System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting and storing user profile information in a database; means for periodically obtaining and storing worldwide fashion trend information in a database; means for analyzing user profile information and trend information and generating personalized product designs; means for suggesting the generated product designs to a user; means for transmitting order information to a partner factory to manufacture a product selected by the user; and means for delivering the manufactured product to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In the traditional apparel purchasing process, consumers must spend a lot of time and effort finding the perfect product for them. Apparel manufacturers also face the problem of excess inventory and waste caused by overproduction, which also creates a significant environmental burden. Our goal is to provide a system that solves these problems, allowing consumers to easily find the perfect apparel product for them and helping manufacturers maintain appropriate production volumes and reduce their environmental impact. [Means for solving the problem]

[0005] a means for collecting and storing user profile information in a database;

[0006] A means to regularly obtain fashion trend information from around the world and store it in a database,

[0007] A means for analyzing user profile information and trend information to generate personalized product designs;

[0008] A means for proposing the generated product design to a user;

[0009] A means for transmitting order information to partner factories to manufacture the products selected by the user;

[0010] The system, which includes a means of delivering manufactured products to users, allows consumers to obtain original products that are best suited to them, and enables manufacturers to avoid overproduction and reduce their environmental impact.

[0011] "Profile Information" is data that includes personal preferences and attributes about a user, such as the user's fashion preferences, size and color preferences, and past purchasing history.

[0012] "Trend information" refers to information showing the latest fashion trends around the world, and is data including popular items, colors, design patterns, and the like.

[0013] "Personalized product design" refers to apparel product designs created specifically for each user by AI based on the user's profile information and trend information.

[0014] An "affiliated factory" is a factory that receives order information, actually manufactures apparel products, and delivers the finished products to users.

[0015] "Database" means a collection of data that stores profile information, trend information, and other necessary data and is organized to enable efficient access and processing.

[0016] "Order information" refers to detailed information about the product selected by the user, including data such as design, size, and color required for manufacturing.

[0017] "Terminal" means the device used by a User to enter information and receive personalized product offers. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] This invention relates to a system that collects user profile information, combines it with trend information from around the world, and proposes personalized apparel products. This system utilizes AI to generate and propose products based on the user's preferences, and then manufactures and delivers the ordered products.

[0040] System Overview

[0041] The system includes the following main components:

[0042] 1. Terminal: A device where users input information, receive product suggestions, and place orders. This can be a smartphone or a PC.

[0043] 2. Server: A centralized system that collects and analyzes information sent by users and trend information, and generates personalized product recommendations.

[0044] 3. Database: Installed on the server, it stores user profile information, trend information, and order information.

[0045] 4. Affiliated Factory: A facility that actually manufactures apparel products based on the order information sent from the server and delivers them to users.

[0046] Processing flow

[0047] 1. User Registration and Information Collection

[0048] A user launches the application and registers, entering basic information such as name, email address, and password.

[0049] The device receives this information and sends it to a server, along with other details such as the user's fashion preferences, size, color, and past purchase history.

[0050] The server stores the received information in a database and generates a user ID to associate with the profile information.

[0051] 2. Obtaining trend information

[0052] The server periodically collects fashion trend information from around the world through API, including popular items, design patterns, colors, etc.

[0053] The collected trend information is stored in a database and used for analysis.

[0054] 3. Personalized product recommendations

[0055] The server analyzes the user's profile information and trend information, and uses AI algorithms to generate personalized product designs for each user.

[0056] A list of suggested products is created based on the generated product design and sent to the terminal.

[0057] 4. Product Selection and Ordering

[0058] The user can view the list of suggested products on the device, select the product they like, and then press the "Purchase" button to confirm the order.

[0059] The terminal transmits order information for the selected product to the server.

[0060] 5. Manufacturing and Delivery

[0061] The server sends the order information to the partner factory, which includes the detailed data (design, size, color) required for manufacturing.

[0062] The partner factory will manufacture the product according to the specified design, and once production is complete, the product will be shipped directly to the customer.

[0063] The server notifies the terminal of the start and completion of delivery.

[0064] Specific examples

[0065] For example, if you're looking for a casual shirt for spring:

[0066] 1. User Registration and Information Collection

[0067] The user enters the following information: "Favorite style: casual," "Favorite colors: blue, white," and "Size: M."

[0068] The terminal sends this information to the server, which stores it in a database.

[0069] 2. Obtaining trend information

[0070] The server obtains the latest fashion information via an API and determines that "checkered shirts for spring" are in fashion.

[0071] 3. Personalized product recommendations

[0072] Based on the user's preferences and the latest trends, the server uses AI to generate a "blue checked casual shirt" and adds it to the list of suggested products.

[0073] This list is sent to the user's device.

[0074] 4. Product Selection and Ordering

[0075] The user checks the suggested products, selects the "blue checked casual shirt," and presses the purchase button.

[0076] The terminal transmits the order information to the server.

[0077] 5. Manufacturing and Delivery

[0078] The server sends the order information to a partner factory, which then produces the shirt, which is then shipped directly to the user.

[0079] The server notifies the user's terminal of the delivery information.

[0080] This system allows users to smoothly obtain apparel products that match their preferences and trends, and enables apparel manufacturers to reduce unnecessary inventory and lighten the burden on the environment.

[0081] The processing flow will be explained below.

[0082] Step 1:

[0083] The user launches the application and enters basic information such as name, email address, and password on the new registration screen.

[0084] Step 2:

[0085] The device receives this basic information and sends it to the server, which stores it in a database and generates a user ID.

[0086] Step 3:

[0087] The user then inputs their fashion preferences (e.g., casual style), size (e.g., medium size), color preferences (e.g., blue, white), and past purchasing history.

[0088] Step 4:

[0089] The device collects these details and sends them to a server, which stores them in a database.

[0090] Step 5:

[0091] The server periodically accesses an external API that provides fashion trend information and collects the latest fashion trend data, including popular items, design patterns, colors, etc.

[0092] Step 6:

[0093] The server stores the collected trend information in a database and prepares it for later analysis.

[0094] Step 7:

[0095] The server uses an AI algorithm to analyze the user's profile information and the latest trends to generate the optimal product design for the user. For example, it generates a "blue checked casual shirt" based on the user's preferred combination of blue and the latest checked pattern.

[0096] Step 8:

[0097] The server creates a list of suggested products based on the generated product design and transmits this list to the user's terminal.

[0098] Step 9:

[0099] Users can view the list of suggested products on their device, view details, select the product they like, and press the "Purchase" button.

[0100] Step 10:

[0101] To confirm the purchase, the terminal sends the order information (item ID, size, color) of the selected item to the server.

[0102] Step 11:

[0103] The server sends the order information to the partner factory and issues manufacturing instructions. The order information includes detailed data required for manufacturing (design, size, color).

[0104] Step 12:

[0105] The partner factory manufactures the product based on the order information received from the server. For example, the partner factory manufactures a "blue checked casual shirt."

[0106] Step 13:

[0107] The partner factory will then arrange for the completed product to be delivered directly to the user.

[0108] Step 14:

[0109] The server notifies the user's device of delivery start and completion information, allowing the user to check the delivery status of the product.

[0110] This series of steps allows users to obtain original products that are best suited to them, while allowing manufacturers to maintain appropriate production volumes and reduce environmental impact.

[0111] Example 1

[0112] 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."

[0113] Conventional apparel product recommendation systems have difficulty in proposing personalized products that reflect the individual preferences and latest trends of users. Furthermore, they lacked efficient methods for quickly manufacturing and delivering the products selected by users. As a result, it was difficult to increase user satisfaction, and manufacturers also faced the challenge of increasing the burden of inventory management.

[0114] 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.

[0115] In this invention, the server includes means for collecting user profile information and storing it in a database, means for periodically obtaining fashion trend information from around the world and storing it in a database, means for analyzing user profile information and trend information and generating personalized product designs using a generative AI model, means for sending the generated product designs to a terminal and proposing them to the user, means for sending order information for products selected by the user on the terminal to an affiliated manufacturing facility, means for delivering the manufactured products to the user, and means for notifying the user's terminal of manufacturing and delivery information. This enables users to efficiently propose, select, and order personalized products based on their preferences and the latest trends, and also enables manufacturers to achieve efficient production and delivery, thereby reducing the burden of inventory management.

[0116] "User" means an entity that utilizes the System to provide profile information and receive personalized product offers.

[0117] "Profile Information" refers to general information about a user, such as the user's fashion preferences, size and color preferences, and past purchasing history.

[0118] "Fashion trend information" refers to information about fashion that is popular around the world at each time and region, such as popular items, colors, and design patterns.

[0119] "Generative AI model" refers to an artificial intelligence model that analyzes user profile information and fashion trend information to automatically generate personalized product designs.

[0120] "Affiliated manufacturing facility" refers to a facility that manufactures the apparel products selected by the user based on the order information sent from the server.

[0121] "Terminal" refers to the device used by a user to enter information, receive product suggestions, and place an order, specifically a smartphone or computer.

[0122] "Server" refers to the centralized system that collects and analyzes information sent by users and trend information, and generates and manages personalized product proposals.

[0123] "Database" refers to an information management system installed on a server for storing user profile information, trend information, and order information.

[0124] This invention relates to a system that collects user profile information, combines it with global fashion trend information, and proposes personalized apparel products. The system utilizes a generative AI model to generate and propose products based on the user's preferences, and then manufactures and delivers the ordered products.

[0125] System Overview

[0126] The system includes the following major components:

[0127] 1. Terminal: A device on which a user enters profile information, receives product suggestions, and places an order. Specifically, a smartphone or PC is used.

[0128] 2. Server: A centralized system that collects and analyzes information sent by users and trend information, and generates personalized product recommendations.

[0129] 3. Database: Installed on the server, it stores user profile information, trend information, and order information.

[0130] 4. Affiliated manufacturing facility: This is the facility that actually manufactures the apparel products based on the order information sent from the server and delivers them to the user.

[0131] Hardware and software used

[0132] Devices: smartphones, computers

[0133] Server: Centralized system

[0134] Database: A cloud database for managing user information, trend information, and order information.

[0135] Generative AI model: An algorithm that analyzes user preferences and trend information to generate personalized product designs

[0136] Processing flow

[0137] 1. User Registration and Information Collection

[0138] A user launches the application and registers, entering basic information such as name, email address, and password.

[0139] The device receives this information and sends it to a server, along with other details such as the user's fashion preferences, size, color, and past purchase history.

[0140] The server stores the received information in a database and generates a user ID to associate with the profile information.

[0141] 2. Obtaining trend information

[0142] The server periodically collects the latest fashion trend information from around the world through API, specifically information on popular items, design patterns, colors, etc.

[0143] The server stores the collected trend information in a database and tags it for analysis.

[0144] 3. Personalized product recommendations

[0145] The server uses the generative AI model to analyze the user's profile information and trend information. For example, it inputs a prompt statement such as "Suggest spring casual shirts for users who like blue."

[0146] The generative AI model uses user preferences and trend information to generate personalized product designs.

[0147] The server adds the generated product design to a list of proposed products and transmits this list to the terminal.

[0148] 4. Product Selection and Ordering

[0149] The user can check the list of suggested products on the device, select the desired product, and press the "Purchase" button to confirm the order.

[0150] The terminal transmits order information for the selected product to the server.

[0151] 5. Manufacturing and Delivery

[0152] The server sends the order information to the partner manufacturing facility, including the details required for manufacturing (design, size, color).

[0153] The partner manufacturing facility will produce the product according to the specified design and ship the product directly to the user once production is complete.

[0154] The server notifies the user's device of the start and completion of delivery, for example, by sending a notification that the product has been shipped.

[0155] Specific examples

[0156] For example, if you're looking for a casual shirt for spring:

[0157] 1. The user enters the following profile information: "Favorite style: casual," "Favorite colors: blue, white," and "Size: M."

[0158] 2. The device sends this information to the server, which stores it in a database.

[0159] 3. The server retrieves the latest fashion information via the API and determines that "checkered shirts for spring" are in fashion.

[0160] 4. Based on the user's preferences and the latest trends, the server uses a generative AI model to generate a "blue checked casual shirt" and adds it to the list of suggested products.

[0161] 5. Send this list to the user's device.

[0162] 6. The user checks the suggested products, selects the "blue checked casual shirt," and presses the purchase button.

[0163] 7. The terminal sends the order information to the server.

[0164] 8. The server sends the order information to a partner manufacturing facility, which produces the shirt. Once production is complete, the shirt is shipped directly to the customer.

[0165] 9. The server notifies the user's terminal of the delivery information.

[0166] This system allows users to smoothly obtain apparel products that match their preferences and trends, and enables apparel manufacturers to reduce unnecessary inventory and lighten the burden on the environment.

[0167] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0168] Step 1: Collect user information

[0169] Subject: User

[0170] How it works: The user launches the dedicated application and clicks "Sign Up." After entering their name, email address, and password, they click "Next."

[0171] Input: User's name, email address, password

[0172] Output: Sends the entered basic information to the terminal

[0173] Subject: Terminal

[0174] How it works: The device encrypts the information entered and sends it to the server using a secure communication protocol (HTTPS).

[0175] Input: Encrypted user basic information

[0176] Output: Basic information sent to the server

[0177] Subject: User

[0178] How it works: Next, the user enters their "favorite style," "favorite color," "size," and "past purchase history."

[0179] Input: Fashion preferences, size, color preferences, past purchase history

[0180] Output: Sends the entered details to the terminal

[0181] Subject: Terminal

[0182] How it works: The device re-encrypts these details and sends them to the server.

[0183] Input: Encrypted details

[0184] Output: Details sent to the server

[0185] Subject: Server

[0186] How it works: The server stores the submitted information in a database and generates a user ID to associate with the profile information.

[0187] Input: Basic information and detailed information received

[0188] Data processing / data calculation: Save to database, generate user ID, associate with profile information

[0189] Output: Profile information stored in the database

[0190] Step 2: Obtaining trend information

[0191] Subject: Server

[0192] How it works: The server uses the fashion API to periodically obtain the latest fashion trend information from around the world, specifically information on popular items, design patterns, colors, etc.

[0193] Input: Fashion trend information obtained through API

[0194] Data processing / data calculation: Shaping and tagging trend information

[0195] Output: Trend information stored in a database

[0196] Step 3: Generate personalized product suggestions

[0197] Subject: Server

[0198] How it works: The server inputs the user's profile information and trend information into the generative AI model for analysis. A prompt such as "Suggest a spring casual shirt that is recommended for a user who likes the color blue" is used.

[0199] Input: User profile information, fashion trend information, prompt text

[0200] Data processing / data calculation: Analysis by generative AI models and generation of personalized product designs

[0201] Output: Suggested product list

[0202] Subject: Server

[0203] Operation: The generated product design is added to a list of suggested products and this list is sent to the user's device.

[0204] Input: Generated product design

[0205] Output: Suggested product list sent to the terminal

[0206] Step 4: Select and order

[0207] Subject: User

[0208] How it works: The user reviews the list of suggested products on their device, selects the product they want, for example, "Blue Checkered Casual Shirt," and clicks the "Buy" button.

[0209] Input: User selected product

[0210] Output: Order information for selected items

[0211] Subject: Terminal

[0212] How it works: The terminal encrypts the order information and sends it to the server.

[0213] Input: Encrypted order information

[0214] Output: Order information sent to the server

[0215] Step 5: Manufacturing and Delivery

[0216] Subject: Server

[0217] Operation: The server sends order information to a partner manufacturing facility, including details needed for manufacturing (design, size, color).

[0218] Input: Detailed data based on order information

[0219] Data processing / data calculation: Formatting and sending order information

[0220] Output: Order information sent to partner manufacturing facility

[0221] Subject: Affiliated manufacturing facility

[0222] How it works: The partner manufacturing facility uses the information received to produce the product according to the specified design.

[0223] Input: Detailed data of the specified design

[0224] Output: Manufactured goods

[0225] Subject: Affiliated manufacturing facility

[0226] What it does: Once production is complete, the product is shipped directly to the customer.

[0227] Input: Manufactured goods

[0228] Output: Item delivered to user

[0229] Subject: Server

[0230] Operation: The server notifies the user's device of the start and completion of delivery, for example, sending a notification that "the product has been shipped."

[0231] Input: Delivery start and completion information

[0232] Output: Notification sent to the user's device

[0233] (Application example 1)

[0234] 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."

[0235] Today's consumers demand apparel that matches their personalities and preferences, and the fashion industry must respond quickly to frequently changing trends. However, meeting these needs requires effectively analyzing large amounts of data and proposing the right products for each user, which is a challenge. Furthermore, an efficient system is needed to collect relevant trend information and quickly manufacture and deliver personalized products.

[0236] 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.

[0237] In this invention, the server includes means for collecting user profile information and storing it in a database, means for periodically obtaining fashion trend information from around the world and storing it in a database, means for analyzing the user profile information and trend information and generating personalized product designs, means for proposing the generated product designs to the user, means for sending order information to a manufacturing facility to manufacture the products selected by the user, means for delivering the manufactured products to the user, means including an algorithm for recommending products based on the user's preferences and the latest trend information, means including a mobile terminal on which an application for proposing personalized fashion items to the user is installed, and means for obtaining trend information from an external API. This makes it possible to effectively combine user preferences with the latest trend information to quickly propose, manufacture, and deliver personalized products.

[0238] "User profile information" refers to individual information such as a user's fashion preferences, size and color preferences, and past purchasing history.

[0239] "Database" refers to a recording medium for storing collected user profile information and fashion trend information from around the world.

[0240] "Trend information" refers to information regarding popular fashion items, colors, and design patterns that is regularly obtained from around the world.

[0241] "Product design" refers to personalized apparel product designs generated by analyzing user profile information and trend information.

[0242] "Proposal" refers to the act of presenting the generated product design to the user.

[0243] "Order Information" refers to the detailed data (e.g., design, size, color, etc.) required to manufacture the product selected by the User.

[0244] "Manufacturing facility" refers to a factory or manufacturing base that actually produces apparel products based on order information received from the server.

[0245] "Delivery" refers to the act of delivering manufactured products to a location designated by the user.

[0246] "Algorithm" refers to the calculation methods and analytical means used to recommend products based on user preferences and the latest trend information.

[0247] "Mobile device" refers to an information terminal that can be carried by a user, such as a smartphone or tablet.

[0248] "External API" refers to a programmatic interface that provides an access point for obtaining trend information from other systems or databases.

[0249] "Personalization" refers to customizing products and services to suit individual user characteristics and preferences.

[0250] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will now be described in detail with reference to the accompanying drawings.

[0251] Overall system picture

[0252] This invention is a system that collects user profile information and stores it in a database. It also periodically acquires and stores fashion trend information from around the world in the same database. It uses artificial intelligence (AI) to analyze the user's profile information and trend information and generate personalized product designs. The generated product designs are then presented to the user via their mobile device (such as a smartphone or tablet).

[0253] Hardware and Software Configuration

[0254] The server is equipped with a database, AI algorithms, and software that executes external API calls. The database that stores user profile information and trend information uses, for example, MySQL or PostgreSQL. Trend information is retrieved from external APIs using, for example, the Requests library. The AI ​​algorithm is a calculation method for recommending products based on user preferences and trend information, and is implemented using, for example, Scikit-learn or TensorFlow.

[0255] The application is installed on the user's device, and the backend of the application is built using the Flask framework (or Django, etc.) to communicate with the server. The user interface is built using HTML, CSS, and JavaScript.

[0256] System operation flow

[0257] 1. User Registration and Information Collection

[0258] A user launches an application on their mobile device and registers, entering information such as their name, email address, password, fashion preferences, size and color preferences, and past purchase history. The application receives this information and sends it to the server, which stores it in a database and generates a user ID that is associated with their profile information.

[0259] 2. Obtaining trend information

[0260] The server periodically collects fashion trend information from around the world through external APIs, including popular items, design patterns, colors, etc. The collected trend information is stored in a database for later analysis.

[0261] 3. Personalized product recommendations

[0262] The server analyzes the user's profile information and trend information, and uses an AI algorithm to generate personalized product designs. Based on the generated product designs, it creates a list of suggested products and sends them to the user's device.

[0263] 4. Product Selection and Ordering

[0264] The user checks the list of suggested products on the terminal and selects the product they like. The order information is then confirmed and sent to the server to purchase the selected product. The server then sends the received order information to the manufacturing facility, providing detailed data (design, size, color, etc.).

[0265] 5. Manufacturing and Delivery

[0266] The manufacturing facility manufactures the product based on the order information sent from the server. Once manufacturing is complete, the manufacturing facility delivers the product to the user. The server notifies the user's device of the start and completion of delivery.

[0267] Examples and prompts

[0268] For example, if you're looking for a casual shirt for spring:

[0269] 1. User Registration and Information Collection

[0270] The user enters the following information: "Favorite style: casual," "Favorite colors: blue, white," and "Size: M."

[0271] The application sends this information to the server, which stores it in a database.

[0272] 2. Obtaining trend information

[0273] The server obtains the latest fashion information via an external API and determines that "checkered shirts for spring" are in fashion.

[0274] 3. Personalized product recommendations

[0275] Based on the user's preferences and the latest trends, the server uses AI to generate a "blue checked casual shirt" and adds it to the list of suggested products.

[0276] This list is sent to the user's device.

[0277] 4. Product Selection and Ordering

[0278] The user checks the suggested products, selects the "blue checked casual shirt," and presses the purchase button.

[0279] The application sends the order information to the server.

[0280] 5. Manufacturing and Delivery

[0281] The server sends the order information to a manufacturing facility, which produces the shirt and then ships it directly to the user.

[0282] The server notifies the user's terminal of the delivery information.

[0283] Example prompt sentence:

[0284] When a user prefers casual shirts, suggest the latest items based on current trends.

[0285] User Preferences: "Casual shirt", "Size M", "Blue"

[0286] Latest Trends: {"item": "blue plaid shirt", "category": "shirt"}

[0287] Result: Recommends "Blue Checkered Casual Shirt" to the user.

[0288] This system allows users to smoothly obtain apparel products that match their preferences and trends, and enables apparel manufacturers to reduce unnecessary inventory and lighten the burden on the environment.

[0289] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0290] Step 1:

[0291] User registration and information collection

[0292] Input: The user enters their name, email address, password, fashion preferences, size and color preferences, and past purchase history into their mobile device.

[0293] How it works: The device sends the entered information to the server, which parses it and stores it in a database. It also generates a user ID and associates it with the user's profile information.

[0294] Output: A message that user registration is complete is displayed on the terminal.

[0295] Step 2:

[0296] Obtaining trend information

[0297] Input: The server sends a request to get the latest fashion trend information from an external API.

[0298] How it works: The Trends API provides data on currently popular items, colors, and design patterns to a server that retrieves this information and stores it in a database.

[0299] Output: The latest trend information is stored in a database.

[0300] Step 3:

[0301] Generate personalized product recommendations

[0302] Input: User profile and trend information stored in a database.

[0303] How it works: The server uses AI algorithms to analyze user profile information and trend information. Based on this analysis, it generates personalized product designs for users.

[0304] Output: A list of generated product designs is generated and sent to the user's device.

[0305] Step 4:

[0306] Product selection and ordering

[0307] Input: Product information selected by the user from the suggested product list displayed on the device.

[0308] How it works: The user confirms the selected items on the terminal and presses the purchase button. The terminal sends the order information for the selected items to the server. The server receives the order information and sends it to the manufacturing facility.

[0309] Output: An order confirmation message is displayed on the user's device. Detailed order information (design, size, color, etc.) is sent to the manufacturing facility.

[0310] Step 5:

[0311] Manufacturing and Delivery

[0312] Input: Order information sent from the server to the manufacturing facility.

[0313] Operation: The manufacturing facility manufactures the product based on the order information received from the server. After the manufacturing is completed, the manufacturing facility delivers the product to the user. The server tracks the delivery status and notifies the user's device when the delivery has started and completed.

[0314] Output: The manufactured product is delivered to the address specified by the user. The delivery status is notified to the terminal.

[0315] By following these detailed, step-by-step processing steps, personalized product suggestions that combine the user's preferences with the latest trend information are realized.

[0316] 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.

[0317] The present invention relates to a system that uses an emotion engine to recognize user emotions in addition to user profile information and trend information, and then proposes personalized apparel products. This system is characterized by incorporating user emotion data to achieve more advanced personalization.

[0318] System Overview

[0319] The system includes the following main components:

[0320] 1. Terminal: A device where users input information, receive product suggestions, and place orders. This can be a smartphone or a PC.

[0321] 2. Server: A centralized system that collects and analyzes information sent by users and trend information, and generates personalized product recommendations.

[0322] 3. Database: Installed on the server, it stores user profile information, trend information, and order information.

[0323] 4. Affiliated Factory: A facility that actually manufactures apparel products based on the order information sent from the server and delivers them to users.

[0324] 5. Emotion Engine: A system for recognizing user emotions and adding and saving that information to profile information.

[0325] Processing flow

[0326] 1. User Registration and Information Collection

[0327] A user launches the application and registers, entering basic information such as name, email address, and password.

[0328] The device receives this information and sends it to the server, which stores it in a database and generates a user ID.

[0329] The user then enters details such as their fashion preferences, size and color preferences, and past purchasing history, which the device then transmits to the server.

[0330] The server stores this information in a database.

[0331] 2. Collecting emotional information

[0332] The emotion engine recognizes the user's emotions through facial expression recognition, voice analysis, and text analysis.

[0333] The device automatically collects emotional data while the user is operating the device or browsing products, and sends it to the server.

[0334] 3. Obtaining trend information

[0335] The server periodically collects fashion trend information from around the world through API, including popular items, design patterns, colors, etc.

[0336] The collected trend information is stored in a database and used for analysis.

[0337] 4. Personalized product recommendations

[0338] The server uses AI algorithms to analyze user profile information, emotional data, and trend information to generate product designs that are optimal for each user.

[0339] For example, if a user expresses the emotion "happy" while shopping, the system will take that emotional data into consideration and suggest products with particularly eye-catching designs or that suit their preferences.

[0340] The server creates a list of suggested products based on the generated product design and transmits this list to the user's terminal.

[0341] 5. Product Selection and Ordering

[0342] The user can view the list of suggested products on the device, select the product they like, and then press the "Purchase" button to confirm the order.

[0343] The terminal sends the order information (product ID, size, color) of the selected product to the server.

[0344] 6. Manufacturing and Delivery

[0345] The server sends order information to partner factories and issues manufacturing instructions. The order information includes detailed data (design, size, color) required for manufacturing.

[0346] The partner factory manufactures the product based on the order information received from the server. For example, the partner factory manufactures a "blue checked casual shirt."

[0347] The partner factory will then arrange for the completed product to be delivered directly to the user.

[0348] The server notifies the user's device of delivery start and completion information, allowing the user to check the delivery status of the product.

[0349] Specific examples

[0350] For example, if you're looking for a casual shirt for spring:

[0351] 1. User Registration and Information Collection

[0352] Users enter information such as "Favorite style: casual," "Favorite colors: blue, white," and "Size: M."

[0353] The terminal sends this information to the server, which stores it in a database.

[0354] 2. Collecting emotional information

[0355] The emotion engine recognizes emotions such as "happy" or "interested" from the user's facial expressions and adds them to the profile information.

[0356] 3. Obtaining trend information

[0357] The server obtains the latest fashion information via an API and determines that "checkered shirts for spring" are in fashion.

[0358] 4. Personalized product recommendations

[0359] The server generates a "blue checked casual shirt" based on the user's preferences, emotional data, and trend information, adds it to a list of suggested products, and sends this list to the user's device.

[0360] 5. Product Selection and Ordering

[0361] The user checks the suggested products, selects the "blue checked casual shirt," and presses the purchase button.

[0362] The terminal transmits the order information to the server.

[0363] 6. Manufacturing and Delivery

[0364] The server sends the order information to a partner factory, which then produces the shirt, which is then shipped directly to the user.

[0365] The server notifies the user's terminal of the delivery information.

[0366] This system allows users to seamlessly obtain personalized apparel products based on their preferences and emotions, while manufacturers can reduce wasteful inventory and lighten their environmental impact.

[0367] The processing flow will be explained below.

[0368] Step 1:

[0369] The user launches the application and enters basic information such as name, email address, and password on the new registration screen.

[0370] Step 2:

[0371] The device receives basic information and sends it to the server, which stores it in a database and generates a user ID.

[0372] Step 3:

[0373] The user then enters details such as their fashion preferences (e.g., casual style), size (e.g., medium), color preferences (e.g., blue, white), and past purchasing history.

[0374] Step 4:

[0375] The device collects these details and sends them to a server, which stores them in a database.

[0376] Step 5:

[0377] The emotion engine is activated and analyzes emotional data from the user's facial expressions, voice, and text input. For example, it analyzes facial expressions through the camera and recognizes emotions such as "happy" or "interested."

[0378] Step 6:

[0379] The device acquires the emotion data and sends it to the server, which stores it in a database and adds it to the profile information.

[0380] Step 7:

[0381] The server periodically accesses an external API that provides fashion trend information and collects the latest fashion trend data, including popular items, design patterns, colors, etc.

[0382] Step 8:

[0383] The server stores the collected trend information in a database for later analysis.

[0384] Step 9:

[0385] The server uses an AI algorithm to analyze the user's profile information, emotional data, and trend information to generate optimal product designs for the user. For example, if a user expresses the emotion "happy," the server will take that emotional data into account to generate particularly eye-catching designs and products that suit their preferences.

[0386] Step 10:

[0387] The server creates a list of suggested products based on the generated product design and transmits this list to the user's terminal.

[0388] Step 11:

[0389] The user can check the list of suggested products on the device, view the details, select the product they like, and press the "Purchase" button.

[0390] Step 12:

[0391] To confirm the purchase, the terminal sends the order information (item ID, size, color) of the selected item to the server.

[0392] Step 13:

[0393] The server sends the order information to the partner factory and issues manufacturing instructions. The order information includes detailed data (design, size, color) required for manufacturing.

[0394] Step 14:

[0395] The partner factory will manufacture the product based on the order information received from the server. For example, it will manufacture a "blue checked casual shirt."

[0396] Step 15:

[0397] The partner factory will then arrange for the completed product to be delivered directly to the user.

[0398] Step 16:

[0399] The server notifies the user's device of delivery start and completion information, allowing the user to check the delivery status of the product.

[0400] This series of steps allows users to seamlessly obtain personalized apparel products based on their preferences and emotions, while helping manufacturers reduce wasted inventory and lighten their environmental impact.

[0401] Example 2

[0402] 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."

[0403] Conventional apparel product recommendation systems only use a user's basic profile information and purchase history to suggest products, making it difficult to reflect the user's momentary emotions or current trends. This results in low personalization accuracy and fails to increase user satisfaction. Furthermore, as a result, users often lose interest in the suggested products, leading to a decline in purchasing motivation.

[0404] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0405] In this invention, the server includes means for collecting user profile information and storing it in a database, means for periodically obtaining fashion trend information from around the world and storing it in a database, means for collecting user emotion information using an emotion engine and storing it in a database, means for analyzing the user's profile information, emotion information, and trend information and generating personalized product designs, means for proposing the generated product designs to the user, means for sending order information to a manufacturing facility to manufacture the products selected by the user, and means for delivering the manufactured products to the user. This enables advanced personalization based on emotion data and the latest trend information in addition to the user's basic profile information, thereby increasing the user's desire to purchase.

[0406] "User Profile Information" means basic information provided by a User, including fashion preferences, size and color preferences, and past purchasing history.

[0407] "Fashion trend information" refers to the latest trends in the fashion industry that are regularly acquired, including popular items, colors, and design patterns.

[0408] The "emotion engine" is a system for recognizing user emotions and has the ability to collect emotional data through facial expression recognition, voice analysis, and text analysis.

[0409] "Personalized product design" refers to a product design optimized for a user, generated based on the user's profile information, emotional information, and fashion trend information.

[0410] A "suggested product list" is a list of products generated based on personalized product designs and suggested to users.

[0411] "Order Information" means detailed information required for manufacturing the product selected by the user, including product ID, size, color, etc.

[0412] "Manufacturing facility" refers to a factory or production line that produces the product selected by the user.

[0413] The present invention relates to a system that integrates a user's profile information, emotion information, and fashion trend information to recommend personalized apparel products. The system includes a means for collecting user profile information and storing it in a database, a means for collecting user emotion information using an emotion engine, and a means for acquiring fashion trend information from around the world and storing it in a database. The system also includes a means for analyzing this information to generate personalized product designs and recommend them to the user. The system also includes a means for sending order information to a manufacturing facility to manufacture the products selected by the user, and a means for delivering the manufactured products to the user.

[0414] First, a user launches the application and registers, which collects user profile information. The user enters information such as their name, email address, password, fashion preferences, size and color preferences, and past purchase history, and the device sends this information to the server. The server stores the received information in a database and generates a user ID.

[0415] Next, the emotion engine collects user emotion data through facial expression recognition, voice analysis, and text analysis. When a user views or interacts with a product, the device automatically collects emotion data and sends it to the server. The emotion engine analyzes emotions using open source libraries (e.g., OpenCV) and voice analysis APIs (e.g., Google Cloud Speech-to-Text API) and sends the results to the server. The server stores the data in a database.

[0416] The server also periodically uses external APIs (e.g., FashionAPI) to collect the latest fashion trend information and stores it in the database. Trend information includes popular items, design patterns, colors, etc., and is used for analysis.

[0417] To make personalized product suggestions, the server integrates and analyzes the user's profile information, emotional data, and trend information, and uses AI algorithms (e.g., TensorFlow or PyTorch) to generate product designs that are optimal for the user. The generated product designs are sent to the user's device as a list of suggested products. For example, if a user is looking for a "casual blue shirt for spring," and the server recognizes that the user's emotion is "happy," it will suggest a casual blue shirt based on that information.

[0418] The user checks the list of suggested products on their device, selects their favorite product, and presses the "Purchase" button. The order information (product ID, size, color) is sent to the server, which then sends the order information to a partner factory and instructs it to manufacture the product. The manufacturing facility manufactures the product based on the order information and delivers the completed product directly to the user. The server then notifies the user of the delivery information on their device, allowing the user to check the delivery status.

[0419] As an example of this system, consider the following prompt: "I'm looking for a casual blue shirt for spring. I'm happy shopping. Please suggest products that fit this criteria."

[0420] This allows users to efficiently find the best products based on their profile information, emotions, and the latest fashion trends, while manufacturers can reduce unnecessary inventory and lighten their environmental impact.

[0421] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0422] Step 1: User registration and information collection

[0423] The user starts the application and enters information such as name, email address, password, fashion preferences, size and color preferences, and past purchase history on the new registration screen. This information is the input data.

[0424] The device serializes the input information and sends it to the server in JSON format via an HTTP POST request. This request is the input, and the user ID received in response is the output.

[0425] The server parses the received JSON data and stores each field (such as name, email address, and fashion preferences) in a database. The server generates a new user ID and returns it to the device as an HTTP response. The device notifies the user.

[0426] Step 2: Collecting emotional information

[0427] Users use the application to browse products.

[0428] The emotion engine recognizes the user's facial expressions using a camera and analyzes their voice using a microphone. It also analyzes text input. This is the input data.

[0429] The device collects emotion data in real time and sends it to the server in JSON format. The emotion data is the input, and the database records are the output.

[0430] The server stores the received emotion data in a database, for example, as a record with fields such as "date and time," "emotion label," and "emotion intensity."

[0431] Step 3: Obtaining trend information

[0432] The server runs a cron job periodically every day and calls an external API (e.g., FashionAPI) to obtain the latest fashion trend information. The obtained trend information is the input data.

[0433] The server stores this trend information in a database in JSON format. The stored trend information is the output. The database contains fields such as item name, design pattern, color, and popularity.

[0434] Step 4: Personalized product recommendations

[0435] The server integrates user profile information, sentiment data, and trend information, and analyzes them using AI algorithms (e.g., TensorFlow, PyTorch). This is the input data.

[0436] As a result of the analysis, the server generates the optimal product design for the user. The generated product design is the output and is sent to the terminal as a list of suggested products. For example, a blue casual shirt may be suggested.

[0437] Step 5: Select and order

[0438] The user checks the list of suggested products on the terminal, selects the product that suits their taste, and presses the "Purchase" button. This is the input data.

[0439] The terminal sends JSON data including the selected product information (product ID, size, color) to the server. The order information is output.

[0440] The server stores the received order information in a database and updates the order status to "Not yet manufactured."

[0441] Step 6: Manufacturing and Delivery

[0442] The server sends the order ID and detailed information (design, size, color) in JSON format to the partner factory. This is the input data.

[0443] The partner factory manufactures the product based on the order information, for example, by cutting the fabric using a CNC cutting machine and sewing it. The manufactured product is the output.

[0444] The partner factory hands over the completed product to the delivery company and notifies the server of the delivery information.

[0445] The server notifies the user of delivery information (e.g., tracking number) on the user's device, allowing the user to check the delivery status.

[0446] This allows users to efficiently find the best products based on their profile information, emotions, and the latest fashion trends, while manufacturers can reduce unnecessary inventory and lighten their environmental impact.

[0447] (Application example 2)

[0448] 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."

[0449] Conventional apparel recommendation systems suggest products based on user profile information and trend information, but they have the problem of not being able to make optimal personalized recommendations because they do not take the user's emotions into account. Therefore, there is a need to provide a system that can make personalized apparel recommendations that correspond to the user's actual emotional state.

[0450] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0451] In this invention, the server includes means for collecting user profile information and storing it in a database, means for periodically obtaining fashion trend information from around the world and storing it in a database, means for analyzing the user profile information and trend information to generate personalized product designs, means for proposing the generated product designs to the user, means for collecting user emotion data using an emotion engine that recognizes the user's emotions and adding it to the profile information, means for sending order information to a manufacturing facility to manufacture the product selected by the user, and means for delivering the manufactured product to the user. This enables more sophisticated personalized proposals based on the user's emotional state.

[0452] "User Profile Information" refers to basic data about a User, such as the User's fashion preferences, size and color preferences, and past purchasing history.

[0453] "Trend information" refers to data that includes information on fashion items, design patterns, colors, etc. that are popular within a certain period of time.

[0454] "Emotion engine" refers to a system that recognizes and analyzes a user's emotions through facial expression recognition, voice analysis, and text analysis.

[0455] "Personalized product design" refers to apparel product designs optimized for specific users, generated based on the user's profile information, trend information, and emotional data.

[0456] "Manufacturing facility" refers to a facility that actually manufactures apparel products based on order information from users.

[0457] "Database" means an electronic repository for storing data used by the System, such as user profile information, trend information, sentiment data, and order information.

[0458] The system for implementing this invention includes the following main hardware and software: The hardware used includes smart glasses, a camera, and a server, and the software used includes Python, OpenCV (an image processing library), and Requests (an HTTP request library).

[0459] This system uses user profile information, trend information, and emotion data to provide personalized product recommendations. The process is explained below.

[0460] First, the user puts on the smart glasses and activates the system. The camera in the smart glasses captures an image of the user's face. This image data is analyzed by the emotion engine to recognize the user's emotional state. The emotion engine combines facial, voice, and text analysis to obtain emotion data.

[0461] The server then periodically retrieves information on fashion trends from around the world via API and stores it in a database, including information on popular items, design patterns, colors, etc. This trend information is combined with user profile information and analyzed to generate personalized product designs.

[0462] The generated product designs are then presented to the user in real time, displayed on the smart glasses display. The user selects the desired product from the list of suggested products and completes the purchase process. This selection is then sent to the server and transmitted to the manufacturing facility.

[0463] The manufacturing facility manufactures the product based on the received order information, and once production is complete, the product is delivered directly to the user. The server manages delivery information and allows the user to check the delivery status of the product.

[0464] This system allows users to seamlessly select and purchase apparel that best suits their preferences and emotional state, while also helping manufacturing facilities reduce wasteful inventory and contribute to reducing environmental impact.

[0465] For example, consider the following prompt:

[0466] "If a user who has a casual style and likes blue or white clothing is wearing smart glasses, and emotion recognition reveals that the user looks happy, the glasses will suggest products based on the latest fashion trends, such as a casual blue shirt or white pants."

[0467] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0468] Step 1:

[0469] The user puts on the smart glasses and starts the system.

[0470] Input: None

[0471] Output: The smart glasses are turned on and the camera is ready to use.

[0472] Specific operation: The user turns on the smart glasses. The system automatically starts the camera.

[0473] Step 2:

[0474] The camera on the device (smart glasses) captures an image of the user's face.

[0475] Input: An image of the user's face

[0476] Output: Captured image (JPEG format)

[0477] Specific operation: The camera captures the user's face, takes a picture, and temporarily stores the image data in memory.

[0478] Step 3:

[0479] The terminal transmits the captured image data to the server.

[0480] Input: Captured image (JPEG format)

[0481] Output: Image data sent

[0482] Specific operation: The device uploads the stored image data to a server via the Internet.

[0483] Step 4:

[0484] The server passes the received image data to an emotion engine and analyzes the user's emotion.

[0485] Input: Received image data

[0486] Output: User emotion data (e.g. happy, interesting)

[0487] Specific operation: The emotion engine analyzes image data and recognizes the user's emotional state from their facial expressions.

[0488] Step 5:

[0489] The server acquires fashion trend information from around the world and stores it in a database.

[0490] Input: Trend information acquisition request

[0491] Output: Trend information (e.g. popular items, colors, design patterns)

[0492] Specific operation: The server periodically obtains the latest fashion trend information using an external API and stores it in a database.

[0493] Step 6:

[0494] The server analyzes the user's profile information, emotion data, and trend information to generate personalized product designs.

[0495] Input: User profile information, sentiment data, trend information

[0496] Output: Personalized product design (e.g., blue checked casual shirt)

[0497] How it works: The server uses AI algorithms to analyze this data and generate optimal product designs for the user.

[0498] Step 7:

[0499] The server sends the generated product design to the user's device (smart glasses).

[0500] Input: Generated product design

[0501] Output: Submitted product design

[0502] Specific operation: The server sends the product design to the terminal via the Internet and displays it on the smart glasses display.

[0503] Step 8:

[0504] The user selects the desired product from the list of suggested products and completes the purchase procedure.

[0505] Input: Suggested product list

[0506] Output: Selected product information (e.g. product ID, size, color)

[0507] Specific operation: The user operates the interface of the smart glasses to select the desired product and press the "Purchase" button.

[0508] Step 9:

[0509] The terminal transmits the product information selected by the user to the server.

[0510] Input: Selected product information

[0511] Output: Order information sent

[0512] Specific operation: The terminal uploads the selected product information to the server via the Internet.

[0513] Step 10:

[0514] The server transmits the order information to a manufacturing facility and issues instructions for manufacturing the goods.

[0515] Input: Order Information

[0516] Output: Manufacturing instructions

[0517] Specific operations: The server sends manufacturing instructions to the manufacturing facility based on the received order information.

[0518] Step 11:

[0519] The manufacturing facility manufactures the goods based on the received order information and delivers them to the user.

[0520] Input: Order Information

[0521] Output: Manufactured goods

[0522] What happens: The manufacturing facility produces the goods based on the order, processes the delivery, and delivers the product directly to the user.

[0523] Step 12:

[0524] The server notifies the user's terminal of the delivery information.

[0525] Input:Shipping information

[0526] Output: Delivery status notification

[0527] Specific operation: The server sends the delivery progress status to the user's terminal via the Internet, allowing the user to check the delivery status.

[0528] 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.

[0529] 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.

[0530] 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.

[0531] [Second embodiment]

[0532] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0533] 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.

[0534] 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).

[0535] 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.

[0536] 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.

[0537] 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).

[0538] 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.

[0539] 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.

[0540] 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.

[0541] 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.

[0542] 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.

[0543] 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."

[0544] This invention relates to a system that collects user profile information, combines it with trend information from around the world, and proposes personalized apparel products. This system utilizes AI to generate and propose products based on the user's preferences, and then manufactures and delivers the ordered products.

[0545] System Overview

[0546] The system includes the following main components:

[0547] 1. Terminal: A device where users input information, receive product suggestions, and place orders. This can be a smartphone or a PC.

[0548] 2. Server: A centralized system that collects and analyzes information sent by users and trend information, and generates personalized product recommendations.

[0549] 3. Database: Installed on the server, it stores user profile information, trend information, and order information.

[0550] 4. Affiliated Factory: A facility that actually manufactures apparel products based on the order information sent from the server and delivers them to users.

[0551] Processing flow

[0552] 1. User Registration and Information Collection

[0553] A user launches the application and registers, entering basic information such as name, email address, and password.

[0554] The device receives this information and sends it to a server, along with other details such as the user's fashion preferences, size, color, and past purchase history.

[0555] The server stores the received information in a database and generates a user ID to associate with the profile information.

[0556] 2. Obtaining trend information

[0557] The server periodically collects fashion trend information from around the world through API, including popular items, design patterns, colors, etc.

[0558] The collected trend information is stored in a database and used for analysis.

[0559] 3. Personalized product recommendations

[0560] The server analyzes the user's profile information and trend information, and uses AI algorithms to generate personalized product designs for each user.

[0561] A list of suggested products is created based on the generated product design and sent to the terminal.

[0562] 4. Product Selection and Ordering

[0563] The user can view the list of suggested products on the device, select the product they like, and then press the "Purchase" button to confirm the order.

[0564] The terminal transmits order information for the selected product to the server.

[0565] 5. Manufacturing and Delivery

[0566] The server sends the order information to the partner factory, which includes the detailed data (design, size, color) required for manufacturing.

[0567] The partner factory will manufacture the product according to the specified design, and once production is complete, the product will be shipped directly to the customer.

[0568] The server notifies the terminal of the start and completion of delivery.

[0569] Specific examples

[0570] For example, if you're looking for a casual shirt for spring:

[0571] 1. User Registration and Information Collection

[0572] The user enters the following information: "Favorite style: casual," "Favorite colors: blue, white," and "Size: M."

[0573] The terminal sends this information to the server, which stores it in a database.

[0574] 2. Obtaining trend information

[0575] The server obtains the latest fashion information via an API and determines that "checkered shirts for spring" are in fashion.

[0576] 3. Personalized product recommendations

[0577] Based on the user's preferences and the latest trends, the server uses AI to generate a "blue checked casual shirt" and adds it to the list of suggested products.

[0578] This list is sent to the user's device.

[0579] 4. Product Selection and Ordering

[0580] The user checks the suggested products, selects the "blue checked casual shirt," and presses the purchase button.

[0581] The terminal transmits the order information to the server.

[0582] 5. Manufacturing and Delivery

[0583] The server sends the order information to a partner factory, which then produces the shirt, which is then shipped directly to the user.

[0584] The server notifies the user's terminal of the delivery information.

[0585] This system allows users to smoothly obtain apparel products that match their preferences and trends, and enables apparel manufacturers to reduce unnecessary inventory and lighten the burden on the environment.

[0586] The processing flow will be explained below.

[0587] Step 1:

[0588] The user launches the application and enters basic information such as name, email address, and password on the new registration screen.

[0589] Step 2:

[0590] The device receives this basic information and sends it to the server, which stores it in a database and generates a user ID.

[0591] Step 3:

[0592] The user then inputs their fashion preferences (e.g., casual style), size (e.g., medium size), color preferences (e.g., blue, white), and past purchasing history.

[0593] Step 4:

[0594] The device collects these details and sends them to a server, which stores them in a database.

[0595] Step 5:

[0596] The server periodically accesses an external API that provides fashion trend information and collects the latest fashion trend data, including popular items, design patterns, colors, etc.

[0597] Step 6:

[0598] The server stores the collected trend information in a database and prepares it for later analysis.

[0599] Step 7:

[0600] The server uses an AI algorithm to analyze the user's profile information and the latest trends to generate the optimal product design for the user. For example, it generates a "blue checked casual shirt" based on the user's preferred combination of blue and the latest checked pattern.

[0601] Step 8:

[0602] The server creates a list of suggested products based on the generated product design and transmits this list to the user's terminal.

[0603] Step 9:

[0604] Users can view the list of suggested products on their device, view details, select the product they like, and press the "Purchase" button.

[0605] Step 10:

[0606] To confirm the purchase, the terminal sends the order information (item ID, size, color) of the selected item to the server.

[0607] Step 11:

[0608] The server sends the order information to the partner factory and issues manufacturing instructions. The order information includes detailed data required for manufacturing (design, size, color).

[0609] Step 12:

[0610] The partner factory manufactures the product based on the order information received from the server. For example, the partner factory manufactures a "blue checked casual shirt."

[0611] Step 13:

[0612] The partner factory will then arrange for the completed product to be delivered directly to the user.

[0613] Step 14:

[0614] The server notifies the user's device of delivery start and completion information, allowing the user to check the delivery status of the product.

[0615] This series of steps allows users to obtain original products that are best suited to them, while allowing manufacturers to maintain appropriate production volumes and reduce environmental impact.

[0616] Example 1

[0617] 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."

[0618] Conventional apparel product recommendation systems have difficulty in proposing personalized products that reflect the individual preferences and latest trends of users. Furthermore, they lacked efficient methods for quickly manufacturing and delivering the products selected by users. As a result, it was difficult to increase user satisfaction, and manufacturers also faced the challenge of increasing the burden of inventory management.

[0619] 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.

[0620] In this invention, the server includes means for collecting user profile information and storing it in a database, means for periodically obtaining fashion trend information from around the world and storing it in a database, means for analyzing user profile information and trend information and generating personalized product designs using a generative AI model, means for sending the generated product designs to a terminal and proposing them to the user, means for sending order information for products selected by the user on the terminal to an affiliated manufacturing facility, means for delivering the manufactured products to the user, and means for notifying the user's terminal of manufacturing and delivery information. This enables users to efficiently propose, select, and order personalized products based on their preferences and the latest trends, and also enables manufacturers to achieve efficient production and delivery, thereby reducing the burden of inventory management.

[0621] "User" means an entity that utilizes the System to provide profile information and receive personalized product offers.

[0622] "Profile Information" refers to general information about a user, such as the user's fashion preferences, size and color preferences, and past purchasing history.

[0623] "Fashion trend information" refers to information about fashion that is popular around the world at each time and region, such as popular items, colors, and design patterns.

[0624] "Generative AI model" refers to an artificial intelligence model that analyzes user profile information and fashion trend information to automatically generate personalized product designs.

[0625] "Affiliated manufacturing facility" refers to a facility that manufactures the apparel products selected by the user based on the order information sent from the server.

[0626] "Terminal" refers to the device used by a user to enter information, receive product suggestions, and place an order, specifically a smartphone or computer.

[0627] "Server" refers to the centralized system that collects and analyzes information sent by users and trend information, and generates and manages personalized product proposals.

[0628] "Database" refers to an information management system installed on a server for storing user profile information, trend information, and order information.

[0629] This invention relates to a system that collects user profile information, combines it with global fashion trend information, and proposes personalized apparel products. The system utilizes a generative AI model to generate and propose products based on the user's preferences, and then manufactures and delivers the ordered products.

[0630] System Overview

[0631] The system includes the following major components:

[0632] 1. Terminal: A device on which a user enters profile information, receives product suggestions, and places an order. Specifically, a smartphone or PC is used.

[0633] 2. Server: A centralized system that collects and analyzes information sent by users and trend information, and generates personalized product recommendations.

[0634] 3. Database: Installed on the server, it stores user profile information, trend information, and order information.

[0635] 4. Affiliated manufacturing facility: This is the facility that actually manufactures the apparel products based on the order information sent from the server and delivers them to the user.

[0636] Hardware and software used

[0637] Devices: smartphones, computers

[0638] Server: Centralized system

[0639] Database: A cloud database for managing user information, trend information, and order information.

[0640] Generative AI model: An algorithm that analyzes user preferences and trend information to generate personalized product designs

[0641] Processing flow

[0642] 1. User Registration and Information Collection

[0643] A user launches the application and registers, entering basic information such as name, email address, and password.

[0644] The device receives this information and sends it to a server, along with other details such as the user's fashion preferences, size, color, and past purchase history.

[0645] The server stores the received information in a database and generates a user ID to associate with the profile information.

[0646] 2. Obtaining trend information

[0647] The server periodically collects the latest fashion trend information from around the world through API, specifically information on popular items, design patterns, colors, etc.

[0648] The server stores the collected trend information in a database and tags it for analysis.

[0649] 3. Personalized product recommendations

[0650] The server uses the generative AI model to analyze the user's profile information and trend information. For example, it inputs a prompt statement such as "Suggest spring casual shirts for users who like blue."

[0651] The generative AI model uses user preferences and trend information to generate personalized product designs.

[0652] The server adds the generated product design to a list of proposed products and transmits this list to the terminal.

[0653] 4. Product Selection and Ordering

[0654] The user can check the list of suggested products on the device, select the desired product, and press the "Purchase" button to confirm the order.

[0655] The terminal transmits order information for the selected product to the server.

[0656] 5. Manufacturing and Delivery

[0657] The server sends the order information to the partner manufacturing facility, including the details required for manufacturing (design, size, color).

[0658] The partner manufacturing facility will produce the product according to the specified design and ship the product directly to the user once production is complete.

[0659] The server notifies the user's device of the start and completion of delivery, for example, by sending a notification that the product has been shipped.

[0660] Specific examples

[0661] For example, if you're looking for a casual shirt for spring:

[0662] 1. The user enters the following profile information: "Favorite style: casual," "Favorite colors: blue, white," and "Size: M."

[0663] 2. The device sends this information to the server, which stores it in a database.

[0664] 3. The server retrieves the latest fashion information via the API and determines that "checkered shirts for spring" are in fashion.

[0665] 4. Based on the user's preferences and the latest trends, the server uses a generative AI model to generate a "blue checked casual shirt" and adds it to the list of suggested products.

[0666] 5. Send this list to the user's device.

[0667] 6. The user checks the suggested products, selects the "blue checked casual shirt," and presses the purchase button.

[0668] 7. The terminal sends the order information to the server.

[0669] 8. The server sends the order information to a partner manufacturing facility, which produces the shirt. Once production is complete, the shirt is shipped directly to the customer.

[0670] 9. The server notifies the user's terminal of the delivery information.

[0671] This system allows users to smoothly obtain apparel products that match their preferences and trends, and enables apparel manufacturers to reduce unnecessary inventory and lighten the burden on the environment.

[0672] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0673] Step 1: Collect user information

[0674] Subject: User

[0675] How it works: The user launches the dedicated application and clicks "Sign Up." After entering their name, email address, and password, they click "Next."

[0676] Input: User's name, email address, password

[0677] Output: Sends the entered basic information to the terminal

[0678] Subject: Terminal

[0679] How it works: The device encrypts the information entered and sends it to the server using a secure communication protocol (HTTPS).

[0680] Input: Encrypted user basic information

[0681] Output: Basic information sent to the server

[0682] Subject: User

[0683] How it works: Next, the user enters their "favorite style," "favorite color," "size," and "past purchase history."

[0684] Input: Fashion preferences, size, color preferences, past purchase history

[0685] Output: Sends the entered details to the terminal

[0686] Subject: Terminal

[0687] How it works: The device re-encrypts these details and sends them to the server.

[0688] Input: Encrypted details

[0689] Output: Details sent to the server

[0690] Subject: Server

[0691] How it works: The server stores the submitted information in a database and generates a user ID to associate with the profile information.

[0692] Input: Basic information and detailed information received

[0693] Data processing / data calculation: Save to database, generate user ID, associate with profile information

[0694] Output: Profile information stored in the database

[0695] Step 2: Obtaining trend information

[0696] Subject: Server

[0697] How it works: The server uses the fashion API to periodically obtain the latest fashion trend information from around the world, specifically information on popular items, design patterns, colors, etc.

[0698] Input: Fashion trend information obtained through API

[0699] Data processing / data calculation: Shaping and tagging trend information

[0700] Output: Trend information stored in a database

[0701] Step 3: Generate personalized product suggestions

[0702] Subject: Server

[0703] How it works: The server inputs the user's profile information and trend information into the generative AI model for analysis. A prompt such as "Suggest a spring casual shirt that is recommended for a user who likes the color blue" is used.

[0704] Input: User profile information, fashion trend information, prompt text

[0705] Data processing / data calculation: Analysis by generative AI models and generation of personalized product designs

[0706] Output: Suggested product list

[0707] Subject: Server

[0708] Operation: The generated product design is added to a list of suggested products and this list is sent to the user's device.

[0709] Input: Generated product design

[0710] Output: Suggested product list sent to the terminal

[0711] Step 4: Select and order

[0712] Subject: User

[0713] How it works: The user reviews the list of suggested products on their device, selects the product they want, for example, "Blue Checkered Casual Shirt," and clicks the "Buy" button.

[0714] Input: User selected product

[0715] Output: Order information for selected items

[0716] Subject: Terminal

[0717] How it works: The terminal encrypts the order information and sends it to the server.

[0718] Input: Encrypted order information

[0719] Output: Order information sent to the server

[0720] Step 5: Manufacturing and Delivery

[0721] Subject: Server

[0722] Operation: The server sends order information to a partner manufacturing facility, including details needed for manufacturing (design, size, color).

[0723] Input: Detailed data based on order information

[0724] Data processing / data calculation: Formatting and sending order information

[0725] Output: Order information sent to partner manufacturing facility

[0726] Subject: Affiliated manufacturing facility

[0727] How it works: The partner manufacturing facility uses the information received to produce the product according to the specified design.

[0728] Input: Detailed data of the specified design

[0729] Output: Manufactured goods

[0730] Subject: Affiliated manufacturing facility

[0731] What it does: Once production is complete, the product is shipped directly to the customer.

[0732] Input: Manufactured goods

[0733] Output: Item delivered to user

[0734] Subject: Server

[0735] Operation: The server notifies the user's device of the start and completion of delivery, for example, sending a notification that "the product has been shipped."

[0736] Input: Delivery start and completion information

[0737] Output: Notification sent to the user's device

[0738] (Application example 1)

[0739] 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."

[0740] Today's consumers demand apparel that matches their personalities and preferences, and the fashion industry must respond quickly to frequently changing trends. However, meeting these needs requires effectively analyzing large amounts of data and proposing the right products for each user, which is a challenge. Furthermore, an efficient system is needed to collect relevant trend information and quickly manufacture and deliver personalized products.

[0741] 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.

[0742] In this invention, the server includes means for collecting user profile information and storing it in a database, means for periodically obtaining fashion trend information from around the world and storing it in a database, means for analyzing the user profile information and trend information and generating personalized product designs, means for proposing the generated product designs to the user, means for sending order information to a manufacturing facility to manufacture the products selected by the user, means for delivering the manufactured products to the user, means including an algorithm for recommending products based on the user's preferences and the latest trend information, means including a mobile terminal on which an application for proposing personalized fashion items to the user is installed, and means for obtaining trend information from an external API. This makes it possible to effectively combine user preferences with the latest trend information to quickly propose, manufacture, and deliver personalized products.

[0743] "User profile information" refers to individual information such as a user's fashion preferences, size and color preferences, and past purchasing history.

[0744] "Database" refers to a recording medium for storing collected user profile information and fashion trend information from around the world.

[0745] "Trend information" refers to information regarding popular fashion items, colors, and design patterns that is regularly obtained from around the world.

[0746] "Product design" refers to personalized apparel product designs generated by analyzing user profile information and trend information.

[0747] "Proposal" refers to the act of presenting the generated product design to the user.

[0748] "Order Information" refers to the detailed data (e.g., design, size, color, etc.) required to manufacture the product selected by the User.

[0749] "Manufacturing facility" refers to a factory or manufacturing base that actually produces apparel products based on order information received from the server.

[0750] "Delivery" refers to the act of delivering manufactured products to a location designated by the user.

[0751] "Algorithm" refers to the calculation methods and analytical means used to recommend products based on user preferences and the latest trend information.

[0752] "Mobile device" refers to an information terminal that can be carried by a user, such as a smartphone or tablet.

[0753] "External API" refers to a programmatic interface that provides an access point for obtaining trend information from other systems or databases.

[0754] "Personalization" refers to customizing products and services to suit individual user characteristics and preferences.

[0755] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will now be described in detail with reference to the accompanying drawings.

[0756] Overall system picture

[0757] This invention is a system that collects user profile information and stores it in a database. It also periodically acquires and stores fashion trend information from around the world in the same database. It uses artificial intelligence (AI) to analyze the user's profile information and trend information and generate personalized product designs. The generated product designs are then presented to the user via their mobile device (such as a smartphone or tablet).

[0758] Hardware and Software Configuration

[0759] The server is equipped with a database, AI algorithms, and software that executes external API calls. The database that stores user profile information and trend information uses, for example, MySQL or PostgreSQL. Trend information is retrieved from external APIs using, for example, the Requests library. The AI ​​algorithm is a calculation method for recommending products based on user preferences and trend information, and is implemented using, for example, Scikit-learn or TensorFlow.

[0760] The application is installed on the user's device, and the backend of the application is built using the Flask framework (or Django, etc.) to communicate with the server. The user interface is built using HTML, CSS, and JavaScript.

[0761] System operation flow

[0762] 1. User Registration and Information Collection

[0763] A user launches an application on their mobile device and registers, entering information such as their name, email address, password, fashion preferences, size and color preferences, and past purchase history. The application receives this information and sends it to the server, which stores it in a database and generates a user ID that is associated with their profile information.

[0764] 2. Obtaining trend information

[0765] The server periodically collects fashion trend information from around the world through external APIs, including popular items, design patterns, colors, etc. The collected trend information is stored in a database for later analysis.

[0766] 3. Personalized product recommendations

[0767] The server analyzes the user's profile information and trend information, and uses an AI algorithm to generate personalized product designs. Based on the generated product designs, it creates a list of suggested products and sends them to the user's device.

[0768] 4. Product Selection and Ordering

[0769] The user checks the list of suggested products on the terminal and selects the product they like. The order information is then confirmed and sent to the server to purchase the selected product. The server then sends the received order information to the manufacturing facility, providing detailed data (design, size, color, etc.).

[0770] 5. Manufacturing and Delivery

[0771] The manufacturing facility manufactures the product based on the order information sent from the server. Once manufacturing is complete, the manufacturing facility delivers the product to the user. The server notifies the user's device of the start and completion of delivery.

[0772] Examples and prompts

[0773] For example, if you're looking for a casual shirt for spring:

[0774] 1. User Registration and Information Collection

[0775] The user enters the following information: "Favorite style: casual," "Favorite colors: blue, white," and "Size: M."

[0776] The application sends this information to the server, which stores it in a database.

[0777] 2. Obtaining trend information

[0778] The server obtains the latest fashion information via an external API and determines that "checkered shirts for spring" are in fashion.

[0779] 3. Personalized product recommendations

[0780] Based on the user's preferences and the latest trends, the server uses AI to generate a "blue checked casual shirt" and adds it to the list of suggested products.

[0781] This list is sent to the user's device.

[0782] 4. Product Selection and Ordering

[0783] The user checks the suggested products, selects the "blue checked casual shirt," and presses the purchase button.

[0784] The application sends the order information to the server.

[0785] 5. Manufacturing and Delivery

[0786] The server sends the order information to a manufacturing facility, which produces the shirt and then ships it directly to the user.

[0787] The server notifies the user's terminal of the delivery information.

[0788] Example prompt sentence:

[0789] When a user prefers casual shirts, suggest the latest items based on current trends.

[0790] User Preferences: "Casual shirt", "Size M", "Blue"

[0791] Latest Trends: {"item": "blue plaid shirt", "category": "shirt"}

[0792] Result: Recommends "Blue Checkered Casual Shirt" to the user.

[0793] This system allows users to smoothly obtain apparel products that match their preferences and trends, and enables apparel manufacturers to reduce unnecessary inventory and lighten the burden on the environment.

[0794] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0795] Step 1:

[0796] User registration and information collection

[0797] Input: The user enters their name, email address, password, fashion preferences, size and color preferences, and past purchase history into their mobile device.

[0798] How it works: The device sends the entered information to the server, which parses it and stores it in a database. It also generates a user ID and associates it with the user's profile information.

[0799] Output: A message that user registration is complete is displayed on the terminal.

[0800] Step 2:

[0801] Obtaining trend information

[0802] Input: The server sends a request to get the latest fashion trend information from an external API.

[0803] How it works: The Trends API provides data on currently popular items, colors, and design patterns to a server that retrieves this information and stores it in a database.

[0804] Output: The latest trend information is stored in a database.

[0805] Step 3:

[0806] Generate personalized product recommendations

[0807] Input: User profile and trend information stored in a database.

[0808] How it works: The server uses AI algorithms to analyze user profile information and trend information. Based on this analysis, it generates personalized product designs for users.

[0809] Output: A list of generated product designs is generated and sent to the user's device.

[0810] Step 4:

[0811] Product selection and ordering

[0812] Input: Product information selected by the user from the suggested product list displayed on the device.

[0813] How it works: The user confirms the selected items on the terminal and presses the purchase button. The terminal sends the order information for the selected items to the server. The server receives the order information and sends it to the manufacturing facility.

[0814] Output: An order confirmation message is displayed on the user's device. Detailed order information (design, size, color, etc.) is sent to the manufacturing facility.

[0815] Step 5:

[0816] Manufacturing and Delivery

[0817] Input: Order information sent from the server to the manufacturing facility.

[0818] Operation: The manufacturing facility manufactures the product based on the order information received from the server. After the manufacturing is completed, the manufacturing facility delivers the product to the user. The server tracks the delivery status and notifies the user's device when the delivery has started and completed.

[0819] Output: The manufactured product is delivered to the address specified by the user. The delivery status is notified to the terminal.

[0820] By following these detailed, step-by-step processing steps, personalized product suggestions that combine the user's preferences with the latest trend information are realized.

[0821] 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.

[0822] The present invention relates to a system that uses an emotion engine to recognize user emotions in addition to user profile information and trend information, and then proposes personalized apparel products. This system is characterized by incorporating user emotion data to achieve more advanced personalization.

[0823] System Overview

[0824] The system includes the following main components:

[0825] 1. Terminal: A device where users input information, receive product suggestions, and place orders. This can be a smartphone or a PC.

[0826] 2. Server: A centralized system that collects and analyzes information sent by users and trend information, and generates personalized product recommendations.

[0827] 3. Database: Installed on the server, it stores user profile information, trend information, and order information.

[0828] 4. Affiliated Factory: A facility that actually manufactures apparel products based on the order information sent from the server and delivers them to users.

[0829] 5. Emotion Engine: A system for recognizing user emotions and adding and saving that information to profile information.

[0830] Processing flow

[0831] 1. User Registration and Information Collection

[0832] A user launches the application and registers, entering basic information such as name, email address, and password.

[0833] The device receives this information and sends it to the server, which stores it in a database and generates a user ID.

[0834] The user then enters details such as their fashion preferences, size and color preferences, and past purchasing history, which the device then transmits to the server.

[0835] The server stores this information in a database.

[0836] 2. Collecting emotional information

[0837] The emotion engine recognizes the user's emotions through facial expression recognition, voice analysis, and text analysis.

[0838] The device automatically collects emotional data while the user is operating the device or browsing products, and sends it to the server.

[0839] 3. Obtaining trend information

[0840] The server periodically collects fashion trend information from around the world through API, including popular items, design patterns, colors, etc.

[0841] The collected trend information is stored in a database and used for analysis.

[0842] 4. Personalized product recommendations

[0843] The server uses AI algorithms to analyze user profile information, emotional data, and trend information to generate product designs that are optimal for each user.

[0844] For example, if a user expresses the emotion "happy" while shopping, the system will take that emotional data into consideration and suggest products with particularly eye-catching designs or that suit their preferences.

[0845] The server creates a list of suggested products based on the generated product design and transmits this list to the user's terminal.

[0846] 5. Product Selection and Ordering

[0847] The user can view the list of suggested products on the device, select the product they like, and then press the "Purchase" button to confirm the order.

[0848] The terminal sends the order information (product ID, size, color) of the selected product to the server.

[0849] 6. Manufacturing and Delivery

[0850] The server sends order information to partner factories and issues manufacturing instructions. The order information includes detailed data (design, size, color) required for manufacturing.

[0851] The partner factory manufactures the product based on the order information received from the server. For example, the partner factory manufactures a "blue checked casual shirt."

[0852] The partner factory will then arrange for the completed product to be delivered directly to the user.

[0853] The server notifies the user's device of delivery start and completion information, allowing the user to check the delivery status of the product.

[0854] Specific examples

[0855] For example, if you're looking for a casual shirt for spring:

[0856] 1. User Registration and Information Collection

[0857] Users enter information such as "Favorite style: casual," "Favorite colors: blue, white," and "Size: M."

[0858] The terminal sends this information to the server, which stores it in a database.

[0859] 2. Collecting emotional information

[0860] The emotion engine recognizes emotions such as "happy" or "interested" from the user's facial expressions and adds them to the profile information.

[0861] 3. Obtaining trend information

[0862] The server obtains the latest fashion information via an API and determines that "checkered shirts for spring" are in fashion.

[0863] 4. Personalized product recommendations

[0864] The server generates a "blue checked casual shirt" based on the user's preferences, emotional data, and trend information, adds it to a list of suggested products, and sends this list to the user's device.

[0865] 5. Product Selection and Ordering

[0866] The user checks the suggested products, selects the "blue checked casual shirt," and presses the purchase button.

[0867] The terminal transmits the order information to the server.

[0868] 6. Manufacturing and Delivery

[0869] The server sends the order information to a partner factory, which then produces the shirt, which is then shipped directly to the user.

[0870] The server notifies the user's terminal of the delivery information.

[0871] This system allows users to seamlessly obtain personalized apparel products based on their preferences and emotions, while manufacturers can reduce wasteful inventory and lighten their environmental impact.

[0872] The processing flow will be explained below.

[0873] Step 1:

[0874] The user launches the application and enters basic information such as name, email address, and password on the new registration screen.

[0875] Step 2:

[0876] The device receives basic information and sends it to the server, which stores it in a database and generates a user ID.

[0877] Step 3:

[0878] The user then enters details such as their fashion preferences (e.g., casual style), size (e.g., medium), color preferences (e.g., blue, white), and past purchasing history.

[0879] Step 4:

[0880] The device collects these details and sends them to a server, which stores them in a database.

[0881] Step 5:

[0882] The emotion engine is activated and analyzes emotional data from the user's facial expressions, voice, and text input. For example, it analyzes facial expressions through the camera and recognizes emotions such as "happy" or "interested."

[0883] Step 6:

[0884] The device acquires the emotion data and sends it to the server, which stores it in a database and adds it to the profile information.

[0885] Step 7:

[0886] The server periodically accesses an external API that provides fashion trend information and collects the latest fashion trend data, including popular items, design patterns, colors, etc.

[0887] Step 8:

[0888] The server stores the collected trend information in a database for later analysis.

[0889] Step 9:

[0890] The server uses an AI algorithm to analyze the user's profile information, emotional data, and trend information to generate optimal product designs for the user. For example, if a user expresses the emotion "happy," the server will take that emotional data into account to generate particularly eye-catching designs and products that suit their preferences.

[0891] Step 10:

[0892] The server creates a list of suggested products based on the generated product design and transmits this list to the user's terminal.

[0893] Step 11:

[0894] The user can check the list of suggested products on the device, view the details, select the product they like, and press the "Purchase" button.

[0895] Step 12:

[0896] To confirm the purchase, the terminal sends the order information (item ID, size, color) of the selected item to the server.

[0897] Step 13:

[0898] The server sends the order information to the partner factory and issues manufacturing instructions. The order information includes detailed data (design, size, color) required for manufacturing.

[0899] Step 14:

[0900] The partner factory will manufacture the product based on the order information received from the server. For example, it will manufacture a "blue checked casual shirt."

[0901] Step 15:

[0902] The partner factory will then arrange for the completed product to be delivered directly to the user.

[0903] Step 16:

[0904] The server notifies the user's device of delivery start and completion information, allowing the user to check the delivery status of the product.

[0905] This series of steps allows users to seamlessly obtain personalized apparel products based on their preferences and emotions, while helping manufacturers reduce wasted inventory and lighten their environmental impact.

[0906] Example 2

[0907] 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."

[0908] Conventional apparel product recommendation systems only use a user's basic profile information and purchase history to suggest products, making it difficult to reflect the user's momentary emotions or current trends. This results in low personalization accuracy and fails to increase user satisfaction. Furthermore, as a result, users often lose interest in the suggested products, leading to a decline in purchasing motivation.

[0909] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0910] In this invention, the server includes means for collecting user profile information and storing it in a database, means for periodically obtaining fashion trend information from around the world and storing it in a database, means for collecting user emotion information using an emotion engine and storing it in a database, means for analyzing the user's profile information, emotion information, and trend information and generating personalized product designs, means for proposing the generated product designs to the user, means for sending order information to a manufacturing facility to manufacture the products selected by the user, and means for delivering the manufactured products to the user. This enables advanced personalization based on emotion data and the latest trend information in addition to the user's basic profile information, thereby increasing the user's desire to purchase.

[0911] "User Profile Information" means basic information provided by a User, including fashion preferences, size and color preferences, and past purchasing history.

[0912] "Fashion trend information" refers to the latest trends in the fashion industry that are regularly acquired, including popular items, colors, and design patterns.

[0913] The "emotion engine" is a system for recognizing user emotions and has the ability to collect emotional data through facial expression recognition, voice analysis, and text analysis.

[0914] "Personalized product design" refers to a product design optimized for a user, generated based on the user's profile information, emotional information, and fashion trend information.

[0915] A "suggested product list" is a list of products generated based on personalized product designs and suggested to users.

[0916] "Order Information" means detailed information required for manufacturing the product selected by the user, including product ID, size, color, etc.

[0917] "Manufacturing facility" refers to a factory or production line that produces the product selected by the user.

[0918] The present invention relates to a system that integrates a user's profile information, emotion information, and fashion trend information to recommend personalized apparel products. The system includes a means for collecting user profile information and storing it in a database, a means for collecting user emotion information using an emotion engine, and a means for acquiring fashion trend information from around the world and storing it in a database. The system also includes a means for analyzing this information to generate personalized product designs and recommend them to the user. The system also includes a means for sending order information to a manufacturing facility to manufacture the products selected by the user, and a means for delivering the manufactured products to the user.

[0919] First, a user launches the application and registers, which collects user profile information. The user enters information such as their name, email address, password, fashion preferences, size and color preferences, and past purchase history, and the device sends this information to the server. The server stores the received information in a database and generates a user ID.

[0920] Next, the emotion engine collects user emotion data through facial expression recognition, voice analysis, and text analysis. When a user views or interacts with a product, the device automatically collects emotion data and sends it to the server. The emotion engine analyzes emotions using open source libraries (e.g., OpenCV) and voice analysis APIs (e.g., Google Cloud Speech-to-Text API) and sends the results to the server. The server stores the data in a database.

[0921] The server also periodically uses external APIs (e.g., FashionAPI) to collect the latest fashion trend information and stores it in the database. Trend information includes popular items, design patterns, colors, etc., and is used for analysis.

[0922] To make personalized product suggestions, the server integrates and analyzes the user's profile information, emotional data, and trend information, and uses AI algorithms (e.g., TensorFlow or PyTorch) to generate product designs that are optimal for the user. The generated product designs are sent to the user's device as a list of suggested products. For example, if a user is looking for a "casual blue shirt for spring," and the server recognizes that the user's emotion is "happy," it will suggest a casual blue shirt based on that information.

[0923] The user checks the list of suggested products on their device, selects their favorite product, and presses the "Purchase" button. The order information (product ID, size, color) is sent to the server, which then sends the order information to a partner factory and instructs it to manufacture the product. The manufacturing facility manufactures the product based on the order information and delivers the completed product directly to the user. The server then notifies the user of the delivery information on their device, allowing the user to check the delivery status.

[0924] As an example of this system, consider the following prompt: "I'm looking for a casual blue shirt for spring. I'm happy shopping. Please suggest products that fit this criteria."

[0925] This allows users to efficiently find the best products based on their profile information, emotions, and the latest fashion trends, while manufacturers can reduce unnecessary inventory and lighten their environmental impact.

[0926] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0927] Step 1: User registration and information collection

[0928] The user starts the application and enters information such as name, email address, password, fashion preferences, size and color preferences, and past purchase history on the new registration screen. This information is the input data.

[0929] The device serializes the input information and sends it to the server in JSON format via an HTTP POST request. This request is the input, and the user ID received in response is the output.

[0930] The server parses the received JSON data and stores each field (such as name, email address, and fashion preferences) in a database. The server generates a new user ID and returns it to the device as an HTTP response. The device notifies the user.

[0931] Step 2: Collecting emotional information

[0932] Users use the application to browse products.

[0933] The emotion engine recognizes the user's facial expressions using a camera and analyzes their voice using a microphone. It also analyzes text input. This is the input data.

[0934] The device collects emotion data in real time and sends it to the server in JSON format. The emotion data is the input, and the database records are the output.

[0935] The server stores the received emotion data in a database, for example, as a record with fields such as "date and time," "emotion label," and "emotion intensity."

[0936] Step 3: Obtaining trend information

[0937] The server runs a cron job periodically every day and calls an external API (e.g., FashionAPI) to obtain the latest fashion trend information. The obtained trend information is the input data.

[0938] The server stores this trend information in a database in JSON format. The stored trend information is the output. The database contains fields such as item name, design pattern, color, and popularity.

[0939] Step 4: Personalized product recommendations

[0940] The server integrates user profile information, sentiment data, and trend information, and analyzes them using AI algorithms (e.g., TensorFlow, PyTorch). This is the input data.

[0941] As a result of the analysis, the server generates the optimal product design for the user. The generated product design is the output and is sent to the terminal as a list of suggested products. For example, a blue casual shirt may be suggested.

[0942] Step 5: Select and order

[0943] The user checks the list of suggested products on the terminal, selects the product that suits their taste, and presses the "Purchase" button. This is the input data.

[0944] The terminal sends JSON data including the selected product information (product ID, size, color) to the server. The order information is output.

[0945] The server stores the received order information in a database and updates the order status to "Not yet manufactured."

[0946] Step 6: Manufacturing and Delivery

[0947] The server sends the order ID and detailed information (design, size, color) in JSON format to the partner factory. This is the input data.

[0948] The partner factory manufactures the product based on the order information, for example, by cutting the fabric using a CNC cutting machine and sewing it. The manufactured product is the output.

[0949] The partner factory hands over the completed product to the delivery company and notifies the server of the delivery information.

[0950] The server notifies the user of delivery information (e.g., tracking number) on the user's device, allowing the user to check the delivery status.

[0951] This allows users to efficiently find the best products based on their profile information, emotions, and the latest fashion trends, while manufacturers can reduce unnecessary inventory and lighten their environmental impact.

[0952] (Application example 2)

[0953] 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."

[0954] Conventional apparel recommendation systems suggest products based on user profile information and trend information, but they have the problem of not being able to make optimal personalized recommendations because they do not take the user's emotions into account. Therefore, there is a need to provide a system that can make personalized apparel recommendations that correspond to the user's actual emotional state.

[0955] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0956] In this invention, the server includes means for collecting user profile information and storing it in a database, means for periodically obtaining fashion trend information from around the world and storing it in a database, means for analyzing the user profile information and trend information to generate personalized product designs, means for proposing the generated product designs to the user, means for collecting user emotion data using an emotion engine that recognizes the user's emotions and adding it to the profile information, means for sending order information to a manufacturing facility to manufacture the product selected by the user, and means for delivering the manufactured product to the user. This enables more sophisticated personalized proposals based on the user's emotional state.

[0957] "User Profile Information" refers to basic data about a User, such as the User's fashion preferences, size and color preferences, and past purchasing history.

[0958] "Trend information" refers to data that includes information on fashion items, design patterns, colors, etc. that are popular within a certain period of time.

[0959] "Emotion engine" refers to a system that recognizes and analyzes a user's emotions through facial expression recognition, voice analysis, and text analysis.

[0960] "Personalized product design" refers to apparel product designs optimized for specific users, generated based on the user's profile information, trend information, and emotional data.

[0961] "Manufacturing facility" refers to a facility that actually manufactures apparel products based on order information from users.

[0962] "Database" means an electronic repository for storing data used by the System, such as user profile information, trend information, sentiment data, and order information.

[0963] The system for implementing this invention includes the following main hardware and software: The hardware used includes smart glasses, a camera, and a server, and the software used includes Python, OpenCV (an image processing library), and Requests (an HTTP request library).

[0964] This system uses user profile information, trend information, and emotion data to provide personalized product recommendations. The process is explained below.

[0965] First, the user puts on the smart glasses and activates the system. The camera in the smart glasses captures an image of the user's face. This image data is analyzed by the emotion engine to recognize the user's emotional state. The emotion engine combines facial, voice, and text analysis to obtain emotion data.

[0966] The server then periodically retrieves information on fashion trends from around the world via API and stores it in a database, including information on popular items, design patterns, colors, etc. This trend information is combined with user profile information and analyzed to generate personalized product designs.

[0967] The generated product designs are then presented to the user in real time, displayed on the smart glasses display. The user selects the desired product from the list of suggested products and completes the purchase process. This selection is then sent to the server and transmitted to the manufacturing facility.

[0968] The manufacturing facility manufactures the product based on the received order information, and once production is complete, the product is delivered directly to the user. The server manages delivery information and allows the user to check the delivery status of the product.

[0969] This system allows users to seamlessly select and purchase apparel that best suits their preferences and emotional state, while also helping manufacturing facilities reduce wasteful inventory and contribute to reducing environmental impact.

[0970] For example, consider the following prompt:

[0971] "If a user who has a casual style and likes blue or white clothing is wearing smart glasses, and emotion recognition reveals that the user looks happy, the glasses will suggest products based on the latest fashion trends, such as a casual blue shirt or white pants."

[0972] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0973] Step 1:

[0974] The user puts on the smart glasses and starts the system.

[0975] Input: None

[0976] Output: The smart glasses are turned on and the camera is ready to use.

[0977] Specific operation: The user turns on the smart glasses. The system automatically starts the camera.

[0978] Step 2:

[0979] The camera on the device (smart glasses) captures an image of the user's face.

[0980] Input: An image of the user's face

[0981] Output: Captured image (JPEG format)

[0982] Specific operation: The camera captures the user's face, takes a picture, and temporarily stores the image data in memory.

[0983] Step 3:

[0984] The terminal transmits the captured image data to the server.

[0985] Input: Captured image (JPEG format)

[0986] Output: Image data sent

[0987] Specific operation: The device uploads the stored image data to a server via the Internet.

[0988] Step 4:

[0989] The server passes the received image data to an emotion engine and analyzes the user's emotion.

[0990] Input: Received image data

[0991] Output: User emotion data (e.g. happy, interesting)

[0992] Specific operation: The emotion engine analyzes image data and recognizes the user's emotional state from their facial expressions.

[0993] Step 5:

[0994] The server acquires fashion trend information from around the world and stores it in a database.

[0995] Input: Trend information acquisition request

[0996] Output: Trend information (e.g. popular items, colors, design patterns)

[0997] Specific operation: The server periodically obtains the latest fashion trend information using an external API and stores it in a database.

[0998] Step 6:

[0999] The server analyzes the user's profile information, emotion data, and trend information to generate personalized product designs.

[1000] Input: User profile information, sentiment data, trend information

[1001] Output: Personalized product design (e.g., blue checked casual shirt)

[1002] How it works: The server uses AI algorithms to analyze this data and generate optimal product designs for the user.

[1003] Step 7:

[1004] The server sends the generated product design to the user's device (smart glasses).

[1005] Input: Generated product design

[1006] Output: Submitted product design

[1007] Specific operation: The server sends the product design to the terminal via the Internet and displays it on the smart glasses display.

[1008] Step 8:

[1009] The user selects the desired product from the list of suggested products and completes the purchase procedure.

[1010] Input: Suggested product list

[1011] Output: Selected product information (e.g. product ID, size, color)

[1012] Specific operation: The user operates the interface of the smart glasses to select the desired product and press the "Purchase" button.

[1013] Step 9:

[1014] The terminal transmits the product information selected by the user to the server.

[1015] Input: Selected product information

[1016] Output: Order information sent

[1017] Specific operation: The terminal uploads the selected product information to the server via the Internet.

[1018] Step 10:

[1019] The server transmits the order information to a manufacturing facility and issues instructions for manufacturing the goods.

[1020] Input: Order Information

[1021] Output: Manufacturing instructions

[1022] Specific operations: The server sends manufacturing instructions to the manufacturing facility based on the received order information.

[1023] Step 11:

[1024] The manufacturing facility manufactures the goods based on the received order information and delivers them to the user.

[1025] Input: Order Information

[1026] Output: Manufactured goods

[1027] What happens: The manufacturing facility produces the goods based on the order, processes the delivery, and delivers the product directly to the user.

[1028] Step 12:

[1029] The server notifies the user's terminal of the delivery information.

[1030] Input:Shipping information

[1031] Output: Delivery status notification

[1032] Specific operation: The server sends the delivery progress status to the user's terminal via the Internet, allowing the user to check the delivery status.

[1033] 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.

[1034] 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.

[1035] 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.

[1036] [Third embodiment]

[1037] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1038] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1039] 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).

[1040] 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.

[1041] 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.

[1042] 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).

[1043] 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.

[1044] 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.

[1045] 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.

[1046] 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.

[1047] 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.

[1048] 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."

[1049] This invention relates to a system that collects user profile information, combines it with trend information from around the world, and proposes personalized apparel products. This system utilizes AI to generate and propose products based on the user's preferences, and then manufactures and delivers the ordered products.

[1050] System Overview

[1051] The system includes the following main components:

[1052] 1. Terminal: A device where users input information, receive product suggestions, and place orders. This can be a smartphone or a PC.

[1053] 2. Server: A centralized system that collects and analyzes information sent by users and trend information, and generates personalized product recommendations.

[1054] 3. Database: Installed on the server, it stores user profile information, trend information, and order information.

[1055] 4. Affiliated Factory: A facility that actually manufactures apparel products based on the order information sent from the server and delivers them to users.

[1056] Processing flow

[1057] 1. User Registration and Information Collection

[1058] A user launches the application and registers, entering basic information such as name, email address, and password.

[1059] The device receives this information and sends it to a server, along with other details such as the user's fashion preferences, size, color, and past purchase history.

[1060] The server stores the received information in a database and generates a user ID to associate with the profile information.

[1061] 2. Obtaining trend information

[1062] The server periodically collects fashion trend information from around the world through API, including popular items, design patterns, colors, etc.

[1063] The collected trend information is stored in a database and used for analysis.

[1064] 3. Personalized product recommendations

[1065] The server analyzes the user's profile information and trend information, and uses AI algorithms to generate personalized product designs for each user.

[1066] A list of suggested products is created based on the generated product design and sent to the terminal.

[1067] 4. Product Selection and Ordering

[1068] The user can view the list of suggested products on the device, select the product they like, and then press the "Purchase" button to confirm the order.

[1069] The terminal transmits order information for the selected product to the server.

[1070] 5. Manufacturing and Delivery

[1071] The server sends the order information to the partner factory, which includes the detailed data (design, size, color) required for manufacturing.

[1072] The partner factory will manufacture the product according to the specified design, and once production is complete, the product will be shipped directly to the customer.

[1073] The server notifies the terminal of the start and completion of delivery.

[1074] Specific examples

[1075] For example, if you're looking for a casual shirt for spring:

[1076] 1. User Registration and Information Collection

[1077] The user enters the following information: "Favorite style: casual," "Favorite colors: blue, white," and "Size: M."

[1078] The terminal sends this information to the server, which stores it in a database.

[1079] 2. Obtaining trend information

[1080] The server obtains the latest fashion information via an API and determines that "checkered shirts for spring" are in fashion.

[1081] 3. Personalized product recommendations

[1082] Based on the user's preferences and the latest trends, the server uses AI to generate a "blue checked casual shirt" and adds it to the list of suggested products.

[1083] This list is sent to the user's device.

[1084] 4. Product Selection and Ordering

[1085] The user checks the suggested products, selects the "blue checked casual shirt," and presses the purchase button.

[1086] The terminal transmits the order information to the server.

[1087] 5. Manufacturing and Delivery

[1088] The server sends the order information to a partner factory, which then produces the shirt, which is then shipped directly to the user.

[1089] The server notifies the user's terminal of the delivery information.

[1090] This system allows users to smoothly obtain apparel products that match their preferences and trends, and enables apparel manufacturers to reduce unnecessary inventory and lighten the burden on the environment.

[1091] The processing flow will be explained below.

[1092] Step 1:

[1093] The user launches the application and enters basic information such as name, email address, and password on the new registration screen.

[1094] Step 2:

[1095] The device receives this basic information and sends it to the server, which stores it in a database and generates a user ID.

[1096] Step 3:

[1097] The user then inputs their fashion preferences (e.g., casual style), size (e.g., medium size), color preferences (e.g., blue, white), and past purchasing history.

[1098] Step 4:

[1099] The device collects these details and sends them to a server, which stores them in a database.

[1100] Step 5:

[1101] The server periodically accesses an external API that provides fashion trend information and collects the latest fashion trend data, including popular items, design patterns, colors, etc.

[1102] Step 6:

[1103] The server stores the collected trend information in a database and prepares it for later analysis.

[1104] Step 7:

[1105] The server uses an AI algorithm to analyze the user's profile information and the latest trends to generate the optimal product design for the user. For example, it generates a "blue checked casual shirt" based on the user's preferred combination of blue and the latest checked pattern.

[1106] Step 8:

[1107] The server creates a list of suggested products based on the generated product design and transmits this list to the user's terminal.

[1108] Step 9:

[1109] Users can view the list of suggested products on their device, view details, select the product they like, and press the "Purchase" button.

[1110] Step 10:

[1111] To confirm the purchase, the terminal sends the order information (item ID, size, color) of the selected item to the server.

[1112] Step 11:

[1113] The server sends the order information to the partner factory and issues manufacturing instructions. The order information includes detailed data required for manufacturing (design, size, color).

[1114] Step 12:

[1115] The partner factory manufactures the product based on the order information received from the server. For example, the partner factory manufactures a "blue checked casual shirt."

[1116] Step 13:

[1117] The partner factory will then arrange for the completed product to be delivered directly to the user.

[1118] Step 14:

[1119] The server notifies the user's device of delivery start and completion information, allowing the user to check the delivery status of the product.

[1120] This series of steps allows users to obtain original products that are best suited to them, while allowing manufacturers to maintain appropriate production volumes and reduce environmental impact.

[1121] Example 1

[1122] 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."

[1123] Conventional apparel product recommendation systems have difficulty in proposing personalized products that reflect the individual preferences and latest trends of users. Furthermore, they lacked efficient methods for quickly manufacturing and delivering the products selected by users. As a result, it was difficult to increase user satisfaction, and manufacturers also faced the challenge of increasing the burden of inventory management.

[1124] 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.

[1125] In this invention, the server includes means for collecting user profile information and storing it in a database, means for periodically obtaining fashion trend information from around the world and storing it in a database, means for analyzing user profile information and trend information and generating personalized product designs using a generative AI model, means for sending the generated product designs to a terminal and proposing them to the user, means for sending order information for products selected by the user on the terminal to an affiliated manufacturing facility, means for delivering the manufactured products to the user, and means for notifying the user's terminal of manufacturing and delivery information. This enables users to efficiently propose, select, and order personalized products based on their preferences and the latest trends, and also enables manufacturers to achieve efficient production and delivery, thereby reducing the burden of inventory management.

[1126] "User" means an entity that utilizes the System to provide profile information and receive personalized product offers.

[1127] "Profile Information" refers to general information about a user, such as the user's fashion preferences, size and color preferences, and past purchasing history.

[1128] "Fashion trend information" refers to information about fashion that is popular around the world at each time and region, such as popular items, colors, and design patterns.

[1129] "Generative AI model" refers to an artificial intelligence model that analyzes user profile information and fashion trend information to automatically generate personalized product designs.

[1130] "Affiliated manufacturing facility" refers to a facility that manufactures the apparel products selected by the user based on the order information sent from the server.

[1131] "Terminal" refers to the device used by a user to enter information, receive product suggestions, and place an order, specifically a smartphone or computer.

[1132] "Server" refers to the centralized system that collects and analyzes information sent by users and trend information, and generates and manages personalized product proposals.

[1133] "Database" refers to an information management system installed on a server for storing user profile information, trend information, and order information.

[1134] This invention relates to a system that collects user profile information, combines it with global fashion trend information, and proposes personalized apparel products. The system utilizes a generative AI model to generate and propose products based on the user's preferences, and then manufactures and delivers the ordered products.

[1135] System Overview

[1136] The system includes the following major components:

[1137] 1. Terminal: A device on which a user enters profile information, receives product suggestions, and places an order. Specifically, a smartphone or PC is used.

[1138] 2. Server: A centralized system that collects and analyzes information sent by users and trend information, and generates personalized product recommendations.

[1139] 3. Database: Installed on the server, it stores user profile information, trend information, and order information.

[1140] 4. Affiliated manufacturing facility: This is the facility that actually manufactures the apparel products based on the order information sent from the server and delivers them to the user.

[1141] Hardware and software used

[1142] Devices: smartphones, computers

[1143] Server: Centralized system

[1144] Database: A cloud database for managing user information, trend information, and order information.

[1145] Generative AI model: An algorithm that analyzes user preferences and trend information to generate personalized product designs

[1146] Processing flow

[1147] 1. User Registration and Information Collection

[1148] A user launches the application and registers, entering basic information such as name, email address, and password.

[1149] The device receives this information and sends it to a server, along with other details such as the user's fashion preferences, size, color, and past purchase history.

[1150] The server stores the received information in a database and generates a user ID to associate with the profile information.

[1151] 2. Obtaining trend information

[1152] The server periodically collects the latest fashion trend information from around the world through API, specifically information on popular items, design patterns, colors, etc.

[1153] The server stores the collected trend information in a database and tags it for analysis.

[1154] 3. Personalized product recommendations

[1155] The server uses the generative AI model to analyze the user's profile information and trend information. For example, it inputs a prompt statement such as "Suggest spring casual shirts for users who like blue."

[1156] The generative AI model uses user preferences and trend information to generate personalized product designs.

[1157] The server adds the generated product design to a list of proposed products and transmits this list to the terminal.

[1158] 4. Product Selection and Ordering

[1159] The user can check the list of suggested products on the device, select the desired product, and press the "Purchase" button to confirm the order.

[1160] The terminal transmits order information for the selected product to the server.

[1161] 5. Manufacturing and Delivery

[1162] The server sends the order information to the partner manufacturing facility, including the details required for manufacturing (design, size, color).

[1163] The partner manufacturing facility will produce the product according to the specified design and ship the product directly to the user once production is complete.

[1164] The server notifies the user's device of the start and completion of delivery, for example, by sending a notification that the product has been shipped.

[1165] Specific examples

[1166] For example, if you're looking for a casual shirt for spring:

[1167] 1. The user enters the following profile information: "Favorite style: casual," "Favorite colors: blue, white," and "Size: M."

[1168] 2. The device sends this information to the server, which stores it in a database.

[1169] 3. The server retrieves the latest fashion information via the API and determines that "checkered shirts for spring" are in fashion.

[1170] 4. Based on the user's preferences and the latest trends, the server uses a generative AI model to generate a "blue checked casual shirt" and adds it to the list of suggested products.

[1171] 5. Send this list to the user's device.

[1172] 6. The user checks the suggested products, selects the "blue checked casual shirt," and presses the purchase button.

[1173] 7. The terminal sends the order information to the server.

[1174] 8. The server sends the order information to a partner manufacturing facility, which produces the shirt. Once production is complete, the shirt is shipped directly to the customer.

[1175] 9. The server notifies the user's terminal of the delivery information.

[1176] This system allows users to smoothly obtain apparel products that match their preferences and trends, and enables apparel manufacturers to reduce unnecessary inventory and lighten the burden on the environment.

[1177] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1178] Step 1: Collect user information

[1179] Subject: User

[1180] How it works: The user launches the dedicated application and clicks "Sign Up." After entering their name, email address, and password, they click "Next."

[1181] Input: User's name, email address, password

[1182] Output: Sends the entered basic information to the terminal

[1183] Subject: Terminal

[1184] How it works: The device encrypts the information entered and sends it to the server using a secure communication protocol (HTTPS).

[1185] Input: Encrypted user basic information

[1186] Output: Basic information sent to the server

[1187] Subject: User

[1188] How it works: Next, the user enters their "favorite style," "favorite color," "size," and "past purchase history."

[1189] Input: Fashion preferences, size, color preferences, past purchase history

[1190] Output: Sends the entered details to the terminal

[1191] Subject: Terminal

[1192] How it works: The device re-encrypts these details and sends them to the server.

[1193] Input: Encrypted details

[1194] Output: Details sent to the server

[1195] Subject: Server

[1196] How it works: The server stores the submitted information in a database and generates a user ID to associate with the profile information.

[1197] Input: Basic information and detailed information received

[1198] Data processing / data calculation: Save to database, generate user ID, associate with profile information

[1199] Output: Profile information stored in the database

[1200] Step 2: Obtaining trend information

[1201] Subject: Server

[1202] How it works: The server uses the fashion API to periodically obtain the latest fashion trend information from around the world, specifically information on popular items, design patterns, colors, etc.

[1203] Input: Fashion trend information obtained through API

[1204] Data processing / data calculation: Shaping and tagging trend information

[1205] Output: Trend information stored in a database

[1206] Step 3: Generate personalized product suggestions

[1207] Subject: Server

[1208] How it works: The server inputs the user's profile information and trend information into the generative AI model for analysis. A prompt such as "Suggest a spring casual shirt that is recommended for a user who likes the color blue" is used.

[1209] Input: User profile information, fashion trend information, prompt text

[1210] Data processing / data calculation: Analysis by generative AI models and generation of personalized product designs

[1211] Output: Suggested product list

[1212] Subject: Server

[1213] Operation: The generated product design is added to a list of suggested products and this list is sent to the user's device.

[1214] Input: Generated product design

[1215] Output: Suggested product list sent to the terminal

[1216] Step 4: Select and order

[1217] Subject: User

[1218] How it works: The user reviews the list of suggested products on their device, selects the product they want, for example, "Blue Checkered Casual Shirt," and clicks the "Buy" button.

[1219] Input: User selected product

[1220] Output: Order information for selected items

[1221] Subject: Terminal

[1222] How it works: The terminal encrypts the order information and sends it to the server.

[1223] Input: Encrypted order information

[1224] Output: Order information sent to the server

[1225] Step 5: Manufacturing and Delivery

[1226] Subject: Server

[1227] Operation: The server sends order information to a partner manufacturing facility, including details needed for manufacturing (design, size, color).

[1228] Input: Detailed data based on order information

[1229] Data processing / data calculation: Formatting and sending order information

[1230] Output: Order information sent to partner manufacturing facility

[1231] Subject: Affiliated manufacturing facility

[1232] How it works: The partner manufacturing facility uses the information received to produce the product according to the specified design.

[1233] Input: Detailed data of the specified design

[1234] Output: Manufactured goods

[1235] Subject: Affiliated manufacturing facility

[1236] What it does: Once production is complete, the product is shipped directly to the customer.

[1237] Input: Manufactured goods

[1238] Output: Item delivered to user

[1239] Subject: Server

[1240] Operation: The server notifies the user's device of the start and completion of delivery, for example, sending a notification that "the product has been shipped."

[1241] Input: Delivery start and completion information

[1242] Output: Notification sent to the user's device

[1243] (Application example 1)

[1244] 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."

[1245] Today's consumers demand apparel that matches their personalities and preferences, and the fashion industry must respond quickly to frequently changing trends. However, meeting these needs requires effectively analyzing large amounts of data and proposing the right products for each user, which is a challenge. Furthermore, an efficient system is needed to collect relevant trend information and quickly manufacture and deliver personalized products.

[1246] 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.

[1247] In this invention, the server includes means for collecting user profile information and storing it in a database, means for periodically obtaining fashion trend information from around the world and storing it in a database, means for analyzing the user profile information and trend information and generating personalized product designs, means for proposing the generated product designs to the user, means for sending order information to a manufacturing facility to manufacture the products selected by the user, means for delivering the manufactured products to the user, means including an algorithm for recommending products based on the user's preferences and the latest trend information, means including a mobile terminal on which an application for proposing personalized fashion items to the user is installed, and means for obtaining trend information from an external API. This makes it possible to effectively combine user preferences with the latest trend information to quickly propose, manufacture, and deliver personalized products.

[1248] "User profile information" refers to individual information such as a user's fashion preferences, size and color preferences, and past purchasing history.

[1249] "Database" refers to a recording medium for storing collected user profile information and fashion trend information from around the world.

[1250] "Trend information" refers to information regarding popular fashion items, colors, and design patterns that is regularly obtained from around the world.

[1251] "Product design" refers to personalized apparel product designs generated by analyzing user profile information and trend information.

[1252] "Proposal" refers to the act of presenting the generated product design to the user.

[1253] "Order Information" refers to the detailed data (e.g., design, size, color, etc.) required to manufacture the product selected by the User.

[1254] "Manufacturing facility" refers to a factory or manufacturing base that actually produces apparel products based on order information received from the server.

[1255] "Delivery" refers to the act of delivering manufactured products to a location designated by the user.

[1256] "Algorithm" refers to the calculation methods and analytical means used to recommend products based on user preferences and the latest trend information.

[1257] "Mobile device" refers to an information terminal that can be carried by a user, such as a smartphone or tablet.

[1258] "External API" refers to a programmatic interface that provides an access point for obtaining trend information from other systems or databases.

[1259] "Personalization" refers to customizing products and services to suit individual user characteristics and preferences.

[1260] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will now be described in detail with reference to the accompanying drawings.

[1261] Overall system picture

[1262] This invention is a system that collects user profile information and stores it in a database. It also periodically acquires and stores fashion trend information from around the world in the same database. It uses artificial intelligence (AI) to analyze the user's profile information and trend information and generate personalized product designs. The generated product designs are then presented to the user via their mobile device (such as a smartphone or tablet).

[1263] Hardware and Software Configuration

[1264] The server is equipped with a database, AI algorithms, and software that executes external API calls. The database that stores user profile information and trend information uses, for example, MySQL or PostgreSQL. Trend information is retrieved from external APIs using, for example, the Requests library. The AI ​​algorithm is a calculation method for recommending products based on user preferences and trend information, and is implemented using, for example, Scikit-learn or TensorFlow.

[1265] The application is installed on the user's device, and the backend of the application is built using the Flask framework (or Django, etc.) to communicate with the server. The user interface is built using HTML, CSS, and JavaScript.

[1266] System operation flow

[1267] 1. User Registration and Information Collection

[1268] A user launches an application on their mobile device and registers, entering information such as their name, email address, password, fashion preferences, size and color preferences, and past purchase history. The application receives this information and sends it to the server, which stores it in a database and generates a user ID that is associated with their profile information.

[1269] 2. Obtaining trend information

[1270] The server periodically collects fashion trend information from around the world through external APIs, including popular items, design patterns, colors, etc. The collected trend information is stored in a database for later analysis.

[1271] 3. Personalized product recommendations

[1272] The server analyzes the user's profile information and trend information, and uses an AI algorithm to generate personalized product designs. Based on the generated product designs, it creates a list of suggested products and sends them to the user's device.

[1273] 4. Product Selection and Ordering

[1274] The user checks the list of suggested products on the terminal and selects the product they like. The order information is then confirmed and sent to the server to purchase the selected product. The server then sends the received order information to the manufacturing facility, providing detailed data (design, size, color, etc.).

[1275] 5. Manufacturing and Delivery

[1276] The manufacturing facility manufactures the product based on the order information sent from the server. Once manufacturing is complete, the manufacturing facility delivers the product to the user. The server notifies the user's device of the start and completion of delivery.

[1277] Examples and prompts

[1278] For example, if you're looking for a casual shirt for spring:

[1279] 1. User Registration and Information Collection

[1280] The user enters the following information: "Favorite style: casual," "Favorite colors: blue, white," and "Size: M."

[1281] The application sends this information to the server, which stores it in a database.

[1282] 2. Obtaining trend information

[1283] The server obtains the latest fashion information via an external API and determines that "checkered shirts for spring" are in fashion.

[1284] 3. Personalized product recommendations

[1285] Based on the user's preferences and the latest trends, the server uses AI to generate a "blue checked casual shirt" and adds it to the list of suggested products.

[1286] This list is sent to the user's device.

[1287] 4. Product Selection and Ordering

[1288] The user checks the suggested products, selects the "blue checked casual shirt," and presses the purchase button.

[1289] The application sends the order information to the server.

[1290] 5. Manufacturing and Delivery

[1291] The server sends the order information to a manufacturing facility, which produces the shirt and then ships it directly to the user.

[1292] The server notifies the user's terminal of the delivery information.

[1293] Example prompt sentence:

[1294] When a user prefers casual shirts, suggest the latest items based on current trends.

[1295] User Preferences: "Casual shirt", "Size M", "Blue"

[1296] Latest Trends: {"item": "blue plaid shirt", "category": "shirt"}

[1297] Result: Recommends "Blue Checkered Casual Shirt" to the user.

[1298] This system allows users to smoothly obtain apparel products that match their preferences and trends, and enables apparel manufacturers to reduce unnecessary inventory and lighten the burden on the environment.

[1299] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1300] Step 1:

[1301] User registration and information collection

[1302] Input: The user enters their name, email address, password, fashion preferences, size and color preferences, and past purchase history into their mobile device.

[1303] How it works: The device sends the entered information to the server, which parses it and stores it in a database. It also generates a user ID and associates it with the user's profile information.

[1304] Output: A message that user registration is complete is displayed on the terminal.

[1305] Step 2:

[1306] Obtaining trend information

[1307] Input: The server sends a request to get the latest fashion trend information from an external API.

[1308] How it works: The Trends API provides data on currently popular items, colors, and design patterns to a server that retrieves this information and stores it in a database.

[1309] Output: The latest trend information is stored in a database.

[1310] Step 3:

[1311] Generate personalized product recommendations

[1312] Input: User profile and trend information stored in a database.

[1313] How it works: The server uses AI algorithms to analyze user profile information and trend information. Based on this analysis, it generates personalized product designs for users.

[1314] Output: A list of generated product designs is generated and sent to the user's device.

[1315] Step 4:

[1316] Product selection and ordering

[1317] Input: Product information selected by the user from the suggested product list displayed on the device.

[1318] How it works: The user confirms the selected items on the terminal and presses the purchase button. The terminal sends the order information for the selected items to the server. The server receives the order information and sends it to the manufacturing facility.

[1319] Output: An order confirmation message is displayed on the user's device. Detailed order information (design, size, color, etc.) is sent to the manufacturing facility.

[1320] Step 5:

[1321] Manufacturing and Delivery

[1322] Input: Order information sent from the server to the manufacturing facility.

[1323] Operation: The manufacturing facility manufactures the product based on the order information received from the server. After the manufacturing is completed, the manufacturing facility delivers the product to the user. The server tracks the delivery status and notifies the user's device when the delivery has started and completed.

[1324] Output: The manufactured product is delivered to the address specified by the user. The delivery status is notified to the terminal.

[1325] By following these detailed, step-by-step processing steps, personalized product suggestions that combine the user's preferences with the latest trend information are realized.

[1326] 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.

[1327] The present invention relates to a system that uses an emotion engine to recognize user emotions in addition to user profile information and trend information, and then proposes personalized apparel products. This system is characterized by incorporating user emotion data to achieve more advanced personalization.

[1328] System Overview

[1329] The system includes the following main components:

[1330] 1. Terminal: A device where users input information, receive product suggestions, and place orders. This can be a smartphone or a PC.

[1331] 2. Server: A centralized system that collects and analyzes information sent by users and trend information, and generates personalized product recommendations.

[1332] 3. Database: Installed on the server, it stores user profile information, trend information, and order information.

[1333] 4. Affiliated Factory: A facility that actually manufactures apparel products based on the order information sent from the server and delivers them to users.

[1334] 5. Emotion Engine: A system for recognizing user emotions and adding and saving that information to profile information.

[1335] Processing flow

[1336] 1. User Registration and Information Collection

[1337] A user launches the application and registers, entering basic information such as name, email address, and password.

[1338] The device receives this information and sends it to the server, which stores it in a database and generates a user ID.

[1339] The user then enters details such as their fashion preferences, size and color preferences, and past purchasing history, which the device then transmits to the server.

[1340] The server stores this information in a database.

[1341] 2. Collecting emotional information

[1342] The emotion engine recognizes the user's emotions through facial expression recognition, voice analysis, and text analysis.

[1343] The device automatically collects emotional data while the user is operating the device or browsing products, and sends it to the server.

[1344] 3. Obtaining trend information

[1345] The server periodically collects fashion trend information from around the world through API, including popular items, design patterns, colors, etc.

[1346] The collected trend information is stored in a database and used for analysis.

[1347] 4. Personalized product recommendations

[1348] The server uses AI algorithms to analyze user profile information, emotional data, and trend information to generate product designs that are optimal for each user.

[1349] For example, if a user expresses the emotion "happy" while shopping, the system will take that emotional data into consideration and suggest products with particularly eye-catching designs or that suit their preferences.

[1350] The server creates a list of suggested products based on the generated product design and transmits this list to the user's terminal.

[1351] 5. Product Selection and Ordering

[1352] The user can view the list of suggested products on the device, select the product they like, and then press the "Purchase" button to confirm the order.

[1353] The terminal sends the order information (product ID, size, color) of the selected product to the server.

[1354] 6. Manufacturing and Delivery

[1355] The server sends order information to partner factories and issues manufacturing instructions. The order information includes detailed data (design, size, color) required for manufacturing.

[1356] The partner factory manufactures the product based on the order information received from the server. For example, the partner factory manufactures a "blue checked casual shirt."

[1357] The partner factory will then arrange for the completed product to be delivered directly to the user.

[1358] The server notifies the user's device of delivery start and completion information, allowing the user to check the delivery status of the product.

[1359] Specific examples

[1360] For example, if you're looking for a casual shirt for spring:

[1361] 1. User Registration and Information Collection

[1362] Users enter information such as "Favorite style: casual," "Favorite colors: blue, white," and "Size: M."

[1363] The terminal sends this information to the server, which stores it in a database.

[1364] 2. Collecting emotional information

[1365] The emotion engine recognizes emotions such as "happy" or "interested" from the user's facial expressions and adds them to the profile information.

[1366] 3. Obtaining trend information

[1367] The server obtains the latest fashion information via an API and determines that "checkered shirts for spring" are in fashion.

[1368] 4. Personalized product recommendations

[1369] The server generates a "blue checked casual shirt" based on the user's preferences, emotional data, and trend information, adds it to a list of suggested products, and sends this list to the user's device.

[1370] 5. Product Selection and Ordering

[1371] The user checks the suggested products, selects the "blue checked casual shirt," and presses the purchase button.

[1372] The terminal transmits the order information to the server.

[1373] 6. Manufacturing and Delivery

[1374] The server sends the order information to a partner factory, which then produces the shirt, which is then shipped directly to the user.

[1375] The server notifies the user's terminal of the delivery information.

[1376] This system allows users to seamlessly obtain personalized apparel products based on their preferences and emotions, while manufacturers can reduce wasteful inventory and lighten their environmental impact.

[1377] The processing flow will be explained below.

[1378] Step 1:

[1379] The user launches the application and enters basic information such as name, email address, and password on the new registration screen.

[1380] Step 2:

[1381] The device receives basic information and sends it to the server, which stores it in a database and generates a user ID.

[1382] Step 3:

[1383] The user then enters details such as their fashion preferences (e.g., casual style), size (e.g., medium), color preferences (e.g., blue, white), and past purchasing history.

[1384] Step 4:

[1385] The device collects these details and sends them to a server, which stores them in a database.

[1386] Step 5:

[1387] The emotion engine is activated and analyzes emotional data from the user's facial expressions, voice, and text input. For example, it analyzes facial expressions through the camera and recognizes emotions such as "happy" or "interested."

[1388] Step 6:

[1389] The device acquires the emotion data and sends it to the server, which stores it in a database and adds it to the profile information.

[1390] Step 7:

[1391] The server periodically accesses an external API that provides fashion trend information and collects the latest fashion trend data, including popular items, design patterns, colors, etc.

[1392] Step 8:

[1393] The server stores the collected trend information in a database for later analysis.

[1394] Step 9:

[1395] The server uses an AI algorithm to analyze the user's profile information, emotional data, and trend information to generate optimal product designs for the user. For example, if a user expresses the emotion "happy," the server will take that emotional data into account to generate particularly eye-catching designs and products that suit their preferences.

[1396] Step 10:

[1397] The server creates a list of suggested products based on the generated product design and transmits this list to the user's terminal.

[1398] Step 11:

[1399] The user can check the list of suggested products on the device, view the details, select the product they like, and press the "Purchase" button.

[1400] Step 12:

[1401] To confirm the purchase, the terminal sends the order information (item ID, size, color) of the selected item to the server.

[1402] Step 13:

[1403] The server sends the order information to the partner factory and issues manufacturing instructions. The order information includes detailed data (design, size, color) required for manufacturing.

[1404] Step 14:

[1405] The partner factory will manufacture the product based on the order information received from the server. For example, it will manufacture a "blue checked casual shirt."

[1406] Step 15:

[1407] The partner factory will then arrange for the completed product to be delivered directly to the user.

[1408] Step 16:

[1409] The server notifies the user's device of delivery start and completion information, allowing the user to check the delivery status of the product.

[1410] This series of steps allows users to seamlessly obtain personalized apparel products based on their preferences and emotions, while helping manufacturers reduce wasted inventory and lighten their environmental impact.

[1411] Example 2

[1412] 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."

[1413] Conventional apparel product recommendation systems only use a user's basic profile information and purchase history to suggest products, making it difficult to reflect the user's momentary emotions or current trends. This results in low personalization accuracy and fails to increase user satisfaction. Furthermore, as a result, users often lose interest in the suggested products, leading to a decline in purchasing motivation.

[1414] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1415] In this invention, the server includes means for collecting user profile information and storing it in a database, means for periodically obtaining fashion trend information from around the world and storing it in a database, means for collecting user emotion information using an emotion engine and storing it in a database, means for analyzing the user's profile information, emotion information, and trend information and generating personalized product designs, means for proposing the generated product designs to the user, means for sending order information to a manufacturing facility to manufacture the products selected by the user, and means for delivering the manufactured products to the user. This enables advanced personalization based on emotion data and the latest trend information in addition to the user's basic profile information, thereby increasing the user's desire to purchase.

[1416] "User Profile Information" means basic information provided by a User, including fashion preferences, size and color preferences, and past purchasing history.

[1417] "Fashion trend information" refers to the latest trends in the fashion industry that are regularly acquired, including popular items, colors, and design patterns.

[1418] The "emotion engine" is a system for recognizing user emotions and has the ability to collect emotional data through facial expression recognition, voice analysis, and text analysis.

[1419] "Personalized product design" refers to a product design optimized for a user, generated based on the user's profile information, emotional information, and fashion trend information.

[1420] A "suggested product list" is a list of products generated based on personalized product designs and suggested to users.

[1421] "Order Information" means detailed information required for manufacturing the product selected by the user, including product ID, size, color, etc.

[1422] "Manufacturing facility" refers to a factory or production line that produces the product selected by the user.

[1423] The present invention relates to a system that integrates a user's profile information, emotion information, and fashion trend information to recommend personalized apparel products. The system includes a means for collecting user profile information and storing it in a database, a means for collecting user emotion information using an emotion engine, and a means for acquiring fashion trend information from around the world and storing it in a database. The system also includes a means for analyzing this information to generate personalized product designs and recommend them to the user. The system also includes a means for sending order information to a manufacturing facility to manufacture the products selected by the user, and a means for delivering the manufactured products to the user.

[1424] First, a user launches the application and registers, which collects user profile information. The user enters information such as their name, email address, password, fashion preferences, size and color preferences, and past purchase history, and the device sends this information to the server. The server stores the received information in a database and generates a user ID.

[1425] Next, the emotion engine collects user emotion data through facial expression recognition, voice analysis, and text analysis. When a user views or interacts with a product, the device automatically collects emotion data and sends it to the server. The emotion engine analyzes emotions using open source libraries (e.g., OpenCV) and voice analysis APIs (e.g., Google Cloud Speech-to-Text API) and sends the results to the server. The server stores the data in a database.

[1426] The server also periodically uses external APIs (e.g., FashionAPI) to collect the latest fashion trend information and stores it in the database. Trend information includes popular items, design patterns, colors, etc., and is used for analysis.

[1427] To make personalized product suggestions, the server integrates and analyzes the user's profile information, emotional data, and trend information, and uses AI algorithms (e.g., TensorFlow or PyTorch) to generate product designs that are optimal for the user. The generated product designs are sent to the user's device as a list of suggested products. For example, if a user is looking for a "casual blue shirt for spring," and the server recognizes that the user's emotion is "happy," it will suggest a casual blue shirt based on that information.

[1428] The user checks the list of suggested products on their device, selects their favorite product, and presses the "Purchase" button. The order information (product ID, size, color) is sent to the server, which then sends the order information to a partner factory and instructs it to manufacture the product. The manufacturing facility manufactures the product based on the order information and delivers the completed product directly to the user. The server then notifies the user of the delivery information on their device, allowing the user to check the delivery status.

[1429] As an example of this system, consider the following prompt: "I'm looking for a casual blue shirt for spring. I'm happy shopping. Please suggest products that fit this criteria."

[1430] This allows users to efficiently find the best products based on their profile information, emotions, and the latest fashion trends, while manufacturers can reduce unnecessary inventory and lighten their environmental impact.

[1431] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1432] Step 1: User registration and information collection

[1433] The user starts the application and enters information such as name, email address, password, fashion preferences, size and color preferences, and past purchase history on the new registration screen. This information is the input data.

[1434] The device serializes the input information and sends it to the server in JSON format via an HTTP POST request. This request is the input, and the user ID received in response is the output.

[1435] The server parses the received JSON data and stores each field (such as name, email address, and fashion preferences) in a database. The server generates a new user ID and returns it to the device as an HTTP response. The device notifies the user.

[1436] Step 2: Collecting emotional information

[1437] Users use the application to browse products.

[1438] The emotion engine recognizes the user's facial expressions using a camera and analyzes their voice using a microphone. It also analyzes text input. This is the input data.

[1439] The device collects emotion data in real time and sends it to the server in JSON format. The emotion data is the input, and the database records are the output.

[1440] The server stores the received emotion data in a database, for example, as a record with fields such as "date and time," "emotion label," and "emotion intensity."

[1441] Step 3: Obtaining trend information

[1442] The server runs a cron job periodically every day and calls an external API (e.g., FashionAPI) to obtain the latest fashion trend information. The obtained trend information is the input data.

[1443] The server stores this trend information in a database in JSON format. The stored trend information is the output. The database contains fields such as item name, design pattern, color, and popularity.

[1444] Step 4: Personalized product recommendations

[1445] The server integrates user profile information, sentiment data, and trend information, and analyzes them using AI algorithms (e.g., TensorFlow, PyTorch). This is the input data.

[1446] As a result of the analysis, the server generates the optimal product design for the user. The generated product design is the output and is sent to the terminal as a list of suggested products. For example, a blue casual shirt may be suggested.

[1447] Step 5: Select and order

[1448] The user checks the list of suggested products on the terminal, selects the product that suits their taste, and presses the "Purchase" button. This is the input data.

[1449] The terminal sends JSON data including the selected product information (product ID, size, color) to the server. The order information is output.

[1450] The server stores the received order information in a database and updates the order status to "Not yet manufactured."

[1451] Step 6: Manufacturing and Delivery

[1452] The server sends the order ID and detailed information (design, size, color) in JSON format to the partner factory. This is the input data.

[1453] The partner factory manufactures the product based on the order information, for example, by cutting the fabric using a CNC cutting machine and sewing it. The manufactured product is the output.

[1454] The partner factory hands over the completed product to the delivery company and notifies the server of the delivery information.

[1455] The server notifies the user of delivery information (e.g., tracking number) on the user's device, allowing the user to check the delivery status.

[1456] This allows users to efficiently find the best products based on their profile information, emotions, and the latest fashion trends, while manufacturers can reduce unnecessary inventory and lighten their environmental impact.

[1457] (Application example 2)

[1458] 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."

[1459] Conventional apparel recommendation systems suggest products based on user profile information and trend information, but they have the problem of not being able to make optimal personalized recommendations because they do not take the user's emotions into account. Therefore, there is a need to provide a system that can make personalized apparel recommendations that correspond to the user's actual emotional state.

[1460] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1461] In this invention, the server includes means for collecting user profile information and storing it in a database, means for periodically obtaining fashion trend information from around the world and storing it in a database, means for analyzing the user profile information and trend information to generate personalized product designs, means for proposing the generated product designs to the user, means for collecting user emotion data using an emotion engine that recognizes the user's emotions and adding it to the profile information, means for sending order information to a manufacturing facility to manufacture the product selected by the user, and means for delivering the manufactured product to the user. This enables more sophisticated personalized proposals based on the user's emotional state.

[1462] "User Profile Information" refers to basic data about a User, such as the User's fashion preferences, size and color preferences, and past purchasing history.

[1463] "Trend information" refers to data that includes information on fashion items, design patterns, colors, etc. that are popular within a certain period of time.

[1464] "Emotion engine" refers to a system that recognizes and analyzes a user's emotions through facial expression recognition, voice analysis, and text analysis.

[1465] "Personalized product design" refers to apparel product designs optimized for specific users, generated based on the user's profile information, trend information, and emotional data.

[1466] "Manufacturing facility" refers to a facility that actually manufactures apparel products based on order information from users.

[1467] "Database" means an electronic repository for storing data used by the System, such as user profile information, trend information, sentiment data, and order information.

[1468] The system for implementing this invention includes the following main hardware and software: The hardware used includes smart glasses, a camera, and a server, and the software used includes Python, OpenCV (an image processing library), and Requests (an HTTP request library).

[1469] This system uses user profile information, trend information, and emotion data to provide personalized product recommendations. The process is explained below.

[1470] First, the user puts on the smart glasses and activates the system. The camera in the smart glasses captures an image of the user's face. This image data is analyzed by the emotion engine to recognize the user's emotional state. The emotion engine combines facial, voice, and text analysis to obtain emotion data.

[1471] The server then periodically retrieves information on fashion trends from around the world via API and stores it in a database, including information on popular items, design patterns, colors, etc. This trend information is combined with user profile information and analyzed to generate personalized product designs.

[1472] The generated product designs are then presented to the user in real time, displayed on the smart glasses display. The user selects the desired product from the list of suggested products and completes the purchase process. This selection is then sent to the server and transmitted to the manufacturing facility.

[1473] The manufacturing facility manufactures the product based on the received order information, and once production is complete, the product is delivered directly to the user. The server manages delivery information and allows the user to check the delivery status of the product.

[1474] This system allows users to seamlessly select and purchase apparel that best suits their preferences and emotional state, while also helping manufacturing facilities reduce wasteful inventory and contribute to reducing environmental impact.

[1475] For example, consider the following prompt:

[1476] "If a user who has a casual style and likes blue or white clothing is wearing smart glasses, and emotion recognition reveals that the user looks happy, the glasses will suggest products based on the latest fashion trends, such as a casual blue shirt or white pants."

[1477] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1478] Step 1:

[1479] The user puts on the smart glasses and starts the system.

[1480] Input: None

[1481] Output: The smart glasses are turned on and the camera is ready to use.

[1482] Specific operation: The user turns on the smart glasses. The system automatically starts the camera.

[1483] Step 2:

[1484] The camera on the device (smart glasses) captures an image of the user's face.

[1485] Input: An image of the user's face

[1486] Output: Captured image (JPEG format)

[1487] Specific operation: The camera captures the user's face, takes a picture, and temporarily stores the image data in memory.

[1488] Step 3:

[1489] The terminal transmits the captured image data to the server.

[1490] Input: Captured image (JPEG format)

[1491] Output: Image data sent

[1492] Specific operation: The device uploads the stored image data to a server via the Internet.

[1493] Step 4:

[1494] The server passes the received image data to an emotion engine and analyzes the user's emotion.

[1495] Input: Received image data

[1496] Output: User emotion data (e.g. happy, interesting)

[1497] Specific operation: The emotion engine analyzes image data and recognizes the user's emotional state from their facial expressions.

[1498] Step 5:

[1499] The server acquires fashion trend information from around the world and stores it in a database.

[1500] Input: Trend information acquisition request

[1501] Output: Trend information (e.g. popular items, colors, design patterns)

[1502] Specific operation: The server periodically obtains the latest fashion trend information using an external API and stores it in a database.

[1503] Step 6:

[1504] The server analyzes the user's profile information, emotion data, and trend information to generate personalized product designs.

[1505] Input: User profile information, sentiment data, trend information

[1506] Output: Personalized product design (e.g., blue checked casual shirt)

[1507] How it works: The server uses AI algorithms to analyze this data and generate optimal product designs for the user.

[1508] Step 7:

[1509] The server sends the generated product design to the user's device (smart glasses).

[1510] Input: Generated product design

[1511] Output: Submitted product design

[1512] Specific operation: The server sends the product design to the terminal via the Internet and displays it on the smart glasses display.

[1513] Step 8:

[1514] The user selects the desired product from the list of suggested products and completes the purchase procedure.

[1515] Input: Suggested product list

[1516] Output: Selected product information (e.g. product ID, size, color)

[1517] Specific operation: The user operates the interface of the smart glasses to select the desired product and press the "Purchase" button.

[1518] Step 9:

[1519] The terminal transmits the product information selected by the user to the server.

[1520] Input: Selected product information

[1521] Output: Order information sent

[1522] Specific operation: The terminal uploads the selected product information to the server via the Internet.

[1523] Step 10:

[1524] The server transmits the order information to a manufacturing facility and issues instructions for manufacturing the goods.

[1525] Input: Order Information

[1526] Output: Manufacturing instructions

[1527] Specific operations: The server sends manufacturing instructions to the manufacturing facility based on the received order information.

[1528] Step 11:

[1529] The manufacturing facility manufactures the goods based on the received order information and delivers them to the user.

[1530] Input: Order Information

[1531] Output: Manufactured goods

[1532] What happens: The manufacturing facility produces the goods based on the order, processes the delivery, and delivers the product directly to the user.

[1533] Step 12:

[1534] The server notifies the user's terminal of the delivery information.

[1535] Input:Shipping information

[1536] Output: Delivery status notification

[1537] Specific operation: The server sends the delivery progress status to the user's terminal via the Internet, allowing the user to check the delivery status.

[1538] 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.

[1539] 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.

[1540] 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.

[1541] [Fourth embodiment]

[1542] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1543] 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.

[1544] 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).

[1545] 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.

[1546] 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.

[1547] 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).

[1548] 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.

[1549] 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.

[1550] 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.

[1551] 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.

[1552] 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.

[1553] 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.

[1554] 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."

[1555] This invention relates to a system that collects user profile information, combines it with trend information from around the world, and proposes personalized apparel products. This system utilizes AI to generate and propose products based on the user's preferences, and then manufactures and delivers the ordered products.

[1556] System Overview

[1557] The system includes the following main components:

[1558] 1. Terminal: A device where users input information, receive product suggestions, and place orders. This can be a smartphone or a PC.

[1559] 2. Server: A centralized system that collects and analyzes information sent by users and trend information, and generates personalized product recommendations.

[1560] 3. Database: Installed on the server, it stores user profile information, trend information, and order information.

[1561] 4. Affiliated Factory: A facility that actually manufactures apparel products based on the order information sent from the server and delivers them to users.

[1562] Processing flow

[1563] 1. User Registration and Information Collection

[1564] A user launches the application and registers, entering basic information such as name, email address, and password.

[1565] The device receives this information and sends it to a server, along with other details such as the user's fashion preferences, size, color, and past purchase history.

[1566] The server stores the received information in a database and generates a user ID to associate with the profile information.

[1567] 2. Obtaining trend information

[1568] The server periodically collects fashion trend information from around the world through API, including popular items, design patterns, colors, etc.

[1569] The collected trend information is stored in a database and used for analysis.

[1570] 3. Personalized product recommendations

[1571] The server analyzes the user's profile information and trend information, and uses AI algorithms to generate personalized product designs for each user.

[1572] A list of suggested products is created based on the generated product design and sent to the terminal.

[1573] 4. Product Selection and Ordering

[1574] The user can view the list of suggested products on the device, select the product they like, and then press the "Purchase" button to confirm the order.

[1575] The terminal transmits order information for the selected product to the server.

[1576] 5. Manufacturing and Delivery

[1577] The server sends the order information to the partner factory, which includes the detailed data (design, size, color) required for manufacturing.

[1578] The partner factory will manufacture the product according to the specified design, and once production is complete, the product will be shipped directly to the customer.

[1579] The server notifies the terminal of the start and completion of delivery.

[1580] Specific examples

[1581] For example, if you're looking for a casual shirt for spring:

[1582] 1. User Registration and Information Collection

[1583] The user enters the following information: "Favorite style: casual," "Favorite colors: blue, white," and "Size: M."

[1584] The terminal sends this information to the server, which stores it in a database.

[1585] 2. Obtaining trend information

[1586] The server obtains the latest fashion information via an API and determines that "checkered shirts for spring" are in fashion.

[1587] 3. Personalized product recommendations

[1588] Based on the user's preferences and the latest trends, the server uses AI to generate a "blue checked casual shirt" and adds it to the list of suggested products.

[1589] This list is sent to the user's device.

[1590] 4. Product Selection and Ordering

[1591] The user checks the suggested products, selects the "blue checked casual shirt," and presses the purchase button.

[1592] The terminal transmits the order information to the server.

[1593] 5. Manufacturing and Delivery

[1594] The server sends the order information to a partner factory, which then produces the shirt, which is then shipped directly to the user.

[1595] The server notifies the user's terminal of the delivery information.

[1596] This system allows users to smoothly obtain apparel products that match their preferences and trends, and enables apparel manufacturers to reduce unnecessary inventory and lighten the burden on the environment.

[1597] The processing flow will be explained below.

[1598] Step 1:

[1599] The user launches the application and enters basic information such as name, email address, and password on the new registration screen.

[1600] Step 2:

[1601] The device receives this basic information and sends it to the server, which stores it in a database and generates a user ID.

[1602] Step 3:

[1603] The user then inputs their fashion preferences (e.g., casual style), size (e.g., medium size), color preferences (e.g., blue, white), and past purchasing history.

[1604] Step 4:

[1605] The device collects these details and sends them to a server, which stores them in a database.

[1606] Step 5:

[1607] The server periodically accesses an external API that provides fashion trend information and collects the latest fashion trend data, including popular items, design patterns, colors, etc.

[1608] Step 6:

[1609] The server stores the collected trend information in a database and prepares it for later analysis.

[1610] Step 7:

[1611] The server uses an AI algorithm to analyze the user's profile information and the latest trends to generate the optimal product design for the user. For example, it generates a "blue checked casual shirt" based on the user's preferred combination of blue and the latest checked pattern.

[1612] Step 8:

[1613] The server creates a list of suggested products based on the generated product design and transmits this list to the user's terminal.

[1614] Step 9:

[1615] Users can view the list of suggested products on their device, view details, select the product they like, and press the "Purchase" button.

[1616] Step 10:

[1617] To confirm the purchase, the terminal sends the order information (item ID, size, color) of the selected item to the server.

[1618] Step 11:

[1619] The server sends the order information to the partner factory and issues manufacturing instructions. The order information includes detailed data required for manufacturing (design, size, color).

[1620] Step 12:

[1621] The partner factory manufactures the product based on the order information received from the server. For example, the partner factory manufactures a "blue checked casual shirt."

[1622] Step 13:

[1623] The partner factory will then arrange for the completed product to be delivered directly to the user.

[1624] Step 14:

[1625] The server notifies the user's device of delivery start and completion information, allowing the user to check the delivery status of the product.

[1626] This series of steps allows users to obtain original products that are best suited to them, while allowing manufacturers to maintain appropriate production volumes and reduce environmental impact.

[1627] Example 1

[1628] 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."

[1629] Conventional apparel product recommendation systems have difficulty in proposing personalized products that reflect the individual preferences and latest trends of users. Furthermore, they lacked efficient methods for quickly manufacturing and delivering the products selected by users. As a result, it was difficult to increase user satisfaction, and manufacturers also faced the challenge of increasing the burden of inventory management.

[1630] 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.

[1631] In this invention, the server includes means for collecting user profile information and storing it in a database, means for periodically obtaining fashion trend information from around the world and storing it in a database, means for analyzing user profile information and trend information and generating personalized product designs using a generative AI model, means for sending the generated product designs to a terminal and proposing them to the user, means for sending order information for products selected by the user on the terminal to an affiliated manufacturing facility, means for delivering the manufactured products to the user, and means for notifying the user's terminal of manufacturing and delivery information. This enables users to efficiently propose, select, and order personalized products based on their preferences and the latest trends, and also enables manufacturers to achieve efficient production and delivery, thereby reducing the burden of inventory management.

[1632] "User" means an entity that utilizes the System to provide profile information and receive personalized product offers.

[1633] "Profile Information" refers to general information about a user, such as the user's fashion preferences, size and color preferences, and past purchasing history.

[1634] "Fashion trend information" refers to information about fashion that is popular around the world at each time and region, such as popular items, colors, and design patterns.

[1635] "Generative AI model" refers to an artificial intelligence model that analyzes user profile information and fashion trend information to automatically generate personalized product designs.

[1636] "Affiliated manufacturing facility" refers to a facility that manufactures the apparel products selected by the user based on the order information sent from the server.

[1637] "Terminal" refers to the device used by a user to enter information, receive product suggestions, and place an order, specifically a smartphone or computer.

[1638] "Server" refers to the centralized system that collects and analyzes information sent by users and trend information, and generates and manages personalized product proposals.

[1639] "Database" refers to an information management system installed on a server for storing user profile information, trend information, and order information.

[1640] This invention relates to a system that collects user profile information, combines it with global fashion trend information, and proposes personalized apparel products. The system utilizes a generative AI model to generate and propose products based on the user's preferences, and then manufactures and delivers the ordered products.

[1641] System Overview

[1642] The system includes the following major components:

[1643] 1. Terminal: A device on which a user enters profile information, receives product suggestions, and places an order. Specifically, a smartphone or PC is used.

[1644] 2. Server: A centralized system that collects and analyzes information sent by users and trend information, and generates personalized product recommendations.

[1645] 3. Database: Installed on the server, it stores user profile information, trend information, and order information.

[1646] 4. Affiliated manufacturing facility: This is the facility that actually manufactures the apparel products based on the order information sent from the server and delivers them to the user.

[1647] Hardware and software used

[1648] Devices: smartphones, computers

[1649] Server: Centralized system

[1650] Database: A cloud database for managing user information, trend information, and order information.

[1651] Generative AI model: An algorithm that analyzes user preferences and trend information to generate personalized product designs

[1652] Processing flow

[1653] 1. User Registration and Information Collection

[1654] A user launches the application and registers, entering basic information such as name, email address, and password.

[1655] The device receives this information and sends it to a server, along with other details such as the user's fashion preferences, size, color, and past purchase history.

[1656] The server stores the received information in a database and generates a user ID to associate with the profile information.

[1657] 2. Obtaining trend information

[1658] The server periodically collects the latest fashion trend information from around the world through API, specifically information on popular items, design patterns, colors, etc.

[1659] The server stores the collected trend information in a database and tags it for analysis.

[1660] 3. Personalized product recommendations

[1661] The server uses the generative AI model to analyze the user's profile information and trend information. For example, it inputs a prompt statement such as "Suggest spring casual shirts for users who like blue."

[1662] The generative AI model uses user preferences and trend information to generate personalized product designs.

[1663] The server adds the generated product design to a list of proposed products and transmits this list to the terminal.

[1664] 4. Product Selection and Ordering

[1665] The user can check the list of suggested products on the device, select the desired product, and press the "Purchase" button to confirm the order.

[1666] The terminal transmits order information for the selected product to the server.

[1667] 5. Manufacturing and Delivery

[1668] The server sends the order information to the partner manufacturing facility, including the details required for manufacturing (design, size, color).

[1669] The partner manufacturing facility will produce the product according to the specified design and ship the product directly to the user once production is complete.

[1670] The server notifies the user's device of the start and completion of delivery, for example, by sending a notification that the product has been shipped.

[1671] Specific examples

[1672] For example, if you're looking for a casual shirt for spring:

[1673] 1. The user enters the following profile information: "Favorite style: casual," "Favorite colors: blue, white," and "Size: M."

[1674] 2. The device sends this information to the server, which stores it in a database.

[1675] 3. The server retrieves the latest fashion information via the API and determines that "checkered shirts for spring" are in fashion.

[1676] 4. Based on the user's preferences and the latest trends, the server uses a generative AI model to generate a "blue checked casual shirt" and adds it to the list of suggested products.

[1677] 5. Send this list to the user's device.

[1678] 6. The user checks the suggested products, selects the "blue checked casual shirt," and presses the purchase button.

[1679] 7. The terminal sends the order information to the server.

[1680] 8. The server sends the order information to a partner manufacturing facility, which produces the shirt. Once production is complete, the shirt is shipped directly to the customer.

[1681] 9. The server notifies the user's terminal of the delivery information.

[1682] This system allows users to smoothly obtain apparel products that match their preferences and trends, and enables apparel manufacturers to reduce unnecessary inventory and lighten the burden on the environment.

[1683] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1684] Step 1: Collect user information

[1685] Subject: User

[1686] How it works: The user launches the dedicated application and clicks "Sign Up." After entering their name, email address, and password, they click "Next."

[1687] Input: User's name, email address, password

[1688] Output: Sends the entered basic information to the terminal

[1689] Subject: Terminal

[1690] How it works: The device encrypts the information entered and sends it to the server using a secure communication protocol (HTTPS).

[1691] Input: Encrypted user basic information

[1692] Output: Basic information sent to the server

[1693] Subject: User

[1694] How it works: Next, the user enters their "favorite style," "favorite color," "size," and "past purchase history."

[1695] Input: Fashion preferences, size, color preferences, past purchase history

[1696] Output: Sends the entered details to the terminal

[1697] Subject: Terminal

[1698] How it works: The device re-encrypts these details and sends them to the server.

[1699] Input: Encrypted details

[1700] Output: Details sent to the server

[1701] Subject: Server

[1702] How it works: The server stores the submitted information in a database and generates a user ID to associate with the profile information.

[1703] Input: Basic information and detailed information received

[1704] Data processing / data calculation: Save to database, generate user ID, associate with profile information

[1705] Output: Profile information stored in the database

[1706] Step 2: Obtaining trend information

[1707] Subject: Server

[1708] How it works: The server uses the fashion API to periodically obtain the latest fashion trend information from around the world, specifically information on popular items, design patterns, colors, etc.

[1709] Input: Fashion trend information obtained through API

[1710] Data processing / data calculation: Shaping and tagging trend information

[1711] Output: Trend information stored in a database

[1712] Step 3: Generate personalized product suggestions

[1713] Subject: Server

[1714] How it works: The server inputs the user's profile information and trend information into the generative AI model for analysis. A prompt such as "Suggest a spring casual shirt that is recommended for a user who likes the color blue" is used.

[1715] Input: User profile information, fashion trend information, prompt text

[1716] Data processing / data calculation: Analysis by generative AI models and generation of personalized product designs

[1717] Output: Suggested product list

[1718] Subject: Server

[1719] Operation: The generated product design is added to a list of suggested products and this list is sent to the user's device.

[1720] Input: Generated product design

[1721] Output: Suggested product list sent to the terminal

[1722] Step 4: Select and order

[1723] Subject: User

[1724] How it works: The user reviews the list of suggested products on their device, selects the product they want, for example, "Blue Checkered Casual Shirt," and clicks the "Buy" button.

[1725] Input: User selected product

[1726] Output: Order information for selected items

[1727] Subject: Terminal

[1728] How it works: The terminal encrypts the order information and sends it to the server.

[1729] Input: Encrypted order information

[1730] Output: Order information sent to the server

[1731] Step 5: Manufacturing and Delivery

[1732] Subject: Server

[1733] Operation: The server sends order information to a partner manufacturing facility, including details needed for manufacturing (design, size, color).

[1734] Input: Detailed data based on order information

[1735] Data processing / data calculation: Formatting and sending order information

[1736] Output: Order information sent to partner manufacturing facility

[1737] Subject: Affiliated manufacturing facility

[1738] How it works: The partner manufacturing facility uses the information received to produce the product according to the specified design.

[1739] Input: Detailed data of the specified design

[1740] Output: Manufactured goods

[1741] Subject: Affiliated manufacturing facility

[1742] What it does: Once production is complete, the product is shipped directly to the customer.

[1743] Input: Manufactured goods

[1744] Output: Item delivered to user

[1745] Subject: Server

[1746] Operation: The server notifies the user's device of the start and completion of delivery, for example, sending a notification that "the product has been shipped."

[1747] Input: Delivery start and completion information

[1748] Output: Notification sent to the user's device

[1749] (Application example 1)

[1750] 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."

[1751] Today's consumers demand apparel that matches their personalities and preferences, and the fashion industry must respond quickly to frequently changing trends. However, meeting these needs requires effectively analyzing large amounts of data and proposing the right products for each user, which is a challenge. Furthermore, an efficient system is needed to collect relevant trend information and quickly manufacture and deliver personalized products.

[1752] 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.

[1753] In this invention, the server includes means for collecting user profile information and storing it in a database, means for periodically obtaining fashion trend information from around the world and storing it in a database, means for analyzing the user profile information and trend information and generating personalized product designs, means for proposing the generated product designs to the user, means for sending order information to a manufacturing facility to manufacture the products selected by the user, means for delivering the manufactured products to the user, means including an algorithm for recommending products based on the user's preferences and the latest trend information, means including a mobile terminal on which an application for proposing personalized fashion items to the user is installed, and means for obtaining trend information from an external API. This makes it possible to effectively combine user preferences with the latest trend information to quickly propose, manufacture, and deliver personalized products.

[1754] "User profile information" refers to individual information such as a user's fashion preferences, size and color preferences, and past purchasing history.

[1755] "Database" refers to a recording medium for storing collected user profile information and fashion trend information from around the world.

[1756] "Trend information" refers to information regarding popular fashion items, colors, and design patterns that is regularly obtained from around the world.

[1757] "Product design" refers to personalized apparel product designs generated by analyzing user profile information and trend information.

[1758] "Proposal" refers to the act of presenting the generated product design to the user.

[1759] "Order Information" refers to the detailed data (e.g., design, size, color, etc.) required to manufacture the product selected by the User.

[1760] "Manufacturing facility" refers to a factory or manufacturing base that actually produces apparel products based on order information received from the server.

[1761] "Delivery" refers to the act of delivering manufactured products to a location designated by the user.

[1762] "Algorithm" refers to the calculation methods and analytical means used to recommend products based on user preferences and the latest trend information.

[1763] "Mobile device" refers to an information terminal that can be carried by a user, such as a smartphone or tablet.

[1764] "External API" refers to a programmatic interface that provides an access point for obtaining trend information from other systems or databases.

[1765] "Personalization" refers to customizing products and services to suit individual user characteristics and preferences.

[1766] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will now be described in detail with reference to the accompanying drawings.

[1767] Overall system picture

[1768] This invention is a system that collects user profile information and stores it in a database. It also periodically acquires and stores fashion trend information from around the world in the same database. It uses artificial intelligence (AI) to analyze the user's profile information and trend information and generate personalized product designs. The generated product designs are then presented to the user via their mobile device (such as a smartphone or tablet).

[1769] Hardware and Software Configuration

[1770] The server is equipped with a database, AI algorithms, and software that executes external API calls. The database that stores user profile information and trend information uses, for example, MySQL or PostgreSQL. Trend information is retrieved from external APIs using, for example, the Requests library. The AI ​​algorithm is a calculation method for recommending products based on user preferences and trend information, and is implemented using, for example, Scikit-learn or TensorFlow.

[1771] The application is installed on the user's device, and the backend of the application is built using the Flask framework (or Django, etc.) to communicate with the server. The user interface is built using HTML, CSS, and JavaScript.

[1772] System operation flow

[1773] 1. User Registration and Information Collection

[1774] A user launches an application on their mobile device and registers, entering information such as their name, email address, password, fashion preferences, size and color preferences, and past purchase history. The application receives this information and sends it to the server, which stores it in a database and generates a user ID that is associated with their profile information.

[1775] 2. Obtaining trend information

[1776] The server periodically collects fashion trend information from around the world through external APIs, including popular items, design patterns, colors, etc. The collected trend information is stored in a database for later analysis.

[1777] 3. Personalized product recommendations

[1778] The server analyzes the user's profile information and trend information, and uses an AI algorithm to generate personalized product designs. Based on the generated product designs, it creates a list of suggested products and sends them to the user's device.

[1779] 4. Product Selection and Ordering

[1780] The user checks the list of suggested products on the terminal and selects the product they like. The order information is then confirmed and sent to the server to purchase the selected product. The server then sends the received order information to the manufacturing facility, providing detailed data (design, size, color, etc.).

[1781] 5. Manufacturing and Delivery

[1782] The manufacturing facility manufactures the product based on the order information sent from the server. Once manufacturing is complete, the manufacturing facility delivers the product to the user. The server notifies the user's device of the start and completion of delivery.

[1783] Examples and prompts

[1784] For example, if you're looking for a casual shirt for spring:

[1785] 1. User Registration and Information Collection

[1786] The user enters the following information: "Favorite style: casual," "Favorite colors: blue, white," and "Size: M."

[1787] The application sends this information to the server, which stores it in a database.

[1788] 2. Obtaining trend information

[1789] The server obtains the latest fashion information via an external API and determines that "checkered shirts for spring" are in fashion.

[1790] 3. Personalized product recommendations

[1791] Based on the user's preferences and the latest trends, the server uses AI to generate a "blue checked casual shirt" and adds it to the list of suggested products.

[1792] This list is sent to the user's device.

[1793] 4. Product Selection and Ordering

[1794] The user checks the suggested products, selects the "blue checked casual shirt," and presses the purchase button.

[1795] The application sends the order information to the server.

[1796] 5. Manufacturing and Delivery

[1797] The server sends the order information to a manufacturing facility, which produces the shirt and then ships it directly to the user.

[1798] The server notifies the user's terminal of the delivery information.

[1799] Example prompt sentence:

[1800] When a user prefers casual shirts, suggest the latest items based on current trends.

[1801] User Preferences: "Casual shirt", "Size M", "Blue"

[1802] Latest Trends: {"item": "blue plaid shirt", "category": "shirt"}

[1803] Result: Recommends "Blue Checkered Casual Shirt" to the user.

[1804] This system allows users to smoothly obtain apparel products that match their preferences and trends, and enables apparel manufacturers to reduce unnecessary inventory and lighten the burden on the environment.

[1805] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1806] Step 1:

[1807] User registration and information collection

[1808] Input: The user enters their name, email address, password, fashion preferences, size and color preferences, and past purchase history into their mobile device.

[1809] How it works: The device sends the entered information to the server, which parses it and stores it in a database. It also generates a user ID and associates it with the user's profile information.

[1810] Output: A message that user registration is complete is displayed on the terminal.

[1811] Step 2:

[1812] Obtaining trend information

[1813] Input: The server sends a request to get the latest fashion trend information from an external API.

[1814] How it works: The Trends API provides data on currently popular items, colors, and design patterns to a server that retrieves this information and stores it in a database.

[1815] Output: The latest trend information is stored in a database.

[1816] Step 3:

[1817] Generate personalized product recommendations

[1818] Input: User profile and trend information stored in a database.

[1819] How it works: The server uses AI algorithms to analyze user profile information and trend information. Based on this analysis, it generates personalized product designs for users.

[1820] Output: A list of generated product designs is generated and sent to the user's device.

[1821] Step 4:

[1822] Product selection and ordering

[1823] Input: Product information selected by the user from the suggested product list displayed on the device.

[1824] How it works: The user confirms the selected items on the terminal and presses the purchase button. The terminal sends the order information for the selected items to the server. The server receives the order information and sends it to the manufacturing facility.

[1825] Output: An order confirmation message is displayed on the user's device. Detailed order information (design, size, color, etc.) is sent to the manufacturing facility.

[1826] Step 5:

[1827] Manufacturing and Delivery

[1828] Input: Order information sent from the server to the manufacturing facility.

[1829] Operation: The manufacturing facility manufactures the product based on the order information received from the server. After the manufacturing is completed, the manufacturing facility delivers the product to the user. The server tracks the delivery status and notifies the user's device when the delivery has started and completed.

[1830] Output: The manufactured product is delivered to the address specified by the user. The delivery status is notified to the terminal.

[1831] By following these detailed, step-by-step processing steps, personalized product suggestions that combine the user's preferences with the latest trend information are realized.

[1832] 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.

[1833] The present invention relates to a system that uses an emotion engine to recognize user emotions in addition to user profile information and trend information, and then proposes personalized apparel products. This system is characterized by incorporating user emotion data to achieve more advanced personalization.

[1834] System Overview

[1835] The system includes the following main components:

[1836] 1. Terminal: A device where users input information, receive product suggestions, and place orders. This can be a smartphone or a PC.

[1837] 2. Server: A centralized system that collects and analyzes information sent by users and trend information, and generates personalized product recommendations.

[1838] 3. Database: Installed on the server, it stores user profile information, trend information, and order information.

[1839] 4. Affiliated Factory: A facility that actually manufactures apparel products based on the order information sent from the server and delivers them to users.

[1840] 5. Emotion Engine: A system for recognizing user emotions and adding and saving that information to profile information.

[1841] Processing flow

[1842] 1. User Registration and Information Collection

[1843] A user launches the application and registers, entering basic information such as name, email address, and password.

[1844] The device receives this information and sends it to the server, which stores it in a database and generates a user ID.

[1845] The user then enters details such as their fashion preferences, size and color preferences, and past purchasing history, which the device then transmits to the server.

[1846] The server stores this information in a database.

[1847] 2. Collecting emotional information

[1848] The emotion engine recognizes the user's emotions through facial expression recognition, voice analysis, and text analysis.

[1849] The device automatically collects emotional data while the user is operating the device or browsing products, and sends it to the server.

[1850] 3. Obtaining trend information

[1851] The server periodically collects fashion trend information from around the world through API, including popular items, design patterns, colors, etc.

[1852] The collected trend information is stored in a database and used for analysis.

[1853] 4. Personalized product recommendations

[1854] The server uses AI algorithms to analyze user profile information, emotional data, and trend information to generate product designs that are optimal for each user.

[1855] For example, if a user expresses the emotion "happy" while shopping, the system will take that emotional data into consideration and suggest products with particularly eye-catching designs or that suit their preferences.

[1856] The server creates a list of suggested products based on the generated product design and transmits this list to the user's terminal.

[1857] 5. Product Selection and Ordering

[1858] The user can view the list of suggested products on the device, select the product they like, and then press the "Purchase" button to confirm the order.

[1859] The terminal sends the order information (product ID, size, color) of the selected product to the server.

[1860] 6. Manufacturing and Delivery

[1861] The server sends order information to partner factories and issues manufacturing instructions. The order information includes detailed data (design, size, color) required for manufacturing.

[1862] The partner factory manufactures the product based on the order information received from the server. For example, the partner factory manufactures a "blue checked casual shirt."

[1863] The partner factory will then arrange for the completed product to be delivered directly to the user.

[1864] The server notifies the user's device of delivery start and completion information, allowing the user to check the delivery status of the product.

[1865] Specific examples

[1866] For example, if you're looking for a casual shirt for spring:

[1867] 1. User Registration and Information Collection

[1868] Users enter information such as "Favorite style: casual," "Favorite colors: blue, white," and "Size: M."

[1869] The terminal sends this information to the server, which stores it in a database.

[1870] 2. Collecting emotional information

[1871] The emotion engine recognizes emotions such as "happy" or "interested" from the user's facial expressions and adds them to the profile information.

[1872] 3. Obtaining trend information

[1873] The server obtains the latest fashion information via an API and determines that "checkered shirts for spring" are in fashion.

[1874] 4. Personalized product recommendations

[1875] The server generates a "blue checked casual shirt" based on the user's preferences, emotional data, and trend information, adds it to a list of suggested products, and sends this list to the user's device.

[1876] 5. Product Selection and Ordering

[1877] The user checks the suggested products, selects the "blue checked casual shirt," and presses the purchase button.

[1878] The terminal transmits the order information to the server.

[1879] 6. Manufacturing and Delivery

[1880] The server sends the order information to a partner factory, which then produces the shirt, which is then shipped directly to the user.

[1881] The server notifies the user's terminal of the delivery information.

[1882] This system allows users to seamlessly obtain personalized apparel products based on their preferences and emotions, while manufacturers can reduce wasteful inventory and lighten their environmental impact.

[1883] The processing flow will be explained below.

[1884] Step 1:

[1885] The user launches the application and enters basic information such as name, email address, and password on the new registration screen.

[1886] Step 2:

[1887] The device receives basic information and sends it to the server, which stores it in a database and generates a user ID.

[1888] Step 3:

[1889] The user then enters details such as their fashion preferences (e.g., casual style), size (e.g., medium), color preferences (e.g., blue, white), and past purchasing history.

[1890] Step 4:

[1891] The device collects these details and sends them to a server, which stores them in a database.

[1892] Step 5:

[1893] The emotion engine is activated and analyzes emotional data from the user's facial expressions, voice, and text input. For example, it analyzes facial expressions through the camera and recognizes emotions such as "happy" or "interested."

[1894] Step 6:

[1895] The device acquires the emotion data and sends it to the server, which stores it in a database and adds it to the profile information.

[1896] Step 7:

[1897] The server periodically accesses an external API that provides fashion trend information and collects the latest fashion trend data, including popular items, design patterns, colors, etc.

[1898] Step 8:

[1899] The server stores the collected trend information in a database for later analysis.

[1900] Step 9:

[1901] The server uses an AI algorithm to analyze the user's profile information, emotional data, and trend information to generate optimal product designs for the user. For example, if a user expresses the emotion "happy," the server will take that emotional data into account to generate particularly eye-catching designs and products that suit their preferences.

[1902] Step 10:

[1903] The server creates a list of suggested products based on the generated product design and transmits this list to the user's terminal.

[1904] Step 11:

[1905] The user can check the list of suggested products on the device, view the details, select the product they like, and press the "Purchase" button.

[1906] Step 12:

[1907] To confirm the purchase, the terminal sends the order information (item ID, size, color) of the selected item to the server.

[1908] Step 13:

[1909] The server sends the order information to the partner factory and issues manufacturing instructions. The order information includes detailed data (design, size, color) required for manufacturing.

[1910] Step 14:

[1911] The partner factory will manufacture the product based on the order information received from the server. For example, it will manufacture a "blue checked casual shirt."

[1912] Step 15:

[1913] The partner factory will then arrange for the completed product to be delivered directly to the user.

[1914] Step 16:

[1915] The server notifies the user's device of delivery start and completion information, allowing the user to check the delivery status of the product.

[1916] This series of steps allows users to seamlessly obtain personalized apparel products based on their preferences and emotions, while helping manufacturers reduce wasted inventory and lighten their environmental impact.

[1917] Example 2

[1918] 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."

[1919] Conventional apparel product recommendation systems only use a user's basic profile information and purchase history to suggest products, making it difficult to reflect the user's momentary emotions or current trends. This results in low personalization accuracy and fails to increase user satisfaction. Furthermore, as a result, users often lose interest in the suggested products, leading to a decline in purchasing motivation.

[1920] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1921] In this invention, the server includes means for collecting user profile information and storing it in a database, means for periodically obtaining fashion trend information from around the world and storing it in a database, means for collecting user emotion information using an emotion engine and storing it in a database, means for analyzing the user's profile information, emotion information, and trend information and generating personalized product designs, means for proposing the generated product designs to the user, means for sending order information to a manufacturing facility to manufacture the products selected by the user, and means for delivering the manufactured products to the user. This enables advanced personalization based on emotion data and the latest trend information in addition to the user's basic profile information, thereby increasing the user's desire to purchase.

[1922] "User Profile Information" means basic information provided by a User, including fashion preferences, size and color preferences, and past purchasing history.

[1923] "Fashion trend information" refers to the latest trends in the fashion industry that are regularly acquired, including popular items, colors, and design patterns.

[1924] The "emotion engine" is a system for recognizing user emotions and has the ability to collect emotional data through facial expression recognition, voice analysis, and text analysis.

[1925] "Personalized product design" refers to a product design optimized for a user, generated based on the user's profile information, emotional information, and fashion trend information.

[1926] A "suggested product list" is a list of products generated based on personalized product designs and suggested to users.

[1927] "Order Information" means detailed information required for manufacturing the product selected by the user, including product ID, size, color, etc.

[1928] "Manufacturing facility" refers to a factory or production line that produces the product selected by the user.

[1929] The present invention relates to a system that integrates a user's profile information, emotion information, and fashion trend information to recommend personalized apparel products. The system includes a means for collecting user profile information and storing it in a database, a means for collecting user emotion information using an emotion engine, and a means for acquiring fashion trend information from around the world and storing it in a database. The system also includes a means for analyzing this information to generate personalized product designs and recommend them to the user. The system also includes a means for sending order information to a manufacturing facility to manufacture the products selected by the user, and a means for delivering the manufactured products to the user.

[1930] First, a user launches the application and registers, which collects user profile information. The user enters information such as their name, email address, password, fashion preferences, size and color preferences, and past purchase history, and the device sends this information to the server. The server stores the received information in a database and generates a user ID.

[1931] Next, the emotion engine collects user emotion data through facial expression recognition, voice analysis, and text analysis. When a user views or interacts with a product, the device automatically collects emotion data and sends it to the server. The emotion engine analyzes emotions using open source libraries (e.g., OpenCV) and voice analysis APIs (e.g., Google Cloud Speech-to-Text API) and sends the results to the server. The server stores the data in a database.

[1932] The server also periodically uses external APIs (e.g., FashionAPI) to collect the latest fashion trend information and stores it in the database. Trend information includes popular items, design patterns, colors, etc., and is used for analysis.

[1933] To make personalized product suggestions, the server integrates and analyzes the user's profile information, emotional data, and trend information, and uses AI algorithms (e.g., TensorFlow or PyTorch) to generate product designs that are optimal for the user. The generated product designs are sent to the user's device as a list of suggested products. For example, if a user is looking for a "casual blue shirt for spring," and the server recognizes that the user's emotion is "happy," it will suggest a casual blue shirt based on that information.

[1934] The user checks the list of suggested products on their device, selects their favorite product, and presses the "Purchase" button. The order information (product ID, size, color) is sent to the server, which then sends the order information to a partner factory and instructs it to manufacture the product. The manufacturing facility manufactures the product based on the order information and delivers the completed product directly to the user. The server then notifies the user of the delivery information on their device, allowing the user to check the delivery status.

[1935] As an example of this system, consider the following prompt: "I'm looking for a casual blue shirt for spring. I'm happy shopping. Please suggest products that fit this criteria."

[1936] This allows users to efficiently find the best products based on their profile information, emotions, and the latest fashion trends, while manufacturers can reduce unnecessary inventory and lighten their environmental impact.

[1937] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1938] Step 1: User registration and information collection

[1939] The user starts the application and enters information such as name, email address, password, fashion preferences, size and color preferences, and past purchase history on the new registration screen. This information is the input data.

[1940] The device serializes the input information and sends it to the server in JSON format via an HTTP POST request. This request is the input, and the user ID received in response is the output.

[1941] The server parses the received JSON data and stores each field (such as name, email address, and fashion preferences) in a database. The server generates a new user ID and returns it to the device as an HTTP response. The device notifies the user.

[1942] Step 2: Collecting emotional information

[1943] Users use the application to browse products.

[1944] The emotion engine recognizes the user's facial expressions using a camera and analyzes their voice using a microphone. It also analyzes text input. This is the input data.

[1945] The device collects emotion data in real time and sends it to the server in JSON format. The emotion data is the input, and the database records are the output.

[1946] The server stores the received emotion data in a database, for example, as a record with fields such as "date and time," "emotion label," and "emotion intensity."

[1947] Step 3: Obtaining trend information

[1948] The server runs a cron job periodically every day and calls an external API (e.g., FashionAPI) to obtain the latest fashion trend information. The obtained trend information is the input data.

[1949] The server stores this trend information in a database in JSON format. The stored trend information is the output. The database contains fields such as item name, design pattern, color, and popularity.

[1950] Step 4: Personalized product recommendations

[1951] The server integrates user profile information, sentiment data, and trend information, and analyzes them using AI algorithms (e.g., TensorFlow, PyTorch). This is the input data.

[1952] As a result of the analysis, the server generates the optimal product design for the user. The generated product design is the output and is sent to the terminal as a list of suggested products. For example, a blue casual shirt may be suggested.

[1953] Step 5: Select and order

[1954] The user checks the list of suggested products on the terminal, selects the product that suits their taste, and presses the "Purchase" button. This is the input data.

[1955] The terminal sends JSON data including the selected product information (product ID, size, color) to the server. The order information is output.

[1956] The server stores the received order information in a database and updates the order status to "Not yet manufactured."

[1957] Step 6: Manufacturing and Delivery

[1958] The server sends the order ID and detailed information (design, size, color) in JSON format to the partner factory. This is the input data.

[1959] The partner factory manufactures the product based on the order information, for example, by cutting the fabric using a CNC cutting machine and sewing it. The manufactured product is the output.

[1960] The partner factory hands over the completed product to the delivery company and notifies the server of the delivery information.

[1961] The server notifies the user of delivery information (e.g., tracking number) on the user's device, allowing the user to check the delivery status.

[1962] This allows users to efficiently find the best products based on their profile information, emotions, and the latest fashion trends, while manufacturers can reduce unnecessary inventory and lighten their environmental impact.

[1963] (Application example 2)

[1964] 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."

[1965] Conventional apparel recommendation systems suggest products based on user profile information and trend information, but they have the problem of not being able to make optimal personalized recommendations because they do not take the user's emotions into account. Therefore, there is a need to provide a system that can make personalized apparel recommendations that correspond to the user's actual emotional state.

[1966] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1967] In this invention, the server includes means for collecting user profile information and storing it in a database, means for periodically obtaining fashion trend information from around the world and storing it in a database, means for analyzing the user profile information and trend information to generate personalized product designs, means for proposing the generated product designs to the user, means for collecting user emotion data using an emotion engine that recognizes the user's emotions and adding it to the profile information, means for sending order information to a manufacturing facility to manufacture the product selected by the user, and means for delivering the manufactured product to the user. This enables more sophisticated personalized proposals based on the user's emotional state.

[1968] "User Profile Information" refers to basic data about a User, such as the User's fashion preferences, size and color preferences, and past purchasing history.

[1969] "Trend information" refers to data that includes information on fashion items, design patterns, colors, etc. that are popular within a certain period of time.

[1970] "Emotion engine" refers to a system that recognizes and analyzes a user's emotions through facial expression recognition, voice analysis, and text analysis.

[1971] "Personalized product design" refers to apparel product designs optimized for specific users, generated based on the user's profile information, trend information, and emotional data.

[1972] "Manufacturing facility" refers to a facility that actually manufactures apparel products based on order information from users.

[1973] "Database" means an electronic repository for storing data used by the System, such as user profile information, trend information, sentiment data, and order information.

[1974] The system for implementing this invention includes the following main hardware and software: The hardware used includes smart glasses, a camera, and a server, and the software used includes Python, OpenCV (an image processing library), and Requests (an HTTP request library).

[1975] This system uses user profile information, trend information, and emotion data to provide personalized product recommendations. The process is explained below.

[1976] First, the user puts on the smart glasses and activates the system. The camera in the smart glasses captures an image of the user's face. This image data is analyzed by the emotion engine to recognize the user's emotional state. The emotion engine combines facial, voice, and text analysis to obtain emotion data.

[1977] The server then periodically retrieves information on fashion trends from around the world via API and stores it in a database, including information on popular items, design patterns, colors, etc. This trend information is combined with user profile information and analyzed to generate personalized product designs.

[1978] The generated product designs are then presented to the user in real time, displayed on the smart glasses display. The user selects the desired product from the list of suggested products and completes the purchase process. This selection is then sent to the server and transmitted to the manufacturing facility.

[1979] The manufacturing facility manufactures the product based on the received order information, and once production is complete, the product is delivered directly to the user. The server manages delivery information and allows the user to check the delivery status of the product.

[1980] This system allows users to seamlessly select and purchase apparel that best suits their preferences and emotional state, while also helping manufacturing facilities reduce wasteful inventory and contribute to reducing environmental impact.

[1981] For example, consider the following prompt:

[1982] "If a user who has a casual style and likes blue or white clothing is wearing smart glasses, and emotion recognition reveals that the user looks happy, the glasses will suggest products based on the latest fashion trends, such as a casual blue shirt or white pants."

[1983] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1984] Step 1:

[1985] The user puts on the smart glasses and starts the system.

[1986] Input: None

[1987] Output: The smart glasses are turned on and the camera is ready to use.

[1988] Specific operation: The user turns on the smart glasses. The system automatically starts the camera.

[1989] Step 2:

[1990] The camera on the device (smart glasses) captures an image of the user's face.

[1991] Input: An image of the user's face

[1992] Output: Captured image (JPEG format)

[1993] Specific operation: The camera captures the user's face, takes a picture, and temporarily stores the image data in memory.

[1994] Step 3:

[1995] The terminal transmits the captured image data to the server.

[1996] Input: Captured image (JPEG format)

[1997] Output: Image data sent

[1998] Specific operation: The device uploads the stored image data to a server via the Internet.

[1999] Step 4:

[2000] The server passes the received image data to an emotion engine and analyzes the user's emotion.

[2001] Input: Received image data

[2002] Output: User emotion data (e.g. happy, interesting)

[2003] Specific operation: The emotion engine analyzes image data and recognizes the user's emotional state from their facial expressions.

[2004] Step 5:

[2005] The server acquires fashion trend information from around the world and stores it in a database.

[2006] Input: Trend information acquisition request

[2007] Output: Trend information (e.g. popular items, colors, design patterns)

[2008] Specific operation: The server periodically obtains the latest fashion trend information using an external API and stores it in a database.

[2009] Step 6:

[2010] The server analyzes the user's profile information, emotion data, and trend information to generate personalized product designs.

[2011] Input: User profile information, sentiment data, trend information

[2012] Output: Personalized product design (e.g., blue checked casual shirt)

[2013] How it works: The server uses AI algorithms to analyze this data and generate optimal product designs for the user.

[2014] Step 7:

[2015] The server sends the generated product design to the user's device (smart glasses).

[2016] Input: Generated product design

[2017] Output: Submitted product design

[2018] Specific operation: The server sends the product design to the terminal via the Internet and displays it on the smart glasses display.

[2019] Step 8:

[2020] The user selects the desired product from the list of suggested products and completes the purchase procedure.

[2021] Input: Suggested product list

[2022] Output: Selected product information (e.g. product ID, size, color)

[2023] Specific operation: The user operates the interface of the smart glasses to select the desired product and press the "Purchase" button.

[2024] Step 9:

[2025] The terminal transmits the product information selected by the user to the server.

[2026] Input: Selected product information

[2027] Output: Order information sent

[2028] Specific operation: The terminal uploads the selected product information to the server via the Internet.

[2029] Step 10:

[2030] The server transmits the order information to a manufacturing facility and issues instructions for manufacturing the goods.

[2031] Input: Order Information

[2032] Output: Manufacturing instructions

[2033] Specific operations: The server sends manufacturing instructions to the manufacturing facility based on the received order information.

[2034] Step 11:

[2035] The manufacturing facility manufactures the goods based on the received order information and delivers them to the user.

[2036] Input: Order Information

[2037] Output: Manufactured goods

[2038] What happens: The manufacturing facility produces the goods based on the order, processes the delivery, and delivers the product directly to the user.

[2039] Step 12:

[2040] The server notifies the user's terminal of the delivery information.

[2041] Input:Shipping information

[2042] Output: Delivery status notification

[2043] Specific operation: The server sends the delivery progress status to the user's terminal via the Internet, allowing the user to check the delivery status.

[2044] 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.

[2045] 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.

[2046] 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.

[2047] 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.

[2048] 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.

[2049] 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.

[2050] 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).

[2051] 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.

[2052] 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."

[2053] 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.

[2054] 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).

[2055] 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.

[2056] 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.

[2057] 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.

[2058] 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.

[2059] 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.

[2060] 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.

[2061] 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.

[2062] 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.

[2063] 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.

[2064] 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.

[2065] The following is further disclosed regarding the above embodiment.

[2066] (Claim 1)

[2067] a means for collecting and storing user profile information in a database;

[2068] A means to regularly obtain fashion trend information from around the world and store it in a database,

[2069] A means for analyzing user profile information and trend information to generate personalized product designs;

[2070] A means for proposing the generated product design to a user;

[2071] A means for transmitting order information to partner factories to manufacture the products selected by the user;

[2072] a means for delivering the manufactured goods to the user;

[2073] A system including:

[2074] (Claim 2)

[2075] 10. The system of claim 1, wherein the profile information includes the user's fashion preferences, size and color preferences, and past purchasing history.

[2076] (Claim 3)

[2077] The system of claim 1, wherein the fashion trend information includes popular items, colors, and design patterns.

[2078] "Example 1"

[2079] (Claim 1)

[2080] a means for collecting and storing user profile information in a database;

[2081] A means to regularly obtain fashion trend information from around the world and store it in a database,

[2082] A means for analyzing user profile information and trend information and generating personalized product designs using a generative AI model;

[2083] A means for transmitting the generated product design to a terminal and proposing it to a user;

[2084] A means for transmitting order information for the product selected by the user on the terminal to a partner manufacturing facility;

[2085] a means for delivering the manufactured goods to the user;

[2086] A means for notifying the user of production and delivery information on the user's device;

[2087] A system including:

[2088] (Claim 2)

[2089] 10. The system of claim 1, wherein the profile information includes the user's fashion preferences, size and color preferences, and past purchasing history.

[2090] (Claim 3)

[2091] The system of claim 1, wherein the fashion trend information includes popular items, colors, and design patterns.

[2092] "Application Example 1"

[2093] (Claim 1)

[2094] a means for collecting and storing user profile information in a database;

[2095] A means to regularly obtain fashion trend information from around the world and store it in a database,

[2096] A means for analyzing user profile information and trend information to generate personalized product designs;

[2097] A means for proposing the generated product design to a user;

[2098] means for transmitting order information to a manufacturing facility for manufacturing the items selected by the user;

[2099] a means for delivering the manufactured goods to the user;

[2100] means including an algorithm that recommends products based on user preferences and current trend information;

[2101] A means including a mobile terminal having installed thereon an application that suggests personalized fashion items to a user;

[2102] A means of obtaining trend information from external APIs;

[2103] A system including:

[2104] (Claim 2)

[2105] 10. The system of claim 1, wherein the profile information includes the user's fashion preferences, size and color preferences, and past purchasing history.

[2106] (Claim 3)

[2107] The system of claim 1, wherein the fashion trend information includes popular items, colors, and design patterns.

[2108] "Example 2: Combining Emotion Engines"

[2109] (Claim 1)

[2110] a means for collecting and storing user profile information in a database;

[2111] A means to regularly obtain fashion trend information from around...

Claims

1. a means for collecting and storing user profile information in a database; A means to regularly obtain fashion trend information from around the world and store it in a database, A means for analyzing user profile information and trend information to generate personalized product designs; A means for proposing the generated product design to a user; A means for transmitting order information to partner factories to manufacture the products selected by the user; a means for delivering the manufactured goods to the user; A system including:

2. 10. The system of claim 1, wherein the profile information includes the user's fashion preferences, size and color preferences, and past purchasing history.

3. The system according to claim 1 , wherein the fashion trend information includes popular items, colors, and design patterns.

Images