually customized styling suggestions is created. Furthermore, by providing a means for generating customized outfit suggestions for the user based on the input information and the latest fashion information, styling that always reflects the latest trends is provided. By including a means for identifying necessary items based on the suggested outfit and listing potential purchases, the user can easily purchase items that are missing. Furthermore, by adding a means for storing data on the user's existing possessions and identifying missing items based on the suggested outfit, existing items can be efficiently combined with newly purchased items. Furthermore, by providing a means for securely storing information entered by the user in a database and analyzing the stored data to generate customized outfit suggestions in real time, the user can receive timely and accurate styling information.

[0006] "User" refers to a consumer who inputs information into the system to receive fashion coordination and purchase suggestions.

[0007] "Personal information" refers to basic information related to an individual's identification, such as a user's name, age, gender, and contact details.

[0008] "Fashion style information" refers to information that represents a user's personal fashion preferences, such as the type, design, and color preferences of clothing.

[0009] "Lifestyle information" refers to information including a user's lifestyle and daily activity patterns, such as commuting frequency and leisure preferences.

[0010] "Coordination suggestions" refer to clothing combination suggestions for the user that are generated based on the input fashion style information and lifestyle information.

[0011] "Purchase Suggestions" refers to a list of items that are needed for the suggested outfit but that the user does not yet own, and are presented as available for purchase.

[0012] "Inventory data" refers to historical information about fashion items such as clothes and accessories that a user currently owns.

[0013] "Database" refers to a structured information management system for storing and managing various data such as a user's personal information, fashion style information, lifestyle information, and belongings data.

[0014] A "generative AI model" refers to an algorithm that learns fashion-related trend information and the user's personal data, and automatically generates customized outfit suggestions and purchase candidates. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] This system provides customized coordination suggestions and purchase candidates based on the user's personal information, fashion style information, and lifestyle information. This system provides optimal fashion advice to users in real time by communicating between a server and the user's terminal.

[0037] First, a user accesses the system using a terminal and creates a new account. The user enters basic personal information such as name and email address. This information is sent from the terminal to the server, which stores it in a database and sends a confirmation email to the user.

[0038] Next, the user uses the profile setting screen to enter information about their fashion style and lifestyle, such as "I like casual style," "I commute five days a week," and "I enjoy outdoor activities on weekends." This data is also sent from the device to the server, which stores and updates it in the database.

[0039] Once a user's profile is set, the server uses a generative AI model to generate outfit suggestions. The generative AI model generates multiple outfit options based on the user's fashion style and lifestyle information, while referencing the latest fashion trends. The server then sends the generated outfit suggestions to the user's device and displays them for the user to visually confirm.

[0040] The server also considers the user's inventory data and identifies any missing items based on the proposed outfit. Based on this, it generates a list of potential purchases and sends it to the user's device. The user can then view this list and easily purchase the items they need from the online shop.

[0041] For example, if a user specifies that they prefer a "casual style," that they commute five times a week, and that they "go outdoors on weekends," the server will use the generative AI model to suggest casual outfits suitable for commuting and outfits suitable for weekend outdoor activities. If the items needed for these suggestions are not in the user's inventory, they will be added to a list of purchase candidates and displayed on the user's device.

[0042] In this way, the system of the present invention provides personalized fashion suggestions in real time that are tailored to the user's needs and preferences, and supports the user in creating attractive styling that is fun and easy.

[0043] The processing flow will be explained below.

[0044] Specific explanation of program processing

[0045] User Registration

[0046] Step 1:

[0047] User: Launches the app, enters their name, email address, and password in the "New Registration" form, confirms the information entered, and presses the "Submit" button.

[0048] Step 2:

[0049] Terminal: Temporarily stores the entered information and sends a transmission request to the server.

[0050] Step 3:

[0051] Server: Stores the received user information in the database and generates a confirmation email if the save is successful.

[0052] Step 4:

[0053] Server: Generates the content of the confirmation email and sends it to the registered email address.

[0054] Fashion and lifestyle information input

[0055] Step 1:

[0056] User: On the profile settings screen, enter your preferred fashion style (e.g., "casual" or "business") and lifestyle (e.g., "commuting five days a week" or "outdoors on weekends").

[0057] Step 2:

[0058] Device: Temporarily stores the entered fashion style and lifestyle information and waits for the "Save" button to be pressed.

[0059] Step 3:

[0060] User: Presses the "Save" button.

[0061] Step 4:

[0062] Terminal: Sends input data to the server.

[0063] Step 5:

[0064] Server: Stores the received user fashion style and lifestyle information in a database and updates the user profile.

[0065] Generating outfit suggestions

[0066] Step 1:

[0067] Server: Retrieves user profile information (fashion style and lifestyle information) from the database.

[0068] Step 2:

[0069] Server: Based on the acquired information, a generative AI model is used to generate outfit suggestions, taking into account trends, the season, and existing personal belongings.

[0070] Step 3:

[0071] Server: Sends the generated coordination plan to the user device.

[0072] Step 4:

[0073] Device: Visually displays the received coordination suggestions to the user.

[0074] Purchase suggestion

[0075] Step 1:

[0076] Server: Retrieves user's inventory data from the database.

[0077] Step 2:

[0078] Server: Based on the obtained outfit suggestions and inventory data, identify items that the user does not have.

[0079] Step 3:

[0080] Server: Generates a list of available items for purchase.

[0081] Step 4:

[0082] Server: Sends the list of potential purchases to the user's device.

[0083] Step 5:

[0084] On the device: The received shopping list is visually displayed to the user and a link to the shopping site is provided.

[0085] Specific examples

[0086] User Registration

[0087] Step 1:

[0088] User: The user launches the app, enters their name, email address, and password in the "New Registration" form, and presses the "Submit" button.

[0089] Step 2:

[0090] Terminal: Sends input information to the server.

[0091] Step 3:

[0092] Server: Stores the received information in a database and sends a confirmation email.

[0093] Fashion and lifestyle information input

[0094] Step 1:

[0095] User: In the app, select "Casual," "Commute 5 days a week," and "Outdoors on weekends," then press the "Save" button.

[0096] Step 2:

[0097] Terminal: Sends input data to the server.

[0098] Step 3:

[0099] Server: Analyzes the input information and stores it in a database.

[0100] Generating outfit suggestions

[0101] Step 1:

[0102] Server: Generates "casual commuting style" and "weekend style suitable for outdoor activities" based on the user profile.

[0103] Step 2:

[0104] Server: Sends coordination proposals to the user device.

[0105] Step 3:

[0106] Terminal: Visually display the suggested outfits to the user.

[0107] Purchase suggestion

[0108] Step 1:

[0109] Server: Obtains the user's belongings data based on the coordination suggestions.

[0110] Step 2:

[0111] Server: Identifies missing items and generates a purchase list.

[0112] Step 3:

[0113] Server: Sends the list of potential purchases to the user's device.

[0114] Step 4:

[0115] On your device: Display suggested purchases and provide links to appropriate online stores.

[0116] Example 1

[0117] 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."

[0118] Today's users want to coordinate their fashion to suit their own style and lifestyle, and meeting these needs requires processing a large amount of information in real time to provide individually customized suggestions.However, existing systems are unable to adequately provide individually customized coordination suggestions, identify missing items, or provide purchase candidates, making it difficult to increase user satisfaction.

[0119] 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.

[0120] In this invention, the server includes means for storing personal information input by the user, means for inputting and storing information about the user's fashion style and lifestyle, means for generating coordinated outfit suggestions customized for the user using a generative AI model based on the input information and the latest fashion information, means for identifying necessary items using the output of the generative AI model based on the coordinated outfit suggestions and creating a list of purchase candidates, and means for transmitting the generated list of purchase candidates to the user terminal. This makes it possible to provide fashion suggestions tailored to the individual needs of the user in real time, identify missing items, and provide support up to the purchase stage.

[0121] "Personal Information" refers to basic information such as a user's name, email address, and password.

[0122] "Fashion style information" refers to information including the type of fashion that the user prefers (for example, casual, business, sporty, etc.).

[0123] "Lifestyle information" refers to information about a user's lifestyle and activities (for example, the number of times they commute per week, their weekend activities, etc.).

[0124] "Generative AI model" refers to an artificial intelligence algorithm that generates customized outfit suggestions for users based on input data.

[0125] A "prompt" refers to a question or instruction input to a generative AI model.

[0126] "Database" refers to a storage device for safely storing a user's personal information, fashion style information, lifestyle information, etc.

[0127] "Outfit suggestions" refer to clothing combinations generated by a generative AI model that match the user's fashion style and lifestyle.

[0128] "Purchase Candidates" refer to items that the user does not own but may purchase, identified based on the suggested outfit.

[0129] "User terminal" refers to a device (e.g., a smartphone, PC, etc.) used by a user to access the system.

[0130] "Visually displaying" refers to outputting information to a user's device in a form that can be visually confirmed.

[0131] This invention is a system that provides customized coordination suggestions and purchase candidates based on a user's personal information, fashion style information, and lifestyle information. The system aims to provide users with optimal fashion advice in real time by communicating between a server and user terminals.

[0132] A user accesses the system using a terminal and creates a new account. The user enters basic personal information, such as name and email address, which is then sent from the terminal to the server. The server stores this information in a database and sends a confirmation email to the user.

[0133] Next, the user enters their fashion style and lifestyle information on the profile setting screen. This information includes, for example, "I like casual style," "I commute five days a week," and "I enjoy outdoor activities on weekends." This data is also sent from the device to the server, where it is saved and updated in the database.

[0134] Once the user's profile is set, the server uses a generative AI model to generate outfit suggestions. The generative AI model generates a number of outfit options based on the user's fashion style and lifestyle information, taking into account the latest fashion trends. The server then sends the generated outfit suggestions to the user's device and displays them for the user to visually confirm.

[0135] The server also considers the user's personal belongings and identifies the items needed for the proposed outfit. Based on this, it generates a list of missing items and sends it to the user's device as purchase candidates. The user can then view this list of purchase candidates and easily purchase the items they need from the online shop.

[0136] For example, if a user specifies that they prefer a "casual style," commute five times a week, and spend their weekends outdoors, the server will use the generative AI model to generate the following prompt:

[0137] Example prompt sentence:

[0138] User input:

[0139] Fashion Style: Casual

[0140] Lifestyle: Commuting 5 days a week, outdoor activities on weekends

[0141] Prompt the generative AI model:

[0142] "Please suggest fashion coordination suitable for a user who likes casual style, commutes five days a week, and enjoys outdoor activities on the weekends."

[0143] The generative AI model suggests casual outfits for commuting or weekend outdoor activities. If the user does not have the necessary items in their inventory, they will be added to a list of purchase options and displayed on their device.

[0144] In this way, the system of the present invention provides personalized fashion suggestions in real time that are tailored to the user's needs and preferences, and supports the user in creating attractive styling that is fun and easy.

[0145] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0146] Step 1:

[0147] A user accesses the system from a terminal and enters the personal information required to create an account (such as name, email address, and password). The entered personal information is sent from the terminal to the server. The server stores the received personal information in a database and sends a confirmation email to the user. Specifically, the user enters information into a web form, and that information is sent to the server via an HTTP request.

[0148] Step 2:

[0149] The user goes to a profile setting screen and inputs information about their fashion style and lifestyle. Specifically, they input information such as "I like casual style," "I commute five days a week," and "I enjoy outdoor activities on weekends." This information is sent from the device to the server, which then stores and updates the received information in a database. Input is done via the user interface, and the transmitted data is analyzed and stored on the server.

[0150] Step 3:

[0151] The server generates a prompt for the generative AI model based on the user's personal information, fashion style information, and lifestyle information stored in the database. For example, the prompt might be, "Please suggest a fashion coordination suitable for a user who likes casual style, commutes to work five days a week, and enjoys outdoor activities on weekends." The prompt is sent to the generative AI model.

[0152] Step 4:

[0153] The generative AI model generates outfit suggestions based on the received prompt. The model uses the information provided by the user and the latest fashion trends to output customized outfit options. The generated outfit suggestions are returned to the server. Specific operations include data processing and model inference.

[0154] Step 5:

[0155] The server sends the outfit suggestions received from the generative AI model to the user's device, which then displays the outfit suggestions so that they can be visually confirmed. Specifically, the server sends the data using a communication protocol and the suggestions are displayed on the user interface.

[0156] Step 6:

[0157] The server queries the user's belongings data and identifies missing items based on the generated outfit suggestions. The server generates a shopping candidate list including the identified missing items and transmits it to the user terminal. Specifically, a database query is executed to generate the shopping candidate list.

[0158] Step 7:

[0159] The user terminal displays the list of potential purchases received from the server. The user can check the list and easily purchase the items they need from the online shop. Specifically, the list is displayed on the user interface and a link to the online shop is provided.

[0160] In this way, data processing and calculations are performed at each step based on user input, and the system provides personalized fashion suggestions in real time.

[0161] (Application example 1)

[0162] 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."

[0163] In recent years, the spread of online shopping has made it easy for consumers to purchase a wide variety of fashion items. However, it is not easy for individual users to find a coordinated outfit that suits their style and lifestyle, especially since it is difficult to check the final look before wearing it. Furthermore, considering how to combine an outfit with items already owned is a time-consuming and labor-intensive task for users. Given this situation, there is a demand for a system that allows users to easily receive personalized fashion suggestions in real time from the comfort of their own home and make appropriate purchasing decisions by virtually trying on the items.

[0164] 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.

[0165] In this invention, the server includes means for storing personal information entered by the user, means for inputting and storing information about the user's fashion style and lifestyle, means for generating coordinated outfit suggestions customized for the user based on the input information and the latest fashion information, means for visually presenting the suggested coordinated outfits through a virtual try-on simulation, and means for identifying necessary items based on the suggested coordinated outfits and listing potential purchases. This allows users to easily receive fashion suggestions suited to them from the comfort of their own homes, as well as to check actual coordinated outfits through the virtual try-on and smoothly purchase the necessary items.

[0166] "Personal Information" means information that can identify you as an individual, such as your name, email address, address, and age, provided by you.

[0167] "Fashion style information" is information about the style of clothing that the user prefers, such as type, color, and design.

[0168] "Lifestyle information" refers to information about the user's daily life patterns, such as their lifestyle, work habits, hobbies, and activities.

[0169] A "coordination suggestion" is a combination of specific fashion items that is generated based on the user's fashion style information and lifestyle information.

[0170] "Virtual try-on simulation" is a system that allows users to try on selected fashion items in a virtual space and visually check them.

[0171] "Purchase candidates" is a list of fashion items that the user does not own but should consider purchasing based on the suggested outfit.

[0172] "Inventory data" refers to information about fashion items that a user already owns.

[0173] A "generative AI model" is an algorithm that analyzes data entered by the user and the latest fashion information to generate appropriate coordination suggestions.

[0174] "Real-time" refers to a process that responds immediately to user actions and provides instant results.

[0175] This system provides customized coordination suggestions and purchase candidates based on the user's personal information, fashion style information, and lifestyle information. This system provides optimal fashion advice to users in real time by communicating between a server and the user's terminal.

[0176] System configuration

[0177] 1. Server

[0178] The server securely stores the personal information, fashion style information, and lifestyle information provided by the user in a database.

[0179] It uses a generative AI model to generate customized outfit suggestions based on the information you enter.

[0180] The suggested outfits are sent to the user's device as a virtual try-on simulation.

[0181] Based on the user's inventory data, it identifies missing items and creates a list of potential purchases.

[0182] 2. User Device

[0183] Users access the system using a terminal and create a new account.

[0184] You enter basic personal information such as your name and email address, which is sent from your device to a server that stores it in a database.

[0185] Users use a profile setting screen to enter their fashion style and lifestyle information, such as "I like casual style," "I commute five days a week," and "I enjoy outdoor activities on weekends."

[0186] Once a profile is set up, users can visually check suggested outfits through a virtual try-on simulation.

[0187] Processing flow

[0188] 1. Enter your personal information

[0189] The personal information entered by the user is sent to the server and stored in a database.

[0190] 2. Enter your fashion style and lifestyle information

[0191] The user inputs fashion style and lifestyle information, which is also sent to the server.

[0192] 3. Coordination Proposal Generation

[0193] The server uses a generative AI model to generate customized outfit suggestions based on the input information.

[0194] The suggested outfits are sent to the user's device and can be visually confirmed through a virtual try-on simulation.

[0195] 4. Identify and list missing items

[0196] The server considers the user's inventory data and identifies missing items based on the suggested outfit.

[0197] The items listed as potential purchases are sent to the user's device.

[0198] Specific examples

[0199] For example, if a user specifies that they prefer a "casual style," that they commute five times a week, and that they "go outdoors on weekends," the server will use the generative AI model to suggest casual outfits suitable for commuting and outfits suitable for weekend outdoor activities. If the items needed for these suggestions are not in the user's inventory, they will be added to a list of purchase candidates and displayed on the user's device.

[0200] Example prompts to input to the generative AI model

[0201] User profile:

[0202] Name: Yamada Taro

[0203] Email address: example@example.com

[0204] Fashion Style: Casual

[0205] Lifestyle:

[0206] Commuting five times a week

[0207] Outdoor activities on the weekend

[0208] Based on this profile information, the app will suggest outfits and list items you don't have in your inventory as potential purchases.

[0209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0210] Step 1:

[0211] Entering and saving personal information

[0212] A user accesses the system through a terminal and creates a new account. The user enters basic personal information such as name and email address. This information is sent from the terminal to the server, which stores it in a database. Example input data is the user's name "Taro" and email address "example@example.com". As output, the server sends a confirmation email to the user.

[0213] Step 2:

[0214] Enter and store fashion style and lifestyle information

[0215] Users use the profile settings screen on their device to enter information about their fashion style and lifestyle, such as "I like casual style," "I commute five days a week," and "I enjoy outdoor activities on weekends." This data is also sent from the device to the server, which stores and updates it in a database.

[0216] Step 3:

[0217] Generating outfit suggestions

[0218] Once the user's profile is set, the server uses a generative AI model to generate outfit suggestions. The input data here is the user's fashion style and lifestyle information. The generative AI model generates a number of outfit options based on the user's input and the latest fashion trend information. The output data is customized outfit suggestions (text and images), which the server sends to the user's device.

[0219] Step 4:

[0220] Providing virtual try-on simulations

[0221] The user visually checks the suggested outfits on their device through a virtual try-on simulation. The server generates virtual try-on simulation data based on the generated outfit suggestions and sends it to the user's device. The input data is the outfit suggestions, and the output data is a visual representation of the virtual try-on. This includes specific actions by the user to operate the avatar and check the suggested outfits.

[0222] Step 5:

[0223] Identifying and listing missing items

[0224] The server considers the user's inventory data and identifies missing items based on the suggested outfit. The input data is the user's inventory data and outfit suggestions, and the output data is a list of identified missing items. The server generates this list and sends it to the user's device.

[0225] Step 6:

[0226] Deciding and notifying potential purchasers

[0227] The user terminal displays the items listed as potential purchases based on the list of missing items received from the server. The user can view this list and take specific actions such as purchasing the necessary items from an online shop. The input data is the list of missing items, and the output data is the list of potential purchases.

[0228] 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.

[0229] This system provides customized outfit suggestions and purchase candidates based on the user's personal information, fashion style information, lifestyle information, and emotions. This system communicates between a server and the user's terminal, providing the user with optimal fashion advice in real time.

[0230] First, a user accesses the system using a terminal and creates a new account. The user enters basic personal information such as name and email address. This information is sent from the terminal to the server, which stores it in a database and sends a confirmation email to the user.

[0231] Next, the user uses the profile setting screen to enter information about their fashion style and lifestyle, such as "I like casual style," "I commute five days a week," and "I enjoy outdoor activities on weekends." This information is sent from the device to the server, which then stores and updates it in a database.

[0232] Once a user's profile is set, the server uses a generative AI model to generate outfit suggestions. The generative AI model generates multiple outfit options based on the user's fashion style and lifestyle information, while referencing the latest fashion trends. The server then sends the generated outfit suggestions to the user's device and displays them for the user to visually confirm.

[0233] The system also incorporates an emotion engine. The user's emotions are detected using cameras and sensors, and the emotion engine analyzes this information to recognize them. For example, the system can detect the emotion a user expresses when wearing a particular piece of clothing, and by learning this information, it can generate optimal outfits based on the user's emotions. The server also takes this emotional information into consideration to suggest outfits that are in line with the user's emotions.

[0234] The server also stores the user's inventory data and identifies missing items based on the suggested outfit. Based on this, a list of potential purchases is generated and sent to the user's device. The user can then easily purchase the items they need from the online shop by viewing this list. The list can also be optimized based on the user's emotions. For example, when a user is busy and stressed, the system suggests items with a simple, relaxed style.

[0235] For example, if a user specifies that they prefer a "casual style," that they commute five times a week, and that they "go outdoors on weekends," the server will use the generative AI model to suggest casual outfits suitable for commuting and outfits suitable for weekend outdoor activities. If the items required for these suggestions are not included in the user's inventory, they will be added to a list of purchase candidates and displayed on the user's device. Furthermore, the emotion engine analyzes the user's emotions, learns which suggestions the user responded most positively to, and reflects this in future outfit suggestions.

[0236] In this way, the system of the present invention provides personalized fashion suggestions in real time based on the user's needs, preferences, and emotions, helping users create attractive styling in a fun, easy way.

[0237] The processing flow will be explained below.

[0238] Specific explanation of program processing

[0239] User Registration

[0240] Step 1:

[0241] User: Launches the app, enters their name, email address, and password in the "New Registration" form, confirms the information entered, and presses the "Submit" button.

[0242] Step 2:

[0243] Terminal: Temporarily stores the entered information and sends a transmission request to the server.

[0244] Step 3:

[0245] Server: Stores the received user information in the database and generates a confirmation email if the save is successful.

[0246] Step 4:

[0247] Server: Generates the content of the confirmation email and sends it to the registered email address.

[0248] Fashion and lifestyle information input

[0249] Step 1:

[0250] User: On the profile settings screen, enter your preferred fashion style (e.g., "casual" or "business") and lifestyle (e.g., "commuting five days a week" or "outdoors on weekends").

[0251] Step 2:

[0252] Device: Temporarily stores the entered fashion style and lifestyle information and waits for the "Save" button to be pressed.

[0253] Step 3:

[0254] User: Presses the "Save" button.

[0255] Step 4:

[0256] Terminal: Sends input data to the server.

[0257] Step 5:

[0258] Server: Stores the received user fashion style and lifestyle information in a database and updates the user profile.

[0259] Generating outfit suggestions

[0260] Step 1:

[0261] Server: Retrieves user profile information (fashion style and lifestyle information) from the database.

[0262] Step 2:

[0263] Server: Based on the acquired information, a generative AI model is used to generate outfit suggestions, taking into account trends, the season, and existing personal belongings.

[0264] Step 3:

[0265] Device: Captures the user's facial image and voice data through the camera and microphone.

[0266] Step 4:

[0267] Device: Sends the acquired emotion data to the server.

[0268] Step 5:

[0269] Server: Analyzes the user's emotions using the emotion engine. Based on the analysis results, the server adjusts the outfit suggestions.

[0270] Step 6:

[0271] Server: Sends the adjusted coordination plan to the user device.

[0272] Step 7:

[0273] Device: Visually displays the received coordination suggestions to the user.

[0274] Purchase suggestion

[0275] Step 1:

[0276] Server: Retrieves user's inventory data from the database.

[0277] Step 2:

[0278] Server: Based on the obtained outfit suggestions and inventory data, identify items that the user does not have.

[0279] Step 3:

[0280] Server: Generates a list of available items for purchase.

[0281] Step 4:

[0282] Server: Optimize the shopping list based on the analysis results of the emotion engine. For example, if the user is feeling stressed, prioritize items with a relaxed style.

[0283] Step 5:

[0284] Server: Sends the optimized shopping list to the user's device.

[0285] Step 6:

[0286] On the device: The received shopping list is visually displayed to the user and a link to the shopping site is provided.

[0287] Specific examples

[0288] User Registration

[0289] Step 1:

[0290] User: The user launches the app, enters their name, email address, and password in the "New Registration" form, and presses the "Submit" button.

[0291] Step 2:

[0292] Terminal: Sends input information to the server.

[0293] Step 3:

[0294] Server: Stores the received information in a database and sends a confirmation email.

[0295] Fashion and lifestyle information input

[0296] Step 1:

[0297] User: In the app, select "Casual," "Commute 5 days a week," and "Outdoors on weekends," then press the "Save" button.

[0298] Step 2:

[0299] Terminal: Sends input data to the server.

[0300] Step 3:

[0301] Server: Analyzes the input information and stores it in a database.

[0302] Generating outfit suggestions

[0303] Step 1:

[0304] Server: Generates "casual commuting style" and "weekend style suitable for outdoor activities" based on the user profile.

[0305] Step 2:

[0306] Device: The camera captures the user's facial image and sends it to the server as emotion data.

[0307] Step 3:

[0308] Server: Analyzes emotions using an emotion engine and adjusts outfit suggestions to match the user's mood.

[0309] Step 4:

[0310] Server: Sends the adjusted coordination proposal to the user device.

[0311] Step 5:

[0312] Terminal: Visually display the suggested outfits to the user.

[0313] Purchase suggestion

[0314] Step 1:

[0315] Server: Obtains the user's belongings data based on the coordination suggestions.

[0316] Step 2:

[0317] Server: Identifies missing items and generates a purchase list.

[0318] Step 3:

[0319] Server: Optimize purchase candidates based on user sentiment data.

[0320] Step 4:

[0321] Server: Sends the optimized shopping list to the user's device.

[0322] Step 5:

[0323] On your device: Display suggested purchases and provide links to appropriate online stores.

[0324] Example 2

[0325] 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."

[0326] Conventional fashion recommendation systems could only provide generalized suggestions without fully considering the individual preferences and lifestyles of users. Furthermore, they lacked personalized coordination suggestions that took into account the user's emotions and actual possessions, making it difficult to provide specific and practical fashion advice.

[0327] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for storing personal information input by the user, a means for inputting and storing the user's fashion style and lifestyle information, a means for generating a coordinated outfit proposal customized for the user using a generative AI model based on the input information and the latest fashion information, a means for generating a prompt sentence for the generative AI model and generating the coordinated outfit proposal, a means for identifying missing items based on the proposed outfit and listing purchase candidates, and a means for analyzing the user's emotions using an emotion detection device and optimizing the coordinated outfit proposal based on the emotion information. This makes it possible to provide specific and practical coordinated outfit proposals based on the user's individual preferences and lifestyle.

[0328] "Means for storing personal information" refers to devices or software used to store and manage basic information such as a user's name, email address, and password.

[0329] "Means for inputting and storing fashion style and lifestyle information" refers to devices and software that allow users to input information about their preferred fashion style and daily living patterns, and to store and manage this information.

[0330] A "generative AI model" is an artificial intelligence system that generates new information or suggestions based on input data, for example, using machine learning algorithms.

[0331] A "means for generating prompt sentences" is a device or software that formats and inputs instructions and information to generate appropriate coordination suggestions for a generative AI model.

[0332] A "means for generating outfit suggestions" is a device or software that uses user input information and a generative AI model to create a series of fashion suggestions and outfit ideas.

[0333] An "emotion detection device" is a device or software that detects and analyzes a user's emotional state, such as one that uses a camera or sensor.

[0334] "Means for optimizing outfit suggestions based on emotional information" refers to devices or software that adjust outfit suggestions made by a generative AI model based on the user's emotional information obtained by an emotion detection device, thereby providing more appropriate suggestions.

[0335] "Means for identifying missing items and listing potential purchase items" refers to a device or software that compares the user's possession data with the generated coordination suggestions, identifies the missing items, and lists them as potential purchase items.

[0336] This is a system that provides customized outfit suggestions and purchase candidates based on a user's personal information, fashion style information, lifestyle information, and emotions. This system communicates between a server and the user's terminal, providing the user with optimal fashion advice in real time.

[0337] Create an account and enter your personal information

[0338] First, the user accesses the system using their own device (e.g., smartphone or PC). The user enters their name, email address, and password on the new account creation screen. This information is sent to the server using the HTTPS protocol. The server stores the received personal information in a database (e.g., MySQL or MongoDB) and sends a confirmation email to the user using a mail server (e.g., Amazon SES or SendGrid).

[0339] Profile Settings

[0340] Next, the user uses their device to access a profile setting screen and enter information about their fashion style and lifestyle. For example, they might say, "I like casual style," "I commute five days a week," or "I enjoy outdoor activities on weekends." The device then sends this information to the server, which then stores and updates the received information in a database.

[0341] Generating outfit suggestions

[0342] Once the user's profile information is set, the server uses a generative AI model (e.g., GPT-4) to generate outfit suggestions. The server creates a prompt for the generative AI model. An example of a prompt is, "The user's fashion style is casual, they commute to work five days a week, and enjoy outdoor activities on weekends. Please suggest the best outfit for the user." Based on this prompt, the generative AI model generates appropriate outfit ideas. The server sends the generated outfit suggestions to the user's device and displays them so that the user can visually confirm them.

[0343] Applying the Emotion Engine

[0344] The system also incorporates an emotion engine. The user's emotions are detected using the device's built-in camera and sensors. The device then sends the emotion data to the server. The server's emotion engine then analyzes the user's emotions using image processing libraries such as OpenCV. Based on the analysis results, the server generates outfit suggestions that are in line with the user's emotions. The server then generates prompts for the AI ​​model to generate outfit suggestions. The server then sends the new suggestions to the user's device.

[0345] Generate a purchase candidate list

[0346] The server retrieves the user's inventory data from a database and compares the items required with the generated outfit suggestions. If the required items are not included in the user's inventory, the server adds them to a list of potential purchases. The server then sends the list of potential purchases to the user's device and displays it for the user to review. The user can then easily purchase the items they need from the online shop by viewing the list of potential purchases.

[0347] In this way, the system can provide specific and practical outfit suggestions in real time based on the user's individual preferences and lifestyle.

[0348] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0349] Step 1:

[0350] The user accesses the system using a terminal and enters their name, email address, and password on the new account creation screen. The entered personal information is sent to the server using the HTTPS protocol. The server stores the received personal information in a database and sends a confirmation email.

[0351] Input: User's name, email address, and password

[0352] Data processing: Personal information is stored in a database

[0353] Output: A confirmation email from the server to the user

[0354] Step 2:

[0355] Users use their devices to access a profile setting screen and input their fashion style and lifestyle information. This information is sent from the device to the server, which then stores and updates it in a database.

[0356] Input: Fashion style information (e.g., casual), lifestyle information (e.g., commute five days a week, spend weekends outdoors)

[0357] Data processing: Save and update the entered information in the database

[0358] Output: Profile information is saved in the server database

[0359] Step 3:

[0360] The server generates a prompt for the generative AI model based on the user's profile information. The generative AI model generates outfit suggestions based on the prompt. The server then sends the generated outfit suggestions to the user's device.

[0361] Input: User profile information

[0362] Data processing: Enter prompts into the generative AI model to generate outfit suggestions

[0363] Output: Send the generated coordination proposal to the user's device

[0364] Step 4:

[0365] The device uses an emotion detection device (e.g., a camera or sensor) to capture the user's emotional data and send it to the server. The server's emotion engine analyzes this emotional data and recognizes the user's emotion. Based on the recognized emotional information, the device generates a new prompt and creates new coordination suggestions.

[0366] Input: User emotion data (e.g. camera footage)

[0367] Data processing: Emotion analysis using an emotion engine, prompt generation, and coordination suggestion update

[0368] Output: Optimized outfit suggestions

[0369] Step 5:

[0370] The server retrieves the user's belongings data from the database and checks the items required for the generated coordination proposal. If the required items are not included in the user's belongings, it generates a list of purchase candidates and sends it to the user's terminal.

[0371] Input: User's belongings data, generated coordination suggestions

[0372] Data processing: Compare inventory data with suggestions and create a list of missing items

[0373] Output: Send the purchase candidate list to the user's device

[0374] (Application example 2)

[0375] 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."

[0376] Conventional fashion suggestion systems make suggestions based on a user's personal information and fashion style information, but they are unable to take into account the user's emotions or real-time reactions, and therefore are unable to fully increase user satisfaction. Furthermore, the process of creating a list of potential purchases based on suggested outfits is not automated, which is a time-consuming process for the user. Thus, there is a need for a system that provides personalized fashion suggestions in real time and in line with the user's emotions.

[0377] The specification processing by the specification 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 storing personal information entered by the user, means for inputting and storing information about the user's fashion style and lifestyle, means for generating coordinated outfit suggestions customized for the user based on the input information and the latest fashion information, means for analyzing the user's emotions using a smartphone camera or sensor and optimizing the coordinated outfit suggestions based on that information, and means for identifying necessary accessories based on the proposed outfit and listing purchase candidates. This enables real-time fashion suggestions based on the user's emotions and preferences and efficient presentation of purchase candidates.

[0378] "Personal information" refers to information that users enter to identify themselves, such as their name, email address, and address.

[0379] "Fashion style" refers to the type of clothing and accessory coordination based on the user's preferences, such as casual or business style.

[0380] "Lifestyle information" refers to information related to the user's daily life and habits, such as the frequency of commuting and the activities they do on their days off.

[0381] A "smartphone" is an evolved form of a mobile phone, and is a portable information terminal with many functions, including Internet access, application execution, and camera functionality.

[0382] A "camera" is a device for taking images or videos, and in this case refers to the one built into a smartphone.

[0383] A "sensor" is a device that detects a physical quantity (such as light, temperature, or motion) and converts it into an electrical signal.

[0384] "Analyzing emotions" means observing the user's facial expressions and behavior and determining their current emotional state (for example, joy, sadness, surprise, etc.) based on that data.

[0385] "Coordination suggestions" suggest clothing combinations and accessory selections based on the user's fashion style and lifestyle information.

[0386] "Purchase candidates" is a list of items that are recommended for the user to purchase based on the coordination suggestions.

[0387] A "database" is a system for efficiently storing, managing, and searching structured data, and is used to organize and store large amounts of data such as user information.

[0388] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate new outfit suggestions based on user information.

[0389] A "prompt sentence" is a sentence that describes an input request for a generative AI model, and is text used to give specific instructions to the AI.

[0390] This invention is a system that provides customized coordination suggestions and purchase candidates based on the user's personal information, fashion style information, lifestyle information, and user emotions. This system communicates between a server and the user's terminal and provides optimal fashion advice in real time.

[0391] System configuration

[0392] The system consists of the following main components:

[0393] 1. User Device

[0394] A smartphone is a device that allows users to input personal information, fashion style, and lifestyle information.

[0395] Smartphones are equipped with cameras and sensors that are used to detect the user's emotions.

[0396] 2. Server

[0397] Personal information and style information submitted by users is stored in a database.

[0398] Use generative AI models to generate outfit suggestions.

[0399] Use a sentiment analysis engine to optimize suggestions based on user sentiment.

[0400] Program processing

[0401] To operate this system, the following data processing and calculation are performed.

[0402] Hardware and Software Use

[0403] 1. Hardware

[0404] Smartphones: Used for data entry and emotion detection.

[0405] Server: Used for data storage, running generative AI models, and sentiment analysis.

[0406] 2. Software

[0407] Database Management System: Software for storing user information and fashion information.

[0408] Generative AI model (e.g., TensorFlow or Keras): An algorithm for generating outfit suggestions based on user information.

[0409] Sentiment analysis engine (e.g., OpenCV): Software for analyzing user emotions based on images acquired from a smartphone camera.

[0410] Data processing and calculation flow

[0411] 1. Entering and saving user information

[0412] Users use their smartphones to enter personal information, fashion style, and lifestyle information, which is then sent to a server, which stores the information in a database.

[0413] 2. Coordination Proposal Generation

[0414] The server uses a generative AI model to generate multiple outfit suggestions based on the user's input and the latest fashion information.

[0415] 3. Sentiment analysis and recommendation optimization

[0416] The smartphone's camera and sensors detect the user's emotions and send the data to a server, which then uses an emotion analysis engine to optimize suggestions based on the user's emotions.

[0417] 4. Presenting potential purchases

[0418] Based on the proposed outfit, the server checks the user's inventory data, identifies missing items, and creates a list of potential purchases, which is then sent to the user's device for viewing.

[0419] Examples of specific examples and prompts

[0420] For example, if a user specifies that they prefer a "casual style" and enters "commuting five days a week" and "outdoors on weekends," the server will send the following prompt to the generative AI model:

[0421] User Information:

[0422] Name: Your name

[0423] Fashion Style: Casual

[0424] Lifestyle: 5-day commute, weekend outdoor activities

[0425] Prompt for generating outfit suggestions:

[0426] Suggest a casual style suitable for commuting five days a week, as well as an outfit suitable for outdoor activities on the weekends, taking into account the latest fashion trends and the preferences of the user.

[0427] Based on these prompts, a generative AI model generates detailed outfit suggestions, which are then delivered to the user in real time.

[0428] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0429] Step 1:

[0430] Users use their smartphones to input their personal information, fashion style information, and lifestyle information, such as their name, email address, preferred style (e.g., casual), and daily life information (e.g., commuting five days a week and outdoor activities on weekends). This data is then sent to the server as input information.

[0431] Step 2:

[0432] The server stores the received user personal information, fashion style information, and lifestyle information in a database. Specifically, the server stores the user information in a "user information table" and the fashion style and lifestyle information in a "style information table" to maintain consistency of the database.

[0433] Step 3:

[0434] The server generates outfit suggestions using a generative AI model based on the user's saved fashion style and lifestyle information. The server creates a prompt sentence like the following and inputs it into the generative AI model:

[0435] User Information:

[0436] Name: User

[0437] Fashion Style: Casual

[0438] Lifestyle: 5-day commute, weekend outdoor activities

[0439] Prompt for generating outfit suggestions:

[0440] Suggest a casual style suitable for commuting five days a week, as well as an outfit suitable for outdoor activities on the weekends, taking into account the latest fashion trends and the user's preferences.

[0441] This allows the generative AI model to suggest the best outfits for the user.

[0442] Step 4:

[0443] Users can use their smartphone's camera and sensors to detect their own emotional state. For example, if they smile while looking in a mirror, the camera captures their facial expression. This emotional data is then sent from the user device to the server.

[0444] Step 5:

[0445] The server inputs the received emotional data into its emotion analysis engine and analyzes the user's emotional state. The analysis results identify the user's current emotion (e.g., joy, excitement, etc.). Based on the results of this emotion analysis, the server optimizes the generated outfit suggestions and selects the suggestion that best matches the user's emotion.

[0446] Step 6:

[0447] The server sends the optimized outfit suggestions to the user's device, where the user can visually check the suggested outfits on their smartphone screen. Specifically, among the multiple outfits presented by the application's UI, the most suitable suggestion based on the results of emotion analysis is displayed first.

[0448] Step 7:

[0449] The server references the user's inventory database and identifies any missing items based on the proposed outfit. For example, if the proposed outfit requires a "white shirt" and the user does not own one, the white shirt is identified as a missing item. This information is added to the purchase candidate list.

[0450] Step 8:

[0451] The server sends a list of missing items to the user's device. The user can then check the list on their smartphone and purchase the items they need directly from the online shop. Specifically, by tapping an item on the list, a link to the online shop's purchase page is provided.

[0452] The above processing steps realize real-time personalized fashion suggestions based on the user's personal information and emotions, and further enable the user to easily purchase items based on the suggestions.

[0453] 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.

[0454] 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.

[0455] 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.

[0456] [Second embodiment]

[0457] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0458] 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.

[0459] 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).

[0460] 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.

[0461] 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.

[0462] 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).

[0463] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0464] 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.

[0465] 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.

[0466] 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.

[0467] 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.

[0468] 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."

[0469] This system provides customized coordination suggestions and purchase candidates based on the user's personal information, fashion style information, and lifestyle information. This system provides optimal fashion advice to users in real time by communicating between a server and the user's terminal.

[0470] First, a user accesses the system using a terminal and creates a new account. The user enters basic personal information such as name and email address. This information is sent from the terminal to the server, which stores it in a database and sends a confirmation email to the user.

[0471] Next, the user uses the profile setting screen to enter information about their fashion style and lifestyle, such as "I like casual style," "I commute five days a week," and "I enjoy outdoor activities on weekends." This data is also sent from the device to the server, which stores and updates it in the database.

[0472] Once a user's profile is set, the server uses a generative AI model to generate outfit suggestions. The generative AI model generates multiple outfit options based on the user's fashion style and lifestyle information, while referencing the latest fashion trends. The server then sends the generated outfit suggestions to the user's device and displays them for the user to visually confirm.

[0473] The server also considers the user's inventory data and identifies any missing items based on the proposed outfit. Based on this, it generates a list of potential purchases and sends it to the user's device. The user can then view this list and easily purchase the items they need from the online shop.

[0474] For example, if a user specifies that they prefer a "casual style," that they commute five times a week, and that they "go outdoors on weekends," the server will use the generative AI model to suggest casual outfits suitable for commuting and outfits suitable for weekend outdoor activities. If the items needed for these suggestions are not in the user's inventory, they will be added to a list of purchase candidates and displayed on the user's device.

[0475] In this way, the system of the present invention provides personalized fashion suggestions in real time that are tailored to the user's needs and preferences, and supports the user in creating attractive styling that is fun and easy.

[0476] The processing flow will be explained below.

[0477] Specific explanation of program processing

[0478] User Registration

[0479] Step 1:

[0480] User: Launches the app, enters their name, email address, and password in the "New Registration" form, confirms the information entered, and presses the "Submit" button.

[0481] Step 2:

[0482] Terminal: Temporarily stores the entered information and sends a transmission request to the server.

[0483] Step 3:

[0484] Server: Stores the received user information in the database and generates a confirmation email if the save is successful.

[0485] Step 4:

[0486] Server: Generates the content of the confirmation email and sends it to the registered email address.

[0487] Fashion and lifestyle information input

[0488] Step 1:

[0489] User: On the profile settings screen, enter your preferred fashion style (e.g., "casual" or "business") and lifestyle (e.g., "commuting five days a week" or "outdoors on weekends").

[0490] Step 2:

[0491] Device: Temporarily stores the entered fashion style and lifestyle information and waits for the "Save" button to be pressed.

[0492] Step 3:

[0493] User: Presses the "Save" button.

[0494] Step 4:

[0495] Terminal: Sends input data to the server.

[0496] Step 5:

[0497] Server: Stores the received user fashion style and lifestyle information in a database and updates the user profile.

[0498] Generating outfit suggestions

[0499] Step 1:

[0500] Server: Retrieves user profile information (fashion style and lifestyle information) from the database.

[0501] Step 2:

[0502] Server: Based on the acquired information, a generative AI model is used to generate outfit suggestions, taking into account trends, the season, and existing personal belongings.

[0503] Step 3:

[0504] Server: Sends the generated coordination plan to the user device.

[0505] Step 4:

[0506] Device: Visually displays the received coordination suggestions to the user.

[0507] Purchase suggestion

[0508] Step 1:

[0509] Server: Retrieves user's inventory data from the database.

[0510] Step 2:

[0511] Server: Based on the obtained outfit suggestions and inventory data, identify items that the user does not have.

[0512] Step 3:

[0513] Server: Generates a list of available items for purchase.

[0514] Step 4:

[0515] Server: Sends the list of potential purchases to the user's device.

[0516] Step 5:

[0517] On the device: The received shopping list is visually displayed to the user and a link to the shopping site is provided.

[0518] Specific examples

[0519] User Registration

[0520] Step 1:

[0521] User: The user launches the app, enters their name, email address, and password in the "New Registration" form, and presses the "Submit" button.

[0522] Step 2:

[0523] Terminal: Sends input information to the server.

[0524] Step 3:

[0525] Server: Stores the received information in a database and sends a confirmation email.

[0526] Fashion and lifestyle information input

[0527] Step 1:

[0528] User: In the app, select "Casual," "Commute 5 days a week," and "Outdoors on weekends," then press the "Save" button.

[0529] Step 2:

[0530] Terminal: Sends input data to the server.

[0531] Step 3:

[0532] Server: Analyzes the input information and stores it in a database.

[0533] Generating outfit suggestions

[0534] Step 1:

[0535] Server: Generates "casual commuting style" and "weekend style suitable for outdoor activities" based on the user profile.

[0536] Step 2:

[0537] Server: Sends coordination proposals to the user device.

[0538] Step 3:

[0539] Terminal: Visually display the suggested outfits to the user.

[0540] Purchase suggestion

[0541] Step 1:

[0542] Server: Obtains the user's belongings data based on the coordination suggestions.

[0543] Step 2:

[0544] Server: Identifies missing items and generates a purchase list.

[0545] Step 3:

[0546] Server: Sends the list of potential purchases to the user's device.

[0547] Step 4:

[0548] On your device: Display suggested purchases and provide links to appropriate online stores.

[0549] Example 1

[0550] 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."

[0551] Today's users want to coordinate their fashion to suit their own style and lifestyle, and meeting these needs requires processing a large amount of information in real time to provide individually customized suggestions.However, existing systems are unable to adequately provide individually customized coordination suggestions, identify missing items, or provide purchase candidates, making it difficult to increase user satisfaction.

[0552] 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.

[0553] In this invention, the server includes means for storing personal information input by the user, means for inputting and storing information about the user's fashion style and lifestyle, means for generating coordinated outfit suggestions customized for the user using a generative AI model based on the input information and the latest fashion information, means for identifying necessary items using the output of the generative AI model based on the coordinated outfit suggestions and creating a list of purchase candidates, and means for transmitting the generated list of purchase candidates to the user terminal. This makes it possible to provide fashion suggestions tailored to the individual needs of the user in real time, identify missing items, and provide support up to the purchase stage.

[0554] "Personal Information" refers to basic information such as a user's name, email address, and password.

[0555] "Fashion style information" refers to information including the type of fashion that the user prefers (for example, casual, business, sporty, etc.).

[0556] "Lifestyle information" refers to information about a user's lifestyle and activities (for example, the number of times they commute per week, their weekend activities, etc.).

[0557] "Generative AI model" refers to an artificial intelligence algorithm that generates customized outfit suggestions for users based on input data.

[0558] A "prompt" refers to a question or instruction input to a generative AI model.

[0559] "Database" refers to a storage device for safely storing a user's personal information, fashion style information, lifestyle information, etc.

[0560] "Outfit suggestions" refer to clothing combinations generated by a generative AI model that match the user's fashion style and lifestyle.

[0561] "Purchase Candidates" refer to items that the user does not own but may purchase, identified based on the suggested outfit.

[0562] "User terminal" refers to a device (e.g., a smartphone, PC, etc.) used by a user to access the system.

[0563] "Visually displaying" refers to outputting information to a user's device in a form that can be visually confirmed.

[0564] This invention is a system that provides customized coordination suggestions and purchase candidates based on a user's personal information, fashion style information, and lifestyle information. The system aims to provide users with optimal fashion advice in real time by communicating between a server and user terminals.

[0565] A user accesses the system using a terminal and creates a new account. The user enters basic personal information, such as name and email address, which is then sent from the terminal to the server. The server stores this information in a database and sends a confirmation email to the user.

[0566] Next, the user enters their fashion style and lifestyle information on the profile setting screen. This information includes, for example, "I like casual style," "I commute five days a week," and "I enjoy outdoor activities on weekends." This data is also sent from the device to the server, where it is saved and updated in the database.

[0567] Once the user's profile is set, the server uses a generative AI model to generate outfit suggestions. The generative AI model generates a number of outfit options based on the user's fashion style and lifestyle information, taking into account the latest fashion trends. The server then sends the generated outfit suggestions to the user's device and displays them for the user to visually confirm.

[0568] The server also considers the user's personal belongings and identifies the items needed for the proposed outfit. Based on this, it generates a list of missing items and sends it to the user's device as purchase candidates. The user can then view this list of purchase candidates and easily purchase the items they need from the online shop.

[0569] For example, if a user specifies that they prefer a "casual style," commute five times a week, and spend their weekends outdoors, the server will use the generative AI model to generate the following prompt:

[0570] Example prompt sentence:

[0571] User input:

[0572] Fashion Style: Casual

[0573] Lifestyle: Commuting 5 days a week, outdoor activities on weekends

[0574] Prompt the generative AI model:

[0575] "Please suggest fashion coordination suitable for a user who likes casual style, commutes five days a week, and enjoys outdoor activities on the weekends."

[0576] The generative AI model suggests casual outfits for commuting or weekend outdoor activities. If the user does not have the necessary items in their inventory, they will be added to a list of purchase options and displayed on their device.

[0577] In this way, the system of the present invention provides personalized fashion suggestions in real time that are tailored to the user's needs and preferences, and supports the user in creating attractive styling that is fun and easy.

[0578] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0579] Step 1:

[0580] A user accesses the system from a terminal and enters the personal information required to create an account (such as name, email address, and password). The entered personal information is sent from the terminal to the server. The server stores the received personal information in a database and sends a confirmation email to the user. Specifically, the user enters information into a web form, and that information is sent to the server via an HTTP request.

[0581] Step 2:

[0582] The user goes to a profile setting screen and inputs information about their fashion style and lifestyle. Specifically, they input information such as "I like casual style," "I commute five days a week," and "I enjoy outdoor activities on weekends." This information is sent from the device to the server, which then stores and updates the received information in a database. Input is done via the user interface, and the transmitted data is analyzed and stored on the server.

[0583] Step 3:

[0584] The server generates a prompt for the generative AI model based on the user's personal information, fashion style information, and lifestyle information stored in the database. For example, the prompt might be, "Please suggest a fashion coordination suitable for a user who likes casual style, commutes to work five days a week, and enjoys outdoor activities on weekends." The prompt is sent to the generative AI model.

[0585] Step 4:

[0586] The generative AI model generates outfit suggestions based on the received prompt. The model uses the information provided by the user and the latest fashion trends to output customized outfit options. The generated outfit suggestions are returned to the server. Specific operations include data processing and model inference.

[0587] Step 5:

[0588] The server sends the outfit suggestions received from the generative AI model to the user's device, which then displays the outfit suggestions so that they can be visually confirmed. Specifically, the server sends the data using a communication protocol and the suggestions are displayed on the user interface.

[0589] Step 6:

[0590] The server queries the user's belongings data and identifies missing items based on the generated outfit suggestions. The server generates a shopping candidate list including the identified missing items and transmits it to the user terminal. Specifically, a database query is executed to generate the shopping candidate list.

[0591] Step 7:

[0592] The user terminal displays the list of potential purchases received from the server. The user can check the list and easily purchase the items they need from the online shop. Specifically, the list is displayed on the user interface and a link to the online shop is provided.

[0593] In this way, data processing and calculations are performed at each step based on user input, and the system provides personalized fashion suggestions in real time.

[0594] (Application example 1)

[0595] 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."

[0596] In recent years, the spread of online shopping has made it easy for consumers to purchase a wide variety of fashion items. However, it is not easy for individual users to find a coordinated outfit that suits their style and lifestyle, especially since it is difficult to check the final look before wearing it. Furthermore, considering how to combine an outfit with items already owned is a time-consuming and labor-intensive task for users. Given this situation, there is a demand for a system that allows users to easily receive personalized fashion suggestions in real time from the comfort of their own home and make appropriate purchasing decisions by virtually trying on the items.

[0597] 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.

[0598] In this invention, the server includes means for storing personal information entered by the user, means for inputting and storing information about the user's fashion style and lifestyle, means for generating coordinated outfit suggestions customized for the user based on the input information and the latest fashion information, means for visually presenting the suggested coordinated outfits through a virtual try-on simulation, and means for identifying necessary items based on the suggested coordinated outfits and listing potential purchases. This allows users to easily receive fashion suggestions suited to them from the comfort of their own homes, as well as to check actual coordinated outfits through the virtual try-on and smoothly purchase the necessary items.

[0599] "Personal Information" means information that can identify you as an individual, such as your name, email address, address, and age, provided by you.

[0600] "Fashion style information" is information about the style of clothing that the user prefers, such as type, color, and design.

[0601] "Lifestyle information" refers to information about the user's daily life patterns, such as their lifestyle, work habits, hobbies, and activities.

[0602] A "coordination suggestion" is a combination of specific fashion items that is generated based on the user's fashion style information and lifestyle information.

[0603] "Virtual try-on simulation" is a system that allows users to try on selected fashion items in a virtual space and visually check them.

[0604] "Purchase candidates" is a list of fashion items that the user does not own but should consider purchasing based on the suggested outfit.

[0605] "Inventory data" refers to information about fashion items that a user already owns.

[0606] A "generative AI model" is an algorithm that analyzes data entered by the user and the latest fashion information to generate appropriate coordination suggestions.

[0607] "Real-time" refers to a process that responds immediately to user actions and provides instant results.

[0608] This system provides customized coordination suggestions and purchase candidates based on the user's personal information, fashion style information, and lifestyle information. This system provides optimal fashion advice to users in real time by communicating between a server and the user's terminal.

[0609] System configuration

[0610] 1. Server

[0611] The server securely stores the personal information, fashion style information, and lifestyle information provided by the user in a database.

[0612] It uses a generative AI model to generate customized outfit suggestions based on the information you enter.

[0613] The suggested outfits are sent to the user's device as a virtual try-on simulation.

[0614] Based on the user's inventory data, it identifies missing items and creates a list of potential purchases.

[0615] 2. User Device

[0616] Users access the system using a terminal and create a new account.

[0617] You enter basic personal information such as your name and email address, which is sent from your device to a server that stores it in a database.

[0618] Users use a profile setting screen to enter their fashion style and lifestyle information, such as "I like casual style," "I commute five days a week," and "I enjoy outdoor activities on weekends."

[0619] Once a profile is set up, users can visually check suggested outfits through a virtual try-on simulation.

[0620] Processing flow

[0621] 1. Enter your personal information

[0622] The personal information entered by the user is sent to the server and stored in a database.

[0623] 2. Enter your fashion style and lifestyle information

[0624] The user inputs fashion style and lifestyle information, which is also sent to the server.

[0625] 3. Coordination Proposal Generation

[0626] The server uses a generative AI model to generate customized outfit suggestions based on the input information.

[0627] The suggested outfits are sent to the user's device and can be visually confirmed through a virtual try-on simulation.

[0628] 4. Identify and list missing items

[0629] The server considers the user's inventory data and identifies missing items based on the suggested outfit.

[0630] The items listed as potential purchases are sent to the user's device.

[0631] Specific examples

[0632] For example, if a user specifies that they prefer a "casual style," that they commute five times a week, and that they "go outdoors on weekends," the server will use the generative AI model to suggest casual outfits suitable for commuting and outfits suitable for weekend outdoor activities. If the items needed for these suggestions are not in the user's inventory, they will be added to a list of purchase candidates and displayed on the user's device.

[0633] Example prompts to input to the generative AI model

[0634] User profile:

[0635] Name: Yamada Taro

[0636] Email address: example@example.com

[0637] Fashion Style: Casual

[0638] Lifestyle:

[0639] Commuting five times a week

[0640] Outdoor activities on the weekend

[0641] Based on this profile information, the app will suggest outfits and list items you don't have in your inventory as potential purchases.

[0642] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0643] Step 1:

[0644] Entering and saving personal information

[0645] A user accesses the system through a terminal and creates a new account. The user enters basic personal information such as name and email address. This information is sent from the terminal to the server, which stores it in a database. Example input data is the user's name "Taro" and email address "example@example.com". As output, the server sends a confirmation email to the user.

[0646] Step 2:

[0647] Enter and store fashion style and lifestyle information

[0648] Users use the profile settings screen on their device to enter information about their fashion style and lifestyle, such as "I like casual style," "I commute five days a week," and "I enjoy outdoor activities on weekends." This data is also sent from the device to the server, which stores and updates it in a database.

[0649] Step 3:

[0650] Generating outfit suggestions

[0651] Once the user's profile is set, the server uses a generative AI model to generate outfit suggestions. The input data here is the user's fashion style and lifestyle information. The generative AI model generates a number of outfit options based on the user's input and the latest fashion trend information. The output data is customized outfit suggestions (text and images), which the server sends to the user's device.

[0652] Step 4:

[0653] Providing virtual try-on simulations

[0654] The user visually checks the suggested outfits on their device through a virtual try-on simulation. The server generates virtual try-on simulation data based on the generated outfit suggestions and sends it to the user's device. The input data is the outfit suggestions, and the output data is a visual representation of the virtual try-on. This includes specific actions by the user to operate the avatar and check the suggested outfits.

[0655] Step 5:

[0656] Identifying and listing missing items

[0657] The server considers the user's inventory data and identifies missing items based on the suggested outfit. The input data is the user's inventory data and outfit suggestions, and the output data is a list of identified missing items. The server generates this list and sends it to the user's device.

[0658] Step 6:

[0659] Deciding and notifying potential purchasers

[0660] The user terminal displays the items listed as potential purchases based on the list of missing items received from the server. The user can view this list and take specific actions such as purchasing the necessary items from an online shop. The input data is the list of missing items, and the output data is the list of potential purchases.

[0661] 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.

[0662] This system provides customized outfit suggestions and purchase candidates based on the user's personal information, fashion style information, lifestyle information, and emotions. This system communicates between a server and the user's terminal, providing the user with optimal fashion advice in real time.

[0663] First, a user accesses the system using a terminal and creates a new account. The user enters basic personal information such as name and email address. This information is sent from the terminal to the server, which stores it in a database and sends a confirmation email to the user.

[0664] Next, the user uses the profile setting screen to enter information about their fashion style and lifestyle, such as "I like casual style," "I commute five days a week," and "I enjoy outdoor activities on weekends." This information is sent from the device to the server, which then stores and updates it in a database.

[0665] Once a user's profile is set, the server uses a generative AI model to generate outfit suggestions. The generative AI model generates multiple outfit options based on the user's fashion style and lifestyle information, while referencing the latest fashion trends. The server then sends the generated outfit suggestions to the user's device and displays them for the user to visually confirm.

[0666] The system also incorporates an emotion engine. The user's emotions are detected using cameras and sensors, and the emotion engine analyzes this information to recognize them. For example, the system can detect the emotion a user expresses when wearing a particular piece of clothing, and by learning this information, it can generate optimal outfits based on the user's emotions. The server also takes this emotional information into consideration to suggest outfits that are in line with the user's emotions.

[0667] The server also stores the user's inventory data and identifies missing items based on the suggested outfit. Based on this, a list of potential purchases is generated and sent to the user's device. The user can then easily purchase the items they need from the online shop by viewing this list. The list can also be optimized based on the user's emotions. For example, when a user is busy and stressed, the system suggests items with a simple, relaxed style.

[0668] For example, if a user specifies that they prefer a "casual style," that they commute five times a week, and that they "go outdoors on weekends," the server will use the generative AI model to suggest casual outfits suitable for commuting and outfits suitable for weekend outdoor activities. If the items required for these suggestions are not included in the user's inventory, they will be added to a list of purchase candidates and displayed on the user's device. Furthermore, the emotion engine analyzes the user's emotions, learns which suggestions the user responded most positively to, and reflects this in future outfit suggestions.

[0669] In this way, the system of the present invention provides personalized fashion suggestions in real time based on the user's needs, preferences, and emotions, helping users create attractive styling in a fun, easy way.

[0670] The processing flow will be explained below.

[0671] Specific explanation of program processing

[0672] User Registration

[0673] Step 1:

[0674] User: Launches the app, enters their name, email address, and password in the "New Registration" form, confirms the information entered, and presses the "Submit" button.

[0675] Step 2:

[0676] Terminal: Temporarily stores the entered information and sends a transmission request to the server.

[0677] Step 3:

[0678] Server: Stores the received user information in the database and generates a confirmation email if the save is successful.

[0679] Step 4:

[0680] Server: Generates the content of the confirmation email and sends it to the registered email address.

[0681] Fashion and lifestyle information input

[0682] Step 1:

[0683] User: On the profile settings screen, enter your preferred fashion style (e.g., "casual" or "business") and lifestyle (e.g., "commuting five days a week" or "outdoors on weekends").

[0684] Step 2:

[0685] Device: Temporarily stores the entered fashion style and lifestyle information and waits for the "Save" button to be pressed.

[0686] Step 3:

[0687] User: Presses the "Save" button.

[0688] Step 4:

[0689] Terminal: Sends input data to the server.

[0690] Step 5:

[0691] Server: Stores the received user fashion style and lifestyle information in a database and updates the user profile.

[0692] Generating outfit suggestions

[0693] Step 1:

[0694] Server: Retrieves user profile information (fashion style and lifestyle information) from the database.

[0695] Step 2:

[0696] Server: Based on the acquired information, a generative AI model is used to generate outfit suggestions, taking into account trends, the season, and existing personal belongings.

[0697] Step 3:

[0698] Device: Captures the user's facial image and voice data through the camera and microphone.

[0699] Step 4:

[0700] Device: Sends the acquired emotion data to the server.

[0701] Step 5:

[0702] Server: Analyzes the user's emotions using the emotion engine. Based on the analysis results, the server adjusts the outfit suggestions.

[0703] Step 6:

[0704] Server: Sends the adjusted coordination plan to the user device.

[0705] Step 7:

[0706] Device: Visually displays the received coordination suggestions to the user.

[0707] Purchase suggestion

[0708] Step 1:

[0709] Server: Retrieves user's inventory data from the database.

[0710] Step 2:

[0711] Server: Based on the obtained outfit suggestions and inventory data, identify items that the user does not have.

[0712] Step 3:

[0713] Server: Generates a list of available items for purchase.

[0714] Step 4:

[0715] Server: Optimize the shopping list based on the analysis results of the emotion engine. For example, if the user is feeling stressed, prioritize items with a relaxed style.

[0716] Step 5:

[0717] Server: Sends the optimized shopping list to the user's device.

[0718] Step 6:

[0719] On the device: The received shopping list is visually displayed to the user and a link to the shopping site is provided.

[0720] Specific examples

[0721] User Registration

[0722] Step 1:

[0723] User: The user launches the app, enters their name, email address, and password in the "New Registration" form, and presses the "Submit" button.

[0724] Step 2:

[0725] Terminal: Sends input information to the server.

[0726] Step 3:

[0727] Server: Stores the received information in a database and sends a confirmation email.

[0728] Fashion and lifestyle information input

[0729] Step 1:

[0730] User: In the app, select "Casual," "Commute 5 days a week," and "Outdoors on weekends," then press the "Save" button.

[0731] Step 2:

[0732] Terminal: Sends input data to the server.

[0733] Step 3:

[0734] Server: Analyzes the input information and stores it in a database.

[0735] Generating outfit suggestions

[0736] Step 1:

[0737] Server: Generates "casual commuting style" and "weekend style suitable for outdoor activities" based on the user profile.

[0738] Step 2:

[0739] Device: The camera captures the user's facial image and sends it to the server as emotion data.

[0740] Step 3:

[0741] Server: Analyzes emotions using an emotion engine and adjusts outfit suggestions to match the user's mood.

[0742] Step 4:

[0743] Server: Sends the adjusted coordination proposal to the user device.

[0744] Step 5:

[0745] Terminal: Visually display the suggested outfits to the user.

[0746] Purchase suggestion

[0747] Step 1:

[0748] Server: Obtains the user's belongings data based on the coordination suggestions.

[0749] Step 2:

[0750] Server: Identifies missing items and generates a purchase list.

[0751] Step 3:

[0752] Server: Optimize purchase candidates based on user sentiment data.

[0753] Step 4:

[0754] Server: Sends the optimized shopping list to the user's device.

[0755] Step 5:

[0756] On your device: Display suggested purchases and provide links to appropriate online stores.

[0757] Example 2

[0758] 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."

[0759] Conventional fashion recommendation systems could only provide generalized suggestions without fully considering the individual preferences and lifestyles of users. Furthermore, they lacked personalized coordination suggestions that took into account the user's emotions and actual possessions, making it difficult to provide specific and practical fashion advice.

[0760] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for storing personal information input by the user, a means for inputting and storing the user's fashion style and lifestyle information, a means for generating a coordinated outfit proposal customized for the user using a generative AI model based on the input information and the latest fashion information, a means for generating a prompt sentence for the generative AI model and generating the coordinated outfit proposal, a means for identifying missing items based on the proposed outfit and listing purchase candidates, and a means for analyzing the user's emotions using an emotion detection device and optimizing the coordinated outfit proposal based on the emotion information. This makes it possible to provide specific and practical coordinated outfit proposals based on the user's individual preferences and lifestyle.

[0761] "Means for storing personal information" refers to devices or software used to store and manage basic information such as a user's name, email address, and password.

[0762] "Means for inputting and storing fashion style and lifestyle information" refers to devices and software that allow users to input information about their preferred fashion style and daily living patterns, and to store and manage this information.

[0763] A "generative AI model" is an artificial intelligence system that generates new information or suggestions based on input data, for example, using machine learning algorithms.

[0764] A "means for generating prompt sentences" is a device or software that formats and inputs instructions and information to generate appropriate coordination suggestions for a generative AI model.

[0765] A "means for generating outfit suggestions" is a device or software that uses user input information and a generative AI model to create a series of fashion suggestions and outfit ideas.

[0766] An "emotion detection device" is a device or software that detects and analyzes a user's emotional state, such as one that uses a camera or sensor.

[0767] "Means for optimizing outfit suggestions based on emotional information" refers to devices or software that adjust outfit suggestions made by a generative AI model based on the user's emotional information obtained by an emotion detection device, thereby providing more appropriate suggestions.

[0768] "Means for identifying missing items and listing potential purchase items" refers to a device or software that compares the user's possession data with the generated coordination suggestions, identifies the missing items, and lists them as potential purchase items.

[0769] This is a system that provides customized outfit suggestions and purchase candidates based on a user's personal information, fashion style information, lifestyle information, and emotions. This system communicates between a server and the user's terminal, providing the user with optimal fashion advice in real time.

[0770] Create an account and enter your personal information

[0771] First, the user accesses the system using their own device (e.g., smartphone or PC). The user enters their name, email address, and password on the new account creation screen. This information is sent to the server using the HTTPS protocol. The server stores the received personal information in a database (e.g., MySQL or MongoDB) and sends a confirmation email to the user using a mail server (e.g., Amazon SES or SendGrid).

[0772] Profile Settings

[0773] Next, the user uses their device to access a profile setting screen and enter information about their fashion style and lifestyle. For example, they might say, "I like casual style," "I commute five days a week," or "I enjoy outdoor activities on weekends." The device then sends this information to the server, which then stores and updates the received information in a database.

[0774] Generating outfit suggestions

[0775] Once the user's profile information is set, the server uses a generative AI model (e.g., GPT-4) to generate outfit suggestions. The server creates a prompt for the generative AI model. An example of a prompt is, "The user's fashion style is casual, they commute to work five days a week, and enjoy outdoor activities on weekends. Please suggest the best outfit for the user." Based on this prompt, the generative AI model generates appropriate outfit ideas. The server sends the generated outfit suggestions to the user's device and displays them so that the user can visually confirm them.

[0776] Applying the Emotion Engine

[0777] The system also incorporates an emotion engine. The user's emotions are detected using the device's built-in camera and sensors. The device then sends the emotion data to the server. The server's emotion engine then analyzes the user's emotions using image processing libraries such as OpenCV. Based on the analysis results, the server generates outfit suggestions that are in line with the user's emotions. The server then generates prompts for the AI ​​model to generate outfit suggestions. The server then sends the new suggestions to the user's device.

[0778] Generate a purchase candidate list

[0779] The server retrieves the user's inventory data from a database and compares the items required with the generated outfit suggestions. If the required items are not included in the user's inventory, the server adds them to a list of potential purchases. The server then sends the list of potential purchases to the user's device and displays it for the user to review. The user can then easily purchase the items they need from the online shop by viewing the list of potential purchases.

[0780] In this way, the system can provide specific and practical outfit suggestions in real time based on the user's individual preferences and lifestyle.

[0781] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0782] Step 1:

[0783] The user accesses the system using a terminal and enters their name, email address, and password on the new account creation screen. The entered personal information is sent to the server using the HTTPS protocol. The server stores the received personal information in a database and sends a confirmation email.

[0784] Input: User's name, email address, and password

[0785] Data processing: Personal information is stored in a database

[0786] Output: A confirmation email from the server to the user

[0787] Step 2:

[0788] Users use their devices to access a profile setting screen and input their fashion style and lifestyle information. This information is sent from the device to the server, which then stores and updates it in a database.

[0789] Input: Fashion style information (e.g., casual), lifestyle information (e.g., commute five days a week, spend weekends outdoors)

[0790] Data processing: Save and update the entered information in the database

[0791] Output: Profile information is saved in the server database

[0792] Step 3:

[0793] The server generates a prompt for the generative AI model based on the user's profile information. The generative AI model generates outfit suggestions based on the prompt. The server then sends the generated outfit suggestions to the user's device.

[0794] Input: User profile information

[0795] Data processing: Enter prompts into the generative AI model to generate outfit suggestions

[0796] Output: Send the generated coordination proposal to the user's device

[0797] Step 4:

[0798] The device uses an emotion detection device (e.g., a camera or sensor) to capture the user's emotional data and send it to the server. The server's emotion engine analyzes this emotional data and recognizes the user's emotion. Based on the recognized emotional information, the device generates a new prompt and creates new coordination suggestions.

[0799] Input: User emotion data (e.g. camera footage)

[0800] Data processing: Emotion analysis using an emotion engine, prompt generation, and coordination suggestion update

[0801] Output: Optimized outfit suggestions

[0802] Step 5:

[0803] The server retrieves the user's belongings data from the database and checks the items required for the generated coordination proposal. If the required items are not included in the user's belongings, it generates a list of purchase candidates and sends it to the user's terminal.

[0804] Input: User's belongings data, generated coordination suggestions

[0805] Data processing: Compare inventory data with suggestions and create a list of missing items

[0806] Output: Send the purchase candidate list to the user's device

[0807] (Application example 2)

[0808] 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."

[0809] Conventional fashion suggestion systems make suggestions based on a user's personal information and fashion style information, but they are unable to take into account the user's emotions or real-time reactions, and therefore are unable to fully increase user satisfaction. Furthermore, the process of creating a list of potential purchases based on suggested outfits is not automated, which is a time-consuming process for the user. Thus, there is a need for a system that provides personalized fashion suggestions in real time and in line with the user's emotions.

[0810] The specification processing by the specification 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 storing personal information entered by the user, means for inputting and storing information about the user's fashion style and lifestyle, means for generating coordinated outfit suggestions customized for the user based on the input information and the latest fashion information, means for analyzing the user's emotions using a smartphone camera or sensor and optimizing the coordinated outfit suggestions based on that information, and means for identifying necessary accessories based on the proposed outfit and listing purchase candidates. This enables real-time fashion suggestions based on the user's emotions and preferences and efficient presentation of purchase candidates.

[0811] "Personal information" refers to information that users enter to identify themselves, such as their name, email address, and address.

[0812] "Fashion style" refers to the type of clothing and accessory coordination based on the user's preferences, such as casual or business style.

[0813] "Lifestyle information" refers to information related to the user's daily life and habits, such as the frequency of commuting and the activities they do on their days off.

[0814] A "smartphone" is an evolved form of a mobile phone, and is a portable information terminal with many functions, including Internet access, application execution, and camera functionality.

[0815] A "camera" is a device for taking images or videos, and in this case refers to the one built into a smartphone.

[0816] A "sensor" is a device that detects a physical quantity (such as light, temperature, or motion) and converts it into an electrical signal.

[0817] "Analyzing emotions" means observing the user's facial expressions and behavior and determining their current emotional state (for example, joy, sadness, surprise, etc.) based on that data.

[0818] "Coordination suggestions" suggest clothing combinations and accessory selections based on the user's fashion style and lifestyle information.

[0819] "Purchase candidates" is a list of items that are recommended for the user to purchase based on the coordination suggestions.

[0820] A "database" is a system for efficiently storing, managing, and searching structured data, and is used to organize and store large amounts of data such as user information.

[0821] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate new outfit suggestions based on user information.

[0822] A "prompt sentence" is a sentence that describes an input request for a generative AI model, and is text used to give specific instructions to the AI.

[0823] This invention is a system that provides customized coordination suggestions and purchase candidates based on the user's personal information, fashion style information, lifestyle information, and user emotions. This system communicates between a server and the user's terminal and provides optimal fashion advice in real time.

[0824] System configuration

[0825] The system consists of the following main components:

[0826] 1. User Device

[0827] A smartphone is a device that allows users to input personal information, fashion style, and lifestyle information.

[0828] Smartphones are equipped with cameras and sensors that are used to detect the user's emotions.

[0829] 2. Server

[0830] Personal information and style information submitted by users is stored in a database.

[0831] Use generative AI models to generate outfit suggestions.

[0832] Use a sentiment analysis engine to optimize suggestions based on user sentiment.

[0833] Program processing

[0834] To operate this system, the following data processing and calculation are performed.

[0835] Hardware and Software Use

[0836] 1. Hardware

[0837] Smartphones: Used for data entry and emotion detection.

[0838] Server: Used for data storage, running generative AI models, and sentiment analysis.

[0839] 2. Software

[0840] Database Management System: Software for storing user information and fashion information.

[0841] Generative AI model (e.g., TensorFlow or Keras): An algorithm for generating outfit suggestions based on user information.

[0842] Sentiment analysis engine (e.g., OpenCV): Software for analyzing user emotions based on images acquired from a smartphone camera.

[0843] Data processing and calculation flow

[0844] 1. Entering and saving user information

[0845] Users use their smartphones to enter personal information, fashion style, and lifestyle information, which is then sent to a server, which stores the information in a database.

[0846] 2. Coordination Proposal Generation

[0847] The server uses a generative AI model to generate multiple outfit suggestions based on the user's input and the latest fashion information.

[0848] 3. Sentiment analysis and recommendation optimization

[0849] The smartphone's camera and sensors detect the user's emotions and send the data to a server, which then uses an emotion analysis engine to optimize suggestions based on the user's emotions.

[0850] 4. Presenting potential purchases

[0851] Based on the proposed outfit, the server checks the user's inventory data, identifies missing items, and creates a list of potential purchases, which is then sent to the user's device for viewing.

[0852] Examples of specific examples and prompts

[0853] For example, if a user specifies that they prefer a "casual style" and enters "commuting five days a week" and "outdoors on weekends," the server will send the following prompt to the generative AI model:

[0854] User Information:

[0855] Name: Your name

[0856] Fashion Style: Casual

[0857] Lifestyle: 5-day commute, weekend outdoor activities

[0858] Prompt for generating outfit suggestions:

[0859] Suggest a casual style suitable for commuting five days a week, as well as an outfit suitable for outdoor activities on the weekends, taking into account the latest fashion trends and the preferences of the user.

[0860] Based on these prompts, a generative AI model generates detailed outfit suggestions, which are then delivered to the user in real time.

[0861] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0862] Step 1:

[0863] Users use their smartphones to input their personal information, fashion style information, and lifestyle information, such as their name, email address, preferred style (e.g., casual), and daily life information (e.g., commuting five days a week and outdoor activities on weekends). This data is then sent to the server as input information.

[0864] Step 2:

[0865] The server stores the received user personal information, fashion style information, and lifestyle information in a database. Specifically, the server stores the user information in a "user information table" and the fashion style and lifestyle information in a "style information table" to maintain consistency of the database.

[0866] Step 3:

[0867] The server generates outfit suggestions using a generative AI model based on the user's saved fashion style and lifestyle information. The server creates a prompt sentence like the following and inputs it into the generative AI model:

[0868] User Information:

[0869] Name: User

[0870] Fashion Style: Casual

[0871] Lifestyle: 5-day commute, weekend outdoor activities

[0872] Prompt for generating outfit suggestions:

[0873] Suggest a casual style suitable for commuting five days a week, as well as an outfit suitable for outdoor activities on the weekends, taking into account the latest fashion trends and the user's preferences.

[0874] This allows the generative AI model to suggest the best outfits for the user.

[0875] Step 4:

[0876] Users can use their smartphone's camera and sensors to detect their own emotional state. For example, if they smile while looking in a mirror, the camera captures their facial expression. This emotional data is then sent from the user device to the server.

[0877] Step 5:

[0878] The server inputs the received emotional data into its emotion analysis engine and analyzes the user's emotional state. The analysis results identify the user's current emotion (e.g., joy, excitement, etc.). Based on the results of this emotion analysis, the server optimizes the generated outfit suggestions and selects the suggestion that best matches the user's emotion.

[0879] Step 6:

[0880] The server sends the optimized outfit suggestions to the user's device, where the user can visually check the suggested outfits on their smartphone screen. Specifically, among the multiple outfits presented by the application's UI, the most suitable suggestion based on the results of emotion analysis is displayed first.

[0881] Step 7:

[0882] The server references the user's inventory database and identifies any missing items based on the proposed outfit. For example, if the proposed outfit requires a "white shirt" and the user does not own one, the white shirt is identified as a missing item. This information is added to the purchase candidate list.

[0883] Step 8:

[0884] The server sends a list of missing items to the user's device. The user can then check the list on their smartphone and purchase the items they need directly from the online shop. Specifically, by tapping an item on the list, a link to the online shop's purchase page is provided.

[0885] The above processing steps realize real-time personalized fashion suggestions based on the user's personal information and emotions, and further enable the user to easily purchase items based on the suggestions.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] [Third embodiment]

[0890] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0891] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0892] 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).

[0893] 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.

[0894] 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.

[0895] 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).

[0896] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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."

[0902] This system provides customized coordination suggestions and purchase candidates based on the user's personal information, fashion style information, and lifestyle information. This system provides optimal fashion advice to users in real time by communicating between a server and the user's terminal.

[0903] First, a user accesses the system using a terminal and creates a new account. The user enters basic personal information such as name and email address. This information is sent from the terminal to the server, which stores it in a database and sends a confirmation email to the user.

[0904] Next, the user uses the profile setting screen to enter information about their fashion style and lifestyle, such as "I like casual style," "I commute five days a week," and "I enjoy outdoor activities on weekends." This data is also sent from the device to the server, which stores and updates it in the database.

[0905] Once a user's profile is set, the server uses a generative AI model to generate outfit suggestions. The generative AI model generates multiple outfit options based on the user's fashion style and lifestyle information, while referencing the latest fashion trends. The server then sends the generated outfit suggestions to the user's device and displays them for the user to visually confirm.

[0906] The server also considers the user's inventory data and identifies any missing items based on the proposed outfit. Based on this, it generates a list of potential purchases and sends it to the user's device. The user can then view this list and easily purchase the items they need from the online shop.

[0907] For example, if a user specifies that they prefer a "casual style," that they commute five times a week, and that they "go outdoors on weekends," the server will use the generative AI model to suggest casual outfits suitable for commuting and outfits suitable for weekend outdoor activities. If the items needed for these suggestions are not in the user's inventory, they will be added to a list of purchase candidates and displayed on the user's device.

[0908] In this way, the system of the present invention provides personalized fashion suggestions in real time that are tailored to the user's needs and preferences, and supports the user in creating attractive styling that is fun and easy.

[0909] The processing flow will be explained below.

[0910] Specific explanation of program processing

[0911] User Registration

[0912] Step 1:

[0913] User: Launches the app, enters their name, email address, and password in the "New Registration" form, confirms the information entered, and presses the "Submit" button.

[0914] Step 2:

[0915] Terminal: Temporarily stores the entered information and sends a transmission request to the server.

[0916] Step 3:

[0917] Server: Stores the received user information in the database and generates a confirmation email if the save is successful.

[0918] Step 4:

[0919] Server: Generates the content of the confirmation email and sends it to the registered email address.

[0920] Fashion and lifestyle information input

[0921] Step 1:

[0922] User: On the profile settings screen, enter your preferred fashion style (e.g., "casual" or "business") and lifestyle (e.g., "commuting five days a week" or "outdoors on weekends").

[0923] Step 2:

[0924] Device: Temporarily stores the entered fashion style and lifestyle information and waits for the "Save" button to be pressed.

[0925] Step 3:

[0926] User: Presses the "Save" button.

[0927] Step 4:

[0928] Terminal: Sends input data to the server.

[0929] Step 5:

[0930] Server: Stores the received user fashion style and lifestyle information in a database and updates the user profile.

[0931] Generating outfit suggestions

[0932] Step 1:

[0933] Server: Retrieves user profile information (fashion style and lifestyle information) from the database.

[0934] Step 2:

[0935] Server: Based on the acquired information, a generative AI model is used to generate outfit suggestions, taking into account trends, the season, and existing personal belongings.

[0936] Step 3:

[0937] Server: Sends the generated coordination plan to the user device.

[0938] Step 4:

[0939] Device: Visually displays the received coordination suggestions to the user.

[0940] Purchase suggestion

[0941] Step 1:

[0942] Server: Retrieves user's inventory data from the database.

[0943] Step 2:

[0944] Server: Based on the obtained outfit suggestions and inventory data, identify items that the user does not have.

[0945] Step 3:

[0946] Server: Generates a list of available items for purchase.

[0947] Step 4:

[0948] Server: Sends the list of potential purchases to the user's device.

[0949] Step 5:

[0950] On the device: The received shopping list is visually displayed to the user and a link to the shopping site is provided.

[0951] Specific examples

[0952] User Registration

[0953] Step 1:

[0954] User: The user launches the app, enters their name, email address, and password in the "New Registration" form, and presses the "Submit" button.

[0955] Step 2:

[0956] Terminal: Sends input information to the server.

[0957] Step 3:

[0958] Server: Stores the received information in a database and sends a confirmation email.

[0959] Fashion and lifestyle information input

[0960] Step 1:

[0961] User: In the app, select "Casual," "Commute 5 days a week," and "Outdoors on weekends," then press the "Save" button.

[0962] Step 2:

[0963] Terminal: Sends input data to the server.

[0964] Step 3:

[0965] Server: Analyzes the input information and stores it in a database.

[0966] Generating outfit suggestions

[0967] Step 1:

[0968] Server: Generates "casual commuting style" and "weekend style suitable for outdoor activities" based on the user profile.

[0969] Step 2:

[0970] Server: Sends coordination proposals to the user device.

[0971] Step 3:

[0972] Terminal: Visually display the suggested outfits to the user.

[0973] Purchase suggestion

[0974] Step 1:

[0975] Server: Obtains the user's belongings data based on the coordination suggestions.

[0976] Step 2:

[0977] Server: Identifies missing items and generates a purchase list.

[0978] Step 3:

[0979] Server: Sends the list of potential purchases to the user's device.

[0980] Step 4:

[0981] On your device: Display suggested purchases and provide links to appropriate online stores.

[0982] Example 1

[0983] 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."

[0984] Today's users want to coordinate their fashion to suit their own style and lifestyle, and meeting these needs requires processing a large amount of information in real time to provide individually customized suggestions.However, existing systems are unable to adequately provide individually customized coordination suggestions, identify missing items, or provide purchase candidates, making it difficult to increase user satisfaction.

[0985] 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.

[0986] In this invention, the server includes means for storing personal information input by the user, means for inputting and storing information about the user's fashion style and lifestyle, means for generating coordinated outfit suggestions customized for the user using a generative AI model based on the input information and the latest fashion information, means for identifying necessary items using the output of the generative AI model based on the coordinated outfit suggestions and creating a list of purchase candidates, and means for transmitting the generated list of purchase candidates to the user terminal. This makes it possible to provide fashion suggestions tailored to the individual needs of the user in real time, identify missing items, and provide support up to the purchase stage.

[0987] "Personal Information" refers to basic information such as a user's name, email address, and password.

[0988] "Fashion style information" refers to information including the type of fashion that the user prefers (for example, casual, business, sporty, etc.).

[0989] "Lifestyle information" refers to information about a user's lifestyle and activities (for example, the number of times they commute per week, their weekend activities, etc.).

[0990] "Generative AI model" refers to an artificial intelligence algorithm that generates customized outfit suggestions for users based on input data.

[0991] A "prompt" refers to a question or instruction input to a generative AI model.

[0992] "Database" refers to a storage device for safely storing a user's personal information, fashion style information, lifestyle information, etc.

[0993] "Outfit suggestions" refer to clothing combinations generated by a generative AI model that match the user's fashion style and lifestyle.

[0994] "Purchase Candidates" refer to items that the user does not own but may purchase, identified based on the suggested outfit.

[0995] "User terminal" refers to a device (e.g., a smartphone, PC, etc.) used by a user to access the system.

[0996] "Visually displaying" refers to outputting information to a user's device in a form that can be visually confirmed.

[0997] This invention is a system that provides customized coordination suggestions and purchase candidates based on a user's personal information, fashion style information, and lifestyle information. The system aims to provide users with optimal fashion advice in real time by communicating between a server and user terminals.

[0998] A user accesses the system using a terminal and creates a new account. The user enters basic personal information, such as name and email address, which is then sent from the terminal to the server. The server stores this information in a database and sends a confirmation email to the user.

[0999] Next, the user enters their fashion style and lifestyle information on the profile setting screen. This information includes, for example, "I like casual style," "I commute five days a week," and "I enjoy outdoor activities on weekends." This data is also sent from the device to the server, where it is saved and updated in the database.

[1000] Once the user's profile is set, the server uses a generative AI model to generate outfit suggestions. The generative AI model generates a number of outfit options based on the user's fashion style and lifestyle information, taking into account the latest fashion trends. The server then sends the generated outfit suggestions to the user's device and displays them for the user to visually confirm.

[1001] The server also considers the user's personal belongings and identifies the items needed for the proposed outfit. Based on this, it generates a list of missing items and sends it to the user's device as purchase candidates. The user can then view this list of purchase candidates and easily purchase the items they need from the online shop.

[1002] For example, if a user specifies that they prefer a "casual style," commute five times a week, and spend their weekends outdoors, the server will use the generative AI model to generate the following prompt:

[1003] Example prompt sentence:

[1004] User input:

[1005] Fashion Style: Casual

[1006] Lifestyle: Commuting 5 days a week, outdoor activities on weekends

[1007] Prompt the generative AI model:

[1008] "Please suggest fashion coordination suitable for a user who likes casual style, commutes five days a week, and enjoys outdoor activities on the weekends."

[1009] The generative AI model suggests casual outfits for commuting or weekend outdoor activities. If the user does not have the necessary items in their inventory, they will be added to a list of purchase options and displayed on their device.

[1010] In this way, the system of the present invention provides personalized fashion suggestions in real time that are tailored to the user's needs and preferences, and supports the user in creating attractive styling that is fun and easy.

[1011] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1012] Step 1:

[1013] A user accesses the system from a terminal and enters the personal information required to create an account (such as name, email address, and password). The entered personal information is sent from the terminal to the server. The server stores the received personal information in a database and sends a confirmation email to the user. Specifically, the user enters information into a web form, and that information is sent to the server via an HTTP request.

[1014] Step 2:

[1015] The user goes to a profile setting screen and inputs information about their fashion style and lifestyle. Specifically, they input information such as "I like casual style," "I commute five days a week," and "I enjoy outdoor activities on weekends." This information is sent from the device to the server, which then stores and updates the received information in a database. Input is done via the user interface, and the transmitted data is analyzed and stored on the server.

[1016] Step 3:

[1017] The server generates a prompt for the generative AI model based on the user's personal information, fashion style information, and lifestyle information stored in the database. For example, the prompt might be, "Please suggest a fashion coordination suitable for a user who likes casual style, commutes to work five days a week, and enjoys outdoor activities on weekends." The prompt is sent to the generative AI model.

[1018] Step 4:

[1019] The generative AI model generates outfit suggestions based on the received prompt. The model uses the information provided by the user and the latest fashion trends to output customized outfit options. The generated outfit suggestions are returned to the server. Specific operations include data processing and model inference.

[1020] Step 5:

[1021] The server sends the outfit suggestions received from the generative AI model to the user's device, which then displays the outfit suggestions so that they can be visually confirmed. Specifically, the server sends the data using a communication protocol and the suggestions are displayed on the user interface.

[1022] Step 6:

[1023] The server queries the user's belongings data and identifies missing items based on the generated outfit suggestions. The server generates a shopping candidate list including the identified missing items and transmits it to the user terminal. Specifically, a database query is executed to generate the shopping candidate list.

[1024] Step 7:

[1025] The user terminal displays the list of potential purchases received from the server. The user can check the list and easily purchase the items they need from the online shop. Specifically, the list is displayed on the user interface and a link to the online shop is provided.

[1026] In this way, data processing and calculations are performed at each step based on user input, and the system provides personalized fashion suggestions in real time.

[1027] (Application example 1)

[1028] 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."

[1029] In recent years, the spread of online shopping has made it easy for consumers to purchase a wide variety of fashion items. However, it is not easy for individual users to find a coordinated outfit that suits their style and lifestyle, especially since it is difficult to check the final look before wearing it. Furthermore, considering how to combine an outfit with items already owned is a time-consuming and labor-intensive task for users. Given this situation, there is a demand for a system that allows users to easily receive personalized fashion suggestions in real time from the comfort of their own home and make appropriate purchasing decisions by virtually trying on the items.

[1030] 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.

[1031] In this invention, the server includes means for storing personal information entered by the user, means for inputting and storing information about the user's fashion style and lifestyle, means for generating coordinated outfit suggestions customized for the user based on the input information and the latest fashion information, means for visually presenting the suggested coordinated outfits through a virtual try-on simulation, and means for identifying necessary items based on the suggested coordinated outfits and listing potential purchases. This allows users to easily receive fashion suggestions suited to them from the comfort of their own homes, as well as to check actual coordinated outfits through the virtual try-on and smoothly purchase the necessary items.

[1032] "Personal Information" means information that can identify you as an individual, such as your name, email address, address, and age, provided by you.

[1033] "Fashion style information" is information about the style of clothing that the user prefers, such as type, color, and design.

[1034] "Lifestyle information" refers to information about the user's daily life patterns, such as their lifestyle, work habits, hobbies, and activities.

[1035] A "coordination suggestion" is a combination of specific fashion items that is generated based on the user's fashion style information and lifestyle information.

[1036] "Virtual try-on simulation" is a system that allows users to try on selected fashion items in a virtual space and visually check them.

[1037] "Purchase candidates" is a list of fashion items that the user does not own but should consider purchasing based on the suggested outfit.

[1038] "Inventory data" refers to information about fashion items that a user already owns.

[1039] A "generative AI model" is an algorithm that analyzes data entered by the user and the latest fashion information to generate appropriate coordination suggestions.

[1040] "Real-time" refers to a process that responds immediately to user actions and provides instant results.

[1041] This system provides customized coordination suggestions and purchase candidates based on the user's personal information, fashion style information, and lifestyle information. This system provides optimal fashion advice to users in real time by communicating between a server and the user's terminal.

[1042] System configuration

[1043] 1. Server

[1044] The server securely stores the personal information, fashion style information, and lifestyle information provided by the user in a database.

[1045] It uses a generative AI model to generate customized outfit suggestions based on the information you enter.

[1046] The suggested outfits are sent to the user's device as a virtual try-on simulation.

[1047] Based on the user's inventory data, it identifies missing items and creates a list of potential purchases.

[1048] 2. User Device

[1049] Users access the system using a terminal and create a new account.

[1050] You enter basic personal information such as your name and email address, which is sent from your device to a server that stores it in a database.

[1051] Users use a profile setting screen to enter their fashion style and lifestyle information, such as "I like casual style," "I commute five days a week," and "I enjoy outdoor activities on weekends."

[1052] Once a profile is set up, users can visually check suggested outfits through a virtual try-on simulation.

[1053] Processing flow

[1054] 1. Enter your personal information

[1055] The personal information entered by the user is sent to the server and stored in a database.

[1056] 2. Enter your fashion style and lifestyle information

[1057] The user inputs fashion style and lifestyle information, which is also sent to the server.

[1058] 3. Coordination Proposal Generation

[1059] The server uses a generative AI model to generate customized outfit suggestions based on the input information.

[1060] The suggested outfits are sent to the user's device and can be visually confirmed through a virtual try-on simulation.

[1061] 4. Identify and list missing items

[1062] The server considers the user's inventory data and identifies missing items based on the suggested outfit.

[1063] The items listed as potential purchases are sent to the user's device.

[1064] Specific examples

[1065] For example, if a user specifies that they prefer a "casual style," that they commute five times a week, and that they "go outdoors on weekends," the server will use the generative AI model to suggest casual outfits suitable for commuting and outfits suitable for weekend outdoor activities. If the items needed for these suggestions are not in the user's inventory, they will be added to a list of purchase candidates and displayed on the user's device.

[1066] Example prompts to input to the generative AI model

[1067] User profile:

[1068] Name: Yamada Taro

[1069] Email address: example@example.com

[1070] Fashion Style: Casual

[1071] Lifestyle:

[1072] Commuting five times a week

[1073] Outdoor activities on the weekend

[1074] Based on this profile information, the app will suggest outfits and list items you don't have in your inventory as potential purchases.

[1075] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1076] Step 1:

[1077] Entering and saving personal information

[1078] A user accesses the system through a terminal and creates a new account. The user enters basic personal information such as name and email address. This information is sent from the terminal to the server, which stores it in a database. Example input data is the user's name "Taro" and email address "example@example.com". As output, the server sends a confirmation email to the user.

[1079] Step 2:

[1080] Enter and store fashion style and lifestyle information

[1081] Users use the profile settings screen on their device to enter information about their fashion style and lifestyle, such as "I like casual style," "I commute five days a week," and "I enjoy outdoor activities on weekends." This data is also sent from the device to the server, which stores and updates it in a database.

[1082] Step 3:

[1083] Generating outfit suggestions

[1084] Once the user's profile is set, the server uses a generative AI model to generate outfit suggestions. The input data here is the user's fashion style and lifestyle information. The generative AI model generates a number of outfit options based on the user's input and the latest fashion trend information. The output data is customized outfit suggestions (text and images), which the server sends to the user's device.

[1085] Step 4:

[1086] Providing virtual try-on simulations

[1087] The user visually checks the suggested outfits on their device through a virtual try-on simulation. The server generates virtual try-on simulation data based on the generated outfit suggestions and sends it to the user's device. The input data is the outfit suggestions, and the output data is a visual representation of the virtual try-on. This includes specific actions by the user to operate the avatar and check the suggested outfits.

[1088] Step 5:

[1089] Identifying and listing missing items

[1090] The server considers the user's inventory data and identifies missing items based on the suggested outfit. The input data is the user's inventory data and outfit suggestions, and the output data is a list of identified missing items. The server generates this list and sends it to the user's device.

[1091] Step 6:

[1092] Deciding and notifying potential purchasers

[1093] The user terminal displays the items listed as potential purchases based on the list of missing items received from the server. The user can view this list and take specific actions such as purchasing the necessary items from an online shop. The input data is the list of missing items, and the output data is the list of potential purchases.

[1094] 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.

[1095] This system provides customized outfit suggestions and purchase candidates based on the user's personal information, fashion style information, lifestyle information, and emotions. This system communicates between a server and the user's terminal, providing the user with optimal fashion advice in real time.

[1096] First, a user accesses the system using a terminal and creates a new account. The user enters basic personal information such as name and email address. This information is sent from the terminal to the server, which stores it in a database and sends a confirmation email to the user.

[1097] Next, the user uses the profile setting screen to enter information about their fashion style and lifestyle, such as "I like casual style," "I commute five days a week," and "I enjoy outdoor activities on weekends." This information is sent from the device to the server, which then stores and updates it in a database.

[1098] Once a user's profile is set, the server uses a generative AI model to generate outfit suggestions. The generative AI model generates multiple outfit options based on the user's fashion style and lifestyle information, while referencing the latest fashion trends. The server then sends the generated outfit suggestions to the user's device and displays them for the user to visually confirm.

[1099] The system also incorporates an emotion engine. The user's emotions are detected using cameras and sensors, and the emotion engine analyzes this information to recognize them. For example, the system can detect the emotion a user expresses when wearing a particular piece of clothing, and by learning this information, it can generate optimal outfits based on the user's emotions. The server also takes this emotional information into consideration to suggest outfits that are in line with the user's emotions.

[1100] The server also stores the user's inventory data and identifies missing items based on the suggested outfit. Based on this, a list of potential purchases is generated and sent to the user's device. The user can then easily purchase the items they need from the online shop by viewing this list. The list can also be optimized based on the user's emotions. For example, when a user is busy and stressed, the system suggests items with a simple, relaxed style.

[1101] For example, if a user specifies that they prefer a "casual style," that they commute five times a week, and that they "go outdoors on weekends," the server will use the generative AI model to suggest casual outfits suitable for commuting and outfits suitable for weekend outdoor activities. If the items required for these suggestions are not included in the user's inventory, they will be added to a list of purchase candidates and displayed on the user's device. Furthermore, the emotion engine analyzes the user's emotions, learns which suggestions the user responded most positively to, and reflects this in future outfit suggestions.

[1102] In this way, the system of the present invention provides personalized fashion suggestions in real time based on the user's needs, preferences, and emotions, helping users create attractive styling in a fun, easy way.

[1103] The processing flow will be explained below.

[1104] Specific explanation of program processing

[1105] User Registration

[1106] Step 1:

[1107] User: Launches the app, enters their name, email address, and password in the "New Registration" form, confirms the information entered, and presses the "Submit" button.

[1108] Step 2:

[1109] Terminal: Temporarily stores the entered information and sends a transmission request to the server.

[1110] Step 3:

[1111] Server: Stores the received user information in the database and generates a confirmation email if the save is successful.

[1112] Step 4:

[1113] Server: Generates the content of the confirmation email and sends it to the registered email address.

[1114] Fashion and lifestyle information input

[1115] Step 1:

[1116] User: On the profile settings screen, enter your preferred fashion style (e.g., "casual" or "business") and lifestyle (e.g., "commuting five days a week" or "outdoors on weekends").

[1117] Step 2:

[1118] Device: Temporarily stores the entered fashion style and lifestyle information and waits for the "Save" button to be pressed.

[1119] Step 3:

[1120] User: Presses the "Save" button.

[1121] Step 4:

[1122] Terminal: Sends input data to the server.

[1123] Step 5:

[1124] Server: Stores the received user fashion style and lifestyle information in a database and updates the user profile.

[1125] Generating outfit suggestions

[1126] Step 1:

[1127] Server: Retrieves user profile information (fashion style and lifestyle information) from the database.

[1128] Step 2:

[1129] Server: Based on the acquired information, a generative AI model is used to generate outfit suggestions, taking into account trends, the season, and existing personal belongings.

[1130] Step 3:

[1131] Device: Captures the user's facial image and voice data through the camera and microphone.

[1132] Step 4:

[1133] Device: Sends the acquired emotion data to the server.

[1134] Step 5:

[1135] Server: Analyzes the user's emotions using the emotion engine. Based on the analysis results, the server adjusts the outfit suggestions.

[1136] Step 6:

[1137] Server: Sends the adjusted coordination plan to the user device.

[1138] Step 7:

[1139] Device: Visually displays the received coordination suggestions to the user.

[1140] Purchase suggestion

[1141] Step 1:

[1142] Server: Retrieves user's inventory data from the database.

[1143] Step 2:

[1144] Server: Based on the obtained outfit suggestions and inventory data, identify items that the user does not have.

[1145] Step 3:

[1146] Server: Generates a list of available items for purchase.

[1147] Step 4:

[1148] Server: Optimize the shopping list based on the analysis results of the emotion engine. For example, if the user is feeling stressed, prioritize items with a relaxed style.

[1149] Step 5:

[1150] Server: Sends the optimized shopping list to the user's device.

[1151] Step 6:

[1152] On the device: The received shopping list is visually displayed to the user and a link to the shopping site is provided.

[1153] Specific examples

[1154] User Registration

[1155] Step 1:

[1156] User: The user launches the app, enters their name, email address, and password in the "New Registration" form, and presses the "Submit" button.

[1157] Step 2:

[1158] Terminal: Sends input information to the server.

[1159] Step 3:

[1160] Server: Stores the received information in a database and sends a confirmation email.

[1161] Fashion and lifestyle information input

[1162] Step 1:

[1163] User: In the app, select "Casual," "Commute 5 days a week," and "Outdoors on weekends," then press the "Save" button.

[1164] Step 2:

[1165] Terminal: Sends input data to the server.

[1166] Step 3:

[1167] Server: Analyzes the input information and stores it in a database.

[1168] Generating outfit suggestions

[1169] Step 1:

[1170] Server: Generates "casual commuting style" and "weekend style suitable for outdoor activities" based on the user profile.

[1171] Step 2:

[1172] Device: The camera captures the user's facial image and sends it to the server as emotion data.

[1173] Step 3:

[1174] Server: Analyzes emotions using an emotion engine and adjusts outfit suggestions to match the user's mood.

[1175] Step 4:

[1176] Server: Sends the adjusted coordination proposal to the user device.

[1177] Step 5:

[1178] Terminal: Visually display the suggested outfits to the user.

[1179] Purchase suggestion

[1180] Step 1:

[1181] Server: Obtains the user's belongings data based on the coordination suggestions.

[1182] Step 2:

[1183] Server: Identifies missing items and generates a purchase list.

[1184] Step 3:

[1185] Server: Optimize purchase candidates based on user sentiment data.

[1186] Step 4:

[1187] Server: Sends the optimized shopping list to the user's device.

[1188] Step 5:

[1189] On your device: Display suggested purchases and provide links to appropriate online stores.

[1190] Example 2

[1191] 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."

[1192] Conventional fashion recommendation systems could only provide generalized suggestions without fully considering the individual preferences and lifestyles of users. Furthermore, they lacked personalized coordination suggestions that took into account the user's emotions and actual possessions, making it difficult to provide specific and practical fashion advice.

[1193] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for storing personal information input by the user, a means for inputting and storing the user's fashion style and lifestyle information, a means for generating a coordinated outfit proposal customized for the user using a generative AI model based on the input information and the latest fashion information, a means for generating a prompt sentence for the generative AI model and generating the coordinated outfit proposal, a means for identifying missing items based on the proposed outfit and listing purchase candidates, and a means for analyzing the user's emotions using an emotion detection device and optimizing the coordinated outfit proposal based on the emotion information. This makes it possible to provide specific and practical coordinated outfit proposals based on the user's individual preferences and lifestyle.

[1194] "Means for storing personal information" refers to devices or software used to store and manage basic information such as a user's name, email address, and password.

[1195] "Means for inputting and storing fashion style and lifestyle information" refers to devices and software that allow users to input information about their preferred fashion style and daily living patterns, and to store and manage this information.

[1196] A "generative AI model" is an artificial intelligence system that generates new information or suggestions based on input data, for example, using machine learning algorithms.

[1197] A "means for generating prompt sentences" is a device or software that formats and inputs instructions and information to generate appropriate coordination suggestions for a generative AI model.

[1198] A "means for generating outfit suggestions" is a device or software that uses user input information and a generative AI model to create a series of fashion suggestions and outfit ideas.

[1199] An "emotion detection device" is a device or software that detects and analyzes a user's emotional state, such as one that uses a camera or sensor.

[1200] "Means for optimizing outfit suggestions based on emotional information" refers to devices or software that adjust outfit suggestions made by a generative AI model based on the user's emotional information obtained by an emotion detection device, thereby providing more appropriate suggestions.

[1201] "Means for identifying missing items and listing potential purchase items" refers to a device or software that compares the user's possession data with the generated coordination suggestions, identifies the missing items, and lists them as potential purchase items.

[1202] This is a system that provides customized outfit suggestions and purchase candidates based on a user's personal information, fashion style information, lifestyle information, and emotions. This system communicates between a server and the user's terminal, providing the user with optimal fashion advice in real time.

[1203] Create an account and enter your personal information

[1204] First, the user accesses the system using their own device (e.g., smartphone or PC). The user enters their name, email address, and password on the new account creation screen. This information is sent to the server using the HTTPS protocol. The server stores the received personal information in a database (e.g., MySQL or MongoDB) and sends a confirmation email to the user using a mail server (e.g., Amazon SES or SendGrid).

[1205] Profile Settings

[1206] Next, the user uses their device to access a profile setting screen and enter information about their fashion style and lifestyle. For example, they might say, "I like casual style," "I commute five days a week," or "I enjoy outdoor activities on weekends." The device then sends this information to the server, which then stores and updates the received information in a database.

[1207] Generating outfit suggestions

[1208] Once the user's profile information is set, the server uses a generative AI model (e.g., GPT-4) to generate outfit suggestions. The server creates a prompt for the generative AI model. An example of a prompt is, "The user's fashion style is casual, they commute to work five days a week, and enjoy outdoor activities on weekends. Please suggest the best outfit for the user." Based on this prompt, the generative AI model generates appropriate outfit ideas. The server sends the generated outfit suggestions to the user's device and displays them so that the user can visually confirm them.

[1209] Applying the Emotion Engine

[1210] The system also incorporates an emotion engine. The user's emotions are detected using the device's built-in camera and sensors. The device then sends the emotion data to the server. The server's emotion engine then analyzes the user's emotions using image processing libraries such as OpenCV. Based on the analysis results, the server generates outfit suggestions that are in line with the user's emotions. The server then generates prompts for the AI ​​model to generate outfit suggestions. The server then sends the new suggestions to the user's device.

[1211] Generate a purchase candidate list

[1212] The server retrieves the user's inventory data from a database and compares the items required with the generated outfit suggestions. If the required items are not included in the user's inventory, the server adds them to a list of potential purchases. The server then sends the list of potential purchases to the user's device and displays it for the user to review. The user can then easily purchase the items they need from the online shop by viewing the list of potential purchases.

[1213] In this way, the system can provide specific and practical outfit suggestions in real time based on the user's individual preferences and lifestyle.

[1214] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1215] Step 1:

[1216] The user accesses the system using a terminal and enters their name, email address, and password on the new account creation screen. The entered personal information is sent to the server using the HTTPS protocol. The server stores the received personal information in a database and sends a confirmation email.

[1217] Input: User's name, email address, and password

[1218] Data processing: Personal information is stored in a database

[1219] Output: A confirmation email from the server to the user

[1220] Step 2:

[1221] Users use their devices to access a profile setting screen and input their fashion style and lifestyle information. This information is sent from the device to the server, which then stores and updates it in a database.

[1222] Input: Fashion style information (e.g., casual), lifestyle information (e.g., commute five days a week, spend weekends outdoors)

[1223] Data processing: Save and update the entered information in the database

[1224] Output: Profile information is saved in the server database

[1225] Step 3:

[1226] The server generates a prompt for the generative AI model based on the user's profile information. The generative AI model generates outfit suggestions based on the prompt. The server then sends the generated outfit suggestions to the user's device.

[1227] Input: User profile information

[1228] Data processing: Enter prompts into the generative AI model to generate outfit suggestions

[1229] Output: Send the generated coordination proposal to the user's device

[1230] Step 4:

[1231] The device uses an emotion detection device (e.g., a camera or sensor) to capture the user's emotional data and send it to the server. The server's emotion engine analyzes this emotional data and recognizes the user's emotion. Based on the recognized emotional information, the device generates a new prompt and creates new coordination suggestions.

[1232] Input: User emotion data (e.g. camera footage)

[1233] Data processing: Emotion analysis using an emotion engine, prompt generation, and coordination suggestion update

[1234] Output: Optimized outfit suggestions

[1235] Step 5:

[1236] The server retrieves the user's belongings data from the database and checks the items required for the generated coordination proposal. If the required items are not included in the user's belongings, it generates a list of purchase candidates and sends it to the user's terminal.

[1237] Input: User's belongings data, generated coordination suggestions

[1238] Data processing: Compare inventory data with suggestions and create a list of missing items

[1239] Output: Send the purchase candidate list to the user's device

[1240] (Application example 2)

[1241] 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."

[1242] Conventional fashion suggestion systems make suggestions based on a user's personal information and fashion style information, but they are unable to take into account the user's emotions or real-time reactions, and therefore are unable to fully increase user satisfaction. Furthermore, the process of creating a list of potential purchases based on suggested outfits is not automated, which is a time-consuming process for the user. Thus, there is a need for a system that provides personalized fashion suggestions in real time and in line with the user's emotions.

[1243] The specification processing by the specification 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 storing personal information entered by the user, means for inputting and storing information about the user's fashion style and lifestyle, means for generating coordinated outfit suggestions customized for the user based on the input information and the latest fashion information, means for analyzing the user's emotions using a smartphone camera or sensor and optimizing the coordinated outfit suggestions based on that information, and means for identifying necessary accessories based on the proposed outfit and listing purchase candidates. This enables real-time fashion suggestions based on the user's emotions and preferences and efficient presentation of purchase candidates.

[1244] "Personal information" refers to information that users enter to identify themselves, such as their name, email address, and address.

[1245] "Fashion style" refers to the type of clothing and accessory coordination based on the user's preferences, such as casual or business style.

[1246] "Lifestyle information" refers to information related to the user's daily life and habits, such as the frequency of commuting and the activities they do on their days off.

[1247] A "smartphone" is an evolved form of a mobile phone, and is a portable information terminal with many functions, including Internet access, application execution, and camera functionality.

[1248] A "camera" is a device for taking images or videos, and in this case refers to the one built into a smartphone.

[1249] A "sensor" is a device that detects a physical quantity (such as light, temperature, or motion) and converts it into an electrical signal.

[1250] "Analyzing emotions" means observing the user's facial expressions and behavior and determining their current emotional state (for example, joy, sadness, surprise, etc.) based on that data.

[1251] "Coordination suggestions" suggest clothing combinations and accessory selections based on the user's fashion style and lifestyle information.

[1252] "Purchase candidates" is a list of items that are recommended for the user to purchase based on the coordination suggestions.

[1253] A "database" is a system for efficiently storing, managing, and searching structured data, and is used to organize and store large amounts of data such as user information.

[1254] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate new outfit suggestions based on user information.

[1255] A "prompt sentence" is a sentence that describes an input request for a generative AI model, and is text used to give specific instructions to the AI.

[1256] This invention is a system that provides customized coordination suggestions and purchase candidates based on the user's personal information, fashion style information, lifestyle information, and user emotions. This system communicates between a server and the user's terminal and provides optimal fashion advice in real time.

[1257] System configuration

[1258] The system consists of the following main components:

[1259] 1. User Device

[1260] A smartphone is a device that allows users to input personal information, fashion style, and lifestyle information.

[1261] Smartphones are equipped with cameras and sensors that are used to detect the user's emotions.

[1262] 2. Server

[1263] Personal information and style information submitted by users is stored in a database.

[1264] Use generative AI models to generate outfit suggestions.

[1265] Use a sentiment analysis engine to optimize suggestions based on user sentiment.

[1266] Program processing

[1267] To operate this system, the following data processing and calculation are performed.

[1268] Hardware and Software Use

[1269] 1. Hardware

[1270] Smartphones: Used for data entry and emotion detection.

[1271] Server: Used for data storage, running generative AI models, and sentiment analysis.

[1272] 2. Software

[1273] Database Management System: Software for storing user information and fashion information.

[1274] Generative AI model (e.g., TensorFlow or Keras): An algorithm for generating outfit suggestions based on user information.

[1275] Sentiment analysis engine (e.g., OpenCV): Software for analyzing user emotions based on images acquired from a smartphone camera.

[1276] Data processing and calculation flow

[1277] 1. Entering and saving user information

[1278] Users use their smartphones to enter personal information, fashion style, and lifestyle information, which is then sent to a server, which stores the information in a database.

[1279] 2. Coordination Proposal Generation

[1280] The server uses a generative AI model to generate multiple outfit suggestions based on the user's input and the latest fashion information.

[1281] 3. Sentiment analysis and recommendation optimization

[1282] The smartphone's camera and sensors detect the user's emotions and send the data to a server, which then uses an emotion analysis engine to optimize suggestions based on the user's emotions.

[1283] 4. Presenting potential purchases

[1284] Based on the proposed outfit, the server checks the user's inventory data, identifies missing items, and creates a list of potential purchases, which is then sent to the user's device for viewing.

[1285] Examples of specific examples and prompts

[1286] For example, if a user specifies that they prefer a "casual style" and enters "commuting five days a week" and "outdoors on weekends," the server will send the following prompt to the generative AI model:

[1287] User Information:

[1288] Name: Your name

[1289] Fashion Style: Casual

[1290] Lifestyle: 5-day commute, weekend outdoor activities

[1291] Prompt for generating outfit suggestions:

[1292] Suggest a casual style suitable for commuting five days a week, as well as an outfit suitable for outdoor activities on the weekends, taking into account the latest fashion trends and the preferences of the user.

[1293] Based on these prompts, a generative AI model generates detailed outfit suggestions, which are then delivered to the user in real time.

[1294] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1295] Step 1:

[1296] Users use their smartphones to input their personal information, fashion style information, and lifestyle information, such as their name, email address, preferred style (e.g., casual), and daily life information (e.g., commuting five days a week and outdoor activities on weekends). This data is then sent to the server as input information.

[1297] Step 2:

[1298] The server stores the received user personal information, fashion style information, and lifestyle information in a database. Specifically, the server stores the user information in a "user information table" and the fashion style and lifestyle information in a "style information table" to maintain consistency of the database.

[1299] Step 3:

[1300] The server generates outfit suggestions using a generative AI model based on the user's saved fashion style and lifestyle information. The server creates a prompt sentence like the following and inputs it into the generative AI model:

[1301] User Information:

[1302] Name: User

[1303] Fashion Style: Casual

[1304] Lifestyle: 5-day commute, weekend outdoor activities

[1305] Prompt for generating outfit suggestions:

[1306] Suggest a casual style suitable for commuting five days a week, as well as an outfit suitable for outdoor activities on the weekends, taking into account the latest fashion trends and the user's preferences.

[1307] This allows the generative AI model to suggest the best outfits for the user.

[1308] Step 4:

[1309] Users can use their smartphone's camera and sensors to detect their own emotional state. For example, if they smile while looking in a mirror, the camera captures their facial expression. This emotional data is then sent from the user device to the server.

[1310] Step 5:

[1311] The server inputs the received emotional data into its emotion analysis engine and analyzes the user's emotional state. The analysis results identify the user's current emotion (e.g., joy, excitement, etc.). Based on the results of this emotion analysis, the server optimizes the generated outfit suggestions and selects the suggestion that best matches the user's emotion.

[1312] Step 6:

[1313] The server sends the optimized outfit suggestions to the user's device, where the user can visually check the suggested outfits on their smartphone screen. Specifically, among the multiple outfits presented by the application's UI, the most suitable suggestion based on the results of emotion analysis is displayed first.

[1314] Step 7:

[1315] The server references the user's inventory database and identifies any missing items based on the proposed outfit. For example, if the proposed outfit requires a "white shirt" and the user does not own one, the white shirt is identified as a missing item. This information is added to the purchase candidate list.

[1316] Step 8:

[1317] The server sends a list of missing items to the user's device. The user can then check the list on their smartphone and purchase the items they need directly from the online shop. Specifically, by tapping an item on the list, a link to the online shop's purchase page is provided.

[1318] The above processing steps realize real-time personalized fashion suggestions based on the user's personal information and emotions, and further enable the user to easily purchase items based on the suggestions.

[1319] 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.

[1320] 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.

[1321] 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.

[1322] [Fourth embodiment]

[1323] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1324] 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.

[1325] 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).

[1326] 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.

[1327] 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.

[1328] 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).

[1329] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1330] 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.

[1331] 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.

[1332] 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.

[1333] 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.

[1334] 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.

[1335] 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."

[1336] This system provides customized coordination suggestions and purchase candidates based on the user's personal information, fashion style information, and lifestyle information. This system provides optimal fashion advice to users in real time by communicating between a server and the user's terminal.

[1337] First, a user accesses the system using a terminal and creates a new account. The user enters basic personal information such as name and email address. This information is sent from the terminal to the server, which stores it in a database and sends a confirmation email to the user.

[1338] Next, the user uses the profile setting screen to enter information about their fashion style and lifestyle, such as "I like casual style," "I commute five days a week," and "I enjoy outdoor activities on weekends." This data is also sent from the device to the server, which stores and updates it in the database.

[1339] Once a user's profile is set, the server uses a generative AI model to generate outfit suggestions. The generative AI model generates multiple outfit options based on the user's fashion style and lifestyle information, while referencing the latest fashion trends. The server then sends the generated outfit suggestions to the user's device and displays them for the user to visually confirm.

[1340] The server also considers the user's inventory data and identifies any missing items based on the proposed outfit. Based on this, it generates a list of potential purchases and sends it to the user's device. The user can then view this list and easily purchase the items they need from the online shop.

[1341] For example, if a user specifies that they prefer a "casual style," that they commute five times a week, and that they "go outdoors on weekends," the server will use the generative AI model to suggest casual outfits suitable for commuting and outfits suitable for weekend outdoor activities. If the items needed for these suggestions are not in the user's inventory, they will be added to a list of purchase candidates and displayed on the user's device.

[1342] In this way, the system of the present invention provides personalized fashion suggestions in real time that are tailored to the user's needs and preferences, and supports the user in creating attractive styling that is fun and easy.

[1343] The processing flow will be explained below.

[1344] Specific explanation of program processing

[1345] User Registration

[1346] Step 1:

[1347] User: Launches the app, enters their name, email address, and password in the "New Registration" form, confirms the information entered, and presses the "Submit" button.

[1348] Step 2:

[1349] Terminal: Temporarily stores the entered information and sends a transmission request to the server.

[1350] Step 3:

[1351] Server: Stores the received user information in the database and generates a confirmation email if the save is successful.

[1352] Step 4:

[1353] Server: Generates the content of the confirmation email and sends it to the registered email address.

[1354] Fashion and lifestyle information input

[1355] Step 1:

[1356] User: On the profile settings screen, enter your preferred fashion style (e.g., "casual" or "business") and lifestyle (e.g., "commuting five days a week" or "outdoors on weekends").

[1357] Step 2:

[1358] Device: Temporarily stores the entered fashion style and lifestyle information and waits for the "Save" button to be pressed.

[1359] Step 3:

[1360] User: Presses the "Save" button.

[1361] Step 4:

[1362] Terminal: Sends input data to the server.

[1363] Step 5:

[1364] Server: Stores the received user fashion style and lifestyle information in a database and updates the user profile.

[1365] Generating outfit suggestions

[1366] Step 1:

[1367] Server: Retrieves user profile information (fashion style and lifestyle information) from the database.

[1368] Step 2:

[1369] Server: Based on the acquired information, a generative AI model is used to generate outfit suggestions, taking into account trends, the season, and existing personal belongings.

[1370] Step 3:

[1371] Server: Sends the generated coordination plan to the user device.

[1372] Step 4:

[1373] Device: Visually displays the received coordination suggestions to the user.

[1374] Purchase suggestion

[1375] Step 1:

[1376] Server: Retrieves user's inventory data from the database.

[1377] Step 2:

[1378] Server: Based on the obtained outfit suggestions and inventory data, identify items that the user does not have.

[1379] Step 3:

[1380] Server: Generates a list of available items for purchase.

[1381] Step 4:

[1382] Server: Sends the list of potential purchases to the user's device.

[1383] Step 5:

[1384] On the device: The received shopping list is visually displayed to the user and a link to the shopping site is provided.

[1385] Specific examples

[1386] User Registration

[1387] Step 1:

[1388] User: The user launches the app, enters their name, email address, and password in the "New Registration" form, and presses the "Submit" button.

[1389] Step 2:

[1390] Terminal: Sends input information to the server.

[1391] Step 3:

[1392] Server: Stores the received information in a database and sends a confirmation email.

[1393] Fashion and lifestyle information input

[1394] Step 1:

[1395] User: In the app, select "Casual," "Commute 5 days a week," and "Outdoors on weekends," then press the "Save" button.

[1396] Step 2:

[1397] Terminal: Sends input data to the server.

[1398] Step 3:

[1399] Server: Analyzes the input information and stores it in a database.

[1400] Generating outfit suggestions

[1401] Step 1:

[1402] Server: Generates "casual commuting style" and "weekend style suitable for outdoor activities" based on the user profile.

[1403] Step 2:

[1404] Server: Sends coordination proposals to the user device.

[1405] Step 3:

[1406] Terminal: Visually display the suggested outfits to the user.

[1407] Purchase suggestion

[1408] Step 1:

[1409] Server: Obtains the user's belongings data based on the coordination suggestions.

[1410] Step 2:

[1411] Server: Identifies missing items and generates a purchase list.

[1412] Step 3:

[1413] Server: Sends the list of potential purchases to the user's device.

[1414] Step 4:

[1415] On your device: Display suggested purchases and provide links to appropriate online stores.

[1416] Example 1

[1417] 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."

[1418] Today's users want to coordinate their fashion to suit their own style and lifestyle, and meeting these needs requires processing a large amount of information in real time to provide individually customized suggestions.However, existing systems are unable to adequately provide individually customized coordination suggestions, identify missing items, or provide purchase candidates, making it difficult to increase user satisfaction.

[1419] 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.

[1420] In this invention, the server includes means for storing personal information input by the user, means for inputting and storing information about the user's fashion style and lifestyle, means for generating coordinated outfit suggestions customized for the user using a generative AI model based on the input information and the latest fashion information, means for identifying necessary items using the output of the generative AI model based on the coordinated outfit suggestions and creating a list of purchase candidates, and means for transmitting the generated list of purchase candidates to the user terminal. This makes it possible to provide fashion suggestions tailored to the individual needs of the user in real time, identify missing items, and provide support up to the purchase stage.

[1421] "Personal Information" refers to basic information such as a user's name, email address, and password.

[1422] "Fashion style information" refers to information including the type of fashion that the user prefers (for example, casual, business, sporty, etc.).

[1423] "Lifestyle information" refers to information about a user's lifestyle and activities (for example, the number of times they commute per week, their weekend activities, etc.).

[1424] "Generative AI model" refers to an artificial intelligence algorithm that generates customized outfit suggestions for users based on input data.

[1425] A "prompt" refers to a question or instruction input to a generative AI model.

[1426] "Database" refers to a storage device for safely storing a user's personal information, fashion style information, lifestyle information, etc.

[1427] "Outfit suggestions" refer to clothing combinations generated by a generative AI model that match the user's fashion style and lifestyle.

[1428] "Purchase Candidates" refer to items that the user does not own but may purchase, identified based on the suggested outfit.

[1429] "User terminal" refers to a device (e.g., a smartphone, PC, etc.) used by a user to access the system.

[1430] "Visually displaying" refers to outputting information to a user's device in a form that can be visually confirmed.

[1431] This invention is a system that provides customized coordination suggestions and purchase candidates based on a user's personal information, fashion style information, and lifestyle information. The system aims to provide users with optimal fashion advice in real time by communicating between a server and user terminals.

[1432] A user accesses the system using a terminal and creates a new account. The user enters basic personal information, such as name and email address, which is then sent from the terminal to the server. The server stores this information in a database and sends a confirmation email to the user.

[1433] Next, the user enters their fashion style and lifestyle information on the profile setting screen. This information includes, for example, "I like casual style," "I commute five days a week," and "I enjoy outdoor activities on weekends." This data is also sent from the device to the server, where it is saved and updated in the database.

[1434] Once the user's profile is set, the server uses a generative AI model to generate outfit suggestions. The generative AI model generates a number of outfit options based on the user's fashion style and lifestyle information, taking into account the latest fashion trends. The server then sends the generated outfit suggestions to the user's device and displays them for the user to visually confirm.

[1435] The server also considers the user's personal belongings and identifies the items needed for the proposed outfit. Based on this, it generates a list of missing items and sends it to the user's device as purchase candidates. The user can then view this list of purchase candidates and easily purchase the items they need from the online shop.

[1436] For example, if a user specifies that they prefer a "casual style," commute five times a week, and spend their weekends outdoors, the server will use the generative AI model to generate the following prompt:

[1437] Example prompt sentence:

[1438] User input:

[1439] Fashion Style: Casual

[1440] Lifestyle: Commuting 5 days a week, outdoor activities on weekends

[1441] Prompt the generative AI model:

[1442] "Please suggest fashion coordination suitable for a user who likes casual style, commutes five days a week, and enjoys outdoor activities on the weekends."

[1443] The generative AI model suggests casual outfits for commuting or weekend outdoor activities. If the user does not have the necessary items in their inventory, they will be added to a list of purchase options and displayed on their device.

[1444] In this way, the system of the present invention provides personalized fashion suggestions in real time that are tailored to the user's needs and preferences, and supports the user in creating attractive styling that is fun and easy.

[1445] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1446] Step 1:

[1447] A user accesses the system from a terminal and enters the personal information required to create an account (such as name, email address, and password). The entered personal information is sent from the terminal to the server. The server stores the received personal information in a database and sends a confirmation email to the user. Specifically, the user enters information into a web form, and that information is sent to the server via an HTTP request.

[1448] Step 2:

[1449] The user goes to a profile setting screen and inputs information about their fashion style and lifestyle. Specifically, they input information such as "I like casual style," "I commute five days a week," and "I enjoy outdoor activities on weekends." This information is sent from the device to the server, which then stores and updates the received information in a database. Input is done via the user interface, and the transmitted data is analyzed and stored on the server.

[1450] Step 3:

[1451] The server generates a prompt for the generative AI model based on the user's personal information, fashion style information, and lifestyle information stored in the database. For example, the prompt might be, "Please suggest a fashion coordination suitable for a user who likes casual style, commutes to work five days a week, and enjoys outdoor activities on weekends." The prompt is sent to the generative AI model.

[1452] Step 4:

[1453] The generative AI model generates outfit suggestions based on the received prompt. The model uses the information provided by the user and the latest fashion trends to output customized outfit options. The generated outfit suggestions are returned to the server. Specific operations include data processing and model inference.

[1454] Step 5:

[1455] The server sends the outfit suggestions received from the generative AI model to the user's device, which then displays the outfit suggestions so that they can be visually confirmed. Specifically, the server sends the data using a communication protocol and the suggestions are displayed on the user interface.

[1456] Step 6:

[1457] The server queries the user's belongings data and identifies missing items based on the generated outfit suggestions. The server generates a shopping candidate list including the identified missing items and transmits it to the user terminal. Specifically, a database query is executed to generate the shopping candidate list.

[1458] Step 7:

[1459] The user terminal displays the list of potential purchases received from the server. The user can check the list and easily purchase the items they need from the online shop. Specifically, the list is displayed on the user interface and a link to the online shop is provided.

[1460] In this way, data processing and calculations are performed at each step based on user input, and the system provides personalized fashion suggestions in real time.

[1461] (Application example 1)

[1462] 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."

[1463] In recent years, the spread of online shopping has made it easy for consumers to purchase a wide variety of fashion items. However, it is not easy for individual users to find a coordinated outfit that suits their style and lifestyle, especially since it is difficult to check the final look before wearing it. Furthermore, considering how to combine an outfit with items already owned is a time-consuming and labor-intensive task for users. Given this situation, there is a demand for a system that allows users to easily receive personalized fashion suggestions in real time from the comfort of their own home and make appropriate purchasing decisions by virtually trying on the items.

[1464] 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.

[1465] In this invention, the server includes means for storing personal information entered by the user, means for inputting and storing information about the user's fashion style and lifestyle, means for generating coordinated outfit suggestions customized for the user based on the input information and the latest fashion information, means for visually presenting the suggested coordinated outfits through a virtual try-on simulation, and means for identifying necessary items based on the suggested coordinated outfits and listing potential purchases. This allows users to easily receive fashion suggestions suited to them from the comfort of their own homes, as well as to check actual coordinated outfits through the virtual try-on and smoothly purchase the necessary items.

[1466] "Personal Information" means information that can identify you as an individual, such as your name, email address, address, and age, provided by you.

[1467] "Fashion style information" is information about the style of clothing that the user prefers, such as type, color, and design.

[1468] "Lifestyle information" refers to information about the user's daily life patterns, such as their lifestyle, work habits, hobbies, and activities.

[1469] A "coordination suggestion" is a combination of specific fashion items that is generated based on the user's fashion style information and lifestyle information.

[1470] "Virtual try-on simulation" is a system that allows users to try on selected fashion items in a virtual space and visually check them.

[1471] "Purchase candidates" is a list of fashion items that the user does not own but should consider purchasing based on the suggested outfit.

[1472] "Inventory data" refers to information about fashion items that a user already owns.

[1473] A "generative AI model" is an algorithm that analyzes data entered by the user and the latest fashion information to generate appropriate coordination suggestions.

[1474] "Real-time" refers to a process that responds immediately to user actions and provides instant results.

[1475] This system provides customized coordination suggestions and purchase candidates based on the user's personal information, fashion style information, and lifestyle information. This system provides optimal fashion advice to users in real time by communicating between a server and the user's terminal.

[1476] System configuration

[1477] 1. Server

[1478] The server securely stores the personal information, fashion style information, and lifestyle information provided by the user in a database.

[1479] It uses a generative AI model to generate customized outfit suggestions based on the information you enter.

[1480] The suggested outfits are sent to the user's device as a virtual try-on simulation.

[1481] Based on the user's inventory data, it identifies missing items and creates a list of potential purchases.

[1482] 2. User Device

[1483] Users access the system using a terminal and create a new account.

[1484] You enter basic personal information such as your name and email address, which is sent from your device to a server that stores it in a database.

[1485] Users use a profile setting screen to enter their fashion style and lifestyle information, such as "I like casual style," "I commute five days a week," and "I enjoy outdoor activities on weekends."

[1486] Once a profile is set up, users can visually check suggested outfits through a virtual try-on simulation.

[1487] Processing flow

[1488] 1. Enter your personal information

[1489] The personal information entered by the user is sent to the server and stored in a database.

[1490] 2. Enter your fashion style and lifestyle information

[1491] The user inputs fashion style and lifestyle information, which is also sent to the server.

[1492] 3. Coordination Proposal Generation

[1493] The server uses a generative AI model to generate customized outfit suggestions based on the input information.

[1494] The suggested outfits are sent to the user's device and can be visually confirmed through a virtual try-on simulation.

[1495] 4. Identify and list missing items

[1496] The server considers the user's inventory data and identifies missing items based on the suggested outfit.

[1497] The items listed as potential purchases are sent to the user's device.

[1498] Specific examples

[1499] For example, if a user specifies that they prefer a "casual style," that they commute five times a week, and that they "go outdoors on weekends," the server will use the generative AI model to suggest casual outfits suitable for commuting and outfits suitable for weekend outdoor activities. If the items needed for these suggestions are not in the user's inventory, they will be added to a list of purchase candidates and displayed on the user's device.

[1500] Example prompts to input to the generative AI model

[1501] User profile:

[1502] Name: Yamada Taro

[1503] Email address: example@example.com

[1504] Fashion Style: Casual

[1505] Lifestyle:

[1506] Commuting five times a week

[1507] Outdoor activities on the weekend

[1508] Based on this profile information, the app will suggest outfits and list items you don't have in your inventory as potential purchases.

[1509] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1510] Step 1:

[1511] Entering and saving personal information

[1512] A user accesses the system through a terminal and creates a new account. The user enters basic personal information such as name and email address. This information is sent from the terminal to the server, which stores it in a database. Example input data is the user's name "Taro" and email address "example@example.com". As output, the server sends a confirmation email to the user.

[1513] Step 2:

[1514] Enter and store fashion style and lifestyle information

[1515] Users use the profile settings screen on their device to enter information about their fashion style and lifestyle, such as "I like casual style," "I commute five days a week," and "I enjoy outdoor activities on weekends." This data is also sent from the device to the server, which stores and updates it in a database.

[1516] Step 3:

[1517] Generating outfit suggestions

[1518] Once the user's profile is set, the server uses a generative AI model to generate outfit suggestions. The input data here is the user's fashion style and lifestyle information. The generative AI model generates a number of outfit options based on the user's input and the latest fashion trend information. The output data is customized outfit suggestions (text and images), which the server sends to the user's device.

[1519] Step 4:

[1520] Providing virtual try-on simulations

[1521] The user visually checks the suggested outfits on their device through a virtual try-on simulation. The server generates virtual try-on simulation data based on the generated outfit suggestions and sends it to the user's device. The input data is the outfit suggestions, and the output data is a visual representation of the virtual try-on. This includes specific actions by the user to operate the avatar and check the suggested outfits.

[1522] Step 5:

[1523] Identifying and listing missing items

[1524] The server considers the user's inventory data and identifies missing items based on the suggested outfit. The input data is the user's inventory data and outfit suggestions, and the output data is a list of identified missing items. The server generates this list and sends it to the user's device.

[1525] Step 6:

[1526] Deciding and notifying potential purchasers

[1527] The user terminal displays the items listed as potential purchases based on the list of missing items received from the server. The user can view this list and take specific actions such as purchasing the necessary items from an online shop. The input data is the list of missing items, and the output data is the list of potential purchases.

[1528] 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.

[1529] This system provides customized outfit suggestions and purchase candidates based on the user's personal information, fashion style information, lifestyle information, and emotions. This system communicates between a server and the user's terminal, providing the user with optimal fashion advice in real time.

[1530] First, a user accesses the system using a terminal and creates a new account. The user enters basic personal information such as name and email address. This information is sent from the terminal to the server, which stores it in a database and sends a confirmation email to the user.

[1531] Next, the user uses the profile setting screen to enter information about their fashion style and lifestyle, such as "I like casual style," "I commute five days a week," and "I enjoy outdoor activities on weekends." This information is sent from the device to the server, which then stores and updates it in a database.

[1532] Once a user's profile is set, the server uses a generative AI model to generate outfit suggestions. The generative AI model generates multiple outfit options based on the user's fashion style and lifestyle information, while referencing the latest fashion trends. The server then sends the generated outfit suggestions to the user's device and displays them for the user to visually confirm.

[1533] The system also incorporates an emotion engine. The user's emotions are detected using cameras and sensors, and the emotion engine analyzes this information to recognize them. For example, the system can detect the emotion a user expresses when wearing a particular piece of clothing, and by learning this information, it can generate optimal outfits based on the user's emotions. The server also takes this emotional information into consideration to suggest outfits that are in line with the user's emotions.

[1534] The server also stores the user's inventory data and identifies missing items based on the suggested outfit. Based on this, a list of potential purchases is generated and sent to the user's device. The user can then easily purchase the items they need from the online shop by viewing this list. The list can also be optimized based on the user's emotions. For example, when a user is busy and stressed, the system suggests items with a simple, relaxed style.

[1535] For example, if a user specifies that they prefer a "casual style," that they commute five times a week, and that they "go outdoors on weekends," the server will use the generative AI model to suggest casual outfits suitable for commuting and outfits suitable for weekend outdoor activities. If the items required for these suggestions are not included in the user's inventory, they will be added to a list of purchase candidates and displayed on the user's device. Furthermore, the emotion engine analyzes the user's emotions, learns which suggestions the user responded most positively to, and reflects this in future outfit suggestions.

[1536] In this way, the system of the present invention provides personalized fashion suggestions in real time based on the user's needs, preferences, and emotions, helping users create attractive styling in a fun, easy way.

[1537] The processing flow will be explained below.

[1538] Specific explanation of program processing

[1539] User Registration

[1540] Step 1:

[1541] User: Launches the app, enters their name, email address, and password in the "New Registration" form, confirms the information entered, and presses the "Submit" button.

[1542] Step 2:

[1543] Terminal: Temporarily stores the entered information and sends a transmission request to the server.

[1544] Step 3:

[1545] Server: Stores the received user information in the database and generates a confirmation email if the save is successful.

[1546] Step 4:

[1547] Server: Generates the content of the confirmation email and sends it to the registered email address.

[1548] Fashion and lifestyle information input

[1549] Step 1:

[1550] User: On the profile settings screen, enter your preferred fashion style (e.g., "casual" or "business") and lifestyle (e.g., "commuting five days a week" or "outdoors on weekends").

[1551] Step 2:

[1552] Device: Temporarily stores the entered fashion style and lifestyle information and waits for the "Save" button to be pressed.

[1553] Step 3:

[1554] User: Presses the "Save" button.

[1555] Step 4:

[1556] Terminal: Sends input data to the server.

[1557] Step 5:

[1558] Server: Stores the received user fashion style and lifestyle information in a database and updates the user profile.

[1559] Generating outfit suggestions

[1560] Step 1:

[1561] Server: Retrieves user profile information (fashion style and lifestyle information) from the database.

[1562] Step 2:

[1563] Server: Based on the acquired information, a generative AI model is used to generate outfit suggestions, taking into account trends, the season, and existing personal belongings.

[1564] Step 3:

[1565] Device: Captures the user's facial image and voice data through the camera and microphone.

[1566] Step 4:

[1567] Device: Sends the acquired emotion data to the server.

[1568] Step 5:

[1569] Server: Analyzes the user's emotions using the emotion engine. Based on the analysis results, the server adjusts the outfit suggestions.

[1570] Step 6:

[1571] Server: Sends the adjusted coordination plan to the user device.

[1572] Step 7:

[1573] Device: Visually displays the received coordination suggestions to the user.

[1574] Purchase suggestion

[1575] Step 1:

[1576] Server: Retrieves user's inventory data from the database.

[1577] Step 2:

[1578] Server: Based on the obtained outfit suggestions and inventory data, identify items that the user does not have.

[1579] Step 3:

[1580] Server: Generates a list of available items for purchase.

[1581] Step 4:

[1582] Server: Optimize the shopping list based on the analysis results of the emotion engine. For example, if the user is feeling stressed, prioritize items with a relaxed style.

[1583] Step 5:

[1584] Server: Sends the optimized shopping list to the user's device.

[1585] Step 6:

[1586] On the device: The received shopping list is visually displayed to the user and a link to the shopping site is provided.

[1587] Specific examples

[1588] User Registration

[1589] Step 1:

[1590] User: The user launches the app, enters their name, email address, and password in the "New Registration" form, and presses the "Submit" button.

[1591] Step 2:

[1592] Terminal: Sends input information to the server.

[1593] Step 3:

[1594] Server: Stores the received information in a database and sends a confirmation email.

[1595] Fashion and lifestyle information input

[1596] Step 1:

[1597] User: In the app, select "Casual," "Commute 5 days a week," and "Outdoors on weekends," then press the "Save" button.

[1598] Step 2:

[1599] Terminal: Sends input data to the server.

[1600] Step 3:

[1601] Server: Analyzes the input information and stores it in a database.

[1602] Generating outfit suggestions

[1603] Step 1:

[1604] Server: Generates "casual commuting style" and "weekend style suitable for outdoor activities" based on the user profile.

[1605] Step 2:

[1606] Device: The camera captures the user's facial image and sends it to the server as emotion data.

[1607] Step 3:

[1608] Server: Analyzes emotions using an emotion engine and adjusts outfit suggestions to match the user's mood.

[1609] Step 4:

[1610] Server: Sends the adjusted coordination proposal to the user device.

[1611] Step 5:

[1612] Terminal: Visually display the suggested outfits to the user.

[1613] Purchase suggestion

[1614] Step 1:

[1615] Server: Obtains the user's belongings data based on the coordination suggestions.

[1616] Step 2:

[1617] Server: Identifies missing items and generates a purchase list.

[1618] Step 3:

[1619] Server: Optimize purchase candidates based on user sentiment data.

[1620] Step 4:

[1621] Server: Sends the optimized shopping list to the user's device.

[1622] Step 5:

[1623] On your device: Display suggested purchases and provide links to appropriate online stores.

[1624] Example 2

[1625] 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."

[1626] Conventional fashion recommendation systems could only provide generalized suggestions without fully considering the individual preferences and lifestyles of users. Furthermore, they lacked personalized coordination suggestions that took into account the user's emotions and actual possessions, making it difficult to provide specific and practical fashion advice.

[1627] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for storing personal information input by the user, a means for inputting and storing the user's fashion style and lifestyle information, a means for generating a coordinated outfit proposal customized for the user using a generative AI model based on the input information and the latest fashion information, a means for generating a prompt sentence for the generative AI model and generating the coordinated outfit proposal, a means for identifying missing items based on the proposed outfit and listing purchase candidates, and a means for analyzing the user's emotions using an emotion detection device and optimizing the coordinated outfit proposal based on the emotion information. This makes it possible to provide specific and practical coordinated outfit proposals based on the user's individual preferences and lifestyle.

[1628] "Means for storing personal information" refers to devices or software used to store and manage basic information such as a user's name, email address, and password.

[1629] "Means for inputting and storing fashion style and lifestyle information" refers to devices and software that allow users to input information about their preferred fashion style and daily living patterns, and to store and manage this information.

[1630] A "generative AI model" is an artificial intelligence system that generates new information or suggestions based on input data, for example, using machine learning algorithms.

[1631] A "means for generating prompt sentences" is a device or software that formats and inputs instructions and information to generate appropriate coordination suggestions for a generative AI model.

[1632] A "means for generating outfit suggestions" is a device or software that uses user input information and a generative AI model to create a series of fashion suggestions and outfit ideas.

[1633] An "emotion detection device" is a device or software that detects and analyzes a user's emotional state, such as one that uses a camera or sensor.

[1634] "Means for optimizing outfit suggestions based on emotional information" refers to devices or software that adjust outfit suggestions made by a generative AI model based on the user's emotional information obtained by an emotion detection device, thereby providing more appropriate suggestions.

[1635] "Means for identifying missing items and listing potential purchase items" refers to a device or software that compares the user's possession data with the generated coordination suggestions, identifies the missing items, and lists them as potential purchase items.

[1636] This is a system that provides customized outfit suggestions and purchase candidates based on a user's personal information, fashion style information, lifestyle information, and emotions. This system communicates between a server and the user's terminal, providing the user with optimal fashion advice in real time.

[1637] Create an account and enter your personal information

[1638] First, the user accesses the system using their own device (e.g., smartphone or PC). The user enters their name, email address, and password on the new account creation screen. This information is sent to the server using the HTTPS protocol. The server stores the received personal information in a database (e.g., MySQL or MongoDB) and sends a confirmation email to the user using a mail server (e.g., Amazon SES or SendGrid).

[1639] Profile Settings

[1640] Next, the user uses their device to access a profile setting screen and enter information about their fashion style and lifestyle. For example, they might say, "I like casual style," "I commute five days a week," or "I enjoy outdoor activities on weekends." The device then sends this information to the server, which then stores and updates the received information in a database.

[1641] Generating outfit suggestions

[1642] Once the user's profile information is set, the server uses a generative AI model (e.g., GPT-4) to generate outfit suggestions. The server creates a prompt for the generative AI model. An example of a prompt is, "The user's fashion style is casual, they commute to work five days a week, and enjoy outdoor activities on weekends. Please suggest the best outfit for the user." Based on this prompt, the generative AI model generates appropriate outfit ideas. The server sends the generated outfit suggestions to the user's device and displays them so that the user can visually confirm them.

[1643] Applying the Emotion Engine

[1644] The system also incorporates an emotion engine. The user's emotions are detected using the device's built-in camera and sensors. The device then sends the emotion data to the server. The server's emotion engine then analyzes the user's emotions using image processing libraries such as OpenCV. Based on the analysis results, the server generates outfit suggestions that are in line with the user's emotions. The server then generates prompts for the AI ​​model to generate outfit suggestions. The server then sends the new suggestions to the user's device.

[1645] Generate a purchase candidate list

[1646] The server retrieves the user's inventory data from a database and compares the items required with the generated outfit suggestions. If the required items are not included in the user's inventory, the server adds them to a list of potential purchases. The server then sends the list of potential purchases to the user's device and displays it for the user to review. The user can then easily purchase the items they need from the online shop by viewing the list of potential purchases.

[1647] In this way, the system can provide specific and practical outfit suggestions in real time based on the user's individual preferences and lifestyle.

[1648] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1649] Step 1:

[1650] The user accesses the system using a terminal and enters their name, email address, and password on the new account creation screen. The entered personal information is sent to the server using the HTTPS protocol. The server stores the received personal information in a database and sends a confirmation email.

[1651] Input: User's name, email address, and password

[1652] Data processing: Personal information is stored in a database

[1653] Output: A confirmation email from the server to the user

[1654] Step 2:

[1655] Users use their devices to access a profile setting screen and input their fashion style and lifestyle information. This information is sent from the device to the server, which then stores and updates it in a database.

[1656] Input: Fashion style information (e.g., casual), lifestyle information (e.g., commute five days a week, spend weekends outdoors)

[1657] Data processing: Save and update the entered information in the database

[1658] Output: Profile information is saved in the server database

[1659] Step 3:

[1660] The server generates a prompt for the generative AI model based on the user's profile information. The generative AI model generates outfit suggestions based on the prompt. The server then sends the generated outfit suggestions to the user's device.

[1661] Input: User profile information

[1662] Data processing: Enter prompts into the generative AI model to generate outfit suggestions

[1663] Output: Send the generated coordination proposal to the user's device

[1664] Step 4:

[1665] The device uses an emotion detection device (e.g., a camera or sensor) to capture the user's emotional data and send it to the server. The server's emotion engine analyzes this emotional data and recognizes the user's emotion. Based on the recognized emotional information, the device generates a new prompt and creates new coordination suggestions.

[1666] Input: User emotion data (e.g. camera footage)

[1667] Data processing: Emotion analysis using an emotion engine, prompt generation, and coordination suggestion update

[1668] Output: Optimized outfit suggestions

[1669] Step 5:

[1670] The server retrieves the user's belongings data from the database and checks the items required for the generated coordination proposal. If the required items are not included in the user's belongings, it generates a list of purchase candidates and sends it to the user's terminal.

[1671] Input: User's belongings data, generated coordination suggestions

[1672] Data processing: Compare inventory data with suggestions and create a list of missing items

[1673] Output: Send the purchase candidate list to the user's device

[1674] (Application example 2)

[1675] 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."

[1676] Conventional fashion suggestion systems make suggestions based on a user's personal information and fashion style information, but they are unable to take into account the user's emotions or real-time reactions, and therefore are unable to fully increase user satisfaction. Furthermore, the process of creating a list of potential purchases based on suggested outfits is not automated, which is a time-consuming process for the user. Thus, there is a need for a system that provides personalized fashion suggestions in real time and in line with the user's emotions.

[1677] The specification processing by the specification 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 storing personal information entered by the user, means for inputting and storing information about the user's fashion style and lifestyle, means for generating coordinated outfit suggestions customized for the user based on the input information and the latest fashion information, means for analyzing the user's emotions using a smartphone camera or sensor and optimizing the coordinated outfit suggestions based on that information, and means for identifying necessary accessories based on the proposed outfit and listing purchase candidates. This enables real-time fashion suggestions based on the user's emotions and preferences and efficient presentation of purchase candidates.

[1678] "Personal information" refers to information that users enter to identify themselves, such as their name, email address, and address.

[1679] "Fashion style" refers to the type of clothing and accessory coordination based on the user's preferences, such as casual or business style.

[1680] "Lifestyle information" refers to information related to the user's daily life and habits, such as the frequency of commuting and the activities they do on their days off.

[1681] A "smartphone" is an evolved form of a mobile phone, and is a portable information terminal with many functions, including Internet access, application execution, and camera functionality.

[1682] A "camera" is a device for taking images or videos, and in this case refers to the one built into a smartphone.

[1683] A "sensor" is a device that detects a physical quantity (such as light, temperature, or motion) and converts it into an electrical signal.

[1684] "Analyzing emotions" means observing the user's facial expressions and behavior and determining their current emotional state (for example, joy, sadness, surprise, etc.) based on that data.

[1685] "Coordination suggestions" suggest clothing combinations and accessory selections based on the user's fashion style and lifestyle information.

[1686] "Purchase candidates" is a list of items that are recommended for the user to purchase based on the coordination suggestions.

[1687] A "database" is a system for efficiently storing, managing, and searching structured data, and is used to organize and store large amounts of data such as user information.

[1688] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate new outfit suggestions based on user information.

[1689] A "prompt sentence" is a sentence that describes an input request for a generative AI model, and is text used to give specific instructions to the AI.

[1690] This invention is a system that provides customized coordination suggestions and purchase candidates based on the user's personal information, fashion style information, lifestyle information, and user emotions. This system communicates between a server and the user's terminal and provides optimal fashion advice in real time.

[1691] System configuration

[1692] The system consists of the following main components:

[1693] 1. User Device

[1694] A smartphone is a device that allows users to input personal information, fashion style, and lifestyle information.

[1695] Smartphones are equipped with cameras and sensors that are used to detect the user's emotions.

[1696] 2. Server

[1697] Personal information and style information submitted by users is stored in a database.

[1698] Use generative AI models to generate outfit suggestions.

[1699] Use a sentiment analysis engine to optimize suggestions based on user sentiment.

[1700] Program processing

[1701] To operate this system, the following data processing and calculation are performed.

[1702] Hardware and Software Use

[1703] 1. Hardware

[1704] Smartphones: Used for data entry and emotion detection.

[1705] Server: Used for data storage, running generative AI models, and sentiment analysis.

[1706] 2. Software

[1707] Database Management System: Software for storing user information and fashion information.

[1708] Generative AI model (e.g., TensorFlow or Keras): An algorithm for generating outfit suggestions based on user information.

[1709] Sentiment analysis engine (e.g., OpenCV): Software for analyzing user emotions based on images acquired from a smartphone camera.

[1710] Data processing and calculation flow

[1711] 1. Entering and saving user information

[1712] Users use their smartphones to enter personal information, fashion style, and lifestyle information, which is then sent to a server, which stores the information in a database.

[1713] 2. Coordination Proposal Generation

[1714] The server uses a generative AI model to generate multiple outfit suggestions based on the user's input and the latest fashion information.

[1715] 3. Sentiment analysis and recommendation optimization

[1716] The smartphone's camera and sensors detect the user's emotions and send the data to a server, which then uses an emotion analysis engine to optimize suggestions based on the user's emotions.

[1717] 4. Presenting potential purchases

[1718] Based on the proposed outfit, the server checks the user's inventory data, identifies missing items, and creates a list of potential purchases, which is then sent to the user's device for viewing.

[1719] Examples of specific examples and prompts

[1720] For example, if a user specifies that they prefer a "casual style" and enters "commuting five days a week" and "outdoors on weekends," the server will send the following prompt to the generative AI model:

[1721] User Information:

[1722] Name: Your name

[1723] Fashion Style: Casual

[1724] Lifestyle: 5-day commute, weekend outdoor activities

[1725] Prompt for generating outfit suggestions:

[1726] Suggest a casual style suitable for commuting five days a week, as well as an outfit suitable for outdoor activities on the weekends, taking into account the latest fashion trends and the preferences of the user.

[1727] Based on these prompts, a generative AI model generates detailed outfit suggestions, which are then delivered to the user in real time.

[1728] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1729] Step 1:

[1730] Users use their smartphones to input their personal information, fashion style information, and lifestyle information, such as their name, email address, preferred style (e.g., casual), and daily life information (e.g., commuting five days a week and outdoor activities on weekends). This data is then sent to the server as input information.

[1731] Step 2:

[1732] The server stores the received user personal information, fashion style information, and lifestyle information in a database. Specifically, the server stores the user information in a "user information table" and the fashion style and lifestyle information in a "style information table" to maintain consistency of the database.

[1733] Step 3:

[1734] The server generates outfit suggestions using a generative AI model based on the user's saved fashion style and lifestyle information. The server creates a prompt sentence like the following and inputs it into the generative AI model:

[1735] User Information:

[1736] Name: User

[1737] Fashion Style: Casual

[1738] Lifestyle: 5-day commute, weekend outdoor activities

[1739] Prompt for generating outfit suggestions:

[1740] Suggest a casual style suitable for commuting five days a week, as well as an outfit suitable for outdoor activities on the weekends, taking into account the latest fashion trends and the user's preferences.

[1741] This allows the generative AI model to suggest the best outfits for the user.

[1742] Step 4:

[1743] Users can use their smartphone's camera and sensors to detect their own emotional state. For example, if they smile while looking in a mirror, the camera captures their facial expression. This emotional data is then sent from the user device to the server.

[1744] Step 5:

[1745] The server inputs the received emotional data into its emotion analysis engine and analyzes the user's emotional state. The analysis results identify the user's current emotion (e.g., joy, excitement, etc.). Based on the results of this emotion analysis, the server optimizes the generated outfit suggestions and selects the suggestion that best matches the user's emotion.

[1746] Step 6:

[1747] The server sends the optimized outfit suggestions to the user's device, where the user can visually check the suggested outfits on their smartphone screen. Specifically, among the multiple outfits presented by the application's UI, the most suitable suggestion based on the results of emotion analysis is displayed first.

[1748] Step 7:

[1749] The server references the user's inventory database and identifies any missing items based on the proposed outfit. For example, if the proposed outfit requires a "white shirt" and the user does not own one, the white shirt is identified as a missing item. This information is added to the purchase candidate list.

[1750] Step 8:

[1751] The server sends a list of missing items to the user's device. The user can then check the list on their smartphone and purchase the items they need directly from the online shop. Specifically, by tapping an item on the list, a link to the online shop's purchase page is provided.

[1752] The above processing steps realize real-time personalized fashion suggestions based on the user's personal information and emotions, and further enable the user to easily purchase items based on the suggestions.

[1753] 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.

[1754] 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.

[1755] 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.

[1756] 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.

[1757] 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.

[1758] 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.

[1759] 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).

[1760] 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.

[1761] 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."

[1762] 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.

[1763] 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).

[1764] 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.

[1765] 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.

[1766] 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.

[1767] 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.

[1768] 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.

[1769] 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.

[1770] 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.

[1771] 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.

[1772] 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.

[1773] 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.

[1774] The following is further disclosed regarding the above embodiment.

[1775] (Claim 1)

[1776] A means of retaining personal information entered by users;

[1777] means for inputting and retaining user fashion style and lifestyle information;

[1778] means for generating coordinated outfit suggestions customized for a user based on input information and the latest fashion information;

[1779] A means to identify necessary items based on the suggested outfits and make a list of potential purchases;

[1780] A system including:

[1781] (Claim 2)

[1782] 10. The system of claim 1, further comprising means for maintaining data on the user's existing belongings and identifying missing items based on the suggested outfit.

[1783] (Claim 3)

[1784] 10. The system of claim 1, further comprising means for securely storing user-entered information in a database and analyzing the stored data to generate customized outfit suggestions in real time.

[1785] "Example 1"

[1786] (Claim 1)

[1787] A means of retaining personal information entered by users;

[1788] means for inputting and retaining user fashion style and lifestyle information;

[1789] A means for generating customized coordination suggestions for a user using a generative AI model based on input information and the latest fashion information;

[1790] A means of identifying necessary items and creating a list of potential purchases using the output of a generative AI model based on the suggested outfit;

[1791] A means for transmitting the generated purchase candidate list to a user terminal;

[1792] A system including:

[1793] (Claim 2)

[1794] 10. The system of claim 1, further comprising means for maintaining data on the user's existing belongings and identifying missing items based on the suggested outfit.

[1795] (Claim 3)

[1796] 10. The system of claim 1, further comprising means for securely storing user-entered information in a database and analyzing the stored data to generate customized outfit suggestions in real time.

[1797] "Application Example 1"

[1798] (Claim 1)

[1799] A means of retaining personal information entered by users;

[1800] means for inputting and retaining user fashion style and lifestyle information;

[1801] means for generating coordinated outfit suggestions customized for a user based on input information and the latest fashion information;

[1802] A means for visually providing suggested outfits through virtual try-on simulations;

[1803] The system includes a means for identifying necessary items based on the suggested coordination and listing potential purchases.

[1804] (Claim 2)

[1805] 10. The system of claim 1, further comprising means for maintaining data on the user's existing belongings and identifying missing items based on the suggested outfit.

[1806] (Claim 3)

[1807] 10. The system of claim 1, further comprising means for securely storing user-entered information in a database and analyzing the stored data to generate customized outfit suggestions in real time.

[1808] "Example 2: Combining Emotion Engines"

[1809] (Claim 1)

[1810] A means of retaining personal information entered by users;

[1811] means for inputting and retaining user fashion style and lifestyle information;

[1812] A means for generating customized coordination suggestions for a user using a generative AI model based on input information and the latest fashion information;

[1813] A means for generating a prompt sentence for the generative AI model and generating a coordination suggestion;

[1814] A way to identify missing items based on the suggested outfits and create a list of potential purchases.

[1815] means for analyzing a user's emotions using an emotion detection device and optimizing coordination suggestions based on the emotion information;

[1816] A system including:

[1817] (Claim 2)

[1818] 10. The system of claim 1, further comprising means for maintaining data on the user's existing belongings and identifying missing items based on the suggested outfit.

[1819] (Claim 3)

[1820] 10. The system of claim 1, further comprising means for securely storing user-entered information and analyzing the stored data to generate customized outfit suggestions in real time.

[1821] "Application example 2 when combining emotion engines"

[1822] (Claim 1)

[1823] A means of retaining personal information entered by users;

[1824] means for inputting and retaining user fashion style and lifestyle information;

[1825] means for generating coordinated outfit suggestions customized for a user based on input information and the latest fashion information;

[1826] A means of analyzing user emotions using a smartphone camera or sensor and optimizing outfit suggestions based on that information;

[1827] A means for identifying necessary accessories based on the proposed coordination and making a list of potential purchases;

[1828] A system including:

[1829] (Claim 2)

[1830] 10. The system of claim 1, further comprising means for maintaining data on the user's existing belongings and identifying missing accessories based on the suggested outfit.

[1831] (Claim 3)

[1832] A means for securely storing user-entered information in a database and analyzing the stored data to generate customized outfit suggestions in real time;

[1833] a means for generating detailed outfit suggestions based on the user's information using a generative AI model and sending a request to the model using a prompt;

[1834] The system of claim 1 further comprising: [Explanation of symbols]

[1835] 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 of retaining personal information entered by users; means for inputting and retaining user fashion style and lifestyle information; means for generating coordinated outfit suggestions customized for a user based on input information and the latest fashion information; A means to identify necessary items based on the suggested outfits and make a list of potential purchases; A system including:

2. The system of claim 1 further comprising means for maintaining data on the user's existing belongings and identifying missing items based on the suggested outfit.

3. 10. The system of claim 1, further comprising means for securely storing user-entered information in a database and analyzing the stored data to generate customized outfit suggestions in real time.

Images